EPA/620/R-93/014
October 1993
Environmental Monitoring and Assessment Program
Agroecosystem Pilot Field Program Plan - 1993
(October 15, 1993)
by
C. Lee Campbell, Technical Director
Jeff Bay
Carol D. Franks
Anne S. Hellkamp
Norman P. Helzer
George R. Hess
Michael J. Munster
Deborah Neher
Gail L. Olson
Steven L. Peck
John O. Rawlings
Brian Schumacher
Mark B. Tooley
This study was conducted in cooperation with
U.S. Department of Agriculture
Agricultural Research Service
Raleigh, NC 27711
U.S. Environmental Protection Agency
Office of Research and Development
Washington, D.C. 20460
Environmental Monitoring Systems Laboratory
Las Vegas, NV 89193
Printed on Recycled Paper
-------
NOTICE
This research has been funded by the United States Environmental Protection
Agency through its Office of Research and Development (ORD) under Interagency
Agreements # DW12934170 with the U.S. Department of Agriculture, Agricultural
Research Service (USDA ARS), # DW12934747 with the USDA National Agricultural
Statistics Service (USDA NASS), and # DW12936168 with the USDA Soil
Conservation Service (USDA SCS) and by the USDA ARS. It was conducted with
our research partners under the management of the Environmental Monitoring
Systems Laboratory - Las Vegas in support of the Environmental Monitroing and
Assessment Program (EMAP). This report has been subjected to ORD's peer and
administrative review and has been approved for publication as an EPA document.
Neither the U.S. EPA nor ORD nor USDA ARS endorses or recommends any trade
name or commercial product mentioned in this product to the exclusion of others.
They are mentioned solely for the purpose of description or clarification.
Proper citation of* this document is:
Campbell, C. L., J. Bay, C. D. Franks, A. S. Hellkamp, N. P. Helzer, G. R. Hess, M. J.
Munster, D. Neher, G. L. Olson, S. L. Peck, J. O. Rawlings, B. Schumacher, and M. B.
Tooley. 1994. Environmental Monitoring and Assessment Program - Agroecosystem
Pilot Field Program Plan - 1993. EPA/620/R-93/014. U.S. Environmental Protection
Agency, Washington, D.C.
11
-------
TABLE OF CONTENTS
Notice . . . .; ii
List of Figures v
List of Tables vi
Glossary of Acronyms . vii
Acknowledgements ix
1. Introduction 1
1.1. Overview of The Agroecosystem Program 1
1.2. Cooperative Interaction with the National Statistics Service (NASS) 3
1.3. Cooperative Interactions with Other National Programs and Agencies 4
1.4. Implementation Schedule for a National Agroecosystem Monitoring
Program 5
2. Rationale and Objectives 6
2.1. Rationale 6
2.2. Objectives 7
3. Societal Values, Assessment Questions, and Indicators 9
3.1. Conceptual Model .9
3.2. Sustainability 11
3.3. Societal Values 11
3.4. ' Assessment Questions 13
3.5. Indicators to Address Assessment Questions 14
4. Design and Statistical Considerations 19
4.1. Pilot Sampling Designs 19
4.2. Within Segment Sampling Protocols 23
4.3. Inclusion Probabilities and Extension 31
4.4. Analysis 35
5. Description of Specific Indicators for The Pilot Project 40
5.1. Crop Productivity 40
5.2. Soil Quality - Physical and Chemical 54
5.3. Soil Biotic Diversity 75
5.4. Land Use and Cover 87
5.5. Agricultural Pest Management ...;." . 95
in
-------
6. Quality Assurance 99
6.1. Introduction 99
6.2. NASS Survey Data Collection 100
6.3. Field Sampling 1°3
6.4. Laboratory Analyses 106
6.5. Soil Audit Materials 106
6.6. Additional Data 107
7. Logistics 108
7.1. Introduction 108
7.2. Logistics and the NASS 109
7.3 Specific Logistics Elements 109
8. Information Management 123
8.1. Introduction 123
8.2. Information Sources and Flow 125
8.3. Confidentiality of Data .. . 128
8.4. Data Integration and Management 130
8.5. Data Access 13°
8.6. Computing Strategy 131
Literature Cited L-l
Appendix 1. Agroecosystem Resource Group Members Al-1
Appendix 2. NASS Survey Questionnaires , , A2-1
Appendix 3. Enumerator Manual for Sampling Soil A3-1
Appendix 4. Methods Manual for Soil Conservation Service Efforts
in the Pilot A4'l
IV
-------
LIST OF FIGURES
Figure 3-1. Societal values considered in the monitoring of agroecosystems. 9
Figure 3-2. Conceptual model of an agroecosystem in its surroundings 10
Figure 3.3. Detail of agroecosystem level of conceptual model 12
Figure 4.1. Hexagon replicates for use in choosing the NASS Segments; the 77 hexagons
were found in 58 countries 21
Figure 4-2. Transect sampling of field . 28
Figure 4-3. Transect sampling of field (Bounce rules) 29
Figure 5.1-1. Some factors which influence crop productivity : . . . ~ 41
Figure 5.3-1. Log-in sheet for the laboratory to record the date samples were' received,
extracted, identified, and preserved and the conditions that they received
the samples at the time of delivery : : . 80
Figure 5.3-2. Example data sheet that will be completed by the nematode enumeration
laboratory and sent to the soil biologist of ARG 82
Figure 7.1. Row chart of logistics activities for the 1993 Pilot Field Program 108
Figure 8-1. Overview of the flow of data through the AIC. 123
Figure 8-1. Use of existing data to perform validity checks on data. 124
Figure 8-2. Flow of data collected by NASS ,,. 125
v
-------
LIST OF TABLES
Table 3-1. Societal values and related assessment questions 14
Table 3-2. Association between Agroecosystem indicators and societal values 15
Table 3-3. vital statistics on indicators for the Agroecosystem program 16
Table 4-1. Stratification of NASS segments in Nebraska for 1993 22
Table 4-2. Resource classes ineligible for selection in the Agroecosystem 1993 pilot 24
Table 4-3. Degrees of freedom for field sampling components of variance on soil
chemical measures 30
Table 4-4. Total number of soil samples for chemical and nematode analysis. 31
Table 5.1.1. Principal crops or land uses eligible for selection in the Agroecosystem
1993 Region 7 Pilot in Nebraska 43
Table 5.1-2. Elements of metadata to be recorded in association with data for the
crop productivity indicators, not including ancillary data such as weather 45
Table 5.1-3. Example output table for an indicator or corp productivity 53
Table 5.2-1. Research indices of soil quality 56
Table 5.2-2. Requested data elements from the SCS State Soil Survey Database 57
Table 5.2-3. Ratings of soil PH ^ : 59
Table 5.2-6. Contents of the enumerator kit 63
Table 5.2-7. Soil analytical laboratory parameters to be measured hi the 1993 Pilot Field
Program 66
Table 5.2-8. Reporting units and precision of soil physical and chemical measurements 68
Table 5.2-9. Metadata for chemical and physical analysis of soils 71
Table 5.2-10. Examples of soil assessments 74
Table 5.3-1. Classification of nematode genera and families by trophic groups and their
respective c-p value as defined by Bongers (1990) for calculation of the
Maturity Index separately for free-living and plant parasitic nematodes.
Classification and trophic groups determined by M. Noffsinger (personal
observation), Maggenti (1982, 1991) and Yeats (1971). (Table taken
from Neher et al. 1993) 76
Table 5.3-2. Reporting Units, Precision and Expected Ranges for Nematode Indices across
Regions of North Carolina (December 1991) 83
Table 5.3-3. Data Quality Objectives for Within Sample and Within Field Variance. Data
objectives based on a preliminary experiment (Neher et al. 1993) 84
Table 5.3-4. Metadata for Biological Analysis Soils in the 1993 Region 7 Pilot 84
Table 5.4-1. Steps to convert NASS area frame to ARC format 90
Table 5.4-2. NASS JES land use classification 91
Table 5.4-3. Steps to acquire JES data 91
Table 5.5-1. Key pests for several major crops in Nebraska 96
Table 7-1. Logistical issues in the 1993 Agroecosystem Pilot. 108
Table 7-2. Activities in the 1993 Agroecosystem Pilot Program 110
Table 7-3. Tasks with schedule for conducting the 1993 Pilot Field Program in Nebraska. . Ill
Table 7-4. Description of seven-digit soil sample identifier 114
Table 8-1. Examples of existing data to be used for the 1993 Pilot Project 127
Table 8-2. Summary of confidentiality provisions of several government agencies with data of
value to the Agroecosystem Resource Group 126
Table 8-3. Examples of integration keys for new and existing data 132
VI
-------
Glossary of Acronyms
AIC
APHIS
ARG
ARS
ASCS
ASTM
AVHRR
AWC
BRG
C
CEC
CGIA
CRP
DAT
DBAPE
DC
DLG
DQO
EC
ECD
EIC
ELISA
EMAP
EPA
ERL
ERS
ESP
FPD
GI
GIS
Hall ECD
HI
HQ
HT
ID
IM
IMC
INEL
JES
LAI
LAN
LCG
LPT
MLRA
NAPP
NASDA
NASS
NAWQA
NC
NC-R
NC-S
Agroecosystem Information Center
Animal and Plant Health Inspection Service
Agroecosystem Resource Group
Agricultural Research Service (USDA)
Agricultural Stabilization and Conservation Service
American Society for Testing and Materials
Advanced Very High Resolution Radiometer
Available water capacity
Business Resources Group, Inc.
Carbon
Cation exchange capacity; Commission for European Communities
Center for Geographic Information and Analysis
Conservation Reserve Program
Digital audio tape
Database Analyzer and Parameter Estimator
District of Columbia
Digital line graph
Data quality objective
Electrical conductivity
Electron capture detector
EMAP Information Center
Enzyme-linked immunosorbent assay
Environmental Monitoring and Assessment Program
Environmental Protection Agency ,
Environmental Research Laboratory
Economic Research Service (USDA)
Exchangeable sodium percentage
Flame photometric detector
Greenness index
Geographic information system
Hall electrolytic conductivity detector
Harvest index
Headquarters
Horvitz-Thompson
Identification/identifier
Information management
Information Management Committee
Idaho National Engineering Laboratory
June Enumerative Survey .
Leaf area index
Local area network
Landscape Characterization Group
Landscape pattern type
Major Land Resource Area
National Aerial Photography Program
National Association of State Departments of Agriculture
National Agricultural Statistics Service (USDA)
National Water-Quality Assessment
North Carolina; North Central
Oj-resistant clone of white clover
Oj-sensitive clone of white clover
vn
-------
Glossary of Acronyms cont'cL
NCCGIA
NCDA
NCSU
NDVI
ME
NGO
NOAA
NPD
NPP
NRI
03
OM
OMB
ORD
PAR
PCR
PSU
QA
QA/QC
RDBMS
RUSTIC
SAR
SAS
SCS
SE
SI
SO
SOP
SSSD
T
TD
TM
USDA
USDC
USDI
USGS
USLE
UV
WE
WRI
North Carolina Center for Geographic Information and Analysis
North Carolina Department of Agriculture
North Carolina State University
Normalized Difference Vegetation Index
Northeast
Non-governmental organization
National Oceanic and Atmospheric Administtation (USDC)
Nitrogen-phosphorus detector
Net primary productivity
National Resources Inventory (USDA/SCS)
Ozone
Organic matter
Office of Management and Budget
Office of Research and Development (EPA)
Photosynthetically active radiation
Post column reaction
Primary Sampling Unit
Quality assurance
Quality assurance/quality control
Relational database management system
Risk of Unsaturated/Saturated Transport and Transformation of Chemical Concentrations
Sodium absorption ratio
Statistical Analysis System
Soil Conservation Service (USDA)
Southeast
Systeme International (version of the metric system) :
South
Standard operating procedure
State Soil Survey Database .
Soil erosion tolerance factor
Technical Director
Thematic Mapper
United States Department of Agriculture
United States Department of Commerce
United States Department of Interior
United States Geological Survey (USDI)
Universal Soil Loss Equation
Ultraviolet
West
World Resources Institute
Vlll
-------
Acknowledgements
The members of the Agroecosystem Resource Group have interacted with many people
during the development of this Plan. Dozens of people have shared information and expertise
during various Agroecosystem workshops. Many more have responded to our written inquiries
and telephone calls. The level of interest and cooperation from other government agencies,
universities and private organizations is appreciated. We would like to express our gratitude
to all who have contributed to our effort.
We would like to thank Joe Miller, Research Leader, USDA Agricultural Research
Service, the personnel of the ARS Area Support Office in Raleigh, and the many other
administrators and staff of ARS with whom we interact in Athens, Georgia and Washington,
D.C. for their continued assistance and support for the EMAP Agroecosystems Program.
A special note of thanks is in order for Steve Manheimer, Jim Gibson, Jaki Stanley,
Becky Cross, and Sarah Hoffman, and those who work with them at USDA National
Agricultural Statistics Service. They have become ah integral part of the EMAP
Agroecosystem Resource Group, bringing with them years of experience in developing spatial
sampling techniques for agricultural statistics. Their insight has proven invaluable and helped
us avoid many of the pitfalls associated with a program as broad and diverse as EMAP. We
would also like to thank Ray Halley, our administrative contact at USDA NASS, who has
given much support to NASS' involvement with the Program. Perhaps most important have
been the lessons learned during the 1992 pilot and the cooperation of Craig Hayes, Tom
Sabel, and David Luckenbach at the North Carolina office of NASS. The cooperation with the
Nebraska office is also off to a good start, thanks to the skill and interest of Deputy State
Statistician Bill Dobbs.
Special thanks go to Wayne Marchant, Rick Linthurst and Hal Kibby for their
programmatic suggestions, ideas and support; to Sue Franson and Ann Pitchford for their
dedicated administrative management support to the Program; and to Walter Heck for his past
leadership, his continued support and current efforts to link the terrestrial EMAP groups.
One goal of the 1993 Pilot has been to increase the cooperation between the
Agroecosystem Resource Group and the USDA Soil Conservation Service. Our two
administrative contacts, Robert Smith and Bill Roth, have provided valuable guidance in this
regard. The technical assistance of the personnel of the Nebraska SCS office is also
acknowledged.
Finally, we would like to thank Tracey Diggs, who spent long hours at the computer
keyboard revising and compiling this manuscript; Tammy Self and Carla Tutor, who copied
and collated the many versions; and our librarian, Phyllis Gams, who kept literature searches
coming.
IX
-------
-------
1. Introduction
In 1993 members of the Environmental Monitoring and Assessment Program's (EMAP)
Agroecosystem Resource Group (ARG) will conduct a Pilot Field Program in Region 7
(Nebraska). EMAP is an Environmental Protection Agency (EPA) initiative administered by
EPA's Office of Research and Development and supported by several U.S. government
agencies. The Technical Director of the ARG is with the U.S. Department of Agriculture's
(USD A) Agricultural Research Service (ARS). USD As National Agricultural Statistics
Service (NASS) cooperates in the development and data collection aspects of the
Agroecosystem program. The USDA Soil Conservation Service (SCS) provides expertise and
support in soil characterization and analysis for the program. These four agencies are the
principal cooperators in the 1993 Pilot; the pilot is an important developmental step towards
the implementation of a plan for monitoring the ecological condition of agroecosystems in the
United States. This document is an implementation plan for the Pilot Field Program. Every
attempt has been made to include pertinent information in this document; however, as plans
continue to develop and methods become refined, changes will necessarily be made.
1.1. Overview of The Agroecosystem Program
This section provides a brief overview of the Agroecosystem component of the EMAP
program. A more detailed description of the program is in Environmental Monitoring and
Assessment Program (EMAP) - Agroecosystem Monitoring and Research Strategy (Heck.et
al. 1991).
1.1.1. Establishment
In the past decade, environmental scientists have identified the need for relevant and
accessible ecological data, and the EPA has been encouraged to adopt an ecological
perspective in which the ecosystem is the fundamental unit of research and monitoring. In
1988, EPA, in cooperation with other federal agencies and organizations, initiated EMAP to
provide estimates of the condition of U.S. ecological resources and to report, with statistical
confidence, changes and trends in these resources (Kutz and Linthurst 1990).
Agroecosystems is one of eight resource categories within EMAP. The ARG was
established in 1988 with Roy E. Cameron (Lockheed Environmental Systems and
Technologies Co., Las Vegas) as Acting Technical Director. Walter W. Heck (USDA ARS)
was Technical Director from 1989-1992 and currently serves as Associate Director of EMAP
1
-------
for Terrestrial Systems. C. Lee Campbell [Department of Plant Pathology, North Carolina
State University (NCSU)] was Associate Director from 1989-1992 and worked with Heck in
the development of the interagency, interdisciplinary group of federal, state and private
scientists (Appendix 1) that comprise the ARG. Campbell (now USDA ARS) currently serves
as Technical Director.
1.1.2. Mission, Objectives, Definition, and Societal Values
The mission of the ARG is "to develop and implement a program to monitor and evaluate
the long-term status and trends of the Nation's agroecosystems from an ecological perspective
through an integrated, interagency process" (Heck et al. 1991). The developmental stages of
this national program include a 1992 Pilot Field Program conducted in Region 4 (North
Carolina) (Heck et al. 1992), this 1993 Pilot Field Program, and subsequent regional pilot and
demonstration field programs.
The specific objectives of the agroecosystem program parallel the overall EMAP program
objectives but focus on agroecosystems. When fully implemented the program will meet the
following objectives:
o Estimate the status and trends in selected indicators of the condition of the Nation's
agroecosystems on a regional basis with known statistical confidence.
o Estimate the geographic coverage and extent of the Nation's agroecosystems with
known statistical confidence.
o Seek associations between selected indicators of natural and anthropogenic stresses and
indicators of the condition of agroecosystems.
o Provide annual statistical summaries and periodic assessments of the Nation's
agroecosystems.
For EMAP, an agroecosystem is a dynamic association of crops, pasture, livestock, other
flora and fauna, atmosphere, soils, and water.
The ARG recognizes that the sustainability of agroecosystems is of primary importance to
the people of the United States and the world. Ecological sustainability, the principal focus for
the Agroecosystem monitoring effort, is the ability to maintain or enhance over the long term:
quality of air, water and soil; productivity; and biodiversity. Because these components of
ecological sustainability are defined in relation to people and society, we refer to them as
societal values; they parallel the "assessment endpqints" stated in the 1991 Research Strategy
-------
Plan (Heck et al. 1991). These values serve as a focus for development of the overall strategy
for agroecosystem monitoring and for the selection of specific biotic and abiotic condition
indicators (measurements) of ecological condition of the resource. Although not mentioned
specifically, socioeconomic factors are recognized as being inherent in these societal values.
1.1.3. Relationship to Other EMAP Resource Groups and Cross-cutting Activities
EMAP comprises eight ecosystem resource groups, four integration groups, and five
coordination groups (Table 1-1). Interdisciplinary and interagency groups of scientists in each
resource group are responsible for the collection, analysis, and integration of data from their
ecological resource. The integration and coordination groups have been established to assist
the resource groups and to ensure uniform quality management, consistency, and integration
across the program.
Presently, the resource groups are in various stages of developing plans for
implementation of monitoring activities. The intent is for all resource groups to be ready to
implement a national monitoring program by 1998.
The ARG communicates with other resource groups, particularly the terrestrial groups,
concerning cross-cutting activities such as indicator development, landscape characterization,
design, statistics, logistics, and QA/QC. Joint efforts in pilot field programs have also been
discussed with all resource groups except Great Lakes and Estuaries. As pilot plans develop,
interactions with the coordination and integration groups will intensify to ensure that the ARG
program is consistent and compatible with other activities within EMAP.
1.2. Cooperative Interaction with the National Agricultural Statistics Service (NASS)
The ARG maintains extensive cooperative interactions with personnel of USDA NASS,
both at the operational and administrative levels. Several members of the USDA NASS staff
serve as members of the ARG and as liaison between NASS and the Agroecosystem
Technical Director (Appendix 1).
The association with NASS is an integral component of the Agroecosystem program,
which will utilize NASS's established and well-accepted national sampling frame and NASS's
long experience in performing site visits and interviews with farmers. Over the past 30 years,
NASS has developed a network of enumerators and administrators experienced in conducting
successful national surveys and monitoring activities. This nationwide force of trained
-------
Table 1-1. Resource, integration and coordination groups of EMAP.
Resource Groups
Agroecosystems
Arid Ecosystems
Estuaries
Forests
Great Lakes
Surface Waters
Wetlands
Landscape Ecology
Integration Groups
Air & Deposition
Assessment and Reporting
Landscape Characterization
Statistics and Design
Coordination Groups
Indicators
Information Management
Logistics
Methods
Quality Assurance
enumerators, with its proven administrative organization, will be utilized for much of the field
assessment in the Agroecosystem component of EMAP.
It is important to the program that growers throughout the U.S. are familiar with and have
confidence in NASS personnel. This confidence has been maintained largely through NASS's
data confidentiality requirement. This requirement is established by law, is well accepted in
the agricultural community, and is essential to the success of the ARG in working with
growers in the U.S. NASS also has an established, well-respected program for tracking,
processing and summarizing data acquired in the field. The ARG is developing the
Agroecosystem program to make maximum use of these aspects of NASS.
1.3. Cooperative Interactions with Other National Programs and Agencies
Plans are underway to have a USDA SCS scientist join the ARG and to establish
cooperative activities'between SCS and the ARG for the 1993 Pilot. Specific plans include
characterizing soils at sample sites for the 1993 Pilot and laboratory analyses (on a fee basis)
at the SCS National Soil Survey Laboratory (Lincoln, NE). Also, ARG and SCS personnel are
-------
exploring ways to integrate data from the SCS National Resource Inventory (NRI, which is
conducted every 5 years) and the Agroecosystem program.
Discussions are also being held with the USDA Economic Research Service (USDA ERS)
to establish a mutually beneficial working relationship between the ARG and USDA ERS,
which would complement existing and planned ARG activities and would continue to
recognize the importance of socioeconomic factors in the sustainability of agroecosystems.
1.4. Implementation Schedule for a National Agroecosystem Monitoring Program
The ARG has developed a multiyear approach, contingent on availability of funding, to
implement a national monitoring program with an initial suite of indicators by 1998. These
indicators will address the societal values and associated assessment questions for
agroecosystems. The first stage of the program (1990) evaluated: 1) statistical designs, 2)
existing monitoring programs (NASS, SCS, and ERS), 3) indicators and associated
measurements (for their availability, validity, variability, and cost) (Campbell et al. 1990), 4)
data management and analysis techniques, and 5) derived outputs (Meyer et al. 1990). During
1990, a national monitoring strategy was developed (Heck et al. 1991). In 1991 in-depth
examinations were conducted of several areas critical to the planning and implementation of
the 1992 Pilot Field Program: 1) statistical design options, 2) measurements associated with
specific indicators, 3) sampling protocols, 4) cooperation with NASS, 5) logistics, 6) total
quality management, and 7) information management. Discussion and re-examination of these
areas will continue through 1993.
Experience from the 1992 and 1993 Pilot Field Programs will be utilized to develop pilot
and demonstration field programs in the Southeast (part of Region 4), the mid-Atlantic
(Region 3) or the Midwest (Region 7) when funds are available. The ARG will also
participate in a multiple resource group demonstration project planned for 1995 in a region to
be determined.
-------
2. Rationale and Objectives
This section provides an overview of the rationale for the 1993 Agroecosystem Pilot Field
Program and presents the specific objectives of the project. The rationale is in keeping with
the overall program approach outlined in Section 2 of the Agroecosystem Monitoring and
Research Strategy (Heck et al. 1991).
2.1. Rationale
Agroecosystems are managed intensively for the benefit of people. As a result, activities
in agroecosystems are often influenced by government programs (e.g., Conservation Reserve
and Crop Quotas) and regulations (e.g., wetlands preservation and changes in permissible
pesticide use) and by socioeconomic factors. These perturbations and social and economic
factors are beyond the realm of traditional ecology and provide a series of challenges to the
establishment of an ecologically-oriented monitoring program for agroecosystems.
Both ecological and more traditional agricultural information is included in the ARG
monitoring program. This includes an evaluation of windbreaks, hedgerows, and farm ponds
and their function in preserving and supporting production of crops and livestock and
providing habitat for insects and wildlife. The EMAP-Agroecosystem Program, which is
ecologically-based, does not duplicate current federal programs. Wherever possible, the ARG
seeks to use existing data and cooperate with other agencies charged with monitoring specific
agroecosystem components.
The Agroecosystem monitoring program will be carried out through a combined survey
and sampling approach. Information on inputs and management practices will be obtained
directly from the grower, soil and water samples will be collected, and on-site measurements
will be made as needed; all of this information will be integrated into indices such as crop
productivity,
production efficiency, soil quality, water quality, and habitat suitability.
Pilot Field Programs serve to resolve several issues prior to regional or national
implementation. These issues include the evaluation of indicators, the establishment of a
sampling frame and sample sizes sufficient to meet target data quality objectives (DQOs), the
evaluation of logistics and quality assurance and control procedures, the development of
information management procedures (including provisions for data confidentiality), and the
establishment of data analysis, summarization, and reporting formats. The 1993 Pilot Field
-------
Program will continue to address these issues at a geographic scale that is large enough to
provide reliable answers to specific questions concerning the operation of the monitoring
program, but is small enough to be physically and fiscally manageable. However, not every
aspect of the monitoring program will be evaluated in the 1993 pilot. For example, because of
the status of indicator development and fiscal constraints, only a limited suite of indicators
will be evaluated in 1993. Also, some components of the pilot include research and
development activities that, if successful, will lead to the inclusion of additional indicators and
procedures into regional demonstrations and eventually into the national monitoring program.
Region 7 and the State of Nebraska were selected for the 1993 Pilot Field Program for
several reasons, given in order of importance:
1. The physiographic diversity of the state is representative of typical midwestern
agroecosystems (intensively cropped areas) and western agroecosystems (sparsely
cropped areas); the state contains a transition zone between these types of
agroecosystems; and the state contains an area (Platte River Basin) where intensively
managed agroecosytems intrude into an area of nonintensively managed systems.
2. Nebraska contains a transition between agroecosystems and arid ecosystems, which
will allow for the careful definition of the areas of responsibility of the ARG and the
Arid Ecosystems Resource Group.
3. EPA Region 7 expressed strong interest in the Agroecosystem monitoring program.
2.2. Objectives
The 1993 Pilot Field Program is designed to provide information that will allow for the
evaluation of specific aspects of the Agroecosystem monitoring program. There are four major
objectives:
1. Empirically evaluate an initial suite of indicators to
o evaluate the ability of an indicator to address the assessment questions and societal
values of interest;
o establish an initial range of values and variance for each indicator across a
midwestern region;
o assess components of variability of indicators within and among sample units;
-------
o identify the usefulness and sensitivity of each indicator in determining ecological
condition; and
o determine the cost-effectiveness for each indicator.
2. Compare the relative efficiency, in terms of cost and precision, of the EMAP Hexagon
Design and the NASS Rotational Panel Design for use in a national agroecosystem
monitoring program.
3. Develop and refine plans for key components of the monitoring program, including
o sampling
o logistics
o total quality management
o data analysis, summarization, and reporting
o information management
4. Develop and evaluate additional measurements that will address specific indicators,
including
o soil quality - biological component
o landscape structure
Experience and information from the 1993 Pilot Field Program will facilitate, expansion to
demonstration projects and implementation. The pilot will address issues vital to the success
of the Agroecosystem program. Meanwhile, members of the ARG will:
o refine assessment questions and the indicator strategy for agroecosystem monitoring;
o analyze, summarize, and evaluate the results of the 1992 Pilot Field Program in North
Carolina;
o evaluate or develop new ecological indicators for beneficial and pest insects, farm
ponds, and wildlife in agroecosystems; and
o plan for 1994 pilot and demonstration activities.
-------
3. Societal Values, Assessment Questions, and Indicators
The goal of the Agroecosystem program is to assess the long-term status and trends of the
condition of the nation's agroecosystems from an ecological perspective. Sustainability of
agroecosystems is the principal concern in this assessment. However, agroecosystem
condition and sustainability cannot be measured directly; such measurement requires
information related to the three societal values: quality of air, water and soil; productivity; and
biodiversity (Figure 3-1).
The EMAP-Agroecosystem monitoring effort is based on assessment questions related to
these three societal values. These assessment questions have been formulated based on agency
values, workshops with agroecological and agricultural experts, peer review, small group
meetings, conceptual models, and "extensive literature review: Biotic and abiotic condition
indicators are being developed and evaluated to answer each assessment question. Condition
indicators will be identified here and specific metrics associated with each indicator will be
discussed in Chapter 5.
Quality of
Air, Water, and Soil
Biodiversity
3.1 Conceptual Model
The Agroecosystem Resource Group has
developed a conceptual model of
agroecosystems (Fig. 3-2) to assist in
formulating assessment questions, identifiying
appropriate measures for each indicator, and
identifying possible relationships among
measurements in the development of indices of
sustainability.
With respect to the conceptual model:
An agroecosystem (Fig. 3-2) is a dynamic
association of crops, pasture, livestock, other
plants and animals, atmosphere, soils, and
water. The agroecosystem includes not only the
field, but also the associated border areas such Figure 3-1. Societal values considered in the
as windbreaks, fence rows, ditch banks, and monitoring of agroecosystems.
farm ponds.
Status & Trends in
Agroecosystem
Condition
Productivity
-------
REST OF THE
KEY
INFORMATION AND POLICY
EMAP INFORMATION
MATERIAL AND ENERGY FLOW
FLOW MANAGED BY FARMER
Figure 3-2. Conceptual model of an agroecosystem in its surroundings.
10
-------
Agroecosystems interact with larger landscapes (Fig. 3-3), which include uncultivated
land, drainage networks, human communities, and wildlife. The landscape is the area that
directly affects the ecology of the agroecosystem and is directly affected by agroecosystem
processes. The landscape boudary depends upon, and varies with, the process being
considered.
Materials and energy are brought to the agroecosystem from afar and exported to distant
points (rest of the world - Fig. 3-3).
Agroecosystems also belong to farms (Fig. 3-3). A farm is an economic entity in which
an operator that uses information about market conditions and resources available to manage
agroecosystems for economic gain.
Government policy, programs and regulations influence decisions made at all levels. In
particular, policies are intended to change people's actions and to realize the interests of
society. Policies may be implemented within the framework of prescribed government
programs and regulations.
3.2 Sustainability
The sustainability of agroecosystems is of primary importance to the people of the United
States and the world. Although there are several aspects of sustainability, the ARG is
interested in the ecological sustainability of agroecosystems. An agroecosystem is ecologically
sustainable if it maintains or enhances its own long-term productivity and biodiversity, the
biodiversity of surrounding ecosystems, and the quality of air, water, and soil.
Two other facters of sustainability, which are addressed by other federal agencies such as
the USDA Economics Research Service (ERS) and MASS, are economic and social
sustainability. A farm is economically sustainable if it is economically viable of the long term.
An agricultural system is socially sustainable if it meets the basic food and fiber needs of
society and maintains or enhances the quality of life for farmers and rural communities.
3.3. Societal Values
The three societal values for agroecosystems are the components of ecological
sustainability - quality of air, water and soil; productivity; and biodiversity. Agroecosystem
performance is a function of the quality of the air, water, and soil entering and within the
11
-------
MANAGEMENT
PRACTICES
CLIMATE
\
WEATHER
BIOTIC
FIELD/ORCHARD/PASTURE
AGROECOSYSTEM
-f AIR, WATER, SOIL
-f ORGANISMS
+ CHEMICALS
+ AGRICULTURAL COMMODITIES
LANDSCAP
REST.QF WQB
Figure 3.3 Detail of agroecosystem level of conceptual model.
12
-------
agroecosystem. In addition, agricultural practices can affect surrounding air, water, and soil
of the agroecosystem and surrounding ecosystems.
In the traditional ecological sense productivity is defined as grams of carbon (biomass) per
unit area per unit time. Society has a compelling interest in the portion of that biomass that is
harvested for food and fiber. In this context, productivity means more than mass per area per
time. It can sometimes be a realtive concept (e.g., certain soils and climates being better
suited to certain plants and animals) or it can involve the efficiency with which resources
such as nitrogen are used in the agroecosystem. Additional aspects of agroecosystem
productivity include biomass production in pastures and growth increment of trees in ecotonal
area such as windbreaks.
The biodiversity (including abundance) of plants, animals and microbes in a field, in areas
that border fields, and in the surrounding landscape affects agroecosystem function and is
affected by agricultural practices. Abundance and diversity of some species in the
agroecosytem, including pollinators and insect predators, can positively affect plant
productivity, whereas diversity of others, such as pests, can have negative effects on both
plants and animals. Genetic diversity is important as the raw material for imporved varieties
and in the prevention of devastating epidemics and must be conserved and protected.
3.4. Assessment Questions for the 1993 Agroecosystem Pilot in Region 7
By focusing on specific aspects of societal values, assessment questions form the link
between these values and indicators. They guide the Integration of data from agroecosystems
into more general information about the three societal values and sustainability over the
region. Assessment questions related to the three societal values and appropriate for EMAP
are being developed by the ARG. The list of assessment questions for the Agroecosystem
monitoring program is not yet complete; however, a primary goal in 1993 is to assemble a
complete list to guide the continued development of the monitoring program.
The primary question for EMAP Agroecosystems is: What proportion of agroecosystems
are ecologically sustainable? The assessment questions presented here (Table 3-1) are a subset
of the total list that will be developed and illustrate the types of questions that EMAP will
answer.
13
-------
Table 3-1. Societal values and related assessment questions.
Societal Value
Assessment Questions
Quality of Air, Water,
and Soil
° What proportion of agroecosystems has soil quality sufficient to sustain
productivity (crop and non-crop)?
Productivity
° What proportion of agroecosystems is attaining projected productive
capacity (as determined by soil map unit, climate, historic levels of
production etc.)?
° What proportion of agroecosystems has acceptable production
efficiency for crops?
Biodiversity
o What proportion of agroecosystems has associated noncrop areas with
habitat wuitable for wildlife species of interest?
° What proportion of agroecosystems have acceptable diversity of soil
microbres?
3.5. Indicators to Address Assessment Questions
Assessment questions are answered with information from indicators, which are measures
of the environment that reflect the condition of an ecological resource or its exposure to
stress. For EMAP two types of indicators are recognized: condition indicators and stressor
indicators.
A condition indicator is a characteristic of the environment that reflects the condition of
an ecological resource. A biotic condition indicator reflects the condition of the biotic
component of the resource and an abiotic condition indicator reflects the condition of the
physical or chemical component of the resource. A stressor indicator is a characteristic of the
environment that is believed to affect the condition of the resource.
An initial list of fifteen indicators has been identified for possible use in the
Agroecosystem monitoring program (Table 3-2). Although the list is comprehensive, it can be
changed. Also, because of fiscal and logistic limitations, it may not be possible to retain all of
the indicators within the regional and national monitoring program. Table 3-3 presents a
summary of the current status of the indicators identified for use and development by the
ARC.
14
-------
Table 3-2. Association between Agroecosystem indicators and societal values.
Indicator
Soil Quality: Physical/Chemical
Soil Biotic Diversity
(Nematode indices)
Crop Productivity
Land Use and Cover
Pest Management
(including Agrichemical Use)
Water Quality: Ponds
and Existing Wells
Landscape Structure
Biological Ozone Indicator
(Clones of white clover)
Insect Diversity
Groundwater/Well Comparisons
Socioeconomic Health
Habitat Quality
Symptoms of Foliar Injury
Genetic Diversity
Livestock Productivity
Quality
of Air, Water,
and Soil
X
X
X
X
X
X
X
X
X
X
Productivity
X
X
X
X
X
X
X
X
X
X
X
X
X
Biodiversity
X
X
X
X
X
X
X
X
X
15
-------
Table 3-3. Vital statistics on indicators for the Agroecosystem program.
Indicator
Soil Quality: Physical/Chemical
Soil Biotic Diversity
(Nematode indices)
Crop Productivity
Land Use and Cover
Pest Management
(including Agrichemical Use)
Water Quality: Ponds
and Existing Wells
Landscape Structure
Biological Ozone Indicator
(Clones of white clover)
Insect Diversity
Groundwater/Well Comparisons
Wildlife Diversity
Habitat Quality
Symptoms of Foliar Injury
Genetic Diversity
Livestock Productivity
Source of
Data
Sample
Sampling
Survey
Survey4
Survey
5
Remote4
Sampling'
Sampling
5
Sample Design
from which
data will come
Both3
Rotational
Both
All of JES
Both
Off-frame
Off-frame
Off-frame
Index Period*
Fall
Fall
Fall
May-June
Fall
Several
Spring/
Summer
?
Responsible
Party
ARG/NASS
/SCS
ARG/NASS
ARG/NASS
ARG/NASS
ARG/NASS
ARG
ARS
cooperators
ARG
Stage of
Develop-
ment2
1
1
1
1
1
1 '
2
' 2
2
2
3
3
4
' 4
4
1 Period during which data are collected (Note: survey data may actually represent earlier events)
2 l=rescarch 2=off-frame research 3=under consideration 4=proposed
Numbers 1 and 2 will be included in the 1993 Pilot.
* Both=segments from both the hexagon design and the NASS rotational panel
4 Will also make use of the NASS strata for Nebraska (developed in 1978)
5 Included in 1992 Pilot but not in 1993 Pilot
16
-------
3.5.1. Selection of Indicators for The 1993 Pilot Field Program
One of the objectives of the 1993 Pilot Field Program is to empirically evaluate an initial
suite of indicators that will address the assessment questions identified in Section 3.4.
3.5.1.1. Rationale for Selection of Pilot Indicators
Indicator selection for the 1993 Pilot Field Program was based on the likelihood of
success in collecting and interpreting data. Criteria for judging this likelihood were: 1) the
ability of NASS enumerators to collect the required survey data and samples; 2) the
availability of analytical and assay procedures that fit within the quality assurance, quality
control, and fiscal standards of the ARG; 3) the ability of the ARG to use and interpret the
data obtained; and 4) experience from the 1992 pilot.
The first criterion is essential because one goal of the 1993 Pilot is to refine the
relationship established between the ARG and NASS in 1992. The ARG still plans for NASS
enumerators to serve as the primary grower contacts and as the primary field personnel for
acquiring specific samples (e.g., soil, water). In the pilot it is essential to confirm this as a
viable approach.
The second criterion reflects the challenge to the ARG of assembling a suite of indicators
that will answer the assessment questions, is scientifically credible and informative, and meets
budget constraints.
The third criterion acknowledges the difficulty of combining and interpreting data from
diverse sources. From the perspective of the design and statistics component of the pilot,
indicators need to have a relatively clear, known interpretation with manageable variability
within and among sample units.
The fourth criterion acknowledges the aid that prior experience will provide in judging the
suitability of specific measurements or indicators.
3.5.1.2. Indicators Selected for the 1993 Pilot Field Program
Based upon the four criteria identified in Section 3.5.1.1, five candidate indicators have
been selected for use in the 1993 Pilot Field Program:
17
-------
o Crop Productivity
o Soil Quality: Physical and Chemical Components
o Soil Biotic Diversity
o Land Use and Cover
o Agricultural Pest Management
These indicators are discussed in detail in Chapter 5. All three societal values are
addressed by this group of indicators. The specific values addressed by each indicator were
identified in Table 3-2.
18
-------
4. Design and Statistical Considerations
Statistical considerations for the 1993 Pilot Project fall under the two topics of sampling
design and protocols, and data analysis. The basic issues associated with these topics are
discussed in more detail in the 1991 Agroecosystem Monitoring and Research Strategy (Heck
et al. 1991).
In the 1993 Nebraska Pilot, the ARG will continue to evaluate the two sampling designs
implemented in the North Carolina 1992 Pilot. The designs are designated the Hexagon
Sampling Plan and the Rotational Panel Plan.Two independent samples, one from each design,
will again be used to provide further information on,cost and variance from which to compare
these two plans. (Key information on temporal correlations needed for a more complete
comparison of the plans, however, cannot be obtained from either of these one-year pilots.)
The basic unit of sampling in both plans is the NASS segment, which in the Great Plains
Region has an approximate size of 2.6 km2 (1 square mile); however, the selection of the
segment differs between the two plans. Each segment contains an unknown number of
agricultural fields and a protocol for obtaining a random sample of agricultural fields with
known probabilities of inclusion has been developed. Some indicators require sampling the
geographical area defined by the field. A protocol is outlined for this within-field sampling,
which also will provide information on relevant components of variance.
Data analysis and reporting will include (in addiion to a simple statistical summary of the
indicator results): 1) estimation of indicator variance components to help determine future
field sampling strategies; 2) correlation analysis to understand relationships among indicators
as well as spatial patterns of the indicators; and 3) comparison of the variance and cost
efficiencies of the two sampling plans.
4.1. Pilot Sampling Designs
The 1993 Nebraska Pilot will provide a second comparison of the EMAP Hexagon
Sampling Plan and the Rotational Panel Sampling Plan on an area with very different
physiography and cropping regimes from that found in the North Carolina 1992 Pilot. Both
cost and precision will be considered in evaluating the relative efficiencies of the two
sampling plans. Because the two sample sizes differ slightly, efficiencies will be expressed in
a standardized manner such as information per unit cost.
19
-------
Since each Pilot is a one-year test, comparisons from the Pilot will not provide
information on the relative efficiency of the two sample plans for estimation of changes or
trends over time. Consideration of the relative efficiencies for the estimation of time trends
must be based on theoretical results (e.g., Lesser 1992) or simulation studies.
Each sampling plan under consideration will use the NASS Area Frame segments as the
basic sampling units (Heck etal., 1991). The NASS area frame segments were defined by first
stratifying the state of Nebraska based on amount of agriculture (See Section 4.1.2). Each
stratum is divided into Primary Sampling Units (PSUs). A random sample of PSU's is then
divided into six to eight sample segments, with segment size dependent'on strata. For
example, segment size is approximately 0.26 km2 for urban strata, 2.6 km2 for agricultural
strata and 10.4 km2 for rangeland strata.
The EMAP Hexagon Plan selects one replicate of the EMAP hexagons and uses the
centroid of these selected hexagons to identify the NASS segment that will be used for
indicator sampling. The Rotational Panel Plan uses a subset of segments from the NASS June
Enumerative Survey (JES). Both plans will be evaluated in the 1993 Pilot Project.
Besides the samples collected in the two sampling plans, 20 rangeland sites may be
identified for research samples to further evaluate the nematode indicator. These sites will be
identified from land use data obtained during the JES.
