EPA/600/R-94/067
AprU 1994
ASSESSMENT OF ALTERNATIVE
MANAGEMENT PRACTICES AND POLICIES
AFFECTING SOIL CARBON IN AGROECOSYSTEMS
OF THE CENTRAL UNITED STATES
by
A.S. Donigian, Jr.1
T.O. Barnwell, Jr.2
R.B. Jackson, IV2
A.S. Patwardhan1
K.B. Weinrich3
A.L. Rowell3
R.V. Chinnaswamy1
C.V. Cole4
AQUA TERRA Consultants1
Mountain View, CA 94043
Environmental Research Laboratory2
U.S. Environmental Protection Agency
Athens, GA 30605
Computer Sciences Corporation3
Athens, GA 30605
Natural Resources Ecology Laboratory4
Fort Collins, CO 80523
EPA Contract No. 68-CO-0019
Work Assignment No. 13
Technical Project Manager
Thomas O. Barnwell, Jr.
Assessment Branch
Environmental Research Laboratory
Athens, GA 30605
ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
ATHENS, GA 30605
<$S Printed on Recycled Paper
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DISCLAIMER
The work presented in this document has been funded by the U. S. Environmental Protection
Agency under Contract No. 68-CO-0019 to AQUA TERRA Consultants, under Contract No. 68-
WO-0043 to Computer Sciences Corporation, and by Cooperative Agreement CR818652-01-0
to Colorado State University. It has been subjected to the Agency's peer and administrative
review and has been approved as an EPA document. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
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FOREWORD
Over the past several years, interest in the issue of global warming has increased within the
scientific community. Gaining understanding of this complex environmental phenomenon
requires the research contributions of specialists in such fields as atmospheric chemistry,
ecology, hydrology, forestry, and agriculture. Terrestrial carbon cycle dynamics, agricultural
soil carbon management and conservation, and water resources impacts are important areas of
research in the global warming issue.
This report is the first major product of the BIOME Agroecosystem Assessment Providing
preliminary estimates of carbon sequestration potential for the central United States including
the Corn Belt, the Great Lakes, and portions of the Great Plains. This Study Region comprises
44% of the land area and 60%-70% of the agricultural cropland of the conterminous United
States. The assessment methodology includes the integration of the RAMS economic model, the
CENTURY soil carbon model, meteorologic and soils data bases, and GIS display and analysis
capabilities. It assesses the impacts of current agricultural trends and conditions, alternative
tillage practices, use of cover crops, and Conservation Reserve Program (CRP) policy on soil
carbon.
This report provides evidence of the value of agricultural residue management practices in
relation to CO2 emissions to the atmosphere. By doing so, the report strongly supports current
USEPA and USDA policy in this area.
Rosemarie C. Russo, Ph.D.
Director
Environmental Research Laboratory
Athens, Georgia
m
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ABSTRACT
The goal of the U.S. EPA BIOME Agroecosystems Assessment Project is to evaluate the degrefe
to which agroecosystems can be technically managed, on a sustainable basis, to conserve and
sequester carbon, reduce the accumulation of CO2 in the atmosphere, and provide reference
datasets and methodologies for agricultural assessment. Agroecosystems play an important role
in the global carbon cycle. They contain about 12% of the terrestrial soil carbon, and
conservation of this pool is essential to the health of the earth. Application of certain
agroecosystem management practices could increase this pool by reducing the build-up of
atmospheric carbon. In particular, alternative or sustainable agricultural practices increase soil
carbon content. BIOME will assess the potential agroecosystem contribution to global carbon
conservation and sequestration.
The carbon sequestration potential of agroecosystems is being estimated through an integration
of tasks including application of existing soil carbon mass balance models, analysis of agri-
cultural production cycles, development of data bases for agroecosystem carbon pools and
dynamics, development of spatial data bases for assessment of agricultural carbon sequestration
potential, and development of cost factors.
This report, the first major product of BIOME, provides preliminary estimates of carbon
sequestration potential for the central United States including the Corn Belt, the Great Lakes,
and portions of the Great Plains. This Study Region comprises 44% of the land area and 60%
to 70% of the agricultural cropland of the conterminous United States. The assessment
methodology includes the integration of the RAMS economic model, the CENTURY soil carbon
model, meteorologic and soils data bases, and GIS display and analysis capabilities in order to
assess the impacts on soil carbon of current agricultural trends and conditions, alternative tillage
practices, use of cover crops, and Conservation Reserve Program (CRP) policy.
The study results indicate a 26% to 53 % increase in soil carbon for the 40-year projection period
from 1990 through 2030 under the methodology assumptions representing a continuation of
current (circa 1989-90) agricultural trends and alternative policy scenarios. This represents a
potential gain of 1 to 2 GtC in the Study Region by 2030. Conservation tillage alternatives and
cover crops appear to have significant potential for increased carbon sequestration. Assessment
of soil carbon changes on Conservation Reserve land indicates mixed results across the Study
Region with both increases and decreases. However, further study is needed before these
conclusions can be fully confirmed. This report provides recommendations to further evaluate
and confirm the study results, perform additional model field testing, refine model components,
extend the methodology to other regions of the United States, and investigate selected research
issues to improve the assessment procedures.
IV
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CONTENTS
Page
Disclaimer ii
Foreword iii
Abstract iv
Figures vii
Tables « . . . ix
Acknowledgments xi
1.0 INTRODUCTION AND OVERVIEW 1
1.1 Introduction 1
1.2 Assessment Framework 3
1.3 Study Conclusions and Recommendations 5
1.4 Revisions to the Draft Report Included in this Final Report 10
1.5 Format of Report 13
2.0 AGROECOSYSTEM CARBON POOLS, DYNAMICS, AND MODELING ..... 14
2.1 Rationale , 14
2.2 Loss and Sequestration of Carbon from Central U.S. Ecosystems ... 16
2.3 Project Plan . 22
2.4 Modeling Soil Organic Matter 22
3.0 MODELING METHODOLOGY FOR SOIL CARBON SEQUESTRATION 40
3.1 Introduction 40
3.2 CENTURY/DNDC Models Overview and Testing 42
3.3 RAMS Model Overview 48
3.4 Initial Study Region and Production Areas 51
3.5 Project Methodology . 51
4.0 DATA BASES AND MODEL INPUT DEVELOPMENT 58
4.1 Climate Divisions and Data Bases 58
4.2 Soils Data Development 66
4.3 Cropping, Land Use, and Agricultural Practice Data 73
4.4 Crop Yield Information 76
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5.0 MODELING SCENARIOS - BASELINE AND ALTERNATIVES 79
5.1 Modeling Agricultural Production Systems 79
5.2 Development of Initial SOC Conditions and Baseline Projections ... 97
5.3 Status Quo/Baseline Conditions 103
5.4 Alternative Policy Scenarios . 103
5.5 Operational Procedures For Generating SOC Levels 116
6.0 MODEL SIMULATIONS AND ASSESSMENT RESULTS 123
6.1 Impacts of Agricultural Production Systems on Soil Carbon 123
6.2 Climate Division and Study Region Impacts of Modeled Scenarios . 140
6.3 Preliminary Assessment of Conservation Reserve Program
(CRP) Land 158
6.4 Study Region Impacts of Alternative Yield Increases 169
7.0 PROJECT FINDINGS AND RECOMMENDED FUTURE WORK 172
7.1 Review of Methodology and Modeling Assumptions 172
7.2 Capsule Summary of Study Findings . . . . 178
7.3 Recommended Future Study Efforts 180
8.0 REFERENCES 184
9.0 APPENDICES 194
A. Simulation of Wheat-Fallow Cropping Systems A-l
B. Application of the CENTURY Model to the Lexington, KY Site ... B-l
C. Tabulated Simulation Results by CD C-l
D. Maps and Displays of Simulation Results and Study Region
Characteristics D-l
E. Land Use and Soil Physical Properties for All Climate Divisions ... E-l
VI
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FIGURES
Page
1.1 Study Region with Production Areas and State Boundaries . 2
1.2 Assessment of Agricultural Carbon Sequestration Potential 4
1.3 Simulated Total Soil Carbon Levels for the Study Region under the Status Quo
Scenario for Three Alternative Levels of Future Crop Yield Increases ....... 6
1.4 Typical Timeline of Soil, Residue, and Plant Carbon Changes During the Year . 11
2.1 An Approach to the Study of Regional Agroecosystems 16
2.2 Agroecosystem Carbon Pools Site Network 17
3.1 Modeling System for Assessment of Agricultural Carbon Sequestration 41
3.2 Flow Diagram for the CENTURY Soil Organic C Submodel 44
3.3- Structure and State Variables for the CENTURY N Submodel 45
3.4 RAMS Study Region, Production Areas and State Boundaries 49
3.5 Soil Carbon Modeling Methodology 53
3.6 Production Areas and Climate Divisions in the RAMS Study Area 54
3.7 Weighted Average and Individual Soil Carbon 56
4.1 Climate Station Locations In and Near the Study Region 59
4.2 Average Maximum Temperature Contours [°C] for the Study Region 60
4.3 Average Annual Precipitation Contours [cm] for the Study Region ......... 61
4.4 Climate Division Boundaries for the Study Region 63
4.5 Counties for Which Soil Textures Were Taken From DBAPE 67
4.6 Counties for Which Soil Textures Were Taken From 1982 NRI 68
4.7 Counties for Which Soil Textures Were Taken From 1987 NRI 69
4.8 USDA Soil Textural Triangle Showing Weighted Textural Distribution 70
4.9 Observed Corn and Soybean Yields 77
4.10 Observed Wheat and Hay Yields 78
5.1 Cropland Distribution for the Study Region 81
5.2 CENTURY Crop Model Diagram 89
5.3 Cover Crop Land Distribution Within the Study Region 109
5.4 CRP Distribution Within the Study Region 114
Vll
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6.1 Simulated vs Actual and Projected Crop Yields for Selected CDs and
C/R/T Combinations 124
6.2 Simulated vs Actual and Projected Crop Yields for Selected CDs and
C/R/T Combinations 125
6.3 Locations of CDs listed in Table 6.2 131
6.4 Changes and Impacts of Agricultural Production Systems on Total SOC
in CD 413, 1907 - 2030 132
6.5 Changes and Impacts of Agricultural Production Systems on Total SOC
in CD 643, 1907 - 2030 132
6.6 Changes and Impacts of Agricultural Production Systems on Total SOC
in CD 473, 1907 - 2030 133
6.7 Changes and Impacts of Cover Crops on Total SOC in CD 413, 1907 - 2030 . . 138
6.8 Changes and Impacts of Cover Crops on Total SOC in CD 603, 1907 - 2030 . . 138
6.9 Impacts of Cover Crops on Corn and Soybean Yields in CD 413 140
6.10 Simulated 1990 Soil Carbon (gC/m2) Distribution Within the
Study Region 148
6.11 Simulated 1990 Soil Carbon (gC/m2) Distribution Within the Study Region
Weighted by Cropland Distribution 149
6.12 Simulated 2030 Soil Carbon (gC/m2) Distribution Within the Study
Region 150
6.13 Simulated 2030 Soil Carbon (gC/m2) Distribution Within the Study Region
Weighted by Cropland Distribution 151
6.14 Increase in Soil Carbon (gC/m2) Within the Study Region From 1990 to
2030 under the Status Quo Scenario 152
6.15 Increase in Soil Carbon (gC/m2) Within the Study Region From 1990 to
2030 under the Status Quo Scenario Weighted by Cropland Distribution 153
6.16 Percent Change in Soil Carbon Within the Study Region From 1990 to
2030 under the Status Quo Scenario 155
6.17 Percent Difference in Soil Carbon Within the Study Region for 2030
for High Conservation Relative to the Status Quo Scenario 156
6.18 Percent Difference in Soil Carbon Within the Study Region for 2030
for Cover Crops Relative to the Status Quo Scenario 157
6.19 Impacts of CRP Scenarios on Total SOC in CDs 413 and 643 160
6.20 Impacts of CRP Scenarios on Total SOC in CDs 473 and 581 160
6.21 Percent Difference in Soil Carbon Within the Study Region for 2030
for CRP1 Relative to the Dominant Rotation 166
6.22 Percent Difference in Soil Carbon Within the Study Region for 2030
for CRP2 Relative to the Dominant Rotation 167
6.23 Simulated Total SOC Impacts for Selected Scenarios and Alternative Annual Crop
Yield Increases . 171
vm
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TABLES
Page
2.1 Corn Belt Sites and Cooperators 23
2.2 Great Plains Sites and Cooperators 25
2.3 Sites Outside of the Great Plains and Corn Belt 26
2.4 Soil Carbon Model Review Form 28
2.5 List of Soil Carbon Models Reviewed 29
2.6 Evaluation Criteria and Capabilities for Comparison of Soil
Carbon Models 30
2.7 Comparison of Soil Carbon Model Features 35
3.1 Activities and Constraints in RAMS . . . . 50
4.1 Statistics of Climate Variables for all Climate Divisions 64
4.2 Soil Textural Distribution of Agricultural Soils in RAMS Study
Region 71
4.3 Texture Categories and Groups for RAMS Study Region . 72
4.4 Selected Physical Properties for the Soil Textural Classes in the
RAMS Study Area 74
4.5 Soil Physical Properties, Weights, and Ranks for Selected CDs 75
5.1 Crop and Cropland Distribution for Each PA and the RAMS Study Region ... 82
5.2 Complete List of Crop Rotations in the Study Region 84
5.3 Final List of Crop Rotations for Modeling with CENTURY 87
5.4 List of Modeled and Original Crop Rotation Numbers and Crop
Sequences gg
5.5 Tillage Parameters Used in the CENTURY Model 91
5.6 Type and Number of Tillage Operations for 1907-2030 92
5.7 Summary of Planting and Harvesting Dates for RAMS Study Region ....... 93
5.8 Harvest Parameters for Corn-Grain, Soybeans, and Small Grain
(Wheat, Barley, and Oats) (assuming only GRAINS are harvested) 94
5.9 Harvest (Grazing) Parameters for Corn-Silage and Hay . 95
5.10 Dominant Crop Rotations within Each PA 99
5.11 Schedule of Climate and Management Practices for Estimating
Soil Organic Carbon 100
5.12 Percentage of Acres Considered Highly Erodible for Alternative
El Criteria by PA and the Entire Study Region 106
5.13 Tillage Distributions for Policy Scenarios 107
IX
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5.14 Tillage Practice Distribution by PA for Modeled Scenarios 108
5.15 Cover Crop Land Distribution by PA 107
5.16 Nationwide CRP Cumulative Enrollment by Crop Under CRP1,
Thousand Acres, 1996-2005 Ill
5.17 Nationwide CRP Current and Projected Annual Enrollment Under
CRP2, Thousand Acres, 1986-1995 . . 112
5.18 CRP Acres by PA Within the Study Region 113
6.1 Comparison of CENTURY (1980-1990) SOC with Values Estimated From
Kern and Johnson Report 126
6.2 Total Soil Carbon (gC/m2) and Percent Change in Total Soil Carbon For Selected
Climate Divisions 128
6.3 Total Soil Carbon (gC/m2) and Percent Change in Total Soil Carbon For Crop
Rotations With and Without Cover Crops for Selected Climate Divisions 136
6.4 Absolute and Percent Change in Total Soil Carbon from 1990 to 2030 in the
Study Region for Status Quo and Alternative Scenarios 142
6.5 Absolute and Percent Difference in Total Soil Carbon Relative to Status
Quo in 2030 , 144
6.6 Total Soil Carbon (gC/m2) and Percent Change in Total Soil Carbon For CRP and
Dominant Crop Rotations for Selected Climate Divisions 159
6.7 Absolute and Percent Change in Total Soil Carbon from 1990 to 2030 in
the Study Region for CRP Scenarios and the Dominant Crop Rotation 162
6.8 Absolute and Percent Difference in Total Soil Carbon for the CRP Scenarios
Relative to the Dominant Rotation . 164
6.9 Impacts of Alternative Annual Crop Yield Increases on Study Region Total SOC
for Status Quo and Policy Scenarios 170
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ACKNOWLEDGMENTS
The work described in this report was a collaborative effort with participation from a number
of groups and individuals. The work was sponsored by the EPA Environmental Research
Laboratory in Athens, GA with active cooperation from the EPA Office of Policy and Program
Evaluation (OPPE) in Washington, D.C. Mr. Thomas Barnwell of EPA-Athens was the EPA
Project Manager providing detailed technical guidance and participation in meetings and project
decisions. Mr. Steve Winnett was the administrative and technical liaison with OPPE. Both
individuals were instrumental in the successful completion of the effort; their assistance is
gratefully acknowledged.
The Project Team assembled for this study included AQUA TERRA Consultants, EPA-Athens,
Computer Sciences Corporation, the Natural Resources Ecology Laboratory (NREL) in Fort
Collins, CO, the Center for Agriculture and Rural Development (CARD) at Iowa State
University, and selected consultants. The participation by CARD staff was sponsored by EPA
OPPE. The report authors are drawn from the first three organizations, but many others
contributed to the numerous technical discussions and decisions needed throughout the study.
Staff from the NREL played an advisory role in the development of the study methodology and
assisted in the application, refinement, testing, and parameterization of the CENTURY-model
for use in this study. Numerous colleagues at NREL who were involved in portions of the work
included Dr. William Parton, Dr. Ted Elliott, Dr. Alister Metherell, and Ms. Laura Harding.
Dr. Parton provided technical guidance in the use of the CENTURY model, including a training
workshop, advice on parameter estimation, and technical direction for model refinements. Dr.
Elliott participated in project meetings and provided administrative overview. Dr. Metherell,
assisted by Ms. Harding, performed model testing on the Sydney, NE site, provided parameter
guidance, and implemented model refinements to represent complex crop rotations. Ms.
Harding was instrumental in model coding and testing. We are especially grateful for the
assistance provided by this group, which was critical to the completion of the study.
Dr. Klaus Flach, consultant to the study and formerly of the SCS (retired), assisted in the soil
characterization and parameter development efforts, and was a key member of the Project Team.
Dr. Keith Paustian, formerly of Michigan State University and currently with NREL, provided
detail review of our CENTURY model testing work and general advice on parameter estimation.
Drs. Wylbur Frye and Robert Blevins, University of Kentucky, provided data for CENTURY
model testing at Lexington, KY, and review comments on the testing results. At CARD, Mr.
Derald Holtkamp was our key contact for RAMS, model output and its interpretation for use.with
CENTURY; he also provided written material for portions of the report. Other CARD staff
who participated at various stages of the study included Dr. Jason Shogren, Dr. Aziz Bouzaher,
XI
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and Mr. Phil Gassman.
contributions.
All of these individuals are acknowledged for their important
Among the authors at EPA, Mr. Barnwell was the Project Manager providing overall project
direction. Mr. Robert Jackson was responsible for the meteorologic and soils data development,
directed the model runs for the Study Region, processed the model output, and wrote associated
portions of the report.
For Computer Sciences Corporation, Mr. Kevin Weinrich developed and applied the automated
calibration procedure for CENTURY and performed the model runs, while Mr. Allen Rowell
assisted in data base processing and prepared all the GIS displays and analysis.
Dr. Vern Cole of NREL and USDA-ARS provided critical input to the study methodology and
modeling effort; his insight and guidance was instrumental to the study.
For AQUA TERRA Consultants, Mr. Anthony Donigian was the Project Manager responsible
for development of the study methodology, overall technical direction of the modeling, analysis
of the results, and preparation of the final report. Mr. Avinash Patwardhan was the Project
Engineer who directed the CENTURY model application and testing, reviewed model results and
operation, and prepared sections of the report. He was assisted by Mr. Radha Chinnaswamy
who prepared model input files, executed model runs, and performed model testing on the
Lexington site. Ms. Dorothy Inahara performed word processing throughout the study and for
the final report.
The contributions from all these individuals, both authors and others, was critical to the
successful collaborative effort in this study.
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SECTION 1
INTRODUCTION AND OVERVIEW
1.1 INTRODUCTION
The goal of the U.S. EPA BIOME Agroecosystems Assessment Project is to evaluate the degree
to which agroecosystems can be technically managed, on a sustainable basis, to conserve and
sequester carbon, thereby reducing the accumulation of CO? in the atmosphere, and to provide
reference datasets and methodologies for agricultural assessment. Agroecosystems play an
important role in the global carbon cycle because they contain 12% of the terrestrial soil carbon.
Conservation of this pool is essential to the health of the earth and to the sustainability of
agriculture. Application of proper agroecosystem management practices could increase this pool
by reducing the build-up of atmospheric carbon. In particular, alternative or sustainable
agricultural practices could maintain or perhaps even increase soil carbon content. BIOME
attempts to assess the potential agroecosystem contribution to global carbon conservation and
sequestration.
This report focuses on the potential impacts of alternative management practices on agricultural
soil C in the major agricultural regions of the central United States (Figure 1.1). These impacts
are estimated by a soil C mass balance modeling study that incorporates policy considerations
and economics in the analysis. A literature review of soil C modeling and impacts of
management practices was used to guide the model selection and parameter estimation effort.
The models selected for use and/or modification to meet the needs of representing soil C cycles
in agroecosystems and impacts of management practices are CENTURY and DNDC (see Section
2.4 for model descriptions). These models share a common ability to examine the impacts of
alternative management practices on soil organic C, and are readily accessible to potential users.
The model results reported herein are derived from CENTURY model simulations; the DNDC
model was used for comparison and evaluation purposes for only selected test regions and its
results have been reported in a separate document (Li and Cialella, 1993).
An important aspect of this effort is the development of the assessment framework and
methodology that defines: (1) the agricultural production systems and scenarios (i.e., crop-soil-
climate combinations) to be assessed as impacted by national policy, (2) the integration of the
model needs with available databases, and (3) the operational mechanics of evaluating C
sequestration potential with the integrated model/database system. We worked closely with
EPA's Office of Policy and Program Evaluation to define a reasonable set of policy alternatives
for this assessment, focusing on policy that might be effected through a revised Farm Bill, such
as incentives to selectively promote conservation tillage, crop rotations, cover crops, and/or good
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F'gure 1.1 Study region with production areas and state boundaries
2
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stewardship of the conservation reserve. Policy alternatives are translated into basic data for use
in soil C models through economic models. These data, including such elements as agricultural
practices, fertilization rates, and production levels are used in the soil C models to produce net
carbon changes on a per unit area basis. The unit-area emissions are combined with areal-extent
data using data base programming and CIS capabilities to produce an estimate of total carbon
and nitrogen changes, and thus estimate changes in greenhouse emissions and associated impacts.
While data from long-term field studies have provided much information about the possible rates
and directions of change in soil C under various management regimes, our current knowledge
base is very fragmented. Most of what we know concerning soil organic matter (SOM)
dynamics has been obtained by studying SOM losses; we understand less about SOM
accumulation and how it varies across soil types, climatic regions and management regimes.
A network of field sites in the Corn and \Vheat Belts has been organized through a cooperative
agreement with Colorado State University (CSU) and Michigan State University (MSU).
Emphasis is on collection, preparation, and evaluation of soil C data. Sites where comparisons
of management practices are possible have been given highest priority. Outputs of the studies
will be useful in future validation of the results of this modeling study as well as providing a
better understanding of the response of SOM to management.
1.2 ASSESSMENT FRAMEWORK
As noted above, a key element in this effort was the development of the assessment framework
and modeling methodology to integrate the economic analysis, the soil carbon modeling, and
the available data bases into an operational mechanism for accomplishing the goals of the study.
Figure 1.2 represents our operational assessment strategy and framework. We needed to define
agricultural production systems arid scenarios (i.e., crop-soil-climate-practice combinations) to
be assessed in terms that would be sensitive to national and international policy considerations.
The integration of the model needs with available databases was a critical feature to insure
reasonable temporal and spatial representation of agricultural practices, and the feasibility of
the operational mechanics for evaluating C sequestration potential with the integrated model and
data base system.
The objective of the assessment is to estimate the greenhouse gas emissions from agriculture and
to estimate the relative impact of various policy alternatives on these emissions. We have
worked closely with the U.S. EPA's Office of Policy and Program Evaluation (OPPE) and Iowa
State University's Center for Agricultural and Rural Development (CARD) to define a
reasonable set of policy alternatives for this assessment. An example set of policy alternatives
is shown in the box labeled "OPPE" in Figure 1.2. Included are several national policy
alternatives and their possible effect on agricultural production levels, especially agriculture
policy alternatives that might be effected through a revised Farm Bill, such as incentives to
selectively promote conservation tillage, sustainable agriculture, or good stewardship of the
conservation reserve.
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As shown in the box labeled "CARD", these policies are used to drive economic models that
predict national production levels using models such as the Basic Linked System (BLS), a set
of national and regional models linked by policy and a world price determination process.
CARD'S Resource Adjustment Modeling System (RAMS) is being used to translate policy
alternatives into basic data for use in soil C models. The CSU/MSU box in Figure 1.2 shows
the data collection sites and the input of this component to the agricultural soil carbon modeling
effort.
The 'center' box in Figure 1.2 includes the primary steps in the modeling methodology to
produce the estimates of soil C sequestration, which is the central focus of this report. The data
produced by the RAMS analysis, including such elements as crop rotation acreages, tillage
practices, irrigation areas, and cover crops are used in the soil C models to produce net soil C
changes and greenhouse gas emissions on a per unit area basis. Data are produced for a range
of "Production Areas" (PAs), which are hydrologic areas defined by the Water Resources
Council that are used for aggregated economic reporting. The economic models also provide
statistics on crop acres, acreage in the conservation reserve, and other agricultural land usage.
These data, along with soils, climate, and crop yield data, are used by the CENTURY and
DNDC soil C models (described in Section 2.4) to simulate the impacts of various agricultural
production systems on soil C and N levels as a basis for calculating greenhouse gas emissions.
Model simulations are performed for Climate Divisions (CDs), which are subsets of the PAs
developed to allow a finer spatial, representation of both climate and soils characteristics. The
unit-area emissions produced by the CENTURY and DNDC models are combined with areal-
extent data using GIS and data base programming techniques to produce estimates of total
changes in soil carbon over time for each alternative policy scenario. As noted above, the
results reported herein are based on the CENTURY model simulations; the DNDC model
simulations were limited to seven selected CDs for testing and evaluation purposes, and its
results have been reported by Li and Cialella (1993). Section 7.1.2 includes a brief discussion
of the DNDC model results.
1.3 STUDY CONCLUSIONS AND RECOMMENDATIONS
During the course of the study, thousands of simulation runs were performed as part of the
model testing, calibration, and evaluation of alternative policy scenarios. Considering model
runs for each of 80 Climate Divisions, with 4 to 6 crop rotations and multiple crops, up to 6 soil
types, 4 alternative tillage practices, and multiple policy alternatives, no simple summary can
adequately describe the range of conditions and impacts identified. Section 6.0, along with
Appendices C and D, presents and discusses details of the study conclusions and
recommendations. Figure 1.3 attempts to aggregate a portion of the study results into a timeline
of agricultural soil carbon (SOC),, predicted by the CENTURY model, for the Study Region for
the 120-year simulation period, beginning with conversion of native vegetation to agricultural
production in about 1907, through current conditions (i.e., 1988-89); three alternative
projections through the year 2030 are included for different levels of annual crop yield increases.
These results were obtained by summing the products of the unit area changes in SOC under
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LEGENDS: I "air 1.5% Yld. Increase HBh 1.0% Yield hcreose -H- 0.5% Yield Increase
7500
PROJECTIONS
(Status Quo)
Conversion lo Agriculture
From Nolive Vegelalion, 1907
110%
-100%
90%
-80%
-70%
-60%
-50%
o
o
o
i-
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en
3000
T r= 1 1 1 i 1 1 1 1 1 1 h40%
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030
YEAR
Figure 1.3 Simulated Total Soil Carbon Levels for the Study Region under the Status Quo
Scenario For Three Alternative Levels of Future Crop Yield Increases.
each crop/rotation/tillage combination and the area associated with each combination within each
CD, then summing the values for all CDs in the Study Region, and dividing by the entire Study
Region area; thus the values are in units of grams C per square meter (gC/m2) for a 20-cm
soil layer.
The Total SOC values for the projection period of 1990 to 2030 in Figure 1.3 are for the 'Status
Quo' condition, which represents a continuation of current 1989-90 cropping, rotation, and
tillage practices through 2030, along with the impacts of the three alternative levels of annual
increases in crop yields — 1.5%, 1.0%, 0.5%. The historical portion of the curve shows the
characteristic, and well-documented, decrease in SOC following land conversion to agriculture
in about 1907, a continuing drop in SOC until about 1950, a period of stable and slightly
increasing SOC through 1970, and then significant SOC increases throughout the remainder of
the period through 2030. Allison (1973), identifies a period with stable or slightly increasing
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SOC which is consistent with the 1950-70 simulations, but subsequent increases of the magnitude
shown in Figure 1.3 from 1972 through 1990 have yet to be confirmed. These results reflect
the impacts of increasing crop yields during this period, modeling assumptions that represent an
associated increase in residues remaining and returning to the soil, and a decrease in the level
and intensity of tillage beginning in 1972.
For the 1990-2030 projection period, the continuing increase in SOC is based on the mix of
cropping and tillage practices identified by the RAMS analysis for the current 1989-90
timeframe, along with the assumed levels of crop yield increases. The curves show steady,
almost linear increases in SOC from 1970 through 2030. For the historical period of 1970
to 1990 the increase occurs at an annual rate of about 0.6% per year, which is also about the
same rate as for the 1990-2030 increase under the 0.5% crop yield increase assumption. For the
1.5% and 1.0% assumed yield increase levels during the 1990-2030 projection period, the Total
SOC increases at 1.2% and 0.9% per year, respectively. If this general pattern is accurate,
agricultural SOC within the Study Region made a comeback from a low of about 50% of
original (i.e., native vegetation) levels in 1950-70, to about 60% of these levels by 1990.
Continuing the increase would lead to 2030 Total SOC levels that approach 75% to 90% of
the original, SOC prior to the onset of agricultural production (circa 1900).
Below are capsule conclusions and recommendations derived from the study results, which are
further expanded and discussed in Sections 6.0 and 7.0:
1. Reasonable extrapolation of current agricultural practices and trends will lead to
an increase (sequestration) of about 1 to 2 Gt C within the Study Region by the
year 2030, or about 25 to 50 Mt C per year. This represents about a 25% to
50% increase over current 1990 levels. Nationwide the increase could be 50%
greater since our Study Region includes only 60-70% of total U.S. cropland.
2. The key assumption underlying these predictions is the projection of annual crop
yield increase from 1990 to 2030; the lower range reflects an increase of 0.5%
per year, while the upper limit of 50% increase reflects a 1.5% per year crop
yield increase. The validity of this assumption needs to be re-assessed or
confirmed, and if valid, policies and research should be promoted to support the
chances of agriculture attaining these levels of yield increase.
3. Conservation tillage practices can significantly increase soil carbon, but the
impacts are highly variable across the Study Region. The degree of impact is due
to complex interactions of combinations of crops and rotations, soils, and climate.
For many combinations, SOC increased 10% to 15% for Reduced Till and up to
50% for No Till, while much lower changes occurred in other C/R/Ts and CDs.
4. The overall impact of increased Reduced Till (RT) and No Till (RT) practices in
terms of Total SOC change for the Study Region, ranged from 2% to 3%
higher than Status Quo under the Medium Conservation Policy, and 6% to 11%
-------
higher under the High Conservation Policy. These conclusions should be
considered preliminary given the capabilities of current models and our limited
knowledge of the quantitative impacts of tillage practices. Moreover, current (i.e.
1992) nation-wide levels of No Till and Conservation Tillage (i.e. combined NT
and RT) exceed the levels included under the Medium Conservation scenario;
within the Cornbelt both NT and RT currently approach the levels included in the
High Conservation scenario. Thus, the conservation scenarios assume relatively
modest changes in practices as compared to more current CTIC data. Further
evaluation of the study procedures, conservation tillage usage, model parameter
sensitivity, and more research is needed in this area of tillage impacts before
these preliminary conclusions can be confirmed.
Cover Crops can lead to significant increases in soil carbon in crop, soil and
climate regimes where they are feasible and appropriate. Although only 12% of
the Study Region cropland included cover crops (under the Cover Crop scenario),
this increased soil carbon by 140 Mt through 2030. Since southern and eastern
portions of our Study Region were most appropriate for cover crops, this may be
an attractive alternative for promoting carbon gains in the South and Southeastern
U.S.
The results of the CRP simulations are mixed. In many cases, 20 years of CRP
leads to SOC values higher than the dominant rotation by the year 2030, and
usually higher than under continuous CRP. In other CDs, the dominant rotation
maintains the highest SOC throughout the projection period. The key factor is
likely the relative carbon inputs of the dominant rotation as compared to the CRP
conditions. When the dominant rotation is a corn-based rotation, its 2030 SOC
is usually the highest. However, this is not always true, especially if only one
year of corn is in the rotation. The percent difference for the CRP simulations
can range from a few percentage points to up to 20% or higher.
The following modeling and methodology recommendations are derived from the study results,
and are further discussed in Section 7.0:
1. The model predictions, especially related to historical trends, impacts of tillage
practices and effects of crop yield increases need to be further evaluated and
assessed in coordination with the soil carbon database efforts at CSU and MSU.
2. Extension of the methodology to other portions of the United States, and possibly
other agricultural regions of the world, should be considered. Application to
selected portions of an entire region may be sufficient to estimate region-wide
impacts of alternative agricultural practices and production systems.
-------
We need to review and establish expected increases in crop yields, plus crop
biomass and residues, for major crops and regions of the country in order to
determine how best to include this aspect of predicting future SOC conditions,
and associated carbon sequestration, under alternative policy scenarios. The
importance of these issues is evident in the study results.
We need to consider projections of the impacts of future climate scenarios on the
assessment of carbon sequestration under alternative policy scenarios. The
current study results are based on future climatic conditions that are the same as
historic conditions,, An assessment of how the study results would change if
climate changes, and associated feedbacks to agricultural production systems are
considered is needed.
Development of appropriate data bases and modeling procedures for
representation of the C and N balances in agricultural systems impacted by animal
waste application is needed. Accurate databases on animal waste'production,
including sources, quantities, composition, treatment, storage, and application to
croplands are not readily available.
We need to develop and/or aggregate databases for improved historical crop
yield, cropping aind crop rotations, agronomic practices, and economic
(production) data, especially as related to soil and climate conditions, in order to
evaluate and improve the historical simulations of SOC changes.
Continuing investigations are needed into the impacts of cropping and tillage
practices on SOQ, and development of appropriate modeling procedures to
accurately represent these effects. In particular, the impacts of No Till on the
entire range of soil physical, chemical and biological processes demand further
study.
We need to study further the potential impacts of CRP with particular focus on
the land characteristics (e.g. soils, topography, cropping and crop rotations) of
the areas taken out of production. To more accurately assess CRP changes,
including increases or decreases in CRP land, more detailed cropping information
is needed to define the production systems from which, and to which, CRP land
is transferred.
We need to provide a more detailed representation of the N cycle in agricultural
soils in close integration with the soil carbon cycle, including calculation of N2O
emissions, as impacted by various agricultural production systems. Possible
integration of CENTURY and DNDC capabilities in this area should be
considered.
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10. We need to investigate the ultimate fate and disposition of erosional SOC losses
in terms of both field and landscape level impacts with regard to greenhouse gas
emissions from agricultural systems. Estimates of historical erosion losses and
associated agricultural practices are needed to better represent the historical
pattern of SOC changes and current condition, as a basis for projecting future
changes, directions, and alternative policy impacts.
To establish the current state of knowledge on many of these research issues, and to help direct
future assessments and needed research, we recommend convening a workshop of both regional
and national experts on agronomy, plant physiology, soil carbon processes, tillage practices,
agricultural policy, and SOC/SOM modeling. The focus of the workshop would be on the issues
identified above to: (1) identify improvements to the assessment methodology; (2) assist in
determining parameter adjustments to best represent regional and crop differences in tillage
practices and impacts; (3) identify both available and needed databases on tillage practices, crop
rotations, animal waste production and disposition, soils characteristics, climate, etc.; (4)
establish regional historical agricultural practices and erosional impacts on SOC; and (5) identify
associated ongoing research efforts and priorities for future research activities.
1.4 REVISIONS TO THE DRAFT REPORT INCLUDED IN THIS FINAL REPORT
In May 1993, the draft report on this study was prepared and distributed to the Project Team
members, and a limited number of outside reviewers, in order to obtain comprehensive review
comments on both the entire methodology and the preliminary study results. A Project Review
meeting was then held in My 1993, in Fort Collins, CO, to discuss the initial results and the
review comments, and address any technical issues related to the study methodology, the
operational modeling procedures, and the parameter values associated with alternative tillage
practices and policy scenarios/From this review meeting, a number of methodology and
operational changes were implemented prior to performing the final model simulations, and
subsequent analyses, included in this final report. Although the changes resulted in significantly
different model results, the general nature of the conclusions are essentially the same as in the
May 1993 Draft Report; those draft results were also reported by Donigian et al., (1993) at the
International Symposium on Greenhouse Gas Emissions and Carbon Sequestration, held in
Columbus, Ohio, April 5-9, 1993.
The primary differences from the Draft Report implemented in the modeling procedures for the
final study results presented herein, are as follows:
a. The CENTURY model output variable used to assess soil carbon storages and changes
was revised to include both plant root and surface residues. All analyses in this report
are based on this quantity, referred to as Total Soil Carbon (with the same acronym,
Total SOC), unless otherwise specified. Total SOC is a better indicator of the carbon
sequestration potential of alternative agricultural practices and policies because root and
10
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residue amounts will differ as a function of the practices and alternatives. Figure 1.4
shows the typical timeline of how Total Carbon, Total SOC, and the various pools vary
during the year. In order to avoid the complication of the dynamic variation in plant
carbon during the year, the annual value of Total SOC used in our analyses is the
minimum during the year of the sum of soil carbon, plant root carbon, and the
surface residue carbon pools; this also allowed a more consistent means of assessing
SOC changes for a wide range of crop rotations (e.g. wheat-fallow) and policy scenarios
(e.g. cover crops).
The yield calibration procedure for the projection time period of 1989 to 2030 was
adjusted to maintain the HIMAX parameter as a constant based on values calibrated for
the recent historical period of 1971 to 1988. HIMAX controls the amount of plant C that
is included in the grain, and subsequently harvested for cash crops. As HIMAX
increases, more plant C is included in the grain and likely lost from the soil system
through harvest. The preliminary results in the Draft Report were based on HIMAX
being calibrated for the projection period and for alternative tillage practices; this lead
to lower C content of residues, lower C inputs to the soil, especially for reduced tillage
and no-till practices, resulting in lower SOC for these practices.
Investigations of the No Tillage simulations from the Draft Report indicated an over-
estimation of the affect of crop residues on soil temperature, leading to lower soil
temperatures and a retarding impact on crop growth and associated residues. For the
CENTURY model results in this report, this adjustment to soil temperature was not
included in the simulations in order to eliminate any bias in the comparisons of tillage
alternatives.
A key issue in the Draft Report was the uncertainty associated with the assumption of a
1.5% annual yield increase for all crops during the projection period. Because of the
critical impact of this assumption, and the automated simulation capabilities of the
Athens-EPA GIS group, we also evaluated the impacts of alternative annual yield
increases of 1.0% and 0.5%. Although the detailed results for crops, rotations, and
tillage practices are presented only for the 1.5% increase assumption, Section 6.4
discusses the Study Region impacts of the alternative yield increase assumptions and
Section 1.3 summarized the key conclusions.
The following selected changes were made to CENTURY model parameters and/or
capabilities as a result of further discussions and investigation of the Draft Report results:
1. The factor that indicates the impact of chisel plowing on soil decomposition was
decreased from 1.6, the value used for moldboard plowing, to 1.4 to represent
less disruption and lower decomposition for the Reduced Tillage options that
include chisel plowing.
11
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CM
E
o
o»
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residue amounts will differ as a function of the practices and alternatives. Figure 1.4
shows the typical timeline of how Total Carbon, Total SOC, and the various pools vary
during the year. In order to avoid the complication of the dynamic variation in plant
carbon during the year, the annual value of Total SOC used in our analyses is the
minimum during the year of the sum of soil carbon, plant root carbon, and the
surface residue carbon pools; this also allowed a more consistent means of assessing
SOC changes for a wide range of crop rotations (e.g. wheat-fallow) and policy scenarios
(e.g. cover crops).
The yield calibration procedure for the projection time period of 1989 to 2030 was
adjusted to maintain the HIMAX parameter as a constant based on values calibrated for
the recent historical period of 1971 to 1988. HIMAX controls the amount of plant C that
is included in the grain, and subsequently harvested for cash crops. As HIMAX
increases, more plant C is included in the grain and likely lost from the soil system
through harvest. The preliminary results in the Draft Report were based on HIMAX
being calibrated for the projection period and for alternative tillage practices; this lead
to lower C content of residues, lower C inputs to the soil, especially for reduced tillage
no-till practices, resulting in lower SOC for these practices.
Investigations of the No Tillage simulations from the Draft Report indicated an over-
estimation of the affect of crop residues on soil temperature, leading to lower soil
temperatures and a retarding impact on crop growth and associated residues. For the
CENTURY model results in this report, this adjustment to soil temperature was not
included in the simulations in order to eliminate any bias in the comparisons of tillage
alternatives.
A key issue in the Draft Report was the uncertainty associated with the assumption of a
1.5% annual yield increase for all crops during the projection period. Because of the
critical impact of this assumption, and the automated simulation capabilities of the
Athens-EPA GIS group, we also evaluated the impacts of alternative annual yield
increases of 1.0% and 0.5%. Although the detailed results for crops, rotations, and
tillage practices are presented only for the 1.5% increase assumption, Section 6.4
discusses the Study Region impacts of the alternative yield increase assumptions and
Section 1.3 summarized the key conclusions.
The following selected changes were made to CENTURY model parameters and/or
capabilities as a result of further discussions and investigation of the Draft Report results:
1. The factor that indicates the impact of chisel plowing on soil decomposition was
decreased from 1.6, the value used for moldboard plowing, to 1.4 to represent
less disruption and lower decomposition for the Reduced Tillage options that
include chisel plowing.
11
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E
o
0)
D>
O
c
o
o
a
TOTAL CARBON
TOTAL SOC
( THIS STUDY )
SOIL C
JUL
FEB
JAN
Figure 1.4.
MAR APR MAY JUN
Typical Timeline of Soil, Residue,
During the Year
AUG SEP OCT NOV DEC
and Plant Carbon Changes
2. The crop model in CENTURY includes an option to calculate fertilizer nitrogen
needs based on a user-defined nitrogen level in the crop. This level was changed
from the minimum nitrogen concentration to the maximum nitrogen concentration
so that the resulting nitrogen concentration of the crop residues would not be so
low as to inhibit residue decomposition.
3. The irrigation calculation was modified to initiate supplemental irrigation when
the root zone soil moisture fell below a user-specified level, as opposed to the
moisture in the entire soil profile as used in Draft Report results.
d. Due to EPA printing regulations, the color maps of the model results shown in the Draft
Report have been replaced with multiple gray-scale tones in this report. Appendix D
includes a complete set of the GIS-mapped CENTURY model results.
12
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1.5 FORMAT OF REPORT
Section 2.0 describes the separate, supporting soil carbon data aggregation effort by CSU and
MSU in terms of the carbon pools, dynamics, and modeling technology on which the data effort
is focused. Section 3.0 describes the overall project methodology that was developed to
integrate the SOC modeling, the RAMS output, and soils/climate data bases to provide an
operational pathway for assessing agricultural SOC sequestration potential. Section 4.0 describes
the data bases and data manipulation for preparing the model input, while Section 5.0 discusses
in detail the procedures and parameter values used to represent baseline agricultural production
systems and alternative scenarios. The simulation results are presented and discussed in Section
6.0, with references to Appendices C and D, which contain tabulated model results and a
complete compilation of the GIS displays of the Study Region for all policy alternatives and
selected characteristics. Section 7.0 reviews the key methodology and modeling assumptions to
set the stage for recommendations for directing, future model development and testing,
methodology development, and research efforts.
13
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SECTION 2.0
AGROECOSYSTEM CARBON POOLS, DYNAMICS, AND MODELING
The objectives of ithis portion of the BIOME study were to (1) conduct field and laboratory
investigations to develop defensible data for evaluating carbon pools and dynamics for
agroecosystems, (2) identify critical soil and climatic parameters that might affect those
dynamics, and (3) review available soil carbon models for their ability to assess the impact of
alternative agricultural management practices on carbon storage and fluxes in agroecosystems.
The data base being developed in support of this effort will be used to test and validate the
models under a range of soil, climatic, and agronomic conditions. The first two objectives are
being accomplished through a joint study of agroecosystems in the Great Plains and the Corn
Belt of the United States by Colorado State University (CSU) and Michigan State University
(MSU). The model review was conducted as part of this effort in order to select an appropriate
tool for assessing carbon sequestration potential for the Study Region. This section describes
both of these efforts and is abstracted from the study Interim Report by Barnwell et al. (1991);
Sections 2.1 through 2.3 discuss the data base development effort currently being conducted
jointly by CSU and MSU; these sections are abstracted from the proposal for that project, and
were authored by the investigators at those institutions. Section 2.4 describes the results of the
soil carbon model review.
The CSU/MSU studies focus on corn, soybeans, wheat and rangelands. Priority is being given
to sites with historical data concerning the effects of management practices on SOM and
supporting data pertaining to climatic conditions. The data will be used to parameterize and
evaluate carbon budget models. Emphasis is on collection, preparation, and evaluation of carbon
budget data. Sites where comparisons of management practices are possible have been given
highest priority. Because of its spatial and temporal variability, CO2 and other greenhouse gas
flux measurements have been inferred from soil storages rather than being measured. Outputs
of the studies will be data useful in the quantification of soil and plant organic carbon pools and
dynamics for the dominant agricultural crops, including rangeland, in the two regions.
2.1 RATIONALE
2.1.1 Project Objectives
The objective of the CSU/MSU research project is to develop credible data sets concerning the
potential for agroecosystems of the Great Plains and Corn Belt to sequester carbon. To
accomplish this, CSU and MSU are: (1) reviewing the national and international literature on
the impact of driving variables, including management, on SOM and producing a document
14
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summarizing the results; (2) holding workshops where the scientific cooperators of this project
present summarized data from thear sites, which will be further integrated into a form that can
be used to parameterize and validate SOM models; and (3) reviewing the experiments presented
by the cooperators to obtain a uniform characterization of soil organic matter and organic matter
input relating to specific pools useful for parameterizing and validating SOM models. The
results of this data effort are planned for publication in book and diskette form in late 1993 (Paul
and Eliott, In Preparation).
2.1.2 Project Perspective
The plan for data collection allow s CSU and MSU to evaluate the potential impact of different
management practices on the sequestration of C in soil organic matter of the Central United
States. They believe that it is also necessary to have spatially explicit information about the
regional distribution of ecosystem driving variables, including management, to make quantitative
estimates of C sequestration. It is essential to know the size of the different SOM pools and the
total amount of SOM because almost all existing SOM models use two to three different SOM
pools. Until recently, it has not been possible to separate these SOM pools analytically. With
recent breakthroughs, it is possible to characterize two conceptually important pools: the slow
pool (Elliott and Cambardella, 1993) and the active pool (Boyle and Paul, 1989) across
geographic zones and soil types within different management treatments that have potential for
increased SOM levels.
2.1.3 An Approach to the Study of Regional Ecosystem Properties
Several levels of resolution of time and space must be considered in order to integrate
information on SOM at the regional level (Figure 2.1). Generalized agroecosystem models are
used to integrate information on processes and properties (e.g., Parton et al., 1987). Where
information gaps are observed, appropriate new experiments can be designed and data bases
collected or collated.
Agroecosystem models must be parameterized and validated over a wide range of management
practices, climates, soils and time scales. Fortunately, appropriate data are available from field
plot experiments that have been ongoing for many years. These experiments are located on the
research sites of the USDA-ARS, State Experiment Stations, Agriculture Canada and
corresponding studies in other countries (Figure 2.2). As a result, much of the information
needed to validate agroecosystem level models already exists. However, this information
generally is not available as extant data bases and not all necessary kinds of information have
been collected at each site. It will take a concerted effort to collate this information and collect
pertinent new data from existing sites in a form that is useful for agroecosystem model
validation. This is the main focus of the data base development effort of the CSU/MSU study.
15
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[PROCESS INFORMATION
ExoerI rents
REGIONAL INFORMATION
Potential Sequestration of C
n Central U.S. Agroecosystems
Syiliesls
laboratory nlcrocosns
Growth cttanber experlnents
Intensive field sites
{iiiiriuitil 11111.1.111.1)
Extensive field site
I.I.IIIM.I irul.i.ul
Ozone, UV-B, 50 response
Others
I'recess Models
Ecosysten
Heat transfer
Solutetvater flow
Gas flux
Crop
Anlnal
Other (and nodules of above)
TERRESTRIAL ECOSYSTEM
ASSFSSUFHT unnpi
Villditlan
ParaneterIzctlai
|
[REGIONAL SITE NETWORKS
EPA AGHOECOSYSTEUS SITE HETHORC
(this proposal)
(organized by CIS)
SCS-Pedon database
SCS-Natural Resource Inventory
SCS-Solls 5/solIs6 database
ARS-CImate databases
Biological surveys
Land use databases
Climate Model Result Archives (CSMP)
Figure 2.1 An Approach to the Study of Regional Agroecosystems (modified from Elliot
and Cole, 1989)
2.2 LOSS AND SEQUESTRATION OF CARBON FROM CENTRAL U.S. ECOSYSTEMS
The conversion of native ecosystems to agricultural lands in the Great Plains and Corn Belt
regions of the Central United States was perhaps the most extensive ecological disturbance
known in North America during the past 150 years (Wilson, 1978). A major consequence of
this land conversion has been the substantial loss of organic matter from soils across the regions
(Haas et al., 1957; Campbell 1978). These soil C losses have been a primary anthropogenic
source of carbon dioxide, second only to fossil fuel combustion in contributing to historical
increases of global CO2 concentrations (Post et al., 1990).
Since soil carbon levels in most Great Plains and Corn Belt agricultural soils have been reduced
to levels well below those existing prior to the establishment of agriculture, the potential should
exist to increase present soil C levels, thereby providing a sink for atmospheric CO2. At present
there is considerable debate whether increases in atmospheric CO2 concentrations and global
warming will result, independently, in significant changes in soil C storage (e.g., Prentice and
Fung, 1990; Schlesinger, 1990). While not discounting the importance of these factors,
increasing agricultural soil C levels may be more dependent on agricultural management
practices and land use changes.
16
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EPA AGROECOSYSTEM CARBON POOLS
Site Network
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Many of the agricultural practices that have been traditionally applied in the Great Plains and
Corn Belt [e.g., moldboard plowing, fallow with no plant cover, removal or burning of crop
residues and monoculture cropping (Cole et al., 1989)], represent a "worst-case" for soil C
maintenance, by minimizing organic matter inputs and promoting a high degree of soil organic
matter decomposition. Currently, agricultural management in the United States is undergoing
rapid change in response to a variety of pressures, including high costs of energy and chemical
inputs, environmental concerns about nutrient and pesticide pollution and soil erosion, and
product quality demands by consumers (National Research Council, 1989). It is encouraging
to note that many of the techniques designed to address these issues have also demonstrated a
potential for increasing soil C levels above those maintained under conventional management.
Such practices include reduced or no-till management (Blevins et al., 1983; Dick, 1983; Lamb
et al., 1985); reduction in the proportion of fallow relative to crop in semiarid regions (Wood
et al., 1991); and use of cover crops, green manure and animal manure (Paustian et al., 1992;
Vitosh et al., 1973). However, the extent to which soil C levels can be regulated through the
application of various combinations of these practices for different cropping systems, soil types,
and climatic regions is not yet clear.
In addition to changing management trends on production land, land use changes may have a
considerable impact on region-wide C storage potential. Over the past 50 years, significant
acreage in cropland has been abandoned or converted back into perennial grassland and forest.
For example, more than 100,000 ha of farmland in northeastern Colorado alone were abandoned
during the 1930's, and in more recent years, large areas of Great Plains cropland have been
converted to grassland under the Conservation Reserve Program (Joyce and Skold, 1988).
Similar changes have occurred in regions of the Corn Belt, in particular in the Great Lakes
region and in eastern areas bordering on the Appalachians. For example, from 1950 to 1990,
the agricultural land base of Michigan declined by 7 million acres, much of which was
previously in corn, soybean and wheat production (Michigan Dept. Agric., 1990). To date,
there has been little systematic evaluation of how these shifts in land use have affected regional
soil C balances or of what effect future changes may have.
While data from long-term field studies have provided much information about the possible rates
and directions of change in soil C under various management regimes, our current knowledge
base is very fragmented. Most of what is known concerning SOM dynamics has been obtained
by studying SOM losses. We understand little about controls on SOM accumulation and how
they vary across soil types, climatic regions and management regimes. Such information is
crucial for estimating the potential for carbon sequestration in agricultural soils of the Great
Plains and Corn Belt regions.
2.2.1 Driving Variables. Processes and Properties
The proposed approach in the CSU/MSU study to assembling an appropriate database for
assessing the potential for C sequestration in agroecosystems is based on the general concept of
relating key driving variables to ecosystem processes and properties. Jenny (1941, 1980)
popularized the concept of driving variable control to explain soil properties and their geographic
18
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distribution. Originally, he proposed five driving variables, i.e., climate, parent material, relief,
organisms and time, as determinants of soil processes. His framework acknowledged that soils
are one of many interrelated ecosystem components.
Observations have been made that describe the empirical relationships between independent
driving variables and dependent ecosystem properties. For example, the distribution of soils and
vegetation have often been related to patterns of temperature and precipitation. Ultimately, the
origin of ecosystem properties, such as soil C content, is the outcome of the multifarious
interactions of a suite of independent driving variables. Since the response variables (properties)
of such descriptions are autocorrelated, it becomes impossible to unambiguously relate ecosystem
properties to each other, even though these properties are usually the ecosystem components we
measure in our studies. External variables drive the system but the components of the system
interact in complicated ways and are also subject to feedback effects that further complicate
interpretation of relationships among components. While statistical analyses may be able to
account for some of the complexity, higher order interactions are notoriously difficult to
understand even though we accept that they have important influences on system properties.
Thus, the statistical approach cannot appropriately account for feedbacks among components.
However, these feedbacks can be accounted for through the use of simulation models.
s^ •
2.2.2 Regional Patterns and Controls on Soil Organic Matter
SOM content is determined by the balance of inputs (primary production in most cases) and
outputs (decomposition and erosion). The primary factors controlling production and
decomposition in agroecosystems are related to Jenny's (1941) state factors, i.e., temperature
and precipitation and their timing (climate), parent material (often represented by texture), relief
(landscape position), organisms (particularly the plant community) and management (Elliott et
al., 1993). These factors seldom act alone, but rather they interact in complex ways. In
addition, these factors may influence production and decomposition differentially across gradients
of these factors.
Regression analysis of 500 rangeland and 300 cultivated soils across the semi-arid Central United
States indicated that organic C increased with precipitation and clay content and decreased with
temperature (Burke et al., 1989). Losses of soil C due to cultivation increased as precipitation
increased, with relative losses being lower in clay soils. Soil C estimates made with these
regression models agreed well with estimates made using the CENTURY model for this region
(Burke et al., 1989). It remains to be determined whether these relationships hold for the more
mesic Corn Belt region. Steady-state simulations were made for 24 grassland sites in the U.S.
Great Plains and compared with data from each site (Parton et al., 1987). Regional trends in
SOM were adequately predicted using four site-specific variables — temperature, moisture, soil
texture, and plant lignin content. Grazing intensity during soil development also had a
significant impact on soil C levels. Sites within close proximity, thus having similar climate,
have been shown to be strongly influenced by soil texture (resulting from different parent
material) (Tiessen et al., 1982; Schimel et al., 1985; Aguilar and Heil, 1988). Relief also
determines levels of SOM (Honeycutt, 1990a, 1990b; Schimel, et al. 1986; Yonker et al., 1988)
19
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but is often confounded with the influence of texture. Similarly, the influence of the vegetation
on SOM is difficult to isolate because it can also be a response variable to other state factors
such as climate. However, there are instances, such as the grassland-tree transition zone in the
Central United States, where trees and grass exist under the same set of driving variables and
impart different influences on SOM (Jenny, 1941; Anderson, 1987).
2.2.3 Agricultural Management and Soil Organic Matter
In agricultural systems, many factors influencing SOM levels are subject to management control
— through tillage, crop selection, addition of fertilizer and organic amendments, irrigation and
residue treatment (i.e., removal, burning). For example, cropping practices and residue
management directly affect both the amount and quality of C inputs to soil. Soil C levels in
several long-term experiments were highly correlated with residue input rates (Larson et al.,
1972; Rasmussen et al., 1980; Paustian et al., 1992) and there is clear evidence that differences
in the quality of organic inputs in combination with N fertilizer levels influence SOM levels
(Parton et al., 1983; Paustian et al., 1992).
Other management practices can have a more indirect influence on C inputs, C losses or both.
Tillage affects water and heat fluxes in soil; e.g., no-till soils tend to be cooler and wetter than
under conventional tillage. Crop growth, which is of course the target of most management
efforts, can interact strongly with soil water dynamics and thereby influence decomposition
processes. In semiarid ecosystems, such as the Great Plains, high plant water-use efficiency and
subsequent moisture limitation of decomposition may tend to favor production over
decomposition. This may explain the high levels of SOM that occur in semiarid grasslands
(Elliott and Cole, in press). With increased moisture, as one moves to the corn growing
regions, declines in SOM are observed even while production increases, perhaps because water
residence time in soil is longer and decomposition remains high. In the wetter regions, factors
that enhance production, such as fertilization, may stimulate increases in SOM because water
controls on decomposition become less important. Of course, in poorly drained soils SOM may
again increase.
Change in management, whether conversion from native ecosystems or shifts to more productive
or environmentally sound practices, influences SOM by affecting either decomposition,
production or more likely, both. Reduced soil tillage intensity, as with no-till management, has
been shown to decrease the rate of SOM loss when plowed out of native sod compared with
more intensive tillage management (Lamb et al., 1985) and to increase the rate of SOM
mineralization (Rice et al., 1986). We have found greater losses of total C and N with increased
tillage intensity (Cambardella and Elliott, 1992) with the greatest proportion of loss from the
particulate fraction. On land previously under conventional tillage, introduction of no-till
invariably results in a more surficial organic matter distribution (Doran, 1987) and in many cases
higher overall soil C levels (Blevins et al., 1983; Dick et al., 1990). Lower C losses (or greater
C gains) under no-till could be the result of decreased decomposition because of reduced soil
disturbance, less contact of litter with soil, cooler soils, less erosion or (in drier environments)
it could be the result of increased production due to greater water availability to plants.
20
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In semiarid systems, where a bare fallow period is inserted into the rotation to store enough soil
water to obtain a crop every other year, SOM losses after plowing can be great and continue for
more than 50 years (Haas et al., 1957). The soil in the fallow part of the rotation is warmer and
wetter and there are no organic matter inputs from plants. With the use of no-till, crops can be
grown more frequently than the conventional once-every-two-years for the wheat-fallow rotation.
With increases in cropping frequency SOM levels can show significant increases in periods as
short as 4 years (Wood et al., 1991) with concomitant increases in potentially mineralizable C
and N (Wood et al., 1993). Rates of SOM loss are reduced with increased frequency of cropping
(Janzen, 1987). In the more mesic sites in the Corn Belt region, replacing winter fallow practices
with cover crops or including perennial legumes or grass in the rotation promotes higher levels
of SOM (Power, 1987; Dick et al., 1990).
2.2.4 Representation of Regional Data
One of the chief difficulties in representing information on regional ecosystems is determining
what constitutes representative data. Information .obtained from a single site, even though
carefully replicated at that site, is essentially pseudoreplicated (Hurlbert 1984) if that site is
intended to represent an entire region. Yet, if one moves too far from the original site for a
replicate site, state factors such as soil texture, temperature or precipitation may change resulting
in lack of good representation. In some sense, it is not possible to have replicated ecosystems.
It is arbitrary as to what class of variables are similar enough to represent a region. If enough
samples are taken within a region to adequately represent it, we have a good representation of
that region but we do not know whether that region is representative of all potential regions
developing under that specific set of driving variables. At the very least, we should be reluctant
to extrapolate our predictions to regions developing under different sets of driving variable.
It is best to have a high sampling density spanning gradients of driving variables to obtain
representation of a region if we are to make predictions pertaining to that region. That is why
CSU and MSU have chosen to use an extensive network of sites. There are, of course, tradeoffs
between the number of sites and the number of properties obtainable from each site. If indeed
there are only a few pertinent state factors (driving variables) (Parton et al., 1987), it should be
possible to represent those sites with these state variables and characterizations of initial
conditions. They favor an extensive site network with measurement of important and reliably
determined variables rather than detailed and hard-to-interpret analyses run on samples from a
few sites. It is equally important to have samples taken in as similar a fashion as possible at all
sites (preferably by one or a few field researchers) to ensure comparability of data. The study
is limited by the availability of long-term sites that have adequate ongoing experiments and data
sets but CSU and MSU have brought together an extensive set of sites they believe are
representative of the grassland, wheat and corn growing regions of the Central United States.
21
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2.3 PROJECT PLAN
2.3.1 Site Network Overview
The CSU/MSU study is simple in concept but has been a challenge to organize. As the main
component of the data collection project, they have brought together a group of cooperating
scientists that collectively have access to a network of sites (Figure 2.2) covering the Corn Belt
and Great Plains of the Central United States. The establishment of this network of 39 long-
term sites provides the background necessary for understanding carbon storage relative to past
and possible future management practices. The sites represent north-south and east-west
gradients, an approximate five-fold range in soil carbon, and an excellent range in native
vegetation and soil texture. A number of the sites have stored samples and or access to present
day native sites. Tables 2.1, 2.2, and 2.3 contain summaries of sites, their locations, the period
of data available at each, and the treatments at each.
2.4 MODELING SOIL ORGANIC MATTER
Simulation modeling provides a means of integrating the numerous factors affecting SOM and
of predicting changes in soil C as a consequence of altered management practices and/or changes
in climatic conditions. Although various SOM models have been developed and refined over
the past 15 years (e.g., Jenkinson and Rayner, 1977; van Veen and Paul, 1981; Parton et al.,
1983; Jenkinson et al., 1987; Parton et al., 1987; 1988, 1989; Paustian et al., 1992), several
key features are common to all. These include multiple SOM fractions that differ in decomposi-
tion rate, temperature and moisture as the primary climatic driving variables, effects of soil
physical properties on organic matter stabilization, and the influence of the quantity and quality
of crop residues on the formation of new soil organic matter. Thus, based on past experience
in modeling SOM, the suite of existing data and new measurements has been designed to be
well-suited for the planned modeling efforts.
To explain the rationale for data acquisition in relation to modeling requirements, we will briefly
describe the assumptions and data requirements of the CENTURY model (Parton et al., 1987),
probably the most widely applied SOM model currently in use. The model was developed at
CSU and members of the MSU group have been active collaborators in the application and
further development of the model. The model has been used to simulate SOM dynamics in both
grassland and agricultural ecosystems (Parton et al., 1983; Parton et al. 1987, 1988; Cole et al.,
1989; Paustian et al., 1992) and has also formed the basis for larger-scale regional analyses of
SOM patterns in Great Plains grasslands (Burke et al., 1989, 1990; Parton et al., 1987, 1989).
The CENTURY model characterizes SOM in terms of three fractions each having characteristic
decomposition rates or turnover times ranging from 1.5 years (active) to 50 years (slow) to 1000
years (passive), which are modified by climatic variables (temperature and moisture), soil
texture, and tillage disturbance. Fresh organic matter (crop residues, manure, plant litter)enters
the soil where its decomposition rate is influenced by chemical composition, expressed through
lignin and N content, as well as climatic conditions and location (surface or incorporated in soil).
22
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TABLE 2.1 Corn Belt sites and cooperators
Name
KBS/LTER
KBS/Interactions
KBS/Forest
Saginaw Research Farm
MSU Old Soils Farm
Suborn Plots
Kentucky Agricultural
Experiment Station Farm
Morrow Plots
Rodale
Location
Hickory Corners, MI
Hickory Corners, MI
Hickory Corners, MI
Thomas Township,
Saginaw County,
Michigan
East Lansing, MI
Columbia, MO
Lexington, KY
Urbana, IL
Kutztown, PA
Period
1989-
1986-
1942-
1972-
1963-
1982
1888-
1970-
1876-
1981-
Treatments
Randomized block design (6 reps) w/
8 cropping systems;
Previously cultivated soil:
Continuous corn - 2 tillages, 2
fertilizer rates
On virgin soil (never-tilled)
No-till continuous corn with
0 N fertilizer vs. Native
grass vegetation
6 treatments
1 1 permanent points were randomly
established in 1942, to serve as loci
around which soil would be sampled
over the next 50 years at 10 year
intervals.
Crop rotation trials consisting of 4
systems X 3 rotation lengths,
including corn, sugar beets, navy
bean, oats, alfalfa
Split-block design with 3
replicates. Whole plots
with 5 nutrient addition
treatments (continuous corn)
Never-tilled woodlot is located
within 300 m
Treatment combinations consist of
various crop rotation and fertility
amendments, (treatments on 9 of the
original 39 plots have been
unchanged since 1888)
Split block design (4 reps per
treatment)
Continous corn
2 tillage treatments
X
4 N fertilizer levels
Control plots w/ bluegrass sod
3 rotations as main plots.
Nutrient-additions in
subplots of main plots;
various combinations of
manure, liming and NPK to
subplots within each rotation.
Randomized block with
split-plots (rotation entry
points as split-plot),
3 cropping systems
Contact Person and
Affiliation
G. Robertson
MSU
G. Robertson
MSU
K. Pregitzer
MSU
D. Christenson
MSU
M. Vitosh
MSU
J. Brown
G. Wagner
G. Buyanovsky
Univ. of Missouri
W. Frye
Univ. of Kentucky
R.Darmody
T.Peck
Univ. of Illinois
R. Janke
S. Peters
Rodale
23
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Koch Farm -VICM
Plots
Clarion-Webster
Research Farm
Galua-Primghor
Experiment Farm
Purdue LTSF Plots
Purdue LTT Plots
Purdue IPM Plots
Hoytville Plots
Wooster Plots
Crosby Plots
Lamberton, MN
Kanawha, IA
Sutherland, IA
West Lafayette, IN
West Lafayette, IN
West Lafayette, IN
Wood County, OH
Wooster, OH
South Charleston
1989-
1954-
1957-
1952-
1975-
1981-
1963
1962-
1962-
Randomized block with
split-plot design, 2 rotations
X
4 management levels
The experiment is repeated on
another area of the farm with a
history of high fertilizer and pesticide
inputs.
Randomized block with split-plot
design
5 crop rotations (2 reps
within year, all years in
rotation as main plots)
X
4 N fertilizer levels
(split-plot)
Randomized block with split-plot
design
6 crop rotations (2 reps
within year, all years in
rotation as main plots)
X
5 N fertilizer levels
(split-plot)
Split-block design
21 combinations of P-K
fertilization, applied to
Corn-Soybean-Wheat-Corn (2
reps per treatment)
3 Residue levels
2 N levels (0 and 200 kg N
per ha)
Split block design (4 reps per
treatment)
Corn-Soybean rotation with 4
tillage treatments
Split-plot design; whole
plot-tillage & rotation,
spllt-plot=weed mgmt.
3 rotations (each crop in each
year for a total of 7
sequences)
Randomized block design
3 Tillage treatments
X
3 rotations
Randomized block design
3 Tillage treatments
X
3 rotations
Randomized block design
Continuous corn
X
3 Tillage treatments
R. Crookston
W. Nelson
Univ. of Minnesota
J. Pesek
ISU
J. Pesek
ISU
D. Stott
D. Mengel
R. Turco
Purdue University
D. Stott
D. Mengel
R. Turco
Purdue University
D. Stott
- D. Mengel
R. Turco
Purdue University
W. Dick
OSU
W. Dick
OSU
W. Dick
OSU
24
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Darlington
Lancaster
NTRM Plots
Organic Conventional
Management
Darlington, WI
Lancaster, WI
Rosemont, MN
Mead, ME
1958-
1967-
1980-
1975-
Continuous corn with three N rates
(plowed)
Crop rotations by N rates (plowed)
Three cultivation types and three N
levels under continuous corn.
Four crop rotations by fertilizer levels
(including manure)
L. Bundy
L. Bundy
Univ.. of Wisconsin
E. Klapp
ARS
G. Lesoing
J. Doran
Univ. of Nebraska
TABLE 2.2 Great Plains sites and cooperators
Name
Long-Term Tillage
Experiment
Rotation x N
Experiment
Long-Term Tillage
Studies C&D
North Platte Cropping
Systems
Graded Terraces
Mini-Bench Site
Tillage-Rotation
Experiment
Dryland
Agroecosystems in
Eastern Colorado
Central Plains
Experimental Range -
(USDA-ARS)
Long-Term Ecological
Research - (NSF)
Location
USDA-ARS Central
Great Plains Research
Station
Akron, CO
USDA-ARS Central
Great Plains Research
Station
Akron, CO
High Plains
Agricultural Lab
Sidney, NE
University of Nebraska
West Central Research
& Extension Center
No. Platte, NE
USDA-ARS,
Conservation &
Production Research
Lab, Bushland, TX
USDA-ARS, Southern
Great Plains
Bushland, TX
USDA-ARS, Northern
Great Plains Research
Laboratory
Mandan, ND
Sterling, Stratum, &
Walsh, CO
Nunn, CO
Period
1967-
1985-
1970-
1981-
1958-
1982-
1984-
1985-
1930-
(ARS)
1981-
(LTER)
Treatments
4 tillage with 5 crop rotations
No-tillage with 2 rotations and
5 N levels
4 tillages with wheat/fallow
rotation
Minimum tillage (irrigation)
with 4 crop rotations
2 tillages with 3 crop rotations
2 tillages with 4 crop rotations
3 tillage with 2 rotations and 3
fertilizer levels
No-till with 3 sites and 5
rotation treatments (2 reps)
Several grazing intensities
Contact Person and
Affiliation
A. Halvorson
ARS
A. Halvorson
ARS
D. Lyon
Univ. of Nebraska
G. Hergert
Univ. of Nebraska
B. Stewart
O. Jones
ARS
B. Stewart
O. Jones
ARS
A. Black
D. Tanaka
ARS
G. Peterson
D. Westfall
CSU
J. Welsh
W. Lauenroth
ARS/CSU
25
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B»rthwatch Sites
Swift Current
Indian Head
Melfort
Elon
Delhi
Dryland Rotations
Restorative dryland
rotations
Long-term no-till
Breton Plot
Brown County
Riley County
Pawnee National
Grasslands, Weld
County, CO
Swift Current, Canada
Indian Head, Canada
Melfort, Canada
Guelph, Canada
Guelph, Canada
Lethbridge, Canada
Lelhbridge, Canada
Lethbridge, Canada
Edmonton, Canada
Brown County, Kansas
Riley County, Kansas
1990
1966-
1958-
1957-
1965-
1989
1951-
1951-
1967-
1939-
1981-
1974-
1975-
Secondary succession or no
disturbance (13 paired native
vs go-back sites). More to be
established.
Twelve crop rotations in all
phases
Eleven crop rotations from
1958 with three additional
from 1968. All phases
present.
Eight crop rotations with all
phases present
Three tillages under continuous
corn
Three rotations and two
tillages, constructed for long-
term use with stable isotope
labelled plots.
Three crop rotations with
fertilizer treatments
Thirteen treatments (rotations)
including native grass
Two tillage and two crop
rotations
Two long-term rotation
treatments (64 years) and three
rotation treatments on two soil
types (1 1 yrs)
Three crop rotations and two
tillages
Three crop rotations and two
tillage treatments
D. Coffin
CSU
C. Campbell
R. Zentner
Ag Canada
G. Lafond
Ag Canada
A. Moulin
L. Townley-Smith
Ag Canada
P. Voroney
R. Beyaert
Univ. of Guelph
P. Voroney
R. Beyaert
Univ. of Guelph
H. Janzen
Ag Canada
H. Janzen
Ag Canada
H. Janzen
Ag Canada
N. luma
Univ. of Alberta
J. Havlin
KSU
J. Havlin
KSU
TABLE 2.3 Sites outside of the Great Plains and Corn Belt
Name
Watkinsville
Pendleton
Location
Watkinsville, GA
Pendleton, OR
Period
1974
1982-
1930,
1950,
1960-
Treatments
Soybeans with conventional
and no-till on similar soils
Several crop rotations, tillage
and ammendment treatments.
Contact Person and
Affiliation
R. Bruce
ARS
P. Rasmussen
ARS
26
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In addition to the driving variables, the amount and composition of the existing SOM (i.e., size
of each of the three SOM fractions) are necessary initial conditions for the model and will
influence the trajectories of soil C changes under a given set of driving variables.
Standard climatic data are available for all the sites in the proposed network, including additional
information such as pan evaporation and soil moisture in some cases. Crop yield data can be
used as the basis for estimating productivity and thereby residue input rates; in many instances,
direct measurements of aboveground residues are available although data on below-ground inputs
are scanty. Uncertainty about below-ground organic matter inputs is admittedly an area of
concern; however, standard productivity data together with information from the literature on
how yield indices and root production vary as a function of crop type and growing conditions
can be used to obtain reasonable estimates of input rates. Similarly, data on crop N content,
crop type and stage of maturity (to estimate lignin content) enables one to characterize residue
input quality. One of the tasks of the existing data synthesis and literature review effort is to
provide such additional information. The major component missing from the existing data that
is an ecologically meaningful fractionation of SOM across all the sites. Therefore, the sampling
and soil analyses planned for future study address this lack of information.
/
2.4.1 Review of Available Soil Carbon Models
As part of the work effort, a literature review was conducted with the key focus on the dynamics
and modeling of SOM and soil carbon, soil carbon budgets, and impacts of soil and agricultural
management practices. Barnwell et al. (1991) provides a summary of major carbon modeling
codes identified in the literature survey. Table 2.4 illustrates the information contained in the
model reviews; Table 2.5 presents the models and references reviewed and summarized in
Barnwell et al. (1991). A list of evaluation criteria and capabilities for comparison of soil
carbon models was developed and is presented in Table 2.6. These criteria were used in the
selection of an appropriate model for use in this investigation for evaluation of agricultural soil
carbon sequestration.
Below we have provided capsule summaries of the six major models reviewed, followed by an
intercomparison of the key capabilities used in the evaluation.
CENTURY Model -
As noted earlier, Parton et al. (1988) developed the CENTURY model to simulate the dynamics
of C, N, P, and S in cultivated and uncultivated grassland soils. Recent model refinements have
resulted in the Agroecosystem Version 4.0 of CENTURY to better represent complex cropping
patterns, rotations, and management practices (Metherell et al., 1993). The model uses monthly
time steps for simulation, and model runs can be performed for longer time periods ranging
from 100 to 10,000 years. The model simulates three soil organic matter fractions: 1) active
fraction (turnover time of 1-5 yr); 2) a protected fraction that is resistant to decomposition
(turnover time 20-40 yr); and 3) a physically protected and chemically resistant fraction
(turnover time of 200-1500 yr). SOM dynamics, nutrient mineralization, and plant production
27
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1. Name of the Model -
2.
3.
4.
Type of Model
Surface and Vadose zone model:
Surface and Vadose zone model:
Vadose zone model:
Vadose zone model:
Vadose and Groundwater model:
Vadose and Groundwater model:
Multi-media model:
Multi-media model:
Simple Approach
Detailed Approach
Simple Approach
Detailed Approach
Simple Approach
Detailed Approach
Simple Approach
Detailed Approach
Processes Simulated
Simulate soil hydrologic balance
Simulate soil moisture
Simulate soil temperature
Simulate crop/plant seasonal vegetation changes
Simulate carbon cycle in soils
Simulate carbon and nitrogen cycles in soils
Simulate carbon, nitrogen and phosphorus cycles in soils
Simulate other nutrients in soils
Purpose/Scope of model
Purpose: BRIEF DESCRIPTION OF PURPOSE OF MODEL
Predict emission of greenhouse gases
C02
CH4
Unique Features:
Simulates effects of changing climate
Simulates effects of soil temperature
Simulates effects of residue management
Simulates effects of soil physical properties
Simulates soil organic matter dynamics
Simulates crop growth as a function of moisture, temperature, nutrients, etc
Simulates changing cropping/tillage practices
Integral Database/Database manager
Integral Input/Execution Manager
Integral graphic capabilities
5. Description of method/techniques
6. Compartments of soil processes simulated
Single soil layer
Multiple soil layers
7. Data requirements/Availability
8. Output options available
9. Limitations
10. Ease of application
System setup: mandays manweeks
Assessments: mandays manweeks
11. Assumptions
12. Hardware/Software requirements
13. Experience
14. Validation/Review
15. Model code maintenance and update
16. Contact
17. References
mat-months
manmonths
manyear
manyear
Table 2.4 - Soil carbon model review form.
28
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Model Name
CENTURY Model
DNDC Model
NCSOIL Model
OEM Model
PHOENIX Model
SIB Model
TEM Model
Box Model
Frissel and van Veen Model
Gildea et al. Model
Jenkinson and Raymer Model
Moore et al. Model
Oeschger et al. Model
Smith Model
Reference
Parton et al., 1988
Li et al., 1991
Molina et al., 1983
Esser, 1990
McGill et al., 1981
Sellers et al., 1986
Raich et al., 1991
Box, 1988
Frissel and van Veen, 1981; van Veen and
Paul, 1981; van Veen et al., 1984
Gildea et al., 1986
Jenkinson and Raymer, 1977; Jenkinson
1990, Jenkinson et aI., 1991
Moore et al., 1981
Oeschger et aI., 1975
Smith, 1979
Table 2.5 List of soil carbon models reviewed
as impacted by cultivation practices were simulated for a 10,000 year run. The results indicate
that weathering of mineral P and S from the parent material is the primary source of soil P and
S, in contrast to soil N where greater than 95% is found in the organic form. The primary
source for soil N is atmospheric deposition and biological fixation. The C:N ratio for SOM was
reported to be fairly constant and "varying C:P and C:S ratios as a function of the available soil
P and S levels is an important aspect of the model and allows it to simulate the impact of
different levels of available P and S on the cycling of P and S in the soil".
Schimel et al. (1990) studied grassland biogeochemistry and its link to atmospheric processes.
Model simulations obtained using the CENTURY model adequately represented the observed
geography of C and N. It was stated that "increases in temperature and associated changes in
precipitation caused increases in decomposing and long-term emission of CQz from grassland
soils". The release of nutrients through the loss of organic matter resulted in increased net
primary production. This conclusion demonstrates the fact that nutrient interactions play a major
role over vegetation response to climate change.
29
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Attribute
Sponsoring Agency
Simulation Time Step
Rainfall/Runoff Analysis
Erosion
Soil Hydrologic Balance
Soil Moisture
Soil Temperature Capability
Soil Processes (Carbon, Nitrogen, Phosphorus, Sulphur)
Fertilizer Input Capability
Tillage/Residue Management Capability
Cropping Practices Capability
Crop Type Capability'
Crop Growth Simulation
Available on Microcomputer
Data and Staff Requirements
Output Options (C02, H20, CH4)
Overall Model Complexity
Table 2.6 Evaluation criteria and capabilities for comparison of soil carbon models.
Paustian et al. (1992) performed model analyses using the CENTURY model for studying the
influence of organic amendments and N-fertilization on SOM in long-term plots. Changes in
soil C and N after 30 years were predicted within ± 30% of observed values. External organic
matter input either increased or maintained the SOM levels; however, a decrease in SOM was
observed when crop roots were the only source of fresh organic matter. The N losses simulated
were 7 to 20% of total N inputs and the N balances calculated by the model were in close
agreement with observed data.
A GIS system along with the CENTURY model was used for regional modeling of grassland
geochemistry (Burke et al. 1990). The driving variables for the model consisted of soil texture,
monthly precipitation, and monthly minimum and maximum temperatures which were included
in the GIS system. Net primary production was affected primarily by climatic factors and it
closely followed spatial variation in precipitation. However, it was reported that SOM is
controlled by soil texture.
DNDC Model -
Li et al. (1992a) developed a physical based model called DNDC (Denitrification Decomposition
Model) for estimating nitrous oxide (N2O) and dinitrogen (N2) evolution from soils. The
submodels included in the model are thermal-hydraulic flow, decomposition and denitrification.
The model calculates dynamic soil temperature and moisture profiles, and estimates shift of
30
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aerobic-anaerobic conditions in soils using climatic data of rainfall events and air temperature.
The dominant processes between rainfall events are the decomposition of organic matter and the
oxidation reactions. These processes affect levels of total organic carbon, soluble carbon and
nitrate in soils between rainfall event. However, during rainfall the dominant process is that of
denitrification resulting in production of nitrogenous gases of N2O and N2. The major input data
requirements for the denitrification-decomposition (DNDC) consist of climatic scenario, soil
texture and its properties, and initial organic matter and nitrate in soil. The DNDC model can
aid in estimating the following (Li, 1992a):
1) Effects of changing organic C in soil on emissions of CO2 and N2O from soils.
