&EPA
United States Office of Research and EPA/620/R-9
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EPA/620/R-95/004
January 1995
Environmental Monitoring and
Assessment Program
Agricultural Lands Pilot Field Program Report
1993
by
Anne S. Hellkamp
Jeff M. Bay
Karen N. Easterling
George R. Hess
Betty F. McQuaid
Michael J. Munster
Deborah A. Neher
Gail L Olson
Kurex Sidik
LA. Stefanski
Mark B. Tooley
C. Lee Campbell
AGRieULRJRAL LANDS
This study was conducted in cooperation with
U.S. Department of Agriculture
Agricultural Research Service
National Agricultural Statistics Service
Natural Resources Conservation Service
Raleigh, NC 27606
U.S. Environmental Protection Agency
Office of Research and Development
Washington, DC 20460
Characterization Research Division - Las Vegas
National Etposure Research Laboratory
Printed on Recycled Paper
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Notice
This research has been funded by the United States Environmental Protection Agency (EPA)
through its Office of Research and Development (ORD) under Interagency Agreements
#DW1 29341 70 with the U.S. Department of Agriculture (USDA) Agricultural *"^ .
(ARS) #DW1 2934747 with the USDA National Agricultural Statistics Service (USDA NASS), and
#DWl'2936168 with the USDA Natural Resources Conservation Service (USDA NRCS), and by the
USDA ARS It was conducted with our research partners under the management of the
Characterization Research Division of the National Exposure Research Laboratory in mortal tfป
Environmental Monitoring and Assessment Program (EMAP). Ne.ther U.S. EPA nor USDA ARS
endorses or recommends any trade name or commercial product mentioned in this document to the
exclusion of others. They are mentioned solely for the purpose of description or clarrfication.
Proper citation of this document is:
Hellkamp, A.S.. J.M. Bay, K.N. Easterling, G.R. Hess, B.F. McQuaid, M.J. Munster, D.A Neher,
G.L Olson, K. Sidik, LA. Stefanski, M.B. Tooley, and C.L. Campbell. 1995. Environmental
Monitoring and Assessment Program - Agricultural Lands Pilot Field Program Report - 1993.
EPA/620/R-95/004. U.S. Environmental Protection Agency, Washington, DC.
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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Table of Contents
Notice
Table of Contents
List of Figures '"'.;''"'
List of Tables iv
Glossary of Acronyms vi
Acknowledgements ' .' ' vii
L yiii
Preface
Executive Summary
IX
XI
1. Introduction
10
10
10
2. Methods
2.1 Sampling Design ' 3
2.1.1 Rotational Panel i ' 3
2.1.2 Hexagon ........ 3
2.1.3 Sample Unit Selection ... 3
2.2 Data Collection ! 4
2.3 Quality Assurance and Quality Control '.'.'.'.'. 6
2.4 Information Management ' 6
2.5 Analysis including "How to Read our CDFs" ... .....'. Q
3. Results
3.1 Extent of Agricultural Resources i
3.1.1 Extent and Geographic Distribution of Annually' Harvested Herbaceous Crops'
3.1.2 Extent and Geographic Distribution of Farm Ponds iซ
3.2 Condition of Agricultural Resources '
3.2.1 Crop Productivity " 21
3.2.2 Soil Quality .... " \ '. 21
' ' . . .. . 27
4. Evaluation
4.1 Extent of Agricultural Resources | 36
4 I 2 E^en! ปnซ ^^ Distribution of' Annua.l'y Harvested Herbaceous Crops .' 36
4.1.2 Extent and Geographic Distribution of Farm Ponds ... ^
4.1.3 Rangeland vs. Permanent Pasture
4.2 Condition of Agricultural Resources , 38
4.2.1 Crop Productivity 39
4.2.2 Soil Quality ......... ' 39
4.3 Design Comparison . 42
56
5. Conclusions and Future Directions
! eo
Literature Cited
- 62
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List of Figures
2
1.1 Nebraska is a Great Plains state '' 2
1.2 Ecological regions of Nebraska
2 1 Location of Hexagon and Rotational Panel segments '."".5
22 Sample selection for Hexagon and Rotational Panel designs ; . y
s rsr^rdSr^^
in this report
3.1 Landscape types and area of annua,lyhan,estedhe*aoeous crops. ,n Nebraska ........ 11
32 Proportion ol land area per segment planted to AHHC 14
ซ
crop 16
o c Prrvnnrtion of fields of size S or smaller 17
M Son of total area of AHHC accounted for by fields of size S or smaller ........... 17
3 7 Common field sizes ... 19
3.8 Number and extent of farm ponds, by use ' ' .... 20
3.9 Cumulative distribution of farm pond area for Nebraska ^
* m Area of farm ponds by landscape type .'"'.'' I VJuAo iซ '' '
3.1? The observed/expected yield ratio, combined for the five predominant AHHCs in ^
Mohrfl^kfl .**""""""*""" O^
3 12 The observed/expected yield ratio for three AHHCs in Nebraska ^
3'.13 The nitrogen efficiency index for land in seven s eed ซops. - '.'.'.'.'.'.'.'.'. 24
^ 1A The nitroaen efficiency index for Nebraska land in three AHHCs .
3.15 l^^Nebraska land in AHHCs with various rotation lengths, as reported by the ^
3.16 PeTcentoi Nebraska AHHC.and in which the 1993 crop'had last' been planted 1, 2, and ^
3,7 p^^STAHHC land with one/two! and ihree'unique CroPs or'land uses ^
over the three seasons 1991-1993 27
3.18 Box plot example 28
3 19 Clay contents in Nebraska surface soils ' ' 2Q
320 Relation between CEC and clay in Nebraska surface so.ls ^
3.21 Cation exchange capacity in Nebraska surface soils 3Q
3.22 Base saturations in Nebraska surface soils i ..... 30
3.23 pH in Nebraska surface soils ' ' ' ' 31
3 24 Phosphorus in Nebraska surface soils ' ' ' g1
3 25 Organic carbon content in Nebraska surface soils ' ' 32
3 26 Integrated assessment ratings for Nebraska surface soils ;'': 34
3 27 Maturity index for free-living nematodes in surface aoil on land planted to AHHCs 34
3.S Maturity index for plant-parasitic nematodes in surface so,. ^^^^
3.29 Shannon's trophic diversity index for nematodes ,n surface soil on land planted to ^
AHHCs "
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List of Figures
4.1
4.2
4.3
4.4
4.5
Association of rotation plan with the values of two rotation indices based on land use over
the three seasons 1991-1993 I
Location of soil pit sites in 1993 Nebraska Pilot ' '
Mean field and 3SD SRPGs with 95% confidence intervals . . .'..........
Proportion of ratings in each suitability group for each method . ^ ........
Mean and 95% confidence intervals for observed-to^expected SRPG ratios . . .
41
42
44
45
45
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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List of Tables
1.1 Indicator categories examined in the Pilot
'. . . . 3
2.1 MASS strata for Nebraska 6
2.2 Selected and completed sample sizes . . . .
3 1 Annually harvested herbaceous crops eligible for selection in the Pilot 10
32 Extent of annually harvested herbaceous cropland (AHHC) in Nebraska - - - ^
s'.3 Sample sizes from the June Agricultural Survey for each landscape type ^
34 Number and extent of farm ponds in Nebraska, by use ; ' '
3.5 Ratings for each of the soil properties used in the integrated assessment of surface soil ... 32
4.1 Completeness of the observed/expected index and the nitrogen efficiency index 39
4.2 Distribution of sample units among two rotation indices ^
4.3 Parameters in Soil Rating for Plant Growth '" ' 43
4.4 Suitability groups from SRPG ratings
4.5 Indicator ranges for Low and Very high plant growth potential groups in southern and ^
eastern Nebraska ; AQ
4 6 Scoring for indicators in Low and Very high plant growth potential groups .
47 Examples of Soil Quality Report Card for sites in Low and Very high plant growth ^
4.8 Coefficten^from a principal component analysis of variables of soil properties and ^
nematode community indices ' '
4.9 Spearman correlations between indices and trophic groups (n=156) of soilborne ^
nematodes 54
410 Variances for five nematode indices ;''. ;' ' "
4.11 Reliability ratios for several indices of nematode community structure for various ^
4 12 EsซrnatedPrerative'efficiencies of the Hexagon design to the Rotational Panel design ...... 56
413 Costs of the Hexagon and Rotational Panel sampling designs ^....... v. ... w
4.U Estimated relative efficiencies of the two designs with respect to the estimation of extent ^
of AHHC and number of farm ponds
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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Glossary of Acronyms
3SD
AHHC
AIC
ARG
ARS
CDF
CEC
EC
EMAP
EPA
JAS
NASS
NRCS
NSSL
QA/QC
SAR
SASฎ
SCS
SRPG
USDA
SCS State Soil Survey Database
annually harvested herbaceous crop
Agricultural Lands Information Center
Agricultural Lands Resource Group
Agricultural Research Service (USDA)
cumulative distribution function
cation exchange capacity
electrical conductivity
Environmental Monitoring and Assessment Program
Environmental Protection Agency
June Agricultural Survey
National Agricultural Statistics Service (USDA)
Natural Resources Conservation Service* (USDA)
National Soil Survey Laboratory
Quality Assurance/Quality Control
sodium adsorption ratio
Statistical Analysis System
Soil Conservation Service* (USDA)
Soil Rating for Plant Growth
United States Department of Agriculture
Note on NRCS and SCS: In October 1994, the USDA Soil Conservation Service (SCS) became the
USDA Natural Resources Conservation Service (NRCS). At the time thfe report was prepared the
Agency was known as NRCS; however, at the time this study was conducted (1993) the Agency was
known as SCS, and thus we refer to it as SCS throughout this report
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
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Acknowledgements
The Agricultural Lands Program would have
gone nowhere without the support of many
individuals and agencies across the country.
We would like to thank the following people
who helped us in the preparation and execution
of the 1993 Pilot Field Program.
First of all, we acknowledge the other members
of the Agricultural Lands Resource Group: Dan
Fiscus, computer support; Charles Harper,
logistical and technical support; John Rawlings,
statistical support and advice; Gyanendra
Dhakhwa, indicator development support; Steve
Peck, moral support; and Brian Schumacher,
quality assurance officer.
Outside of the ARG, two EMAP members to
whom we owe the most are Walt Heck, our
leader in terrestrial matters, and Sue Franson,
our project officer. They have endeared
themselves to us as capable and personable
administrators.
Our headquarters are located at the Air
Resources Research Consortium on the
campus of North Carolina State University. We
wish to thank those here who support our
presence, including Clara Edwards, office
manager; Phyllis Gam's, librarian; and Joseph
Miller, site administrator. We also owe thanks
to Susan Davis and the rest of the local USDA
Area Support Office.
The relationship between the Agricultural Lands
Group and the USDA National Agricultural
Statistics Service continues to be strong. We
thank Sarah Hoffman and Jim Gibson for
technical assistance and coordination, Becky
Cross for all her work in drawing the sample,
and Jaki Stanley for overseeing the
questionnaire and enumerator's manual. We
also thank Craig Hayes and his staff for their
continued interest and support. For
administrative support, we thank Ray Halley
and Steve Manheimer, whom we remind that
ants are easy to catch.
Equally vital was the cooperation of the NASS
office in Lincoln, Nebraska. Current and former
state statisticians Bill Dobbs and Jack
Aschwege were supportive of the ARG's first
activities in the Midwest. The skill and efforts
of Lynn Gentrup deserve special credit. It was
his thoughtful planning and day-to-day
coordination that made the Pilot run as
smoothly as it did. His extra efforts made our
lives easier. Also, we thank the 28 or so
enumerators who collected questionnaire
information and soil samples, and the farmers
who took time to answer questions and allow
soil sampling.
The USDA Soil Conservation Service was very
active in this Pilot. We had help from both the
National Soil Survey Center and the National
Soil Survey Laboratory. Among the staff there
we thank Ellis Benham, Carol Franks, Terri
Hywood, Fred Kaisaki, Lea Ann Pytlik, and Tom
Reinsch. We are also indebted to the
Nebraska SCS office, including Norm Helzer,
Renee Gross, and those scientists whd did site
and soil descriptions and collected samples
from a subset of our selected fields. For
administrative support, we thank Bill Roth and
Maury Mausbach.
We owe appreciation, too, to Mae Noffsinger
and her staff for taking on the major task of
nematode identification and enumeration for
this project.
Finally, thanks to all who carefully reviewed this
manuscript and provided helpful comments.
vni
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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Preface
The goal of the Environmental Monitoring and
Assessment Program (EMAP) is to track the
condition of our environment. EMAP was
initiated in 1989 by the U.S. Environmental
Protection Agency (EPA) to address the
question, "Do environmental policies work and
what new ones do we need?" To answer this,
it is necessary to understand the extent and
severity of environmental problems. Many
monitoring efforts are site- or problem-specific
and do not allow assessment of condition over
large regions with statistical confidence. EMAP
is designed to collect relevant information to
help policy makers decide how to allocate
limited resources among different
environmental problems. The effort reaches
beyond the EPA to include other federal
agencies, universities, and scientists from a
wide variety of disciplines.
The mission of EMAP's Agricultural Lands
Resource Group (ARG) is to develop and
implement a program that will, in the long term,
monitor and assess the condition and extent of
the nation's agricultural lands from an
ecological perspective through an interagency
process. The specific objectives of the
Agricultural Lands program are to:
Estimate the status, trends, and changes in
selected indicators of the condition of the
nation's agricultural lands on a regional
basis with known confidence.
Estimate the geographic coverage and
extent of the nation's agricultural lands with
known confidence.
Seek associations between selected
indicators of natural and anthropogenic
stresses and indicators of the condition of
agricultural lands.
Provide annual statistical summaries and
periodic assessments of the nation's
agricultural lands.
For EMAP-Agricultural Lands' monitoring efforts
to be useful to decision makers, the program
must answer specific questions that are related
to values that society bestows on agricultural
lands. EMAP has identified three broad
categories of values for all resources:
consumptive uses, nonconsumptive uses, and
biological integrity. These reflect either what
society values or characteristics of the
environment that support what society values.
Whether societal values are being met on
agricultural lands depends in part upon the
condition of these lands, as well as on the
prospects for that condition in the future. The
ARG is addressing three aspects of condition
productivity; quality of air, water, and soil;
and biodiversity as well as the ecological
sustainability of agricultural lands. Assessment
questions, currently under development,
address the critical aspects of condition and
sustainability. Indicators are being developed
to answer each assessment question; indicators
are measures that reflect the condition of an
ecological resource or its exposure to stress.
For a more detailed discussion of EMAP-ARG's
conceptual approach, see EMAP -
Agroecosystem Pilot Field Program Plan - 1993
(Campbell et al., 1994a).
The early stages of development for each
EMAP Resource Group involve conducting Pilot
Field Programs. The primary purpose of these
is indicator development, but they also provide
an opportunity to illustrate the kinds of ^
assessments that can be accomplished using
EMAP data. In addition, they provide a setting
in which to develop cooperative working
relationships with other federal agencies.
In 1992, the ARG conducted its first Pilot Field
Program, in North Carolina. Among its
objectives were the comparison of two sampling
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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frames; the evaluation, of an initial suite of
indicators; the development of plans for
sampling, logistics, and other critical activities;
and the establishment of field procedures in
association with the U.S. Department of
Agriculture's National Agricultural Statistics
Service (USDA-NASS). For the results of the
1992 Pilot, see EMAP-Agroecosystem Pilot
Field Program Report -1992 (Campbell et al.,
1994b).
In 1993, the ARG launched its second Pilot
Field Program, in Nebraska. Although the
objectives were similar to those of the 1992
Pilot, Nebraska is much different than North
Carolina, allowing further development of the
Agricultural Lands program in a variety of
ecological settings.
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
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Executive Summary
The Environmental Monitoring and Assessment
Program (EMAP) is being designed to help
policy makers decide how to allocate limited
resources among environmental problems.
Many monitoring efforts are site- or problem-
specific and do not allow assessment of
condition over large regions with statistical
confidence. EMAP will track the condition of
our environment on a regional basis.
The mission of EMAP's Agricultural Lands
Resource Group (ARG) is to develop and
implement a program that will, in the long term,
monitor and assess the condition and extent of
the nation's agricultural lands. The specific
objectives are to:
Estimate status and trends in condition.
Estimate geographic coverage and extent.
Seek stressor-condition associations.
Provide summaries and assessments.
The ARG conducted its second Pilot Field
Program, in Nebraska, in 1993. There were
three major objectives: (1) test a suite of
indicators in these categories: Land Use and
Cover, Crop Productivity, and Soil Quality;
(2) compare the relative efficiency of two
sampling designs; (3) develop and refine plans
for key components of the monitoring program.
Nebraska was selected primarily because of the
presence of both typical midwestern intensively
cropped lands and western sparsely cropped
lands. Addressing ecological condition in
widely varying settings is crucial for developing
a suite of indicators that can be used nationally.
EMAP uses probability sampling frames to
choose sample sites, allowing statistically valid
statements to be made for a region. Two
different frames were used; 288 sites in
Nebraska were chosen, all planted to annually
harvested herbaceous crops (AHHCs). After
the 1993 fall harvest, data were collected on
crop yields, soil characteristics, and
management. In addition, data on land use
and cover were provided from the National
Agricultural Statistics Service's June Agricultural
Survey.
Land Use and Cover
AHHCs are planted on over 7.4 million ha in
Nebraska, covering 37% of the state. Most are
found in the extensively cultivated lands in
eastern and southern Nebraska; western
Nebraska is predominantly sparsely cropped
rangeland. Corn is the most common crop
(45% of cropland); soybeans are the second
most common crop (14%).
Crop diversity was; measured as the number of
different crops in a given area and their relative
abundance. In the extensively cropped lands,
one third of the sample areas (260 ha each) '
contained four crops; in half the areas, a single
crop (usually corn) accounted for more than
55% of the total cropped acreage. How
diversity changes with time will be of interest:
decreasing diversity would signal increasing
vulnerability to pests and diseases.
Approximately 75% of all fields in Nebraska's
extensively cropped lands are 16 ha or smaller;
this differs from the usual impression of
Nebraska as a state covered by large fields.
Approximately 35%, of the extensively cropped
lands are covered by fields in this size range.
There are nearly 75,000 farm ponds in
Nebraska, covering almost 58,000 ha. More
than half are less than 0.3 ha in area. Water
for livestock is the single largest use of farm
ponds, followed by erosion and flood control.
Crop Productivity
Is cropland producing the yields we expect?