4.1.1. The Hexagon Sampling Plan
There were 317 EMAP hexagons (40 km2) with their centroids within the state boundaries
of Nebraska that were eligible for selection for the 1993 Pilot hexagon sample. These
hexagons were divided into four interpenetrating replicates according to procedures outlined in
the EMAP Design Report (Overton et al. 1991) where specific replicates are to be used in
particular years. The designated 1993 EMAP replicate was selected for the 1993 Pilot. The
77 hexagons hi the selected replicate fell in 58 of the 93 counties in Nebraska (Figure 4-1).
The coordinates of the centroids of these 77 hexagons were forwarded to NASS for
identification of the NASS sample segments according to the following procedures.
o The primary sampling unit (PSU) that surrounds the centroid is identified along with
its ID number, i.e., stratum, substratum, county and NASS replicate. A NASS
20
-------
technician will divide the PSU into segments according to NASS' standard criteria.
Special care will be taken to ensure that the technician does not know the location of
the centroid within the PSU to avoid bias while delineating the segment boundaries.
o After segments within the selected PSU have been delineated, the segment containing
the centroid is identified and designated as a sample segment.
o Characteristics described at the time of segmentation include the area of the PSU, the
area of the selected segment and, if possible, the estimated cultivated acreage and an
estimate of the number of fields within the segment.
o The boundaries of the PSU and the selected segment are delineated on an aerial photo
and on a county highway map for use by NASS field staff and the enumerators during
data collection. Duplicate photos and maps may be prepared for the ARG according
to the NASS confidentiality guidelines.
o Accurate time and cost records are maintained by NASS for each step of the
operations described above.
EMAP Hexagons in Nebraska
All hacigons < 12,600 density)
Figure 4-1. Hexagon replicates for use in
choosing the NASS Segments; the 77 hexagons
were found in 58 counties.
21
-------
Nearly half of Nebraska is covered by rangeland that, agriculturally, is used primarily for
grazing livestock and from which some wild hay is harvested. These areas are minimally
managed. Of the 77 hexagons selected for the 1993 pilot, 37 fell within this area. It is
expected that the EMAP Hexagon sample segment in these rangeland areas will have limited
cropped acreage. All segments identified by the 77 hexagon centroids will be included in the
sample regardless of whether they fall in rangeland areas. All agricultural fields identified in
all segments, despite the predominant nature of the landscape, are included in the list of fields
for selection. The method of selecting sample fields is described in section 4.3.1.
4.L2. The Rotational Panel Sampling Plan
The complete 1993 NASS sample for their June Enumerative Survey (JES) in Nebraska
has.390 segments stratified as shown in Table 4-1. The selection of segments in the
Rotational Panel Plan for the 1993 Pilot differs somewhat from that of the 1992 Pilot. In the
1992 North Carolina Pilot, the newest NASS replicate was used for all but two of the selected
Table 4-1. Stratification of NASS segments in Nebraska for 1993.
Stratum
Total
I.D. Definition Sq mile1
Number
11
12
20
31 '
32
40
50
Total
>80% Cultivated
51-80% Cultivated
15-50% Cultivated
>20 home/mi2 Agri-Urban
>20 home/mi2 Commercial
<15% Cultivated
50 Non-Agricultural
30,112
8755
9531
649
168
27,695
180
77,120
Number of
Seg
Size Reps2
1.00
1.00
2.00
0.25
0.10
4.00
1.00
15
10
5
5
5
10
5
Pilot
Segments Segments
225
70
35
10
5
40
5
390
-135
-42
-21
-6
-3
-24
-3
-234
'Although metric units are the standard for EMAP, all NASS stratification reports are made in miles
and square miles; in order to be consistent with those reports, miles and square miles are used as units
in this table.
2 Note: These replications, while they include the replications discussed in the text, which are rotated
on a 5 year basis, also include replication within the Sub Strata to give the appropriate number of
sample segments.
22
-------
sample segments. This was done to allow comparability between the hexagon sample, which
had all new segments, and the NASS sample of which only the new replication contained new
segments. In addition, the newest replicate would have the longest life in the NASS sample.
However, because of the limited number of segments in the 1992 sample relative to the
number of fields to be sampled, some respondents were selected multiple times; this resulted
in excessive respondent burden. As a result, three NASS replicates will be used in the 1993
pilot rather than one as was done in the 1992 pilot. There will also be a corresponding
reduction in the number of fields sampled per segment. Replicate years 1, 3 and 5 of the
NASS 1993 JES sample, where the number represents the number of years that the replication
has been in the rotation, will be used to draw the Rotational Panel Plan sample. These
replicates were chosen because they represent the full range of time sample units are in the
rotation. The newest replication provides a direct comparison with the Hexagon Plan since
both the newest replication in the Rotational Panel Plan Sample and the Hexagon sample will
have approximately the same expected number of fields and the segments within each of these
will be visited for the first time by NASS enumerators during the 1-993 Pilot. This will allow
the Rotational Panel and Hexagon designs to be more comparable with respect to the
interview conducted during the JES. In addition, use of sample segments from the three
replicate years provides for an evaluation of the effects of the length of time the segment has
been in the sample.
Maps and aerial photos will be prepared for the Nebraska NASS Field Office for each of
the 390 NASS sample segments in the 1993 June Enumerative Survey. For many NASS
segments in Nebraska county highway maps are used instead of aerial photography.
Duplicate aerial photographs or maps of the segments selected from the 1993 pilot will be
sent to the ARG. Records of the cost of these photos and maps and the cost of their
preparation is maintained by NASS.
4.2. Within Segment Sampling Protocols
Most of the indicators for the 1993 Pilot Program will be measured either on individual
fields (e.g., fertilizer and pesticide applications) or on a random small area within the field
(nematode assay and soil properties). Field sampling protocols will be divided into two parts:
selecting fields within the segments and sampling within those fields. The field selection
procedure and field sampling protocols are the same for both sampling designs.
23
-------
4.2.1 Field Selection
The field identification and acreage data taken in the JES will be used to randomly select
a subset of fields over all selected segments in the Hexagon Plan and, separately, over all
segments within rotation years 1, 3, and 5 for the Rotational Panel Plan. All fields that
contain crops that have not been excluded froni the 1993 Pilot will be included in the
ordering.
Selection of fields is based on selection of random "acres" from the total expanded
cropland hectares contained in the sample of segments. During the JES-, NASS enumerators
will obtain land use information on all areas of each sample segment. The location of each
cultivated field in each sample segment will be mapped on an aerial photograph or a county
highway map and its identification number and its hectareage recorded. For the 1993 Pilot
the population of eligible fields will contain a single resource class, defined as the planted
acreage in any field that contains annually harvested herbaceous crops. Summer fallow will
be included. The agroecosystem resource classes that are not being considered for inclusion
in the study are listed hi Table 4-2. These were excluded largely because condition indicators
for these resource classes have not reached a state of development where they are ready to be
tested in a pilot study.
Table 4-2. Resource classes ineligible for selection in the Agroecosystem 1993 pilot.
Perennial fruit and nut crops
Permanent managed pastures
Other agricultural lands (including farmsteads, windbreaks, farm ponds, ditches, farm roads,
grassed water ways, confined feeding operations, and woodlots)
In both sample designs, a systematic sample with a random start will be used to select
fields for inclusion in the pilot with probability proportional to expanded size, where
expanded size is the planted acreage of the field multiplied by the segment expansion factor.
In the 1992 North Carolina Pilot, all fields within sampled segments were ordered arbitrarily
first by crop and then by segments (for a given stratum for the Rotational Panel sample).
24
-------
This ordering was used to provide for a better distribution of the sample across crops, and,
while this ordering accomplished the objective, it compromised the spatial distribution of the
sample. Therefore, in the 1993 Nebraska pilot Rotational Panel Plan, fields will be ordered
first by NASS replication and then by segment. Within each rotation year there are either
one, two or three replications, depending on the stratum (Cotter and Nealon 1987). When
there are two (three) replications, they are numbered such that rotation year one has
replications 1 and 6 (1, 6 and 11), rotation year two has replications 2 and 7 (2, 7 and 12),
etc. The ordering by replication will provide for similar representation in each rotation year.
NASS's use of substrata ensures a reasonable spatial distribution of their selected segments
across the state (Cotter & Nealon, 1987). Because segments are numbered sequentially within
a substratum, ordering of fields by segment will allow our subsample of fields to retain a
reasonable spatial distribution. In the Hexagon Plan, the fields will be ordered by segment.
For both designs, the field selection procedure uses the expanded cropland hectareage of
all fields within the selected segments. A sample of random cropland hectares is then selected
from an ordered list of all fields using a random 0.4-ha start and uniform step size to obtain
that selected step size. Let n be the number of fields to be drawn, then the step size, k; is
determined as where the numerator is the estimated total population cropland acreage,
where Acijkl is the cropland hectareage in field 1 in segment ijk. For the Rotational Panel
Plan, the subscripts ijk designate segment k in replicate j in strata i. For the Hexagon Plan,
the subscript ijk can be replace with a single subscript to designate the hexagon.
25
-------
For the Rotational Panel Plan, rotation years 1, 3 and 5 will be those from which the
sample will be selected. Balancing the need for an adequate sample size and cost
considerations, we suggest that in the Rotational Plan the number of fields be 216. Given that
there will be approximately 234 segments in the three NASS replicates chosen for the
Rotational Plan this gives an expectation of 0.92 fields per segment. This number of fields
seems reasonable for exploring the components of variance and to estimate population
parameters for the indicator values in which we are interested. There will be about 72 fields
per rotation year in the Rotational Panel Plan. For the Hexagon Sample, a total of 72 fields
will also be sampled. This will allow a comparable number of sample fields in the newest
replication of the Rotational Panel Plan and in the Hexagon Plan. To obtain the desired 72
fields out of the 77 hexagons an expected number of .94 fields per segment will be needed.
Because approximately 37 of these hexagons will be located in rangeland strata and with little
or no cropland, it is expected that enough hexagons will have multiple fields per segment to
allow estimation of the "among segment" and "between field within segment" components of
variance. The sampling plan gives a.total of 288 fields that will be sampled in the 1993
Nebraska Pilot. Soil samples for nematode analysis will be done only on the Rotational Panel
plan.
Once the step size has been selected using the method outlined above, it will be adjusted
to provide the desired sample size when the land use data from the JES are available. A
random integer between 1 and k will be chosen as the random start and then every integer
m+ck, c^0,l,2, . . . until m+ck > AT, will designate a selection. Field i is selected for
sampling if A,., < m+ck i A,. In double cropped fields where the hectareage of the two crops
differ, the maximum of the two planted hectareages will be used in field selection.
The fields that have been selected will be identified and marked on the aerial photographs
or on county highway maps for use by the NASS enumerators in collecting the field data.
The 24 rangeland sites will be selected in rangeland that is near cropped fields that were
included in the 1993 Pilot.
4.2.2 Sampling Within Fields
Soil sampling to determine soil physical and chemical properties and nematode densities
will require within-field sampling. A sample of 20 soil cores composited for each field will
provide sufficient soil for both physical and chemical analysis and nematode density assays.
26
-------
The protocol for the soil sampling will be as performed in the 1992 North Carolina Pilot. In
addition, the Soil Conservation Service (SCS) will take a selected number of fields on which
two detailed, shallow-soil profiles will be done to provide further information on the soil
structure (See section 5.2 for more details). Plans are currently being made with SCS to
determine the number of fields on which these soil profiles will be performed and how the
fields will be selected.
It would be desirable to have a method that would sample the entire field, however, field
size often will make this impractical. Consequently, the entire field will serve as the soil
sampling unit only if it is 0.4 ha or less in size. For fields larger than 0.4 ha, a pseudo-
random 0.4-ha subregion of the field will be chosen. If a field is chosen randomly for the
collection of more than one soil sample, an independent 0.4-ha subregion will be chosen for
each sampling. The one-acre subregion will be sampled with 20 soil cores of approximately
20 cm depth taken at equal distances along a 100-m transect that represents the diagonal of
the one-acre subregion. The diagonal transect, as opposed to another method such as a grid
placement, was chosen primarily because of its ease of implementation.
NASS's procedures for locating objective yield plots will be adopted with slight
modifications for use in locating the sampling transect. According to these procedures, if the
field is smaller than 24 ha, the field is divided into quarters. If the field is larger than 24 ha,
it is divided into ninths. The objective yield plot is then located in only one of these
subregions. The subregion that is selected for location of the objective yield plot is identified
by the first corner of the field that the enumerator encounters as he or she approaches the
field. While this is not a random choice, in NASS's experience they have found this
procedure to be satisfactory.
The modifications to the above procedure for objective yield for locating the transect for
the soil samples for the EMAP-Agroecosystem Pilot are as follows. If the field is 0.4 ha or
less in size, it will not be subdivided before location of the transect; otherwise, subdivision of
the field will be as described above. A random point will be located in the subdivision based
on a random number of rows and paces along rows from the corner of the selected
subsection. If rows are not present in the field, a random number of paces will be used. The
enumerator using NASS procedures will locate the point in the field. This point will
designate the midpoint of the transect to be used for soil sampling, and the transect will run at
a 45° angle to the direction being walked by the enumerator (Figure 4-2). From this center
point on the transect, the enumerator will take 20 soil cores in each direction along the
27
-------
transect with the first core being 1.75 m from the center point and each succeeding core being
an additional 2.5 m away. For example, if the enumerator had come to the selected point
from due south, then the transect would run from the center point approximately 50 m to the
northeast and 50 m to the southwest
S'-yj.'-i-H-iiy-yi" ^^ ' '
paces
Figure 4-2. Transect sampling of field.
If the transect intersects the boundary of a field then a set of "bounce" rules will be
initiated. Upon reaching the field margin the enumerator will reflect off the boundary at an
angle of 90 degrees (turning back into the field and not out of the field) from the direction of
the transect (Figure 4-3). This will continue for every boundary encountered until the entire
distance of the transect has been traversed.
28
-------
Bounce rules
Samp ling starting
point
paces
Figure 4-3. Transect sampling of field (Bounce rules).
To ensure that the sampling is not biased by the subjective placement of the core,
enumerators will mark the end of each 5-pace interval with a wooden stake, and before
sampling, they will lay a marked stick along the transect at the stake. The soil sample will
then be taken at either 45 or 90 cm from the stake, depending on whether the stake is odd or
even. Appendix 6 gives additional detail on this sampling procedure.
*
4.2.3 Sources of Variation in Field Sampling
There are three principal sources of variation in field sampling: between-field variation,
within-field variation, and the variation in laboratory analyses. To obtain estimates of these
components of variation, the following design will be used. Except for the possibility that
numbers of segments may differ, the same procedure applies to both sampling designs. Data
from both sampling plans may be combined to evaluate components of variance.
For soil samples, every j* field will be sampled twice to get an estimate of the within
field variability. This will be accomplished by choosing a second independent transect from
the same field using NASS protocols. The next most accessible corner of the field is chosen
and the sampling is repeated on a new transect defined with a new starting point. Considering
29
-------
only the transects from these twice-sampled fields, the soil sample from every s* transect will
be split for duplicate laboratory analysis. To ensure enough soil for the split sampling, two
cores will be drawn at each sampling point in these particular transects.
Let N be the number of fields sampled in the pilot; this should be about N=288. Using
the ordering of the fields given previously, every j* field will be sampled twice with
independent transects. (Other fields will be sampled twice simply due to their size being
greater than the step size in the field sampling process). This will give 2N/j soil samples
from these twice-sampled fields. (There will be (-i^)JV soil samples from the other
fields.) Of the 2N/j soil samples from the twice-sampled fields, every 8th soil sample will be
split for laboratory determinations. This procedure gives N/j and 2N/js degrees of freedom
for the "samples in fields" and "determinations in samples" mean squares. The two mean
squares have equal degrees of freedom if s=2. With N=288 and s=2, the analysis of variance
(ignoring strata) is found in Table 4-3 for the soil measures and for the nematode indicator.
Table 4-3. Degrees of freedom for field sampling components of variance on soil chemical
measures.
Source
Samples(fields)
Det(samples)
Total
df
N-l
N/j
2N/js
y 3****
js
(j,s)=(5,2)
287
58
58
404 c
(j,s)=(6,2)
287
48
48
384
Current plans are to use j=6 and s=2; that is, every sixth field sampled will use two
independent transects and the soil from the second of these transects in every twelfth field
will be split to provide duplicate laboratory determination for soil chemical analysis and
nematode assay. These numbers may be modified by budget considerations. Additional
known samples to determine laboratory accuracy also will be included (see Section 5.2.5).
The total numbers of samples that will require laboratory analysis are found in Table 4-4.
30
-------
Table 4-4. Total number of soil samples for chemical and nematode analysis.
Sample Source
Chemical
Nematode (All
NASS)
Fields
Duplicate Samples (k)
Lab Splits
Rangeland Research Sites
Total Soil Samples
216 NASS
72 Hex
36 NASS
12 HEX
18 NASS
6 Hex
24
384
216
36
18
24
294
4.3 Inclusion Probabilities and Estimation
The general procedures for estimation and computation of variance based on the EMAP
hexagon design are being developed by the EMAP Statistical Design Team. The approach
relies on the Horvitz-Thompson estimation procedures (Horvitz and Thompson 1952). The
two sampling plans being tested by the. ARG are not direct applications of the EMAP
Hexagon Design but the general methods of estimation will be applied with appropriate
modifications to take into account the specifics of the sampling designs. The emphasis in the
estimation is on the construction of the cumulative distribution function (CDF), and its point-
wise confidence limits, for each indicator. Many indicators are of the type that would
normally be estimated with a ratio estimator (e.g., pesticide use per acre of cropland).
However, because of emphasis on the CDF, the indicator will be computed as the appropriate
ratio at the observation level. There are some indicators, such as land use indicators, for
which the CDF is not appropriate. In these cases, simple population estimates (totals or
means standardized to specific units of measure) and their standard errors will be computed.
In both sampling plans, data are obtained at two levels, segment-level and field-level data.
Probabilities of inclusion and the estimation procedures are outlined separately for the two
levels of information.
31
-------
4.3.1 Segment-Level Estimation
4.3.1.1 Hexagon Sampling Design
Although the actual Hexagon Design sample consists of NASS segments, the selection of
the segments is determined by the location of the centroids of selected hexagons. The
inclusion probability associated with a particular hexagon in the original EMAP design is I/A,
where A=634.5 km2 is the area of the large hexagon. One of the four interpenetrating
replicates will be used in the 1993 Nebraska pilot giving a probability of inclusion of the
sampled hexagons of 1/4A. Conditional on the selected hexagon, the NASS segment is
selected with probability proportional to the total area, %, of the segment. Thus, the
unconditional probability of inclusion for the ith NASS segment in the Hexagon Plan is
ps=a/4A
and the expansion factor (or weight) associated with the z'th segment is
The Horvitz-Thompson (HT) formula for estimating a population total,
attribute y, is given as
, for any
es
where the summation is over the set of sample segments, S, in the population of interest and
yj is the value of the attribute for the sampled segment. The number of units in the
population is estimated by setting yt = 1. The number of units in the population having
values of indicator X less than or equal to some value x is estimated by defining y~l if X^x
and ypO, otherwise. The proportion of units having values of indicator X less than or equal
to some value x is estimated by the ratio of the estimated number of units having X^x to the
estimate of the total number of units in the population.
The HT variance formula provides unbiased estimates of variance if all pairwise
probabilities are strictly positive. Systematic sampling leads to a large number of zero
2 (n-
32
-------
pairwise inclusion probabilities. A modification of the variance formula has been shown to
perform satisfactorily where the pairwise inclusion probabilities, Tty, have been approximated
(Stehman and Overton 1987) by
from which
The variance formula with this approximation is:
It has been suggested recently that the Yates-Grundy estimator of variance might be more
appropriate (S. Overton, personal communication). This suggestion is being investigated by
the EMAP Statistical Design Team. Also, procedures are being developed to determine the
variance of the estimates empirically by means of facsimile population bootstrap (Overton
1991).
4.3.1.2 The Rotational Panel Sampling Design
The segment level inclusion probabilities for the Rotational Panel Sampling Design will
make use of the 7Cijk, or expansion factors wijk=l/7iijk,'already established by NASS for the JES.
(The subscripts i, j, k identify stratum, replicate, and segment, respectively.) These inclusion
probabilities will be adjusted downward by the conditional probability of including a JES
segment in the EMAP subsample. Current plans call for using approximately 3/5ths of the
NASS JES sample segments in the EMAP sample so that the conditional probability for
including the segment in the EMAP sample will be approximately 0.6. The conditional
probability will be determined by stratum and, consequently, may not be a constant. In
general, let this conditional probability be /?ijk for the i/fcth segment. Then, the inclusion
probability for the /th segment in the EMAP sample is 7^=^%; or the expansion factor is
33
-------
Estimation of population quantities will follow the general procedures outlined above for
the Hexagon plan except that the stratification of the sample design will be taken into
account. Likewise, estimates of the measures of precision will be based on the standard
formulae for a stratified random sample.
4.3.2 Field-level Estimation
With selection of fields being based on random selection of "expanded" acres as described
in Section 4.2.1, each expanded acre has probability 1/r of being selected. To relate this to
the inclusion probabilities for the segments, we note that each cropland acre in segment ijk
represents w*ijk expanded acres so that the conditional probability of a random cropland acre in
segment ijk being selected is /7r(acrelsegment)=w*;jl/r. Thus, the unconditional probability of a
random cropland acre being selected is
pr(segment)*/7r(acrelsegment)=(l/w*ijk)*(w*uk/r)=l/r
for all i, j, k. Or, the expansion factor for each sampled acre is r. The above is written using
the notation for the Rotational Panel sample design but applies equally to the Hexagon design.
Thus, expanding the acreage of the fields by their expansion factors prior to selection of
the fields provides a self-weighting sample and simplifies estimation. The expansion factor r,
however, is a random variable so that measures of precision must take Var(r) into account or
be clearly expressed as being conditional on r. Similarly, in a stratified sample, as in the
Rotational Panel design, the numbers of observations per stratum become random variables
even when the total sample size is fixed; this variability needs to be taken into account in
computing the variances of the estimates if the advantages of stratification are to be
incorporated into the measures of precision. Alternatively, the stratification can be ignored
even if present and the resulting variance of the estimates based on the nonstratified
computations should provide a conservative estimate of precision. The appropriate method of
taking into account the randomness of the strata sample sizes needs to be clarified. It is
informative to look at three types of estimators based on this self-weighting sample.
1. Estimation of population total:
Thus, Var(yr) must take into account both the Var(4c) and Var(y) for estimates of
population totals obtained from the field-level data. However, nearly all of the indicators
34
-------
for which population totals are desired will be estimated from the segment-level data.
(The segment-level data are not self-weighting and the more general weighted estimator
must be used.)
2. Estimation of population mean:
This is the simple arithmetic mean of the sample observations; the constant expansion
factor drops out.
3. Estimation of population proportion of area having X
-------
Confidence intervals for the CDF will be done following the methods developed by the
EMAP Statistical Design Team. Details on the theory of constructing these confidence
intervals, and algorithms for their use, are found in EMAP Status Estimation Procedures and
Algorithms (Lessor and Overton, draft, 1992 version).
A problem arises in estimating a population CDF when the indicator variable is subject to
appreciable measurement error, as is expected for the Agroecosystem indicators. Without
adjustment, the empirical CDF obtained from the indicator data estimates an overly dispersed
CDF. Methods of disentangling the estimated CDF from measurement error are being
developed by the EMAP Statistical Design Team.
It is anticipated that the two sampling plans will provide similar information on the
population distribution of the indicators. The estimated CDF from the two sampling plans
will be compared with nonparametric tests and, if compatible, a combined CDF will be
presented. Other methods of displaying key features of several CDFs which may simplify or
enhance the CDF's interpretation will be explored.
In addition, the statistical summary will include displays of the spatial patterns of key
indicators. The displays will be of sufficient resolution to develop contour plots or shaded
maps of the value of the indicator. The precision of the kriged surface can also be displayed.
4.4.2. Variance Estimation
Estimation of precision (variance) of population estimates is determined by the sampling
design. For each sampling design, the precision attained by the 1993 pilot survey for each of
the various population estimates will be computed as appropriate for that design. These
measures of precision will be repeated in the statistical summary and will be used to compare
the variance efficiencies of the two designs for estimation of status and to help in defining
attainable data quality objectives for measures of status for the Agroecosystem Program. For
definition of data quality objectives and comparison of variance efficiencies for measures of
change or trend, assumptions on the magnitude of temporal correlations must be made.
Components of variance will be estimated for key indicator variables. The components of
variance will be used with cost estimates to explore strategies for future allocation of
sampling effort: numbers of fields per segment, samples per field, and determinations per
36
-------
sample. Because several indicators are involved, each with its own variance component
structure, any sampling strategy adopted would necessarily be a compromise. Excessively
large variance components may suggest problems in the definition of specific indicators.
4.4.3. Analysis of Correlation Structure
Analysis of the correlation structure of the indicators will address four basic questions:
o What are the relationships among the indicators and what implications do these
relationships have with respect to defining the set of indicators to be used?
o Are the principal components of the indicators useful for defining health of the
agroecosystem?
o What is the nature of the spatial correlation structure of the indicators?
o Is there also potential to use double sampling techniques to enhance the Agroecosystem
information with the correlated information collected on other variables from the full
JES sample of 16,000 units a year?
Principal component analysis and biplots will be used to investigate the relationships
among the condition indicators. Correlations reveal pairwise linear association of the
indicators. Principal component analyses and the biplot reveal multivariate associations, i.e.,
groups of indicators that tend to behave similarly within sets. Similar behavior of several
indicators may suggest redundancies in the definition of indicators or may suggest a definition
for one dimension of health of the ecosystem. Different groups may be addressing different
dimensions of health. Interpretation of the principal component analysis will require close
collaboration with indicator leads.
The nature of the spatial correlation structure will be investigated by fitting variogram
models that depend on the spatial relationships of the observations. This will require
knowledge of the site locations and must be done in conformity with the confidentiality
requirements of NASS. The variogram information will be used in constructing spatial
displays of regional patterns. The spatial correlation structure and some method of spatial
interpolation also may be used as one possible way of masking the true locations of the
37
-------
sample points (maintaining confidentiality) while retaining data that have properties similar to
the original observations.
Correlations between the agroecosystem indicator values and variables measured by NASS
in the full JES sample will be explored to determine if any of the correlations are of sufficient
magnitude to warrant use of double sampling techniques.
4.4.4. Comparison of the two Sampling Plans
Both sampling designs, the Hexagon Plan and the Rotational Panel Plan, sample exactly
the 'Same reference population and both are proper probability samples. Consequently, the
estimates of population totals or means obtained from the two designs are estimating the same
population quantities; any differences would be due to sampling error. The primary
differences in the two designs are the degree of uniformity of spatial coverage, the use of
stratification, and the rotation of segments out of the sample. Both stratification and the
systematic spatial coverage may affect precision of all estimates. The precision of the
estimates of status for the two plans will be adjusted for differences in sample size before
being compared. The direct effect of stratification on precision also will be determined by
applying post-stratification to the Hexagon sample.
The rotation of the sample segments will affect precision estimates of change or trend;
the magnitude of the effect will depend on the magnitude of the temporal correlations. The
temporal correlations cannot be estimated from a single-year study, so any comparisons of
precision of change or trend estimation will necessarily require assumptions about the
temporal correlation.
Records of costs at each step of the process will be maintained. NASS will maintain
records of costs for operations they do such as segmentizing the hexagon sample, delineating
sample segments on aerial photos, and visiting sample sites. The ARG will main, in costs for
operations they perform. The types of activities required to prepare the sample have been
noted in Section 4.1. There will be similar field costs in training enumerators and in
collecting the data. The entire field cost for the Hexagon sample will be assigned to that Plan
since it is outside the scope of the regular JES. Costs assigned to the Rotational Plan sample
will be prorated to include miscellaneous costs associated with the conduct of the survey.
38
-------
There should be little or no incremental cost in preparing the Rotation Panel sample.
NASS may choose to allocate pro rata costs for the development of the JES sample and
perhaps even some costs for the development of the NASS area frame in Nebraska. These
costs may have to be negotiated, but whatever they are determined to be, they will be
considered in the comparison with the Hexagon Plan.
The costs per observation will differ considerably between the two plans due to the closer
coordination of the Rotational Panel Plan with the ongoing JES. Costs will be determined for
each sampling plan and combined with the measures of precision to obtain information per
unit cost for each indicator population estimate. Having costs in two regions (North Carolina
and Nebraska) with very different cropping systems and physiography will facilitate the cost
evaluation and indicate how costs may differ across regions.
This empirical comparison of the two designs will be limited because only one realization
of the sampling process in two different regions will be available and because variances in the
limited regions covered by the 1992 and 1993 Pilots may not adequately reflect variances for
other regions. Consequently, simulation may also be used for a more thorough comparison of
the designs and these results will be compared with the theoretical work of Lesser (1992).
39
-------
5. Description of Specific Indicators for The Pilot Project
5.1. Crop Productivity
5.1.1. Introduction
When people are concerned about agriculture, crop production is often the focus. This
concern is embodied in the question Will There Be Enough Food?, the title of the 1981
Yearbook of Agriculture (USDA 1981). Crop productivity indicators are being developed to
address the broader value "productivity", but they will also give an indication of the quality of
air, water, and soil. In applying the first societal value, the ARG is focusing on field-level
sustainability: whether a given piece of land, under existing management, can continue to
produce at least as much food and fiber as it is producing now. The status quo is the
reference point because we have not defined "adequate" production or "sufficient" production
efficiency. To do so would involve broad ecological issues of land allocation, human
population and diet, which go beyond the scope of EMAP. Field-level sustainability is
inherently a trend issue and cannot be assessed in a one-year pilot. Still, it is important to
attempt the calculations and to look at spatial variability and differences among species. Note
that certain soil properties or management practices may give advance warning about whether
productivity can be sustained, but these are not crop productivity indicators, per se.
Indicators of crop productivity will be used to answer two assessment questions. The first
is whether the land is meeting its productive capacity, in other words, are fields producing as
expected based on best management practices for their climate and soils? This is relevant to
the first societal value, with less of a connection to the quality of air, water, and soil.
Traditional yield expectations, which assume high levels of management, may not be
appropriate when the quality of natural resources is an important consideration.
The second assessment question for crop productivity is: in what proportion of
agroecosystem units (fields, in this case) is production efficiency rising (or falling)? Although
this assessment question is most closely tied to the value of productivity, there are also
implications for the quality of air, water, and soil. For example, if nitrogen fertilizer is
applied at a higher rate than it is taken up by the crop, there is excess available for leaching
(Angle et al. 1993). Also, all other things being equal, vigorous and efficient crop growth
should be a sign of high quality soil, water, and air.
40
-------
Factors Influencing Crop Productivity
Natural Factors
I Ttmp»r«tur« I
I Pr.dpil.ton |
^T
Management Inputs
\ CropVirfcty |
| Ftrtt'IirtraJ
I SoUr FUdi»tion|
I lrrlg«ti«
I Tilliga
Crop Productivity
i Soil Eroaion
""
Costs I
mmodity Pgmj.|
[Cworv'n R»*»tvo|
Anthropogenic Stressors
Socio-economic Factors
Figure 5.1-1.
productivity.
Some factors which influence crop
In addition to its crucial
importance to human society, the
crop plant also provides food for
soil microbes, plant-eating insects,
and other organisms. Crop
productivity is thus an important
ecological parameter and a
potentially important response
indicator of agroecosystem
condition. One obstacle to the
realization of this potential is that
so many factors affect crop
productivity (Fig. 5.1-1). By
definition, crop plants are highly influenced by human activity. In industrial agriculture, the
marketable yield of crop plants is being optimized through management (tillage, planting
dates, fertilization, irrigation, etc.), with a view toward economic profit. Mitchell (1984)
points out that in monocropping, the economic-agricultural system determines which crop
varieties are planted and what yields are obtained. Crop productivity could be measured in
either economic or ecological terms; it is the latter which are of interest in EMAP. An
indicator of productivity should be responsive to environmental stresses such as air pollution,
climate change, soil degradation, and water contamination.
Crop yields have been surveyed and reported for decades, but yield alone is not a
sufficient indicator of agroecosystem condition or sustainability. If one field produces a
higher yield than another, is that field therefore "healthier"? What if the second field has a
higher yield but also received additional fertilizer? To answer these questions, it is necessary
to make allowance for the effects of management inputs and perhaps for the influence of
weather and soil type. During the Pilot, several such standardized indicators will be tested,
including input/output indices and adjusted yields.
Adjusted or not, crop yield only reflects part of a plant community's productivity;
however, net primary productivity is not being developed actively by the ARG because of
anticipated problems in finding reliable conversion factors and because of uncertainty in how
to interpret the results. Remote sensing provides an even broader perspective. The use of a
remotely sensed greenness index was tested during the 1992 Pilot, when the ARG
commissioned a study of the use of the normalized difference vegetation index (NDVI) for
41
-------
evaluating productivity of plants in North Carolina agricultural landscapes. Based on results
from the 1992 study, the ARG will not try to use the greenness index in 1993. Several
technical and statistical uncertainties make the NDVI, as currently calculated from two-week
composites, questionable. Problems include cloud interference that is not removed by
compositing, possible viewing angle effects, and boundary uncertainties in GIS coverages
(memo from Steven Walsh et al. of the Department of Geography, the University of North
Carolina at Chapel Hill, to Sharon LeDuc et al. at the Atmospheric Research and Exposure
Assessment Laboratory, Research Triangle Park, NC).
Productivity indicators will first be calculated separately for each crop species. A later
step will be to standardize and combine data from different crops into overall productivity
indices. It may be possible to weight and then directly combine indi viduaf values if they are
on a common scale. For indicators that are not on a common scale, the ARG must first
establish baseline indicator values for each crop. These baselines can then be used for
standardization and for detecting trends. "Normalized yield", as described in Section 5.1.7.4,
will be a test of this sort of calculation.
5.1.2. Data to be Collected by NASS
The National Agricultural Statistics Service (NASS) will gather essential information
about both inputs and outputs. They will ask farmers about the production from the harvested
area of each sample field. The Agroecosystem 1993 Pilot Questionnaire will contain
questions about timing and amounts of fertilizer, lime, and pesticide applications; about the
tillage system; and about irrigation (Appendix 5). The questionnaire will be very much like
the 1992 Fall Questionnaire used in North Carolina. To reduce confusion, one difference will
be that the date of each field operation or chemical application will be asked, as well as the
crop for which the operation was intended. For the 1993 Pilot, sample fields will be drawn
from the population of acres of annually harvested herbaceous crops in Nebraska, excluding
wild hay (see Table 5.1-1). These areas will be determined in the June Enumerati e Survey.
Land in summer fallow at the time of the JES will also be included in the sample because it
is a part of the cropland resource and soil samples should be collected there. Conceptually,
the fallow and all inputs during the fallow period are inputs to the subsequent crop. How to
incorporate this consideration into productivity calculations has not been determined. Land
that is idled for longer periods of time will be counted in June, but will not be included in the
fall sample.
42
-------
The 1993 Pilot will be restricted to one resource class, land with annually harvested
herbaceous crops, because the ARG lacks the personnel to develop indicators this year for
such resource classes as pastures and woody crops. For the 1993 Pilot, no distinction will be
made between arid and non-arid cropland areas; however, discussions have been initiated with
the EMAP-Arid group concerning measures of productivity (and soils) in arid and non-arid
lands. A question on the June Survey will attempt to distinguish between pasture and
rangeland (Section 5.4.2).
The NASS enumerators will also be collecting soil samples (Section 5.2). Data from
soil analyses may or may not be used in the process of standardizing yields.
Table 5.1-1. Principal crops or land uses eligible for selection in the Agroecosystem 1993
Region 7 Pilot in Nebraska.
Barley
Bean, Dry Edible
Corn
Hay
Oat
PotatoRye
Rye
Sorghum (Milo)
Soybean
Sugar Beet
Summer Fallow
Winter Wheat
5.1.3. Essential Complementary Data
5.1.3.1. Weather Data
Some productivity indicators should be adjusted for year-to-year weather fluctuations.
This will most likely require the use of weather data and some sort of crop growth model.
The ARG is now trying to find a source of geographically-referenced weather data for 1992
for North Carolina. A similar effort will be made for the 1993 Nebraska weather. These
data will include daily values for precipitation, maximum and minimum temperature, solar
radiation, and perhaps relative humidity and wind speed. The High Plains Climate Center will
be investigated as a possible source of data.
43
-------
5.7.3.2. Production Practices Not Queried
Certain values needed for calculations will not be asked on the Survey Questionnaire or
will not be known by some farmers, so the values must be obtained from other sources. For
example, some crop models require the depth to which seed was planted, planting density
(population per unit area), etc. If unavailable from the questionnaire, typical values will be
obtained from the literature or from the Agricultural Extension Service. Industry standards for
moisture content of the major U.S. crops will be obtained from NASS.
5.1.3.3. Reference Yield Values
For purposes of calculating normalized yield, county average yields for the reference
period of 1980-1989 will be used (see Section 5.1.7.4). These will be obtained from NASS
for the major crops in Nebraska. Yield is not the main indicator of interest, but it will
provide some practice for standardizing across crops, if time is available for such an exercise.
5.1.3.4. Soil Map Unit
Some models may require more detailed information about the soil than the data which
the ARG will get from each field. Also, expected yields are available by^oil map unit.
Procedures are being developed to determine the soil map units in each sample field.. This
will allow access to information from the State Soil Survey Database (SSSD). One method
will be to compare aerial photos (with the sample fields outlined) to soil survey maps. As a
check, USDA Soil Conservation Service (SCS) personnel will determine the map units on the
small subset of fields which they will be visiting (see Chapters 5.2 and 9).
5.1.4. Logistics
The field-level data needed for the crop productivity indicators will be taken from the
Agroecosystem Survey Questionnaire to be administered by NASS in Fall 1993 (Appendix 5).
Logistics for soil sampling are described in Section 5.2. As mentioned above, some soil data
will be derived from the SSSD. Complementary data from the literature and other sources
will be obtained by the ARG. The indicator lead for. crop productivity and the ARG
information manager are working together to obtain weather data.
44
-------
5.1.5. Quality Assurance
Quality Assurance (QA) procedures for data collected by NASS are discussed in Section
6. For weather data, QA procedures will be discussed with the supplier of the database. It is
anticipated that the complementary data will present several QA problems. For example,
matching aerial photographs to soil survey maps involves an estimation error. QA for
indicators derived using crop growth models will also require development.
5.1.6. Metadata Requirements
Because data from so many sources will be needed, metadata requirements will be
extensive. They will fall into different groups, depending oh their level of. applicability (Table
5.1-2). The QA/QC procedures will be part of the metadata.
Table 5.1-2. Elements of metadata to be recorded in association with data for the crop
productivity indicators, not including ancillary data such as weather.
1. Data keys
o Year
o PSU/segment ID (also identifies the frame as hexagon or NASS)
o Tract i
o Sample (field) number .
2. Other metadata
o Description of variable (including the form in which chemical species are
expressed, such as P vs. P2O5)
o Coding tables for pesticide active ingredient codes, etc.
o Units for various values (yields, fertilizer, manure, pesticides)
o Moisture content of harvested material
o Units, source, and base period for averages or expectations for standardizing
yields (if used)
Data keys will be needed to identify the sample: date (year), PSU/segment ID and sample
(field) number. The segment ID also indicates the frame (NASS or hexagon) from which the
sample was drawn. Although the association of the sample number with a particular field
must be kept confidential, some geographic information will be needed. In particular, the
45
-------
name of the county will be needed if county averages are used to normalize yields. Fields
may also be assigned to subregions for summarization (e.g. land resource regions).
Sometimes this can be done if only the county is known, but other times a more specific
location may be needed. Some geographic reference will be needed for drawing maps.
Certain metadata items will be the same for each record in the entire database. These
include the descriptions of each variable. In many cases, the description will be the question
which was asked in the questionnaire. The information will include the units in which the
quantity is expressed, for example "acres" for the area of a field or area under irrigation and
"inches" for the amount of irrigation water. Data from NASS are in U.S. units and will be
converted to metric units unless the figures are used in a calculation that causes the units to
cancel. Some quantities, such as moisture content and fertilizer composition, will be
dimensionless ratios and should be expressed as decimals (not percentages). The description
of fertilizer analysis should indicate the chemical form in which the analysis is expressed
(e.g., P vs. P2O5). All final summary statistics will be expressed in metric units or as
dimensionless ratios.
Another category of metadata which will be the same for the entire database will be the ,
translation tables for those variables that are recorded by code numbers: crop and land use
codes; fertilizer timing and application method; pesticide product code, timing, application
method, and applicator; type of manure; tillage system; erosion control methods; irrigation
system; and source of irrigation water. The method of storing yes/no responses must be
documented (e.g., l=yes 0=no).
Some of the metadata will be associated with a particular crop, land use, or input. For
example, NASS will provide yield data in pounds per acre along with the weight of the
customary unit, so that comparisons can be made to reference values. The descriptions of the
conversion factor variables will need to tell the quantity to which they apply and what units
are being changed to what other units. If production inputs are to be standardized, ..he
conversion factors or weights will likewise need to be documented. The source and version
of crop growth models used for calculating indicators also must be recorded.
Other metadata include the units for fertilizer, manure, and pesticides. Where reference
or expected yields are used, the units, source, and date(s) must be recorded. Most of these
need not be stored with the individual record, but must be indexed by the particular county
and crop.
46
-------
5.7.7. Data Analysis and Integration
Any these crops or land uses listed in Table 5.1-1 are eligible for sampling in the fall of
1993. However, many occupy such small areas that few fields will be selected. Sample
fields will be drawn according to the protocol outlined in Section 4. Indicator values will be
calculated separately for each crop so that when trends are measured, shifts in the
productivity of one crop do not mask shifts in the productivity of others. Keeping the
indicators separate is also a way of recognizing the important differences among the
requirements and adaptation of different crop plants. NASS distinguishes between irrigated
and nonirrigated situations for each crop. Ideally, the ARG will not make such a split, but
will take irrigation into consideration. A later step will be to combine index values across
crop species. With only one year's data, this will not be possible except for the indices
described below as "normalized yield" and "comparative yield" (a type of adjusted yield).
I
For certain crops, it may be impossible to calculate some or all of the productivity
indicators. For example, the sampling scheme might draw too few rye fields for a reliable
estimate, or existing crop growth models may be inadequate for calculating adjusted yields of
potato. It is not known how often these problems will occur, but it is unlikely that indicators
will be reported for all of the crops found on the sample fields.