2) Rate of carbon accumulation in soils, based of current residue inputs, and both
physical and chemical soil properties.
3) Soil management changes affecting accumulation rates of carbon, and emissions
of CO2 and N2O from soils.
4) Effects of climate change (monthly average air temperature and precipitation) on
carbon accumulation rates, and CO2 and N2O emissions from soils.
Li et al. (1992b) conducted sensitivity and simulation analyses using the DNDC model, which
provided the following conclusions:
1) Increasing denitrifier biomass from 0.0001 to 1 kg C/ha results in slight increases
in N2O emission; also, there is a gradual increase in N2 emission.
2) During rainfall events, the soluble carbon increases and the change in
denitrification rate depends on NO3' content in soils.
3) If soil NO3" is low (1 mg N/kg) then even with a 5000-fold increase in soluble
carbon (simulated condition), there is a little increase in denitrification.
However, if soil NO3~ is high (100 mg N/kg), then denitrification increases
significantly with increasing soluble carbon.
4) Decreasing soil pH gradually decreases denitrification value to 0 kg N/ha at pH
of 3.75.
5) Denitrification rate increases when soil temperature increases from 0 to 30°C and
decreases sharply between 30 and 40°C.
6) N2O emissions increase as in soil profile thickens and then stabilize at a constant
value after which there is no effect of soil thickness. This "effective" soil
thickness depends on the duration of rainfall and is reported to be less than 20 cm
if rainfall duration is not longer than 20 hours.
31
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7) A linear increase in N2O emission occurs with increasing rainfall duration.
8) Soluble carbon concentration is highly depend on initial organic carbon and
residue content in soil.
9) The trends and magnitude of simulated N2O emissions are consistent with the
results obtained in field experiments.
Li et al. (1992c) applied the DNDC model for estimating N2O emissions from fallow, grass and
sugarcane fields at Belle Glade, FL. Good agreement was reported between simulated and
observed results of N2O emissions, nitrogen mineralization rates, and changes in nitrate and
ammonium at soil surface. High emissions of N2O were observed following rainfall events;
however, rainfall was not only the factor controlling N2O emissions. Emissions also depended
on nitrate concentration distribution in the soil profile. Soil surface NO3 concentrations were
inversely related to rainfall as they decreased with rainfall and gradually increased after rains
ceased. The following conclusions were noted in regards to vegetation and the way it affects
N2O emissions:
1) Higher canopy interception will decrease denitrification.
2) A decrease of nitrate content due to plant uptake and an increase in soluble
carbon resulting from root secretion will change the ratio of N2O to N2 emitted
in soil.
Jenkinson and Rayner Model -
Jenkinson and Rayner (1977) developed a model to simulate the behavior of SOM that was
divided into five fractions. These fractions consisted of decomposable plant material (DPM),
resistant plant material (RPM), biomass (BIO), humified organic matter (HUM) and inert
organic matter (IOM). The model assumes that each fraction contains a single species that
undergoes biological decomposition by the first-order process. On a monthly basis, the addition
of plant carbon from crop residues is represented by the DPM and RPM fractions. In the
model, the organic carbon inputs are assumed to enter the DPM or the RPM pools, which
decompose forming CO2, microbial biomass, and humified organic matter. It is assumed that,
for all soils, the ratio of microbial biomass to humified organic matter remains the same during
biological activity. In the model, decomposition of humified organic matter by biological
activity results in CO2, microbial biomass, and residual humified matter. The inert component
does not undergo any transformation during biological activity.
The model has been validated on Rothamsted soils for organic carbon content and total N content
in the top 23 cm of soil. Good correlation between observed data and simulated results is
reported. The current model (Jenkinson, 1990) was also validated for both organic carbon and
microbial biomass contents, and simulated and observed results were in good agreement.
32
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Frissel and van Veen Model -
Frissel and van Veen (1981) developed an N transformation model based on four stages of
organic matter transformation. The stages used in the model are:
1) C/N ratio controls mineralization and immobilization.
2) Consideration was made for differences in decomposition rates of organic
compounds in plant residues for amino acids, cellulose, lignin fractions and
microbial mass (in this stage the authors assumed that the organic matter
decomposition is controlled by C uptake by the biomass rather than the C/N
ratio).
3) N transformations were incorporated into a multi-layer soil model.
4) SOM fractions were incorporated into the C and N pool.
The model was based on the assumption that SOM can be represented by several carbon and
nitrogen pools. The biomass growth rate was controlled by the carbon availability from the
added soil pool, and it was assumed that there was no change in the microbial population if no
carbon was added to the soil. Nitrogen immobilization was proportional to biomass growth and
it was assumed that mineralization occurred simultaneously and independently of immobilization.
The authors used the C/N ratio to characterize mineralization and immobilization. They
assumed that if the C/N ratio was less than 20 to 30, then net mineralization occurs; otherwise,
net immobilization occurs.
OEM Model -
Esser (1990) developed the Osnabruck Biosphere Model (OEM) to simulate global terrestrial
sources and sinks of CO2 with special reference to soil organic matter. The model is grid-based
and estimates the influences of "(i) the rising atmospheric CO2 level, (ii) the temperature change
due to greenhouse effect, and (iii.) the clearing of forests and other natural vegetation, on the
development of the biospheric pools, the SOC, and the atmospheric CO2" (Esser, 1990). Model
simulations for the period of 1860-1980 and for 1980 only were performed. Five conclusions
were drawn from the study:
1) The effect of CO2 fertilization is very important.
2) The landuse database should be improved to reflect changing landuse practices.
3) SOC pool can be predicted using the simple assumption that the lignin fraction
of litter contributes to SOC.
33
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4) SOC losses after clearing are sufficiently predictable considering the reduced net
primary productivity of field crops.
5) The effects of landuse changes and clearings on SOC and litter are, on a global
scale, compensated by the CO2 fertilization effect.
The model results have not been validated with independent data.
NCSWAP Model -
Clay et al. (1985) developed the NCSWAP (Nitrogen Carbon Soil Water and Plant) model for
estimating nitrogen and carbon transformations in soils. NCSWAP calculates dynamics of
carbon and nitrogen organics, ammonium, and nitrate that result from the processes of residue
decomposition, mineralization, immobilization, nitrification and denitrification. The model
divides the active soil organic phase in two dynamic pools, one labile and one resistant. These
pools are defined by their kinetic rate constants and are handled separately in the model, each
decomposing exponentially. A variable is introduced in the model that determines whether
residue decomposition results in N mineralization or immobilization.
The soil moisture is modeled using the Green-Ampt infiltration equation, and the model also
accounts for redistribution of water in the soil profile. Subroutines to calculate soil temperature
and plant growth are also included in the model. The model results are in good agreement with
observed data.
TEM Model -
Raich et al. (1991) developed the grid-based Terrestrial Ecosystem Model (TEM) to simulate
the C and N pool sizes and fluxes at continental to global scale using soils, vegetation, and
climate data to predict net primary productivity. The dimensions of each grid are 0.5° latitude
by 0.5° longitude. The model assumes that vegetation and detritus is distributed homogeneously
within grid cells. Each grid cell is classified by vegetation type and soil texture. The model
simulates interactions among terrestrial ecosystems and environmental variables at continental
or global scales using a monthly time step. Nutrient pools are divided into five state variables:
carbon in living vegetation, nitrogen in living vegetation, organic carbon in detritus and soils,
organic nitrogen in detritus and soils, and available inorganic nitrogen. The model includes all
dead plant material in the soil pool and all living plant material (roots, shoots, and leaves) in the
vegetation pool.
2.4.2 Other Models and Inter-Model Comparisons
McCracken (1991) also provides a review of computer models that aid in determining carbon
dioxide release into the atmosphere. The models reviewed were those by Aber et al. (1982),
Jenny (1980), Pastor and Post (1986). The author concluded from modeling results and
34
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observations that "soil N levels are correlated with soil organic C, statements made for C tend
to apply also to soil N". This conclusion is in agreement with Stevenson's (1986) findings.
Models developed by Box (1988), Gildea et al. (1986), Moore et al. (1981), and Smith (1979)
were also reviewed, details of which are provided in Barnwell et al (1991). The Box model
(Box, 1988) is a spreadsheet type model that estimates global CO2 fluxes under varying climatic
and soil conditions. Gildea et al,, (1986) developed a model to estimate terrestrial nitrogen
loadings that can be used with an aquatic nutrient cycling model. The purpose of the Moore et
al. model (1981) is to estimate annual net exchange of carbon between terrestrial ecosystems and
the atmosphere. Smith (1979) developed a general model to simulate the C, N, P, and K
dynamics in soils.
A comparison of the models reviewed is presented in Table 2.7. All the models reviewed
simulate the simultaneous C and N cycles in soils except OBM, Box, and Moore et al. which
only simulate the C cycle, and the Gildea et al. model which only simulates the N cycle. All
of the models reviewed were developed for the purpose of research and, except for CENTURY,
no application of the models other than by the developers was found. This raises a question as
to whether the models can be used effectively by others. From the group of the models
reviewed<1the CENTURY and Jenkinson and Rayner models have undergone the most field
testing and validation to date.
To study the carbon sequestration potential of various agricultural production systems it is
important that the models allow simulations of various crops and cropping practices under
various soil and climatic conditions. All of the models account for the type of crop grown;
however, only CENTURY, DNDC, NCSWAP, and Gildea et al. models allow simulation of
varying cropping practices.. Soil moisture and temperature play an important role in
transformation reactions that affect C and N storages in soils. All the models include the effects
of varying soil moisture on C and N cycles; however, the same is not true for soil temperature
effect, which is ignored in the Jenkinson and Rayner, Box, and the Gildea et al. models. As
crop growth consumes nitrogen and respires CO2 during growth, it is important that the models
account for crop growth with relation to changing C and N storages in soils.
Information regarding model availability to general users, data requirements, and model
complexity is incomplete for mztny models, and is often difficult to obtain due to limited
documentation. However, from the models reviewed, the CENTURY, DNDC, Jenkinson and
Rayner, Frissel and van Veen, and the NCSWAP models standout as candidate models that can
aid in evaluating the carbon sequestration potential as they account for changing soil-crop-climate
systems. Potential model users need to obtain more detailed information regarding data
requirements and availability, model code availability, and system requirements in order to
assess their ultimate utility for evaluating alternative agricultural production systems.
35
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Table 2.7 Comparison of Soil Carbon Model Features
Attribute/Model
Model
Developer
Simulation Time
Step
Rainfall/Runoff
Analysis
Irrigation Application
Erosion
Soil Hydrologie
Balance
Soil Moisture
Soil Temperature
Soil Processes
Carbon
Nitrogen
Phosphorus
Sulphur
Fertilizer
Tillage/Residue
Management
Cropping Practices
Crop Type
Crop Growth Simulation
Available on
Microcomputer
Data and Staff
Requirements
Output Options
C02
NjO
CH4
Validation
Status
Overall Model
Complexity
CENTURY
Colorado
State Uni.
Monthly
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Low
Yes
Yes
No
Medium
Medium
DNDC
Bruce Company
Daily
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Medium
Yes
Yes
No
Low
Medium
Jenkinson & Rayner
Rothamsted Experiment
Station
Monthly
Yes
?
No
Yes
Yes
No
Yes
Yes
No
No
Yes
Yes
No
Yes
No
Yes
?
Yes
?
No
Medium
?
Frissel & van Veen
Research Institute
Wageningen
?
Yes
?
No
Yes
Yes
Yes
Yes
Yes
No
No
?
Yes
No
Yes
No
?
?
?
?
No
Low
?
36
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Table 2.7 (cont'd.)
Attribute/Model OBM
Model General Ecol.
Developer Group, F.R.G.
Osnabruck
Simulation Time ?
Step
Rainfall/Runoff Yes
Analysis
. Irrigation Application ?
Erosion No
Soil Hydrologic Yes
Balance
Soil Moisture ?
Soil Temperature ?
Soil Processes
Carbon Yes
Nitrogen No
Phosphorus No
Sulphur No
Fertilizer ?
Tillage/Residue Yes
Management
Cropping Practices No
Crop Type Yes
Crop Growth Simulation No
Available on
Microcomputer ?
Data and Staff ?
Requirements
Output Options
COt ?
N2O No
CH« No
Validation Low
Status
Overall Model ?
Complexity
NCSWAP TEM
Uni. of Ecosystem Center
Minnesota Marine Bio. Lab.
St. Paul, MN Woods Hole, MA
Daily Monthly
Yes Yes
Yes ?
? Yes
Yes Yes
Yes Yes
Yes ?
Yes Yes
Yes Yes
No No
No No
Yes ?
Yes ?
Yes ?
Yes Yes
Yes Yes
? ?
? ?
Yes ?
No ?
No No
Low Low
? ?
BOX
Uni. of Georgia
Athens, GA
Monthly
Yes
?
No
Yes
Yes
No
Yes
No
No
No
?
No
No
Yes
No
?
?
Yes
No
Low
?
37
-------
Table 2.7 (cont'd.)
Attribute/Model
Model
Developer
Simulation Time
Step
Rainfall/Runoff
Analysis
Irrigation Application
Eroiion
Soil Hydrologic
Balance
Soil Moisture
Soil Temperature
Soil Processes
Carbon
Nitrogen
Phosphorus
Sulphur
Potassium
Fertilizer
Tillage/Residue
Management
Cropping Practices
Crop Type
Crop Growth Simulation
Available on
Microcomputer
Data and Staff
Requirements
Output Options
CO,
NjO
CH4
Validation
Status
Overall Model
Complexity
Gildea et al.
Univ. of New
Hampshire
?
Yes
?
No
Yes
?
No
No
Yes
No
No
No
Yes
Yes
Yes
Yes
No
?
?
No
?
No
Low
?
Moore et al.
Univ. of New
Hampshire
?
?
No
No
?
?
?
Yes
No
No
No
No
No
No
No
Yes
No
?
?
Yes
No
No
Low
7
Smith
ORNL Oak
Ridge
Tennesse
?
Yes
?
No
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
?
Yes
Yes
?
?
Yes
?
No
Low
?
38
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2.4.3 Closure
The focus of the literature search and model review was the identification and documentation
of available soil carbon models, and their abilities to investigate soil carbon budgets,
transformation, and pathways in agroecosystems, and impacts of tillage and cropping practices
on the soil carbon cycle. The initial evaluation reduced the number of 'candidate models' to
four or five primary models that were further Investigated. Unfortunately, essentially all existing
models are primarily 'research models' in that few have undergone more than limited field
testing and validation. One of the key issues in the selection of a model for this study was to
provide a level of model complexity (and associated data requirements) that was consistent with
the project needs and was compatible with the national level databases available (discussed in
the next section) for providing the model input. As a result of this assessment, the CENTURY
model was selected as the primary tool for use and/or modification to meet the needs of
representing soil carbon cycles in agroecosystems and impacts of management practices. The
DNDC model was selected for application to limited portions of the Study Region as a
consistency check of the soil process representation. Section 5.0 describes the development of
the modeling framework and methodology to define the agricultural production systems and
scenarios (i,e., crop-soil-climate combinations) to be assessed, the integration of the model needs
with available databases, and the operational mechanics of evaluating carbon sequestration
potential with the integrated model/database system.
39
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SECTION 3.0
MODELING METHODOLOGY FOR SOIL CARBON SEQUESTRATION
Li order to effectively use one or more models to assess the soil carbon sequestration potential
of agricultural regions of the United States, a framework or methodology was needed to
address issues of model integration, data needs and availability, temporal and spatial scales of
analysis, detail of representation of agricultural practices, and associated technical model
application questions. This section provides the integration of these concerns in the form of the
overall project methodology that was followed to assess both current conditions of soil carbon,
and SOC, in agricultural systems, and projected changes. Changes were evaluated under a
continuation of current conditions (i.e., status quo) and selected alternative futures derived from
policy changes. Individual components of the methodology are discussed in subsequent sections.
Section 4.0 describes the component databases and data analyses performed in support of the
methodology. Section 5.0 discusses our approach to modeling agricultural production systems
and the various scenarios we analyzed to project future changes in SOC. Section 6.0 describes
the preliminary results and conclusions from application of this methodology.
3.1 INTRODUCTION
In Section 1.0 the overall operational strategy for the study was discussed in terms of the high-
level integration of models, databases, and their linkage for assessment of policy impacts on
agriculture and resulting soil carbon levels. Figure 3.1 shows the expanded 'modeling box' of
the high-level assessment strategy flowchart (Figure 1.2). As noted earlier, the objective of the
study was to estimate the greenhouse gas emissions and C sequestration potential of agricultural
production systems, and the impact of various policy alternatives designed to reduce emissions
by increasing soil C levels.
The modeling system shown in Figure 3.1 includes four primary components:
• Resource Adjustment Modeling System (RAMS) to provide baseline cropping
practices and changes in practices as impacted by policy alternatives,
• CENTURY and DNDC soil models to provide unit-area (i.e., per hectare)
changes in soil carbon and greenhouse gas emissions for each designated
agricultural production system,
• EPA Agroecosystem Carbon Pools database developed by CSU and MSU to
provide field site data for model testing and validation, and
40
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• GIS capabilities to integrate the unit-area soil C and emission values with
cropping acreages for display and analysis of impacts of baseline and policy
alternatives.
The results of the soil carbon model review described in Section 2 led to the selection of the
CENTURY and DNDC models for use in the modeling component of the study. Although soil
carbon/soil organic matter models are still largely 'research-type' tools, these two models have
been applied and tested on a variety of sites throughout the world, and have shown an acceptable
degree of success in reflecting observed soil data. However, both models needed further
refinement in this project to represent the range of crops, cropping/tillage practices, and crop
rotations common to U.S. agriculture. Furthermore, the CENTURY model was the primary soil
model used in the methodology because of its greater execution efficiency, validation experience,
and flexibility in representing complex production systems; the DNDC model provided a more
mechanistic simulation of the nitrogen cycle to allow consistency checks with the CENTURY
model results for selected scenarios in selected sections of the Study Region.
A number of critical issues needed to be resolved in order to develop an effective and reasonable
methodology for assessing SOC levels ~ past, current, and future ~ as impacted by all aspects
of agricultural production systems and the variability of soils and climate characteristics.
Agricultural production systems needed to be defined in terms of crops, rotations, tillage,
chemical inputs, etc., in a manner consistent with the CENTURY model. Soil and climate data
needed to be transformed to a level of spatial and temporal detail consistent with the spatial
detail of the assessment. Since the RAMS and CENTURY models were both part of the
integrated methodology, differences in spatial scales needed to be considered in establishing the
appropriate scale for the assessment. In order to estimate future changes to soil carbon in
agroecosystems, we needed to know the current status as impacted by past cropping practices,
tillage, historical yields, etc., as a baseline condition.
Following discussions of the component models in the next two sub-sections, the remaining
portions of Section 3 discuss the specific details of the 'Project Methodology' including the
critical issues identified above.
3.2 CENTURY AND DNDC MODELS - OVERVIEW AND TESTING
3.2.1 CENTURY Model Overview
As noted in Section 2.0, Parton et al. (1988) developed the CENTURY model to simulate the
dynamics of C, N, P, and S in cultivated and uncultivated grassland soils. Recent model
refinements have resulted in the Agroecosystem Version 4.0 of CENTURY to better represent
complex cropping patterns, rotations, and management practices (Metherell et al., 1993). The
model uses monthly time steps for simulation, and model runs can be performed for time periods
ranging from 10 to 10,000 years. The model is composed of five organic matter pools, two
represent litter or crop residues and three represent SOM. The fresh organic matter, which
includes plant material and animal manure, is divided into metabolic and structural litter
42
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fractions, depending on its lignin: nitrogen ratio. The SOM is divided into three fractions: 1)
an "active" fraction that has a rapid turnover rate (1-5 years) and consists of microbial biomass
and metabolites; 2) a "slow" fraction with an intermediate turnover time (20-40 years) that
represents stabilized decomposition products; and 3) a "passive" fraction that represents the
highly stabilized, recalcitrant organic matter (200-1500 years turnover).
In the model, soil moisture is calculated as a function of the ratio of monthly precipitation (plus
irrigation) to monthly evapotranspiration, and soil water movement is calculated using a simple
piston flow formulation. The soil temperature term is calculated as a function of average
monthly soil temperature for the 20-cm soil layer surface. First-order rate constants depending
on soil temperature and moisture are used to represent the decomposition rates. The other
factors that affect the organic matter transformation include soil texture and the lignin content
of the litter. This lignin content of the litter pool influences the relative amount of the structural
material that is assimilated by microorganisms (transferred to active pool) or gets incorporated
into the slow pool. The soil texture influences the relative amount of decomposing organic
matter that is either mineralized to CO2 or enters the slow pool as decomposition products.
The carbon cycle, as represented in CENTURY, is depicted in Figure 3.2, and Figure 3.3 shows
the associated nitrogen cycle. The carbon that is released due to decomposition gets partly
mineralized ,and the remaining gets added as a decomposition product to one or more organic
matter pools. The carbon flow dictates the release of nitrogen, as nitrogen is considered to be
mainly in C-bonded forms. The C:N ratios of materials entering the soil organic pools are used
to calculate the mineralization and immobilization of N. The model allows the representation
of C:N ratios of the material entering the organic pool to be constant or to vary at specified time
intervals. If the N content of decomposition products entering a given pool is less than the
minimum required for the recipient pool, then mineral N is immobilized, and conversely if
organic N content is in excess, then that amount is mineralized. The model uses simple
equations to represent N inputs by atmospheric deposition and through plant and soil fixation.
The losses of N forms represented include leaching, gaseous losses, crop removal, and erosion.
The CENTURY model has submodels for estimating plant growth and soil water balance. These
submodels provide information on plant litter inputs and climatic variables for SOM
computations. Plant growth is expressed as a function of a potential growth rate moisture,
temperature, shading and seedling modifiers. The potential plant growth rate is used to calculate
the crop nutrient demand. If nutrient availability is insufficient to satisfy nutrient demand, then
the crop production is reduced accordingly. The soil water content is calculated as a function
of the ratio of monthly precipitation (plus irrigation and stored water) to monthly potential
evapotranspiration. Options in the model allow the user to either specifically define fertilizer
and irrigation applications, or direct the model to calculate (and satisfy) the plant nutrient and
moisture needs based on user-defined criteria, e.g., fraction of optimum production or moisture
deficits at which irrigation is initiated. These options are discussed in more detail in Section
5.0.
43
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3.2.2 CENTURY Model Testing and Application
Much of the model testing to date has been done on grassland and wheat systems. The
CENTURY model has been validated by Parton et al. (1987) by simulating steady state C and
N levels and aboveground plant production for 24 sites in the Great Plains. <• The simulation
results were compared with mapped plant production and soil C and N levels for fine, medium,
and sandy textured soils. The authors found that the model overestimated the C and N soil
levels for fine textured soils, underestimated for sandy soils, and did an excellent job for
medium textured soils. The effect of soil texture and climate on the soil C and N levels in the
Great Plains was adequately represented by the model. The model results of aboveground plant
production were in excellent agreement with the observed values.
Schimel et al. (1990) studied grassland biogeochemistry and its link to atmospheric processes.
Model simulations obtained using the CENTURY model adequately represented the observed
geography of C and N. It was stated that "increases in temperature and associated changes in
precipitation caused increases in decomposing and long-term emission of COj from grassland
soils". The release of nutrients through the loss of organic matter resulted in increased net
primary production. This conclusion demonstrates the fact that nutrient interactions play a major
role over vegetation response to climate change.
Paustian et al. (1991) have used the model to study the influence of organic amendments and N-
fertilization on SOM. Schimel et al. (1990) used the CENTURY model to study grassland
biogeochemistry and its link to the atmospheric processes. Burke et al. (1990) used CENTURY
coupled with a geographic information system to study the spatial variability in storage and
fluxes of C and N within grassland ecosystems. The model has also been used by Cole et al.
(1989) to study the effects of SOM dynamics in the North American Great Plains. Parton et al.
(1989) used the CENTURY model to simulate the regional patterns of soil C, N, and P
dynamics in the U.S. central grasslands.
In the current study, CENTURY model capabilities in the Agroecosystem Version 4.0 for
representing additional crops (e.g., legume hay and soybeans), complex cropping patterns and
multi-year crop rotations were added (Metherell et al., 1993), and additional model testing was
performed. Appendices A and B, respectively, describe CENTURY testing efforts on a wheat-
fallow rotation field site in Sydney, NE, and a continuous corn (with winter cover crop) site in
Lexington, KY. An additional test site for a corn-soybean rotation in Kanawha, IA was also
selected but data availability precluded completion of the testing for this study.
The additional model testing has shown that CENTURY can adequately represent soil carbon
levels as impacted by tillage practices, nitrogen fertilization levels, and cover cropping practices,
all of which are significant needs for the current study. Further model testing and development
is needed to improve the soil nitrogen cycle representation and its impacts on crop yields; see
Appendices A and B for further details on the CENTURY model testing.
46
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3.2.3 DNDC Model Overview
Li et al. (1992a) developed the physically based Denitrification Decomposition (DNDC) model
for estimating nitrous oxide (N2O) and nitrogen (N^ evolution from soils. The submodels
included in the model represent thermal-hydraulic flow, decomposition and denitrification. The
model calculates dynamic soil temperature and moisture profiles, and estimates shift of aerobic-
anaerobic conditions in soils using climatic data of rainfall events and air temperature. The
dominant processes between rainfall events are the decomposition of organic matter and the
oxidation reactions. These processes affect levels of total organic carbon, soluble carbon and
nitrate in soils between rainfall events. However, during rainfall the dominant process is that
of denitrification resulting in production of nitrogenous gases of N2O and N2. The major input
data requirements for the denitrification-decomposition (DNDC) consist of the climatic scenario,
soil texture and its properties, and initial organic matter and nitrate in soil. The DNDC model
can aid in estimating the following (Li, 1991a):
1) Effects of changing organic C in soil on emissions of CO2 and N2O from soils.
2) Rate of carbon accumulation in soils, based on current residue inputs, and both
physical and chemical soil properties.
3) Soil management changes affecting accumulation rates of carbon, and emissions
of CO2 and N2O from soils.
4) Effects of climate change (monthly average air temperature and precipitation) on
carbon accumulation rates, and CO2 and N2O emissions from soils.
Li et al. (1992b) applied the DNDC model for estimating N2O emissions from fallow, grass and
sugarcane fields at Belle Glade, FL. Good agreement was reported between simulated and
observed results of N2O emissions, nitrogen mineralization rates, and changes in nitrate and
ammonium at the soil surface. High emissions of N2O were observed following rainfall events;
however, rainfall was not the only factor controlling N2O emissions. Emissions also depended
on the nitrate concentration distribution in the soil profile. Soil surface NO3 concentrations were
inversely related to rainfall as they decreased with rainfall and gradually increased after rains
ceased.
As evident by its name, DNDC was designed primarily to represent the nitrogen cycle and
simulate N emissions. Thus, much of the model testing to date has focused on N emissions,
although sites in Germany (uncultivated grass) and Missouri (fertilized winter wheat) for CO2
emissions were included in the initial model testing (Li et al., 1992b). As part of this study,
testing was performed on additional field sites world-wide, including the 150-year records of the
well-known Rothamsted Experimental Station in England, and the model results were generally
consistent with the observed data (Li, C., personal communication, 1992).
47
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3.3 RAMS MODEL OVERVIEW
The Resource Adjustment Modeling System (RAMS) was developed in 1990 by the Center for
Agricultural and Rural Development (CARD) at Iowa State University. RAMS is a regional,
short-term, static, profit maximizing, linear programming model of agricultural production
defined at the producing area (PA) level. The function of RAMS is to provide quantitative
estimates of optimal levels of production activities. It is assumed that those performing the
production activities are trying to maximize profits while being constrained by technology,
government policy, and the resource base (e.g. land). Considerable detail of the technology and
policy environments of crop production at the regional level makes RAMS a useful instrument
for the analysis of policy changes at a disaggregated level. PA's are hydrological areas,
adjusted to county boundaries, representing aggregated subareas defined by the U.S. Water
Resources Council (1970). The areas are small enough so that the assumption of homogenous
production across the area can be reasonably made. RAMS has been previously used to evaluate
restrictions on the use of atrazine and other triazines in corn and sorghum production (Bouzaher
et al., 1992). For this earlier use, RAMS was constructed to interface with plant and
geophysical process models. For the present analysis RAMS is interfaced with the CENTURY
and DNDC models. Information about production patterns and cropping systems is passed to
these models. Such linking of environmental and economic realms generates a more
comprehensive evaluation of policies designed to sequester carbon in agricultural soils.
RAMS is composed of 27 crop producing areas. Each region is assumed to be a separate
decision-making entity with the model solved in each region independent of the outcomes in the
others. The outline of the production regions forms the boundary for the Study Region (See
Figure 3.4 and Section 3.4).
Cropping systems are characterized in the model by activities that specify crop rotation, tillage,
contour management, and irrigation. Each activity is defined by a combination of the four
dimensions. Table 3.1 lists the possibilities for each. Winter cover crops are a new addition
to RAMS, and were added as part of this study to investigate carbon sequestration alternatives.
Cover crops are included only for a subset of all crop sequences within each rotation.
Furthermore, the subset depends on the geographic location of the PA. In general, the choice
of crop sequences amenable to cover crops in any PA depends upon the time period in which
cover crops can grow and the scarcity of moisture. The growing period for winter cover crops
is greater following crops with earlier harvest dates and/or succeeding crops with early planting
dates. Southern PA's typically have longer, and warmer, growing seasons allowing substantial
growth of a winter cover crop. Some western PA's simply do not have enough water to support
both a commodity and a winter cover crop (Cruse, 1992, personal communication).
RAMS also has a detailed weed control sub-sector. More than 400 chemical and mechanical
weed control alternatives are specified. Each alternative consists of a tank-mix of chemicals,
and details about how and when they are applied. A government commodity program sub-sector
incorporates the major rules and provisions of commodity programs and conservation compliance
from the Food, Agriculture and Trade Act of 1990 (FACTA)(House of Representatives, 1990).
48
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Figure 3.4 RAMS study region, production areas, and state boundaries
49
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Table 3.1 Activities and Constraints in RAMS
Crop Production Activities
Defined by:
Rotation Management - ten to twenty-five rotations per PA
•up to 6 year rotation of following crops
barley, com, corn silage, cotton, non-legume hay, legume hay oats, peanuts,
sorghum, sorghum silage, soybeans, sunflower, spring wheat, winter wheat
•rye or hairy vetch winter cover crops for designated crop sequences in some PA's
Tillage Management
•conventional till - fall plow
•conventional till - spring plow
•conservation (or reduced) till
•no-till
Contour Management
•straight row
•contouring
•strip cropping
•terracing
Irrigation Management
•dryland
•irrigated - surface water source
•irrigated - groundwater source
Other Activities
Weed Control Activities- one activity for each strategy (317 for com, 98 for sorghum)
Strategies include one or more of following chemicals (generic names)
•atrazine, alachlor, metolachlor, propachlor, pendimethalin, EPTC, Butylate, cyanazine,
simazine, dicamba, bromoxynil, 2,4-D, bentazon, glyphosate, nicosulfuron, primisulfuron
Commodity Program Activities
Single activity for Conservation Reserve Program
Deficiency payments and set-aside requirements for following program crops
•barley, corn, cotton, oats, sorghum, spring wheat and winter wheat
Crop Marketing Activities - for each crop grown
Resource and Institutional Constraints
•Single class of land available for production or idling under commodity programs
•Government commodity program participation base for each program crop
•Conservation Reserve Program enrollment base
•Seven levels of highly erodible land reflecting degrees of erodibility
•Calibration bounds on tillage, crop acres, and chemical use
50
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The government commodity program activities simulate deficiency payments, along with set-
aside and conservation compliance restriction, and land retirement from crop production under
the Conservation Reserve Program. Commodity marketing activities are included, and resource
and institutional constraints bound the profit maximizing solution in each PA.
The outcomes of the model include the indicators used to evaluate the economic costs and
benefits as well as the production patterns, and choice of cropping systems, which are fed to the
CENTURY and DNDC models. Primary economic indicators include net returns to production,
acreage planted, tillage practices, rotations, erosion, and fertilizer and herbicide use. A more
detailed description of RAMS can be found in Bouzaher et al. (1992).
3.4 INITIAL STUDY REGION AND PRODUCTION AREAS
Although the intent of the overall agroecosystem component of the EPA Climate Change study
is to include all U.S. agriculture, the need to integrate both economic and policy implications
with soil carbon modeling led to restrictions in the spatial scope of this initial effort.
Consequently, this assessment was restricted to the RAMS Study Region shown in Figure 3.4;
this region encompasses the traditional agricultural regions of the Corn Belt, the Lakes States,
and portions of the Northern Plains. Approximately 216 million acres of cropland are included
in the RAMS region, representing 60% to 70% of the total U.S. cropland, depending on specific
definitions of harvested versus total cropland acres (USDA, 1992). In addition, the RAMS
region is 25% of the continental United States, and the 216 million cropland acres represents
44% of the entire Study Region.
Also shown in Figure 3.4 are the RAMS 'Production Areas' (PAs) representing the basic spatial
unit of analysis for the economic and policy analysis; typically the RAMS results are aggregated
to state or some other political unit for analysis, or the results are summarized for the entire
Study Region. These PAs are defined as hydrologic areas delineated by the U.S. Water
Resources Council (1970), except that the boundaries are adjusted to conform to county
boundaries, for aggregated economic reporting of various economic and agricultural statistics.
The PA boundaries do not coincide with state boundaries, and there may be multiple PAs within
a state. As noted earlier, the RAMS model performs its analysis on each PA producing
aggregated crop yields, rotations, agronomic practices, and economic variables for the entire PA.
The modeling of soil carbon and agricultural practices in this study required a finer spatial
resolution based on climate and soils data; this is discussed in the sections below on the Project
Methodology and in Section 4.0.
3.5 PROJECT METHODOLOGY
This section describes the modeling methodology developed to integrate the issues of spatial
detail of the required data, modeling of agricultural practices, estimation of current or initial soil
carbon conditions, and procedures for assessing alternative future conditions resulting from
policy scenarios. An overriding concern throughout the work was to strike a balance between
the number of combinations of soils, climate, crop rotations, tillage practices, and associated
51
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model runs needed to adequately represent the heterogeneities of the study region, within the
time and resource constraints of the assessment.
Figure 3.5 shows the basic steps in applying the methodology to the RAMS Study Region; these
steps are discussed in more detail below:
1. Divide the Study Region into 'Climate Divisions' (CD) that can be represented
with individual climate timeseries of the monthly precipitation and max/min air
temperature needed by the models. The CD boundaries were defined to coincide
with both Production Area (PA) and county boundaries to facilitate the
transformations (i.e., aggregation/disaggregation) of cropping practice and soils
data. Figure 3.6 shows the climate divisions along with PA and state boundaries.
The 'Climate Division' is the basic spatial unit of analysis for this study.
Section 4.0 describes the component climate and soils data bases, and the
procedures and assumptions used to delineate the CD boundaries.
2. For each CD, identify the distribution of soil textures on the agricultural
cropland. Section 4.2 describes how we used the NRI/SOILS5 database (Goebel,
1987) and the EPA DBAPE system (Imhoff et al., 1991) to determine the surface
soil textural distributions for all counties in each CD for six major textural
groups: sand/loamy sand, sandy loam/sandy clay loam, loam, silty loam, silty
clay loam/silty clay, clay/clay loam.
3. For each CD, identify the primary crops/rotation/tillage (C/R/T) scenarios as
defined by the RAMS output for the 'status quo' condition. The RAMS output
identified 80 different rotations used in the Study Region; these were grouped into
35 rotations for modeling purposes, based on similarity of crops, cropping
sequence, and modeling capabilities. Of the 35 rotations simulated, 13 represent
almost 85% of the cropland in the Study Region; thus the primary rotation
sequences are represented in the reduced list.
The RAMS output for each PA (and each policy alternative) is transformed to a
CD based on the ratio of 'county cropland' to 'PA cropland' and then aggregated
for the specific counties in each CD (see discussion in Section 4.3).
4. Establish Initial Conditions (Current, i.e., 1988-90) for SOC (and other needed
model variables) to be used for each C/R/T for each soil texture within each CD.
Section 5.2 describes the procedure for this based on calibrating model results to
historical crop yields from 1907 through 1988 for the major rotations and
practices in each CD. Since SOC is a direct function of soil texture and historical
practices (primarily crop yields and residue management) within a defined climate
regime, it was necessary to evaluate these controlling factors in our estimation of
initial SOC value for each CD.
52
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SOIL CARE3ON MODELING METHODOLOGY
Establish
Climate Divisions (CD) for
Study Region
Define Soil
Texture Groups and Dist
for Each CD
Identify
C/R/T Scenarios
for Each CD
Legend:
SOC - Soil
Carbon
C/R/T-Crop/
Rotation/
Tillage
Establish
Initial Conditions (SOC.N) for
Each CD
Perform
Model Runs for Each Soil-C/R/T>«-
Combination
Weight the
Model Output by Soil Texture
Distribution
Multiply
^Weighted Curve by C/R/T
Areas
Analyze
and Rank C/R/T
Impacts
Sum C/R/T
Values to Get CD and Study
Region Totals
No change in
C/R/T values
Evaluate
^Alternatives by Changes to SOC
and N Emissions
Alternative
Affects
C/R/T values
AQUA TERRA Consultants
Figure 3.5 Soil carbon modeling methodology
53
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co
Figure 3.6 Production areas and climate divisions in the RAMS study area
54
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8.
For each CD, extend the historical CENTURY model runs (from Step 4, above)
through the 40-year projection period of 1990-2030 for each C/R/T and each soil
texture group, and output SOC (year-end values, gC/m2) and N Emissions
(annual total, gN/m2/yr) for analysis. We also outputted and analyzed other
model variables (e.g., yields, primary productivity, carbon inputs) to check the
validity of the simulations in each CD.
A key factor in the modeling of SOC during the projection period of 1990 to
2030 is the assumption of changes, primarily increases, in crop yields. Based on
U.S.D.A. estimates derived from the deliberations of a group of agronomic and
agricultural research experts, an annual increase of 1.5% in yields was assumed
for each crop throughout the 40-year period (U.S.D.A., 1990). Li addition, the
impacts of alternative yield increase levels (i.e., 1.0%/year and 0.5%/year) were
evaluated for region-wide impacts. The yield increase assumption has a critical
impact on projected changes in SOC due to its impact on carbon inputs to the
soil; higher yields will provide increased carbon inputs and increased SOC. This
issue is discussed further in Section 5.0 where the assumptions for both baseline
and alternative scenarios are presented.
Weight the output from each soil-C/R/T (CENTURY) run by the soil texture
distribution for that CD to get one timeseries of SOC (and each other output
variable) for each C/R/T within each CD. Figure 3.7 is an example for a CD
in Iowa where a corn-soybean rotation was run for three soil textures and a
weighted curve was developed.
For each CD, we then multiply the weighted curve (e.g., for SOC, gC/m2; N
emission, gN/m2/yr) by the area for each C/R/T combination; these areas are
provided by the RAMS output for each PA, which is transformed to a CD basis.
The resulting values of SOC or N emissions for each C/R/T are then analyzed to
determine the total SOC changes and N emissions for each CD. Since the
projections are available for each year of the projection period, the analysis can
be done on an annual basis, in 10-year increments (i.e., 1990-2000, 2000-2010,
2010-2020, 2020-2030), or for the entire 40-year period (i.e., 1990-2030).
The totals for each CD are then summed to provide totals for the entire Study
Region for the entire projection period or any subset thereof.
The evaluation of 'Status Quo' or baseline conditions, and comparison with each
alternative scenario, is based on the relative impacts on SOC changes and N
emissions. However, not all alternative scenarios require execution of new or
additional model runs. Alternatives that simply change the acreages of the
rotations (i.e., each C/R/T combination) do not require additional model runs;
Step 7 would be repeated for each new set of rotation acreages, in a simple
spreadsheet-type calculation. Alternatives that lead to changes in the individual
55
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Weighted Average & Individual Soil Carbon - CD413
9000
Q.
0)
Q
'6
CO
o
o
CM
cvT
o"
o
•8
as
O
*o
CO
Silt Loam
CR186 (CRN.SOY) - GTSP
--E3--
SCL/SL
Weighted
Weights: Silt Loam - 0.39, Loam-0.34, Sandy Clay Loam/Sandy Loam - 0.27
1000
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030
YEAR
Figure 3.7 Weighted average and individual soil carbon
56
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C/R/T curves (e.g., changes in tillage practices, new rotations, addition of cover
crops) require new model runs, so Steps 5 through 7 are all repeated, along with
calculation of the total SOC changes and N emissions.
Application of the methodology to the entire Study Region required an extensive effort in the
development of the input data for soils, meteorology, and agricultural practices. Also, we
needed to define the important components of agricultural production systems for both current
baseline, or status quo, conditions, and for alternative future conditions resulting from selected
policy mechanisms. The data base and model input development is described in Section 4.0,
followed by the description of, and assumptions in, modeling agricultural systems with the
CENTURY model, in Section 5.0, for the baseline and alternative scenarios.
57
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SECTION 4.0
DATA BASES AND MODEL INPUT DEVELOPMENT
As part of the EPA BIOME Agroecosystems Assessment project, an extensive array of data
bases and GIS coverages were identified, collected, and reviewed by the EPA-Athens staff, and
implemented on their computer system for potential use in this study. The Interim Project
Report (Barnwell et al., 1991) provides details of the climate, soils, land use/land cover, fossil
fuels, energy, ecoregion, life zones, topography, vegetation, CO2 emissions, and other data bases
available at the EPA Athens Laboratory (Barnwell et al., 1991). This section focuses on the
specific data bases used to develop input data needed by the soil carbon models, and
manipulation of these data to derive selected model parameter values appropriate to the scale of
the assessment. In this section, we discuss the sources and the manner in which we manipulated
the climate, soils, agricultural (i.e., cropping, land use, practice), and crop yield data used in
the modeling and project methodology.
4.1 CLIMATE DIVISIONS AND DATA BASES
The models require monthly values for maximum and minimum temperature (CENTURY), or
average temperature (DNDC) and total precipitation. These values are usually provided to the
models as historically recorded input from a user-supplied file. In addition, CENTURY can also
stochastically generate precipitation data using historical mean and skewness values computed
from a user-supplied file or input directly by the user. The goal of this task was to develop the
appropriate temperature and precipitation timeseries for the models at a spatial scale that would
allow consideration of climate variability throughout the Study Region, would not require
excessive project resources, and would be consistent with the spatial resolution of the soils and
agricultural practice scenarios.
A collection of historical climate data for the United States (Wallis et al., 1991) was used to
obtain 41 years (1948-1988) of monthly maximum and minimum temperature and total
precipitation data for 589 climate stations (CS) in and near the Study Region; Figure 4.1 shows
the locations of the climate stations. These stations are operated by the National Oceanic and
Atmospheric Administration (NOAA) and are a subset of the original 1036 stations for which
daily data were provided by the authors on CD-ROM. From these daily data, average monthly
maximum temperature and average annual precipitation values were derived for each of the
stations. A Geographic Information System (GIS) was used to create contour maps of the annual
temperature and precipitation data, as shown in Figures 4.2 and 4.3, respectively. The
temperature contours (using 2°C contour intervals) and precipitation contours (with 10-cm
58
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Figure 4.1 Climate station locations in and near the study region
59
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<
1-iUi
*
-
iS _ « PI
8'Si =
o> m Qj *•
* S 5 »•
< ee < <
\ •••**•>
*«
•*?fcn
^t**
V 'Jv* »-i^c^-1<-^'
Figure 4.2 Average maximum temperature contours [°C] for the study region
60
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Figure 4.3 Average annual precipitation contours [cm] for the study region
61
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contour intervals) were overlayed to produce areas of similar climate. Areas bounded by either
type of contour were assumed to experience similar climate within the accuracy of the contour
intervals. These values of 2°C temperature and 10-cm precipitation were derived from
sensitivity analyses with CENTURY, recommendations and prior experience from the model
authors (Burke et al., 1991), and prior experience in similar modeling investigations (Donigian
and Mulkey, 1992).
The temperature and precipitation isopleths shown in Figures 4.2 and 4.3 were used to delineate
the Climate Division (CD) boundaries, which were adjusted to coincide with county boundaries.
This was done primarily to simplify analysis of the county-level soils data, and the allocation
of the crop rotation and agricultural practice data from the PAs of the RAMS model to the CD.
Direct overlay of the contours did identify subregions of similar climate; however, some of the
regions were not contiguous and varied greatly in size and shape. Therefore, working from
these GIS-created subregions, we altered the CD boundaries manually to follow and not cross
PA boundaries. This resulted in each PA being divided into from two to six CDs, for a total
of 80 CDs in the study region; Figure 4.4 shows the final CD boundaries, along with state and
PA boundaries. As noted earlier, the CDs are the basic spatial unit of analysis for this study.
They provide a reasonable compromise in spatial analysis between the detailed county-level soils
data, the 589 CSs within the study region, and the relatively large PAs which supply the basic
driving factors of the assessment.
To create a single climate data set for each CD, data from multiple CSs were averaged.
Initially, all of the CSs that fell within the boundary of a CD were allocated to the CD for data
averaging. However, when a CD did not encompass any CSs, or when their number and/or
locations were not geographically representative of the entire CD, one or more additional nearby
CSs were manually assigned to the CD to improve the basis for calculating the needed climate
timeseries. For each CD the average of the monthly climate values-from each of the allocated
CSs was saved in a single file for input to the models.
Table 4.1 shows the minimum, maximum, average and standard deviations of the climate
variables for the 41-year period for each CD in the Study Region. The precipitation values are
derived from the average annual values of the CSs assigned to each CD for the 1948-1988
period. The minimum (MIN) and maximum (MAX) values are those of the CSs with the lowest
and highest annual values that are assigned to the CD, while the average (AVE) and standard
deviation (STD) are calculated from all the CS annual averages. The MAX-MIN column in
Table 4.1 represents the range of average annual precipitation values calculated as the difference
of the MAX and MIN columns (with some round off).
The temperature values are calculated in an analogous fashion based on average maximum
monthly temperatures calculated from the daily temperatures for each CS for the 41-year period.
Thus, in Table 4.1, the MIN column is the lowest maximum temperature and the MAX column
is the highest maximum temperature for the CSs assigned to the CD. The AVE and STD
columns are the statistics of average maximum values for the CSs, and the MAX-MIN column
is the range within the CD.
62
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co
Figure 4.4 Climate division boundaries for the study region
63
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Table 4.1 Statistics of Climate Variables for all Climate Divisions
PRECIPITATION (cm) MAXIMUM TEMPERATURE (°C)
CD#
MIN. MAX. AVE. STD. MAX-MIN
MIN. MAX. AVE. STD. MAX-MIN
221
222
223
231
232
241
242
251
252
253
261
262
271
272
281
282
311
312
313
314
321
322
323
341
342
351
352
353
391
392
393
401
402
403
411
412
413
414
415
416
421
422
431
432
433
441
442
443
70.0
77.7
80.4
75.6
74.4
82.3
89.9
71.5
75.8
71.5
71.5
76.4
74.7
85.5
90.8
87.8
91.1
95.6
102.7
103.5
90.9
90.1
95.6
108.5
115.0
94.7
92.9
107.9
64.1
61.3
72.0
74.2
74.2
75.3
73.1
80.5
76.4
85.7
84.2
84.5
87.9
88.3
99.2
99.8
105.9
113.7
120.3
131.1
77.7
80.4
89.6
80.7
77.6
87.4
91.9
89.6
95.4
87.8
76.4
77.6
83.8
95.3
111.7
104.9
101.1
113.6
110.7
109.9
103.5
103.6
107.8
118.6
126.7
96.9
110.7
120.1
71.0
77.0
76.7
81.8
84.0
80.7
85.5
83.5
85.5
91.5
91.0
92.1
95.1
100.8
101.3
106.8
122.2
122.2
126.1
132.9
74,8
79.2
85.0
78.5
75.9
84.3
91.1
81.3
89.9
81.2
74.0
77.1
78.3
91.2
99.8
97.5
96.8
106.2
105.1
106.3
96.5
96.6
101.9
113.2
120.5
95.8
103.0
113.2
68.3
70.1
73.9
78.7
79.5
79.0
79.2
82.4
80.9
88.6
87.1
89.5
90.9
95.6
100.4
103.5
111.6
118.0
122.5
132.1
3.6
1.4
4.2
2.6
1.4
2.8
1.1
9.1
5.9
8.6
3.5
0.6
4.8
3.0
8.8
7.9
3.8
7.6
3.7
3.1
5.5
5.8
5.2
4.1
5.5
1.0
5.6
3.8
2.4
5.9
2.2
3.2
4.4
2.5
4.2
1.7
3.8
2.1
3.0
3.1
2.4
3.9
1.1
3.4
9.1
6.1
2.5
0.9
7,6
2.8
9.1
5.1
3.2
5.2
1.9
18.0
19.6
16.3
4.9
1.2
9.0
9.8
20.9
17.2
10.0
18.0
8.0
6.4
12.6
13.5
12.2
10.1
11.7
2.3
17.8
12.2
0.9
15.7
4.7
7.6
9.8
5.4
12.4
3.0
9.1
5.9
. 6.8
7.6
7.2
12.5
2.1
6.9
16.2
8.6
5.7
1.9
10.0
10.9
10.3
11.7
13.0
13.4
14.8
10.3
13.8
11.7
11.7
12.6
12.6
14.8
13.3
13.4
15.6
17.6
18.1
16.6
15.6
14.7
16.6
17.6
18.9
15.2
16.1
17.6
10.8
11.6
12.7
11.1
12.3
13.1
12.2
13.8
13.8
14.2
14.7.
15.6
15.5
16.9
17.1
17.6
18.9
19.5
19.0
19.5
11.5
11.5
11.3
12.7
13.5
14.0
15.9
11.7
15.3
13.4
12.6
14.1
14.7
15.9
14.1
15.8
17.2
18.0
20.0
18.2
17.2
17.5
18.2
19.0
19.8
15.8
18.0
18.7
11.9
13.1
14.0
12.3
14.0
15.2
14.0
14.2
15.0
15.6
16.3
16.4
16.6
18.3
18.0
19.1
19.1
20.6
21.1
21.8
10.8
11.2
10.9
12.1
13.2
13.6
15.2
11.0
14.4
12.8
12.2
13.5
13.6
15.3
13.7
14.8
16.1
17.8
18.7
17.4
16.0
16.3
17.6
18.5
19.3
15.6
16.8
18.2
11.3
12.5
13.3
11.6
12.9
14.0
13.4
14.0
14.5
14.9
15.6
16.1
16.0
17.4
17.5
18.3
19.0
20.0
20.3
20.5
0.6
0.3
0.4
0.5
0.3
0.3
0.6
0.7
0.5
1.0
0.7
0.8
1.0
0.4
0.4
U
0.7
0.3
0.9
0.7
0.6
1.CL
0.7
0.5
0.4
0.3
0.6
0.3
0.4
0.5
0.6
0.6
0.8
0.9
0.6
0.2
0.5
0.6
0.8
0.3
0.4
0.5
0.5
0.7
0.1
0.7
0.9
1.0
1.5
0.6
1.0
1.0
0.5
0.6
1.1
1.4
1.5
1.7
0.9
1.5
2.0
1.1
0.8
2.4
1.7
0.5
1.9
1.6
1.5
2.8
1.6
1.4
0.9
0.6
1.9
1.1
1.1
1.5
1.3
1.2
1.7
2.1
1.8
0.4
1.2
1.4
1.6
0.8
1.1
1.4
0.9
1.5
0.2
1.1
2.1
2.3
64
-------
Table 4.1 (contd.)
CD#
PRECIPITATION (cm)
MIN. MAX. AVE. STD. MAX-MIN
MAXIMUM TEMPERATURE (°C)
MIN. MAX. AVE. STD. MAX-MIN
443
444
471
472
473
474
475
531
532
533
561
562
571
572
573
581
582
583
584
591
592
601
602
603
611
612
631
632
633
634
641
642
643
Mean
Max.
Min.
131.6
126.1
36.7
46.2
51.5
44.3
55.3
41.6
46.1
55.2
64.1
67.1
68.2
78.9
84.9
39.2
52.2
57.1
66.6
69.9
85.1
85.6
88.3
94.8
107.1
113.2
38.1
51.7
59.9
75.9
75.9
104.5
110.6
80.5
131.1
36.7
132.9
132.9
44.4
58.5
68.7
55.3
62.9
52.4
55.5
66.8
78.9
77.7
78.9
90.0
94.4
48.6
59.1
70.4
85.1
77.7
99.4
92.0
99.8
107.1
122.2
126.1
48.5
69.9
75.9
87.3
99.4
113.2
125.7
90.8
132.9
44.4
132.1
129.9
40.8
50.2
59.0
49.7
59.5
46.1
51.2
62.4
68.6
74.1
74.1
84.2
88.5
44.2
55.3
63.1
74.8
74.8
92.8
89.5
94.2
101.6
114.1
119.2
42.8
59.2
67.3
80.8
92.3
107.8
115.4
85.7
132.1
40.8
0.9
2.7
3.2
5.7
6.2
5.5
3.3
5.6
4.5
3.8
6.9
4.8
4.6
5.3
4.6
3.9
2.9
5.3
8.1
2.8
6.3
3.5
5.8
4.3
6.2
6.3
4.3
7.9
7.2
4.3
8.0
2.9
6.0
4.4
9.1
0.6
1
6
7
12
17
11
7
10
9
11
14
10
10
11
9
9
6
13
18
7
14
6
11
12
15
12
10
18
16
11
23
8
15
10
23
1
.9
.9
.8
.3
.1
.0
•5
.7
.4
.6
.8
.6
.6
.2
.4
.5
.9
.3
•5
.9
.4
.4
.5
.2
.2
.8
.4
.2
.0
.4
.5
.7
.1
.3
.5
.2
19.5
.20.5
9.9
9.7
9.9
12.0
10.8
11.4
12.2
13.0
15.6
16.3
13.4
16.1
17.3
17.8
18.2
18.0
17.1
16.3
18.2
16.0
17.0
17.7
18.8
20.2
19.8
19.8
19.4
19.4
18.4
18.6
19.9
15.2
20.5
9.7
21.8
22.5
11.6
12.0
12.0
12.8
12.5
13.0
14.6
16.2
16.3
17.3
16.1
17.2
18.3
19.3
19.4
19.5
19.5
17.3
18.8
16.3
17.9
20.1
20.6
21.1
21.3
22.3
20.7
21.8
21.4
21.5
22.3
16.7
22.5
11.3
20.5
21.7
10.9
10.5
10.9
12.4
11.7
12.1
13.7
15.1
16.0
16.6
14.7
16.8
17.8
18.7
18.7
18.7
18.3
17.0
18.5
16.2
17.5
19.0
19.8
20.8
20.5
20.6
19.9
20.9
20.3
20.4
21.6
16.0
21.7
10.5
1.0
0.9
0.8
1.0
0.8
0.4
0.7
0.8
0.9
1.1
0.3
0.4
1.3
0.4
0.4
0.7
0.5
0.7
1.1
0.3
0.3
0.2
0.4
0.7
0.8
0.4
0.7
1.2
0.6
1.1
1.1
1.0
1.0
0.6
1.3
0.1
2.3
2.0
1.7
2.3
2.1
0.7
1.7
1.6
2.4
3.2
0.6
0.9
2.7
1.0
1.1
1.5
1.1
1.5
2.5
0.9
0.6
0.4
1.0
2.4
1.7
0.9
1.5
2.5
1.3
2.4
3.0
2.8
2.4
1.5
3.2
0.2
-------
The bottom three lines of Table 4.1 show the averages of the climate variables for all CDs. The
average differences between the minimum and maximum values of the annual precipitation and
maximum temperature are 10.3 cm and 1.48 °C, respectively; the associated ranges are about
2 to 23 cm for precipitation and 0.4 to 3.2 °C. We believe these differences provide reasonably
uniform climate conditions consistent with the spatial scale of the assessment.
4.2 SOILS DATA DEVELOPMENT
CENTURY and DNDC require input data on soil texture (percent sand, silt, and clay), bulk
density, field capacity, wilting point, and initial soil C and N levels. This section discusses the
soils, data bases, and associated model parameters, and Section 5.2 describes the procedures for
estimating initial soil carbon and nutrient levels.
County resolution information was acquired for all of the soil data. The data were taken from
three soils data bases - Data Base Analyzer and Parameter Estimator (DBAPE) (Imhoff et al.,
1991), the 1982 National Resources Inventory (NRI) (Goebel and Dorsch, 1982), and the 1987
NRI/Soils-5 data base (Goebel, 1987). To develop soil texture information for the entire Study
Region, the three data bases were utilized independently, meaning that DBAPE data were used
for all counties contained in the data base (Figure 4.5), and data for any missing counties were
obtained from either the 1982 NRI data (Figure 4.6), or 1987 NRI/Soils-5 data (Figure 4.7).
Data extracted from the each data base were limited to agricultural soils classes (i.e. PO, PI,
and P4 categories), including agricultural soils under both irrigated and nonirrigated conditions.
The soil texture information taken from the 1982 NRI Data base was contained in the 1982
NRI/Soils-5 identification block (fields 76-78) in the 1982 NRI file. The file contained a code
for which a texture could be determined from the National Soils Handbook (Soil Conservation
Service, 1983). Texture information in both DBAPE and the 1987 NRI data bases was listed
as ranges of percent sand and clay. To assign a textural classification for the surface 20-cm
layer, the high and low values from these ranges were averaged and their sum subtracted from
100 to obtain percent silt. This was done for each layer of soil for every soil type in the
counties of the study region identified in DBAPE and the 1982 NRI data bases. Where multiple
soil layers comprised the 20 cm depth, the soil texture values were averaged using depth-
weighting (i.e., using the soil layer depth as the weight) of percent sand, silt, or clay to the 20-
cm depth from the surface of the soil. The resulting depth-weighted sand, silt, and clay
percentages were then used with the Soil Conservation Service Textural Triangle, Figure 4.8,
(Brady, 1990) to assign one of the twelve textural classes based on the depth-weighted
distribution.
Combining the files developed from the three data bases entailed only the sorting of county
federal information processing (FIPS) codes, a unique numerical code for each county within
the United States. Since each of the data bases provided information on the area of each soil
within the county, and each soil had an associated texture class (as described above), we could
determine the area (and distribution) of soil textures in each county of the Study Region. The
66
-------
S
2
cu>
ff»
co
Figure 4.5 Counties for which soil textures were taken from.DBAPE
67
-------
"I5
e i5 w =
01 W 09 *"
Jj I
Figure 4.6 Counties for which soil textures were taken from 1982 NRI
68
-------
Figure 4.7 Counties for which soil textures were taken from 1987 NRI
69
-------
•& <§>
-£ <£>
Percent sand
• - Weighted textural distribution
Figure 4.8 USDA soil textural triangle showing, weighted
textural distribution
70
-------
format of the final result was " County PIPS, Texture, Area " for all of the counties in the Study
Region (i.e., each county could potentially have from 1 to 12 different soil textures).
Table 4.2 lists the percentage of the agricultural soils in RAMS study area defined by each of
the twelve soil textural classes; note that the textural classes of Silt and Sandy Clay are not
present in the Study Region. The distribution of the soil textural percentages shown in Table
4.2 was generally consistent for all the states within the RAMS study region. Table 4.2 also
shows the range of percentages from a state-level analysis; the Study Region consists of 21
states. For the Study Region as a whole, the dominant textures are sandy loam, loam, silt loam,
and silty clay loam. The state-level percentages show that significant areas of some states are
also represented by sand, loamy sand, and silty clay textures. Based on this analysis, and the
need to represent the full range of textural classes, we combined the soils into the six texture
groups, as shown in Table 4.3. The following criteria were used to develop the six soil textural
groups:
a. Percent distribution each soil textural class within the entire RAMS Study Region;
b. Proximity of the grouped soil textural classes on the Soil Conservation
Service Textural Triangle;
c. Sensitivity of CENTURY model to percent sand, silt, and clay; and
d. Burke et al.'s (1990) application of CENTURY for regional analysis
of grassland biogeochemistry where the authors assigned five soil
textural groups based on sand percentage of 0-20%, 20-40%, etc.
Table 4.2 Soil Textural Distribution of Agricultural Soils in RAMS Study Region
Texture
Sand
Loamy Sand
Sandy Loam
Sandy Clay Loam
Loam
Silt
Silt Loam
Silty Clay Loam
Silty Clay
Sandy Clay
Clay Loam
Clay
Percentage of RAMS
Study Area
1
5
20
1
13
0
42
11
3
0
2
3
Range of Percentages
from All States in Study Region
0 - 17
1-26
5 - 39
0- 1
1-29
0
11-72
4-26
1-13
0
0- 7
0- 4
71
-------
Table 4.3 Texture Categories and Groups for RAMS Study Region
Texture Categories* Combined Texture Groups*
Sand
Loamy_Sand
SandyJLoam
Sandy_Clay_Loam
Loam
SiltyJLoam
Silty_Clay_Loam
Silty_Clay
Clay_Loam
Clay
Sand/Loamy_Sand
Sandy_Loam/Sandy_Clay_Loam
Loam
Silty_Loam
Silty_Clay_Loam/Silty_Clay
Clay/Clay_Loam
* Note: Sandy_Clay and Silt were not found in 'the study area.
4.2.1 Derivation of Model Soil Parameters
The overall approach to developing model soil parameters was to estimate appropriate values for
the ten SCS soil texture classifications listed in Table 4.3, before transforming the parameter
values to the six texture groupings used in this study. As noted above, the analysis of the soils
data resulted in " County PIPS, Texture, and Area " for all the counties in the Study Region.
The data from counties in each CD were then grouped together to obtain the distribution of the
area in each of the possible ten soil textural classes for all 80 CD's. The next step involved
the calculation of the following soil physical properties for the ten soil textural classes found in
the RAMS Study Area:
1. Percent Sand
2. Percent Silt
3. Percent Clay
4. Bulk Density
5. Field Capacity
6. Wilting Point
Flach (1992, personal communications) analyzed the SCS-USDA (1991) soils data base for the
states in the Study Region to estimate the soil physical properties listed above. He calculated
the mean distributions (i.e., % sand, % silt, % clay) for almost 2300 soils within the Study
Region, for each of the 10 textural classes. He then mapped the values for each of the 2300
soils onto the SCS soil textural triangle to demonstrate that the textural distribution of 'real-
world' soils are not evenly distributed throughout the soil triangle. Figure 4.8 depicts the
calculated means of percent sand, silt, and clay for all the textural classes. Note that the sand,
72
-------
silt, and clay percentages of the soil textural groups, for the soils in our Study Region, do not
often fall at the center of the textural class. Table 4.4 lists the percent sand, silt, and clay
associated with each soil textural class along with the field capacity, wilting point, and bulk
density estimated from the SCS-USDA (1991) soils database.
The next step consisted of calculating soil physical properties for each CD from the values listed
in Table 4.4. Instead of averaging the properties for the classes included in the six soil textural
groups listed in Table 4.3, we calculated area-weighted soil physical properties for all the
combined soil textural groups. For example, if the 'sand/loamy, sandy' soil group was
comprised of 20% sand and 80% sandy loam in a specific CD, we would use these percentages
to calculate weighted properties for that CD. Table 4.5 lists the weighted soil physical
properties calculated for a few selected CD's; a complete listing for all the CD's can be found
in Appendix E. In Table 4.5 we also list the ranking and the weights for each of the six soil
textural classes. The ranking refers to the area! distribution of a particular soil texture in a CD.
A rank of 1 was given to the most common soil texture in the CD, and a rank of six was given
to the least common soil texture. The weight column in Table .4.5 refers to the percent of the
area expressed as the fraction occupied by each soil textural group in the CD. The relation
between ranking and weight is that the highest weight is assigned a rank of 1 in a CD and vice
versa. ,This ranking and weight information was needed for the calibration of the CENTURY-
simulated yields with historical observed yields, and is explained in detail in Section 5.0.
4.3 CROPPING, LAND USE, AND AGRICULTURAL PRACTICE DATA
All cropping, land use and agricultural practice data for each policy option were provided by
CARD as part of the RAMS output. Table 3.1 in Section 3.3 listed the crop production
activities included in RAMS specifying the crop rotation, tillage category, contour category, and
irrigation conditions. Except for the contour category, the remaining characteristics are
discussed in Section 5.2 under the discussion of 'modeling agricultural production systems'.
Essentially RAMS provided the area for each crop rotation practiced in each PA, along with the
required tillage and irrigation categories needed to accurately model the rotations. The
information from RAMS is current as of the 1990-91 crop year; thus it provides an appropriate
starting point for projection of soil carbon changes over the next 40 years.
The contour categories are not represented in the models because their primary impact is on
erosion control, and erosion is not explicitly represented in the modeling methodology. Section
7.1 includes a discussion of the reasons and effects of ignoring the impacts of erosion in this
study of atmospheric carbon emissions from agriculture.
Since all the crop production information from RAMS is developed on a PA basis, it was
necessary to transform or disaggregate the information to a CD level for use in the assessment.
As noted earlier, the CD boundaries were specifically adjusted to coincide with both county and
PA boundaries so that the CDs are entirely contained within one PA. Consequently, the
transformation is simply to determine how the information for each PA is to be distributed to
each of the enclosed CDs. This disaggregation is based on the distribution of harvested cropland
73
-------
Table 4.4 Selected Physical Properties for the Soil Textural Classes in the RAMS Study Area
Texture
Sand Silt
Clay
Bulk Field Wilting
Density Capacity Point
g/cc % %
Sand
Loamy Sand
Sandy Loam
Loam
Silty Loam
Sandy Clay Loam
Clay Loam
Silty Clay Loam
Silty Clay
Clay
92
81
61
41
12
51
28
7
6
11
5
13
27
41
68
25
41
60
48
35
3
5
12
19
20
24
31
32
46
55
1.58
1.54
1.52
1.42
1.43
1.41
1.41
1.37
1.28
1.18
11.0
13.9
23.5
30.0
33.0
32.5
32.3
36.1
41.6
44.7
3.5
5.3
8.9
12.0
13.0
18.2
17.1
19.8
26.0
28.3
Source: Flach, 1992, personal communication
in the CD compared to the PA; thus the ratio of the cropland within a CD to the cropland within
the PA was used to transform all the RAMS information on crop rotations and practices to the
CD level. County-level cropland area and PA area data from the 1987 Census of Agriculture
(DOC/COA, 1987) was used, in conjunction with a listing of the counties contained within each
CD and PA, to calculate the appropriate transformation factors. This approach assumes that the
cropland distribution is a reasonable surrogate for all the cropping, rotation, irrigation, and
tillage practices used within the PA; we believe this is an appropriate assumption for the scale
of this assessment.
Section 5.0 describes the details of the cropping, land use, and practice information from RAMS
as part of the methodology for modeling the range of agricultural production systems within the
Study Region.
74
-------
Table 4.5 Soil Physical Properties, Weights, and Ranks for Selected CD's
% Sand % Silt % Clay
CD-221
Clay/Clay Loam 11 35 54
Loam 41 41 18
Loamy Sand/Sand 85 10 5
Sandy Clay Loam/Sandy Loam 61 27 12
Silty Clay/Silty Clay Loam 7 55 39
Silt Loam 12 68 20
BD
FC
WP
Weight
CD-272
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/Silty Clay Loam
Silt Loam
CD-341
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/Silty Clay Loam
Silt Loam
CD-413
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/Silty Clay Loam
Silt Loam
CD-562
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/Silty Clay Loam
Silt Loam
CD-603
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/Silty Clay Loam
Silt Loam
CD-643
Clay/Clay Loam
Loam
Loamy Sand/Sand
Silty Clay/Silty Clay Loam
Silt Loam
13
41
83
61
7
12
20
41
91
61
7
12
281
41
81
61
7
12
28
41
81
61
7
12
11
41
60
7
12
11.
41.
81.
7
12
36
41
11
27
58
68
38
41
6
27
57
68
41
41
13
27
60
68
41
41
13
27
50
68
35
41
27
56
68
35
41
13
58
68
52
18
5
12
35
20
43
18
3
12
37
20
31
18
6
12
33
20
31
18
6
12
44
20
54
18
13
37
20
54
18
6
36
20
1.18
1.42
1.56
1.52
1.33
1.43
1.20
1.42
1.55
1.52
1.36
1.43
1.30
1.42
1.58
1.52
1.34
1.43
1.41
1.42
1.54
1.52
1.37
1.43
1.41
1.42
1.54
1.52
1.29
1.43
1.18
1.42
1.51
1.34
1.43
.18
.42
.54
.35
.43
0.45
0.30
0.13
0.23
0.39
0.33
0.44
0.30
0.13
0.23
0.37
033
0.39
0.30
0.11
0.23
0.38
0.33
0.32
0.30
0.14
0.23
0.36
0.33
0.32
0.30
0.14
0.23
0.41
0.33
0.45
0.30
0.24.
0.38
0.33
0.45
0.30
0.14
0.37
0.33
0.28
0.12
0.05
0.09
0.23
0.13
0.27
0.12
0.05
0.09
0.21
0.13
0.23
0.12
0.04
0.09
0.22
0.13
0.17
0.12
0.05
0.09
0.20
0.13
0.17
0.12
0.05
0.09
0.25
0.13
0.28
0.12
0.10
0.22
0.13
0.28
0.12
0.05
0.21
0.13
0.10
0.24
0.08
0.20
0.07
0.31
0.22
0.07
0.09
0.32
0.15
0.15
0.02
0.15
0.10
0.29
0.14
0.30
0.02
0.34
0.01
0.20
0.04
0.39
0.03
0.18
0.01
0.17
0.07
0.54
0.02
0.18
0.25
0.10
0.45
0.01
0.34
0.39
0.05
0.21
Rank
4
2
5
3
6
1
2
6
5
1
4
3
5
3
2
4
1
5
2
1
4
3
75
-------
4.4 CROP YIELD INFORMATION
As noted in the Project Methodology (Section 3.5), historical crop yield data were needed as part
of the procedures for estimating initial soil carbon levels in order to predict changes through the
projection period; Section 5.2 discusses the details of these procedures. The historical yield data
at a state level of resolution were obtained for corn, corn-silage, soybean, wheat, and hay for
the period beginning in 1928 and ending in 1988 on 5-year intervals from United States
Department of Agriculture - National Agricultural Statistics Service (NASS)( 1928-88) and U.S.
Department of Commerce CENSUS of Agriculture (1972-88) for all the states associated in the
study region. However, for the period beginning in 1972 and ending in 1988, yearly county
level yield data were obtained for all crops listed above from NASS computer tapes (USDA-
NASS Tapes, 1988). The yearly county level hay yield was only available for the period 1982
to 1988.
The next step consisted of calculating CD-level yield data. The state level yield data were
assigned as is to CDs that were entirely encompassed within a state; however, if the CD crossed
state boundaries, an area-weighted (based on the area of the CD in each state) yield was
estimated for all the crops hi the period from 1924 to 1971. The yearly county level yield data
were averaged for all the counties that were associated with a given CD. These two data sets
were then combined to obtain a single observed yield data set for all the crops grown in all of
the 80 CD's. The observed yield data for selected CDs are shown in Figure 4.9 for corn and
Soybeans, and Figure 4.10 for wheat and hay.
76
-------
1000
1920
1930
1940
1S50 19EO
YEAR
1970
1980
1990
3000
1930
1930
1990
Figure 4.9 Observed Com and Soybean Yield
77
-------
8000
7000
6000
!?5000
^>-
1
t 4000
> 3000
JD
o
2000
1000
CD272 -S-CD341 T*r CD473 -i-CD603
1920
1930
1940
1950 1960
YEAR
1970
1980
1990
C0272 -B-CD341 -^-CD473 -1- C0603
1920
1930
1940
1950 1960
YEAR
1970
1980
1990
Figure 4.10 Observed Hay and Wheat Yield
78
-------
SECTION 5.0
MODELING SCENARIOS - BASELINE AND ALTERNATIVES
This section describes our approach to representing agricultural production systems within the
Study Region using the CENTURY model and the underlying assumptions and procedures used
to assess SOC changes. We discuss the definition and parameterization of agricultural
production systems, the estimation of initial soil carbon (and nitrogen) values as a basis for
projecting future changes, the assumptions used to define both baseline (i.e., current) and
alternative future conditions reflecting the modeling scenarios developed by RAMS, and the
operational procedures employed for the CENTURY model runs. The focus is on how the
models are used, including underlying model assumptions, application assumptions, and
parameter adjustments, to approximate expected future conditions associated with each defined
scenario.
5.1 MODELING AGRICULTURAL PRODUCTION SYSTEMS
An 'agricultural production system' is a complex combination of (1) specific crops grown in
defined rotation patterns; (2) seasonally defined tillage and harvest practices using specified
implements (equipment) that control the soil, crop, and residue disposition; and (3) agronomic
inputs including fertilizers, manure, and/or organic amendments along with irrigation water and
pesticides, as appropriate. All these components are integrated within the local climate and soil
resource environment, and are ultimately influenced by the policy and market (economic)
mechanisms that drive the demand for the products produced by the system.
This section describes the details of our approach for representing the physical aspects of
agricultural production systems within the RAMS study region using the CENTURY model; the
policy and market mechanisms are represented by RAMS and are discussed in Sections 5.3 and
5.4. The following subsections describe each of the three major components of these systems
noted above — crops and rotations, tillage and residue management, inputs — within the
limitations of the modeling capabilities and data available for this investigation. Clearly, as in
any modeling exercise, representing agriculture on 216 million acres with 100,000 farms or
more, requires some simplification of the 'real system' to make the assessment feasible.. Thus,
our approach was to focus on the dominant crops, rotations, and tillage practices, and represent
the soil carbon impacts of these combinations within the spatial variability of the climate and
soils conditions of the Study Region.
79
-------
S.I.I Crops and Rotations
Cropping information for the RAMS Study Region was provided by CARD for each modeling
scenario. Figure 5.1 shows the cropland distribution by CD for the Study Region; this
distribution was used for all policy scenarios. Table 5.1 tabulates the crop distribution by PA,
i.e., % of PA area for each crop, for the Status Quo scenario, along with the corresponding
distribution for the study region; the '% cropland' in each PA mapped in Figure 5.1 is also
listed. As expected, the dominant crops include corn, soybeans, hay, and wheat which
collectively comprise almost 90% of the cropland in the study region. Combining this
information with the crop modeling capability of CENTURY was the basis for identifying the
specific crops to be simulated.
Unfortunately, not all crops are currently included in CENTURY i.e., appropriate parameter
values have not been developed, tested, and confirmed with field site applications. Based on
past and current model testing, similarities of crop species, and the cropping patterns of the
study region, we selected six crops for simulation. We've noted below how the parameters for
these crops are used to represent other crops lacking parameter values:
Corn Corn is used to represent all corn, sorghum, and other crops occupying
minor areas within the PAs (.e.g., cotton, sunflowers). Different crop
parameter values are used for Corn-Grain and Corn-Silage due to the
difference in biomass produced. Appendix B reports on CENTURY
testing on a continuous corn site in Lexington, KY.
Soybean Soybean parameters were initially developed and tested by NREL as part
of this study; further testing is needed and planned, possibly on a corn-
soybean rotation site in Iowa.
Wheat Wheat parameters are used to represent all small grains, e.g., wheat, oats,
and barley. CENTURY has been extensively tested for various wheat
species, including some testing on a site in Sydney, NE as part of this
study (see Appendix A)
Legume-Hay Legume hay parameters were developed and tested by NREL for selected
temperate grasses with N fixation characteristics.
Non-Legume Non-legume hay parameters were developed by NREL for selected
Hay temperate grasses.
Grassland Grassland parameter values have been developed by NREL for numerous
species over a number of years of model testing. These parameters are
used in the CRP scenario.
80
-------
Figure 5.1 Cropland distribution for the study region
81
-------
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Since crops are primarily grown within a specific rotation sequence, the next step was to identify
the rotation sequences practiced in the region. Table 5.2 shows the crop rotations for the study
region obtained from the RAMS output, along with their acreage and '% of Study Area'; more
than 80 different rotations are used in different PAs of the region, ranging from continuous
cropping (usually only for corn, hay, wheat or sorghum) to 2- to 6- year rotations of up to 14
different crops. However, only 18 different rotations exceed 1.0% of the cropland area in the
region, and the most prevalent rotation is corn-soybean which makes up 23.9%.
Considering the specific crops simulated by CENTURY, we analyzed the crop rotation
information and developed rotation combinations, or groupings, to minimize the number of
individual rotations that must be simulated for each defined subregion, i.e., climate division.
Table 5.3 shows the final list of 35 rotations selected for simulation, reduced from the original
80 rotations listed in Table 5.2. Table 5.4 lists the original rotations included with the modeled
rotations.
Our general approach to grouping the less frequent rotations was to eliminate any rotation that
was less than 5% of a PA, unless it was a significant rotation (i.e., more than 5%) in another
PA, and would therefore have to be simulated anyway. With this final list, each PA contains
2 to 8 rotations, and most have only 4 or 5. Table 5.3 also shows the grouped acreage and the
corresponding ' % of Study Region' resulting from the grouping. Although we have retained the
designations for barley, oats, and sorghum in Table 5.3, as noted above, barley and oats are
represented with the wheat parameters and sorghum uses the corn parameters in CENTURY.
5.1.2 Tillage and Residue Management Practices
Soil carbon is a direct function of the carbon inputs to the soil derived from the crop or
vegetation, both as roots and crop residues. Thus, the impacts of tillage and harvest practices
that control the disposition of the crop biomass, and associated carbon, must be accurately
represented to mimic the carbon balance of the soil. This section discusses the procedures and
parameters used to model the impacts of tillage and harvest practices for the crops and rotations
in the Study Region.
An overview of the crop model is needed in order to understand the potential impacts of the
model parameters. Figure 5.2 is a diagram of the crop model in CENTURY identifying the
various carbon pools represented; each pool also has an associated N pool (and other nutrients).
The various pools can be grouped as follows:
Live Plant Material
Dead Plant Residue
Above Ground
- Grain
- Shoots
- Roots
Below Ground
- Standing dead
- Surface litter
1
- Root residue
83
-------
Table 5.2 Complete List of Crop Rotations in the Study Region
CR# Crop Rotation Acreages
2 BAR,BAR,SOY 4,190,350
4 BAR,BAR,SMF 390,970
8 BAR,CRN,CRN,CRN 395,228
11 BAR, CSL 63,860
14 BAR,CSL,CSL * 0
21 BAR,HLH,HLH,CRN,CRN 4,464,886
70 BAR,SOY 58,495
78 BAR,SUN,SMF,SWT 1,584,520
80 BAR,SWT 124,394
81 BAR,SWT,SUN,SWT 2,611,154
100 CRN 18,097,382
105 CRN,CRN,CRN,OTS,HLH,HLH * 0
107 CRN,CRN,CRN,OTS,NLH,NLH 216,102
109 CRN,CRN,CRN,SWT 2,695
111 CRN,CRN,CRN,WWT,HLH 155,153
115 CRN,CRN,CRN,HLH,HLH,HLH 713,294
122 CRN,CRN,OTS,HLH,HLH 244,365
123 CRN,CRN,OTS,HLH,HLH,HLH 2,316,860
125 CRN,CRN,OTS,NLH,NLH 1,164,545
131 CRN,CRN,SOY 15,395,588
132 CRN,CRN,SOY,OTS,HLH 2,666,886
133 CRN,CRN,SOY,OTS,HLH,HLH * 0
136 CRN,CRN,SOY,SRG 574,704
137 CRN,CRN,SOY,WWT 552,384
138 CRN,CRN,SOY,WWT,HLH 2,008,010
142 CRN,CRN,WWT * 0
144 CRN,CRN,WWT,HLH,HLH 1,747,290
145 CRN,CRN,WWT,HLH,HLH,HLH 604,068
162 CRN,OTS,HLH,HLH,HLH 861,163
168 CRN,OTS,NLH,NLH,NLH 172,299
170 CRN,OTS,WWT 101,876
178 CRN,SRG 116,479
186 CRN,SOY 51,681,598
189 CRN,SOY,CRN,WWT,HLH,HLH 1,325,780
196 CRN,SOY,OTS,NLH,NLH 588,494
201 CRN,SOY,WWT 12,991,752
202 CRN,SOY,WWT,HLH * 0
203 CRN,SOY,WWT,HLH,HLH,HLH 3,447,955
210 CRN,SWT 223,820
% of Study Region
1.9
0.2
0.2
0.0
0
2.1
0.0
0.7
0.1
1.2
8.4
0
0.1
0.0
0.1
0.3
0.1
1.1
0.5
7.1
1.2
0
0.3
0.3
0.9
0
0.8
0.3
0.4
0.1
0.0
0.1
23.9
0.6
0.3
6.0
0
1.6
0.1
84
-------
Table 5.2 (contd.)