We calculated the ratio of the yield reported for
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
XI
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each sample field to the county average yield
for that crop over the period 1980-1989
(irrigated fields compared to the average for
irrigated fields; nonirrigated compared to
nonirrigated). For the five predominant crops,
1993 was slightly better than average;
soybeans did better than corn or sorghum.
Is cropland requiring increasing subsidies of
nonrenewable inputs? We examined nitrogen
use efficiency by calculating the quantity of
nitrogen applied for each unit of harvested
material. Not only soybeans (a legume) but
also wheat shows more efficient use of applied
nitrogen than the predominant crop, corn.
Are crops being managed for plant health? We
examined crop rotation in three ways.
(1) Rotation plans. We estimate that nearly
half of the AHHC land in Nebraska is not
covered by any planned rotation; of the land in
rotation, half the plans are two years in length.
(2) How long since the 1993 crop was grown in
that field? Over half the AHHC land had the
same crop in 1993 as in 1992, and 72% of the
corn acreage had been planted to com in 1992.
This is a substantial lack of rotation for a crop
that occupies half of the AHHC acres in
Nebraska. (3) How many crops were grown in
the past three years? Forty-two percent of the
AHHC land had the same crop every year out
of three; corn again predominated.
Soil Quality
Surface soil samples were analyzed for a set of
physical, chemical, and biological indicators.
Median values for clay, organic carbon, and
cation exchange capacity are lowest in
northwestern and highest in eastern Nebraska;
median pH values follow an opposite trend,
with the highest in the northwest. An integrated
rating showed better quality surface soils in
eastern and southern Nebraska than in the
northwest. This pattern corresponds to the
trend in land use, with most cultivated land in
the eastern part of the state.
A maturity index for free-living nematodes,
which reflects the degree of stability of soil
biota, showed that the relative health of the
eastern and southern regions of Nebraska was
similar, as did the Shannon index of trophic
diversity. A maturity index for plant-parasitic
nematodes showed healthier soils in the east,
although its interpretation is controversial.
Soil pits were dug at 26 sites to examine
subsurface indicators. Two methods for
assessing soil quality, the Soil Rating for Plant
Growth (SRPG) and the Soil Quality Report
Card, underwent preliminary development using
these data. Although different in their
approaches, both provide ways of determining
whether soils are meeting their potential. The
SRPG will be best used for regional monitoring,
whereas the Report Card will be more suitable
for monitoring specific sites.
Future Directions
The ARG will continue to develop indicators for
use in a national monitoring program. This
includes further work on the indicators explored
here, development of new indicators (we are
testing an insect indicator in 1994), and
consideration of pasture-livestock systems,
windbreaks, and other components of the
agricultural landscape. In addition, we will
address cross-resource issues by working with
other EMAP Resource Groups in the mid-
Atlantic region in 1994-1997. We are also
expanding our partnerships with other federal
agencies, primarily USDA's National Agricultural
Statistics Service and Natural Resources
Conservation Service.
EMAP continues to play an important role in
the national effort to base environmental policy
decisions on sound scientific information. The
monitoring and assessment techniques being
developed by EMAP are intended to form the
basis for any regional or national monitoring
effort where determining the nature and scope
of environmental problems is of interest.
EMAP-Agricultural Lands Pilot Field Program Report -1993
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1. Introduction
In 1993, the Environmental Monitoring and
Assessment Program-Agricultural Lands
Resource Group (EMAP-ARG) conducted its
second Pilot Field Program, in Nebraska. This
Pilot provided an opportunity to continue work
on indicators already under development, while
examining the behavior of these indicators in
different ecological settings than those in which
earlier research took place (North Carolina).
Addressing ecological condition in widely
varying settings is crucial to the development of
a suite of indicators that can be used on a
nationwide basis.
There were three major objectives in the 1993
Pilot Field Program:
1. Test a suite of indicators (Table 1.1) to:
evaluate the ability of each indicator to
address the assessment questions and
societal values of interest;
establish initial means, ranges, and
variances for each indicator across a
midwestern region;
assess components of variability of
indicators within and among sample
units;
determine the cost-effectiveness for
each indicator.
2. Compare the relative efficiency, in terms of
cost and precision, of two alternative
sampling designs for use in a national
agricultural lands monitoring program.
3. Develop and refine plans for key
components of the monitoring program,
including:
sampling
logistics
total quality management
data analysis, summarization, and
reporting
information management
Table 1.1. Indicator categories examined in the Pilot.
EXTENT OF AGRICULTURAL RESOURCES
Land Use and' Cover
Annually harvested herbaceous crops
extent of cropped land
crop diversity and dominance
field size
Farm ponds
number and area
uses
CONDITION OF AGRICULTURAL RESOURCES
Crop Productivity
observed/expected yield index
nitrogen use efficiency
crop rotation
Soil Quality
soil texture
cation exchange capacity
base saturation and electrical conductivity
pH
phosphorus
organic matter
sodium adsorption ratio
stability ofpiotic communities
diversity of biotic communities
An additional objective of the 1993 Pilot was to
further develop the ARG's relationship with
USDA-NASS and SCS at both state and
national levels. ;
One-state Pilot Field Programs are designed to
address questions; over an area large enough
to provide reliable answers but small enough to
be affordable and manageable. In 1993,
Nebraska was selected for several reasons:
1. The state contains both typical midwestern
agricultural lands (intensively cropped
areas) and western agricultural lands
(sparsely cropped areas).
2. Nebraska contains a transition between
agricultural lands and rangelands. This
allowed EMAP to begin the process of
carefully defining the areas of
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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/. Introduction
responsibility of the ARG and the
Rangelands Resource Group.
3. EPA Region 7 expressed strong interest in
the Agricultural Lands monitoring program.
Nebraska is a Great Plains state with an area
of approximately 20 million hectares (Figure
1.1). It falls in three distinct ecological regions
that correspond to the Land Resource Regions
of the USDA-Soil Conservation Service (SCS).
In this report, they are called the northwestern,
eastern, and southern regions (Figure 1.2)1.
Figure 1.1. Nebraska is a Great Plains state.
Climate and soils vary among the three
regions. Northwestern Nebraska has the least
rainfall and eastern Nebraska the most;
average annual temperatures are more variable
In northwestern Nebraska than in the other two
regions. Northwestern Nebraska has
predominantly sandy, alkaline soils;
southern Nebraska, silt loam soils that are
moderately alkaline to slightly acid; and eastern
Nebraska, silt loam and silty clay soils that are
slightly acid to acid (Elder 1969).
Figure 1.2. Ecological regions of Nebraska.
This report presents the results for some of the
Pilot Field Program objectives. Specifically, this
report:
gives a brief overview of the methods
employed in the Pilot (Chapter 2);
describes the findings for each indicator
in terms of extent and condition :of
agricultural resources in Nebraska
(Chapter 3);
evaluates each indicator and the two
sampling designs (Chapter 4);
draws conclusions about these
indicators and suggests future
directions for the program (Chapter 5).
'The SCS designations for the three regions are:
Western Great Plains Range and Irrigated
(northwestern); Central Feed Grains and Livestock
(eastern); and Central Great Plains Winter Wheat
and Range (southern) (USDA-SCS 1993a).
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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2. Methods
EMAP sample sites are chosen using
probability sampling designs, allowing
statistically valid statements to be made about
populations over a region. In the 1993 Pilot,
the ARG compared two probability sampling
designs to make an evaluation of their
precision, cost, and efficiency: the USDA-
National Agricultural Statistic Service's (NASS)
Rotational Panel design (Cotter and Nealon
1987), and a design modified from the EMAP
Hexagon design (Overton et al., 1991).
Data collection for the ARG is performed
primarily by NASS enumerators. NASS has a
well established, nationwide network of
enumerators and administrators experienced in
conducting agricultural surveys. Growers
throughout the U.S. are familiar with NASS
personnel and have confidence in NASS
because of its data confidentiality provisions.
NASS conducts many agricultural surveys each
year, including the June Agricultural Survey
(JAS). The area survey component of the JAS
includes some questions added for EMAP, and
all of the data from this area survey are
available to the ARG. Information from the
survey is used to select fields for the Fall
Survey, which NASS conducts specifically for
EMAP (for copies of the survey questionnaires,
see Campbell et al., 1994a). Soil Conservation
Service (SCS) personnel also collect data for
the ARG during the fall sampling period.
2.1 Sampling Design
2.1.1 Rotational Panel
Table 2.1. NASS strata
for Nebraska.
>80% Cultivated
51-80% Cultivated
15-50% Cultivated
<15% Cultivated
Urban/Agriculture
Commercial
Non-agricultural
NASS uses area frame
probability sampling
designs to conduct its
area surveys. For the
JAS, a state's land is
stratified by the
proportion of land
under cultivation (Table
2.1); extensively
cultivated areas are
sampled at greater
intensity than sparsely cultivated areas. A
sampling frame is developed for each stratum
in a two-stage process. First, the stratum is
divided into primary sampling units; then a
random subset of these is divided into smaller
units called segments. Segments are chosen
at random for detailed data collection
(enumeration) in June. A segment stays in the
JAS sample for 5 years and is then rotated out
to reduce respondent burden. Consequently,
one fifth of the entire sample is replaced each
year (hence the name "Rotational Panel").
During its first year in the sample, a segment is
in rotation year 1; during its second year, in
rotation year 2; eind so on.
The complete 1993 NASS sample for the JAS
in Nebraska had 390 segments. The
Rotational Panel sample used for this Pilot
consisted of segments from rotation years 1, 3,
and 5 and had a total of 234 segments (Figure
2.1).
2.1.2 Hexagon
Under the EMAP Hexagon design, the United
States is covered by a triangular point grid;
each point defines the centroid of a hexagon
that is approximately 40 km2. There are 317 of
these hexagons with their centroids in
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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2. Methods
EMAP Hexagon (n=77)
NASS Rotational Panel (n=234)
Figure 2.1. Location of Hexagon and Rotational Panel segments. Grey lines are boundaries of
ecological regions.
Nebraska. EMAP divides the grid into four
interpenetrating replicates, with one replicate to
be sampled each year, so that all points are
sampled over a 4-year period (Overton et al.,
1991). The 1993 replicate in Nebraska had 77
hexagons (Figure 2.1).
The modification of the Hexagon design by the
ARG made use of NASS segments. NASS
identified the segment containing the centroid
of each hexagon and added it to the sample of
segments visited in June.
2.1.3 Sample Unit Selection
During the JAS, NASS enumerators obtained
complete land use data for each sample
segment. The location of each field in each
sample segment was mapped on an aerial
photograph and its identification number and
acreage (determined during the visit) recorded.
Fields eligible for the Fall Survey were those
that contained annually harvested herbaceous
crops. Of the 234 Rotational Panel segments,
202 had at least one field with annually
harvested herbaceous crops and were eligible
to be sampled during the Fall Survey; of the 77
Hexagon segments, 54 were eligible (Figure
2.2).
Each segment has an associated expansion
factor, which is the inverse of the probability of
that segment being included in the sample
(Cotter and Nealon 1987). Multiplying an acre
by its segment's expansion factor converted
that acre to the number of acres in Nebraska
that it represented. Prior to drawing the
samples for the Fall Survey, field areas for both
the Hexagon and Rotational Panel designs
were expanded using the expansion factors to
obtain lists of acres of annually harvested
herbaceous crops in Nebraska for 1993. By
expanding the field areas, each list represented
EMAP-Agricultural Lands Pilot Field Program Report -; 1993
-------
Modified Hexagon Design
317
HEXAGONS
IN NEBRASKA
77
HEXAGONS
IN 1993 REPLICATE YEAR
NASS identifies segments containing
Hexagon centroids
Performs June Agricultural Survey
54
HEXAGON SEGMENTS
WITH ANNUALLY HARVESTED
HERBACEOUS CROPS
(eligible for Fall Survey)
Expands acreage and selects 72 random acres
Rotaitionati Pake/ Design
390
ROTATIONAL PANEL SEGMENTS
IN NEBRASKA
2.34
ROTATIONAL PANEL SEGMENTS,
IN ROTATION YE/\RS 1, 3, & 5
NASS performs June Agricultural Survey
202
ROTATIONAL PANEL SEGMENTS
WITH ANNUAU.YHARVESTED
HERBACEOUS CROPS
(eligible for Fall Survey)
Expands acreage and selects 216 random acres
SITES SELECTED IN
40
HEXAGON SEGMENTS
(72 acres)
_
SITES SELECTED IN
175
ROTATIONAL PANEL SEGMENTS I
(216 acres)
RgUre 2-2- Sample selection for Hexagon and Rotational Panel designsT
an estimate of the population of annually
harvested herbaceous crop acres in Nebraska.
The two lists were then ordered by segment
number. Each list was sampled systematically
with random starts in order to identify the fields
to be visited under each of the two designs for
the Fall Survey. A predetermined number of
random acres were targeted for selection For
the Hexagon design, 72 acres were selected-
these fell in 40 segments. For the Rotational
Panel design, 216 acres were selected; these
fell in 175 segments (Figure 2.2). Thus, a total
of 288 acres were selected. Each randomly
chosen acre identified a field to be visited,
although the acre that was actually sampled
could fall anywhere in that field. The fields
selected were identified and marked on the
aerial photographs for use by NASS
enumerators in collecting field data during the
Fall Survey. A random sampling site in each
field was identified upon arrival at the field'.
Thirty-nine sample acres in the Hexagon design
were selected to have soil pits dug by Soil
Conservations Service (SCS) scientists so that
subsurface analyses could be done for these
sites.
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
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2.2 Data Collection
Data collection for the 1993 Nebraska Pilot
consisted of three primary activities:
The Nebraska June Agricultural Survey for
land use and cover information, farm pond
characteristics, and information used to
select the fields for the Fall Survey,
conducted by NASS enumerators.
The Fall Survey for crop yields and
agricultural management information,
conducted by NASS enumerators.
Fall soil sampling for physical and chemical
characteristics and nematode enumeration;
NASS enumerators collected surface soil
samples and SCS personnel dug soil pits.
Samples were sent via Federal Express to
the SCS National Soil Survey Laboratory
(NSSL) in Lincoln, NE, for physical and
chemical analyses, and to N & A
Nematode Identification Service in Davis,
CA, for nematode enumeration.
i
Copies of the survey questionnaires and details
of the sampling methods are in EMAP -
Agroecosystem Pilot Field Program Plan - 1993
(Campbell et al., 1994a). Table 2.2 shows the
number of sites selected and actually sampled.
Table 2.2. Selected and completed sample sizes.
Original Sample Size
Completed
Questionnaires
Number of Soil Samples
Original Pit Sample Size
Completed Pits
Rotational
Panel
216.
168
156
'
Hexagon
72
52
53
39
;29a
"These were at 26 sites; three sites had two pits each.
2.3 Quality Assurance and Quality Control
The quality assurance/quality control (QA/QC)
procedures for this Pilot are detailed in the
Quality Assurance Project Plan (Schumacher
1993). This Plan has four basic requirements:
ensure that field and laboratory collection
and measurement procedures are
standardized among all participants;
monitor the performance of various
measurement systems being used to
maintain statistical control and to provide
rapid feedback so that corrective measures
can be taken before data quality is
compromised;
complete a periodic assessment of the
performance of these measurement
systems and their components;
verify and validate that reported da;ta are
sufficiently representative, unbiased,
precise, and complete so as to be!suitable
for their intended use.
These requirements were met by:
documenting and distributing all field
evaluation and sampling techniques,
analytical methods, and data management
procedures prior to the field season;
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
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2. Methods
conducting training workshops on field
procedures for all participants prior to the
field season;
using field and laboratory assessment
samples and procedures to verify and
assure the quality of the data;
conducting technical system audits in the
field and laboratories;
establishing adequate verification and
validation techniques for the data;
evaluating the QA data using established
statistical methods, and documenting data
quality.
These activities resulted in the following
outcomes:
Field and laboratory audits were conducted.
Minor problems were found and corrected
on site. No problems were found that
would compromise data quality.
Audit soil samples ("knowns") were
submitted arid assayed; values are being
used to calculate accuracy windows.
Questionnaire data were validated and
verified by NASS using their own data
quality assurance procedures. >
2.4 Information Management
Planning for data management consisted of
NASS-ARG interactions to determine process,
schedule, and flow of the survey data from the
field to the ARG (Figure 2.3). Processes were
monitored carefully and adjustments made as
necessary. Once results were received in
digital format from the National Soil Survey
Laboratory and the nematode lab, they were
merged with the survey and sample design
data. Verification and validation routines were
performed on the data.
The ARG made use of external data during the
1993 Pilot. SCS's Land Resource Regions
were used as data summarization regions
(Chapters 3 and 4; see also Chapter 1). NASS
stratification was used as the basis for
landscape categories for Land Use and Cover
indicators (Section 3.1). NASS aerial
photography was used for soil map unit
identification, and the Nebraska State Soil
Survey Database was used in development of
new soil quality assessment methods (Section
4.2.2).
Sunrays to NASS
Subject to NASS data
confidentiality provisions
Figure 2.3. Data flow for the 1993 Agricultural Lands
Pilot. AIC = Agricultural Lands Information Center.
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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2. Methods
2.5 Analysis
One of the statistical tools used to summarize
indicator data from the 1 993 Pilot is the
cumulative distribution function (CDF). The
Statistical Analysis System (SASฎ) (SAS
Institute, Inc. 1989) was used for calculating the
CDFs and performing all data analyses.
Data were collected using both the Hexagon
and the Rotational Panel designs, except for
nematodes, which were only sampled from
fields chosen in the Rotational Panel design.
For indicators using both designs, CDFs
computed from each design were combined to
form the CDFs reported in Chapter 3. An
average variance was computed for each
design using the variances at the observed
values for the combined dataset. The
combined CDF was defined as:
where
F(t) = estimated CDF from both designs
combined
F,p(t) = estimated CDF from Rotational Panel
design
Fbซ(t) - estimated CDF from Hexagon design
w s JJ / (1+p) = the weighting used
{I
the ratio of the average variance of the
Hexagon design to the average variance of
the Rotational Panel design
conducted every year in each of the 48
contiguous states. Thus, although Land Use
and Cover data were collected in June using
the Hexagon design, CDFs for these indicators
represent estimates based only on the
complete JAS performed using the Rotational
Panel design. For JAS and Fall Survey data,
variances computed from the Rotational Panel
design took its stratification into account.
The above weighting provides an approximately
optimal combination of the two CDFs in terms
of overall variability.