The four types of indicators proposed here range from simple and straightforward to
complex but potentially more useful. Simple yield is a key building block of the other
indicators. Input/output relationships and adjusted yield directly support the assessment
questions discussed in Section 5.1.1., unmasking inherent productivity differences that are
hidden by management or other variables. Normalized yield is an easy way to try to combine
data across crops.
5.1.7.1. Simple yield
Yield estimates (production per harvested acre per year) are routinely reported by
agencies such as NASS. Nevertheless, there are reasons for the ARG to examine simple
agronomic yields:
o As a QA check, the values can be compared to the estimates that NASS derives from
a much larger sample.
47
-------
o Yield is the starting point for calculating adjusted yield. It will be interesting to see
the spatial distributions and CDF's of such yields before and after adjustment.
o A simple assessment can be done by plotting yields over time and comparing them to
changes in inputs over time. This may not be the most powerful use of the data,
however, since inputs and output will be known on a field-by-field basis.
o If sample size permits, aggregation over regions that do not follow state boundaries
can be tested with simple yield. There are 13 Major Land Resource Areas (MLRAs)
in Nebraska, among three land resource regions.
5.1.7.2. Adjusted Yield
One way to develop an indicator of crop health in agroecosystems would be to estimate
what the yield on each field would have been if a standard set of inputs and set of weather
conditions had occurred. Such adjustments would come from existing research findings on
the response of yield to inputs. In particular, such information is contained in models of crop
growth. A similar method would be to build an indicator from the difference or ratio between
each field's yield and the yield predicted by a statistical or process model. Two different
models which will be tested will be the Erosion/Prgductivity Impact Calculator (EPIC) and
SOYGRO. The adjusted values can be aggregated, mapped, or displayed as cumulative
distribution functions. Much work remains to be done in this area. The two critical steps are
(1) deciding which inputs, natural and anthropogenic, should be accounted for and (2) finding
the means to make those adjustments. For example, not all indicators should be adjusted for
weather fluctuations. That adjustment may reduce the variability inherent in yield data, but if
all weather variations were accounted for, it might be more difficult to detect changes caused
by global climate change, except through shifts in land use.
Rather than using the predicted yield from a model, an indicator could be calculated from
the ratio or difference of observed yield to the expected yield for a given crop on a given soil.
This index will be called "comparative yield". Differences between soil types and regions
would be removed, but not the influence of weather in an individual season. Expected values
are published by the SCS for the six or eight major crops in each soil survey; however, these
are not updated often, and the updates occur at staggered intervals (USD A Soil Conservation
Service 1970, 1983). If this approach is to be useful, a set of estimates would be needed that
were made within a few years of each other, covering the entire state and eventually the
48
-------
country. The SCS has such information in its State Soil Survey Database, but the yields
apply to large regions, not counties as in soil surveys (Clayton Lee, SCS, personal
communication). Alternatively, estimates of yield trends over time can be used to adjust the
expected yields to a common year (Joyce Scheyer, SCS, personal communication) or yield
tables can be scaled to one another by choosing a reference combination of crop and map unit
(Clayton Lee, personal communication) within the same MLRA (Betty McQuaid, SCS,
personal communication). Despite these obstacles, comparative yield is worth testing as an
indicator and may be more easily combined across species than some of the other indices.
There is an important assumption in the method described above: that the yield reported
for the sample field is at the same moisture content as the standard yield. This assumption
also applies to the calculation of normalized yield but is probably justified in that case,
because the average yields are provided by the same agency that collects the EMAP-
Agroecosystem data. For some potential indicators, moisture corrections will be needed
before combining across crops.
5.1.7.3. Input/Output Relationships
One way of measuring plant health using ARG data is to look at the response of yield or
net primary productivity to various inputs, either singly or collectively. This might involve
single-factor or multi-factor productivity indices. Single-factor indices are most appropriate'
when they are based on the resource that is most limiting or of most concern in a given
situation (Marten 1988, Lai 1991). One issue is whether to calculate ratios before or after
summing across sample units. The latter is preferred for statistical reasons but may not make
sense in all cases. Another possibility would be to use Agroecosystem data to determine the
coefficients relating yield to inputs (Lin et al. 1991). Two single-factor indices will be
calculated with 1993 data: applied nitrogen vs. yield and applied irrigation water vs. yield.
The input (nitrogen or water) will be used in the numerator. If it were in the denominator,
fields with zero input would have to be excluded from the calculation. Total water use
efficiency would be an interesting indicator, but cannot be calculated without measurement of
the rainfall on each sample field.
Within the concept of a multi-factor index, one approach to aggregating the input data
would be to put them on a common energy scale. Ideally, this would be done using process
analysis and on the basis of energy resource depletion (Southwell and Rothwell 1977) or
some similar philosophy. Various types of energy input/output ratios have been used in
49
-------
agriculture (Fluck and Baird 1980); however, the validity of the energy ratio (energy output
per energy input) has been questioned, and energy productivity (e.g., kg of production per unit
of input energy) has been suggested as a better measure (Fluck 1979). Neither of these
approaches will be tried in the 1993 Pilot, because of the initial effort needed to find
conversion factors and because of the continued effort needed to update such conversion
factors as technology changes.
5.1.7.4. Normalized yield
For practice in combining indicators across crops, normalized yield (Y') may be
calculated for each field. This requires the field's yield per acre (Y = production from
field/area harvested), the county average for the arbitrary reference period 1980-1989 (Yref),
and the standard deviation of that average yield (s). Similar to a standard normal variate, the
calculation will be
YY
The number 5 is added arbitrarily so that the distribution of Y' will have a mean of 5, and
negative values will be conveniently rare. The advantage of this method of standardization is
that both means and variances of different crops are put on a similar scale. For simplicity, s
will be calculated from temporal (year-to-year) variation in the county means. Values from
each field will be used to calculate regional means, quartiles, etc. Estimates will be self-
weighting, because of the sample selection procedure (see Section 3).
The following is an example of how normalized yield would be calculated, given the
following hypothetical yields for 1993.
Hypothetical corn yield in sample field: 80 bu/A
Hypothetical soybean yield in sample field: 26 bu/A
Assume mean yields and standard deviations in County A for the reference period 1980-1989:
Com: mean = 71 bu/A, std. dev. = 20 bu/A
Soybean: mean = 25 bu/A, std. dev. = 7 bu/A
YY
Normalized yield Corn: (80 -71)720 + 5 = 5.45
Soybean: (26-25)77 + 5 = 5.14
These will be calculated for each field.
50
-------
5.1.8. Interpretation of Indicators
Sustainability will be no easier to assess than it is to define. The goal of the ARG is to
measure the status and trends of productivity indicators, along with other agroecosystem
indicators, to obtain a picture of the condition of U.S. agroecosystems with regard to
sustaining both the supply of agricultural commodities and the ecological integrity of the
system. Because of the great differences among crop species, most indicators will be
interpreted separately for each crop before a composite index for all crops is calculated.
Classification of indicator values nominal or subnominal can only be done in reference to
specific criteria. Unfortunately, criteria for crop productivity may be difficult to find initially.
Thus, the main use of productivity indicators will be for following trends. It may be difficult
to detect any trends, and innovative ways must be developed to distinguish the effects of
changes in the natural resource base from other effects. Complicating factors include
production decisions caused by price shifts and changes in government programs (W.E.
Foster, NCSU, personal communication). The easiest starting point for data interpretation will
be to look at the graph of simple yields over time alongside the graphs of various inputs such
as fertilizer and land use. The more complex indicators"will then be examined.
A secondary type of assessment will be to look for associations among indicators and for
associations between indicators and the forces that may drive them. Spatial maps of the crop
productivity indicators can be used to overlay the maps of other agroecosystem indicators and
other data.. For example, maps for productivity indicators could be used to overlay maps of
soil quality or ozone concentrations. This technique can serve the ARG's primary goal (i.e.,
using multiple indicators to get a picture of agroecosystem condition in various regions) and it
might also be used to make hypotheses about indicator patterns. Similarly, trends in each
condition indicator over time can be compared with trends in other condition indicators and
trends in stressor indicators (including inputs). This may preserve some of the information
that is lost when indicators are adjusted for other factors.
Some of the pitfalls of interpretation can be illustrated for the case of normalized yield.
Because each value is relative to a county average, most spatial (county-to-county) variability
will have been removed. The normalized yield will show whether a particular region is
producing above, below, or at about the same level as it did during the reference period.
Because yields vary widely from year to year, it will take some time to determine if such
51
-------
differences are true trends. The calculation of normalized yield does nothing to account for
changes in technology, climate, or cropping pattern, so such factors will be reflected in the
trends that are found. Despite these concerns, this indicator will be a test of one method of
combining data across crops. Historic yield data allow this to be done in the Pilot, while
standardization of other indicators may need to wait until baselines have been established.
Comparative yield (observed vs. expected) has 'the second-greatest chance of being
summarized across crop species.
An output table (such as Table 5.1-3) will be generated for each indicator, as will a graph
of the cumulative distribution function and a box plot (example not shown).
5.1.9. Research Goals and Applications
As mentioned above, extensive research is required before adjusted yield and input/output
relationships become useful indicators. For example, yield response to nitrogen input is
curvilinear. What does that mean for the interpretation of simple ratios, which have an
implicit assumption of a linear response? Can a more sophisticated indicator be calculated?
One limitation of the Pilot statistical design is the small sample size. Ways of using a
larger NASS sample for indicator calculations will be investigated* When associations with
other indicators are tested, only the values from the Pilot sample can be used.
It is not known what region should serve as the basis for means and standard deviations
used in normalizing the various indicators. Counties will serve this purpose for the
normalized yield indicator in the Pilot, but,there may never be enough data to do county-by-
county standardization of other indicators. Therefore, larger regions should be tested for
normalizing yield. Regions should be chosen to reduce the variability of the normalized
indicator.
52
-------
Table 5.1-3. Example output table for an indicator of crop productivity
Average Normalized Yield in Nebraska "
Crop27 N Mean 1st Ouartile Median 3rd Quartile
Corn (grain)
Soybeans
Wheat (grain)
Hay (all)
Sorghum (grain)
Corn (silage)
Oats (grain)
Beans, Dry Edible
Sorghum (silage)
Sugar Beets
Rye (grain)
Barley (grain)
Potatoes
Composite Index
For AH Crops
v Assuming that no regions smaller than the state will be used for summarization.
2/ It may not be possible to calculate all indicators for all crops. See Section 5.1.7.
Note: Pastures, idle land, and woody perennials will not be included in the 1993 Pilot.
53
-------
5.2 Soil Qualify - Physical and Chemical
5.2.1 Introduction
Soils function as sinks and sources of biogeochemical elements, as filters for pollutants,
and as an environment for growth of plants and other biological communities. They are liable
to change, gradually or abruptly, and partly irreversibly, due to human use. Soil structure is
especially sensitive to human activities (Kay 1989). The main activities anthropogenic
affecting soils in agroecosystems are vehicular traffic, tillage, use of agricultural chemicals,
waste disposal and land use. To protect and conserve agricultural soils from degradative
processes, specific practices such as conservation tillage, residue management, crop rotation,
careful selection of crops for specific soils, and use of organic amendments are widely
implemented on U.S. cropland. The long-term goal of soil quality monitoring and assessment
in agroecosystems is to provide a regional assessment of the cumulative soil response to these
conservation efforts.
The following questions will be addressed of soil quality in the 1993 pilot study in
Region 7:
What proportion of agroecosystem units have erosion exceeding tolerable limits as defined
by the revised USLE (Universal Soil Loss Equation) and T value and the Watershed Erosion
Prediction Project?
What proportion of agroecosystem units have soil alkalinity and salinity values that
impact productivity?
What proportion of agroecosystem units have leachable soils and are receiving
applications of leachable pesticides?
What proportion of agroecosystem units have soil quality sufficient to sustain productivity
of crop and noncrop plants?
What proportion of agroecosystem units have soils with a physical structure that can
support soil microbes?
The main short-term objective in the assessment of soil quality is to determine the range
and frequency distribution (in proportion of land area) of individual measurements and to
evaluate how well the chosen measurements and derived indices will reflect changing
conditions. Because standards of soil quality will vary with climate and soil, determination of
the rate of change of soil quality will be an important long-term objective. A second long-
term objective is to combine indicator measurements into quantitative indices so that general
54
-------
statements about soil quality on a regional basis can be made. Several possible indices
include structure, tilth, fertility, contamination, and productivity (Table 5.2-1). Thirdly, soil
quality information will be combined with other pilot data into a picture of overall
agroecosystem health. A fourth long-term objective is to integrate information on the health
of agricultural soils in the U.S. with information on soils in forests and arid lands to provide
an overall picture of soil quality across terrestrial ecosystems.
5.2.2 Data Sources
Data for soil quality assessment in the pilot will come from soil samples taken by
National Agricultural Statistics Service (NASS) enumerators. Data will also be obtained from
the Soil Conservation Service (SCS) State Soil Survey Database (SSSD) and Natural
Resources Inventory (NRI).
The State Soil Survey Database (SSSD) databases are currently being compiled at a state
level by linking the information from the Soil Interpretations Record Data Base (commonly
known as the Soils-5 database), with the specific map unit identified in the county-level soil
surveys (compiled in the Soils-6 database). The SSSD is, therefore, a more refined and
accurate source of information about a specific soil than the Soils-5 database because the
information is linked to a specific geographic location (SCS National Soil Survey Lab,
Lincoln, NE, personal communication, 1991).
Selected data elements from the SSSD to be used in the pilot are listed in Table 5.2-2.
Useful data elements from this database to be used in statistical analyses include grouping
variables, such as taxonomic classification, Major Land Resource Area, and soil depth. The
SSSD data will be linked to Agroecosystem data by identifying the map unit of the sample
point on NASS aerial photos and the appropriate SCS county soil map, and requesting the
selected data elements for each mapping unit from the state SCS office. State SCS personnel
will confirm the identity of the soil map unit at each sampling location during a site visit.
SSSD data can also be used to determine expected ranges in each state of many soil
properties, including pH, bulk density, available water capacity, organic matter, permeability
and clay content. The Agroecosystem Resource Group (ARG) has acquired data previously
from the North Carolina Soil Conservation Service Office for this purpose.
55
-------
Table 5.2-1 Research indices of soil quality.
Assessment or index
Measurements
Contamination/Toxins
Anthropogenic
Nonanthropogenic
salinization and
alkalinization
Soil structure
(tilth, porosity)
Soil fertility
Leaching Potential/
Adsorption Potential/
Run-off Potential
(SCS ratings)
Sensitivity to
degradation from
intensive agriculture
Erosion
Productivity
Lead/cadmium/mercury/zinc
and other trace metal contaminants
PH
Exchangeable sodium percentage
Base saturation
Electrical conductivity or calcium carbonate equivalent
Bulk density
Available water capacity
Porosity
Organic carbon
clay content
Base saturation
Extractable phosphorus
Organic matter
pH
Slope .
Infiltration (Hydrologic group)
Horizon depth
Organic carbon
K factor
Texture
Drainage
Erosion rate
Erodibility index (R*K*L*S/T)
Soil depth
Rooting depth
Depth to water table
Restrictive soil layers
Landscape position (hillslope)
taxonomic order or suborder
"Highly Erodible Land' rating-water
"Highly Erodible Land1 rating-wind
Erosion rate (USLE)
Erosion index (USLE/T)
Soil properties such as bulk density, organic carbon, pH,
exchangeable cations, and cation exchange capacity
56
-------
Table 5.2-2 Requested data elements from the SCS State Soil Survey Database
Data element
Definition
MLRA
survey area ID
map unit ID
map unit symbol
map unit name
class code
soil layer
soil layer
soil layer
available water capacity
available water capacity
bulk density
bulk density
cation exchange capacity
cation exchange capacity
clay
clay
organic matter
organic matter
permeability
permeability
pH
pH
K factor
T factor
SCS LCC
SCS LCC
slope
slope
hydrologic group
drainage
prime farmland
depth
depth
code for Major Land Resource Area
code for state+FIPS (state soil survey area)
stssaid+musym: uniquely identifies a map unit within a state
map unit symbol
map unit name
code for taxonomic classification of the soil
identifies the original layers on the Soils-5 record
depth to the lower boundary of the soil layer (inches)
depth to the upper boundary of the soil layer (inches)
maximum value for the range of awe (inches/in)
minimum value for the range in awe (inches/in)
maximum value for the range in moist bulk density (g/cm3)
minimum value for the range in moist bulk density (g/cm3)
maximum value for the range in CEC
minimum value for the range in CEC
maximum value for the clay content (% in less than 2 mm fraction)
minimum value for the clay content (% in less than 2 mm fraction)
maximum value for the range in O.M. (% by weight)
minimum value for the range in O.M. (% by weight)
maximum value for the range in permeability (inches/hour)
minimum value for the range in permeability (inches/hour)
maximum value for the range in pH
minimum value for 'the range in pH
credibility factor; can be used in USLE (tons/acre)
soil loss tolerance factor; can be used to interpret USLE (tons/acre)
SCS Land Capability Class rating (nonirrigated)
SCS Land Capability Class- subclass rating
maximum value for the range of slope within a map unit (%)
minimum value for the range of slope within a map unit (%)
the SCS hydrologic group
code identifying the natural drainage condition/frq+duration when
saturation-free
SCS prime farmland classification
depth to water table
depth to bedrock
57
-------
5.2.3 Indicators
The initial set of physical and chemical indicators of soil quality to be measured in the
Pilot are described in some detail below. These indicators were chosen for evaluation because
they are known to be important to the functioning of the soil system, are affected by
anthropogenic stresses, and are likely to be measurable in a single sampling period on a
regional basis. Many are key variables in soil productivity models.
5.2.3.1 Organic carbon
The organic matter of surface soils range from 0.1% in mineral soils to nearly 100% in
organic soils (Schnitzer 1982). Organic matter is considered important for the long-term
physical, chemical and biological functioning of soils; it stabilizes soil structure, increases the
cation exchange capacity and water-holding capacity of sandy soils, and supplies nutrients for
plants and microorganisms. Loss of organic matter is increased by tillage and affected by
management practices such as choice of crops, stubble mulching, fallowing and use of organic
amendments. Organic matter is lost due to soil erosion, often accompanied by a loss in
nutrients, deterioration of soil structure and diminished soil workability (Pierce et al 1991,
Frye et al 1982). Depletion of soil organic matter and erosion are interdependent because a
decrease in organic matter increases the susceptibility of a soil to erosion (Pierce et al. 1991).
Changes in land management, such as the increasing implementation of no-till practices, may
affect rates of organic matter loss (Coleman et al. 1990). Organic carbon will be used as a
surrogate measure of organic matter.
5.2.3.2 Clay content
Soil clay content is the weight percentage of the particle size class smaller than 0.002 mm
diameter that is present in the < 2 mm soil fraction. Clay may have thousands of times more
surface area per gram than silt or sand and is, therefore, the most chemically and pnysically
active part of the mineral soil (USDA, SCS 1983).
Under conditions of accelerated erosion, the subsurface soil layers are increasingly
incorporated into the plow layer (Indorante et al. 1991, Frye et al. 1982, Stone et al. 1985,
Pierce et al. 1991). This is due to selective removal of fine particles during the erosion
process, and to mixing of subsoil into the surface layer. The implications of changing the
surface soil texture on crop productivity can be significant. The kind and amount of clay
58
-------
affects available water capacity, permeability, credibility and workability (Frye et al. 1982,
Lai 1987, Pierce et al. 1991).
As in the case of organic matter, an "ideal" or "healthy" standard of clay content in soil
does not exist. The indicator is intended to provide a broad-scale, long-term picture of clay
content in the top 20 cm of agricultural soils. An increase in clay content would be
interpreted as an indicator of soil loss and a warning of decline in soil quality.
5.2.3.3 SoilpH
Soil pH is an indicator of possible chemical constraints to the growth of roots and other
biological communities. Chemical constraints usually associated with pH include the
availability of inhibitory compounds (e.g., aluminum, salts), or a nutrient deficiency (e.g.,
phosphorus fixation), (Pierce et al. 1991). As soil weathering and leaching processes
progress, base cations are removed from soil and the pH declines. The amount of rainfall,
rate of percolation, and evaporation leave a definite impression on pH and on the morphology
of the soil profile. Classes of soil pH used by the SCS are listed in Table 5.2-3. These
ratings could be used to give a qualitative interpretation of pH values. An increase in land in
highly acid or highly alkaline classes would be interpreted as a warning of decline in soil
quality.
Table 5.2-3. Ratings of soil pH if
Class
ultra acid
extremely acid
very strongly acid
strongly acid
moderately acid
slightly acid
neutral
mildly alkaline
moderately alkaline
strongly alkaline
very strongly alkaline
pH value
<3.5
3.5-4.4
4.5-5.0
5.1-5.5
5.6-6.0
6.1-6.5
6.6-7.3
7.4-7.8
7.9-8.4
8.5-9.0
>9.0
From: TJSDA, Soil Conservation Service 1983
59
-------
5.2.3A Calcium carbonate equivalent
In some soils of Nebraska, naturally occurring carbonates may be found in surface and
subsurface horizons. The distribution and amount of CaCO3 are important for fertility,
erosion, and available water holding capacity of the soil. High CaCO3 content at or near the
soil surface can be an potential indicator of erosional loss of the original soil surface horizons.
The loss of CaCO3 from the soil profile at depth may indicate acidic inputs from either
natural decomposition of organic matter, anthropogenic inputs of fertilizers (e.g. urea), or
atmospheric deposition. The determination of carbonates in the soil is generally reported as a
calcium carbonate equivalent on a per weight basis, although magnesium carbonates may be
present in the soil as well as the calcium carbonates.
5.2.5.5 Cation exchange capacity and exchangeable cations
The cation exchange capacity (CEC) of a soil is defined as the sum total of exchangeable
cations that a soil can adsorb (USDA SCS 1992). The CEC is a reversible reaction in the soil
solution and may arise from permanent charges or pH-dependent sites on organic and mineral
matter (predominantly of the clay minerals although some CEC resides on silt-sized particles).
Since the CEC is dependent upon the negatively charged sites of organic and mineral matter,.
any loss of organic matter, silt, and/or clay content will lead to a decrease in the CEC of the
soil and may indicate accelerated soil erosion or poor tillage practices that leave the soil
susceptible to erosion by wind or water. Conversely, a sudden increase in the CEC of the
surface horizons may be an indication of erosional loss of Hie natural A horizon or deep
tillage practices, both of which lead to the subsequent incorporation of subsurface material
into the topsoil.
Exchangeable cations are those positively charged elements which are readily exchanged
from the charged sites in a soil. The exchangeable cations generally include Ca, Mg, K, and
Na. Exchangeable cations are generally extracted from the soil during the determination of
CEC by displacement on the exchange sites with NH4+. They are commonly used to
characterize the soil fertility status and thus provide information on readily available plant
nutrients. Exchangeable cation contents are also used in the calculations/determinations of
other parameters including: percent base saturation, sodium adsorption ratio (commonly used
to determine the quality of irrigation water in relation to Na salt buildup in the soil), and
exchangeable sodium percentage (used to indicate high Na content in the soil which leads to
60
-------
detrimental plant effects, such as osmotic stress, specific ion effects, and nutritional
imbalances).
5.2.3.6 Extractable phosphorus
Phosphorus is a macronutrient that directly effects plant growth and health and is thus a
common component in a fertilization program on any given field. Conversely, phosphorus in
excess quantities that reaches a stream, pond, or lake via overland runoff or reaches the
groundwater via leaching is a common fresh water pollutant. In the soil, phosphorus is
generally strongly bound to the soil particles in the subsurface horizons but has a relatively
slow release rate through time. Over the years of standard phosphate fertilizer application, the
potential for the creation of a large pool of P in the subsurface horizons of soil exists. Thus,
in the agriculture system, the monitoring of P hi the subsurface horizons can indicate: 1) if P
is accumulating and could become a potential pollutant if released during erosion events or
leached into groundwater; 2) if P contents are remaining at steady concentration levels; or 3)
if P is being "mined" by the plant uptake through time.
5.2.3.7 Aggregate stability
An aggregate is a group of primary particles that cohere to each other more strongly than
to the surrounding soil (USDA, SCS 1992). Aggregate stability is a function of whether thfe
cohesive forces between the particles can withstand the applied disruptive forces such as
plowing and raindrop impact. Aggregate stability is influenced by soil particle-size
distribution, clay mineralogy, structure, organic matter content, and microbial population.
Erodibility of the soil increases as aggregate stability decreases, thus, this parameter can be
used as an indicator of soil erosion potential and the "physical health" of the soil.
5.2.3.8 Available water capacity (AWC)
Available water capacity is the capacity of a soil to hold water available to plants; it is
usually expressed in inches of water per inch of soil depth. AWC is the amount of water held
by the soil at tensions between field capacity and wilting point (-33 and -1500 kPa); and is
mainly determined by the pore size distribution of the soil.
61
-------
5.2.3.9 Bulk density
Bulk density is an indicator of how well plant roots are able to extend into the soil
(USDA, SCS 1983). Bulk density is expressed as soil weight per volume dry soil and
generally ranges from about 1.0 to about 2.0 g/cm3 in agricultural soils. Because bulk density
is defined as the volume of both solids and pores, soils that are loose and porous will have
low weights per volume (bulk density) and those that are compact will have higher bulk
densities (Brady 1974). Soils that contain organic matter and have good aggregation have low
bulk densities.
Bulk density is used as a parameter most closely related to mechanical impedance of root
growth in models that relate soil properties to soil productivity (Kiniry et al. 1983, Pierce et
al. 1983). Crop rotation and soil management of a given soil affects the bulk density,
especially of the surface layers. Accelerated erosion and intensive cultivation increases bulk
density; adding crop residues, manure or planting cover crops tends to lower it (Frye et al.
1982, Brady 1974, Groenevelt et al. 1984).
Nonlimiting, critical and root-limiting bulk densities are generally known, and vary with
the texture class of the soil (USDA SCS 1975, Pierce et al. 1983). An increase in the
proportion of soils reaching critical bulk density values within their texture class would be
interpreted as an indication of decline in soil quality. Bulk density would be an important
component in a soil structure index.
5.2.4. Logistics
Each NASS enumerator will sample approximately 10-15 segments using a kit containing
the items listed in Table 5.2-6 received at the NASS training session. Within the enumerator
kit will be a soil sampler/probe set. In the probe set, three tips will be available for the core
tube for sampling soil under a range of conditions. The regular (2 notches), mini ,1 notch),
and super (4 notches) duty tips are for sampling moist, dry, and stony soils, respectively.
Extra parts will be available at the Nebraska State Office of NASS and can be shipped by
overnight express delivery upon demand. Several phone numbers, where someone could be
reached, will be included in the enumerator's manual for use in the event of equipment loss or
breakage.
62
-------
Table 5.2-6. Contents of the enumerator kit
Item
Purpose
Manual describing sampling methods
Instruction sheet indicating type of field
Record keeping sheet
3-foot hinged ruler
stakes (20 red, 10 yellow)
36-inch Oakfield probe set
[contains 12" handle, 2 12" extension rods, 12" tube
which extracts a sample 8" long x 13/16" diameter,
3/4 " tips for moist, dry, and stony soil, a footstep
for dry or compacted soils, and a fiberboard case]
Extra 12" core tube
14-qL plastic bucket with handle
Wooden block with bolt
Screwdriver
Sharpshooter shovel
1 500-ml plastic beaker
4 x 2 x 12" plastic bags
3 qL plastic bags
Paper-wire tags
inform enumerator whether field has two sampling
lines and whether 20 or 40 cores should be sampled
from each sampling line
keep a record of sample number and respective
Federal Express airbill number, date samples were
collected and mailed, and whether the field was
cultivated or not at the time of sampling
measure 45° angles for transect
marking transect and location of soil cores along the
transect
collect 20 20-cm deep cores
spare part in case the tube in the kit becomes twisted
collect and homogenize soil cores
facilitate removal of soil core from sampling tube
facilitate removal of soil core from sampling tube
facilitate insertion of probe into tight or dry soils
measure volume of soil for nematode analysis
contain 500 ml soil sample for nematode enumeration
at the moisture content of the field
contain chemical analysis samples for shipping to
analytical laboratory at the moisture content of the
field
tag on which bag labels placed
63
-------
Bag labels
contain random numbers for analytical laboratories to
track samples; random numbers without location
identifiers are used to maintain confidentiality of data
pro-addressed shipping labels
10 1/2" x 16" Jiffy-Lite bubble bags
8 1/2" x 12" Jiffy-Lite bubble bags
Roll of strapping tape with cutting edge
1 48-qt. plastic ice chest
First-aid kit
insure sample is sent to the correct address; the 10-
digrLnumber on the airbill is recorded on the record
keeping forms to permit tracking of lost samples.
sending samples for chemical/physical analysis
directly to analysis laboratory
sending samples for enumeration of nematodes
directly to the laboratory
packaging samples for mail
lightweight, insulated container for storage of samples
in an environment to prevent temperatures lethal to
nematodes
safety (note: NE enumerators already have kits)
Sample collection. For each field, the enumerator will be given the following information
printed on an instruction sheet placed within their questionnaire: the sample number(s),
whether or not a second composite sample must be collected in that field, the number of cores
to be collected from each transect, and the number of paces along and into the field to
determine the midpoint of the sampling transect. Two labels, each with a different
identification number, will be provided for composite samples that will be divided into
duplicate samples. All labels will be printed in cooperation with the Nebraska State Office of
NASS. The sampling design was constructed to
include measures of within-field variability (a second composite sample collected for every
sixth field sampled) and within-sample or laboratory variability (duplicates are taken from the
second composite sample from every twelfth field). The enumerator will collect soil cores
according to the sampling design described in Section 4.2.2. Example instructions for the
NASS enumerators are listed in Appendix 3.
Twenty cores (2-cm diameter) of soil are necessary to provide enough soil (1256 cm3) for
the required analyses. Total soil volume of each composite sample must exceed 1000 cm3,
half required for chemical/physical analysis and the other half for nematode enumeration
64
-------
(Section 5.3.3). When a field is selected for a duplicate sample, 40 cores per transect will be
required to collect enough soil for all laboratory determinations.
Within each field, one core will be taken at each of 20 locations, except for duplicate
samples where two cores will be taken at each of 20 locations, equally spaced along a 100-
yard diagonal transect (Section 4). For each core, the soil tube will be pushed straight down
into the soil, without twisting, to the depth that fills the length of the tube (20 cm). The tube
will be pulled up and the soil core placed into a plastic bucket. If the core is unsatisfactory
(see Appendix 6), another core will be taken in the same location within 15 cm. When all 20
cores have been deposited into the bucket the enumerators will be instructed to mix the soil
thoroughly by hand, breaking up soil clumps gently. A preliminary study indicated variability
associated with subsampling of composite samples that were hand-mixed was not significantly
different than for those mixed with a riffle sampler. Any rocks larger than 2-cm in diameter
will be discarded, but all surface organic matter should be kept as part of the soil sample.
When appropriate, soil for nematode enumeration (Section 5.3) will be removed first.
Samples will be placed first in plastic bags closed by a paper-wire tag and then enveloped in
a pre-addressed padded mailing container and stored in an insulated container (ice chest).
Samples will be mailed directly to the analysis laboratories the same day they are collected or
first thing the next day through Federal Express (1-800-238-5355 for pick-up). Postage will be
paid using a Federal Government account through the Air Resources Research Consortium at
North Carolina State University.
SCS soil scientists will visit approximately 36 of the sample fields (chosen from the
hexagon-selected fields). They will determine the map unit composition of the field, the map
units crossed by the the transect described above, and the classes of accelerated erosion on the
field. They will also dig a 51cm (20 inch) deep pit on each map unit that is crossed by the
transect. Samples from up to four horizons will be collected for determination of certain soil
physical properties that have potential as indicators (see below). USDA SCS (1983) gives the
details of the methods to be used by SCS personnel.
Laboratory analysesphysical and chemical. The analytical laboratory will analyze the
soil samples for the specified chemical and physical parameters using the prescribed
procedures (Table 5.2-7). Soil samples collected by the enumerators along the transect and
the samples collected from the 51 cm pits (divided by soil horizon and up to four samples per
pit) will be analyzed for particle-size distribution (sand, silt, and clay), pH (1:1 soil:water),
organic carbon, exchangeable cations, cation exchange capacity, extractable phosphorus (Bray
65
-------
n), and calcium carbonate equivalent (in soils with pH>7.0). In addition, bulk density, 15-bar
water retention, and available water capacity will be measured on all soil horizons sampled
from pits and aggregate stability will be measured only on A horizon samples collected from
pits. Detailed laboratory procedures are described in SCS methods manual (USDA, SCS
1992). Reporting units and precision are listed in Table 5.2-8. Soil preparation and analyses
will be completed by the National Soil Survey Laboratory of the SCS in Lincoln, Nebraska.
Table 5.2-7. Soil analytical laboratory parameters to be measured in the 1993 Pilot Field
Program.
Parameter
Description of Parameter
SAND Sand is the portion of the sample with particle diameter between 0.05 mm and 2.0
mm; it is measured using the sieve and pipette method
SILT Silt is the portion of the sample with particle diameter between 0.002 mm and 0.05
mm; it is calculated as [100 - (SAND + CLAY)]
CLAY Clay is the portion of the sample with particle diameter less than 0.002 mm; it is
measured using the sieve and pipette method
ORG_C Organic carbon determined by rapid oxidation through dry combustion followed by
infrared or thermal detection using an automated CHN analyzer
CEC Cation exchange capacity using the buffered (pH 7.0) ammonium acetate solution
using a 1:13 soil to solution ratio; analyzed for ammonium by one of the following
three methods: automated distillation/titration; manual distillation/titration; or
ammonium displacement/flow injection analysis
PHJH20 pH determined in a 1:1 deionized watensoil extract; it is measured with a pH meter
and combination electrode
XCA Exchangeable calcium in a buffered (pH 7.0) ammonium acetate solution using a
1:13 soil to solution ratio; cation content quantified using either atomic absorption
spectrometry or inductively coupled argon plasma atomic emission spectrometry
XMG Exchangeable magnesium in a buffered (pH 7.0) ammonium acetate solution using a
1:13 soil to solution ratio; cation content quantified using either atomic absorption
spectrometry or inductively coupled argon plasma atomic emission spectrometry
66
-------
XK
Exchangeable potassium in a buffered (pH 7.0) ammonium acetate solution using a
1:13 soil to solution ratio; cation content quantified using either atomic absorption
spectrometry or inductively coupled argon plasma atomic emission spectrometry
XNA
Exchangeable sodium in a buffered (pH 7.0) ammonium acetate solution using a 1:13
soil to solution ratio; cation content quantified using either atomic absorption
spectrometry or inductively coupled argon plasma atomic emission spectrometry
AWC
WRET
AGO
BD
CACO3
Extractable phosphorus determined in a Bray & Kurtz No. 2 extractant using a 1:13
soil to solution ratio; quantified using a colorimetric procedure and autoanalyzer
Available water capacity determined on saran-coated clods at -33 and -1500 kPa (-
0.3 and 15 bars) soil matric potential using a pressure plate
Water retention determined on saran-coated clods at -1500 kPa (-15 bars) soil matric
potential using a pressure plate
Aggregate stability determined by retention of air-dry aggregates on a 0.5 mm sieve
after sample has been submerged overnight followed by agitation of the sample;
gravimetric determination
Bulk density at field soil water content at time of sampling determined using a
natural clod with a saran resin coating method
Calcium carbonate equivalent determined by treating the samples with HC1 and
measuring the evolved CO2 manometrically
67
-------
Table 5.2-8. Reporting units and precision of soil physical and chemical measurements
Parameter
Reporting units17
Reporting precision2'
SAND
SILT
CLAY
ORG_C
CEC
PH_H20
XCA
XMG
XK
XNA
P mg/kg
AWC
WRET
AGO
BD
CACO3
" All values expressed on an oven-dry soil
y Number of decimal places that each unit
wt%
wt%
wt%
wt%
meq/lOOg
pH units
meq/lOOg
meq/lOOg
meq/lOOg
meq/lOOg
1.0
cm cm'1
wt%
wt%
gem'3
weight basis.
should be determined for
1.0
1.0
1.0
1.0
1.00
1.0
1.00
1.00
1.00
1.00
1.0
1.00
1.00
5.2.5 Quality Assurance I Quality Control
Samples. Each sample will be enclosed in a pre-labeled container, with a unique sample
number. The code number will not reveal the actual location of the field where the sample
was collected. The containers will not contain contaminants that would bias or interfere with
detection of chemical parameters. The date the sample was collected and mailed, the Federal
Express tracking number, and whether the field was cultivated or not at the time of sampling
will be recorded on the record keeping sheet provided with the questionnaire to facilitate
tracking samples. The record sheets will be mailed to the Nebraska State Office of NASS.
As each sample is received, the date of receipt and condition will be recorded by laboratory
personnel in a log that later will be returned to ARG personnel. The analysis laboratory will
send a diskette file with a list of sample numbers and dates received to ARG personnel on a
weekly basis for purposes of tracking lost samples.
68
-------
As indicated in section 5.2.4, to assess the field variability error component, replicate and
split samples will be collected. Replicate samples may be defined as two unique samples
collected from different areas within a given field. These samples are used to assess the
variability across the field and the long-range heterogeneity. The split samples are two
separate samples collected from the same composite sample and can be used to assess the
short-range heterogeneity component and as an indication of the in-field
homogenization/compositing process. A more complete discussion of the EMAP-A QA
program may be found in Chapter 6.
Laboratoiy analyses. An interagency agreement has been prepared with the SCS National
Soil Survey Laboratory (Lincoln, Nebraska) to address the following topics: requested analysis
procedures, quality assurance/quality control procedures, costs, and times of sample analysis
completion (i.e., turn-around time). Laboratory QA/QC practices will include requirements to
assess laboratory accuracy, precision, detection limits, calibration - both initial and ongoing,
and blanks to check for potential laboratory contamination. Acceptance limits for each of
these criteria will be specified in the quality assurance project plan that will be created prior
to the initiation of the field sampling and subsequent laboratory analytical season. A more
complete discussion of the EMAP-A QA program may be found in chapter 6.
Reporting. The data from chemical and physical analyses will be sent as an ASCII file on
diskette and as a printed copy to the ARG information manager. The ARG will perform
validation tests on the data to determine whether the values for each parameter fit within the
expected range and for completeness of the submitted database. Additionally, validation tests
may be developed in conjunction with the QA program to allow for the evaluation of the
quality of the data.
All samples must pass the laboratory QA/QC requirements. Submitted samples will be
archived at the analysis laboratory until laboratory personnel are notified that all analyses have
met the QA/QC requirements. After all data have been collected, validated, and transformed
(as needed), the ARG information manager will work with NASS personnel to integrate the
soils data into the larger Agroecosystem Pilot dataset at the North Carolina State Statistical
Office of NASS.
69
-------
5.2.6. Metadata requirements
In addition to the analysis data, metadata will be recorded to permit future interpretation of
the database. Metadata will include methods of analysis, reporting units, whether data are
numeric or characters, name of analysis laboratory, and comments recorded during sampling
or processing procedures (Table 5.2-9).
5.2.7. Data Analysis
Cumulative distribution functions (CDF) will be calculated for each indicator of soil
quality. Calculations of CDF are useful in determining the range and distribution of indicator
values as a proportion of cropland area. Categories or classes of condition can be defined
with the CDF.
Variance components (see section 4.4.2) will be quantified for 1) among and within NASS
strata, 2) among transects within fields, and 3) among composite samples. These components
will help in evaluation of the sampling design to determine how easily change of a particular
indicator can be detected. For example, change among NASS strata can be detected more
easily if the variance attributed to fields and samples is less than that for strata than if
variance attributed to fields and samples is greater than that for strata.
Power curves will be calculated to quantify the power of indicators to detect trends as a
function of the variance components from the pilot. These curves will be used to determine
the number of sampling sites required to detect change in condition in future studies.
Determination of the rate of change of soil quality (change in the proportion of land area
with specific ranges in indicator values) is an important long-term objective. Because the
program is designed to give regional estimates of each indicator and standards of s, Jl quality
will vary with climate and soil, some grouping of the data will probably be necessary (see
Section 5.2.7.1) (Webster and Oliver, 1990).
70
-------
Table 5.2-9. Metadata for chemical and physical analysis of soils.
Variable
SAND
SILT
CLAY
ORG_C
CEC
EC
PH_H20
PH_BUFF
XCA
XMG
XK
XNA
P
AWC
WRET
AGO
BD
CACO3
Type
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Numeric
Unit1'
wt%
wt%
wt%
wt%
meq/lOOg
dS/m
pH units
pH units
meq/lOOg
meq/lOOg
meq/lOOg
meq/iOOg
mg/kg
cm cm"1
cm cm"1
wt%
g cm'3
wt%
v All values expressed on an oven-dry soil
y Used as an example laboratory.
Anal. Method
sieve/pipette
sieve/pipette
sieve/pipette
dry combustion
NH4OAc
1:1 soil:solution
1:1 soil:solution
l:2soil:0.01MCaC!2
NH4OAc
NH4OAc
NH4OAc
NH4OAc
Bray-II
pressure plate
pressure plate
wet sieve
saran clod
manometric
weight basis.
Lab2 Comments
scs
scs
scs
scs
scs
scs
scs
scs
scs
scs
scs
scs
scs
scs
scs
scs
scs
scs
5.2.7.1. Soil spatial variability and statistical approaches
The spatial and temporal variability of many soil properties is large and may make
changes in soil quality difficult to detect. Because EMAP is designed to provide regional
estimates of indicator values, some aggregation will likely be necessary to minimize the broad
71
-------
inherent differences among agricultural soils. Several methods used to group soils according
to taxonomic classes or soil properties would be appropriate for EMAP data. These include:
o Derived geographic classifications such as the Land Resource Regions and Major Land
Resource Areas (USDA, SCS 1981)
o Taxonomic order or suborders
o General landscape position or slope (Stone et al. 1985, Larson et al. 1983)
o Soil depth (Larson et al. 1983, 1985).
These aggregation groups are described in detail in Heck et al. (1991). The 1993 Pilot,
like the 1992 Pilot, was not designed to address how many samples are needed for different
aggregation approaches, but the range of data values across the three physiographic regions of
North Carolina and the two physiographic regions of Nebraska may allow some initial
exploration of these approaches. Data not collected directly that would be required to group
each sample into the suggested aggregations are available in the SSSD.