CR # Crop Rotation Acreages
215 CRN,SWT,SWT 2,190,769
218 CRN.WWT 746,696
231 CRN,NLH,NLH 227,696
232 CRN,NLH,NLH,NLH 423,464
235 CSL 527,634
239 CSL,CSL,CSL,SWT 414,132
242 CSL,CSL,OTS,HLH 916,740
243 CSL,CSL,OTS,HLH,HLH 1,631,370
244 CSL,CSL,OTS,HLH,HLH,HLH 1,353,307
246 CSL,CSL,OTS,NLH,NLH 255,575
250 CSL,CSL,SOY 447,257
252 CSL,CSL,WWT,NLH,NLH 5,290
259 CSL,OTS 541,752
261 CSL,OTS,HLH,HLH 284,299
262 CSL,OTS,HLH,HLH,HLH 1,677,935
265 CSL,OTS,HLH,HLH,SWT 428,316
276 CSL,SOY,CSL,OTS,NLH,NLH 87,435
277 CSL,SOY,HLH,HLH,HLH,HLH 444,915
280 CSL,SWT 411,200
325 COT.SOY 2,455,120
339 HLH,HLH,HLH,HLH,SRG,SOY 941,592
350 OTS,HLH,HLH,HLH 809,412
354 OTS,HLH,HLH,HLH,SRG,SRG 451,784
366 OTS,NLH,NLH,NLH 2,294,135
372 OTS,NLH,NLH,NLH,SOY 137,963
379 OTS,NLH,NLH,SSL,SOY,SSL 20,359
384 OTS,OTS,SMF 1,090,070
409 SRG 1,211,234
410 SRG,SRG,SOY * 0
411 SRG,SRG,SOY,WWT * 0
415 SRG,SOY 1,483,385
416 SRG,SOY,SOY 1,987,066
417 SRG,SOY,WWT 224,954
419 SRG,SOY,HLH,HLH,HLH,HLH 202,938
424 SRG,WWT 3,304,256
432 SRG,NLH,NLH 23,971
434 SSL,SSL,WWT,NLH,NLH 3,110
439 SSL,WWT 99,701
452 SOY,SWT,SWT 1,051,320
454 SOY,WWT 260,553
% of Study Region
1.0
0.3
0.1
0.2
0.2
0.2
0.4
0.8
0.6
0.1
0.2
0.0
0.3
0.1
0.8
0.2
0.0
0.2
0.2
1.1
0.4
0.4
0.2
1.1
0.1
0.0
0.5
0.6
0
0
0.7
0.9
0.1
0.1
1.5
0.0
0.0
0.1
0.5
0.1
85
-------
Table 5.2 (contd.)
CR # Crop Rotation
458 SOY,WWT,SOY
459 SOY,WWT,WWT,WWT
462 SMF,NLH,NLH,NLH,NLH
463 SMF,SWT
471 SMF,WWT *
478 SWT,HLH,HLH,HLH
490 WWT
503 HLH,HLH,HLH,HLH
508 NLH,NLH,NLH,NLH
Total RAMS Study Area =
Acreages
884,594
1,677,812
43,065
11,744,092
0
51,908
2,848,742
18,250,303
18,057,440
216,481,059
% of Study Region
0.4
0.8
0.0
5.4
0
0.0
1.3
8.4
8.3
100.0
Modeled under Tillage and Cover Crop Policies
86
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Table 5.3 Final List of Crop Rotations for Modeling with CENTURY
CR # Crop Rotation Acreages
2 BAR,BAR,SOY 5,241,670
4 BAR,BAR,SMF 3,065,560
100 CRN 19,425,089
115 CRN,CRN,CRN,HLH,HLH,HLH 713,294
125 CRN,CRN,OTS,NLH,NLH 1,552,951
131 CRN,CRN,SOY 16,365,526
138 CRN,CRN,SOY,WWT,HLH 5,382,433
144 CRN,CRN,WWT,HLH,HLH 6,456,545
145 CRN,CRN,WWT,HLH,HLH,HLH 4,117,983
186 CRN,SOY 55,678,535
189 CRN,SOY,CRN,WWT,HLH,HLH 1,325,780
196 CRN,SOY,OTS,NLH,NLH 588,494
201 CRN,SOY,WWT 13,216,715
203 CRN,SOY,WWT,HLH,HLH,HLH 3,447,955
215 CRN,SWT,SWT 5,025,740
218 CRN,WWT 4,252,531
232 CRN,NLH,NLH,NLH 651,160
235 CSL 527,634
239 CSL,CSL,CSL,SWT 416,827
243 CSL,CSL,OTS,HLH,HLH 2,548,110
244 CSL,CSL,OTS,HLH,HLH,HLH 2,055,728
246 CSL,CSL,OTS,NLH,NLH 571,056
250 CSL,CSL,SOY 447,257
262 CSL,OTS,HLH,HLH,HLH 1,928,414
280 CSL,SWT 1,016,813
339 HLH,HLH,HLH,HLH,SRG,SOY 1,144,530
350 OTS,HLH,HLH,HLH 861,321
366 OTS,NLH,NLH,NLH 2,294,135
416 SRG,SOY,SOY 1,987,066
458 SOY,WWT,SOY 1,145,147
459 SPY,WWT,WWT,WWT 1,677,812
463 SMF,SWT 11,744,092
490 WWT 2,848,742
503 HLH,HLH,HLH,HLH 18,695,218
508 NLH,NLH,NLH,NLH 18,065,842
Total RAMS Study Area = 216,481,020
% of Study
Region
2.4
1.4
9.0
0.3
0.7
7.6
2.5
3.0
1.9
25.7
0.6
0.3
6.1
1.6
2.3
2.0
0.3
0.2
0.2
1.2
0.9
0.3
0.2
0.9
0.5
0.5
0.4
1.1
0.9
0.5
0.8
5.4
1.3
8.6
8.3
100.0
87
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Table 5.4. List of Modeled and Original Crop Rotation Numbers and Crop Sequence
Modeled Original
Rotation Rotation
Number Number
2 2,452
4 4,78,384
100 100,178,409
115 115,105
125 125,107,168
131 131,8,136,142,410,411
138 138,111,132,137,133
137 137
144 144,21,122
145 145,80,123,162,354
186 186,70,325,415
189 189
196 196
201 201,417,202
203 203
215 215,81,210
218 218,170,424,439
232 232,231
235 235
239 239,109
243 243,242
244 244,265
246 246,276,379,434,252
250 250
262 262,261
277 277
280 280,11,259,14
339 339,419
350 350,478
366 366,372,432,462
416 416
458 458,454
459 459
463 463,471
490 490
503 503,277
508 508,252,434
Crop Rotation Modeled
BAR,BAR,SOY
BAR,BAR,SMF
CRN
CRN,CRN,CRN,HLH,HLH,HLH
CRN,CRN,OTS,NLH,NLH
CRN,CRN,SOY
CRN,CRN,SOY,WWT,HLH
CRN,CRN,SOY,WWT
CRN,CRN,WWT,HLH,HLH
CRN,CRN,WWT,HLH,HLH,HLH
CRN,SOY
CRN,SOY,CRN,WWT,HLH,HLH
CRN,SOY,OTS,NLH,NLH
CRN,SOY,WWT
CRN,SOY,WWT,HLH,HLH,HLH
CRN,SWT,SWT
CRN,WWT
CRN,NLH,NLH,NLH
CSL
CSL,CSL,CSL,SWT
CSL,CSL,OTS,HLH,HLH
CSL,CSL,OTS,HLH,HLH,HLH
CSL,CSL,OTS,NLH,NLH
CSL,CSL,SOY
CSL,OTS,HLH,HLH,HLH
CSL,SOY,HLH,HLH,HLH,HLH
CSL,SWT
HLH,HLH,HLH,HLH,SRG,SOY
OTS,HLH,HLH,HLH
OTS,NLH,NLH,NLH
SRG,SOY,SOY
SOY,WWT,SOY
SOY,WWT,WWT,WWT
SMF,SWT
WWT
HLH,HLH,HLH,HLH
NLH,NLH,NLH,NLH
-------
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Through the various tillage and harvest parameters in CENTURY, we identify the impact on,
and disposition of, the C and N content of these pools for specific operations. Since these
parameters control the amount of C input to the soil from the plant, they have a major impact
on SOC; thus, their values are critical to adequately representing the SOC impacts of cropping
and tillage practices.
Tillage Parameters and Practices -
The CENTURY model allows the user to define the impacts of individual tillage operations
through a suite of model parameters that define the amount, or fractions, of plant biomass, and
associated C and N contents, that are transferred between the various above and below ground
pools as a result of the tillage operation. Table 5.5 shows the parameter values used to represent
a variety of tillage equipment typically associated with conventional, reduced tillage and no
tillage practices. Thus, for example, a mold board plowing operation would transfer 90% of
the live and dead plant material to the soil layer and 10% would remain on the soil surface;
whereas, a chisel plow leaves 58% of the plant residue on the surface and incorporates 42% into
the soil layer. Most of these parameter values were developed by the CENTURY authors
through model applications for grassland and wheat systems. The parameters for tillage
equipment associated more with other cropping systems (i.e., corn, soybeans) were added under
this study based on similar parameters from the EPIC model (Sharpley and Williams, 1990).
In addition to the impact of individual tillage operations, the timing and frequency of all
operations - primary tillage, planting, cultivation, harvest - must be defined throughout the year
for all crops in each rotation. The frequency of tillage operations is shown in Table 5.6 for both
the historical time period, 1907-1988, and the projection period, 1989-2030. The practices
simulated for the projection period, based on the categories defined by RAMS, include:
Conventional Tillage
(Fall Plow)
Conventional Tillage
(Spring Plow)
Reduced Tillage
No-Till
CTFP
CTSP
RT
NT
For each of theses practices, the number of times a specific tillage operation is performed is
shown by the numbers in Table 5.6; thus, conventional tillage includes two disk cultivations,
for seedbed preparation, and two row cultivations for weed control during the growing season,
while reduced tillage uses a chisel plow plus one operation each of disk and row cultivation.
The no-till practice uses herbicides, a no-till drill operation, and no other tillage operations.
These assumptions for the type and number of operations were developed through consultation
with agronomy experts at Colorado State and Iowa State during discussions with the study team.
They are meant to represent the general nature and frequency of tillage operations for the crops
grown in the study region, not the exact practices and variations utilized by all the individual
fanners.
90
-------
Table 5.5 Tillage Parameters Used in the CENTURY Model
Tillage Options1
Fraction
transferred
Multiplicative
Effect on Soil
From
Live
Dead
Surface
Roots
Decomposition
To
Dead
Surface
Soil
Surface
Soil
Soil
Soil
1 - Abbreviations: MBP
CP
PDC
DC
RC
NTD
HB
D
-
MBP
0.00
0.10
0.90
0.10
0.90
0.90
1.00
1.60
CP
0.00
0.58
0.42
0.58
0.42
0.42
1.00
1.40
PDC
0
0
.00
.05
0.95
0
0
0
1
1
.05
.95
.95
.00
.60
DC
0.00
0.50
0.50
0.50
0.50
0.50
1.00
1.60
RC
0.00
0.00
0.00
0.00
0.00
0.50
0.00
1.30
NTD HB
0.05 1.00
0.05 0.00
0.00 0.00
0.05 0.00
0.05 0.00
0.05 0.00
0.10 1.00
1.00 1.00
D
0.05
0.05
0.10
0.05
0.15
0.20
0.20
1.10
Mold Board Plow
Chisel Plow
Combined Mold Board Plow
and Disk
Cultivator
Disk Cultivator
Row Cultivator
-
-
No-Till Drill
Herbicide
Planting Drill
91
-------
Table 5.6 Type and Number of Tillage Operations for 1907-2030
Time Period 1907-70
Tillage
1971-88 1989-2030
CTSP CTFP RT NT
CC
Mold Board
Plow (fall)
Mold Board
Plow (spring)
Chisel Plow
Disk
Cultivator
Planting
Drill
Row
Cultivator
Herbicide
No Till
Drill
1
Note: The numbers in the column under a particular tillage practice reflect the number of times
a particular tillage operation will be performed.
Abbreviations:
CTSP -
CTFP -
RT
NT
CC -
Conventional Till Spring Plow
Conventional Till Fall Plow
Reduced Till
No Till
Crop Rotations with Cover Crops
92
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The timing of operations was developed in relation to expected planting and harvesting dates for
each of the simulated crops for each Climate Division (CD). The U.S.D. A Handbook No. 628,
Usual Planting and Harvesting Dates for U.S. Field Crops. (U.S.D.A., 1984) provided this
information for the Study Region for each individual state. Plowing was set in the month either
prior to planting, for spring plowing, or following harvest for fall plowing. Field cultivations
were set at monthly intervals following planting. The monthly time step in CENTURY
precluded a more accurate representation of the seasonal variation in planting and harvesting
dates across the Study Region since the dates had to be adjusted to the most appropriate month,
although differences of a few weeks were common among the CDs. Consequently, the timing
of operations was essentially the same in much of the study region, except for earlier planting
in a few of the southern CDs. Table 5.7 summarizes the planting and harvesting date
information.
Table 5.7 Summary of Planting and Harvesting Dates for RAMS Study Region
Northern CDs Southern CDs*
Crops
CENTURY
Planting
Date
CRN
CSL
SOY
SWT
WWT
HAY
5/1
5/1
5/1
5/1
11/1
4/1
CENTURY
Harvesting
Date
10/1
10/1
10/1
9/1
8/1
CENTURY
Planting
Date ,
4/1
4/1
5/1
3/1
10/1
6/1,8/1,10/1** 3/1
CENTURY
Harvesting
Date
9/1
9/1
10/1
7/1
7/1
6/U8/l,10/l**
* - Represents CDs in Arkansas, Kansas, Missouri, Oklahoma and Tennessee.
All other CDs are represented as Northern CDs.
** - Represents three harvest events.
Abbrevations:
CRN - Corn
CSL - Corn Silage
SOY - Soybean
SWT - Spring Wheat
WWT - Winter Wheat
HAY - Legume and Non-Legume Hay
93
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Harvest and 'Grazing' Parameters -
The harvest and grazing parameters in CENTURY are used to specify the disposition of the total
crop biomass, including both grain and shoots, as a result of harvest operations for both forage
and non-forage crops. These parameters have a major impact on soil carbon because they
determine the amount of plant material remaining after harvest that is subsequently available to
replenish or increase soil carbon levels. For non-forage crops, like corn-grain, soybeans, and
small grains, we assume that all the grain is removed during the harvest operation, that none of
the plant residue is removed, and that 50% of the residue remaining after the operation is left
in the 'standing dead' pool (see Figure 5.2). These assumptions are reflected in the harvest
parameter values shown in Table 5.8. The values shown were developed primarily based on
experience with wheat in the Great Plains, but they are generally appropriate for other crops and
regions. The assumption that none of the plant residue is removed has the most impact on soil
carbon levels as this allows all plant material, except the harvested grain, to ultimately return
to the soil. The assumption that 50% of the residue remains as standing dead has little impact
since subsequent tillage operations re-distribute the residue among the various pools, except for
No-Till, which lacks the operations for residue incorporation.
Table 5.8
Model
Parameter
AGLREM
BGLREM
FLGHRV
RMVSTR
REMWSD
HIBG
Harvest parameters for Corn-Grain. Soybeans, and Small Grains (Wheat. Barley.
and Oats') (assuming only GRAINS are harvested)
Definition
Fraction of above ground live which will not
be affected by harvest operations
Fraction of below ground live which will not
be affected by harvest operations
Flag for grain harvest
(indicating harvest of grains)
Fraction of the above ground residue that will be
removed
Fraction of the remaining residue that will be left
standing
Fraction of roots that will be harvested
Value
0.00
0.00
1
0.00
0.50
0.00
94
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For the forage crops, i.e., corn-silage and hay, a different approach was used. Since these crops
are grown primarily for animal (cattle, hogs) production, we devised an approach that attempts
to account for the animal waste returned to the field from use of these crops as animal feed.
Unfortunately, we were unable to locate or identify any regional or national scale data base that
would allow us to define animal waste application rates (C and N) for all the cropland in our
CDs. However, there is a clear correlation between animal populations and forage crop acres
for counties in our study region. Based on this information, -we assumed that all forage crops
grown within a CD are fed to animals within the same CD and the resulting waste is
returned to the same forage cropland as fertilizer. Although this is an approximation, it is
appropriate for the scale of our assessment, and it allows us to maintain a mass balance for C
and N for these crops that is consistent with the available data.
The NREL staff recommended the use of the 'grazing option' in CENTURY to represent the
direct return of animal waste derived from the harvested forage crops. Table 5.9 shows the
parameter values used to represent the internal link between the harvested forage crops and the
animal waste C and N returned to the field. In effect, the values were set to represent a pseudo-
harvest, or grazing, event as removing 90% of the crop and returning 40% of the N content,
Table 5.9 Harvest (Grazing) parameters for Corn-Silage and Hay.
Model Definition
Parameter
FLGREM Fraction of live shoots removed by a grazing
event
FDGREM Fraction of standing dead removed by a grazing
event
GFCRET Fraction of removed material returned by a
grazing event for carbon
GRET(l) Fraction of N in removed material returned
by a grazing event
GRZEFF Effect of grazing on production
0 - No direct effect
1 - Moderate effect (linear decrease in
production)
2 - Intensively grazed production effect
(quadratic effect on production)
FECF(l) Fraction of N in material returned as feces
(added to litter pool, rest added to mineral N)
FECLIG Lignin content of feces
Value
0.90
0.90
0.20 (com)
0.30 (hay)
0.40
0.0
0.80
0.25
95
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and 20% to 30% of the C content to the field; the value of 20% was used for corn-silage and
30% for hay, due to the larger biomass and C/N ratio of the corn-silage.
The return percentages were derived from a brief review of the limited literature on this subject
~ primarily reports of N losses associated with the transformation of feed to animal waste and
its return to the field. In studies on N balances for grasslands, Woodmansee (1979) cites
literature to support a 17% uptake of N from ingested feed, with the remaining 83% eliminated
in the waste. He also assumed a 50% loss due to volatilization of the inorganic N (primarily
NH3), which is consistent with estimates of handling and application losses by Donigian et al.
(1991) and Mosier (personal communication, 1992). Further assuming a minimal 5% loss for
runoff/leaching from feedlots, we see that the three loss mechanisms combine to produce about
40% of crop N returned in the applied waste (i.e., 0.83 x 0.50 x 0.95 = 0.39, rounded to
40%.)- Using estimated C/N ratios for corn-silage (30-40), hay (20-30), and animal waste (15-
20), we estimated a 20% return of C for corn-silage and 30% return for hay.
Clearly, this approach is an approximation to the complex transformations of C and N in animal
wastes, and the impacts of potential feed and waste transfers (arid associated C balances) among
the counties and subregions of the study area. Further refinement of this approach, or an
alternative, possibly integrating animal population data and use of small grains as feed stocks,
should be investigated in future efforts. However, for this initial effort, we believe this
simplified assessment is reasonable for the scale of our CDs and the limited extent of corn-silage
and hay croplands in the study region.
5.1.3 Fertilizer and Irrigation Inputs
The primary inputs to agricultural production systems that affect soil carbon levels include
fertilizers, animal waste, pesticides, and irrigation. The procedures for accommodating animal
waste applications and use of pesticides (primarily herbicides) to replace tillage operations were
described above. This section discusses the procedures used to represent fertilizer and irrigation
applications. For both of these inputs, CENTURY allows the user to select 'automatic' options
that allow the model to calculate nutrient and water inputs as a function of crop and soil moisture
conditions, associated crop needs, and selected fractions of optimum levels (farmer control)
during the growing season. Thus, based on model parameter values selected by the user,
CENTURY calculates the needed nutrient and irrigation water inputs and makes them available
to the crop.
For the automatic fertilizer option, CENTURY allows the user to define the fraction of
maximum potential carbon production desired and then determines the nutrient application rate
needed to attain that level while maintaining crop nutrient concentrations at appropriate levels.
Thus, if the user desires a fertilization level corresponding to 80% of maximum crop (i.e.,
carbon) production, he sets the input parameter (aufert=A80) to indicate 80% of production and
the model applies the appropriate amount of nutrient. Since we are only modeling C and N in
this study, N was the only nutrient added as fertilizer.
96
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Similarly for the automatic irrigation option, CENTURY allows the user to define the soil
moisture level, as a fraction of available water holding capacity(AWC), at which irrigation will
be applied to raise the level to field capacity. Thus, if the user defines the trigger level at 50%,
the model will add enough water whenever the moisture level reaches 50% of AWC to raise the
level to field capacity. This option was only used for the CDs and crop rotations receiving
supplemental irrigation as identified by the RAMS model.
CENTURY provides a number of other options, including defining specific fertilizer and
irrigation applications; however, the options described above were the ones chosen for this
study. The lack of reliable, comprehensive data bases for fertilizer and irrigation (as with
animal waste) applications for all crops, rotations, and regions of the study area precluded any
possible reliance on use of actual data for these inputs. The study team decided that the use of
these automatic options within CENTURY were reasonable and appropriate for the purposes of
this study.
5.2 DEVELOPMENT OF INITIAL SOC CONDITIONS AND BASELINE PROJECTIONS
Significant changes in soil organic matter, either depletion or accumulation, usually occur slowly
in agricultural systems with timeframes on the order of decades. In estimating equilibrium soil
carbon levels under native grassland and rangeland conditions, the CENTURY model is often
run for 5000 to 10,000 years in order to calculate stable values (Parton et al., 1989; Burke et
al., 1990). In agricultural systems, changes in soil carbon depend on soil texture, regional
climate, and carbon inputs to the soil, which are determined by cropping practices, crop yields,
and management (i.e., rotations, tillage, fertilizer applications, residue/harvest practices). The
challenge for this study was to develop a procedure for estimating initial soil carbon levels for
each of our CDs, consistent with historical agricultural practices within each CD, as a basis for
projecting future changes in SOC under both baseline (Status Quo) and alternative policy
scenarios. A correlated issue is how to define the assumptions that establish the baseline
condition over the projection period, i.e., do we assume agricultural practices will not change
over the next 40 years or do we incorporate changes that are expected even without additional
policy changes? Both issues are discussed below.
Although a number of soils data bases exist that include soil carbon, or organic matter, their
utility for estimating initial soil carbon levels is limited. The primary limitation is that the data
are usually associated with a specific soil series but without any definition of the variation in
SOC due to the specific historical practices. Pedon data bases that provide values for defined
sample sites also suffer from not having a history of the land use at the site. Moreover, the
values in the data bases are derived from samples collected over the past 10 to 20 years, so they
are not really 'current' conditions. Even if appropriate soil carbon data were available,
assigning values to the individual carbon pools in CENTURY would be difficult because the
pools are conceptually defined based on turnover rates.
97
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The approach developed to estimate starting SOC conditions was suggested by Dr. Vern Cole
of the USDA Agricultural Research Service (Fort Collins, CO) based partly on his past work
with CENTURY (Cole et al., 1989), studies by Paustian et al. (1992) on impacts of carbon
inputs on soil carbon, and current studies modeling historical yields and SOC on wheat-fallow
systems in Colorado (V. Cole, personal communication, 1992). The underlying concept is to
model a prior historic time period of agricultural activity, and calibrate to historic crop yields,
so that the carbon inputs to the soil, the major factor determining soil carbon levels, are
accurately represented.
To apply this general concept to the study region, we developed historical scenarios for cropping
practices, tillage practices, fertilizer use, and harvest practices, and then attempted to match
historical yield trends within each CD so that carbon inputs and SOC could be estimated with
CENTURY. The overall steps in the methodology for estimating starting conditions are listed
below, followed by a discussion of the underlying assumptions:
1. Use the IVAUTO option in CENTURY to start model runs in 1907 assuming a native
grassland condition prior to the onset of agriculture. The IVAUTO option allows the
user to estimate beginning (1907) SOC conditions based on regression models developed
by Burke et al. (1989) that estimate SOC as a function of soil texture and regional
climate. Although the data used by Burke et al covered only the western fringes of our
Study Region, we believe the 81-year simulation period from 1907 to 1988 will minimize
the impact of any discrepanices in the 1989 starting values for our projection period.
2. Identify and select the dominant rotation in each of the CDs based on the distribution
and area of rotations provided by RAMS. Table 5.10 lists the dominant rotations for
each PA, the corresponding CDs within the PA, and the percent of PA cropland occupied
by the dominant rotation.
3. Run CENTURY for each dominant rotation in each CD for each of the 6 soil texture
groups existing in the CD for the period 1907 to 2030; Table 5.11 lists the primary
assumptions for weather data and historical management practices for defined time
periods, which are discussed further below.
4. Calibrate the CENTURY model crop parameters to historic crop yield data for the 1907
to 1988 time period, for all crops and for the dominant soil type within each rotation, so
that historic carbon inputs are represented.
5. Use the 1988 year-end conditions for SOC (and all other CENTURY required state
variables) for each soil texture group and rotation as the starting conditions for the
1989-2030 projections; starting in 1989 allows a 1- to 2-year period to adjust any
inconsistencies between the initial conditions and the simulated scenario.
98
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Table 5.10 Dominant Crop Rotations within Each PA
PA # CD #
22
23
24
25
26
27
28
31
32
34
35
39
40
41
42
43
44
47
53
56
57
58
59
60
61
63
64
221 to 223
231 to 232
241 to 242
251 to 253
261 to 262
271 to 272
281 to 282
311 to 314
321 to 323
341 to 342
351 to 353
391 to 393
401 to 403
411 to 416
421 to 422
431 to 433
441 to 444
471 to 475
531 to 533
561 to 562
571 to 573
581 to 584
591 to 592
601 to 603
611 to 612
631 to 634
641 to 643
CR239
CR145
CR100
CR186
CR145
CR186
CR503
CR508
CR203
CR144
CR186
CR186
CR100
CR186
CR186
CR201
CR186
CR463
CR463
CR131
CR131
CR508
CR100
CR508
CR508
CR503
CR131
CR # Crop Rotation Sequence
CSL,CSL,CSL,SWT
CRN,CRN,WWT,HLH,HLH,HLH
CRN
CRN,SOY
CRN,CRN,WWT,HLH,HLH,HLH
CRN,SOY
HLH,HLH,HLH,HLH
NLH,NLH,NLH,NLH
CRN,SOY,WWT,HLH,HLH,HLH
CRN,CRN,WWT,HLH,HLH
CRN,SOY
CRN,SOY
CRN
CRN,SOY
CRN,SOY
CRN,SOY,WWT
CRN,SOY
SMF.SWT
SMF,SWT
CRN,CRN,SOY
CRN,CRN,SOY
NLH,NLH,NLH,NLH
CRN
NLH,NLH,NLH,NLH
NLH,NLH,NLH,NLH
HLH9HLH,HLH,HLH
CRN,CRN,SOY
%of
PA
Area
88.9
45.0
25.5
42.8
46.8
38.4
35.0
27.2
56.0
87.1
71.2
40.4
36.4
58.6
54.1
51.8
45.1
36.6
30.9
71.8
70.5
60.2
53.4
35.0
45.8
75.3
70.0
99
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6. Run CENTURY for the 1989-2030 projection period for all rotations within the CD
based on the RAMS data, using the starting conditions from 1988 (Step 5 above). For
the projection period, calibrate crop yields to approximate a 1.5% per year increase in
yields expected due to both crop technology and genetic advances (U.S.D.A., 1990); see
further discussions below. To evaluate the impacts of alternative yield increase levels
(e.g., 1.0% and 0.5%), this step is repeated for each level.
7. Weight the model results by the soil texture area distribution (i.e., fraction of cropland
associated with each soil texture group) for each simulated scenario in each CD to get
the final model projections.
5.2.1 Underlying Assumptions for Starting Conditions and Baseline Projections
Table 5.11 lists the underlying assumptions used to approximate the evolutionary changes in
agricultural practices over the 120-year simulation period, including the past 80 years and the
40-year projection period, so that we could use CENTURY to attempt to estimate changes in soil
carbon. Clearly, we did not have the resources nor the data to accurately track historical
changes in agricultural practices for the 489-million-acre Study Region. Our goal was to
develop a reasonable pattern of the timing and nature of changes in agriculture that have a direct
effect on soil carbon, in order to arrive at a reasonable starting point for assessing future
changes.
Climate -
The 41-year climate data base discussed in Section 4.0 was used in simulating the actual time
period of data 1948-88, and both the prior and following time periods of 1907-47 and 1989-
2030. Although CENTURY includes an option to stochastically generate monthly
precipitation, testing showed that this produced much greater projected year-to-year
variability in crop yields than observed. Also, since an analogous capability did not exist for
monthly air temperature, use of the observed data was judged to be the best approach.
Tillage -
The type and number of tillage operations were shown in Table 5.6 for each of the simulation
time periods. Conventional tillage was assumed through 1970, when a gradual shift to
various types of conservation tillage began. Larson and Osborne (1982) cite statistics
showing that conservation tillage was only 2.3% of harvested cropland nationwide in 1965,
and this increased to 16% in 1979. Citing data from the Conservation Tillage Information
Center (CTIC), Mannering et al. (1987) show that conservation tillage was increasing but was
still less than 10 to 15 million acres nationwide in the late 1960s. The RAMS data showed
conventional tillage to currently still be 70% of the study region harvested cropland, i.e.,
1989-90. For the 1971-88 simulation period, we reduced the number of tillage operations
to accommodate the trend toward less tillage, but maintained conventional tillage as the
dominant practice.
101
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Fertilizer and Irrigation -
Dramatic increases in fertilizer use in the 1940s and following WWII is cited as one of the
reasons for significant yield increases for the major production crops (Tisdale et al., 1985).
As shown in Table 5.11, we assumed the use of fertilizers began about 1936 with levels
leading to 50% of maximum production for 1936-74, 80% of maximum production for 1948-
70, and 100% of maximum production after 1970.
For irrigation applications, the Council for Agricultural Science and Technology (CAST)
noted significant increases in irrigation water use in the Great Plains and the East starting in
the mid-to-late 1940s (CAST, 1992). We assumed that irrigation began extensively in the
time period beginning in 1948, and that irrigation water was applied when the moisture
deficit reached 50% of available water holding capacity. The RAMS output identified the
specific crops, rotations, and PAs where irrigation was practiced.
Harvest and Manure Applications -
The impacts of harvest operations on plant residues and the procedures for accommodating
the C and N return from forage crops was discussed above in Section 5.1. In Table 5.11 we
note that, for the initial time period of 1907-35, we assume that 50% of the plant residue is
removed along with the grain at harvest time, and that, beginning in 1936, only the grain is
harvested with all the plant residue remaining in the field. These assumptions were derived
from Cole et al. (1989) for wheat-fallow cropping systems in the northern Great Plains to
approximate the advent of harvesting combines and their greater selectivity of grain harvest.
The validity of these assumptions for other crops and regions is unknown and should be
further investigated.
Crop Varieties and Yields -
As noted above, the procedure we followed to estimate initial SOC levels involved calibrating
crop yields to match historic levels during the simulation period. This led to lower yielding
varieties (and associated crop parameters in CENTURY) for the 1907-47 time periods,
increasing yields from 1948-70, and the highest historic yields in the latest period of 1971-
88. The crop yield data (discussed in Section 4.0) clearly showed the sometimes dramatic
trends of increasing crop yields; Flach et al. (1992) cite references supporting annual
increases in crop yields averaging 1.8% per year during that past 40 years. For the
projection period 1989-2030, a 1.5% per year increase was assumed based on U.S.D.A.
projections (U.S.D.A., 1990). Although the CAST report indicates that this level of yield
increase may be obsolete (the projections were made in the early 1980s) and optimistic since
increases in the 1980s were considerably less, it concluded that they were plausible (CAST,
1992).
This assumption of a 1.5% per year increase in crop yields is a critical factor in
projecting soil carbon levels, and associated carbon sequestration, over the next 40
102
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years. To assess the general sensitivity of the study conclusions to this assumption,
additional model runs were performed on the entire Study Region assuming annual yield
increases of 1.0% and 0.5% (see Section 6.4).
5.3 STATUS QUO/BASELINE CONDITIONS
The Status Quo, or Baseline, scenario represents the projection of changes in SOC from 1990
to 2030 under current cropping practices and current trends, the most critical of which is the
assumed annual 1.5% increase in crop yields. As noted earlier, the output of the RAMS model
included the acreages for each crop rotation practiced within each PA, along with the tillage
practices and areas receiving supplemental irrigation. The other assumptions used in simulating
each crop rotation under the Status Quo conditions with CENTURY were discussed above in
terms of model parameter values selected. The baseline for the RAMS model is the 1991
growing season. Commodity program parameters for the 1991 programs were used. Input
prices for 1990 were used. 1991 data were not available, but, crop inputs such as seed and
fertilizer are often purchased in the previous year for tax purposes or because discounts are
offered. Acreages and chemical usage were calibrated to 1990/91 data. Commodity prices were
estimated as averages for the 1991 calendar year, using projected prices for the later months.
crop yields, were estimated for 1991.
5.4 ALTERNATIVE POLICY SCENARIOS
Three policy scenarios were analyzed, including increased use of Conservation Tillage,
increased use of Cover Crops, and impacts of the Conservation Reserve Program (CRP).
The first two of the three scenarios were represented in RAMS by (1) targeting adoption of no-
till and reduced tillage practices to levels suggested by tightening the criteria for defining highly
erodible land for conservation compliance purposes, and (2) targeting planting of winter cover
crops following sorghum, silage and small grains. The third involved identifying 'likely' changes
in the CRP over the 1990-2030 projection period. In the case of conservation compliance,
targeting of the most erosive land offers a dual benefit of reducing erosion and increasing storage
of carbon in agricultural soils. Therefore, we are exploiting mutually beneficial policy outcomes
by selecting the appropriate instrument.
5.4.1 Constricting the Criteria for Defining Highly Erodible Land
Conservation compliance, initiated in the 1985 farm bill, ties receipt of a long list of financial
benefits from the government to the development and implementation of plans to reduce erosion
on tracts of land determined to be eroding excessively (i.e., highly erodible lands). The criteria
for defining highly erodible lands, eventually rested with EI>8, where El is an "credibility
index" equal to RKLS/T for water erosion. R, K, L, and S are the Universal Soil Loss Equation
(USLE) (Wischmeier and Smith, 1978) coefficients and T is the theoretical amount of soil loss
that if exceeded will lead to losses in productivity. Only water erosion is considered and is
clearly the dominant form of erosion in the study region. El > 8 is merely a guideline which
individual counties followed while identifying highly erodible land.
103
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Once a tract of land is labeled highly credible, conservation practices must be outlined in a plan
and approved by local SCS officials. A separate criteria was involved in deciding which
practices were acceptable. The most restrictive criteria demands that erosion be reduced to T.
Individual producers proving economic hardship were allowed to meet a less restrictive criteria.
With the more restrictive criteria, the following relationships must hold on highly erodible land:
(1) E..EI,
where:
E
T
RKLS
CP
El
erosion measured in tons/acre
allowable soil loss for which productivity is maintained
USLE coefficients for climate, soils, and topography
USLE coefficients for specific production practices
erodibility index
Modeling conservation compliance in RAMS involved estimation of El values from the National
Resource Inventory (NRI) and other sources for all land in the study area. Six "highly erodible
land groups" (HELG) based on ranges of the estimated El values are defined, as follows:
El Range HELG
0-8 not highly erodible
9-15 1
16-22 2
23-29 3
30-36 4
37-43 5
>43 6
The amount of land falling into each HELG is estimated from NRI and other data. CP values
estimated for all production activities in RAMS are evaluated for each group of highly erodible
104
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land. From equation (6), if El* < 1/CP, where El* is the minimum of the range for each
HELG, the practice is considered to meet the conservation compliance requirements for that
HELG.
For the present analysis, the amount of land falling into one of the six highly erodible land
groups was used as the target for different levels of conservation tillage. This was modeled by
constraining the sum of No-Till plus Reduced Tillage to be equal to the amount of highly
erodible land. Naturally, as the minimum El for land to be considered highly erodible was
reduced from 8 to 5 to 2, the amount of land required to be in conservation tillage increased.
Table 5.12 gives the amount of land considered highly erodible as a percentage of the total
cropped acres for various levels of El for the entire Study Region. Based on this analysis, the
El target values of 8, 5 and 2 were chosen to represent three alternative levels of conservation
tillage implementation within the Study Region. The RAMS analysis then provided the specific
changes in tillage practices for all the crop rotations practiced within each PA. Table 5.13
summarizes the tillage distribution for the Study Region for each of these three — low, medium,
high — levels of conservation tillage implementation; Table 5.14 shows the same information by
PA.
5.4.2 Winter Cover Crops
With guidance from CARD, Dr. Rick Cruse, Professor of Agronomy at Iowa State University,
gathered data and developed agronomic practices for winter cover crops in the Study Region.
While cover crops will likely improve the soil carbon content of the soil (all else equal), reduce
soil erosion and fix nitrogen available to the subsequent crop in the case of hairy vetch, cover
crops also cost money and, because of competition for moisture, reduce yields in the subsequent
crop. Further, establishment of a reasonable stand of cover crop is not feasible with every crop
sequence in every PA of the Study Region. Dr. Cruse defined the crop sequence, geographic
area and application method combinations that are feasible candidates for establishing cover
crops.
Figure 5.3 shows the PAs where cover crops were deemed to be feasible along with the '% of
cropland' that includes rotations with cover crops, as determined by RAMS. Table 5.15 shows
the cover crop acreages and ' % of cropland' by PA and for the Study Region. Although only
12% of the Study Region includes rotations with cover crops, in some PAs the proportion
reaches 40 to 50%, and it averages 23% for the PAs where cover crops are viable. Cover
crops were considered feasible primarily in the southern half of the Study Region, with the
greatest concentration in the southcentral area in the states of Missouri, Arkansas, southern
Illinois, and western Tennessee. Note that the specific mix of rotations in PA 32 were not
considered supportive of cover crops.
Following the identification exercise, the data needed in RAMS were collected. Because of the
crop sequence and geographic specific nature of utilizing cover crops, the time needed to collect
and process the large amount of data was greater than initially anticipated. Once the necessary
data were collected and incorporated into RAMS, a new baseline with cover crops was run.
105
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Table 5.12 Percentage of Acres Considered Highly Erodible for Alternative El Criteria by
PA and the Entire Study Region
% Considered Highly Erodible
EI=8
6.91%
10.46%
17.01%
10.92%
1.80%
6.41%
24.66%
50.20%
22.36%
48.17%
13.94%
5.77%
32.32%
27.57%
18.13%
29.84%
20.86%
0.88%
6.40%
34.37%
45.29%
7.16%
24.21%
46.14%
23.92%
3.50%
17.18%
EI=7
8.15%
13.10%
19.50%
13.09%
2.00%
7.60%
29.22%
53.73%
25.32%
51.54%
16.33%
6.93%
35.65%
30.18%
20.48%
33.20%
23.04%
1.23%
7.61%
38.31%
48.48%
9.20%
28.52%
50.69%
25.74%
4.73%
23.77%
EI=6
9.53%
16.74%
23.51%
16.47%
2.63%
9.70%
35.29%
56.54%
30.23%
55.86%
19.72%
8.61%
39.01%
33.39%
23.15%
36.69%
25.24%
1.79%
9.38%
40.97%
51.85%
11.79%
33.00%
55.12%
28.36%
7.01%
32.40%
EI=5
10.99%
21.40%
28.03%
20.35%
4.01%
13.48%
41.30%
60.73%
36.20%
60.86%
23.76%
11.12%
44.65%
37.20%
26.60%
42.08%
29.01%
2.31%
11.78%
44.99%
55.93%
16.24%
37.85%
61.19%
31.49%
10.44%
44.45%
EI=4
17.92%
28.15%
33.66%
26.07%
6.15%
18.14%
48.94%
66.15%
43.90%
65.33%
30.12%
14.80%
50.95%
42.31%
32.58%
47.84%
35.62%
3.20%
15.83%
48.49%
60.29%
21.06%
45.22%
67.26%
37.87%
16.58%
60.88%
EI=3
24.38%
39.27%
42.50%
33.83%
9.62%
23.46%
59.07%
73.70%
55.06%
72.30%
39.94%
20.75%
61.12%
50.76%
41.01%
57.65%
46.17%
5.05%
21.06%
54.13%
66.35%
30.03%
52.64%
73.15%
51.63%
25.59%
75.99%
El =2
37.29%
55.63%
57.04%
47.96%
15.35%
33.15%
73.12%
82.08%
69.21%
83.62%
56.21%
31.52%
73.50%
64.93%
57.20%
75.75%
68.68%
9.89%
33.02%
65.40%
75.78%
49.39%
69.08%
80.55%
79.77%
45.34%
89.04%
PA
22
23
24
25
26
27
28
31
32
34
35
39
40
41
42
43
44
47
53
56
57
58
59
60
61
63
64
Study 18.04%
Region
20.32% 23.16% 26.97% 32.18% 39.78% 53.39%
106
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Table 5.13 Tillage Distributions for Policy Scenarios
Modeled
Scenarios
Status Quo/Baseline
Low Conservation
Medium Conservation
High Conservation
Cover Crops
CTFP
Tillage Practice
CTSP RT
NT
23%
23%
23%
13%
22%
47%
43%
41%
31%
48%
27%
29%
29%
30%
27%,
3%
5%
8%
26%
3%
Table 5.15 Cover Crop Land Distribution by PA
PA#
24
27
28
31
34
35
41
42
43
44
56
57
59
60
61
64
Cropland
(acres)
2,164,040
6,356,361
1,237,701
2,701,337
5,124,066
13,904,872
25,216,417
13,576,737
4,878,079
8,982,813
2,649,217
10,156,826
5,444,927
8,975,078
864,007
4,144,596
Cover Crop
(acres)
597,924
1,472,307
83,901
442,785
8,021
2,815,670
3,321,539
2,508,280
2,282,049
4,456,571
546,271
1,471,673
933,370.
3,728,537
406,235
1,236,308
Cover Crop
as Percent of
Cropland
27.63%
23.16%
6.78%
16.39%
0.16%
20.25%
13.17%
18.47%
46.78%
49.61%
20.62%
14.49%
17.14%
41.54%
47.02%
29.83%
Total 116,377,074 26,311,441 22.61%
Area
Percent under Cover Crop for the RAMS Study Area = 12.15%
107
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108
-------
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Figure 5.3 Cover crop land distribution within the study region
109
-------
Including rotations with cover crops increased the number of activities in RAMS by 10 to 30
percent and increased the time required to find the optimal solution for each run.
Targeting cover crops to be grown following sorghum, silage and small grain is accomplished
by including a constraint in RAMS to force activities meeting this criterion into the model. The
level of the target is specified as the sum of the sorghum, silage and small grain acreage grown
under the baseline. While production activities with cover crops for any of the crop sequences
modeled are allowed, only those with cover crops grown after one of these crops will count
toward satisfying the constraint. One important outcome of this approach is the value of the
shadow price on the cover crop constraint. The interpretation of the shadow price is the dollars
per acres subsidy needed, at the margin, to get producers to willingly plant the target level of
winter cover crops. That is, if a single per-acre incentive payment was offered in the PA, the
shadow price is what this incentive payment would need to be to achieve the target level of
cover crops.
For the PAs (and CDs), crop rotations, and crop sequences identified by CARD as viable for
cover crops, the CENTURY model input was modified to add legume hay as a cover crop
following harvest of the previous crop, with the entire crop returned to the soil prior to planting
of the subsequent crop in the rotation. Legume hay was chosen to represent cover crops to
avoid the need to define fertilizer applications, and initial testing showed that legume hay would
be consistent with the biomass produced by the rye or hairy vetch cover crop species
recommended by CARD. In the spring following the winter cover crop period, the normal
tillage practices were followed, i.e., moldboard plow for CT, chisel plow for RT, and herbicides
for NT. Note that Table 5.13 shows a slight decrease in CTFP and a slight increase in CTSP
for the Study Region as a whole as a result of cover crops requiring spring plowing in place of
fall plowing.
5.4.3 Conservation Reserve Program (CRP)
In order to assess the potential impacts of CRP land on SOC, two alternative scenarios were
developed and simulated with CENTURY. Below we discuss the two scenarios — CRP1 and
CRP2 — in terms of their nationwide significance and interpretation, followed by a discussion
of how the scenarios would be implemented within the Study Region and are represented by the
CENTURY model.
CRP1
Through discussions with several researchers familiar with the policy process at USD A,
researchers at CARD determined a "likely" outcome of the CRP program, as current contracts
expire (see Table 5.16). It is assumed that the current program reaches 40 million acres
nationwide by 1995. This scenario would then maintain CRP coverage on 15 to 20 million acres
to include (a) 2.7 million acres of trees, (b) 8.3 million acres of environmentally sensitive
grassland, and (c) 4 to 9 million acres of additional grassland. This yields an average of 17.5
110
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Table 5.16 Nationwide CRP Cumulative Enrollment by Crop Under CRP1, 1996-2005 .
(Thousand Acres). . • ~
Wheat Corn Sorghum Barley Oats Cotton Rice Soybean Other Annual
Total
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
11677
11339
9330
7682
6720
5896
5832
5679
5489
5299
5109
4914
4829
3616
3324
3020
2788
2731
2613
2459
2304
2150
2669
2539
1979
1628
1458
1342
1327
1301
1256
1212
1168
3088
3010
2473
2034
1766
1551
1534
1503
1453
1402
1351
1509
1466
1220
1023
890
782
768
740
713
687
660
1482
1453
1097
907
800
748
732
706
687
668
648
17
17
16
15
12
10
10
10
9
8
8
4972
4804
3791
3337
2998
2727
2678
2576
2443
2311
2175
9672
9418
7638
6286
5560
5075
4988
4842
4638
4433
4232
40000
38875
31161
26236
23224
20919
20601
19971
19147
18324
17500
million acres to remain in the CRP program through the end of the study period, and 22.5
million acres of CRP land to return to production as contracts from the current program expire.
As contracts expire, beginning in 1996, a portion of the total CRP enrollment in 1986 returns
to crop production. The land remaining in CRP from 1986 contracts is determined as ACRP11996
= 17.5 * (ACRPi986 / 40) where ACRPi986 is the total CRP enrollment during 1986. Acreage
remaining in the new CRP from contracts expiring in later years is determined similarly. The
amount of land returning to crop production as each contract expires is then equal to ACRPlt+i0
- ACRPt.
Table 5.16 details the distribution of land in the baseline CRP among its alternative uses as
existing contracts expire, old contracts are renewed, and some new land is brought into the
program. This distribution reflects the use of CRP land before it entered the original 40 million
acre CRP. The land returning to crop production is assigned to production of each of the seven
program crops (corn, wheat, sorghum, barley, oats, rice, and cotton) and to soybeans according
to the proportion of that crop's acreage reduction attributable to CRP in the crop year it entered
the original CRP. These reductions are outlined in the FAPRI 1992 Outlook (Johnson et al.,
1992) and are based on estimates provided by USDA.
CRP2
CRP2 is simply a continuation of the current program, assuming that the goal of 40 million acres
is reached by 1995; all contracts are renewed. No additions or reductions were made to the BLS
projections for this scenario, except as provided for by the FAPRI projections in the "fine
111
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tuning" process. Table 5.17 gives the breakdown of the current CRP by area reductions
attributed to particular crops. Only the seven program crops and soybeans were explicitly
accounted for in the BLS during this analysis. Acreages of other crops were judged to be too
small to have significant impacts in the model, and were thus considered in the "other" total.
For the analysis, the CRP structure projected through the final sign-up was maintained in every
simulation year after 1995.
Table 5.17 Nationwide CRP Current and Projected Annual Enrollment Under CRP2,
1986 - 1995 (Thousand Acres).
Wheat Corn Sorghum Barley Oats Cotton Rice Soybean Other
Annual
Total
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
600
3571
2930
1710
1465
114
272
338
338
338
151
2156
520
540
413
101
209
275
275
275
231
995
624
303
206
27
46
79
79
79
139
954
780
477
382
30
55
90
90
90
76
437
351
236
192
25
50
47
47
47
50
633
339
190
92
28
46
35
35
35
1
2
2
4
4
0
1
1
1
1
300
1800
807
604
482
86
182
236
235
242
452
3165
2403
1291
862
154
260
363
364
357
2000
13713
8756
5355
4098
565
1121
1464
1464
1464
Crop 1677 4914 2669 3088
Total
1509 1482 17 4972 9672 40000
Source: Johnson, et al., FAPRI1992 U.S. Agricultural Outlook.
RAMS provides the current CRP acres in each PA. Table 5.18 lists these CRP acres and '%
of cropland' for each PA and for the Study Region, totaling 15 million acres. Figure 5.4 shows
the distribution of these acres throughout the Study Region, as the ' % of cropland' listed in
Table 5.18 even though the CRP acres are not included in the Study Region cropland total
of 216 million acres. CRP ranges from less than 1% to almost 15% of cropland in the PAs,
and totals almost 7% of the cropland in the Study Region.
CRP Simulation with CENTURY
For each PA, CENTURY CRP simulations were performed for two conditions to represent the
CRP scenarios discussed above. CRP1 is represented by a conversion to CRP in 1986 and a
return to crop production in 2006, while CRP2 is represented by the land remaining in CRP
112
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Table 5.18 CRP Acres by PA Within the Study Region
PA#
CRP
(acres)
Cropland
(acres)
22
23
24
25
26
27
28
31
32
34
35
39
40
41
42
43 -
44
47
53
56
57
58
59
60
61
63
64
705
62,475
61,805
168,216
42,950
110,951
17,550
33,601
188,731
277,762
325,903
761,558
438,578
1,623,548
464,764
234,917
511,144
2,139,753
1,469,394
167,275
747,068
1,347,404
234,926
1,317,292
12,122
2,048,974
262,849
468,736
2,850,672
2,164,038
4,293,392
2,475,260
6,356,362
1,237,701
2,701,336
6,156,615
5,124,067
13,904,865
12,206,423
6,072,917
25,216,406
13,576,758
4,878,077
8,982,813
19,937,310
14,409,609
2,649,214
10,156,825
13,760,145
5,444,932
8,975,086
864,009
17,472,893
4,144,601
CRP Acres
as Percent of
Cropland
0.15%.
2.19%
2.86%
3.92%
1.74%
1.75%
1.42%
1.24%
3.07%
5.42%
2.34%
6.33%
7.22%
6.44%
3.42%
4.82%
5.69%
10.73%
10.20%
6.31%
7.36%
9.79%
4.31%
14.68%
1.40%
11.73%
6.34%
Total
Area
15,072,216
216,481,104
6.96%
113
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Figure 5.4 CRP distribution within the study region
114
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from 1986 through 2030. The primary assumptions in representing CRP land in CENTURY are
as follows:
Native grass grown (50% warm and 50% cold weather grass species)
No annual tillage
No harvest or cutting of grass
No fertilizer application
Atmospheric N deposition is included
Crop senescence in the month of November
For CRP simulations, the initial SOC conditions were determined as described in Section 5.2.
The dominant crop rotation was simulated from 1907 through 1985 when conversion to CRP was
assumed. Under CRP1 when crop production resumes in 2006, the simulation returns to the
dominant crop rotation in each PA. Under CRP2, the converted land remains in CRP through
2030. The only exceptions to these procedures were for PAs where the dominant rotation is
continuous hay; all the other PAs had dominant rotations with a minimum of 40% cash crop,
i.e. at least 2 out of 5 years in corn, soybeans, wheat, etc. (see Table 5.10). For the PAs with
dominant hay rotations, we chose the next most common rotation that included at least 40% of
the years in a cash crop; we refer to this as the dominant cash crop rotation. The impacted
PAs and dominant cash crop rotations are listed below:
PA
DOMINANT CASH CROP ROTATION
PA28 CR144 CRN,CRN,WWT,HLH,HLH
PA31 CR145 CRN,CRN,WWT,HLH,HLH,HLH
PA58 CR100 CRN
PA60 CR201 CRN,SOY,WWT
PA61 CR186 CRN,SOY
PA63 CR218 CRN,WWT
TILLAGE
CTSP
CTSP
CTSP
CTSP
CTFP
CTFP
For the above PAs, the starting 1986 conditions for the CRP scenarios were estimated by
running the dominant rotation for the 1907-1970 time period and then converting to the dominant
cash crop rotation for the 1971 to 1985 period. This approach provided consistency with the
assumptions on historical practices for all the PAs and allowed a 15-year period for the cash
crop rotation prior to CRP conversion.
In summary the CRP scenarios are as follows:
CRP1 Dominant rotation, or dominant cash crop rotation, is converted to CRP in
1986. In 2006, after 20 years under CRP, 56% of 15 million enrolled acres
in the Study Region (i.e., 8.4 million acres) are converted back to the dominant
rotation (or cash crop rotation), with the remaining 44% (6.6 million acres)
staying in CRP through 2030.
115
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CRP2 Land remains in CRP from 1986 through 2030 at the 15 million acre level.
5.5 OPERATIONAL PROCEDURES FOR GENERATING SOC LEVELS
As discussed in the Section 3.5, the Project Methodology involved matching of historical and
projected crop yield data with CENTURY yield predictions, under assumed crop residue and
management practices, as key elements in estimating soil carbon levels for the historical,
current, and projection time periods. The basic premise was that adjusting CENTURY model
parameters to accurately portray historical crop yield data, and using reasonable assumptions for
historical (and future) tillage and management practices, would result in accurate estimates of
carbon inputs into the soil environment, and would thus provide a sound basis for estimating soil
carbon.
Since the Study Region is divided into 80 climatic divisions (CDs), each CD with at least four
crop/rotation/tillage combinations, and calibrations were required for each crop throughout the
120-year simulation period, a large number of CENTURY simulations were performed.
Consequently, an automated calibration procedure was developed to calibrate the CENTURY
crop yield model to predict both historical and projected future crop yield levels. The following
section discusses the operational steps that were followed for the CENTURY crop yield
calibrations and the overall simulations of historical practices and alternative policy scenarios.
5.5.1 Operational Procedure for CENTURY Simulations
Eight steps were executed in order to perform the CENTURY model yield calibrations and
simulations to estimate soil organic carbon levels in each CD for every crop/rotation/tillage
combination in the Study Region.
STEP 1.
STEP 2.
The first step consisted of creating weather files for the 80 CDs, as defined by
the meteorologic data analyses discussed in Section 4.1. Using the procedure
discussed in Section 4.1, weather files consisting of 41 years of monthly
precipitation (cm/month), maximum temperature (°C), and minimum
temperature (°C) were created for each CD.
The crop and agricultural management inputs to the CENTURY model are
provided by a 'scheduling' file. The following information is contained in each
scheduling file:
a. Sequence and crops grown in crop rotation.
b. Crop variety grown (e.g., high yield or low yield).
c. Planting and harvesting dates for each crop in a rotation.
d. Time and type of tillage practice.
e. Amount of fertilizer application (calculated by CENTURY).
116
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STEP 3.
f. Amount of irrigation application (calculated by CENTURY).
g. Starting and ending year of simulation.
A scheduling file was created for each crop/rotation/tillage combination. The
CENTURY model allows the user to simulate different crop rotations during
different time periods of the simulation, e.g., a crop rotation can be simulated
from year 1 to year 4, and then the conditions at the end of year 4 are the
starting conditions for simulations starting in year 5. The crop rotation starting
in year 5 can be different from the crop rotation simulated from years 1 to 4.
This option in the CENTURY model is called the EXTEND option.
As discussed in the Project Methodology (Section 3.5), scheduling files were
created for the dominant crop rotation in each CD beginning in 1907 and
ending in 1988, this file was named the EXTEND scheduling file. For all the
other crop rotations in the CD, including the dominant crop rotation, new
scheduling files were created, with the simulation start year of 1989 and ending
in 2030. As an example, if four crop rotations were practiced in a particular
CD, then we prepared five scheduling files: one EXTEND scheduling file for
the dominant crop rotation for the period 1907 to 1988 and four scheduling
files for all crop rotations for the period 1989 to 2030.
The crop rotations practiced in each CD are listed in Table D. 1 in Appendix
D; these rotations are identified by a crop rotation # (CR) which is defined
in Table 5.3. The crop varieties (i.e., as a function of yield level) along with
the historical and current management practices are listed in Tables 5.6 and
5.11.
The 'site-specific' file for CENTURY contains information about site latitude,
longitude, weather statistics, and soil physical properties, all of which were
estimated for each CD. This information is used by CENTURY to calculate
initial soil C and N conditions when the IVAUTO option is selected (see
Section 5.2). The weather statistics required in the site specific files were
calculated using the FILE100.EXE program. The input to this program is the
CD weather file and the output from the program is the weather statistics
consisting of the 41 year average of monthly precipitation (cm/month),
maximum temperature (°C), and minimum temperature (°C), along with the
monthly standard deviation and skewness for precipitation. The weather
statistics information is output in the CD###. 100 file, where ### refers to the
CD number, which is the site specific file for the CD. The FILE100 program
was executed for all the CDs. The latitude and longitude of the centroid of
each CD, which is used to estimate evapotransporation, was estimated and
input to each CD file.
117
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The following soil physical properties are contained in this file, and were
estimated for each of the soil texture groups found in each CD, as discussed
in Section 4:
a. Percent Sand
b. Percent Silt
c. Percent Clay
d. Bulk density
e. Field capacity moisture content
f. Wilting point moisture content
STEP 4. In Section 5, Table 5.11 lists the crop yield varieties that were assumed to be
grown in the various time periods during both the historical and projection
period simulation. The information about crop variety and its associated crop
parameters is contained in the CROP. 100 file. Two crop parameters —
namely, PRDX (potential above ground monthly production) and HIMAX
(harvest index maximum - fraction of above ground live carbon in grain) —
differentiate between the low, medium, and high yielding crop varieties. The
yearly crop biomass production is controlled by the PRDX parameter; low
yielding crop varieties have a lower PRDX and vice versa. The HIMAX
parameter controls the amount of carbon that goes into grain production.
These two parameters control the amount of crop yield and were used in the
calibration of CENTURY simulated crop yields to observed (i.e., historical)
crop yields in each CD. For the projection period, only PRDX was increased
while HIMAX was maintained at the values for the 1971-88 period.
For the dominant crop rotation (CR) in each CD, five crop yield varieties were
created in the CROP. 100 file; however, for the crop rotations other than the
dominant crop rotation only two crop varieties during the period 1989 to 2030
were created. The relative crop yield varieties simulated during these various
time periods are listed below:
Period 1907-47
Period 1948-70
Period 1971-88
Period 1989-2010
Period 2011-2030
Low Yield
Medium Yield
High Yield
Higher Yield
Highest Yield
The limits on the HIMAX parameter used for yield calibration are:
Corn - 0.35 to 0.60
Soybean - 0.22 to 0.50
Wheat - 0.22 to 0.48 (includes oats, barley, and small grains)
118
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These limits on HIMAX values for the major crops were obtained from various
literature sources (Russell, 1991; Jones, 1983) and discussions with NREL staff
(A. Metherell, NKEL' personal communication, 1992). No limit was set on
the PRDX value as this was the only CENTURY parameter which controlled
biomass production. The recommended procedure was to increase or decrease
the PRDX and HIMAX values, within the above limits, to match observed crop
yields (C.V. Cole, personal communication, 1993). For projected yields, only
PRDX was increased. This is discussed below in greater detail.
STEP 5. After the completion of steps 1 through 4, the CENTURY model crop yield
calibrations were performed. The calibration procedure is discussed below.
Historical Period (1907-88)
Only crops in the dominant crop rotation were calibrated for this historical time
period, and only for the dominant soil type in the CD. However, if the
dominant soil type in the CD was loamy sand/sand, then the calibration was
also performed for a silt or clay loam textured soil, since these loam soils were
generally dominant throughout the Study Region. The calibration procedure
consisted of comparing the mean simulated crop yield for each crop grown in
the crop rotation for the time periods listed in Step 4, with the mean observed
(or projected) crop yield value of the respective time period. If the mean value
of the simulated crop production was within ± 1 percent of the observed mean
value, then the calibration was assumed to be complete.
While calibrating for crop production if the simulated, yields were lower than
observed yields, then the PRDX and HIMAX values in the CROP. 100 file were
increased simultaneously, while insuring that the limits of HIMAX were not
violated. Once the maximum value of HIMAX was reached, then only the
PRDX parameter was increased. The above procedure was followed in the
reverse order if simulated yields were higherthan the observed crop yields.
The calibrated PRDX and HIMAX values for the dominant soil type were then
used without further calibration for the remaining soil types in the CD.
Once the dominant crop rotation was calibrated for the historical period, the
remaining crop rotations along with the dominant rotation in the CD were then
simulated and calibrated for the projection period.
Projection Period (1989-2030)
As noted earlier, a 1.5% annual increase in crop yield was assumed, initially;
the procedures were than repeated with 1.0% and 0.5% yield increases.
Based on the projected increase, yield targets for the projection period were
calculated for each crop in each crop rotation and CD. The calibration
119
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STEP 6.
procedures for both the historical and projection periods were implemented by
an automated iteration procedure using UNIX optimization script commands.
These commands changed the PRDX*and HIMAX simultaneously for the
historical period, as discussed above, and then re-ran the model; for the
projection period, HIMAX was maintained constant at the values calibrated for
the 1971-88 period, and only PRDX was adjusted in the caibration. The
iteration was continued until the calibration tolerance of „+ 1 % was attained.
The projection period crop rotations were simulated using the EXTEND option
of the CENTURY model, which allowed us to initialize the 1989 year with the
1988 year-ending values. The dominant crop rotation was simulated for the
historical period through 1988 for each soil type in the CD, and then
EXTENDED into the projection period for each of the remaining crop rotations
in the CD. This extension was performed separately for each soil texture
group in the CD. The CENTURY output for each CD was then area-weighted
by multiplying the output from each soil type by the fraction of area they
occupied in the CD, and then summing the results to obtain the weighted
results for each crop rotation.
The mean simulated weighted yields for each time period were again compared
with the observed (and projected) yields, and if the simulated yields were
within about ± 5 percent of the observed yields then the yields were assumed
to be calibrated. Otherwise, the CENTURY model was re-calibrated for the
time periods where the differences in observed and simulated yields were
greater than this tolerance.
The CENTURY output variables printed for each model run, along with a brief
definition, are:
CENTURY
Output
Variable
TOTC
SOMTC
SOMSC
CGRAIN
Definition
Total Soil Organic Carbon (includes carbon stored in soil.
surface and root litter)
Soil Organic Carbon (includes carbon stored in soil and root
litter)
Soil Organic Carbon (includes carbon stored in soil only)
Economic Yield of Carbon in Grain, used to calculate Grain
Yields: YIELD (grains) kg/ha = CGRAIN*28.75
120
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SHREMA Annual accumulator of Carbon removed from shoots during
grazing
SDREMA Annual accumulator of Carbon removed from standing dead
during grazing, used to calculate Hay and Silage Yields,
YIELD (Hay) kg/ha = (SHREMA+SDREMA)*30
YIELD (Silage) kg/ha = (SHREMA+SDREMA)*35
CPRODA Net Primary Production
CINPUTS Total Soil Carbon Inputs
For Hay and Silage Crops -
CINPUT = CPRODA + GRETA * (SHREMA*SDREMA)
- (SHREMA + SDREMA)
For Grain Crops -
CINPUT = CPRODA - (CGRAIN+CRMVST)
/
VOLEXA Accumulator for nitrogen volatilization as a function of
nitrogen remaining after uptake by plants
VOLGMA Accumulator for nitrogen volatilized as a function of gross
mineralization
Used to calculate total N emissions from soils,
TOTAL N Volatilized = (VOLEX A+VOLGMA)
CRMVST Amount of Carbon removed through straw during harvest
FERTOT(l) Accumulator of Nitrogen applied during the year
STEP 7. The calibration procedure in Step 5 was used for simulating all the status quo
C/R/T combinations in all the 80 CDs. Once the status quo simulations were
completed, the three policy alternatives, i.e, increased conservation tillage, use
of cover crops, and CRP alternatives were simulated. The policies are
described in detail in Section 5.4., and their impacts were simulated only for
the projection period.
Increased Conservation Tillage
In this policy alternative, combinations of C/R/T reflecting the selected targets
for conservation tillage were estimated by RAMS. The C/R/T combinations
were essentially identical to those in the status quo simulations except for the
a few rotations where tillage practices were changed. The necessary scheduling
files with changes in tillage practice were created and then using the EXTEND
option, the results obtained for the dominant crop rotation simulations
121
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STEPS.
performed in the Status Quo scenario were extended into the projection period
for each of these combinations with changed tillage practices. The projection
period calibration procedure as stated above was performed again for crops
included in the new C/R/T combinations. As noted in Section 5.4, three
policies consisting of low, medium, and high levels of conversation tillage were
simulated.
Cover Crop
The identification of C/R/T combinations reflecting the targets on cover crops
was provided by CARD. As noted earlier, we used legume hay as the cover
crop and developed revised scheduling files for the projection period for the
C/R/T combinations with cover crops added. The EXTEND option of the
CENTURY model was used again for extending the results of the dominant
crop rotation into the appropriate C/R/T combinations with cover crops. There
was no calibration needed for the cover crop simulations as the C/R/T
combinations which now supported cover crops were already simulated and
calibrated without cover crops in the Status Quo scenario.
CRP Acres
The RAMS study area includes 15 million acres which are enrolled under the
CRP program, this area was not included in the 216 million acres of cropland
simulated in the Status Quo scenario. Since the CRP program was started in
1986, we simulated the dominant crop (or cash crop) rotation from 1907 to
1985, and then used the EXTEND option of the CENTURY model to simulate
two CRP scenarios as discussed in Section 5.4. The CRP land was simulated
using grass as the native crop. No calibration was necessary as the dominant
crop rotation was already calibrated in the Status Quo scenario.
The final step involved processing and analyzing the CENTURY output.
CENTURY output for every policy scenario, for all of the C/R/T's, weighted
by soil type, comprised approximately 1500 files and occupied 15 million bytes
of disk space. Each C/R/T storage or emission factor was multiplied by its
area in the CD, which varied according to the policy scenario that was being
run. After multiplication by the area, weighted storages and emissions totals
were computed by multiplying the output by the area of the C/R/T combination
in the CD. The post-processing was performed using two programs
TABLES.BAS and FACTORS.BAS written in Quick BASIC. The program
extracted from each datafile (one datafile per C/R/T) the result values for the
years 1980, 1990, 2000, 2010, 2020, and 2030. The extracted results were
multiplied by the area of the specific C/R/T in the CD.
122
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SECTION 6.0
MODEL SIMULATIONS AND ASSESSMENT RESULTS
This section presents the simulation results for both the Baseline/Status Quo conditions and the
alternative scenarios described in Section 5.0. Considering the extent of the Study Region, the
number of CDs, and the alternative combinations of crops, rotations, and tillage practices, the
vast quantity of output generated by the models could be analyzed in a variety of ways. For this
initial assessment we have focussed primarily on soil carbon (SOC) values, and changes in SOC
as impacted by the agricultural production systems and the variation in soils and climate
conditions throughout the Study Region. We have analyzed these impacts in two ways: (1) unit
area impacts, in terms of gC/m2, to assess local impacts of the practices, and (2) aggregated
impacts, in terms of total grams C, to evaluate the net change in C (i.e., sequestration potential)
due to both practice and land use changes. These results are presented in Sections 6.1 and 6.2,
respectively. Since the area in the Conservation Reserve Program (CRP) is separate from the
cropland acres in our Study Region, the CRP alternatives are discussed separately in Section 6.3.
Whereas, the results in Sections 6.1 through 6.3 are based on the 1.5%/year yield increase,
Section 6.4 provides summary results for the whole Study Region for alternative yield increase
levels.
6.1 IMPACTS OF AGRICULTURAL PRODUCTION SYSTEMS ON SOIL CARBON
Lacking long-term soil carbon databases to confirm the CENTURY model predictions, we
included two 'consistency' checks as part of the modeling effort to insure that the model results
are reasonable and consistent with the general literature, observations, and impressions of carbon
dynamics within the Study Region. These two checks included comparisons of simulated and
observed crop yields, and simulated soil carbon levels in selected CDs compared to mapped
estimates developed by Kern and Johnson (1991) in a recent study.
Figures 6.1 and 6.2 show comparisons of simulated and both observed (1924-88) and projected
(1989-2030) crop yields for selected CDs and C/R/T combinations throughout the Study Region;
more than 3000 of these types of comparisons were made for each crop within each CD and
C/R/T combination. Complete results for all CDs and C/R/T combinations are available from
the EPA Athens Laboratory.
Although these are calibrated results within a relatively tight tolerance of+. 5%, the model
shows good agreement and year-to-year variability compared to observations during the historic
period of 1924-1988. Since carbon inputs to the soil are a direct function of crop yields (and
residue management practices), accurate yield simulation provides a sound basis for accurately
123
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12000
CD401_Corn-Graln_RT_CR100
Observed, ooooo Simulated
8000 -
o»
11 ii m ii 11 ii nu i ii it 11 m i ii 11 u 111111 ii 111 n 11111 n 1111 u 11111 n
1890 1910 1930 1950 1970 1990 2010 2030
Year
1 5000 n
CD41 3_Corn-Grain_RT_CR1 86
..... Observed, ooooo Simulated
O
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1890 1910 1930 1950 1970 1990 2010 2030
Year
c
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1890 1910 1930 1950 1970 1990 2010 2030
Year
CD581_Non-Legume-Hay CTFP CR508
Observed, ooooo Simulated
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.....Observed, ooooo Simulated
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90 1910 1930 1950 1970 1990 2010 2030
Year
Figure 6.1 Simulated vs. actual and projected crop yields for selected CDs and C/R/T
Combinations
124
-------
CD221_Corn-Silage__CTFP_CR239
• •.* » Observed, ooooo Simulated
50000 q
40000 i
30000 -.
20000 -.
10000 :
0 111II n 11 i|ini M nil I lin IIIII in HI I " Ml "I' " mui |lll 11III H
1890 1910 1930 1950 1970 1990 2010 2030
Year
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5000 -
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A * * Observed, ooooo Simulated
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1890 1910 1930 1950 1970 1990 2010 2030,
Year
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Figure 6.2 Simulated vs. actual and projected crop yields for selected CDs and C/R/T
Combinations
125
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simulating SOC. Note that during the projection period of 1989-2030, the year-to-year
variability is generally greater than during the historic period. During this period, the model
was calibrated to the annual yields resulting from the assumed 1.5% annual increase; the greater
variability indicates greater sensitivity to actual annual variations in climatic conditions at these
higher levels of potential crop production. Thus, at high potential crop production levels,
climate variations may lead to greater fluctuations in actual crop yields.
Table 6.1 shows a comparison of soil carbon levels for selected test CDs with corresponding
values estimated by Kern and Johnson (1991), who developed a national map of agricultural soil
carbon from the 1982 and 1987 NRI database (Goebel and Dorsch, 1982; Goebel, 1987), the
1982 SCS Soil Interpretation Records, and the SCS Pedon Database. The Kern and Johnson
values shown in Table 6.1 were estimated from their published maps and adjusted to a 20-cm
depth to be consistent with the CENTURY model predictions. The CENTURY values shown
in the table are a range for all C/R/T combinations in the test CD for the 1980 to 1990 time
period. Except for two test CDs (CD473 and CD643), the CENTURY estimates are
consistently in or near the low end of the estimated range from Kern and Johnson. The general
spatial pattern of SOC from both estimates is similar, with the highest values in the central and
northern portions of the Study Region and the lowest values in the western, southern, and
eastern periphery (see Figure 6.10 for spatial display of 1990 SOC values). For the two CDs
where the CENTURY values are about 40% too low, it is interesting to note that the
neighboring CDs produced SOC values within the Kern and Johnson range; differences in soil
texture and climate used in the two methods are possible reasons for this spatial shift and
differences in SOC.
Table 6.1 Comparison of CENTURY (1980-1990) SOC with Values
Estimated From Kern and Johnson Report
272
341
413
473
603
632
643
Location
N.E. OH
N.E. KY
Central IA
No. MN
Central MO
S.W. KS
N.W. AR
SOC, gC/m2
Kern/Johnson CENTURY. 1980-90
4000-5000
3000-4000
5000-7000
6000-8000
3000-5000
3000-4000
3000-4000
4160-4450
3080-3280
4500-4800
3800-3920
3050-3130
3000-3030
1820-1970
Note: CENTURY values in this table are for soil carbon without root or crop residues
included.
126
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In sum, both of the consistency checks indicated reasonable model behavior when compared to
selected observed data points for crop yields and soil carbon. Although further validation is
needed and highly recommended (see Section 7.0), these consistency checks were deemed
sufficient to justify use of the modeling procedures and methodology for this preliminary
assessment.
6.1.1 Impacts of Crops. Rotations, and Tillage Practices
Table 6.2 shows model predictions of SOC for rotations and tillage combinations for selected
CDs distributed throughout the Study Region;" the locations of these CDs are shown in Figure
6.3. The values in Table 6.2 are year-end conditions at ten-year intervals from 1980 to 2030,
and the percent change is calculated for each ten-year period starting in 1990 and for the entire
40-year projection period, 1990-2030. As noted in Section 3.2, the SOC values are the Total
Soil Carbon in the top 20 cm of the soil profile, including both root and surface residues,
and are presented as unit area impacts in gC/m2. Table C.I in Appendix C includes results for
all CDs within the Study Region.
Figures 6.4, 6.5, and 6.6 are examples of the CENTURY model predictions for SOC for the
entire simulation period of 1907-2030 for CDs 413, 473, and 632, respectively. These are
presented to demonstrate the historical and future trends in SOC predicted by CENTURY under
the model application assumptions discussed in Section 5.0.
From the model results shown in these figures, Table 6.2, and Appendix C, the following
observations are provided:
1. Values in Table 6.2 are generally representative of those for all CDs and C/R/T
combinations; the percent change for the 40-year projection period is typically
about 30% to 80%. For all CDs and C/R/T combinations, the average increase
was 55%, with an average minimum of 25% and an average maximum of 87%
(from Table C.2). However, selected C/R/T combinations have much larger and
smaller changes, including a few (primarily wheat-fallow rotations) with negative
changes i.e., loss of SOC during the projection period.
The greatest percent changes, ranging up to 150% to 200%, usually occur when
a dominant hay or grain rotation, simulated during the historic period (i.e. 1907r
88) is followed by a corn or corn-silage based rotation during the 40-year
projection period. This is due to the large biomass production of corn, and the
resulting high level of carbon inputs to the soil for these rotations. Conversely,
the smallest increases, and some decreases, in SOC are for the reverse situation.
127
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131
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9000
19001910192019301940195019601970198019902000201020202030
YEAR
Figure 6.4. Changes and Impacts of Agricultural Production Systems
On Total SOC in CD413, 1907-2030
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t
19001910192019301940195019601970198019902000201020202030
YEAR
Figure 6.5. Changes and Impacts of Agricultural Production Systems
On Total SOC in CD643, 1907-2030
132
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CR262 - CSL. OTS. HLH. HLH, HLH
T9001910192019301940195019601970198019902000201020202030
YEAR
Figure 6.6. Changes and Impacts of Agricultural Production Systems
on Total SOC in CD473, 1907-2030
2. Historical practices, as represented by the model assumptions used in this study,
have led to decreases in SOC until about the 1940's and 1950's. Since then the
model predictions of SOC are increasing for most crop production systems that
leave significant amounts of crop residue on the field. At this time, these
predicted trends appear to be reasonable and consistent with the application
assumptions and the available literature. A variety of studies have noted the
typical steep decrease in SOC when forest and grasslands are converted to
agricultural production (Mann, 1986; Schlesinger, 1984; Odell et al, 1984), but
increases in SOC have also been documented when soils are initially low in
carbon (Mann, 1986) or under intensive fertilization (Odell et al, 1984).
Houghton et al (1983) have shown idealized curves of SOC following conversion
of native lands to agriculture that are similar to the pattern in Figures 6.4 - 6.6.
Paustian (1992), and others, have noted increases in SOC with increased carbon
inputs due to amendments and residue management that retain most of the crop
residue on the field.
133
-------
Thus, the pattern of decreasing SOC following conversion of native land to
agriculture, and subsequent increases with more intensive fertilization and
increased carbon inputs is generally consistent with the literature; however, we
have no direct confirmation of these CENTURY model predictions at the scale
of the Study Region at this time. Hopefully, the ongoing data consolidation effort
by CSU-NREL and MSU will help to validate these conclusions when it is
available.
3. The model results show consistent and significant increases in SOC throughout
the projection period, except for a few hay or grain-based rotations in CDs
located in the southern and western portions of the Study Region. The increases
are due primarily to the assumption of a 1.5% annual increase in crop yields and
subsequent increases in carbon inputs, as noted in Section 5.2.1. The sensitivity
of the Study Region results to alternative annual yield increase levels is discussed
in Section 6.4.
4. The impacts of the tillage practices -- Conventional Till, Reduced Till, No-Till -
- are significant and highly variable across the Study Region. The degree of the
impact is a function of the complex interactions of specific crops and rotations,
climate and soil characteristics. We generally see significant increases in SOC as
one moves from CT to RT to NT. In many cases, the increase in SOC due to RT
may be 10% to 15% higher, and for NT up to 50 % higher, than for CT;
however, in other C/R/T and CD combinations the changes are much less, and
often less than generally assumed by conventional wisdom. For example, the
changes in SOC for selected CDs for a corn-soybean rotation (CR186) are shown
below for selected CDs where all three tillage alternatives are practiced:
Percent Change in SOC, 1990-2030 for Corn-Soybean Rotations and Tillage
Practices
GD272
CTSP 45%
RT 50%
NT 61%
CD413
20%
23%
36%
CD321
53%
58%
68%
CD392
37%
40%
52%
CD603
64%
69%
78%
In some CDs, the percent change does not adequately describe the real impact;
for example, in CD392 the percent change for RT is only 3% more than for
CTSP, but the absolute difference in SOC from 1990 to 2030 is about 190
gC/m2. Differences of a few percent among practices are probably not significant
within the context of this study.
134
-------
These results on the impacts of tillage should be considered preliminary for a
number of reasons: (1) we simply do not quantitatively understand all the impacts
of tillage alternatives as a function of soils and climate on crop yields, residue
decomposition, and organic matter dynamics; (2) the parameter changes in current
models, like CENTURY, to distinguish RT and NT are uncertain and require
further investigation; and (3) some mechanisms for movement and disposition of
surface residues under alternative tillage practices (e.g., earthworm activity, new
tillage implements, etc.) are not well represented by the model. In fact, some
members of the Study Team questioned whether we knew enough about the
differences in processes and parameter changes to warrant modeling both RT and
NT as separate, distinct alternatives. This area warrants further investigation
before our conclusions on the impacts of alternative tillage practices can be
confirmed (see Section 7.0).
6. The impact of irrigation was primarily on maintenance of crop yields; information
from RAMS identified the CDs and rotations where irrigation was practiced. For
many of the southern CDs (e.g., 632, 643 in Table 6.2) essentially all the
cropland was irrigated. However, for some of the northern and eastern CDs, the
RAMS output identified rotation acres both with and without irrigation; this
occurred in CD 262, as shown by 'dryland' and 'irrigated' conditions in Table
6.2 following the tillage type designation. For CD 262, the irrigated condition
produced slightly lower SOC levels (although the percent difference was higher)
due to higher CO2 emissions and SOC decomposition under the higher soil
moisture levels of the irrigated land.
7. Differences in the 1980 SOC conditions (Table C. 1 in Appendix C) within a PA,
i.e., among CDs with the same first two digits (e.g., 411, 412, 413), are due
entirely to soil and climate differences, and historical crop yield levels, since the
historical management practices and dominant rotations are the same within the
PA.
6.1.2 Impacts of Cover Crops
Implementation of cover crops in appropriate CDs and C/R/T combinations identified by the
CARD analysis resulted in dramatic increases in SOC in many cases. Table 6.3 shows the SOC
and percent change for selected CDs and for C/R/T combinations both with and without cover
crops; Table C.3 in Appendix C includes complete results for. all CDs and rotations with cover
crops, while Table C.4 shows the average, minimum, and maximum percent change for each
CD with cover crops. Figures 6.7 and 6.8 graphically show the SOC changes for two selected
rotations in CDs 413 and 603, respectively.
As noted in Section 5.4, only 45 CDs out of the total number of 80 were considered conducive
to cover crops due to climate, crops, and length of the growing season. These CDs were located
in the central and southern portions of the Study Region (Figure 5.2).
135
-------
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10000
9000
8000
7000
6000
5000
4000
_ 3000
_o
"o
2000
1000
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030
YEAR
Figure 6.7. Changes and Impacts of Cover Crops on Total SOC in CD413
1907-2030
9000
19001910192019301940195019601970198019902000201020202030
YEAR
Figure 6.8. Changes and Impacts of Cover Crops on Total SOC
in CD603, 1907-2030
138
-------
A review of these results provides the following observations:
1. Use of cover crops can significantly increase SOC for many C/R/T combinations,
especially for CDs located in the southern portions of the Study Region. For
example, in CDs 603 and 612 the largest increases during the 40-year projection
period are on the order of 100% to 150% greater with cover crops. For all CDs
and C/R/T combinations with cover crops, the average increase was 63%, with
an average minimum of 29% and an average maximum of 117%. For some
CDs and C/R/T combinations, the increase is two to three times greater with
cover crops. However, many of these situations are for corn-silage and hay
rotations where the 'manure-return' assumption, combined with cover crops,
leads to the large increase.