Regardless of which sampling design is
ultimately selected by the Agricultural Lands
Resource Group, Land Use and Cover
indicators will be computed from NASS's June
Agricultural Survey in order to take advantage
of its large sample size and the fact that it is
8
EMAP-Agricultural Lands Pilot Field Program Report- 1993
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2. Methods
How to Read our CDFs
The CDF for a particular indicator gives the
estimated proportion of the population that has
values less than or equal to any specified
value. Typically, in this report, the population is
the area of land in Nebraska, or in the eastern,
southern, or northwestern region of Nebraska,
cultivated with annually harvested herbaceous
crops. Maps or labels indicate the region (or
other population) represented by the CDF. If
none are presented, the CDF represents all
possible samples for that measurement or
index.
A CDF, by definition, will range from 0 to 1 on
the vertical axis. An interpolation procedure is
used to smooth the empirical step-function CDF
by connecting the midpoints of the steps for
each observed value; this procedure is
incorporated to remove bias. Hence, the
estimated CDFs will not begin at 0 or end at 1.
For many indicators, this is not noticeable.
Ninety percent confidence bands are added to
the CDFs to indicate the precision of each point
estimate.
As an example, consider the estimated CDF for
the maturity index for free-living nematodes
(Figure 2.4). This CDF was calculated from 86
observations and represents the southern
region of Nebraska. The percentile lines show
that 25% of the population of annually
harvested herbaceous cropland have maturity
index values of approximately 2.2 or less; 50%
of the population have values of approximately
2.35 or less; and 75% have values of
approximately 2,6 or less. The confidence
bands around the CDF tell us that, for example,
the proportion of annually harvested
herbaceous cropland in southern Nebraska with
a maturity index of 2.6 or less is estimated to
be 0.75 with a 90% confidence interval of
approximately (0.67, 0.83).
Upperboundof
90% confidence
Cumulative
distribution
function
Lower bound of 90%
confidence interval
Reference lines showing
25th, 50th, and 75th percentiles
Map shows region
/ covered by CDF
1 2 3 4
Maturity Index
for Free -living Nematodes Number of observations
from which CDF
was generated
Figure 2.4. Annotated cumulative distribution function, showing key features of the CDFs
presented in this report.
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
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3. Results
This section presents the results from indicator
measurements. These results fall into two
categories: Extent of Agricultural Resources,
which includes Land Use and Cover, and
Condition of Agricultural Resources, which
includes Crop Productivity and Soil Quality.
The description of indicator results in terms of
Extent and Condition of Agricultural Resources,
and the presentation in a question and answer
format, serves as the prototype for future
annual statistical summaries.
3.1 Extent of Agricultural Resources
One of the ARG's goals is to estimate and
report on the geographic coverage and extent
of the nation's agricultural lands. In addition, it
is useful to have some description of the
agricultural landscape as an aid in interpreting
results from condition indicators.
The following sections describe the extent and
geographic distribution of two types of
agricultural resources in Nebraska, annually
harvested herbaceous crops and farm ponds.
3.1.1. Extent and Geographic Distribution of Annually
Harvested Herbaceous Crops
Annually harvested herbaceous crops are found
throughout Nebraska, but most are found in the
intensively cultivated lands in eastern and
southern Nebraska and along the Platte River
in western Nebraska (Figure 3.1). Western
Nebraska is predominantly sparsely cropped
rangeland. For this Pilot, we examined the
land planted in annually harvested herbaceous
crops. Annually harvested herbaceous crops
(AHHC) are herbaceous plants that are
harvested every year, regardless of whether the
plant itself is annual or perennial (Table 3.1).
Although summer fallow is not a crop, it is
considered part of the AHHC resource because
it is grown in a regular rotation with AHHCs
(usually wheat). Annually harvested
herbaceous crops are planted on over 7.4
million hectares in Nebraska (Table 3.2),
covering some 37% of the state. Corn,
accounting for 45% of the annually harvested
herbaceous cropland, is the most common
crop; soybeans are the second most common
crop, accounting for 14%.
Table 3.1. Annually harvested herbaceous crops
eligible for selection in the Pilot.
Amaranth
Barley
Canola
Corn
Dry beans
Hay
Alfalfa
Other
Millet
Oats
Potatoes
Popcorn
Rye
Safflower
Sorghum
Summer fallow
Sunflowers
Triticale
Vegetables (all)
Winter wheat
Soybeans
10
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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3.LI Results: Extent: Annually Harvested Herbaceous Crops
Extensive cropland (>50% Cultivated)
10,074,326 ha. (51 % of Nebraska)
Sparse cropland (<15% Cultivated)
7,178,544 ha. (36% of Nebraska)
Cropland mosaic (15-50% Cultivated)
2,470,435 ha. (12% of Nebraska)
Non-Agricultural (Cities, water, etc.)
258,422 ha. (1% of Nebraska)
Area and proportion of land in each of four different types of landscape.
Estimated area
and proportion of
annually harvested
herbaceous crops
found in the four
types of landscape
shown above.
6,619,727 ha. (89% of AHHC)
Non-Agricultural
42,846 ha (1% of AHHC)
Sparse cropland
121,989 ha. (2% of AHHC)
Cropland mosaic
625,396 ha. (8% of AHHC)
Figure 3.1. Landscape types and area of annually harvested herbaceous crops in Nebraska.
EMAP-Agricultural Lands Pilot Field Program Report - 1993
11
-------
3.LI Results: Extent: Annually Harvested Herbaceous Crops
Table 3.2. Extent of annually harvested herbaceous
cropland (AHHC) in Nebraska.
Landscape Type
NASS Strata
Extensive cropland
>80% Cultivated
51-80% Cultivated
Cropland mosaic
15-50% Cultivated
Sparse cropland
<15% Cultivated
Non-Agricultural
Urban/Agriculture
Commercial
Non-agricultural
State Total
Extent of AHHC (ha)
(with 95% confidence)
6,619,727+244,429
625,396 ฑ 177,027
121 ,989 ฑ70,734
42,846 + 29,200
7,409,958 ฑ 306,470
Information about the extent and distribution of
annually harvested herbaceous crops was
obtained by analyzing data from the complete
NASS June Agricultural Survey. The JAS
design is based on stratification of land by
intensity of cultivation (see Chapter 2). Each
stratum is subdivided into segments; segment
size varies with stratum (Table 3.3).
What follows is a description of the extent and
distribution of land devoted to annually
harvested herbaceous crops in Nebraska.
Information is presented at the scale of broad
landscapes (we have aggregated the NASS
strata into several different types of landcapes
[Table 3.2]), smaller areas within similar
landscapes (based on segments within strata),
and individual fields (based on fields within
segments). The sample size is not constant
(Table 3.3), and changes reflect differences in
both the questions and the scale of ;
observation.
The other indicators summarized in Chapter 3
are reported for regions that differ from the
landscapes shown in Figure 3.1 and Table 3.2.
The reasons for these differences are
discussed under the heading Reporting on
Consistent Regions in Section 4.1.1.
Table 3.3. Sample sizes from the June Agricultural Survey for each landscape type.
Landscape Type
Extensive cropland
Cropland mosaic
Sparse cropland
Non-Agricultural
State Total
Average
Segment
Size (haa)
260
520
1040
65
Number
of
Segments
295
35
40
20
390
Number of
Segments
with AHHC
290
31
13
9
343
Number
of
Fields
4030
396
87
60
4573
"260 hectares is 1 square mile
12
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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3.1.1 Results: Extent: Annually Harvested Herbaceous Crops
How much of the land is cropped?
If you look at a random segment of land in
Nebraska, how much of that area would you
expect to be planted in annually harvested
herbaceous crops? Because crops are
distributed unevenly, the answer depends on
which part of the state you are in. For
example, in extensive cropland, three quarters
of the segments are at least 50% cropped,
whereas in sparse cropland, more than 95% of
the segments are less than 10% cropped.
Figure 3.2 shows the proportion of segments in
which P% or less of the area is cropped.
1 . O
0.8
,_ O . 6
0.4
co
O . 2
. O
Extensive Cropland
n = 295
O 2O 4 O 60 SO 100
0.8
,_ 0 . 6
o>
>
O
CO
. 4
0 . 2
Cropland Mosaic
n = 35
2O 4 O 6O SO 1 O O
4 O
O
O . 6
o>
0
0 . 2
O . O
Sparse Cropland
n = 40
2O 40 60 SO 100
P = Area Cropped (%)
Figure 3.2. Proportion of land area per segment
planted to AHHC.
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
13
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3.1.1 Results: Extent: Annually Harvested Herbaceous Crops
How diverse are the state's
croplands?
A more diverse agriculture may be less
vulnerable to disease, pest infestation, and
changing economic conditions. The diversity of
a collection of items in this case, crops is
usually measured in two ways: the number of
different items, and their relative abundance.
When comparing the diversity of crops in
several areas, each area should be of equal
size; diversity can change with area in
surprising ways. Because extensive cropland,
cropland mosaic, and sparse cropland all have
different segment sizes (Table 3.3), these
diversities should not be compared.
Instead, the issue of interest is how the
diversity of each of these landscapes changes
with time. Decreasing diversity would be
undesirable from an ecological perspective,
signalling increasing vulnerability to pests and
disease. Examining this issue is not possible
with the current dataset; multiple years must be
sampled.
First, the number of different items can be
examined. In a random segment, how many
different annually harvested herbaceous crops
would you be likely to find? The answer
depends on which part of the state you are in
(Figure 3.3). For example, no segment in
Nebraska's extensively cropped lands contains
more than eight crops; approximately one-third
of these segments contain four different crops.
Most of the segments in Nebraska's sparsely
cropped lands contain no crops (approximately
68%).
Extensive Cropland
n = 295
0123456789
Cropland Mosaic
n = 35
0.8n
01 23456789
Sparse Cropland
n = 40
0.1 23456789
Number of Different Crops
Figure 3.3. Number of annually harvested
herbaceous crops per segment. Black lines
represent 90% confidence intervals.
14
EMAP-Agricultural Lands Pilot Field Program Report -1993
-------
3.1.1 Results: Extent: Annually Harvested Herbaceous Crops
Do these cropped areas tend to be dominated
by a single crop? The answer to this question
provides a measure of relative abundance, the
second aspect of diversity. For example, two
segments of equal size may each contain corn
and soybeans. In one segment, corn may
account for 90% of the cropped land, with 10%
in soybeans; in the second segment, corn may
account for 50% of the land. Corn dominates
cropped land in the first segment; in the second
segment, the crops have a more even diversity.
Large areas dominated by a single crop,
particularly if crop rotation is not practiced, may
be more vulnerable to disease and pest
infestations.
For the segments in which annually harvested
herbaceous crops occur, is there a single crop
that dominates the cropped area? Dominance
for a segment is calculated as
Area of most common crop within segment
Area of AHHC within segment
Figure 3.4 shows the proportion of segments in
which P% of the annually harvested
herbaceous cropland is accounted for by a
single crop the one with the largest area.
For example, in 50% of the extensive cropland
segments, a single crop (usually corn) accounts
for more than 55% of the total cropped
acreage; in 25% of the segments, more than
70% of the cropped acreage is dominated by a
single crop. Because extensive cropland,
cropland mosaic, and sparse cropland all have
different segment sizes, these diversities
should not be compared. Instead, the
comparison of interest is how the diversity of
each of these landscapes changes with time.
1 . O
O
O . 8
O
a.
ฐ 0.6
O- '
d>
0.4
*-
CO
e o . 2
O.Q
1 . O
O
O . 8
O
a.
ฑ0.6
a*
0.4
*-
CO
e o . 2
=3
ฐ-ฐ2
1 . O
O
0.8
0
Q-
ฑ0.6
Q_
a>
> .
O . 4
ca
e o . 2
0 . O
Extensive Cropland ^^
n 3so f''^'/
- : /
JSf
T
, r ^~*~ . , ,
0 3O,4O 50 6O 70 8O 9O10
Cropland Mosaic
n 31
/
/
1 A-
' ~ J '
*' / '
' ^
r*
0
O 3O 4O SO 6O VO SO 9O1OO
Sparse Cropland
n - 13 /- - ,
/.
/
/
i
\ i
\
: i{
: /,'
/7
s
I
1
/
0 30
5O 6O 70 8 O 90100
P = Area Of Most Common Crop (%)
Figure 3.4. Percent of cropped area covered by the
most common annually harvested herbaceous crop.
EMAP-Agricultural Lands Pilot Field Program Report - 1993
15
-------
3.1.1 Results: Extent: Annually Harvested Herbaceous Crops
How large are the crop fields?
If you were to measure the size of a random
field of an annually harvested herbaceous crop,
how large would you expect the field to be? A
field Is a continuous area of land devoted to
one crop or land use (USDA-NASS 1992). It
includes only the cropped land and excludes
noncrop areas such as ditches and grassed
waterways.
The distributions in Figure 3.5 show the
proportion of fields of size S or smaller.
Although half of the segments in Nebraska's
extensively cropped lands are more than 75%
cultivated in AHHC (Figure 3.2), 50% of the
fields are 8 ha or smaller. However, fields of
this size account for less than 25% of the area
planted to AHHC (Figure 3.6).
1 . O
o
O . 8
O . 6
O . 4
co
O . O
1 . O
Extensive Cropland
25. 5 O 75 1 O O 1 2 5
25 5 O 75 1OO 125
v_ O . 6
O . 4
ป
CO
O . 2
O . 0
Sparse Cropland
O 25 5O 75 100,125
S Field Size (Hectares)
Figure 3.5. Proportion of fields of size S or smaller.
16
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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3.1.1 Results: Extent: Annually Harvested Herbaceous Crops
How do different sized fields contribute to the
total area of annually harvested herbaceous
cropland? In other words, what proportion of
the total area of annually harvested herbaceous
cropland occurs in fields of a given size or
smaller? The distributions in Figure 3.6 show
the proportion of the total area of annually
harvested herbaceous cropland accounted for
by fields of size S or smaller.
The sizes of some common fields are shown in
Figure 3.7. Approximately 75% of all fields in
Nebraska's extensively cropped lands are one-
sixteenth section or smaller (Figure 3.5). This
differs from the stereotypical impression of
Nebraska as a state covered by large fields. In
the extensively cropped landscapes, there are
jumps in the area of cropland accounted for by
fields of 16, 51, and 65 hectares (Figure 3.6).
These represent one-sixteenth section, one-
quarter section center pivot irrigated, and one-
quarter section fields, respectively. In
extensively cropped land, approximately 35% of
the cropped area is accounted for by fields the
size of one-sixteenth sections or smaller
(Figure 3.6).
ion
(40 ac)
Center pivot corner
3.5 ha (8.7 ac)
i . o
Extensive Cropland
n 4O3O
25 50 75 100 125
25 50 75 100 125
S = l=ield Size (Hectares)
Figure 3.6. Proportion of total area of AHHC
accounted for by fields of size S or smaller.
Figure 3.7. Common field sizes.
EMAP-Agricultural Lands Pilot Field Program Report - 1993
17
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3.1.2 Extent and Geographic Distribution of Farm Ponds
Farm ponds provide or collect water for farm
uses, including livestock watering, irrigation,
and erosion control. A farm pond is a
permanent, still body of water less than 16.2
hectares (40 acres) that contains water at least
seasonally. We estimated the number and
extent of farm ponds using data collected
during the June Agricultural Survey. The
number and extent of farm ponds in Nebraska,
by use, is reported in Table 3.4 and Figure 3.8.
Our goals in this effort were to determine how
extensive this resource is and what kinds of
uses are intended for farm ponds. With this
information, we can decide whether or not to
begin development of farm pond indicators and,
if we decide affirmatively, use the pond uses to
guide indicator development.
Table 3.4. Number and extent of farm ponds in Nebraska, by use. Uses separated by dashed lines are
grouped in Figure 3.8. Upper and lower bounds represent 95% confidence intervals, except where noted.
Primary Intended Pond Use
Water for livestock
Erosion control
Flood control
Catchment
Irrigation re-use pit
Other irrigation use
Waste lagoons
Water for wildlife
Recreation
None or other uses*
TOTAL
Number of Ponds
lower Estimate upper
bound bound
30,226
5,588
1,554
1b
6,514
47
1,192
866
5b
803
61,479
40,808
8,578
3,118
136
9,488
1,208
2,969
2,479
646
5,203
74,633
51,389
11,569
4,681
425
12,462
2,370
4,746
4,092
1,222
9,602
87,787
Extent of Ponds (ha)
lower Estimate upper
bound bound
16,863
3,949
42ฐ
0.4C
3,043
9ฐ
644
97
6ฐ
484
45,564
24,016
9,448
5,549
55
4,488
1,252
3,950
3,660
798
4,690
57,906
31,170
14,946
10,457
172
5,933
2,718
7,257
7,223
1,533;
8,896
70,248
* Most of these ponds had no Intended use. Other uses recorded were gravel pit and vacant pasture.
" The lower bound Is the number seen in our sample.
e The lower bound Is the extent seen in our sample.
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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3.1.2 Results: Extent: Farm Ponds
Estimated
number of
farm ponds
and proportion
of total,
by use.
Erosion and
11,832poi
Water for Livestock
40,808 ponds (55%)
Recreation
/ 646 ponds (1%)
Water for Wildlife
2,479 ponds (3%)
- Lagoons for Waste
2,969 ponds (4%)
Other Uses.
5,203 ponds (7%)
Irrigation
10,696 ponds (14%)
Estimated
area of
farm ponds
and proportion
of total,
by intended
use.
Erosion and
15,052 heel
Water for Livestock
24,016 hectares (41%)
Recreation
798 hectares (1%)
Water for Wildlife
3,660 hectares (6%)
.- Lagoons for Waste
3,950 hectares (7%)
No or Other Uses
Irrigation 4,690 hectares; (8%)
5,740 hectares (10%)
Figure 3.8. Number and extent of farm ponds, by use. Some of the categories are combinations of two or three
pond use categories as described in Table 3.4.
EMAP-Agricultural Lands Pilot Field Program Report - 1993
19
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5.7.2 Results: Extent: Farm Ponds
How large are the farm ponds?
More than half of the farm ponds in Nebraska
are less than 0.3 hectares in area (Figure 3.9).
However, the distribution of pond areas varies
with landscape type (Figure 3.10). For
example, there is a greater proportion of ponds
larger than 7 hectares in sparsely cropped
landscapes than in cropland mosaic landscapes
(p=0.058).
i. o
o
0.8
O
ex.
2 0.6
CL.
0 . 0
Statewide
n 378
5 10 15 2O 25
P = Area of pond (hectares)
Figure 3.9. Cumulative distribution of farm pond area
for Nebraska.
i.o
<=
o
0.8
o
a.
ฐ 0 . 6
~ O . 4h
E 0 . 2|
0 . Oil
Extensive Cropland
n 294
10 15 20 25
1 . 0
O . 8
O
ฐ 0.6
rป
O>
O . 4
CO
E 0 . 2
O . O
/
/
-'
;
1
''/.
