AN ABSTRACT OF THE THESIS OF
Aya Ogishi for the degree of Master of Science in Agricultural and Resource Economics
presented on January 5, 996. Title: Economic Evaluation of Alternative Agricultural
Resources Conservation Policies.
Redacted for Privacy
Abstract approved:
Stanley F. Miller
The agriculture production system in the post-war United States is characterized
as highly specialized and increasingly dependent on off-farm inputs. Although the US
has traditionally supported agriculture with numerous government commodity programs
such as target prices, nonrecourse loans, and deficiency payments, agriculture today is
perceived as one of the major sources of environmental degradation.
In this thesis research, the conservation compliance provision in the Food
Security Act of 1985 and 1990, as well as various other conservation policy alternatives,
are evaluated for five representative farms in terms of its effectiveness in preserving the
agricultural resources and protecting the environment. F-iighly erodible lands in Western
Oregon is the study area of this thesis since the agricultural lands are used for the
production of a variety of crops and are considered susceptible to environmental damage.
The conservation compliance provision performs fairly well in reducing soil
erosion and protecting water quality. But its performance depends on the characteristics
of the representative farms. Alternative policies are found to be more cost effective in
reducing these externalities, although no single policy is the most cost-effective for all
types of farms in reducing all environmental outputs. Trade-offs exist between net return
and environmental output production as well as among environmental externalities.
The nature of the environmental externalities such as nonpoint source pollution
and the uncertain parameters for biosirnulation and economic models are limiting
features of the study.
©Copyright by Aya Ogishi
January 5. 1996
All Rights Reserved
Economic Eva! uation of Alternative Agricultural Resources Conservation Policies.
by
Aya Ogishi
A THESIS
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Master of Science
Completed January 5, 1996
Commencement June 1996
Master of Science thesis of Aya Ogishi presented on January 5. 1996
APPROVED:
Redacted for Privacy
Major Pro
riculfüraTand Rsource Economics
Redacted for Privacy
Chair of Department of Agricultural and Resource Economics
Redacted for Privacy
Dean of Graduat'Schoo1
I understand that my thesis will become part of the permanent collection of Oregon State
University libraries. My signature below authorized release of my thesis to any reader
upon request.
Redacted for Privacy
Aya Ogishi, Author
ACKNOWLEDGEMENT
I would like to take this opportunity to thank everyone who has helped me
complete this thesis. I would like to thank especially my major professor, Dr. Stanley
Miller who has guided me through all the way, and my committee members, Dr. Ludwig
Eisgruber and Dr. Greg Perry.
I have received many helpful information and advice from various individuals
including Ken Hale, Fred Gelderman, and Dr. Larry l3oersma. Jeff Lee, Georgie
Mitchell, Paul Pedone have also given me advice with regard to running the simulation
program.
Many people have made my life in Corvallis special, including my roommate,
Nancy Bergeron, whom I shared most of my time and thought with, and all of my other
very good friends. Wherever we will be, I hope that our friendship will last for a
lifetime. And of course, special thanks go to my parents, who are always there for me
whenever I need them, Their continual support has helped me go though all the tough
times.
Lastly, 1 would like to thank my brother who has been my inspiration since I was
little. Without his patient tutoring during my early school years, I would not he here
finishing my Master degree.
TABLE OF CONTENTS
Page
INTRODUCTION
1
1.1 Nonpoint Source Pollution (NPS)
3
1.1.1 SoilErosion
1.1.2 Water Pollution
3
1.2 Requirements for Appropriate Economic Analysis
1.2.1 Farm Level Data
1.2.2 Use of Nonpoint Production Functions
1.2.3 Stochastic Risks
1.2.4 Bioeconomic Model
4
4
5
5
5
6
1.3 Justification
7
1.4 Objectives
8
15 Thesis Organization
9
BACKGROUND 1: AGRICULTURAL PRODUCTION SYSTEM
2.1 Physical Farm Characteristics
2.1.1 Soil Characteristics
2.1.2 Climate and Weather Effects
2.2 Farm Inputs
10
10
10
11
12
2.2.1 Plant Nutrients and Fertilizer
12
2.2.1.1 Nitrogen
2.2.1.2 Phosphorus
2.2.1.3 Potassium
13
2.2.2 Pesticides
13
14
14
TABLE OF CONTENTS (Continued)
2.3 Farm Management
2.3.1 Crop Rotation
2.3.2 Tillage Practices and Crop Residue Management
2.3.3 Pesticide and Fertilizer Applications
2.3.4 Other Farming Practices
Pace
15
is
16
18
19
BACKGROUND II: POLICIES AND ECONOMICS
20
3.1 History of Soil and Water Conservation
20
3.1.1 Soil Conservation Policies
3.1.2 Water Quality Protection
3.2 The Conservation Title of Food Security Act of 1985
3.2.1 Determination for HEL lands
3.2.2 Conservation Reserve Program
3.2.3 Conservation Easement
3.2.4 Conservation Compliance. Sodbuster, and Swampbuster
Provisions
3.3 Various Conservation Related Programs
3.3.1 Long-standing Assistance Programs
3.3.2 Provisions of 1985 and 1990 Farm Acts
21
23
25
25
26
29
29
30
30
32
3.4 Appropriate Economic Theory underlying the Erosion and
Pollution Control
33
3.4.1 Profit Maximization (Unconstrained Case)
3.4.2 Profit Maximization (Constrained Case)
3.4.3 Externalities
34
36
37
PROCEDURE AND MODEL DESCRIPTION
4.1
General Procedure
39
39
TABLE OF CONTENTS (Continued)
4.2 Site Setting: Willarnette Valley in the Western Oregon
4.2.1 Physical Characteristics
4.2.2 Main Crops in Production
4.2.2.1 Small Grains
4.2.2.2 Grass Seed
4.2.2.3 Vegetable Crops
4.3 Collection of Farm-Level Data
Page
40
41
41
42
42
43
43
4.3 1 Enterprise Budget and Prices
43
4.3.2 Conservation Compliance Plans
4.3.3 Mail Survey
44
45
4.4 introduction of Biophysical Model: EPIC
47
4.4.1. Selection of the Model
4.4.2 Data Sources for EPIC Parameters
47
4.4.2.1 Weather
4.4.2.2 Soils
4.4.2.3 Crops
4.4.2.4 Other Parameters
51
4.4.3 Simulation
51
52
52
54
54
4.5 Using Linear Programming as a Tool to Obtain Results
56
4.5.1 Linear Programming
4.5.2 The Profit Maximizing Model: CAMS
56
57
5 RESULTS
63
5.1 Survey Result
5.1.1 Soil Characteristics
5.1.2 Farming Operations of Pre- and Post-Compliance plans
63
63
64
TABLE OF CONTENTS (Continued)
Page
5.2 Representative Farms
68
5.2.1 Soil Grouping
5.2.2 Description of Representative Farms
52.3 Adjustments to the Farming Operations of Representative
68
69
Farms
71
5.3 EPIC Simulation Results
78
5.3.1 Pesticide Index
5.3.2 Pre-Compliance Situations
5.3.3 Potential Farming Adjustments
78
81
83
5.3.3.1 Case of Continuous Wheat Rotation in Farm A
84
5.3.3.2 Case of Wheat-Annual Ryegrass Rotation in Farm B
85
86
5.3.3.3 Case of Wheat-Fescue Rotation in Farm C
5.3.3.4 Case of Wheat-Corn-Beans Rotation in Farm D
86
5.3.3.5 Case of Wheat-Perennial Ryegrass Rotation in Farm E 87
.
5.4 GAMS Maximization Result
87
5.4.1 The Effects of Compliance Provision
5.4.2
87
5.4.1.1 Profit Maximizing Behavior of Farm A
5.4.1.2 Profit Maximizing Behavior of Farm B
5.4.1.3 Profit Maximizing Behavior of Farm C
5.4.1.4 Profit Maximizing Behavior of Farm D
5.4.1.5 Profit Maximizing Behavior of Farm E
89
90
90
Alternative Policies
92
91
92
95
5.4.2.1 Evaluation of Alternative Policies: Case of Farm A.
5.4.2.2 Evaluation of Alternative Policies: Case of Farm B . .. 99
5.4.2.3 Evaluation of Alternative Policies: Case of FanTn C . 102
5.4.2.4 Evaluation of Alternative Policies: Case of Farm D.. . 105
5.4.2.5 Evaluation of Alternative Policies: Case of Farm E . 108
.
.
.
.
6 SUMMARY
6.1 Conclusions
.
.
.
.
111
111
TABL[ OF CONTENTS (Continued)
Page
6.2 Limitation
6.2.1 EPIC
6.2.2 Linear Programming Model
113
113
115
BIBLIOGRAPHY
116
APPENDICES
123
Appendix A Crop Field Operations
Appendix B The HEL Conservation Plan Survey
Appendix C EPIC Runs Results
123
136
143
LIST OF FIGURES
Figure
Page
5.1
Cost-Effectiveness of Alternative Policies on Farm A
97
5.2
Cost-Effectiveness of Alternative Policies on Farm B
101
5.3
Cost-Effectiveness of Alternative Policies on Farm C
104
5.4
Cost-Effectiveness of Alternative Policies on Farm D
107
5.5
Cost-Effectiveness of Alternative Policies on Farm E
110
LIST OF TABLES
Table
Page
The Recommended Seeding Dates and Crop Residue Levels to
Achieve the Specified % of December 1 Ground Cover
46
4.2
Description of Water Quality computer Models
49
4.3
Dry Matter Production and Seed Yields
53
4.4
LS values for the Standard USLE and for Adjusted USLE for Oregon
56
4.5
The Annual Maximum Soil Loss Level (T-value)
62
5.1
Characteristics of Soils of HEL Farms
65
5.2
Survey Result Summary: Percentage of Producers who Have
MadeVarious Farming Adjustments and Have Observed the Change
in Yield
67
5.3
Classification of Soils in the Study Area
70
5.4
Representative Farms
71
5.5
Distributions of Soil Type and Slope for Different Farm
72
5.6a
Farming Operations for Representative Farm A
73
5.6b
Farming Operations for Representative Farm B
74
5.6c
Farming Operations for Representative Farm C and B
75
5.6d
Farming Operations for Representative Farm D
76
5.6e
Farming Operations for Representative Farm E
77
5.7
Pesticide Characteristics
79
5.8a
Toxicity Value Assigned for Pesticide Runoff and Pesticide Remaining
in the Sediment
80
4.1
LIST OF TABLES (Continued)
Table
Page
5.8b
Toxicity Value Assigned for Pesticide Leaching
80
5.9
Per Acre Crop Yields and Environmental Outputs for Pre-Compliance
Situation
82
5.10
Land Rent Estimates for Representative HEL Farms
88
5.11
Percentage of RM Returns with repsect to Gross Income
88
5. 12a Optimal Field Operations and Corresponding Fifteen-Year (Per Acre)
Averaged Net Return and Environmental Outputs: Before Compliance
89
12b Optimal Field Operations and Corresponding Fifteen-Year (Per Acre)
Averaged Net Return and Environmental Outputs: After Compliance
89
5.
5.13
Proposed Alternative Policies
93
5. 14a Fifteen-Year Averaged (Per Acre) Profit, Environmental Outputs,
and Field Operations with Pre- and Post-Compliance and Alternative
Policies: Case of Farm A
96
5. 14b Fifteen-Year Averaged (Per Acre) Profit, Environmental Outputs,
and Field Operations with Pre- and Post-Compliance and Alternative
Policies: Case of Farm B
100
5. 14c Fifteen-Year Averaged (Per Acre) Profit, Environmental Outputs,
and Field Operations with Pre- and Post-Compliance and Alternative
Policies: Case of Farm C
103
5. 14d Fifteen-Year Averaged (Per Acre) Profit. Environmental Outputs,
and Field Operations with Pre- and Post-Compliance and Alternative
Policies: Case of Farm D
106
5. 14e Fifteen-Year Averaged (Per Acre) Profit, Environmental Outputs,
and Field Operations with Pre- and Post-Compliance and Alternative
Policies: Case of Farm E
109
LIST OF APPENDIX TABLES
Table
Page
Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation I (Continuous Winter
Wheat) on Representative Farm A: Woodburn 16%
144
Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 1 (Continuous Winter
Wheat) on Representative Farm A: Helmick 16%
145
Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation I (Continuous Winter
Wheat) on Representative Farm A: Nekia 16%
146
Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 2 (2 year Annual Ryegrass
and 1 year Winter Wheat) on Representative Farm B: Woodburn 8%
147
C.2.2 Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 2 (2 year Annual Ryegrass
and 1 year Winter Wheat) on Representative Farm B: Helmick 8%
148
C.2.3 Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 2 (2 year Annual Ryegrass
and 1 year Winter Wheat) on Representative Farm B: Steiwer 8%
149
C. 1.1
C. 1.2
C. 1.3
1
1
Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 3 (4 year Tall Fescue
and 1 year Winter Wheat) on Representative Farm B: Woodburn 8%
150
C.3.2 Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 3 (4 year Tall Fescue
and 1 year Winter Wheat) on Representative Farm B: Helmick 8%
151
C.3.3 Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 3 (4 year Tall Fescue
and 1 year Winter Wheat) on Representative Farm B: Steiwer 8%
152
LIST OF APPENDIX TABLES (Continued)
Table
C.4. 1
Page
Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 3 (4 year Tall Fescue
and 1 year Winter Wheat) on Representative Farm C: Woodburn 16%
C.4.2 Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 3 (4 year Tall Fescue
and 1 year Winter Wheat) on Representative Farm C: Helmick 16%
C.4.3
Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 3 (4 year Tall Fescue
and 1 year Winter Wheat) on RepresentativeFarm C: Steiwer 16%
C.4.4 Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 3 (4 year Tall Fescue
and 1 year Winter Wheat) on Representative Farm C: Chehulpum 16%
CS. 1
153
154
155
156
Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 4 (1 year Corn, 1 year
Beans, and 1 year Wheat) on Representative Farm D: Woodburn 8%
157
C.5.2 Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 4 ( lyear Corn, 1 year
Beans, and 1 year Wheat) on Representative Farm D: Willamette 8%
158
C.5.3 Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 4 ( lyear Corn, 1 year
Beans, and 1 year Wheat) on Representative Farm D: Jory 16%
159
Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 4 ( lyear Corn, 1 year
Beans, and 1 year Wheat) on Representative Farm D: Nekia 8%
160
C.5.5 Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 4 (1 year Corn, 1 year
Beans, and 1 year Wheat) on Representative Farm D: Helmick 16%
161
C. 5.4
LIST OF APPENDIX TABLES (Continued)
Table
C.6. 1
Page
Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 5 (4 year Perennial
Ryegrass and 1 year Winter Wheat) on Representative Farm E: Jory 8%
162
C6.2 Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 5 (4 year Perennial
Ryegrass and 1 year Winter Wheat) on Representative Farm E: Nekia 8%
163
C.63 Fifteen-Year Averaged Environmental and Crop Outputs (per acre)
for Different Farming Operations of Rotation 5 (4 year Perennial
Ryegrass and 1 year Winter Wheat) on Representative Farm E: Nekia 16%
164
Economic Evaluation of Alternative Agricultural Resources Conservation Policies
1. INTRODUCTION
Recent findings show that agriculture is the largest single source of nonpoint
pollution in the United States (various). About one-fifth of the U.S. croplands are
considered susceptible to soil erosion. Erosion causes not only on-site damages by
reducing soil productivity, but also off-site damages by depositing suspended sediments
in surface water (National Research Council, 1989: 115-119). Bound with sediments or
dissolved in runoff water, nutrients, pesticides, salts, and manure are delivered to surface
water, further degrading water quality. Agricultural production now is estimated to
generate roughly 50% of surface water pollution (National Research Council, 1989: 98).