5.2.8. Research Goals and Applications
Long-term assessment of soil quality has become a high priority for agroecologists
(McCracken et al. 1985, Shirley 1991, Pierce et al. 1991, Haberern 1991). The World
Resources Institute listed soil condition and extent of degradation as high priority
environmental information needed for decision makers (WRI 1991). The earliest soil
assessments for agroecosystems attempted to develop numerical ratings of soil productivity
and were motivated by the need to compare different soils for purposes of land use planning
and tax assessments. These ratings were based primarily on crop yield (Htiddleston 1984).
Several newer soil productivity models are based on soil properties such as bulk density and
texture, often with the goal of predicting the effect of accelerated soil erosion on long-term
crop yields (Williams et al. 1984, Pierce et al. 1983, Kiniry et al. 1983, Huddleston 1982).
The Soil Conservation Service is currently developing a new Soil Rating for Plant Growth,
which is also based on soil properties (Ray Sinclair, SCS, Lincoln, NE, personal
communication). Because soil structure is central to the functioning of soils and is susceptible
to long-term damage from intensive agriculture, attention is also being given to conceptual
models that characterize soil structure and the rate of change due to agricultural land
management (Kay 1989, Gibbs and Reid 1988, Thomasson 1978).
72
-------
Table 5.2-10 lists several examples of published work on soil assessments that will be
useful for identification of ranges for indicator values (e.g., USD A SCS 1983) and in the
development of indices of soil quality (e.g. Lai 1991, Singh et al. 1992, Pierce et al. 1983,
Kiniry et al. 1983, Huddleston 1982, Thomasson 1978). The basis for establishing rating
scales to interpret indicator values ultimately should include not only the capacity of the soil
to sustain crop production, but also should allow for interpretation of how changes in soil
indicators affect soil organisms, nutrient cycling, soil resiliency, vulnerability to erosion and
thresholds of irreversible change. For example, soil porosity values, and changes over time,
could be interpreted in the context of microbial ecology as well as adequate aeration for root
growth.
Many of the assessments listed in Table 5.2-10 combine and query GIS databases on a
regional or national scale (Burke et al. 1989, Turner et al. 1986, Nielsen and Lee 1987, Bliss
and Reybold 1989). Examples of soil assessments conducted on a regional scale include soils
or land area likely to be sensitive to intensive agricultural use (Federoff 1987, Yassoglou
1987), sensitive to acid deposition (Turner et al. 1986) or susceptible to organic matter loss
(Burke et al. 1989). Goss (1991) developed a rating scheme of the soil leaching potential of
agricultural chemicals that has been applied to a national assessment of groundwater
vulnerability (Nielsen and Lee 1987). This scheme .(Goss 1991) combines chemical and
physical information on soils and on pesticides and can be used as a management tool to
"match" appropriate types and rates of agricultural chemicals to soils in an effort to keep
runoff and residues out of water systems. This is one of the many soil quality assessment
questions that could be addressed using pilot data.
73
-------
Table 5.2-10. Examples of soil assessments.
Productivity indices
Erosion Productivity Impact Calculator
Tilth index
Changes in soil structure due to cropping systems
Extent of erosion and land degradation
Soil leaching potential/groundwater vulnerability
Sensitivity of soil to acidification from acid deposition
Land use effects on soil organic matter dynamics
Organic matter dynamics
Sustainability index: production per unit soil loss or per unit
decline in soil properties
Sensitivity of soil to degradation
Soil ratings for specific uses
Global change
Berger et al. 1952
Storie 1978
Kiniry et al. 1983
Larson et al. 1983
Pierce et al. 1983
Gersmehl and Brown 1986
Huddleston 1984
Huddleston 1982
Williams et al. 1984
Singh et al. .1992
Kay 1989
Gibbs and Reid 1988
Thomasson 1978
USDA SCS RCA Appraisal 1989
Goss 1991
Nielsen and Lee 1987
Turner et al. 1986
Cole et al. 1989
Burke et al. 1989
Lai 1991
Federoff 1987
Yassoglou 1987
USDA SCS 1983
Bliss 1990
Sombreck 1990
74
-------
5.3. Soil Biotic Diversity
Nematodes (free-living and plant parasitic) are a group of soil fauna that have promise for use
as an indicator of pollution exposure and the restoration capacity of soil ecosystems (Schouten et al.
1990). This indicator will be evaluated for its ability to answer the question, "what proportion of
agroecosystem units have soils that cannot support diverse communities of soil microbiota?"
Nematodes have the following attributes that make them useful as ecological indicators (Freckman
1988).
o Nematodes are small with short generation times, allowing them to respond quickly to
changes in food supply; they are ubiquitous, even in polluted or disturbed areas; they
are frequently the last animals to die.
o Nematodes have the ability to survive desiccation and revive with moisture.
o Populations are relatively stable with soil, thus any change is viewed as the result of an
environmental perturbation.
o Perturbation of nematode populations usually reflect a change of trophic structure.
o Trophic, or functional, groups can be separated easily, primarily by anterior structures
associated with various modes of feeding (Yeates and Coleman 1982, Freckman 1988).
Therefore, species identification is not necessary and the cost associated with
identification is relatively small.
o Abundance and size of nematodes makes sampling easier and less costly than for other
microflora and fauna.
Functional groups of nematodes are present in three positions of food webs in soil. Plant-
parasitic nematodes are herbivores, feeding on plant roots and are, therefore, consumers,of primary
production. Bacterivores and fungivores consume bacteria and fungi (including mycorrhizae),
respectively, and are, thus, involved directly with decomposition and nitrogen mineralization
(Parmelee and Alston 1986, Seastedt et al. 1988, Sohlenius et al. 1988, Moore and de Ruiter 1991).
Omnivores add "connectedness" to the food web (Coleman et al. 1983) by feeding on more than one
food source, including bacteria, flagellates and amoeba. Predaceous nematodes feed upon all the
75
-------
other functional groups of nematodes (Moore and de Ruiter 1991). Calculation of a diversity index
of trophic groups would describe the relative abundance and evenness of the distribution of
nematodes across trophic groups.
A Maturity Index is a measure based on ecological life history strategy characteristics of
nematode taxa (Bongers 1990). Nematode families are classified on a scale of 1-5, with colonizers
(short life cycle, high reproductive rates, tolerant to disturbance)=l, and persisters (long life cycles,
low colonization ability, few offspring, sensitive to disturbance)=5. A maturity index value is given
to each family. Maturity indices should be calculated separately for families of plant parasitic (PPI)
and free-living (MI) (Bongers 1990). Families identified in the North Carolina Survey studies and
their respective maturity index weights (c-p values) are illustrated in Table 5.3-1.
Table 5.3-1. Classification of nematode genera and families by trophic groups and their respective c-p value as
defined by Bongers (1990) for calculation of the Maturity Index separately for free-living and plant parasitic
nematodes. Classification and trophic groups determined by M. Noffsinger (personal observation), Maggenti (1982,
1991) and Yeates (1971). (Table taken from Neher et al. 1993).
Trophic group
Family
Genera
Bacterial feeders Alaimidae
Bastianidae
Cephalobidae
Diploscapteridae
Microlaiinidae
Monhysteridae
Panagrolaimoidea,
Prismatolaimidae-
Plectidae
Rhabdltidae.
Tcratocephalidae
Fungal feeders Anguinidae
'
Aphelenchidae
Aphelenchoididae
Diphtherophoridae
Paraphelenchidae
Tylencholaimidae
Alaimus
Amphidelus
Bastidnia
Acrobeles
AcroBeloides
Cephalobus
Chiloplacus
Eucephalbbus
Diploscapter'
Mierolaimus
Morihystera
Monhystrella,
Theristus
Panagrolaimus
Prismatolaimus
Anaplectus
Plectus
Wilsonema
Bursilla
Cruznema
Poikilolaimus
Rfiabditis
Teratorhabditis
Euteratocephalidae
Ditylenchus
Pseudhdlenchus
Aphelenchus
Aphelenchoides
Diphtherophora
Paraphelenchus
Tylencholaimus
4
4
3
2
2.
2:
Z
Z
1:
3
1
t'
1
1
3
^
2.
2
1
1
1
1:
1-
3"
2
2
2
2
3
2
4
76
-------
Tylencholaimellidae
Tylencholaimellus
Algal feeders/Omnivores
Belondiridae
Cyatholaimidae
Dorylaimellidae
Dorylaimidae
Labronema
Leptonchidae
Plectidae
Tripylidae
Axonchmm
Belondira
Achromadora
Dorylaimellus
Eudorylaimus
4
Mesodorylaimus
Pungentus
Thornenema
Leptonchus
Chronogaster
Tobrilus
Tripyla
5
5
3
5
4
4
4
4
4
2
3
3
Predators
Anatonchidae
Aphelenchoididae
Diplogasteridae
Mesodiplogaster
Dorylaimidae
lotonchulidae
Mononchidae
Mylonchulidae
Nygolaimidae
Anatonchus
Seinura
Butlerius
1
Monocholaimellus
Aporcelaimus
Discolaimus
Miconchus
lotonchus
Mononchus
Prionchulus
Granonchulus
Mylonchulus
Nygolaimus
Sectonema
4
2
1
1
4
4
4
4
4
4
4
4
5
5
Plant feeders
Belonolaimidae
Criconematidae
Hemicycliophoridae
Heteroderidae
Hoplolaimidae
Longidoridae
Pratylenchidae
Trichodoridae
Tylenchidae
Merlinius
Tylenchorhynchus
Criconemella
Hemicycliophora
Heterodera
Meloidogyne
Helicotylenchus
Hoplolaimus
Scuttellonema
Xiphinema
Pratylenchus
Paratrichodorus
Trichodorus
Aglenchus
Atylenchus
Basiria
Boleodorus
Coslenchus
Ecphyadophora
Filenchus
Psilenchus
Tylenchus
2
2
3
3
3
3
3
3
3
5
3
4
4
2
2
2
2
2
2
2
2
2
77
-------
The indices developed to describe nematode community structure from the 1992 North Carolina
pilot in 1992 will be compared with those in Region 7 in 1993. The specific questions to be
answered by the Region 7 pilot are:
Is nematode community structure similar between NE and NC? Are the population distributions
of index values (e.g., maturity index, diversity index) comparable between the two states?
Does the variance among regions, among fields, and within fields for this assessment endpoint
allow data quality objectives to be met with a reasonable sample size?
Are the indices of nematode community structure which indicate soil biological health generally
applicable? Are the soil properties that influence nematode density and community structure the
same between NE and NC? Is the relationship between soil chemistry and nematode community
structure similar in the two areas?
5.3.1. Data to be Collected
Populations of nematodes in soil will be quantified by family by N & A Nematode Identification
Service (Davis, California). Families will be grouped subsequently by feeding preference as
illustrated in Table 5.3-1. Mae Noffsinger, an internationally recognized taxonomist, will count and
classify the nematodes. Typical transformations associated with count data, such as In (x + 1), or
adjustments for soil moisture are not necessary because the indices applied are either weighted
frequency means or already have logarithms incorporated in the index calculation (see Section 5.3-6).
5.5.2
Essential Complementary Data
Various soil characteristics influence populations of nematodes. Soil parameters measured will
include organic carbon, exchangeable calcium, exchangeable sodium, pH, electrical conductivity, soil
texture, and gravimetric soil moisture (see Section 5.2). In addition, data concerning 1) application
of nematicides, by tradename and formulation, within the past 2, 2-4, or 4-12 months; 2) crop(s)
planted; 3) cropping history; and 4) tillage practices will be obtained from the NASS Questionnaire
(see Appendix 5). The NASS Questionnaire will also include questions regarding applications of
herbicides and pesticides that may be used to interpret observed community patterns of nematodes
(Section 5.3.7).
78
-------
5.3.3. Logistics
Sample collection. Only soil sampled from the Rotational Panel Design will be analyzed for
nematode populations. An autumn sampling period was selected to coincide with generally higher
populations of nematodes at that time of year. Populations of bacterivorous and fungivorous
nematodes are favored at this time because 1) crop residues are incorporated into soil by cultivation
(Kastner and Germershausen 1989), and 2) temperatures are favorable (15-20 C) (Stinner and
Crossley 1982). Samples should not be collected from saturated soils; otherwise anaerobic conditions
would develop in the plastic bags during storage and transport. Anaerobic conditions could decrease
the estimates of nematode populations.
Although there are few quantitative studies describing the spatial patterns of bacterial-feeding
nematodes (McSorley et al. 1985), populations are probably aggregated around plant roots and
organic debris in a manner similar to plant-parasitic nematodes. Therefore, ridges, furrows, and plant
rows should be sampled with equal probability within a field. Because nematode populations are
aggregated spatially, soil samples will be collected using a systematic design described in Section 4
and Appendix 6. Except for fields that are chosen for two composite samples, 20 cores (2-cm
diameter), taken to 20-cm depth, will be collected along a diagonal transect described in Section
4.2.2, across a 0.4-ha area, chosen at random, and pooled as one composite sample for estimation of
field populations. After all cores have been collected in a bucket and gently homogenized (excessive
pressure or abrasion will damage or kill nematodes), a 500-cm3 subsample will be transferred to a 4
x 2 x 12 inch plastic bag. The bag will be closed with a pre-labeled wire tag with the appropriate
identification code and stored in an insulated container or at temperatures < 30 C (Barker 1985b)
until mailed, to avoid temperatures that may affect estimates of nematode populations. All equipment
necessary for collection of the samples will be included in the enumerator kit (Table 5.2-6 in Section
5.2).
Samples will be mailed using either Federal Express (call 1-800-238-5355 for pickup) or United
Parcel Service either the day of sampling or the following morning to the enumeration laboratory (N
& A Nematode Identification Service, 251 Quarter Circle, Davis, CA 95616). Prior to mailing, the
soil sample will be placed in a padded (with bubble wrap) envelope, which is pre-addressed and
postage-paid to the enumeration laboratory. Previous studies suggested there was no significant
effect of mailing on nematode populations and no significant effect of mailing an ice pack with the
soil sample (Neher et al 1993).
79
-------
Samples will be mailed between Monday and Thursday so that they are delivered the day after
mailing. Samples collected over the weekend should be stored indoors at room temperature or in a
cooler and mailed first thing on Monday morning. These steps are necessary to minimize the
possibility that the soil samples would be exposed to temperatures lethal to nematodes. As samples
are received by the enumeration laboratory, the date of receipt and their condition upon receipt will
be recorded on log sheet pages provided by ARG (Figure 5.3-1). Samples will be stored at 15 C and
processed within 14 days of receipt.
Figure 5.3-1. Log-in sheet for the laboratory to record the date samples were received, extracted;
identified, and preserved and the conditions that they received the samples at the time of delivery.
EMAP-Agroecosystems, 1509 Varsity Drive, Raleigh, NC 27695, 919/515-3311
1993 Pilot Study in Region 7
Page
of
SAMPLE
1100
1111
1127
1131
1134
1140
1143
1148
1163
1177
1185
CONDITION
RECEIVED
EXTRACTED
IDENTIFIED
PRESERVED
Laboratoiy analyses. Nematodes will be extracted from 500 cm3 soil using a modified
Cobb's sifting and gravity method (Thome 1961, Ayoub 1980, M. Noffsinger, personnal
communication) followed by a modified sugar centrifugal-flotation (Caveness and Jensen
1955; T. Burlando and M. Noffsinger, personnal experience). Cobb's method was chosen as
the extraction method because of its 90-95% extraction efficiency (Neher et.al., 1993; M.
Noffsinger, personnal communication) and its ability to extract both live and dead nematodes,
which is important if samples have been mishandled before reaching the enumeration
80
-------
laboratory. A compound-light microscope will be used as an aid to identify and enumerate
nematodes in soil by taxonomic family.
5.3.4 Quality Assurance
Samples. Samples will be shipped in pre-labeled containers with unique sample numbers
and will be logged on an inventory sheet as received by the enumeration laboratory as
described in Section 5.2.5. Duplicate samples will be submitted to the enumeration laboratory
for determination of within-laboratory and within-field variability. The variability will be
compared to expected ranges, standard deviations and percent coefficients of variation
established from preliminary surveys conducted in 1990 and 1991 (Tables 5.3-2, 5.3-3). It is
impossible to submit known blanks with the field samples because of complex inoculation,
handling and storage procedures involved in handling biological organisms.
Laboratory analyses. Typically, counts are made on 20% of the nematodes extracted
from the 500 ml soil sample. For every 20th sample identification and enumeration will be
repeated 1) on the same subsample (20% of total) and 2) on a second subsample from the
same soil sample. These repeated measurements permit estimation of the components of
variance associated with identification and within-sample variability, respectively.
A representative sample of nematode families from each sample will be preserved in 10%
formalin in 25 ml vials with 0.5 to 1.0 ml of glycerin added and the vial sealed with paraffin
wax (M. Noffsinger personal communication) and stored at room temperature (Daykin and
Hussey 1985). The preserved samples will be kept for four years and utilized if further
information or confirmation is needed.
The data from the nematode enumeration laboratory will be sent as a hardcopy (Figure
5.3-2) to the soil biologist of ARG who will arrange to have the data key-punched and make
the necessary index calculations. After the data are entered into the computer, verified and
validated manually and examined for conformity to expected ranges of values, they will be
combined with the other soils data described in Section 5.2.4 under deputization by NASS at
their State Office.
81
-------
Figure 5.3-2. Example data sheet that will be completed by the nematode enumeration
laboratory and sent to the soil biologist of ARG.
EMAP-Agroecosystems, 1509 Varsity Drive, Raleigh, NC 27695, 919/515-3311
1993 Pilot Study in Region 7
Page
of
FAMILY
Sample
Alaimidae
Anatonchidae
Anguinidae
Aphelenchidae
Aphelanchoididae
Bastianidae
Belondiridae
Betonolaimidae
Cophalobidae
Criconematidae
Cyatholalmidae
Diphtherophoridae
Diplogastoridao
Dfploscapteridae
Dorylaimellidae
Dorylairnidae
Hemicycliophoridae
Heteroderidae
Hoplolaimidae
lotonchuldae
Leptonchidae
Longidoridae
NUMBER PER 500 ML OF SOIL
#
#
#
#
#
#
#
82
-------
Microlaimidae
Monhysteridae
Mononchidae
Mylonchulidae
Nygolaimidae
Panagrolaimoidea
Plectidae
Pratylenchidae
Prismatolaimidae
Rhabditidae
Teratocephalidae
Trichodoridae
Tripylidae
Tylenchidae
Tylencholaimellidae
Tylencholaimidae
'
-
Table 5.3-2. Reporting Units, Precision and Expected Ranges for Nematode Indices across
Region's of North Carolina (December 1991)
Parameter
Reporting precision3
Expected range (median)b
Shannon0
MI
PPI
1.00
1.00
1.00
1.38-3.79 (2.77)
d
0-4.36 (3.03)
a Number of significant decimal places ;
b Expected concentration ranges in reporting units for soil samples, based on the 1st, 95th, and
(50th) percentiles of data collected from the December 1991 survey; n = 122,
0 Diversity of trophic groups.
d Couldn't calculate
83
-------
Table 5.3-3. Data Quality Objectives for Within Sample and Within Field Variance. Data
objectives based on a preliminary experiment (Neher et al. 1993).
Within Samole3
Parameter
Shannon6
MI
PPI
SDC
0.27
0.06
0.06
%cvd
3.04
9.27
7.68
Within Field"
SD
0.16
0.48
0.42
%cv ,
7.37
15.64
12.15
a median values of 20 pairs of duplicate, composite samples from two fields
b average values for 30 composite samples collected for each of two fields
c standard deviation
d (standard deviation/mean) x 100
0 diversity of trophic groups
5.5.5. Metadata Requirements
In addition to data used for analysis, metadata will be recorded to permit future
interpretation of the database. Metadata will include methods of analysis, reporting unijs,
whether data are numeric or characters, name of analytical laboratories, and comments
recorded during sampling or processing procedures (Table 5.3-4) as well as names of families,
taxonomic references for trophic groups and maturity groups.
Table 5.3-4. Metadata for Biological Analysis of Soils in the 1993 Region 7 Pilot
Variable
Type
Anal. Method
Lab
Comments
Shannon"
MI
PPI
Numeric
Numeric
Numeric
Cobb's/c_f
Cobb's/c_f
Cobb's/c_f
NOFF
NOFF
NOFF
8 diversity of trophic groups
84
-------
5.3.6. Data Analysis and Integration
Several indices will be computed for the nematode community in each soil sample.
Cluster or biplot analyses (Hodda 1986, Rawlings 1988) will be used to determine how well
the indices behave to characterize relative health of soil. Based on preliminary studies, the
follow indices had the lowest within sample variation and highest between field variation and
are, therefore, chosen for comparison in the 1993 Region 7 Pilot Study:
o Shannon index of trophic diversity (Shannon and Weaver 1949, Ludwig and
Reynolds 1988);
o Maturity index for free-living nematodes (MI) (Bongers 1990); and
o Maturity index for plant parasitic nematodes (PPI) (Bongers 1990).
Diversity of taxonomic families and feeding preferences will be estimated using the
Shannon diversity index, (A/7), a diversity measure giving more weight to rare taxa, Nl= exp
[-ZP,.(lnP,.)], where P, is the proportion of trophic group i in the total nematode community
(Ludwig and Reynolds 1988). The MI is calculated as the weighted mean of the values
assigned constituent nematode families (and the genera and species they contain) (Bongers
1990):
MI or PPI= (L v; * yj.)/nwhere v~the colonizer-persister (c-p) value assigned to family i,.f=ibs
frequency of family i in a sample, and n=total number of individuals in a sample.
The variance structure will be characterized into within field variation and within
sample variation using cumulative distribution! functions (section 4.4.1). The CDFs are useful
when the cumulative extent of some resource is less than, or equal to, a specified percentile of
the data.
Biplots and canonical correlation procedures will be used to explore associations between *
nematode community indices and associated soil properties (Section 5.3.2). Biplots
graphically illustrate principle components; associated properties have vectors that project in a
similar direction and length in space. Canonical correlation can quantify the ability of soil
properties to predict nematode community indices and vice versa.
Interpretation of indices. Generally, higher diversity index values indicate greater
diversity. Although the association of diversity to stability of an ecosystem is debated, greater
85
-------
diversity may have other implications when applied as the diversity of trophic groups of
nematode communities in agroecosystems. Agricultural fields are characterized by an
abundance of bacterivorous and plant-parasitic nematodes, moderate abundance of fungivorous
nematodes, and a low frequency of omnivores (< 5% total nematodes) and predators (< 2% of
total nematodes) (Wasilewska 1979). Theoretically, small increases in heterotroph (omnivore)
biomass help re-establish system equilibrium and counteract perturbation (O'Neill 1976). The
presence of predators lengthens food-chains resulting in greater stability of the soil ecosystem,
and their numbers increase when conditions are stable (Wasilewska 1979). Given this
theoretical background, higher diversity of trophic groups may imply a presence and relative
abundance of omnivores and predators. Due to the typically unequal distribution of trophic
groups within nematode communities in agroecosystems, the Shannon index may be more
applicable than the Simpson index of diversity (Simpson 1949) because the Shannon index
gives weight to rare taxa or in this case, omnivores and predators. Nevertheless, high
numbers of bacterivores and fungivores infer rapid decomposition rates (especially when
Rhabditis spp. are abundant), and may be associated with low organic matter and with either
low or high populations of bacteria or fungi. Microbial populations may be decreased by
nematode feeding or increased by the feeding activity and feces of nematodes (Wasilewska
1979).
Maturity indices are based on whether particular nematode families are colonists
(generalists or r-strategists) or persisters (specialists or ^-strategists) (Bongers 1990, Ricklefs
1990) and, therefore, reflect relative disturbance. Disturbances include cultivation, agricultural
chemical applications and soil compaction. A lower maturity index for free-living nematodes
indicates more disturbance i.e., mostly colonizers and few persisters. A higher maturity index
for plant-parasitic nematodes reflects increased plant production.
For comparative purposes, nematode community indices will be calculated for 24
rangeland sites in addition to the fields of annually harvested herbaceous crops. Rangeland
sites will be selected that occur near sampled crop fields within a NASS segment (~.se Section
4). Rangeland sites would not be disturbed in the manner of cropped land and therefore
provide baseline values for maturity and diversity indices. Comparing nematode communities
in rangeland and cropped land will also allow evaluation of this indicator across ecosystems.
86
-------
5.4. Land Use and Cover
5.4.1. Introduction
A concept central to the field of landscape ecology is that the spatial structure of a
landscape affects the flow of energy and materials, and the movement of organisms, among
its components. The influence of a landscape's spatial structure is apparent at many scales.
There are interactions within an agroecosystem, such as those between a hedgerow and an
adjacent field; and the cumulative effects of agroecosystems in a landscape on, for example,
water quality and the habitat of far-ranging species. /Landscapes are also characterized by
temporal patchiness on many scales. There are annual changes in agricultural land use, as well
as decades-long processes as land cycles between agriculture and other uses.
Changes in land use patterns may foreshadow ecological change. For example:
o a change in acreage planted to chemical-intensive crops might affect water quality in
surrounding areas;
o removal of hedgerows, windbreaks, and shelterbelts may lead to increased soil erosion;
o bringing marginal farmland into production may lead to reduced productivity and
increased erosion; and
o changes in the amount and spatial structure of non-cropped land areas in the landscape
may affect populations of plants and animals that utilize those areas.
Land use changes may also reflect changing ecological conditions. For example:
o global climatic changes may bring about major shifts in cropping regions or cropping
patterns within regions; and
o degradation of soil or water quality may lead to the abandonment of cropped land.
Two closely related indicators address these issues:
o Land use and cover: an accounting of the amount of land in various land use and cover
categories. The remainder of this section focuses on this indicator.
87
-------
o Landscape structure: an analysis of the spatial structure of the various components of
landscapes. This research effort is part of the 1992 pilot in North Carolina and will be
continued as part of that pilot. No new work in this area is planned for the 1993 pilot.
5.4.2. Resource Classes
Agroecosystems may be comprised of several broad categories of land use and cover
which have been defined as resource classes. Resource classes are:
o Annually harvested herbaceous crops
o Perennial fruit and nut crops
o Permanent managed pasture (including grazing animals)
For purposes of agroecosystem extent, the land area in the above resources will ultimately be
summed. However, in 1993 only annually harvested herbaceous cropland will be accounted
for using June Enumerative Survey (JES) data collected by NASS. Also, numbers and uses of
farm ponds will be determined in the JES.
During the 1993 JES an initial attempt will be made to. distinguish rangeland from
permanent pasture based on frequency of fertilization;' NASS presently does not differentiate
between them.
NASS area frame materials and AVHRR data will be used to produce maps showing the
location of Nebraska's agricultural lands.
-------
5.4.3. Data Acquisition
Area Frame Material
The NASS area frame (Cotter and Nealon 1987) provides complete coverage of the
conterminous United States and Hawaii. Sampling frames are developed by state and are
currently updated every 15-20 years. The components of the area frame are summarized
below. Detailed information, including the strata used for the Nebraska frame, may be found
in the Design and Statistical Considerations section of this document, and in Cotter and
Nealon (1987).
o Strata: A state's land area is stratified according to intensity of cultivation.
Stratification is performed by county.
o PSU: Strata are further subdivided into primary sampling units (PSU). The size of the
PSU's varies by stratum, but is 15-20 square kilometers for most cultivated strata. A
random sample of PSU's is drawn to represent each stratum. PSU boundaries are
digitized by NASS.
o Segment: All selected PSU's are further subdivided into segments of approximately 2.6
square kilometers each. One segment is selected at random from each selected PSU.
The resulting set of segments comprise the NASS sample.
The NASS stratification of land area provides a framework in which to analyze long-term
changes in land use patterns over large geographic areas. Strata maps for the state can be
created by combining county strata maps using GIS techniques. Because the strata represent
very broad categories of land use intensity (e.g., 15-50% agriculture), these maps will directly
reflect only large changes in agricultural land use intensity. For the 1992 pilot, a procedure
for creating a geographic information system (GIS) coverage based on the NASS strata was
developed using the area frame for North Carolina. This procedure will be used in 1993 to
develop a strata map for Nebraska based on the current frame for Nebraska, which was
developed in 1983. Table 5.4-1 summarizes the steps needed to create an ARC coverage of
the NASS area frame.
89
-------
Table 5.4.1. Steps to convert NASS area frame to ARC format.
Step
1) For each county, NASS Area Frame Division registers PSU
map to latitude / longitude. QA checks applied by NASS.
2) For each county, NASS Area Frame Division converts PSU
map from internal to DLG format.
3) DLG files shipped to ARG with paper map and PSU area
listing for each county.
4) DLG maps converted by ARG to ARC format and strata map
for each county produced. If a county does not convert cleanly,
the county map is plotted on paper and returned to NASS for
clarification.
5) ARC county coverages edge-matched by ARG to provide
seamless PSU map for the state. QA check of boundaries with
other coverages of state and county borders.
6) Dissolve PSU boundaries between like strata to provider
seamless strata map for the state.
7) Provide complete documentation of procedure used .to create
coverages. Also document all GIS files created according to
ASTM standards.
Target
Completion
3/30/93
4/15/93
4/30/93
7/15/93
8/15/93
9/15/93
10/15/93
Actual
Completion
-
AVHRR Data
Land cover maps derived from AVHRR data at the USGS EROS data center may also be
used to analyze broad-scale land cover patterns. Although AVHRR data may some day allow
detection of changes in land cover over large geographic areas, the data have yet to be
assessed for accuracy by the USGS (work is underway). Change detection is not possible
without information about the accuracy of the data, so use of AVHRR will be limited to maps
showing the approximate location of agroecosystem resources.
June Enumerative Survey (JES) Data
Land use data for all selected sample segments are collected annually by NASS during the
June Enumerative Survey (JES). Land within each segment is classified into the categories
shown in Table 5.4-2. As described in the Design and Statistical Considerations section of
this document and in Cotter and Nealon (1987), these values are expanded to give land use
90
-------
Land Use Classification
estimates for the entire state. It is anticipated that JES data for Nebraska will be available
from NASS in August 1993. The entire
Table 5.4.2. NASS JES land use classification. Nebraska JES sample will be utilized to
calculate land cover estimates. Each JES
record is uniquely identified by segment
number and field ID; the latitude and
longitude of the segment centroid will
also be provided. These data provide
extensive information about the land
; used for agricultural production and very
, little information about other components
of the landscape. Consequently, these
data will be used primarily to analyze
changes in land used for agricultural
production.
Cropland, by crop
Permanent pasture (includes rangeland)
Pastured cropland
Idle cropland
Occupied farmstead or dwelling
Other (woods, waste, roads, ditches, etc.)
Table 5.4.3. Steps to acquire JES data.
Step
1) ARG develops survey instrument with NASS.
2) NASS obtains OMB approval.
3) JES data collected by NASS enumerators.
4) JES data released to ARG.
Target
Completion
1/15/93
3/30/93
6/15/93
8/15/93
Actual
Completion
1/31/93
Unknown
6/15/93
For 1993, JES data will be used to estimate the extent of annually harvested herbaceous
cropland and farm ponds in Nebraska. Also, an initial attempt to distinguish rangeland from
permanent pasture will be made during the 1993 June Survey. During a workshop in January
1993, it was determined that fertilization is what distinguished rangeland from permanent
pasture, with permanent pasture being fertilized at least once in a five year period and
rangeland being fertilized less frequently than once every five years. For all land identified as
permanent pasture, the NASS enumerator will mark off areas that meet this definition of
rangeland and record their acreage.
91
-------
5.4.4. Logistics and Quality Assurance
No special field sampling is required. Some QA aspects are discussed under data
acquisition. Standard NASS QA procedures will be used during administration of the JES.
QA procedures used by NASS for area frame development are documented in Cotter and
Nealon (1987).
Documentation of QA procedures used to develop GIS coverages from remotely sensed
data will be obtained from the providers.
These documents will be available at the ARG headquarters in Raleigh, NC.
5.4.5. Metadata Requirements
GIS Coverages
All GIS coverages will be documented in accordance with ASTM Draft Proposed
Specifications for Meta-Data Support in Geographic Information Systems (August 1991),
which has been adopted as a standard by the GIS Team of the EMAP Information
Management Task Group. The manner in which these data will be stored has not been
determined.
JES Data
NASS will provide metadata for the June Enumerative Survey. For each data element, at
least the following must be provided:
Variable name
Brief description
Data type (integer, real, character)
Measurement type (categorical, nominal, ordinal, interval, ratio)
Definition of categories (for categorical and nominal data)
Units (for ordinal, interval and ratio data)
Data collection method
Error information
92
-------
5.4.6. Data Analysis
An indicator of Agricultural Land Use Intensity, based on the NASS area frame, will be
calculated for the state of Nebraska. The area frame will also be used to develop a GIS
coverage of the NASS strata that may be used as a data layer in other GIS analyses.
Indicator 1) Agricultural Land Use Intensity: report area and area! proportion of land in each
stratum.
Source of data: NASS Area Fram Design Information
Summary statistic for segment: not applicable
Sampling method: entire state covered; not sampled
Variance structure: base map accuracy, digitizing, land cover classification
Trend to be detected: long-term (15-20 years) changes in land use
Base period: 1983, year of current area frame for Nebraska
Nominal and Subnominal: not appropriate
JES data will be used to calculate indicators of Annually Harvested Herbaceous Cropland
Extent and Annually Harvested Herbaceous Cropland Diversity. The number of different
crops within a sample unit is obtained in the JES and will be used in the diversity calculation.
The values of these indicators will be reported and tracked over time. The entire Nebraska
JES sample will be utilized to calculate these indicators.
Indicator 2) Annually Harvested Herbaceous Cropland Extent: report estimated total area of
annually harvested herbaceous cropland for Nebraska. Crops qualifying as annually harvested
herbaceous cropland are the same ones from which fall samples are selected. (See Table 5.1-
1)
Source of data: JES
Summary statistic for segment: acres of annually harvested herbaceous cropland
Sampling method: see Design and Statistical Considerations, Section 4
Variance structure: see Design and Statistical Considerations, Section 4
Trend to be detected: annual changes in production land use
Base period: 1993
Nominal and Subnominal: not appropriate
Indicator 3) Annually Harvested Herbaceous Cropland Diversity: calculate a crop diversity
index for each segment and for the entire state using the Shannon-Wiener diversity formula.
93
-------
Source of data: JES
Summary statistics for segment: proportion, p{, of annually harvested herbaceous cropland
in each annually harvested herbaceous crop. This will be calculated at the segment level (i.e.,
one for each segment) and for the entire state. Plot the distribution of segment level diversity.
Sampling method: see Design and Statistical Considerations, Section 4
Variance structure: The variance formula for the Shannon-Wiener index is valid only for
diversity of fields (i.e., N is the number of fields in the sample); it does not apply when the
diversity is weighted by land area, as it is here. The variance for this is unknown and will
likely be calculated using a Monte-Carlo method.
Calculations: Shannon-Wiener formula
Trend to be detected: annual changes in production land use diversity
Base period: 1993
Nominal and Subnominal: unknown
Indicator 4) Farm Pond Extent: report estimated total number and area farm ponds in
Nebraska.
Source of data: JES
Summary statistic for segment: number and acres of farm ponds
Sampling method: see Design and Statistical Considerations, Section 4
Variance structure: see Design and Statistical Considerations, Section 4
Trend to be detected: annual changes in farm pond extent
Base period: ,1993
Nominal and Subnominal: not appropriate
94
-------
5.5. Agricultural Pest Management
5.5./. Introduction
In the 1993 Nebraska Pilot, pest management information will be collected in the fall
survey questionnaire (Appendix 5). Information on the type and amount of each pesticide
used on the selected field will be collected from the grower. The target insect pest also will
be identified for each insecticide application. In addition, questions will be asked about the
fanner's familiarity with the concepts and practices associated with Integrated Pest
Management (IPM). No in-field assessments are planned for pest management until the 1994
pilot studies.
The pesticide information will be used primarily as an associative variable during this
year's pilot. For example, the soil biotic diversity (Section 5.3), being assessed using indices
of nematode community structure, will use pesticide (especially nematicide) information as a
covariate in statistical analyses. Pesticide information also may be used in the development of
the crop productivity index (Section 5.1); however, the mechanics of how this will be
implemented have not yet been determined. The pesticide information possibly will serve
also as a stressor indicator. For example, the pesticides may be divided into groups based on
toxicity to certain faunal groups, such as those pesticides toxic to beneficial insects,
pollinators, birds, and mammals. The value of such groupings is currently being explored in
the analysis of the data from the 1992 EMAP-Agroecosystem Pilot.
Pesticide application information is inherently difficult to collect. Growers can be
reluctant to disclose this information because of the public's perception of pesticide problems.
Another purpose for including the pesticide questions in the 1993 pilot will, therefore, be to
determine our ability to collect this information.
In this year's pilot, we will be asking the grower the target insect pest for each insecticide
application (See table 5.5.1). An important idea in insect pest management is that of the "key
pest." A particular crop is usually attacked at any one time by only one or two insect or
mite species in numbers such that the pest needs to be managed by chemical inputs. These
are called the key pests and are usually crop specific.
95
-------
Table 5.5.1. Key pests for several major crops in Nebraska
Small grain: Barley, Rye, Oat, Winter Wheat
Russian wheat aphid
Wheat curl mite
Greenbug
Hessian fly
Chinch bug
Soil cutworms
Bean, Dry Edible
Mexican bean beetle
Flea beetle
Colorado potato beetle
Soil cutworms
Green peach aphid
Com
Corn rootwonn (adult)
Corn rootwonn (larvae)
European Corn Borer
Spider mite
Western bean cutworm
Soil cutworm
Alfalfa Hay
Alfalfa weevil
Potato leafhopper
Soil cutworms
Clover leaf weevil
Blister beetles
Potato
Wireworms
Flea beetles
Colorado potato beetle
Soil Cutworms
Green peach aphid
Sorghum (Milo)
Cinch Bugs
Greenbug
Wireworm
Corn leaf aphids
Soybeans
Bean leaf beetle
Green clover worm
Looper caterpillar
Woolybear caterpillar
Spider mites
Sugar Beet
Sugar beet root maggot
Sugar beet root aphid
Army cutworm
Wireworms
Source: Robert Right, University of Nebraska and Gary Hain, Nebraska Extension, Personal Communication
Insecticides are often used for a specific pest. By asking the growers which chemicals were
used and what the target organisms were, crop specific assessments can be made about the
spatial extent and pattern of pest problems. This, of course, does not provide an assessment
of the intensity of the pest problem. Also, using questionnaire data of this type to determine
which pests are present is based on grower perception and attitude and is not a direct
measurement of which pests may actually be in the field. For example, one problem with
using insecticide application and target pest as a surrogate of pest spectrum is that growers
may apply pesticides prophylactically in anticipation of a pest problem that does not currently
exist. This potential problem will be explored by asking the level of grower's awareness of
and participation in the practices of integrated pest management (IPM). The IPM concept
96
-------
promotes the use of chemicals only after the pest has caused crop damage above some
economic threshold. Scouting, crop rotation, the use of beneficial insects, and adjusting time
of planting are a few of the strategies used to reduce the amount of pesticide that is used on a
particular crop. By knowing the pesticide applied to the field, the target pest, and whether
IPM practices are being used, an indication of the pest spectrum may be obtained for the state
of Nebraska.
The use of IPM is thought to be a sustainable agricultural practice. The spatial extent and
frequency of implementation of IPM by the grower may be indicative of lower pesticide use
and improved condition in agroecosystems. Therefore, IPM will be explored as a correlative
measure with other condition indicators.
5.5.2. Data to be collected by NASS
With the fall EMAP questionnaire, NASS enumerators will collect data on the type, rate
and frequency of pesticide use including insecticides, fungicides, nematicides, and herbicides.
The crop treated, the number of acres treated, the mode of application and the time of
application will be recorded. NASS enumerators also will inquire about the target pest for
each insecticide application. The farmer's awareness of IPM concepts will be determined both
by asking about his or her familiarity with the term and whether typical IPM practices are
being used in pest management decisions.
5,5.3. Essential Complementary Data
Several data items will not be obtained in the fall questionnaire. For example, the
chemical grouping in terms of persistence, toxicity, mode of action and chemical formulation
will have to be obtained from published sources. .The spectrum of plants and pests against
which the pesticides are effective and the crops and pests for which they are registered in the
state of Nebraska will also be important for some planned analyses. This information will be
obtained from the Nebraska Department of Agriculture or Cooperative Extension Service
personnel.
5.5.4. Logistics
Logistics of administering the fall questionnaire will be handled by NASS. (See Chapter
7 for more details)
97
-------
5.5.5. Quality Assurance
Quality Assurance will be handled primarily by NASS (see Chapter 6 for more details);
however; the ARG will also evaluate the reliability of the data be checking 10% of the
response to compare the appropriateness of the target pest listed and the insecticide
application reported.
5.5.6. Data Analysis and Integration
Data analysis will involve the classification of pesticides into ecologically meaningful
groups such as persistence, toxicity, chemical formulation, mode of action, and spectrum of
pests affected. The spatial distribution of pesticide use will be explored using standard
statistical methods. Much of the effort for the pesticide information will be focused on
exploring correlations and associations between pesticide use and key indicators.
The prevalence and spatial distribution of IPM practices also will be assessed as will its
correlation with key indicators and the pesticide information.
5.5.7. Research Goals and Applications
We hope to answer several questions by taking this pesticide information:
1. Are response rates high enough to gather reliable information from growers on
pesticide use?
2. What is the frequency and spatial distribution of IPM practices?
3. Can pesticide use and target pest knowledge be used as an initial estimate of the pest
spectrum?
In addition to exploring these questions we will use this information as a covariate in the
analysis of other indicators such as the index of soil biotic diversity. It also may be used in a
crop productivity index; however, its utility in this application is still being investigated.
98
-------
6. Quality Assurance
6.1. Introduction
Decision makers, the public, and other EMAP data users must have a high degree of
confidence in the data and statistics generated by the EMAP-Agroecosystems Program. The
purpose of quality assurance is to ensure that the data will yield sound and unbiased
conclusions related to the principal questions being addressed. Quality assurance (QA) for the
Agroecosystem Program is being developed to assure the reliability and comparability of its
measurements within a given pilot and between various terrestrial EMAP resource groups. It
is recognized that the development of a QA program is an iterative process and must be
flexible to accommodate changes as new situations are encountered. Thus, information and
improvements learned from the initial 1992 Pilot project will be incorporated into the pilot
and quality assurance project plans for the 1993 Pilot project.
The quality assurance policy of the U.S. EPA requires that every monitoring and
measurement project have a written and approved quality assurance project plan (QAPjP).