'
'V
' /
Cropland Mosaic
n 35
D 5 10 15 20 2 '.
i . o
ej
0 . O
5 1O 15 20' 25
P = Area of pond (hectares)
Figure 3.10. Area of farm ponds by landscape type.
20
EMAP-Agricultural Lands Pilot Field Program Report - 1993
-------
3.2 Condition of Agricultural Resources
One of the ARG's goals is to estimate and
report on the condition of the nation's
agricultural lands. Development of condition
indicators for agricultural lands and the
methods to interpret results from these
indicators is at the heart of the EMAP-
Agricultural Lands program. The following
sections present the results from Crop
Productivity and Soil Quality, the two condition
indicator categories tested in Nebraska, along
with some preliminary interpretations. In a few
cases, thresholds for acceptable (nominal),
marginal, and unacceptable (subnominal)
conditions are presented; in most cases
thresholds are still under development and are
not presented.
3.2.1 Crop Productivity
The concept of sustainable agriculture has
attracted many adherents and nearly as many
definitions. This principle that we must meet
the needs of today without undermining the
ability of future generations to meet their needs
is the lens through which we view the
consumptive and nonconsumptive uses of
agricultural lands. A key component of
consumptive use is the harvest of crops for
food and fiber. The third EMAP value,
biological integrity, is represented by our belief
that agriculture is better when it makes use of
ecosystem processes rather than trying to
substitute for them. For example, it is good to
rotate crops (for soil maintenance, pest
management, and a yet-unexplained "rotation
effect"), to have legumes in the rotation (for
nitrogen fixation), and not to pump ground
water faster than it can be replenished.
Sustainability is an economic and social issue
as well as an ecological one (e.g., Lowrance et
al., 1986); for instance, economic viability of
farms and quality of life in rural communities
must be considered as part of the broad
picture. However, the ARG focuses on
ecological sustainability.
Crops and fields fit within a landscape, so a
complete assessment would have to consider
whether the choice of crops or livestock on a
given area of land is ecologically appropriate
and sustainable,, Population trends and the
demand for food and fiber are other critical
pieces to be considered. Nevertheless, the
current status of crop productivity, and more
importantly its trends, are worth monitoring.
With 1 year's data from a pilot project, no
trends can be analyzed; the interpretation of
crop productivity measures as status indicators
is still being developed, including the
establishment of thresholds for acceptable and
unacceptable condition.
3.2.1.1 Issues and Questions
At least three dilferent aspects of crop
productivity are of concern in the context of
agricultural resources. These lead to specific
questions that can be addressed by indicators.
An added category concerning crop rotations is
placed here but could also be included in the
cropland diversity section (3.1.1).
The first issue is: Are agricultural lands (soils,
water, and air) able to produce plentiful crops
for a growing demand? It is beyond the scope
of this program to look at the supply and
demand aspects of this question, but we can
ask the assessment question: "On what
proportion of cropland are crop yields meeting
our expectations, based on each field's soil
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
21
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5.2.7 Results: Condition: Crop Productivity
type, the region's climate, the current year's
weather, and the management practices used?"
The yields for sample fields in the 1993 Pilot
were determined by interviews with farm
operators. Expected yields can come from
various sources. The USDA Soil Conservation
Service determines expected yields of the
major crops for each soil map unit. Expected
yields can also be generated from process
models or statistical models. Models have
some drawbacks, but have the major
advantage that they may allow for adjustment
for the effects of temperature and precipitation
in a given growing season. A third source for
expected yields is historical data; this type is
used below for the observed/expected index.
Use of the first two types of expected values
will be explored in other work by the ARG.
The second issue is: Even if yields are steady
or increasing, are they doing so only because
of large and increasing subsidies to the
agroecosystem from nonrenewable inputs? It
has been asserted that a deteriorating
agroecosystem will require increasing inputs
such as fertilizer and pest control to maintain
production (NRC 1993; Cox 1984). Even
current levels of inputs are a concern if we
know they will not be available in the long run.
We can focus on nitrogen, for example, a
primary plant nutrient and one controlled by
management practices, and ask: "In what
proportion of cropland is nitrogen use efficiency
increasing, holding steady, or declining over
time?" One way to express nitrogen use
efficiency would be to estimate the nitrogen
additions to and withdrawals from the field, and
to see whether they are in balance. This may
eventually be done with two of the largest
fluxes: farmer-applied nitrogen and the
nitrogen removed in harvested material. For
this report, nitrogen use efficiency was
calculated as the quantity of nitrogen applied
for each unit of harvested material.
The third issue is: Are crops being bred and
managed in such a way that the plants are
healthy, or do they suffer from nutrient
deficiencies, pests, or disease? Are they bred
and managed in such a way as to minimize the
possibility of future outbreaks of pests or
disease and reduce reliance on agrichemical
inputs? This issue can be examined from two
perspectives: the management practices or the
results (plant health). We are not measuring
plant health directly, but crop rotation is an
important component of management which
has implications for plant health; several
measures are reported below. These address
biodiversity in time especially important
considering that there is little diversity in a crop
field within a single season due to the
requirements of the mechanized agriculture
commonly practiced in the United States.
3.2.1.2 Indicators
Sample units were not used in calculations if
they were not annually harvested herbaceous
cropland (AHHC) (see Table 3.1). Summer
fallow was considered an eligible land use for
this study because it is in rotation with annual
crops, but data from summer-fallowed fields
could not be included in yield-based indices.
Observed/Expected Yield
An index was calculated as the ratio of the
yield reported for each sample field to the
county average yield for that crop over the
period 1980-1989. County yields were
obtained from the National Agricultural Statistics
Service office in Nebraska for the five crops
that represented 95% of the yield data from the
Fall Survey: corn for grain, sorghum for grain,
alfalfa hay, soybeans, and winter wheat.
Average yields from nonirrigated fields were
used for nonirrigated sample fields, and
averages from irrigated fields were used for
irrigated sample fields.
22
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
-------
3.2.7 Results: Condition: Crop Productivity
The index, being dimensionless, can be
combined across all crops. Figure 3.11
represents the cumulative distribution function
(CDF) for this index for the state of Nebraska.
By interpolation, 56.7% of the land area had
yields greater than the 1980-1989 mean, with a
90% confidence interval of (50.5%, 62.9%).
Thus, 1993 was a slightly better-than-average
year for crop yields, despite the flooding that
affected some areas. Very few acres of
annually harvested herbaceous crops (AHHC),
around 5%, had yields of less than half of the
expectation. The distributions for each of the
three ecological regions of the state are similar
(data not shown). The CDFs also show little
difference among crop species (three are
shown in Figure 3.12). With its predominance
in the sample, the distribution for com is
essentially identical to the overall distribution.
Soybeans appeared to have higher, and thus
better, index values than other crops. We
compared the proportions of each crop's area
having index values greater than 1.0 (e.g., 0.55
for corn). This proportion is significantly greater
for soybeans than for corn (p=0.051) or
sorghum (p=0.037) (data not shown).
1.0
CJ
0 . 0
1 . 0
ฐ 0.6
to
>>
0.4
to
=3
S O . 2
13
C3
o . o
Soybeans
1 . O
o . o
Major Crops
(combined)
0 1 2 3 4 5
Observed/Expected Yield Index
Figure 3.11. The observed/expected yield ratio,
combined for the five predominant AHHCs in
Nebraska (corn for grain; soybeans; wheat; sorghum
for grain; alfalfa hay).
1 . 0
0 . 8
*_ 0 . 6
Q_
to
0.4
CO
E O . 2
0 . 0
Wheat
n=19
12345
Observed/Expected Yield Index
Figure 3.12. The observed/expected yield ratio for
three AHHCs in Nebraska.
EMAP-Agricultural Lands Pilot Field Program Report - 1993
23
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3.2.1 Results: Condition: Crop Productivity
Nitrogen Efficiency Index
Figure 3.13 shows the CDF for the nitrogen
efficiency index, combined across seed crops.
This index is the ratio of the weight of nitrogen,
applied to the field as chemical fertilizer or
manure, to the dry weight of harvested output.
Because the nitrogen input is in the numerator
of the index, smaller index values represent
greater efficiencies. In terms of individual
crops, not only soybeans (a legume), but even
wheat shows more efficient use of applied
nitrogen than the predominant crop, corn
(Figure 3.14). Comparisons of median index
values for these three crops show significant
differences between com and soybeans
(p<0.001) and com and wheat (p=0.007).
Of the nitrogen applied to AHHC land in
Nebraska, only 4.6% of the 1993-total came
from manure. The standard error of this
estimate is about 2.8%, and the 95%
confidence interval is (0,10.1%).
1 . 0
1 . O
O . O T
O.OO O.O5 0.1O O.15 O.2O
Nitrogen Efficiency Index
Figure 3.13. The nitrogen efficiency index for land in
seven seed crops: com for grain, sorghum,
soybeans, wheat, dry beans, millet, and popcorn.
Larger index values indicate lower efficiency.
~ 0.4
ce
O
O . O
Soybeans
11-21
O.OO O.O5 O.1O 0.15 O.2O
1 . O
E=
O
.
t: 0.8
ea
Q.
O
*- 0.6
o_
~ O . 4
CO
=3
E O . 2
CJ>
n n
r' ' S'
'/'
1 1
1
j
Lj
/
j
1
\
1
f
( :
I
:
Wheat
n-ie
0.00 0.05 0.1O 0.15 0.20
Nitrogen Efficiency llndex
Figure 3.14. The nitrogen efficiency index for
Nebraska land in three AHHCs. Larger index values
indicate lower efficiency.
24
EMAP-Agricultural Lands Pilot Field Program Report - 1993
-------
5.2.7 Results: Condition: Crop Productivity
Crop Rotation Indices
There are two ways to consider crop rotation: in
terms of intent or in terms of results. The fall
questionnaire asked whether there was a crop
rotation plan for the sample field, and if so, how
many years were in the rotation. Nearly half of
the AHHC in Nebraska (mostly corn) is not
covered by any planned rotation (Figure 3.15).
Of the AHHC in rotation, half the plans are only
2 years in length. Note: Rotation plans do not
always call for crops to change every year, nor
does the absence of a plan mean that the
same crop is grown continuously on that field.
no rotation plan
(45ฑ3%)
r 4 years
(5ฑ2%)
\ ' 5 years
6-8 years <5ฑ1%)
Figure 3.15. Percent of Nebraska land in AHHCs
with various rotation lengths, as reported by the
farmer. n=213
How long?
The fall questionnaire obtained land use
information for the 1993, 1992, and 1991 crop
years, so the length of time that has passed
since the current (1993) crop was last grown on
the field can be estimated. Different uses of
the same crop were considered to be the same
crop; for example, sorghum for grain and
sorghum for silage were both considered
"sorghum." This How long? index takes on
integer values of 1, 2, and 3 years (Figure
3.16). An index value of 3 was assigned if the
1993 crop did not appear in 1992 or 1991,
signifying that the crop had last appeared three
or more years ago, if ever. Over half of the
land in AHHC was planted in 1993 to the same
crop as in 1992, and 72ฑ4% of the 1993 corn
acreage had been planted to corn in 1992.
This is a substantial lack of rotation for a crop
that occupies half of the AHHC acres in the
state of Nebrasska. This index may underreport
rotation because a 1993 crop on any part of the
field in a previous year was counted as if it had
occupied the entire field. That bias occurred in
no more than 7% of the cases, however.
rs
fallow
in 1993
Figure 3.16. Percent of Nebraska AHHC land in
which the 1993 crop had last been planted 1, 2, and
3 or more years prior. n=208
EMAP-Agricultural Lands Pilot Field Program Report - 1993
25
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3.2.1 Results: Condition: Crop Productivity
How many?
Another index was calculated as the number of
different crops (or land uses, including fallow)
on the sample field in the 3-year period
covered by the questionnaire1. Forty-two
percent of the AHHC had the same crop every
year out of the three (Figure 3.17). Most of
those (32% of AHHC) were com. Continuous
sorghum accounted for 4% and continuous
alfalfa hay another 4%. Successive years of
alfalfa would be more ecologically sustainable
than continuous row crops; however, this index
indicates diversity without consideration of the
relative merits or liabilities of different crop
species.
Figure 3.17. Percent of Nebraska AHHC land with
one, two, and three unique crops or land uses over
the three seasons 1991-1993. n=205
Consider a theoretically "good" 6-year rotation
of 4 years of alfalfa followed by 1 year of a
small grain and then 1 year of corn. Collecting
the Fall Questionnaire data on a random ;
sample of such fields at different stages in the
rotation would provide average values of 2.0 for
both the How long? and the How many?
indices. (Individual samples, being sets of 3
years of data, would have values of 1, 2, or 3.)
The same result would occur if the rotation
were 3 years of alfalfa followed by 1 year of
small grain and 2 years of corn. Rotations that
include that much hay or pasture would be
beneficial for reducing erosion and improving
certain soil properties such as organic matter
(Bullock 1992), but can hardly be
recommended for all of Nebraska. Yet the
existing proportions of crops may not rule but
diverse rotations. Hypothetical rotations of 4 to
6 crops can be devised that approximate the
actual distribution of crops in the various
regions of Nebraska and give average rotation
indices greater than 2.0 (data not shown).
Therefore, it seems that index values of 3,
while representing the maximum diversity, may
not be necessary for a field to be in good
condition. Provisionally, a value of 2 will be
considered nominal for either index. This
cannot be applied to the index values from
individual fields because even with a good
rotation, there may be stretches of 3 years for
which there is no diversity. For the state of
Nebraska, the average values for all fields are
1.55 for the first index and 1.67 for the second,
which are significantly less than 2.00 (one-
tailed t-test, pซ0.01). This would appear to
represent a subnominal condition, but this is a
verypreliminary appraisal.
Vor simplicity, if there were two or more crops side-
by-side In the field in any year, the crop occupying
the larger area was used. In three cases, multiple
crops over several years caused enough confusion
that the sample units were excluded from the index
calculation. Such decisions result in the variation in
n for the different indices.
26
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
-------
3.2.2 Soil Quality
Soil quality relates to the ability of the soil to
perform certain functions in terrestrial
ecosystems. Various interest groups have
promoted their own definition of soil quality and
function (see Doran et. al. (1994) for a review).
Regardless of definition, the functions of
interest depend on the particular use of the soil,
whether it is supporting an agricultural field, a
residential area, a golf course, or a forest.
Each use carries with it a unique set of
desirable soil attributes, as well as unique
indicators of soil condition.
EMAP has identified values of consumptive
(e.g., production of food and fiber) and non-
consumptive (e.g., environmental buffering of
contaminants and pathogens) uses of
agricultural lands, which have been used to
formulate assessment questions. In this Pilot,
we focused on soil quality for supporting plant
growth. Some of the assessment questions
that we have attempted to answer are:
What proportion of Nebraska's agricultural
lands have productive soils?
What proportion of Nebraska's agricultural
soils show signs of physical or chemical
deterioration?
What proportion of Nebraska's agricultural
lands have soils that support diverse
communities of soil microbiota?
Answers to these questions were developed
using several approaches. National Agricultural
Statistics Service (NASS) enumerators
collected surface soil samples from 209 sites
(random acres) across the state. Thirty-one
samples were from northwestern Nebraska,
110 from southern Nebraska, and 68 from
eastern Nebraska. At each site, surface
samples were composited from 20 subsamples
(8 inch [20 cm] cores) taken along a 100-m
transect. These composite surface samples
were analyzed for a set of physical, chemical,
and biological indicators. Data are summarized
for each of Nebraska's three ecological regions.
Section 3.2.2.1 provides an evaluation of each
of the physical and chemical indicators
individually, and an integrated analysis of
several indicators, which represents a rating for
the physical and chemical condition of the
surface soil. Results for several indicators are
presented as box plots (Figure 3.18), where the
bottom and top of the grey
box represent the 25th and
75th quantiles, respectively
(0.7 and 4.6 in the example),
and the solid line through the
box represents the median
(1.6 in the example).
Quantiles were estimated
using CDFs.
Figure 3.18. Box
plot example.
In addition to composite surface samples, we
studied subsurfaice soil indicators to find out
more about the ability of the soil to perform
varied functions (Section 4.2.2).
Soil biotic diversity was addressed by
examining stability and diversity of nematode
communities (Section 3.2.2.2). In addition, soil
measurements were combined with
management data to evaluate the effects of
management practices on nematode
communities (Section 4.2.2.4).
3.2.2.1 Composite Surface Soil
Sarapl.es
In general, results from the analyses of the
composited surface soil samples show that the
median values for clay, organic carbon, and
cation exchange capacity are lowest in
northwestern Nebraska and highest in eastern
Nebraska. Median pH values follow an
opposite trend, with northwestern Nebraska the
highest and eastern Nebraska the lowest. An
integrated rating showed higher quality surface
EMAP-Agricultural Lands Pilot Field Program Report - 1993
27
-------
5.2.2 Results: Condition: Soil Quality
soils in eastern and southern Nebraska than in
northwestern Nebraska.
Soil Texture
Soil texture describes the relative amount of
sand, silt, and clay in a soil, and is a nearly
permanent property of the soil. Although soil
texture may change through the loss of fine
particles to erosion or deposition from floods,
such changes would be difficult to detect in a
regional monitoring program such as EMAP;
consequently, soil texture is not a good
indicator of soil quality. Nonetheless, soil
texture is vital for the interpretation of soil.
indicators because it has a strong influence on
such properties as pore size distribution and
biological activity, water-holding capacity,
available water, infiltration, nutrient retention
and cycling, and bulk density.
The clay fraction is the most reactive of the
three soil fractions because it has a large
surface area, and the surface is where
chemical exchange processes take place. In
general, the best agricultural soils have
medium-texture and contain about 18-35% clay.
Soils with too much or too little clay require
special considerations for tillage and water
management. Soils with too little clay are
vulnerable to compaction and wind erosion,
have low water-holding capacity, and have
greater leaching potentials than finer-textured
soils of comparable mineralogical composition.
Conversely, soils with too much clay can be
difficult to till when wet and have low available
water and low infiltration rates.
There are significant differences between the
surface soil textures in the three regions
(p<0.01 for each pairwise comparison of
median clay content).
We grouped the soils into low (<18%), medium
(18-35%), and high (>35%) clay contents
(Figure 3.19). About 75% of the cropped
surface soils in northwestern Nebraska are low
in clay content, compared to about 20-30%' in
the other two regions.
LOW 7(4+4%)
(2818%),/ ~
Figure 3.19. Clay contents in Nebraska
surface soils.