2. For the CDs located in the central portion of the Study Region (which
corresponds to the northern tier of cover crop CDs), the SQC increase is much
less than in the other crop cover regions. These points are evident in the values
in Table 6.3 and the curves in Figure 6.7. CDs 241, 272, 341, and 413 show
smaller differences for cover crops than the other CDs.
As noted above, crop rotation 186 in CD 413 shows a relatively small increase
in SOC when a cover crop is implemented. This is partly due to yield reductions
in the crops following the cover crop, as shown in Figure 6.9. CARD had noted
that the northern tier of cover crop CDs were considered areas where the
economic benefits of cover crops might be 'marginal' at best, and subsequent
yield reductions are expected.
3. Even if climate conditions are favorable for cover crops, the extent of the impact
on SOC depends on the specific crop rotation sequence. Thus, as discussed in
Section 5.4, crop rotation 350 (CSL,CSL,SOY) can support cover crops in each
winter (inter-crop) period, whereas rotation 186 (CRN,SOY) includes a cover
crop only after the soybean crop, i.e., only 50% of the time. Clearly, the more
often a cover crop is grown, the greater the potential impact on SOC. Longer
rotations, such as CR 262 (CSL,OTS,HLH,HLH,HLH), show the least impact
because they often have fewer opportunities for cover crop growth; for CR 262
a cover crop is only grown after CSL, so only one out of five years (i.e., 20%
of the time) includes a cover crop.
139
-------
2
£
16
U
10
| 8
Corn-No Cover
Soybeon-Cover
Corn-Cover
Corn-Observed
Soybeon— No Cover
Soybean-Observed
CR1B6 -CRN, SOY
* as
*
•
1980
1990
2000 2010
YEAR
2020
2030
Figure 6.9. Impact of Cover Crops on Corn and Soybean Yields in CD413
6.2 CLIMATE DIVISION AND STUDY REGION IMPACTS OF MODELED SCENARIOS
6.2.1 Total SOC Impacts for CDs and the Study Region
To evaluate the aggregate impact on SOC within each CD and the entire Study Region, the unit
SOC soil storage values discussed above were converted to total SOC. i.e., gC, by multiplying
unit values times the appropriate areas associated with each C/R/T combination for each modeled
scenario. The result of this conversion is included in Table 6.4, which shows both the absolute
and percent change in SOC for the Status Quo conditions and each modeled scenario (except
CRP which is discussed in Section 6.3, below). The absolute values represent the difference in
total SOC storage between 1990 and 2030 in each CD for each modeled scenario, while the
percent change relates the difference to the 1990 Status Quo values.
140
-------
The end of Table 6.4 includes summary statistics for each column; below we have listed some
of the key numbers from those statistics.
Gain in Total SOC, 1990-2030
Gigatonnes C Percent of 1990 Value
Status Quo/Baseline
Low Conservation
Medium Conservation
High Conservation
Cover Crops
1.795
1.808
1.825
1.904
1.944
49.1%
49.4%
49.9%
52.0%
53.1%
In Table 6.5 we've calculated the absolute and percent difference in SOC relative to Status Quo
conditions in 2030 for each CD for each modeled scenario. These numbers indicate how much
different 2030 SOC would be for each alternative, compared to the Status Quo.
From these results the following observations are provided:
1. Under the Status Quo or Baseline scenario, 1.8 Gt C will be retained or
sequestered within the Study Region from 1990 to 2030 under the Status Quo
assumptions described earlier. This represents a 49% increase over the current
1990 level of SOC of 3.66 Gt C in the Study Region based on the model
calculations.
2. The range in increase in SOC among the CDs goes from a minimum of 22% to
a maximum of 101 % during the 40-year projection period, under the Status Quo
scenario.
3. The three conservation compliance (tillage) alternatives had a small, but
significant, impact on SOC or carbon sequestration in the Study Region as a
whole. The differences shown above, especially for the High Conservation
alternative, although small are considered significant because of the consistency
of the increase and the relatively modest level of conservation tillage represented
by the scenario. For the High Conservation scenario, the SOC change relative
to the Status Quo ranges from +36% to -13%, with an average increase of 8%
(Table 6.5); thus the impacts vary among the CDs. However, the CDs with
negative changes showed consistent shifts away from corn-based rotations to grain
and soybean based rotations; thus the SOC decreases were due more to crop
changes than to tillage impacts.
141
-------
Table 6.4 Absolute and Percent Change in Total Soil Carbon from 1990 to 2030 in the Study
Region for Status Quo and Alternative Scenarios
CD
ft
CD221
CD222
C0223
C0231
C0232
CD241
CD242
C0251
CD252
CD253
CD261
CD262
CD271
CD272
CD281
CD2S2
C0311
CD312
CD313
C0314
C0321
C0322
CD323
CD341
C0342
C0351
CD352
C0353
CD391
CD392
C0393
C0401
CD402
C0403
C0411
C0412
C0413
C0414
C0415
C0416
CD421
CD422
CD431
CD432
CD433
C0441
C0442
CD443
CD444
C0471
CD472
CD473
C0474
C0475
CD531
CD532
CD533
CD561
C0562
CD571
CD572
CD573
C0581
COS82
CD583
C0584
Status Quo
Absolute
IMtCJ
2.23
0.61
0.64
4.80
33.00
5.10
9.00
0.10
24.50
4.60
2.27
15.80
7.60
49.80
4.20
5.40
9.20
7.80
1.91
9.70
32.90
17.00
6.40
8.60
18.40
26.70
80.00
23.80
3.02
65.00
35.60
5.80
22.20
30.40
46.00
13.50
42.00
60.00
24.90
34.90
66.00
64.00
9.40
16.10
13.40
0.34
16.20
8.00
14.80
53.10
80.00
36.20
42.00
23.20
18.70
39.00
37.80
13.60
10.70
28.20
39.10
16.80
12.60
56.80
31.60
20.10
Percent
[%]
26.02%
26.18%
26.45%
46.15%
61.57%
29.65%
34.22%
28.65%
45.71%
31 .08%
40.18%
53.56%
41 .99%
50.20%
35.29%
41.86%
58.97%
73.58%
63.67%
69.29%
50.77%
49.28%
52.03%
33.33%
37.63%
49.54%
50.00%
44.32%
32.54%
39.39%
47.21%
31.87%
35.35%
40.75%
44.66%
42.06%
35.00%
44.78%
46.63%
46.47%
46.15%
52.89%
46.08%
50.16%
46.85%
22.37%
34.91%
36.20%
47.44%
67.30%
77.67%
66.18%
101.45%
67.25%
46.52%
52.70%
57.10%
56.20%
59.12%
48.62%
52.20%
51.06%
33.60%
60.94%
49.14%
53.60%
Low
Absolute
CMtC]
2.47
0.68
0.71
4.80
33.00
5.10
9.00
0.10
24.50
4.60
2.27
15.80
7.60
49.80
4.20
5.40
9.70
8.30
2.01
10.10
34.00
17.40
6.60
9.60
20.20
26.70
80.00
23.80
3.02
65.00
35.60
5.80 •
22.20
30.40
46.00
13.50
42.00
60.00
24.90
34.90
66.00
64.00
9.40
16.10
13.40
0.34
16.20
8.00
14.80
53.10
80.00
36.20
42.00
23.20
18.70
39.00
37.80
13.70
10.70
28.90
41.00
17.30
12.60
56.80
31.60
20.10
Cons.
Percent
[%]
28.62%
28.81%
28.98%
46.15%
61.57%
29.65%
34.22%
28.65%
45.71%
31.08%
40.18%
53.56%
41.99%
50.20%
35.29%
41.86%
62.18%
78.30%
67.00%
71 .63%
52.39%
50.43%
53.66%
37.07%
41.22%
. 49.54%
50.00%
44.32%
32.54%
39.39%
47.21%
31.87%
35.35%
40.75%
44.66%
42.06%
35.00%
44.78%
46.63%
46.47%
46.15%
52.89%
46.08%
50.16%
46.85%
22.37%
34.91%
36.20%
47.44%
67.30%
77.67%
66.18%
101.45%
67.25%
46.52%
52.70%
57.10%
56.38%
59.12%
49.74%
54.67%
52.42%
33.60%
60.94%
49.14%
53.60%
Hed.
Absolute
EMtC]
2.47
0.68
0.71
4.80
33.00
5.10
9.00
0.10
24.50
4.60
2.27
15.80
7.60
49.80
4.40
5.50
9.60
8.10
1.98
10.10
34.00
17.40
6.60
10.00
20.90
26.70
80.00
23.80
3.02
65.00
35.60
5.90
22.80
31.40
46.00
13.50
42.00
60.00
24.90
34.90
66.00
64.00
9.60
16.30
13.60
0.35
16.50
8.20
15.00
53.10
80.00
36.20
42.00
23.20
18.70
39.00
37.80
14.40
11.10
30.40
41.90
18.30
12.60
56.80
31.60
20.10
Cons.
Percent
[%]
28.62%
28.81%
28.98%
46.15%
61 .57%
29.65%
34.22%
28.65%
45.71%
31.08%
40.18%
53.56%
41.99%
50.20%
36.97%
42.64%
61.54%
76.42%
65.78%
71 .63%
52.39%
50.43%
53.66%
38.61%
42.65%
49.54%
50.00%
44.32%
32.54%
39.39%
47.21%
32.42%
36.25%
42.09%
44.66%
42.06%
35.00%
44.78%
46.63%
46.47%
46.15%
52.89%
47.06%
50.78%
47.55%
23.03%
35.56%
37.10%
47.92%
67.30%
77.67%
66.18%
101.45%
67.25%
46.52%
52.70%
57.10%
59.26%
61 .33%
52.23%
55.79%
55.45%
33.60%
60.94%
49.14%
53.60%
High
Absolute
[MtCJ
2.47
0.68
0.71
5.10
34.40
5.50
10.10
0.11
25.50
5.00
2.27
15.80
7.60
49.80
4.90
5.20
8.70
7.30
1.75
9.10
35.90
18.30
7.00
10.80
22.10
28.60
83.00
24.90
3.02
65.00
35.60
6.40
24.80
34.20
52.00
14.70
48.00
67.00
28.20
38.70
71.00
68.00
10.10
17.30
14.50
0.43
20.90
10.40
18.50
53.10
80.00
36.20
42.00
23.20
19.00
39.00
37.80
15.70
11.60
32.60
45.70
19.80
11.40
52.20
27.40
18.40
Cons.
Percent
C%]
28.62%
28.81%
28.98%
49.04%
64.06%
31 .98%
38.26%
30.37%
47.49%
33.78%
40.18%
53.56%
41.99%
50.20%
41.18%
40.31%
55.77%
68.87%
58.14%
64.54%
55.15%
52.89%
56.91%
41.70%
45.01%
52.96%
51.88%
46.20%
32.54%
39.39%
47.21%
34.97%
39.30%
45.72%
50.49%
45.65%
39.67%
50.00%
52.71%
51 .39%
49.65%
56.20%
49.27%
53.56%
50.35%
28.10%
44.75%
46.85%
58.92%
67.30%
77.67%
66.18%
101.45%
67.25%
47.26%
52.70%
57.10%
64.61%
63.74%
55.92%
60.69%
59.82%
30.48%
56.25%
42.75%
49.07%
Cover
Absolute
[HtC]
2.23
0.61
0.64
4.80
33.00
5.40
9.80
0.10
24.50
4.60
2.27
15.80
7.80
52.70
4.30
5.40
9.80
9.10
2.40
10.80
32.90
17.00
6.40
8.60
18.40
28.20
85.00
26.30
3.02
65.00
35.60
5.80
22.20
30.40
51.00
14.80
48.00
66.00
27.60
39.00
72.00
66.00
11.80
22.30
16.00
0.79
32.80
17.50
30.40
53.10
80.00
36.20
42.00
23.20
18.70
39.00
37.80
15.60
11.50
28.80
42.00
17.70
12.60
56.80
32.40
20.10
Crop
Percent
[%]
26.02%
26.18%
26.45%
46.15%
61.57%
31 .40%
37.26%
28.65%
45.71%
31.08%
40.18%
53.56%
43.09%
53.07%
36.13%
41.86%
62.82%
85.85%
80.00%
77.14%
50.77%
49.28%
52.03%
33.33%
37.63%
52.32%
53.13%
48.88%
32.54%
39.39%
47.21%
31.87%
35.35%
40.75%
49.51%
46.11%
40.00%
49.25%
51.69%
52.00%
50.35%
54.55%
57.84%
69.47%
55.56%
51.97%
70.69%
79.55*
97.44%
67.30%
77.67%
66.18%
101.45%
67.25%
46.52%
52.70%
57.10%
64.20%
63.54%
49.66%
56.00%
53.80%
33.60%
60.94%
52.26%
53.60%
142
-------
Table 6.4 (contd.)
Low Cons.
Absolute Percent
[MtC] t%]
Med. Cons.
Absolute Percent
[HtC] [%]
High Cons.
Absolute Percent
[HtC] [X]
Cover Crop
Absolute Percent
[HtC] [%]
CD591
CD592
CD601
C0602
CD603
CD611
CD612
CD631
CD632
CD633
CD634
CD6A1
CD642
CD643
Average
Maximum
Minimum
Total
Study
Region
Change
39.40
13.40
6.90
23.60
33.80
1.84
1.29
16.90
27.50
17.50
24.40
7.80
12.90
4.67
22.44
80.00
0.10
1795.02
49.50%
49.26%
42.33%
52.80%
51.06%
36.36%
35.44%
51.52%
44.28%
40.51%
39.74%
42.39%
54.66%
73.78%
47.32%
101.45%
22.37%
49.08%
39.40
13.40
7.10
24.00
34.80
1.95
1.40
17.10
27.90
17.80
24.60
7.80
12.90
4.67
22.59
80.00
0.10
1807.52
49.50%
49.26%
43.56%
53.69%
52.57%
38.54%
38.46%
52.13%
44.93%
41.11%
40.07%
42.39%
54.66%
73.78%
47.93%
101.45%
22.37%
49.41%
40.30
14.00
7.40
25.20
36.80
1.99
1.44
17.10
27.90
17.80
24.60
9.10
14.30
5.35
22.81
80.00
0.10
1824.56
50.56%
51.28%
45.12%
56.38%
55.59%
39.25%
39.56%
52.13%
44.93%
41.11%
40.07%
49.46%
60.34%
84.25%
48.64%
101.45%
23.03%
49.86%
44.90
15.30
7.70
25.50
36.80
2.25
1.75
18.60
29.20
18.90
26.20
9.30
15.00
5.82
23.80
83.00
0.11
1903.66
56.05%
55.84%
46.95%
56.92%
55.59%
44.47%
48.08%
56.71%
47.02%
43.65%
42.60%
50.27%
63.29%
91.22%
50.56%
101.45%
28.10%
51 .96%
44.50
15.30
7.40
30.20
47.80
2.36
2.77
16.90
27.50
17.50
24.40
10.50
16.10
6.36
24.30
85.00
0.10
1943.95
55.97%
56.25%
45.40%
67.71%
72.21%
46.64%
76.10%
51.52%
44.28%
40.51%
39.74%
57.07%
68.22%
100.32%
53.25%
101.45%
26.02%
53.13%
from 1990
143
-------
Table 6.5
Absolute and Percent Difference in Total Soil Carbon Relative to Status Quo in 2030
CO
C0221
C0222
C0223
C0231
C0232
CD241
CD242
CD251
C0252
C0253
C0261
C0262
C0271
C0272
CD281
C0282
C0311
C0312
C0313
CD314
C0321
C0322
C0323
CD341
CD342
C0351
CD352
CD353
C0391
C0392
C0393
CD401
CD402
CD403
C0411
C0412
CD413
CD414
CD415
C0416
CD421
C0422
C0431
CD432
C0433
C0441
C0442
C0443
C0444
C0471
CD472
CD473
C0474
C0475
C0531
CD532
C0533
C0561
C0562
C0571
C0572
C0573
C0581
C0582
C0583
C0584
Relative Change
Low Cons.
Absolute Percent
[MtCJ IX]
Relative Change
High Cons.
Absolute Percent
[HtC] [%]
Relative Change
Cover Crop
Absolute Percent
[MtCJ [%]
0.24
0.07
0.07
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.50
0.50
0.10
0.40
1.10
0.40
0.20
1.00
1.80
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.10
0.00
0.70
1.90
0.50
0.00
0.00
0.00
0.00
10.76%
11.48X
10.94%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
5.43%
6.41%
5.24%
4.12%
3.34%
2.35%
3.12%
11.63%
9.78%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.74%
0.00%
2.48%
4.86%
2.98%
0.00%
0.00%
0.00%
0.00%
0.24
0.07
0.07
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.20
0.10
0.40
0.30
0.07
0.40
1.10
0.40
0.20
1.40
2.50
0.00
0.00
0.00
0.00
0.00
0.00
0.10
0.60
1.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.20
0.20
0.20
0.01
0.30
0.20
0.20
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.80.
0.40
2.20
2.80
1.50
0.00
0.00
0.00
0.00
10.76%
11.48%
10.94%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
4.76%
1.85%
4.35%
3.85%
3.66%
4.12%
3.34%
2.35%
3.12%
16.28%
13.59%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
1.72%
2.70%
3.29%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
2.13%
1.24%
1.49%
2.94%
1.85%
2.50%
1.35%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
5.88%
3.74%
7.80%
7.16%
8.93%
0.00%
0.00%
0.00%
0.00%
0.24
0.07
0.07
0.30
1.40
0.40
1.10
0.01
1.00
0.40
0.00
0.00
0.00
0.00
0.70
-0.20
-0.50
-0.50
-0.16
-0.60
3.00
1.30
0.60
2.20
3.70
1.90
3.00
1.10
0.00
0.00
0.00
0.60
2.60
3.80
6.00
1.20
6.00,
7.00
3.30
3.80
. 5.00
4.00
0.70
1.20
1.10
0.09
4.70
2.40
3.70
0.00
0.00
0.00
0.00
0.00
0.30
0.00
0.00
2.10
0.90
4.40
6.60
3.00
-1.20
-4.60
-4.20
-1.70
10.76%
11.48%
10.94%
6.25%
4.24%
7.84%
12.22%
6.00%
4.08%
8.70%
0.00%
0.00%
0.00%
0.00%
16.67%
-3.70%
-5.43%
-6.41%
-8.38%
-6.19%
9.12%
7.65%
9.37%
25.58%
20.11%
7.12%
3.75%
4.62%
0.00%
0.00%
0.00%
10.34%
11.71%
12.50%
13.04%
8.89%
14.29%
11.67%
13.25%
10.89%
7.58%
6.25%
7.45%
7.45%
8.21%
26.47%
29.01%
30.00%
25.00%
0.00%
0.00%
0.00%
0.00%
0.00%
1.60%
0.00%
0.00%
15.44%
8.41%
15.60%
16.88%
17.86%
-9.52%
-8.10%
-13.29%
-8.46%
--
--
-- '
--
--
0.30
0.80
--
--
--
--
--
0.20
2.90
0.10
0.00
0.60
1.30
0.49
. 1.10
--
--
--
0.00
0.00
1.50
5.00
2.50
--
--
--
--
--
--
5.00
1.30
6.00
6.00
2.70
4.10
6.00
2.00
2.40
6.20
2.60
0.45
16.60
9.50
15.60
--
--
--
--
--
• --
--
--
2.00
0.80
0.60
2.90
0.90
--
--
--
--
—
--
--
--
--
5.88%
8.89%
--
--
--
--
--
2.63%
5.82%
2.38%
0.00%
6.52%
16.67%
25.65%
11.34%
--
--
--
0.00%
0.00%
5.62%
6.25%
10.50%
--
--
--
--
--
--
10.87%
9.63%
14.29%
10.00%
10.84%
11.75%
9.09%
3.13%
25.53%
38.51%
19.40%
132.35%
102.47%
118.75%
105.41%
--
--
--
--
--
--
--
--
14.71%
7.48%
2.13%
7.42%
5.36%
--
--
--
--
144
-------
Table 6.5 (contd.)
Relative Change
Lou Cons.
Relative Change
Heel. Cons.
Relative Change
High Cons.
Relative Change
Cover Crop
CD
#
CD591
CD592
CD601
CD602
CD603
CD611
C0612
CD631
CD632
CD633
CD634
CD641
CD642
C0643
Average
Maximum
Minimum
Absolute
[MtC]
0.00
0.00
0.20
0.40
1.00
0.11
0.11
0.20
0.40
0.30
0.20
0.00
0.00
0.00
0.16
1.90
0.00
Percent
[%]
0.00%
0.00%
2.90%
1.69%
2.96%
5.98%
8.53%
1.18%
1.45%
1.71%
0.82%
0.00%
0.00%
0.00%
1 .54%
11.63%
0.00%
Absolute
[MtC]
0.90
0.60
0.50
1.60
3.00
0.15
0.15
0.20
0.40
0.30
0.20
1.30
1.40
0.68
0.37
3.00
0.00
Percent
C%]
2.28%
4.48%
7.25%
6.78%
8.88%
8.15%
11.63%
1.18%
1.45%
1.71%
0.82%
16.67%
10.85%
14.56%
3.07%
16.67%
0.00%
Absolute
[MtC]
5.50
1.90
0.80
1.90
3.00
0.41
0.46
.70
.70
.40
.80
.50
2.10
1.15
1.36
7.00
-4.60
Percent
[%]
13.96%
14.18%
11.59%
8.05%
8.88%
22.28%
35.66%
10.06%
6.18%
8.00%
7.38%
19.23%
16.28%
24.63%
8.16%
35.66%
-13.29%
Absolute
[MtC]
5.10
1.90
0.50
6.60
14.00
0.52
1.48
--
--
--
--
2.70
3.20
1.69
1.85
16.60
0.00
Percent
[%]
12.94%
14.18%
7.25%
27.97%
41.42%
28.26%
114.73%
--
--
--
--
34.62%
24.81%
36.19%
13.87%
132.35%
0.00%
145
-------
Moreover, as shown in Table 5.13, the High Conservation Scenario was based
on a 26% level for NT, while the Medium Conservation scenario assumed only
an 8% NT level; the corresponding levels of conservation tillage (i.e. combined
RT and NT) were 37% and 56%, respectively for Medium and High scenarios.
Data for 1992 from CTIC indicate that NT was practiced on 19% of cropland
within the Cornbelt, and combined conservation tillage was 43%; also, dramatic
increases in both practices are expected for 1993 (Dan McCain, CTIC, personal
communication, 1993). Thus, the levels of RT and NT derived from the RAMS
simulations are modest when compared to more recent data. Increased levels of
RT and NT practices would show a greater increase in the Study Region SOC
than indicated by the three scenarios evaluated. Although these results of tillage
impacts are encouraging, further study is needed before we can confirm these
conclusions (See Section 7.0). In addition, conservation tillage has other benefits
(e.g., water quality improvement) not considered in this study.
4. Cover crops have a significant impact on SOC in the Study Region leading to
a 53% increase from 1990, compared to 49% for Status Quo, and an additional
0.14 Gt C sequestered in the Study Region. Although these numbers are not
large, they are the result of cover crops being implemented on only 12% of the
cropland in the Study Region. Among the CDs with cover crops, the average
SOC increase over the Status Quo was 14% with a range from 0% to 132%
(Table 6.5).
6.2.2 Spatial Assessment of Study Region SOC Impacts
To assess and analyze spatial variations in the simulation results, the GSIS capabilities of the
Environmental Research Laboratory in Athens, GA were employed to display the results
discussed above for all CDs within the Study Region. The accompanying figures generated by
the Athens GIS staff are as follows:
Figure 6.10
Figure 6.11
Figure 6.12
Figure 6.13
Figure 6.14
Figure 6.15
Figure 6.16
Simulated 1990 Soil Carbon (gC/m2) Distribution Within the Study Region
Simulated 1990 Soil Carbon (gC/m2) Distribution Within the Study Region
Weighted by Cropland Distribution
Simulated 2030 Soil Carbon (gC/m2) Distribution Within the Study Region
Simulated 2030 Soil Carbon (gC/m2) Distribution Within the Study Region
Weighted by Cropland Distribution
Increase in Soil Carbon (gC/m2) Within the Study Region From 1990 to
2030 under the Status Quo Scenario
Increase in Soil Carbon (gC/m2) Within the Study Region From 1990 to
2030 under the Status Quo Scenario Weighted by Cropland Distribution
Percent Change in Soil Carbon Within the Study Region From 1990 to 2030
under the Status Quo Scenario
146
-------
Figure 6.17 Percent Difference in Soil Carbon Within the Study Region For
2030 For High Conservation Relative to the Status Quo Scenario
Figure 6.18 Percent Difference in Soil Carbon Within the Study Region For
2030 For Cover Crops Relative to the Status Quo Scenario
Appendix D includes a complete set of the model results maps, plus additional maps, (e.g.,
cropland and CRP distribution, additional tillage and cover crop scenario maps), that help the
reader to interpret the GIS-displayed model results.
As noted above, Figures 6.10 through 6.13 show the 1990 SOC, 2030 SOC, and the difference
or gain in SOC during the 40-year projection period, respectively, under the Status Quo
scenario. The units are gC/m2, as opposed to the units of MtC in Tables 6.4 and 6.5, in order
to eliminate the impact of the amount of cropland area. The spatial patterns in these figures are
quite complex and difficult to analyze quantitatively. However, some trends and observations
are evident, as follows:
1. The pattern of higher values of SOC (dark shades) in the central and northern
portions of the Study Region, and lower values (light shades) in the eastern,
southern, and western peripheries, is consistent with other soil carbon mapping
efforts (e.g., Kern and Johnson, 1991). .
2. The 1990 (Figure 6.10) and 2030 (Figure 6.12) maps of SOC show the same
general spatial pattern, although with significantly higher values for 2030. This
is indicated by the increase in CDs with the darker shades in Figures 6.12 and
6.13.
3. The regions with the greatest absolute change (Figure 6.14) and percent change
(Figure 6.15) appear to be those CDs with dominant corn-based rotations and/or
those located in the central and northern portions of the Study Region. The
smallest absolute increases are generally in the south.
4. Figures 6.11, 6.13, and 6.15, respectively, show the 1990 and 2030 SOC values,
and the increase from 1990 to 2030, weighted by cropland area in order to
account for the relative distribution of cropland within the Study Region; these
figures were generated by multiplying the SOC values in Figures 6.10, 6.12, and
6.14 by the fraction of area representing cropland (shown in Figure 5.1).
These figures are a more accurate spatial representation of SOC distribution
within the Study Region since they show the net impact of both unit area changes
and cropland distribution. Thus, comparing Figure 6.10 and 6.11 shows the
greatest concentration of SOC in the central area i.e., the Cornbelt and Plains
portions of Study Region; northern areas, such as PA 22, have large unit area
values but comprise a small portion of the total SOC because of the small area
of cropland. Figure 6.15 shows the largest increases generally in the central
portion of the Study Region, with the exception of CDs 473 and 474. In PA 47,
147
-------
Figure 6.10 Simulated 1990 Soil Carbon (gC/m2) distribution within the study
region
148
-------
Figure 6.11 Simulated 1990 Soil Carbon (gC/m2) distribution within the study
region weighted by cropland distribution
149
-------
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co • O>
S o "5 i-
IH?
LU j_ » S
V O> Ol m
5 s a >
< oc < o
Figure 6.12 Simulated 2030 Soil Carbon (gC/m2) distribution within the study
region
150
-------
Figure 6.13 Simulated 2030 Soil Carbon (gC/m2) distribution within the study
region weighted by cropland distribution
151
-------
Figure 6.14 Increase in Soil Carbon (gC/m2) within the study region from 1990
to 2030 under the Status Quo Scenario
152
-------
Figure 6.15 Increase in Soil Carbon (gC/m2) within the study region from 1990
to 2030 under the Status Quo Scenario weighted by cropland
distribution --,,
-------
the dominant rotation is wheat-fallow, but 60% of the PA area is in corn, barley,
or soybean-based rotations. The large simulated increase results from this change
in rotations between the historical and projection periods. Figure 6.16 shows the
increase weighted by the cropland distribution.
5. Under the Status Quo scenario, the absolute increase in SOC was generally in
the range of 1500 to 2500 gG/m2, as shown by the prevalence of the medium
range of dark shades in Figure 6.14; Figure 6.15 shows smaller values in the
range of 1000 to 2000 gC/m2 due to the weighting by the cropland area. This
range of values corresponds to a 50% to 70% increase in SOC (in the top 20 cm
of the soil) as shown by the medium dark shaded areas in Figure 6.16. Some of
the largest increases, such as in the CDs in PA 31, PA 47, and PA 53, are due
to dominant rotations of wheat-fallow or hay, followed by corn-based rotations
that lead to significant SOC increases during the projection period, as noted
above.
6. Figure 6.17 shows the percent difference in SOC in 2030 for the High
Conservation scenario compared to the Status Quo scenario. Except for a few
CDs with negative differences (shown by the cross-hatched areas), the High
Conservation scenario produced increased higher SOC throughout the Study
Region with differences mostly in the range of 5% to 25%. The largest
differences are generally in the southern and central portions of the Study Region.
Most of the CDs show differences of 10% to 20%, with some of the southern
and areas showing differences of more than 25% to 35%. As noted above, these
differences are based on rather modest increases in RT and NT areas compared
to more recent data on increasing usage of conservation tillage practices.
The few PAs that show negative differences, as compared to the Status Quo
scenario, are regions where the predicted changes in crop rotations (by the RAMS
model) lead to significant increases in wheat-fallow rotations (i.e. PA 58) or
increases in longer rotations with fewer years of corn and corn-silage (i.e. PA 31)
(See Table E.I in Appendix E). Independent confirmation of the likelihood of
these types of rotational changes is needed in order to validate tillage scenario
impacts in these regions.
7. The percent difference in SOC in 2030 due to cover crops is shown in Figure
6.18. Cover crops uniformly increased SOC throughout the Study Region (in
areas where cover crops were included), with the differences ranging from 0%
to more than 130%. In most of the central area, the differences are less than
15%, but increase to 30% and more for the southern CDs. These patterns
indicate an increase in the gain in SOC due to climatic conditions from the central
to the southern areas. Most of the PAs with small increases include crop
rotations that provide little opportunity for cover crops, and/or are located where
winter climate restricts cover crop growth.
154
-------
S .
-•st- LS~>
NX NX' NX NX XX
CO
II
XN.
Figure 6.16 Percent change in Soil Carbon within the study region from 1990 to
2030 under the Status Quo Scenario
155
-------
Figure 6.17 Percent difference in Soil Carbon within the study region for 2030
for High Conservation relative to the Status Quo Scenario
156
-------
CO
"is
"35
•— *•'
.S r»»
i?
a» o
i g
3 £
UTS
•^*-
NX" NX NX"
N/'
ura <=> e=>
ur>
X/' t-o
yr» "
'*'" II
CO
s »
1
s '8
JSI I
< oc < o
Figure 6.18 Percent difference in Soil Carbon within the study region for 2030
for Cover Crops relative to the Status Quo Scenario
157
-------
6.3 PRELIMINARY ASSESSMENT OF CONSERVATION RESERVE PROGRAM (CRP)
LAND
The procedures and assumptions for modeling SOC on CRP lands were described in Section 5.4.
This section presents the results of modeling two CRP scenarios, CRP1 and CRP2, and
compares the results to SOC conditions under the dominant rotation, or dominant cash crop
rotation, for each CD in the Study Region. As noted in Section 5.4, CRP1 assumes a 20-year
CRP enrollment of approximately 15 million acres within the Study Region starting in 1986,
with 56% converted back to the dominant rotation in 2007, and the remaining 44% staying in
CRP through 2030. CRP2 assumes that the 15 million acres remains in CRP throughout the
projection period, i.e., through 2030.
Initially, a third CRP scenario was to be evaluated to represent a 25% increase in CRP
enrollment to 18.75 million acres. However, in order to do the assessment we needed to know
where the additional 3.75 million acres would come from, i.e., which C/R/T combinations are
taken out of production (from the 216 million acres), and how do the remaining C/R/T acres
change in each PA. This information was not available for this study.
6.3.1 Unit Area Impacts of CRP Land Conversion on SOC
Table 6.6 summarizes the changes in SOC on a unit area basis, i.e., in terms of gC/m2, for the
dominant rotation, CRP1 simulation, and CRP2 simulation for eight selected CDs (whose
locations are shown in Figure 6.1). Table C.5 hi Appendix C includes results for all CDs.
Figures 6.19 and 6.20 graphically show the pattern of SOC changes during the projection period
for four of these CDs. We've included the dominant rotation in the tables and figures as a
baseline to which the CRP changes can be compared; obviously, the actual change would depend
on which crops (and their rotations) are taken out of production.
From these preliminary results, the following observations are provided:
1. The results of the CRP simulations are mixed. In many cases, CRP1 leads to
SOC values higher than the dominant rotation by the year 2030, and usually
higher than CRP2. In other CDs, the dominant rotation maintains the highest
SOC throughout the projection period. The key factor is likely the relative carbon
inputs of the dominant rotation as compared to the CRP simulations. When the
dominant rotation is a corn-based rotation, its 2030 SOC is usually the highest
(e.g. CDs 262, 272, 632, and 643 in Table 6.6). However, this is not always
true, especially if only one year of corn is in the rotation (e.g. CD 413, 603).
The percent difference for the CRP simulations can range from a few percentage
points to up to 20% or higher.
158
-------
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159
-------
8000
1000
U T 1 1 1 1 r
1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030
YEAR
Figure 6.19. Impacts of CRP Scenarios on Total SOC in CDs 413
and 643
7000
6000
O
E
S5000
C-4
I
4000
o
3000
CD473
CDS 81 I DOMINANT ROTATION - CASH CROP |
CR100 - CRN
T~
1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030
YEAR
Figure 6.20. Impacts of CRP Scenarios on Total SOC in CDs 473
and 581
160
-------
2. As shown in Figures 6.19 and 6.20, the SOC values for CRP1 change at the end
of the 20-year enrollment when the grass is plowed under in preparation of the
return to crop production. The CRP2 curves do not show this change since the
land remains as uncultivated grassland.
3. The CRP2 values are often, but not always, the lowest in terms of the change
during the projection period. The relative positions depend on many factors,
including the nature of the dominant rotation, irrigation practices, and soil and
climate conditions. In Figure 6.20, for CD 473 the CRP2 curve leads to the
greatest increase by 2030; the dominant rotation in this CD is wheat-fallow with
relatively low carbon inputs. For CD 581, CRP2 is the lowest and the Dominant
Rotation is the highest by 2030 because the rotation is continuous corn with high
carbon inputs.
6.3.2 Study Region Impacts of CRP Scenarios
To obtain the Study Region impacts, the CRP1 and CRP2 CENTURY simulations were
multiplied by the appropriate areas under CRP and dominant rotation conditions for each CRP
scenario, as discussed above and in Section 5.4. Table 6.7 shows the absolute and percent
change in soil carbon for both CRP scenarios and the dominant crop rotation, as a base for
comparison, while Table 6.8 shows the relative changes, both absolute and percent, for CRP1
and CRP2 compared to the dominant rotation for SOC values in 2030. Figures 6.21 and 6.22
show the spatial variation in the percent difference for CRP1 and CRP2, respectively, compared
to the dominant rotation in 2030. Maps of the absolute and weighted differences, and percent
differences are included in Appendix D.
Some observations from these results are as follows:
1. The average percent changes from 1990 to 2030 for the Dominant Rotation,
CRP1 and CRP2 are 32%, 30% and 19.0%, respectively. Thus, both CRP
scenarios show a decrease in SOC over the dominant rotation, but the overall
Study Region differences are small. Among the CDs, the maximum changes for
the three scenarios were 70%, 63%, and 62%, respectively, while the minimum
changes were 11%, 12%, and 7%.
2. From the bottom of Table 6.7, the net difference for CRP1 is 4.0 MtC, or 4.9%
less than the SOC for the dominant rotation. For CRP2, the net difference is
31.4 MtC, or 39% less than the Dominant Rotation.
3. There are great differences among the various CDs in terms of CRP impacts
relative to the Dominant Rotation, ranging from -35% to +65% difference in
SOC for CRP1, and -78% to +141% for CRP2, compared to the dominant
rotation. The averages of these ranges for CRP1 and CRP2 are -5% and -27%,
respectively, as shown at the end of Table 6.8.
161
-------
Table 6.7 Absolute and Percent Change in Total Soil Carbon from 1990 to 2030
in the Study Region for CRP Scenarios and Dominant Crop Rotation
CD
#
221
222
223
231
232
241
242
251
252
253
261
262
271
272
281
282
311
312
313
314
321
322
323
341
342
351
352
353
391
392
393
401
402
403
411
412
413
414
415
416
421
422
431
432
433
Dominant
Absolute
[ktCj
3.70
1.03
1.09
74.91
469.48
191.61
323.47
2.79
740.73
136.51
28.11
200 . 24
111.47
778.47
44.79
56.06
72.54
55.55
13.52
75.24
902.02
469.66
175.78
455.47
988.42
558.89
1726.06
518.91
163.02
3808.90
2158.03
436.80
1895.40
2463.04
2218.69
627.63
1285.44
2798.92
1159.70
1622.88
2003.97
1976.62
457.15
818.74
693.88
Rotation
Percent
[%]
28.90%
29.44%
30.13%
33.03%
39.95%
39.57%
43.53%
20.43%
35.27%
23.57%
28.75%
39.04%
35.35%
45.05%
24.18%
28.27%
35.43%
37.89%
32.51%
40.02%
45.39%
44.40%
46 . 74%
32.58%
37.21%
44.27%
46.07%
41.19%
28.26%
36.83%
45 . 84%
33.46%
41.99%
46.23%
33.31%
30.45%
16.59%
32.59%
33.79%
33.66%
40.99%
47.73%
46.66%
53.06%
50.65%
CRP1
Absolute
[ktC]
3.00
0.84
0.86
54.85
314.49
124.80
222.57
3.08
628.17
146.98
19.95
135.48
80.77
551.90
52.97
64.56
66.57
56.91
15.11
74.03
657.14
329.99
132.32
585.34
1185.64
420.38
1292.68
436.55
128.21
2725.43
1443.52
336.21
1349.94
1760.00
1755.19
524.34
2029.81
2427.24
970.38
1398.72
1508.58
1504.25
348.25
617.00
569.24
Percent
22.05%
22.56%
21.87%
22.58%
25.07%
25.57%
29.34%
21.96%
28.78%
24.35%
18.84%
24 . 04%
25 . 14%
30.66%
27.04%
30 . 74%
29.77%
35 . 58%
33.03%
36.10%
30.20%
28.30%
32.05%
39.64%
42 . 04%
31.82%
33.14%
33.01%
21.43%
25.55%
29.69%
25.57%
29.68%
32.84%
25.48%
24.57%
25.26%
27.15%
27.02%
27.87%
29.62%
35.23%
34.38%
38.43%
39.80%
CRP2
Absolute
[ktC]
1.83
0.51
0.52
40.78
199.18
58.03
127.76
2.68
449 . 71
134.81
15.85
100 . 94
52.91
364.60
38.25
41.78
49.44
46.96
15.17
55.13
511.75
253.69
107.58
477.98
911.89
299.88
900.36
365.74
92.62
1676.90
777.93
148.79
516.49
618.45
1099.82
362.93
1265.16
1656.64
667.64
986.66
1056.82
1025.44
244.77
438.83
457.90
Percent
[%]
13.49%
13.71%
13 . 14%
16.77%
15.85%
11.85%
16.79%
19 . 11%
20.53%
22 . 27%
14.94%
17.84%
16.42%
20.17%
19.44%
19.74%
22.00%
29.21%
33.01%
26.75%
23.39%
21.62%
25.89%
32.08%
31.92%
22.60%
22.92%
27.46%
15.48%
15.71%
15.98%
11.31%
11.35%
11.52%
15.95%
16.97%
15.72%
18.48%
18.53%
19.58%
20.68%
23.88%
24.04%
27.19%
31.79%
162
-------
Table 6.7 (contd.)
CD
#
441
442
443
444
471
472
473
474
475
531
532
533
561
562
571
572
573
581
582
583
584
591
592
601
602
603
611
612
631
632
633
634
641
642
643
Avg.
Max.
Min.
Dominant
Absolute
[ktC]
15.81
914.56
413.47
803.08
1344.70
1902.08
885.06
665.04
493.52
531.93
1060.00
741.28
784.74
643.20
1828.83
2558.25
1154.71
1693.71
2984.69
2003.30
1377.00
1879.02
711.68
532.39
2220.87
3179.04
21.29
18.79
2490.64
3332.36
1973.16
2796.27
432.03
797.88
280.18
1015.37
3808.90
1.03
Rotation
Percent
[%]
18.22%
34.68%
33.03%
45.25%
16.02%
17 . 30%
15.16%
15.19%
13.49%
13.08%
14.16%
11.07%
51.53%
56.67%
43 . 14%
46.86%
48.23%
43.63%
27.57%
28.19%
31.38%
55.24%
61 . 26%
22.51%
29.85%
29.12%
25.22%
32.96%
50 . 29%
35.90%
31.69%
30.55%
37.07%
53.24%
69.87%
35.61%
69.87%
11.07%
CRP1
32.23%
Absolute
[ktC]
24.29
1089.00
558.22
921.26
1080.82
1805.00
892.09
704.81
542.67
570.01
1092.60
1133.62
723.57
519.22
1443.73
2032.45
997.34
1153.56
4929.43
2795.94
2034.14
1410.45
612.47
718.18
2818.97
4121.74
29.67
26.47
1752.39
3256.45
2036.86
3045.57
384.07
695.09
268.99
965.94
4929.43
0.84
77275.40
Percent
26.23%
38.93%
41.25%
47.99%
12.21%
15.72%
14.73%
15.07%
14.11%
12.83%
13 . 14%
15.48%
46.53%
44.89%
32.96%
35 . 34%
40.01%
29.36%
47.10%
40.03%
47 . 11%
41.27%
51.50%
28.62%
36.60%
35.80%
32.78%
41.90%
34.96%
35.26%
32.33%
32.28%
31.73%
44.54%
62.72%
30.98%
62.72%
12.21%
29.58%
CRP2
Absolute
[ktC]
29.72
1176.50
675.43
994.84
1233.96
1958.40
1111.54
873.12
731.36
753.27
1605.00
1785.40
27 ft-. 70
198.65 .
670.41
1036.80
624.09
619.65
714.95
802.10
630.18
432.07
238.52
528.52
1980.14
3303.36
29.88
33.28
536.71
1341.57
1082.16
1887.42
356.59
561.69
266.03
622.47
3303.36
0.51
Percent
[%]
31.88%
41.70%
49.40%
51.30%
13.94%
17.06%
18 . 36%
18.66%
19.01%
,16.95%
19.30%
24.38%
17.66%
17.17%
15.31%
18.03%
25 . 04%
15.75%
6.81%
11.45%
14.54%
12.64%
20.06%
20.98%
25.61%
28.54%
32.81%
52.30%
10.70%
14.51%
17.16%
19.92%
29.43%
35.95%
61.94%
21.89%
61 . 94%
6.81%
49797.48
19.02%
Total 81229.86
Study
Region Change
from 1990
CRP1 - Represents scenario under CRP from 1986 to 2030 (20 years) and converted
back to dominant crop rotation from 2007 to 2030.
CRP2 - Represents scenario under CRP from 1986 to 2030.
163
-------
Table 6.8 Absolute and Percent Difference in Total Soil Carbon for
the CRP Scenarios Relative to Dominant Crop Rotation
Relative Change
CRP1
Absolute Percent
[ktC]
221
222
223
231
232
241
242
251
252
253
261
262
271
272
281
282
311
312
313
314
321
322
323
341
342
351
352
353
391
392
393
401
402
403
411
412
413
414
415
416
421
422
431
432
433
-0.71
-0.18
-0.23
-20.06
-154.98
-66.81
-100.89
0.28
-112.55
10.47
-8.16
-64.76
-30.70
-226.57
8.18
8.50
-5.97
1.36
1.60
-1.21
-244.87
-139.67
-43.46
129.88
197.22
-138.51
-433.37
-82.36
-34.81
-1083.47
-714.51
-100.59
-545.45
-703.04
-463.50
-103.29
744.37
-371.68
-189.32
-224.17
-495.39
-472.37
-108.90
-201.74
-124.64
-19.08%
-17.78%
-21.35%
-26.78*
-33.01%
-34.87%
-31.19%
10.20%
-15.20%
7.67%
-29.04%
-32.34%
-27.54%
-29.10%
18.27%
15.17%
-8.23%
2.45%
11.81%
-1.60%
-27.15%
-29.74%
-24.73%
28.52%
19.95%
-24.78%
-25.11%
-15.87%
-21.35%
-28.45%
-33.11%
-23.03%
-28.78%
-28.54%
-20.89%
-16.46%
57.91%
-13.28%
-16.32%
-13.81%
-24.72%
-23.90%
-23.82%
-24.64%
-17.96%
Relative Change
CRP2
Absolute Percent
[ktC]
-1.87
-0.51
-0.58
-34.14
-270.29
-133.58
-195.70
-0.11
-291.02
-1.70
-12.26
-99.31
-58.56
-413.87
-6.54
-14.29
-23.10
-8.59
1.65
-20.11
-390.27
-215.97
-68.20
22.51
-76.53
-259.01
-825.70
-153.17
-70.40
2132.00
1380.10
-288.02
1378.91
1844.60
1118.87
-264.69
-20.28
1142.28
-492.06
-636.22
-947.15
-951.18
-212.38
-379.91
-235.98
-50.47%
-50.00%
-52.72%
-45.57%
-57.57%
-69.72%
-60.50%
-4.04%
-39.29%
-1.25%
-43.62%
-49.59%
-52.54%
-53.16%
-14.60%
-25.48%
-31.85%
-15.47%
12.24%
-26.73%
-43.27%
-45.98%
-38.80%
4.94%
-7.74%
-46.34%
-47.84%
-29.52%
-43.18%
-55.97%
-63.95%
-65.94%
-72.75%
-74.89%
-50.43%
-42.17%
-1.58%
-40.81%
-42.43%
-39.20%
-47.26%
-48.12%
-46.46%
-46.40%
-34.01%
164
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Table 6.8 (contd.)
Relative Change
CRP1
Absolute Percent
[ktC] [%]
Relative Change
CRP2
Absolute Percent
[ktC] [%]
441
442
443
444
471
472
473
474
475
531
532
533
561
562
571
572
573
581
582
583
584
591
592
601
602
603
611
612
631
632
633
634
641
642
643
Ave.
Max.
Min.
8.48
174.44
144.75
118.18
-263.88
-97.08
7.03
39.77
49.15
38.08
32.60
392.34
-61.17
-123.98
-385.09
-525.80
-157.37
-540.15
1944 . 74
792.64
657.14
-468.57
-99.21
185.79
598.10
942.70
8.38
7.67
-738.25
-75.91
63.70
249.30
-47.96
-102.79
-11.19
-49.43
1944.74
-1083.47
53.60X
19.07X
35.01X
14.72X
-19.62X
-5.10X
0.79X
5 . 98X
9.96X
7 . 16X
3.08%
52.93%
-7.80%
-19.28%
-21.06%
-20.55%
-13.63%
-31.89%
65 . 16%
39 . 57%
47.72%
-24.94%
-13.94%
34.90%
26.93%
29.65%
39.37%
40.84%
-29.64%
-2.28%
3.23%
8.92%
-11.10%
-12.88X
-3.99%
-4.51%
65.16%
-34.87%
13.91
261.94
261.96
191.76
-110.74
56.32
226.48
208.08
237.84
221.34
545.00
1044.12
-510.04
-444.56
1158.42
1521.45
-530.62
1074.06
2269.74
1201.20
-746.82
1446.95
-473.16
-3.88
-240.73
124.32
8.59
14.48
1953.93
1990.79
-891.00
-908.85
-75.44
-236.19
-14.14
-392.90
1044.12
2269.74
87.96%
28.64X
63.36X
23.88X
-8.24X
2.96X
25.59X
31.29%
48.19%
41.61%
51.42%
140.85%
-64.99%
-69.12%
-63.34%
-59.47%
-45.95%
-63.41%
-76.05%
-59.96%
-54.24%
-77.01%
-66.48%
-0.73%
-10.84%
3.91%
40.35%
77.07%
-78.45%
-59.74%
-45.16%
-32.50%
-17.46%
-29.60%
-5.05%
-26.53%
140.85%
-78.45%
165
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Figure 6.21 Percent difference in Soil Carbon within the study region for 2030
for CRP1 relative to the Dominant Rotation
166
-------
Figure 6.22 Percent difference in Soil Carbon within the study region for 2030
for CRP2 relative to the Dominant Rotation
167
-------
4. The spatial variation in the relative percent difference for CRP1 and CRP2,
shown in Figures 6.21 and 6.22, respectively, are the net results of the effects of
climate, soil conditions, and differences between CRP lands and the dominant
rotation for each CD. Most of the Study Region shows decreases in SOC, as
shown by the cross-hatched areas. The greatest increases (both absolute and
percent) are generally in the southern and western portions of the Study Region,
while the smallest and negative changes are often in the north and east.
However, the patterns are quite complex due to the combined impacts noted
above.
5. For CRP1 (Figure 6.21), the differences are mostly in the range of -10% to -20%
compared to the Dominant Rotation, although a number of CDs scattered
throughout the southern and western areas show significant increases, (see Table
6.8). For CRP2 (Figure 6.22), the Study Region shows a more even display of
SOC losses (i.e. negative percent differences) throughout the Study Region mostly
with greater losses as compared to the Dominant Rotation. As expected, many
of the same CDs that gained SOC under CRP1 also gained under CRP2. CRP2
also shows a much greater range of impacts among the CDs than CRP1, as
shown by Figures 6.21, 6.22, and those in Appendix D.
These initial results are considered preliminary, for a number of reasons:
a. The CRP land of 15 million acres is separate from the Study Region cropland
area of 216 million acres. Any comprehensive consideration of carbon
sequestration on CRP land, and its impacts on the Study Region, must include the
land character and conditions prior to and following CRP, and the relative
changes in SOC on these areas. Thus, the CRP land should be included as part
of the Study Region cropland in any future assessments.
b. Our methodology assumes that the CRP land is converted from the Dominant
Rotation, or Dominant Cash Crop Rotation, in 1986, and under CRP1 reverts
back to that rotation in 2005. This assumption was necessary because the CRP
land was not included as part of the RAMS analysis for the Status Quo and
alternative scenarios. To accurately assess the SOC impacts of these changes in
land use, CARD needs to identify the specific rotations in each PA that are
converted to CRP. Similarly, the return of CRP land to crop production must be
defined in terms of the specific crops and rotations expected for the returned
lands. Thus for each PA, we need to know the crop rotation distribution both
with and without CRP in order to effectively evaluate the SOC impacts of these
scenarios. If an increase in CRP land is proposed, then similar information is
needed for the new, or additional land converted to CRP.
c. The specific nature of the agronomic activities, vegetation, and soils associated
with CRP land needs better definition, along with the variation in these
168
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characteristics throughout the Study Region. This assessment assumed the same
soils characteristics on CRP land as for the other croplands, growth of native
temperate grasses with the same species characterization throughout the area, and
no human intervention related to agronomic activities. These assumptions need
to be confirmed and/or revised to more realistically represent the SOC on CRP
lands.
All of these issues need to be resolved before we can identify needed changes to the
methodology for a more realistic assessment of the impacts of CRP on carbon sequestration
within the Study Region.
6.4 STUDY REGION IMPACTS OF ALTERNATIVE YIELD INCREASES
The critical importance of the assumption of the annual crop yield increase has been noted
earlier in Section 3, Section 5, and above in the discussion of the study results produced with
an annual increase of 1.5 %. Because of the impact of this assumption, and given the automated
calibration and Study Region modeling capabilities developed by the EPA Athens Laboratory,
the entire suite of CENTURY simulations for all 80 CDs, all C/R/T combinations, and all
policy scenarios (excluding CRP) were re-done for the 1990-2030 projection period for two
additional levels of crop yield increases: 1.0% and 0.5 % per year. Although all detailed
results in this report are derived from the model runs with the 1.5% annual crop yield increase
assumption, the same information has been generated for these two additional yield levels.
Table 6.9 shows the summary results of those simulations, along with the results for the
1.5%/year increase level, in terms of both the Total SOC change (in GtC) over the 40-year
projection period, and the percent change from the 1990 value and relative to the Status Quo
scenario. Figure 6.23 graphiczilly shows the timeline of Total SOC changes for the projection
period for selected combinations of scenarios and annual yield increase levels that bracket the
range of SOC increases.
The Status Quo scenario results for the three yield levels were shown earlier in Figure 1.3 for
the projection period, along with the entire historical simulation from 1907 through 2030. The
curves in Figure 1.3 show steady, almost linear increases in SOC from 1970 through 2030.
For the historical period of 1970 to 1990 the increase occurs at an annual rate of about 0.6%
per year, which is also about the same rate as for the 1990-2030 increase under the 0.5% crop
yield increase. For the 1.5% aind 1.0% yield increase levels during the 1990-2030 projection
period, the Total SOC increases at 1.2% and 0.9% per year, respectively. If this general pattern
is accurate, agricultural SOC within the Study Region is making a comeback from a low of
about 50% of original (i.e., native vegetation) levels in 1950-70, to about 60% of these levels
in 1990. Continuing the increaise would lead to 2030 Total SOC levels that approach 75% to
90% of the original SOC prior to the onset of agricultural production (circa 1900).
169
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Table 6.9 Impacts of Alternative Annual Crop Yield Increases on Study Region
Total Soil Organic Carbon for Status Quo and Policy Scenarios
Status Quo
1.5 % Yield Increase
1.0 % Yield Increase
0.5% Yield Increase
Low Conservation
1.5 % Yield Increase
1.0 % Yield Increase
0.5% Yield Increase
Medium Conservation
1.5 % Yield Increase
1.0 % Yield Increase
0.5% Yield Increase
High Conservation
1.5 % Yield Increase
1.0 % Yield Increase
0.5% Yield Increase
Cover Crops
1.5 % Yield Increase
1.0 % Yield Increase
0.5% Yield Increase
Total SOC, GtC
1990 2030 1990-2030
Gain
Percent Increase
from from
1990 Status
Value Quo
3.66
3.66
3.66
5.45
5.01
4.60
1.80
1.36
0.95
49.1
37.1
26.0
3.66
3.66
3.66
5.47
5.02
4.62
1:81
1.37
0.96
49.4
37.4
26.2
0.7
0.9
1.1
3.66
3.66
3.66
5.48
5.04
4.63
1.82
1.39
0.97
49.9
37.9
26.6
1.7
2.2
2.6
3.66
3.66
3.66
5.57
5.13
4.72
1.90
1.47
1.06
52.0
40.1
28.8
6.1
8.2
11.2
3.66
3.66
3.66
5.60
5.14
4.74
1.94
1.49
1.08
53.1
40.7
29.7
8.3
9.9
14.2
170
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6500
High Conservation
@ 1.5% Yield
4000
1990
2030
Figure 6.23 Simulated Total SOC Impacts for Selected Scenarios and Alternative Annual
Crop Yield Increases
Table 6.9 shows that changing the annual level of crop yield increase from 0.5 % to 1.5 %
leads to almost a doubling of the Total SOC gain during the 40-year projection period, i.e.
changing the annual yield increase by a factor of 3 leads to about a 80% to 90% gain in Total
SOC. This is fairly consistent for all policy scenarios. The absolute gain ranges from about 1
GtC to 2 GtC across all scenario and yield increase combinations; this corresponds to a
percentage gain of about 30% to 50% compared to the 1990 Total SOC levels. For the 0.5%
yield increase level, the Total SOC gains range from 26% for the Status Quo to 30% for Cover
Crops, with the alternative tillage scenarios in between. Similarly, for the 1.5% level, the range
is 49% to 53%. In Figure 6.23, the timelines for Status Quo, High Conservation, and Cover
Crops are plotted for yield increases of 1.5% and 0.5% per year; these combinations cover the
range of Total SOC gains listed in Table 6.9.
Comparing the policy scenarios to the Status Quo condition, each level of conservation tillage
leads to increased gains in Total SOC, and these gains also increase with each higher level of
increasing crop yields. The final column in Table 6.9 shows the percent change from the Status
Quo scenario. Although the absolute gain increases with the yield increase, the percent change
is higher for the lower level of crop yield increase because the gain in SOC is divided by a
smaller absolute SOC level. Thus, for Cover Crops, at the 0.5% yield level the SOC is 14.2
% higher than the Status Quo, and only 8.3 % higher at the 1.5% yield level.
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SECTION 7.0
PROJECT FINDINGS AND RECOMMENDED FUTURE WORK
This section summarizes the study findings and provides recommendations for future research
and investigations needed to improve and evaluate the methodology and modeling procedures.
To set the stage for this discussion, we review some of the key assumptions in both the
methodology and the modeling in Section 7.1 below. The study findings are summarized in
Section 7.2, and Section 7.3 discusses future recommended efforts to extend this methodology
for assessment of carbon sequestration in other parts of the U.S., to perform further field site
model testing, and to pursue selected research issues identified during the course of the work.
7.1 REVIEW OF METHODOLOGY AND MODELING ASSUMPTIONS
The study results presented in Section 6.0 are based on a variety of both methodology and
modeling assumptions. Sections 3.0 and 5.0 described the methodology and scenario
assumptions, respectively, which were employed to represent the range of climatic, soils, and
agronomic conditions throughout the Study Region, and projections of these conditions under
alternative future scenarios. The modeling assumptions are basic to the CENTURY model
framework, structure, and algorithms, and were described briefly in Sections 2.4 and 3.2.
Below we discuss each of these two categories of assumptions so that the reader can have a more
informed basis for evaluating the current study results.
7.1.1 Key Methodology Assumptions and Impacts
Annual 1.5% Increase in Crop Yield: The assumed 1.5% annual increase in crop yields
during the projection period may be the most critical assumption in terms of the projected
level of increase in SOC throughout the projection period. As noted in Sections 3.5 and
5.2, this assumed increase was applied uniformly for all crops and rotations; the resulting
yield levels were then used as targets for calibration of the CENTURY crop production
(PRDX) parameter. Section 6.4 presented summary results for the entire Study Region
of alternative annual yield increase levels of 1.0% per year and 0.5% per year. Even
at the lowest level of 0.5% per year, SOC increased approximately 26% to 30% from
1990 to 2030, while the increase for the 1.5% level was 49% to 53%.
Because of this sensitivity, this assumption needs to be re-evaluated and/or confirmed in
order to establish the reliability of the study projections. Even if the final judgement is
that the 1.5% per year increase is the best we can predict at this time, the increase
should be re-assessed individually for each crop and differentially for different portions
172
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of the Study Region. In addition, we need to determine if yield increases will be
accompanied by similar increases in crop biomass and residues, so that appropriate
changes in the model crop parameters can be implemented.
Equal Yields for All Tillage Practices: As noted in Section 6.1, the yield calibration involved
adjusting crop parameters for each rotation so that individual crop yields would match
both the historical data and the future projected yields, within a small tolerance of a few
percent. The CENTURY model then simulates crop yields that are essentially the same
for all tillage practices, within a reasonable tolerance. The sentiment among the Project
Team members was that farmers would likely adjust practices, such as fertilizer
applications, so that yields would not diminish under RT and NT. The impact of this
assumption is to eliminaie yield variations due to tillage as a consideration in projecting
alternative future conditions. The large body of conflicting evidence on this subject
would appear to warrant this type of approach for this study.
Automatic Fertilizer and Irrigation Applications: In Section 5.1, we described
CENTURY model options used in this study to automatically calculate the fertilizer N
and irrigation needs of each crop, based on user-defined control parameters. The
automatic fertilizer option was used for all crops, except legume hay and CRP lands,
while the irrigation option was only used in CDs and rotations identified by CARD as
being irrigated. The impact of this is to eliminate nutrient stress conditions for all crops
and CDs, and moisture stress conditions for the irrigated CDs and crop rotations. The
fertilizer option effectively eliminated over-fertilization as all nutrient sources, including
mineralization, atmospheric deposition, manure applications on forage crops (see below),
etc. are available for satisfying crop needs.
Animal Waste Applied to Forage Crops: Section 5.1 described the assumptions and
procedures used for returning animal waste nutrients (C and N) to forage croplands that
produce animal feeds. These procedures were applied only to corn-silage, legume hay,
and non-legume hay croplands which collectively comprise 26% of the cropland (see
Table 5.1). They were needed due to the lack of an appropriate database of animal waste
production and application for the Study Region. Considering the spatial extent of our
CDs and the fact that animal waste, corn-silage, and hay are not usually transported great
distances, the return of C and N associated with the harvested crop to the same CD in
which it was grown is a reasonable assumption for this study. However, future efforts
should consider a more detailed assessment of this component of the C and N balances
within the CDs, including distributions of animal populations, actual animal waste
production and application, and waste resulting from grain production for animal feeds,
which was not considered here.
173
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Erosion Impacts: Although CENTURY allows the model user to input monthly erosion rates,
erosion was not included in our simulations. The reasons for this were as follows:
a. Carbon sequestration was estimated as the change in SOC over the 40-year
projection period. Including erosion losses would under-estimate
sequestration since SOC losses by erosion would lead to smaller increases
in SOC; these field level losses are more likely to be landscape-level
movement as the C associated with erosion leaves the field and is
deposited in other field areas, wetlands, and waterbodies.
b. Simple calculations using the range of typical erosion rates, SOC levels
for surface soils, and enrichment ratios show that SOC losses by erosion
could reach 0.5% to 1.0% per year, producing up to 20% to 40% loss of
SOC over a 40-year projection period. Modeling this extent of soil and
SOC loss would need to consider elevation and horizon shifts in the soil
surface, a capability not included in CENTURY nor most other SOM
models.
c. The RAMS output provides average annual erosion for each crop rotation,
but not for each crop-year within the rotation. Thus additional effort
would have been required to develop reasonable erosion rates for each
crop under different rotations, tillage practices, and climatic conditions in
each CD.
Based on the above, we feel it is appropriate to ignore erosion in this study; however,
the impacts of this assumption on the study results need to be considered. The primary
impacts appear to be related to the effects of alternative tillage practices and calculation
of initial SOC conditions for projection purposes. Ignoring erosion may have reduced
the real impacts of RT and NT on SOC changes. Since erosion rates (and associated
SOC losses) would be significantly greater for CT than either RT or NT, the increases
in SOC through 2030 for all tillage practices would have been much less if erosion had
been included, but the relative impacts for CT would have been greater than for RT or
NT. In other words, ignoring erosion eliminates one of the key differences among the
three tillage alternatives.
The impact of ignoring erosion on the calculation of initial SOC conditions is even more
complex, and may need to be reconsidered. The historical pattern of changes in SOC
for different crop rotations for the 1907-1988 time period was discussed in Section 6.2;
the overall pattern was shown to be generally consistent with the literature, and the
consistency checks for crop yields and SOC observations indicated reasonable agreement.
The pattern resulted from the simulations performed under the historical management
practices summarized in Table 5.11 and discussed in Section 5.2. If erosion had been
included as part of the simulation, the initial SOC conditions (i.e., in 1989) for the
projection period would have been lower and may have a subsequent impact on the SOC
174
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projections. In effect, the assumed historical practices appear to have masked the impact
of ignoring erosion. In future efforts to refine and re-evaluate these results, it may be
appropriate to consider including erosion in conjunction with a review of the historical
practice assumptions and their impacts on the starting SOC conditions.
Climatic Data Assumptions: As described in Section 5.1, the climatic data used in the model
simulations was extracted from a national data base for the period of 1948 to 1988, and
the 41-year data base was repeated for time periods prior to and following the historical
time span. Although CENTURY includes an option to stochastically generate monthly
precipitation for any time period, testing with this option showed much greater year-to-
year variations in the simulation results than using the historic data. Also, the historic
air temperature data would still be needed since the stochastic option is not available for
air temperature.
The net effect of this assumption is that the projections and historical SOC conditions are
estimated under unchanging climatic patterns. That is, the 40-year projection is made
assuming that the climate for the next 40 years will not be different from the last 40
years, with an analogous assumption for the historic climate pattern.
Spatial Scale of the Assessment: Section 4.0 described the development of the
Climate Divisions (CD) as the basic spatial unit of the analysis. The CD boundaries
were developed from analysis of the precipitation and air temperature patterns, and were
adjusted to conform to county boundaries to be consistent with the soils data and RAMS
agronomic information. The CDs, which are aggregates of counties, are a reasonable
spatial compromise between a county-level analysis and a PA-level analysis; simulating
crop rotations in each of the 1300 counties in the Study Region would not have been
feasible, and the PAs used in the RAMS assessment were too large for an accurate
representation of meteorologic and soils variability. Greater spatial detail from the
RAMS assessment would be needed, perhaps at the county level, before a finer spatial
analysis would be warranted.
Representation of Tillage Practices: The modeling of alternative tillage practices with
CENTURY was discussed in Section 5.1.2. Although CENTURY provides relatively
detailed mechanisms for representing alternative practices, the impacts of practices on
SOC are a direct result of how the individual mechanisms are parameterized, i.e., the
specific parameter values used in the model will determine the simulated impacts on
SOC. Some of the specific areas where parameters values need to be re-evaluated are
as follows:
a. Incorporation of Surface Residues - Table 5.6 listed the type and
number of tillage operations used for each tillage practice, and Table 5.5
provided the parameters for each operation controlling their impact on the
incorporation and disposition of the residue. It is clear from these tables
that NT has only two growing season operations, neither of which
175
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incorporate crop residues into the soil; whereas, both CT and RT have
numerous operations that move the residue into the soil column. The
harvest operation transforms 50% of the total residue into the 'standing
dead' pool and 50% to the 'surface residue' pool (see Table 5.8 and
Figure 5.2). Under NT, the parameters that control the disposition of
these pools and the rate of C transfer to the soil need to be re-evaluated.
It may be appropriate to consider periodic inversion (i.e., moldboard
plowing) every 5 to 10 years for NT rotations, or a constant annual rate
of surface residue incorporation to account for processes not specifically
included in the model (e.g., earthworm activity).
Tillage Equipment Parameters - Table 5.5 listed the parameters that
control the impacts of individual tillage equipment operations in terms of
the disposition of crop residues and effects on decomposition. The chisel
plow and cultivator operation parameters need to be re-evaluated as these
equipment are used for RT and NT practices, and prior testing of
CENTURY has not focussed extensively on these types of operations.
Parameters for Different Crops - An underlying concern related to both
Items b and c above is that much of the past field testing experience with
CENTURY has focussed primarily on grasslands and wheat-dominated
cropping systems. Many of the model parameters are derived from this
past experience and therefore may not accurately represent the corn and
soybean dominated rotations that are prevalent throughout the Study
Region. Consequently, all model parameters that may vary as a function
of crop type and associated tillage operations (e.g., standing dead fall rate,
surface residue decomposition rate, residue incorporation, etc.) need to be
re-assessed and confirmed. Experience in the central and eastern portions
of the Study Region (e.g., Paustian et al., 1992; Blevins et id., 1983)
would be especially relevant in this effort.
As part of the EPA BIOME Study, Li and Cialella (1992) applied the DNDC model
(discussed in Section 2.4) to selected CDs in seven different states within the Study
Region. Using fertilizer applications and initial conditions calculated by CENTURY,
they ran simulations for the 40-year projection period for each dominant rotation under
the four different tillage practices (CTFP, CTSP, RT, NT). Their results showed
consistent and significant trends as a function of tillage practice, i.e., conventional tillage
enhanced SOC losses, while RT and NT conserved SOC. For the corn-based rotations
in the seven CDs, SOC increased 24% under conventional tillage and 58% under No-
Till. For non-legume hay and wheat-fallow rotations, in MO and MN, respectively,
SOC decreased 28% under Conventional Tillage and only 12% under No-Till.
176
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Although these results are generally consistent with conventional expectations and the
CENTURY model results (with some exceptions and unknowns, discussed below), they
further reinforce the need to examine the process representation and parameterization of
models before accepting their results for decision-making purposes. The DNDC model
provides detailed algorithms for simulating soil N processes, but uses relatively simple
assumptions for the impacts of tillage. Thus, tillage impacts are modeled by the
following changes in the model parameters (C. Li, 1993, personal communication):
a.
b.
c.
Depth of tillage is 0 cm, 15 cm, and 25 cm, respectively for NT, RT, and
both CTFP and CTSP, regardless of the type of tillage equipment used.
Decomposition rates are increased by a factor of 3.0 for conventional
tillage, and by a factor of 1.5 for RT, as compared to NT, which is
represented by the default rate in the model.
Denitrifier populations are decreased by 30% for both conventional and
RT,as compared to NT.
In contrast, the CENTURY model parameters used in this study were based on only a
60% increase in the decomposition rate for moldboard, a 40% increase for chisel plows,
and a 30% increase for a row cultivator. Furthermore, there is no indication of the crop
yields simulated by DNDC, or how yields varied across the four tillage treatments and
for future projections through 2030. As noted earlier, the crop yield increase has a
critical impact on SOC changes for any C/R/T combination. Consequently, although the
two models represent tillage impacts in different ways, the differences in model results
could be due entirely to the differences assumed for the decomposition effect, or
differences in simulated crop yields. These differences in model parameterization need
to be resolved as part of further work on assessing the complex impacts of alternative
tillage practices.
7.1.2 Key Model Assumptions and Impacts
Nitrogen Cycle Representation: The N cycle component of CENTURY is a relatively
simplified representation of soil N processes based on parameters that calculate process
fluxes as constant rates or adjusted for selected soil and/or environmental conditions.
Also, the parameter values in the model are derived primarily from experience and
testing on native grassland and wheat cropping systems; how well the parameters
represent corn and soybean systems is unclear. The CENTURY model tests performed
by Metherell (Appendix A) and Chinnaswamy et al. (Appendix B) both noted problems
with the soil N simulation related to soil mineral N storages, crop N uptake, impacts of
fertilization and tillage practices, volatile N losses, and non-symbiotic N fixation. Since
N2O is an important greenhouse gas and since CENTURY does not separately represent
N2O emissions from total volatile N emissions, future model development and refinement
177
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efforts should consider improvements to the soil N model. CSU/NREL staff have
indicated that research activities related to the N model are currently in progress (W.
Parton, 1992, personal communication). The type of soil N process modeling algorithms
in the DNDC model may be appropriate for incorporation into CENTURY.
Monthly Simulation Time Step: The monthly time step in CENTURY is one of the reasons
the model can be run efficiently for long periods of time such as the 120-year span of
our model runs. However, the monthly time step also presented some difficulties in this
study in attempting to represent temporal differences in planting, tillage, and harvest
times among the CDs in the Study Region (see Section 5.1). Also, representation of
selected agronomic practices, such as double cropping, cover crops, etc. that have two
or more operations within a calendar month are difficult to include with a monthly time
step. Thus, including a capability for a shorter time step, such as daily or weekly,
should be considered in future model refinements; Paustian (1993, personal
communication) has indicated that a research version of CENTURY with a daily time
step is currently being tested. This finer temporal resolution will help to better
differentiate and represent agricultural production systems within the Study Region and
other parts of the U.S.
20 cm Soil Layer Depth: Currently, CENTURY. uses a 20 cm soil layer depth for
representation of all carbon and nutrient processes, whereas many soil nutrient models
use a larger soil depth (e.g., 30 cm in DNDC, up to 45 cm in Frissel and van Veen), or
one that is user-defined (e.g., NCSWAP, EPIC). Crop rooting depths and some tillage
operations extend below the 20 cm depth for many parts of the Study Region. Metherell
(Appendix A) noted potential problems related to mineralization of N below the 20 cm
depth available for crop uptake that could impact the simulated N balance. Future
refinements to CENTURY should consider use of a deeper, or variable, soil depth,
possibly with multiple layers to better represent root zone soil processes and tillage
practices.
SOC Changes Do Not Impact Crop Yields: CENTURY does not currently include a linkage
between changes in SOC and crop yields or productivity. In this study, we imposed an
increase in yields during the projection period due to improvements in crop management
and genetics. Future improvements in CENTURY should consider a direct impact of
changes in SOC on crop yields.
7.2 CAPSULE SUMMARY OF STUDY FINDINGS
Below are capsule conclusions and recommendations derived from the study results:
1. Reasonable extrapolation of current agricultural practices and trends will lead to
an increase (sequestration) of about 1 to 2 Gt C within the Study Region by the
year 2030. This represents about a 25% to 50% increase over current 1990
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levels. Nationwide the increase could be 50% greater since our Study Region
includes only 60-70% of total U.S. cropland.
The key assumption underlying these predictions is the projection of annual crop
yield increase from 19% to 2030; the lower range reflects an increase of 0.5%
per year, while the upper limit of 50% increase reflects a 1.5% per year crop
yield increase. The validity of this assumption needs to be re-assessed or
confirmed, and if valid, policies and research need to be promoted to support the
chances of agriculture attaining this level of yield increase.
Conservation tillage practices can significantly increase soil carbon, but the
impacts are highly variable across the Study Region. The degree of impact is due
to complex interactions of combinations of crops and rotations, soils, and climate.
For many combinations, SOC increased 10% to 15% for Reduced Till and up to
50% for No Till, while much lower changes occurred in other C/R/Ts and CDs.
The overall impact of increased Reduced Till (RT) and No Till (NT) practices in
terms of Total SOC change for the Study Region, ranged from 2% to 3%
higher than Status Quo under the Medium Conservation Policy, and 6% to 11%
higher under the High Conservation Policy. These conclusions should be
considered preliminary, given the capabilities of current models and our limited
knowledge of the quantitative impacts of tillage practices. Moreover, current (i.e.
1992) nation-wide levels of No Till and Conservation Tillage (i.e. combined NT
and RT) exceed the levels included under the Medium Conservation scenario;
within the Cornbelt both NT and RT currently approach the levels included in the
High Conservation scenario. Thus, the conservation scenarios assume relatively
modest changes in practices as compared to more current CTIC data. Further
evaluation of the study procedures, conservation tillage usage, model parameter
sensitivity, and more research is needed in this area of tillage impacts before
these preliminary conclusions can be confirmed.
Cover Crops can lead to significant increases in soil carbon in crop, soil and
climate regimes where they are feasible and appropriate. Although only 12% of
the Study Region cropland included cover crops (under the Cover Crop scenario),
this increased soil carbon by 140 Mt through 2030. Since southern and eastern
portions of our Study Region were most appropriate for cover crops, this may be
an attractive alternative for promoting carbon gains in the South and Southeastern
U.S.
The results of the CRP simulations are mixed. In many cases, 20 years of CRP
leads to SOC values higher than the dominant rotation by the year 2030, and
usually higher than under continuous CRP. In other CDs, the dominant rotation
maintains the highest SOC throughout the projection period. The key factor is
likely the relative carbon inputs of the dominant rotation as compared to the CRP
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conditions. When the dominant rotation is a corn-based rotation, its 2030 SOC
is usually the highest. However, this is not always true, especially if only one
year of corn is in the rotation. The percent difference for the CRP simulations
can range from a few percentage points to up to 20% or higher.
7.3 RECOMMENDED FUTURE STUDY EFFORTS
7.3.1 Extension of Methodology to Other Portions of the U.S.
Part of the original objective of the U.S. EPA BIOME program was to develop an assessment
methodology for agricultural production systems that could be used to project changes in soil
carbon for all regions of the U.S. The methodology developed in this study satisfies that
objective in that it can be applied to other sections of the U.S. given the availability of the
required input data. In this study, we focussed the assessment on the RAMS study region
because the RAMS output provided the information on cropping systems, rotations, tillage
practices, etc. needed to define the agricultural production systems in sufficient detail for
CENTURY simulations. Since RAMS has not been applied to the remaining portions of the
U.S., a significant effort would be required to apply RAMS to the remaining regions so that the
study methodology developed herein could be used (D. Holtkamp, 1993, personal
communication). The climate and soils data bases used in this study provide national coverage,
so the cropping system information provided by RAMS (noted above), and changes in this
information for alternative policy scenarios, is the only missing element needed to attempt a
national-level analysis.
It may be possible to assess SOC changes under the Status Quo Scenario for other regions by
developing the needed cropping system information from other sources, such as the CTIC, ERS,
ASCS, etc. This would allow application of the study methodology to project, on a national
level, the SOC impacts of current trends in crop yields, tillage practices, and possibly cover
crops and CRP; additional CENTURY model testing for other crops and rotations would be
needed. Evaluation of alternative future conditions, such as increased Reduced Till and No Till
could also be evaluated in terms of resulting SOC changes and associated carbon sequestration.
However, the direct linkage to policy and alternative policy scenarios would require the complete
RAMS analysis for the entire U.S. for a national scope assessment similar to the effort in this
study.
A possible alternative to application of the full study methodology, to all land areas of a new
study region, would be to apply the methodology to pre-selected test CDs as .was done in the
early testing stages of this effort. With careful selection of these test CDs, based on the degree
to which soils, climate, and cropping practices are representative of the entire study region, the
results of the methodology applied to the test CDs can be extrapolated to the larger study
region. For example, the seven PAs that included the test CDs of our study represented about
40% of the cropland of the entire Study Region. The change in SOC under Status Quo
conditions for these seven PAs was almost exactly 40% of the change for the entire Study
Region. This was the result of careful selection of the test CDs to represent the geographic
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extent of the Study Region, while including the range of variation in climate, soils, and cropping
practices (as noted in Section 5.0). For other potential study regions within the United States,
the required soils and climate databases are available and need only be supplemented with
cropping information in order to have the data needed for selection of potential test CDs.
7J3.2 Future Model Field Site Testing
As part of this study, field site testing of CENTURY was performed for a wheat-fallow rotation
in Sydney, NE (Appendix A) and a corn rotation in Lexington, KY (Appendix B). In addition,
consistency checks for the model results were performed with observed crop yields and regional
estimates of agricultural SOC by Kern and Johnson (1991). Further model testing and evaluation
is needed in two primary areas:
1. Comparison of the historical pattern of SOC changes predicted by the
methodology used for estimating starting 1989 SOC levels with long-term field
data is needed.
2. Testing and evaluation of model behavior and parameter values for selected crops
(e.g., soybeans), regions, and tillage practices is highly recommended.
The SOC data base currently being consolidated by Paul and Elliott (1993, in press), as part of
the EPA BIOME study, will be an extremely valuable resource for both testing efforts.
Although CENTURY has undergone more field site testing than any other currently operational
SOC model, testing on legumes and in selected portions of the Study Region (i.e., eastern and
southeastern regions) has been limited. In addition, as noted above, many of the parameter
values were originally derived from experience with grassland and wheat-based crop rotations.
Further model testing for a wider range of crop, climate, soils, and tillage practices is warranted
for increased confidence in the model predictions for our Study Region and other agricultural
sectors of the U.S.
7.3.3 Future Research Issues for Investigation
In addition to the model and methodology testing recommended above, a number of research
issues have been identified during this study that may require longer term investigations. These
issues, some of which have also been noted under the modeling assumptions above, include:
a. Review and establishment of expected increases in crop yields, plus crop biomass
and residues, for major crops and regions of the country in order to determine
how best to include this aspect of predicting future SOC conditions, and
associated carbon sequestration, under alternative policy scenarios. The
importance of these issues is evident in the study results.
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b. Projections of the impacts of future climate scenarios on the assessment of carbon
sequestration under alternative policy scenarios. The current study results are
based on future climatic conditions that are the same as historic conditions. An
assessment is needed of how the study results would change if climate changes,
and asssociated feedbacks to agricultural production systems should also be
considered. Additional model capabilities, such as yield changes due to increased
CO2, may need to be implemented.
c. Development of appropriate data bases and modeling procedures for
representation of the C and N balances in agricultural systems impacted by animal
waste applications. Accurate databases on animal waste production, including
sources, quantities, composition, treatment, storage, and applications to croplands
are non-existent.
d. Development and/or aggregation of databases for improved historical crop yield,
cropping and crop rotations, agronomic practices, and economic (production)
data, especially as related to soil and climate conditions, in order to evaluate and
improve the historical simulations of SOC changes.
e. Continuing investigation into the impacts of cropping and tillage practices on
SOC, and development of appropriate modeling procedures to accurately
represent these effects. In particular, the impacts of No Till on the entire range
of soil physical, chemical and biological processes demands further study; we
currently have very limited detailed (quantitative) knowledge of these impacts
which have critical effects on SOC.
f. Further study of the potential impacts of CRP with particular focus on the land
characteristics (e.g. soils, topography, cropping and crop rotations) of the areas
taken out of production. To more accurately assess CRP changes, including
increases or decreases in CRP land, more detailed cropping information is needed
(perhaps from the RAMS assessment) to define the production systems from
which, and to which, CRP land is transferred.
g. More detailed representation of the N cycle in agricultural soils in close
integration with the soil carbon cycle, including calculation of N2O emissions, as
impacted by various agricultural production systems.
h. Investigation into the ultimate fate and disposition of erosional SOC losses in
terms of both field and landscape level impacts with regard to greenhouse gas
emissions from agricultural systems. In conjunction with item c (above),
estimates of historical erosion losses and associated agricultural practices are
needed to better represent the historical pattern of SOC changes and current
conditions, as a basis for projecting future changes, directions, and alternative
policy impacts.