The purpose of the QAPjP is to specify the policies, organization, objectives, and quality
assurance and quality control activities needed to achieve the data quality requirements of the
EMAP-Agroecosystems program. The overall EMAP-Agroecosystems QAPjP will be
prepared, submitted, and approved prior to the field sampling season of the 1993 Pilot project.
The QAPjP will include discussion on the required topics including: project description;
project organization and responsibilities; QA objectives; sampling procedures; sample custody;
calibration procedures and frequency; analytical procedures; data reduction, validation, and
reporting; internal quality control checks; performance and system audits; preventative
maintenance; procedures for assessing measurement system data quality; corrective actions;
and QA reports to management.
The 1993 Pilot Project in Region 7 is being developed as a cooperative effort between the
USDA ARS, USDA NASS, USDA SCS, and the U.S. EPA. Several different types of efforts
that require quality assurance/quality control measures to be implemented include: verbal
survey data collection by NASS, soil sample collection by both NASS and the SCS, and
analytical laboratory data that will be generated by the National Soil Survey Laboratory of the
SCS in Lincoln, Nebraska. The QA/QC procedures to be implemented for each of these areas
will be discussed in the following sections.
99
-------
6.2. NASS Survey Data Collection
The EMAP-Agroecosystems QA program will take advantage of QA procedures already
employed by NASS during the collection of verbal survey data from the JES and during the
Fall Sampling Survey. NASS views quality control as the process of eliminating as many
survey errors as possible. To limit errors, every survey process must be associated with some
type of quality assurance/quality control procedure. The major survey processes in the
Agroecosystem 1993 Pilot Project amenable to quality control considerations include: 1. area
sampling frame - their construction, maintenance, and sampling; 2. survey specifications and
questionnaire design; 3. training; 4. survey data collection; and 5. data processing, editing, and
review.
6.2.1. Area Frame Development
i.
General procedures for selection of Pilot segments according to either the NASS
rotational panel or EMAP hexagon scheme are presented in Section 4. Prior to drawing
segments, however, the area frame must be constructed. This activity is handled by NASS
and will be performed following standardized NASS protocols and QA procedures. Some of
the quality assurance methods in frame development are documented in Area Frame Design
for Agricultural Surveys (Cotter and Nealon 1987). These procedures are important for
ensuring that no land area is double-counted or unintentionally omitted, and that strata are
correctly identified. The GIS lead within the ARG will verify the selected PSUs to assure
mat they contain the appropriate EMAP hexagon centroid.
6.2.2. Survey Specifications and Questionnaire Design
Verbal survey data for the EMAP-Agroecosysterris program will come from two surveys
administered by NASS, namely, the JES and the Fall Survey. The JES is an annual NASS
survey designed to collect land use and management practice data. A standard set of survey
questions and specifications has been designed and used by NASS in the performance of the
JES. The ARG and NASS will work together to ensure that all the appropriate questions
required for both organizations are incorporated in the questionnaire to be used by the NASS
enumerators. It will.be the responsibility of NASS to ensure that the questions (special to the
EMAP-Agroecosystems program) are incorporated into the JES questionnaire forms.
100
-------
In contrast to the JES, the Fall Survey is a set of questions especially developed for the
EMAP-Agroecosystems program. Additional information needed by the EMAP-
Agroecosystems program, mainly in the areas of post-harvest concerns, such as crop yields
and management practices, will be presented to the farmers by the NASS enumerators to
obtain this information. The ARG and NASS will again work together to ensure that all the
appropriate questions required for both, organizations are incorporated in the questionnaire to
be used by the NASS enumerators and that the questions are in a proper format to obtain the
required data.
Both sets of survey questionnaires are included in this document in Appendix 2.
6.2.3. Training
For the JES, a national workshop was held in April 1993 and the state training school
was held in May 1993. NASS is responsible for conducting the workshops and for training
their enumerators to collect the required verbal survey data. NASS will also provide each
enumerator with an enumerator's handbook containing the questionnaires and other appropriate
forms to conduct the JES survey.
The Fall Survey, which will probably start in October (post-harvest sampling), will consist
of both verbal survey questions and soil sample collection. These two activities generally run
concurrently with each other or with the questions (and permission to soil sample) being
asked one day followed by soil sampling on the next day. NASS, the ARG, and EMAP-
Agroecosystems QAO will cooperate in planning and running the training session for these
activities.
Similar to the JES, a training session will be held within one month prior to the initiation
of each field sampling program for the Fall Survey. During the training school, the
enumerators will review the questionnaire for clarity and completeness, learn how to collect
the soil samples by hands-on training in the field, and be provided with all the appropriate
handbooks, forms, and equipment necessary to start the Fall Survey sampling program.
6.2.4. Survey Data Collection
NASS has an established QA program that will be used during the collection of the verbal
survey data for the June Enumerative Survey (JES) and Fall Survey used by the EMAP-
101
-------
Agroecosystems program. Copies of the appropriate QA documents and survey evaluation
reports will be obtained from NASS and maintained by the EMAP-Agroecosystems QAO and
the ARG during 1993. A brief description of the NASS QA program is provided in the
following paragraphs.
Once field interviews begin, the supervisory enumerators are responsible for assuring that
data are taken correctly. The supervisors accompany new enumerators on their first day of
interviewing and meet with experienced enumerators after the first few interviews of each
survey. If there are any problems, the supervisor either instructs the enumerator individually
or holds a re-training meeting, if needed.
For both surveys, approximately two interviews from each enumerator's workload will be
checked by telephone follow-up. Questions from a worksheet will be asked, to verify that the
interviewer did contact the farmer, that a particular crop was grown, etc. For the JES, the
supervisory enumerator does an on-the-ground check of a couple of random farmers from
each enumerators workload, to be sure that field boundaries were drawn correctly. The
responsibilities of supervisory enumerators are given in the NASDA Supervisory Enumerator
Handbook (USDA NASS, 1990).
A third aspect of the NASS QA program is the measurement of crop objective yields.
The determination of objective yields is performed by specially trained NASS personnel.
These trained surveyors will visit a given field during the growing/harvest season and quantify
the yields of the given crop. The primary purpose for this determination is for comparison
with the farmer's verbally reported yield values.
6.2.5. Data Processing, Editing, and Review
Survey data are subject to a three-stage processing, editing, and review program. First,
the supervisory NASS enumerator checks the data for reasonableness and complete less. If
deficiencies are identified, the supervisor will return the questionnaire back to the enumerator
for resolution of the identified problems.
Once the revised and completed questionnaires are received by NASS, the questionnaires
are reviewed and edited by a statistician. The statistician reviews the forms for extraneous or
apparently erroneous values. Again, if unsatisfactory data are identified, they are returned to
102
-------
the supervisory enumerator who is responsible for correcting identified problem areas or
returning the questionnaires to the field enumerators for further editing.
After these two manual edits, data are entered into the computer where another detailed
edit is performed. Data entry is performed using a double-entry system in which two data
managers will enter the same dataset. The computer will then perform a comparison between
the two entered datasets and provide a print-out indicating where differences existed. When
differences are identified, the two data managers will be responsible for the resolution of the
problem and correction of the inaccurately entered data. Additionally, a computer program
will be run to verify that responses are appropriate to questions, to check for internal
consistency, and to determine if the data are within the expected ranges for Region 7 -
Nebraska. Problems discovered at this level are brought to the attention of the statistician for
resolution.
6.3. Field Sampling
Field sampling of soils for the 1993 Pilot Project in Region 7 will be performed by NASS
and the SCS. NASS enumerators will be responsible for the collection of the upper 20 cm (8
inches) of soil using a corer, whereas the SCS will be responsible for the digging of soil pits
(to approximately a 0.6 m depth) and the collection of special samples to be used for the
determination of the "physical health" of the soil. Surface horizon samples collected by the
SCS will be used for comparison purposes with the core samples collected by NASS to
determine the representativeness and validity of the core sample collection program. Each of
the quality assurance/quality control procedures associated with each organization's sampling
program will be discussed in the following sections.
6.3.1. NASS Soil Sampling
One of the first steps in assuring the quality and integrity of the collected soil samples
will be the development of a standard protocol for the collection of the samples. The protocol
developed for the 1992 Pilot Program in North Carolina will be used as the framework for
this effort. The 1992 Pilot Program NASS sampling protocol will be modified to include
necessary changes and' suggestions (for clarity) made by the NASS enumerators during and
after the 1992 Pilot sampling season was completed. The standard sampling protocol will be
included as an appendix in the QAPjP to be prepared for the 1993 Pilot Project.
103
-------
The NASS, the ARG, and the EMAP-Agroecosystems QAO will be responsible for the
training of the NASS enumerators prior to any field sampling for soils. This training will
occur during the month prior to initiation of the field sampling season. The training session
will include a verbal explanation of the purpose, goals, and procedures for the collection of
the soil samples followed by a hands-on field training session. During the field training
session, each enumerator will be required to set up the required transects and collect,
composite, homogenize, and bag soil samples as if he/she were collecting actual field samples.
Bach NASS enumerator will be provided with a copy of the sampling protocol manual during
the training sessions.
To ensure that the EMAP-Agroecosystems sampling protocols are being followed properly
by the NASS enumerators, at least two field system audits will be performed by the EMAP-
Agroecosystems QAO, NASS, and possibly members of the ARG. Field system audits will
be performed on at least two different field sampling crews. Additional audits may be
scheduled if time permits or if serious deficiencies are identified.
6.3.2. SCS Soil Sampling
Similar to the NASS soil sampling efforts, one of the first steps in assuring the quality
and integrity of the collected soil samples is the development of a standard protocol for the
collection of the samples. Due to the vast experience in the collection of soils by the SCS,
the standard SCS sampling protocol will be used for their soil sampling collection efforts.
The EMAP-Agroecosystems QAO will obtain a copy of the SCS soil sampling protocols and
maintain a copy on file for reference. Additionally, the standard sampling protocol will be
included as an appendix in the QAPjP to be prepared for the 1993 Pilot Project.
The SCS will be responsible for the training of the SCS soil samplers prior to any field
sampling for soils for the EMAP-Agroecosystems program. The soil scientists sent to the field
will be capable of collecting the necessary soil samples, characterizing/describing the soil
(from the pit), and will have sufficient field experience in determining the major (or
dominant) soil series of the map unit in which the sampling site is located. The selection of
the soil scientists to perform these functions will be at the discretion of the SCS.
Prior to field soil sampling, the soil series of each sample field will be determined by
comparing the NASS aerial photograph (on which the field has been outlined) with the most
recent SCS soil survey report. During the digging of the sampling pits (to be discussed), the
104
_
-------
SCS soil scientist will verify the identification of the soil series from which samples are being
collected.
The goal of the SCS soil sampling effort will be to dig small pits and collect specialized
samples by soil horizon (instead of just the top 20 cm). These samples will be analyzed for
the same parameters as the core samples plus bulk density, aggregate stability, available water
capacity (water retention difference), which cannot be determined on disturbed core samples.
The data from these samples will be compared to the data from the core samples to establish
the correlation and usefulness of collecting only the surface 20 cm of soil regardless of soil
horizon or if subsurface samples are needed, in conjunction with the surface core samples, to
monitor changes in the agroecosystem.
To ensure that the EMAP-Agroecosystems sampling protocols are being followed properly
by the SCS soil scientists, at least one field system audit will be performed by the EMAP-
Agroecosystems QAO, SCS, and possibly members of the ARG. Additional audits may be
scheduled if time permits or if needed.
6.3.3. Field Quality Assurance Measures
Two sources of field variation will be examined and assessed during the collection Of the
soil samples, namely, large-scale heterogeneity across a given field and short-range
heterogeneity within a given composite sample. To assess the natural variability of the entire
measurement system (i.e., the large-scale heterogeneity), on every sixth field, a second
complete transect will be collected and composited to form an additional sample. The
collection method will be identical to that used in the first transect sampled except that the
entry into the field will be from a different starting point (e.g., field corner). Additional detail
on the sampling procedure may be found in Section 4.2.
To assess the error component associated within a given composite sample (NOTE: a
composite sample in this case, is composed of the forty 20-cm core samples placed hi the
sampling bucket, disaggregated by hand, and mixed), split samples will be collected from
every twelfth field. Upon completion of the field preparation, the sample will be divided into
approximately two equal portions. Both portions will be submitted to the analytical laboratory
for analysis as two unique, double-blind, replicate samples. The split samples will also allow
for the assessment of the effectiveness of the in-field sample preparation process as it relates
to potential differences that may occur during subsampling the aliquots for nematode
105
-------
identification and enumeration and for soil physical and chemical characterization. Additional
detail on the sampling procedure may be found in Section 4.2.
6.4. Laboratory Analyses
To assess the error components associated with the analytical laboratory performing
physical and chemical characterizations, measurements of both QA (blind to the analytical
laboratory) and QC (known to the laboratory) samples and measurements will be performed.
A QA soil audit material may be included in with the routine soil sample batches to assess the
accuracy of the analytical laboratory. This sample is an actual soil sample that has undergone
extensive characterization and has established acceptance windows. The audit material will be
prepared to mimic the condition of the routine field samples and will be submitted at random
to the analytical laboratory in conjunction with the routine samples submitted by the NASS
enumerators.
The QC samples used to assess various error components at the analytical laboratory will
include: analytical replicates, reagent blanks, ongoing calibration check samples, and low-
level detection check samples. The QC measurements of instrument detection limits and
initial instrument calibration will also be established within the EMAP-Agroecosystems QA
program. The frequency of use and acceptance criteria for each QA/QC sample or
measurement will be specified prior to any sample analysis in the QAPjP to be prepared for
the 1993 Pilot Project.
To assess the error components associated with analytical laboratories performing
biological identifications and enumerations, such as are being performed for soil biotic
diversity (nematodes), the QA/QC procedures may include the collection of replicate field
samples, analytical replicates, examination of the relative abundance of organism identified
(for nematode communities) and secondary confirmation of organism identification by
independent experts.
6.5. Soil Audit Materials
During the 1993 Pilot Project, bulk A horizon for use in the development of a soil QA
material will be collected. This sample will be prepared and submitted to the analytical
laboratory with the routine soil samples to initiate the development of accuracy windows on
an actual soil sample. Upon completion of its initial characterization in 1993, the remaining
106
-------
soil will be used as a material to assess the accuracy of the analytical laboratory. During each
of the pilot and demonstration projects, a new bulk A horizon (and potentially some
subsurface B horizons) will be collected, prepared, analyzed, and used in a similar manner
during future pilot or demonstration projects.
6.6. Additional Data
Additional data will be collected for use in the EMAP-Agroecosystems program that
includes weather data from the National Oceanic and Atmospheric Administration (NOAA)
and conversion factors from either the SCS, NASS, or the USD A Cooperative Extension
Service (such as net primary productivity conversion factors and moisture contents, see
Section 5.1). The collection of these data are not within the scope of the EMAP-
Agroecosystems program; however, information on the QA/QC procedures used in the
collection and processing of these data will be obtained and data quality will be evaluated.
107
-------
7. Logistics
7.1. Introduction
Implementation of the Agroecosystem Pilot
Program will require detailed logistics planning,
including coordination and oversight of all support
and data collection activities. Table 7-1 lists the key
issues to be considered. The logistics activities are
closely tied to both QA and information management
for the pilot program. Logistics considerations for
each indicator are included in each indicator
subsection (Sections 5.1 through 5.5 of this
document). A schematic of the logistics for the pilot
is given in Figure 7-1. A Logistics Notebook is
being maintained by the ARG.with details on
logistics for the pilot.
Staffing
Design of Survey Questionnaires
Communications
Training
Safety
Sampling Schedule
Site Access and Reconnaissance
Procurement and Inventory Control
Field Operations
Laboratory Operations
Waste Disposal and Sanitation
Information Management
Quality Assurance
Cost Tracking
Review of Logistics
Table 7-1. Logistical issues in the
1993 Agroecosystem Pilot.
Figure 7.1. Flow chart of logistics activities for the 1993 Pilot Field Program.
UK JES data to plan for
Fall survey and sampling:
Select field*
Jrac Enumerate S*nty
plui added qtteitloas
(JM Katatioaal Panel
+77 Htiicim segments)
Conduct Fall survey and
sample so
Resolve Issues;
Finalize questionnaire;
Train/prepare for Fall
survey and sampling
(216 Relational Panel and/or
72 Hexaton segments)
Land use /land cover indicator
(entire JES sample in Nebraska)
\ARG CIS Iead\
Compile and edit
survey data
Analyze soil and
enumerate nematodei
Description of map units on a
subset of fields, and sampling
of soil from shallow pits
Calculate Indices and
summary statistics;
Supplemental studies
ea research indicators
Nematode Indices in
permanent pasture
Obtain external data
URGwilbARG-lMV
(sites near sample fields)
VARC soil blodh-eriity lead\
108
-------
7.2. Logistics and the NASS
A major goal of the pilot programs (1992 in Region 4, 1993 in Region 7) is to determine
whether the NASS enumerators can collect most of the field data required for the indicators
being tested in the pilot. The enumerators, operating within the NASS organization, will use
procedures selected and developed jointly by the ARG and NASS. From the standpoint of
logistics, working with NASS has several benefits. Based on the integrity and reliability of
their personnel, NASS has developed a good relationship with the agricultural community;
this relationship will greatly facilitate data collection. Additionally, NASS has an
infrastructure for the collection of agricultural data, including well-developed logistical
procedures and strict quality controls. Use of this infrastructure greatly reduces the resources
that would be needed for the ARG to develop similar procedures. The ARG is using the
pilots to define more completely the interactions between NASS and the ARG and to further
develop and refine logistics procedures for the long-term monitoring of our agricultural
resources. Some NASS procedures are documented in non-published sources such as the
Interviewer's Manual. Copies of these documents will be available for review at the ARG
headquarters in Raleigh, North Carolina.
7.3 Specific Logistics Elements
Any EMAP logistics planning needs to consider fifteen elements (Baker and Merritt
1991). These elements are discussed below as they apply to three pilot activities: the June
Enumeratiye Survey (JES), the fall survey, and soil sampling. Soil sampling will take place
during the fall survey period. An additional activity is discussed separately at the end of this
chapter: characterization of a subset of fields by scientists from the Soil Conservation Service
(SCS).
7.3.1. Overview
Table 7-2 lists the major activities involved in developing the 1993 Agroecosystem Pilot
Program and identifies the responsible party for each activity. The flow of the major
activities planned for the 1993 pilot is diagrammed in Figure 7-1, and a schedule is presented
in Table 7-3.
109
-------
AC27W3Y
KESPONSWUE PARTY
Developing indicators
Statistical design
Developing segments
Obtaining ancillary data
Testing sampling procedures
Developing survey questionnaires
Writing manuals:
- Interviewer's/Enumerator's
- Supervising & Editing
Training enumerators:
. - June Enumerative Survey. (JES)
- Fall survey and sampling
Equipment procurement - Survey
Equipment procurement - Sampling
Conducting the JES
Selection of sample fields
Fall survey
Data entry: surveys
Calculation of cost estimates
Soil sampling
Soil processing and analysis:
physical/chemical
nematode extraction and enumeration
Compiling of data
Indicator calculations and analysis
Sample Statistical Summary
Quality Assurance
ARG Indicator leads
ARG Statistical Team
NASS
ARGIM
NASS/ARG
ARG/NASS
NASS/ARG
NASS.
NASS/ARG
NASS
ARG / NASS / SCS
NASS Enumerators
NASS, according to
ARG specifications
NASS Enumerators
NASS
NASS.
NASS Enumerators
/ SCS Personnel
SCS Soil Survey
Laboratory (Nebr.)
N&A Nematode Identifi-
cation Service (Calif.)
ARG IM / NASS
ARG
ARG
NASS/EPA
Table 7-2. Activities in the 1993 Agroecosystem Pilot Program
7.3.2. Staffing
The Agroecosystem Resource Group will maintain a scientific and statistical staff for the
analysis and synthesis of the pilot data. The ARG consists of a group of scientists located in
Raleigh, North Carolina and a number of other individuals at locations such as Las Vegas,
Nevada; Athens, Georgia; and Washington, DC. Appendix 1 lists the names and addresses of
the Agroecosystem Resource Group members. Most of the Raleigh group are employed
110
-------
through North Carolina State University (NCSU). The Technical Director is on assignment to
the USDA-Agricultural Research Service (ARS) from NCSU.
Pilot activities will be coordinated from Raleigh where the Technical Director and most
of the indicator leads are stationed. Secondary coordination will come from the NASS state
office in Nebraska. Responsibility for the development of indicators and indices of
agroecosystem health will reside with the ARG staff.
Table 7-3. Tasks with schedule for conducting the 1993 Pilot Field Program in Nebraska.
Tasks
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
Develop fall questionnaire.
Supply enumerator training manual for fall survey and sampling.
Assure that logistics, QA, and information management strategies
are in place.
NASS enumerator training schools: (a) for the June Survey; (b)
for fall survey and sampling.
Obtain all necessary equipment and materials for the pilot.
Administer questionnaires and take field samples: (a) June
Enumerative Survey; (b) fall survey and sampling. Perform QA
audits.
Send soil samples from the field to contract laboratories for
analysis.
Work with NASS on data management and analysis.
Receive data from laboratories on (a) soil physical/chemical
characteristics and (b) soil biotic diversity (nematodes).
Compare the cost, variance, and biases of the two design
approaches.
Data analysis: provide statistical summaries, compare cumulative
distribution functions, explore spatial patterns, and examine
statistical properties. (
Prepare pilot report and prototype annual statistical summary.
Schedule
May - July 1993
August - October 1993
March - September
1993
May 1993;
October 1993
April - August 1993
June 1993; October -
November 1993
October - November
1993
August 1993 - March
1994
January - April 1994
February - June 1994
February - July 1994
May - July 1994
August 1994
111
-------
Interagency agreements are in place between EPA and ARS, NASS and SCS. the SCS.
An SCS employee will be joining the ARG in November 1993. The work of administering
questionnaires and collecting soil samples will be done by NASS enumerators, hired on a
part-time basis through the National Association of State Departments of Agriculture
(NASDA). Enumerators are often local farmers, members of farm families, retired rural
residents, or other persons with an interest in agriculture. They are located throughout each
state, which makes data collection quicker and more efficient. NASDA and NASS will be
responsible for hiring and supervising enumerators and will handle the corresponding payroll
and other administrative functions.
For the Agroecosystem pilot, there will be a slight expansion of the standard JES ~ 77
segments will be sampled on the EMAP hexagon design that would not otherwise have been
visited. These hexagon centroids (1/4 sample) fall within 58 of Nebraska's 93 counties. The
EMAP June questions will be included on all hexagon segments and on all 390 NASS JES
segments. All of these segments will be eligible for selection for the fall survey and
sampling. The expected number of fields to be sampled in the fall is 288 (216 on the
Rotational Panel plus 72 from the hexagon design). It is expected that at most 35
enumerators will be needed in the fall.
Many hexagon-selected segments in the western part of the state are expected to be in
rangeland. If a segment is visited in June and has only rangeland, it will not be eligible for
selection in the fall (see Table 4-2 and Table 5.1-1).
Soil sampling requires a certain-amount of strength, but this should not-be a problem for
most enumerators. It is most likely that enumerators will work alone during the survey and
sampling. They will follow NASS guidelines, including those regarding confidentiality, and?
will follow procedures outlined hi the manuals to be developed by the ARG and NASS. Soil
physical/chemical analyses will be performed by the SCS National Soil Survey Laboratory in
Lincoln, Nebraska. Separate samples will be sent to the N&A Nematode Identification
Service in Davis, California for extraction and enumeration of nematodes. These laboratories
are responsible for their own staffing. Teams from the Nebraska office of the SCS will visit a
subsample of fields to do a more detailed description and to dig shallow pits for soil
characterization (Section 7.3.17 and Appendix 4).
112
-------
7.3.3. Design of Survey Questionnaires
Two questionnaires will be used for data collection during the pilot. The first will be
during the June Enumerative Survey (JES), an annual NASS activity. The ARG will obtain
some fine-scale land use/ land cover data from the JES (Section 5.4). In cooperation with
NASS, the ARG has developed supplementary questions for the June survey. These have
been added to the regular questionnaire for the rotational panel. A shorter survey was devised
for the hexagon segments (see Appendix 2). The added questions are: 1) line 7a of the Crops
and Land Uses page (idle land in government programs); 2) line lOa of that same page (acres
irrigated/ to be irrigated by well water); 3) the table for recording size and primary use of
each pond; and 4) a question to distinguish rangeland from other permanent pasture, based on
the absence of fertilizer over the last five years. Note that question 10. (acres irrigated and to
be irrigated) was an addition to the North Carolina JES in 1992 but is standard in Nebraska.
The overall goal of the fall questionnaire will be the same as in 1992: to provide data on
crop yields, cropping sequence, fertilizer, pest management, irrigation, tillage system and other
practices. These questions will be asked for selected fields (see Sections 4.3 and 5.1.2 for
selection criteria). The ARG is working closely with NASS to design the Agroecosystem
1993 pilot questionnaire (i.e., fall survey). A copy of the questionnaire is found in
Appendix 2.
During the fall interview, the enumerators will ask farmers for permission to do one or
more additional activities. On all selected fields, they will ask permission to take soil samples
(Sections 5.2 and 7.3.10). On forty of the hexagon-selected fields, they will ask for the
operator's written, informed consent to a visit by SCS scientists, who will describe the sites
and dig small pits for detailed soil characterization (see Sections 5.2 and 7.3.17).
7.3.4 Communications
During surveys, communication will follow NASS procedures for maintaining contact
between the Nebraska NASS office, supervisory enumerators and the other enumerators" in the
field. This will include reporting of work hours and mileage, progress of surveys, and similar
information. The ARG will work through its liaison hi the NASS office.
113
-------
The seven digits of the id represent five attributes.
If the number is ABBBCDE, then
Sample and equipment tracking. NASS will assign a seven-digit number to each sample.
The number will contain certain information, such as whether the sample comes from the
rotational panel or hexagon
frame and whether it is from
a second transect or a split
sample (Table 7-4). The
analytical laboratory will not
know locations of sample
sites. The sample number,
which for convenience may
be prefixed by the letter "C"
(for chemical/physical) or
"N" (for nematode assay),
forms the soil sample ID.1
Each soil sample ID will be
preprinted on two labels: one
for the tag on the sample bag
and the other for the record-
digit
A
BBB
C
D
E
represents
type of analysis
sample number
design/sampling
scheme (see App. 3)
transect #
split code
having values of
1 = chemical
2 = nematode
sequential
1,2,3 = NASS 1,2,3
4,5,6 = HEX4,5,6
1 or 2
0, 1, or 2
Table 7-4. Description of seven-digit soil sample identifier.
keeping form at the end of the questionnaire. Other labels will be prepared for the face page
of the questionnaire and for the kit envelopes given to enumerators. NASS will print all
labels. The record-keeping form will have space for recording five pieces of data next to
each label: date sampled, date mailed, shipper tracking number, whether the field was
recently tilled (y/n), and comments. Record-keeping sheets will be mailed to the Nebraska
NASS office at the end of each week, where the information will be entered into a data set.
A portion of these data (sample id, date sampled, date mailed, and tracking number) will be
sent on a diskette to the ARG logistics coordinator.
For the transect samples taken by NASS, the SCS Soil Survey Laboratory will provide
the ARG with a weekly log on diskette. It will contain date received, sample ID, nd
comments. The exact format will be decided by negotiation among laboratories, the ARG
logistics officer, and the ARG information manager. The nematode enumeration laboratory
'Note that a "soil sample" is a physical item, but a "sample" or "field" is a draw from
the statistical frame. Even the latter may occur more than once in an agricultural field, but it
is unlikely, given the sampling density.
114
-------
will fax a weekly list of sample numbers and dates received. With the lists of samples sent
and received, the ARG logistics officer will look for missing samples, transcription errors, etc.
Emergencies and problems. Emergency communication in the field will be handled
according to NASS protocol. The first priority is the health and safety of the enumerators.
There are non-emergency problems which will inevitably arise in the field. Enumerators will
be given spares of those parts of the sampling kits that are most likely to break. Some spare
equipment will be kept at the NASS Nebraska office. If necessary, other equipment can be
ordered on short notice by having the NASS office contact the ARG.
7.3.5. Training
NASS enumerators are part-time employees with a wide range of educational
backgrounds. Prior to participating in any data collection efforts, they undergo training in
sample and data collection methods. Training of enumerators will be the joint responsibility
of the ARG and NASS. The NASS will be responsible for enumerator training for survey
questionnaires. A training school was held in Lincoln in mid-May for the Nebraska June
Enumerative Survey (JES). Training includes background information and a review of the
survey instrument. Group and one-on-one practice exercises are conducted to strengthen the
enumerators' knowledge of the questions. The Interviewer's Manual (USDA NASS 1993) is
an important supplement and aid to the training.
A two-day training session for fall activities will be held in Lincoln in mid-October 1993.
Earlier, there will have been a practice training session with several enumerators, to identify
weaknesses and make necessary changes in the training or procedures. NASS will be
responsible for training enumerators for the collection of post-harvest survey data; the ARG
will assist in training enumerators in soil sampling techniques. These techniques will be
taught in the classroom and field. An enumerator's manual for the fall collection of
Agroecosystem data will be developed by NASS and the ARG, using the 1992 manual as a
starting point. A first draft will be prepared by mid-August, with the completed version to be
ready in October. It will include information on the background and objectives of the pilot
program and will define specific interview and sampling procedures for the fall survey and
sampling period.
115
-------
7.3.6. Safety
The safety of NASS enumerators is a prime concern. Conduct of surveys will .not
present any hazards that are unfamiliar to the enumerators, but soil sampling will be a new
activity for many of them. Work in Agroecosystems is generally less dangerous than for
other EMAP resource groups, which often operate in remote locations. Enumerators live in
the same area that they work, and should know if and where such hazards as poisonous
snakes, bee stings, and tick-borne diseases (e.g., Lyme, Rocky Mountain Spotted Fever) may
be encountered. Common sense should guide decisions about dressing for the weather and
working under extreme conditions (e.g., lightning storms). Each enumerator has a first aid kit
and a snake-bite kit from previous surveys. Safe use of equipment will be taught in training.
For example, improper use of a soil probe (stomping on it) can cause knee injury. Also, all
enumerators will wear blaze orange vests while taking soil samples in areas where hunters
may be active.
According to the Guidelines for Preparing Logistics Plans, "First aid and CPR training
are required for all personnel, especially those who will be working in remote locations"
(Baker and Merritt 1991, Section 2, Page 11 of'20). The guidelines also say that the safety
plan is supposed to designate the American Red Cross First Aid textbook as a guide for first
aid and CPR. Whether these are binding directives and how they should be applied within
the Agroecosystem Program must be investigated. Note that no first aid training was done
before the 1992 pilot hi North Carolina.
The soil analysis and nematode enumeration laboratories willlbe responsible for their own
safety plans.
7.5.7. Sampling Schedule
Data will be collected by NASS enumerators for the ARG during the June Enumerative
Survey and during the fall. The period of field activity for the JES is mid-May to mid-June.
Administering the Agroecosystem questionnaire and taking soil samples will begin in mid-
October. The work will last two or three weeks, depending on the progress of the crop
harvest. NASS will be responsible for monitoring progress of the work. Cooperators from
SCS will work during the same fall period. They will visit each of the approximately 36
"informed consent" fields after NASS has done its work. They should come as soon as
possible thereafter, but never more than two weeks later.
116
-------
7.3.8. Site Access and Reconnaissance
NASS has an excellent record with the agricultural community at the national, state, and
local levels. During the JES, enumerators locate and interview all farm owners or operators
in sampled segments. A subset of fields will be drawn for the fall survey and sampling.
Physical access to fields is seldom a problem, but other factors can prevent a sample or
questionnaire data from being collected. A farmer might decline to answer the survey and/or
refuse to allow soil samples to be taken, or the enumerator may be unable to locate the farm
operator, or the farm operator may be deceased. In 1992 inaccessibility, refusals, and
misclassification resulted in 32 missing sample units from the questionnaire data (9%), plus
another 10 that were partially incomplete, and the absence of 25 sample units for soil (7%).
Enumerator error resulted in the loss of soil data for only two sample units (1%). Based upon
previous NASS experience, refusal rates in Nebraska are expected to be higher than they were
in North Carolina.
A key tool in the location of a sample field is the segment's aerial photograph, on which
fields are outlined. Since the photos are re-used in subsequent NASS surveys, slides will be
made of them for archiving. These will be kept at NASS offices for confidentiality reasons.
One set will stay at the Nebraska office and the other at the North Carolina office (because of
proximity to the ARG). The original photographs will also be used to locate the sample fields
on county soil surveys, so that the soil map units can be determined. This will probably take
place in Nebraska, but who will do this and when has yet to be decided.
7.3.9. Procurement and Inventory Control
The ARG will provide equipment and supplies for the collection of soil samples and for
the shipping of samples to contract analytical laboratories. NASS will provide all supplies
associated specifically with the questionnaires (e.g. forms, labels, aerial photos). A list of the
equipment to be found in the soil sampling kits is found in Section 5.2.4. Pre-printed labels
for each sample will be produced by NASS in consultation with the ARG (see Section 7.3.4).
Each piece of equipment (or each set of pieces, e.g., for soil probe kits) will be marked
"Agroecosystems". Enumerators will sign for their materials when they receive them at the
training school. Much of the equipment (coolers,.soil probes, etc.) needed in Region 7 are
already on hand from the North Carolina pilot, though more will need to be purchased. The
117
-------
completed kits will be stored in Raleigh, at NCSU Research Unit II, until being sent to
Nebraska. Other equipment and expendable supplies (mailing envelopes, plastic bags, wire
tags, etc.) will be ordered by the ARG but shipped to the NASS Nebraska office, which will
be responsible for storing, sorting, and distributing them. Equipment will be returned at the
debriefing following the pilot and will be stored by NASS for use in the 1994 Pilot Field
Program.
7.3.10. Field Operations
NASS will be responsible for all data and sample collection activities during both the
June Enumerative Survey and the fall survey. The ARG will be responsible for all field
activities involved in the development of new indicators during the initial stages of testing
(not described here).
Surveys. Survey logistics will be the responsibility of NASS, which has years of
experience in this area. Details on how the enumerators are to conduct the surveys will be
found in the Agricultural Surveys: Interviewer's Manual (USDA NASS 1993) and the
Environmental Monitoring and Assessment Program (EMAP): Interviewer's Manual (in
preparation). The latter is often called the enumerator's manual. The period of field activity
for the JES is mid-May to mid-June. The fall survey and associated sampling will take place
from mid-October through November. Questions on the fall survey will apply to individual
fields. NASS will select fields from the June survey sample, using the scheme described in
Section 4.2.1, and will identify them by referencing the aerial photographs that will be given
to the enumerators.
Soil sampling. Soil sampling will be done during the same period as the fall survey. To
save time and travel, soil samples will be taken right after the questionnaire is completed and
permission is obtained. Soil sampling will be done as outlined in Sections 4.2.2, 5.2.4, 5.3.3
and Appendix 3. A very brief description is presented here.
The field will be sampled with 20 soil cores taken at equal distances along a straight-line
transect. The total length of the transect will be approximately 90 m long, depending on the
length of the enumerator's stride. Enumerators will have the aerial photographs, sampling
equipment (including probes, bags, labels and mailers) and the number of paces (printed on a
label on the survey form) needed to locate the transect midpoint. This midpoint will be
determined using a modification of the NASS method for locating objective.yield plots. The
118
-------
enumerator will take the assigned number of paces along and into the field, and will orient the
transect at a 45° angle to his or her path of entry into the field. Samples will be taken at
intervals of five paces along the transect. The 20 cores will then be composited and mixed.
Soil clumps are to be broken apart gently. Samples for both nematode enumeration (500 cm3)
and soil physical/chemical analysis (700-800 cm3) will be drawn from the composited sample,
labeled, and packaged. The enumerator will ship the samples that same day or early the next
day via either Federal Express, UPS, or the U.S. Postal Service. The final decision on
shipper(s) will be made by 1 August. Postage will be paid through an ARG account, set up
through the USD A. Nematode samples require special treatment, as it is preferable that the
nematodes reach the laboratory alive. These subsamples must be kept in an insulated box (ice
chest) away from extreme heat or cold until they can be shipped to the enumeration
laboratory. They will be sent between Monday and Thursday via overnight delivery so that
they reach the laboratory on a weekday.
The words "SOIL SAMPLE" will be written on each package, following the instructions
of the Animal and Plant Health Inspection Service (APHIS). Also, the word
"Agroecosystems" on each package will alert the SCS laboratory as to the identity of these .
samples. After shipping any sample, the enumerator will complete the record-keeping form,
on the back of the questionnaire. During the 1992 pilot in North Carolina, soil samples were
sent by Federal Express "government overnight" service, and in most cases they reached the
laboratories in Ohio and California the next day.
Special cases. There are two situations which will require special attention by the
enumerator. On a subset of the hexagon-selected fields, enumerators will ask the farm
operator's permission for a follow-up visit by SCS soil scientists to further characterize the
site and its soil (Appendix 4). The signed consent form will be returned to the NASS office,
giving them permission to release the farmer's name and address to the SCS. In such cases,
the enumerator will need to mark the aerial photograph and the site in such a way that SCS
can locate the field and the transect within that field (Appendix 3). The second special
situation will be that there will be twenty samples taken from rangeland sites near the sample
fields. This is for testing of the nematode indices, though other soil properties will also be
measured. Sampling procedure will be the same as on the cultivated fields, and the farmer
will be asked a few short questions about management and plant composition on the pasture
(see Appendix 2).
119
-------
7.3.11. Laboratory Operations
Analysis of soil samples will be performed by cooperating/contract laboratories, as
mentioned above (SCS Soil Survey Laboratory and N&A Nematode Identification Service).
Log books for sample tracking will be required of the laboratories. The laboratory procedures
to be used for soil physical and chemical analysis are listed in Table 5.2-7; detailed
descriptions may be found in (USDA SCS 1983). Details on nematode enumeration are given
in Sections 5.3.3 and 5.3.4. The enumeration laboratory will log in samples and store them at
15C. Samples will be extracted within 14 days of receipt. Nematodes will then be identified'
to taxonomic family and counted.
7.3.12. Waste Disposal and Sanitation
During sampling. There are few hazards associated with the soil samples, but some
precautions need to be taken. Soil will be cleaned from probes, buckets, and other items
before the next field is visited. This will reduce the chance of spreading weeds, nematodes,
and other soil-borne pests. There are no federal quarantines regarding movement of soil out
of Nebraska, but soybean cyst nematode is found in some counties: Burt, Cass, Douglas,
Gage, Johnson, Nemaha, Otoe, Pawnee, Richardson, Sarpy, and Wayne. While taking soil
samples in these counties, enumerators will wear plastic disposable boots over their shoes.
They will change the boots between fields, place the used boots in a plastic garbage bag, and
dispose of them in the trash. This same precaution will be taken in fields where sugar beets
are grown in the rotation. This is because of concern over a soil-borne disease called
rhizomania (caused by beet necrotic yellow vein virus) that has been recently discovered in
western Nebraska. Soil from all counties will be packaged securely for shipping.
At the laboratories. The first disposal issue for the laboratories is that they not discard
any sample until data have been determined to meet quality objectives (Section 5.2.5). The
SCS Soil Survey Laboratory routinely archives approximately 500 cm3 of each soil sample
indefinitely. This volume may be somewhat smaller for EMAP samples. At the nematode
laboratory, some extracted nematodes will be preserved and stored, in case more detailed
identification is needed later (Section 5.3.4).
All laboratories handling samples from the pilot will be required to follow specified
procedures before disposing of soil. These procedures specify the temperature and duration to
heat the soil (or soil screenings) to kill pests. Both laboratories have compliance agreements
120
-------
with APHIS. Waste disposal of any other hazardous materials, such as reagents used to test
soil, at analytical and enumeration laboratories is to be done according to the methods
generally used by those laboratories.
7.3.75. Information Management
Data collected during the pilot, as well as massive amounts of existing data (e.g., Natural
Resources Inventory, Agricultural Census, State Soil Survey Database, weather data, GIS
data) will need to be managed by the ARG Information Manager, through the Agroecosystem
Information Center (AIC). Cooperation with NASS and the logistics of data transfer between
MASS and the AIC are being developed. Pilot questionnaire data will go first to NASS,
while soil and nematode analysis data will come first to the ARG. A critical concern is to
ensure the confidentiality of data from individual farms. These issues, along with hardware
and software requirements, are covered in Section 8. Procedures for the acquisition of the
data to be used for land use/ land cover and landscape descriptors are covered in Section 5.4.
An additional information/logistics issue is how to report data back to the farm operators.
Some of the soil test results will be returned to the cooperating farmers. This will be handled
by NASS, to avoid breaking the confidentiality of the respondents. Some interpretation will
be provided in a cover letter. If the individual would like further information, he or she will
be directed to the local extension agent.
7.3.14. Quality Assurance
A streamlined logistical operation helps to ensure that data are collected properly and that
good records are kept. Also, a number of QA procedures for the pilot will have to be
incorporated into the logistics area. Some of these are already part of NASS operations (e.g.,
the work of supervisory enumerators and call-backs to verify questionnaires). Others are
sampling operations that will be added for QA purposes. For example, a second composite
soil sample will be taken from every sixth field. Half of those second composite samples (or
every 12th field) will contain 40 soil cores, two from each point on the transect. This larger
sample has enough soil that it can be split in the field and sent as duplicate samples to the
analysis laboratory. Section 6 gives further details on Quality Assurance for the 1992 pilot.
121
-------
7.3.15. Cost Tracking
One of the objectives of the pilot is to compare the efficiency (cost and precision) of the
NASS Rotational Panel and the EMAP Hexagon Design (Section 4.1). Information on costs
will be recorded by NASS for every step in the pilot: frame development, sample selection,
preparation of maps and aerial photographs, and conduct of the sampling and surveys. It is
standard practice for NASS enumerators to report their time and mileage during surveys, but
the cost of office work by NASS also will need to be included. Allowance will be made for
the fact that certain activities (e.g., soil sampling for nematodes) will be done only on one
design or the other. The Nebraska and Washington, D.C. offices of NASS will report their
costs to the ARG Technical Director.
7.3.16. Review of Logistics
After the pilot, enumerators will be debriefed to determine strengths and weaknesses in
the logistics. Enumerators will return sampling equipment at this time. Post-pilot discussions
will also be held with NASS administrators.
The report on the Pilot Field Program will include a section on logistics, which will
document problems and propose solutions. An important task for both NASS and the ARG
will be to evaluate whether EMAP sampling is an appropriate job for NASS enumerators and
whether the enumerator corps can do this type of job effectively.