Pesticides and nitrates from fertilizers and manure also are increasingly being detected in
groundwater (National Research Council, 1989: 105).
Several laws concerning soil and water quality control have been passed by
Congress to curb unlimited environmental degradation. Many, however, have addressed
environmental components such as soil erosion and surface water pollution individually
and separately. As the qualities of soil and water resources are naturally related, laws
need to focus on improving not just one aspect of the environment but the overall quality
of the environment.
The main interest of the study, conservation compliance (CC), is an example of
regulations which target one environmental component. CC was established for the
purpose of controlling erosion on highly erodible lands (HEL), although it carries
2
obvious implications for water quality. CC is the first mandatoiy erosion control
measure, even though the enforcement applies only to those producers who are enrolled
in Federal commodity or assistance programs. CC. it is believed, will reduce erosion as
it discards the traditionally-carried agenda behind most conservation policies: voluntary
participation, financial support to farmers, and crop productivity enhancement.
However, to determine its overall environmental impact, the cost-effectiveness of this
regulation on changing water quality as well as reducing erosion needs to be evaluated.
To reduce the environmental degradation without having to reduce producers
income substantially, a committee on Long-Range Soil and Water Conservation,
convened by the Board on Agriculture of the National Research Council, recommends
that the current agricultural soil and water resource policy should:
conserve and enhance soil quality as a fundamental first step to
environmental improvement;
increase nutrient, pesticide, and irrigation use efficiencies
in farming systems;
3 increase the resistance of farming systems to erosion and
runoff
4. make greater use of field and landscape buffer zones.
(National Research Council, 1993: 36)
In this introductory chapter, the characteristics of nonpoint source pollution as
well as the approaches available to deal with these characteristics are briefly described.
The objective, procedure and organization of the thesis then are presented.
3
1.1 Nonpoint Source Pollution (N PS)
It is almost impossible to trace nonpoint pollution back to an individual farm and
quantify the farms contribution to the total pollution. Erosion and water pollution rates
vary in time and location. They are influenced by many factors including climate,
vegetation, slope, and soil materials. Their rates also can be accelerated substantially by
land uses. Nonpoint pollution at the farm has the following characteristics:
NPS loadings cannot be monitored at the individual farm or
source level, since the loadings enter water bodies over a
dispersed area, rather than at fixed, identifiable points
there is imperfect knowledge about the relationship between
loadings and farm-level input choices and management
practices
loadings depend in part on random variables such as wind,
rainfall, and temperature
(Malik, Larson, and Ribaudo, 1993 :6)
Segerson (1988) states that because of the uncertainty with regard to climatic and
topographic conditions, there will be a range of possible ambient levels associated with
any given abatement practice or discharge level at any given time."
1.1.1 SoilErosion
Soil erosion is defined as the physical movement of soil by water or wind.
Eroded soils have been replaced by new soil formation in many situations prior to human
cultivation of the soils or early years of farming. As the farming system of intensive
cultivation has become more intense, however, the erosion rates have greatly surpassed
4
the soil formation rates. These excessive rates of erosion are putting heavy pressure on
the land resource.
1.1.2 Water Pollution
Almost all states identify NPS water pollution as a serious environmental
problem (USDA-EPA, 1984). The largest pollution sources are agricultural by-products
of nutrients (nitrogen and phosphorus), pesticide residuals, and topsoil sediments in the
surface and ground water. Over the past thirty years, the use of high-yielding varieties
(H1V) of crops and the continuous production of a single crop on the same parcel of land
have become the common farming practices. Monoculture practices are accompanied by
the increased application rates of fertilizer and pesticides. Federal programs and policies
are generally supportive of this increased use of fertilizer and pesticides (National
Research Council, 1989:3 8). These phenomena have been escalating the negative
impacts on water quality.
1.2 Requirements for Appropriate Economic Analysis
The nature of nonpoint source pollution makes the measurement or control of the
individual loading impossible, or very costly. A variety of methods with different sets of
determinants have been used in calculating the costs of these environmental damages.
Several factors need to be considered when evaluating the effect of agricultural
production systems on various environmental services.
5
1.2.1 Farm Level Data
The site-specific nature of nonpoint source pollution requires detailed
information at the farm or regional level (Johnson, Adams, Perry, 1991; Stevens, 1988;
Anderson, Opaluch, and Sullivan, 1985). Taylor, Adams, and Miller (1992) found that
optimal farm management could be different among farms because of their erosive
characteristics. Different soil types, climate, and crops in production affect pollution
rates separately as well as interactively. The collection of the farm-level data and
evaluation of pollution production at this level are thus crucial for a valid economic
benefit-cost analysis.
1.2.2 Use of Nonpoint Production Functions
When the observation of the actual pollution loading is difficult, a nonpoint
production function should be used to estimate the loading. Griffin and Bromley (1982)
state that only when an appropriate nonpoint production function such as the Universal
Soil Loss Equation (USLE) is used, can an optimal solution to environmental quality
control be identified.
1.2.3 Stochastic Risks
To control agricultural nonpoint source (NPS) pollution, Shortle and Dunn (1986)
state that two uncertainties about pollution load need to be considered: imperfect
information about effluent amount and ex-ante uncertainty about weather conditions.
6
Farmers and policy makers can have different information. Farmers are not always eager
to provide information when they know that the disclosure of the information might
lower their incomes. Weather condition, which is stochastic in nature, is one of the
primary factors which influence the loadings of eroding soils and polluted water. A few
storms can cause large effects on the environmental outputs. Segerson (1988)
demonstrates the more accurate information on tabatement cost estimates, estimates of
damages from ambient pollution, and estimates of how each polluters abatement affects
the distribution of those ambient levels" when structuring incentive mechanism of
controlling NPS pollution.
1.2.4 Bioeconomic Model
Increasingly sophisticated biosimulation models are used to evaluate NPS issues
in agriculture. Biosimulation models generally take stochasticity into consideration and
estimate the loadings of nonpoint source pollution using nonpoint production functions.
Although such models do not accurately predict actual pollution rates, they are useful in
"diminishing the uncertainty about nonpoint loadings' (Shortle and Dunn. 1986).
Linking biological/hydrological and economic aspects are gaining increasing popularity
and respect in the field of agricultural economics for the use of benefit-cost analysis
(Taylor, Adams, and Miller, 1992). Roberts and Swintori (1994) state that 'existing
economic optimization methods [usually Linear Programming or Dynamic Programming
related] linked to biophysical simulation hold the greatest promise for evaluating the
7
tradeoffs among profitability, environmental impact, and stability (both financial and
environmental)."
1.3 Justification
Although the reduction of sedimentation, improvement in water quality, and
enhancement of wildlife habitat are secondary objectives of the Federal Security Act, it is
doubtful that the Act can succeed in providing an integrated policy of soil and water
quality improvement. Conservation Compliance, especially, seems to focus only on the
erosion control. Because soil and water qualities are both major concerns when
exploring the optimal total environmental quality, how policies affect these qualities
needs to be evaluated together. The development of a systematic approach which
simultaneously improves both environmental qualities is necessary for sustainable
agricultural production and clean environment.
The 1990 GAO reports on the implementation status of the Conservation
Compliance program. However, the dynamic effects of conservation compliance on such
things as soil and water resources and the farm income for different farm types are not
fully evaluated. Therefore, it is necessary to determine the relationships among different
environmental services and cost-effectiveness in terms of environmental protection in
establishing this conservation program. Alternative conservation policies, which may
serve better in reducing the environmental damages at lower private and social costs
needs to be examined as well.
8
In this thesis, the stochastic nature of NPS is taken into consideration in the
evaluation process. Erosion and pollution control policies which target highly erodible
lands are evaluated for the resource protection efficiency and cost-effectiveness. Farmlevel analysis is conducted for representative farms which are developed from survey
results and conservation compliance plans. The bio-physical model which uses nonpoint
production functions is incorporated into an economic optimization model. Then,
various management options are evaluated in terms of the impacts on soil and water
resources as well as on the income.
The results of this thesis, hopefully, will provide insight into the issue of long-
term, sustainable food supply and clean environment. Exploring the better relationships
among different environmental services can induce the public as well as policy makers to
become aware of the needs to better integrate conservation policies. Policy makers also
can benefit from the information on cost-effectiveness of the alternative policies.
1.4 Objectives
The objective of this study is to estimate the cost of conservation compliance and
compare its cost-effectiveness in reducing various environmental pollutants with
alternative regulations for representative NFL farms in Western Oregon. The specific
procedure to achieve this objective is:
Examine the changes in tiliage practices, crop production mixes, and other field
practices resulting from the need to develop and implement a compliance plan
Determine the differences in environmental outputs of various farming practices
for each HEL farm
9
Evaluate the effects of conservation compliance on environmental outputs and
cost effectiveness in reducing soil erosion and water pollution rates
4)
Introduce and evaluate the effects of alternative policies on environmental
outputs and cost effectiveness in reducing soil erosion and water pollution rates
1.5 Thesis Organization
In chapters 2 and 3, background information for the study is provided. Chapter 2
discusses the general agricultural production system and its relation to the highly
erodible lands. Chapter 3 discusses the various past and present agricultural
conservation policies and how they interact with one another. Economic theory behind
the policies also is briefly discussed as well.
Chapter 4 outlines the data collection procedures and the model development. It
includes a detailed description of two main models: biosimulation (Erosion Productivity
Impact Calculator (EPIC)) and linear programming. The results of the simulation and
optimization models as well as the survey results are given in Chapter 5. The last chapter
summarizes the results and lists several limitations of the study.
10
2. BACKGROIJND I: AGRICULTURAL PRODUCTION SYSTEM
Understanding the environmental problems related to agriculture requires an
examination of the whole agricultural production process system. in the first section of
this chapter, physical farm characteristics that affect the loading of the nonpoint
pollution are first described. The following sections discuss the farm inputs which
significantly contribute to pollution loading and the farming practices which can be used
to reduce the loading.
2.1 Physical Farm Characteristics
Many physical characteristics, particularly weather and soil properties, are
associated with a specific farm. They stochastically affect the loading of soil loss and
water pollution.
2.1.1 Soil Characteristics
Soil erodibility and runoff differ considerably based on soil type. Soils are less
erodible when their properties make soil detachment and transportation difficult. Soil
properties such as soil aggregate size, structural stability, infiltration rate, and
permeability are considered some of the significant determinants of water erosion and
runoff (Troeh, Hobbs, and Donahue. 1991: 8 1-83). Soils with large stable structural
aggregates and rapid permeability and infiltration rates are more resistant to water
erosion and runoff (Miller and Donahue, 1990: 456). The permeability and infiltration
11
rates are greater for finer-textured soils high in clay and organic matter than coarser-
textured soils (National Research Council, 1993: 320). Cation exchange complex, type
of clay mineral, organic matter content, cementing material other than clay and organic
matter, and cropping history all affect aggregate sizes and stability (Troeh, Hobbs, and
Donahue, 1991: 81).
Soil erosion decreases the soil quality through surface soil loss, plant nutrient
loss, textural change, and structural change (Troeh, Hobbs, and Donahue, 1991: 69).
Surface soil is generally !!more friable, more permeable to water, air, and roots, and
higher in organic matter and fertility" than the lower soil layers (Troeh, Hobbs, and
Donahue, 1991: 69). As surface soil erodes away, subsurface soils with lower
permeability and infiltration rates are exposed, and as the result, erosion and runoff
accelerate (Troeh, Hobbs, and Donahue, 1991: 69). Various quality attributes including
organic carbon, available water-holding capacity, PH, and bulk density are degraded
(National Research Council, 1993: 222-226). A large amount of nitrogen, phosphorus.
and potassium are in addition, lost with the sediments.
2.1.2 Climate and Weather Effects
Falling raindrops and running water are the major contributors to water erosion.
Kinetic energy of the raindrop is related to mass and velocity of the raindrop as:
E = 1/2mv2
where E is kinetic energy, m is mass of falling drop, and v is velocity. When the
raindrops strike the surface, the energy released breaks soil aggregates, splatters away
12
soil grains, and compacts the soil surface. They detach individual grains, transport them,
and reduce the infiltration rate (Troeh, Hobbs, and Donahue, 1991: 72).
Erosiveness of runoff water depends on the depth, velocity, turbulence, and
abrasive material content. Runoff occurs when rainfall intensity is greater than soil
infiltration rate. The velocity depends on soil gradient (steepness), length, shape, and
aspect (Troeh, Hobbs, and Donahue, 1991: 75-78).