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To establish the current state of knowledge on many of these research issues, and to help direct
future assessments and needed research, we recommend convening a workshop of both regional
and national experts on agronomy, plant physiology, soil carbon processes, tillage practices,
agricultural policy, and SOC/SOM modeling. The focus of the workshop would be on the issues
identified above to: (1) identify improvements to the assessment methodology; (2) assist in
determining parameter adjustments to best represent regional and crop differences in tillage
practices and impacts; (3) identify both available and needed databases on tillage practices, crop
rotations, animal waste production and disposition, soils characteristics, climate, etc.; (4)
establish regional historical agricultural practices and erosional impacts on SOC; and (5) identify
associated ongoing research efforts and priorities for future research activities.
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SECTION 8.0
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Parton, W.J., J. Persson, and D.W. Anderson. 1983. Simulation of Soil Organic Matter
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Parton, W.J., D.S. Schimel, C.V. Cole, and D.S. Ojima. 1987. Analysis of Factors
Controlling Soil Organic Matter Levels in Great Plains Grasslands. Soil Sci. Soc. Am.
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Parton, W.J., C.V. Cole, J.W.B. Stewart, D.S. Ojima, and D.S. Schimel. 1989. Simulating
Regional Patterns of Soil C, N, and P Dynamics in the U.S. Central Grasslands Region.
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Parton, W.J., J.W.B. Stewart, and C.V. Cole. 1988. Dynamics of C, N, P and S in Grassland
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Prentice, K.C., and I.Y. Fung. 1990. The Sensitivity of Terrestrial Carbon Storage to Climate
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193
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SECTION 9.0
APPENDICES
A. Simulation of Wheat-Fallow Cropping Systems
B. Application of the CENTURY Model to the Lexington, KY Site
C Tabulated Simulation Results by CD
D. Map Displays of Simulation Results and Study Region
Characteristics . .
E. Land Use and Soil Physical Properties for All Climate Divisions
194
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APPENDIX A
SIMULATION OF WHEAT-FALLOW CROPPING SYSTEMS
A-l
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SIMULATION OF WHEAT-FALLOW CROPPING SYSTEMS1
Alister Metherell2
Natural Resource Ecology Laboratory
Colorado State University
Fort Collins, CO 80523
INTRODUCTION
Management interacts with natural driving variables in an agroecosystem to have a major impact
on the processes that control the ecosystem properties. Soil organic matter is an important
component of the ecosystem, influencing both physical and chemical soil properties. Soil organic
matter is also a major pool in the global C cycle. The management of agricultural systems has
had a major impact on soil organic matter with the cultivation of soils in the Great Plains of
North America resulting in large losses of C and N (Burke et al, 1989). This raises questions
as to whether the loss of organic matter will continue and whether alternative agricultural
management practices can reduce the rate of loss or even reverse the trend. The CENTURY soil
organic matter model (Parton et al, 1987; Parton et al, 1988) integrates the effects of climate
and soil driving variables with agricultural management to simulate C, N, and water dynamics
in the soil-plant system, with an emphasis on long-term soil organic matter dynamics. A new
agroecosystem version of the CENTURY model has been designed for the simulation of complex
agricultural management systems including crop rotations, a variety of tillage practices, fertilizer,
and irrigation.
Moisture is the main limitation to agricultural production in the semi-arid western half of the
Great Plains of the United States and Canada. Consequently the majority of dryland farmers use
summer fallow, in a wheat - fallow "rotation", where in alternate years all plant growth is
controlled by cultivation or herbicides, to conserve moisture for the succeeding crop. Production
in one season is forfeited in anticipation that there will be at least partial compensation by
increased crop production the next season. With this system of farming, in contrast to continuous
cropping, the variation in crop yields due to climatic factors is minimized, risk is reduced and
the farm business has greater economic stability (Haas et al, 1974).
The key to successful summer fallow management is the control of weed growth, which would
otherwise result in transpiration of water from the soil profile. Traditionally fanners have used
i
From a thesis submitted to the Academic Faculty of Colorado State University in
partial fulfillment of the requirements for the degree of Doctor of Philosophy
2 Present address: AgResearch, Canterbury Agriculture & Science Centre,
P.O. Box 60, Lincoln, New Zealand.
A-2
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cultivation for weed control. Cultivation methods have evolved from the "dust mulch" with
moldboard plowing and frequent harrowing after each significant rain, through "conventional bare
fallow," which utilizes shallow disking and bare rodweeders, to "stubble-mulch," which uses
implements, which undercut the soil surface, killing the weeds but leaving crop residues on the
soil surface (Greb, 1979). The surface residues reduce the evaporative loss of water thus
increasing the efficiency of water storage over the fallow period and also provide protection from
wind and water erosion (Greb et al., 1979). The fallow water storage efficiency is greatest with
no-till management where herbicides are used to control the weed growth (Smika and Wicks,
1968). With no-till management the accumulation of surface residues is maximized and there
is no moisture loss associated with stirring of the soil during tillage.
Summer fallow, particularly when associated with intensive cultivation, increases the
mineralization of soil organic matter (Haas et al., 1957) resulting in the degradation of soil
structure, reduced water infiltration, and increased susceptibility to erosion. Furthermore, because
a crop is only grown every second year, the inputs of C to the soil are also considerably lower
than with grassland or continuous cropping. Thus the agricultural management of the semi-arid
Great Plains has resulted in a dramatic loss of soil organic matter (Haas et al., 1957; Burke et
al, 1989).
The prediction of the effects of different agricultural management strategies on soil organic
matter levels is one of the main uses of the CENTURY model (Parton et al, 1987). However, the
model had not been formally validated for the effects of tillage methods on crop production and
soil organic matter dynamics. The long-term tillage experiments at Sidney, Nebraska, provide
an ideal data set for verification, calibration and validation of the revised CENTURY model
described in Chapter 2 (Metherell 1992, Ph.D. Dissertation).
METHOD
The Sidney tillage experiments
In 1969 and 1970 experiments were initiated at two locations on the High Plains Agricultural
Laboratory research area near Sidney, Nebraska to compare the effects of fallow tillage methods
on the wheat-fallow cropping system (Fenster and Peterson, 1979). The first location was on an
Alliance silt loam, a fine silty, mixed, mesic Aridic Argiustoll, derived from mixed loess and
loamy calcareous residuum over weathered sandstone. The land had been farmed from 1920 to
1957, when it was reseeded to crested wheatgrass (Agropyron desertorum (L.) Gaertn.). The
experimental site was broken out of sod with a moldboard plow in 1967. This location has
frequently been referred to as the "reseeded sod" or "previously cultivated" site. The second
location was on a Duroc loam, a fine silty, mixed, mesic Pachic Haplustoll derived from mixed
loess and alluvium. The land had remained in native grasses until the sod was broken with a
moldboard plow in 1970. Hence, this location has been called the "native sod site". The
experiments at this location have a unique feature in that strips of the native sod were left intact
and incorporated into the experimental design. The predominant grasses on the site are western
A-3
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wheatgrass (Pascopyrum stnithii Rybd. A. Love), needleandthread (Stipa comata Trin. & Rupr.),
blue grama (Bouteloua gracilis (H.B.K.) Lag. ex Steud.), sideoats grama (Bouteloua curtipendula
(Michx.) Torn) and sand dropseed (Sporobolus cryptandrus (Torr.) Gray) (Lyon et al, 1992).
Each location was divided into two major blocks, which were fallowed or grew wheat in alternate
years (Figure Al). On the Alliance soil, site A has had wheat harvested in even years and been
fallowed in odd years, while site B has had wheat harvested in odd years and been fallowed in
even years. Sites A and B were first sown to wheat in 1969 and 1970, respectively. On the
Duroc soil, site C, also referred to as the west side, has had wheat harvested in odd years and
been fallowed in even years, while site D, on the east side, has had wheat harvested in even years
and been fallowed in odd years. Proso millet was planted on site D after plowing of the native
sod in the spring of 1970. The site was fallowed in the summer of 1971 and wheat was sown
in September 1971. Wheat was planted on site C in September 1970, but the crop failed and
millet was sown in the spring of 1971 (G.A. Peterson, pers. comm.).
"Reseeded site"
Alliance silt loam
Site A
"Native sod site'
Duroc loam
Wheat harvested
Even years
Site
Odd ye
B
ars
Site C
Wheat harvested
Even years
Site D
Odd years
Treatments
Main plots
No-till
Stubble mulch
Plow
Sub-plots
/ 0 kg N / ha
^45
<°
^45
Treatments
Native sod
No-till
Stubble mulch
Plow
Figure Al Layout of the Sidney tillage experiments.
A-4
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Both experiments compared bare fallow or plow tillage with stubble-mulch and no-till treatments
(Fenster and Peterson, 1979). On each site (A, B, C and D) there were three replicates of the
tillage treatments in a randomized block design. On the Alliance soil (sites A and B), each
tillage treatment plot was split with one half receiving ammonium nitrate fertilizer at 45 kg N/ha
during wheat growth in April. The bare fallow and stubble-mulch plots were tilled with
conventional farm machinery. Bare fallow plots were moldboard plowed in the spring, followed
by two to three operations of the field cultivator, and then one to two operations with the rotary
rodweeder. The stubble-mulch plots were tilled with 90-150 cm V-blades (sweep) from two to
four times, then one to two times with the rotary rodweeder. Initial tillage operations were 10
to 15 cm in depth and each subsequent operation was at a decreasing depth to develop a firm
seedbed. Weeds were sprayed in the no-till plots when they were 5 to 10 cm tall. Grassy weeds
were controlled after harvest and/or in the spring with paraquat and later in the summer with
glyphosate. If only broadleaf weeds were present 2,4-D plus dicamba was used.
All wheat plots were seeded each September with a drill equipped with large coulters, slot
openers for the seed, and press wheels spaced 30 cm apart. The drill causes minimal disturbance
in the no-till plots (G.A. Peterson, pers. comm.). For the period 1969 to 1983, Centurk hard red
winter wheat was sown. Since then a variety of cultivars have been used. Wheat was harvested
each July with a combine. Grain yield, grain protein, and crop residue (standing stubble plus
surface litter) were measured.
The experiments have been the subject of a large number of studies looking at different aspects
of soil organic matter, soil chemistry, soil microbiology, soil physics, soil water and nutrient
cycling. I have collated many of these results from various sources to initialize, and compare
with predictions from, the CENTURY model (Lyon et al., 1992).
Simulation strategy
Sites C and D, which incorporate the native sod plots, have been the subject of many soil organic
matter studies (Broder et al, 1984; Lamb et al, 1985b; Mielke et al, 1986; Follet and Peterson,
1988; Cambardella and Elliott, 1992; J. Doran, unpublished; C. Cambardella, unpublished).
Therefore these plots were used for validation of the CENTURY model's predictions of soil organic
matter dynamics, while sites A and B, which incorporate the N fertilizer treatment were used to
calibrate the wheat growth sub-model. No information from any of these sites was used for
parameterization of the soil organic matter decomposition sub-model, except that the control on
the maximum possible C/N ratio for the slow pool was set at 18, rather than 20, in order that the
model gave a better match to the measured C/N ratios in the native sod plots. The most recent
estimates for decomposition parameters were made by simulating a variety of grassland and forest
ecosystems (WJ. Parton, pers. comm.). The parameters for the effect of cultivation on
decomposition rates were set on the basis of a subjective assessment of the degree of stirring for
the various tillage implements.
A-5
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Weather data
The mean monthly precipitations and temperatures used as the climate driving variables for the
CENTURY model were based on the Sidney 6NNW weather station located at the High Plains
Agricultural Laboratory. Historical daily weather records were obtained from computer laser
optical disk (Earthlnfb, 1992). Records were available for the Sidney 6NNW station from 1971
to 1991. To create a weather record from 1949 to 1970 and to fill in missing days for the 1971
to 1991 period, I derived equations using multiple linear regression of the monthly precipitation
sums, and minimum and maximum temperature means for the Sidney 6NNW station against data
from Sidney 3S, Potter and Dalton weather stations for the period since 1971. Only those months
that had a complete record (up to 5 missing days were allowed for temperatures) were used in
the regressions. I then used the equation, which incorporated the maximum number of stations
with a complete record for a particular month, to create the monthly weather file from 1948 to
1970 and to fill in missing daily values from 1971 to 1991. Mean annual precipitation and mean
annual temperature at the High Plains Agricultural Laboratory are 38 cm and 8.2 °C respectively,
with maximum precipitation during the summer months (Figure A2). Standard deviation and
skewness values were calculated from the monthly precipitation record to drive the CENTURY
model's stochastic precipitation generator, used for the simulation period prior to 1949.
cm / month
10
8
o
'•5 6
o
-------
Atmospheric N deposition
Wet deposition data (National Atmospheric Deposition Program (NRSP-3)/National Trends
Network, 1991) were obtained for the Pawnee and North Platte sites, located approximately 155
km west south west and 184 km east of Sidney, respectively. Ammonium deposition was
adjusted by a factor of 1.58 to account for NH4+ losses prior to analysis (Ramundo and Seastedt,
1990). On average 67% of the total N deposition occurred as NH4*. Total N (NH^-N plus
NO3"-N) wet deposition for these two sites showed little relationship to annual precipitation
(Figure A3) and had an overall mean value of 0.32 g N / m2. The only relevant result for annual
dry deposition of N was for one year at the Pawnee site (Meyers et al., 1991). The value of 0.22
g N / m2 indicated a substantial fraction of atmospheric N inputs occurs as dry deposition. For
the simulations reported here, I used a value of 0.5 g N / m2 / year for total atmospheric N
deposition.
0.5
8 0.4
0.3
0.2
-B-
D
D
D
Sidney
_L
_L
"20 25 30 35 40 45
Precipitation cm / year
D Pawnee • North Platte
50
55
60
Figure A3 Annual nitrogen wet atmospheric deposition (adjusted for NH/ losses) versus
annual precipitation for Pawnee, Colorado and North Platte, Nebraska
NADP/NTN sites.
A-7
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Soil texture
Soil samples to 20 cm depth were collected for soil texture analyses on site C in 1990
(Cambardella, 1992) and site D in 1991 (Cambardella, unpublished). Some texture analyses with
samples taken to shallower depths on site C have shown differences between treatments
(O'Halloran, 1987; Elliott, pers. comm.). This was apparently due to mixing of horizons in the
cultivated plots because the difference between treatments was not statistically significant for the
20 cm deep samples. However, there was a distinct difference between site C and D (Table Al),
even though the plots are adjacent to each other. To parameterize the CENTURY model for sites
A and B, the data for 0 - 15 cm samples published by Mielke et al. (1986) were used.
The depth for the soil water sub-model was set at 120 cm for sites A and B and at 180 cm for
sites C and D.
Table Al Soil texture (%, 0-20 cm) on the long-term tillage plots at the High Plains
Agricultural Laboratory near Sidney, Nebraska.
Sites A & B Site C
Site D
Sand
Silt
Clay
37.1
40.0 :
- 22.9
37.5
31.8
30.7
47.8
31.2
21.0
Soil organic matter initialization
The CENTURY model was run for 5000 years to reach equilibrium under native grassland
conditions for each site, using the stochastic precipitation generator and mean monthly
temperatures. The grass type representing a 50/50 mix of warm and cool season species was
used (Parton et al, 1992). For sites C and D no grazing was simulated after 1940. Because the
historical grazing intensity is largely unknown, this factor was used to calibrate the model to
closely match the soil organic matter C and N measured on sites C and D in 1990 (Cambardella
and Elliott, 1992) and 1991 (Cambardella, .unpublished), respectively (Figures 13 and 14). I also
ensured that the grazing intensity gave appropriate levels of consumption and residual biomass
for the live and dead pools for summer and winter periods. During summer months, the grazing
option was set to remove 27% of live shoots and 2% of the standing dead per month and in
winter months removal rates were 15% of live shoots and 7% of the standing dead per month.
For both periods, 30% of the C and 80% of the N was returned as excreta with 50% of the N
returned in faecal (organic) matter. The CENTURY model grazing option, which has a negative
linear effect on potential plant production as live biomass removal increases (Parton et al., 1992)
was used.
A-8
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For sites A and B a sequence of historical crop management events was simulated from 1920 to
1956 followed by 10 years in cool season grasses. The historical management was chosen to be
appropriate to the period, to give grain yields similar to the county average yields and to result
in soil organic matter levels similar to those measured on the experimental plots. However, there
is some uncertainty regarding the initial soil organic matter C and N levels for sites A and B
because the single measurement made in 1969 (Fenster and Peterson, 1979) is lower in C and
especially N, than most other analyses for samplings between 1978 and 1981. The simulated
historical management sequence from 1920 to 1938 involved continuous wheat with low harvest
index varieties (maximum harvest index = 0.30), plow cultivation, no fertilizer, and 50% of the
straw removed. From 1939 to 1956 it involved wheat fallow with medium harvest index varieties
(maximum harvest index = 0.35), bare fallow cultivation, N fertilizer and no straw removal. N
fertilizer was added to this sequence because of the apparent lack of N in the subsequent
simulation of the experimental treatments, but soil organic N was only increased 6 g N / m2 by
this addition. The simulated soil organic matter C and N levels prior to wheat planting in 1969
were 2791 g C / m2 and 275 g N / m2.
Simulation treatments
Management operations were scheduled using the CENTURY model's event scheduler, EVENT 100,
to simulate, as closely as possible, the management of the field experiments. Native grass
simulations were set up to grow from April to October with senescence hi November. Wheat
simulations had October as the planting month and July as the harvest month. To represent
modern wheat varieties the maximum harvest index was set at 0.42. In the simulation of the
stubble mulch and plow treatments, weeds were allowed to grow after wheat harvest until the
start of fallow tillage, that is, August to March inclusive. The bare fallow or plow treatment was
represented by using the model options for a plow in April, a field cultivator in May, June and
July, and a rodweeder in August and September. Stubble mulch simulations used a sweep in
April, May, June, and July, and a rodweeder in August and September. All three treatments used
a no-till drill with minimal disturbance in October. The different tillage implements have
different parameters for the transfer of material between the various plant and litter pools and
different multipliers for the effect of cultivation on decomposition rate (Table A2).
N at 4.5 g N/m2 was added to the plus N treatment on sites A and B in April. For sites C and
D the following events were also considered: planting of millet on site D in 1970 and on site C
after the failure of the wheat crop in 1971, floods in June 1974 and July 1981, use of a
conventional drill with greater soil and residue disturbance on site C in 1984, winterkill of the
wheat crop on the plow treatment on site C in 1976-77 and on the stubble mulch and no-till
treatments in 1978-79, no harvest due to hail on site D in 1988 and the no-till treatment on site
D in 1980, and no harvest due to drought and downy brome infestation on site C in 1989.
A-9
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RESULTS
Simulation of sites A and B
The primary variables used for calibration of the plant production sub-model were wheat yields
and grain N content. However, simulation results were also compared to field data for crop
residue levels, soil water, and mineral N in the soil profile.
I was able to calibrate the wheat parameters so that the model matched the field data for grain
yield and grain N content for the plus N fertilizer treatment, but CENTURY underestimated both
yields and N content for the zero fertilizer treatment (Figures A4 and A5). The field results
showed no response to N during the first 10 years of the experiment, whereas in the second 10
years, there were 11% and 14% responses in the stubble mulch and no-till treatments,
respectively (Brown et al., 1991). Similarly, the effect of fertilizer on grain N content only
reached statistical significance in the second ten years. However, the CENTURY model predicted
a large response to the application of 4.5 g N / m2 from the beginning of the experiment A test
simulation using the automatic fertilizer option in CENTURY predicted that on average a fertilizer
application of 3.0 g N / m2 / crop would be required to achieve maximum wheat yields at
minimum plant N concentrations.
The N deficit in the CENTURY simulations was also reflected in the results for soil mineral N
(Figure A6). Field results (Lamb et al., 1985a) showed that there was a large accumulation of
nitrate in the soil profile during the fallow, but the model only predicted a small accumulation
Table A2 CENTURY model cultivation options and parameter values used for the
simulation of the Sidney sites.
From.
To
transfered
Live
Dead
Surface
Roots
Dead
Surface
Soil
Surface
Soil
Soil
SoH
O.O
0.1
O.9
O.I
O.9
0.9
l.O
O.O
0.5
O.5
O.5
O.5
0.5
1.0
0.75
O.25
O.O5
O.I
O.I
0-1
l.O
O.4
O.4
O.2
O.2
O.2
0.2
1.0
O.O5
O.05
O.I
O.O5
O.15
0.2
O.2
O.O5
O.O5
O
O.O5
O.O5
0.05
O.I
l.O
O
O
O
O
0
l.O
Multiplicative effect
on decomposition rate
1.5
1.5
1.3
1.3
1.1
l.O
l.O
A-10
-------
-N 4-N -N -4-N —N + N
No—till Stubble Mulch Plow
1969-1979
• Field
— N +N —N +N -N + N
No-till Stubble, Mulch Plow
198O-199O
I Model
Figure A4
Simulated versus actual grain yields for two periods by three fallow tillage
treatments by two nitrogen treatments for the Sidney A and B sites.
CD
35
30
25
20
15
10
5
-N +N -N +N -N +N
No-till Stubble Mulch Plow
1969-1979
m Field
-N +N -N +N -N +N
No-till Stubble Mulch Plow
1980-1990
Bi Model
Figure AS Simulated versus actual grain nitrogen concentrations for two periods by three
fallow tillage treatments by two nitrogen fertilizer treatments for the Sidney A
and B sites.
A-ll
-------
14-
12-
10-
8
6
4-
2
1981
1982
1983
1984
Field Model
NO N45 NO N45
No-till 0 *
Stubble Mulch A A
Plow d •
Figure A6 Simulated versus measured mineral nitrogen in the soil profile at Sidney site A.
of mineral N. In seven out of eight years, on sites A and B, in which nitrate was measured in
the soil profile in the fall close to the time of wheat planting, the model underestimated mineral
N in the profile by about 2 to 9 g N / m2.
The simulated crop residue levels (standing dead plus surface litter) showed the seasonal patterns
expected from the different tillage treatments and were of a similar magnitude to the field data
(Fenster and Peterson, 1979) (Figure A8).
From field measurements of total soil water (Fenster and Peterson, 1979), I estimated wilting
point by calculating the mean of the measurements made after wheat harvest. To estimate
available soil water at wheat planting, for comparison with the model output, this mean was
subtracted from total soil water measured at wheat planting. A moderate amount of weed growth
was included in the simulations of the stubble mulch and plow treatments, from after wheat
harvest until the start of the spring cultivations, in order to reduce the water storage during this
period. The simulated soil water status showed the expected biennial cycle, with the maximum
levels of available soil water being similar to those estimated from the field data (Figure A7).
However, the model failed to predict the low water storage in all treatments on site B in 1972,
and in the plow treatment and, to lesser extent, the stubble mulch treatment in 1974. Such
discrepancies may have been due to variations in the efficiency of weed control in the field
experiments. In many years, the model did not predict very large differences between the tillage
treatments in soil water storage, although, where differences did occur, they were in the expected
descending order of no-till, stubble mulch, and plow.
A-12
-------
30 :
25-
20;
is-
le-
s'
1S
-
0
M;'-,
i i
/ (A
1 1 1'
/I 1 //fl°
,-? l-'/b
/ i i i
f J If I'
}71 1973 1975
Kin— till
A
n/s /""•
/' * n \ \ •» * *
/ " ~""\ ?'' l«ft •' T'l
* * ii / - - 1 * ' I**
i / i //i JJ 1 ,;~~J I \x
/ ii i /it" ! * I '* I
J l ._,' \|' ^-— II I ' |
/ If / RJ
•w .\y-
1977 1979 19£
Fteld Model
A
Stubble Mulch A
Plow Q
Figure A7 Simulated versus measured available soil water for the no nitrogen treatments
at Sidney site B.
300
200
M
E
\
0
100-
0-
\
"*N
^*,
tl
y
\^ n
V=rj* \
X^""- v 1
9
A
I \t\
ya
V l*^»
n*^_ j ^t
b
v^^ —
,
\\
y
*
~~»»t
Cj
I ^%
d
§
1971 1973 1975 1977 197
Held Model
NO N4S NO N45
Figure A8 Simulated versus measured crop residue (standing dead plus surface litter) at
Sidney site A.
A-13
-------
Simulation of sites C and D
In view of the fact that the land had previously been cultivated for 36 years, it was unusual that
the field experiment on sites A and B did not show a greater response to N fertilizer. Sites C
and D had a much higher fertility status, having been in native sod until the start of the
experiment and it seemed unlikely that the simulation would predict a N deficiency. Thus,
despite the problem with the N deficit in the simulations of sites A and B we decided to use the
same parameters and schedules of management events for the simulations of sites C and D, in
order to validate the CENTURY model's predictions of soil organic matter dynamics.
Notwithstanding the above factors, the model again underpredicted the accumulation of mineral
N during the fallow (Figure A9) indicating that the problem was not associated with the initial
soil organic matter level at the beginning of the experiment.
However, simulated wheat yields were not markedly reduced by N deficiency, except in the first
crop on site C immediately after breaking the sod. Simulated yields tended to be slightly higher
than observed yields (Figure A10) and were only moderately correlated (r2 = 0.36).
The CENTURY model predicted a gradual decline in soil organic matter in all three wheat fallow
tillage treatments (Figures Al 1 and A12). The relative change was greater for C than for N
resulting in a narrowing of the C/N ratio. The decline was greatest in the plow treatment and
least in the no-till treatment. Almost all of the field data gave the same ranking of the
treatments, but there was considerable variation between sampling dates in the absolute C and
N levels measured. The data were compiled from a variety of published and unpublished sources
and this variation is assumed to be due to four factors: different sampling methods, some
differences in depth sampled, although the results presented are all nominally for 20 cm depth,
differences in sample preparation, especially the amount of residue and roots removed, and
differences in analytical methods. There were two data sets in which we had more confidence
in the results. The first set was for samples collected in 1981 and 1982 with results first
published by Lamb et al. (1985b). The soil samples had been stored and we reanalyzed them
for C, obtaining much higher results than originally published, but more in line with the other
data. The N results reported here are those originally published. The second dataset was for
samples that were collected from site C in 1990 (Cambardella and Elliott, 1992) and site D in
1991 (Cambardella, unpublished). As only small changes would be expected in soil organic
matter in the native sod treatment between 1981/82 and 1990/91 it can be seen that the results
for these two datasets are consistent with each other (Figures A13 and A14).
In the native sod treatment simulated soil organic matter levels increased slightly from 1970 to
1992, reflecting the change from an equilibrium established with grazing prior to 1940, to
ungrazed conditions after 1940. The simulated and actual soil organic matter C and N levels in
the native sod treatment were lower on site D than on site C reflecting the higher sand content
on site D. While there had been some calibration of grazing intensity in order to match the
simulated and field soil organic matter data, the fact that the simulation results agreed fairly
A-14
-------
30-f
CM
E
20-
'c?
10-
1970
30-f
20-
z
I
en
10-
Site C
D
O
Bo
D
A
a
1975
1980
1985
Site D
A
O
P
1970
1975
Field
Native sod +
Notill o
Stubble mulch A
Plow a
1980
Model
1985
Figure A9 Simulated mineral nitrogen and measured nitrate nitrogen to a depth of 180 cm.
A-15
-------
Model
1SO
14O
1OO
^ 80
CJ>
6O
4O
20
04
_ 9 O ,
- A * "° • A
A Q n A
- a
. , i , i , r . i . i . i . i . i .-•-
3 , 2O 4O 6O SO 1OO 12O 14O 1GO 18O
r, r. / mz -Field
Site C
Site D
No-till Stubble Mulch Plow
a o A
Figure A10 Simulated versus observed wheat yields for sites C and D.
closely with the field data for the native sod plots on both sites for the 1981/82 and 1990/91
sampling dates indicate that CENTURY correctly predicted the effect of soil texture on native
grassland soil organic matter levels. The model predicted C/N ratios of 11.9 and 12.5 for sites
C and D, respectively, compared to measured C/N ratios of 11.6 and 11.1. Thus, on site D, the
C levels were slightly overestimated, while N levels were slightly underestimated.
For the 1981/82 and 1990/91 datasets, the simulated soil organic C (Figure A13) and N (Figure
A14) agreed fairly closely with the field data for all treatments. Differences between simulated
and observed data were mostly within the degree of variation in the field data, shown on the
figures as the standard error of the difference between treatments (SED). The largest discrepancy
was for results for the plow treatment in 1981/82. In general, there was more differentiation
between the tillage treatments than was predicted by the model. Coefficients of determination
(r2) for simulated versus observed data were 0.86 and 0.69 for soil organic C and N, respectively.
In both cases, the slopes of the regression lines were not significantly different (p > 0.05) from
1.0 and the intercepts were not significantly different (p > 0.05) from 0.0.
The CENTURY model predicted that the average annual effect on decomposition rate was 1.66,
2.06, and 2.34 times greater than the native sod treatment for no-till, stubble mulch, and plow,
respectively (Figure A15). The field data suggest that the decline hi soil organic matter had been
rapid and then levels stabilized, whereas the model predicts a more gradual and continual decline.
A-16
-------
4500
4000-
CM
o
3500
3000
2500
1967
CM
o
4500H
4000
3500
3000
2500-
1967
Site C
/^-^
1972
1977 1982
Site D
1987
1992
1972
1977
1982
Model
1987
1992
Field
Native sod *
Kotill o
Stubble mulch A
Plow n
Figure All The effect of tillage treatment on simulated and measured soil organic carbon
to 20 cm depth.
A-17
-------
Site C
440-
400-
NE
\ 360-
z,
320-
280-
1S
, 400-
360-
ot
£,
\ 320-
z
280
240
1
o
0
A $ + *
A' 0 ^ **
V- .
D
a
a • ,
67 1972 1977 1982 1987 1992
Site D ,.
A « °*
* 8 *o-"-4
-
/— -— «. '• "" n
CI "~\-3sr^ri"l^ ^ A
967 1972 1977 1982 1987 1992
Field Model
Native sod + —
Kin+;n A
Stubble mulch A
Plow a
Figure A12 The effect of tillage treatment on simulated and measured soil organic nitrogen
to 20 cm depth.
A-18
-------
1982
SiteC
1990
Native No-tili Stubble Plow
sod mulch
Native No-till Stubble Plow
sod mulch
Figure A13 The effect of tillage treatment on simulated and measured soil organic carbon
to 20 cm depth for the 1981/82 and 1990/91 datasets. SED = standard error of the
difference for field data treatment means.
400
1982
SiteC
1990
200
Native No-till Stubble Plow
sod mulch
Native No-till Stubble Plow
sod mulch
Figure A14 The effect of tillage treatment on simulated and observed soil organic nitrogen
to 20 cm depth for the 1981/82 and 1990/91 datasets. SED = standard error of the
difference for field treatment means ;
A-19
-------
The soil organic matter pools in the CENTURY model are conceptual, kinetically defined pools that
do not correspond exactly with chemical or physical soil organic matter fractions. However, soil
organic matter fractionation schemes based on combinations of physical methods (Elliott and
Cambardella, 1991) are likely to yield fractions related to the aggregate structure and soil organic
matter quality, and hence, its rate of turnover. Thus, it is of considerable interest to compare the
changes in the sizes of the CENTURY model pools with the soil organic matter fractions isolated
from soil collected on Sidney site C in 1990 (Cambardella and Elliott, 1992). Particulate organic
matter is organic matter which, after dispersion of the soil with sodium hexametaphosphate, is
retained on a 53 |om sieve. Most of the paniculate organic matter has a density less than 1.85
g / cm3. The material that passes through the sieve is termed mineral associated organic matter.
Within the mineral associated organic matter of macroaggregates Cambardella (1992) isolated,
by gentle sonication, sieving, and flotation in sodium polytungstate a series of size-density
fractions. The fraction of fine-silt sized particles (2-20 ^m), with a density of 2.07-2.21 g / cm3,
termed the enriched labile fraction, contained 10-20% of the total soil C and 8-28% of the total
J FMAMJ JASOND J FMAMJ JASOND
0.0
Native sod No-till Stubble mulch Plow
Figure A15 The average monthly combined effect of temperature, moisture, anaerobic
conditions and cultivation on the decomposition rate multiplier for each tillage
treatment.
A-20
-------
soil N, depending upon the treatment. The specific rate of N mineralization in this fraction was
much greater than for the intact aggregates, suggesting a labile, but physically protected pool.
The mineralization rate of the enriched labile fraction was much higher in the native sod
treatment than in the cropped soils.
Almost all of the differences in the amounts of soil organic matter in the four treatments could
be accounted for by the changes in the size of the paniculate organic matter fraction. This result
was analogous to the CENTURY model's prediction that the change in size of the slow pool would
account for almost all of the difference between treatments (Figures A16 and A17). However,
the size of. the slow pool is much larger than the size of the particulate organic matter fraction.
The enriched labile fraction, which is physically protected from decomposition in the aggregate
structure, may possibly account for most of the rest of the slow pool.
Stable C isotope measurements also indicated that the soil organic matter dynamics were
associated with the particulate organic matter fraction rather than the mineral fraction. The
change in vegetation from native grassland, which is a mixture of C3 and C4 species, to wheat,
which is a C3 plant, has changed the stable C isotope signature of the vegetation, expressed as
8I3C values, from approximately -2l%o to -27%o. This, in turn, will induce a change in the
400
300 -
CM
£
CD
CENTURY
pools
ill Metabolic
H Structural
H Active
• Slow
• Passive
•200 -
Physical
fractions
100 -
m
t,
u>
IT;
w
X
v
s\
cl
S§
§s
ii
m
Particulate
Organic
Matter
Enriched
Labile
Fraction
Mineral
Associated
Organic
Matter
Native
Sod
Notill
Stubble
Mulch
Plow
Figure A16 CENTURY model soil organic nitrogen pools compared with physical fractions
of soil organic matter at Sidney site C, 1990 (after Camberdella, 1992)
A-21
-------
5000
Native
Sod
CENTURY
pools
Notill
Stubble
Mulch
Plow
Figure A17 CENTURY model soil carbon pools compared with physical fractions of soil
organic matter for Sidney site C, 1990 (after Camberdella, 1992).
signature of the soil organic matter (Balesdent et al, 1988). Analysis of the paniculate and
mineral associated fractions (Cambardella, 1992) indicated that the signature had only changed
in the paniculate fraction (Figure A18). The CENTURY model predicted that the greatest change
in 513C values would occur in the metabolic, structural, and active pools with inputs directly from
plant material and in the pools with the fastest turnover times. There was a small, but definite
change in the signature of the slow pool. No change in the 813C value of the passive pool
corresponds to there being no change in the mineral associated organic matter isolated from the
field soil samples. The model predictions do vary from the measurements of the paniculate
fraction in which the plow treatment was less depleted in 13C than the no-till and stubble mulch
treatments.
Under native grassland vegetation the lowest 813C value occurred in the structural pool reflecting
the input of lignin, which is depleted in 13C, to the structural pool. The 813C value was lower
in the slow pool than in the passive pool, reflecting inputs to the slow pool from the structural
pool. Fractionation during microbial respiration resulting in enrichment in 13C as soil organic
matter ages would also tend to result in a higher 513C value in the passive pool. A much lower
613C value in the paniculate organic matter compared to the mineral organic matter is also likely
to be a result of the 40 to 53% lignin content (Cambardella and Elliott, 1992) of the paniculate
organic matter.
A-22
-------
813c
CENTURY model pools Physical fractions
Passive Slow Active Structural Metabolic Mineral Partkulate
Native sod
vegetation
Native sod H No-till g3 Stubble mulch £2 Plow
Figure A18 'Stable carbon isotope ratios for the CENTURY model soil carbon pools and the
physical fractions of soil organic matter for Sidney site C, 1990.
DISCUSSION
Nitrogen balance
The largest discrepancy between field data and model results was in the prediction of mineral N
during the fallow period. The accumulation of mineral N in the soil profile was underestimated
by amounts ranging from about 2 to over 20 g N / m2. The low mineral N levels then reduced
crop N uptake, and at sites A and B, in the absence of N fertilizer, the crop yields. Similar
problems with the N balance have been experienced in the simulation of crop rotations at
Pendleton, Oregon (WJ. Parton, pers. comm.). There is field evidence from sites C and D at
Sidney that indicates that hi the no-till treatment there was a positive N budget (Lamb et at,
1985b) suggesting that there was an unaccounted N input. In that study, total mineralization of
N was estimated by calculating the difference in total N, in the top 30 cm of soil, between the
native sod and the wheat treatments. The loss of N from the surface 30 cm of the soil was less
A-23
-------
than the N in the grain removed. When the difference in the amount of N leached to a depth
of 7 or 15 m was considered, the budget became even more positive and amounted to additional
inputs to the no-till treatment, over 11 or 12 years, of 24 and 38 g N / m2 for sites C and D
respectively. The N budget was balanced for the stubble mulch treatment on both sites, and for
the plow treatment on site D. However, on site C there was an unaccounted loss of 51 g N / m2
from the plow treatment. No estimate was made of volatile N losses.
The discrepancy between the CENTURY model's prediction of mineral N and the field
measurements cannot be ascribed to a gross underestimation of mineralization rates in the surface
soil because the model did a reasonably good job of predicting the decline in soil organic N in
the surface 20 cm. Nor can the difference be explained by incorrect estimation of the effect of
cultivation on mineralization rates because the problem occurred with no-till as well as the tilled
treatments. Three processes are suggested that could have contributed to the simulated N
deficiency.
Firstly, the CENTURY model is parameterized to simulate soil organic matter dynamics in the
surface 20 cm of soil only. Although the concentration of organic matter is usually lower in soil
layers below 20 cm there may still be a considerable reservoir of organic N, which, if
mineralized, would be measured as nitrate in the deep soil samples and would be available for
plant uptake by wheat which has a deep root system. Some evidence for N mineralization in
deep soil layers comes from a study at Mandan, North Dakota (Haas et al, 1957). After 30
years cropping, there had been a loss of organic N and C in the 15-30 cm and 30-60 cm layers
(Table A3).
However, the data of Lamb et al. (1985b) for Sidney sites C and D do not support this
hypothesis. In soil samples taken in 1981 and 1982 there was no significant difference between
treatments in the quantities of total N and organic C (samples reanalyzed in 1991) in the 20-30
cm layer (Table A4). Although the C concentration was significantly higher in the native sod
plots on site C, the difference in bulk density between treatments reduced the magnitude of the
effect. These results do not rule out the hypothesis because they only measure a net effect and
Table A3 Percent loss of C and N in 3 soil layers after 30 years cropping at Mandan, North
Dakota. Results are means for a variety of crop rotations including small grains, corn and
fallow years (after Haas et al, 1957).
Nitrogen
Carbon
0-15
29.2
32.9
Depth cm
15-30
9.9
10.3
30-60
5.9 :
7.8
A-24
-------
some mineralization of soil organic matter would have been balanced by inputs of organic matter
from roots below 20 cm, and the net losses over 11 or 12 years could have been smaller than
could be detected statistically. From data for an Alliance soil in the EPIC model soils database
(Sharpley and Williams, 1990), I calculated that there is about 300 g N / m2 in soil organic
matter between 20 and 66 cm depth. The CENTURY model predicts that about one percent of the
organic N in the surface 20 cm mineralizes each year. The proportion mineralizing in lower soil
layers is likely to be lower than in the surface soil. The average age cf the soil organic matter
is greater in subsurface layers (Jenkinson et al., 1992) indicating greater resistance to
decomposition. If one half of one percent of the N in the 20 to 66 cm layer mineralized each"
year then 1.5 g N / m2 / year would be released. This would be sufficient to make up the deficit
for crop production, but would not match the measured accumulation of nitrate in the profile
during the fallow period. However, there would be a positive feedback from having more N
cycling through the plants and crop residue.
The second process that could have contributed to the simulated N deficiency is an
overestimation of volatile N losses. Note that CENTURY did, not predict any deep leaching of
mineral N, so that leaching losses were in fact underestimated. In the present formulation of the
CENTURY model, volatile N losses are predicted from three sources. Firstly, volatile N losses due
to nitrification are estimated as being 5% of gross mineralization. Secondly, denitrification losses
are estimated as 5ln% of the residual mineral N in the soil at the end of each month after all
other processes have been accounted for. Thirdly, for crops there is a volatile loss of 4% of the
N in the shoots, calculated when the crop is harvested. The first mechanism accounts for most
of the volatile N loss. For Sidney sites C and D, the volatile losses associated with nitrification
averaged 0.45, 0.67, 0.84, 0.97 g N / m2 / year for native sod, no-till, stubble mulch, and plow
treatments, respectively. Denitrification losses were insignificant, while under wheat-fallow the
volatilization from shoots averaged 0.10 g N / m2 / year. The nitrification loss was originally
calibrated to balance the N budget in native grasslands, with volatile N losses being
approximately equal to external N inputs. However, volatile losses from senescing herbage were
ignored. Recent research (Zachariassen, 1992) has shown that volatilization of ammonia from
native grasslands is greatest at the end of the season when the herbage is senescing. The net
ammonia flux over the entire season was estimated to be 0.0593 g N / m2 at the Konza prairie
and 0.0744 g N / m2 at the Pawnee grasslands. Given that there was net downward flux during
the main growing season, the volatile losses during senescence could probably account for up to
0.15 g N / m2 or approximately 15% of the N in the standing crop at the end of the season. If
this pathway of N losses was accounted for in the grassland simulations, the volatile losses
associated with .nitrification could be reduced from 5 to 3% of gross mineralization. Hence, the
simulated volatile N losses with wheat-fallow management would be reduced by up to 0.4 g N
/ m2 / year. The simulated equilibrium soil organic matter level in native grasslands is very
sensitive to the rate of N loss because net primary production is limited by N. Hence, further
field research and model testing are necessary to establish the amounts and mechanisms of N
losses in native grasslands.
Clearly, neither of the above mechanisms could account for all of the discrepancy between
simulated and measured mineral N, I suggest that higher than predicted rates of non-symbiotic
A-25
-------
Table A4 The effect of tillage on total soil N (from Lamb et al, 1985b) and organic C (samples
reanalyzed in 1991) at 3 soil depths.
Site C 1982
0-10 cm
10-20 cm
20-30 cm
0-3Q em
Site D- 1981
0-10 cm
10-20 cm
20-30 em
0-30 cm
Site C 1982
0-10 cm
10-20, cm
20-30 cm
Site D 1981
0-10 cm
10-20 cm
20-30 cm
Site C 1982
0-10 cm
10-20 era
20-30 cm
0-20 cm
0-30 cm
Site D 1981
0-10 cm
10-20 cm
20-30 cm
0-20 cm
0-30 cm
Plow
147
143
96
386
129
116
114
360
17.6
14.5
9.5
14.1
12.1
9.2
1755
1544
1201
3299
4500
1400
1289
1155
2688
3843
Stubble
Mulch
189
136
99
424
148
118
104
369
22.0
12.9
9.5
17.7
9,7
8.8
2071
1526
1216
3597
4814
1667.
1149
1131
2815
3946
No-till
o N / m2 —
207
138
107
452
183
114
109
405
0 P / IfO
g V, / Kg —
22,3
12.2
9.4
19.8
10.9
9.3
2305
1409
1176
3714
4889
2049
1259
1163
3293
4472
Native
Sod
222
135
103
460
176
140
103
419
27,9
14.4
10.8
23.2 .
11.6
9.6
2483
1695
1287
4178
5465
2071
1373
1151
3443
4595
SED
7.8
7.4
5.3
15.1
5.7
5.7
2.9
12.2
1.19
0.45
0.38
0.94
0.78
0.35
-
113
52
47
146
179
89
90
45
170
201
**
ns
ns
**
**
*
ns
*
**
**
*
**
ns
ns
**
**
ns
**
**
**
ns
ns
*
*
• significant p < 0.01, * = significant p < 0.05, ns.= not significant
A-26
-------
N2 fixation may have occurred in the wheat-fallow plots. Currently the CENTURY model predicts
non-symbiotic N2 fixation as a linear function of annual precipitation with no fixation when
precipitation is less than 33 cm / year. The N inputs go directly into the mineral pool. At
Sidney this varies between 0 and 0.51 g N / m2 / year and averages 0.15 g N / m2 / year.
However, in the wheat-fallow system, which has a marked biennial cycle in soil moisture status,
tying non-symbiotic N2 fixation to annual precipitation is erroneous. Furthermore, there is some
evidence that non-symbiotic N2 fixation is enhanced by the decomposition of cellulose rich
residues, such as wheat straw, when anaerobic microsites are available for N fixing anaerobes,
such as Clostridium species (Rice and Paul, 1972; Lynch and Harper, 1983). N2 fixation
efficiencies ranged from 2.1 to 5.0 mg N / g straw added or from 11.5 to 16.1 mg N / g substrate
consumed. During the fallow period the soil moisture status rises and there is the required
combination of decomposing wheat straw and anaerobic microsites. If a gain of 5 mg N / g
straw is assumed, and given that at Sidney the straw yields are approximately 400 g DM / m2
7 crop, there could be a potential for 1 g N2 fixation / m2 / year. In contrast to this calculation,
Lamb et al. (1987) estimated from laboratory QHj reduction assays of the soils from the Sidney
experiments, that the non-symbiotic N2 fixation potential was only 0.033 g N / m2 / year.
However, because the incubations were conducted in complete darkness, which would not have
allowed N2 fixation by cyanobacteria, the authors concluded that the non-symbiotic N2 fixation
potential may have been underestimated.
In the assays of Lamb et al. (1987) the N2 fixation potential was positively correlated with soil
moisture content and was twice as high in the no-till treatment compared to the plowed soil. In
soil samples collected during spring growth of wheat at Sidney the population and activity of
anaerobic denitrifying organisms was greater in the surface soil of the no-till plots than in the
cultivated plots (Broder et al., 1984; Linn and Doran, 1984). These results were attributed to the
greater water filled pore space in the no-till treatment. A higher rate of non-symbiotic N2
fixation activity in the no-till plots helps to explain the difference in N budgets between
treatments found by Lamb et al. (1985b).
The combined effect of the three mechanisms described above would result in additional 2.9 g
N / m2 / year. Combined with the positive feedback of producing more residue with a higher N
content a large part of the discrepancy between model and field results could be accounted for.
Soil Organic Matter
The CENTURY model, within a reasonable degree of error, simulated the changes in soil organic
C and N when native sod was converted to a wheat-fallow system on sites C and D. There was
an indication that the model did not sufficiently differentiate between the tillage treatments. In
particular, soil organic matter levels in the plow treatment declined at faster rate than was
predicted by the model. An increase in the factor for the effect of cultivation on decomposition
rates seems justified for the tillage practices with the greatest degree of soil stirring, such as
plowing and use of a field cultivator.
A-27
-------
Comparison of the CENTURY model pools with physical fractionations of soil organic matter
indicated strong similarities between paniculate soil organic matter and that part of the slow pool
which is derived from the structural pools. I recommend that future development of the CENTURY
model separate the slow pool into two parts. Material derived from the structural pools is very
different in physical form and chemical composition from material derived from the active and
surface microbe pools. The former is very likely to correspond with paniculate soil organic
matter and would thus provide a relatively simple laboratory technique for initialization and
validation of changes in one of the pools. The latter part of the slow pool may correspond to
the enriched labile fraction or similar components of the mineral associated organic matter.
A-28
-------
REFERENCES
Balesdent, J., Wagner, G.H., Mariotti, A. 1988. Soil organic matter turnover in long-term field
experiments as revealed by carbon-13 natural abundance. Soil Science Society of
America Journal 52:118-124.
Broder, M.W., Doran, J.W., Peterson, G.A., Fenster, C.R. 1984. Fallow tillage influence on
spring populations of soil nitrifiers, denitrifiers, and available nitrogen. Soil Science
Society of America Journal 48:1060-1067.
Brown, R.E., Havlin, J.L., Lyons, D.J., Fenster, C.R., Peterson, G.A, 99L Long term tillage
and nitrogen effects on wheat production in a wheat fallow rotation. Agronomy Abstracts
1991 Annual Meetings American Society of Agronomy. 326.
Burke, I.C., Yonker, CM., Parton, W.J., Cole, C.V., Flach, K., Schimel, D.S, 1989. Texture,
climate, and cultivation effects on soil organic matter content in U.S. grassland soils.
Soil Science Society of America Journal 53:800-805.
Cambardella, C.A. 1992. Physical separation and characterization of soil organic matter
fractions. Ph.D. Dissertation. Colorado State University, Fort Collins. 135pp.
Cambardella, C.A., Elliottt, E.T. 1992. Paniculate soil organic-matter changes across a
grassland cultivation sequence. Soil Science Society of America Journal 56:777-783.
Earthlnfo. 1992. Daily observations of precipitation, snowfall, maximum temperature, minimum
temperature, and evaporation for over 25,000 present and historical stations in the NCDC
Cooperative Observers network (TD-3200 file). CD ROM Laser disk.
Elliott, E.T., Cambardella, C. A. 1991. Physical separation of soil organic matter. Agriculture,
Ecosystems and Environment 34:407-419.
Elliott, E.T., Cole, C.V. 1989. A perspective on agroecosystem science. Ecology 70:1597-1602.
Fenster, C.R., Peterson, G.A. 1979. Effects of no-tillage fallow as compared to conventional
tillage in a wheat-fallow system. University of Nebraska; Agricultural Experiment Station
Research Bulletin 289. 28pp.
Greb, B.W. 1979. Reducing drought effects on croplands in the west-central Great Plains.
United States Department of Agriculture Agricultural Information Bulletin No.420. 31
PP-
Greb, B.W., Smika, D.E., Welsh, J.R. 1979. Technology and wheat yields in the Central Great
Plains Experiment Station Advances. Journal of Soil and Water Conservation 34:264-268.
A-29
-------
Haas, H.J., Evans, C.E., Miles, E.F. 1957. Nitrogen and carbon changes in Great Plains soils
as influenced by cropping and soil treatments. USDA Technical Bulletin 1164. U.S.
Government Printing Office. Washington D.C..
Haas, H.J., Willis, W.O., Bond, J.J. 1974. Chapter 1. Introduction. In "Summer fallow in the
western United States. USDA-ARS Conservation Research Report No. 17." 1-11.
Jenkinson, D.S., Harkness, D.D., Vance, E.D., Adams, D.E., Harrison, A.F. 1992. Calculating
net primary production and annual input of organic matter in to soil from the amount and
radiocarbon content of soil organic matter. Soil Biology and Biochemistry 24:295-308.
Lamb, J.A., Peterson, G.A., Fenster, C.R. 1985a. Fallow nitrate accumulation in a wheat-fallow
rotation as affected by tillage system. Soil Science Society of America Journal
49:1441-1446.
Lamb, J.A., Peterson, G.A., Fenster, C.R. 1985b. Wheat fallow tillage systems effect on a
newly cultivated grassland soils' nitrogen budget. Soil Science Society of America
49:352-356.
Linn, D.M., Doran, J.W. 1984. Aerobic and anaerobic microbial populations in no-till and
plowed soils. Soil Science Society of America Journal 48:794-799.
Lynch, J.M. Harper, S.H.T. 1983. Straw as a substrate for cooperative nitrogen fixation.
Journal of General Microbiology 129:251-253.
Lyon, D., Monz, C.A., Brown, R., Metherell, A.K. (In prep). Soil organic matter changes over
two decades of winter wheat-fallow cropping in western Nebraska. In "Soil organic
matter in temperate agricultural ecosystems: A site network approach." Paul, E.A., Cole,
C.V. (eds). Lewis Publishers, Chelsea, Michigan.
Metherell, A.K. 1992. Simulation of soil organic matter dynamics and nutrient cycling in
agroecosystems. Ph.D. Dissertation. Colorado State University, Fort Collins, Colorado.
Meyers, T.P., Hicks, B.B., Hosker, R.P., Womack, J.D., Satterfield, L.C. 1991. Dry deposition
inferential measurement techniques-JJ. Seasonal and annual deposition rates of sulfur and
nitrate. Atmospheric Environment 25A:2361-2370. . •
Mielke, L.N., Doran, J.W., Richards, K.A. 1986. Physical environment near the soil surface of
plowed and no-tilled soils. Soil and Tillage Research 7:355-366.
National Atmospheric Deposition Program (NRSP-3)/National Trends Network, 1991.
NADP/NTN Coordination Office, Natural Resource Ecology Laboratory, Colorado State
University, Fort Collins, CO 80523
A-30
-------
O'Halloran, I.P., Stewart, J.W.B., de Jong, E. 1987. Changes in P forms and availability as
influenced by management practices. Plant and Soil 100:113-126.
Parton, W.J., Schimel, D.S., Cole, C.V., Ojima, D.S. 1987. Analysis of factors controlling soil
organic matter levels in Great Plains grasslands. Soil Science Society of America Journal
51:1173-1179.
Parton, W.J., Stewart, J.W.B., Cole, C.V. 1988.. Dynamics of C,N,P and S in grassland soils:
a model. Biogeochemistry 5:109-131.
Parton, W.J., McKeown, B., Kirchner, V., Ojima, D. 1992. Users guide for the CENTURY model.
Colorado State University.
Ramundo, R.A., Seastedt, T.R. 1990. Site-specific underestimation of wetfall NH4+ using NADP
v data. Atmospheric Environment 24A:3093-3095.
Rice, W.A., Paul, E.A. 1972. The organisms and biological processes involved in asymbiotic
nitrogen fixation in waterlogged soil amended with straw. Canadian Journal of
Microbiology 18:715-723.
Sharpley, A.N., Williams, J.R. 1990. "EPIC-Erosion/Productivity Impact Calculator 1. Model
Documentation. U.S. Department of Agriculture Technical Bulletin No. 1768." 235pp.
Smika, D.E., Wicks, G.A. 1968. Soil water storage during fallow in the central Great Plains as
influenced by tillage and herbicide treatments. Soil Science Society of America
Proceedings 32:591-595.
Zachariassen, J. 1992. Ammonia exchange above grassland canopies. Ph.D. Dissertation.
Colorado State University, Fort Collins, Colorado.
A-31
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-------
APPENDIX B
APPLICATION OF THE CENTURY MODEL TO THE LEXINGTON, KY, SITE
B-l
-------
APPLICATION OF THE CENTURY MODEL TO THE LEXINGTON, KY, SITE
prepared by
R.V. Chinnaswamy
A.S. Patwardhan
A.S. Donigian, Jr.
AQUA TERRA Consultants
Mountain View, CA 94043
B.I INTRODUCTION
The CENTURY model developed by Parton et al. (1987, 1988) has been used for various field
applications. Initially, the studies were limited to grassland conditions since the model did not
have capabilities to represent crops such as corn, millet, soybean and wheat. During the past
year model capabilities have been greatly enhanced by researchers at NREL at Colorado State
University (Metherell, 1992), by incorporating crop parameters and algorithms to properly
represent multiple commercial crops, crop rotations, and tillage practices, along with interactions
with the various nutrient cycles simulated by the model.
The EPA Climate Change Study Region consists of the Corn Belt, the Great Lakes area and
some portions of the Great Plains. The crops grown in this region are primarily corn, soybeans,
and wheat. Within the study region, agronomic practices consist of complex cropping patterns,
rotations, and tillage practices with these crops. Since the CENTURY model was being used
for assessing the carbon sequestration potential of agroecosystems in the study region, field
testing of the model (with the new crops) within the study region was necessary. Furthermore,
the field testing could be used as a tool in estimating the model performance, particularly, in
simulating the soil organic matter dynamics under various cropland and management conditions.
In other words, the model testing and validation will help evaluate the accuracy of the
assessment of carbon sequestration potential of agricultural lands based on the model predictions.
Hence, two sites were chosen for field testing of the model: a continuous corn site with rye
cover crop in Lexington, KY, and a wheat fallow rotation site in Sydney, NE (discussed in
Appendix A).
^
The detailed information about the Lexington field experiments was provided by Drs. Keith
Paustian, Robert Blevins and Wilbur Frye. Dr. Blevins clarified many doubts about the field
experiments. Dr. Paustian provided valuable suggestions and greater insight into the model
application during the study. Their indispensable help is gratefully acknowledged. The pioneers
in the CENTURY model development, Drs. William Parton and Vernon Cole, and Dr. Alister
Metherell offered many helpful suggestions.
The CENTURY model and its site data requirements are discussed in Section B.2. The
Lexington, KY site and field experiments are then discussed briefly in Section B.3. Section B.4
B-2
-------
•f
presents the application of the CENTURY model to the site; results and discussions, and
recommendations and conclusions are discussed in Sections B.5 and B.6, respectively.
B.2 CENTURY MODEL OVERVIEW
The CENTURY model simulates the dynamics of C, N, P and S in cultivated and.grassland soils
using a monthly time step for model simulations. The model is composed of five organic matter
pools, two of which represent litter or crop residues and the remaining three representing soil
organic matter. The fresh organic matter, which includes plant material and animal manure, is
divided into metabolic and structural litter fractions, depending on its ligninmitrogen ratio. The
soil organic matter is divided into three fractions: 1) an "active" fraction that has a rapid
turnover rate and consists of microbial biomass and metabolites, 2) a "slow" fraction with an
intermediate turnover time that represents stabilized decomposition products, and 3) a "passive"
fraction that represents the highly stabilized, recalcitrant organic matter.
In the model, soil moisture is calculated as a function of the ratio of monthly precipitation to
monthly evapotranspiration, the soil temperature term is calculated as a function of average
monthly soil temperature at the soil surface. First-order rate constants depending on soil
temperature and moisture are used to represent the decomposition rates. The other factors that
affect the organic matter transformation include soil texture and the lignin content of the litter.
The soil texture influences the relative amount of decomposing organic matter that is either
mineralized to CO2 or enters the slow pool as decomposition products. The lignin content of
the litter pool influences the relative amount of the structural material that is assimilated by
microorganisms (transferred to active pool) or gets incorporated into the slow pool.
B.2.1 C and N cycles
:ENTURY
The carbon cycle is depicted in Figure B.I, and Figure B.2 shows the nitrogen cycle. The
carbon that is released due to decomposition gets partly mineralized and the remaining gets
added as decomposition product to one or more organic matter pools. The carbon flow dictates
the release of nitrogen as nitrogen is considered to be mainly in C-bonded forms (organic N).
The C:N ratios of materials entering the soil organic pools are used to calculate the
mineralization and immobilization of N. The model allows the representation of C:N ratios of
the material entering the organic pool to be constant or to vary at specified time intervals. If
the N content of decomposition products entering a given pool is less than the minimum required
for the recipient pool, then mineral N is immobilized, and conversely if organic N content is in
excess, then that amount is mineralized. The model uses simple equations to represent N inputs
by atmospheric deposition and through plant and soil fixation. The losses of N forms
represented include leaching, gaseous losses, crop removal, and erosion.
The CENTURY model has submodels for estimating plant growth and soil water balance. These
submodels provide information on plant litter inputs and climatic variables for soil organic
B-3
-------
B-4
-------
oo
OO
^ tfl
4J
cd
13
9
U
9
l-i
4J
ttl
CM
•
pq
0)
i-i
3>
•H
CO
B-5
-------
matter computations. Plant growth is expressed as a function of the growing season
precipitation. The potential plant growth rate is used to calculate the crop nutrient demand. If
nutrient availability is insufficient then the crop production is reduced based on the nutrient
availability. The soil water content is calculated as a function of the ratio of monthly
precipitation to monthly potential evapotranspiration.
Paustian et al. (1992) have applied the model to study the influence of organic amendments and
N-fertilization on soil organic matter. Schimel et al. (1990) employed the CENTURY model
to study grassland biogeochemistry and its link to the atmospheric processes. Burke et al. (1990)
used CENTURY coupled with a geographic information system to study the spatial variability
in storage and fluxes of C and N within grassland ecosystems. The model has also been used
by Cole et al. (1989) to study the effects of soil organic matter dynamics in the North American
Great Plains. Parton et al. (1989) applied the CENTURY model to simulate the regional
patterns of soil C, N, and P dynamics in the U.S. central grasslands.
The CENTURY model has been validated by Parton et al. (198.7) by simulating steady state C
and N levels and aboveground plant production for 24 sites in the Great Plains. The simulation
results were compared with mapped plant production and soil C and N levels for fine, medium,
and sandy textured soils. The authors found that the model overestimated the C and N soil
levels for fine textured soils, underestimated for sandy soils, and did an excellent job for
medium textured soils. The effect of soil texture and climate on the soil C and N levels in the
Great Plains was adequately represented by the model. The model results of aboveground plant
production were in excellent agreement with the observed values.
B.2.2 Application of CENTURY model to Lexington Site
The application of the CENTURY model in this study was limited to the simulation of carbon
and nitrogen dynamics in the soil profile. The crops that were simulated include corn and a
winter cover crop of wheat; although rye was the winter cover crop at Lexington, it is not
currently represented in the model. The application of CENTURY model consisted of
simulating grassland conditions and the actual field experiments. The grassland conditions were
simulated to achieve similar initial conditions for model inputs as represented by field
measurements of soil organic carbon taken in 1970 at the beginning of the field experiment.
B.2.3 Data Requirements
The nutrient inputs along with organic matter (C and N) are estimated from equilibrium or
steady state simulations performed for a period of 5000 years. The steady state conditions are
. achieved by simulating historic ecological conditions at the site and the initial organic matter and
nutrient pools are estimated using regression equations developed by Burke et al. (1990). The
following is a list of site-specific data requirements for the application of CENTURY to a
particular field site:
B-6
-------
Soils:
% sand, % silt, % clay
Bulk density, pH
Number of soil layers
Drainage characteristics of soils
Expected baseflow and storm flow fractions
Meteorological:
Crops:
Monthly precipitation
Monthly maximum temperature
Monthly minimum temperature
Crops grown
Planting and harvesting months
Timing of tillage and cultivation operations
Timing and rate of fertilizer applications
B.3 LEXINGTON FIELD EXPERIMENTS
In the following sections, the Lexington site, the field experiments and the available data that
are necessary for testing and validation of CENTURY model are described in detail.
B.3.1 Lexington Site Description
A field experiment was initiated in the spring of 1970 (Blevins et al., 1977, 1983a) at the
Kentucky Agricultural Experiment Station Farm in Lexington. The soil type at the site was
Maury silt loam which is classified as Typic Paleudalf (fine, mixed, mesic) with a 1 to 3%
slope. The Maury soil is deep, well-drained and originated in residuum of phosphatic limestone.
The experimental area occupies a broad ridgetop that has been under bluegrass pasture for
approximately 50 years before the start of. the experiment (Blevins 1992, personal
communication). The management practices on the pasture area consisted of low intensity
grazing with cattle and horses, and occasional mowing. The ecosystem was altered from the
permanent bluegrass pasture to continuous corn production in April 1970.
B.3.2 Field Experiment
The objective of the experiment was to study what effects the conventional and no-till systems
have on chemical and physical properties of soils. The experimental design consisted of split-
block design with four replications. The experimental area (46.1 x 48.4 m2) was divided
laterally into four strip blocks receiving four levels of ammonium nitrate fertilizer (0, 84, 168,
and 336 kgN/ha), which was applied randomly at planting time to quarters split vertically within
each block. The block was further split laterally, with conventional tillage and no-tillage
randomly assigned across the entire block.
B-7
-------
The plots under conventional tillage system were plowed in late April, about 1 to 2 weeks before
corn plantation. The tillage implements consisted of a four bottom, 16 inch trailing moldboard
plow. The depth of plowing was 15 to 18 cm. A tandem disk harrow was used for performing
secondary tillage operation to depth of 10 cm. Corn was planted in May, and rye was broadcast
on all plots in mid September, 2 weeks prior to harvesting corn, so as to produce a cover crop
and additional mulch. In the beginning of 1985 half of the plots were seeded to rye and other
half to hairy vetch. No cultivation practices were used for weed control, and no irrigation
practices were followed.
The plots under no-till system were sprayed with herbicide in late April before planting corn.
Com was usually planted in May using No-till drill and rye was broadcast on all plots in mid-
September, two weeks prior to harvesting corn in order to produce a cover crop and additional
mulch. Table B. 1 lists the management schedule at the site.
B.3.3 Available data
Following is a description of the type of data that was available to perform the CENTURY
simulations:
Meteorologic Data:
The meteorologic data for the period of 1978-1991 was taken at
the site. Data for the time periods of 1970-1989 is available
at Lexington Airport which is approximately 4 miles west of
the site. The data from the airport was used for CENTURY
simulations.
Soils Data:
The following data was available at the beginning of the
experiment in 1970 for the untreated plots
Depth
cm
0-5
5-15
15-30
Sand
%
7.1
7.1-
5.3
Silt
%
69.9
69.9
70.2
Clay
%
23.0
23.0
24.5
Bulk
Density
g/cm3
1.27
1.28
Organic C
g/m2
2419.35
2214.40
1728.00
Atmospheric Deposition:
NADP (National Atmospheric Deposition Program) wet deposition
data either on a seasonal basis (summer, fall, winter, and
B-8
-------
Table B.I. Management History at the Long-term Continuous Corn-Tillage Study at
Lexington, KY (Blevins 1992, unpublished data)
Year
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
Corn
Plant
date
5/6
5/10
5/17
5/17
5/17
5/23
5/24
5/20
5/18
5/17
5/15
5/21
5/13
5/23
5/17
4/30
5/13
5/7
5/20
5/11
5/14
5/8
Corn
harvest
date
9/15
9/28
9/28
9/27
9/27
9/30
10/1
9/29
9/21
9/9
9/25
10/2
9/24
9/152
9/26
9/20
9/25
9/28
10/5
10/3
10/5
9/18
Corn
Variety
SX-29
Pioneer 3369A
Pioneer 3369A
Pioneer 3369A
Pioneer 3369A
Pioneer 3369A
Pioneer 3369A
Pioneer 3369A
Pioneer 3369A
Pioneer 3369A
Pioneer 3369A
Pioneer 3369A
Pioneer 3369A
B73xPA91
B73xPA91
B73xPA91
B73xPA91
B73xPA91
B73xPA91
Pioneer 3165
Pioneer 3165
Pioneer 3165
Seed
rate
per
ac
25,000
18,000
22,500
22,500
22,500
22i500
22,500
22,500
22,500
22,500
22,500
22,500
22,500
22,500
22,500
22,500
22,500
22,500
22,500
22,500
22,500
22,500 .
Amount(date)
cover crop
overseeded
Rye1
Rye
Rye
Rye
Rye
Rye
Rye
Rye(9/20)
Rye
Rye
Rye
Rye
Rye
Rye
Rye
Rye+H.V
Rye+H.V
Rye+H.V
Rye+H.V
Rye+H.V
Rye+H.V
Rye+H.V
.3 (9/8)
. (9/10)
. (9/14)
. (9/15)
. (9/14)
. (9/25)
. (9/12)
Fertilizer
K2O Ib/ac
50
175 '
175
100
100
100
100
100
100
100
100
100
100
100
100
100
Rye seeded at 2 bu/ac
For silage only
Hairy vetch overseeded at 30 Ib/ac
B-9
-------
spring) or an annual basis is available for the period of 1984
-1991. There are a total of four sites in Kentucky where data
is collected. These sites, Lilley Cornett Woods, Clark State Fish
Hatchery, Mackville and Land between Lakes are located
approximately 100 miles south east, 100 miles north east, 25 miles west
and 200 miles south west of Lexington, respectively. Based on the
observed data, a constant atmospheric deposition of 0.6 gN/m2/yr was used
in the model.
Crop and Nutrient Data:
The observed data available for the conventional and no till
experiments conducted at the Lexington field sites is listed
in Tables B.2 and B.3. The observed data of corn grain yields in
Mg/ha for four fertilizer application rates representing the
period of 1970-1991 is listed in Table B.2, note that there is
no yield data for 1983 which was reported to be a drought year.
Table B.3 lists organic carbon and organic nitrogen measurements
taken at three depths (0-5 cm, 5-15 cm, and 15-30 cm) in the
year 1975 and 1989 for the four fertilizer application rates.
Observed soil organic matter content for the depths of 0-5 cm
and 5-15 cm in 1975 and 1980 (Blevins et al., 1983a) is listed
in Table B.4.
B.4 CENTURY MODEL APPLICATION
The CENTURY model was used to simulate the historic grassland conditions and then the long-
term continuous corn-tillage experiments. The simulation of grassland conditions provided initial
values for various organic matter pools that were in good agreement with the field data measured
at the beginning of the experiment in 1970.
B.4.1 Simulation of Grassland Conditions and Analysis
As described in Section B.3, the native vegetation in the experimental area was mostly bluegrass
pasture for about 50 years before the start of the experiment in 1970. The following
assumptions were made regarding the past site conditions.
a. The site was under grassland conditions.
b. The period between April and October was the growing season for grass and November
was the senescence month.
c. Grazing events occurred throughout the year with winter grazing during winter months
of November, December, and January through March. We assumed that during winter
grazing, 15 and 7% of the live and standing-dead biomass, respectively, were removed.
For the remaining months, grazing events occured with. 25 and 3% removal of the live
,B-10
-------
Table B.2.
Year
Corn Grain Yields in Mg/ha from Lexington, KY, site (Paustian, 1992, Personal
Communication).
Conventional Tillage
Fertilizer Rates (kgN/ha)
0
84
168 336
No-Till
Fertilizer Rates (kgN/ha)
0
84
168 338
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1984
1985
1986
1987
1988
1989
1990
1991
5.71
9.47
8.15
4.14
8
4
5
5
4
4
5
4
3
3
1
2
2
3
4
3
1
.09
.89
.33
.52
.2
.26
.96
.95
.32
.45
.88
.63
.82
.32
.2
.2
.51
5.64
11.29
10.10
7
10
5
8
7
6
8
7
6
5
4
5
4
4
4
6
5
5
.71
.16
.02
.09
.71
.27
.15
.9
.96
.52
.64
.71
.01
.89
.08
.96
.46
.52
5.64
9.97
9.97
8.09
10.22
5.14
8.84
7.97
6.08
7.78
8.08
7.78
6.08
5.39
6.08
4.2
4.95
3.64-
8.03
8.34
6.9
5.
10.
10.
8.
10.
6.
8.
8.
6.
7.
8.
8.
6.
5.
7.
4.
64
16
35
47
16
02
84
28
27
71
84
97
27
02
46
2
5.39
3.51
7.53
7.34
7.21
5.64
6.21
7.40
4.14
5.58
3.76
4.33
3.64
2.07
4.58
3.64
4.95
3.14
3,76
3.14
3.14
3.07
3.26
6.08
4.33
3.32
6.21
10.41
9.60
7.46
9.66
6.08
9.03
6.65
4.89
7.4
7.09
7.34
4.14
5.77
5.33
4.33
5.14
4.33
8.53
5.96
6.65
6.21
10.66
9.35
7.9
10.35
6.27
9.78
6.84
5.33
•7.71
8.53
9.03
5.96
6.9
6.9
4.89
6.15
4.89
8.66
7.65
8.66
6.59
10.85
9.72
7.59
10.47
6.65
10.66
7.21
6.21
7.59
7.84
9.41
5.77
5.9
8.03
5.21
5.33'
5.21
9.35
6.08
8.72
B-ll
-------
Table B.3. Observed Organic C and N data for Lexington, KY Site (Paustian, 1992, Personal
Communication)
Org-C g/kg Org-N g/kg
0-5cm 5-15cm 15-30cm 0-5cm 5-15cm 15-30cm
Conventional Tillage
Fertilizer Rates
1975 Data
0
84
168
336
kgN/ha
kgN/ha
kgN/ha
kgN/ha
13.70
15.10
16.20
16.80
12.90
14.70
15.10
14.60
6.80
9.10
8.50
7.90
1.60 1.50 0.80
1.60 1.40 1.00
1.70 1.60 1.00
1.60 1.50 1.00
1989 Data
0 kgN/ha
84 kgN/ha
168 kgN/ha
336 kgN/ha
No-Till
1975 Data
0 kgN/ha
84 kgN/ha
168 kgN/ha
336 kgN/ha
1989 Data
0
84
168
336
kgN/ha
kgN/ha
kgN/ha
kgN/ha
19.20 20.70
20.30 21.30
19.60 23.10
21.90 24.30
21.30 12.90
23.00 13.20
23.80 12.50
26.30 14.30
32.80 20.80
39.50 22.60
34.30 22.20
43.20 23.40
9.50
12.10
11.20
12.40
7.90
7.90
7.20
8.40
8.90
9.00
8.70
10.70
2.10 1.30 1.00
2.20 1.30 1.10
2.20 1.40 1.10
2.50 1.50 1.10
B-12
-------
Table B.4. Soil Organic Matter Content (adapted from Blevins et al., 1983a)
N-Rate
kg/ha
Untreated
Sod Plots
NT
Organic Matter Content %
1975
CT NT
1980
CT
0-5 cm
0
84
168
336
0
84
168
336
5.18
2.47
3.68
3.96
4.11
4.53
2.22
2.28
2.15
2.46
2.37
2.60
2.78
2.79
2.23
2.53
2.60
2.52
3.70
5.08
4.82
5.05
5-15 cm
1.88
2.21
2.34
1.98
2.16
2.41
2.40
2.52
2.38
2.40
2.31
2.57
B-13
-------
and dead biomass, respectively.
d. A fire event occurred once every ten years in July. The fire intensity was assumed to
be moderate.
An input (scheduler) file containing the above assumptions was setup and the model was run for
a period of 5000 years using the option for stochastic generation of climate data in the model.
The initial soil carbon levels in the various pools at the beginning of the historic run were
established using the IVAUTO option in the CENTURY model. The IVAUTO option allows
the user to estimate the beginning SOC conditions based on regression models developed by
Burke et al., (1989) that estimate SOC as a function of soil texture and regional climate.
The analysis consisted of simulating the dynamics of carbon and nitrogen. The state variables
such as total soil organic carbon and nitrogen from this run were then compared with the field
data observed in 1970. The model under estimated the total soil organic carbon approximately
by 4% and over estimated total soil organic nitrogen by 23%. The fact that the model-simulated
organic nitrogen value is much higher than the observed value raised a question about the
validity of the observed value corresponding to 1970. Therefore, an approximate organic
nitrogen value for 1970 was estimated using the C/N corresponding to 1975. It is based on the
fact that the C:N ratio does not vary much over a short period (Metherell, 1992, personal
communications). The difference between the estimated organic nitrogen value (1970) and
model simulated value was negligible (0.04%). Since the difference between the model
estimation and the field observation (1970) was minimal, the conditions simulated by the model
(at the end of 5000 yr) were used as the initial conditions for the simulation of tillage
experiments.
B.4.2 Simulation of Tillage Experiments
The conventional tillage and no-till experiments conducted at the site between 1970 and 1991
were simulated using the CENTURY model. For each tillage type, the model was run with four
input scenarios consisting of four fertilizer application rates (0, 84, 168, 336 kg N/ha). The
scheduling of events in the simulation of experiments are presented in Tables B.5 and B.6. The
following simulated variables were analyzed and compared with the available observed data:
a. Grain Yield
b. Soil Organic Carbon and Nitrogen
c. Concentration of N in Grain
d. Nitrogen uptake by plants
e. Available water in the soil.
f. Net carbon input into the soil
Initially, the simulation runs indicated that there was a considerable difference between the
predicted values and observed data. For instance, the simulated organic carbon values for the
three fertilizer rates, 84, 168, and 336 kg/ha, increased from the beginning of the simulation
which was in contrast to the field data. This suggested the need for a reexamination of the
B-14
-------
Table B.5. Schedule of Crops and Various Operations for the Simulation of CT
Experiments.
Month Crop(s)
1970 1971-91
Jan Grass Wheat
Feb Grass Wheat
Mar Grass Wheat
April Grass Wheat
May Corn Corn
June Corn Corn
July Corn Corn
Aug Com Corn
Sep Corn Corn
Oct Wheat Wheat
Nov Wheat Wheat
Dec Wheat Wheat
Table B.6. Schedule
Fertilizer
Rate kg/ha
0
0
0
0
a
0
0
0
0
0
0
0
of Crops and
Tillage/Harvest
Operation
Winter grazing in 1970
Winter grazing in 1970
Winter grazing in 1970
Plowed in
Cultivator used for planting
-
. •
-
Harvested
No-Till Drill used for planting
-
-
Various Operations for the Simulation of NT
Experiments.
Month Crop(s)
1970 1971-91
Jan Grass Wheat
Feb Grass Wheat
Mar Grass Wheat
April Grass Wheat
May Com Corn
June Corn Corn
July Com Corn
Aug Corn Corn
Sep Corn Corn
Oct Wheat Wheat
Nov Wheat Wheat
Dec Wheat Wheat
Fertilizer
Rate kg/ha
0
0
0
0
a
0
0
0
0
0
0
0
Tillage/Harvest
Operation
Winter grazing in 1970 ,
Winter grazing in 1970
Winter grazing in 1970
Herbicide used for killing
No-Till Drill used for planting
-
-
-
Harvested
No-Till Drill used for planting
-
a - N fertilizer rates of 0, 84, 168, 336 kg/ha were used in four experiments.
B-15
-------
default parameterization of the rate constants. Further, the net carbon input into the soil for the
highest fertilizer rate was very high (900-1000 gC/m2). An approximate estimation, assuming
a harvest index of about 50%, indicated that the residue input rate for a maximum yield of about
10 Mg/ha would be about 450-650 gC/m2. As a result, the crop production parameter (PRDX)
for corn was reduced from 500 to 450 and maximum harvest index (HIMAX) was set to 0.53.
In addition, the slow pool decomposition rate constant was doubled and .set to 0.4.
In the site specific file, the initial amounts of the litter (active) pool and slow pool from the
equilibrium analysis appeared to be low and high, respectively. The initial C in the active pool
was doubled and the C in the slow pool was decreased by the corresponding amount (Paustian,
1992, Personal Communication). The crop\tillage\management scenarios were simulated with
these changes.
B.5. RESULTS AND DISCUSSION
In the following subsections the results obtained from the simulation analysis are presented and
discussed.
B.5.1. Conventional Tillage Experiments
Corn Grain Production
The crop production parameters (crop production and harvest index) were calibrated to observed
crop yields under the conventional tillage system, for the highest fertilizer application rate of 336
kg N ha"1. These parameters were then used for both conventional tillage and no-till and for all
the four fertilizer application rates. This approach was adopted to maintain consistency between
tillage levels and to approximate the nitrogen stress induced at the lower nitrogen fertilizer
applications. This procedure resulted in generally good agreement between the observed and
simulated crop yields for most of the tillage and fertilizer treatments.
For all the N fertilizer rates, the model over estimated the average grain yield by approximately
6 to 10% (Table A.7). The simulated grain yields increased with the increasing N fertilizer
rates as expected. Further, the variations in the annual yield values corresponding to low
fertilizer rates (0 and 84 kg/ha) appeared to be maximum (Figure B.3) where as their
counterparts at high fertilizer rates were minimum. These variations in the annual corn yield
suggest that corn production was limited by nitrogen availability for crop growth. At higher
fertilization levels, corn production was not limited by nitrogen availability. However, it may
have been limited by the maximum crop production parameter as specified in the crop input file.
Soil Organic Carbon and Nitrogen
Simulated soil organic carbon (SOC) was compared with the observed data (Figure B.4). In
general, the simulated carbon values for all the fertilizer rates followed the trend of the observed
carbon values. However, the simulated carbon values corresponding to low fertilizer rates were
B-16
-------
10000
Corn Grain Yield, Lexington, KY
Conventional Tillage-No Pert. Applied
1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990
YEAR
Figure B.3. Simulated Versus Actual Grain Yields for Conventional
Tillage Treatment Without Fertilizer Addition.
B-17
-------
Table B.7. Simulated and Observed Average Corn Yields (kg ha'1, 21-year average, 1970-91)
Fertilizer
Rates
Simulated
Average
S.D.1
Observed
Average S.D.1
% Difference
Average S.D.1
Conventional Tillage
0
84
168
336
No-Till
0
34
168
336
4947
7156
7780
7803
5306
6900
6953
6962
924
679
699
714
979
744
782
783
4619
6752
7103
7316
4247
6762
7553
7638
2004
1991
1856
1891
1279
1808
1688
1820
9.2
6.7
9.3
7.1
19.9
2.9
-8.1
-8.8
35.0
27.3
24.9
24.4
26.3
26.6
26.8
27.5
Note: -ve
S.D. stands for Standard Deviation
Sign indicates under estimation
more in line with the observed data than those at the higher rates, indicating that CENTURY
has a greater SOC response for higher fertilization levels. Nearly identical carbon inputs were
simulated for the fertilization levels of 168 and 336 kg N ha'1 resulting in identical SOC for
these two fertilizer rates.