7.3.17. Soil Pits and Site Characterization by SCS
Members of the Nebraska SCS will visit approximately 36 of the sample fields
(chosen from the hexagon-selected fields). This only will be done with the written permission
of the farmer, obtained during the fall survey. The soil scientists will determine tr map unit
composition of the field, the map units crossed by the transect from which NASS enumerators
collected samples, and the classes of accelerated erosion on the field. They will also dig a
51cm (20 inch) deep pit on each map unit that is crossed by the transect. Samples from up to
four horizons will be collected for determination of certain soil physical properties that have
potential as indicators (see Section 5.2.4). Appendix 4 gives the details of the field methods
to be used by SCS personnel.
122
-------
8. Information Management
8.1. Introduction
The Agroecosystem 1993 Pilot Field Program will require that large quantities of data be
obtained, stored, manipulated, integrated and analyzed. These new and existing data will come
from many sources, including joint ARG-NASS data collection efforts, other government
agencies, cooperating non-governmental organizations (NGOs), and academic institutions
(Figure 8-1 and Table 8-1). Information collected during the pilot and existing data must be
integrated in such a way as to make meaningful analysis possible. The focal point of this
integration will be the Agroecosystem Information Center (AIC). The AIC, consisting of
hardware, software, and data communications network, will be refined further during the Pilot
to provide a major component of the integration, analysis, and reporting activities .
Agroecosystem
Data Collected
WithNASS
Agroecosystem
Information ;
Center
Other
EMAP
Data
Agroecosystem Resource Group
PRODUCTS
o Reports
o Data
Figure 8-1. Overview of the flow of data through the AIC.
123
-------
Raw Data
Verified Data
J
Validated Data
Check Values for Reasonableness
eg.: Range Check
Duplicate Entries
Format Check
Inappropriate Codes
Internal Consistency
Check Data for Reasonableness
eg: Comparison with Historical Data
Statistical Analyses
Figure 8-1. Use of existing data to perform validity checks on data.
A major emphasis of the Pilot Field Program will be the continued development of a close
working relationship with USDA NASS (Section 1.2). As discussed in Section 4.1, NASS
uses an area frame to gather data on crop acreage, cost of production, farm expenditures, crop
yield, specialty crops, livestock production, chemical usage, irrigation, water quality and other
items of interest to the agricultural community. Statistics are compiled and reported annually
from the June Enumerative Survey (JES) using some. 16,000 Primary Sampling Units
nationwide. A primary objective in the Pilot will be developing and fine tuning the logistics
and cooperation required for moving and integrating data from several sources into a cohesive
data management system. Group members will perform statistical, modeling, geographical and
other types of analyses using the data.
The confidentiality of data, and consequently data security, are particularly critical issues
to in the agroecosystem program. Meeting the program objectives requires that data be
collected from individual farm operators. Because these NASS data, at some level of
summarization, are then to be available outside the confines of NASS facilities, there must be
a policy and mechanism which continues to protect the privacy of the individual respondents.
124
-------
As a part of the Pilot, the ARG will work closely with NASS to refine existing methods and
procedures for maintaining strict confidentiality and security of all microdata (i.e., data which
can be associated with individual growers and operations).
8.2. Information Sources and Flow
Information that will be used for analyses and reporting in the pilot will 'originate from
two general sources: those data actively collected at the farm field sites and those existing
data (both current and historical) that have been collected by other agencies.
Data for the 1993 Pilot will be collected
under an Interagency Agreement developed
with USDA-NASS under which NASS
enumerators collect all of the agricultural
field level information. This information
will consist of both survey data and physical
samples for laboratory analysis (Figure 8-2).
The enumerators will operate within the
NASS organization, using procedures
selected and developed jointly by the ARG
and NASS. The survey data will be
entered, verified, validated, and stored on
NASS computers. The analysis results from
the soil samples will be sent directly to the
ARG from the laboratory on magnetic
media. The ARG will perform final
verification and validation procedures on all
of the data. Once validation is complete,
the soil analyses data will be integrated with
several data sets including the NASS survey
data, the State Soil Survey Database from
Nebraska, and the National Resources Inventory.
Surveys to NASS
Data
[Aggregation!
|~ Statistical
I Analyses
Report |
Generation i
Subjact to NASS data
confident aSly provisions
Figure 8-2. Flow of data collected by NASS.
125
-------
From the standpoint of information management, working with NASS is important for a
number of reasons.
o Over time, NASS has developed a relationship with the agricultural community which
will greatly facilitate the collection of data.
o NASS provides the assurance of data confidentiality to individual farm operators.
This allows the organization to collect data which farmers might otherwise be
reluctant to supply for fear of legal or regulatory action.
o NASS has a fully developed infrastructure for the collection, recording,
summarization, analysis and publication of agricultural data, including strict quality
controls. Use of this infrastructure greatly reduces the expenditure of resources on the
development of duplicate logistics and QA procedures.
o NASS has developed the computer resources to organize, analyze, and quickly report
on large volumes of data. Use of these resources may reduce the overall need for
data processing within the ARG.
In addition to field data, a broad array of existing data will be required for the 1993 Pilot
Field Program (Table 8-1). The ARG is committed to the use of existing data whenever
Table 8-2. Summary of confidentiality provisions of several government agencies with data of value
to the Agroecosystem Resource Group.
Organization
USDA National Agricultural
Statistics Service
USDC Census of Agriculture
USDA Soil Conservation Service
(National Resource Inventory)
US Environmental Protection
Agency
Policy
Public Law 99-198.
No release of data with identity of individual
respondent.
US Code Title 13.
No release of data with identity of individual
respondent.
No release of exact location (Primary Sampling
Unit) at which data are collected.
Freedom of Information Act requests handled on a
case-by-case basis.
Lowest Level of
Aggregation Noimally
Available
County
Zip Code (5 ~igit)
County
Varies
126
-------
possible, assuming the scope and quality of the data are sufficient for our needs. Although
there may be some effort required to transform existing data to conform with EMAP
standards, this effort is usually substantially less than that required to collect new data.
Several efforts are underway within EMAP to address the issue of using existing data. The
existing data we anticipate using fall into two major categories and have a variety of uses.
These are:
Table 8-1. Examples of existing data to be used for the 1993 Pilot Project.
Description
Weather and Climate Data
State Soil Survey Database
(Nebraska derivation of SOILS-5)
National Resources Inventory
Herbicide Use Database
Ag. Land Use and Cover Data
Census of Agriculture
Soil Ratings for Pesticide Loss
Major Land Resource Areas
AVHRR Coverages
Aerial Photography
Source
NOAA
SCS
SCS
Resources for the Future
NASS/EMAP-LC
USDC
BRG/Data & SCS
SCS
EROS/USGS
ASCS
o Physical and biological parameters: used to provide complementary data, verify values
of collected data, provide a basis for the implementation of summarized data
(validation), and for indicator research.
o Geographic and remotely sensed data: used for boundary establishment, identification
of geographic features, spatial distributions, geographic visualization, and for indicator
research.
127
-------
The use of existing data also permits the analysis of historical trends. In this way, it may
be feasible to validate and correlate measurements associated with specific indicators by
predicting present conditions using historical data. Existing data will be used to develop
expected values for performing verification and validation of both survey and sample data
(Figure 8-3). The ARG will import data as needed and appropriate from other EMAP efforts
as well as other agencies and organizations to support pre- and post-Pilot activities. It is
expected that more effort will be expended in order to acquire additional existing data to
support the Region 7 Pilot.
8.3. Confidentiality of Data
In order to protect the rights of individual respondents, legal confidentiality provisions
(Table 8-2) apply to all data collected by NASS. NASS cannot release microdata; data are
currently available, in most cases, at the county level. The United States Department of
Commerce's (USDC) Census of Agriculture is also legally subject to confidentiality
provisions, and the USDA Soil Conservation Service's (SCS) National Resource Inventory
(NRI) follows confidentiality restrictions. Table 8-2. summarizes some of these policies. The
rationale behind such assurances is clear. Confidentiality laws protect individual respondents
from prosecution which might otherwise result, from their participation in a data collection
effort. Without such assurances, respondents may be hesitant to comply with any survey or
data collection efforts, either voluntary or legally required. Also, without these assurances,
respondents may be more likely to falsify information on surveys. Violation of this confidence
would result in loss of NASS credibility with survey respondents and seriously hamper future
data collection efforts. Hence, NASS is very serious about maintaining data confidentiality.
The current view of the ARG with respect to these confidentiality provisions is positive.
In a review of confidentiality in EMAP, Franson (1990) writes that the EPA is presently
unable to issue a blanket statement of confidentiality for EMAP data. Requests for EMAP
data from the
regulatory arm of EPA, or from other agencies, corporations, and individuals, must be
reviewed on a case-by-case basis. Farmers are unlikely to provide data without a
confidentiality agreement. In fact, the question of whether farmers would cooperate with any
team identifying themselves with the EPA, even with promises of confidentiality, should not
be lightly dismissed.
128
-------
Although the microdata from agencies employing confidentiality provisions are not
available, there are solutions which allow the ARG, and other components of EMAP, to make
use of the data collected by NASS.
Aggregated data: Whereas many agencies will not release their microdata, they will all
release data aggregated at various levels (Table 8-2). The goal is to aggregate the data in such
a way that individuals cannot be identified. Obviously, for the Agroecosystem Program, the
lower the level of aggregation, the better (i.e., county-level data are better than state-level
data). ', . '
Analysis requests: The owning agency may accept requests for tabulation and analysis from
another agency. The analysis would be performed by the owning agency's personnel, the
results returned to the requesting agency. Release of any confidential-material would be
strictly avoided.
Deputization: It is possible, at least with some government agencies (including NASS), for
an individual to be deputized by that agency. Deputization requires completion of a non-
disclosure agreement. A deputized individual is permitted to access the data for the purpose of
performing analyses. Typically, the analysis would have to be performed at the owning
agency's facilities. Only aggregated results may be removed, and are subject to confidentiality
screening.
Location stripping and data screening: Another alternative being discussed for the Region 7
Pilot, is to strip the data sets of locational identifiers .for most analyses. After these are
removed, the data sets would be screened to be certain that no "context sensitive" information
was contained within them. Context sensitive information is that which might identify a farm
operator strictly by the uniqueness of the information contained in the data record.
All of these possibilities will be explored by the ARG with NASS, and any other agencies
with such provisions, during the Pilot program. Currently, the view of the ARG is to treat this
as a true Pilot Field Program; data will not be released, in any form, to anyone outside of the
ARG until it has been summarized into a publishable format. Regardless of the
confidentiality provisions, it is our belief that because of the preliminary nature of these data,
they would not be of use to others, except in a final publication format.
129
-------
8.4. Data Integration and Management
The integration of existing data with data subject to confidentiality provisions presents a
unique challenge that can be resolved only through close interagency cooperation. The NASS
data are used for economic forecasts which have the potential for affecting the livelihood of
many people. They are closely guarded and access is severely restricted and carefully
monitored. No microdata may be removed from NASS facilities, and computers containing
microdata may not be connected to foreign networks. During the 1992 Pilot, NASS allowed
the ARG to use a PC at the NASS facilities in Raleigh, NC. ARG members were able to
examine and analyze NASS microdata using the PC. A similar arrangement will be pursued
for the 1993 Pilot. Although this mode of operation will suffice during the pilot program,
other approaches will be explored for future pilots, demonstrations and implementations.
Because of the data confidentiality and security requirements discussed previously, the
task of data management becomes paramount. In order to coordinate and facilitate the
movement, integration and selection of collected and ancillary data, a full-featured relational
database management system (RDBMS) must be employed. This becomes especially critical
when ARG members require different "views" of the data so that different analyses can be
performed on various subsets. Carefully constructed data dictionaries are essential to
maintaining flexible access to all of the data. Another important concept to be established
and tested during the pilot is one of maintaining metadata associated with the collected data.
The metadata are information about the data. Examples are: sample methods, QA/QC
procedures, etc. The volume of metadata is expected to be substantial and its management a
challenge. Cooperative efforts are underway within EMAP-IM to standardize metadata
requirements and management tools.
8.5. Data Access
Providing ARG members access to the pilot and ancillary data in a convenient and
organized manner will prove to be a challenge of the AIC. Individual ARG members are
currently located in several cities throughout the United States (Appendix 1). Because of the
requirements for data storage locations (Figures 8-2 and 8-3), the logistics of locating,
accessing and transporting that data to the individual investigator will require a carefully
planned and designed information system.
130
-------
Although we anticipate no release of pilot data outside of EMAP, eventually aggregated
data from demonstration projects and implementation will be made available to outside groups
and agencies. This will necessitate identifying what is available, where it is located, its
characteristics (metadata) and how to obtain it. Development of this reference data base, the
data directory/catalog/dictionary (D/C/D), will be a component of the Pilot Field Program.
Plans call for working cooperatively with the Information Management Task Group of EMAP,
which is attempting to standardize the process for cataloging EMAP data.
8.6. Computing Strategy
Hardware: The AIC is composed of a heterogeneous network of Sun workstations, PC's and
Apple Macintoshes. They all use the Network File System (NFS) running the TCP/IP
protocol on an Ethernet topology. The NASS computing system is anchored by a remote
time-sharing IBM mainframe and is supplemented by local mini- and microcomputers.
Software: Plans are to procure the Oracle RDBMS for the Region 7 Pilot. This would serve
as a storage and management tool for most of the data. The ARC/Info GIS will be used to
store and analyze the geographic and remote sensing data. SAS will be used to do the
statistical analyses and much of the reporting. Both ARC/Info and SAS have "hooks" into
Oracle that permit them to retrieve data from the Oracle data base structure. Current plans
call for the construction of an information system for the ARG integrating these three
software packages, utilizing the strengths of the individual tools.
Data integration: The survey data, lab data, and ancillary data all need to be related to each
other in some meaningful way so that analyses and reports can be generated. Table 8-3 lists
some of the data sets and how they will be joined together in order to perform integrated
analyses. Pre- and post-pilot activities will focus upon validating many of these relationships
between data.
131
-------
Table 8-3. Examples of integration keys for new and existing data.
Description
Weather and Climate Data
State Soil Survey Database
(Nebraska derivation of SOILS-5)
National Resources Inventory
NASS Questionnaire Data
Ag. Land Use and Cover Data
Census of Agriculture
Soil Samples
AVHRR Coverages
Aerial Photography
Integration K?Y
kriged location
soil-map unit
Lasnd Resource Region
segment/tract/field
resource class/crop type
county name
segment/tract/field
resource class
segment
132
-------
LITERATURE CITED
Alexander, M. (1985) Ecological constraints on nitrogen fixation in agricultural ecosystems.
Adv. Microbial Ecol 8:163-183.
American Society for Testing and Materials. (1991) Draft Proposed Specification for Meta-Data
Support in Geographical Information Systems. August 1991. ASTM Committee D.I8.01.05.
Angle, J.S., Gross, C.M., Hill, R.L., and Mclntosh, M.S. 1993. Soil nitrate concentrations under
corn as affected by tillage, manure, and fertilizer application. J. Environ. Qual. 22:141-147.
Ayoub, S. M. 1980. Plant Nematology, An Agricultural Training Aid. California Department of
Food and Agriculture. NemaAid Publications, Sacramento, California.
Baker, D.E. and Chesnin, L. (1975) Chemical monitoring of soils for environmental quality and
animal and human health. Adv. Agon. 27:305-374.
Baker, J.R. and Merritt, G.D. (1991) Environmental Monitoring and Assessment Program:
Guidelines for Preparing Logistics Plans. May 1991. U.S. Environmental Protection Agency.
Office of Research and Development. Environmental Monitoring Systems Laboratory. Las Vegas,
Nevada. EPA/600/4-91/001.
Baker, D.E., Amacher, M.C. and Leach, R.M. (1979) Sewage sludge as a source of cadmium
in soil-plant-animal systems. Environ. Health Perspectives 28:45-49.
Barker, K.R. (1985) Sampling nematode communities. In: An Advanced Treatise on Meloidogyne
Vol. II: Methodology, ed. K.R. Barker, C.C. Carter and J.N. Sasser. North Carolina State
University Graphics, Raleigh, pp. 2-17.
Berger, K.C., Hole, F.D. and Beardsley, J.M. (1952) A soil productivity score card. Soil Sci.
Soc. Am, Proc. 16:307-309.
Bliss, N.B. (1990) A hierarchy of soil databases for calibrating models of global climate change.
In: Soils and the Greenhouse Effect, ed. A.F. Bouwman. John Wiley and Sons, Ltd. pp. 529-533.
Bliss, N.B. and Reybold, W.U. (1989) Small-scale digital soil maps for interpreting natural
resources. Journal of Soil and Water Conervation (Jan.-Feb.):30-34.
Bongers, T. (1990) The maturity index: an ecological measure of disturbance based on nematode
species competition. Oecologia 84:14-19.
Box, E.G., Holben, B.N. and Kalb, V: (1989) Accuracy of the AVHRR vegetation index as a
predictor of biomass primary productivity and net CO2 flux. Vegetatio 80:71-89.
L-l
-------
Brady, N.C. (1974) The Nature and Properties of Soils. Eighth Edition. MacMillan Publishing
Co. New York.
Brams, E. (1977) Relationships of soil cadmium and lead to the environment peripheral to
industrial cities. In: Proceedings, International Seminar of Soil Environment and Fertility
Management in Intensive Agriculture (SEFMIA), Japan 1977. Soc. of the Science of Soil and
Manure, Tokyo, pp. 369-382. (Cited in Methods of Soil Analysis, Part 2. Agronomy Monograph
No. 9, Second edition. 1982. ASA-SSSA. p. 362. Madison, WI.)
Brookes, P.C. and McGrath, S.P. (1985) Effects of heavy metal accumulation in field soils
treated with sewage sludge on soil microbial processes and soil fertility. In: Microbial
Communities in Soil, ed. V. Jensen, A. Kjoller and L.H. Sorensen. Elsevier Applied Science
Publ., New York. pp. 327-343.
Buol, S.W., Hole, F.D., and McCracken, R.J. 1980. Soil Genesis and Classification. Second
Edition. The Iowa State University Press, Ames.
Burke, I.C., Yonker, C.M., Parton, W.J., Cole, C.V., Flach, K. and Schimel, D.S. (1989)
Texture, climate and cultivation effects on soil organic matter content in U.S. grassland soils.
Soil Sci. Soc. Am. J. 53:800-805.
Campbell, C.L., Heck, W.W., Moser, T.J., Breckenridge, R.P., Hess,G.R. and Meyer, J.R. (1990)
Indicator Fact Sheets for Agroecosystems. In: Environmental Monitoring and Assessment
Program: Ecological Indicators, ed. C.T. Hunsaker and D.E. Carpenter. EPA/600/3-90/060.
U.S.EPA, Office of Research and Development, Washington, DC. pp. F-l to F-23.
Caveness, F. E., and H. J. Jensen. 1955. Modification of the centrifugal-flotation technique for
the isolation and concentration of nematodes and their eggs from soil and plant tissue. Proc.
Helminthol. Soc. 22:87-89.
Cole, C.V., Stewart, J.W.B., Ojima, D.S., Parton, W.J. and Schimel, D.S. (1989) Modelling land
use effects of soil organic matter dynamics in the North American Great Plains. In: Ecology of
Arable Land., ed. M. Clarholm and L. Bergstrom, Kluwer Academic Publishers, pp. 89-98.
Coleman, D.C., Reid, C.P.P. and Cole, C.V. (1983) Biological strategies of nutrient cycling in
soil systems. Adv. in Ecol. Res. 13:1-55.
Cooper, J.E., Wood, M. and Bjourson, A.J. (1985) Influence of acidity and aluminum on the
nodulation of Marsh Trefoil (Lotus pedunculatus) and white clover (Trifolium repens) by
Rhizobia. In: Microbial Communities in Soil, ed. V. Jensen, A. Kjoller and L.H. Sorensen.
Elsevier Applied Science Publishers, New York.
L-2
-------
Cotter, J. and Nealon, J. (1987) Area Frame Design for Agricultural Surveys. U.S. Department
of Agriculture, National Agricultural Statistics Service, Research and Applications Division, Area
Frame Section. Washington, DC.
Daniels, R.B., Gilliam, J.W., Cassel, D.K. and Nelson, L.A. (1987) Quantifying the effect of
soil erosion on present soil productivity. J. Soil Water Conserv. (May-Jun.): 183-187.
Daykin, M.E. and Hussey, R.S. (1985) Staining and histopathological techniques in nematology.
In: An Advanced Treatise on Meloidogvne Vol. II: Methodology, ed. K.R. Barker, C.C. Carter,
and J.N. Sasser. North Carolina State University Graphics, Raleigh, pp. 39-48.
Dean, J.D., Huyakom, P.S., Donigian, A.S., Jr., Voos, K.A., Schanz, R.W., Meeks, Y.J. and
Carsel, R.F. (1989) Risk of Unsaturated/Saturated Transport and Transformation of Chemical
Concentrations (RUSTIC), I. EPA/600/3-89/048a, U.S. EPA, Athens, Georgia.
Donald, C.M. and Hamblin, J. (1976) The biological yield and harvest index of cereals as
agronomic and plant breeding criteria. Adv. Agron. 28:361-405.
Duniway, J.M. (1979) Water relations of water molds. Ann. Rev. Phytopathol. 17:431-460.
Ewel, J.J., Mazzarino, MJ. and Berish, G.W. (1991) Tropical soil fertiity changes under
monocultures and successional communities of different structure. Ecol. Applications 1:289-303.
Federoff, N. (1987) The production potential of soils: Part I. Sensitivity of principal soil types
to the intensive agriculture of north-western Europe. In: Scientific Basis for Soil Protection in
the European Community, ed. H. Barth and P. L'Hermite. Elsevier Applied Science, New York.
pp. 65-85.
Fluck, R.G and Baird, C.D. (1980) Agricultural Energetics. AVI Publishing Co. Inc., Westport,
Connecticut.
Fluck, R.C. (1979) Energy productivity: A measure of energy utilization in agriculture systems.
Agric. Systems 4:29-37.
Franson, S.E. (1990) Data confidentiality in the Environmental Monitoring and Assessment
Program (EMAP): Issues and Recommendations. U.S. EPA Report 600/X-90/129. U.S. EPA,
Las Vegas, NV.
Freckman, D.W. (1988) Bacterivorous nematodes and organic-matter decomposition. Agri.,
Ecosys. and Environ. 24:195-217. .
Frye, W.W., Ebelhar, S.A., Murdock, L.W. and Blevins, R.L. (1982) Soil erosion effects on
properties and productivity of two Kentucky soils. Soil Sci. Soc. Am. J. 46:1051-1055.
L-3
-------
Gersmehl, PJ. and Brown, D.A. (1986) Regional differences in the validity of the concept of
innate soil productivity. Ann. Assoc. of Am. Geographers 76:480-492.
Gibbs, RJ. and Reid, J.B. (1988) A conceptual model of changes in soil structure under
different cropping systems. Adv. in Soil Sci. 8:123-149.
Gorny, M. (1976) The problem of bioindication in soils of degrade biotopes. Wiad. ekol.
22:224-230 (cited by Wasilewska 1979).
Goss, D. (1991) Screening procedure for soils and pesticides relative to potential water quality
impacts. In: Using Computer Simulation Models in Pesticide Registration Decision Making.
Weed Sci. Soc. of Am., Louisville, KY.
Goss, D. and Wauchope, R.D. (1990) The SCS/ARS/CES Pesticide Properties Database II:
Using it with soils data in a screening procedure. In: Pesticides in the Next Decade: The
Challenges Ahead., ed. D.L. Weigmann. pp. 471-493.
Groenevelt, P.H., Kay, B.D. and Grant, C.D. (1984) Physical assessment of a soil with respect
to rooting potential. Geoderma 34:101-114.
Grossman, R.B. (1993) Aggregate Size Distribution. Overview of Certain Soil Characterization
Methods with Emphasis on Use Dependant Temporal Properties (Unpublished)
Haberern, J. (1991) A soil health index. /. Soil and Water Conser. 47:6.
Hall, G.F., Logan, TJ. and Young, K.K. (1985) Criteria for determining tolerable erosion rates.
In: Soil Erosion and Crop Productivity, ed. R.F. Follett and B.A. Stewart. ASA-CSSA-SSSA,
Madision, WI. pp. 173-188.
Heagle, A.S., McLaughlin, M.R., Miller, I.E., and Joyner, R.E. (1992) Response of two white
clover clones to peanut stunt virus and ozone. Phytopathology 82:254-258.
Heagle, A.S., McLaughlin, M.R., Miller, I.E., Joyner, R.E., and Spruill, S.E. (1991) Adaptation
of a white clover population to chronic ozone stress. New Phytol. 119:61-68.
Heagle, A.S., Rebbeck, J., Shafer, S.R., and Heck, W.W. (1989) Effects of long-term ozone
exposure and soil moisture deficit on growth of a ladino clover-tall fescue pasture.
Phytopathology 79:128-136.
Heck, W.W., Campbell, C.L., Moser,T.L, Rawlings, J.O., Hess, G.R., Breckemidge, R.B., Smith,
C.N., Byers, G.E. and Finkner, A.L. (1989) EMAP-Agroecosystem Research Plan. Informational
Report.
L-4
-------
Heck, W.W., Campbell, C.L., Breckenridge, R.P., Byers, G.E., Finkner, A.L., Hess, G.R., Meyer,
J.R., Moser, T.J., Peck, S.L., Rawlings, J.O., and Smith, C.N. (1991) Environmental Monitoring
and Assessment Program, Agroecosystem Monitoring and Research Strategy. U.S. EPA, Office
of Research and Development, Environmental Monitoring Systems Laboratory, Las Vegas, NV.
EPA 600/4-91/013.
Hodda, M. (1986) Cluster analysis of marine nematodes. Nematologica 32:419-438.
Horvitz, D.G. and Thompson, DJ. (1952) A generalization of sampling without replacement
from a finite universe. J. Am. Statist. Assoc. 47:663-685.
Huddleston, J.H. (1984) Development and use of soil productivity ratings in the United States.
Geoderma 32:297-317.
Huddleston, J.H. (1982) Agricultural Productivity Ratings for Soils of the Willamette Valley.
Oregon State University Extension Service, Corvallis, OR.
Hunsaker, C.T. and Carpenter, D.E. eds. (1990) Environmental Monitoring and Assessment
Program: Ecological Indicators. EPA/600/3-90/060. U.S. EPA, Office of Research and
Development, Washington, DC.
Imhoff, J.C., Carsel, R.F., Kittle, J.L. and Hummel, P.R. (1990) Data Base Analyzer and
Parameter Estimator (DBAPE) Interactive Computer Program. User's Manual.
EPA/600/3-89-083, U.S. EPA, Athens, Georgia.
Indorante, S.J., Hammer, R.D. and Maatta, J.M. (1991) Horizontal and vertical distribution of
soil properties on native and eroded central Missouri deep loess soils. Soil and Water Conserva.
Soc. Annual Meeting, p. 21. Abstract.
Jonasson, I.R. and Boyle, R.W. (1971) Geochemistry of mercury. In: Mercury in Man's
Environment. Proc. Royal Soc. Canada Symp., Ottawa, Ontario, Canada. 15-16 Feb 1971.
Geological Survey of Canada, Department of Energy, Mines and Resources, Ottawa, Canada, pp.
5-21.
Kastner, V. A., and Germershausen. 1989. Struktur and Abundanzdynamik der Nematodenfauna
in einem Schwarzerde- Lo|3boden. Hercynia 26:71-93.
Katznelson, H. (1940) Survival of Azotobacter in soil. Soil Sci. 49:21-35.
Kay, B.D. (1989) Rates of change of soil structure. Adv. in Soil Sci. 12:1-52.
Kemper, W.D; and Rosenau, R.C. (1986) Aggregate stability and size distribution. In A. Klute
(ed.) Methods of soil analysis. Part 1. Physical and mineralogical methods. 2nd ed. Agronomy
9:425-422.
L-5
-------
Khorram, S., Cheshire, H., Siderelis, K. and Nagy, Z. (1991) Mapping and CIS Implementation
of Land Use and Land Cover Categories for the Albemarle-Pamlico Drainage Basin. North
Carolina State University Computer Graphics Center and North Carolina Department of
Environment, Health and Natural Resources, Center for Geographic Information and Analysis.
Draft Copy date July 1991.
Kiniry, L.N., Scrivner, C.L. and Keener, M.E. (1983) A Soil Productivity Index Based Upon
Predicted Water Depletion and Root Growth. University of Missouri-Columbia Agricultural
Experiment Station Bulletin 1051.
Klopatek, J.M. (1978) The Energetics of Land Use in Three Oklahoma Counties. Norman: The
University of Oklahoma Graduate College. Dissertation.
Ko, W.H. and Hora, F.K. (1972) Identification of an Al ion as a soil fungitoxin. Soil Sci.
113:42-45.
Korentajer, L. (1991) A review of the agricultural use of sewage sludge: benefits and potential
hazards. Water SA 17:189-196.
Kutz, F.W. and Linthurst, R.A. (1990) A systems-level approach to environmental assessment.
Toxicol. and Environ. Chem. 28:105-114.
Lai, R. (1991) Soil structure and sustainability. J. Sust. Agric. 1:67-92.
Lai, R. (1987) Effects of soil erosion on crop production. CRC Critical Rev. in Plant Sci.
5:303-367.
Larson, W.E., Fenton, T.E., Skidmore, E.L. and Benbrook, C.M. (1985) Effects of soil erosion
on soil properties as related to crop productivity and classification. In: Soil Erosion and Crop
Productivity., ed. R.F. Follett and B.A. Stewart. ASA-CSSA-SSSA, Madision, WI. pp. 189-211.
Larson, W.E., Pierce, FJ. and Dowdy, R.H. (1983) The threat of soil erosion to long-term crop
production. Science 219:458-465.
Lesser, V.M. (1992) A comparison of Periodic Survey Designs Employing Multi-State
Sampling. Chapel Hill: The University of North Carolina. Dissertation.
Lin, B-H, Hansen, L., Daberkow, S. and Dreitzer, M. (1991) Substitutability of crop rotations
for agrichemicals: preliminary results. In: Agricultural Resources: Inputs Situation and Outlook
Report, AR-24. October 1991. USD A, Economic Research Service, Resources and Technology
Division. Washington, DC. pp. 24-29.
Lindsay, W.L. (1979) Mercury. In: Chemical Equilibria in Soils. John Wiley and Sons, New
York. pp. 344-363.
L-6
-------
Ludwig, J.A. and Reynolds, J.F. (1988) Statistical. Ecology: A Primer on Methods and
Computing. John Wiley & Sons, New York. pp. 85-103.
Lunetta, R.S., Congalton, R.G., Fenstermaker, L.K., Jensen, J.R., McGwire, K.Q.and Tinney,
L.R. (1991) Remote sensing and geographic information system data integration: error sources
and research issues. Photogrammetric Eng. and Remote Sensing 57:677-687.
MacArthur, R.H., and MacArthur, J.W. 1961. On bird species diversity. Ecology 42:594-598.
Maggenti, A. R. 1982. Nemata. Pages 878-923 in S. P.. Parker, ed. Synopsis and Classification
of Living Organisms. McGraw-Hill, New York.
Maggenti, A. R. 1991. Nemata: Higher classification. Pages 147-187 in W. R. Nickle, ed. Manual
of Agricultural Nematology. Marcel Dekker, Inc., New York.
Mann, L.K. (1986). Changes in soil carbon storage after cultivation. Soil Sci. 142:279-288.
Marten, G.G. (1988) Productivity, stability, sustainability, equitability and autonomy as properties
for agroecosystem assessment. Agricultural Systems 26: 291-316.
McCracken, R.J., Lee, J.S., Arnold, R.W. and McCormack, D.E. (1985) An Appraisal of Soil
Resources in the USA. In: Soil Erosion and Crop Productivity., ed. R.F. Follett and B.A.
Stewart. ASA-CSSA-SSSA, Madison, WI. pp. 49-66.
McSorley, R., Bankers, W.H., Parrado, J.L. and Reynolds, J.S. (1985) Spatial distribution of
the nematode community on perrine marl soils. Nematropica 15:77-92.
Meyer, J.R., Peck, S.L., Rawlings, J.O. and Campbell, C.L. (1990) Annual Statistical Summary:
Agroecosystems. A Hypothetical Example. EPA/600/X-90/208. U.S. EPA, Environmental
Monitoring Systems Laboratory, Las Vegas, NV.
Mitchell, R. (1984) The ecological basis for comparative primary production. In Agricultural
Ecosystems: Unifying Concepts, ed. R. Lowrance, B.R. Stinner, and G.I. House. Wiley-
Interscience, John Wiley & Sons, New York. pp. 13-53.
Moore, J.C. and de Ruiter, P.C. (1991) Temporal and spatial heterogeneity of trophic
interactions within below-ground foodwebs. Agric., Ecosys. Environ. 34:371-397.
Mutatkar, V.K. and Pritchett, W.L. (1966) Influence of added aluminum on carbon dioxide
production in tropical soils. Soil Sci. Soc. Am Proc. 30:343-346.
National Soil Survey Center. (1993) MLRA: Soil Survey by Geographic Area (DRAFT).
USDA-SCS. Lincoln, Nebraska.
L-7
-------
Nebraska Agricultural Statistics Service. (1991) 1990-1991 Nebraska Agricultural Statistics.
Nebraska Department of Agriculture, P.O. Box 80169, Lincoln.
Neher, D. A., Peck, S. L., Rawlings, J. O., and Campbell, C. L. 1994. Measures of nematode
community structure for an agroecosystem monitoring program and sources of variability among
and within agricultural fields. Plant and Soil (submitted).
Neher, D. A., and Campbell, C. L. 1994. Comparison of nematode communities and microbial
biomass in soils with annual and perennial crops. Applied Soil Ecology (submitted).
Nelson, D.W. and Sommers, L.E. (1982) Total carbon, organic carbon, and organic matter.
Methods of Soil Analysis, Part 2. Agronomy Monograph No. 9, 2nd Edition. ASA-SSSA
Madison, WI.
Nielsen, E.G. and Lee, L.K. (1987) Magnitude and Costs of Groundwater Contamination from
Agricultural Chemicals: A National Perspective. USDA Economic Research Service Report
AGES870318. USDA/ERS, Washington, DC.
North Carolina Agricultural Statistics Division. (1991) North Carolina Agricultural Statistics:
1991. North Carolina Department of Agriculture, Raleigh.
O'Neill, R.V. (1976) Ecosystem persistence and heterotrophic regulation. Ecology 57:1244-1253.
Ovalles, F.A. and Collins, M.E. (1986) Soil-landscape relationships and soil variability in North
Central Florida. Soil Scl Soc. Am J. 50:401-408.
Overton, S.O. (1991) Probability Sampling and Population Inference in Monitoring Programs.
(unpublished manuscript)
Overton, S.O., White, D. and Stevens, D.L., Jr. (1991) Design Report for EMAP Environmental
Monitoring and Assessment Program. U.S. EPA/600/xxx/xxx. (draft)
Page, A.L. and Ganje, T.J. (1970) Accumulation of lead in soils for regions of high and low
motor vehicle traffic density. Environ. Sci. Technol. 4:140-142.
Parmelee, R.W. and Alston, D.G. (1986) Nematode trophic structure in conventional and no-
tillage agroecosystems. /. Nematology 18:403-407.
Parrish, R.S. and Smith, C.N. (1990) A method for testing whether model predictions fall within
a prescribed factor of'true values, with an application to pesticide leaching. Ecological Modelling
51:59-72.
L-8
-------
Pierce, F.J. and Lai, R. (1991) Monitoring soil erosion's impact on crop productivity. In:
Research Methods in Soil Erosion. Second Edition, (in press).
Pierce, F.J., Larson, W.E., Dowdy, R.H. and Graham, W.A.P. (1983) Productivity of soils:
Assessing long-term changes due to erosion. / Soil Water Conserv. Soc. (Jan.-Feb.):39-44.
Rebbeck, J., Blum, U. and Heagle, A.S. (1988) Effects of ozone on the regrowth and energy
reserves of a ladino clover-tall fescue pasture. /. Applied Ecology 25:659-681.
Ricklefs, R. E. 1990. Ecology. 3rd edn. Chiron Press, Inc., New York.
Rosswall, T., Schnurer, J. and Soderlund, S. (1985) Interactions of acidity, aluminum ions and
microorganisms. In: Microbial Communities in Soil., ed. V. Jensen, A. Kjoller and L.H.
Sorensen. Elsevier Applied Science Publishers, New York. pp. 395-410.
Russell, E.W. (1973) Soil Conditions and Plant Growth. Longman Group Ltd., London.
Schnitzer, M. (1982) Organic matter characterization. Methods of Soil Analysis, Part 2.
Agronomy Monograph No. 9, 2nd Edition. ASA-SSSA, Madison, WI.
Schouten, A.J., van Breemen, E.M. and Slob, W. (1990) Estimating nematode abundance in
natural sandy forest soil; a comparison of two soil samplers. Pedobiologia 34:239-246.
Schroeder, R.L., Cable, T.T, and Haire, S.L. 1992. Wildlife species richness in shelterbelts: test
of a habitat model. Wildlife Society Bulletin 20:264-273.
Schroeder, RL. 1986. Habitat suitability index models: wildlife species richness in shelterbelts.
U.S. Fish & Wildlife Service Biological Report 82(10.128). 17 pages.
Seastedt, T.R., James, S.W. and Todd, T.C. (1988) Interactions among soil invertebrates,
microbes and plant growth in the tallgrass prairie. Agric., Ecosys. and Environ. 24:219-228.
Shannon, C.E. and Weaver, W. (1949) The Mathematical Theory of Communication. Univ.
Illinois Press, Urbana.
Sharp, D.D., Lieth, H., Noggle, G.R. and Gross, H.D. (1976) Agricultural and Forest Primary
Productivity in North Carolina 1972-1973. Tech. Bui. No. 241, North Carolina Agricultural
Experiment Station, Raleigh, NC.
Shirley, C. (1991) Experts to issue "Soil Report Card". The New Farm. (Sep.-Oct.):5-6.
Simpson, E.H. (1949) Measurement of diversity. Nature 163:688.
L-9
-------
Singh, K.K., Colvin, T.S., Erbach, D.C. and Mughal, A.Q. (1992) Tilth Index: An approach to
quantifying soil tilth. Trans. ASAE (in press).
Sohlenius, B., Bostrom, S. and Sandor, A. (1988) Carbon and nitrogen budgets of nematodes
in arable soil. Biol Pert. Soils 6:1-8.
Soil Survey Laboratory Staff. (1992) Soil Survey Laboratory Methods Manual, Soil Survey
Investigations Report No. 42, Version 2.0. USDA-SCS. Washington, D.C.
Soil Suvey Laboratory Staff. (1983) Principles and Procedures for Using Soil Survey
Laboratory Data. USDA-SCS. Washington, D.C.
Sombreck, W.G. (1990) Geographic quantification of soils and changes in their properties. In:
Soils and the Greenhouse Effect, ed. A.F. Bouwman. John Wiley and Sons, Ltd. pp. 225-244.
Southwell, P.H. and Rothwell, T.M. (1977) Report on Analysis of Output/Input Energy Ratios
of Food Production in Ontario. March 31, 1977. School of Engineering, University of Guelph,
Ontario, Canada. Contract serial number OSW76-00048.
Stehman, S.V. and Overton. W.S. (1987) Estimating the variance of the Horvitz-Thompson
estimator in variable probability, systematic samples. Proceedings of the Section on Survey
Research Methods of the American Statistical Association. American Statistical Assoc.,
Alexandria, VA.
Stevenson, FJ. and Elliott, E.T. (1985) Methodologies for assessing the quantity and quality of
soil organic matter. In: Dynamics of Soil Organic Matter in Tropical Ecosystems, ed. D.C.
Coleman, J.M. Oades and G. Uehara. University of Hawaii Press, Niftal Project.
Stewart, J.W.B. and Bettany, J.R. (1982) Mercury. In: Methods of Soil Analysis, Part 2.
Agronomy Monograph No. 9, Second Edition. ASA-SSSA, Madison, WI.
Stinner, B.R. and Crossley, D.A., Jr. (1982) Nematodes in no-tillage agroecosystems. In:
Nematodes in Soil Ecosystems, ed D.W. Freckman. University of Texas Press, Austin, pp. 14-28.
Stone, J.R., Gilliam, J.W., Cassel, O.K., Daniels, R.B., Nelson, L.A. and Kleiss, H.J. (1985)
Effect of erosion and landscape position on the productivity of Piedmont soils. Soil Sci. Soc. Am.
J. 49:987-991.
Storie, R. (1978) Storie Index Rating. Special Publications, Division of Agricultural Sciences,
University of California.
Suter, G.W.II. (1990) Endpoints for regional ecological risk assessments. Environ. Manage. 14:9-
23.
L-10
-------
Thomasson, A.J. (1978) Towards an objective classification of soil structure. ^J. Soil Science
29:38-46.
Thorne, G. 1961. Principles ofNematology. McGraw-Hill Inc., New York.
Tisdale, S.L. and Nelson, W.L. (1975) Soil Fertility and Fertilizers, 3rd Edition. MacMillian
Publishing House, New York.
Tivy, J. (1990) Agricultural Ecology. Longman Scientific & Technical; Harlow, Essex, England.
Turner, M.G. (1989) Landscape ecology: The effect of pattern on process. Annual Review of
Ecolog. System. 20:171-197.
Turner, R.S., Olson, R.J. and Brandt, C.C. (1986) Areas having soil characteristics that may
indicate sensitivity to acidic deposition under alternative forest damage hypotheses.
Environmental Sciences Division Report No. 2720, Oak Ridge National Laboratory, Oak Ridge,
Tennessee.
Turner, M.G. and Gardner, R.H. (1991) Quantitative Methods in Landscape Ecology. Spinger-
Verlag, Berlin.
Tyler, G., Balsberg Pahlsson, A.-M., Bengtsson, G., Baath, E. and Tranvik, L. (1989) Heavy
metal ecology of terrestrial plants, microorganisms and invertebrates. Water, Air Soil Pollut.
47:189-215.
United States Department of Agriculture. (1992) Keys to Soil Taxonomy. Soil Conservation
Service, Soil Management Support Services Technical Monograph No. 19. Pocahontas Press, Inc.
Blacksburg, Virginia.
United States Department of Agriculture. (1991) Agricultural Chemical Usage: 1990 Field
Crops Summary. USDA National Agricultural Statistics Service and USDA Economic Research
. Service. U.S. Government Printing Office: 1991, 282-964/40207. Washington, DC.