2.2 Farm Inputs
Nutrients, especially those added in the form of fertilizer, and pesticides are often
associated with the environmental problems because they bind with sediments
discharged from the land or dissolve into the surface running or leaching water. Soil and
water qualities are significantly degradedby these processes.
2.2.1 Plant Nutrients and Fertilizer
Many soil nutrients are essential to plant growth and development: nitrogen,
phosphorous, potassium, boron, calcium, chlorine, cobalt, copper, iron, magnesium,
magnesium molybdenum, sulfur, and zinc (National Research Council, 1989: 141-142).
Except for the first three soil-derived plant nutrients, most of the nutrients are available
in sufficient amount in the soil (National Research Council, 1989: 141). Depending on
the soil and cropping history, nitrogen, phosphorous, and potassium may need to be
added to the soil. It is essential for vigorous plant growth to have the adequate amount of
these soil nutrients available. Although adding fertilizers helps plant growth, they also
can negatively affect the environment, especially water quality.
22.1.1 Nitrogen
Nitrogen is often the most limiting soil-derived nutrient in the United States
(National Research Council, 1989: 144). Producers used to supply nitrogen by
incorporating leguminous crops in their rotation of corn and small grains and by adding
animal manure. When they started continuously planting high nitrogen responsive
varieties of corn or small grains, the demand for nitrogen fertilizer dramatically increased
(National Research Council, 1989: 42). Nitrogen fertilizer is provided to crops usually in
the form of ammonium ions (NH4). When ammonium ions undergo biological
nitrification and are oxidized, nitrate ions (NO3-) and hydrogen ions (1-1+) are produced.
Hydrogen ions acidify the soil. Because nitrate ions have a negative charge, they are not
absorbed into the soil cation exchange system. Although nitrate ions are readily
available as nutrients for plants, if not absorbed by plants, these highly water soluble ions
easily enter the ground or surface water (National Research Council, 1 989: 145).
2.2.1.2 Phosphorus
When soluble phosphorous is applied as a fertilizer and not used by plants
quickly, it soon binds either with aluminum, iron oxide, and calcium, or with clay
colloids. As it does, it no longer remains easily available for plant uptake. Leaching is
not a problem, but phosphorus which is bound to sediment in runoff water is often
14
implicated in eutrophication and decline in surface water quality" (National Research
Council, 1989).
2.2.1.3 Potassium
Potassium fertilizer is needed for highly organic soils, in humid region.
Potassium, having a net positive charge, is absorbed on the soil cation exchange
complex. Although it is more water soluble and more readily available for plant uptake
than phosphorus, it has limited mobility in water and leaches only in sandy soils
(National Research Council, 1989:154).
2.2.2 Pesticides
In the 1 940s, the synthetic chemical pesticides such as DDT (dichloro diphenyl
trichioroethane), BHC (benxene hexachloride), and 2,4-D (2,4-Dichlorophenoxy acetic
acid) were developed. They contributed substantially to decreased pest damage which
resulted in increased crop yields through the 1960s (National Research Council, 1989:
175). When pesticides were first marketed as low-priced pest control substitutes, they
appeared to be cost-effective. However, the perceived benefits of pesticides have
become less clear when one recognizes that pesticides often kill beneficial species as
well as harmful ones. Also pests have become more resistant to chemicals, which in turn
has resulted in increased application rates (National Research Council, 1989: 175). In
addition, many pesticides are known to be harmful to the environment and human health
(National Research Council, 1989: 175).
15
Pesticides fate is largely determined by their specific properties such as
solubility, absorption, and persistence. Pesticides can be either degraded, lost to the
atmosphere, absorbed to soil particles, leached, or removed through surface runoff by
rainfall or irrigation (National Research Council, 1989: 317). Pesticides with low
sorption, long life, and high water solubility have a high potential to contaminate the
groundwater (National Research Council, 1989: 317). Pesticides that bind strongly with
soils do not leach, but can be discharged to surface runoff or in the sediments (National
Research Council, 1989: 317).
2.3 Farm Management
Several farming practices can reduce agriculture-related environmental problems.
These include crop rotation, residue management, cross slope farming, contouring,
modification of the timing and the rates of pesticide and fertilizer applications, among
others.
2.3.1 Crop Rotation
Crop rotation is the "successive planting of different crops in the same field." It
is contrasted to planting the same crop every year as in continuous cropping (National
Research Council. 1989: 138). Beside the economic benefit of reducing the risk
associated with fluctuating markets, crop rotations offer many benefits in farming:
increased soil organic matter, pest control, and increased availability of certain nutrients.
The largest contribution of crop rotation is the control of weeds, insects, and diseases.
16
The planting of different crops interrupts the reproduction of pests, especially insects and
diseases that feed on plant roots. Because a healthier rooting system absorbs nutrients in
the soil more effectively, smaller amounts of nutrients are free to leach out of the root
zone and fertilizer application can be reduced. Certain types of rotation crops serve to
provide additional nutrients to succeeding crops. Some deep-rooted crops may bring up
nutrients from deep in the soil profile, while legumes fix nitrogen from the atmosphere
(National Research Council, 1989: 138-141).
Crop rotations, however, can have some disadvantages: potential decrease in the
base acreage for the federal program, lower net revenue from the inclusion of low market
value crops in the rotation, and necessity of owning and operating more equipment for
the greater diversity of new crops that enter in the rotation (National Research Council,
1989: 141).
2.3.2 Tillage Practices and Crop Residue Management
Crop Residue Management (CRM) is a conservation practice designed to leave
surface residue on the ground to reduce wind and water erosion. Factors that determine
the residue amount left are the previous crops, the harvest practices, and the type of
tillage operations. CRM usually involves conservation tillage, or reduction in the
number of passes over the field with tillage implements and/or in the intensity of tillage
operations, including the elimination of plowing (inversion of the surface layer of soil)
(USDA-ERS, 1993: 31). CRM can also include farming practices such as the use of
cover crop or early planting of the crop.
17
Conservation tillage is defined as a tillage and planting system that maintains
either uat least 30% of the crop residue in reducing water erosion' or "at least 1,000 lbs
per acre of flat, small grain residue equivalent in reducing wind erosion on the ground"
(USDA-ERS, 1993: 31). Out of about 300 million total acres planted in the United
States, conservation tillage was implemented on 89 million acres in 1992. This number
is estimated to increase as the CRM becomes more widely adopted in conservation
compliance plans, in reducing production costs, and in improving environmental quality
(USDA-ERS, 1993: 31).
Crops can be planted earlier than usual in the effort to increase the green cover
during the winter months. For regions with the high winter precipitation rates such as
Western Oregon, early planting is an especially important erosion control practice.
Tillage practices that leave crop residue on the ground enhance rainfall
infiltration and reduce the amount of sediment and sediment-related chemical (fertilizers
and pesticides) losses through rainfall and runoff to surface waters. Some of the positive
effects of residue management are:
Cleaner runoff resulting from the filtering action of the
increased organic matter associated with higher levels of crop
residue
Greater opportunity for captured chemicals to break down
into harmless components through the action of microorganisms
contained in organic matter in the residue or in the top layer of
soil and exposure to air and sunlight
(USDA-ERS, 1993: 38-41)
While conservation tillage may reduce soil erosion and water runoff, it may
require increased use of fertilizers and pesticides. Conservation tillage releases nutrients
18
relatively slowly but evenly compared to conventional tillage. Plant nutrients tend to be
more stratified and concentrated in the upper soil layer. A layer of crop residue on the
ground can serve as a favorable habitat to some pests. Diseases, insects, and weeds can
rejuvenate in spring as they overwinter in the bed of residue. Soils tend to be compacted
and to be cooler in spring, and thus contribute to the slower plant growth, although cooler
and moist soil conditions in the summer as well as the higher concentration of soil
microbes and earthworms positively affect plant growth (National Research Council,
1993: 156-160).
2.3.3 Pesticide and Fertilizer Applications
Changing the rate, timing, and mode of pesticide and fertilizer applications may
affect the fate of the chemicals (Huang, Un, and Hansen, 1994) (Johnson, Adams, and
Perry, 1991). The potential exists to reduce the current rates of over-application. Inputoutput balance of nutrients and pesticides can be further studied. Application right
before irrigation and heavy rainfall season should be avoided as it increases the runoff
and leaching rates of chemicals (National Research Council, 1993: 321-322). Pesticide
losses differ with the mode of application. Pesticide loss through soil-incorporation is
generally lower compared with the loss through spraying (National Research Council,
1993: 323).
19
2.3.4 Other Farming Practices
Contouring, cross-slope farming, and terraces can be used to combat erosion and
runoff, particularly on the hilly slopes. Contouring and cross-slope farming, however,
may not be economical if the farm does not have a uniform landscape, since they take
large amounts of labor and machinery. Establishing terraces also requires large initial
investments.
Creating and managing field and landscape buffer zones in addition are potential
farming practices which encourage soil conservation and improve water quality. The
buffer zones can work as the "landscape sinks that trap or immobilize sediments,
nutrients, pesticides. and other pollutants before they reach surface water or
groundwater" (National Research Council, 1993: 105).
20
3. BACKGROUND it: POLICIES AND ECONOMICS
A number of government policies have been established to protect the nation1s
resources. In 1992, USDA and state and local government spent $3.6 billions for
conservation purposes. This number was estimated to have risen to $3.9 billion in 1993
(USDA-ERS, 1993). Rental and easement payments comprised one half of USDA
conservation expenditures, while the total share of technical assistance and extension
was one quarter of these expenditures. Cost sharing for installation, conservation data
and research, and project conservation accounted for 10.4%, 8.1%, and 4.9%,
respectively (USDA-ERS, 1993:18). In this chapter, selected past and present policies
are first presented. The economic theory underlying pollution control, which needs to be
considered in making any policy decisions, is then discussed.
3.1 History of Soil and Water Conservation
Early conservation programs were often influenced by traditional agricultural
policies to provide financial support to farmers and to enhance agricultural productivity.
The goal of reducing soil erosion and water pollution was often linked with that of price
support through supply control. Voluntary participation instead of mandatory controls
was emphasized since farmers were perceived to be have property rights to soil and water
resources. Cost sharing and technical assistance also were common practices in
conservation planning.
21
3.1.1 Soil Conservation Policies
The strong public awareness of the need for soil conservation developed after the
Dust Bowl era in the Great Plains of the 1930s. This era led the government to start the
first major nationwide campaign to battle soil erosion. The Soil Conservation Act was
enacted in 1935, and the Soil Conservation Service (SCS, presently called the Natural
Resource Conservation Service or NRCS) was established as a permanent agency in the
Department of Agriculture. Administered by the SCS, the Conservation Assistance
Program (CTA) provided voluntarily participating farmers with technical assistance for
conservation planning. The CTA was followed by the Agricultural Conservation
Program (ACP), which was authorized by the Soil Conservation and Domestic Allotment
Act of 1936. This program also was based on voluntary and cost-sharing principles for
conservation practices and administrated through another agency, the Agricultural
Stabilization and Conservation Service (ASCS). Producers were paid to set aside
acreage from !!soildepleting crops! and replace them with ?soiIconserving crops such
as grasses and legumes. Because the !soildepleting crops!? at the time were often in
surplus, the program accomplished two goals: reduced surplus and conserved soils
(National Research Council, 1993: 152). In 1956, the Great Plains Conservation
Program (GPCP) was initiated under the SCS. It targeted interested farmers and
provided long-term technical and cost-sharing aids for conservation planning (Hanley,
1991: 70-71).
In 1956, the Soil Bank was established as the first conservation reserve program
administered by USDA. The main purpose of this program, based on voluntary
22
participation, was to reduce the excess supply of major crops by diverting acreage from
production. The program required producers to establish an approved vegetative cover
on the ground. Participants received cost-sharing, technical assistance, and annual rental
payments in return (Berg, 1994: 7). The Soil Bank was, however, repealed in 1965, and
its 3-10 year contracts resulted in only short-term erosion reductions.
Beginning in the 1970s, the criteria for monitoring erosion and for evaluating
conservation program was established by the creation of National Resources Inventory
(NRI) and the Soil and Water Resources Conservation Act (RCA) appraisals. Through
the 1972 amendment to the Rural Development Act, USDA was required to conduct the
National Resources Inventory (NRI) every five years (Berg, 1994:7). NRI provided
various data for program evaluation, such as erosion rates, levels of appropriate
conservation, and land-use conversions (Steiner, 1990: 8). The RCA of 1977 and the
Agriculture, Rural Development, and Related Agencies Appropriation Act of 1979 were
made into law to require performance evaluation of each conservation program and to
monitor the effective allocation of funds to appropriate farmers (Harlin and Berardi,
1987: 325-27). The NRI indicated that rate of soil erosion had increased to levels higher
than during the Dust Bowl. The primary cause was intensive cultivation resulting from
price increases stemming from the export boom and the termination of soil bank program
(Berg, 1994: 7). US General Accounting Office (GAO), in addition found that USDAs
conservation programs were not effectively reducing soil erosion and 'targeting of
technical assistance on certain lands would be required (Berg, 1994:8). These NRI and
GAO reports reveal USDNs historical view toward a conservation program as only "a
residual to other USDA policies" (Berg, 1994: 8).
23
3.1.2 Water Quality Protection
Along with the soil conservation, water quality enhancement was needed. It has
resulted in the establishment of the three major clean water acts as of today. These acts,
the Federal Water Pollution Control Act Amendments of 1972, the Clean Water Act of
1977, and the Water Quality Act of 1987, required state governments to set up plans to
control water pollution (Steiner. 1990:7-8). The familiar approaches of voluntary and
cost-sharing were followed.
The 1972 amendments to the Federal Water Pollution Control Act (FWPCA)
were the first federal act to take a control approach to reduce nonpoint source (NPS)
water pollution as well as point source pollution. Each state was responsible for
identifying NPS problems, specifying control methods, and implementing the controls.