28
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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5.2.2 Results: Condition: Soil Quality
Cation Exchange Capacity
Cation exchange capacity (CEC) is a measure
of the soil's ability to adsorb cations, including
most of the primary and secondary plant
nutrients and micronutrients. Soils with low
CEC (<15 cmol(+)/kg) hold fewer cations and
may require more frequent applications of
fertilizer than soils with high CEC (Broderson
1991; Brady 1984).
The CEC values of the surface soil samples are
highly correlated with clay contents (r2 = 0.92)
(Figure 3.20). CEC is generally lower in
northwestern Nebraska, which has lower clay
contents, than in eastern or southern Nebraska
(all three medians are significantly different,
p<0.01) (Figure 3.21).
35
8
2ฐ
o
K)
,. '.",>.
. .3?
ซ...,*
i *ซ V*
.,ป . ป*x *
444 Northwestern
xxx southern
a Eastern
% CLAY
Figure 3.20. Relation between CEC and clay in
'Nebraska surface soils.
cmol /10Og
25
15 --'-
10 .---J
I
ft
Figure 3.21. Cation exchange capacity in Nebraska
surface soils.
Base Saturation and Electrical
Conductivity
Base saturation is defined as the percentage of
total CEC occupied by basic cations such as
calcium, magnesium, sodium, and potassium.
The SCS laboratory (NSSL Staff 1992)
calculates base; saturation by:
Base saturation (%)=
NHjQAc extractable bases
CEC (buffered)
Most crops grow best when base saturation is
above 60% (R. Mikkelson, personal
communication). L.ow base saturations may
pose fertility problems because the soil
exchange sites are occupied by cations (e.g., H
or Al) other than the four basic cations
measured (Ca, Mg, Na, K). Base saturation
exceeding 100% could be an indicator of
limitations due to climate, salinity, or soil
reaction, and resultant nutrient availability or
micronutrient toxicity problems. In most areas
in Nebraska, base saturations exceeding 100%
are a result of free carbonates in the soil (T.
Reinsch, personal communication); free
carbonates are measured as cations, but do
not occupy exchange sites. These soils may
be deficient in nutrients such as phosphorus,
iron, and manganese, and the high carbonates
in some soils may pose limitations for some
crops.
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
29
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3.2.2 Results: Condition: Soil Quality
High base saturations are problematic when the
exchange sites are dominated by soluble salts
(major dissolved inorganic solutes), which is
indicated with a high electrical conductivity
(EC > 2 dS m'1). Electrical conductivity reveals
the amount of electrolytes or salts in the soil
solution. Salts in soil cause drought-like
conditions for plants by imposing higher
osmotic potential and drawing the water away
from the roots. Excessive amounts of any one
of the ions (e.g., Na+) may be toxic. There may
also be nutritional effects, e.g., NaCI will inhibit
NO3~ uptake and reduce plant yields.
Most of the soils in Nebraska (98%) have base
saturation higher than 60% and low electrical
conductivity (EC < 2 dS m'1). Twenty-six
percent have very high base saturations
exceeding 100%. Proportionally more soils
with very high base saturations are in
northwestern Nebraska (Figure 3.22), which
follows regional trends of increasing alkalinity
and increasing irrigation from east to west. The
median base saturation for northwestern
Nebraska is significantly higher than the
median for eastern Nebraska (p<0.01), but not
significantly different from southern Nebraska.
70
pH
Soil pH controls many chemical and biological
processes in the soil, including nutrient
availability. Most crops and many microbes
prefer a pH between 6 and 7.5. At pH values
outside this range, some nutrients become
limited, and many microbes that contribute to
nutrient cycling cannot flourish (Broderson
1991; Brady 1984). Trends in soil pH reflect
the effects of some management practices,
such as fertilization without liming.
In Nebraska, pH values of the surface samples
range from less than 6 to more than 8 (for pH
measured in water). The median value was
significantly higher in northwestern Nebraska
than in eastern Nebraska (p<0.01) (Figure
3.23), but was not significantly different than
southern Nebraska. The low pH in eastern
Nebraska may be due to higher precipitation, to
crop removal of bases, or both. The pH can be
raised by liming the soil, so the lower pH
signals a need for management input. :
Figure 3.23. pH in Nebraska surface soils.
Figure 3.22. Base saturations in Nebraska surface
soils.
30
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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1
3.2.2 Results: Condition: Soil Quality
Phosphorus
Phosphorus levels in soil are one indication of
soil fertility. Trends in phosphorus might
indicate whether the soil's nutrients are being
depleted without being restored.
Although the median values for phosphorus for
the three regions were all significantly different
from each other (p<0.01) (Figure 3.24), they fall
well within the medium range (20-39 u.g/g) of
soil test phosphorus for Nebraska soils
(E. Kamprath, personal communication).
Phosphorus ftig/g)
40
Figure 3.24. Phosphorus in Nebraska surface soils.
Organic Matter
Organic matter improves soil infiltration and
serves to enhance the nutrient retention
capabilities of the soil. Generally, more organic
matter is better. The presence of organic
matter reflects residue-preserving management
practices (USDA-SCS 1993b).
Organic matter is estimated by analysis of
organic carbon content (NSSL 1992). The
median value of organic carbon for the samples
analyzed was 1.28%. Soils in northwestern
Nebraska had the lowest median value for
organic Carbon, and eastern Nebraska had the
highest (all three medians are significantly
different, p<0.01) (Figure 3.25).
percent ^_
1.5 BD|B
i-*
0.5
Figure 3.25. Organic carbon content in Nebraska
surface soils.
Sodium Adsorption Ratio
Sodium is an essential element in plant
nutrition, but it is toxic to plants and destructive
to soil structure in high concentrations. Sodium
is a problem in arid areas, where sufficient
water does not pass through the soil to leach
accumulations of the sodium ion to greater
depths (McQuaid et. al., 1987). The sodium
adsorption ratio (SAR) is a measure of the
amount of sodium in the soil relative to calcium
and magnesium. SAR is calculated by:
SAR -
iVa
vi(Ga + Mg)l2
Soils that have values of 13 or more may have
an increased dispersion of organic matter and
clay particles, reduced permeability and
aeration, and a. general degradation of soil
structure (Soil Survey Staff 1993). None of the
surface samples tested had SAR values greater
than 3.9. There were no significant regional
differences.
EMAP-Agricultural Lands Pilot Field Program Report - 1993
31
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5.2.2 Results: Condition: Soil Quality
Integrated Assessment of the
Measured Values
We attempted to calculate one number for each
sample that would reflect the condition of the
surface soil. Values for each of six soil
indicators were rated from 1 (low) to 3 (high)
(Table 3.5). We then averaged the six indicator
values and compiled the data for the three
regions.
Table 3.5. Ratings for each of the soil properties
used In the integrated assessment of surface soil.
Indicator
Clay
CEC
Base
Saturation
Organic Matter
(organic carbon
x1.6)
PH
SAR
1=Low
<18%
>35%
<60%
0.1-2%
2=
Moderate
<16
>100%
2-5%
>7.5
<6.1
>4
3=Hlgh
18-35%
>16
60-100%
>5%
6.1-7.5
<4
This rating follows the same general trend as
seen with the individual indicator values (Figure
3.26). By this rating scheme, soils in
northwestern Nebraska are significantly worse
than those in eastern or southern Nebraska
(p<0.01); the eastern and southern regions are
not significantly different from each other. This
trend in soil quality corresponds to the trend in
land use: most cultivated land is in the eastern
part of the state, and the least amount in the
northwest.
Figure 3.26. Integrated assessment ratings for
Nebraska surface soils (means and 95%
confidence intervals shown).
32
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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5.2.2 Results: Condition: Soil Quality
3.2.2.2 Soil Biotic Diversity
Nematodes (free-living and plant-parasitic) are
a group of soil fauna that show promise for use
as an indicator of pollutant exposure and
restoration of soil ecosystems (Schouten et al.,
1990). We plan to use nematode communities
to answer the question, "What proportion of
Nebraska's agricultural lands have soils that
support diverse communities of soil
microbiota?" Specifically, we must quantify the
stability (or maturity) and diversity of biota in
soils.
Nematodes are found in most soils throughout
the world. They are diverse in their feeding
habits and occur as central members of the soil
food web. Nematodes can be classified as
bacterivores, fungivores, omnivores, plant-
parasites, and predators. Predaceous and
omnivorous nematodes occur higher in the
trophic hierarchy than bacterivores and
fungivores and are more sensitive to
disturbance. Because of the range of feeding
habits, the trophic structure of nematode
communities reflects the relative degree of
stability of or disturbance to the biota.
What is the degree of stability (or
maturity) in nematode communities
in soil?
A highly stable, or mature, community of
nematodes would indicate that minimal
disturbance or contamination has occurred in
the soil. Maturity indices depend on whether
nematode communities are dominated by
colonists (generalists or r-strategists) or
persisters (specialists or /(-strategists) (Bongers
1990; Ricklefs 1979) and, therefore, reflect
relative disturbance. The maturity index for
free-living nematodes depends oh the
establishment of omnivores and bacterial-,
algal-, and fungal-feeding nematodes.
Disturbances such as cultivation, additions of
fertilizer and manure, and application of
herbicides all result in an increase in the
amount of microbial activity and, hence, food
available for colonizing taxa, which feed on
microbes directly. Thus, a lower maturity index
for free-living nematodes indicates more
disturbance, i.e., mostly colonizers and few
persisters (Bongers 1990; Neher and Campbell
1994; Yeates 1994). In contrast, the maturity
index for plant-parasitic nematodes includes
only obligate pilant-feeding nematodes. This
index should increase with fertilization and
increased primary, particularly root, production.
A lower value for the maturity index for plant-
parasitic nematodes may (Neher and Campbell
1994; Yeates 1994) or may not (Bongers 1990;
Freckman and Ettema 1993) indicate more
disturbance. Eiongers (1990) claims that the
two indices give different information because
they reflect two different aspects of the soil
community. Each maturity index is rated on a
scale of 2 to 5.
Results from the maturity jndex for free-living
nematodes show that only a small proportion of
communities of soil nematodes on annually
harvested herbaceous cropland in southern and
eastern Nebraska had a high degree of stability
or maturity (Figure 3.27). Although the
medians for this index were similar for the
southern (2.36) and eastern (2.35) regions, the
median for the maturity index for plant-parasitic
nematodes wa,s greater in the eastern (2.41)
than southern (2.23) region (p=0.01) (Figure
3.28). Thus, biased on the maturity index for
free-living nematodes, the relative health of
soils in the eastern and southern regions was
similar. However, based on the maturity index
for plant-parasitic nematodes and if a lower
value indicates greater disturbance it
appears that eastern soils are slightly healthier
than southern soils. The northwestern region
was not analyzed individually because too few
samples were collected to make statistically
meaningful statements about the region.
EMAP-Agricultural Lands Pilot Field Program Report - 1993
33
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5.2.2 Results: Condition: Soil Quality
1 . 0
c=
o
.-0.6
n_
ฑ0.4
0.2
O
0 . 0
1 . 0
CJ>
0 . 0
1 . 0
t=
o
ti 0 . 8
o
ex.
o
_ 0 . 6
du
o>
^0.2
=9
O
0 . 0
1 . 0
~ 0 . 8
o
Q.
0
.-0.6
ฑ0.4
0 . 2
0 . O
1 . 0
e=
O
"E 0 . 8
O
ex
o
0 . 6
a.
a>
ฑ0.4
CO
=9
(=0.2
0 . 0
/S^'
I
n 156
12345
Maturity Index
for Free living Nematodes
Rgure 3.27. Maturity index for free-living nematodes
In surface soil on land planted to AHHCs.
1 . O
t: o . s
O
a.
0
.-0.6
ฑ0.4
0 . 2
=3
O
0 . 0
t
n 156
1234
Maturity Index
for Plant-Parasitic Nematodes
Figure 3.28. Maturity index for plant-parasitic
nematodes in surface soil on'land planted to AHHCs.
34
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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5.2.2 Results: Condition: Soil Quality
What is the degree of diversity of
nematode communities in soil?
A diversity index such as the Shannon index of
trophic diversity (Shannon and Weaver 1949)
describes the relative abundance and evenness
of the distribution of nematodes across trophic
or feeding-preference groups. In agricultural
fields, higher diversity of trophic groups is
correlated with an increase in the less
abundant trophic groups (fungivores,
omnivores, and predators) relative to the more
abundant trophic groups (bacterivores and
plant-parasitic nematodes).
Trophic diversity (Figure 3.29) values were
similar for land area cultivated with AHHC in
the eastern and southern regions (medians
were not significantly different), suggesting the
relative biotic health of soils in the eastern and
southern regions was similar. However, the
eastern region had more variable index values;
for instance, there were values below 2 in the
east, but none in the south.
1 . 0
tl! ฐ
o
0 . 6
0 . 2
o
0 . 0
^k
tl ฐ
o
O
ป_0
CD
~ ฐ
co
0.2-
CJ
A
//'
'/'
I/'
0 . O
1.0
O
~ 0
O . 6
-------
4. Evaluation
As with any kind of preliminary study, this Pilot
has had its successes and challenges. The
following sections deal specifically with the
indicators tested in this Pilot, and we evaluate
their potential and performance for use in
EMAP. A comparison of the two sampling
designs is also presented.
4.1 Extent of Agricultural Resources
4.1.1 Annually Harvested Herbaceous Crops
Successes
"Other crop" identification
During the 1993 pilot, MASS improved its
information management procedures to provide
EMAP with more data about crops identified as
"other" in the June Agricultural Survey. As a
result, there are fewer observations in the
"other crop" category. This allows us to do a
better job calculating diversity measures.
MASS is currently undergoing changes in the
way it manages information, which should
further improve the content of the data we
receive.
Challenges
Measurement error
Each datum used to calculate resource extent
is the area of a field. Although NASS takes
great pains to control the quality of its data,
these measurements are not 100% accurate.
We do not know the frequency and magnitude
of measurement error, and it is not accounted
for in the estimates of variance. The variance
calculations used here assume that each
measurement is completely accurate.
Idle land
We have not decided how idle land fits into our
resource classes. Two possibilities are:
1. Include it with cropland or pasture,
depending on its previous (and intended
future) use. This solution would mean that
soil indicators would be interpreted with
reference to cropland standards. It is;.
unclear how productivity indicators would
be measured and interpreted for idle land.
2. Establish an idle land resource class. This
solution would necessitate the development
of a sampling frame and indicators for idle
land.
For 1993, idle fields were excluded from both
extent and sampling. However, there were four
annually harvested herbaceous crop fields in
the JAS that also contained idle land. When
NASS created the list of eligible acreage from
which our fall sample was drawn (Section 2.1),
it did not include the idle acreage within these
four fields. Nevertheless, the idle land within
these fields are included in our estimate of
extent because, had those fields been selected
for our fall survey, the enumerators would have
been unable to separate the idle land from the
cropped land. In the future, we will adjust the
36
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
-------
4.1 Evaluation: Extent of Agricultural Resources
sample selection protocol to account for this
contingency.
Reporting on consistent regions
The regions used to summarize extent and
geographic distribution were different from
those used to summarize other indicators.
Most indicators were summarized by Land
Resource Region; extent and distribution were
reported by aggregated NASS strata. The
geographic coordinates of the JAS sample
points arrived too late to reliably place our
samples in the proper Land Resource Region.
There are also statistical issues to be
addressed when post-aggregating the data.
We will address the issue of reporting on
consistent regions in future studies.
Are these indicators working?
Information about the extent and distribution of
agricultural resources has two roles. The first
role is to provide a context for the development
and interpretation of indicators by establishing
where the resource is (so that it can be
sampled) and how extensive it is. The current
presentation performs these tasks well. The
second role is to act as indicators in their own
right. This applies particularly to crop diversity,
which has been cited as an indicator of
everything from soil formation and productivity
to the potential for pest infestation and damage
from disease (Barrett et al., 1990; Altieri 1994).
Loss of diversity is considered undesirable, so
trends in these measures will provide valuable
information. Yet, exactly how diversity relates
to the health of agricultural lands is an open
question. As our program develops we will
look for correlation between crop diversity and
other indicators! of system health.
4.1.2 Farm ponds
Successes
Although there were problems estimating the
number and extent of farm ponds, even our
lowest estimates indicate that there are a large
number of farm ponds in Nebraska. They also
clearly indicate that livestock watering is the
predominant intended use of these ponds.
Farm ponds represent a significant ecological
resource that should eventually be monitored.
Until then, we will continue to estimate the
number, extent, and use of farm ponds in our
surveys. We will also initiate discussion with
the EMAP-Surface Waters Resource Group to
determine if there is overlap between the farm
pond resource and the lake resource.
Challenge!?
The farm pond sample was small and had a
very skewed distribution of the number of
ponds per segment. This resulted in wide and
negative confidence intervals. These problems
are partly a result.of drawing the sample from a
frame stratified with respect to a different
criterion (intensity of cultivation).
EMAP-Agriculturol Lands Pilot Field Program Report - 1993
37
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4.1 Evaluation: Extent of Agricultural Resources
4.1.3 Rangeland vs. Permanent Pasture
Background
Our sampling frame is based on the NASS
June Agricultural Survey. NASS does not
differentiate between rangeland and pasture,
but instead between cropland pasture and
permanent pasture (USDA-NASS 1991).
Cropland pasture is land that is in a crop
rotation but is being grazed in the survey year.
Permanent pasture includes all land that is
grazed and never cropped, including rangeland.
During the 1993 Pilot, we tried to distinguish
rangeland from permanent pasture so that we
could properly apportion responsibility between
the Rangelands (for rangeland) and Agricultural
Lands (for permanent pasture) Resource
Groups. During an EMAP-Agricultural Lands
meeting in Lincoln, Nebraska (11-13 January
1993) we considered various definitions of
rangeland and decided that fertilization was the
main factor that differentiated range from
pasture. Grazing land fertilized more than once
every 5 years would be called pasture; less
than once every 5 years was rangeland. A
question was added to the June Agricultural
Survey: "Of the permanent pasture inside this
blue tract boundary, how many acres have
been fertilized at least once during the last 5
years?"
Results
This question failed to distinguish rangeland
from permanent pasture in a way that agreed
with most people's impression of whether the
land was actually rangeland or pasture. Many
areas that are considered by most people to be
pasture, particularly in the eastern part of the
state, were classified as rangeland by this
definition. The areas in question were
considered pasture by most people because
they had higher stocking rates that those ;
usually associated with rangeland, and because
they were often covered with exotic grasses.
Our current definition is therefore unacceptable
and we will consider alternative methods for
separating rangeland from permanent pasture.