Soil organic carbon values were greater with increasing N fertilizer rates. This is probably due
to the fact that an increase in N fertilizer rate increases the crop production which in turn
increases the surface residue after harvest. Incorporation of this residue into the soil results in
increasing the soil organic matter. Blevins et al. (1977) reported similar observations in their
conventional tillage experiments. Organic carbon corresponding to all the fertilizer rates
indicated a net SOC loss at the end of the simulation compared to the original pasture soil, with
the minimum loss for the highest fertilizer application and vice versa.
A decrease in soil organic matter is generally found when grassland or permanent pasture is
cultivated (Allison, 1973). Plowing may increase decomposition rates in the disturbed plow
layer and conversion of grassland to annual crops may also result in lower net carbon inputs of
carbon to soil due to the export of organic matter in the harvested products. In simulations of
B-18
-------
Soil Organic Carbon, Lexington, KY
Conventional Tillage
6000
0 kgN/ha-Observed
a
84 kgN/ho-Observed
1970 1972 1974 1976 1978 1980 1982 1984 1985 1988 1990 1992
YEAR
Soil Organic Carbon, Lexington, KY
Conventional Tillage '
6000
168 kgN/ha-Observed I
°
336 kgM/ha-Observed
i 1 1 1—i 1 1 1 1—i—i 1—i—T—i—i—t-—i r
1970 1972 1974 1976 1978 1980 1982- 1934 1985 1988 1990 1992
YEAR
Figure B.4. Simulated and Measured Soil Organic Carbon at
Different Nitrogen Fertilizer Additions.
B-19
-------
long-term field experiments in Sweden, Paustian et al. (1992) also reported that simulated and
measured SOM decreased in treatments receiving only crop residues as carbon inputs. Also,
Blevins et al. (19835) reported a similar observation at the end of a 10-year field (Lexington)
experiment. The net SOC loss could be due to the fact that plowing increases the oxidation of
the organic matter at least in the top 20 cm. This increased oxidation could increase the
decomposition rate of the organic matter which in turn decreases the SOC.
For the three fertilizer N rates, 84, 168 and 336 kg/ha, the model over-estimated the carbon
values at the end of the simulation by 5 to 10%. Error bars associated with the observed data,
if available, would be useful in evaluating the model results more accurately.
Simulated soil organic nitrogen was compared with the observed data (Figure B.5). The organic
N followed the trend of the observed data for the non fertilizer case. For the other three
fertilizer rates, 84, 168 and 336 kg/ha, the organic N increased with increasing N fertilizer rates.
It should be noted that the organic N data for 1990 and 1992 were not reported by the
researchers at the Lexington field experiment station; these data points would be useful, if they
were measured and available, in evaluating the model performance. In other words, additional
field data will be useful in better establishing the trend of the observed organic N in the field
and thereby provide a better basis for assessing the model predictions.
Grain Nitrogen Concentration and N Uptake by Plants
Simulated grain nitrogen concentration compared well with the observed data for the year 1980
(Figure B.6). On the other hand, they were not in good agreement for the year 1981. In
comparison to literature reported range of 1.35 to 1.75 (Follet et al., 1991), the model under
estimated the grain nitrogen concentrations for the low fertilizer rates. However, for the high
fertilizer rates, 168 and 336 kg/ha, the model predictions were in the expected range.
The nitrogen uptake by plants increased with increasing N fertilizer rates (Figure B.6) with the
highest uptake corresponding to the highest fertilizer rate. The average plant uptake for both
tillage systems and for all fertilizer rates are presented in Table B.8. Typical corn plant N
uptake as reported in the literature ranges from 135-168 kg N ha"1 (Tisdale et al., 1985). The
observed plant N uptake was reported to be 100-160 kg N ha"1. The model results compared
well with the observed values for the N fertilizer rate of 84 kg/ha. For the N fertilizer rate of
168 kg/ha, the simulated value compared well with the observed value in 1980. However, the
model under estimated the N uptake in the following year. The error bars associated with the
observed N uptake by plants could be useful, if available, in validating the model results more
accurately. Nevertheless, model's ability in predicting the N uptake by the plant (for
conventional tillage) appears to be good. Paustian et al. (1992) reported similar observations.
Net Carbon Input into the Soil
The net carbon input into the soil increased with increasing N fertilizer rates (Table B.8). In
comparison to the zero fertilizer case, the net carbon input increased approximately twice as
B-20
-------
Soil Organic Nitrogen, Lexington, KY
Conventional Tillage
800 -
700 -
^600 -
v_^ .
c
a>
5' 400
i_
2
.Si OOO
c
o
D>
i_
O onrv
200 -]
100
'
_^ '"
* * ~
0 kgN/ha-MODEL
a
0 kgN/ha-FIELD
34 kgN/ha-MODEL
A
84 kgN/ha-FIELO
1969 1972 1975 1978 1981 1984 1987 1990 1993
YEAR
Soil Organic Nitrogen, Lexington, KY
Conventional Tillage
BUU -
700
^600-
N
Z500
^^
c
a>
2 400
i-
iz
£ 300 "
c
o
o>
t_
zoo
100 -
-
19
^_
^^>^^
[xi a A
A O
69 1972 1975 1978 1981 1984 1987 1990 . 19
YEAR
93
168 kgN/ha-MODEL
D
168 kgN/ha-FIELO
336 kgN/ha-MOOEL
»5ib kgN/na— MLLU
Figure B.5. Simulated and Measured Soil Organic Nitrogen at
Different Nitrogen Fertilizer Additions.
B-21
-------
Grain Nitrogen Concentration
Conventional Tillage .
2 ~
.8
1.4-
c
0>
DJ
0 ,
— 1
z
•g 0.8
6
0.6 •
0.4-
.L
Observed Data* k~" -K
• i
1970 1972 1974 1976 1978 1980 1982 1984 19861988 1990 1992
YEAR
Nitrogen Uptake by Plants
Conventional Tillage
200 -
1 80 -
150"
•jr
QJ
^f
o 100 -
$
o
*- en
nr 60
Z
40 -
Field Data* 'R •+
]r^/\ ^^-^^\ _/\ ^^^
~ X ~ ^^Z^^ ^~^^( * P^^,
^i^ M • \ A ^
cf A a
/"^ V" M A A ^ '
/ \ / \ A 71 »—Tk
" Blevins et al., 1986
Tfcr-
0 kgN/ha-MODEL
-B-
84 kgN/ha-MODEL
X
84 kgN/ha-FIELD
168 kgN/ha-MOOEL
168 kgN/ha-FIELD
-3-
336 kgN/ha-MODEL
0 -| r- 1 1 1 1 1 1 1
1969 1972 1975 1978 1981 1984 1987 1990 1993
YEAR
Figure B.6. Simulated and Measured a) Grain Nitrogen Concentration
and b) Nitrogen Uptake by Plants, at Different Fertilizer
Additions.
B-22
-------
much for the other three N fertilizer rates. This is probably due to the fact that higher fertilizer
rate results in higher crop growth which in turn results in higher crop residue after harvest. The
incorporation of this residue into the soil results in higher carbon input into the soil. The
maximum net carbon input corresponding to the highest fertilizer rate was in the range of 400-
500 gC/m2, as expected.
Table B.8. Average Carbon Inputs and Plant Nitrogen Uptake, Predicted by CENTURY.
Conventional Tillage
Fertilizer
Rates
kg N ha'1
0
84
168
336
Carbon
Inputs
kg C ha'1
2570
4100
4680
4710
Plant N
Uptake
kg N ha'1
68
118
136
143
No-Till
Carbon
Inputs
kg C ha'1
2590
3740
3790
3800
Plant N
Uptake
kg N ha'1
65
110
120
121
B.5.2 No-Till Experiments
Corn Grain Production
Simulated corn grain yields were compared with the observed data. The model over estimated
the average grain yield by approximately 20% for the non fertilizer case (Table B.7). However,
the model over estimated the average grain yield by approximately 2% for the N rate of 84
kg/ha. On the other hand, the average grain yields corresponding to the high fertilizer
applications were under estimated by the model by approximately 10%. "The simulated grain
yields increased with increasing N fertilizer rates as observed. Additionally, there was
significantly no difference between the average grain yields corresponding to the three fertilizer
rates, 84, 168 and 336 kg/ha. As mentioned before, the crop production parameter limits the
grain yields at high fertilizer rates (168 and 336 kg/ha).
Soil Organic Carbon and Nitrogen
Simulated soil organic carbon was compared with the observed data (Figure B.7). For the
fertilizer rates, 0, 84 and 168 kg/ha, the simulated values appeared to follow the trend of the
observed data. Also, the model predictions compared reasonably well with the observed data
for 1980 and 1992. For the highest fertilizer rate, the simulated values neither followed the
trend of the observed data nor compared well. Additionally, the observed data showed an
increase in the organic C values from 1980.
B-23
-------
Soil Organic Carbon, Lexington, KY
No-Till
6000
. a "
0 kgN/ha-Observed
A
84 kgN/ha-Observed
1970 1972 197-1 1976 1978 1980 1982 1984 19S6 1988 1990 1992
YEAR . , .
Soil Organic Carbon, Lexington, KY
No-Till
6000
a
168 kgVha-Observed
A
336 kgN/ha-Observed
1970 1972 .1974 1976 1978 1980 1982 1984 1986 1988 1990 1992
'YEAR
Figure B.7. Simulated and Measured Soil Organic Carbon at
Different Nitrogen Fertilizer Additions.
B-24
-------
Soil organic carbon levels corresponding to the three fertilizer rates were higher than those of
the unfertilized treatment. However, there was not much difference between the simulated C
values corresponding to the three fertilizer rates (84, 168 and 336 kg/ha). In fact, they appear
to start stabilizing after 1980.
Simulations predicted a net loss in SOC levels for all the fertilizer rates compared to the original
pasture soil. The observed data indicated the same except for the highest fertilizer rate where
the data showed a net increase in SOC. Also, Blevins et al. (1983b) reported that under no-till
system the organic C in the surface increased at the end of a 10-year field experiment. This
suggests that the model could be modified to better represent the changes in the organic matter
dynamics, especially for the no-till system, possibly by extending the model to a depth of 30 cm
and addind a layered soil profile representation.
The comparison of simulated organic nitrogen with the observed data showed that the organic
N did not compare well with the observed data for the three fertilizer rates, (Figure B.8).
However, the soil organic nitrogen increased with increasing N fertilizer rates as observed by
Blevins et al. (1977). Also, fertilizer additions resulted in increasing the organic nitrogen
approximately by 10% whereas zero fertilizer application resulted in 10% decrease. Paustian
et al. (1992) reported similar observations in their CENTURY model application to Swedish
field experiments.
Grain Nitrogen Concentration and Nitrogen Uptake by Plants
Simulated grain nitrogen concentration compared reasonably well with the observed data (Figure
B.9). The model simulated the lowest nitrogen concentrations for the unfertilized treatment as
expected. Based on the literature reported values (Follet et al. 1991), the model predictions
were in the range for the three fertilizer rates (84, 168, and 336 kg/ha).
The nitrogen uptake by plants increased with increasing N fertilizer rates (Figure B.9). The
simulated values did not compare well with the observed data. In general, the model under
estimated the nitrogen uptake by plants.
B.5.3 Comparison of Conventional Tillage and No-till Simulations
The simulated average grain yields corresponding to no-till were less than those of conventional
tillage for all the fertilizer rates except for the non fertilizer case (Table B.7), which is due to
the simulation of lower soil temperatures under the no-till system resulting in retarding crop
germination and hence crop yield. This result was not evident for the non fertilizer case because
the higher crop production parameters (crop production parameters of the highest fertilizer
application which resulted in highest yield were used) offset the effect of soil temperature on
crop germination. These predictions are in contrast to the field data. Blevins et al. (1986)
reported that, at low N fertilizer rates, the 10-year average corn yield was higher for
conventional tillage than for no-till. However, at moderate to high rates of N fertilization,
B-25
-------
800
700
V600
.2300
I
°200
100
Soil Organic Nitrogen, Lexington, KY
No-Till
0 kgN/ha-MODEL
a
0 kgN/ha-FIELD
84 kgN/ha-MODEL
A.
84 kgN/ha-FIELD
1969 1972 1975 1978 198T J984 1987 1990 1993
YEAR
Soil Organic Nitrogen, Lexington, KY
No-Till
BUU-
700
^600-
£
|5oo-
c
0*400-
z
.H OUU
d
O>
zou
n
19
^~~~ - 2
69 1972 1975 1978 1981 1984 1987 1990 19J
168 kgN/ha-MODEL
n
168 kgN/ha-FIELD
336 kgN/ha-MODEL
A
336 kgN/ha-FIELD
YEAR
Figure B.8. Simulated and Measured Soil Organic Nitrogen at
Different Nitrogen Fertilizer Additions.
B-26
-------
Grain Nitrogen Concentration
No-Till
1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992
YEAR
Nitrogen Uptake by Plants
No-Till
0 kgN/ha-MODEL
84 kgN/ha-MODEL
A.
84 kgN/ha-OBS -
168 kgN/ha-MODEL
168 kg/ha-OBS
336 kg/ha-MODEL
0 kgN/ha-MODEL
84 kgN/ha-MODEL
X
84 kgN/ha-FIELD
-X-
168 kgN/ha-MOOEL
168 kg/ha-FIELD
336 kg/ha-MODEL
1969 1972 1975 1978 1981 1984 1987 1990 1993
YEAR
Figure B.9. Simulated and Measured a) Grain Nitrogen Concentration
and b) Nitrogen Uptake by Plants, at Different Fertilizer
Additions.
B-27
-------
average corn yield was equal or higher for no-till. As reported by Hill et al. (1973), no-till
causes an increase in corn yields by making water more readily available to the plants which
is due to the low evaporation rate from the soil surface. On the other hand, in the conventional
tillage system, the water stress resulting from high evaporation rate in the early growing period
could reduce the yields. The model prediction which is quite opposite to the above criteria
suggests that nutrients and other factors could control the yield levels apart from available water.
However, since the predictions deviated by less than 10% (mostly) from the respective observed
data, for both tillage types, the model is performing adequately in predicting the yield levels,
and it should be noted that CENTURY model was not developed to predict yearly crop yields.
The organic carbon values for the no-till simulation were greater than those of conventional
tillage for the fertilizer rates of zero and 84 kg/ha. For the high fertilizer rates, 168 and 336
kg/ha, the reverse was true. This fact is in contrast to the literature reports that the tillage
operations associated with the conventional (tillage) treatment usually promote more rapid
oxidation of organic matter (Blevins et al., 1977) thus resulting in lower organic carbon. As
discussed before, lower soil temperature can limit crop production under a no-till system. Since
the residue inputs, shoot and root, contribute directly to the surface and soil litter, respectively,
a reduction in their quantity would have reduced the SOC levels at high fertilizer rates in the no-
till treatment. The tillage parameters used for no-till result in lower transfer of root and shoot
litter thus producing lower carbon inputs, and resulting in lower SOC than for conventional
tillage.
The organic nitrogen values corresponding to no-till system were approximately less than those
of conventional tillage for all the fertilizer rates except for the non fertilizer case. The more
organic N in conventional tillage system could be due to the incorporation of surface residue by
plowing.
The net carbon input into the soil for conventional tillage was greater than that of no-till for all
the fertilizer rates. This could be due to the incorporation of surface residue by plowing which
is associated with the conventional tillage system.
The difference between the available water for both tillage systems was minimum (Figure B. 10).
As described above, the availability of water under no-till should be higher than that under
conventional tillage. Hence, the model needs to be improved to represent the relative variation
in the soil temperature (or low evaporation rate) resulting from the residue cover on the surface
so as to better simulate the water availability.
B.6 CONCLUSIONS
The following conclusions can be drawn from the simulation of Lexington field experiments
involving two tillage scenarios, conventional tillage and no-till, with four fertilizer rates:
The CENTURY model predicted the corn grain yields reasonably well. At high fertilizer rates,
B-28
-------
—
CD
«
Available Water in the Soil Profile
Conventional Tillage and No-Till
336 kgN/ha-CT
-o- :
336 kgN/ha-NT
1969 19721975 1978 1981 1984 1987 1990 1993
YEAR
Figure B.10. Simulated Available Soil Water for Two Tillage Treatments.
B-29
-------
the crop production parameter appears to limit the crop yield and at low fertilizer rates the
nutrients (nitrogen) limit the crop production. In spite of the nutrient limitation on the crop
production, the CENTURY model predictions, especially, 21-year average yields matched
reasonably well with the field data.
In general, the organic C values predicted by the model adequately represented the dynamics of
the organic matter in the soil. Even though they compared reasonably well with the measured
data for both the tillage systems, the cultivation parameter values associated with each tillage
practice should be reevaluated in order to better represent the impacts of tillage types on the soil
organic matter dynamics. The results from the field testing of Sidney revealed similar findings
(Appendix A).
The model predicted the organic N values within a reasonable degree of error. In fact, the lack
of observed data further limited our ability to assess the accuracy of the model predictions.
However, field testing of Sidney indicated that the model was able to predict organic nitrogen
fairly close to the field data (Appendix A). Nevertheless, the fact that the model did not
differentiate much between the tillage treatments, in both sites, suggests that the cultivation
parameters and their effect on the decomposition rate of soil organic matter need to be further
investigated and improved.
The grain nitrogen concentrations simulated by the model matched adequately with the observed
values. In general, the predicted values were in the range reported by the researchers. The
nitrogen uptake by the plants was reasonably predicted by the. model for the conventional tillage
system. However, the model under estimated the N uptake for no-till. As discussed above,
further development in the parameter values associated with each tillage type and consequent
improvement in the simulation of the soil organic matter dynamics could enhance the capability
of the model to better predict the nitrogen uptake by plants.
Although the CENTURY model did a fairly good job in simulating the hydrology, the
predictions did not differentiate much between the tillage treatments. The non-availability of the
field data limited our ability to assess the model performance. However, the results from field
testing of the model at Sidney site indicated similar observations. In addition, they further
revealed that the model predicted differences between tillage treatments in some years were in
the descending order of no-till, stubble mulch and plow. In light of the above discussions, we
recommend that the hydrological parameter values be reestimated for different tillage types based
on extensive field investigations.
Based on the preceding discussions, it is evident that CENTURY model can simulate soil organic
matter dynamics adequately under various crop-tillage-management scenarios although it could
benefit from improvements and modifications. Additionally, the testing results further confirm
that the CENTURY model can be an excellent tool for assessing carbon sequestration potential
of agroecosystems in the central United States.
B-30
-------
B.7 REFERENCES
Allison, F.E. 1973. Soil Organic Matter and Its Role in Crop Production. Developments in Soil
Science 3. Elsevier, Amsterdam-London-New York.
Blevins, R.L,., G.W. Thomas, and P.L. Cornelius. 1977. Influence of No-tillage and
Nitrogen Fertilization on Certain Soil Properties after 5 Years of Continuous Corn.
Agron. J. 69:383-386.
Blevins, R.L., M.S. Smith, G.W. Thomas, and W.W. Frye. 1983a. Influence of
Conservation Tillage on Soil Properties. J. of Soil and Water Conservation.
38(3):301-305.
Blevins, R.L., G.W. Thomas, M.S. Smith, W.W. Frye, and P.L. Cornelius. 1983b.
Changes in Soil Properties After 10 years Continuous Non-Tilled and Conventionally
Tilled Corn. Soil and Tillage Research. 3 (1983) 135-146.
Blevins, R.L., J.H. Grove, and B.K. Kitur. 1986. Nutrient Uptake of Corn Grown Using
Moldboard Plow or No-Tillage Soil Management. Commun. in Soil Sci. Plant Anal.
17(4), 401-417.
Burke, I.C., D.S. Schimel, C.M. Yonker, WJ. Parton, L.A. Joyce, and
W.K. Lauenroth. 1990. Regional Modeling of Grassland Biogeochemistry using
CIS. Landscape Ecology. 4(l):45-54.
Cole, C.V., J.W.B. Stewart, D.S. Ojima, W.J. Parton, and D.S. Schimel. 1989.
Modeling Land Use Effects of Soil Organic Matter Dynamics in the North
American Great Plains. In: Ecology of Arable Land. M. Clarholm and L.
Bergstrom (eds). Kluwer Academic Publishers. 89-98.
Follett, R.F., D.R. Keeney, and R.M. Cruse. 1991. Managing Nitrogen for Groundwater
Quality and Farm Profitability. Soil Science Society of America, Inc., Madison, WI.
Hill, J.D. and R.L. Blevins. 1973. Quantitative Soil Moisture Use in Corn Grown Under
Conventional and No-Tillage Methods. Agron. J. p. 945-949.
Jones, C.A. 1983. A Survey of the Variability in Tissue Nitrogen and Phosphorus
Concentrations in Maize and Grain Sorghum. Field Crops Research. 6:133-147.
Kitur, B.K., M.S. Smith, R.L. Blevins, and W.W. Frye. 1984. Fate of 15N-Depleted
Ammonium Nitrate Applied to No-tillage and Conventional Tillage Corn.
Agron. J. 76:240-242.
Metherell, A.K. 1992. Simulation of Soil Organic Matter Dynamics and Nutrient Cycling in
B-31
-------
Agroecosystem. Ph.D. Dissertation. Colorado State University, Fort Collins, Colorado.
Parton, W.J., D.S. Schimel, C.V. Cole, and D.S. Ojima. 1987. Analysis of Factors
Controlling Soil Organic Matter Levels in Great Plains Grasslands. Soil
Sci. Soc. Am. J. 51:1173-1179.
Parton, W.J., J.W.B. Stewart, and C.V. Cole. 1*88. Dynamics of C, N, P and S in
Grassland Soils: A Model. Biogeochemistry. 5:109-131.
Parton, W.J., C.V. Cole, J.W.B. Stewart, D.S. Ojima, and D.S. Schimel. 1989.
Simulating Regional Patterns of Soil C, N, and P Dynamics in the U.S.
Central Grasslands Region. In: Ecology of Arable Land. M. Clarholm and L.
Bergstrom (eds). Kluwer Academic Publishers. 99-108.
Paustian, K., W.J. Parton, and J. Persson. 1992. Influence of Organic Amendments
and N-Fertilization on Soil Organic Matter in Long-Term Plots: Model
Analyses. Soil Sci. Soc. of Am. J. 56:476-488.
Schimel, D.S., W.J. Parton, T.G.F. Kittel, D.S. Ojima, and C.V. Cole. 1990.
Grassland Biogeochemistry: Links to Atmospherics Processes. Climatic
Change. 17:13-25:
Tisdale.S.L., W.L. Nelson, and J.D. Beaton. 1985. Soil Fertility and Fertilizers. 4th Edition.
Macmillan Publishing Company, New York, NY. 754p.
B-32
-------
APPENDIX C
Tabulated Simulation Results by CD
C.I Total Soil Carbon (gC/m2) and Percent Change in Total Soil Carbon for the Study
Region
C.2 Maximum, Minimum, and Average Percent Change in Total Soil Carbon for
1990-2030 by CD for the Status Quo Scenario
C.3 Total Soil Carbon (gC/m2) and Percent Change in Total Soil Carbon for Crop
Rotations with Cover Crops for the Study Region
C.4 Maximum, Minimum, and Average Percent Change in Total Soil Carbon for
1990-2030 by CD for Crop Rotations with Cover Crops
C.5 Total Soil Carbon (gC/m2) and Percent Change in Total Soil Carbon for CRP and
Dominant Crop Rotations for the Study Region
C.6 Percent Change in Soil Carbon from 1990-2030 for CRP Scenarios and Dominant
Crop Rotation
C-l
-------
Table C.1
Total Soil Carbon (gC/m2) and Percent Change in Total Soil Carbon for the
Study Region
Total Soil Carbon (gC/m2)
Percent Change in Total Soil Carbon for
Selected Time Periods
CD
#
'221
221
221
221
221
221
222
222
222
222
222
222
223
223
223
223
223
223
231
231
231
231
231
231
231
231
231
232
232
232
232
232
232
232
232
232
241
241
241
241
241
241
241
241
242
242
242
242
242
242
242
242
CR
#
239
239
239
350
350
350
239
239
239
350
350
350
239
239
239
350
350
350
100
100
145
145
145
235
350
366
366
100
100
145
145
145
235
350
366
366
100
100
131
186
201
262
503
508
100
100
131
186
201
262
503
508
TILL
CTFP
RT
NT
CTFP
CTSP
NT
CTFP
RT
NT
CTFP
CTSP
NT
CTFP
RT
NT
CTFP
CTSP
NT
RT
NT
CTSP
RT
NT
RT
CTFP
CTFP
NT
RT
NT
CTSP
RT
NT
RT
CTFP
CTFP
NT
CTSP
NT
RT
CTSP
RT
CTSP
CTSP
CTSP
CTSP
NT
RT
CTSP
RT
CTSP
CTSP
CTSP
1980
7344
7344
7344
7344
7344
7344
6654
6654
6654
6654
6654
6654
6022
6022
6022
6022
6022
6022
5109
5109
5109
5109
5109
5109
5109
5109
5109
5232
5232
5232
5232
5232
5232
5232
5232
5232
5591
5591
5591
5591
5591
5591
5591
5591
4083
4083
4083
4083
4083
4083
4083
4083
1990
7404
7600
7633
7575
7575
7599
6692
6924
6955
6895
6895
6919
6044
6251
6278
6225
6225
6244
5370
5397
5361
5370
5397
5389
5366
5366
5387
5575
5609
5569
5576
5610
5597
5579
5579
5609
5942
6002
5961
6058
6072
6079
6109
6121
4397
4460
4416
4498
4512
4516
4559
4576
2000
7985
8359
8559
7335
7329
7524
7049
7448
7691
6539
6539
6735
6400
6757
6995
5879
5877
6053
6124
6400
5715
5786
5931
6785
5511
5515
5673
6659
6885
6189
6276
6396
7876
5915
5946
6119
6494
6849
6361
6158
6444
6453
6021
6089
4928
5376
4801
4577
4842
4797
4560
4631
2010
8376
8817
9179
7351
7257
7630,
7334
7806
8228
6501
6429
6790
6673
7069
7517
5860
5812
6141
6569
6964
6144
6255
6483
7653
5702
5669
5842
7300
7607
6662
6798
6986
9228
6085
6098
6313
6800
7288
6587
6242
6586
6631
6098
6115
5160
5824
4955
4654
4988
5014
4763
4763
2020
9032
9616
10087
7483
7472
8021
8009
8602
9125
6587
6574
7107
7362
7862
8414
5907
5893
6387
7543
8034
6501
6678
6964
9195
6052
6038
6510
8428
8807
6984
7178
7436
11364
6477
6472
6878
7738
8297
7096
6802
7079
7359
6429
6375
5866
6732
5311
5072
5389
5580
5142
5118
2030 1990/2000 2000/2010
9544
10254
10913
7932
7795
8458
8662
9390
10083
7017
6904
7557
7865
8482
9209
6228
6107
6709
8395
9008
7132
7366
7729
10507
6691
6648
7253
9501
9978
7794
8057
8342
13556
7221
7338
7752
8293
8969
7828
7383
7862
7778
6918
6900
6311
7371
5908
5565
6121
5970
5611
5494
7.84%
9.98%
12.13%
-3.16%
-3.24%
-0.98%
5.33%
7.56%
10.58%
-5.16%
-5.16%
-2.65%
5.89%
8.09%
11.42%
-5.55% .
-5.59%
-3.05%
14.04%
18.58%
6.60%
7.74%
9.89%
25.90%
2.70%
2.77%
5.30%
19.44%
22.74%
11.13%
12.55%
14.01%
40.71%
6.02%
6.57%
9.09%
9.28%
14.11%
6.71%
1.65%
6.12%
6.15%
-1.44%
-0.52%
12.07%
20.53%
8.71%
1.75%
7.31%
6.22%
0.02%
1.20%'
4.89%
5.47%
7.24%
0.21%
-0.98%
1.40%
4.04%
4.80%
6.98%
-0.58%
-1.68%
0.81%
4.26%
4.61%
7.46%
-0.32%
-1.10%
1.45%
7.26%
8.81%
7.50%
8.10%
9.30%
12.79%
3.46%
2.79%
2.97%
9.62%
10.48%
7.64%
8.31%
9.22%
17.16%
2.87%
2.55%
3.17%
4.71%
6.40%
3.55%
1.36%
2.20%
2.75%
1.27%
0.42%
4.70%
8.33%
3.20%
1.68%
3.01%
4.52%
4.45%
2.85%
2010/2020
7.83%
9.06%
9.89%
1.79%
2.96%
5.12%
9.20%
10.19%
10.90%
1.32%
2.25%
4.66%
10.32%
11.21%
11.93%
0.80%
1.39%
4.00%
14.82%
15.36%
5.81%
6.76%
7.41%
20.14%
6.13%
6.50%
11.43%
15.45%
15.77%
4.83%
5.58%
6.44%
23.14%
6.44%
6.13%
8.94%
13.79%
13.84%
7.72%
8.97%
7.48%
10.97%
5.42%
4.25%
13:68%
15.59%
7.18%
8.98%
8.03%
11.28%
7.95%
7.45%
2020/2030
5.66%
6.63%
8.18%
6.00%
4.32%
5.44%
8.15%
9.16%
10.49%
6.52%
5.01%
6.33%
6.83%
7.88%
9.44%
5.43%
3.63%
5.04%
11.29%
12.12%
9.70%
10.30%
10.98%
14.26%
10.55%
10.10%
11.41%
12.73%
13.29%
11.59%
12.24%
12.18%
19.28%
11.48%
13.38%
12.70%
7.17%
8.09%
10.31%
8.54%
11.06%
5.69%
7.60%
8.23%
7.58%
9.49%
11.24%
9.72%
13.58%
6.98%
9.12%
7.34%
1990/2030
28.90%
34.92%
42.97%
4.71%
2.90%
11.30%
29,43%
35.61%
44.97%
1.76%
0.13%
9.22%
30.12%
35.69%
46.68%
0.04%
-1.89%
7.44%
56.33%
66.90%
33.03%
37.16%
43.20%
94.97%
24.69%
23.89%
34.63%
70.42%
77.89%
39.95%
44.49%
48.69%
142.20%
29.43%
31.52%
38.20%
39.56%
49.43%
31 .32%
21.87%
29.47%
27.94%
13.24%
12.72%
43.52%
65.26%
33.78%
23.72%
35.66%
32.19%
23.07%
20.06%
C-2
-------
Table C.I (contd.)
CD CR TILL 1980
Total Soil Carbon (gC/m2)
1990 2000 2010 2020 2030
Percent Change in Total Soil Carbon for
Selected Time Periods
1990/2000 2000/2010 2010/2020 2020/2030 1990/2030
251 100 NT
251 144 CTSP
251 145 CTSP
251 186 CTSP
251 186 RT
251 186 NT
251 366 CTSP
251 366 RT
252 100 NT
252 144 CTSP
252 145 CTSP
252 186 CTSP
252 186 RT
252 186 NT
252 366 CTSP
252 366 RT
253 100 NT
253 144 CTSP
253 145 CTSP
253 186 CTSP
253 186 RT
253 186 NT
253 366 CTSP
253 366 RT
261 100 CTSPD
261 100 CTSP I
261 100 RTD
261 100 RTI
261 100 NTD
261 100 NT I
261 145 CTSP
261 215 RT
261 280 RT
261 366 CTSP
262 100 CTSPD
262 100 CTSPI
262 100 RTD
262 100 RTI
262 100 NTD
262 100 NT I
262 145 CTSP
262 215 RT
262 280 RT
262 366 CTSP
271 100 NT
271 138 CTSP
271 186 CTSP
271 186 RT
271 186 NT
271 201 RT
271 243 CTSP
271 366 CTSP
272 100 NT
272 138 CTSP
272 186 CTSP
272 186 RT
272 186 NT
272 201 RT
272 243 CTSP
272 366 CTSP
4085
4085
4085
4085
4085
4085
4085
4085
3927
3927
3927
3927
3927
3927
3927
3927
3276
3276
3276
3276
3276
3276
3276
3276
3461
3461
3461
3461
3461
3461
3461
3461
3461
3461
3193
3193
3193
3193
3193
3193
3193
3193
3193
3193
4256
4256
4256
4256
4256
4256
4256
4256
4289
4289
4289
4289
4289
4289
4289
4289
4197
4242
4243
4244
4250
4270
4256
4258
4109
4141
4142
4142
4150
4184
4161
4166
3359
3405
3408
3407
3412
3440
3421
3424
3726
3624
3731
3635
3736
3657
3732
3735
3736
3736
3460
3370
3465
3382
3498
3409
3466
3473
3485
3482
4558
4518
4518
4526
4558
4529
4535
4540
4607
4560
4559
4569
4606
4570
4581
4592
4956
4447
4514
4309
4361
4617
4436
4459
4977
4637
4665
4418
4488
4752
4705
4614
4110
3589
3623
3476
3532
3818
3573
3591
3892
3822
3986
3945
4340
4312
3987
4308
4619
3959
3703
3635
3811
3773
4182
4166
3805
4273
4585
3777
5815
4952
4877
4939
5195
5150
5472
5023
6043
5056
5029
5109
5364
5231
5546
5132
5379
4719
4900
4450
4523
4946
4636
4641
5413
5054
5226
4627
4731
5162
5082
4916
4461
3789
3969
3577
3656
4118
3691
3693
3993
3909
4123
4084
4643
4630
4268
4535
5163
4042
3316
3727
3958
3923
4525
4516
4114
4623
5150
3838
6475
5196
5112
5209
5602
5514
6107
5214
6964
5474
5395
5520
5924
5689
6210
5435
6206
5183
5095
4828
4941
5486
5020
5063
6304
5703
5607
5171
5320
5865
5705
5574
5225
4193
4149
3908
4020
4640
3990
4034
4492
4383
4695
4640
5405
5369
4464
5220
6117
4397
4346
4251
4566
4542
5318
5317
4336
5480
6298
4268
7730
5917
5716
5866
6370
6010
7074
5726
8364
6300
6076
6256
6769
6180
7194
6088
6725
5592
5551
5111
5259
5914
5383
5472
6873
6223
6166
5603
5800
6425
6264
6203
5669
4498
4518
4210
4361
5107
4317
4409
4771
4671
5028
4992
5826
5798
4805
5839
6742
4743
4695
4577
4958
4923
5832
5790
4819
6290
7223
4657
8562
6287
6115
6308
6872
6643
7621
6113
9402
6761
6613
6834
7412
6994
7861
6653
18.08%
4.83%
6.38%
1.53%
2.61%
8.12%
4.22%
4.72%
21.12%
11.97%
12.62%
6.66%
8.14%
13.57%
13.07%
10.75%
22.35%
5.40%
6.30%
2.02%
3.51%
10.98%
4.44%
4.87%
4.45%
5.46%
6.83%
8.52%
16.16%
17.91%
6.83%
15.34%
23.63%
5.96%
7.02%
7.86%
9.98%
11.56%
19.55%
22.20%
9.78%
23.03%
31.56%
8.47%
27.57%
9.60%
7.94%
9.12%
13.97%
13.71%
20.66%
10.63%
31.16%
10.87%
10.30%
11.81%
16.45%
14.46%
21.06%
11.75%
8.53%
6.11%
8.55%
3.27%
3.71%
7.12%
4.50%
4.08%
8.76%
8.99%
12.02%
4.73%
5.41%
8.62%
8.01%
6.54%
.8.54%
5.57%
9.55%
2.90%
3.51%
7.85%
3.30%
2.84%
2.59%
2.27%
3.43%
3.52%
6.98%
7.37%
7.04%
5.26%
11.77%
2.09%
3.05%
2.53%
3.85%
3.97%
8.20%
8.40%
8.12%
8.19%
12.32%
1.61%
11.34%
4.92%
4.81%
5.46%
7.83%
7.06%
11.60%
3.80%
15.24%
8.26%
7.27%
8.04%
10.43%
8.75%
11.97%
5.90%
15.37%
9.83%
3.97%
8.49%
9.24%
10.91%
8.28%
9.09%
16.46%
12.84%
7.29%
11.75%
12.44%
13.61%
12.25%
13.38%
17.12%
10.66%
4.53%
9.25%
9.95%
12.67%
8.10%
9.23%
12.49%
12.12%
13.87%
13.61%
16.41%
15.96%
4.59%
15.10%
18.47%
8.78%
13.88%
14.05%
15.36%
15.77%
17.52%
17.73%
5.39%
18.53%
22.29%
11.20%
19.38%
13.87%
11.81%
12.61%
13.70%
8.99%
15.83%
9.81%
20.10%
15.08%
12.62%
13.33%
14.26%
8.63%
15.84%
12.01%
8.36%
7.89%
8.94%
5.86%
6.43%
7.80%
7.23%
8.07%
9.02%
9.11%
9.96%
8.35%
9.02%
9.54%
9.79%
11.28%
8.49%
7.27%
8.89%
7.72%
8.48%
10.06%
8.19%
9.29%
6.21%
6.57%
7.09%
. 7.58%
7.78%
7.99%
7.63%
11.85%
'10.21%
7.86%
8.03%
7.66%
8.58%
8.38%
9.66%
8.89%
11.13%
14.78%
14.68%
9.11%
10.76%
6.25%
6.98%
7.53%
7.88%
10.53%
7.73%
6.75%
12.41%
7.31%
8.83%
9.23%
9.49%
13.17%
9.27%
9.28%
60.23%
31.82%
30.82%
20.42%
23.74%
38.50%
26.48%
28.51%
67.26%
50.27%
48.86%
35.27%
39.75%
53.56%
50.54%
48.89%
68.77%
32.09%
32.57%
23.56%
27.81%
48.45%
26.19%
28.76%
28.04%
28.89%
34.76%
37.33%
55.94%
58.54%
28.75%
56.33%
80.46%
26.95%
35.69%
35.81%
43.08%
45.56%
66.72%
69.84%
39.03%
81.11%
107.25%
33.74%
87.84%
39.15%
35.34%
39.37%
50.76%
46.67%
68.04%
34.64%
104.08%
48.26%
45.05%
49.57%
60.92%
53.04%
71.60%
44.88%
C-3
-------
Table C.1 (contd.)
Total Soil Carbon (gC/m2)
Percent Change in Total Soil Carbon for
Selected Time Periods
CO CR TILL
# #
281 144 CTSP
281 144 RT
281 144 NT
281 186 RT
281 186 NT
281 201 RT
281 201 NT
281 458 RT
281 458 NT
281 503 CTSP
281 508 CTFP
281 508 CTSP
281 508 RT
282 144 CTSP
282 144 RT
282 144 NT
282 186 RT
282 186 NT
282 201 RT
282 201 NT
282 458 RT
282 458 NT
282 503 CTSP
282 508 CTFP
282 508 CTSP
282 508 RT
311 131 RT
311 131 NT
311 138 NT
311 145 CTSP
311 196 CTSP
311 196 RT
311 196 NT
311 250 RT
311 250 NT
311 503 CTSP
311 508 CTSP
312 131 RT
312 131 NT
312 138 NT
312 145 CTSP
312 196 CTSP
312 196 RT
312 196 NT
312 250 RT
312 250 NT
312 503 CTSP
312 508 CTSP
313 131 RT
313 131 NT
313 138 NT
313 145 CTSP
313 196 CTSP
313 196 RT
313 196 NT
313 250 RT
313 250 NT
313 503 CTSP
313 508 CTSP
1980
4892
4892
4892
4892
4892
4892
4892
4892
4892
4892
4892
4892
4892
4711
4711
4711
4711
4711
4711
4711
4711
4711
4711
4711
4711
4711
4385
4385
4385
4385
4385
4385
4385
4385
4385
4385
4385
3543
3543
3543
3543
3543
3543
3543
3543
3543
3543
3543
3105
3105
3105
3105
3105
3105
3105
3105
3105
3105
3105
1990 ,
5007
5007
5007
5007
5007
5007
5007
5007
5007
5007
5007
5007
5007
4909
4909
4910
4909
4910
4909
4910
4909
4909
4910
4910
4910
4910
4501
4503
4503
4495
4496
4501
4503
4503
4503
4503
4503
3606
3629
3629
3596
3596
3605
3628
3625
3634
3622
3629
3129
3149
3149
3121
3121
3129
3149
3147
3158
3146
3150
2000
5660
5740
5822
5755
5887
5830
5951
5621
5719
5319
5384
5387
5425
5593
5673
5762
5570
5742
5623
5774
5341
5472
5329
5399
5404
5444
5511
5632
5281
5182
5015
5046
5176
6378
6359
4779
4820
4596
4709
4366
4243
4045
4069
4181
5771
5721
3734
3735
3829
3964
3665
3571
3423
3442
3532
4867
4834
3211
3206
2010
5983
6082
6276
6092
6349
6247
6444
5863
6084
5524
5606
5477
5519
6019
6123
6333
5890
6178
5999
6207
5479
5744
5716
5791
5678
5733
6165
6394
5915
5903
5370
5402
5614
7521
7640
5126
5106
5234
5445
4982
4916
4441
4472
4656
7290
7233
4013
3961
4230
4477
4073
4048
3649
3665
3799
6040
6025
3381
3331
2020 2
6619
6759
6947
6877
7116
6922
7036
6365
6602
5959
5901
5946
6005
6623
6771
6972
6551
6854
6553
6729
5820
6126
6191
6142
6196
6262
6856
7133
6738
6100
5981
6039
6289
8681
8920
5605
5628
5967
6248
5841
5105
5102
5161
5369
8734
8809
4332
4305
4747
5060
4753
4167
4137
4161
4320
7235
7249
3638
3590
030 1
7175
7366
7545
7449
7692
7626
7712
6861
7063
6381
6628
6377
6447
7196
7396
7582
7094
7397
7255
7375
6217
6478
6748
7039
6722
6797
7710
7986
7218
6828
6452
6520
6791
9860
10285
6071
6072
6925
7221
6380
5883
5504
5602
5862
10095
10400
4819
4733
5415
5758
5166
4736
4470
4519
4750
8413
8526
4026
3902
990/2000 20
13.04%
14.63%
16.27%
14.93%
17.57%
16.43%
18.85%
12.26%
14.22%
6.23%
7.52%
7.58%
8.34%
13.93%
15.56%
17.35%
13.46%
16.94%
14.54%
17.59%
8.80%
11.46%
8.53%
9.95%
10.06%
10.87%
22.43%
25.07%
17.27%
15.28%
11.54%
12.10%
14.94%
41.63%
41.21%
6.12%
7.03%
27.45%
29.76%
20.30%
17.99%
12.48%
12.87%
15.24%
59.20%
57.42%
3.09%
2.92%
22.37%
25.88%
16.38%
14.41%
9.67%
10.00%
12.16%
54.65%
53.07%
2.06%
1.77%
100/2010 ;
5.70%
5.95%
7.79%
5.85%
7.84%
7.15%
8.28%
4.30%
6.38%
3.85%
4.12%
1.67%
1.73%
7.61%
7.93%
9.90%
5.74%
7.59%
6.68%
7.49%
2.58%
4.97%
7.26%
7.26%
5.07%
5.30%
11.86%
13.52%
12.00%
13.91%
7.07%
7.05%
8.46%
17.92%
20.14%
7.26%
5.93%
13.88%
15.62%
14.10%
15.86%
9.78%
9.90%
11.36%
26.32%
26.42%
7.47%
6.05%
10.47%
12.94%
11.13%
13.35%
6.60%
6.47%
7.55%
24.10%
24.63%
5.29%
3.89%
2010/ZOZO ,
10.63%
11.13%
10.69%
12.88%
12.08%
10.80%
9.18%
8.56%
8.51%
7.87%
5.26%
8.56%
8.80%
10.03%
10.58%
10.09%
11.22%-
10.94%
9.23%
8.40%
6.22%
6.65%
8.31%
6.06%
9.12%
9.22%
11.20%
11.55%
13.91%
3.33%
11.37%
11.79%
12.02%
15.42%
16.75%
9.34%
10.22%
14.00%
14.74%
17.24%
3.84%
14.88%
15.40%
15.31%
19.80%
21.78%
7.94%
8.68%
12.22%
13.02%
16.69%
2.93%
13.37%
13.53%
13.71%
19.78%
20.31%
7.60%
7.77%
iUZU/ZUSU n
8.40%
8.98%
8.60%
8.31%
8.09%
10.17%
9.60%
7.79%
6.98%
7.08%
12.31%
7.24%
7.36%
8.65%
9.23%
8.74%
8.28%
7.92%
10.71%
9.60%
6.82%
5.74%
8.99%
14.60%
8.48%
8.54%
12.45%
11.95%
7.12%
11.93%
7.87%
7.96%
, 7.98%
13.58%
15.30%
8.31%
7.88%
16.05%
15.57%
9.22%
15.23%
7.87%
8.54%
9.18%
15.58%
18.06%
11.24%
9.94%
14.07%
13.79%
8.68%
13.65%
8.04%
8.60%
9.95%
16.28%
17.61%
10.66%
8.69%
VVU/dUJU
43.29%
47.11%
50.68%
48.77%
53.62%
52.30%
54.02%
37.02%
41.06%
27.44%
32.37%
27.36%
28.75%
46.58%
50.66%
54.41%
44.51%
50.65%
47.78%
50.20%
26.64%
31 .96%
37.43%
43.36%
36.90%
38.43%
71.29%
77.34%
60.29%
51.90%
43.50%
44.85%
50.81%
118.96%
128.40%
34.82%
34.84%
92.04%
98.98%
75.80%
63.59%
53.05%
55.39%
61.57%
178.48%
186.18%
33 . 04%
30.42%
73.05%
82.85%
64.05%
51.74%
43.22%
44.42%
50.84%
167.33%
169.98%
27.97%
23.87%
C-4
-------
Table C.1 (contd.)
Total Soil Carbon (gC/m2)
CD CR TILL 1980 ! 1990 2000 2010 2020 2030
Percent Change in Total Soil Carbon for
Selected Time Periods
1990/2000 2000/2010 2010/2020 2020/2030 1990/2030
314 131 RT
314 131 NT
314 138 NT
314 145 CTSP
314 196 CTSP
314 196 RT
314 196 NT
314 250 RT
314 250 NT
314 503 CTSP
314 508 CTSP
321 125 CTSP
321 125 RT
321 125 NT
321 131 CTSP
321 131 NT
321 186 CTSP
321 186 RT
321 186 NT
321 203 CTSP
321 203 RT
322 125 CTSP
322 125 RT
322 125 NT
322 131 CTSP
322 131 NT
322 186 CTSP
322 186 RT
322 186 NT
322 203 CTSP
322 203 RT
323 125 CTSP
323 125 RT
323 125 NT
323 131 CTSP
323 131 NT
323 186 CTSP
323 186 RT
323 186 NT
323 203 CTSP
323 203 RT
341 144 CTSP
341 144 RT
341 144 NT
341 186 CTFP
341 186 NT
341 232 CTSP
341 232 RT
342 144 CTSP
342 144 RT
342 144 NT
342 186 CTFP
342 186 NT
342 232 CTSP
342 232 RT
3780
3780
3780
3780
3780
3780
3780
3780
3780
3780
3780
4171
4171
4171
4171
4171
4171
4171
4171
4171
4171
4345
4345
4345
4345
4345
4345
4345
4345
4345
4345
3925
3925
3925
3925
3925
3925
3925
3925
3925
3925
3294
3294
3294
3294
3294
3294
3294
3502
3502
3502
3502
3502
3502
3502
3917
3926
3926
3906
3907
3918
3926
3926
3926
3926
3926
4465
4474
4505
4465
4505
4464
4474
4504
4466
4475
4598
4607
4635
4598
4635
4598
4607
4635
4599
4609
4206
4214
4241
4202
4238
4201
4209
4237
4202
4210
3478
3456
3498
3442
3495
3443
3457
3679
3659
3700
3645
3699
3648
3663
4930
5063
4677
4578
4381
4408
4518
5662
5685
4121
4159
5155
5225
5394
5366
5681
5050
5123
5354
4910
4958
5349
5384
5499
5529
5761
5242
5307
5487
5080
5112
4917
4960
5081
4985
5235
4709
4773
4977
4619
4654
3620
3667
3795
3528
3911
3514
3531
3940
3986
4105
3797
4194
3882
3898
5609
5821
5320
5333
4787
4822
4994
67'46
6824
4520
4506
5630
5TS1
5983
5898
6374
5472
5590
5935
5430
5503
5883
5954
6141
6115
6481
5725
5820
6088
5648
5696
5567
5645
5801
5584
5950
5200
5293
5558
5165
5216
3842
3922
4116
3682
4263
3614
3647
4205
4276
4433
3973
4589
3958
3979
6302
6594
6179
5605
5477
5555
5742
7837
8036
5015
4963
6535
6775
7028
6592
7253
6216
6384
6810
5900
6008
6681
6822
6994
6748
7227
6385
6532
6836
5990
6079
6535
6663
6783
6172
6686
5832
5972
6296
5576
5657
4316
4447
4683
4077
4824
3992
4011
4727
4847
5073
4290
5060
4290
4324
7208
7521
6716
6362
5871
5980
6227
9146
9497
5666
5606
7163
7446
7686
7463
8247
6836
7064
7588
6493
6641
7333
7525
7674
7592
8135
6942
7141
7503
6641
6745
7052
7264
7480
6967
7572
6348
6539
6959
6166
6267
4611
4787
5068
4354
5281
4255
4271
5048
5223
5476
4584
5493
4604
4626
25.86%
28.96%
19.12%
17.20%
12.13%
12.50%
15.07%
44.21%
44.80%
4.96%
5.93%
15.45%
16.78%
19.73%
20.17%
26.10%
13.12%
14.50%
18.87%
9J 94%
10.79%
16.33%
16.86%
18.64%
20.24%
24.29%
14.00%
15.19%
18.38%
10.45%
10.91%
16.90%
17.70%
19.80%
18.63%
23.52%
12.09%
13.39%
17.46%
9.92%
10.54%
4.08%
6.10%
8.49%
2.49%
11.90%
2.06%
2.14%
7.09%
8.93%
10.94%
4.17%
13.38%
6.41%
6.41%
13.77%
14.97%
13.74%
16.49%
9.26%
9.39%
10.53%
19.14%
20.03%
9.68%
8.34%
9.21%
9.68%
10.91%
9.91%
12.19%
8.35%
9.11%
10.85%
10.59%
10.99%
9.98%
10.58%
11.67%
10.59%
12.49%
9.21%
9.66%
10.95%
11.18%
11.42%
13.21%
13.81%
14.17%
12.01%
13.65%
10.42%
10.89%
11.67%
11.82%
12.07%
6.13%
6.95%
8.45%
4.36%
9.00%
2.84%
3.28%
6.72%
7.27%
7.99%
4.63%
9.41%
1.95%
2.07%
12.35%
13.27%
16.14%
5.10%
14.41%
15.20%
14.97%
16.17%
17.76%
10.95%
10.14%
16.07%
18.21%
17.46%
11.76%
13.79%
13.59%
14.20%
14.74%
8.65%
9.17%
13.56%
14.57%
13.89%
10.35%
11.51%
11.52%
12.23%
12.28%
6.05%
6.72%
17.38%
18.03%
16.92%
10.53%
12.36%
12.15%
12.82%
13.27%
7.95%
8.45%
12.33%
13.38%
~-" 13.77%
10.72%
13.15%
10.45%
9.98%
12.41%
13.35%
14.43%
7.97%
10.26%
8.38%
8.67%
14.37%
14.05%
8.69%'
13.50%
7.19%
7.65%
8.44%
16.70%
18.18%
12.98%
12.95%
9. '60%
9.90%
9.36%
13.21%
13.70%
9.97%
10.65%
11.42%
10.05%
10.53%
9.75%
10.30%
9.72%
12.50%
12.56%
8.72%
9.32%
9.75%
10.86%
10.95%
7.91%
9.01%
10.27%
12.88%
13.25%
8.84%
9.49%
10.53%
10.58%
10.78%
6.83%
7.64%
8.22%
6.79%
9.47%
6.58%
6.48%
6.79%
7.75%
7.94%
6.85%
8.55%
7.31%
6.98%
84.01%
91.56%
71.06%
62.87%
50.26%
52.62%
58.60%
132.95%
141 .90%
44.31%
42.79%
60.42%
66.42%
70.61%
67.14%
83.06%
53.13%
57.89%
68.47%
45.38%
48.40%
59.48%
63.33%
65.56%
65.11%
75.51%
50.97%
55.00%
61.87%
44.40%
46.34%
67.66%
72.37%
76.37%
65.80%
78.66%
51.10%
55.35%
64.24%
46.73%
48.85%
32.57%
38.51%
44.88%
26.49%
51.10%
23.58%
23.54%
37.21%
42.74%
47.99%
25.76%
48.49%
26.20%
26.28%
C-5
-------
Table C.1 (contd.)
Total Soil' Carbon (gC/m2)
Percent Change in Total Soil Carbon for
Selected Time Periods
CO CR TILL
# #
351 100 NT
•351 115 CTSP
351 131 NT
351 186 CTSP
351 186 RT
351 186 NT
351 201 RT
351 366 CTSP
352 100 NT
352 115 CTSP
352 131 NT
352 186 CTSP
352 186 RT
352 186 NT
352 201 RT
352 366 CTSP
353 100 NT
353 115 CTSP
353 131 NT
353 186 CTSP
353 186 RT
353 186 NT
353 201 RT
353 366 CTSP
391 131 CTSP
391 131 RT
391 131 NT
391 186 CTSP
391 186 RT
391 186 NT
391 189 CTSP
391 201 CTFP
391 243 CTSP
391 350 CTSP
391 366 CTSP
392 131 CTSP
392 131 RT
392 131 NT
392 186 CTSP
392 186 RT
392 186 NT
392 189 CTSP
392 201 CTFP
392 243 CTSP
392 350 CTSP
392 366 CTSP
393 131 CTSP
393 131 RT
393 131 NT
393 186 CTSP
393 186 RT
393 186 NT
393 189 CTSP
393 201 CTFP
393 243 CTSP
393 350 CTSP
393 366 CTSP
1980
4049
4049
4049
4049
4049
4049
4049
4049
4845
4845
4845
4845
4845
4845
4845
4845
4072
4072
4072
4072
4072
4072
4072
4072
5201
5201
5201
5201
5201
5201
5201
5201
5201
5201
5201
4868
4868
4868
4868
4868
4868
4868
4868
4868
4868
4868
4779
4779
4779
4779
4779
4779
4779
4779
4779
4779
4779
1990
4346
4295
4347
4294
4305
4346
4306
4316
5173
5119
5174
5118
5132
5172
5133
5146
4336
4285
4337
4285
4299
4335
4300
4299
5217
5227
5247
5244
5254
5278
5244
5244
5255
5259
5259
4968
4979
5004
5045
5050
5076
5046
5044
5065
5044
5044
5036
5049
5077
5089
5096
5129
5089
5089
5113
5089
5089
2000
5667
4947
5373
4677
4754
5032
4945
4793
6706
5958
6333
5654
5719
5940
5918
5626
5584
4965
5244
4651
4701
4900
4856
4575
5563
5644
5858
5566
5625
5868
5773
5666
5843
5713
5770
5374
5470
5685
5435
5500
5758
5610
5420
5746
5322
5344
5558
5670
5889
5614
5691
5974
5689
5550
6053
5309
5325
2010
6467
5419
5972
4962
5076
5531
5351
5237
7682
6554
7048
6053
6160
6496
6353
5978
6394
5482
5818
4951
5031
5330
5093
4850
5783
5906
6268
5758
5849
6263
5998
5911
6227
5998
6030
5584
5732
6104
5669
5764
6188
5747
5659
6358
5630
5655
5832
5993
6353
5856
5975
6393
5850
5784
6726
5626
5663
2020 I
7808
6015
6819
5620
5789
6352
5967
6094
9100
7212
7869
6769
6928
7290
6975
6869
7646
6037
6523
5548
5672
6007
5559
5597
6108
6297
6754
6200
6326
6831
6466
6310
6922
6521
6550
5971
6172
6633
6207
6342
6860
6257
6152
7206
6075
6092
6245
6475
6930
6586
6764
7337
6461
6300
7918
6163
6143
2030 1
8859
6746
7770
6195
6410
7066
6825
6724
10443
8177
8989
7476
7698
8124
7998
7540
8604
6824
7362
6050
6220
6616
6306
6024
6532
6775
7293
6726 -
6894
7490
7142
6830
7569
7052
7138
6460
6717
7249
6903
7093
7727
7027
6852
8040
6837
6890
6813
7117
7596
7422
7641
8286
7402
7157
9029
6876
7012
990/2000 2
30.39%
15.18%
23.60%
8.91%
10.42%
15.78%
14.83%
11.05%
29.63%
16.38%
22.40%
10.47%
11.43%
14.84%
15.29%
9.32%
28.78%
15.86%
20.91%
8.54%
9.35%
13.03%
12.93%
6.42%
6.63%
7.97%
11.64%
6.14%
7.06%
11.17%
10.08%
8.04%
11.18%
8.63%
9.71%
8.17%
9.86%
13.60%
7.73%
8.91%
13.43%
11.17%
7.45%
13.44%
5.51%
5.94%
10.36%
12.29%
15.99%
10.31%
11.67%
16.47%
11.79%
9.05%
18.38%
4.32%
4.63%
000/2010
14.11%
9.54%
11.14%
6.09%
6.77%
9.91%
8.21%
9.26%
14.55%
10.00%
11.29%
7.05%
7.71%
9.36%
7.35%
6.25%
14.50%
10.41%
10.94%
6.45%
7.01%
8.77%
4.88%
6.01%
3.95%
4.64%
6.99%
3.44%
3.98%
6.73%
3.89%
4.32%
6.57%
4.98%
4.50%
• 3.90%
4.78%
7.37%
4.30%
4.79%
7.46%
2.44%
4.40%
10.65%
5.78%
5.81%
4.92%
5.69%
7.87%
4.31%
4.99%
7.01%
2.83%
4.21%
11.11%
5.97%
6.34%
2010/2020
20.73%
10.99%
14.18%
13.26%
14.04%
14.84% .
11.51%
16.36%
18.45%
10.03%
11.64%
11.82%
12.46%
12.22%
9.79%
14.90%
19.58%
10.12%
12.11%
12.05%
12.74%
12.70%
9.14%
15.40%
5.61%
6.62%
7.75%
7.67%
8.15%
9.06%
7.80%
6.75%
11.16%
8.71%
8.62%
6.93%
7.67%
8.66%
9.49%
10.02%
10.85%
8.87%
8.71%
13.33%
7.90%
7.72%
7". 08%
8.04%
9.08%
12.46%
13.20%
14.76%
10.44%
8.92%
17.72%
9.54%
8.47%
2020/2030
13.46%
12.15%
13.94%
10.23%
10.72%
11.24%
14.37%
10.33%
14.75%
13.38%
14.23%
10.44%
11.11%
11.44%
14.66%
9.76%
12.52%
13.03%
12.86%
9.04%
9.66%
10.13%
13.43%
7.62%
6.94%
7.59%
7.98%
8.48%
8.97%
9.64%
10.45%
8.24%
9.34%
8.14%
8.97%
8.18%
8.83%
9.28%
11.21%
11.84%
12.63%
12.30%
11.37%
11.57%
12.54%
13.09%
9.09%
9.91%
9.61%
12.69%
12.96%
12.93%
14.56%
13.60%
14.03%
11.56%
14.14%
19VU/ 2U.SU
103.84%
57.06%
78.74%
44.27%
48.89%
62.58%
58.49%
55.79%
101.87%
59.73%
73.73%
46.07%
49.99%
57.07%
55.81%
46.52%
98.43%
59.25%
69.74%
41.19%
44.68%
52.61%
46.65%
40.12%
25.20%
29.61%
38.99%
28.26%
31.21%
41.90%
36.19%
30.24%
44.03%
34.09%
35.72%
30.03%
34.90%
44.86%
36.82%
40.45%
52.22%
39.25%
35.84%
58.73%
35.54%
36.59%
35.28%
40.95%
49.61%
45.84%
49.94%
61 .55%
45.45%
40.63%
76.58%
35.11%
37.78%
C-6
-------
Table C.1 (contd.)
Total Soil Carbon (gC/mZ)
Percent Change in Total Soil Carbon for
Selected Time Periods
CO CR
# #
401 100
401 100
401 186
401 186
401 246
401 246
401 280
401 280
401 366
401 366
401 503
402 100
402 100
402 186
402 186
402 246
402 246
402 280
402 280
402 366
402 366
402 503
403 100
403 100
403 186
403 186
403 246
403 246
403 280
403 280
403 366
403 366
403 503
411 100
411 100
411 115
411 138
411 138
411 186
411 186
411 186
411 186
411 243
411 503
411 508
412 100
412 100
412 115
412 138
412 138
412 186
412 186
412 186
412 186
412 243
412 503
412 508
TILL
CTSP
RT
CTFP
NT
CTSP
NT
CTSP
NT
CTFP
NT
CTFP
CTSP
RT
CTFP
NT
CTSP
NT
CTSP
NT
CTFP
NT
CTFP
CTSP
RT
CTFP
NT
CTSP
NT
CTSP
NT
CTFP
NT
CTFP
CTSP
RT
CTSP
CTSP
RT
CTFP
CTSP
RT
NT
CTSP
CTSP
CTFP
CTSP
RT
CTSP
CTSP
RT
CTFP
CTSP
RT
NT
CTSP
CTSP
CTFP
1980
6396
6396
6396
6396
6396
6396
6396
6396
6396
6396
6396
6241
6241
6241
6241
6241
6241
6241
6241
6241
6241
6241
5565
5565
5565
5565
5565
5565
5565
5565
5565
5565
5565
4972
4972
4972
4972
4972
4972
4972
4972
4972
4972
4972
4972
4566
4566
4566
4566
4566
4566
4566
4566
4566
4566
4566
4566
1990
6695
6711
6742
6795
6775
6827
6777
6828
6761
6792
6720
6590
6606
6667
6722
6688
6742
6687
6742
6674
6709
6608
5953
5972
6074
6135
6107
6170
6102
6164
6087
6132
5988
5244
5252
5245
5244
5252
5244
5244
5252
5290
5272
5314
5328
4804
4815
4805
4805
4816
4804
4804
4815
4850
4822
4849
4859
2000
7225
7380
6704
7125
7574
7704
8026
8190
6807
6976
6546
7351
7523
6774
7196
7572
7687
7928
8075
6756
6913
6393
6677
6867
6312
6769
7093
7228
7443
7611
6221
6371
5731
6257
6411
6000
5640
5705
5615
5676
5759
6056
6307
5524
5566
5681
5822
5468
5241
5304
5091
5151
5224
5495
5999
5013
5051
2010
7540
7780
6784
7423
8220
8411
8947
9184
7018
7171
6795
7789
8062
6903
7553
8214
8412
8834
9084
6889
7050
6595
7037
7323
6413
7120
7637
7935
8138
8436
6347
6604
5948
6849
7077
6458
5894
5998
5835
5927
6049
6497
6972
5968
5985
6137
6340
57'42
5471
5568
5245
5335
5441
5847
6488
5219
5273
2020 2030 1990/2000 2000/2010 2010/2020 2020/2030 1990/2030
8406
8771
7232
8007
9475
9709
10232
10645
7364
7784
6964
8708
9149
7374
8181
9440
9683
10053
10466
7206
7600
6712
8045
8499
6968
7883
8984
9295
9362
9819
6796
7166
6114
7931
8294
7014
6627
6785
6387
6533
6706
7265
8104
6506
6441
7104
7444
6247
6142
6300
5753
5887
6042
6542
7721
5681
5575
8935
9401
7684
8622
10346
10771
11454
12128
8052
8344
7687
9357
9903
7911
8904
10355
10800
11241
11980
7974
8189
7403
8705
9294
7597
8672
10030
10465
10632
11360
7682
7863
6851
8899
9410
7932
7114
7316
6991
7204
7429
8072
9176
7360
7456
7951
8398
6985
6731
6929
6267
6445
6647
7220
8647
6180
6400
7.91%
9.96%
-0.56%
4.85%
11.79%
12.84%
18.42%
19.94%
0.68%
2.70%
-2.58%
11.54%
13.88%
1.60%
7.05%
13.21%
14.01%
18.55%
19.77%
1.22%
3.04%
-3.25%
12.16%
14.98%
3.91%
10.33%
16.14%
17.14%
21 .97%
23.47%
2.20%
3.89%
-4.29%
19.31%
22.06%
14.39%
7.55%
8.62%
7.07%
8.23X
9.65%
14.48%
19.63%
3.95%
4.46%
18.25%
20.91%
13.79%
9.07%
10.13%
5.97%
7.22%
8.49%
13.29%
24.40%
3.38%
3.95%
4.35%
5.42%
1.19%
4.18%
8.52%
9.17%
11.47%
12.13%
3.09%
2.79%
3.80%
5.95%
7.16%
1.90%
4.96%
8.47%
9.43%
11.42%
12.49%
1.96%
1.98%
3.15%
5.39%
6.64%
1.60%
5.18%
7.66%
9.78%
9.33%
10.83%
2.02%
3.65%
3.78%
9.46%
10.38%
7.63%
4.50%
5.13%
3.91%
4.42%
5.03%
7.28%
10.54%
8.03%
7.52%
8.02%
8.89%
5.01%
4.38%
4.97%
3.02%
3.57%
4.15%
6.40%
8.15%
4.10%
4.39%
11.48%
12.73%
6.60%
7.86%
15.26%
15.43%
14.36%
15.90%
4.93%
8.54%
2.48%
11.79%
13.48%
6.82%
8.31%
. 14.92%
15.10%
13.79%
15.21%
4.60%
7.80%
1.77%
14.32%
16.05%
8.65%
10.71%
17.63%
17.13%
15.04%
16.39%
7.07%
8.50%
2.79%
15.79%
17.19%
8.60%
12.43%
13.12%
9.46%
10.22%
10.86%
11.82%
16.23%
9.01%
7.61%
15.75%
17.41%
8.79%
12.26%
13.14%
9.68%
10.34%
11.04%
11.88%
19.00%
8.85%
5.72%
6.29%
7.18%
6.25%
7.68%
9.19%
10.93%
11.94%
13.93%
9.34%
7.19%
10.38%
7.45%
8.24%
7.28%
8.83%
9.69%
11.53%
11.81%
14.46%
10.65%
7.75%
10.29%
8.20%
9.35%
9.02%
10.00%
11.64%
12.58%
13.56%
15.69%
13.03%
9.72%
12.05%
12.20%
13.45%
13.08%
7.34%
7.82%
9.45%
10.27%
10.78%
11.10%
13.22%
13.12%
15.75%
11.92%
12.81%
11.81%
9.58%
9.98%
8.93%
9.47%
10.01%
10.36%
11.99%
8.78%
14.79%
33.45%
40.08%
13.97%
26.88%
52.70%
57.77%
69.01%
77.62%
19.09%
22.85%
14.38%
41.98%
49.90%
18.65%
32.46%
54.82%
60.18%
68.10%
77.69%
19.47%
22.05%
12.03%
46.22%
55.62%
25.07%
41.35%
64.23%
69.61%
74.23%
84.29%
26.20%
28.22%
14.41%
69.69%
79.16%
51.22%
35.65%
39.29%
33.31%
37.37%
41.45%
52.58%
74.05%
38.50%
39.93%
65.50%
74.41%
45.36%
40.08%
43.87%
30.45%
34.15%
38.04%
48.86%
79.32%
27.44%
31.71%
C-7
-------
Table C.I (contd.)
Total Soil Carbon (gC/m2)
Percent Change in Total Soil Carbon for
Selected Time Periods
CO
#
413
413
413
413
413
413
413
413
413
413
413
413
414
414
414
414
414
414
414
414
414
414
414
414
415
415
415
415
415
415
415
415
415
415
415
415
416
416
416
416
416
416
416
416
416
416
416
416
421
421
421
421
421
421
421
421
421
421
CR
*
100
100
115
138
138
186
186
186
186
243
503
508
100
100
115
138
138
186
186
186
186
243
503
508
100
100
115
138
138
186
186
186
186
243
503
508
100
100
115
138
138
186
186
186
186
243
503
508
100
145
186
186
186
201
244
262
503
508
TILL
CTSP
RT
CTSP
CTSP
RT
CTFP
CTSP
RT
NT
CTSP
CTSP
CTFP
CTSP
RT
CTSP
CTSP
RT
CTFP
CTSP
RT
NT
CTSP
CTSP
CTFP
CTSP
RT
CTSP
CTSP
RT
CTFP
CTSP
RT
NT
CTSP
CTSP
CTFP
CTSP
RT
CTSP
CTSP
RT
CTFP
CTSP
RT
NT
CTSP
CTSP
CTFP
CTSP
CTSP
CTSP
RT
NT
RT
CTSP
CTSP
CTSP
CTSP
1980
4677
4677
4677
4677
4677
4677
4677
4677
4677
4677
4677
4677,
4856
4856
4856
4856
4856
4856
4856
4856
4856
4856
4856
4856
4649
4649
4649
4649
4649
4649
4649
4649
4649
4649
4649
4649
4946
4946
4946
4946
4946
4946
4946
4946
4946
4946
4946
4946
4631
4631
4631
4631
4631
4631
4631
4631
4631
4631
1990
4967
4979
4969
4968
4979
4966
4965
4976
5018
4988
5041
5054
5142
5154
5143
5142
5154
5142
5142
5154
5190
5169
5206
5216
4846
4860
4847
4847
4860
4847
4846
4860
4898
4860
4900
4912
5143
5159
5145
5144
5159
5145
5144
5159
5196
5162
5205
5217
4870
4902
4904
4918
4954
4918
4922
4924
4958
4969
2000
5933
6098
5693
5355
5413
5123
5193
5257
5553
5981
5216
5230
6011
6150
5818
5558
5612
5399
5476
5547
5815
6311
5453
5472
5804
5941
5560
5135
5179
5128
5210
5280
5548
5842
5080
5095
6196
6328
5972
5633
5676
5498
5592
5663
5942
6359
5515
5527
5670
5492
5317
5383
5632
5512
6060
5672
5167
5259
2010
6559
6804
6244
5658
5768
5286
5390
5491
5958
6588
5733
5746
6463
6678
6181
5873
5969
5537
5658
5756
6150
6868
5791
5872
6507
6738
6105
5584
5673
5419
5554
5662
6075
6532
5593
5651
6950
7186
6551
6176
6267
5818
5985
6087
6489
7134
6000
6223
6058
6023
5607
5700
6077
5808
6739
6076
5470
5490
2020 2030 1990/2000 2000/2010 2010/2020 2020/2030 1990/2030
7510
7913
6740
6333
6513
5493
5634
5758
6356
7606
6341
6233
7554
7908
6781
6713
6873
6150
6330
6477
6978
8143
6440
6368
7472
7844
6584
6243
6394
5919
6119
6281
6771
7530
6112
6083
7942
8308
7147
6879
7028
6328
6561
6608
7155
8377
6715
6665
6930
6405
6248
6379
6840
6431
. 7402
6987
6001
6022
8442
9012
7590
6741
6958
5790
5980
6133
6815
8592
7134
7114
8593
9093
7733
7310
7499
6818
7062
7270
7836
9239
7148
7509
8499
9041
7529
6651
6825
6485
6752
6971
7543
8480
6783
7027
8912
9387
8122
7373
7547
6877
7177
7210
7907
9474
7472
7583
7506
7253
6914
7103
7647
7342
8570
7688
6665
6678
19.44%
22.47%
14.57%
7.78%
8.71%
3.16%
4.59%
5.64%
10.66%
19.90%
3.47%
3.48%
16.90%
19.32%
13.12%
8.09%
8.88%
4.99%
6.49%
7.62%
12.04%
22.09%
4.74%
4.90%
19.76%
22.24%
14.71%
5.94%
6.56%
5.79%
7.51%
8.64%
13.27%
20.20%
3.67%
3.72%
20.47%
22.65%
16.07%
9.50%
10.02%
6.86%
8.70%
9.76%
14.35%
23.18%
5.95%
5.94%
16.42%
12.03%
8.42%
9.45%
13.68%
12.07%
23.12%
15.19%
4.21%
5.83%
10.55%
11.57%
9.67%
5.65%
6.55%
3.18%
3.79%
4.45%
7.29%
10.14%
9.91%
9.86%
7.51%
8.58%
6.23%
5.66%
6.36%
2.55%
3.32%
3.76%
5.76%
8.82%
6.19%
7.30%
12.11%
13.41%
9.80%
8.74%
9.53%
5.67%
6.60%
7.23%
9.49% ,
11.81%
10.09%
10.91%
12.16%
13.55%
9.69%
9.63%
10.41%
5.82%
7.02%
7.48%
9.20%
12.18%
8.79%
12.59%
6.84%
9.66%
5.45%
5.88%
7.90%
5.37%
11.20%
7.12%
5.86%
4.39%
14.49%
16.29%
7.94%
11.93%
12.91%
3.91%
4.52%
4.86%
6.68%
15.45%
10.60%
8.47%
16.88%
18.41%
9.70%
14.30%
15.14%
11.07%
11.87%
12.52%
13.46%
18.56%
11.20%
8.44%
14.83%
16.41%
7.84%
11.80%
12.70%
9.22%
10.17%
10.93%
11.45%
15.27%
9.27%
7.64%
14.27%
15.61%
9.09%
11.38%
12.14%
8.76%
9.62%
8.55%
10.26%
17.42%
11.91%
7.10%
14.39%
6.34%
11.43%
11.91%
12.55%
10.72%
9.83%
14.99%
9.70%
9.69%
12.41%
13.88%
12.61%
6.44%
6.83%
5.40%
6.14%
6.51%
7.22%
12.96%
12.50%
14.13%
13.75%
14.98%
14.03%
8.89%
9.10%
10.86%
11.56%
12.24%
12.29%
13.45%
10.99%
17.91%
13.74%
15.26%
14.35%
6.53%
6.74%
9.56%
10.34%
10.98%
11.40%
12.61%
10.97%
15.51%
12.21%
12.98%
13.64%
7.18%
7.38%
8.67%
9.38%
9.11%
10.51%
13.09%
11.27%
13.77%
8.31%
13.23%
10.65%
11.34%
11.79%
14.16%
15.77%
10.03%
11.06%
10.89%
69.96%
81.00%
52.74%
35.68%
39.74%
16.59%
20.44%
23.25%
35.81%
72.25%
41.51%
40.75%
67.11%
76.42%
50.35%
42.16%
45.49%
32.59%
37.33%
41.05%
50.98%
78.73%
37.30%
43.96%
75.38%
86.02%
55.33%
37.21%
40.43%
33.79%
39.33%
43.43%
54.00%
74.48%
38.42%
43.05%
73.28%
81 .95%
57.86%
43.33%
46.28%
33.66%
39.52%
39.75%
52.17%
83.53%
43.55%
45.35%
54.12%
47.96%
40.98%
44.42%
54.36%
49.28%
74.11%
56.13%
34.42%
34.39%
C-8
-------
Table C.1 (contd.)
Total Soil Carbon (gC/m2)
Percent Change in Total Soil Carbon for
Selected Time Periods
CO
#
422
422
422
422
422
422
422
422
422
422
431
431
431
431
431
431
431
431
431
431
431
432
432
432
432
432
432
432
432
432
432
432
433
433
433
433
433
433
433
433
433
433
433
441
441
441
441
441
441
441
441
441
441
441
441
441
CR
#
100
145
186
186
186
201
244
262
503
508
100
131
131
186
186
201
201
201
262
503
508
100
131
131
186
186
201
201
201
262
503
508
100
131
131
186
186
201
201
201
262
503
508
186
186
186
218
366
366
416
416
458
459
459
503
508
TILL
CTSP
CTSP
CTSP
RT
NT
RT
CTSP
CTSP
CTSP
CTSP
CTSP
CTSP
NT
RT
NT
CTSP
RT
NT
CTSP
CTSP
CTFP
CTSP
CTSP
NT
RT
NT
CTSP
RT
NT
CTSP
CTSP
CTFP
CTSP
CTSP
NT
RT
NT
CTSP
RT
NT
CTSP
CTSP
CTFP
CTFP
CTSP
NT
NT
CTFP
RT
CTFP
NT
CTFP
RT
NT
CTSP
CTFP
1980
4419
4419
4419
4419
4419
4419
4419
4419
4419
4419
4522
4522
4522
4522
4522
4522
4522
4522
4522
4522
4522
3984
3984
3984
3984
3984
3984
3984
3984
3984
3984
3984
3359
3359
3359
3359
3359
3359
3359
3359
3359
3359
3359
3702
3702
3702
3702
3702
3702
3702
3702
3702
3702
3702
3702
3702
1990
4654
4685
4685
4700
4741
4700 -
4696
4696
4731
4740
4752
4752
4799
4792
4831
4779
4793
4832
4790
4824
4833
4184
4184
4229
4228
4265
4216
4229
4265
4232
4257
4271
3615
3615
3655
3650
3676
3605
3617
3679
3660
3652
3666
3874
3874
3905
3931
3921
3922
3897
3931
3867
3873
3909
3916
3923
2000
5569
5270
5141
5203
5456
5340
5754
5312
4715
4781
5673
5351
5575
5247
5463
5382
5452
5569
5411
4935
4968
4962
4710
4950
4692
4897
4830
4889
5006
4879
4385
4411
4308
4073
4279
3971
4138
4123
4172
4315
4136
3660
. 3684
3922
4007
4150
4424
3901
3841
3997
4269
3846
4190
4238
3752
3758
2010
5999
5804
5452
5539
5895
5601
6371
5686
4898
4929
6111
5671
6000
5612
5935
.5686
5766
5923
5962
5318
5457
5319
4993
5345
5007
5320
5111
5180
5353
5255
4670
4760
4634
4257
4579
4262
4528
4372
4433
4652
4455
3819
3892
3937
4078
4323
4890
4042
3865
4116
4535
3891
4336
4436
3824
3903
2020 2030 1990/2000 2000/2010
6973
6302
6237
6368
6791
6375
7157
6744
5512
5538
7044
6111
6527
6413
6791
6235,
6369
6541
7209
6094
6032
6155
5418
5863
5782
6146
5706
5842
6022
6382
5290
5237
5388
4641
4995
4931
5215
4856
4983
5190
5228
4258
4146
t"
4329
4511
4756
5419
4266
4185
4469
4943
4073
4625
4665
4068
3922
7636
7101
6921
7114
7624
7246
8200
7202
, 6002
5969
7677
6746
7228
7095
7552
7009 ,
7153
7311
7692
6732
6667
6688
5974
6479
6354
6783
6453
6610
6773
6789
5786
5819
5821
5156
5542
5413
5778
5431
5575
5786
5566
4589
4469
4580
4806
5093
6000
4564
4395
4808
5422
4386
5028
5074
4397
4404
19.66%
12.48%
9.73%
10.70%
15.08%
13.61%
22.52%
13.11%
-0.33%
0.86%
19.38%
12.60%
16.17%
9.49%
13.08%
12.61%
. 13.74%
15.25%
12.96%
2.30%
2.79%
18.59%
12.57%
17.04%
10.97%
14.81%
14.56%
15.60%
17.37%
15.28%
3.00%
3.27%
19.17%
12.66%
17.07%
8.79%
12.56%
14.36%
15.34%
17.28%
13.00%
0.21%
0.49%
1.23%
3.43%
6.27%
12.54%
-0.51%
-2.06%
2.56%
8.59%
-0.54%
8.18%
8.41%
-4.18%
-4.20%
7.72%
10.13%
6.04%
6.45%
8.04%
4.88%
10.72%
7.04%
3.88%
3.09%
7.72%
5.98%
7.62%
6.95%
8.63%
5.64%
5.75%
6.35%
10.18%
7.76%
9.84%
7.19%
6.00%
7.97%
6.71%
8.63%
5.81%
5.95%
6.93%
7.70%
6.49%
7.91%
7.56%
4.51%
7.01%
7.32%
9.42% .
6.03%
6.25%
7.80%
7.71%
4.34%
5.64%
0.38%
1.77%
4.16%
10.53%
3.61%
0.62%
2.97%
6.23%
1.17%
3.48%
4.67%
1.91%
3.85%
2010/2020
16.23%
8.58%
14.39%
14.96%
15.19%
13.81%
12.33%
18.60%
12.53%
12.35%
15.26%
7.75%
8.78%
14.27%
14.42%
9.65%
10.45%
10.43%
20.91%
14.59%
10.53%
15.71%
8.51%
9.69%
15.47%
15.52%
11.64%
12.77%
12.49%
21.44%
13.27%
10.02%
16.27%
9.02%
9.08%
15.69%
15.17%
11.07%
12.40%
11.56%
17.35%
11.49%
6.52%
9.95%
10.61%
10.01%
10.81%
5.54%
8.27%
8.57%
8.99%
4.67%
6.66%
5.16%
6.38%
0.48%
2020/2030 1990/2030
9.50%
12.67%
10.96%
11.71%
12.26%
13.66%
14.57%
6.79%
8.88%
7.78%
8.98%
10.39%'
10.74%
10.63%
11.20%
12.41%
12.30% .