Most states took Best Management Practices (BMP) which generally include land-use
controls and land-management practices. The 1977 Clean Water Act authorized the
USDA to provide water quality BMPs with technical and financial assistance (Malik,
Larson, and Ribaudo, 1993). These two acts, however, resulted more in reducing point-
sources than in reducing nonpoint sources, because it was difficult to identify and search
for control method for nonpoint source pollution (Malik, Larson, and Ribaudo, 1993).
The more direct approach to control NPS was introduced in section 319 of the
1987 Water Quality Act. It required States to:
1. identify navigable waters that, without additional action to
control nonpoint sources of pollution, cannot reasonably be
expected to attain or maintain applicable water-quality
standards or goals,
24
identify nonpoint sources that add significant amounts of
pollution to affected waters, and
develop an NPS management plan on a watershed-by-watershed
basis to control and reduce specific nonpoint sources of
pollution
(Malik, Larson, and Ribaudo, 1993: 2)
The designed management plan was required to include a list of appropriate BM1Ps and
implementation schedule.
Seventeen states have applied or are planning to apply agricultural pollution
control as a part of their 319 NPS control plan. Farmers are required to 'have approved
plans for land disturbance (including tillage), to obtain permits for activities that may
cause soil erosion or residual discharges to waterways, to comply with established
permissible soil-loss limits, and to respond to specific complaints" (Malik, Larson, and
Ribaudo, 1993).
The FWPCA, CWA, and WQA, however, have not been very successful in
meeting water quality goals. The Federal Government has a limited role in enforcing the
NPS water pollution control since installing the land-use and water-use regulations are
generally in the hands of the states. States' BMPs are generally in the form of
education, technical assistance, and cost-sharing voluntary approaches. Even with the
section 319 program, states are not held responsible for their slow progress in applying
the controls, and they often compromise using voluntary approaches which are
economically more attractive (Malik, Larson, and Ribaudo, I 993).
25
3.2 The Conservation Title of Food Security Act of 1985
In 1985, the Food Security Act (the Farm Bill) was enacted with the conservation
goals of retiring marginal land, forcing cross compliance of agriculture and conservation
program and preserving the family farm (Steiner, 1990: 9). Erosion control was for the
first time treated as an independent objective of agricultural policies. Although the
income support objective was again linked to conservation effort, the support was
conditional on the adoption of conservation practices on certain highly erodible lands.
The erosion control was planned to be achieved through three approaches: the
Conservation Reserve Program, conservation easements, and the Conservation
Compliance, Sodbuster, and Swampbuster provisions (Steiner, 1990: 18).
3.2.1 Determination for HEL lands
The soil loss tolerance (T value) is defined as the maximum annual average soil
loss in tons per acre which allows continued high levels of crop production and which is
economically feasible (Troeh, Hobbs, and Donahue, 1991: 114). T value ranges from
one to five1 and consists of natural runoff, irrigation, and wind erosion. USDA-SCS
(1986) states that "raindrop splatters, sheet, nil, concentrated flow, and gully erosion
taking place in a field must all be accounted for in determining if average annual erosion
exceeds T' The erodibility Index of a soil map unit is used in identifying highly erodibie
land. The erodibility index is defined as:
1
For the T values for specific soils, see chapter 4.6.2.
26
erodibility index from sheet and nil erosion: RKLS/T
erodibility index from wind erosion: CuT
R, K, and LS are rainfall, soil erodibility, and slope-length factors from Universal Soil
LOss Equation2, while C and I are factors of the wind erosion equation. C is the climatic
characterization of wind speed and surface soil moisture, and I is the susceptibility of the
soil to wind erosion. When the erodibility index of either I) and/or 2) is 8 or larger, the
corresponding soil map unit is considered highly erodible (USDA, 1990: 511 -5-8).
Highly Erodible Land (HEL) status is given to a field when the highly erodible soil map
units are either one-third or more of the field acreage or 50 or more acres of the field
(USDA, 1990: 511-10). Once the HEL determination is given, the entire field is
considered either highly erodible or not, and HEL fields are subject to developing
appropriate conservation plans.
3.2.2 Conservation Reserve Program
The Conservation Reserve Program (CRP), administered by ASCS, provided
producers of eligible highly erodible lands with the subsidy payment for a period of 1015 years in exchange for taking lands out of production and maintaining appropriate land
management during the reserve period. USDA agreed to pay an annual per-acre rent (up
to maximum acceptable rental rates established for each county) and to provide
producers with technical assistance and 50% of the costs to establish appropriate
2
The explanation of USLE and its factors is found in chapter 4.4.3a.
27
conservation practices (often vegetative cover). The primary goal of CRP was to reduce
soil erosion on highly erodible cropland. However, reduction of sedimentation,
improvement in water quality, and enhancement of wildlife habitat as well as sustenance
of food productivity, supply control of commodities, and income support for farmers,
were perceived important considerations when developing and implementing the
program.
The 1985 Act required an enrollment of 40-45 million acres of highly erodible
land by the end of the 1990 crop year. To be considered highly erodible, at least twothirds of the cropland had to meet one of the following criteria:
soil in land capability class VI-V1113
soil in land capability class lI-V and eroding at a rate greater
than 3T (Tthe soil loss tolerance rate), or greater than 2T if
cropland is to be planted to trees or if subject to severe gully
erosion
the soil with an erodibility index (El) greater than 8 and be
eroding at greater than T.
(Young and Osborn, 30)
Program enrollment eligibility also was extended to include land that could cause serious
environmental damage or experience continuous productivity declines because of soil
salinity. Where practicable, at least one-eighth of the acres enrolled was to be planted in
Capability grouping is made according to the suitability of soils for field crops.
Class I soils:
have few limitation on what crops to be produced.
Class II - V soils:
limit the kinds of plants produced and require some
conservation practices.
Class VI - VIII soils: limit the their use mostly to pasture, woodland, and
wildlife, and are unsuitable for the production of field
crops.
(I.JSDA-SCS, 1972)
28
trees. No more than 25% of the total cropland in a county could enter the program unless
the Secretary of Agriculture determined that the larger program participation would not
harm the county's economy.
The CRP was modified overtime to encourage greater participation as well as to
meet other environmental concerns. For signups beginning in 1987, filter strips along
waterways, cropped wetlands, and areas subject to scour erosion could be included in
CRP. These were specifically targeted to improve water quality. In addition, if trees
were to be planted, only one-third, versus the original two-thirds of the cropland, needed
to be classified as highly erodible, and erosion rate decreased from 3T to 2T.
Through the twelve signup periods from 1985 to 1993, 36.5 million acres of
highly erodible lands and associated croplands with environmental concerns were
enrolled in the CRP (USDA-ERS, 1993). The reduction of erosion and runoff resulting
from the implementation of this program is well-documented. According to USDA-ERS
(1993), an average of 19 tons per acre of soils is saved annually for the lands signed up
between 1985 to 1989, with 15 tons per acre saved on average for the lands enrolled after
1991. The CRP also has contributed to several other environmental benefits including
improved surface and groundwater quality and the improved wildlife habitat (Heimlich
and Osborn, 1993). It has at the same time helped farmers to maintain their income and
reduced excess supply of crops and commodity program payments.
The future of the CRP lands is currently under review. It is estimated that over
50 percent of CRP land operators will return their lands to production, and over 25
percent of these lands will not be subject to the Conservation Compliance (Heimlich and
29
Osborn, 1993). Benefits obtained from the CRP will not last long if currently enrolled
lands return to crop production.
3.2.3 Conservation Easement
The conservation easement program allowed landowners who could not repay
Farmers Home Administration (FmHA) loans to receive relief if they established
conservation easements. This program also is voluntary, and only a small minority of
farmers are eligible for easements (Steiner, 1990).
3.2.4 Conservation Compliance, Sodbuster, and Swampbuster Provisions
Conservation Compliance, Sodbuster, and Swampbuster provisions protect 1-IIEL
farmed by the federal program participants, because non-compliers lose eligibility for all
federal program benefits, including price and income supports, crop insurance, disaster
payments, and Farmers Home Administration loans. The Compliance provision required
landowners who cropped highly erodible lands between 1981 and 1985 to have an
approved NRCS conservation plan by 1990 and comply with the plan requirement by
January 1, 1995. The Sodbuster and Swampbuster provisions prohibited farmers or
landowners to plant annually tilled crops on highly erodible fields or wetlands after 1985.
Conservation Compliance originally required the establishment of a conservation
system which would reduce erosion to the soil loss tolerance level (T-value) for a
specified soil type. However, USDA now requires the plan to contain either Resource
Management Systems (RMS), the original standard of Basic Conservation Systems
(BCS), or Alternative Conservation Systems (ACS) (USDA-SCS, 1989). RMS is defined
as a conservation system that meets acceptable soil losses as well as maintaining
acceptable water quality and ecological levels. ACS is the system which reduces erosion
a substantial amount but does not require producers to meet T-value (USDA-SCS, 1989).
As of April 1993, 148 million acres are classified as highly erodible lands (HEL),
and 141 million acres have approved conservation plans on their lands. Sixty-one
percent of approved plans were fully applied, while thirty-nine percent were not fully
applied or the plans were not yet certified (USDA-ERS, 1993: 26). In addition to the
requirement of meeting the compliance deadline of 1995, producers are required to
follow the compliance schedule set by the NRCS. NRCS conducts an annual spot check
on 5 % of plans and those producers who fall behind the schedule lose eligibility for farm
program benefits (USDA-ERS, 1993).
3.3 Various Conservation Related Programs
The following is a list of current government programs designed to reduce
nonpoint pollution, along with the time period in which they were used and the agency
overseeing each program (USDA-ERS, 1993).
3.3.1 Long-standing Assistance Programs
a. Agricultural Conservation Program (ACP), 1936 - present, ASCS
Provides financial assistance of up to $3,500/year or S35,000/I0-year contract to
farmers who carry out approved conservation and environmental protection
practices on agricultural land and farmstead
31
b. Conservation Technical Assistance (CTA), 1936 - present, SCS, local Conservation
Districts
Provides technical assistance to farmers for planning and implementing soil and
water conservation and water quality practices.
C. Extension Service (ES)
Provides information and recommendations on soil conservation and water
quality practices to landowners and farm operators in cooperation with the State
Extension Services and State and local offices of USDA and Conservation
Districts
Small Watershed Program, 1954 -present
Assists local organizations in flood prevention, watershed protection and water
management. Part of this effort involves establishment of measures to reduce
erosion, sedimentation, and runoff
Great Plains Conservation Program (GPCP), 1957- present, SCS
Provides technical and financial assistance (Lip to $35,000 per farmer contract) in
10 Great Plains States for conservation treatment to entire operation units; funds
a water quality special project in the 10 states
Resource Conservation and Development Program (RC&D), 1962 - present
Assists multi-county areas in enhancing conservation, water quality, wildlife
habitat, recreation, and rural development
Water Bank Program, 1970 - present
Provides annual rental payments to farmers for preserving wetlands in important
migratory waterfowl nesting, breeding, or feeding areas.
Colorado River Salinity Control Program, 1974 - present
Established a voluntary on-farm cooperative salinity control program within the
USDA; provides cost-sharing and technical assistance to farmers to improve the
management of irrigated lands to reduce the amount of salt entering the Colorado
River
Forestry Incentives Program, 1978 - present
Provides cost-sharing up to 65% (up to S 10,000 per owner) for tree planting
and timber stand improvement for private forest lands of no more than 1,000
acres
Emergency Conservation Program, 1978 - present
Provides financial assistance to farmers in rehabilitating cropland damaged by
natural disasters
k. Rural Clean Water Program (RCWP), 1980-1995 (expected), 21 selected states
Provides cost-sharing (up to $50,000 per farm) and technical assistance to farmers
who voluntarily implement approved best management practices to improve
water quality
1. Farmers HOme Administration (FmHA)
Provides loans to farmers for soil and water conservation, pollution abatement,
and building or improving water systems; may acquire 50-year conservation
easements as a means of helping farmers reduce outstanding loan amounts; places
conservation easements on foreclosed land being sold, or transfers such lands to
government agencies for conservation purposes
3.3.2 Provisions of 1985 and 1990 Farm Acts
Conservation Reserve Program (CRP)
Allows farmers to voluntarily retire from crop production highly erodible or
environmentally sensitive cropland for a 10- to 15-year period, in exchange
for the annual payment up to S50,000 and 50% cost-share assistance for
establishing vegetative cover on the land
Conservation compliance provision
Requires farmers with highly erodible cropland to have an approved conservation
plan on that land and to fully implement the plan by January, 1995, to maintain
eligibility tbr USDA program benefits
Sodbuster Provision
Requires farmers who convert highly erodible land to commodity production
to have an approved conservation system on that land, to maintain eligibility for
USDA program benefits
Swampbuster Provision
Makes farmers who converts wetlands for, or to make possible, the production of
an agricultural commodity ineligible for USDA program benefits, unless there is
a determination that conversion would have only a minimal effect on wetland
hydrology and biology
Wetlands Reserve Program (WRP)
Provides easement payments and cost sharing to farmers who return farmed or
converted wetland back into a wetland environment on a permanent or long-term
basis. Payments are up to the fair market value of the land less the value of
permitted uses. Up to I million acres may be enrolled in the program by 1995.
f Water Quality incentives Projects (WQIPs)
Provides annual incentive payments up to $3,500 per year for 3-5 years to farmers
who implement a USDA-approved water quality resource management plan
Environmental Easement Program
When implemented, will provides annual payments for up to 10 years (up to
$50,000 per year, $250,000 per farm) and up to I 00?/ cost sharing to farmers who
agree to deed restriction that provide long-term protection to environmentally
sensitive land
Integrated Farm Management Program
Assists producers in adopting farm resource-management plans to conserve
resources and comply with environmental requirements
The goal is to have 3-5 million acres enrolled by 1995.