38
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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4.2 Condition of Agricultural Resources
4.2.1 Crop Productivity
The indicators presented in the crop
productivity section are easily understood and
relate to important aspects of the agricultural
resource. Each indicator has its limitations, and
further development should improve them. One
major limitation for inclusion in annual reports
or summaries is that the crop productivity
indicators are more useful for trends than for
status. No attempt has been made to calibrate
them to other measures of resource condition,
and little effort has been made to determine
acceptable and unacceptable cutoffs for the
indicator values. Comments on each potential
indicator follow.
Observed/Expected Yield Index
Overall completeness (the percentage of
sample units used in the calculations) of the
observed/expected index was 82%, considered
against the number of sample units with
annually harvested herbaceous crops (AHHCs)
(Table 4.1); many of the samples that were not
used were lacking yield data. Of the samples
with yield data, 95% were used (those
containing one or more of the five major crops).
This is the main strength of the index it
combines data across most or all of the crops
in the sample. Another strength is that there is
a nominal/subnominal boundary at the index
value of 1, although it cannot be considered a
rigid boundary because natural variation above
and below that value is expected. A third
useful feature is that the index can be used in
conjunction with other indicators, i.e., it is
important to know what yields are doing when
one is interpreting other indicators.
The major limitation of the observed/expected
yield index is that it is influenced by
management and weather, so that little can be
Table 4.1. Completeness of the observed/expected
index and the nitrogen efficiency index.
Category
Target number of sample units
Questionnaire with valid data
Questionnaire reporting AHHCs
Questionnaire rcsporting AHHCs
with nonmissing, nonzero yields
Used in the combined
observed/expected index
(had one or more of the five
major crops)
Used in the combined nitrogen
efficiency index for seed crops
Number of
Sample Units
288
220
213
184
174
161
said about the overall ecology of the system.
A more robust index would account for weather
effects, and such work is underway with crop
growth models. Even without such a
correction, this index would be interesting to
monitor over time.
Nitrogen Use Efficiency
The statewide CDF for nitrogen use efficiency
for seed crops was generated from 161
observations. This represents 76% of the
AHHC sample units. While less than the
completeness for the previous index, this is
enough to give narrow confidence bands on the
distribution. Although this index is restricted to
seed crops, it includes some additional smaller-
acreage crops Ithat are not in the previous
index: dry beans, millet, and popcorn. It seems
reasonable to combine data only when similar
plant organs ara involved; thus, alfalfa hay is
not included in this index because it is not a
EMAP-Agricultural Lands Pilot Field Program Report - 1993
39
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4,2 Evaluation: Condition: Crop Productivity
seed crop. It is difficult to evaluate this index
further. The main limitation is that there are
still no criteria for declaring nominal and
subnominal condition. A nitrogen balance
index that considers the nitrogen content of the
harvested crop may be developed later. As it
is, the nitrogen use efficiency index will be
primarily useful for trends, in keeping with the
philosophy of the second issue listed in Section
3.2.1.1 ("Even if yields are steady or increasing,
are they doing so only because of large and
Increasing subsidies to the agroecosystem from
nonrenewable inputs?").
Data quality is even more of a concern for this
index than others, considering that conversion
factors are used for moisture correction and for
estimating nitrogen content of manures. Effects
of uncertainty in these factors have not been
explored. The accuracy of farmer-reported
yield data, and the problem of variation in
moisture content of com are other unresolved
challenges.
Crop Rotation Indices
The How long? and How many? indices are
very tightly linked (Table 4.2), but they reflect
slightly different aspects of the system.
Whenever only one crop was found during the
3-year period (How many? =1), the 1993 crop
appeared the previous year (How long? =1).
The other end of the spectrum is just as clear;
when there were three different crops, the first
index also had a value of 3 (meaning 3 or more
years since the last occurrence of the 1993
crop). There was one exception because of
the way split fields were handled.
Because of the way it is calculated, the How
long? Index has some limitations. It is
conservative in the sense that any past planting
of the 1993 crop counts as if the whole field
were planted to that crop that year, thus slightly
underestimating rotation. Because the
questionnaire data cover only 3 years, the
Table 4.2. Distribution of sample units among two
rotation indices.
How
many?
1
2
3
How long?
1
85
24
1
2
0
55
0
3
0
10
16
index cannot have a value greater than 3, even
if the last occurrence of the crop was longer
than 3 years prior. Thus it may underestimate
the time interval. On the other hand, the index
cannot be skewed by very large numbers from
the occasional planting of a rare crop. For
these reasons, the second index, a simple
count of the number of crops in the 3 years, is
probably the better index. The choice of
nominal and subnominal cutoff points can only
be made for a region as a whole, so it may be
impossible to say at any given time what
proportion of a region is in good or bad
condition without a major reworking of the
index. As it is, the cutoff point of 2 (discussed
in Section 3.2.1.2) is so far based only on
hypothetical rotations.
How do the empirical indices compare to the
farmer's rotation plan? A chi-squared test for
independence showed a significant effect of the
length of the planned rotation (as a class
variable) and the values of the two indices
(p<0.001). As can be seen in Figure 4.1, the
difference is primarily between those fields for
which there is no rotation plan and those with
some kind of a plan, even if it is only a 2-year
rotation. ',
No correlations or associations have been
sought between the rotation indices and other
indicators.
40
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
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4.2 Evaluation: Condition: Crop Productivity
1
3.0
2.5
o>
2.0
1.5
1.0
"How Long?" index
"How Many?" Index
no plan 2 years 3 years 4 years 5 years
Length of Rotation Plan
Figure 4.1. Association of rotation plan with the
values of two rotation indices based on land use over
the three seasons 1991-1993. Nebraska annually
harvested herbaceous crops. Sample standard
errors shown.
Data quality is still an issue. For example,
crops may be omitted or misreported
(especially cover crops). One observation was
apparently miscoded when a crop was
recorded in the year of planting instead of
harvest. (This interpretation was given by NASS
to explain the irregular original data, and the
corrected data were used in all rotation
indices.) In another case, a field was listed as
fallow for 3 years in a row. It was probably idle
land rather than fallow, but the data were left in
the calculations as if they were correct.
These rotation indices are rather crude, in part
because they are based on only 3 years of
data and in part because they do not
distinguish whether the specific crops and
rotations on the land are good for the soil or
not. Nevertheless, the indices are easy to
calculate, relate to important aspects of the
resource, and are able to detect the notable
lack of rotation of annually harvested
herbaceous crops in Nebraska, especially for
corn.
EMAP-Agricultural Lands Pilot Field Program Report - 1993
41
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4.2.2 Soil Quality
The indicators presented in Section 3.2.2 are
all important components of the soil and
contribute to our knowledge of soil condition.
However, we are interested in developing an
integrative measure that will give us a better
idea of overall soil condition. In Sections
4.2.2.1 to 4.2.2.3 we present two approaches,
the Soil Rating for Plant Growth and the Soil
Quality Report Card. Soil Biotic Diversity is
evaluated separately in Section 4.2.2.4.
Both the Soil Rating for Plant Growth and the
Soil Quality Report Card rely on information
from below the soil surface. At 26 sites, soil
pits were dug and the soil profiles were
described and sampled by Soil Conservation
Service (SCS) soil scientists (Figure 4.2). The
soil pits were located within randomly selected
NASS transects. The soil profiles described
and sampled represented the dominant soil
type (component) along the transect. Thirteen
of the sites were in southern Nebraska, ten in
eastern Nebraska, and three in northwestern
Nebraska. At three sites in southern Nebraska,
where soil scientists found two distinct soil
components within a transect, two soil profiles
were described and sampled. Thus, a total of
29 soil profiles were described. All soil
horizons were sampled to a depth of 51 cm.
As in the composited samples, the pit samples
were analyzed for a set of physical and
chemical indicators. Additional analyses were
determined by SCS research soil scientists and
were based on field and laboratory findings.
We evaluated the results from the soil profile
investigations using our two novel approaches.
Each of these uses a system of assigning
ratings to the soil properties and combining the
individual soil property ratings for an overall soil
quality rating. Results are presented on a
regional basis.
Rgure 4.2. Location of soil pit sites in 1993 Nebraska Pilot. The dashed line separates
higher rainfall in the east from lower rainfall in the west.
42
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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4.2.2 Evaluation: Condition: Soil Quality
4.2.2.1 Soil Rating for Plant Growth (SRPG) Model
The SRPG Model is under development by the
Natural Resources Conservation Service
(NRCS). We have used version 1.0 of the
SRPG, to which we have made modifications,
in this report. This version uses soil property
data from the SCS State Soil Survey Database
(3SD). The SRPG has been revised and
modified since this early version; it is under
review and no final version has been published.
Information on the early version may be
obtained from the NRCS (Scheyer 1993).
Version 1.0 of the SRPG involves rating soils
on seven factors (Table 4.3) (future versions
may use more factors). Each of the factors
carry ratings ranging from about 0.5
(undesirable) to 1.0 (desirable). The seven
ratings are multiplied in sequence, and the
product is multiplied by 100 to arrive at an
overall rating. The suitability of the soil for crop
growth is rated from low to high (Table 4.4).
Table 4.4. Suitability groups from SRPG ratings.
Soil Rating
0-45
46-70
>70
Suitability Group
Low
Moderate
High
With a multiplicative index, a poor rating on one
factor will depress the overall rating more
drastically than with an averaged index. This is
appropriate for the SRPG because a single
poor factor can seriously hamper plant growth
potential. For instance, a soil that is flooded
but whose other properties are all favorable will
have limited potential due to the flooding.
The model was designed to use data from the
3SD, which contains soil property data ranges
for more than 100,000 soil map units in the
U.S. SRPG ratings can be calculated from the
Table 4.3. Parameters in Soil Rating for Plant
Growth.
Factor
Surface Soil
Soil Profile
Water Features
Toxicity
Soil Reaction
Soil Climate
i
Landscape
Elements from database
(surrogates from field or
laboratory measurements)
Organic matter
Bulk density
Clay content
Available water capacity
PH
Sodium adsorption ratio
Calcium carbonate
Gypsum
Cation exchange capacity
Sh rink-swell
(ratio of oven-dried to
Vs bar bulk density)
Rock fragments
(none [no data])
Depth to restrictive layer
Available water capacity in root
zone
Water table during growing
season
(drainage classes [e.g., well
or poorly drained])
Permeability
Strongly contrasting particle size
classes
Available water capacity
Sodium adsorption ratio
Electrical conductivity
Cation exchange capacity
PH
Soil moisture regime
(irrigated fields received
high ratings)
Soil temperature regime
Moisture x temperature
interaction
Slope
Eroded phase
(classes of erosion [1 to 4])
Channeled or gullied phase
Flooded phase
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
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4.2.2 Evaluation: Condition: Soil Quality
nearly static soil properties recorded in the
database for all documented map units in the
U.S. The soil properties derived from the 3SD
and thus, the SRPG rating, do not account for
management practices or dynamic
environmental factors. As a result, a well
managed soil is assigned the same rating as a
poorly managed soil in the same map unit.
For each of the soil map units at the 26 EMAP
sites, the SOS provided the ARG with a range
midpoint for each soil property from the 3SD
database. From these, we calculated an SRPG
for each map unit. Three sites had two map
units each; for each of these, sites, SRPG
ratings from the two map units were averaged.
We modified some elements of the SRPG
model to develop our own ratings from field
measurements for each of our 26 sites
described by SCS soil scientists (Table 4.3).
For example, in the Landscape category, we
did not have any erosion descriptors (e.g.,
gullies or channels); instead, we used the
erosion classes (noneroded to Class 5 erosion)
to establish a comparable scale ranging from a
1.0 for noneroded soils to a 0.5 for Class 5
erosion. A similar approach was used for
rating limitations associated with drainage, i.e.,
we used the drainage classes rather than
information relating to a seasonal high water
table. We had enough field information and
laboratory data to calculate a rating for each of
the seven categories of the SRPG model,
although in some cases, missing data
compelled us to calculate a category rating
from fewer subcategories than specified
originally.
Mean SRPGs with 95% confidence intervals
are presented in Figure 4.3. Not enough data
are available to present confidence intervals for
the northwestern region; however, the data
from that region are included in the confidence
interval for the state. Eastern Nebraska has
significantly higher 3SD ratings than southern
Nebraska, which is consistent with our
expectations of soil productivity trends for this
Figure 4.3. Mean field and 3SD SRPGs with 95%
confidence intervals.
state. Field SRPG ratings are not significantly
different between the eastern and southern
regions.
The proportions of ratings in each suitability
group are shown for each method in Figure 4.4.
The two methods show the same proportion
with low suitability for crop growth, but the field
method shows far fewer with high suitability
than the 3SD method. That the soils do not
"measure up to their potential" may suggest
that they are degraded. More detailed
analyses are being conducted to evaluate
whether the differences are due to systematic
overestimation of properties in the database, or
whether the lower-than-expected ratings are
linked to poor management practices.
The field SRPGs show that more of the soils in
eastern Nebraska (70%) have high suitability
for crop growth than in southern Nebraska
(15%). The 3SD method "overestimated" these
percentages: 90% in eastern Nebraska, 62% in
southern (data not shown). The wide
discrepancy for southern Nebraska may imply
that, within the high suitability group,
proportionally more of the soils there are
degraded.
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4.2.2 Evaluation: Condition: Soil Quality
Field SRPGs
3SD SRPGs
Figure 4.4. Proportion of ratings in each suitability
group for each method. Data from entire state
included (n=26).
We calculated a ratio of field SRPG rating to
3SD SRPG rating for each site to give an
estimation of relative condition of the soil. First,
we ensured that the ratings in each pair were
comparable. For every missing value in one
SRPG rating, we deleted the companion value
in the other SRPG rating and recalculated all
the SRPG ratings. We compared the two
SRPG ratings for each site by calculating an
observed-to-expected ratio:
SRPG Ratio = Field SRPG
3SD SRPG
Soils with a ratio ฃ1 were estimated to be in
relatively good condition, whereas those with a.
ratio <1 were judged to be in relatively poor
condition. This approach allowed us to make
comparisons among soil map units, and is
comparable to the approach used in calculating
the observed/expected yield index, which
normalized across crops. The benefit of this
approach is that it allows us to avoid continually
assigning the inherently productive soils of the
midwest good ratings and less productive soils
of other regions lower ratings. It also provides
a way of evaluating how well soils are
responding to management and an approach to
the question of whether agricultural practices
are sustainable.
The average ratio values are 1.68 for
northwestern Nebraska, 0.91 for southern
Nebraska, and 0.83 for eastern Nebraska
(mean ratios with 95% confidence intervals for
eastern and southern Nebraska are shown in
Figure 4.5). In Nebraska, 27% of the samples
had a ratio greater than one, suggesting that
they are in relatively good condition. This
includes 31% of the samples from southern
Nebraska and 10% of the samples from
eastern Nebraska.
Figure 4.5. Mean and 95% confidence intervals for
observed-to-expected SRPG ratios.
Evaluation of the SRPG
The use of a holistic model for evaluating soils
data is an attractive concept, but it has several
limitations. Individual ratings within any
category of the SRPG model could differ from
year to year simply due.to natural variability in
the soil map unit, and more importantly, due to
management practices. While we are
interested in detecting differences due to
management, we want a rating that will not
vary much with rotation. Currently, a field's
rating would be much different following
soybeans than following corn. With a large
enough sample size, these differences would
average out for 'the region, but the SRPG is
data-intensive and, hence, expensive to obtain.
The SRPG model may compound its variability
problems by multiplying all the factors together.
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4.2.2 Evaluation: Condition: Soil Quality
The SRPG model may need to be modified for
each region of the country. For example,
salinity might be a factor to measure in
Nebraska, and exchangeable acidity might be
the key factor in the Southeast. The SRPG
model could be generalized from its current
form to accommodate this; however, the
weightings of the factors need to be evaluated
carefully.
*
Constructing ratios of observed to expected
SRPG ratings seems to have merit for
evaluating the relative condition of the soils.
This approach allows comparison across soil
map units, and, with sufficient sample size,
estimation of the soil condition for a region.
Without additional field work, we could
construct prediction intervals from the 3SD for
each soil map unit and provide estimates of
relative condition as field data become
available.
Comparison of the 3SD SRPG and
the Field SRPG
Correlation between the SRPG ratings
calculated from the 3SD database and the
ratings calculated from our field data was poor
(r = 0.4). This may indicate that we are truly
assessing soil condition when we calculate the
ratio of field SRPG rating to 3SD SRPG rating.
We need to test this hypothesis and the
sensitivity of the ratio by measuring well and
poorly managed fields within like soil map units,
and calculating ratios. Differences between
observed-to-expected ratios may actually be
due to variability in the soil map unit. The
database SRPG rating represents a midpoint
value for a given soil map unit within a county,
whereas the field SRPG rating may represent
values anywhere between the extremes. In
addition, many measured values included in the
SRPG model, e.g., available water capacity,
organic carbon, and pH, can have large
variances.
Identifying Differences in Paired
SRPG Ratings
Before we modified the SRPG ratings for
calculating ratios, we used paired t-tests to
compare ratings from the two methods for each
soil profile. In each of the seven categories,
the field SRPG rating was paired with the 3SD
SRPG rating. The ratings obtained using field
data were significantly different than the ratings
obtained using the 3SD database for Water
Features (p = .0087), Toxicity (p = .0484), and
Landscape (p = .0007) categories. In most
cases, the differences were due to missing
values in the database or to use of surrogate
values in the field SRPG.
Most of the differences in Water Features were
attributable to available water capacity; the
measured data tended to have lower values
than the 3SD.
In the Toxicity category, most of the differences
were attributable to missing data in the 3SD
SRPG ratings and to differences in cation
exchange capacity values (which may not
actually belong in this category); the fieldiSRPG
ratings tended to be higher than the 3SD
SRPG ratings.
Differences in Landscape were attributable to
both the slope class and the erosion class; the
field SRPG tended to be lower than the
database SRPG ratings.
SRPG Compared with Yields
There was no correlation between the ratings
and the observed agronomic yields in the field
in 1993 (r < 0.01). This is not a problem,
because yield is largely dependent on
management factors and weather, and is
variable among years despite the same base
resource (soil). It would not be possible to
establish relationships between yield and
productivity potential until management factors
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4.2.2 Evaluation: Condition: Soil Quality
are included in the model. The SRPG is,
however, not designed for predicting yields, but
rather for identifying productive potential of a
soil based on indicators of soil quality.