11.77%
6.69%
10.46%
10.52%
8.65%
10.26%
10.50%
9.89%
10.36%
13.09%
13.14%
12.47%
6.37%
9.37%
11.11%
8.03%
11.09%
10.95%
9.77%
10.79%
11.84%
11.88%
11.48%
6.46%
7.77%
7.79%
5.79%
6.53%
7.08%
10.72%
6.98%
5.01%
7.58%
9.69%
7.68%
8.71%
8.76%
8.08%
12.28%
64.07%
51.56%
47.72%
51 .36%
60.80%
54.17%
74.61%
53.36%
26.86%
25.92%
61 .55%
41.96%
50.61%
48.05%
56.32%
46.66%
49.23%
51.30%
60.58%
39.55%
37.94%
59.84%
42.78%
53.20%
50.28%
59.03%
53.05%
56.30%
58.80%
60.42%
35.91%
36.24%
61.02%
42.62%
51 .62%
48.30%
57.18%
50.65%
54.13%
57.27%
52.07%
25.65%
21.90%
18.22%
24.05%
30.42%
52.63%
16.39%
12.06%
23.37%
37.92%
13.42%
29.82%
29.80%
12.28%
12.26%
C-9
-------
Table C.1 (contd.)
Total Soil Carbon (gC/m2)
Percent Change in Total Soil Carbon for
Selected Time Periods
CD
if
442
4*42
442
442
442
442
442
442
442
442
442
442
442
443
443
443
443
443
443
443
443
443
443
443
443
443
444
444
444
444
444
444
444
444
444
444
444
444
444
CR
#
186
186
186
218
366
366
416
416
458
459
459
503
508
186
186
186
218
366
366
416
416
458
459
459
503
508
186
186
186
218
366
366
416
416
458
459
459
503
508
TILL
CTFP
CTSP
NT
NT
CTFP
RT
CTFP
NT
CTFP
RT
NT
CTSP
CTFP
CTFP
CTSP
NT
NT
CTFP
RT
CTFP
NT
CTFP
RT
NT
CTSP
CTFP
CTFP
CTSP
NT
NT
CTFP
RT
CTFP
NT
CTFP
RT
NT
CTSP
CTFP
1980
2761
2761
2761
2761
2761
2761
2761
2761
2761
2761
2761
2761
2761
2477
2477
2477
2477
2477
2477
2477
2477
2477
2477
2477
2477
2477
2454
2454
2454
2454
2454
2454
2454
2454
2454
2454
2454
2454
2454
1990
2950
2949
2983
3022
2995
2997
2986
3023
2935
2942
2978
2971
2978
2652
2651
2681
2713
2689
2690
2682
2713
2635
2640
2670
2675
2683
2610
2610
2636
2660
2642
2643
2632
2660
2594
2600
2627
2611
2617
2000
3175
3249
3445
3802
3014
2938
3235
3547
2888
3304
3366
2843
2840
2851
2918
3115
3476
2844
2757
2934
3222
2687
3060
3128
2698
2707
2921
2992
3154
3541
2770
2669
2993
3253
2640
3110
3141
2556
2558
2010
3310
3428
3731
4461
3084
2912
3281
3744
2886
3401
3551
2865
2895
2963
3078
3371
4047
2998
2816
2959
3394
2690
3206
3362
2819
2852
3101
3222
3469
4331
2871
2684
3056
3451
2691
3303
3411
2591
2606
2020 ;
3734
3891
4238
5223
3340
3251
3641
4188
3067
3762
3852
3124
2912
3305
3458
3811
4720
3214
3124
3301
3845
2899
3544
3657
3118
2939
3544
3710
3995
5241
3087
2972
3390
3865
2871
3764
3814
2833
2687
>030 1
3973
4180
4589
6060
3575
3373
4051
4757
3263
4163
4274
3308
3223
3528
3730
4122
5254 ,
3497
3201
3626
4303
3051
3915
4056
3303
3239
3791
4008
4359
6034
3379
3111
3846
4470
3096
4267
4347
3008
2963
I990/2000 2
7.62%
10.17%
15.48%
25.81%
0.63%
-1.96%
8.33%
17.33%
-1.60%
12.30%
13.02%
-4.30%
-4.63%
7.50%
10.07%
16.18%
28.12%
5.76%
2.49%
9.39%
18.76%
1.97%
15.90%
17.15%
0.85%
0.89%
11.91%
14.63%
19.65%
33.12%
4.84%
0.98%
13.71%
22.29%
1.77%
19.61%
19.56%
-2.10%
-2.25%
000/2010
4.25%
5.50%
8.30%
17.33%
2.32%
-0.88%
1 .42%
5.55%
-0.06%
2.93%
5.49%
0.77%
1.93%
3.92%
5.48%
8.21%
16.42%
5.41%
2.14%
0.85%
5.33%
0.11%
4.77%
7.48%
4.48%
5.35%
6.16%
7.68%
9.98%
22.31%
3.64%
0.56%
2.10%
6.08%
1.93%
6.20%
8.59%
1.36%
1.87%
2010/2020
12.80%
13.50%
13.58%
17.08%
8.30%
11.64%
10.97%
11.85%
6.27%
10.61%
8.47%
9.04%
0.58%
11.54%
12.34%
13.05%
16.62%
7.20%
10.93%
11.55%
13.28%
7.76%
10.54%
8.77%
10.60%
3.05%
14.28%
15.14%
15.16%
21.01%
7.52%
10.73%
10.92%
11.99%
6.68%
13.95%
11.81%
9.34%
3.10%
2020/2030
6.40%
7.42%
8.28%
16.02%
7.03%
3.75%
11.26%
13.58%
6.39%
10.65%
10.95%
5.88%
10.67%
6.74%
7.86%
8.16%
11.31%
8.80%
2.46%
9.84%
11.91%
5.24%
10.46%
10.91%
5.93%
10.20%
6.96%
8.03%
9.11%
15.13%
9.45%
4.67%
13.45%
15.65%
7.83%
13.36%
13.97%
6.17%
10.27%
1990/2030
34.67%
41.74%
53.83%
100.52%
19.36%
12.54%
35.66%
57.36%
11.17%
41.50%
43.51%
11.34%
8.22%
33.03%
40.70%
53.74%
93.66%
30.04%
18.99%
35.19%
58.60%
15.78%
48.29%
51.91%
23.47%
20.72%
45.24%
53.56%
65.36%
126.84%
27.89%
17.70%
46.12%
68.04%
19.35%
64.11%
65.47%
15.20%
13.22%
471
471
471
471
471
471
471
471
471
471
471
471
2
2
100
125
145
215
215
215
262
463
463
508
CTSP
RT
NT
CTSP
CTFP
CTSP
RT
NT
CTFP
CTSP
RT
CTFP
3746
3746
3746
3746
3746
3746
3746
3746
3746
3746
3746
3746
3779
3780
3787
3769
3774
3769
3776
3785
3778
3714
3714
3782
4853
4910
5048
4610
4558
4919
4968
5205
4456
3907
4442
4158
5316
5396
5626
4991
4887
5610
5703
6061
4783
3963
4768
4352
6379
6508
6856
5678
5375
6690
6872
7433
5662
4186
5536
4697
7347
7525
7754
6044
5924
7782
8045
8817
6165
4309
6107
5226
28.42%
29.89%
33.29%
22.31%
20.77%
30.51%
31.56%
37.51%
17.94%
5.19%
19.60%
9.94%
9.54%
9.89%
11.45%
8.26%
7.21%
14.04%
14.79%
16.44%
7.33%
1.43%
7.33%
4.66%
19.99%
20.60%
21.86%
13.76%
9.98%
19.25%
20.49%
22.63%
18.37%
5.62%
16.10%
7.92%
15.17%
15.62%
13.09%
6.44%
10.21%
16.32%
17.06%
18.61%
8.88%
2.93%
10.31%
11.26%
94.41%
99.07%
104.75%
60.36%
56.96%
106.47%
113.05%
132.94%
63.18%
16.02%
64.43%
38.18%
C-10
-------
Table C.1 (contd.)
Total Soil Carbon (gC/m2>
Percent Change in Total Soil Carbon for
Selected Time Periods
CD CR TILL
# #
472 2 CTSP
472 2 RT
472 100 NT
472 125 CTSP
472 145 CTFP
472 215 CTSP
472 215 RT
472 215 NT
472 262 CTFP
472 463 CTSP
472 463 RT
472 508 CTFP
473 2 CTSP
473 2 RT
473 100 NT
473 125 CTSP
473 145 CTFP
473 215 CTSP
473 215 RT
473 215 NT
473 262 CTFP
473 463 CTSP
473 463 RT
473 508 CTFP
474 2 CTSP
474 2 RT
474 100 NT
474 125 CTSP
474 145 CTFP
474 215 CTSP
474 215 RT
474 215 NT
474 262 CTFP
474 463 CTSP
474 463 RT
474 508 CTFP
475 2 CTSP
475 2 RT
475 100 NT
475 125 CTSP
475 145 CTFP
475 215 CTSP
475 215 RT
475 215 NT
475 262 CTFP
475 463 CTSP
475 463 RT
475 508 CTFP
531 4 CTSP
531 4 RT
531 100 CTSP
531 100 RT
531 100 NT
531 244 CTSP
531 463 CTSP
531 463 RT
531 503 CTFP
531 508 CTSP
1980
4227
4227
4227
4227
4227
4227
4227
4227
4227
4227
4227
4227
3895
3895
3895
3895
3895
3895
3895
3895
3895
3895
3895
3895
3166
3166
3166
3166
3166
3166
3166
3166
3166
3166
3166
3166
3650
3650
3650
3650
3650
3650
3650
3650
3650
3650
3650
3650
3424
3424
3424
3424
3424
3424
3424
3424
3424
3424
1990
4344
4345
4348
4330
4332
4330
4337
4348
4337
4295
4295
4340
3971
3977
3976
3958
3965
3956
3967
3975
3975
3917
3917
3977
-3279
3282
3288
3277
3280
3274
3280
3285
3281
3219
3219
3287
3753
3758
3762
3745
3749
3745
3754
3762
3758
3691
3691
3774
3475
3477
3424
3429
3438
3429
3418
3418
3489
3491
2000
5794
5867
6170
5299
5311
5845
5920
6046
5156
4576
5129
4704
5046
5117
5435
4921
4918
5157
5232
5428
5020
4128
4479
4700
4632
4698
5350
4287
4258
4725
4798
4948
4039
3388
3960
3592
4749
4805
5463
4792
4846
4938
5004
5185
4821
3866
4127
4573
4090
4121
4215
4292
4421
3883
3536
3543
3859
3892
2010
6357
6449
7016
5920
5905
6650
6751
6957
5583
4560
5351
5005
5594
5713
6156
5555
5618
5868
5993
6311
5630
4133
4649
5289
5478
5582
6666
5145
5120
5946
6085
6423
4584
3430
4309
4005
5348
5434
6399
5529
5608
5831
5948
6273
5515
3904
4308
5296
4370
4403
4684
4813
5031
4230
3582
3594
4213
4213
2020 i
7519
7645
8837
7005
6547
8100
8305
8604
6601
4812
6100
5255
6327
6470
7504
6397
6258
6859
7091
7524
6625
4345
5219
5674
6407
6542
8787
6345
5739
7097
7340
7829
5530
3620
5416
4196
5969
6082
7853
6449
6346
6645
6847
7302
6595
4124
4917
5714
4652
4700
5374
5573
5892
4456
3758
3772
4638
4613
2030 1
8892
9110
10650
7846
7694
9835
10177
10741
7463
5038
6891
6178
7277
7508
8698
7265
7215
7948
8317
9106
7597
4511
5758
7133
7865
8070
10755
7376
6799
8821
9181
10002
6516
3708
6207
5162
6781
6954
9160
7347
7216
7734
8069
8762
7490
4189
5290
7058
5484
5571
5863
6123
6530
4907
3865
3885
5446
5274
990/2000 2
33.37%
35.02%
41.90%
22.37%
22.59%
34.98%
36.49%
39.05%
18.88%
6.54%
19.41%
8.38%
27.07%
28.66%
36.69%
24.33%
24.03%
30.35%
31.88%
36.55%
26.28%
5.38%
14.34%
18.17%
41.26%
43.14%
62.71%
30.82%
29.81%
44.31%
46.28%
50.62%
23.10%
5.25%
23.01%
9.27%
26.53%
27.86%
45.21%
27.95%
29.26%
31.85%
33.29%
37.82%
28.28%
4.74%
11.81%
21.17%
17.69%
18.52%
23.10%
25.16%
28.59%
13.24%
3.45%
3.65%
10.60%
11.48%
000/2010 2
9.71% «
9.91%
13.71%
11.71%
11.18%
13.77%
14.03%
15.06%
8.28%
-0.34%
4.32%
6.39%
10.86%
11.64%
13.26%
12.88%
14.23%
13.78%
14.54%
16.26%
12.15%
0.12%
3.79%
12.53%
18.26%
18.81%
24.59%
20.01%
20.24%
25.84%
26.82%
29.81%
13.49%
1.23%
8.81%
11.49%
12.61%
13.09%
17.13%
15.37%
15.72%
18.08%
18.86%
20.98%
14.39%
0.98%
4.38%
15.81%
6.84%
6.84%
11.12%
12.13%
13.79%
8.93%
1.30%
1.43%
9.17%
8.24%
1010/2020
18.27%
18.54%
25.95%
18.32%
10.87%
21 .80%
23.01%
23.67%
18.23%
5.52%
13.99%
4.99%
13.10%
13.25%
21.89%
15.15%
11.39%
16.88%
18.32%
19.22%
17.67%
5.12%
12.26%
7.27%
16.95%
17.19%
31.81%
23.32%
12.08%
19.35%
20.62%
21.89%
20.63%
5.53%
25.69%
4.76%
11.61%
11.92%
22.72%
16.63%
13.15%
13.95%
15.11%
16.40%
19.58%
5.63%
14.13%
7.89%
6.45%
6.74%
14.73%
15.79%
17.11%
5.34%
4.91%
4.95%
10.08%
9.49%
2020/2030
18.26%
19.16%
20.51%
12.00%
17.51%
21.41%
22.54%
24.83%
13.05%
4.69%
12.96%
,17.56%
15.01%
16.04%
15.91%
13.56%
15.29%
15.87%
17.28%
21.02%
14.67%
3.82%
10.32%
25.71%
22.75%
23.35%
22.39%
16.24%
18.47%
24.29%
25.08%
27.75%
17.83%
2.43%
14.60%
23.02%
13.60%
14.33%
16.64%
13.92%
13.70%
16.38%
17.84%
19.99%
13.57%
1.57%
7.58%
23.52%
17.88%
18.53%
9.09%
9.86%
10.82%
10.12%
2.84%
2.99%
17.42%
14.32%
1990/2030
104.69%
109.66%
144.94%
81.20%
77.60%
127.13%
134.65%
147.03%
72.07%
17.29%
60.44%
42.35%
83.25%
88.78%
118.76%
83.55%
81 .96%
100.91%
109.65%
129.08%
91.11%
15.16%
47.00%
79.35%
139.85%
145.88%
227.09%
125.08%
107.28%
169.42%
179.90%
204.47%
98.59%
15.19%
92.82%
57.04%
80.68%
85.04%
143.48%
96.18%
92.47%
106.51%
114.94%
132.90%
99.30%
13.49%
43.32%
87.01%
57.81%
60.22%
71.23%
78.56%
89.93%
43.10%
13.07%
13.66%
56.09%
51.07%
C-11
-------
Table C.1 (contd.)
Total Soil Carbon (gC/m2)
CO
#
532
532
532
532
532
532
532
532
532
532
533
533
533
533
533
533
533
533
533
533
561
561
561
561
561
561
561
561
561
561
561
561
562
562
562
563
562
562
562
562
562
562
562
562
571
571
571
571
571
571
571
571
571
571
571
571
571
571
571
CR
#
4
4
100
100
100
244
463
463
503
508
4
4
100
100
100
244
463
463
503
508
100
131
131
131
145
145
186
201
246
246
262
366
100
131
131
131
145
145
186
201
246
246
262
366
125
131
131
131
138
186
186
218
218
339
339
350
366
508
508
TILL
CTSP
RT
CTSP
RT
NT
CTSP
CTSP
RT
CTFP
CTSP
CTSP
RT
CTSP
RT
NT
CTSP
CTSP
RT
CTFP
CTSP
NT
CTSP
RT
NT
CTSP
RT
CTSP
RT
CTSP
RT
CTSP
CTSP
NT
CTSP
RT
NT
CTSP
RT
CTSP
RT
CTSP
RT
CTSP
CTSP
RT
CTSP
RT
NT
RT
CTFP
RT
CTFP
RT
CTSP
RT
CTSP
CTSP
CTFP
CTSP
1980
2956
2956
2956
2956
2956
2956
2956
2956
2956
2956
2943
2943
2943
2943
2943
2943
2943
2943
2943
2943
3438
3438
3438
3438
3438
3438
3438
3438
3438
3438
3438
3438
3931
3931
3931
3931
3931
3931
3931
3931
3931
3931
3931
3931
4204
4204
4204
4204
4204
4204
4204
4204
4204
4204
4204
4204
4204
4204
4204
1990
3042
3044
3003
3008
3013
3011
2994
2994
3073
3077
3000
3001
2979
2983
2988
2983
2963
2963
3032
3038
3815
3714
3730
3767
3762
3776
3758
3771
3777
3792
3713
3827
4355
4251
4266
4312
4294
4308
4289
4303
4309
4323
4266
4359
4508
4439
4455
4495
4508
4495
4507
4494
,4507
4570
4576
4567
4567
4587
4587
2000
35,56
3575
3754
3796
3899
3579
3176
3181
3594
3633
3389
3400
3988
4021
4103
3907
3083
3086
3674
3693
5668
4338
4441
4698
4461
4525
4425
4490
4649
4825
3874
4016
6128
5026
5116
5270
5015
5070
4964
5058
5185
5358
4466
4548
5384
5066
5193
5422
5249
5093
5279
5057
5280
5014
5052
4869
4979
4892
4910
2010
3867
3888
4169
4252
4448
4018
3231
3237
4127
4091
3563
3578
4503
4591
4759
4596
3084
3090
4239
4183
6911
4641
4809
5237
5075
5218
4882
4818
5197
5478
4077
4228
7437
5424
5575
5850
5752
5872
5507
5472
5816
6113
4688
4755
5888
5368
5559
5953
5671
5452
5730
5397
5741
5654
5661
5419
5512
5498
5534
2020
4039
4068
4794
4937
5233
4253
3340
3348
4767
4782
3669
3690
5250
5413
5686
4895
3160
3166
4919
4853
8713
5034
5262
5848
5248
5472
5575
5381
6367
6648
4428
4511
9364
5937
6186
6568
5986
6191
6290
6068
7267
7557
5143
5159
6827
5764
6034
6531
6554
6063
6467
6069
6550
6276
6311
5877
6045
5771
6172
2030
4720
4784
5187
5382
5722
4760
3418
3432
5480
5495
4192
4237
5711
5930
6302
5591
3291
3301
5894
5693
10717
5628
5946
6546
6055
6350
6465
6349
7368
7660
4725
5026
11661
6660
7000
7320
6950
7197
7408
7223
8473
8778
5520
5710
7786
6354
. 6704
7240
7191
6876
7420
6859
7540
6761
6844
6509
6807
6822
6960
Percent Change in Total Soil Carbon for
Selected Time Periods
1990/2000 2000/2010 2010/2020 2020/2030 1990/2030
16.89%
17.44%
25.00%
26.19%
29.40%
18.86%
6.07%
6.24%
16.95%
18.06%
12.96%
13.29%
33.87%
34.79%
37.31%
30.97%
4.04%
4.15%
21.17%
21.56%
48.57%
16.80%
19.06%
24.71%
18.58%
19.83%
17.74%
19.06%
23.08%
27.24%
4.33%
4.93%
40.71%
18.23%
19.92%
22.21%
16.79%
17.68%
.15.73%
17.54%
20.32%
23.94%
4.68%
4.33%
19.43%
14.12%
16.56%
20.62%
16.43%
13.30%
17.12%
12.52%
17.15%
9.71%
10.40%
6.61%
9.02%
6.64%
7.04%
8.74%
8.75%
11.05%
12.01%
14.08%
12.26%
1.73%
1.76%
14.83%
12.60%
5.13%
5.23%
12.91%
14.17%
15.98%
17.63%
0.03%
0.12%
15.37%
13.26%
21.93%
6.98%
8.28%
11.47%
13.76%
15.31%
10.32%
7.30%
11.78%
13.53%
5.24%
5.27%
21.36%
7.91%
8.97%
11.00%
14.69%
15.81%
10.93%
8.18%
12.16%
14.09%
4.97%
4.55%
9.36%
5.96%
7.04%
9.79%
8.03%
7.04%
8.54%
6.72%
8.73%
12.76%
12.05%
11.29%
10.70%
12.38%
12.70%
4.44%
4.62%
14.99%
16.11%
17.64%
5.84%
3.37%
3.42%
15.50%
16.89%
2.97%
3.13%
16.58%
17.90%
19.47%
6.50%
2.46%
2.45%
16.04%
16.01%
26.07%
8.46%
9.41%
11.66%
3.40%
4.86%
14.19%
11.68%
22.51%
21.35%
8.60%
6.69%
25.91%
9.45%
10.95%
12.27%
4.06%
5.43%
14.21%
10.89%
24.94%
23.62%
9.70%
8.49%
15.94%
7.37%
8.54%
9.70%
15.57%
11.20%
12.86%
12.45%
14.09%
11.00%
11.48%
8.45%
9.66%
4.96%
11.52%
16.86%
17.60%
8.19%
9.01%
9.34%
11.92%
2.33%
2.50%
14.95%
14.91%
14.25%
14.82%
8.78%
9.55%
10.83%
14.21%
4.14%
4.26%
19.82%
17.30%
23.00%
11.79%
12.99%
11.93%
15.37%
16.04%
15.96%
17.98%
15.72%
15.22%
6.70%
11.41%
24.53%
12.17%
13.15%
11.44%
'16.10%
16.24%
17.77%
19.03%
16.59%
16.15%
7.33%
10.68%
14.04%
10.23%
11.10%
10.85%
9.71%
13.40%
14.73%
13.01%
15.11%
7.72%
8.44%
10.75%
12.60%
18.21%
12.76%
55.16%
57.16%
72.72%
78.92%
89.91%
58.08%
14.16%
14.62%
78.32%
78.58%
39.73%
41.18%
91.70%
98.79%
110.91%
87.42%
11.06%
11.40%
94.39%
87.39%
180.91%
51.53%
59.41%
73.77%
60.95%
68.16%
72.03%
68.36%
95.07%
102.00%
27.25%
31.33%
167.76%
56.66%
64.08%
69.75%
61.85%
67.06%
72.72%
67.85%
96.63%
103.05%
29.39%
30.99%
72.71%
43.14%
50.48%
61.06%
59.51%
52.96%
64.63%
52.62%
67.29%
47.94%
49.56%
42.52%
49.04%
48.72%
51.73%
C-12
-------
Table C.1 (contd.)
Total Soil Carbon (gC/m2)
CO
#
572
572
572
572
572
572
572
572
572
572
572
572
572
572
572
573
573
573
573
573
573
573
573
573
573
573
573
573
573
573
581
581
581
581
581
581
581
581
581
581
581
581
581
581
582
582
582
582
582
582
582
582
582
582
582
582
582
582
CR
#
125
131
131
131
138
186
186
218
218
339
339
350
366
508
508
125
131
131
131
138
186
186
218
218
339
339
350
366
508
508
100
100
186
186
186
218
218
218
262
463
490
503
508
508
100
100
186
186
186
218
218
218
262
463
490
503
508
508
TILL
RT
CTSP
RT
NT
RT
CTFP
RT
CTFP
RT
CTSP
RT
CTSP
CTSP
CTFP
CTSP
RT
CTSP
RT
NT
RT
CTFP
RT
CTFP
RT
CTSP
RT
CTSP
CTSP
CTFP
CTSP
RT
NT
CTSP
RT
NT
CTFP
RT
NT
CTSP
RT
RT
CTFP
CTFP
CTSP
RT
NT
CTSP
RT
NT
CTFP
RT
NT
CTSP
RT
RT
CTFP
CTFP
CTSP
1980
3822
3822
3822
3822
3822
3822
3822
3822
3822
3822
3822
3822
3822
3822
3822
3085
3085
3085
3085
3085
3085
3085
3085
3085
3085
3085
3085
3085
3085
3085
2358
2358
2358
2358
2358
2358
2358
2358
2358
2358
2358
2358
2358
2358
4777
4777
4777
4777
4777
4777
4777
4777
4777
4777
4777
4777
4777
4777
1990
4147
4044
4061
4102
4147
4133
4144
4132
4143
4188
4196
4200
4200
4215
4215
3415
3330
3342
3385
3415
3406
3384
3404
3414
3427
3429
3496
3496
3449
3449
2354
2363
2349
2354
2363
2350
2354
2363
2380
2343
2351
2357
2421
2421
4996
5012
4986
4995
5012
4987
4995
5012
4994
4899
4931
4970
5079
5077
2000
4735
4664
4777
5018
4592
4536
4702
4639
4838
4479
4517
4379
4463
4381
4396
3861
3902
4001
4265
3787
3793
3836
3920
4091
3595
3628
3469
3497
3507
3513
2703
2836
2689
2717
2840
2498
2533
2612
2558
2165
2411
2252
2497
2495
6935
6739
5616
5631
5739
5976
6076
5964
5152
4620
5431
4859
5431
5426
2010
5357
4960
5133
5497
5190
4916
5202
4979
5316
4959
5025
4826
4963
4858
.4882
4279
4170
4319
4708
4204
4131
4227
4206
4484
3882
3937
3737
3735
3728
3722
2927
3118
2900
2941
3140
2597
2649
2768
2756
2023
2427
2353
2650
2641
8103
7709
6020
6099
6183
6603
6796
6517
5473
4314
5835
5265
5870
5849
2020 2
6305
5350
5592
6066
7055
5564
5993
5689
6190
5689
5769
5303
5488
5283
5323
4983
4456
4655
5171
5014
4675
4854
4825
5237
4467
4552
4125
4109
4023
4020
3408
3643
3331
3402
3666
2892
2959
3090
3088
2031
2632
2363
2719
2795
10165
9838
6827
6992
7088
7720
8024
8013
6193
4472
6720
5521
6437
6555
030
7273
5939
6261
6728
8412
6452
7049
6706
7391
6044
6168
5873
6189
6402
6139
5638
4936
5200
5726
5455
5345
5672
5652
6219
4583
4698
4357
4388
4722
4497
3676
3952
3613
3702
4020
3113
3201
3336
3281
2032
2809
2489
3110
3081
11164
11608
7446
7677
7674
8630
8998
9491
6559
4586
7491
6005
7446
7425
Percent Change in Total Soil Carbon for
Selected Time Periods
1990/2000 2000/2010 2010/2020 2020/2030 1990/2030
14.17%
15.33%
17.63%
22.33%
10.73%
9.75%
13.46%
12.27%
16.77%
6.94%.
7.65%
4.26%
6.26%
3.93%
4.29%
13.06%
17.17%
19.71%
25.99%
10.89%
11.36%
13.35%
15.15%
19.83%
4.90%
5.80%
-0.77%
0.02%
1.68%
1.85%
14.82%
20.01%
14.47%
15.42%
20.18%
6.29%
7.60%
10.53%
7.47%
-7.59%
2.55%
-4.45%
3.13%
3.05%
38.81%
34.45%
12.63%
12.73%
14.50%
19.83%
21.64%
18.99%
3.16%
-5.69%
10.13%
-2.23%
6.93%
6.87%
13.13%
6.34%
7.45%
9.54%
13.02%
8.37%
10.63%
7.32%
9.88%
10.71%
11.24%
10.20%
11.20%
10.88%
11.05%
10.82%
6.86%
7.94%
10.38%
11.01%
8.91%
10.19%
7.29%
9.60%
7.98%
8.51%
7.72%
6.80%
6.30%
5.94%
8.28% :
9.94%
7.84%
8.24%
10.56%
3.96%
4.57%
5.97%
7.74%
-6.55%
0.66%
4.48%
6.12%
5.85%
16.84%
14.39%
7.19%
8.31%
7.73%
10.49%
11.84%
9.27%
6.23%
-6.62%
7.43%
8.35%
8.08%
7.79%
17.69%
7.86%
8.94%
10.35%
35.93%
13.18%
15.20%
14.25%
16.44%
14.72%
14.80%
9.88%
10.57%
8.74%
9.03%
16.45%
6.85%
7.77%
9.83%
19.26%
13.16%
14.83%
14.71%
16.79%
15.06%
15.62%
10.38%
10.01%
7.91%
8.00%
16.43%
16.83%
14.86%
15.67%
16.75%
11.35%
11.70%
11.63%
12.04%
0.39%
8.44%
0.42%
2.60%
5.83%
25.44%
27.61%
13.40%
14.64%
14.63%
16.91%
18.06%
22.95%
13.15%
3.66%
15.16%
4.86%
9.65%
12.07%
15.35%
11.00%
11.96%
10.91%
19.23%
15.95%
17.62%
17.87%
19.40%
6.24%
6.91%
10.74%
12'.77%
21.18%
15.32%
13.14%
10.77%
11.70%
10.73%
8.79%
14.33%
16.85%
17.13%
18.75%
2.59%
3.20%
5.62%
6.78%
17.37%
11.86%
7.86%
8.48%
8.46%
8.81%
9.65% ,
7.64%
8.17%
7.96%
6.25%
O'.04%
6.72%
5.33%
14.38%
10.23%
9.82%
17.99%
9.06%
9.79%
8.26%
11.78%
12.13%
18.44%
5.90%
2.54%
11.47%
8.76%
15.67%
13.27%
75.37%
46.85%
54.17%
64.01%
102.84%
56.10%
70.10%
62.29%
78.39%
44.31%
46.99%
39.83%
47.35%
51.88%
45.64%
65.09%
48.22%
55.59%
69.15%
59.73%
56.92%
67.61%
66.03%
82.16%
33.73%
37.00%
24.62%
25.51%
36.90%
30.38%
56.15%
67.24%
53.81%
57.26%
70.12%
32.46%
35.98%
41.17%
37.85%
-13.27%
19.48%
5.60%
28.45%
27.26%
123.45%
131.60%
49.33%
53.69%
53.11%
73.04%
80.14%
89.36%
31.33%
-6.38%
51.91%
20.82%
46.60%
46.24%
C-13
-------
Table C.1 (contd.)
Total Soil Carbon (gC/m2)
CD
#
583
-583
583
583
583
583
583
583
583
583
583
583
583
583
584
584
584
584
584
584
584
584
584
584
584
584
584
584
591
591
591
591
591
591
591
591
591
592
592
592
592
592
592
592
592
592
601
601
601
601
601
601
601
601
601
601
601
CR
#
100
100
186
186
186
218
218
218
262
463
490
503
508
508
100
100
186
186
186
218
218
218
262
463
490
503
508
508
100
100
100
186
186
244
339
490
508
100
100
100
186
186
244
339
490
508
131
131
186
186
186
201
201
201
503
508
508
TILL
RT
NT
CTSP
RT
NT
CTFP
RT
NT
CTSP
RT
RT
CTFP
CTFP
CTSP
RT
NT
CTSP
RT
NT
CTFP
RT
NT
CTSP
RT
RT
CTFP
CTFP
CTSP
CTSP
RT
NT
CTFP
RT
CTSP
CTFP
RT
CTSP
CTSP
RT
NT
CTFP
RT
CTSP
CTFP
RT
CTSP
CTSP
NT
CTSP
RT
NT
CTSP
RT
NT
CTSP
CTFP
RT
1980
4626
4626
4626
4626
4626
4626
4626
4626
4626
4626
4626
4626
4626
4626
4496
4496
4496
4496
4496
4496
4496
4496
4496
4496
4496
4496
4496
4496
4635
4635
4635
4635
4635
4635
4635
4635
4635
3891
3891
3891
3891
3891
3891
3891
3891
3891
4176
4176
4176
4176
4176
4176
4176
4176
4176
4176
4176
1990
4786
4803
4775
4785
4803
4777
4785
4803
4794
4727
4763
4774
4903
4903
4485
4515
4477
4485
4514
4477
4485
4514
4490
4418
4450
4482
4576
4576
5054
5076
5150
5132
5148
5170
5185
5043
5210
4179
4194
4247
4265
4276
4291
4304
4151
4321
4234
4280
4297
4308
4342
4298
4310
4343
4354
4367
4366
2000
6152
6026
5113
5176
5262
5394
5496
5429
5019
4269
4935
4393
5153
5143
6281
6196
5097
5154
5244
5260
5370
5359
4717
3994
4581
4408
4743
4752
5886
6067
6294
5541
5712
6328
5215
4823
5081
4987
5127
5330
4453
4567
5300
4249
4027
4243
4724
5032
4653
4716
4965
4699
4755
4945
4442
4494
4533
2010
7006
6819
5343
5454
5594
5767
5958
5841
5333
3880
5124
4596
5489
5456
7279
7028
5425
5539
5653
5776
5999
5874
5063
3691
4796
4917
5083
5034
6301
6613
6886
5853
6183
7251
5441
4796
5131
5360
5579
5834
4699
4919
6122
4484
3983
4396
4977
5428
5017
5121
5500
5021
5098
5362
4794
4930
4866
2020
8404
8303
5988
6110
6277
6551
6825
6671
5992
3895
5721
4791
6038
6200
8851
8880
6189
6321
6395
6673
6973
6811
5724
3698
5375
5089
5563
5670
7267
7762
8008
6630
7150
8138
6137
5121
5505
6176
6536
6833
5278
5604
6559
5091
4164
4836
5349
5937
5568
5717
6163
5274
5399
5731
5257
5171
5335
2030
9191
9417
6504
6678
6804
7263
7615
7604
6355
3987
6255
5193
6943
6907
9839
10391
6702
6896
6991
7375
7766
7912
6065
3739
5783
5523
6334
6199
7846
8474
8823
7475
8181
9636
6375
5434
5895
6739
7232
7469
5789
6243
7617
5343
4342
5258
5874
6564
6171
6381
6913
6018
6208
6546
5759
6080
5945
Percent Change in Total Soil Carbon for
Selected Time Periods
1990/2000 2000/2010 2010/2020 2020/2030 1990/2030
28.54%
25.46%
7.07%
8.17%
9.55%
12.91%
14.85%
13.03%
4.69%
-9.68%
3.61%
-7.98%
5.09%
4.89%
40.04%
37.23%
13.84%
14.91%
16.17%
17.48%
19.73%
18.71%
5.05%
-9.59%
2.94%
-1.65%
3.64%
3.84%
16.46%
19.52%
22.21%
7.96%
10.95%
22.39%
0.57%
-4.36%
-2.47%
19.33%
22.24%
25.50%
4.40%
6.80%
23.51%
-1.27%
-2.98%
-1.80%
11.57%
17.57%
8.28%
9.47%
14.34%
9.32%
10.32%
13.86%
2.02%
2.90%
3.82%
13.88%
13.15%
4.49%
5.37%
6.30%
6.91%
8.40%
7.58%
6.25%
-9.11%
3.82%
4.62%
6.52%
6.08%
15.88%
13.42%
6.43%
7.46%
7.79%
9.80%
11.71%
9.61%
7.33%
-7.58%
4.69%
11.54%
7.16%
5.93%
7.05%
8.99%
9.40%
5.63%
8.24%
14.58%
4.33%
-0.55%
0.98%
7.47%
8.81%
9.45%
5.52%
7.70%
15.50%
5.53%
-1.09%
3.60%
5.35%
7.86%
7.82%
8.58%
10.77%
6.85%
7.21%
8.43%
7.92%
9.70%
7.34%
19.95%
21.76%
12.07%
12.02%
12.20%
13.59%
14.55%
14.20%
12.35%
0.38%
11.65%
4.24%
10.00%
13.63%
21.59%
26.35%
14.08%
14.11%
13.12%
15.52%
16.23%
15.95%
13.05%
0.18%
12.07%
3.49%
9.44%
12.63%
15.33%
17.37%
16.29%
13.27%
15.63%
12.23%
12.79%
6.77%
7.28%
15.22%
17.15%
17.12%
12.32%
13.92%
7.13%
13.53%
4.54%
10.00%
7.47%
9.37%
10.98%
11.63%
12.05%
5.03%
5.90%
6.88%
9.65%
4.88%
9.63%
9.36%
13.41%
8.61%
9.29%
8.39%
10.86%
11.57%
13.98%
6.05%
2.36%
9.33%
8.39%
14.98%
11.40%
11.16%
17.01%
8.28%
9.09%
9.31%
10.52%
11.37%
16.16%
5.95%
1.10%
7.59%
8.52%
13.85%
9.32%
7.96%
9.17%
10.17%
12.74%
14.41%
18.40%
3.87%
6.11%
7.08%
9.11%
10.64%
9.30%
9.68%
11.40%
16.13%
4.94%
4.27%
8.72%
9.81%
10.56%
10.82%
11.61%
12.16%
14.10%
14.98%
14.22%
9.54%
17.57%
11.43%
92.03%
96.06%
36.20%
39.56%
41.66%
52.04%
59.14%
58.31%
32.56%
-15.65%
31.32%
8.77%
41.60%
40.87%
119.37%
130.14%
49.69%
53.75%
54.87%
64.73%
73.15%
75.27%
35.07%
-15.36%
29.95%
23.22%
38.41%
35.46%
55.24%
66.94%
71.32%
45.65%
58.91%
86.38%
22.95%
7.75%
13.14%
61.25%
72.43%
75.86%
35.73%
46.00%
77.51%
24.14%
4.60%
21.68%
38.73%
53.36%
43.61%
48.11%
59.21%
40.01%
44.03%
50.72%
32.26%
39.22%
36.16%
C-14
-------
Table C.1 (contd.)
Total Soil Carbon (gC/m2)
Percent Change in Total Soil Carbon for
Selected Time Periods
CD CR TILL
# #
602 131 CTSP
602 131 NT
602 186 CTSP
602 186 RT
602 186 NT
602 201 CTSP
602 201 RT
602 201 NT
602 503 CTSP
602 508 CTFP
602 508 RT
603 131 CTSP
603 131 NT
603 186 CTSP
603 186 RT
603 186 NT
603 201 CTSP
603 201 RT
603 201 NT
603 503 CTSP
603 508 CTFP
603 508 RT
611 131 CTFP
611 131 NT
611 186 CTFP
611 186 RT
611 186 NT
611 350 CTSP
611 350 RT
611 350 NT
611 458 CTFP
611 458 RT
611 458 NT
611 503 CTSP
611 503 RT
611 508 CTFP
611 508 RT
611 508 NT
612 131 CTFP
612 131 NT
612 186 CTFP
612 186 RT
612 186 NT
612 350 CTSP
612 350 RT
612 350 NT
612 458 CTFP
612 458 RT
612 458 NT
612 503 CTSP
612 503 RT
612 508 CTFP
612 508 RT
612 508 NT
1980
3614
3614
3614
3614
3614
3614
3614
3614
3614
3614
3614
3268
3268
3268
3268
3268
3268
3268
3268
3268
3268
3268
3091
3091
3091
3091
3091
3091
3091
3091
3091
3091
3091
3091
3091
3091
3091
3091
2033
2033
2033
2033
2033
2033
2033
2033
2033
2033
2033
2033
2033
2033
2033
2033
1990
3584
3603
3603
3610
3625
3604
3611
3626
3623
3625
3626
3266
3276
3277
3279
3280
3277
3280
3280
3280
3280
3279
3014
3014
3014
3014
3014
3014
3014
3014
3014
3014
3014
3014
3014
3014
3014
3014
2003
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2004
2004
2005
2000
4388
4528
4195
4239
4363
4252
4287
4361
3646
3648
3659
4002
4132
3810
3847
3975
3865
3901
3977
3340
3334
3338
3778
3915
3559
3656
3757
3332
3289
3321
3264
3304
3375
3144
3155
3001
2990
2992
2525
2732
2325
2403
2536
2265
2234
2267
2157
2200
2283
2115
2123
2009
1986
1991
2010
4742
4931
4599
4675
4851
4662
4710
4828
3846
3891
3K53
4331
4571
4205
4276
4483
4224
4273
4420
3516
3559
3486
4093
4376
3955
4136
4316
3522
3502
3577
3457
3561
3688
3350
3375
3216
3080
3087
2715
3075
2582
2722
2949
2406
2396
2461
2273
2373
2515
2276
2294
2156
2059
2062
2020 c
5306
5561
5312
5429
5618
5275
5378
5482
4205
4110
4133
4796
5107
4839
4944
5205
4719
4821
4970
3924
3823
3825
4514
4873
4539
4816
5021
4049
3980
4083
3696
3853
4049
3669
3714
3365
3337
3329
2986
3451
2995
3211
3516
2869
2819
2917
2480
2626
2855
2540
2570
2272
2224
2218
!030 1
5959
6215
5980
6159
6425
6058
6224
6322
4567
4639
4460
5394
5728
5369
5532
5848
5342
5489
5625
4213
4209
4049
5054
5541
4893
5280
5585
4280
4202
4348
4019
4200
4411
3993
4061
3677
3497
3476
3327
3904
3163
3453
3858
3016
2967
3102
2678
2845
3105
2770
2818
2436
2293
2287
990/2000 21
22.43%
25.67%
16.43%
17.42%
20.35%
17.98%
18.72%
20.27%
0.63%
0.63%
0.91%
22.53%
26.12%
16.26%
17.32%
21.18%
17.94%
18.93%
21.25%
1.82%
1.64%
1.79%
25.34%
29.89%
18.08%
21.30%
24.65%
10.55%
9.12%
10.18%
8.29%
9.62%
11.97%
4.31%
4.67%
-0.43%
-0.79%
-0.72%
26.06%
36.25%
15.96%
19.85%
26.48%
12.96%
11.42%
13.06%
7.58%
9.72%
13.86%
5.48%
5.88%
0.24%
-0.89%
-0.69%
300/2010 ;
8.06%
8.90%
9.63%
10.28%
11.18%
9.64%
9.86%
10.70%
5.48%
6.66%
4.75%
8.22%
10.62%
10.36%
11.15%
12.77%
9.28%
9.53%
11.13%
5.26%
6.74%
4.43%
8.33%
11.77%
11.12%
13.12%
14.87%
5.70%
6.47%
7.70%
5.91%
7.77%
9.27%
6.55%
6.97%
7.16%
3.01%
3.17%
7.52%
12.55%
11.05%
13.27%
16.28%
6.22%
7.25%
8.55%
5.37%
7.86%
10.16%
7.61%
8.05%
7.31%
3.67%
3.56%
2010/2020
11.89%
12.77%
15.50%
16.12%
15.81%
13.14%
14.18%
13.54%
9.33%
5.62%
7.82%
10.73%
11.72%
15.07%
15.62%
16.10%
11.71%
12.82%
12.44%
11.60%
7.41%
9.72%
10.28%
11.35%
14.76%
16.44%
16.33%
14.96%
13.64%
14.14%
6.91%
8.19%
9.78%
9.52%
10.04%
4.63%
8.34%
7.83%
9.98%
12.22%
15.99%
17.96%
19.22%
19.24%
17.65%
18.52%
9.10%
10.66%
13.51%
11.59%
12.03%
5.38%
8.01%
7.56%
ZOZO/Z030 1
12.30%
11.76%
12.57%
13.44%
14.36%
14.84%
15.73%
15.32%
8.60%
12.87%
7.91%
12.46%
12.15%
10.95%
11.89%
12.35%
' 13.20%
13.85%
13.17%
7.36%
10.09%
5.85%
11.96%
13.70%
7.79%
9.63%
11.23%
5.70%
' 5.57%
6.49%
8.73%
9.00%
8.94%
8.83%
9.34%
9.27%
4.79%
4.41%
11.41%
13.12%
5.60%
7.53%
9.72%
5.12%
5.25%
6.34%
7.98%
8.33%
8.75%
9.05%
9.64%
7.21%
3.10%
3.11%
yyu/zusu
66.26%
72.49%
65.97%
70.60%
77.24%
68.09%
72.36%
74.35%
26.05%
27.97%
23.00%
65.15%
74.84%
63.83%
68.70%
78.29%
63.01%
67.34%
71 .49%
28.44%
28.32%
23.48%
67.68%
83.84%
62.34%
75.18%
85.30%
42.00%
39.41%
44.26%
33.34%
39.34%
46.35%
32.48%
34.73%
21.99%
16.02%
15.32%
66.10%
94.71%
57.75%
72.21%
92.41%
50.42%
47.98%
54.71%
33.56%
41.89%
54.86%
38.15%
40.54%
21.55%
14.42%
14.06%
C-15
-------
Table C.1 (contd.)
Total Soil Carbon CgC/m2)
Percent Change in Total Soil Carbon for
Selected Time Periods
CO
#
631
631
631
631
631
631
631
631
631
631
631
631
631
631
632
632
632
632
632
632
632
632
632
632
632
632
632
632
633
633
633
633
633
633
633
633
633
633
633
633
633
633
•634
634
634
634
634
634
634
634
634
634
634
634
634
634
CR
*
186
186
186
218
218
218
218
490
490
503
503
508
508
508
186
186
186
218
218
218
218
490
490
503
503
508
508
508
186
186
186
218
218
218
218
490
490-
503
503
508
508
508
186
186
186
218
218
218
218
490
490
503
503
508
508
508
TILL
CTSP
RT
NT
CTFP
CTSP
RT
NT
RT
NT
CTFP
RT
CTSP
RT
NT
CTSP
RT
NT
CTFP
CTSP
RT
NT
RT
NT
CTFP
RT
CTSP
RT
NT
CTSP
RT
NT
CTFP
CTSP
RT
NT
RT
NT
CTFP
RT
CTSP
RT
NT
CTSP
RT
NT
CTFP
CTSP
RT
NT
RT
NT
CTFP
RT
CTSP
RT
NT
1980
2039
2039
2039
2039
2039
2039
2039
2039
2039
2039
2039
2039
2039
2039
3275
3275
3275
3275
3275
3275
3275
3275
3275
3275
3275
3275
3275
3275
3178
3178
3178
3178
3178
3178
3178
3178
3178
3178
3178
3178
3178
3178
2968
2968
2968
2968
2968
• 2968
2968
2968
2968
2968
2968
2968
2968
2968
1990
2006
2009
2013
2006
2006
2009
2013
2004
2004
2014
2014
2021
2021
2022
3204
3205
3206
3204
3204
3205
3206
3146
3146
3206
3205
3208
3208
3208
3128
3130
3132
3128
3128
3130
3132
3076
3076
3132
3131
3134
3134
3134
2896
2897
2898
2896
2896
2897
2898
2846
2846
2897
2897
2898
2898
2899
2000
2622
2658
2855
2667
2687
2771
2910
2586
2754
2068
2076
2782
2815
2855
4139
4163
4219
4143
4188
4261
4256
3684
3819
3270
3278
4094
4177
4214
3964
4007
4111
3923
3965
4041
4091
3521
3678
3166
3176
3772
3887
3924
3666
3703
3794
3744
3794
3864
3915
3215
3365
2928
2936
3475
3492
3531
2010
2934
2990
3267
3058
3106
3199
3336
2905
3136
2258
2280
2828
2882
2968
4581
4644
4789
4709
4804
4870
4816
3876
4062
3606
3630
4274
4378
4450
4360
4445
4609
4369
4455
4541
4565
3649
3874
3509
3535
4008
4110
4175
4007
4067
4226
4181
4279
4358
4376
3275
3478
3217
3220
3639
3668
3710
2020
3434
3529
3921
3654
3718
3880
4056
3586
3878
2451
2486
3239
3325
3447
5343
5418
5610
5523
5657
5775
5701
4439
4627
3853
3897
4778
4886
4970
5054
5161
5387
5015
5139
5287
5328
4122
4375
3745
3783
4797
4954
5020
4620
4690
4917
4813
4965
5092
5109
3728
3969
3451
3471
4141
4179
4227
2030
3765
3915
4330
4100
4195
4404
4545
4181
4540
2716
2768
3751
3957
4128
5898
6040
6213
6203
6380
6513
6520
5017
5188
4238
4313
5831
5952
6038
5554
5717
5946
5608
5770
5948
5995
4557
4813
4091
4154
5299
5444
5524
5041
5133
5366
5338
5521
. 5687
5661
4112
4348
3772
3815
4605
4645
4696
1990/2000
30.70%
32.30%
41.82%
32.95%
33.94%
37.92%
44.56%
29.04%
37.42%
2.68%
3.07%
37.65%
39.28%
41.19%
29.18%
29.89%
31.59%
29.30%
30.71%
32.94%
32.75%
17.10%
21 .39%
1.99%
2.27%
27.61%
30.20%
31.35%
26.72%
28.01%
31.25%
25.41%
26.75%
29.10%
30.61%
14.46%
19.57%
1.08%
1.43%
20.35%
24.02%
25.20%
26.58%
27.82%
30.91%
29.28%
31.00%
33.37%
35.09%
12.96%
18.23%
1.07%
1.34%
19.91%
20.49%
21 .80%
2000/2010
11.89%
12.49%
14.43%
14.66%
15.59%
15.44%
14.63%
12.33%
13.87%
9.18%
9.82%
1.65%
2.38%
3.95%
10.67%
11.55%
13.51%
13.66%
14.70%
14,29%
13.15%
5.21%
6.36%
10.27%
10.73%
4.39%
4.81%
5.60%
9.98%
10.93%
12.11%
11.36%
12.35%
12.37%
11.58%
3.63%
5.32%
10.83%
11.30%
6.25%
5.73%
6.39%
9.30%
9.82%
11.38%
11.67%
12.78%
12.78%
11.77%
1.86%
3.35%
9.87%
9.67%
4.71%
5.04%
5.06%
2010/2020
17.04%
18.02%
20.01%
19.48%
19.70%
21.28%
21.58%
23.44%
23.66%
8.54%
9.03%
14.53%
15.37%
16.13%
16.63%
16.66%
17.14%
17.28%
17.75%
18.58%
18.37%
14.52%
13.90%
6.84%
7.35%
11.79%
11.60%
11.68%
15.91%
16.10%
16.88%
14.78%
15.35%
1 16.42%
16.71%
12.96%
12.93%
6.72%
7.01%
'19.68%
20.53%
20.23%
15.29%
15.31%
16.35%
15.11%
16.03%
16.84%
16.75%
13.83%
14.11%
7.27%
7.79%
13.79%
13.93%
13.93%
2020/2030
9.63%
10.93%
10.43%
12.20%
12.82%
13.50%
12.05%
16.59%
17.07%
10.81%
11.34%
15.80%
19.00%
19.75%
10.38%
11.48%
10.74%
12.31%
12.78%
12.77%
14.36%
13.02%
12.12%
9.99%
10.67%
22.03%
21.81%
21 .48%
9.89%
10.77%
10.37%
11.82%
12.27%
12.50%
12.51%
10.55%
10.01%
9.23%
9.80%
10.46%
9.89%
10.03%
9.11%
9.44%
9.13%
10.90%
11.19%
11.68%
10.80%
10.30%
9.54%
9.30%
9.91%
11.20%
11.15%
11.09%
1990/2030
87.68%
94.87%
115.10%
104.38%
109.12%
119.21'%
125.78%
108.63%
126.54%
34.85%
37.43%
85.60%
95.79%
104.15%
84.08%
88.45%
93.79%
93.60%
99.12%
103.21%
103.36%
59.47%
64.90%
32.18%
34.57%
81.76%
85.53%
88.21%
77.55%
82.65%
89.84%
79.28%
84.46%
90.03%
91.41%
48.14%
56.46%
30.61%
32.67%
69.08%
73.70%
76.26%
74.06%
77.18%
85.16%
84.32%
90.64%
96.30%
95.34%
44.48%
52.77%
30.20%
31.68%
58.90%
60.28%
61.98%
C-16
-------
Table C.1 (contd.)
Total Soil Carbon (gC/m2)
Percent Change in Total Soil Carbon for
Selected Time Periods
CD
#
641
641
641
641
641
641
641
641
641
642
642
642
642
642
642
642
642
642
643
643
643
643
643
643
643
643
643
CR
#
100
131
131
131
218
218
366
490
503
100
131
131
131
218
218
366
490
503
100
131
131
131
218
218
366
490
503
TILL
NT
CTSP
RT
NT
CTFP
RT
CTSP
RT
CTSP
NT
CTSP
RT
NT
CTFP
RT
CTSP
RT
CTSP
NT
CTSP
RT
NT
CTFP
RT
CTSP
RT
CTSP
1980
2962
2962
2962
2962
2962
2962
2962
2962
2962
2846
2846
2846
2846
2846
2846
2846
2846
2846
1787
1787
1787
1787
1787
1787
1787
1787
1787
1990
3197
3167
3175
3198
3171
3178
3202
3115'
3207
3097
3071
3079
3097
3070
3078
3097
3015
3096
1953
1928
1934
1953
1929
1935
1968
1894
1953
2000
4080
3644
3697
3791
3663
3806
3500
3506
3451
4094
3622
3681
3777
3641
3787
3265
3436
3150
2898
2414
2476
2630
2419
2571
2290
2350
2044
2010
4595
3902
3980
4161
3995
4217
3777
3718
3748
4599
3868
3951
4136
3989
4222
3382
3650
3319
3387
2630
2712
2979
2692
2935
2420
2582
2173
2020 2030 1990/2000 2000/2010 2010/2020 2020/2030 1990/2030
5311
4047
4140
4388
4458
4781
4398
4244
4215
5340
4200
4309
4542
4455
4789
3845
4208
3662
4097
2885
2987
3341
3105
3460
2968
3060
2509
5843
4341
4472
4704
4942
5343
4676
4716
4497
5915
4706
4896
5097
4911
5339
4033
4699
3890
4533
3275
3446
3811
3480
3919
3261
3448
2771
27.61%
15.06%
16.44%
18.54%
15.51%
19.76%
9.30%
12.55%
7.60%
32.19%
17.94%
19.55%
21 .95%
18.59%
23.03%
5.42%
13.96%
1.74%
48.38%
25.20%
28.02%
34.66%
25.40%
32.86%
16.36%
24.07%
4.65%
12.62%
7.08%
7.65%
9.75%
9.06%
" 10.79%
7.91%
6.04%
8.60%
12.33%
6.79%
7.33%
9.50%
9.55%
11.48%
3.58%
6.22%
5.36%
16.87%
8.94%
9.53%
13.26%
11.28%
14.15%
5.67%
9.87%
6.31%
15.58%
3.71%
4.02%
5.45%
11.58%
13.37%
16.44%
14.14%
12.45%
16.11%
8.58%
9.06%
9.81%
11.68%
13.42%
13.69%
15.28%
10.33%
20.96%
9.69%
10.14%
12.15%
15.34%
17.88%
22.64%
18.51%
15.46%
10.01%
7.26%
8.01%
7.20%
10.85%
11.75%
6.32%
11.12%
6.69%
10.76%
12.04%
13:62%
12.21%
10.23%
11.48%
4.88%
11.66%
6.22%
10.64%
13.51%
15.36%
14.06%
12.07%
13.26%
9.87%
12.67%
10.44%
82.76%
37.06%
40.85%
47.09%
55.84%
68.12%
46.03%
51.39%
40.22%
90.99%
53.23%
59.01%
64.57%
59.96%
73.45%
30.22%
55.85%
25.64%
132.10%
69.86%
78.17%
95.13%
80.40%
102.53%
65.70%
82.04%
41.88%
C-17
-------
Table C.2 Maximum, Minimum, and Average Percent Change in Total Soil Carbon
for 1990-2030 by CD for the Status Quo Scenario
CD#
CD221
CD222
CD223
CD231
CD232
CD241
CD242
CD251
CD252
CD253
CD261
CD262
CD271
CD272
CD281
CD282
CD311
CD312
CD313.
CD314
CD321
CD322
CD323
CD341
CD342
CD351
CD352
CD353
CD391
CD392
CD393
CD401
CD402
CD403
Maximum
42.97%
44.97%
46.69%
94.97%
142.20%
49.43%
65.27%
60.23%
67.27%
68.77%
80.46%
107.26%
87.85%
104.08%
50.69%
54.42%
128.40%
186.19%
169.98%
141.90%
70.61%
65.57%
76.37%
51.10%
48.50%
103.84%
101.88%
98.43%
44.03%
58.74%
76.59%
69.01%
68.10%
74.24%
Minimum
2.90%
0.13%
-1.90%
23.89%
29.43%
12.73%
20.06%
20.43%
35.27%
23.57%
26.95%
33.74%
34.65%
44.88%
27.44%
26.64%
34.82%
30.42%
23.87%
42.79%
45.39%
44.40%
46.74%
23.55%
25.76%
44.27%
46.07%
40.13%
25.21%
30.03%
35.11%
13.97%
12.03%
14.41%
Average
19.87%
19.08%
18/74%
48.14%
62.27%
28.20%
34,66%
33.15%
49.36%
37.07%
43.60%
55.79%
50.23%
59.68%
40.96%
43.37%
69.11%
90.98%
79.60%
79.88%
58.79%
56.46%
61.27%
34.39%
36.39%
61.57%
59.59%
54.71%
34.14%
40.48%
47.16%
33.70%
37.18%
43.42%
C-18
-------
Table C.2 (contd.)
CD # Maximum
CD411
CD412
CD413
CD414
CD415
CD416
CD421
CD422
CD431
CD432
CD433
CD441
CD442
CD443
CD444
CD471
CD472
CD473
CD474
CD475
CD531
CD532
CD533
CD561
CD562
CD571
CD572
CD573
CD581
CD582
GD583
CD584
CD591
CD592
CD601
CD602
CD603
CD611
CD612
74.05%
79.32%
72.25%
78.74%
75.38%
83.53%
74.12%
74.62%
61.55%
60.42%
61.02%
30.42%
53.84%
53.75%
65.36%
132.95%
147.03%
129.08%
227.10%
143.49%
89.94%
89.91%
110.91%
102.00%
103.05%
72.72%
78.40%
82.16%
67.25%
131.60%
96.06%
130.14%
86.38%
77.51%
59.21%
77.24%
78.29%
85.30%
92.42%
Minimum
33.31%
27.45%
16.59%
32.59%
33.79%
33.66%
34.39%
25.93%
37.95%
35.92%
21.90%
12.26%
8.23%
15.79%
13.22%
16.02%
17.30%
-15.16%
15.19%
13.49%
13.08%
14.16%
11.07%
27.26%
29.40%
42.52%
39.83%
24.63%
5.60%
20.82%
8.78%
23.23%
7.75%
4.60%
32.27%
26.06%
28.32%
15.33%
14.06%
Average
46.19%
43.95%
39.60%
47.67%
47.96%
50.05%
49.02%
51.05%
49.10%
50.43%
45.95%
20.03%
28.62%
38.90%
79.16%
93.26%
85.72%
130.22%
91.28%
53.19%
57.70%
64.40%
61.48%
62.35%
53.22%
55.03%
48.63%
38.56%
62.81%
47.11%
57.99%
47.59%
46.58%
43.15%
59.32%
57.89%
45.06%
46.76%
C-19
-------
Table C.2 (contd.)
CD # Maximum
CD631
CD632
CD633
CD634
CD641
CD642
CD643
Maximum
Minimum
Average
All CD's
Absolute
115.10%
99.13%
89.85%
90.64%
82.77%
90.99%
132.10%
227.10%
30.42%
87.38%
227.10%
Minimum
34.86%
32.19%
30.62%
30.20%
37.07%
25.65%
41.88%
46.74%
-1.90%
25.26%
-1.90%
Average
92.53%
79.06%
70.21%
68.12%
50.60%
53.56%
78.60%
130.22%
18.74%
53.08%
54.66%
C-20
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Table C.4 Maximum, Minimum, and Average Percent Change in Total Soil Carbon for
1990-2030 by CD for Crop Rotations with Cover Crops
CD#
CD241
CD242
CD271
CD272
CD281
CD282
CD311
CDS 12
CDS 13
CDS 14
CD341
CD342
CD351
CD352
CD353
CD411
CD412
CD413
CD414
CD415
CD416
CD421
CD422
CD431
CD432
CD433
CD441
CD442
CD443
CD444
CD561
CD562
CD571
CD572
CD573
CD591
CD592
Maximum
49.43%
65.27%
87.85%
104.08%
50.69%
54.42%
150.59%
256.71%
268.02%
185.15%
60.23%
61.61%
103.84%
101.88%
98.43%
84.73%
83.02%
83.21%
82.36%
90.21%
94.43%
150.05%
105.09%
108.54%
116.39%
103.51%
98.89%
139.63%
151.17%
196.43%
180.92%
167.76%
78.17%
117.36%
111.54%
89.05%
75.87%
Minimum
12.73%
20.06%
34.65%
44.88%
27.44%
32.55%
34.82%
30.42%
23.87%
42.79%
23.55%
26.21%
44.27%
46.07%
40.13%
33.31%
27.45%
16.59%
32.59%
33.79%
33.66%
34.39%
25.93%
37.95%
35.92%
21.90%
12.26%
8.23%
20.72%
13.22%
31.33%
30.99%
42.52%
39.83%
24.63%
7.75%
4.60%
Average
29.60%
36.87%
50.50%
60.84%
41.68%
44.22%
82.48%
121.47%
115.47%
98.79%
37.22%
40.34%
62.84%
61.37%
56.67%
50.63%
47.10%
43.14%
50.97%
51.60%
53.23%
64.76%
57.12%
56.55%
62.72%
53.06%
41.33%
62.03%
71.72%
85.87%
89.55%
80.64%
54.37%
60.37%
50.78%
51.11%
49.63%
C-26
-------
Table C.4 (contd.)
CD # Maximum
CD601
CD602
CD603
CD611
CD612
CD641
CD642
CD643
Maximum
Minimum
Average
All CD's
Absolute
70.84%
116.06%
131.71%
131.19%
213.22%
103.18%
113.26%
187.59%
268.02%
49.43%
117.19%
268.02%
Minimum
32.27%
26.06%
28.32%
22.00%
21.56%
37.07%
25.65%
41.88%
46.07%
4.60%
28.64%
4.60%
Average
47.05%
76.59%
81.13%
63.50%
91.51%
61.80%
65.72%
102.78%
121.47%
29.60%
62.64%
59.87%
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C-34
-------
Table C.6 Percent Change in Total Soil Carbon from 1990-2030 for CRP Scenarios
and Dominant Crop Rotation
CD#
221
222
223
231
232
241
242
251
252
253
261
262
271
272
281
282
311
312
313
314
321
322
323
341
342
351
352
353
391
392
393
401
402
403
Dominant
Rotation
Percent
Change
28.90%
29.44%
30.13%
33.03%
39.95%
39.57%
43.53%
20.43%
35.27%
23.57%
28.75%
39.04%
35.35%
45.05%
24.18%
28.27%
35.43%
37.89%
32.51%
40.02%
45.39%
44.40%
46.74%
32.58%
37.21%
44.27%
46.07%
41.19%
28.26%
36.83%
45.84%
33.46%
41.99%
46.23%
CRP1
Percent
Change
22.05%
22.56%
21.87%
22.58%
25.07%
25.57%
29.34%
21.96%
28.78%
24.35%
18.84%
24.04%
25.14%
30.66%
27.04%
30.74%
29.77%
35.58%
33.03%
36.10%
30.20%
28.30%
32.05%
39.64%
42.04%
31.82%
33.14%
33.01%
21.43%
25.55%
29.69%
25.57%
29.68%
32.84%
CRP2
Percent
Change
13.49%
13.71%
13.14%
16.77%
15.85%
11.85%
16.79%
19.11%
20.53%
22.27%
14.94%
17.84%
16.42%
20.17%
19.44%
19.74%
22.00%
29.21%
33.01%
26.75%
23.39%
21.62%
25.89%
32.08%
31.92%
22.60%
22.92%
27.46%
15.48%
15.71%
15.98%
11.31%
11.35%
11.52%
C-35
-------
Table C.6 (contd.)
CD#
411
412
413
414
415
416
421
422
431
432
433
441
442
443
444
471
472
473
474
475
531
532
533
561
562
571
572
573
581
582
583
584
591
592
601
602
603
Dominant
Rotation
Percent
Change
33.31%
30.45%
16.59%
32.59%
33.79%
33.66%
40.99%
47.73%
46.66%
53.06%
50.65%
18.22%
34.68%
33.03%
45.25%
16.02%
17.30%
15.16%
15.19%
13.49%
13.08%
14.16%
11.07%
51.53%
56.67%
43.14%
46.86%
48.23%
43.63%
27.57%
28.19%
31.38%
55.24%
61.26%
22.51%
29.85%
29.12%
CRP1
Percent
Change
25.48%
24.57%
25.26%
27.15%
27.02%
27.87%
29.62%
35.23%
34.38%
38.43%
39.80%
26.23%
38.93%
41.25%
47.99%
12.21%
15.72%
14.73%
15.07%
14.11%
12.83%
13.14%
15.48%
46.53%
44.89%
32.96%
35.34%..
40.01%
29.36%
47.10%
40.03%
47.11%
41.27%
51.50%
28.62%
36.60%
35.80%
CRP2
Percent
Change
15.95%
16.97%
15.72%
18.48%
18.53%
19.58%
20.68%
23.88%
24.04%
27.19%
31.79%
31.88%
41.70%
49.40%
51.30%
13.94%
17.06%
18.36%
18.66%
19.01%
16.95%
19.30%
24.38%
17.66%
17.17%
15.31%
18.03%
25.04%
15.75%
6.81%
11.45%
14.54%
12.64%
20.06%
20.98%
25.61%
28.54%
C-36
-------
Table C.6 (contd.)
CD#
611
612
631
632
633
634
641
642
643
Average
Maximum
Minimum
Dominant
Rotation
Percent
Change
25.22%
32.96%
50.29%
35.90%
31.69%
30.55%
37.07%
53.24%
69.87%
35.61%
69.87%
11.07%
CRP1
Percent
Change
32.78%
41.90%
34.96%
35.26%
32.33%
32.28%
31.73%
-44.54%
62.72%
30.98%
62.72%
12.21%
CRP2
Percent
Change
32.81%
52.30%
10.70%
14.51%
17.16%
19.92%
29.43%
35.95%
61.94%
21.89%
61.94%
6.81%
C-37
-------
-------
APPENDIX D
Maps and Displays of Simulation Results and Study Region Characteristics
D.I Climate Station Locations In and Near the Study Region
D.2 Production Areas and State Boundaries
D.3 Production Areas and Climate Divisions in the RAMS Study Area
D.4 Cropland Distribution for the Study Region
D.5 Cover Crop Land Distribution Within the Study Region
D.6 CRP Distribution Within the Study Region
D.7 Simulated 1990 Soil Carbon (gC/m2) Distribution Within the Study Region
D.8 Simulated 1990 Soil Carbon (gC/m2) Distribution Within the Study Region
Weighted by Cropland Distribution
D.9 Simulated 2030 Soil Carbon (gC/m2) Distribution Within the Study Region
D. 10 Simulated 2030 Soil Carbon (gC/m2) Distribution Within the Study Region
Weighted by Cropland Distribution
D. 11 Increase in Soil Carbon (gC/m2) Within the Study Region From 1990 to 2030
under the Status Quo Scenario
D.12 Increase in Soil Carbon (gC/m2) Within the Study Region From 1990 to 2030
under the Status Quo Scenario Weighted by Cropland Distribution
D. 13 Percent Change in Soil Carbon Within the Study Region From 1990 to 2030
under the Status Quo Scenario
D. 14 Increase in Soil Carbon (gC/m2) Within the Study Region For 2030
For High Conservation Relative to the Status Quo Scenario
D.15 Increase in Soil Carbon (gC/m2) Within the Study Region For 2030
For High Conservation Relative to the Status Quo Scenario Weighted by Cropland
Distribution
D.16 Percent Difference in Soil Carbon Within the Study Region For 2030
For High Conservation Relative to the Status Quo Scenario
D.17 Increase in Soil Carbon (gC/m2) Within the Study Region For 2030
For Cover Crops Relative to the Status Quo Scenario
D.I8 Increase in Soil Carbon (gC/m2) Within the Study Region For 2030
For Cover Crops Relative to the Status Quo Scenario Weighted by Cover Crop
Distribution
D. 19 Percent Difference in Soil Carbon Within the Study Region For 2030
For Cover Crops Relative to the Status Quo Scenario
D.20 Increase in Soil Carbon (gC/m2) Within the Study Region For 2030
For CRP1 Relative to the Dominant Rotation
D-l
-------
D.21 Increase in Soil Carbon (gC/m2) Within the Study Region For 2030
For CRP1 Relative to the Dominant Rotation Weighted by CRP Distribution
D.22 Percent Difference in Soil Carbon Within the Study Region For 2030
For CRP1 Relative to the Dominant Rotation
D.23 Increase in Soil Carbon (gC/m2) Within the Study Region For 2030
For CRP2 Relative to the Dominant Rotation
D.24 Increase in Soil Carbon (gC/m2) Within the Study Region For 2030
For CRP2 Relative to the Dominant Rotation Weighted by CRP Distribution
D.25 Percent Difference in Soil Carbon Within the Study Region For 2030
For CRP2 Relative to the Dominant Rotation
D-2
-------
Figure D.I Climate station locations in and near the study region
D-3
-------
Figure D.2 Production areas and state boundaries
D-4
-------
Figure D.3 Production areas and climate divisions in the RAMS study area
D-5
-------
NX -V NX' NX' NX NX.
•s ^ ,= -
•g E o)
VI nl VJ A
Iss s
<£< 5
Figure D.4 Cropland distribution for the study region
D-6
-------
Figure D.5 Cover crop land distribution within the study region
D-7
-------
Figure D.6 CRP distribution within the study region
D-8
-------
Figure D.7 Simulated 1990 Soil Carbon (gC/m2) distribution within the study region
D-9
-------
Figure D.8 Simulated 1990 Soil Carbon (gC/m2) distribution within the study region
weighted by cropland distribution
D-10
-------
Figure D.9 Simulated 2030 Soil Carbon (gC/m2) distribution .within the study region
D-ll
-------
Figure D. 10 Simulated 2030 Soil Carbon (gC/m2) distribution within the study region weighted
by cropland distribution D_12
-------
Figure D. 11 Increase in Soil Carbon (gC/m2) within the study region from 1990 to 2030 under
the Status Quo Scenario D_13
-------
Figure D. 12 Increase in Soil Carbon (gC/m2) within the study region from 1990 to 2030 under
the Status Quo Scenario weighted by cropland distribution
D-14
-------
Figure D. 13 Percent change in Soil Carbon within the study region from 1990 to 2030 under
the Status Quo Scenario
D-15
-------
Figure D.14 Increase in Soil Carbon (gC/m2) within the study region for 2030
for High Conservation relative to the Status Quo Scenario
D-16
-------
Figure D.15 Increase in Soil Carbon (gC/m2) within the study region for 2030 for High
Conservation relative to the Status Quo Scenario weighted by cropland
distribution D~17
-------
Figure D.16 Percent difference in Soil Carbon within the study region for 2030 for High
Conservation relative to the Status Quo Scenario
D-18
-------
Figure D. 17 Increase in Soil Carbon (gC/m2) within the study region for 2030 for Cover
Crops relative to the Status Quo Scenario
D-19
-------
ns * O)
g s-g 2
1 2 § .
g 0,5 o
™ =
III
< ce < o
Figure D.I8 Increase in Soil Carbon (gC/m2) within the study region for 2030 for Cover
Crops relative to the Status Quo Scenario weighted by cover crop distribution
. D-20
-------
Figure D. 19 Percent difference in Soil Carbon within the study region for 2030 for Cover
Crops relative to the Status Quo Scenario
D-21
-------
Figure D.20 Increase in Soil Carbon (gC/m2) within the study region for 2030 for CRP1
relative to the Dominant Rotation
D-22
-------
Figure D.21 Increase in Soil Carbon (gC/m2) within the study region for 2030 for CRP1
relative to the Dominant Rotation weighted by CRP distribution
D-23
-------
Figure D.22 Percent difference in Soil Carbon within the study region for 2030 for CRP1
relative to the Dominant Rotation
D-24
-------
Figure D.23 Increase in Soil Carbon (gC/m2) within the study region for 2030 for CRP2
relative to the Dominant Rotation
D-25
-------
Figure D.24 Increase in Soil Carbon (gC/m2) within the study region for 2030 for CRP2
relative to the Dominant Rotation weighted by CRP distribution
D-26
-------
Figure D.25 Percent difference in Soil Carbon within the study region for 2030 for CRP2
relative to the Dominant Rotation
D-27. '
-------
-------
APPENDIX E
Land Use and Soil Physical Properties for All Climate Divisions
E. 1 Baseline and Conservation Tillage Policy Scenario Crop Rotations, and Acreages
E.2 Baseline and Cover Crop Rotations, Tillage, and Acreages
E.3 Soil Physical Properties, Percentage Weights, and Ranks for all Climate Divisions
E-l
-------
Table E.1 Baseline and Conservation Tillage Policy Scenario Crop Rotations, and Acreages
Baseline
LOW-CST
Mediun-CST High-CST
PA
CR
#
CROP ROT AT I OM
22 239 CSL,CSL,CSL,SWT
22 239 CSL,CSL,CSL,SWT
22 239 CSL,CSL,CSL,SUT
22 350 OTS,HLH,HLH,HLH
22 350 OTS,HLH,HLH,HLH
22 350 OTS,HLH,HLH,HLH
Total PA 22 Area =
23 100 CRN
23 100 CRN
23 145 CRN,CRN,WWT,HLH,HLH,HLH
23 145 CRN,CRN,WWT,HLH,HLH,HLH
23 145 CRN,CRN,UWT,HLH,HLH,HLH
23 235 CSL
23 350 OTS,HLH,HLH,HLH
23 366 OTS,NLH,NLH,NLH
23 366 OTS,NLH,NLH,NLH
Total PA 23 Area =
24 100 CRN
24 100 CRN
24 131 CRN, CRN, SOY
24 186 CRN,SOY
24 201 CRN,SOY,UWT
24 262 CSL,OTS,HLH,HLH,HLH '
24 503 HLH,HLH,HLH,HLH
24 508 NLH,NLH,NLH,NLH
Total PA 24 Area =
25 100 CRN
25 144 CRN,CRN,WWT,HLH,HLH
25 145 CRN,CRN,UWT,HLH,HLH,HLH
25 186 CRN,SOY
25 186 CRN,SOY
25 186 CRN,SOY
25 366 OTS,NLH,NLH,NLH
25 366 OTS,NLH,NLH,NLH
Total PA 25 Area =
26 100 CRN
26 100 CRN
26 100 CRN
26 145 CRN,CRN,UWT,HLH,HLH,HLH
26 215 CRN.SUT.SUT
26 280 CSL.SUT
26 366 OTS,NLH,NLH,NLH
Total PA 26 Area =
27 100 CRN
27 138 CRN,CRN,SOY,WWT,HLH
27 186 CRN,SOY
27 186 CRN,SOY
27 186 CRN,SOY
27 201 CRN.SOY.WUT
27 243 CSL,CSL,OTS,HLH,HLH
27 366 OTS.NLH.NLH.NLH
Total PA 27 Area =
TILLAGE
TYPE
CTFP
RT
NT
CTFP
CTSP
NT
RT
NT
1 CTSP
1 RT
1 NT
RT
CTFP
CTFP
NT
CTSP
NT
RT
CTSP
RT
CTSP
CTSP
CTSP
NT
CTSP
) CTSP
CTSP
RT
NT
CTSP
RT
CTSP
, RT
NT
1 CTSP
RT
RT
CTSP
NT
CTSP
CTSP
RT
NT
RT
CTSP
CTSP
ROTATION
ACRES
414,132
0
2,695
• 40,887
11,023
0
468,736
326,010
83,411
1,158,930
124,394
0
527,634
501,821
128,471
0
2,850,672
487,973
63,320
388,903
529,266
353,556
244,873
69,815
26,332
2,164,038
104,504
1,747,290
455,881
342,912
1,473,020
21,121
148,664
0
4,293,392
21,487
257,928
72,426
1,157,930
223,820
367,488
374,181
2,475,260
10,267
2,283,309
1,124,760
1,141,290
175,720
1,041,609
442,480
136,927
ROTATION
ACRES
252,501
160,817
3,345
38,052
3,649
10,370
468,734
326,010
83,411
1,158,930
124,394
0
527,634
501.821
128,471
0
2,850,671
487,973
63,320
388,903
529,266
353,556
244,873
69,815
26,332
2,164,038
104,504
1,747,290
455,881
342,912
1,473,020
21,121
148,664
0
4,293,392
21,487
257,928
72,426
1,157,930
223,820
367,488
374,181
2,475,260
10,267
2,283,309
1,124,760
1,141,290
175,720
1,041,610
442,480
136,927
ROTATION
ACRES
252,501
160,817
3,345
38,052
3,649
10,370
468,734
326,010
83,411
1,158,930
124,394
0
527,634
501,821
128,471
0
2,850,671
487,973
63,320
388,903
529,266
353,556
244,873
69,815
26,332
2,164,038
104,504
1,747,290
455,881
342,912
1,473,020
21,121
148,664
0
4,293,392
21,487
257,928
72,426
1,157,930
223,820
367,488
374,181
2,475,260
10,267
2,283,309
1,124,760
1,141,290
175,720
1,041,610
442.480
136,926
ROTATION
ACRES
252,501
160,817
3,345
38,052
3,373
10,646
468,734
429,397
358,626
0
14,214
124,302
534,427
1,264,970
0
124,740
2,850,676
59,412
491,881
388,903
529,266
353,556
244,873
69,815
26,332
2,164,037
104,504
1,728,790
456,927
0
1,373,110
481,404
48,752
99,914
4,293,401
21,487
257,928
72,426
1,157,930
223,820
367,488
374,181
2,475,260
10,267
2,283,308
1,124,760
1,141,290
175,720
1,041,610
442,480
136,926
6,356,362 6,356,363 6,356,362 6,356,361
E-2
-------
Table E.1 (contd.)
Baseline Low-CST
Medium-GST High-CST
PA
#
28
28
28
28
28
28
28
28
28
28
28
28
28
31
31
31
31
31
31
-31
31
31
31
31
32
32
32
32
32
32
32
32
32
32
34
34
34
34
34
34
34
35
35
35
35
35
35
35
35
CR CROP ROTATION TILLAGE
it "
144 CRN,CRN,WWT,HLH,HLH
144 CRN.CRN.WWT.HLH.HLH
144 CRN.CRN.WWT.HLH.HLH
186 CRN, SOY
186 CRN, SOY, ,
201 CRN,SOY,UUT
201 CRN.SOY.WWT
458 SOY.WWT.SOY
458 SOY.WWT.SOY
503 HLH.HLH.HLH.HLH
508 NLH.NLH.NLH.NLH
508 NLH.NLH.NLH.NLH
508 NLH.NLH.NLH.NLH
Total PA 28 Area =
131 CRN, CRN, SOY
131 CRN, CRN, SOY
138 CRN,CRN,SOY,WWT,HLH
145 CRN,CRN,WWT,HLH,HLH,HLH
196 CRN,SOY,OTS,NLH,NLH
196 CRN,SOY,OTS,NLH,NLH
196 CRN,SOY,OTS,NLH,NLH
250 CSL,CSL,SOY
250 CSL,CSL,SOY
503 HLH.HLH.HLH.HLH
508 NLH.NLH.NLH.NLH
Total PA 31 Area =
125 CRN,CRN,OTS.NLH,NLH
125 CRN.CRN.OTS.NLH.NLH
125 CRN.CRN.OTS.NLH.NLH
131 CRN, CRN, SOY
131 CRN, CRN, SOY
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
203 CRN.SOY.WWT.HLH.HLH.HLH
203 CRN.SOY.WWT.HLH.HLH.HLH
Total PA 32 Area =
144 CRN,CRN,WWT,HLH,HLH
144 CRN.CRN.WWT.HLH.HLH
144 CRN.CRN.WWT.HLH.HLH
186 CRN, SOY
186 CRN, SOY
232 CRN.NLH.NLH.NLH
232 CRN.NLH.NLH.NLH
Total PA 34 Area =
100 CRN
115 CRN,CRN,CRN,HLH,HLH,HLH
131 CRN, CRN, SOY
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
201 CRN.SOY.WWT
366 OTS,NLH,NLH,NLH
TYPE
CTSP
RT
NT
RT
NT
RT
NT
RT
NT
CTSP
CTFP
CTSP
RT
RT
NT
NT
CTSP
CTSP
RT
NT
RT
NT
CTSP
CTSP
CTSP
RT
NT
CTSP
NT
CTSP
RT
NT
CTSP
RT
CTSP
RT
NT
CTFP
NT
CTSP
RT
NT
CTSP
NT
CTSP
RT
NT
RT
CTSP
ROTATION
ACRES
98,744
75,641
69,980
118,464
, 0
0
0
83,091
0
433,669
358,112
0
0
1,237,701
292,342
0
0
604,068
588,494
0
0
276,398
170,859
34,087
735.089
2,701,336
747,307
152,283
264,955
10,753
0
1,533,360
0
0
3,350,542
97,415
6,156,615
3,441,446
526,685
496.755
4,775
3,246
273,007
378,153
5,124,067
52,432
713.294
0
7,620,040
1,926,500
354,424
2,844,111
394,064
ROTATION
ACRES
98.744
75,641
69,980
118,464
0
0
0
83,091
0
433,669
358,112
0
0
1,237,701
30,233
312,683
0
542,929
0
538,506
0
0
474,779
63,987
738,218
2.701,336
0
887,650
264,955
10,753
0
476,310
1,127,210
0
3,292,322
97,415
6,156,615
2,655,770
1,088,400
1,371,860
0
8,021
0
0
5,124,051
52,432
713,294
0
7,620,040
1,926,500
354,424
2,844,111
394,064
ROTATION
ACRES
0
10,810
233,988
136,347
0
0 '
0
130,040
0
368,405
358,112
0
0
1,237,702
298,712
323,204
0
0
0
270,028
315,072
0
433,623
329,515
731,185
2,701,339
0
887,650
264,955
10,753
0
476,310
1,127,210
0
3.292,320
97,413 ,
6,156,611
2,005,620
1,088,400
2,022.010
0
8,021
0
0
5,124,051
52,432
713,294
0
7,620,040
1,926,500
354,424
2,844,111
394,064
ROTATION
ACRES
0
0
244,787
30,026
7,255
104,335
95,840
119,931
279,914
0
0
332,708
22,905
1,237,701
298,712
0
82,408
0
0
270,028
1,351,230
0
215,004
303,636
180,319
2,701,337
0
0
1,131,570
0
10,753
0
652,754
1,006,510
1,895,510
1,459,520
6,156,617
839,390
1,088,400
3,188,250
0
8,021
0
0
5,124,061
0
713,294
52,432
4,982,120
1.926.500
2,992.330
2,844,120
394,064
Total PA 35 Area =
13,904,865 13,904,865 13,904,865 13,904,860
E-3
-------
Table E.1 (contd.)