Pesticide Recordkeeping provision, May 10, 1993 - present
Requires private applicators of restricted-use pesticides to maintain records
accessible to State and Federal agencies regarding products applied, amount, and
date and location of application
J. Forest Stewardship Program
Provides grants to State forestry agencies for expanding tree planting and
improvement and for providing technical assistance to owners of nonindustrial
private forest lands in developing and implementing forest stewardship plans to
enhance multi-resource use
k. Stewardship Incentive Program (SIP)
Provides cost-sharing up to 75% (up to $10,000 per year per landowner) for
practices of 10 years of more in approved forest stewardship plans for enhancing
multiple uses of non industrial private forest lands
3.4 Appropriate Economic Theory underlying the Erosion and Pollution Control
So far in this chapter, existing conservation policies and programs were
described. To identify the best plan to reduce nonpoint pollution, however, a benefit-cost
analysis needs to be conducted for alternative policies. In this section, the economic
theory behind pollution control is discussed. Neglect of the underlying economic theory
can result in inefficient solutions.
34
3.4.1 Profit Maximization (Unconstrained Case)
The production function of the farm producing one commodity is given in the
form of:
y
f(x)
(1)
where y is output and x is the vector of inputs. Profit is maximized where the difference
between the total revenue and cost is the largest. Since each farmer is assumed to have
no control over input and output prices, prices are assumed constant. The profit function
for the single output thus is written as:
it(x)=pf(x)-.wx
(2)
where p is the market output price, and w is the vector of market prices of the respective
inputs x. The condition of unconstrained profit maximization is described as:
First Order Conditions (FOC):
= pDf(x*)
(3)
and (pDf(x*) w)x* = 0
/
af(x*)
(4)
3f(x*)
where Df(x*)
dx,
)
is the vector of partial derivatives off with respect to each of the inputs. FOCs are
necessary conditions for profit maximization. FOCs state that the resources are used to
35
the point where the marginal revenue from the additional factor is equal to or more than
the marginal cost incurred from adding the extra factor (Equation (3)). Equation (4)
indicates that when the input price is not equal to the value of marginal product, the input
is not used.
Second Order Condition (SOC):
When the production function is known as being convex, FOCs are both
necessary and sufficient conditions for profit maximization. When the shape of
production function is unknown, however, the second order condition (SOC) must be
introduced. This condition is sufficient to establish that the x obtained in FOCs
represents the maximizing arguments.
The SOC states that the matrix of second derivatives of profit function, D2 t(x*)
must be negative semidetinite. This implies that the Hessian matrix of the production
function, D2 f(x*) must be negative semi-definite at the optimal point,
a2 f(x*)
where
D2 f(x*) =
(5)
Equation (5) implies that f <0 for all i. This indicates the law of diminishing returns of
factor inputs.
3.4.2 Profit Maximization (Constrained Case)
In the section above, the unconstrained profit maximization case was explained.
When constraints are added to the model, it takes the form of:
Maximize ic(x)
pf(x) - wx
subjectto g(x)k
(6)
form=l,. .M
.
where gs are constraint functions and ks are the limits on resources available.
The lagrangian function is thus written as:
= pf(x) -wx
g(x)) = 0
Am (km
where As are the marginal values of constrained or limited resources.
The FOCs are:
D2(x*,A*)
pDf(x*) - w-
?
gm(x) = 0
= k - g(x*) =0
The SOCs are satisfied when D2(x*,A*) is negative semi-definite.
(7)
37
3.4.3 Externalities
Baumol and Oats (1988) gives the definition of externalities as follows:
Condition 1. An externality is present whenever some
individuals (say As) utility or production relationships
include real (that is, nonmonetary) variables, whose values
are chosen by others (persons, corporations, governments)
without particular attention to the effects on As welfare.
Condition 2. The decision maker, whose activity affects
others utility levels or enters their production functions,
does not receive (pay) in compensation for this activity an
amount equal in value to the resulting benefits (or costs) to
others.
When an externality like nonpoint source pollution exists, society maximizes profits with
constraints on allowable emissions as well as production capacity. Societyts lagrangian is
written as:
= pf(x) -wx -A (k - g(x)) -
(Z*z(x)) = 0
(10)
where the last terms represent the constraints imposed regarding the pollution emission
rates. With the assumptions of concavity of implicit production function and binding
constraints, FOC describes the societys optimal choices of production activities as
follows (Griffin and Bromley, 1982):
= pf(x) - wi-A g1(x) -
Profit maximizing farms assume
(x) 0
for i = 1, ... , n
(11)
= 0 and equates marginal revenue (first term) and
private marginal cost (second and third terms). The fourth term indicates the marginal
38
cost of externality. This should be added to the private marginal cost to obtain the social
marginal cost.
4. PROCEDURE AND MODEL DESCRIPTiON
In the earlier chapters, the background information necessary to understand the
nature of the environmental problem as well as earlier and theoretical developments in
the abatement of the problem were described. Having reviewed these, the discussion
now turns to the approaches taken for the evaluation of the main issues in this thesis.
4.1 General Procedure
A list of farms in two counties of the Willamette Valley in Western Oregon with
conservation compliance plans was obtained from the Natural Resource Conservation
Services (NRCS), the former Soil Conservation Service (SCS). The two counties
selected, Polk and Marion counties, both have large amounts of highly erodible lands
(HEL), as confirmed by the NRCS.
A mail survey was sent to all HEL producers with compliance plans in the two
selected counties (Appendix B). Producers were asked to identify the adjustments they
had made in their farming operations to implement their compliance plans. The survey
results, combined with the ëorresponding compliance plans, were used to develop two to
three representative farms for each county. The principal characteristics that
differentiate the representative farms were crop rotations used prior to implementing the
compliance plans and soil types.
For each representative farm, a biophysica.! simulator, Erosion-Productivity
Impact Calculator (EPIC), was used to generate the environmental outputs (soil erosion,
40
nitrate leaching and runoff etc.) and crop yield from various farming operations for both
pre- and post-compliance plan situations. EPIC simulates the biophysical processes in
the agricultural production system given soil, weather, crop, and farm management
inputs and data. Crop enterprise budgets. compliance plans, and survey results together
provide necessary parameters for all simulation runs of utilizing EPIC model. EPIC
simulations are conducted for each soil of each representative farm.
The environmental outputs and productivity estimates obtained from the EPIC
simulation runs are inputed into a linear programming model and solved using General
Algebraic Modeling System (GAMS) software. Farm income is maximized subject to
various constraints related to the faims and their operations. Profit maximizing farming
activities are identified for each representative farm before and after the implementation
of the compliance plans.
Alternative policies are introduced by adding different sets of costs and
constraints to linear programming model described earlier. With the new constraints and
costs introduced to GAMS, the optimal field operations and crop mix are determined for
each policy. Cost effectiveness of different policies are determined.
4.2 Site Setting: Willamette Valley in the Western Oregon
As previously stated, two counties in the Willamette Valley, Polk and Marion
counties were selected as the specific locations of the study. Marion county leads
Oregon in terms of gross farm and ranch sales. It also is the largest producer of
vegetable crops in the Valley. Both Marion and Polk counties have a large acreage of
41
highly erodible lands that need conservation plans: approximately 24,000 and 25,000
acres respectively (Gelderman, 1994; Hale, 1994). Currently almost all of these HEL
acres have approved plans.
4.2.1 Physical Characteristics
The Willamette Valley has a modified marine climate of a dry and warm summer
and a wet and cool winter. The weather in the Valley is greatly influenced by the Pacific
Ocean to the west and the Cascade Mountains to the east. Average winter temperature in
Salem is 40 degrees F, while average summer temperature is 63 degrees F. The daily
minimum temperature in winter is 33 degrees on average, and the daily maximum in
summer is about 77 degrees (IJSDA-SCS, 1982). Of the 40-45 inches of annual
precipitation, about eighty-five percent falls between October through March. in the
summer, several weeks can pass by without significant rainfall.
Wide variations in soil characteristics exist in Polk and Marion counties. Four
major land types are: alluvial flood plains, valley terraces, foothills, and the mountainous
areas (USDA-SCS, 1982)(USDA-SCS, 1972). Most of the highly erodible lands (HEL)
are located in the terraces and foothills.
4.2.2 Main Crops in Production
Many types of crops ranging from cereal grains and grass seeds to hay, orchards,
caneberries, and vegetables can be produced on the variety of soils that exist in the
42
Willamette Valley. The main crops on highly erodible lands (1IEL) are grains and grass
seeds with smaller acreage of vegetable crops.
4.2.2.1 Small Grains
In 1994, about 10% of gross farm sales came from grains (of which 88% is
wheat) in Polk County, while Marion Countys grain sales represented only 2.5% of gross
sales (Oregon State University Extension Service, 1995). Wheat is the Oregons fifth
leading agricultural commodities after farm forestry, cattle and calves, nursery crops, and
dairy in terms of gross sales (Oregon State University Extension Service, 1995). About
30% of harvested acreage was planted to wheat in 1994.
4.22.2 Grass Seed
Oregon leads the nation in the production of grass seeds. Of 445,925 harvested
acres of grass and legume seeds in 1994, annual and perennial ryegrass seeds were
estimated to be planted on more than a half (254,370 acres, estimated) of the acreage
(Oregon State University Extension Service, 1995). Tall fescue seed was planted on
about 6.5% percent of the acreage, and it ranks third in the area produced among grass
and legumes, after annual and perennial ryegrass seeds (Oregon State University
Extension Service, 1995). Over 90% of the grass and legume seeds are produced in the
Willamette Valley , and both Polk and Marion Counties are large contributors to their
production.
43
4.2.2.3 Vegetable Crops
Marion County is the leader in producing vegetable crops in the state, especially
sweet corn and snap beans. Marion County devotes over 5,000 harvested acres to
sweet corn and 13,000 acres to snap beans (USDA-Oregon Agricultural Statistics
Service, 1993). Sweet corn and snap beans are the Oregons second and third most
valuable vegetable crop, respectively (USDA-Oregon Agricultural Statistics Service,
1993). Oregon is the second largest producer of snap beans in the nation as well as being
the fourth largest sweet corn producer (USDA-Oregon Agricultural Statistics Service,
1993). About 85 percent of sweet corn is for processing, while the remaining is
marketed fresh. Most snap beans also are for processing.
4.3 Collection of Farm-Level Data
The farm-level data were mainly obtained from three sources: enterprise budgets,
compliance plans, and a mail survey.
4.3 1 Enterprise Budget and Prices
Crop enterprise budgets published by the Oregon State University Extension
Service contain information on general production methods, input needs, and various
costs for producing each crop. The farming operations specified in the budgets were
used to identify basic farming practices in producing each crop. Since the published year
varies for each budget, fertilizer and pesticide costs were adjusted to the current market
44
prices. Few adjustments, however, were made in the machinery costs. Time did not
allow collecting the necessary machinery cost information. Prices of fertilizer, pesticide,
and commodities were obtained from various public sources.
4.3.2 Conservation Compliance Plans
NRCS personnel developed conservation plans individually to fit the specific
nature of each HEL field. The plans list the basic adjustments to the farming operations
necessary to reduce erosion rates to the predetermined level on the HEL fields. The most
important concern in preparing the plan was the need to meet the targetT (acceptable
annual erosion rate). Erosion rates before and after the implementation of the written
plan on each field were calculated using the Universal Soil Loss Equation (USLE).
Although Polk County strictly adheres to the strict target-T requirement, Marion
County uses an Alternative Conservation System (ACS) on about 50% of the HEL plans.
The use of ACS does not require farmers to meet the strict T-constraints as long as
erosion from the farms is reduced substantially. Marion County NRCS personnel
determined that the shallow soils of the HELs would make producers using these soils
face severe economic hardships if they were forced to strictly adhere to the target-T
conservation system.
The mid-west version of USLE was modified to fit Oregons weather and soils.
Location site, soil type, and field acreage as well as R (rainfall), K (soil erodibility), LS
(slope-length), C (crop management). P (erosion control) and T (tolerance level) factors4
4The description of these factors and USLE is found in Section 4.4.3.
45
of the modified version of USLE were specified for each field. C and P factors were the
only factors which change according to the farm practice employed. The most often
adopted field operation that changes C is residue management practice. It includes such
things as early planting and reduced plowing (Hale, 1994). The P factor can be
decreased by cross-slope farming and by the construction of terraces and contouring.
Because erosion is difficult to monitor, crop residue serves as proxy for erosion. Thus,
the plans specify the amount of crop residue to be present on the ground on December 1
(Table 4.1).
Although conservation plans specify the practices which will reduce erosion,
complete adherence to the plans is not required. The plans do not require producers to
follow every listed practice as long as the producers manage to reduce the erosion rate on
their lands to target T or appropriate target specified in the plan (up to 4 times T). In
addition, producers usually have some flexibility on how they achieve their targets. The
plans also do not specify the pre-compliance operations.
4.3.3 Mail Survey
A mail survey was sent to all producers farming Highly Erodible Lands (HEL) in
Polk and Marion Counties. The purpose of the survey was to ask producers the
specific adjustments they made in complying to the conservation plan and to identify the
pre-compliance management system. The producers were asked to list the farming
operations before and after the development of the plans on their largest tract of HEL
46
Table 4. 1 The Recommended Seeding Dates and Crop Residue Levels
to Achieve the Specified % of December 1 Ground Cover5
Ground Cover
by December 1
Planting Date
Residue Level to be left
on Soil Surface
45 %
September 20
October 1
October 15
Spring
leave 20%
leave 30%
leave 40%
leave 45% over winter
55%
September 20
October 1
October 15
Spring
leave 30%
leave 40%
leave 50%
leave 55% over winter
60%
September 20
October 1
October 15
Spring
leave 30°/a
70%
80%
leave 45%
leave 55%
leave 60% over winter
September 20
October 1
October 15
Spring
leave 55%
leave 65%
leave 70% over winter
September 20
October 1
October 15
Spring
leave 50%
leave 65%
leave 75%
leave 80% over winter
leave 400/0
Source: Water Erosion Control: Guidelines for Oregon (IJSDA-SCS, 1986)
lands. Dillman's (1978) surveying method, Total Design Method" was used when
preparing and mailing the survey. A copy of questionnaire is given in Appendix B.