United States Department of Agriculture. (1989) The Second RCA Appraisal. Soil, Water, and
Related Resources on Nonfederal Land in the United States. Analysis of Conditions and Trends.
USDA Misc. Publ. 1482. Washington, DC.
United States Department of Agriculture. (1981) Will There Be Enough Food?: The 1981
Yearbook of Agriculture. U.S. Government Printing Office: 1981 O-354-445.
United States Department of Agriculture. (1975) Soil taxonomy: A basic system of soil
classification for making and interpreting soil surveys. USDA Soil Conservation Service
Agricultural Handbook 436.
L-ll
-------
United States Department of Agriculture (1961) Land capability classification. USDA/SCS
Agricultural Handbook 210. Washington, DC.
United States Department of Agriculture (1951) Soil Survey Manual. U.S. Department of
Agriculture Handbook 18.
United States Department of Agriculture, National Agricutural Statistics Service. (1993)
Agricultural Sm-veys: Interviewer's ManualJune 1993-.May 1994. February 1993. Agricultural
Statistics Board. Washington DC. 20250. (internal document)
United States Department of Agriculture, National Agricutural Statistics Service. (1991a) 1991
Soybean Objective Yield Survey: Interviewer's Manual. May 1991. Agricultural Statistics Board.
Washington DC. 20250. (internal document)
United States Department of Agriculture, National Agricultural Statistics Service. (1991b)
Objective Yield S&E [Supervising and Editing] Manual. June 1991. (internal document)
United States Department of Agriculture, National Agricultural Statistics Service. (1991c)
Area Frame Design Information. Area Frame Section, Fairfax, VA. (internal document)
United States Department of Agriculture, National Agricutural Statistics Service. (1990) NASDA
Supervisory Enumerator Handbook, (internal document)
United States Department of Agriculture, Soil Conservation Service. (1990) The Universal Soil
Loss Equation with Factor Values for North Carolina. Technical Guide Section I-C. Raleigh, NC.
United States Department of Agriculture, Soil Conservation Service. (1983) National Soils
Handbook. 430-VI-NSH. U.S. Government Printing Office, Washington, DC. July 1983.
United States Department of Agriculture, Soil Conservation Service. (1981) Land Resource
Regions and Major Land Resource Areas of the United States. USDA/SCS Agricultural
Handbook 296, Washingtion, DC.
United States Department of Agriculture, Soil Conservation Service. (1970) Soil Survey: Wake
County, North Carolina. November 1970. In cooperation with North Carolina Agricultural
Experiment Station. U.S. Government Printing Office, Washington, DC.
United States Environmental Protection Agency. (1991a) Environmental Monitoring and
Assessment Program: Quality Assurance Program Plan. December 1991. Environmental
Monitoring Systems Laboratory. Cincinnati, Ohio.
Veitch, P.P. (1902) The estimation of soil acidity and the lime requirements of soils. /. Amer.
Chem. Soc. 24:1120-1128.
L-12
-------
Warren, H.V, Delavault, R.E. and Barasko, J. (1966) Some observations on the geochemistry
of mercury as applied to prospecting. Econ. Geol. 61:1010-1028.
Wasilewska, L. (1979) The structure and function of soil nematode communities in natural
ecosystems and agrocenoses. Polish Ecol. Studies 5:97-145.
Webster, R. and Oliver, M.A. (1990) Statistical methods in soil and land resource survey.
Oxford University Press, Oxford.
Wiegand, C.L., Richardson, A.J., Escobar, D.E. and Gerbermann, A.H. (1991) Vegetation
indices in crop assessments. Remote Sens. Environ. 35:105-119.
Williams, J.R., Jones C.A. and Dyke, P.T. (1984) A modeling approach to determining the
relationship between erosion and soil productivity. Trans. ASAE X: 129-144.
Wischmeier, W.H. and Smith, D.D. (1978) Predicting Rainfall Erosion Losses-A Guide to
Conservation Planning. Agriculture Handbook 537, U.S. Department of Agriculture, Washington,
DC.
World Resources Institute. (1991) Moving toward eco-development: generating evnironmental
information for decisionmakers. WRI Issues and Ideas. August 1991:1-8.
Yassoglou, N.J. (1987) The production potential of soils: Part II. Sensitivity of the soil
systems in Southern Europe to degrading influxes. In: Scientific Basis for Soil Protection in
the European Community, ed. H. Earth and P. L'Hermite. Elsevier Applied Science, New York.
pp. 87-122.
Yeates, G.W. (1971) Feeding types and feeding groups in plant and soil nematodes.
Pedobiologia 11:173-179.
Yeates, G.W. and Coleman, D.C. (1982) Nematodes in decomposition. In: Nematodes in Soil
Ecosystems, ed. D.W. Freckman. University of Texas Press, Austin, pp. 55-80.
L-13
-------
-------
APPENDIX 1
AGROECOSYSTEM RESOURCE GROUP MEMBERS
Members
C. Lee Campbell,
Technical Director
USDA/ARS
1509 Varsity Drive
Raleigh, NC 27606
(919)515-3311
E-Mail: CAMPBELL.CHARLES
Internet: lee_campbell @ ncsu.edu
Fax (919) 515-3593
Jeff Bay
Agroecosystem Resource Group
NC State University
1509 Varsity Drive
Raleigh, NC 27606
(919)515-3311
Internet: jeff_bay@ncsu.edu
Fax (919) 515-3593
Roy E. Cameron
Lockheed Environmental
Technologies & Systems Co.
Environmental Monitoring Department
1050 E. Flamingo Road
Las Vegas, NV 89119
(702) 734-3318
Fax (702) 796-8367
Charles R. Harper
Agroecosystem Resource Group
NC State University
1509 Varsity Drive
Raleigh, NC 27606
(919)515-3311
Fax (919) 515-3593
Craig M. Hayes
Dept. of Agric. Statistics
NC Department of Agriculture
1 West Edenton Street
P. O. Box 27761
Raleigh, NC 27601
(919) 856-4394
Fax (919) 856-4139
Allen S. Heagle
USDA/ARS
1509 Varsity Drive
Raleigh, NC 27606
(919) 515-3311
E-Mail: HECK.WALTER
Fax (919) 515-3593
Anne S. Hellkamp
Agroecosystem Resource Group
NC State University
1509 Varsity Drive
Raleigh, NC 27606
(919)515-3311
Internet: anne_hellkamp@ncsu.edu
Fax (919) 515-3593
George R. Hess
Agroecosystem Resource Group
NC State University
1509 Varsity Drive
Raleigh, NC 27606
(919)515-3311
Internet: george_hess@ ncsu.edu
Fax (919)515-3593
Michael J. Munster
Agroecosystem Resource Group
NC State University
1509 Varsity Drive
Raleigh, NC 27606
(919)515-3311
Internet: mike_munster@ ncsu.edu
Fax (919) 515-3593
Deborah A. Neher
Department of Plant Pathology
Box 7616
NC State University
Raleigh, NC 27695-7616
(919)515-669.0
Internet: deb_neher@ncsu.edu
Fax (919) 515-7716
Al - 1
-------
Members, continued
Gail L. Olsen
EG&G
1509 Varsity Drive
Raleigh, NC 27606
(919)515-3311
Internet: gail_olsen@ ncsu.edu
(919) 515-3593
Steven L. Peck
Agroecosystem Resource Group
NC State University
1509 Varsity Drive
Raleigh, NC 27606
(919)515-3311
Internet: steve_peck@ncsu.edu
Fax (919) 515-3593
John O. Rawlings
Department of Statistics
Box 8203
NC State University
Raleigh, NC 27695-8203
(919) 515-1941
Internet: john_rawlings@ ncsu.edu
Fax (919) 515-3593
Brian A. Schumacher
U.S. EPA
EMSL-LV, BAD
P.O. Box 93478
Las Vegas, NV 89193-3478
(702) 798-2242
Internet: EADBAS@VEGAS1.LAS.EPA.GOV
Fax (702) 798-2454
Charles N. Smith
U.S. EPA-ERL
College Station Road
Athens, GA 30613
(404) 546-3175
E-Mail: SMITH.C
Fax (404) 546-3340
Len Stefanski
Department of Statistics
NC State University
Box 8203
Raleigh, NC 27695-8203
(919)515-1945
Internet: len_stefanski@ ncsu.edu
Fax (919) 515-591
Mark Tooley
Agroecosystem Resource Group
NC State University
1509 Varsity Drive
Raleigh, NC 27606
(919)515-3311
E-Mail: TOOLEY.MARK
Internet: mark_tooley@ncsu.edu
Fax (919) 515-3593
Exofficio
John A. Dunning
USDA/ARS
1509 Varsity Drive
Raleigh, NC 27606
(919) 515-2778
515-2779
Fax (919) 515-3593
Susan E. Franson
U.S. EPA
EMSL-LV, BAD
P.O. Box 93478
Las Vegas, NV 89193-3478
(702) 798-2213
E-Mail: FRANSON.SUSAN
Internet: EADSEF @
VEGAS1 .LAS.EPA.GOV
Fax (702) 798-2454
Ray Halley
USDA-NASS
Room 4151 South Bldg.
14th Independence, SW
Washington, DC 20250-2000
(202) 720-2248
Fax (202) 720-0507
Al -2
-------
Ex officio. continued
Walter W. Heck, Associate Director, EMAP
USDA/ARS
1509 Varsity Drive
Raleigh, NC 27606
(919)515-3311
E-Mail: HECK.WALTER
Internet: walt_heck@ncsu.edu
Fax (919) 515-3593
Douglas G. Lewis
DEHNR
Division of Planning and Assessment
P.O. Box 2768
Raleigh, NC 27611-7687
(919) 733-6376
Fax (919) 733-2622
Steve Mannheimer
USDA-NASS
Rm 4801, South Building
14th & Independence Ave, SW
Washington, DC 20250-2000
(202) 720-0684
Fax (202) 720-8738
Bill Roth
USDA-SCS
P.O. Box 2890
South Agricultural Bldg.
Washington, DC 20013
(202) 720-1809
Fax (202) 720-4593
Robert Smith, Jr.
USDA-SCS
P.O. Box 2890
South Agricultural Bldg.
Washington, DC 20013
(202) 720-4452
Fax (202) 690-2019
Al -3
-------
-------
APPENDIX 2
NASS SURVEY QUESTIONNAIRES
The first part of this Appendix contains'the complete NASS questionnaire that was administered
in June 1993 for the Agroecosystem component of EMAP. This version of the June Enumerative
Survey will be used on the segments selected on the Hexagon Design. It contains the five extra
questions (7a, lOa, 51, 52, and 53) which the ARG, with the concurrence of NASS, has added
to the regular JES. Segments selected by the Rotational Panel Design will recieve the full JES,
including the extra questions.
The second part of this Appendix is the 1992 version of the Fall questionnaire. The second part
of this appendix contains the questionnaires used during the fall 1993 survey period.
A2- 1
-------
NATIONAL
AGRICULTURAL
STATISTICS
SERVICE
I U.S. Dept.of Agriculture
|Rm5809
I Washington. O.C 20250
1202-720-7017
1993
JUNE EMAP SURVEY
Authority for collection of information on the June EMAP Survey
is Title 7, Section 2204 of the U.S. Code. The information will be used
to prepare agricultural estimates. Individual reports are confidential.
Response is voluntary.
Form Approved
O.M.8. Number 0535-0089
Approval Expires 5/31/96
Area Version
NEBRASKA
Project Code 920
Segment Number:.
Tract Letter:
.County:.
State
Stratum
Segment
00000
Tract No
00
OFFICE USE - OPTIONAL
407
408
1. f need to make sure we have your (the operator's) correct name and address.
Name of Farm,
Ranch, or Operation:.
Name of Operator:
[first]
[Middle]
[Last]
Address:.
[Route or Street}
ICity]
Telephone: ( )_
[Area Code] [A/umber]
[State]
[Z/pCode]
2. On June 1, were the day-to-day decisions for this tract of land made by
an individual operator, by partners, or by a hired manager?
Q [Individual - enter 1 ]
Q [Partners-enter number of partners, including operator]
Q [Hiredmanager- enter8]
921
-------
PAGE 2
SECTION D - CROPS AND LAND USE ON TRACT
EMAP
How many acres are inside this blue tract boundary drawn on the photo (map)?. - - - -.
Now I would like to ask about each field inside this blue tract boundary and its use during 1993.
FIELDNUMBER
1 . Total acres in field
2. Crop or land use [Specify.]
3. Occupied farmstead or dwelling
4. Woods, roads, ditches, vacant farmstead, etc.
Permanent - not in crop rotation
Cropland - used only for pasture
6. Land in summer fallow
7. Idle cropland - Idle all during 1993
7a. Idle cropland in goverment programs
8. Two crops planted in this field for harvest
this year or two uses of the same crop?
[Specify second crop or use.]
Acres
9. Acres left to be planted?
10. Acres irrigated and to be irrigated
[If double cropped, include acreage
of each crop irrigated.]
a. Acres irrigated/to be irrigated by well water
15. Planted
16. For grain
1 7. Planted and to be planted
Ry
18. For grain
1 9. Planted and to be pla nted
20. For grain
21. Planted and to be planted
22. For grain
23. . Planted and to be planted
fVtrn
24. ' For grain
26. Sorghum Planted and to be planted
, [Exclude crosses
27. with Sudan.] For grain
26. Other uses of grains planted
(Acres abandoned, silage, etc.) Acres
jo Alfalfa and alfalfa mixtures .
' Cut and to be cut for hay
30. Grain -Cut and to be cut for hay
Hay
31. Wild hay -Cut and to be cut for hay
32. Other hay - Cut and to be cut for hay
33. Planted and to be planted
34. Following another crop
40. Dry Edible Beans Planted and to be planted
41 . Sugarbeets Planted and to be planted
43. Planted Oil varieties
Sunflower and to be
44. planted Non-oil varieties
48. Other crops Acres planted or in use
01
28
*
43
41
42
56
»
47
57
45
a Yes a NO
44
10
20
819
541
547
548
533
*
534
535
*
536
530
531
570
571
*
653
656
651
654
600
602
607
*
691
680
681
02
28
9
*1
42
56
~
47
57
45
a Yes a NO
44
;
10
;
620
819
541
547
548 ,
533
534
535
536
530
531
570
571
653
656
651
654
600
602
607
691
680
i
681
*
03
328 <
41
42
56
47
-
57
845
- . *
a Yes O No
44
610
620
819
541
547
548
533
534
535
536
C
530
«
531
e
570
571
e
o
e
656
651
«
654
600
o
602
607
a
691
«
680
*
681
-
04
41
42
56
57
45
D Yes D NO
44
10
20
-
19
41
547
548
533
534
535
536
530
531
570
571
656
651
*
654
600
602
607
691
680
681
05
328
"
^§§§§§$§$^
41
42
56
*
57
45
O Yes D No
44
10
20
19
540
41
47
48
533
534
535
»
536
*
530
531
570
571
*
656
651
654
602
607
691
680
681
»
-------
SECTION D - CROPS AND LAND USE ON TRACT (continued)
PAGES
[Enter total tract acres.]. ...
FIELD NUMBER
1, Total acres in field
. 2, Crop or land use [Specify.]
3. Occupied farmstead or dwelling
4, Woods, roads, ditches, vacant farmstead, etc.
Permanent - not in crop rotation
Cropland - used only for pasture
6, Land in summer fallow
7, Idle cropland -Idle all during 1993
7a, Idle cropland in goverment programs
8, Two crops planted in this field for harvest
this year or two uses of the same crop?
[Specify second crop or use.]
Acres
9. Acres left to be planted?
10, Acres irrigated and to be irrigated
[If double cropped, include acreage
of each crop irrigated.]
a. Acres irrigated / to be irrigated by well water
15. Planted
Winter Wheat
16. For grain
1 7. Planted and to be planted
Rue , ,
18, For grain
19. Planted and to be planted
Oats
20, For grain
21 , Planted and to be planted
Barley
22, For grain
23. Planted and to be planted
Corn
24, For grain
26, Sorghum Planted and to be planted
27. with Sudan.] For grain
28. Other uses of grains planted Use
(Acres abandoned, silage, etc.) Acres
29 Alfalfa and alfalfa mixtures
Cut and to be cut for hay
**« **.**.*«**.» .-. » X
30. Grain - Cut and to be cut for hay
u»u ,
nay
31. Wild hay -Cut and to be cut for hay
32. Other hay -Cut and to be cut for hay
33. Planted and to be planted
Soybeans
34. Following another crop
40. Dry Edible Beans Planted and to be planted
41 . Sugarbcets Planted and to be planted
43. Planted Oil varieties
44, planted Non-oil varieties
48, Other crops Acres planted or in use
06
828
843
841
842
«
856
847
857
845
D Yes D No
844
610
.
620
819
*
540
*
541
547
548
533
534
535
536
530
531
570
*
571
653
656
'
651
*
654
600
602
*
607
*
691
680
681
07
828
^^^^^^
841
842
856
847
'
857
*
845
*
D Yes O No
844
.
610
«
620
819
'
540
541
' .«
547
<
548
533
534
*
535
536
530
531
570
571
653
656
651
654
600
602
607
691
680
681
08
828
»
841
842
856
847
857
845
O Yes O No
844
610
620
819
t
540
541
-
547
548
533
534
535
.«
536
530
531
570
571
653
656
*
651
654
600
602
607
691
680
681
09
828
841
'
842
856
847
«b/
*
845
a Yes a NO
844
610
620
819
540
541
547
548
533
*
534
-
535
.
536
530
531
'
570
571
653
656
*
651
'
654
600
602
607
691
680
681
OFFICE USE -
TOTAL ACRES
00
840
SUM OF
PERMANENT
PASTURE
ACRES
350
I
EMAP
-------
PAGE 4
SECTION D - Continued
[Refer to photo and point out blue tract boundaries]
51. Are there any ponds, either constructed or naturally formed, within the
blue tract boundaries? [Include re-use or tailwater pits and animal
waste lagoons.] [Say if needed to operator: A pond is a permanent still
body of open water less than 40 acres, which has water at least
seasonally.]
D YES D DON'T KNOW * 2
n NO »3
[Enter code, then go to Item S3]
803
a. How many ponds are there within these boundaries ? Dumber
805
52. I would like to ask you a few questions about each of the ponds on this tract.
Pond Number
a. How many acres are in
Pond ?
051
052
053
054
Acres
b. What is the primary use of Pond
Select a code from the Pond Use card.
071
072
073
074
055
075
v *"
POND USE COOES
1- Aquaculture
2- Erosion Control
3- Flood Control
4- Fish or Wildlife
5- Irrigation: Re-Use Pit
6- Irrigation: Other
7- Lagoon or Waste Storage
8- Water for Livestock
9- Sediment or Chemical Catchment
10-Water-based Recreation
11 -None of the above, please specify (_
12-None of the above, please specify (
[Continue Item 52 for additional ponds on tract, then go to Item S3]
53.
Of the permanent pasture inside this blue tract boundary,
how many acres have been fertilized at least once during the
last five years? [Include use of manure and lime.] ,
Acres
804
[Go to Total Acres Operated Section]
EMAP
-------
Section D, Continued
PAGES
POND USE SUPPLEMENT
[Use with Item 52, Page 4, if
more than five ponds on tract]
Pond Number.
a. How many acres are in
Pond I
056
057
058
059
060
-Acres
b. What is the primary use of Pond
Select a code from the Pond Use card.
076
077
078
079
080
POND USE CODES
1- Aquaculture
2- Erosion Control
3- Flood Control
4- Fish or Wildlife
5- Irrigation: Re-Use Pit
6- Irrigation: Other
7- Lagoon or Waste Storage
8- Water for Livestock
9- Sediment or Chemical Catchment
10-Water-based Recreation
11-None of the above, please specify (
12-None of the above, please specify (
[Go to Page 4, Item S3]
EMAP
-------
PAGE 6
NOTES AND CALCULATIONS
EMAP
-------
EMAP
PAGE?
TOTAL ACRES OPERATED
\IF HIRED MANAGER CHECKED ON FACE PAGE (921 = 81 GO TO ITEM 21
1. Now I would like to ask about the total acres operated under this land arrangement.
Include farmstead, all cropland, woodland, pastureland, wasteland, and
government program land.
901
a. On June 1, how many acres did this operation own? +.
902
b. Rent from others? [Exclude land used on an animal unit month (AUM) basis] t.
905
d. Rent to others?.
poo ~~
e. Then the total acres operated under this arrangement was ITEM 1a + 1b-1d : -=->
[GO TO ITEM 3]
2. Now I would like to ask about the total acres operated as a hired manager. u
On June 1, how many acres were operated for others as a hired manager 1904
under this land arrangement? »
3. Does this include the farmstead, all cropland, woodland, pastureland,
wasteland, and government program land? [If not, make corrections]
4. What was the peak number of cattle and calves on hand on this operation *>70
at any time during 1993? ; Head
5. What was the peak number of hogs and pigs on hand on this operation 300
at any time during 1993? Number!
6. During 1993, what was the total storage capacity of all the bins, cribs, sheds,
and other structures normally used to store whole grains and oilseeds on 808
the total acres operated? Bushels
-------
PAGES
EMAP
CONCLUSION
[Check type of respondent and enter code]
D Operator/Manager « 1
D Spouse - 2
D Other [Enter name below] 3
D Obs R =4
D Obs NR - 5
[Record name of respondent if not the Operator or spouse].
101
Enumerator:
Date:
[Notes about respondent's answers or other data collection problems]
May
28-148
29-149
30-150
31-151
June
01-152
02-153
03-154
04-155
June
05-156
07-157
08-158
09-159
10-160
11-161
12-162
13-164
14-165
June
15-166
16-167
17-168
18-169
19-170
20-171
21-172
Enumerator IO
098
Julian Date
987
Off ice Use
Quality Rating
100
-------
1993 JUNE AGRICULTURAL SURVEY
POND USE CARD
[for use with Question 52, all parts]
What is the primary use of Pond
? [Select the code that best fits.]
1- Aquaculture
2- Erosion Control
3- Flood .Control
4- Fish or Wildlife
5- Irrigation:
Re-Use Pit
6- Irrigation:
Other
7- Lagoon or
Waste Storage
8- Water for
Livestock
9- Sediment or
Chemical
Catchment
10- Water-based
Recreation
Fish farming, fish hatcheries, and aquaculture rotated with cropland
(such as: catfish farming and crawfish farming.)
Ponds used to reduce loss of soil from the land (in gullies, etc.)
Dams used to prevent flooding downstream.
Areas designated and managed for fish or wildlife habitat (such as: fish or
game reserves, wildlife parks, or wildlife food, cover, and water facilities.)
Bodies of water used to collect irrigation water so that it may be pumped
back onto the same or another field during future irrigation operations.
Also called tail water pits.
Bodies of water used for artificial watering of land by surface flooding,
sprinkling orsubirrigation methods.
Animal waste lagoons, settling ponds, or other waste storage or treatment
facilities associated with agricultural operations
Uses for water supplies provided to any domestic animal produced or kept
primarily for farm, ranch, or market purposes, including beef and dairy
cattle, hogs, sheep, goats, and horses.
Ponds used to protect water quality downstream by collecting sediment or
chemicals that might be washing off of upstream fields, feedlots, etc.
All water-based recreation (such as: fishing, boating, skiing, swimming,
diving, canoeing, and waterfowl hunting).
ME
-------
NATIONAL
AGRICULTURAL
STATISTICS
SERVICE
U.S. Department
of Agriculture
Washington. OX.
20250
1993
LAND USE STUDY
Form Approved
OMB Number Q535-Q218
Expiration Date 09/3OS96
PROJECT CODE 920
Nebraska
EMAP
State
ID
Tract
Field
Sample No.
CONTACT RECORD
DATE
TIME
NOTES
COMPLETION CODE
3 -COMPLETED i
8 -REFUSAL 001
9 -INACCESSIBLE |
OFFICE USE
010
i INTRODUCTION
j [Introduce yourself and ask for the operator. Rephrase in your own words.]
I The Nebraska Agricultural Statistics Service is conducting a survey of farm chemical use and cropping practices
! as they relate to the environment. Information from this and other surveys will be used to assess the agricultural
! and environmental conditions within Nebraska. This information will be used only for environmental analysis.
; Authority for collection of this data is Title 7, Section 2204 of the U.S. Code. Response to this survey is
i confidential and voluntary, but important to obtain a representative analysis.
i We encourage you to use your farm records during the interview.
BEGINNING TIME [MILITARY]
1002
[Show aerial photograph to respondent and identify sample field.]
1. Did you make any of the day-to-day farming decisions for this field in 1993?
n YES- [Continue.]
Q NO - [Conclude the interview, and ask for the respondent's assistance in
locating the correct operator.]
-------
-2-
A
FIELD IDENTIFICATION
1. How many acres are in this field? (Include woods, waste, etc.)
2. How many acres in this field are considered Cropland?
ACRES
003
004
3. Do you (Does this operation) own this field or rent it?
[Enter code 1 for OWNED;
enter code 2 for RENTED, LEASED or USED RENT FREE.]
CODE
005
JL
1.
LAND USE and TILLAGE HISTORY
Now I'd like to obtain the land use history for this field for the past three years.
Please report all crops grown, including cover crops. Let's start with the 1993 crop
year. What was the field used for in 1993?
[Use a separate line for each use of the field each year.]
LINE
NO.
01
02
03
04
06
07
10
11
1
CROP
YEAR
1993
1993
1993
1993
1992
1992
1992
1992.
1991
1991
1991
1991
2
CROP
or
LAND
USE
! Write in.]
3
CODE
020
021
022
023
024
025
026
027
028
029
030
031
4
How many
acres were
planted?
[If column 2 use is
not a crop, record
acres of reported
land use.]
051
052
053
054
055
056
C>57
058
059
060
061
062
How many
acres were
harvested?
066
067
068
069
6
What
was the
average
yield
per
acre?
081
082
083
084
7
[Record
reported
unit]
8
How much
did the
[unit recorded
in column 7]
weigh!
I If reported unit is
pounds enter 1.]
096
097
098
099
180
CODE
-------
-3-
CROP and LAND USE CODES
1
6
5
110
3
656
11
15
ALFALFA. HAY
CORN,
FIELD
SILAGE
SWEET
DRY BEANS
GRAIN HAY
HAY, ALL OTHER
OATS
SORGHUM,
25
24
26
28
30
34
GRAIN
SILAGE
SOYBEANS
SUGAR BEETS
SUNFLOWERS
WHEAT, ALL
OTHER CROPS (Specify)
land use and tillage historycontinued
OTHER
301
304
901
305
313
314
306
LAND USES
PASTURE
IDLE CROPLAND
FALLOW
WOODLAND
WETLAND
RANGELAND
NON-AG
OTHERCSpeofy)
B
13
What erosion
control methods
were used on this
field?
10
What was
the
seeding
rate per
acre?
11
What was the
12
When was
this crop
harvested?
When was
this crop
planted?
average row
spacing [if
app//cafa/e]?
Erosion Control Codes
NONE
TERRACING
CONTOUR
CROPPING
OR PLOWING
STRIP CROPPING
GRASSED
WATERWAYS
COVER CROPS
OTHER (SPECIFY)
-------
-4-
B
land use and tillage historycontinued
B
For the remainder of this interview we will be asking for information for only
the 1993 crop year.
2. [Ask only if crops, idle cropland and/or government program
land was reported in item /.]
Now I'd like to obtain the tillage history for this field for the
1993 crop year.
1
CROP
OR
LAND USE
ItVnre/nl
2
CROP
CODE
106
107
108
109
3
What type of tillage was
used on this field in 1993?
[fnter highest number if more
than one.]
1 NONE
2 NO-TILL
3 RtDGE-TILL
4 MULCH-TILL (OR OTHER
CONSERVATION TILLAGE)"
5 CONVENTIONAL
(MOLDBOARDPLOW)
6 OTHER CONVENTIONAL
126
127
128
129
3. Has the Soil Conservation Service evaluated this field since 1985?
D YES- [EnterCode 1 and continue.]
D NO - [Skip to Section C, page 6.]
a. Has the Soil Conservation Service classified this field as
"Highly Erodible"?
D YES - [Enter Code 1 and continue.]
D NO - [Skip to Section C, page 6.]
CODE
130
CODE
131
-------
-5 -
NOTES and CALCULATIONS:
-------
-6-
FERTILIZER USAGE HISTORY
MANURE USAGE
1
Was manure applied to this field at any time during the 1993
crop year? (Include lagoon waste and slurry. Exclude sludge.)
Q YES- [Enter Code 1 and continue.]
£] NO- [Go to item 3, page 7.]
COPE
294
2. Now I need to know about the manure applications for all crops
grown in this field this year.
MANURE TYPES
1
2
3
4
5
6
7
8
CATTLE
HOG
SHEEP
GOAT
CHICKEN
TURKEY
HORSE
OTHER
(Specify)
1
CROP
OR
LAND USE
( Write in,]
CROP
CODE
205
206
207
3
What kind
of manure
was applied
during 1993
crop year?
[Enter code.]
201
202
203
4
How much
was applied
per acre?
210
211
212
5
UNIT
CODE
POUNDS = 1
CWT = 2
TONS = 3
219
220
221
6
How many
acres were
treated?
[Acres.}
240
241
243
7
When was
this applied?
MMDDYY
228
229
230
8
Was this
slurry
[enter 1 ] or
solid
[enter 2]?
250
251
252
-------
-7-
fertiiizer usage historycontinued
COMMERCIAL FERTILIZER USAGE
3. Were commercial fertilizers applied to this field at any
time during the 1993 crop year?
[] YES- [E nter Code 1 and continue.]
Q NO - [Skip to Section D, page 8.}
4. For each fertilizer applied to this field in the past year, I
need some information on the analysis applied and the
amount applied. What was the first fertilizer you applied?
(Include sided ressing.)
[Complete table using a separate line for each application.]
CODE
283
T-TYPE
2
TABLE
001
MATERIAL UNIT CODES
1 Pounds of materials
12 Gallons of materials
15 Ounces (liquid)
28 Ounces (dry)
19 Actual nutrients (pounds)
L
I
N
E
01
02
03
04
05
06
07
08
09
10
11
12
1^
14
15
1
CROP
OR
LAND USE
[Write In.]
2
CROP
CODE
284
284
284
284
284
284
284
284
284
284
284
284
284
284
284
3
MATERIAL USED
I Enter percent analysis or
actual pounds of plant nutrients
applied per acre.]
N
NITROGEN
285
285
285
285
285
285
285
285
285
285
285
285
285
285
285
P205
PHOSPHATE
286 '
286
286
286
286
286
286
286
286
286
286
286
286
286
286
K2O
POTASH
287
287
287
287
287
287
287
287
287
287
287
287
287
287
287
4
How much
was applied
per acre per
application?
(Leave this
column blank if
actual nutrients
were reported \
288
288
288
288
288
288
288
288
288
288
288
288
288
288
288
5
[Enter
unit
code.]
289
289
289
289
289
289
289
289
289
289
289
289
289
289
289
6
How
many
acres
were
treated?
290
290 . . '
290
290
290
290
290
290
290
290
290
290
290
290
290
7
When was
this applied?
MMDDYY
291
291
291
291
291
291
291
291
291
291
291
291
291
291
291
T-TYPE
0
TABLE
000
LINE
00
OFFICE USE
-------
-8-
D
PEST MANAGEMENT
D
1. Were any pesticides (such as herbicides, insecticides, fungicides, nematicides,
defoliants or growth regulators) applied to this field in 1993? CODE
Q YES - [Enter Code 1 and complete table.]
Q NO - [Go to item 2, page 11.]
350
T-TYPE
3
TABLE
002
L
1
N
E
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
1
Crop
or
Land USQ
I Write In.]
2
Crop
Code
301
301
301
301
301
301
301
301
301
301
301
301
301
301
301
3
What
product
was
applied to
the [crop]?
(Enter Code,]
302
302
302
302
302
302
302
302
302
302
302
302
302
302
302
4
Was this
product
bought in
liquidordry
form?
[Enter L or D.]
5
When
was it
applied?
MMDDYY
303
303
303
303
303
303
303
303
303
303
303
303
303
303
303
6 C
How much
was applied
per acre
per
application?
RATE
304
304
304
304
304
304
304 ;
304
304
304
«
304
304
*
304
304
304
)R 7
What was
the total
amount
applied
per
application?
305
305
305
305
305
305
305
305
305
305
305
305
*
305
305
305
-
[ENUMERATOR NOTE: If any chemical is reported for which no code is on the listing sheet,
complete the appropriate line in the table above (leaving out the unknown product code), and
record the name and a description of the chemical below.]
LINE NUMBER
CHEMICAL NAME & FORMULATION
LIQUID OR DRY PRODUCT
EPA NUMBER
-------
D
-9-
pest managementcontinued
D
APPLICATION METHODS
1 Aerial
2 Broadcast ( Ground)
3 Foliar Application
4 Irrigation Water
5 Band In/Over Rows
6 Alternate Rows
7 Directed Spray
8 Chiseled/Knifed-in
9 In Furrow
10 Spot Treatment
APPLICATION DECISION REASONS
1 Routine or Preventative Schedule
2 Extension Recommendation
3 Scouting or Observation in the field
(weeds, insects, crop damage)
4 Weather
(Favorable for pests or diseases)
5 Field History
6 Computer Prediction
7 Other (Specify)
L
1
N
*
m
02
fa
04
9*
06
ft?
0*
0$
10
11
12
13
14
15
8
[Enter Unit
Code.]
1 Pound
12 Gallon
13 Quart
14 Pint
15 Ounce
306
306
306
306
306
306
306
306
306
306
306
306
306
306
306
9
How was
it
applied?
[Enter Code.]
307
307
307
307
307
307
307
307
307
307
307
307
307
307
307
10
How
many
acres
were
treated?
ACRES
308
308
308
308
308
308
308
308
308
308
308
308
308
308
308
11
What was the
primary reason
you decided to
make this
application?
(Enter Code. I
309
309
309
309
309
309
309
309
309
309
309
309
309
309
309
12
[If this was an
insecticide],
what was the
primary target
pest?
(Enter Code.)
310
310
310
310
310
310
310
310
310
310
310
310
310
310
310
-------
-10-
NOTES and CALCULATIONS:
-------
-11 -
D
pest managementcontinued
D
T-TYPE
0
TABLE
000
LINE
00
Now I'll be asking about some specific pest management
practices for crops grown in this field this year.
2. Are you currently practicing Integrated Pest Management or (IPM),
for insect or mite control in this field?
D YES- [EnterCode 1.]
D NO- [EnterCode2.]
D DON'T KNOW - [Enter Code 3.]
CODE
320
3. Were insects, mites or their damage monitored in this field?
Q YES- [EnterCode! and continue.]
D NO- [Go to item4.]
321
a. Who did the majority of
pest/damage monitoring?
[Enter code.]
1 Self or Family Member
2 Employee
3 Extension agents
4 Chemical Dealer
5 Commercial Scouting Service
6 Someone else
(Specify)^
323
1 Crop damage observed
b. What was the primary 2 Field insect counts collected
method used to monitor 3 insect traps
pests/damage in this field? 4 Extension reports 324
[Enter code} J5 otner
(Specify)
4. Have you released beneficial insects in this field to control
insects or mites?
Q YES- [EnterCodel and continue.]
rj NO- [Go to item5.] ,
OFFICE USE
a. What beneficial insect was released? [Write in ] j
CODE
I 327
b. What was the primary target pest? [Enter code.]
5. Do you currently have a crop rotation plan for this field? 328
D YESr [Enter Code 1 andcont/nt/e.j
D NO- [Go to Sect/on E, page 13.] YEARS
329
a. What is the length in years of your crop rotation on this field?
OFFICE USE
-------
-12-
NOTES and CALCULATIONS:
-------
-13-
FIELD OPERATIONS
Now I'd like to find out what operations were performed in this field for the 1993 crops. To
do this we'll collect information about each piece of equipment and machinery used on the
field. Let's begin with the first operation performed after the 1992 crop harvest. [If 2 or more
implements were used at the same time, report them on the SAME LINE.]
I,
1
N
£
01
02
03
04
OS
06
0?
Oft
Ofr
$0
*1
1?
13
U
1*
1
What crop
was this
for?
I Write /n. |
2
Crop
Code
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
3
What type of
operation
was done?
I Write ln.\
4
What
implement(s)
was used?
{Enter code.\
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
5
When was
this operation
done?
MMDDYY
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
[ENUMERATOR NOTE: If a machine is reported for which no code is on the listing sheet, complete
the appropriate line in the table above (leaving out the unknown machine code). Record the line
number, the name and a description of the machine below.]
LINE NUMBER MACHINE NAME & DESCRIPTION
-------
-16-
H
SOIL SAMPLE RECORD KEEPING
H
[ENUMERATOR NOTE: Place a record keeping label for each soil sample on the left side
of the page. Then enter the information for that sample on the right.]
1. Was this sample's field tilled before the sample
was taken? [YES = 1]
930
Date Date
Sample Sample
Taken Mailed
Federal
Express
Shipping
Number
r
r
r
r
r~
OFFICE USE
Iqan
-------
1993
LAND USE SURVEY
RANGELAND SUPPLEMENT
State
ID
Tract
Field
Sample No.
3
8
9
COMPLI
-COMPLETED
- REFUSAL
-INACCESSABLE
.TIONCODE
949
1. Has this pasture ever been cropped or tilled?
G YES- What year was the most recent cropping or tillage?.
0 NO- [Continue.]
2. Now I'd like to know about materials applied to this field.
1
Material
Fertilizer
Lime
Manure
2
Was
[mater/a/]
ever
applied?
CYES = il
951
952
953
3
What year was
[mater/a/] last
applied?
YY
954
955
956
950
19
3. What are the main plant species in this pasture?
[Write In up to 2 species.]
OFFICE USE
957
958
-------
-------
APPENDIX 3
ENUMERATOR MANUAL FOR SAMPLING SOIL
GENERAL
This section describes the procedures for collecting soil samples from the sample field for
chemical and nematode analysis. Just as with information from the interview, all information
is confidential. This includes any information about the soil collected from this field. The
laboratories doing the analyses will not know where the samples were obtained or whose land
they are from. Results of the analysis will be sent back to the Nebraska Agricultural
Statistics Office within 6 months and from there they will be sent back to the farmer. Only
authorized personnel will have access to this information.
Equipment to be taken into the field:
right angle
3 yellow stakes
10 red stakes
assembled probe
plastic bucket
screwdriver and/or wooden block with bolt
orange safety vest
disposable shoe covers (if required)
If any equipment is missing or breaks, call the State Office to request replacement parts.
Parts can be delivered by Federal Express the following day.
Before entering the field:
1. Check the instruction sheet (Section IV) included in your enumerator kit to determine
whether the field will be sampled in duplicate or not. Although most fields will have
soil collected from only one sampling line, some fields will require soil from two
different sampling lines. Also, some fields may have twice as much soil collected
from a sampling line. How many sampling lines you will mark and how much soil
will be collected from each is listed on the instruction sheet included in the
enumerator kit for that field.
2. Place the record keeping label from the enumerator kit onto the back of the
questionnaire.
3. Check the number of paces along and into field to determine the midpoint of the
sampling line.
4. If this sample has also been selected for SCS soil samples, and the farmer has signed
the consent form, record the rows and paces onto the consent form, so SCS soil
scientists will be able to locate the sampling line again later. The starting corner,
A3 - 1
-------
rows, and paces and the location of the sampling line will also need to be drawn on
the aerial photo.
I.
LOCATING THE 5-ACRE SAMPLING AREA IN THE FIELD
The starting corner into the field will be the first corner of the field which is reached when
approaching the field. If the field has NO definite corners, begin at the point which is most
accessible by car. If the field is to have two sampling lines marked, the second closest corner
will be the starting point for the second line.
Follow these procedures when locating and laying out sample units. You will locate the
midpoint of the sampling line, then mark out and take samples from 10 places on each side of
the midpoint.
STEP 1 Determine the starting corner. This will be the first corner you reach when
approaching the field.
STEP 2 Walk along the end of the field the required number of rows as marked on the label.
If there
are no discernible rows in the field,
count off paces instead of rows. This
will be your entry point into the field.
In the example in Figure 1 this is 12
paces. If the field border takes an
abrupt turn, follow the border, but do
not count paces that are not in the
initial direction of walking. Continue
counting once you are again moving in
the starting direction (Figure 1).
starting
corner
STEP 3 Turn to face into the field and
then walk the specified number
of paces into the field. Start
your first pace about one and
one-half feet outside the
plowed edge of the field. In
Figure 1 this is 7 paces.
entry
point
1st yellow
stake
Figure 1. Starting point in odd-shaped fields.
*IMPORTANT*
Do not count paces in any areas that are not considered cropland acres
(stop counting at the start of each such area and resume counting at the
other side). However, any blank or unplanted areas in the field that
were not deducted should be included in the pace count.
A3 -2
-------
*EXCEPTIONS*
1. Bounce back rule. When pacing along the edge of the field, or pacing into the
field, if you reach the opposite end or side of the field and still have not taken the
required number of paces, complete as many paces as you can until you are outside
the field, then turn around and walk back in the direction from which you came until
the required number of paces has beeri stepped off. (Remember to count both your
exit and re-entry pace into the field.)
2. Odd-Shaped fields. The bounce back rule still applies. As in Figure 1, you should
count paces along the field edge only while walking in the initial direction.
STEP 4 After you have taken the last of the required paces, you will mark out the center of
the sampling line. Place'a yellow stake at the toe of your shoe. Lay the right-angle
on the ground with the point at the hinge touching the stake. Place a second yellow
stake at the top right corner of the V (Figure 2).
Figure 2. Placement of yellow reference stakes at center of transect.
STEP 5 Flip the triangle 180° so that it forms an upside down V with the hinge point still
touching the stake. Place a third yellow stake at the bottom left corner of the right-
angle to form a straight line with all three stakes (Figure 2).
*IMPORTANT*
Then pick up the right angle and 10 red stakes and carry them with you.
You will need them as you pace off the sampling line.
A3-.3
-------
STEP 6 Use the ten RED stakes to mark out the first half of the sampling line. Beginning at
the center yellow stake, take two and one-half paces, along the straight line made by
the three yellow stakes. Place a red stake at the toe of your shoe. This will be stake
number 1.
STEP 7 Walk five paces from the stake and place a second red stake (stake 2) at the toe of
your shoe.
paces
Figure 3. View of entire diagonal sampling line across the field.
STEP 8 Repeat step 7 until all 10 red stakes have been inserted into the soil. This line
(called the sampling line) should be diagonal across crop rows (Figure 3).