Baseline
Low-CST
Hedium-CST High-CST
PA
#
39
39
39
39
39
39
39
39
39
39
39
40
40
40
40
40
40
40
40
40
40
40
41
41
41
41
41
41
41
41
41
41
41
41
42
42
42
42
42
42
42
42
42
42
CR CROP ROTATION TILLAGE
#
131 CRN, CRN, SOY
131 CRN, CRN, SOY
131 CRN, CRN, SOY
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
189 CRN,SOY,CRN,UUT,HLH,HLH
201 CRN,SOY,UWT
243 CSL,CSL,OTS,HLH,HLH
350 OTS,HLH,HLH,HLH
366 OTS,NLH,NLH,NLH
Total PA 39 Area =
100 CRN
100 CRN
186 CRN, SOY
186 CRN, SOY
246 CSL,CSL,OTS,NLH,NLH
246 CSL,CSL,OTS,NLH,NLH
280 CSL.SWT
280 CSL.SWT
366 OTS,NLH,NLH,NLH
366 OTS,NLH,NLH,NLH
503 HLH.HLH.HLH.HLH
Total PA 40 Area =
100 CRN
100 CRN
115 CRN,CRN,CRN,HLH,HLH,HLH
138 CRN,CRN,SOY,WWT,HLH
138 CRN.CRN,SOY,WWT,HLH
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
243 CSL,CSL,OTS,HLH,HLH
503 HLH.HLH.HLH.HLH
508 NLH,NLH,NLH,NLH
Total PA 41 Area =
100 CRN
145 CRN,CRN,UWT,HLH,HLH,HLH
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
201 CRN.SOY.WWT
244 CSL,CSL,OTS,HLH,HLH,HLH
262 CSL.OTS.HLH.HLH.HLH
503 HLH.HLH.HLH.HLH
508 NLH.NLH.NLH.NLH
TYPE
CTSP
RT
NT
CTSP
RT
NT
CTSP
CTFP
CTSP
CTSP
CTSP
CTSP
RT
CTFP
NT
CTSP
NT
CTSP
NT
CTFP
NT
CTFP
CTSP
RT
CTSP
CTSP
RT
CTFP
CTSP
RT
NT
CTSP
CTSP
CTFP
CTSP
CTSP
CTSP
RT
NT
RT
CTSP
CTSP
CTSP
CTSP
ROTATION
ACRES
395,
2,320,
302.
3,087,
1,799,
44,
1,325,
975,
1,188,
51,
716,
12,206,
127,
2,083,
963.
177,
255,
649,
207,
1,608,
6.072.
4,156.
2,081.
1.017,
6,331,
69,
7,634,
737,
916,
1,966,
304,
25,216.
2,948,
240,
4.601,
2.352.
397.
2.301.
191.
33,
325,
186,
228
303
075
080
120
343
780
345
890
390
869
423
269
560
468
694
575
0
324
0
687
0
340
917
220
0
0
734
390
762
249
120
830
740
396
965
406"
080
286
100
500
254
078
004
820
569
067
ROTATION
ROTATION
ACRES
395,
2,320,
302,
3,087,
1,799,
44,
1,325,
975,
1,188,
51,
716,
12,206,
127,
2,083,
963.
177.
255.
649,
207,
1,608,
6.072.
4.156.
2.081.
1.017,
6,331,
69.
7,634,
737,
916,
228
300
075
080
120
343
780
345
890
390
869
420
269
560
468
694
575
0
324
0
687
0
340
917
220
0
0
734
390
762
249
120
830
740
1,966.395
304,
25,216.
2,948,
240,
4.601.
2.352.
397,
2,301.
191,
33,
325,
186.
965
405
080
286
100
500
254
078
004
820
569
067
2
3
1
1
1
12
2
1
6
4
2
1
6
7
1
25
2
4
2
2
ACRES
395,
,320,
302,
,087,
,799,
44,
.325,
975.
.188,
51,
716.
,206,
,223,
640,
487,
255,
649,
207,
,608,
.072,
,156,
,081,
,017,
,331,
69,
,634,
737,
916,
,966,
304,
,216,
,948,
465,
,601,
,352,
397,
,301,
325,
186,
228
303
075
080
120
343
780
345
890
390
869
423
0
610
520
882
490
0
334
0
732
0
340
908
220
0
0
734
390
762
249
120
830
740
395
965
405
080
109
100
500
254
080
0
0
569
067
2
3
1
1
1
12
2
1
1
6
1
1
3
7
7
1
25
2
1
2
3
2
ROTATION
ACRES
395,228
,320,300
302,075
,087,080
,799,120
44,343
,325,780
975,345
,188,890
51,390
716,869
,206,420
0
,246,930
0
,086,800
0
232,988
0
687,899
1,214
208,737
,608,340
,072,908
,916,370
,001,220
,780,750
552,384
0
2,922
69,249
,650,280
,721,150
442,153
,774,960
304,965
,216,403
,988,410
240,286
,845,550
,352,500
,112,480
,301,070
191,004
33,819
325,569
186,067
Total PA 42 Area =
13,576,758 13,576,758 13,576,759 13,576,755
E-4
-------
Table E.1 (contd.)
Baseline Low-GST
Medium-CST High-CST
PA
#
43
43
43
43
43
43
43
43
43
43
43
44
44
44
44
44
44
44
44
44
44
44
44
44
47
47
47
47
47
47
47
47
47
47
47
47
53
53
53
53
53
53
53
53
53
53
CR CROP ROTATION TILLAGE
# TYPE
100 CRN CTSP
131 CRN, CRN, SOY CTSP
131 CRN.CRN.SOY NT
186 CRN, SOY RT
186 CRN, SOY
201 CRN.SOY.WWT
201 CRN.SOY.WWT
201 CRN.SOY.UWT
262 CSL.OTS.HLH.HLH.HLH
503 HLH.HLH.HLH.HLH
508 NLH,NLH,NLH,NLH
Total PA 43 Area =
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
218 CRN.WWT"
366 OTS,NLH,NLH,NLH
366 OTS,NLH.NLH,NLH
416 SRG.SOY.SOY
416 SRG.SOY.SOY
458 SOY.WWT.SOY
459 SOY.WWT.WWT.WWT
459 SOY.WWT.WWT.WWT
503 HLH.HLH.HLH.HLH
508 NLH.NLH.NLH.NLH
Total PA 44 Area =
2 BAR, BAR, SOY
2 BAR, BAR, SOY
100 CRN
125 CRN.CRN.OTS.NLH.NLH
145 CRN.CRN.WWT.HLH.HLH.HLH
215 CRN.SWT.SWT
215 CRN.SWT.SWT
215 CRN.SWT.SWT
262 CSL.OTS.HLH.HLH.HLH
463 SHF.SWT
463 SMF.SUT
508 NLH.NLH.NLH.NLH
Total PA 47 Area =
4 BAR.BAR.SMF
4 BAR.BAR.SMF
100 CRN
100 CRN
100 CRN
244 CSL, CSL.OTS.HLH.HLH.HLH
463 SHF.SWT
463 SHF.SWT
503 HLH.HLH.HLH.HLH
508 NLH.NLH.NLH.NLH
NT
CTSP
RT
NT
CTSP
CTSP
CTFP
CTFP
CTSP
NT
NT
CTFP
RT
CTFP
NT
CTFP
RT
NT
CTSP
CTFP
CTSP
RT
NT
CTSP
CTFP
CTSP
RT
NT
CTFP
CTSP
RT
CTFP
CTSP
RT
CTSP
RT
NT
CTSP
CTSP
RT
CTFP
CTSP
ROTATION
ACRES
12,901
576,681
0
938,150
142,733
1,791,717
735,471
0
115,942
183,180
381 ,302
4,878,077
2,455,120
1,267,650
328,052
0
204,999
0
1,987,066
0
884,594
1,677,812
0
27,385
150,135
8,982,813
1,051,320
4,190,350
176,275
172,299
405,282
4,377,087
245,827
179,008
1,054,480
6,371,460
919,378
794,545
19,937,310
2.674,590
390,970
116,479
2,926,710
217,153
1.398,143
4,161,760
291,494
0
2,232,310
ROTATION
ACRES
12,901
576,681
0
938,150
142,733
1,791,717
735,471
0
115,942
183,180
381,302
4,878,077
2,455,120
1,267,650
328,052
0
204,999
0
1,987,066
0
884,594
1,677,812
0
27,385
150,135
8,982,813
1,051,320
4,190,350
176,275
172,299
405,282
4,377,087
245,827
179,008
1,054,480
6,371,460
919,378
794,545
19,937,311
2,674,590
390,970
116,479
2,926.710
217,153
1,398,144
4,161,760
291.494
0
2,232,310
ROTATION
ACRES
12,901
576,681
0
704,002
379,248
1,555,210
969,618
0
115,942
183,180
381,302
4,878,084
2,455,120
679,658
928,424
0
294,225
0
1,912,440
0
923,264
1,677,810
0
27,385
84,486
8,982,812
1,051,320
4,190,350
176,275
172,299
405,282
4,377,080
245,827
179,008
1,054,480
6,371,460
919,378
794,545
19,937,304
2,674,590
390,970
116,479
2,926,710
217,153
1,398,144
4,161,760
291,494
0
2,232,310
ROTATION
ACRES
14,878
486,900
19,255
0
1,160,470
0
1,673,620
841,623
117,719
182,316
381,302
4,878,083
2,455,120
0
1,605,080
68,888
330,802
51,004
0
1,895,600
0
1,626,810
922,129
27,385
0
8,982,818
1,051,320
4,190,350
176,275
172,299
405,282
4,377,080
245,827
179,008
1,054,480
6,371,460
919,378
794,545
19,937,304
2,751,340
390,970
116,479
1,999,450
1,148,800
1,122,970
3,382,580
1,218,760
56,872
2,221.390
Total PA 53 Area
14,409,609 14,409.610 14,409,610 14,409.611
E-5
-------
Table E.1 (contd.)
Baseline Low-CST
Medium-CST High-CST
PA
#
56
56
56
56
56
56
56
56
56
56
56
56
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
58
58
58
58
58
58
58
58
58
58
58
58
58
58
59
59
59
59
59
59
59
59
59
CR CROP ROTATION
#
100 CRN
131 CRN, CRN, SOY
131 CRN, CRN, SOY
131 CRN, CRN, SOY
145 CRN,CRN,WWT,HLH,HLH,
145 CRN,CRN,WWT,HLH,HLH,
186 CRN, SOY
201 CRN.SOY.WWT
246 CSL,CSL,OTS,NLH,NLH
246 CSL,CSL,OTS,NLH,NLH
262 CSL,OTS,HLH,HLH,HLH
366 OTS,NLH,NLH,NLH
Total PA 56 Area =
125 CRN,CRN,OTS,NLH,NLH
131 CRN, CRN, SOY
131 CRN, CRN, SOY
131 CRN, CRN, SOY
138 CRN,CRN,SOY,WWT,HLH
186 CRN, SOY
186 CRN, SOY
218 CRN.WWT
218 CRN.WWT
339 HLH,HLH,HLH,HLH,SRG,
339 HLH,HLH,HLH,HLH,SRG,
350 OTS,HLH,HLH,HLH
366 OTS,NLH,NLH,NLH
508 NLH,NLH,NLH,NLH
508 NLH,NLH,NLH,NLH
Total PA 57 Area =
100 CRN
100 CRN
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
218 CRN,UWT
218 CRN.WWT
218 CRN.UWT
262 CSL,OTS,HLH,HLH,HLH
463 SHF.SWT
490 WWT
503 HLH,HLH,HLH,HLH
508 NLH.NLH.NLH.NLH
508 NLH,NLH,NLH,NLH
Total PA 58 Area =
100 CRN
100 CRN
100 CRN
186 CRN, SOY
186 CRN, SOY
244 CSL,CSL,OTS,HLH,HLH,
339 HLH,HLH,HLH,HLH,SRG,
490 UUT
508 NLH,NLH,NLH,NLH
TILLAGE
TYPE
NT
CTSP
RT
NT
HLH CTSP
HLH RT
CTSP
RT
CTSP
RT
CTSP
CTSP
RT
CTSP
RT
NT
RT
CTFP
RT
CTFP
RT
SOY CTSP
SOY RT
CTSP
CTSP
CTFP
CTSP
RT
NT
CTSP
RT
NT
CTFP
RT
NT
CTSP
RT
RT
CTFP
CTFP
CTSP
CTSP
RT
NT
CTFP
RT
HLH CTSP
SOY CTFP
RT
CTSP
ROTATION
ACRES
0
1,602,820
263,518
34,678
177,729
33,770
42,860
224,954
0
107,794
134,538
26,553
2,649,214
216,102
5,131,259
1,786,693
246,912
0
425,206
0
0
848,572
632,251
309,341
252,900
110,116
197,473
0
10,156,825
2,049,830
164,984
83,817
337,128
0
1,275,058
0
0
378,581
0
1,018,680
169,989
8,250,320
31,758
13,760,145
2,468,150
370,376
71,274
853,576
572,148
192i474
202,938
352,391
361,605
ROTATION
ACRES
28,484
1,190,540
405,084
251,909
29,559
0
187,905
224,954
19,318
0
245,735
65,725
2,649,213
0
3,436,520
2,553,900
1,439,650
414,352
903,436
0
34,949
192,448
• 220,497
0
637,284
0
0
323,787
10,156,823
2,049,830
164,984
83,817
337,128
0
1,275,058
0
0
378,581
0
1,018,680
169,989
8,250,320
31,758
13,760,145
2,468,148
370,376
71,274
853,576
572,148
192,475
202,938
352,391
361,605
ROTATION
ACRES
34,515
909,217
405,083
527,205
29,936
0
187,905
224,954
19,318
0
245,359
65,725
2,649,217
0
2,500,090
2,423,750
2,519,800
341,021
729,988
0
0
395,925
802,432
0
120,009
0
0
323,787
10,156,802
2,049,830
164,984
83,817
337,128
0
1,275,060
0
0
378,581
0
1,018,680
169,989
8,250,320
31,758
13,760,147
1,319,597
822,931
766,241
1,307,170
119,594
192,475
202,938
352,391
361,605
ROTATION
ACRES
44,857
368,382
405,083
1,057,690
31,656
0
187,905
224,954
19,318
0
243,638
65,725
2,649,209
0
1,291,250
1,659,420
4,536,080
474,756
90,055
730,904
0
295,624
655,007
0
251,053
0
0
172,686
10,156,835
0
2,343,716
0
0
421,528
0
722,858
625,026
378,581
2.122,180
560,601
169,989
6.383,910
31,758
13,760,147
385,491
58,808
2,466,218
541,296
883,716
192,475
202,938
352,390
361,605
Total PA 59 Area
5,444,932 5,444,931
5,444,942 5,444.937
E-6
-------
Table E.1 (contd.)
Baseline Low-CST
Medium-CST High-CST
PA CR CROP ROTATION
60
60
60
60
60
60
60
60
60
60
60
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
63
63
63
63
63
63
63
63
63
63
63
63
63
63
64
64
64
64
64
64
64
64
64
131
131
186
186
186
201
201
201
503
508
508
131
131
186
186
186
350
350
350
458
458
458
503
503
508
508
508
186
186
186
218
218
218
218
490
490
503
503
508
508
508
100
131
131
131
218
218
366
490
503
CRN, CRN, SOY
CRN, CRN, SOY
CRN, SOY
CRN, SOY
CRN, SOY
CRN.SOY.WWT
CRN.SOY.WWT
CRN.SOY.WWT
HLH.HLH.HLH.HLH
NLH.NLH.NLH.NLH
NLH.NLH.NLH.NLH
Total PA 60 Area
CRN, CRN, SOY
CRN, CRN, SOY
CRN, SOY
CRN, SOY
CRN, SOY
OTS,HLH,HLH,HLH
OTS,HLH,HLH,HLH
OTS,HLH,HLH,HLH
SOY, WWT, SOY
SOY, WWT, SOY
SOY, WWT, SOY
HLH.HLH.HLH.HLH
HLH.HLH.HLH.HLH
NLH.NLH.NLH.NLH
NLH.NLH.NLH.NLH
NLH.NLH.NLH.NLH
Total PA 61 Area
CRN, SOY
CRN, SOY
CRN, SOY
CRN.WWT
CRN.WWT
CRN.WWT
CRN.WWT
WWT
WWT
HLH.HLH.HLH.HLH
HLH.HLH.HLH.HLH
NLH.NLH.NLH.NLH
NLH.NLH.NLH.NLH
NLH.NLH.NLH.NLH
Total PA 63 Area
CRN
CRN, CRN, SOY
CRN, CRN, SOY
CRN, CRN, SOY
CRN.WWT
CRN.WWT
OTS,NLH,NLH,NLH
WWT
HLH.HLH.HLH.HLH
TILLAGE
TYPE
CTSP
NT
CTSP
RT
NT
CTSP
RT
NT
CTSP
CTFP
RT
CTFP
NT
CTFP
RT
NT
CTSP
RT
NT
CTFP
RT
NT
CTSP
RT
CTFP
RT
NT
CTSP
RT
NT
CTFP
CTSP
RT
NT
RT
NT
CTFP
RT
CTSP
RT
NT
NT
CTSP
RT
NT
CTFP
RT
CTSP
RT
CTSP
ROTATION
ACRES
91,638
0
788,444
1,271,970
238,916
1,292,619
1,656,246
0
490,083
3,145,170
0
8,975,086
21,072
0
206,940
0
8,992
3,301
0
0
177,462
0
0
50,128
0
387,714
0
8,400
864,009
42,041
918,236
192,202
1,338,510
99,701
0
0
1,204,860
0
13,162,800
0
514,543
0
0
17,472,893
56,076
2,226,539
674,106
0
690,688
0
50,603
272,811
173,778
ROTATION
ACRES
431,327
377,032
44,218
1,049,420
492,444
722,805
1,878,790
343,791
490,083
3,145,170
0
8,975,080
21,071
0
193,433
8,443
14,282
0
3,343
0
0
206,040
0
50,099
0
358,960
5,236
3,110
864,017
0
961,323
192,202
1,039,770
99,701
338,246
0
1,187,790
0
12,431,700
707,199
0
514,943
0
17,472,874
56,076
2,226,540
674,106
0
690,688,
0
50,602
272,811
173,778
ROTATION
ACRES
115,558
101,745
0
1,132,310
1,316,790
0
1,795,900
1,145,420
490,083
2,877,270
0
8,975,076
21,063
0
165,904
4,852
45,942
0
3,343
0
0
209,630
0
50,099
0
354,829
5,236
3,110
864,008
0
961,323
192,202
1,039,770
99,701
338,246
0
1,187,790
0
12,431,700
707,199
0
514,943
0
17,472,874
565,222
1,540,050
513,001
330,004
538,024
167,580
50,602
266,336
173,778
ROTATION
ACRES
0
91,638
0
0
2,637,580
0
1,369,300
1,572,400
490,083
1,255,170
1,558,910
8,975,081
260
21,973
0
0
226,756
0
0
3,318
66,052
0
150,888
0
50,089
108,510
172,972
63,190
864,007
0
0
1,145,744
0
99,701
338,246
1,201,550
0
1,116,810
9,450,470
3,614,070
0
0
506,310
17,472,901
0
178,121
0
2,743,270
51,902
701,198
50,602
245,718
173,778
Total PA 64 Area =
4,144,601 4,144,601 4,144,597 4.144,589
E-7
-------
Table E.2 Baseline and Cover Crop Rotations, Tillage, and Acreages
BASELINE
PA
#
22
22
22
22
23
23
23
23
23
23
23
24
24
24
24
24
24
24
24
24
24
25
25
25
25
25
25
25
26
26
26
26
26
26
26
27
27
27
27
27
27
27
27
27
CR CROP ROTATION
#
239 CSL,CSL,CSL,SWT
239 CSL,CSL,CSL,SWT
350 OTS.HLH.HLH.HLH
350 OTS.HLH.HLH.HLH
Total PA 22 Area =
100 CRN
100 CRN
145 CRN.CRN.UWT.HLH.HLH,
145 CRN.CRN.UWT.HLH.HLH,
235 CSL
350 OTS.HLH.HLH.HLH
366 OTS.NLH.NLH.NLH
Total PA 23 Area =
100 CRN
100 CRN
131 CRN, CRN, SOY
186 CRN .SOY
201 CRN.SOY.WWT
201 CRN.SOY.WWT
262 CSL, OTS.HLH.HLH.HLH
262 CSL, OTS.HLH.HLH.HLH
503 HLH.HLH.HLH.HLH
508 NLH.NLH.NLH.NLH
Total PA 24 Area =
100 CRN
144 CRN.CRN.WWT.HLH.HLH
145 CRN.CRN.WWT.HLH.HLH.
186 CRN. SOY
186 CRN, SOY
186 CRN, SOY
366 OTS.NLH.NLH.NLH
Total PA 25 Area =
100 CRN
100 CRN
100 CRN
145 CRN.CRN.WWT.HLH.HLH.
215 CRN.SWT.SWT
280 CSL.SWT
366 OTS.NLH.NLH.NLH
Total PA 26 Area =
100 CRN
138 CRN.CRN.SOY.WWT.HLH
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
201 CRN,SOY,UWT
201 CRN.SOY.WWT
243 CSL.CSL.OTS.HLH.HLH
366 OTS.NLH.NLH.NLH
TILLAGE
TYPE
CTFP
NT
CTFP
CTSP
RT
NT
HLH CTSP
HLH RT
RT
CTFP
CTFP
CTSP
NT
RT
CTSP
RT
Cover
CTSP
Cover
CTSP
CTSP
NT
CTSP
HLH CTSP
CTSP
RT
NT
CTSP
CTSP
RT
NT
HLH CTSP
RT
RT
CTSP
NT
CTSP
CTSP
RT
NT
RT
Cover
CTSP
CTSP
ROTATION
ACRES
414,132
2,695
40,887
11,023
468,736
326,010
83,411
1,158,930
124,394
527,634
501,821
128,471
2,850,672
487,973
63,320
388,903
529,266
353,556
0
244,873
0
69,815
26,332
2,164,038
104,504
1,747,290
455,881
342,912
1,473,020
21,121
148,664
4,293,392
21,487
257,928
72,426
1,157,930
223,820
367,488
374,181
2,475,260
10,267
2,283,309
1,124,760
1,141,290
175,720
1,041,609
0
442,480
136,927
COVER-CROPS
TILLAGE ROTATION
TYPE ACRES
CTFP
NT
CTFP
CTSP
RT
NT
CTSP
RT
RT
CTFP
CTFP
CTSP
NT
RT
CTSP
RT
RT Cover
CTSP
CTSP Cover
CTSP
CTSP
NT
CTSP
CTSP
CTSP
RT
NT
CTSP
CTSP
RT
NT'
CTSP
RT
RT
CTSP
NT
CTSP
CTSP
RT
NT
RT
RT Cover
CTSP Cover
CTSP
414,132
2,695
40,886
11,022
468,735
326,010
83,411
1,158,930
124,394
527,634
501,821
128,471
2,850,671
484,857
63,320
388,903
529,266
8,081
345,477
0
252,447
65,357
26,332
2,164,040
104,504
1,747,290
455,881
342,912
1,473,020
21,121
148,664
4,293,392
21,487
257,928
72,426
1,157,930
223,820
367,488
374,181
2,475,260
10,267
2,269,431
1,131,800
1,129,290
175,720
30,619
1,022,990
449,317
136,927
Total PA 27 Area =
6,356,362
6,356,361
E-8
-------
Table E.2 (contd.)
BASELINE
PA CR CROP ROTATION TILLAGE
# #
28 144 CRN.CRN.WWT.HLH.HLH
28 144 CRN.CRN.WWT.HLH.HLH
28 144 CRN.CRN.WWT.HLH.HLH
28 186 CRN. SOY
28 458 SOY.WWT.SOY
28 458 SOY.WWT.SOY
28 503 HLH.HLH.HLH.HLH
28 508 NLH,NLH,NLH,NLH
Total PA 28 Area =
31 131 CRN, CRN, SOY
31 145 CRN,CRN,WWT,HLH,HLH,HLH
31 196 CRN,SOY,OTS,NLH,NLH
31 250 CSL.CSL.SOY
31 250 CSL.CSL.SOY
31 250 CSL.CSL.SOY
31 250 CSL.CSL.SOY
31 503 HLH.HLH.HLH.HLH ,
31 508 NLH.NLH.NLH.NLH
Total PA 31 Area =
32 125 CRN.CRN.OTS.NLH.NLH
32 125 CRN,CRN,OTS,NLH,NLH
32 125 CRN,CRN,OTS,NLH,NLH
32 131 CRN, CRN, SOY
32 186 CRN, SOY
32 203 CRN,SOY,WWT,HLH,HLH,HLH
32 203 CRN,SOY,WWT,HLH,HLH,HLH
Total PA 32 Area =
34 144 CRN,CRN,WWT,HLH,HLH
34 144 CRN.CRN.WWT.HLH.HLH
34 144 CRN.CRN.WWT.HLH.HLH
34 186 CRN, SOY
34 186 CRN, SOY
34 186 CRN, SOY
34 232 CRN.NLH.NLH.NLH
34 232 CRN.NLH.NLH.NLH
Total PA 34 Area =
35 100 CRN
35 115 CRN.CRN.CRN.HLH.HLH.HLH
35 186 CRN, SOY
35 186 CRN, SOY
35 186 CRN, SOY
35 201 CRN.SOY.WWT
35 201 CRN.SOY.WWT
35 366 OTS.NLH.NLH.NLH
TYPE
CTSP
RT
NT
RT
RT
Cover
CTSP
CTFP
RT
CTSP
CTSP
RT
NT
Cover
Cover
CTSP
CTSP
CTSR
RT
NT
CTSP
CTSP
CTSP
RT
CTSP
RT
NT
CTFP
NT
Cover
CTSP
RT
NT
CTSP
CTSP
RT
NT
RT
Cover
CTSP
ROTATION
ACRES
98.744
75,641
69,980
118,464
83,091
0
433,669
358,112
1,237,701
292,342
604,068
588,494
276,398
170,859
0
0
34,087
735,089
2,701,336
747,307
152,283
264,955
10,753
1,533,360
3,350,542
97,415
6,156,615
3,441,446
526,685
496,755
4,775
3,246
0
273,007
378,153
5,124,067
52,432
713,294
7,620,040
1,926,500
354,424
2,844,111
0
394,064
COVER-CROPS
TILLAGE ROTATION
TYPE ACRES
CTSP
RT
NT
RT
RT
RT Cover
CTSP
CTFP
RT
CTSP
CTSP
RT
NT
RT Cover
NT Cover
CTSP
CTSP
CTSP
RT
NT
CTSP
CTSP
CTSP
RT
CTSP
RT
NT
CTFP
NT
NT Cover
CTSP
RT
NT
CTSP
CTSP
RT
NT
RT
RT Cover
CTSP
98,744
75,641
69,980
118,464
0
83,091
• 433,669
358,112
1,237,701
290,201
604,068
595,315
0
6,614
278,539
164,246
34,087
728,267
2,701,337
747,307
152,283
264,955
10,753
1,533,360
3,3507542
97,415
6,156,615
3,446,220
526,685
491,980
0
0
8,021
273,007
378,153
5,124,066
52,432
713,294
7,620,040
1,926,500
354,424
28,448
2,815,670
394,064
Total PA 35 Area
13,9104,865
13,904,872
E-9
-------
Table E.2 (contd.)
PA
BASELINE
t CR CROP ROTATION TILLAGE ROTATION
# TYPE ACRES
39
39
39
39
39
39
39
39
39
39
39
40
40
40
40
40
40
40
40
41
41
41
41
41
41
41
41
41
41
41
41
41
41
41
42
42
42
42
42
42
42
42
42
42
42
42
42
131 CRN, CRN, SOY
131 CRN, CRN, SOY
131 CRN, CRN, SOY
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
189 CRN,SOY,CRN,WWT,HLH,HLH
201 CRN, SOY, WWT
243 CSL,CSL,OTS,HLH,HLH
350 OTS,HLH,HLH,HLH
366 OTS,NLH,NLH,NLH
Total PA 39 Area =
100 CRN
100 CRN
186 CRN, SOY
186 CRN, SOY
246 CSL,CSL,OTS,NLH,NLH
280 CSL.SWT
366 OTS,NLH,NLH,NLH
503 HLH,HLH,HLH,HLH
Total PA 40 Area =
100 CRN
138 CRN,CRN,SOY,WWT,HLH
138 CRN, CRN, SOY, WWT, HLH
137 CRN, CRN, SOY, WWT
137 CRN, CRN, SOY, WWT
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
243 CSL,CSL,OTS,HLH,HLH
243 CSL,CSL,OTS,HLH,HLH
277 CSL,SOY,HLH
503 HLH, HLH, HLH, HLH
508 NLH,NLH,NLH,NLH
Total PA 41 Area =
100 CRN
145 CRN, CRN, WWT, HLH, HLH, HLH
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
201 CRN, SOY, WWT
201 CRN, SOY, WWT
244 CSL,CSL,OTS,HLH,HLH,HLH
244 CSL,CSL,OTS,HLH,HLH,HLH
262 CSL,OTS,HLH,HLH,HLH
262 CSL,OTS,HLH,HLH,HLH
503 HLH, HLH, HLH, HLH
508 NLH,NLH,NLH,NLH
CTSP
RT
NT
CTSP
RT
NT
CTSP
CTFP
CTSP
CTSP
CTSP
CTSP
RT
CTFP
NT
CTSP
CTSP
CTFP
CTFP
CTSP
CTSP
RT
Cover
Cover
CTFP
Cover
CTSP
RT
NT
CTSP
Cover
Cover
CTSP
CTFP
CTSP
CTSP
CTSP
RT
NT
RT
Cover
CTSP
Cover
CTSP
Cover
CTSP
CTSP
395,228
2,320,303
302,075
3,087,080
1,799,120
44,343
1,325,780
975,345
1,188,890
51,390
716,869
12,206,423
127,269
2,083,560
963,468
177,694
255,575
649,324
207,687
1,608,340
6,072,917
4,156,220
2,081,734
1,017,390
0
0
6,331,762
0
69,249
7,634,120
737,830
916,740
0
0
1,966,396
304,965
25,216,406
2,948,080
240,286
4,601,100
2,352,500
397,254
2,301,078
0
191,004
0
33,820
0
325,569
186,067
COVER-CROPS
TILLAGE ROTATION
TYPE ACRES
CTSP
RT
NT
CTSP
RT
NT
CTSP
CTFP
CTSP
CTSP
CTSP
CTSP
RT
CTFP
NT
CTSP
CTSP
CTFP
CTFP
CTSP
CTSP
RT
CTSP
RT
CTFP
PTFP
CTSP
RT
NT
CTSP
CTSP
CTSP
CTSP
CTFP
CTSP
CTSP
CTSP
RT
NT
RT
RT
CTSP
CTSP
CTSP
CTSP
CTSP
CTSP
395,228
2,320,300
302,075
3,087,080
1,799,120
44,343
1,325,780
975,345
1,188,890
51,390
716,869
12,206,420
127,269
2,083,560
963,468
177,694
255,575
649,324
207,687
1,608,340
6,072,917
4,251,550
3,431,450
0
Cover 236,067
Cover 321,200
4,650,540
Cover 2,967
69,249
8,330,300
737,830
0
Cover 217,705
Cover 2,543,600 New
118,994
304,965
25,216,417
2,970,260
240,286 .
4,576,720
2,352,500
397,254
21,950
Cover 2,279,120
0
Cover 189,061
0
Cover 40,099
323,420
186,067 .
Total PA 42 Area =
13,576,758
13,576,737
E-10
-------
Table E.2 (contd.)
BASELINE
PA
L CR CROP ROTATION
#
43 100 CRN
43 131 CRN, CRN. SOY
43 186 CRN, SOY
43 186 CRN, SOY
43 186 CRN, SOY
43 201 CRN.SOY.WUT
43 201 CRN.SOY.UUT
43 201 CRN,SOY,UUT
43 201 CRN.SOY.WWT
43 218 CRN.WWT
43 262 CSL,OTS,HLH.HLH,HLH
43 262 CSL.OTS.HLH.HLH.HLH
43 503 HLH, HLH, HLH, HLH
43 508 NLH,NLH,NLH,NLH
Total PA 43 Area =
44 186 CRN, SOY
44 186 CRN, SOY
44 186 CRN. SOY
44 218 CRN.WWT
44 366 OTS,NLH,NLH,NLH
44 416 SRG.SOY.SOY
44 416 SRG.SOY.SOY
44 45a'SOY,UUT,SOY
44 458 SOY,UUT,SOY
44 459 SOY,UUT,UUT,UWT
44 459 SOY.UUT.UUT.UUT
44 503 HLH, HLH, HLH, HLH
44 508 NLH,NLH,NLH,NLH
Total PA 44 Area =
47 : 2 BAR, BAR, SOY
47 2 BAR, BAR, SOY
47 100 CRN
47 125 CRN,CRN,OTS,NLH,NLH
47 145 CRN, CRN, UUT, HLH, HLH,
47 215 CRN.SUT.SUT
47 215 CRN.SUT.SUT
47 215 CRN.SUT.SUT
47 262 CSL.OTS.HLH.HLH.HLH
47 463 SMF.SUT
47 463 SMF.SUT
47 508 NLH.NLH.NLH.NLH
Total PA 47 Area =
53 4 BAR.BAR.SMF
53 4 BAR.BAR.SMF
53 100 CRN
53 100 CRN
53 100 CRN
53 244 CSL,CSL,OTS,HLH,HLH,
53 463 SMF.SUT
53 463 SMF.SUT
53 508 NLH.NLH.NLH.NLH
TILLAGE
TYPE
CTSP
CTSP
CTSP
RT
NT
CTSP
Cover
RT
Cover
Cover
CTSP
Cover
CTSP
CTFP
CTFP
CTSP
NT
Cover
CTFP
CTFP
Cover
CTFP
Cover
RT
Cover
CTSP
CTFP
•
CTSP
RT
NT
CTSP
HLH CTFP
CTSP
RT
NT
CTFP
CTSP
RT
CTFP
CTSP
RT
CTSP
RT
NT
HLH CTSP
CTSP
RT
CTSP
ROTATION
ACRES
12,901
576,681
0
938,150
142,733
1,791,717
0
735,471
0
0
115,942
0
183,180
381,302
4,878,077
2,455,120
1,267,650
328,052
0
204,999
1,987,066
0
884,594
0
1,677,812
0
27,385
150,135
8,982,813
1,051,320
4,190,350
176,275
172,299
405,282
4,377,087
245,827
179,008
1,054,480
6,371,460
919,378
794,545
19,937,310
2,674,590
390,970
116,479
2,926,710
217,153
1,398,143
4,161,760
291,494
2,232,310
COVER-CROPS
TILLAGE ROTATION
TYPE ACRES
CTSP
CTSP
CTSP
RT
NT
CTSP
CTSP Cover
RT
RT Cover
CTSP Cover
CTSP
CTSP Cover
CTSP
CTFP
CTFP
CTSP
NT
CTFP Cover
CTFP
CTFP
CTFP Cover
CTFP
CTFP Cover
RT
RT Cover
CTSP
CTFP
CTSP
RT
NT
CTFP
CTSP
CTSP
RT
NT
CTFP
CTSP
RT
CTFP
CTSP
RT
CTSP
RT
NT
CTSP
CTSP
RT
CTSP
65,409
603,994
152,126
1,040,970
142,733
0
888,156
26,316
606,333
671,618
0
115,942
183.180
381,302
4,878.079
2,455.120
1.279,160
. 328,052
1,279,800
172,419
0
164,861
0
1,618,710
0
1,483,200
27,385
174,106
8,982,813
1,051,320
4,190,350
176,275
405,282
172,299
4,377,080
245,827
179,008
1,054,480
6.371.460
919,378
794,545
19,937,304
2.674.590
390,970
116,479
2,926,710
217,153
1,398,144
4,161,760
291,494
2,232,310
Total PA 53 Area =
14,409,609
14,409,610
E-11
-------
Table E.2 (contd.)
PA
BASELINE
t
56
56
56
56
56
56
56
56
56
56
56
56
56
56
56
56
57
57
57
57
57
57
57
57
57
57
57
57
57
57
58
58
58
58
58
58
58
58
58
58
59
59
59
59
59
59
59
59
59
59
59
59
CR CROP ROTATION
#
100 CRN
131 CRN, CRN. SOY
131 CRN, CRN, SOY
131 CRN, CRN, SOY
145 CRN, CRN, WWT, HLH, HLH,
145 CRN, CRN, WWT, HLH, HLH,
186 CRN, SOY
186 CRN, SOY
201 CRN, SOY, WWT
201 CRN, SOY, WWT
244 CSL,CSL,OTS,HLH,HLH,
246 CSL,CSL,OTS,NLH,NLH
246 CSL,CSL,OTS,NLH,NLH
262 CSL,OTS,HLH,HLH,HLH
262 CSL,OTS,HLH,HLH,HLH
366 OTS,NLH,NLH,NLH
Total PA 56 Area =
125 CRN,CRN,OTS,NLH,NLH
125 CRN,CRN,OTS,NLH,NLH
131 CRN, CRN, SOY
131 CRN, CRN, SOY
131 CRN, CRN, SOY
186 CRN, SOY
186 CRN, SOY
218 CRN, WWT
218 CRN, WWT
339 HLH, HLH, HLH, HLH, SRG,
339 HLH, HLH, HLH, HLH, SRG,
350 OTS, HLH, HLH, HLH
366 OTS,NLH,NLH,NLH
508 NLH.NLH.NLH.NLH
Total PA 57 Area =
100 CRN
100 CRN
186 CRN, SOY
186 CRN, SOY
218 CRN. WWT
262 CSL, OTS, HLH, HLH, HLH
490 WWT
503 HLH, HLH, HLH. HLH
508 NLH.NLH.NLH.NLH
508 NLH.NLH.NLH.NLH
Total PA 58 Area =
100 CRN
100 CRN
100 CRN
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
218 CRN, WWT
TILLAGE
TYPE
NT
CTSP
RT
NT
HLH CTSP
HLH RT
CTSP
Cover
RT
Cover
HLH Cover
RT
Cover
CTSP
Cover
CTSP
RT
Cover
CTSP
RT
NT
CTFP
Cover
RT
Cover
SOY CTSP
SOY RT
CTSP
CTSP
CTFP
RT
NT
CTSP
RT
CTFP
CTSP
RT
CTFP
CTFP
CTSP
CTSP
RT
NT
CTFP
CTSP
RT
Cover
244 CSL, CSL, OTS. HLH, HLH, HLH CTSP
244 CSL.CSL.OTS.HLH.HLH,
339 HLH. HLH, HLH, HLH, SRG,
490 WWT
508 NLH.NLH.NLH.NLH
HLH Cover
SOY CTFP
RT
CTSP
ROTATION
ACRES
0
1,602,820
263,518
34,678
177,729
33,770
42,860
0
224,954
0
0
107,794
0
134,538
0
26,553
2,649,214
216,102
0
5,131,259
1,786,693
246,912
425,206
0
848,572
0
632,251
309,341
252,900
110,116
197.473
10.156,825
2,049,830
164,984
83,817
337,128
1,275,058
378,581
1,018,680
169,989
8,250,320
31,758
13,760,145
2,468,150
370,376
71,274
853,576
0
572,148
0
192,474
0
202,938
352,391
361,605
COVER-CROPS
TILLAGE ROTATION
TYPE ' ACRES
NT
CTSP
RT
NT
CTSP
RT
CTSP
CTSP
RT
RT
CTSP
RT
RT
CTSP
CTSP
CTSP
RT
RT
CTSP
RT
NT
CTFP
CTFP
RT
RT
CTSP
RT
CTSP
CTSP
CTFP
RT
NT
CTSP
RT
CTSP
CTFP
RT,
CTFP
CTFP
CTSP
CTSP
RT
NT
CTFP
CTSP
RT
CTFP
CTSP
CTSP
CTFP
RT
CTSP
24,808
1,481,960
373,512
9,870
152,472
0
0
Cover 84,902
0
Cover 224,953
Cover 93,596
0
Cover 31,571
0
Cover 111,249
60,324
2,649,217
0
Cover 136,232
5,149,470
1,786,690
246,912
0
Cover 406,998
0
Cover 928,443
632,251
309,341
252,900
110,116
197,473
10,156,826
2,049,830
164,984
83,817
337,128
378,581
1,275,060
1,018,680
169,989
8,250,320
31,758
13,760,147
1,973,377
842,633
71,274
0
683,116
214,182
Cover 640,256
0
Cover 293,114
127,270
238,100
361,605
Total PA 59 Area =
5,444,932
5,444,927
E-12
-------
Table E.2 (contd.)
PA
#
60
60
60
60
60
60
60
60
60
60
60
60
61
61
61
61
61
61
61
61
61
61
61
61
63
63
63
63
63
63
63
63
64
64
64
64
64
64
64
64
64
CR CROP ROTATION
#
131 CRN, CRN, SOY
131 CRN, CRN, SOY
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
201 CRN.SOY.WWT
201 CRN.SOY.WWT
201 CRN.SOY.WWT
201 CRN.SOY.WWT
503 HLH.HLH.HLH.HLH
508 NLH.NLH.NLH.NLH
Total PA 60 Area =
131 CRN, CRN, SOY
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
246 CSL.CSL.OTS.NLH.NLH
350 OTS.HLH.HLH.HLH
458 SOY.WWT.SOY
458 SOY.WWT.SOY
503 HLH.HLH.HLH.HLH
508 NLH.NLH.NLH.NLH
508 NLH.NLH.NLH.NLH
Total PA 61 Area =
186 CRN, SOY
186 CRN, SOY
186 CRN, SOY
218 CRN.WWT
218 CRN.WWT
490 WWT
503 HLH.HLH.HLH.HLH
508 NLH.NLH.NLH.NLH
Total PA 63 Area =
100 CRN
131 CRN. CRN, SOY
131 CRN, CRN. SOY
218 CRN.WWT
218 CRN.UUT
366 OTS.NLH.NLH.NLH
458 SOY.WWT.SOY
490 WWT
503 HLH.HLH.HLH.HLH
BASELINE-
TILLAGE ROTATION
TYPE ACRES
COVER-CROPS
TILLAGE ROTATION
TYPE ACRES
CTSP
Cover
CTSP
Cover
RT
NT
CTSP
Cover
RT
Cover
CTSP
CTFP
CTFP
CTFP
Cover
NT
Cover
Cover
CTSP
CTFP
Cover
CTSP
CTFP
NT
CTSP
RT
NT
CTFP
CTSP
RT
CTFP
CTSP
NT
CTSP
RT
CTFP
Cover
CTSP
Cover
RT
CTSP
91,638
0
788,444
0
1,271.970
238,916
1,292,619
0
1,656,246
0
490,083
3,145,170
8,975,086
21,072
206,940
0
8,992
0
0
3.301
177,462
0
50,128
387,714
8,400
864,009
42,041
918,236
192,202
1,338,510
99,701
1,204,860
13,162,800
514,543
17,472,893
56,076
2,226,539
674,106
690,688
0
50,603
0
272,811
173,778
CTSP
NT
CTSP
CTSP
RT
NT
CTSP
CTSP
RT
RT
CTSP
CTFP
CTFP
CTFP
CTFP
NT
NT
NT
CTSP
CTFP
CTFP
CTSP
CTFP
NT
CTSP
RT
NT
CTFP
CTSP
RT
CTFP
CTSP
NT
CTSP
RT
CTFP
RT
CTSP
CTFP
RT
CTSP
91,638
Cover 238,917
0
Cover 749,430
1,489,750
0
0
Cover 1,331,630
29,900
Cover 1,408,560
490,083
3,145,170
8,975.078
21,071
0
Cover 208,028
0
Cover 8,975
Cover 8,416
3,301
1,897
Cover 180,816
50,128
381,374
0
864,007
42,041
918,236
192,202
1,338,510
99.701
1,204,860
13.162,800
514,543
17,472,893
56,076
2,443,560
32,799
0
Cover 762,644
50,602
Cover 473,664
151,473
173,778
Total PA 64 Area =
4.144,601
4,144,596
E-13
-------
Table E.3 Soil Physical Properties, Percentage Weights, and Ranks for all Climate Divisions
CD-221
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-223
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CO-223
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-231
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silt Loam
CD-232
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
SiLt Loam
CD-241
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-242
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-251
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
X SAND % SILT % CLAY
BD
FC UP Rank Weight
11
41
85
61
7
12
X SAND
41
88
61
7
12
X SAND
41
87
61
7
12
X SAND
28
41
87
61
12
X SAND
11
41
81
60
7
12
X SAND
22
41
61
7
12
% SAND '
25
41
82
61
7
12
X SAND
28
41
89
61
7
12
35
41
10 .
27
55
68
% SILT
41
8
27
55
68
X SILT
41
9
27
58
68
X SILT
41
41
9
27
68
X SILT
35
41
13
27
58
68
X SILT
39
41
27
55
68
X SILT
40
41
12
27
60
68
X SILT
41
41
7
27
60
68
54
18
5
12
39
20
X CLAY
18
4
12
39
20
X CLAY
18
4
12
36
20
X CLAY
31 .
18
5
12
20
X CLAY
54
18
6
14
36
20
X CLAY
39
18
13
38
20
X CLAY
" 36
18
6
12
33
20
X CLAY
31
18
4
12
33
20
1.18
1.42
1.56
1.52
1.33
1.43
BD
1.42
1.57
1.52
1.33
1.43
BD
1.42
1.56
1.52
'1.35
1.43
BD
1.41
1.42
1.56
1.52
t.43
BD
.18
.42
.54
.50
.35
.43
BD
1.33
1.42
1.52
1.34
1.43
BD
1.36
1.42
1.54
1.52
1.37
1-43
BD
1.41
1.42
1.57
1.52
1.37
1.43
0.45
0.30
0.13
0.23
0.39
0.33
FC
0.30
0.12
0.23
0.39
0.33
FC
0.30
0.12
0.23
0.37
0.33
FC
0.32
0.30
0.13
0.23
0.33
FC
0.45
0.30
0.14
0.24
0.37
0.33
FC
0.36
0.30
0.23
0.38
0.33
FC
0.35
0.30
0.14
0.23
0.36
0.33
FC
0.32
0.30
0.12
0.23
0.36
0.33
0.28
0.12
0.05
0.09
0.23
0.13
WP
0.12
0.04
0.09
0.23
0.13
UP
0.12
0.04
0.09
0.21
0.13
UP
0.17
0.12
0.05
0.09
0.13
UP
0.28
0.12
0.05
0.10
0.21
0.13
UP
0.21
0.12
0.09
0.22
0.13
UP
0.19
0.12
0.05
0.09
0.20
0.13
UP
0.17
0.12
0.04
0.09
0.20
0.13
4
2
5
3
6
1
Rank
4
2
3
5
1
Rank
4
1
3
5
2.
Rank
5
2
4
3
1
Rank
6
2
5
3
4
1
Rank
5
4
3
2
T
Rank
4
5
2
3
6
1
Rank
6
2
1
4
5
3
0.10
0.24
0.08
0.20
0.07
0.31
Weight
0.14
0.27
0.15
0.09
0.35
Weight
0.13
0.41
0.16
-0.10
0.20
Weight
0.01'
0.26
0.08
0.19
0.46
Weight .
"0.01
0.28
0.04
0.07
0.05
0.55
Weight
-0.03
" 0.06
0.23
0.29
0.39
Weight
0.05
0.04
0.37
0.13
0.03
0.38
Weight
0.02
0.19
0.42
0.13
0.08
0.16
E-14
-------
Table E.3 (contd.)
CO-252
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-253
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CO-261
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-262
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-271
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-272
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-281
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CO-282
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
X SAND X SILT X CLAY
BD
FC UP Rank Weight
17
41
£13
61
7
12
X SAND
41
86
61
6
12
X SAND
41
87
61
7
12"
X SAND
20
41
87
61
7
12
X SAND
11
41
82
61
6
12
X SAND
13
41
83
61
7
12
X SAND
22
41
31
61
7
12
X SAND
20
41
81
61
7
12
37
41
12
27
55
68
X SILT
41
9
27
52
68
X SILT
41
9
27
60
68
X SILT
38
41
9
27
56
68
X SILT
35
41
12
27
54
68
X SILT
36
41
11
27
58
68
X SILT
39
41
13
27
56
68
X SILT
38
41
13
27
60
68
46
18
5
12
39
20
X CLAY
18
5
12
42
20
X CLAY
18
4
12
33
20
X CLAY
43
18
4
12
37
20
X CLAY
54
18
6
12
40
20
X CLAY
52
18
5
12
35
20
X CLAY
40
18
6
12
37
20
X CLAY
43
18
6
12
33
20
.26
.42
.55
.52
.33
.43
BD
.42
.56
.52
.31
.43
BD
1.42
1.56
1.52
1.37
1.43.
BD
1.30
1.42
1.56
1.52
1.34
1.43
BD
1.18
1.42
1.54
1.52
1.32
1.43
BD
1.20
1.42
1.55
1.52
1.36
1.43
BD
1.32
1.42
1.54
1.52
1.34
1.43
BD
1.30
1.42
1.54
1.52
1.37
1.43
0.41
0.30
0.13
0.23
0.39
0.33
FC
0.30
0.13
0.23
0.40
0.33
FC
0.30
0.12
0.23
0.36
0.33
FC
0.39
0.30
0.12
0.23
0.38
0.33
FC"
0.45
0.30
0.14
0.23
0.39
0.33
FC
0.44
0.30
0.13
0.23
0.37
0.33
FC
0.37
0.30
0.14
0.23
0.38
0.33
FC
0.39
0.30
0.14
0.23
0.36
0.33
0.24
0.12
0.05
0.09
0.23
0.13
WP
0.12
0.05
0.09
0.24
0.13
UP
0.12
0.04
0.09
0.20
0.13
WP
0.23
0.12
0.04
0.09
0.22
0.13
WP
0.28
0.12
0.05
0.09
0.23
0.13
WP
0.27
0.12
0.05
0.09
0.21
0.13
WP
0.21
0.12
0.05
0.09
0.22
0.13
WP
0.23
0.12
0.05
0.09
0.20
0.13
5
6
3
1
4
2
Rank
2
1
3
4
5
Rank
3
1
2
4
5
Rank
6
3
1
2
4
5
Rank
6
5
4
1
2
3
Rank
2
6
5
1
4
3
Rank .
4
5.
6
3
2
1
Rank
5
3
6
4
2
1
0.06
0.10
0.16
0.36
0.14
0.18
Weight
0.28
0.48
0.18
0.03
0.03
Weight
0.12
0.56
0.21
0.06
0.05
Weight
0.02
0.15
0.55
0.23
0.03
0.02
Weight
0.02
0.03
0.11
0.42
0.29
0.13
Weight
0.22
0.07
0.09
0.32
0.15
0.15
Weight
0.08
0.07
0.01
0.09
0.27
0.48
Weight
0.04
0.10
0.01
0.07
0.10
0.68
E-15
-------
Table E.3 (contd.)
CD-311
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Sflty Clay/SiIty Clay Loam
Silt Loam
CD-312
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
SiIty Clay/SiIty Clay Loam
Silt Loam
CD-313
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
SiIty Clay/SiIty Clay Loam
Silt Loam
CD-314
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
SiIty Clay/SiIty Clay Loam
Silt Loam
CD-321
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
SiIty Clay/SiIty Clay Loam
Silt Loam
CD-322
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
SiIty Clay/SiIty Clay Loam
Silt Loam
CD-323
Loam
Loamy Sand/Sand ,
Sandy Clay Loam/Sandy Loam
SiIty Clay/SiIty Clay Loam
Silt Loam
CD-341
Clay/Clay Loam
Loam ,
Loamy Sand/Sand '
Sandy Clay Loam/Sandy Loam
SiIty Clay/SiIty Clay Loam
Silt Loam
X SAND X SILT X CLAY
BD
FC UP Rank Weight
28
41
81
61
7
12
X SAND
28
41
81
61
7
12
X SAND
28
41
81
61
7
12
X SAND
11
41
92
, 61
7
12
% SAND
11
41
61
7
• 12
X SAND
28
41
81
61
7
12
X SAND
41
92
61
7
12
X SAND
20
41
91
61
7
12
41
41
13
27
60
68
X SILT
41
41
13
27
56
68
X SILT
41
41
13
27
59
68
X SILT
35
41
5
27
58
68
X SILT
35
41
27
54
68
X SILT
41
41
13
27
60
68
X SILT
41
5
27
55
68
X SILT
38
41
6
27
57
68
31
18
6
12
33
20
X CLAY
31
18
6
12
38
20
X CLAY
31
18
6
12
34
20
X CLAY
54
18
3
12
35
20
X CLAY
54
18
12
40
20
X CLAY
31
T8
6
12
33
20
X CLAY
18
3
12
39
20
X CLAY
43
18
3
12
37
20
1.41
1.42
1.54
1.52
1.37
1.43
BD
1.41
1.42
1.54
1.52
1.34
1.43
BD
1.41
1.42
1.54
1.52
1.36
1.43
BD
1,18
1.42
1.58
1.52
1.35
1.43
BD
1.18
1.42
1.52
1.33
1.43
BD
1.41
1.42
1.54
1.52
1.37
1.43
BD
1.42
1.58
1.52
1.33
1.43
BD
1.30
1.42
1.58
1.52
1.34
1.43
0.32
0.30
0.14
0.23
0.36
0.33
FC
0.32
0.30
0.14
0.23
0.38
0.33
FC
0.32
0.30
0.14
0.23
0.36
0.33
FC
0.45
0.30
0.11
0.23
0.37
0.33
FC
0.45
0.30
a. 23
0.39
0.33
FC
0.32
0.30
0.14
0.23
0.36
0.33
FC
0.30
0.11
0.23
0.39
0.33
FC
0.39
0.30
0.11
0.23
0.38
0.33
0.17
0.12
0.05
0.09
0.20
0.13
UP
0.17
0.12
0.05
0.09
0.22
0.13
UP
0.17
0.12
0.05
0.09
0.20
0.13
UP
0.28
0.12
0.04
0.09
0.21
0.13
UP
0.28
0.12
0.09
0.23
0.13
UP
0.17
0.12
0.05
0.09
0.20
0.13
UP
0.12
0.04
0.09
0.23
0.13
UP
0.23
0.12
0.04
0.09
0.22
0.13
6
3
5
4
2
1
Rank
. 5
4
6
3
2
1
Rank
5
2
6
3
4
1
Rank
6
4
5
2
3
1
Rank
5
4
2
3
1
Rank
5
3
6
4
2
1
Rank
4
5
3
2
1
Rank
6
3
5
2
4
1
0.03
0.09
0.04
0.05
0.12
0.67
Weight
0.09
0.10
0.02
0.16
0.19
0.44
Weight
0.03
0.28
0.01
0.16
0.16
0.36
Weight
0.01
0.10
0.06
. 0.14
. 0.11
0.58
Weight
0.03
0.05
0.37
0,18
0.37
1.00
Weight
0.02
0,11
• 0.01
0.08
0.14
'. 0.64
Weight
0.14
0.01
0.16
0:16
0.53
Weight
-0.02
0.15
0.10
0,29
0.14
0.30
E-16
-------
Table E.3 (contd.)
CD-342
Clay/Clay Loam
Loam ,
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-351
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-352
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-353
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-391
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-392
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-393
Clay/Clay Loam .
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-401
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
X SAIID X SILT X CLAY
BD
FC
UP
Rank Weight
2:1
41
85
61
7
12
X SAND
11
41
81
61
6
12
X SAND
28
41
81
61
7
12
X SAND
28
41
61
7
12
X SAND
41
82!
60
7
12
X SAND
20
41
83
61
7
12
X SAIID
28
41
81
59
7
12
X SAND
28
41
61
6
12
39
41
10
27
59
68
X SILT
35
41
13
27
52
68
X SILT
41
41
13
27
55
68
X SILT
41
41
27
59
68
X SILT
41
11
27
60
68
X SILT
38
41
12
27
59
68
X SILT
41
41
13
27
57
68
X SILT
41
41
27
48
68
38
18
5
12
35
20
X CLAY
54
18
6
12
42
20
X CLAY
31
18
6
12
38
20
X CLAY
31
18
12
34
20
X CLAY
18
5
13
33
20
X CLAY
41
18
6
12
34
20
X CLAY
31
18
6
15
36
20
X CLAY
31
18
12
46
20
.34
.42
.55
.52
.36
.43
BD
1.18
1.42
1.54
1.52
1.31
1.43
BD
1.41
.42
.54
.52
.33
.43
BD
.41
.42
.52
.36
.43
BD
.42
.55
.51
.37
.43
BD
.31
.42
.55
.52
.36
.43
BD
1.41
.42
.54
.50
.35
.43
BD
1.41
1.42
1.52
1.28
1.43
0.36
0.30
0.13
0.23
0.37
0.33
FC
0.45
0.30
0.14
0.23
0.40
0.33
FC
0.32
0.30
0.14
0.23
0.38
0.33
FC
0.32
0.30
0.23
0.37
0.33
FC
0.30
0.13
0.24
0.36
0.33
FC
0.38
0.30
0.14
0.23
0.36
0.33
FC
0.32
0.30
0.14
0.25
0.37
0.33
FC
0.32
0.30
0.23
0.42
0.33
0.20
0.12
0.05
0.09
0.21
0.13
UP
0.28
0.12
0.05
0.09
0.24
0.13
UP
0.17
0.12
0.05
0.09
0.22
0.13
UP
0.17
0.12
0.09
0.21
0.13
UP
0.12
0.05
0.10
0.20
0.13
UP
0.22
0.12
0.05
0.09
0.20
0.13
UP
0.17
0.12
0.05
0.11
0.21
0.13
UP
0.17
0.12
0.09
0.26
0.13
6
5
4
2
3
1
Rank
6
5
4
1
2
3
Rank
6
3
5
4
1
2
Rank
5
3
4
2
1
Rank
4
3
2
5
1
Rank
5
3
6
2
4
1
Rank
5
1
6
3
4
2
Rank
5
2
3
4
1
0.07
0.11
0.12
0.16
0.16
0.38
Ueight
0.01
0.07
0.14
0.37
0.24
0.17
Ueight
0.01
0.10
0.03
0.08
0.40
0.38
Ueight
0.01
0.10
0.09
0.21
0.59
Ueight
0.11
0.14
0.17
0.02
0.56
Ueight
0.09
0.14
0.07
0.24
0.13
0.33
Ueight
0.11
0.31
0.01
0.18
0.13
0.26
Ueight
0.01
0.17
0.07
0.02
0.73
E-17
-------
Table E.3 (contd.)
CD-402
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-403
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-411
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-412
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-413
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-414
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt. Loam
CD-415
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-416
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
X SAND % SILT X CLAY
BD
FC UP Rank Weight
13
41
81
61
7
12
X SAND
22
.41
81
61
7
12
X SAND
28
41
81
'61
7
12
X SAND
20
41
81
61
7
12
X SAND
28
41
81
61
7
12
X SAND
16
41
81
61
7
12
X SAND
28
41
61
6
12
X SAND
18
41
89
61
6
12
36
41
13
27
58
68
X SILT
39
41
13
27
59
68
X SILT
41
41
13
27
60
68
X SILT
38
41
'13
27
57
68
X SILT
41
41
13
27
60
68
% SILT
37
41
13
27
59
68
X SILT
'41
41
27
51
68
X SILT
37
41
7
27
52
68
51
18
6
12
35
20
X CLAY
39
18
6
12
34
20
X CLAY
31
18
6
12
33
20
X CLAY
4J
18
6
12
36
20
X CLAY
31
18
6
12
33
20
X CLAY
47
18
6
12
34
20
X CLAY
31
18
12
43
20
X CLAY
45
18
4
12
42
20
1.21
1.42
1.54
1.52
1.36
1.43
BD
1.33
1.42
1.54
1.52
1.36
1.43
BD
.41
.42
.54
.52
.37
.43
BD
.30
.42
.54
.52
.35
1.43
BD
.41
.42
.54
.52
.37
.43
BD
1.25
1.42
1.54
1.52
1.36
1.43
BD
1.41
1.42
1.52
1.30
1.43
BD
1.27
1.42
1.57
1.52
1.31
1.43
0.43
0.30
0.14
0.23
0.37
0.33
FC
0.36
0.30
0.14
0.23
0.36
0.33
FC
0.32
0.30
0.14
0.23
0.36
0.33
FC
0.39
0.30
0.14
0.23
0.37
0.33
FC
0.32
0.30
0.14
0.23
0.36
0.33
FC
0.41
0.30
0.14
0.23
0.36
0.33
FC
0.32
0.30
0.23
0.41
0.33
FC
0.40
0.30
0.12
0.23
0.40
0.33
0.26
0.12
0.05
0.09
0.21
0.13
WP
0.21
0.12
0.05
0.09
0.20
0.13
WP
0.17
0.12
0.05
0.09
0.20
0.13
UP
0.23
0.12
0.05
0.09
0.21
0.13
WP
0.17
0.12
0.05
0.09
0.20
0.13
WP
0.25
0.12
0.05
0.09
0.20
0.13
WP
0.17
0.12
0.09
0.25
0.13
WP
0.24
0.12
0.04
0.09
0.24
0.13
2
4
6
5
3
1
Rank
5
4
6
3
2
1
Rank
5
2
6
3
4
1
Rank
6
4
5
3
2
1
Rank
5
2
6
3
4
1
Rank
ft
5
6
2
3
1
Rank
5
2
4
3
1
Rank
5
3
6
4
1
2
0.14
0.08
0.03
0.04
0.12
0.59
Weight
0.06
0.07
0.03
0.17
0.32
0.35
Weight
0.03
0.24
0.01
0.23
. 0.09
0.40
Weight
0.02
0.16
0.12
0.18
0.21
0.31
Weight
0.02
0.34
0.01
0.20
0.04
0.39
Weight
0.10
0.10
0.01
0.22
0.14
0.43
Weight
0.01
0.33
0.13
0.13
0.40
Weight
0.05
0.15
0.04
0.10
0.35
0.31
E-18
-------
Table E.3 (contd.)
CD-421
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-422
Clay/Clay Loam
Loam
Loamy* Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-431
Clay/Clay Loam
Loam
Loamy Sand/Sand
Silty Clay/SiIty Clay Loam
Silt Loam
CD-432
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-433
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-441
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loan
Silt Loam
CO-442
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-443
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loan
Silt Loam
X SAND X SILT X CLAY
BO
FC UP Rank Weight
28
41
83
61
7
12
X SAND
14
41
87
61
7
12
X SAND
28
41
81
7
12
X SAND
28
41
61
7
12
X SAND
18
41
92
61
7
12 ,
X SAND
41
61
7
12
X SAND
20
41
83
61
7
12
X SAND
28
41
81
61
7
12
41
41
12
27
57
68
X SILT
36
41
9
27
58
68
X SILT
41
41
13
59
68
X SILT
41
41
27
57
68
X SILT
37
41
5
27
56
68
X SILT
41
27
60
68
X SILT
38
41
11
27
60
68
X SILT
41
41
13
27
60
68
31
18
6
12
36
20
X CLAY
50
18
5
12
35
20
X CLAY
31
18
6
34
20
X CLAY
31
18
12
37
20
X CLAY
45
18
3
12
38
20
X CLAY
18
12
33
20
X CLAY
41
18
5
12
33
20
X CLAY
31
18
6
12
33
20
1.41
1.42
1.55
1.52
1.35
1.43
BD
1.22
1.42
1.56
1.52
1.36
1.43
BD
.41
.42
.54
.37
.41
BD
1.41
1.42
1.52
1.34
1.43
BD
.27
.42
.58
.52
.34
.43
BD
1.42
1.52
1.37
1.43
BD
1.31
1.42
1.55
1.52
1.37
1.43
BD
1.41
1.42
1.54
1.52
1.37
1.43
0.32
0.30
0.14
0.23
0.37
0.33
FC
0.43
0.30
0.13
0.23
0.37
0.33
FC
0.32
0.30
0.14
0.36
0.33
FC
0.32
0.30
0.23
0.38
0.33
FC
0.40
0.30
0.11
0.23
0.38
0.33
FC
0.30
0.23
0.36
0.33
FC
0.38
0.30
0.13
0.23
0.36
0.33
FC
0.32
0.30
0.14
0.23
0.36
0.33
0.17
0.12
0.05
0.09
0.21
0.13
UP
0.26
0.12
0.05
0.09
0.21
0.13
UP
0.17
0.12
0.05
0.20
0.13
UP
0.17
0.12
0.09
0.22
0.13
UP
0.24
0.12
0.04
0.09
0.22
0.13
UP
0.12
0.09
0.20
0.13
UP
0.22
0.12
0.05
0.09
0.20
0.13
UP
0.17
0.12
0.05
0.09
0.20
0.13
6
4
3
5
2
1
Rank
5
2
6
4
3
1
Rank
4
3
5
2
1
Rank
3
5
2
4
1
Rank
5
3
6
2
4
1
Rank
2
3
4
1
Rank
5
3
4
2
6
1
Rank
3
6
4
2
5
1
0.01
0.09
0.12
0.06
0.23
0.49
Weight
0.06
0.18
0.02
0.08
0.12
0.54
Weight
0.10
0.13
0.02
0.20
0.55
Weight
0.09
0.06
0.15
0.07
0.63
Weight
0.05
0.14
0.02
0.24
0.11
0.44
Weight
0.15
0.08
0.02
0.75
Weight
0.09
0.17
0.09
0.23
0.06
0.36
Weight
0.14
0.01
0.12
0.32
0.07
0.34
E-19
-------
Table E.3 (contd.)
CD-444
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-471
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-472
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-473
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CO-474
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam ,
CD-531
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CO-532
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-533
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
X SAND X SILT X CLAY
BD
FC
UP
Rank Weight
14
41
81
61
7
12
X SAND
28
41
81
61
7.
12
X SAND
11
41
82
61
7
12
X SAND
20
41
82
61
6
12,
X SAND
28
41
81
61
6
12
X SAND
11
41
81
61
7
12
X SAND
20
41
81
\6-\
7-
12
X SAND
28
41
59
6
12
36
41
13
27
60
68
X SILT
41
41
13 '
27
58
68
X SILT
35
41
12
27
56
68
X SILT
38
41
12
27
53
68
X SILT
41
41
13
27
53
68
X SILT
35
41
13
27
58
68
X SILT
38
41
13
27
59
68
X SILT
41
41
27
53
68
50
18
6
12
33
20
X CLAY
31
18
6
12
35
20
X CLAY
54
18
6
12
38
20
X CLAY
43
18
6
12
41
20
X CLAY
31
18
6
12
' 41
20
X CLAY
- 54
18
6
12
35
20
X CLAY
43
18
6
12
34
20
X CLAY
31
18
14
41
20
1.22
.42
.54
.52
.37
.43
BD
1.41
1.42
1.54
1.52
1.36
1.43
BD
1.18
1.42
1.55
1.52
1.34
1.43
BD
.30
.42
.54
.52
.32
1.43
BD
1.41
1.42
1.54
1.52
1.32
1.43
BD
1.18
1.42
1.54
1.52
1.36
1.43
BD
.30
.42
.54
.52
.36
.43
BD
1.41
1.42
1.50
1.32
1.43
0.43
0.30
0.14
0.23
0.36
0.33
FC
0.32
0.30
0.14
0.23
0.37
0.33
FC
0.45
0.30
0.14
0.23
0.38
0.33
FC
0.39
0.30
0.14
0.23
0.39
0.33
FC
0.32
0.30
0.14
0.23
0.40
0.33
FC
0.45
0.30
0.14
0.23
0.37
0.33
FC
0.39
0.30
0.14
0.23
0.36
0.33
FC
0.32
0.30
0.24
0.40
0.33
0.26
0.12
0.05
0.09
0.20
0.13
WP
0.17.
0.12
0.05
0.09
0.21
0.13
WP
0.28
0.12
0.05
0.09
0.22
0.13
WP
6.23
0.12
0.05
0.09
0.23
0.13
WP
0.17
0.12
0.05
0.09
0.24
0.13
WP
0.28
0.12
0.05
0.09
0.21
0.13
WP
0.23;
0.12
0.05
0.09
0.20
0.13
WP
0.17
0.12
0.10
0.24
0.13
3
2
4
5
6
1
Rank
6
4
5
1
3
2
Rank
6
3
5
4
1
2
Rank
6
1
5
4
2
3
Rank
6
4
5
2
3
1
Rank
6
4
5
2
3
1
Rank
5
2
6
4
3
1
Rank .
5
2
3
4
1
0.16
0.16
0.14
0.14
0.13
0.27
Weight
0.01
0.17
0.06
0.31
0.19
0.26
Weight
0.01
0.12
0.08
0.10
0.47
0.22
Weight
0.06
0.24
0.15
0.18
0.19
0.18
Weight
0.01
0.19
0.05
0.26
0.22
0.27
Weight
0.01
0.10
0.03
0.24
0.15
0.47
Weight
0.08
0.21
0.03
0.09
0.14
0.45
Weight
0.02
0.13
0.13
0.05
0.67
E-20
-------
Table E.3 (contd.)
CO-561
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-562
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-571
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CO-572
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CO-573
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-581
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/Silty Clay Loam
Silt Loam
CD-582
Clay/Clay Loam .
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/Silty Clay Loam
Silt Loam
CD-583
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/Silty Clay Loam
Silt Loam
X SAND X SILT X CLAY
BO
FC
UP Rank Weight
28
41
83
61
6
12
X SAND
28
41
81
61
6
12
X SAND
28
41
81
60
6
12
X SAND
28 ,
41
61
6
12
X SAND
22
41
61
6
12
X SAND
28
41
81
61
7
12
X SAND
22
81
61
7
12
X SAND
22
41
61
7
12
41
41
12
27
48
68
X SILT
41
41
13
27
-50
68
X SILT
41
41
13
27
49
68
X SILT
41
41
27
49
68
X SILT
39
41
27
50
68
X SILT
41
41
13
27
59
68
X SILT
39
13
27
60
68
X SILT
39
41
27
60
68
31
18
6
13
46
20
X CLAY
31
18
6
12
44
20
X CLAY
31
18
6
13
45
20
X CLAY
31
18
12
45
20
X CLAY
39
18
12
44
20
X CLAY
31
18
6
12
34
20
X CLAY
39
6
12
33
20
X CLAY
39
18
12
33
20
1.41
1.42
1.55
1.52
1.28
1.43
BO
.41
.42
.54
.52
.29
.43
BD
.41
.42
.54
.51
.29
.43
BD
.41
.42
.52
.29
.43
BD
1.33
1.42
1.52
1.29
1.43
BD
1.41
1.42
1.54
1.52
1.36
1.43
BD
1.33
1.54
1.52
1.37
1.43
BD
1.33
1.42
1.52
1.37
1.43
0.32
0.30
0.14
0.23
0.42
0.33
FC
0.32
0.30
0.14
0.23
0.41
0.33
FC
0.32
0.30
0.14
0.24
0.41
0.33
FC
0.32
0.30
0.23
0.42
0.33
FC
0.36
0.30
0.23
0.41
0.33
FC
0.32
0.30
0.14
0.23
0.36
0.33
FC
0.36
0.14
0.23
0.36
0.33
FC
0.36
0.30
0.23
0.36
0.33
0.17
0.12
0.05
0.09
0.26
0.13
UP
0.17
0.12
0.05
0.09
0.25
0.13
UP
0.17
0.12
0.05
0.10
0.25
0.13
UP
0.17
0.12
0.09
0.26
0.13
UP
0.21
0.12
0.09
0.25
0.13
UP
0.17
0.12
0.05
0.09
0.20
0.13
UP
0.21
0.05
0.09
0.20
0.13
UP
0.21
0.12
0.09
0.20
0.13
5
4
3
2
6
1
Rank
5
2
6
3
4
1
Rank
5
3
6
1
4
2
Rank
5
3
1
4
2
Rank
5
3
1
4
2
Rank
6
5
2
4
1
3
Rank
4
5
3
1
2
Rank
5
3
4
2
1
0.06
0.13
0.21
0.23
0.02
0.35
Weight
0.03
0.18
0.01
0.17
0.07
0.54
1.00
Weight
0.02
0.13
0.02
0.45
0.10
0.28
1.00
Weight
0.03
0.16
0.41
0.13
0.27
Weight
0.03
0.21
0.48
0.07
0.21
Weight
. 0.02
0.10
0.21
0.18
0.30
0.19
Weight
0.03
0.01
0.06
0.75
0.15
Weight
0.03
0.07
0.04
0.28
0.58
E-21
-------
Table E.3 (contd.)
CD-584
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CO-591
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-592
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-601
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-60Z
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-603
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-611
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Silty Clay/SiIty Clay Loam
Silt Loam
CD-612
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silty Clay/Silty Clay Loam,
Silt, Loam
% SAND X SILT X CLAY
BD
FC
UP Rank Weight
23
41
61
7
12
X SAND
28
41
61
7
12
X SAND
12
41
61
7
12
X SAND
28
41
61
7
12
X SAND
13
41
59
7
12
X SAND
11
41
60
7
12
X SAND
11
41
58
7
12
X SAND
14
41
81
60
7
12
39
41
27
55
68
X SILT
41
41
27
60
68
X SILT
35
41
27
60
68
X SILT
41
41
27
56
68
X SILT
36
41
27
54
. 68
X SILT
35
41
27
56
68
X SILT
35
.41
26
57
68
X SILT
36
41
13
27
55
68
38
18
12
38
20
X CLAY
31
18
12
33
20
X CLAY
53
18
12
33
20
X CLAY
31
18
12
38
20
X CLAY
52
18
14
39
20
X CLAY
54
18
13
37
20
X CLAY
54
, 18
16
36
20
X CLAY
50
18
6
13
39
20
.34
.42
.52
.33
.43
BD
1.41
1.42
1.52
1.37
1.43
BD
1.19
1.42
1.52
1.37
1.43
BD
1.41
1.42
1.52
1.34
1.43
BD
1.20
1.42
1.50
1.33
1.43
BD
1.18
1.42
1.51
1.34
1.43
BD
1.18
1.42
1.48
1.35
1.43
BD
1.22
1.42
1.54
1.51
1.33
1.43
0.36
0.30
0.23
0.38
0.33
FC
0.32
0.30
0.23
0.36
0.33
FC
0.44
0.30
0.23
0.36
0.33
FC
0.32
0.30
0.23
0.38
0.33
FC
0.44
0.30
0.25
0.39
0.33
FC
0.45
0.30
0.24
0.38
0.33
FC
0.45
0.30
0.26
0.37
0.33
FC
0.43
0.30
0.14
0.24
0.39
0.33
0.20
0.12
0.09
0.22
0.13
UP
0.17
0.12
0.09
0.20
0.13
UP
0.27
0.12
0.09
0.20
0.13
UP
0.17
0.12
0.09
0.22
0.13
UP
0.27
0.12
0.11
0.23
0.13
UP
0.28
0.12
0.10
0.22
0.13
WP "
0.28
0.12
0.12
0.21
0.13
WP
0.26
0.12
0.05
0.10
0.23
0.13
3
5
2
4
1
Rank
3
2
4
5
1
Rank
1
4
3
5
2
Rank
4
1
5
3
2
Rank
3
5
2
4
2
Rank
5
3
2
4
1
Rank
5
1
4
3
2
Rank
5
4
1
3
6
2
0.07
0.04
0.09
0.05
0.75
Weight
0.16
0.16
0.05
0.02
0.61
Weight
0.33
0.04
0.28
0.03
0.32
Weight
0.10
0.47
0.09
0.11
0.23
Weight
0,20
0.15
0.20
0.17
0.28
Weight
0.02
0.18
0.25
0.10
0.45
Weight
0.01
0.39
0.03
0.19
0.38
Weight
0.12
0.14
0.27
0.16
0.07
0.24
E-22
-------
Table E.3 (contd.)
CO -631
Clay/Clay Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Silt Loam
CD -632
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Si Ity Clay/Si Ity Clay Loam
Silt Loam
CD-633
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Si Ity Clay/Si Ity Clay Loam
Silt Loam
CD -634
Clay/Clay Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Si Ity Clay/Si Ity Clay Loam
Silt Loam
CD-641
Clay/Clay Loam
Loam
Sandy Clay Loam/Sandy Loam
Si Ity Clay/Si Ity Clay Loam
Silt Loam
CD -642
Clay/Clay Loam
Loam
Loamy Sand/Sand
Sandy Clay Loam/Sandy Loam
Si Ity Clay/Si Ity Clay Loam
Silt Loam
CO-643
Clay/Clay Loam
Loam
Loamy Sand/Sand
Si Ity Clay/Si Ity Clay Loam
Silt Loan
% SAND % SILT % CLAY
23
81
61
12
% SAND
21
41
81
61
7
12
% SAND
20
41
81
61
7
12
% SAND
15
41
83
61
6
12
% SAND
11
41
60
7
12
% SAND
28
41
81
61
7
12
% SAND
11
41
81
7
12
BD
FC
UP
Rank Weight
39
13
27
68
% SILT
39
41
13
27
58
68
% SILT
38
41
13
27
57
68
% SILT
37
41
12
27
51
68
% SILT
35
41
27
55
68
% SILT
41
41
13
27
56
68
% SILT
35
41
13
58
68
38
6
12
20
% CLAY
40
18
6
12
36
20
% CLAY
43
18
6
12
36
20
% CLAY
48
18
6
12
43
20
% CLAY
\
54
18
14
39
20
% CLAY
31
18
6
13
38
20
X CLAY
54
18
6
36
20
1.34
1.54
1.52
1.43
BD
1.32
1.42
1.54
1.52
1.35
1.43
BD
1.30
1.42
1.54
1.52
1.35
1.43.
BD
1.24
1.42
1.55
1.52
1.30
1.43
BD
1.18
1.42
1.50
1.33
1.43
BD
1.41
1.42
1.54
1.52
1.34
1.43
BD
1.18
1.42
1.54
1.35
1.43
0.36
0.14
0.23
0.33
FC
0.37
0.30
0.14
0.23
0.37
0.33
FC
0.39
0.30
0.14
0.23
0.38
0.33
FC
0.42
0.30
0.14
0.23
0.40
0.33
FC
0.45
0.30
0.24
0.39
0.33
FC
0.32
0.30
0.14
0.23
0.38
0.33
FC
0.45
0.30
0.14
0.37
0.33
0.20
0.05
0.09
0.13
UP
0.21
0.12
0.05
0.09
0.21
0.13
UP
0.23
0.12
0.05
0.09
0.22
0.13
UP
0.25
0.12
0.05
0.09
0.25
0.13
UP
0.28
0.12
0.10
0.23
0.13
UP
0.17
0.12
0.05
0.09
0.22
0.13
UP
0.28
0.12
0.05
0.21
0.13
3
4
1
2
Rank
4
6
5
2
3
1
Rank
4
3
6
2
5
1
Rank
6
3
4
2
5
1
Rank
4
5
2
3
1
Rank
5
3
6
2
4
1
Rank
5
2
1
4
3
0.07
0.04
0.58
0.31
Weight
0.05
0.02
0.04
0.12
0.05
0.72
Weight
0.04
0.09
0.02
0.26
0.04
0.55
Weight
0.04
0.06
0.06
0.26
0.04
0.54
Weight
0.08
0.02
0.34
0.09
0.47
Weight
0.03
0.16
0.02
0.23
0.08
0.48
Weight
0.01
0.34
0.39
0.05
0.21
E-23 *«.S. GOVERNMENT PRINTING OFFICE: 1994 - 550-001/80383
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