5Ground cover includes green growth and residue from previous crops
47
4.4 Introduction of Biophysical Model: EPIC
The Erosion-Productivity Impact Calculator (EPIC) simulation model generates
and supplies the technical and environmental information necessary for conducting the
economic analysis. The effects of weather, climate, and crop management practices are
evaluated in terms of crop yield, risk, net returns, and soil and water resources in various
research projects (USDA-ARS). There are a variety of biophysical simulation models
which could be chosen to estimate the rates of erosion and nonpoint source water
pollution. The more frequent and standard the bioeconomic approaches become, the
more useful these models will be.
4.4.1. Selection of the Model
The Water Quality Model Evaluation Committee (1 992a- I 992e) evaluated the
performance of five different water quality computer models and one computerized
procedure model in dealing with the problems of nonpoint source pollution from
nutrients, pesticides and sediments. The models evaluated include: Simulator for Water
Resources in Rural Basins - Water Quality (SWRRBWQ), Agricultural Nonpoint Source
Pollution Model (AGNPS). Nitrate Leaching and Economic Analysis Package (NLEAP),
Groundwater Loading Effects of Agricultural Management Systems (GLEAMS),
Erosion-Productivity Impact Calculator (EPIC), and the National Pesticide/Soil Database
and User Decision Support System for Risk Assessment of Ground and Surface Water
Contamination (NPURG). These models predict and determine the effect of
48
management decisions on various components such as sediment, nutrients, and
pesticides, but all have their weaknesses (Table 4.2).
EPIC was selected since it was evaluated to work best for the purpose of the
study. The primary function of the model is to determine the relationship between
erosion and long-term soil productivity. But the model also provides various important
outputs related to the fate of nutrients and pesticides and the timing of these chemicals
leaching and runoff (USDA-SCS, 1992b). EPIC is the only model that combines soil and
water pollution components related to nutrients and pesticides with economics (USDASCS, 1992b).
AGNPS and GLEAMS use a peak flow equation which is mainly applicable to
upstream agricultural fields in the Midwest. This equation is characterized by small
drainage areas, type 116 rainfall distribution, and uniform field surfaces where the field
and channel slopes are close (USDA-SCS, 1992a) (USDA-SCS, 1992c). SWRRBWQ
and EPIC use the modified rational formula which also is applicable only to type II
rainfall distributions. However, a new version of EPIC contains a method which is
applicable to type I, IA, and III as well as type II (Williams, l994). The study area falls
into type TA. Rainfall distributions are necessary to calculate peak flow. Peak flow
greatly influences erosion.
6
Type I and IA -- the Pacific marine climate with wet winters and dry summers
Type III -Gulf of Mexico and Atlantic coastal areas with large rainfall amounts
associated with tropical storms
Type II -the rest of the country
(USDA-ARS, EPIC - User's Guide, Draft)
It is possible that new versions of the other biosimulation models we have evaluated
have adjusted their peak flow equations so that the model outputs can be valid for parts
of the country other than mid-western U.S.
49
Table 4-2 Description of Water Quality Computer Models
Model
Functions
Weakness
SWRRBWQ
Watershed
scale
-includes components of
water, sediment, nutrient,
and pesticide yields at a
subbasin or basin outlet
-includes lake water
quality component
-includes only limited number of soils,
crops, irrigation, and others
-gives unrealistic output because of the
assumption of unique area
characteristics
-uses peak flow equation consistent only
with type II rainfall distribution
AGNPS
Watershed
scale
-single event based
-includes hydrology,
erosion, sediment
transport, nutrient
transport, chemical oxygen
demand (COD)
components
-does not properly determine the flow
path for local peak flow
-determines only suspended sediment
from sheet and nh erosion
-no pesticide model
NLEAP
Field
scale
-includes nutrient (nitrate)
component
-predict nitrate leaching
rate and potential impact
on groundwater quality
-has poor linkage between economics,
yield, and management
GLEAMS
Field
scale
-includes components of
water, sediment, and
pesticide yields at the edge
of field and bottom of the
root zone
--does not properly determine the flow
path for local peak flow
-gives different erosion rate when the
area is changed
-assumes the ratio of suspended
sediment concentration at the boundary
to the average suspended sediment
concentration constant
EPIC
Field
scale
-includes components of
weather, hydrology,
erosion, nutrient cycling,
pesticide fate, soil
temperature, tillage, crop
growth, crop and soil
management, and
economics
-uses too many default values, which
raise some concern for the quality of
output
-uses sediment yield model which
includes bed and bank sediment
materials in the sediment yields
Sources: USDA-SCS, 1992a, 1992b, 1992c, 1992d. and 1992e
50
NLEAP gives realistic nitrate leaching estimates. However, it does not include
pesticide components, as is the case of AGNPS. In addition, NLEAP does not
interconnect economics, yield, and management very well in the model (USDA-SCS,
1992d). For example, deficient nitrogen or water does not reduce crop yield.
SWRRBWQ and GLEAMS have an unrealistic assumption of unique area characteristics
and constant sediment concentration ratio, respectively (USDA-SCS, I 992c) (USDA-
SCS, l992e). Thus the outputs of the models are questionable and not very useful.
Unlike most other models, EPIC has the advantage of being able to simulate
cropping systems for many years, and to evaluate long-term soil productivity (USDA-
ARS). Because the data manipulation of cropping patterns and management practices is
simple, it is possible to compare outputs from current field operations with the ones from
alternatives. Thus, EPIC is a very useful tool in conservation planning (USDA-SCS,
1992b).
EPIC has been used in research to evaluate 'crop productivity, risk of crop
failure, degradation of the soil resource, impacts on water quality, response to different
input levels and management practices, response to spacial variation in climate
and soils, and long-term changes in climate" (USDA-ARS). EPIC's estimates for nutrient
cycling, water and nitrogen percolation, and nutrient sediment losses are found consistent
with the actual field data (Parsons, Pease, and Bosch, 1994). The recent applications
include Mapp, et al (1994), Lakshminarayan, Bouzaher, and Johnson (1994), Larson,
Helfand, and House (1994), Parsons, Pease. and Bosch (1 994),Tay!or, Adams, and
Miller (1992), and Lee, Phillips, and Liu (1993).
51
4.4.2 Data Sources for EPIC Parameters
Various physical and economic parameters are required in order to run the EPIC
model. Although EPIC provides many of the parameters necessary for the program,
these parameters often do not generate reasonable outputs. It is important to replace
these internal default values with localized input data to increase the accuracy of the
estimates (USDA-SCS, 1992b).
4.4.2.1 Weather
EPIC contains weather data from 1041 national weather stations. Data from the
station closest to the selected Natural Resources Inventory (NRI) site were included in
EPIC (Lee, Phillips, and Liu, 1993). Daily weather data are generated internally in EPIC,
given the initial parameters taken from the weather data above. The daily values
generated include precipitation, maximum and minimum temperature, solar radiation,
and wind. The simulated daily weather variables are the same for all simulation runs
using the same weather data (Lee, Phillips, and Liu. 1993). Climate data for the study
were estimated using the Corvallis weather data included in the EPIC program. Corvallis
is located near the center of the Willamette Valley, and its weather is similar to weather
in both Polk and Marion Counties.
52
4.4.2.2 Soils
The EPIC program contains soil parameters for 737 soils nationwide, but it does
not have parameters for many of the important soils in the study area. Most of the soils
listed in the database represent soils in the eastern regions of the United States, which
was EPIC's original focus. The data set included in EPIC program is not adequate for
many applications because the appropriate soil series are not present and/or better sitespecific information is available (USDA-ARS, 1991). The Soils-S database from the
National Resources Inventory (NRI) was used in the study. It contains most of the
required soil parameters for more than 4,000 US soils including all the soils in the study
area. The Soils-5 database has been verified as fairly representative of Oregon soils
(Pedone, 1995) when it is supplemented with the localized field data. When sample
EPIC runs were conducted utilizing Woodburn soil which is present in both the EPICsupplied and Soils-S databases, the outputs obtained using the Soils-S corresponded
reasonably well with the ones obtained using the EPIC supplied database.
4.4.2.3 Crops
Although many crop parameters used in simulation are default values, several
adjustments to the parameters were made to better reflect the crop productivity response
to the local characteristics of the study area. Also, because only a limited number of
crops are included in the EPIC program, simplifying substitutions had to be made to
adequately simulate the crops produced in the study area. For example, even though
grass seed crops are commonly produced in the region, they are not common nationally.
53
EPIC does not provide the crop parameters for these crops. Winter pasture is used as a
substitute for grass seed, because both have similar growth patterns. Seed yield was
estimated from the production of dry matter (Table 4.3).
Although EPIC tracks pesticide fate, it does not accurately assess the effect of
pest damage and pesticide effectiveness on crop yield. Local pest problems thus need to
be incorporated into the simulation. Because winter wheat, especially in continuous
cropping, suffers from several severe local pest problems if planted early, the economic
yield / above-ground biomass in the rotation was adjusted for situations where wheat was
planted earlier than October 15 (the recommended planting date to avoid the pest
problems).
All the biomass and yields of vegetable crops are given in dry weight in the EPIC
program. The wet yields for these crops are obtained by dividing the dry yields by one
minus the appropriate moisture percentage of the crops. When 90 % of these crops
harvested are water, the wet yields are calculated by multiplying dry weight yields by 10.
Table 4.3 Dry Matter Production and Seed Yields
Dry Matter Production
Seed Yield
Annual Ryegrass
3 tons/acre
over 2,200 lbs
Perennial Ryegrass
2.5 - 2.75 tons/acre
900 - 1800 lbs
Tall Fescue
3.5 -4 tons/acre
1200 - 1500 lbs
54
4.4.2.4 Other Parameters
Several other localized data were supplied as the inputs to the simulation runs.
Pesticide parameters are obtained from the GLEAMS documentation (Knisel, 1.993), and
fertilizer parameters are constructed according to the amount of nitrogen and phosphorus
in the different fertilizers used in actual farm practices in the Willamette Valley.
4.4.3 Simulation
Fifteen years of crop production are simulated for the several soil types and
various farm operations. EPIC gives environmental and agricultural outputs for each
simulation. The EPIC outputs become the technical coefficients in the linear
programming model.
In calculating soil loss, EPIC provides several alternative equations to choose
from. The basic structure is:
Y= x (K)(C)(P)(LS)(ROKF)
where Y is the sediment yield in ton/ha
K is the soil erodibility factor
C is the crop management factor
P is the erosion control practice factor
LS is the slope length and steepness factor
ROKF is the coarse fragment factor
However each alternative equation has a different value for x as follows:
x El
(standard USLE)
x= 1.586 (Q*.q)S6AI2
(modified USLE or MTJSLE)
x= 2.5 (Qq,)5
(Second modified USLE or MUST)
55
where
El is rainfall energy factor
Q is runoff volume in mm, and
q is peak runoff rate in mm/h.
USLE depends only on rainfall as an indicator of erosive energy, while MTJSLE and
MUST depend only on runoff variables as indicators. The use of runoff variables
"increased the prediction accuracy [and] eliminated the need for a delivery ratio (used in
the USLE to estimate sediment yield).' MUSLE and MUST also have the advantage that
they provide single storm estimates of sediment yields, while USLE provides only annual
yield estimates (USDA-ARS, 1990). However, sediment yield estimated using MTJSLE
and MUST include bed and bank sediment load material as well as sheet and nil,
interrill, and ephemeral gully erosion (LJSDA-SCS, 1992b). As the bed and bank
sediment materials are not parts of the landscape erosion, the erosion estimate can be
high (USDA-SCS, 1992b). In addition, USLE is the equation used by NRCS personnel
in conservation planning (USDA-SCS, 1991). Therefore, USLE was used to calculate
actual erosion and associated nutrient and sediment runoff with sediments.
USLE used by the NRCS personnel in Oregon is the modified version of the
standard or Midwestern USLE. It was modified to correspond to the Pacific Northwest
Drylands. Although the factors contained in the equations are the same, the formulas in
calculating the factor values, especially R and LS are different. Only one weather data
set was used in the study, and R value falls between 45-50, which is consistent with the
adjusted R value. The EPIC R value is thus assumed to be a reasonable estimate for the
purpose of the study. Only two different LS values are used for evaluation. LS value is
not significantly different between the standard EPIC value and adjusted value for
56
Oregon. In the case of steeper slopes, the erosion rate obtained from EPIC is slightly
overestimated, but the rate is still reasonable even in the absolute value.
Table 4.4 LS values for the Standard USLE and for Adjusted USLE for Oregon
slope-length
8%-400 feet
16%-250 feet
EPIC LS value
(standard USLE)
1.97
4.58
LS value for Oregon
(adjusted USLE)
1.98
4.08
4.5 Using Linear Programming as a Tool to Obtain Results
Yields and environmental outputs obtained from EPIC were incorporated into a
profit-maximizing linear programming model.
4.5.1 Linear Programming
Linear programming is a type of mathematical programming which is linear both
in the objective function and resource constraints. In the world of linear programming,
the production functions have fixed input-output coefficients. This production function
is called a fixed-proportions or Leontief production function. It has the general form:
f(x)=min{cz1x1,
57
where the a values are technical coefficients and x values represent activity levels. The
output level is given by the smallest of a x, and substitution among any of inputs is not
possible. There is no change in output unless the changed input is the limiting resource.
When the input constraint is binding, average and marginal product (AP, MP,
respectively) are positive. MP is 0 when the constraint is not binding. This production
function in addition has the property of constant returns to scale (CRTS). If all the inputs
are increased by a certain proportion, then the output will increase by the same
proportion.
Even though the assumption of fixed input-output coefficients sounds unrealistic.
it improves tractability. The Leontief function is one of the most popular production
function used by economists because of its well-established, easy-to-handle property.
Linear programming is a useful tool to represent reality. Silberberg (1990) states
"assumptions are always simplifications of reality by definition and are incorporated into
the analysis to improve the manageability of the model or theory." An important point
of any model is to be aware of its underlying assumption when applying it to the real
problem.
4.5.2 The Profit Maximizing Model: GAMS
The linear programming model described above is useful to analyze trade-offs of
profit maximizing behaviors and various environmental externalities. GAMS is the
solution algorithm used in the study. Farm profits are maximized subject to soil acreage
58
and farm management constraints. Assuming there are j enterprise activities and k
resources, the profit maximization model is vvTitten as:
Max m= py
subject to Qy b
yo
where p is the j dimension vector of expected return for enterprise activity (positive or
negative), y is thej dimension vetor of the level of activity, Q is thej x k matrix of
technical coefficients for the y enterprise activities, and b is the k dimension vector of
resources available.