*IMPORTANT* 90-Degree Rule. If you reach a border of the field while walking off
the line, turn 90-degrees, so that you are again facing into the field and
proceed with your paces and inserting stakes (Figure 4). Repeat the 90-
degree rule for each border you reach. If the midpoint of the line falls
on a corner or an edge use this rule to make sure both halves of the
sampling line fall in the field.
STEP 9 Now that you have marked out 10 stakes you will turn around and take soil samples
from each one until you are back at the yellow center stakes. To do this, first close
the right angle. When closed, the right angle becomes a 3-foot ruler. Use it to
measure off 3 feet from the last stake (number 10), and take one soil core (Figure 5).
Instructions for taking the soil core begin in Section II below. In some fields, two
cores will be taken from each stake. Instruction sheets in your enumerator kit will
A3-4
-------
Sampling line
midpoint
Figure 4. 90-degree rule for each border encountered.
tell you if double cores need to be taken.) Pull the red stake and place it in the
bucket along with the soil after taking the soil core.
STEP 10 The right angle is also marked with a black line at 1.5 feet. Repeat step 9 at
the next red stake except use the marking on the right angle to take the soil
sample 1.5 feet from the red stake. Repeat this process at each red stake,
alternating between taking samples 3 feet and 1.5 feet from each stake.
Distance Soil Sample
Stakes Taken From Stake
1,3,5,7,9 1.5'
2, 4, 6, 8, 10 3'
STEP 11 Once you have taken the ten samples (twenty for double sampled fields) from
this half of the sampling line you will need to repeat steps 6 through 10,
starting at the center yellow stake, walking in the opposite direction from the
first half of the sampling line to pace out and take ten (again, twenty if double-
sampled) cores at the ten points on the other half of the sampling line.
STEP 12 After taking the second set of ten soil cores, remove the remaining yellow
stakes and exit field. (DO NOT remove all stakes for fields to be sampled by
SCS, see note below.)
*IMPORTANT*
AFTER bagging all the soil samples, make sure all stakes, soil probe,
and bucket are free of soil before leaving the field area. Do not reuse
A3-5
-------
take core
here
take core
here
[*NOTE*
stakes in another field.
If possible, rinse all
equipment thoroughly
with water at the farm.
If you are taking
samples from several
fields it is important to
keep any soil from
one field from mixing
with soil from another
field. If you were
wearing plastic
"boots", remove them
after all work in that
field is complete.
.Place them in a garbage bag for disposal in the garbage or at a landfill.
Do not re-use them.
FOR FIELDS SELECTED FOR ADDITIONAL SOIL CONSERVATION
SERVICE (SCS) SAMPLES:
etc.
sampling line
Figure 5. Even- and odd-numbered cores are taken 3' and
.1.5', respectively, from the yellow stakes.
If the farmer has consented, SCS soil scientists will revisit these fields to take
additional soil samples. SCS personnel will need to know where you took your
samples, so tie some flagging ribbon at your starting corner. After you have taken
your soil sample, DO NOT take all of the stakes out of the field. Leave the yellow
stakes marking the center of the sampling line and the red stakes marking the ends
of the sampling lines in the field. When you have finished, mark the rows, paces,
starting corner and location of the sampling line on the aerial photograph. Deliver
bqth the aerial photo and a copy of the consent form to the nearest SCS office.]
H. TAKING THE SOIL SAMPLE
For each core, push the soil probe straight down into the soil, without twisting, to the depth
that fills the length of the tube (8"). Pull up the tube and push it down onto the bolt (in
wooden block) to empty the core into the bucket; if soil sticks, a large screwdriver may be
necessary to scrape the core out of the tube. If the core is less than 8" in depth, take another
core within 6" of the same location. If there is heavy residue on the field push it aside with
your foot before inserting the probe, but do not scrape.the surface of the soil. Record any
problems on the survey form. Combine all 20 cores from the sampling line in the bucket.
If you pull the probes from the ground and see that the core is less than 8" in depth, discard
that core and take another within 6" of the same location. If you are still unable to get an 8"
core then a 6" core is acceptable. If after several tries you cannot get even a 6" core, collect
two 4" deep cores. Cores MUST be at least 4" in length. If it is impossible to get even a 4"
A3 -6
-------
core, do not take that sample, record that point as missing and move on to the next red stake.
DO NOT take more cores elsewhere to "make up" for the loss. Record any problems on the
survey form. Combine all cores from the sampling line in the bucket.
NOTES:
1. In the probe set, three tips will be available for. the core tube for sampling soil under a
range of conditions. The regular tip (with 2 notches on the rim), should be used for
most soils. If the soil is especially dry or stony you may want to use the mini tip (1
notch) or super duty tip (3> notches). A thin metal rod for changing tips is included in
each probe set, although a pair of vice grips may also be useful.. Decide if you need
another tip before you begin sampling. You will probably not want to change them
very often; they are not easy to change.
2. Discard any rocks larger than 1" diameter. Do not remove plant or other organic
debris from the soil surface, but keep as part of the sample. You may push heavy
crop residue away with your foot, but do not scrape the soil surface as you do it.
3. If the soil is waterlogged, do not take nematode samples. Come back to the field at
another time. Saturated soil in the. plastic bag can become anaerobic (no oxygen) and
the nematodes may die.
III. LABELLING AND TRANSPORTING THE SAMPLE
When all cores have been deposited into the bucket for 1 composite sample, use your hands
to break up the clumps gently (rough treatment of the soil may kill organisms living there) to
clumps about 1/4" or less in diameter. Mix the soil thoroughly. Two different size bags are
included in your enumerator kits for sending soil for laboratory analysis. The larger bags will
be used to hold soil for chemical analysis. The smaller bags will be sent to a different
laboratory for nematode analysis.
Note: The following directions apply to the NASS1 type samples. Variations are discussed
below.
From the bucket of mixed soil, fill the plastic beaker all the way to the top with soil and pour
into a small plastic bag. Close the bag with a wire tag marked with the appropriate sample
number. Pour the rest of the soil into the large bag and close it with another wire tag with
the appropriate sample number. Do not write' on the tag. To ensure confidentiality, no
identifying information about the operation (such as names or addresses) will be associated
with the samples sent to the laboratory.
*IMPORTANT*
Place the record keeping label for each sample on a record keeping
form and fill in the completion code. Also indicate whether, the field
A3 -7
-------
was cultivated or not. A field is considered cultivated unless it contains
untilled stubble or a standing crop. Also enter the date the sample was
taken, the date it was mailed and the Federal Express airbill number
found on the shipping label. (The Federal Express airbill number is 10
digits and is located in the upper right hand corner of the airbill). Mail
the record keeping forms and pink customer copies from the back of
Federal Express airbills to the State Office with your completed
questionnaire.
Store all samples in the cooler (in the shade!) at all times to avoid
temperatures lethal to soil organisms!
Mail the beaker (small) sample in the small mailing envelope also marked for Nematode
samples and the large sample in the large mailing envelope marked for Chemical analysis.
ID numbers beginning with 1 are for Nematode samples that will be analyzed in California.
Soil sample IDs beginning with 2 are for Chemical samples that will be sent to Lincoln,
Nebraska for analysis.
Send one sample per envelope using the pre-addressed, postage-paid envelopes provided. Use
strapping tape to close the mailing envelopes. You will be instructed where to drop off the
samples for Federal Express pickup. All samples should be mailed on the same day of
sampling or first thing the following day. For pickup of Federal Express packages, call 1-
800-238-5355. The time of the latest pick-up time of a day is available from Federal Express
on a 24-hour a day basis by calling the 1-800 number and providing the zip code .of the
pickup address.
*IMPORTANT*
Do not mail nematode samples on Friday. Hold them indoors in the
cooler over the weekend, and send them first thing Monday morning.
IV. EXCEPTIONS FOR MULTIPLE SAMPLES:
Some of the fields sampled will have extra soil collected. These fields will be identified in
the cover sheet included in your enumerator kit for the field.
Some fields will have samples collected from each of TWO different sampling lines (NASS2,
NASS3, HEX5, HEX6). The second closest corner to the original starting corner will be used
as the starting corner for pacing to the midpoint of the second sampling line. The number of
rows and paces to the midpoint will be different for the second sampling line. The sampling
procedure and preparing samples will be the same as described above.
Also, in a small number of fields (NASS3, HEX6), the second sampling line will be used to
take twice as many cores. This soil will be split into two equal-volume samples for both "C"
and "N" samples. Instead of taking one core at each red stake, take TWO 8"-deep cores for a
A3 - 8
-------
total of 40 cores, instead of 20, from the sampling line. Put them all in the same bucket.
Mix the double sample thoroughly by hand. From the bucket, remove two beaker fulls of
soil, each one to fill an "N" bag. Divide the remaining soil evenly between two "C" bags.
Close each bag with a wire tag with the appropriate sample number and mail each bag
SEPARATELY in it's corresponding mailing container (remember only one bag should be
placed in each mailing envelope). There will be a total of 3 chemical and 3 nematode bags
and their respective mailing containers for these fields. Since there will be no nematodes
samples sent from HEX fields, discard the amount of soil you would have used for those
samples before packaging them. That way all of the samples going for chemical analysis will
be about the same weight.
If you have not already done so, attach labels to the bag tags and record keeping sheet for
each bag of soil. The label going on the tag should have nothing but the sample id number.
V. INSTRUCTION SHEETS
Below are examples of the type of instruction sheets that will be included in the enumerator
kit to describe the different types of fields to be sampled.
NASS1
Check to make sure that NASS1 appears on the label for this field. Use the rows and paces
marked on the label to locate the midpoint of the sampling line.
For this field you will mark out:
1_ (one) sampling line
Take 20 (twenty) soil cores (1 from each stake).
This line will have both:
1 (one) chemical analysis bag
1 (one) nematode analysis bag
NASS2
Check to make sure that NASS2 appears on the label for this field. Use the rows and paces
marked on the label to locate the midpoint of each sampling line.
A3-9
-------
For this field you will mark out:
2 (two) sampling lines
SAMPLING LINE 1:
Take 20 (twenty) soil cores Q. from each stake).
This line will have both:
1 (one) chemical analysis bag
1 (one) nematode analysis bag
SAMPLING LINE 2:
Take 20 (twenty) soil cores Q. from each stake).
This line will have both:
1 (one) chemical analysis bag
1 (one) nematode analysis bag
NASS3
Check to make sure that NASS3 appears on the label for this field. Use the rows and paces
marked on the label to locate the midpoint of each sampling line.
For this field you will mark out:
2_ (two) sampling lines
SAMPLING LINE 1:
Take 20 (twenty) soil cores (I from each stake).
This line will have both:
1 (one) chemical analysis bag
1 (one) nematode analysis bag
A3- 10
-------
SAMPLING LINE 2:
Take 40 (forty) soil cores (2 from each stake).
This line will have both:
2 (two) chemical analyses bags
2 (two) nematode analyses bags
HEX 4
Check to make sure that HEX4 appears on the label for this field. Use the rows and paces
marked on the label to locate the midpoint of the sampling line.
For this field you will mark out: i
1 (one) sampling line
Take 20 (twenty) soil cores (T from each stake).
This line will have:
1 (one) chemical analysis bag
*NOTE* No nematode analyses will be done on soil from this field. Do not use any
bags marked for nematode analysis.
HEX 5
Check to make sure that HEX2 appears on the label for this field. Use the rows and paces
marked on the label to locate the midpoint of each sampling line.
For this field you will mark out:
2. (two) sampling lines
SAMPLING LINE 1:
Take 20 (twenty) soil cores (I from each stake).
A3- 11
-------
This line will have:
1 (one) chemical analysis bag
SAMPLING LINE 2:
Take 20 (twenty) soil cores (1_ from each stake).
This line will have:
1 (one) chemical analysis bag
*NOTE* No nematode analyses will be done on soil from this field. Do not use any
bags marked for nematode analysis.
HEX 6
Check to make sure that HEX6 appears on the label for this field. Use the rows and paces
marked on the label to locate the midpoint of each sampling line.
For this field you will mark out:
2 (two) sampling lines
SAMPLING LINE 1:
Take 20 (twenty) soil cores Q. from each stake).
This line will have:
1 (one) chemical analysis bag
SAMPLING LINE 2:
Take 40 (forty) soil cores (2 from each stake).
This line will have:
2 (two) chemical analyses bags
A3 - 12
-------
*NOTE* No nematode analyses will be done on soil from this field. Do not use any
bags marked for nematode analysis.
A3 - 13
-------
-------
APPENDIX 4
METHODS MANUAL for SOIL CONSERVATION SERVICE EFFORTS IN THE
PILOT
The following pages contain a description of the methods to be used by soil
scientists from the Nebraska SCS office. The methods apply to field characterization and
pit sampling on a subset of fields in the 1993 pilot, with the informed consent of the
farmer. Also included are copies of the field data sheets that will be used.
Techniques, methods, and procedures for use by field soil scientists were prepared
by Norman P. Helzer, State Soil Scientist, Nebraska SCS, and Carol D. Franks, Soil
Scientist, National Soil Survey Center, SCS. Modifications by MJ. Munster, EMAP-
Agroecosystems, in part to reflect discussions held after the manual was first prepared.
A4- 1
-------
A4.1. Methods Manual
The manual lists the procedures to be used by SCS soil scientists to identify map units
and map unit components, to locate the 20 inch deep soil pit, to describe and sample
soils, and to assign classes of accelerated erosion.
Procedures to identify map units and map unit components include using remote
sensing and soil transect techniques. (Section A4.2)
Nebraska Agricultural Statistics Service (NASS-NE) will provide the aerial photograph
used to identify the sample field. It will show the field outline as well as identifying
roads or other features needed to locate the field. It will also show the approximate
location of the transect. The instructions for pacing to the center of the transect will
be provided, in case the marking stakes were rentoved or destroyed. NASS
enumerators will drop off the aerial photograph and a copy of the signed consent form
(releasing the farmer's name to SCS) at a county SCS office close to the field. If the
field has not been disturbed, but for some reason the~ stakes marking the transect
cannot be located, do not take the sample, as it is likely that you are not at the correct
location.
The SCS soil scientist will provide the percent composition, down to the map unit
component, of the field in which the EMAP transect is located. These will be
sketched on the photograph and recorded on a special data form (included after
Section A4.4)
For each EMAP transect, the SCS soil scientist will identify the map unit and map unit
component. If the EMAP transect crosses a boundary between different soils, the map
unit and components) at each end of the EMAP transect will be identified. Record
this information on the aerial photograph provided by NASS-NE. (Section A4.2)
Locate, describe and sample the soil. On the EMAP transect, locate the soil pit within
a pedon (soil individual) that is representative of the map unit/map unit component. If
an EMAP transect falls entirely within a single soil, dig and sample one 20 inch soil
pit. This is to be at the center of the transect, preferably, but can be shifted if the
center point is not representative. If an EMAP transect falls across soils with different
properties, as determined in the field, pits will be dug on the transect, with one in each
component. Pits will be 40 feet from the boundary, thus 80 feet apart, to avoid
transition zones between soils. Where an EMAP transect falls in a terraced field,
locate and dig the sample in an undisturbed area between the terraces. (Section A4.3)
Describe the soil solum in its entirety. Complete Form SCS-SOI-232 (232) by hand or
by using PEDON software. (Section A4.4) Sample this soil to a depth of 20 inches
from a pit within the representative pedon. (Section A4.3) If the EMAP transect
A4-2
-------
crosses two map units, describe and sample the two soil pits that are 80 feet apart.
The minimum kinds of soil and site properties to be described are listed in Section
A4.4. Analyses anticipated are particle size analysis, pH, organic carbon, cation
exchange capacity and exchangeable cations, total phosphorous, bulk density, 15 bar
water retention and calcium carbonate equivalent. Aggregate stability will also be
measured as appropriate. Sample at least one horizon and as many as four horizons,
insuring that significant horizons in the 0 to 20 inch depth are sampled. Label the
samples. Write the EMAP identification on page 5 of the 232 in the "Free Form Site
Notes" block. (Section A4.4). Ship soil descriptions, clod samples and soil samples
on a weekly basis directly to the SGS National Soil Survey Laboratory at Lincoln,
Nebraska. One hard copy of each soil description is to be sent to the Nebraska State
Soil Scientist. All samples are to be sent directly to the National Soil Survey
Laboratory using preprinted mailing labels provided by the laboratory.
While on site, assign the class of accelerated erosion to each map unit/map unit
component in the field. (Section A4.5) Choose one erosion class (or noneroded if
applicable) that best describes the field as a whole. Record this information on the
supplemental data form. Also, assign an erosion class to each pedon sampled. Record
this on page 1 of the 232 in the first block. Do not subdivide a component or a single
component map unit into more than one erosion class.
Data taken that is not found on the usual or supplemental forms will need to be
reported on page 5 of the 232 in the "Free Form Site Notes" block. For example, the
EMAP identification is recorded in this block. Reference the EMAP number that
identifies the field.
Samples are received in the Soil Survey Laboratory (SSL) receiving and processing
lab. The samples are laid out and given laboratory sample tracking numbers. The 232
is used to identify samples and give them sample numbers for respective pedons and
horizons.
Upon completion of analysis a diskette in fixed field ASCII format will be sent to the
EMAP-Agroecosystems information manager, Mark Tooley.
These procedures are to be done at a rate of two EMAP fields per day. The quantity
and quality of field work is expected to be high and be managed to fit within a 10
hour maxiflex day. The shipping of samples to Lincoln and the entering of the pedon
data into PEDON may take additional time the next
day or so. Travel authorization and travel budgeting will be handled by the immediate
supervisor of each participating soil scientist.
Several sample sites in fields will be audited by EMAP cooperators. SCS soil
scientists are expected to cooperate with EMAP - Agroecosystems Quality Assurance
Officers doing Quality Assurance Field Audits.
A4 - 3
-------
A4.2. Field Investigation Methods, Landscape and Geomorphic Studies
[(Ref.) National Soils Handbook]
Identifying and delineating major landform units. The soil scientist concentrates on
the identifiable components of the delineated landforms, e.g., in hills, the side slope,
toe slope, and foot slope components. These kinds of components are delineated
consistent with map unit size specifications. Soil patterns commonly coincide with
major landforms and individual soils with individual landform components.
Include the interaction between topography, stratigraphy, and hydrology. This helps to
separate systematic variability from the random variability in mapping associated soil
patterns.
Design map units that represent sets of soil properties repeated on characteristic
landform components. Design map units that meet the users needs for soil
interpretations and management decisions. The following are checked:
- predictive value of soil-landform features;
- internal properties; and
- slope gradient and shape, vegetation, and position on
the landform relative to surrounding soils.
Record map units in sample field. Identify extent and inclusions.
- Remote sensing techniques. Aerial photos have tonal shades and patterns
indicating possible changes in vegetation, drainage, parent materials, etc. [(Ref.) Soil
Survey Manual (SSM)] Sampling of units depends on whether the answers or
relationships we desire are related in a meaningful way with the features of the soil
map units. The actual clues are not necessarily soil properties at all, but are features
of identification that we associate with the unseen soil models. Mapping in most
surveys involves delineating segments of the landscape, cutting out geographic areas,
and putting the boundaries on base maps.
- Some map units designed for specific landforms
include
1. Ridge (includes summit and shoulder)
2. Stream terrace
A4-4
-------
Tonal shades and patterns on aerial photographs are used to indicate possible changes
of vegetation, drainage conditions, materials, and so forth. The patterns of the gray
tones are used to delineate areas on maps. As we look at the existing vegetation, we
see differences that correspond to tones and composition of the species makeup, and
we verify or modify the boundary locations of the units accordingly. Configurations
of the visible surface of the land, stones, and other features are used as evidence of
changes important enough to be recognized as separate areas.
Many schemes have been proposed and tested for determining the composition of map
units. The same can be said for the distribution of properties that exist. It is fairly
well accepted that certain features of soils and of landscapes are not in accord with
existing models of distributions in systematic and predictable ways. Therefore, it is
common to employ soil transects to estimate the composition of map units. These
should not be confused with the transects being'used by NASS enumerators to collect
surface soil for this project. The first aspect of composition is to identify the soil
taxonomic components because taxonomic class is related to soil genesis and
morphology.
- Soil Transects are used to estimate composition and to communicate field
observation by taxonomic component (responses or indicators for interpretive
purposes). Detailed profile descriptions with soil properties recorded support line
separation.
Identify map unit crossed by EMAP transect. Record this information on the
supplemental data form. Procedures include the following techniques:
- Identify landforms
.- Remote sense patterns on aerial photograph
- Field examination of landform; and position and shape within landform
- Use properties identified in the soil and landform features to draw any needed
map boundary line
- If necessary, do a soil transect with soil profile description of solum.
Map Unit Components and Boundaries.
[(Ref.) MLRA ( Major Land Resource Area) Handbook (Draft)]
The following describe the evaluation of soil map units and boundaries (tabular and
spatial information) through transect methods. Information on soil transect and
statistical analysis is included here.
A4-5
-------
Why do we need soil transect information? When mapping, a soil scientist predicts
where soils are likely to change in the landscape and makes observations to test the
prediction. The better the landscape model, the more accurate the predictions are
about changes in soil types across the landscape.
In soil transecting, observations are made based on a predetermined spacing. A larger
number of observations are made in a delineation as compared to mapping. The
observations are of the major soil making up the delineation as" well as included soils.
No sites are ignored as being "unrepresentative."
Determine the composition of map units. This includes an estimate of the amount of
named and similar soils and contrasting soils.
At each point in a soil transect, the taxonomic class of the soil is identified. Other
kinds of data gathered consist of measurements and observations of soil and site
properties. For example, the kinds of data gathered on" a steep, stony unit normally
will be different from data gathered on a level, frequently flooded, poorly drained unit.
Analysis of Soil Transect Data. Determine taxonomic composition. It also is
important to visually inspect your soil transect data and look for patterns or trends.
Existing Soil Map Units. The first step in assessing the quality of existing soil
mapping is to evaluate line placement. Specifically, do the lines conform to natural
landscape breaks and are all important natural soil-landform units delineated?
Map Unit Boundaries. For each map unit, determine what information needs to be
gathered. Information for other properties such as slope, stone cover, depth to
redoximorphic features, surface texture, depth to bedrock, etc. will be observed and
documented when appropriate. This list will vary for each map unit.
A4.3. The sample
[(Ref.) "Principles and Procedures for Using Soil Survey Laboratory Data"]
Selecting the site. Sites representative of the soil in the survey area are purposefully
selected for sampling using the transect site method. In this study, fields and transect
sites are preselected.
Sampling the pedon. The sampling party has responsibility to obtain samples
representative of the pedons selected for characterization. Decisions are necessary on
horizon delineation, thickness of horizon (or interval) sample, what material should be
A4-6
-------
excluded from the sample, and the usefulness of compositing samples. The sampling
party insures that site and pedon descriptions are adequate.
The ideal sample contains all soil materials within the horizon in proportion to their
occurrence in the pedon. The sampler attempts to approximate the ideal by carefully
sampling a selected section of the horizon. The sample is normally taken along a pit
face from horizon boundary to horizon boundary and between arbitrary lateral limits
encompassing short range variability observed at the site.
For this study, sample as appropriate for aggregate stability determination. (See
reference at end of this Appendix for method of sampling).
Bagged samples
The Label "For want of identification, the sample was lost." Too often this
sentiment is expressed. Labeling is critical. Record the identification on page 1 of the
SCS-SOI-232 (232) in the first block. Four elements need to be included to eliminate
confusion.
1. Pedon and horizon number.
2. Depth.
3. Horizon designation.
4. Record the EMAP identification on page 5 of the 232 in block "Free Form
Site Notes"
The soil name is helpful. Use lead pencil or water-proof ink. Label outside of
container. '
Tag Example:
Soil Name
Sample No.
County
Horizon
Depth (inches)
'or plastic bags, staple a tag between folds
at the top of the bagon the outside.
Assignment of Pedon and Horizon
Numbers
Pedon is identified on page 1 of the 232 in the first block. Horizon Numbers are
recorded on page 2 of the 232.
The unique identifier for any sampling site is the soil survey number assigned at the
time of sampling. It is also the number by which the data for that sample may be
retrieved from the pedon data record.
A4- 7
-------
Assign soil survey numbers as follows:
1. Begin with S to indicate special sample.
2. Use the last two digits of the calendar year.
3. List the two-letter state FIPS code followed by a hyphen.
4. List the three-digit county FIPS code followed by a hyphen.
5. Within each calendar year, number each pedon chronologically as sampled in that
county (state soil scientist keeps this record).
This completes the pedon designation. An additional number can be added to identify
the horizon sampled.
(5. Horizons are numbered sequentially from the top on page 2 of the 232. Add a
hyphen and numeral to indicate the horizon sampled. (NOTE: Sequential
horizon numbers remain the same even if all horizons not sampled.)
Samples sent to National Soil Survey Laboratory should be:
- representative of horizon
- at least 4 kg in size
- placed in plastic bag
- labeled with tag on outside of bag (this label matches the information on the 232)
- accompanied with pedon description complete with volume estimates of
coarse fragments
Bulk density samples. Samples for bulk density are approximately fist-sized clods of
undisturbed soil. If clods cannot be secured; i.e., in sands, core samples are taken.
Three clods are collected per horizon.
Sampling (Bulk Density Clods)
1. Take three clods per horizon for bulk density.
2. Make clods spherical to egg-shaped, 6 to 7 cm in diameter.
3. Put fragile clods in a hairnet; put firm clods in a double wire loop.
4. Hang clod; coat once quickly with Saran; allow to dry.
Packaging
1. Assemble box so numbers on lid are on inside.
2. Make cells and lid numbers correspond as shown in diagram. (Number four
cells in box.)
3. Label lid to identify clods in cells.
4. Put coated clod in a plastic bag and place in cell of clod box. DO NOT
REMOVE WIRE OR NET.
5. Use all cells or stuff empty cells with a filler such as newspaper.
6. Secure lid with strapping tape prior to transport. Individual boxes can
be shipped.
A4- 8
-
-------
Additional Information on Soil Sampling and Analysis.
[(Ref.) National Soils Handbook]
Samples are collected by soil horizon. Samples are collected for all horizons.
Sampling is most beneficially conducted from excavations. Use air-dry bulk samples.
Samples need to be large enough to represent the proportion of rock fragments up to
20 mm (3/4 in.) in diameter (determined by weight in the laboratory) and to provide at
least one quart of less than 2 mm material. Proportions of rock fragments larger than
20 mm (3/4 in.) are estimated by volume in the field. The routine method for bulk
density and moisture retention determinations require clod samples which preserve the
field configuration of pore space. Sampling equipment and supplies are bags, tags,
shipping documents.
Sample Collection and Preparation
[(Ref.) Soil Survey Laboratory Methods Manual, Soil Survey Investigation Report #42]
Cardboard boxes are used for .shipping natural clod samples for bulk-density studies.
Bulk samples are collected in 8-mil plastic bags, 23 x 51 cm (9 x 20 in), that are
closed before shipping by stapling the folded opening. The samples are then shipped
in canvas laundry bags.
Field Sampling and Site Selection
Site selection, descriptions of the site and soil, and careful sample collection are
requisite to successful soil analysis. If a transect is to be sampled, care must be taken
to ensure that the variable being tested can be adequately assessed after considerations
of other variables in the transect.
Pedon Sampling
Sample freshly dug pits. Dig the pit at least 20 inches deep. Make supplemental
borings or excavations if necessary to assess and describe pedons larger than the pit.
In laterally uniform pedons, sample in a vertical pattern 30 to 50 cm wide. Each
sample should represent the entire cross section of each horizons Place a 3- to 5-kg
sample, representative of the horizon, in an 8-mil, 4L plastic bag (about three-fourths
full). Fold top of bag, place tag in fold and staple, Mark depth, horizon, and pedon
number on the tag. Clods for determining bulk density and water retention are
normally too small to cover the entire cross section of the horizon and should be taken
from the center. Clods can be packaged in plastic bags and placed in cardboard clod
boxes for transport. If convenient, start sampling at the bottom of the pit to minimize
contamination.
A4-9
-------
If rock fragments >20 mm are present, follow procedures outlined in Appendix C. For
contrasting soil materials, estimate the proportions of each component and record in
the pedon description. Sample components separately, if reasonable. Make arbitrary
subhorizons if morphologically recognizable horizons are more than 30 cm thick in the
upper part of the pedon or more than 60 cm in the lower part. Consider the
requirements of the classification system in locating subhorizon boundaries.
If, for some reason, a pit cannot be dug, samples can be taken with a probe core
technique. Bulk and clod samples can be taken from probe cores; however, cores
smaller than 5 cm are not suitable for bulk density and water content determination.
[(Ref.) Fabric-Related Analyses, Aggregate Stability (4G), Soil Survey Laboratory
Methods Manual]
Application. An aggregate is a group of primary particles that cohere to each other
more strongly than to other surrounding soil particles (Kemper and Rosenau, 1986).
Disintegration of soil mass into aggregates requires the application of a disrupting
force. Aggregate stability is a function of whether the cohesive forces between
particles can withstand the applied disruptive force. Erodibility of soils increased as
aggregate stability decreases (Kemper. and Rosenau, 1986). The datum can serve as a
predictor of soil erosion potential.
[(Ref.) Aggregate Size Distribution. Overview of Certain Soil Characterization
Methods With Emphasis on Use Dependant Temporal Properties, R.B. Grossman]
Aggregate Size Distribution. For the EMAP sample, collect or sample, loose soil
without sieving in the field. Method G001
Sample Loose Soil Without Sieving in the Field. Method G001. The determination is
made on relatively loose near-surface soil which is air dried and passed through a nest
of sieves in the NSSL in Lincoln.
A4.4. Examination and Description of Soils
[(Ref.) Soil Survey Manual]
For rapid investigations of soils, a small pit can be dug and a section of soil removed
with a spade. Knowledge of the internal properties of a soil is derived mainly from
studies of such samples. Complete study of an entire pedon requires the exposure of a
VERTICAL section and the removal of horizontal sections layer by layer. Horizons
are studied hi both horizontal and vertical dimensions.
A4 - 10
-------
The description of a body of soil should record kinds of layers, their depth and
thickness, and the properties of each layer. A pedon represents the desired segment of
its range.
Pedons representative of an extensive mappable area are generally more useful than
pedons that represent the border of an area or a small inclusion. For a soil description,
the vegetation and part of the landscape that the pedon represents should be described.
A pit exposing a vertical face approximately 20 inches is satisfactory for this project.
Sides of the pit are cleaned of all loose material disturbed by digging, the exposed
vertical faces are examined, usually starting at the top and working downward, to
identify significant changes in properties. Boundaries between layers are marked on
the face of the pit, and the layers are identified and described.
Patterns of color within structural units^ variations of particle size from the outside to
the inside of structural units, and the pattern in which roots penetrate structural units
are often seen more clearly in a horizontal section. The depth to a horizon or layer
boundary commonly differs within short distances, even within a pedon.
For this study:
Sample soil pit 20 inches deep.
Describe soil profile to 60 inches in order to classify the whole soil profile. To do this,
expose the entire profile or auger/probe sample from the entire 60 inch depth.
Collect bulk soil sample by horizon (bottom to top)
Package soil sample in plastic bags with identification labels.
Collect bulk density clod samples; dip in saran and box.
Additional guidance for sample collection and preparation is in Appendix B.
Soils with rock fragments
[(Ref.) Soil Taxonomy]
Soil Texture, Coarse Fragments, Stoniness, and Rockiness. Soil Texture refers to the
relative proportions of the various size groups of individual soil grains in a mass of
soil. Specifically, it refers to the proportions of clay, silt, and sand below 2
millimeters in diameter. The presence of coarse particles larger than very coarse sand
(or 2 millimeters) and smaller than 10 inches is recognized by modifiers of textural
class names, like gravelly sandy loam or cobbly loam. General classes of still larger
particles - stones or rock outcrops - are defined in terms of the influence they have on
soil use, and in specific physical terms for individual soil series.
A4- 11
-------
[(Ref.) Soil Survey Manual]
Size limits. The upper size limit for rock fragments is the size of the pedon. The term
"rock fragments" refers to particles 2 mm in diameter or larger and includes all sizes
that have horizontal dimensions less than the size of a pedon. It is not the same as
coarse fragments, which excludes stones and boulders larger than 250 mm. Coarse
fragments are 2 to 250 mm. Laboratory data sheets normally report size classes up to
75 mm or 250 mm. The largest size sent to the laboratory with 3 to 5 kg samples is
20 mm.
Volume estimates. Record in the pedon description, the volume percentage estimates
of rock fragments >250 mm (10 in), 75 to 250 mm (3 to 10 in), and 20 to 75 mm (3/4
to 3 in). Collect a 3 to 5 kg sample of the <20-mm soil material, and store it in a
plastic bag. Calculate the volume percentages of coarse fragments.
Weight estimates. Estimate and record the volume percentage of the >75 mm fraction
as outlined in volume estimates. Collect and weigh a 15 to 20 kg sample of the <75
mm fraction. Record the weights of the 20 to 75 mm fraction and the <75 mm
fraction. Collect a 3 to 5 kg sample from the <20 mm fraction, and store it in a
plastic bag. Calculate the weight percentage of coarse fragments.
Minimum kinds of soil site properties to be described:
1. Soil classification
2. Major Land Resource Area
3. Latitude and longitude
4. Land use, crop or native vegetation
5. Slope (%)
6. Landform
7. Micro relief
8. Aspect
9. Accelerated erosion class
10. Permeability (hydraulic conductivity class)
11. Drainage class
12. Elevation
13. Water table (when applicable)
14. Moisture
15. Roots
16. Rock fragments (%) by volume
17. Miscellaneous land type (%)
18. Parent material
19. Hydrologic group (free form notes)
20. Salt (electrical conductivity) when present (free form notes)
21. Texture (particle size)
A4 - 12
-------
22. Clay %, (free form notes)
23. Sand % (free form notes)
24. Horizons
25. Depth (in.)
26. Color (moist/dry)
27. Consistency (Rupture resistance)
28. Mottles (Redoximorphic features
29. Pores
30. Chemical reaction
a. Ph
b. effervescence class (HCL)
31. Concentrations
32. Structure
33. Crusted (yes/no) (free form notes)
34. Collect Bulk Density clods (free form notes)
A4- 13
-------
Field Worksheet NE-LandUse 10-15-93
Segment #_
Tract #
Field #
SS Name_
Date
County
First Map Unit
Symbol
% of Field
Name
_Erosion Class of Map Unit_
Taxon
% of Mao Unit
Erosion Class
First
Second
Third
etc.
Second Map Unit:
Symbol
% of Field
ComnonentCs^
Name
_Erosion Class of Map Unit_
Taxon
% of Mao Unit
Erosion Class
First
Second
Third
etc.
Third Map Unit:
Symbol
% of Field
Name
_Erosion Class of Map Unit_
Comoonentfs) Taxon
% of Mao Unit
Erosion Class
First
Second
Third
etc.
-------
o
3
O 19
User Define
Properties
Code V
R)
i
Transect Dat
Transect ID
Interval
3
BW
^t V "
o
co 4
^0 ^4
O .
fl ^
O ^
03 ^_
GO 2
:
1
U
II
s
&
u
EO
4)
a w
41 3 a
M u e
Sflt WJ
19 *f~
JJ 4> b
fl Cl4
4)
b >
|
rt '
pj 1 1
O a
t
4 1 1
S
S
3
i C 4) VI 1 1
fl O<
rt Q.
1
1 1
i >
1 1 1
1
1
1 1
'
41 01
a, 4i
«j o a
S -rt 4)
«eo.c &> JJ
u o> cu o fl o
XXX
..
in
^4
a> ..
JJ U
XXX
a
C S JJ 4) JJ
o o^a u
rt JJ O -rt U
OUJiaOD "-* jj c c w 03 M
u (u co o E I-H c o n fl tt'tnu B 4>
U Q, U O i-l C C OTIC 3 U Vi fl S> W j:
4)4)CQCXU4>f-l4>4> -rt Q 3 OO(9*v<4>MJJ
aa. < s rt u K J J niZ.B! -o tj eu a a E« o
O O _E
CO MS
1
i
'i
!
..
.^ *»_ AJ
3. IT
p
~ » CO rH " C E E "
Q< 41 09 i-t 4> O «-l <" U
P O O =c (8 4> J3 tn-rt *O *O 4) 4>
1 a H w acnJRlJJCCCnco
COM O -rt 4) B fl J J £*
4)-uT3CE-«-i>
.Q.QiJ'OOCOM(04>T-iOJJ»-i
J,Si££3cOCuaW2J2U
LJ
1
: p-
' t
: : v
i : o-
£ 1
.. 5 41 g
_| .. "o JJ X e rt 41 ^J
I'm .. -rt p -H.. S 55
! leg -I) C X to C O Z 3
* O tr* * 1 3 rf W O O Vt< P«
SwQ£ocn3coZ§Qia«uuc/5s
..
O »J
I 1
m
44) " O
. -3 S01
O 4> 4) C
4> « "O 41 JJ O
CO B M JJ-0
O *r4 *! Id 4)
14 M CO 04 04
.H y
o s
..
u
4)
cj c a^:
CO f
x e u i
=4 SKf-0
5 i
u
fl
u
u-l
.^4 .. "
« ..QUO
a o co o w .
0
=====
l
= ll
| I
Q 0
i sli
C o io w
S 83 8
a 64 MS
M
b 1
g
o
s s
1
5
«i
B M M
rt-rt JJ
. «Q CO
i.
§
rt
JJ
0
IM
B
H
o "a
JJ U
g
<
X
W
JJ
CO
r-l D.
-H a
4)
JJ A
§ 8
jJ ^
O
S -o
m c
tx £
1
A
Ui
41
a
1
II
!
1
i
II II
.
1
41 ..
*
CO
CU
n
s tna
O C-H >,
N *< *^ JJ
JJ JJ B
Sons
H fl O O
P U Z U
1
1
4)
a
3 .. ..
u ntc
tna £
c
S
1
1
1
u l>
CO Q
1 1
4)
a
3 01 C W Vl
Jj 0) *H 4) T^
iH Q 2 CO Q
JJ
3-
H
County Nan
Quad :
Location:
SS
o
gcs
NN
-co o: E M i
a o rto «
o co 2 co rt
01 M O i
J CO
M
o
CO
S 1
a \
5
j5
1
,
|J JJT3
4) CU C
JJ 0)
(0 a >
(0
10
Q
** \
« 1
U
H
£
o en
I" 55-
^ -ago
O-fa 2
»'
SO ..
vi o
-H fa S
Cl4
ta
fl
Q
'
31
X
fl
Q
^
Cl
fl
Q
~
a 1 1 1
E
£ C S^'
0
W '
1 1 1
&JJJ
41 B § C
H 5 co s
M
rt
0
^
CO
y
a ^
TJ IS
2 2
CU U
-J 41
ae
X fl
£z
-------
C5
CJ
m
-------
C6
CO
EL)
ucnc
M
5 as
PV
Co
XECD4J
H
W
x a,
u ^
0
U.CW
§>m^
8 I
X
M
t
Hr-lOU
E«OJ
o
a.
3
-------
C7
1
OL,
-------
C8
in
8
<
04
CM
n
CM
h 1
$
03
O
t->
0
V
tn
g
£
0)
M
fc.
V]
^
C)
V
^J^
x
5
*;
*^
%:
u
^
0
N
0
N
»-
1
^
P*
^
o
o
0
^J
0
C
o
H
JJ
s
H
>t-l
>H
JJ
§
3
w
-
n
0)
u
o
c
o
N
*H
S
K
a
0)
jj
i
s
-
*
n
V
o
o
o
T!
£
§
f
2
h
^\
^?
X
X
M
^
N
^
>^
e
V
^x
o
vS
s
v
»
, 5
^
C
0
-H
1J
0
IU
C
H
c
0
H
1J
JJ
0)
01
0)
0)
3
z
5 J|
ef
O
u
i
i
-
i
j
i
t
|
\ ;
!
i
i
i ;
i i
'
I
i
i
I
i
o
U
(B
^^ *^
s u
.c o
S to
>i CO
CO
=
=
-------
A4.5. Erosion (by component)
The following classes of erosion will be the standard or reference by which surface
layers are judged. Erosion is often variable, but for this study, the classes of
"noneroded" and "slight" will be combined with normal or uneroded soil. The classes
"moderate" and "severe" will be combined to form the eroded soil category.
[(Ref.) Soil Survey Manual]
Classes of Accelerated Erosion
.The classes of accelerated erosion that follow apply to both water and wind erosion.
They are not applicable to landslip or tunnel erosion. The classes pertain to the
proportion of upper horizons that have been removed. These horizons may range
widely in thickness; therefore, the absolute amount of erosion is not specified.
Noneroded
A noneroded soil has no loss.
This class consists of soils that have lost some, but on the average more than 0 but
less than 25 percent, of the original A and/or E horizons or of the uppermost 20 cm if
the original A and/or E horizons were less than 20 cm thick. Throughout most of the
area, the thickness of the surface layer is within the normal range of variability of the
uneroded soil. Scattered small areas amounting to less than 20 percent of the area
may be modified appreciably.
Evidence for slight erosion includes (1) a few rills (2) an accumulation of sediment at
the base of slopes or in depressions, (3) scattered small areas where the plow layer
contains material from below, and (4) evidence of the formation of widely spaced,
deep rills or shallow gullies without consistently measurable reduction in thickness or
other change in properties between the rills or gullies.
Moderate
This class consists of soils that have lost, on the average, 25 to 75 percent of the
original A and/or E horizons or of the uppermost 20 cm if the original A and/or E
horizons were less than 20 cm thick. Throughout most cultivated areas of moderate
erosion, the surface layer consists of a mixture of the original A and/or E horizons and
material from below. Some areas may have intricate patterns, ranging from uneroded
A4-20
-------
small areas to severely eroded small areas. Where the original A and/or E horizons
were very thick, little or no mixing of underlying material may have taken place.
Severe
This class consists of soils that have lost, on the average, 75 percent or more of the
original A and/or E horizons or of the uppermost 20 cm if the original A and/or E
horizons were less than 20 cm thick. In most areas of severe erosion, material below
the original A and/or E horizons is exposed at the surface in cultivated areas; the
plow layer consists entirely or largely of this material. Even where the original A
and/or E horizons were very thick, at least some mixing with underlying material
generally took place. In some small areas, the original soil can be identified only with
difficulty. Some areas may have an intricate pattern of gullies.
A4 - 21 Sf-U.S. GOVERNMENT PRINTING OFFICE: 1994 - 550-001/80340
-------
------- |