4.2.2.2 Soil Quality Report Card
Researchers from various institutions and
agencies have become interested in developing
a "soil quality report card" (Doran et. al.,
1994). A goal of the EMAP Agricultural Lands
Resource Group is to develop a soil quality
report card that can be used to monitor the
status and trends of soil quality, on a regional
basis, for different functions such as plant
growth, pesticide retention, and general
environmental quality.
The SCS provided a database of soil properties
for the 29 soil map units sampled by pit in
Nebraska (Figure 4.2). The soil properties are
from the 3SD database and represent a
midpoint value for that soil map unit within a
county. The pit sites are well distributed in
eastern and southern Nebraska and represent
22 counties. Northwestern Nebraska was
omitted because there were only three pit sites
in this region. The data were then sorted by
region (southern or eastern Nebraska) to arrive
at an expected range of values for nine soil
properties (called indicators). The indicators
selected were cation exchange capacity (by
NH4OAC), percent organic matter, percent clay,
available water capacity (in/in), pH (by H2O),
percent calcium carbonate, sodium adsorption
ratio, drainage class, and permeability (in/hr).
These indicators were selected because they
were available both in the SCS database and
from our analytical results and have been
mentioned as potential indicators by various
investigators (NRC 1993).
The SCS also provided expected yield data for
both irrigated and nonirrigated corn, wheat, and
winter wheat for southern and eastern
Nebraska. The data are current and were
recently updated by the SCS Nebraska State
Office personnel. The corn yield data were
sorted by soil map unit and region, and
subdivided into five categories, called plant
growth potential groups, based on expected
nonirrigated corn yield for that soil map unit.
These groups were: low (31 -41 bushels per
acre), moderately low (42-62), moderate (64-
67), high (79-94), and very high (115-130).
Corn yields were used instead of wheat yields
because the database had more corn yield
information for line various pit sites.
As expected, the lower plant potential groups
are located in northwestern Nebraska and
those with the highest potential in eastern
Nebraska. Southern Nebraska has
intermediate plant growth potential becoming
lower in a westernly direction across the state.
Plant growth (corn yield) is highly dependent on
rainfall. Rainfall distribution across Nebraska is
highest in eastern Nebraska (28-34 inches),
and decreases to about 14-18 inches in arid
northwestern Nebraska. An imaginary line
extending due south from the notch made by
the Missouri River separates the higher rainfall
areas from the lower rainfall areas in the state
(see dashed line on Figure 4.2). The higher
rainfall area includes all of eastern Nebraska
and the eastern part of southern Nebraska.
Soil taxonomic distributions also follow a trend.
The "udic mollispls" are located to the east of
the dashed line in the higher rainfall area, the
"ustic-udic mollisols" to the southwest of the
line in the intermediate rainfall area, and the
"ustic-aridic mollisols" in northwestern Nebraska
in the lowest rainfall area.
Because plant growth is dependent on rainfall
in Nebraska, an SCS soil scientist (Larry
Ragon, personal communication) was
interviewed to identify "ideal soils" for growing
corn in southern and eastern Nebraska (without
irrigation). He identified the Hall soil series for
southern Nebraska and the Judson soil series
for eastern Nebraska. A range of indicator
values was obtained from the 3SD for these
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4.2.2 Evaluation: Condition: Soil Quality
"ideal soils" to arrive at threshold or optimal
indicator values for each region. Future efforts
will focus on refining these threshold indicator
ranges for crop plants. An expected range for
each indicator within the low (southern
Nebraska) and very high (eastern Nebraska)
plant growth potential groups was then derived.
These ranges are shown in Table 4.5, along
with the ranges for "ideal soils". Expected
indicator ranges for the intermediate plant
growth potential groups were determined but
are not presented in this report.
For simplicity, the two extremes, the low and
the very high plant growth potential groups, will
be discussed. As shown, the indicator ranges
show differences between the two groups. In
general, the very high plant growth potential
group has lower bulk density and pH; has
higher available water capacity, cation
exchange capacity, and organic matter; and is
more slowly permeable and more poorly
drained than the low plant growth potential
group.
Table 4.5. Indicator ranges for Low and Very high plant growth potential groups in southern and eastern Nebraska.
Plant Growth Potential Group
ปLow
Component
Physical
Chemical
Indicator || Expected range
Bulk density (g/cm3)8
Available water capacity
(in/in)
Clay (%)
Drainage class0
Permeability (in/hr)ฐ
PH
Organic matter (%)
Calcium carbonate
equivalent
Sodium adsorption ratio
Cation exchange
capacity (cmo!(+)/kg)
1.1-1.9
0.09-0.24
5-35"
Excessively
to Well
0.6-20
5.6-8.4
0-4
0-3
0
0-20
Very high
Ideal soil || Expected range
1.4-1.6
0.18-0.24
15-35
Well
0.6-2.0
5.6-7.8
1-3
0
0
19-29"
1.2-1.35
0.13-0.23
5-35"
Well to
Moderate
0.6-6.0
5.6-7.8
0.5-5
0-15
0
5-30
Ideal soil
1.3-1.35
0.21-0.23
24-35
Well
0.6-2.0
5.6-7.3
2-5
0
0
25-30
"Surface only, Ap or A horizon; all others represent the widest range within 0-24 inches.
tow and Very high groups have same range for clay content of pit sites in 3SD database.
'From Soil Survey Staff (1993); in/hr units are used for permeability units. Permeability classes used are >20, 20-6.0, 6.0-2.0,
and
2.0-0.6. Drainage classes used are excessively, somewhat excessively, well, moderately well, somewhat poorly, poorly, and
very poorly.
''Not from the 3SD database; from the SCS characterization database.
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4.2.2 Evaluation: Condition: Soil Quality
The initial Soil Quality Report Card consists of
two components: physical and chemical. A
score is assigned to each indicator, based on
the threshold values of the "ideal soil" for its
plant growth potential group (Table 4.6). An
average score is then calculated for each
component (physical and chemical), and the
Soil Quality Index is the average of the two.
Future Report Cards will also contain a
biological component consisting of one or more
of these potential indicators presently being
studied: microbial biomass, the trophic structure
of nematode populations, and respiration.
Table 4.6. Scoring for indicators in Low and Very high plant growth potential groups.
Indicator
Bulk density
Available water
capacity
% clay
Drainage class
Permeability
PH
% organic matter
(organic carbon x 1.6)
Calcium carbonate
equivalent
Sodium adsorption
ratio
Cation exchange
capacity
Scores for
Low plant growth potential group
100
<1.6
0.18-0.24
>15
Well
0.6-2.0
5.6-7.8
>3
0
0
>19
75
1.6-1.9
0.09-0.18
5-15
Moderately
Well
2.0-6.0
7.8-8.4
1-3
0-3
0-2
15-19
50
>1.9
<0.09
<5
Excessively
and Poorly to
Very Poorly
>6.0 or <0.6
>8.4
<1
>3
/
>2
<15
Scores for
Very high plant growth potential group
100
<1.35
>0.21
>24
Well ;
0.6-2.0
6.5-7.3
>2.0
0
0
>25
75
1.35-1.5
0.13-0.21
, 5-24
Moderately
Weil
2.0-6.0
5.6-6.5
0.5-2.0
0-5
0-2
5-25
50
>1.5
<0.13
<5
Excessively
and Poorly to
Very poorly
>6.0 or <0.6
>7.3 or <5.6
<0.5
>5
>2
<5
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4.2.2 Evaluation: Condition: Soil Quality
A Soil Quality Report Card developed for one
pit site, a Hall component in a Hord and Hall
silt loam, 0 to 1% slopes map unit in Harlan
County, is presented as an example (Table
4.7). The indicator values listed are those
measured from soil samples taken at the pit
site. Because the expected nonirrigated corn
yield (41 bushels) of the Hall soil map unit falls
in a low plant growth potential group, the map
unit is compared to the indicator ranges
expected for the low potential group.
A Soil Quality Report Card developed for a
second pit site, a Judson component sampled
in a Judson silt loam, 2 to 7% slopes map unit
in Thurston County, is also shown in Table 4.7.
Because the expected nonirrigated corn yield of
Judson soil map unit (126 bushels) falls in a
very high plant growth potential group, it is
rated against the very high ranges.
Table 4.7. Examples of Soil Quality Report Card for sites in Low and Very high plant growth potential groups.
Component
Physical
Indicator
Bulk density
Available water
capacity
% clay
Drainage class
Permeability
Average score for Physical
Chemical
PH
% organic matter
Calcium carbonate
equivalent
Sodium adsorption
ratio
Cation exchange
capacity
Average score for Chemical
Soil Quality Index
Hall (southern Nebraska)
Low
Data
1.40
0.14-0.20
17.4-30.1
Well
0.6-2.0
Score
100
75
100
100
100
95
6.5-7.2
0.85-3.0
0
0.37-1.02
16.6-24.9
100
75
100
75
75
85
90
Judson (eastern Nebraska)
Very high
Data
1.36
0.06-0.14
33.1-36.6
Well
0.6-2.0
Score
75
50"
100
100
100
85
6.2-6.6
0.85-3.2
0
0
24.5-29
75a
75a
100
100
100
90
87.5
"Fell between two ranges, given lower score.
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4.2.2 Evaluation: Condition: Soil Quality
Evaluation of the Soil Quality Report
Card
The Judson pit site in .the very high plant
growth potential group has an average Soil
Quality Index of 87.5, whereas the Hall pit site,
in the low plant growth potential group, has an
Index of 90. In some indexing methods, where
soils of comparable plant growth potential are
not compared, the Hall site would probably be
judged to have lower soil quality than the
Judson site.
The Report Card could be used to monitor
permanent sites to show trends in soil quality
and possibly to study cause and effect of any
change due to management practices at the
sites. Cumulative distribution functions could
also be used to assess the overall scores. In
future pilots, we will assess the usefulness of
the Soil Quality Report Card as a tool to make
regional assessments.
4.2.2.3 Summary: Evaluation of
Three Methodologies Used
to Assess Soil Quality
We have presented three methods for
assessing soil condition (quality) on a regional
basis: the Integrated Assessment of Measured
Values (Section 3.2.2.1), the SRPG Model, and
the Soil Quality Report Card. The Integrated
Assessment of Measured Values was
determined on composited surface samples,
whereas the SRPG and Soil Quality Report
Card were determined on pit samples. In terms
of cost, the Integrated Assessment of Measured
Values technique is less costly, because only
surface samples are taken. Therefore, more
samples can be taken, and statements about
soil quality can be made on larger regions with
a more accurate prediction of sample (field)
variability. However, by studying only the
surface samples (20 cm depth), we are missing
key differences in soil quality among fields that
are affected by subsoil properties. (We are
also losing information by using composite
rather than intact samples.) Although more
costly to develop, the SRPG and Soil Quality
Report Card methods incorporate subsoil
indicators and, as a result, more accurate
distinctions among individual fields and regions
can be made. From this, we can learn more
about soil quality as it relates to plant growth
and environmental quality.
From our studies in Nebraska, and based on a
limited sample (29), it appears that the SRPG
Model is a better way to arrive at regional soil
condition, wheretas the Soil Quality Report Card
could be better used to monitor and assess the
status of soil condition at specific sites.
Although both methods are based on ratings
derived from measured values and 3SD
database values, the SRPG requires less initial
labor than the Report Card. Thus, despite the
need for some modification for different regions
of the country, the SiRPG would be more
suitable for wide-scale monitoring. In addition,
the Soil Quality Report Card requires
development of productivity potential groups
that are crop-specific and, thus, less suitable for
an entire region; also, the yield data on which
the groupings are based may not be current,
resulting in groupings that may not accurately
reflect the region. For these reasons, the
Report Card would be more suitable for use at
long-term monitoring sites where quantification
of changes in soil quality due to management
are of interest.
Future studies should focus on modifying the
SRPG Model for assessing soil quality;
developing a minimum dataset of easily
measured indicators that can be evaluated at
many more sites;; and linking soil quality
indicators with management systems.
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4.2.2 Evaluation: Condition: Soil Quality
4.2.2.4 Soil Biotic Diversity
Five indices were computed for the nematode
community in each soil sample:
Maturity index for all free-living
nematodes (Ml) (Bongers 1990)
Maturity index for free-living nematodes
excluding opportunists (MINO)
(T. Bongers, personal communication)
Maturity index for all plant-parasitic
nematodes (PPI) (Bongers 1990)
Combined maturity index for free-living
and plant-parasitic nematodes (EMI)
(Yeates 1994)
Shannon index of trophic diversity.
(SHAN) (Shannon and Weaver 1949)
Diversity of nematodes by feeding preference
was estimated using the Shannon diversity
index, N1= exp [-I.P, (InP,)], where P, is the
proportion of trophic group / in the total
nematode community (Ludwig and Reynolds
1988).
The Ml was calculated as the weighted mean
of the values assigned to constituent nematode
families (and the genera and species they
contain) (Bongers 1990): Ml or PPI = (2 v,*f)/n
where v, = the colonizer-persister (c-p) value
assigned to family /, f, = the frequency of family
/ in a sample, and n = total number of
individuals in a sample. C-p values range from
1 to 5; however, plant-parasitic taxa are
assigned c-p values from 2 to 5 because there
are no plant-parasitic colonizers designated as
1 (Bongers 1990). Free-living nematodes with
a c-p value of 1 are considered opportunists.
Opportunist populations increase when nitrogen
fertilizer or organic matter are added to soil
and, therefore, do not necessarily reflect true
changes in soil health (T. Bongers, personal
communication). Therefore, the maturity index
for free-living nematodes was calculated
separately with (Ml) and without (MINO)
opportunists included.
Associations among soil properties
and with nematode communities
Nematode community indices were compared
to various soil chemical and physical properties
thought to influence populations of nematodes,
including clay content, sand content, pH,
organic carbon, extractable phosphorus,
exchangeable potassium, exchangeable
calcium, exchangeable magnesium,
exchangeable sodium, cation exchange
capacity, and water retention at -1500 kPa
matric potential (Table 4.8). To be a cost-
effective program, we must eliminate redundant
indicators; thus, we were searching for
significant correlations between nematode
communities and soil properties.
Principal component analysis involves the linear
transformation of the original set of variables to
a set of orthogonal variables so that the first
principal component accounts for the greatest
amount of the total dispersion, the second
principal component accounts for the greatest
possible amount of the remaining dispersion,
and so on. The first five principal comppnents
accounted for about 78% of the dispersion in
the data. The first, third, and fourth principal
components tended to be comprised of soil
chemical and physical properties (i.e., variables
with the largest coefficients). Conversely, the
second and fifth principal components tended
to consist of nematode community indices.
Nematode indices tend to load on different
principal components than do the soil chemical
and physical properties. Thus, the nematode
indices are not redundant and do provide
information different from that provided by soil
chemical and physical properties.
Soil texture and pore space distribution define'
the physical space inhabitable by nematodes.
Correlations were examined for associations
between percent sand, silt, and clay, and
nematode indices and trophic group
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4.2.2 Evaluation: Condition: Soil Quality
Table 4.8. Coefficients from a principal component analysis of variables of soil properties and nematode community
indices.
Principal
Component
(32%)a 1
(18%) 2
(13%) 3
(10%) 4
(5%) 5
1
2
3
4
5
Ml
-0.181
0.367
0.210
-0,048
0.204
C
0.278
0.087
0.078
0.379
-0.117
MINO
-0.229
0.395
0.194
0.072
0.154
P
0.063
-0.220
0.443
0.189
0.220
PPI
-0.143
0.329
0.127
0.267
-0.51 1
K
0.153
-0.167
0.438
0.350
0.147
EMI
-0.224
0.440
0.172
0.182
-0.156
Ca
0.157
0.129
0.318
-0.494
-0.343
SHAN
-0.050
0.321
-0.015
-0.123
0.652
Mg
0.378
0.133
0.057
-0.020
0.067
Sand
-0.380
-0.173
0.111
-0.045
-0.070
Nai
0.132
-0.167
-0.358
-0.120
0.009
Clay
0.386
0.226
-0.082
-0.070
-0.017
CEC
0.403
0.199
0.004
-0.028
0.042
pH
0.043
-0.065
0.463
-0.527
-0.051
-1500kPa
0.307
0.180
-0.144
0.039
-0.089
"percent of the total variation accounted for by each principal component
abundances. All correlations were weak. The
greatest significant (p<0.05) correlations were
between soil sand content and populations of
omnivorous (r = 0.26), fungivorous (r = -0.33),
and plant-parasitic nematodes (r = -0.30). Soils
with increased sand content, which have
relatively large pores, contained more mature
nematode communities, fewer fungivorous,
fewer plant-parasitic, and more omnivorous
nematodes. It was not possible to detect an
optimal range of sand content for ominvorous
nematode populations. Some data scatter was
evident because some soils with high omnivore
populations had low sand content. In an
attempt to control this scatter, we ran the
correlations separately for different tillage
systems. The correlations between omnivorous
nematode populations and sand content were
similar, but opposite in sign, for conventional
(r = -0.30) and no-till, mulch till, and ridge-till
systems combined (r = 0.25). In future studies,
we plan to explore further the relationship
among omnivorous nematode populations,
tillage practices, and soil texture with the goal
of estimating threshold populations of
omnivorous nematodes for different textured
soils.
Relationships; among nematode
community indices
Among the nematode indices, the combined
maturity index for free-living and plant-parasitic
nematodes (EMI) was correlated positively with
all other forms of the maturity index and the
trophic diversity index (SHAN) (Table 4.9). EMI
has been criticized by some scientists because
it does not tell anything different than either Ml
or PPI, and may have less power of
differentiation, because index values are
intermediate between those of Ml and PPI for
the same sample.
Maturity for free-living nematodes excluding
opportunists (MINO) correlated positively with
Ml, PPI, and SHAN. The relatively high
correlation between MINO and Ml suggests that
both indices are measuring similar aspects of
nematode community structure, as expected.
Many nematode families that feed on bacteria
and fungi are colonists (Bongers 1990; Neher
et al., 1994) and the presence of such families
would reduce the values of maturity indices that
EMAP-Agricultural Lands Pilot Field Program Report - 1993
53
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4.2.2 Evaluation: Condition: Soil Quality
Table 4.9 Spearman correlations between indices and trophic groups (n=156) of soilborne nematodes.
MINO
PPl
SMI
SHAN
Bacterivores*
Fungivores*
Omnivores*
Predators"
Plant parasites*
0.73"*
0.09
0.55*"
0.12
-0.47'**
-0.41***
0.36***
-0.17*
-0.03
Mi
0.21"
0.80***
0.20*
-0.31"*
-0.53***
0.55***
0.18*
-0.03
MINO
0.66***
0.08
-0.08
-0.12
0.11
0.07
-0.05
ppi
0.23**
-0.29***
-0.49***
0.44***
0.15
-0.11
SMI
-0.06
0.12
0.27***
0.41"*
-0.28***
SHAN
0.57***
0.36***
0.06
0.33*"
==:
Bacteri-
vores
0.13
-0.06
0.43"*
Fungi-
vores
0.04
0.26** -0.07
Omni- Predators
vores
"number per 500 ml of soil
pcO.05, "p<0.01, *"p<0.001
include free-living nematodes (Ml, MINO, and
EMI). Bacterivores are generally more
abundant than fungivores in agricultural soils.