When considering the effect of implementing conservation compliance plans, an
additional constraint is necessary. Compliance plans call for maintaining acceptable
maximum average erosion rates on HEL fields each year, which are usually equal to T
value. The values are specified for each soil type and vary from I to 5 tons per acre a
year. Therefore, the following constraint is added to the linear programming model
above:
twhere t is the v dimension vector of T values and E isvthe
x r xj erosion rate for
activity for the state of nature. There are r states of nature and v T-values.
The basic linear programming model contains the following objective function
and constraints:
59
Maximize Profits (Total Sales - Total Input Costs - Risk and Management Returns)
subject to:
Yield-to-Product Balance
Environmental Output-to-Tillage Practice Balance
Fertilizer Needs-to-Ti ilage Practice Balance
Pesticide Needs-to-Tillage Practice Balance
Total Acreage Limit
Soil-to-Farm Balance
Rotation-to-Farm Balance
Tillage Practice-to-Yearly Erosion Balance
Compliance Limit
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Specifically,
Max a
EXP11 * IN l -EXP2 * TN2
PRICE1 X -
subject to
YIELDJS * Y -X
zjs ENVCjS *
FD7 *
PD
zjs
- Qe
- [Ni
*Y
- IN2Y
7jS Y
Yljs
=0
for all i
(1)
0
for all e
(2)
=0
for all f
(3)
0
for all p
(4)
100
- A2 *
A1 *
land7 *
-
=0
(5)
for all j, z
(6)
and all combinations of (s1,s2)
forallj,s
- Yi2js
(7)
and all combinations of(z1,z2)
i:i: STrojs * Yzjs -
lit *DEsro
r1I
DE,
0
100 * T
for all r, s, o
(8)
for all s, o
(9)
(r1 ,r2) = (1,t),
,
(15-t,15)
60
where the activities are:
X is the quantity produced of crop i,
Y is the acre of tillage practice j for rotation crop z on soil s,
Qe is the unit of environmental service e,
1N1 is the unit of fertilizer f,
lN2 is the unit of pesticide p,
DE is the deviation above the erosion limit for year R.
Parameters are described as:
PRICE is the Linit price of crop i,
EXP l is the unit cost of fertilizer f
EXP2 is the unit cost of pesticide p.
LANIDJ is the cost of tillage j for rotational crop z,
YIELDZJS is the yield of crop i for tillage j for rotational crop z on soil s,
ENVS is the unit of environmental service e for tillage j for rotaional
crop z on soil s,
FDfZ is the unit of fertilizer f needed for rotational crop z,
PDPLJ is the unit of pesticide p needed for tillage j for rotational crop z
STrgs is the erosion rate for year r for tillage j for rotaional crop z on soil s
T is T-value for soil s
where i represents the crop, j represents the tillage practice, z is the rotational crop, s is
the soil, e is the environmental service, f is fertilizer, p is pesticide, o is rotation, r is year,
and t is the number of years in rotation.
The objective function states that the profit is to be maximized, that is,
maximizing total sales minus all production costs minus risk and management returns.
The level of long term profit is obtained by subtracting land rent from the above profit.
Since long-term profit is zero in the case of perfect competition model, profit in the
objective function is a pure land rent. The risk and management returns (RMR) for the
unconstrained case are calculated as:
RMR
Total Sales - Total Input Costs - Land Rent
61
where land rent is the estimate obtained from Pease (1995) for each soil type. RMR is
then re-specified as a percentage of the total sales, and this percentage is used to estimate
the RMR for constrained cases (Turner and Periy, 1996). The parameter c is thus
defined as one minus percentage of risk and management returns from the unconstrained
case. This implies that the unconstrained problem always gives the land rent estimates
which are specified in Pease (1995).
The tillage practices activities (Y) determine the various activities such as
pesticide (P) and fertilizer (F), environmental outputs (Q), and yields (X). Constraints
(1), (2), (3), and (4) describes these relationships. Constraint (1) describes the
relationship between crop yield and field practices. Constraint (2) shows the relationship
between environmental output produced and the use of particular tillage operations.
Constraints (3) and (4) show the fertilizer and pesticides required, respectively, in
adopting particular tillage practices.
The next three constraints give the restrictions related to soils and rotations.
Constraint (5) is the farm acreage limit. It forces all farm acreage into production.
Constraint (6) is required since each farm has the proportional acreage of the particular
soil. Constraint (7) is the rotational requirement as all the rotational crops in the rotation
should be produced for the same acreage.
While constraint (8) describes the relationship between annual erosion rate for a
particular soil and tillage operations, constraint (9) shows that the farmers are required
to meet target averaged T-value (Ts) for the span of entire rotation. If the rotation is one
year, T must be met every year. But if farmers have a five-year rotation, they are only
62
required to meet the five-year averaged T-value. T-value for different soil depth is
described in the table 4.5.
When alternative policies are introduced, additional constraints are added. When
alternative "targets" are evaluated, the equation, Qe
Qo is added, where Qo is the
target level of the pollutants, and Qe is the activity level which has the upper limit.
Table 4.5 The Annual Maximum Soil Loss Level (T-value)
Soil Depth
Renewable Soil8
T (tons/acre)
Nonrenewable Soil9
T (tons/acre)
Greater than 60'
5
5
40-60"
4
3
20-40"
3
10-20"
2
Less than 10"
1
1
1
Source: USDA-SCS Water Erosion Control, 1986
Alternative policies which are considered in the study are modified T-constraints,
"targets" and "targets" with T-constraints.
"Soils with favorable substrate that can be renewed by tillage, fertilization, organic
material, and other management practice" (USDA-SCS, 1986).
9"Soils with unfavorable substrata that cannot by renewed by economical means"
(USDA-SCS, 1986).
63
5 RESULTS
In this chapter, the effects of compliance and alternative regulations on producers
income and various environmental services are presented. The results of the HEL
Conservation Plan survey are discussed in the first section. The characteristics of
representative HEL farms developed from the survey results are described in the next
section. The results of EPIC and GAMS models for representative HEL farms follow.
5.1 Survey Result
HEL Conservation Plan Survey questionnaires were mailed to all HEL producers
who have compliance plans in Polk and Marion Counties. The response rate was
approximately 40 percent. Only 75 percent of the returned questionnaires (58 responses
out of 202 questionnaires sent), however, were used for the study. The other 25 percent
lacked some vital information necessary to be included in the study. Since the
information from all the surveyed producers were not available, there is a large potential
for the study results being biased. No effort was made to contact the non-respondents.
5.1.1 Soil Characteristics
According to the survey results, HEL farms with conservation compliance plans
have the following soils: Nekia, Belipine, Jory, Salkum, Chehulpum, Rickreall, Stayton,
Steiwer, Willakenzie, Silverton, Hazelair, Helmick, Suver, Dupee, Willamette,
Woodburn, Santiam, and Helvetia. The soil characteristics of these
are described in Table
5.1
1
8
soils listed above
According to the compliance plans, several HEL farms have
64
Dixonville, Philomath, and Witzel soils. Since no questionnaires were returned from
producers having these soils, however, these three soils are not considered in this study.
Woodburn and Willamette are highly productive soils, while Chehulpum,
Hazelair, and Steiwer soils have limited productivity. A variety of crops are produced on
the former soils. The latter three soils are the "least suitable for farming of any soils in
the survey area" in Marion County (USDA-SCS, 1972), and only certain crops can be
produced profitably on these soils.
5.1.2 Farming Operations of Pre- and Post-Compliance plans
Producers in the survey indicated that the main farming adjustments were crop
rotation, tillage practices, and planting date (Table 5,2). More than half of HEL
producers have changed their tillage practices, and many of these producers also now
plant on cross slope. The new tillage practices include fewer passes over the field and
use machinery which causes less soil disturbance. Because of the change in tillage
practices (for example, from conventional moldboard plowing to chisel plowing, field
cultivation, or no-till), some producers stated that they needed to purchase or rent new
equipment.
About 50 percent of the producers in Polk County indicated that they have
changed crop rotation on their HEL lands, while only 11 percent of producers in Marion
County mentioned the change in their rotation, Many of HEL producers in Marion
County in pre-compliance situation were already producing grass seeds, which generally
Table 5-1 Characteristics of Soils of HEL Farms
Soil
Taxanomy
iydrologic group)
Bellpine
Xeric
Drainage Landscape
class
position
WD
1-laplohumult
D
Chehulpum Entic
WI)
iow foothills,
high rolling
uplands
lo foothills
liaploxeroll
Parent
Material
colluvium from
sedimentary
rocks
weathered
sedimentary rocks
C
Dupee
Aquultic
Ilaplox eralf
SWPD
Aquullic
liaploxeroll
MWD
Aquic
SWPD
C
1-Eazelair
I)
Ilehnick
Xerochrepts
I)
Heletia
Ultic
MWI)
Argixeroll
C
Jory
Xeric
WI)
liaplohumult
C
Nekia
Xeric
WD
I {aplohumult
C
Rickreall
D
Salkum
Xeric
WI)
I laplohurnult
Ultic
1-Iaploxeralfs
C
WD
swales and
depressions ,
lo foothills
io convex
foothills
colluvium from
sedimentary rocks
Surface
Layer
dark reddish
brown silty
clay loam
dark brown
silt loam &
silty clay loam
dark brown
silty loam
dark grayish
brown silt loam,
silty clay loam
convex
dark brown
footsiopes
suit loam &
ridges
silty clay loam
terraces above residuum and
v, dark grayish
the Willamette
colluvium from
brown and dark
River floo dplain sedimen tary rocks brown silt loam
low foothills,
colluvium from
dark reddish
higher rolling
sedimentary rocks brown silt loam,
uplands
and basic rocks
Lsilty clay loam
foothills,
colluvium from
dark reddish
higher uplands
sedimentary rocks brown silty
and basic rocks
clay loam
dark reddish
footslope and
eathered
lo rolling
sedimentary rocks brown silty
foothills
clay loam
old, high
dark brown
weathered
gravelly
terraces
silty clay loam
alluvium
calayey
colluviuin from
sedimentary ro cks
colluvium from
sedimentary rocks
Thick- Penneness
9 in
ability
slow
AWC
max
root depth
w.table
20-40 in 3,5-6 in > 72in
Effective
17-24in
6 in
moderate
9 in
moderately > 60
10-20
slow
2-4
6-13
> 72
32-14
20-26
24-36
lOin
slow
24-130
4-7
14-20
12-24
1 Sin
very slow
20-136
5.5-7.5
14-20
12-24
15 in
moderately > 60
10-12
22-26
36-72
slow
moderately > 60
9-1 1
> 72
slow
25-28
moderately 20-40
4-7
17-24
> 72
>72
20 in
9 in
slow
5 in
slow
12-20
2-3
8-14
l4in
slow
48-60
9-12
Table 5-1 Characteristics of Soils of HEL Farms
Soil
Taxanomy
(hydrologic group)
Santiam
Aquultic
Haploxeraif
C
Silverton
Pachic Ultic
Argixerolls
Drainage Landscape
class
position
MWD
terraces above
the Willarnette
Valley floor
Parent
Material
WD
fine-textured
material that
Contains gravel
C
Stayton
Lithic Umbric WI)
Vitrandeprs
I)
Steiwer
Ultic
Haploxeroll
WI)
Aquic
SWPD
dissected
terraces,
footslopes of
low foothills
foot slopes,
drainageways
of red foothills
low foothills
silty allivium
over claycy
alluvium
Surface
Layer
v.dark grayish
Thick- Perme-Effective AWC
ness
ability
17 in
moderately >40
8-11
slow
20-26
brown. dark
brown silt loam
dark brown
silt loam
root depth
7 in
slow
20-40
5-7
alluvium underlain black silt loam
by besalt
17 in
moderate
15-20
2-4
weathered
sedimentary rocks
vdark grayish
brown silt loam
15 in
moderately 20-40
ridges and
smooth low
foothills
low, broad
valley terraces
residuum and
colluvium from
sedimentary rocks
silty alluvium
dark brown
11 in
very slow
12 in
moderate
low foothills
residuum and
colluvium from
sedimentary rocks
silty alluvium
8 in
moderately 20-40
max
w.table
24-36
3.5-8
16-20
> 72
20-36
4-7.5
14-20
12-24
> 60
12-24
> 72
5-7.5
16-20
> 72
11-13
24-36
slow
C
Suver
Haplohumult
1)
Willamette
Pachic Ultic
Argixerolls
WD
Willakenzie Ultic
Haploxeraif
WD
B
C
Woodburn
C
Aquultic
Argixecoll
MWD
terraces above
the \Telley
tloodp lain
silty clay loam
v.dark
grayish-brown
silt loam
dark reddish
brown silty
clay loam
v,dark grayish
brown. dark
brown silt loam
Slow
17 in
slow
> 60
20-26
67
cause low erosion rates. Relatively more Polk County producers were producing small
grains, which cause substantially higher erosion.
A small number of producers in both counties after compliance plant grains
earlier than before in order to stimulate more green growth prior to the winter rains.
Some grass producers stopped burning grass or started planting cover crops instead of
summer fallowing in the effort of maintaining a certain amount of residues on their lands
all year around.
Ten percent of the survey respondents stated that they increased the use of
chemical pesticides, usually Roundup in the spring as part of their reduced tillage system.
Thirty percent of producers in Polk County claimed that the yield of their crops had
Table 5.2 Survey Result Summary: Percentage of Producers who Have Made Various
Farming Adjustments and Have Observed the Change in Yield
Total Number
POLK
MARION
30 farms
28 farms
Tillage
53%
54%
Rotation
47%
11%
Planting Date
17%
0%
Cross Sloping
47%
29%
Fertilizer
7%
11%
Pesticide
13%
21%
Machinery
20%
14%
Other
0%
21%
Yield
30%
7%
68
decreased as a result of the new field operations, while only seven percent of producers
in Marion County mentioned the decline in their crop yields.