Omnivores and predators generally comprise
<5% of the total nematode community and
reside in a higher position within the food chain
than bacterivores and fungivores. Therefore,
the presence of such families increases values
of both trophic diversity and maturity indices
that include free-living nematodes (the negative
correlation between predators and Ml is
probably due to one family with c-p = 1; they
would not be included in MINO).
The ability to differentiate ecological condition
of soil among fields improves if the variance
among fields exceeds the variance within fields
and within composite samples. Therefore, the
ability of MINO, PPI, ZMI, and SHAN to
measure soil health among fields is greater
than for Ml (Table 4.10). Variance within
composite soil samples was relatively great for
Ml. This may be because Ml includes the early
colonists (c-p = 1), whereas PPI and MINO do
not. These early colonists respond to changes
in nitrogen in soil, which may be due to addition
of organic matter or litter into the soil.
Therefore, their populations are likely to
Table 4.10. Variances for five nematode indices.
Actual variance values are presented.
Indices
Maturity Index
(Ml)
Maturity Index
(MINO)
Plant Parasitic
Index (PPI)
Combined
Maturity Index
(IMI)
Shannon Trophic
Diversity (SHAN)
Among
fields
0.032
0.056
0.124
0.071
0.228
Within
fields
0.050
0.052
0.032
0.027
0.035
Within
samples
0.064
0,027
0.021
0.011
0.103
fluctuate more rapidly than other groups. They
may track nutritional changes in soil more than
ecological stability itself (T. Bongers, personal
communication). Thus, high variance within
composite soil samples for Ml reduces its
acceptability as an indicator of soil health.
Because of this concern, MINO was chosen for
illustration in Section 3.2.2.2 rather than Ml.
54
EMAP-Agricultural Lands Pilot Field Program Report - 1993
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4.2.2 Evaluation: Condition: Soil Quality
A reliability ratio measures the relative ability of
an index to differentiate among sites [reliability
ratio = 0.70
desirable. Results suggest that sampling plans
that have multiple measurements per field may
greatly improve the ability of these indices to
differentiate ecological condition of soil among
fields with statistical confidence (Table 4.11). .
Generally, PPI, ZMI, and SHAN had the
greatest reliability. Ml did not perform
satisfactorily even with multiple samples per
field.
One of the biggest challenges with this
indicator is to train more people who have the
expertise and time to identify nematodes to
taxonomic family, especially nematodes that are
not parasites of higher plants.
Table 4.11. Reliability ratios for several indices of nematode community structure for various sampling plans.
Samples
per
field
1
2
3
Subsamples
per
composite
1
2
3
1
2
3
1
2
3
Reliability Ratios'
Ml
0.219
0.281
0.310
0.360
0.438
0.473
0.457
0.539
0.574
MINO
0.427
0.473
0.491
0.598
0.642
0.658
0.691
0.729
0.743
PPI
0.703
O.747
0.763
0.826
0.855
0.865
0.877
0.898
0.906
EMI
0.655
0.689
0.701
0.792
0.816
0.825
0.851
0.869
0.876
SHAN
0.623
0.725
0.767
0.768
0.841
0.868
0.832
0.888
0.908
"Calculated using the variance estimates in Table 4.10.
EMAP-Agricultural Lands Pilot Field Program Report - 1993
55
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4.3 Design Comparison
The Rotational Panel design and a design
modified from the EMAP Hexagon design
(referred to in this section as the Hexagon
design) were compared for efficiency and cost-
effectiveness. These two designs are
described in Section 2.1.
Design comparisons were performed primarily
for fall questionnaire and soil sample data.
Land Use indicators took advantage of the full
area frame component of the June Agricultural
Survey dataset in order to get the most precise
estimates possible. Other indicators, including
soil quality (physical, chemical, soil biotic .
diversity) and crop productivity indices, were
only calculated from the fall questionnaire and
soil sample data. The design comparison,
then, is most relevant with respect to these
indicators.
Only statewide estimates were used and the
comparison was done both accounting for, and
ignoring, the stratification (Cotter and Nealon
1987) of the Rotational Panel design. Cost and
precision were both considered in the
evaluation of the relative efficiencies of the two
sampling plans.
Typically, the efficiency of one design relative to
another is defined as the ratio of the variances
for the statistic of interest, adjusted for different
sample sizes when necessary. For example,
the relative efficiency of design I to design II is
defined as the variance under design n divided
by the variance under design I with respect to
the parameter of interest. In EMAP, the
parameter of interest is the cumulative
distribution function (CDF). A method that
evaluates the overall precision of the CDF was
used for comparing the two sampling designs
for physical and chemical measures of soil
quality and crop productivity indicators.
Nematode indices could not be used because
nematode data were collected only with the
Rotational Panel design. Crop-specific indices
of crop productivity are not presented because
of relatively small sample sizes. For the
indicators listed in Table 4.12, a CDF was
Table 4.12. Estimated relative efficiencies8 of the Hexagon design to the Rotational Panel design.
Indicator
Soil Quality (Physical and Chemical)
CEC
Clay
Organic Carbon
Crop Productivity
Nitrogen Efficiency - All Seed Crops
(using nitrogen from commercial fertilizer and manure)
Observed/Expected Yield - All Crops
(all crops for which expected yields were available)
Ignoring
Stratification
Relative
Efficiency
1.05
1.04
0.99
1.08
0.81
Relative
Efficiency
with Cost
Factored in
0.66
0.66
0.63
0.63
0.47
Accounting for
Stratification
Relative
Efficiency
1.04
1.03
0.98
1.09
0.81
Relative
Efficiency
with Cost
Factored in
0.66
0.65
0.62
0.63
0.47
* A relative efficiency less than 1 implies that the Rotational Panel design is more efficient.
A relative efficiency more than 1 implies that the Hexagon design is more efficient.
56
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
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4.3 Evaluation: Design Comparison
computed under each plan. Relative efficiency
(RE) was defined as:
where Var^t) is the estimated variance at t for
the CDF under the Rotational Panel design and
Varh(t) is the estimated variance under the
Hexagon design. The sums are over equally
spaced increments ranging from
f, = max (min [Hexagon dataset],
min [Rotational Panel dataset])
to
tw = min (max [Hexagon dataset],
max [Rotational Panel dataset])
where max refers to the maximum of the
observations in parentheses, and min refers to
the minimum. The limits of the sum were
defined as such to prevent the relative
efficiencies from being dominated by variances
in the tails of the CDFs. To adjust for
differences in sample sizes, RE was multiplied
by (n^/nh), the ratio of the sample sizes. This
adjusted value is a measure of the relative
efficiency of the Hexagon sampling design to
the Rotational Panel sampling design, ignoring
costs (Table 4.1 2).
Relative efficiencies greater than 1 indicate that
the Hexagon design is more efficient, and
relative efficiencies less than 1 indicate that the
Rotational Panel design is more efficient. For
example, consider soil chemistry when
stratification is ignored in the variance
calculations under the Rotational Panel design.
For estimating percent clay in soil, the Hexagon
design was estimated to be about 4% more
efficient than the Rotational Panel design.
'When costs were factored in (described below),
the Hexagon design was estimated to be only
66% as efficient as the Rotational Panel design.
Results are nearly the same when variance
calculations accounted for stratification.
The adjusted relative efficiency can be thought
of as the information per sample unit of the
Hexagon design relative to the Rotational Panel
design, where information is defined as the
inverse of the average variance. To factor in
costs, total costs, excluding salaries of the ARG
staff, were documented for each plan using
records kept by NASS and the ARG (Table
4.13). The Hexagon sampling design required
NASS enumerators to visit segments in June
that they normally would not visit and, hence,
costs were more per sample unit. The relative
cost per sample unit of the Hexagon design to
the Rotational Panel design was calculated for
the JAS, the fall questionnaire, and the soil
samples. The estimated relative information
per unit was divided by the relative cost per
unit to obtain the estimated information per
dollar of the Hexagon design relative to the
Rotational Panel design. This index is a
measure of the relative efficiency accounting for
differences in costs (Table 4.12).
Accounting for stratification had virtually no
effect on the estimated relative efficiencies.
This is not surprising because MASS'S
stratification is designed to improve estimates
of extent from segment level data and is not
specifically designed to improve estimates of
field or acre level data, e.g., fall questionnaire
and soil sample indices. When costs were
ignored for soil quality, the estimated relative
efficiencies were approximately 1 (meaning the
designs were about equal) for the soil indices.
The soil measures were, however, not all
independent. The relative efficiencies, ignoring
costs, for the crop productivity indices indicated
that the Rotational Panel design was slightly
less efficient than the Hexagon design for the
nitrogen efficiency index, but more efficient for
the observed/expected yield index. With costs
factored in, the Rotational Panel design was
more efficient for all fall questionnaire and soil
sample indices eixamined.
EMAP-Agricultural Lands Pilot Field Program Report - 1993
57
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4.3 Evaluation: Design Comparison
Table 4.13. Costs of the Hexagon and Rotational Panel sampling designs.3'"
Item
Constructing segments (prorated over 20 years)
June Agricultural Survey
Subtotal
Fall survey costs?:
NASS labor & administration
Equipment, shipping, & lab analyses
Total
Number of fall questionnaires listed as complete
Cost per fall survey sample unit
Number of soil samples
Cost per soil sample unit
Hexagon
$454
$ 13,000
$ 13,454
$ 8,250
$ 5.250
$ 26,954
52
$518
53
$509
Rotational
Panel
0
$ 9.750
$ 9,750
$ 24,750
$ 15,750
$ 50,250
168
$299
156
$322
* Because nematode data were only collected from the Rotational Panel design, nematode costs are
not Included. Nematode lab costs are a fraction of the total sampling cost: $50 per sample,
$65 per QA/QC sample (every 20th sample).
b Table entries are rounded.
c Cost differences for the Fall Survey reflect only differences in sample sizes.
Entries in Table 4.12 are estimated relative
efficiencies and, therefore, are subject to
sampling variability. Assessing the variability in
the estimated relative efficiencies is difficult and
we made no attempt to do so. Two
conclusions can be reliably drawn from Table
4.12. First, the Rotational Panel design
appears to be no less efficient than the
Hexagon design. Second, the Rotational Panel
design is the more cost-effective design. These
results are very similar to results found for the
1992 North Carolina Pilot (Campbell et. al.,
1994b).
In addition to the above comparisons for fall
data, the two sampling designs were compared
with respect to the estimates of the extent of
annually harvested herbaceous crops and the
number of ponds from the June data (Table
4.14). These comparisons were made for the
purpose of illustrating the effect of NASS's
stratification on indicators derived from JAS
data. Relative efficiencies in Table 4.14 were
calculated from the estimated variances of the
estimates and are adjusted for the different
sample sizes used in the two designs. For the
Rotational Panel design, NASS sampled 390
segments and the cost per sample unit was
$25, while for the Hexagon design 77 segments
were sampled and the cost per sample unit
was about $175. The much lower cost for the
Rotational Panel design is because NASS
already visits segments in the Rotational Panel
design as part of their June Survey, whereas
Hexagon segments require additional trips and
interviews that NASS enumerators would not
normally need to do. NASS samples high-
agriculture strata with greater intensity than
low-agriculture strata and, thus, segments in
the Rotational Panel sample tend to be more
agricultural than do segments in the Hexagon
sample. The impact of NASS's stratification is
evident in Table 4.14, where the Rotational
Panel design appears to be considerably more
efficient than the Hexagon design in the
estimation of extent of annually harvested
herbaceous crops. The Rotational Panel
design also seems to be more efficient in
EMAP-Agricultuml Lands Pilot Field Program Report - 1993
-------
4.3 Evaluation: Design Comparison
Table 4.14. Estimated relative efficiencies8 of the two designs with respect to the estimation of extent of AHHC and
number of farm ponds.
Indicator
Extent of annually harvested herbaceous crops
Number of ponds
Ignoring
Stratification
Relative
Efficiency
0.38
0.82
Relative
Efficiency
withCost
Factored in
Cl.05
Ci.12
Accounting for
Stratification
Relative
Efficiency
0.18
0.71
Relative
Efficiency
with Cost
Factored in
0.03
0.10
a A relative efficiency less than 1 implies that the Rotational Panel design is more efficient.
A relative efficiency more than 1 implies that the Hexagon design is more efficient.
estimating the number of ponds, although the
improved efficiency is not as great as for extent
of annually harvested herbaceous crops. This
is most likely because many ponds provide
water for livestock and occur in rangeland
areas, which NASS samples at a lower intensity
than areas with more agriculture.
Both designs provided good spatial coverage of
Nebraska. Due to the stratification of the
Rotational Panel design, a higher percentage of
segments from the Rotational Panel design
were eligible for the Fall Survey (Chapter 2).
Prior to drawing the sample for the Fall Survey,
the lists of acres were ordered by segment
number. Systematic samples with random
starts were then drawn for each of the designs.
Since NASS numbers segments in a serpentine
manner across a state within a stratum (Cotter
and Nealon 1987), a systematic sample from a
list ordered by segment number preserves the
initial stratification and spatial coverage of the
JAS. For the Fall Survey, a higher percentage
of eligible segments were selected for the
Rotational Panel design than for the Hexagon
design (Chapter 2). Thus, the Rotational Panel
design, because it concentrates segments in
agricultural areas, allows for broader spatial
coverage for the Fall Survey, i.e., a smaller
percentage of Fall Survey samples lie within
the same segment for the Rotational Panel
design. ;
In summary, when differences in costs are
considered, the Rotational Panel design
appears to be more efficient than the Hexagon
design. If costs are ignored, the Rotational
Panel design tends to perform as well as the
Hexagon design for most fall questionnaire and
soil sample indices. For estimation of the
extent of annually harvested herbaceous crops
and of the number of ponds from the June
Survey, the Rotational Panel design was more
efficient than the Hexagon design. The
stratified nature of the Rotational Panel design
seems to provide for better spatial coverage
than the Hexagon design for the Fall Survey.
EMAP-Agricultural Lands Pilot Field Program Report - 1993
59
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5. Conclusions and Future Directions
With the 1993 Pilot Field Program in Nebraska,
the EMAP-Agricultural Lands Resource Group
has moved further along the path of indicator
development. We have tested indicators of
land use and cover, crop productivity, and soil
quality in areas ecologically different from those
in which the indicators were first developed.
We have evaluated what the indicators can tell
us about current condition and future
sustainability,- and we have identified the areas
In which they need further work. Consideration
of both the Nebraska and North Carolina
studies has also helped us clarify our future
directions.
Indicators
The three indicator categories tested in this
Pilot have shown both progress and potential.
Land Use and Cover. These indicators provide
Information about the extent, distribution, and
diversity of agricultural resources. This
information provides the context in which to
interpret condition indicators. A future
challenge for these indicators is to develop
them as indicators of condition in their own
right.
Crop Productivity. These indicators address
the critical issues of agricultural productivity,
wise use of resources, and the effect of
management on plant health. While they have
some utility as status indicators (i.e., they can
tell us something about condition at one
moment in time), they will be most useful as
trend indicators when the EMAP-ARG collects
data over several years in a single area. A
future challenge for these indicators is to find
ways to account for different sources of
variability (e.g., weather, management, variation
in corn moisture content, uncertainty in ancillary
data) when constructing the indices.
Soil Quality. We are starting to move beyond
traditional physical and chemical measurements
toward approaches to quantifying soil health
that not only integrate many measures, but also
include biological components. Future
challenges in developing these indicators
include refining and expanding our approaches
to make them useful in a nationwide monitoring
program; developing indicators for various soil
functions such as environmental buffering; and
exploring additional biological and physical
indicators for fields under different management
systems.
A continuing challenge for all of the indicators
is that of establishing threshold values for
acceptable (nominal) and unacceptable
(subnominal) condition. Having these threshold
values will give us greater flexibility in
developing methods for assessments.
Future Directions for the EMAP-
Agricultural Lands Program
In addition to continuing work on the indicators
presented in this report, the ARG will be
initiating several new projects in the future.
Each study both raises and answers questions
that must be addressed as we pursue our
mission. The knowledge and experience
gained in Pilot Field Programs will help us lay
the groundwork for future studies and,
ultimately, a national monitoring program.
Mid-Atlantic Integrated Assessment. We will
work with other EMAP Resource Groups
(Forests, Surface Waters, Estuaries, and
Landscape Ecology) to examine cross-resource
issues. We will also monitor this geographic
area for several consecutive years, allowing us
to address trends in condition. ;
Other agricultural resources. To date, we have
focused solely on annually harvested
60
EMAP-Agricultural Lands Pilot Field Program Report -.1993
-------
5. Conclusions
herbaceous crops. Other resource classes of
interest include perennial fruit and nut crops,
pasture-livestock systems, farm ponds, and
windbreaks. In 1994 and 1995, we will conduct
studies of windbreaks as bird habitat in
Nebraska.
New indicators. Eventually, we hope to have a
suite of indicators for each resource class. The
three categories examined in this report are the
common foundation for assessing the condition
of land in annually harvested herbaceous crops
and other crop resource classes. In 1994, we
will test a set of new soil indicator measures
(many new "in-field" measures as well as new
ways of evaluating the data) and an entirely
new indicator category, insect diversity (using
ants).
Assessment methods. We will continue to
explore methods of combining information from
all indicator categories to create an overall
estimate of the condition and ecological
sustainability of agricultural landscapes.
Interagency Cooperation
In addition to the work we are pursuing within
EMAP, we continue to develop relationships
with other federal agencies. -In particular, the
ARG is building partnerships with USDA-NASS
and NRCS. By drawing on the expertise,
experience, and information bases of these two
agencies, we will be making maximum use of
available resources. We will also ensure that
EMAP's monitoring of agricultural lands is
designed to address issues of concern to
decision makers in numerous federal agencies.
"We hope that eventually these other agencies
will assume responsibility for some components
of the monitoring program, thus making EMAP
the true interagency effort it was envisioned to
be.
EMAP-Agricultuml Lands Pilot Field Program Report -1993
61
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64 EMAP-Agricultural Lands Pilot Field Program Report - 1993
&U& GOVERNMENT PRINTING OFFICE: OK - C5040C/22059
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