5.2 Representative Farms
From these survey results, combined with the corresponding compliance plans,
five representative 1-IEL farms were developed; three for Polk County and two for Marion
County. All the HEL fields were categorized into these five representative farms based
on the crop rotation before implementing the conservation compliance plans. Soil types
and slope of the fields were then used to divide each group of HEL fields into several
different tracts.
5.2.1 Soil Grouping
The 18 soils specified in section 5. 1.1 were grouped into seven model soils
according to their parental material, drainage class, landscape position and other
characteristics. A representative soil was selected to represent each soil group. This
grouping was done to reduce the number of soils to be considered and to reduce the
number of EPIC runs required.
Beilpine, Jory, Nekia, and Salkum are red soils located mainly in the higher
elevations and could have been represented by one soil, Jory. However, the use of Jory
soil to represent Nekia and Belipine soils would have overestimated the value of the
water holding capacity of the latter soils, since the soils have considerably lower root
69
depth than Jory (Huddleston, 1995). Thus, Nekia and Beilpine soils are grouped to form
one soil type, while Jory and Salkum are grouped to form an another.
Foothill well-drained soils consist of Chehulpum, Rickreail, Steiwer, and
Willakenzie. Hazelair, Helmick, Suver, and Dupee are somewhat poorly drained, foothill
soils. Silty soils in the valley terraces include Willamette. Woodburn, Silverton, Stayton,
Santiam, and Helvetia. Root depth is generally lower for Chehulpum and Rickreall (less
than 20 inches) than for Steiwer and Willakenzie (between 20 to 40 inches), thus
necessitating two different soil groups. Silverton has less than 20 inch root depth, while
Stayton has 20-40 inches of depth. They are thus added to the two groups described
above, respectively. Willamette separates itself from the remaining terrace soils because
of its deep water table (more than 72 inches) and different bulk density (Huddleston,
1995).
The seven model soils representing each seven groups are: Nekia, Jory,
Chehulpum, Steiwer, Hazelair, Willamette, and Woodburn (Table 5.3). The selection of
the representative soil in each group was made by the number of farms cultivating the
particular soil.
5.2.2 Description of Representative Farms
Specific characteristics of representative HEL farms are described in Table 5.4.
The percentage of soil type and slope which makes up each farm is derived from the
distributions of soil type and slope in each farm, as shown in Table 5.5. The number of
70
Table 5.3 Classification of Soils in the Study Area
Model Soil
Soils in the Category
Chehulpum
Chehulpum, Rickreall, Stayton
Hazelair
Ha.zelair, Helmick, Suver, Dupee
Jory
Jory, Salkum
Nekia
Nekia, Belipine
Steiwer
Steiwer, WiHakenzie, Silverton
Willamette
Willamette
Woodburn
Woodburn, Santiam, Helvetia
survey respondents whose farms contain the specific soil type and slope is described in
parenthesis next to each soil type-slope.
Farm A through C were assumed to be located in Polk County. Farm A produces
winter wheat continuously on 16%-slope Nekia, Woodburn, and Helmick soils. Farm B
has a rotation of winter wheat and annual ryegrass seed on 8%-slope Steiwer, Woodburn,
and Helmick soils, while farm C has a rotation of winter wheat and tall fescue seed on
16%-slope Chehulpum, Steiwer, Woodburn, and Helmick soils.
Farms D and E are located in Marion County. Farm D has a rotation of winter
wheat and vegetable crops, and Farm B has a rotation of winter wheat and perennial
iyegrass seed. Farm D has 8%-slope Nekia and Willamette soils and 16%-slope Jory,
Helmick, and Woodburn soils, while Farm E has 8%-slope Jory and 8%- and 16°/o-slope
Nekia soils.
71
Table 5.4 Representative Farms
Rotation
Soil Type
Slope
Pct. of
total HEL
Farm A
Polk
continuous small grain
(winter wheat)
Nekia
Helmick
Woodburn
16%
16%
16%
50%
25%
25%
Farm B
Polk
small grain, grass seed
(winter wheat, annual
ryegrass seed)
Steiwer
Helmick
Woodburn
8%
8%
8%
50%
25%
25%
Farm C
Polk
grass seed, small grain
(tall fescue seed,
Chehulpum
Steiwer
Helmick
Woodburn
16%
16%
16%
16%
small grain, row crops
(irrigated)
(winter wheat, sweet
corn, bush beans)
Nekia
Jory
Helmick
Willamette
Woodburn
8%
16%
16?/
8%
8%
25%
25%
grass seed, small grain
(perennial iyegrass
seed, winter wheat)
Nekia
Nekia
Jory
8%
16%
8%
65%
25%
winter wheat)
Farm D
Marion
Farm E
Marion
15%
50%
10%
25%
12.5°/G
12.5%
25%
10%
5.23 Adjustments to the Farming Operations of Representative Farms
Table 5.6a through Table 5.6e list various farming operations of each
representative HEL farm. The first row of each table represents the pre-compliance
farming operation of each corresponding farm. The second row and thereafter represent
possible combinations of adjustment to farming practices resulting from compliance.
The first column of Table 5.6a and 5.6d and first two columns of Table 5.6b, 5.6c, and
5.6e represent the tillage system used in producing a crop specified in parenthesis. Next
72
Table 5.5 Distributions of Soil Type and Slope for Different Farm
Soil Type-Slope (Number of Farms with the Soil)
Farm A
Nekia-8% (I), Nekia-16% (I), Woodbum-16% (1), Suver-l6% (1),
l-lelmick-16% (2), Steiwer-16% (2), Chehulpum-8% (1),
Chehulpum-16% (1), Helvetia-16% (I), Dupee-16% (1)
Farm B
Santiam-8% (1), Willakenzie-8% (2), Hazelair-8% (1)
Farm C
Woodburn-16% (1), Rickreall-16% (1), Bellpine-16% (1),
Helmick-16% (1), Steiwer-8% (3), Steiwer-l6°/ (2), Suver-8% (1),
Flelvetia-16% (1)
Farm D
Nekia-8% (3), Jory-16% (I), Willamette-8% (1), Woodburn-8% (I),
Salkum-16% (1), Hazelair-I6°/4 (1)
Farm B
Nekia-8% (10), Nekia-16% (6), Jory-8% (2), Jory-16% (I),
Silverton-8% (2)
columns specify the planting date for winter wheat and the indication for cross slope.
The last column is the ID or identification code, which is assigned to each farming
operation system containing specific tillage practices and planting date, as described to
the left of this column in the same row.
The pre-compliance field operations for each farm were estimated using the crop
enterprise budgets (OSU Extension Services). Because crop budgets represent typical
fanning practices of a particular crop on productive soils, the practices for the 1-IEL
representative farms of the study were slightJy modified. For example, only a half of the
amount of fertilizers were applied to winter wheat produced on relatively unproductive
soils such as Chehulpum, Helmick, and Steiwer. Field operations of post-compliance
cases, on the other hand, are what were stated in the survey, supplemented with the
practices listed in the developed plans themselves. Listed in Tables 5.6a through 5.6e are
73
different combinations of tillage practices, cross-slope tiliage, and planting date
evaluated in the study.
Table 5.6a Farming Operations for Representative Farm A
Tillage
(wheat)
Planting Date
(wheat)
Conventional
Conventional
Conventional
Conventional
Conventional
Conventional
Reduced Till
Reduced Till
Reduced Till
Reduced Till
Reduced Till
Reduced Till
No Till
No Till
No Till
No Till
No Till
No Till
10/15
10/15
10/1
10/1
9/20
9/20
10/15
10/15
10/1
10/1
9/20
9/20
10/15
10/15
Cross Slope
ID
no
acO3
yes
no
yes
no
yes
no
yes
no
yes
acl3
10/1
10/1
no
yes
no
yes
no
yes
9/20
9/20
no
yes
acO2
acl2
acOl
ad 1
arO3
arl3
arO2
arl2
arOl
an 1
anO3
anl3
anO2
anl2
anOl
an!!
74
Table 5.6h Farming Operations for Representative Farm B
Tillage
(wheat)
Conventional
Conventional
Conventional
Conventional
Conventional
Conventional
Conventional
Conventional
Reduced Till
Reduced Till
Reduced Till
Reduced Till
Reduced Till
Reduced Till
Reduced Till
Reduced Till
No Till
No Till
No Till
No Till
No Till
No Till
No Till
No Till
Tillage
(annual ryegrass)
Burn! No Till
Burni No Till
Burn! No Till
Burn! No Till
Flail/Conventional
Flail/Conventional
Flail/Conventional
Flail/Conventional
Burn! No Till
Burn/No Till
Burn! No Till
Burn! No Till
Flail/Reduced Till
Flail/Reduced Till
Flail/Reduced Till
Flail/Reduced Till
Burn! No Till
Burn/No Till
Burn! No Till
Burn! No Till
Flail/Reduced Till
Flail/Reduced Till
Flail/Reduced Till
Flail/Reduced Till
Planting Date
(wheat)
Cross Slope
10/15
10/15
9/20
9/20
10/15
10/15
no
bcO3
yes
no
yes
no
yes
no
yes
no
bcl3
9/20
9/20
10/15
10/15
9/20
9/20
10/1 5
10/15
9/20
9/20
10/15
10/15
9/20
9/20
10/15
10/15
9/20
9/20
ID
beOl
bcl 1
bcO3b
be I 3b
bc0 lb
bcllb
brO3
yes
no
yes
no
yes
brl3
no
yes
no
yes
no
yes
no
yes
no
yes
brOib
brOl
brl 1
hr03h
brl3b
brl lb
bnO3
bnl3
bn0i
bnl I
bn03b
bnl3h
bn0lb
bn 11 b
75
Table 56c Farming Operations for Representative Farm B and C
Tillage
(wheat)
Conventional
Conventional
Conventional
Conventional
Conventional
Conventional
Conventional
Conventional
Reduced Till
Reduced Till
Reduced Till
Reduced Till
Reduced Till
Reduced Till
Reduced Till
Reduced Till
No Till
No Till
No Till
No Till
No Till
No Till
No Till
No Till
Tillage
(Tall Fescue)
BurniConventjonal
Burn/Conventional
Burn/Conventional
Bum/Conventional
Flail/Conventional
Flail/Conventional
Flail/Conventional
Flail/Conventional
Burn'Reduced Till
Bum/Reduced Till
Burn/Reduced Till
BurnlReduced Till
Flail/Reduced Till
Flail/Reduced Till
Flail/Reduced Till
Flail/Reduced Till
Burn/Reduced Till
Burn/Reduced Till
Bum/Reduced Till
Burn/Reduced Till
Flail/Reduced Till
Flail/Reduced Till
Flail/Reduced Till
Flail/Reduced Till
Planting Date
(wheat)
Cross Slope
10/15
10/15
9/20
9/20
10/15
10/15
9/20
9/20
10/15
10/15
9/20
9/20
10/15
10/15
9/20
9/20
10/15
10/15
no
yes
no
ccO3
yes
no
yes
no
yes
ccl I
9/20
9/20
10/15
10/15
9/20
9/20
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
ID
ccl3
ceO I
ccO3b
ccl3b
ccOlb
ccl lb
cr03
cr13
cr01
cr11
cr03h
crl3b
cr0 lb
cr1 lb
cnO3
cnl3
enO 1
cnl I
cn03b
cnl3h
cnOlb
cnl lb
76
Table 5.6d Farming Operations for Representative Farm D
Tillage
(wheat)
Conventional
Conventional
Conventional
Conventional
Reduced Till
Reduced Till
Reduced Till
Reduced Till
No Till
No Till
No Till
No Till
Planting
Date
(wheat)
10/15
10/15
9/20
9/20
10/15
10/15
9/20
9/20
10/15
lO/lS
9/20
9/20
Cross Slope
ID
no
yes
dcO3
no
dcOI
yes
no
yes
no
yes
no
yes
no
yes
dcli
dcl3
drO3
dri3
drOl
dri I
dnO3
dnl3
dnOl
dnll
77
Table 5.6e Farming Operations for Representative Farm F
Tillage
(wheat)
Conventional
Conventional
Conventional
Conventional
Conventional
Conventional
Conventional
Conventional
Reduced Till
Reduced Till
Reduced Till
Reduced Till
Reduced Till
Reduced Till
Reduced Till
Reduced Till
No Till
No Till
No Till
No Till
No Till
No Till
No Till
No Till
Tillage (Perennial
Ryegrass)
Burn/Conventional
Burn/Conventional
Burn/Conventional
Burn/Conventional
Flail/Conventional
Flail/Conventional
Flail/Conventional
Flail/Conventional
BurniReduced Till
BurnfReduced Till
Burn/Reduced Till
Burn/Reduced Till
Flail/Reduced Till
Flail/Reduced Till
Flail/Reduced Till
Flail/Reduced Till
Burn'Reduced Till
Burn/Reduced Till
BurniReduced Till
Burn/Reduced Till
Flail/Reduced Till
Flail/Reduced Till
Flail/Reduced Till
Flail/Reduced Till
Planting
Date
(wheat)
10/15
10/15
9/20
9/20
10/15
10/15
9/20
9/20
10/15
10/15
9/20
9(20
10/15
10/15
9/20
9/20
10/15
10/15
9/20
9/20
10/15
10/15
9/20
9/20
Cross Slope
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
1D
ecO3
ecl3
ecO I
eel I
ec03b
ecl3b
ecOib
ec jib
erO3
erl3
cr01
cr11
er03b
erl3b
cr01 b
cr1 lb
enO3
enl3
enOl
eni 1
enO3h
enl3b
enOib
enl lb
78
5.3 EPIC Simulation Results
Although EPIC provides various outputs, only those related to soil erosion and
water pollution are considered in this study. These outputs are soil loss, organic nitrogen
in sediment, nitrate runoff, nitrate leaching, phosphorus in sediment, phosphorus runoff,
pesticide in sediment, pesticide runoff, and pesticide leaching. Phosphorus leaching is
excluded from the evaluation since it is relatively unimportant as described in chapter 2.
In this section, results from the EPIC simulation runs for alternative farming operations
on representative HEL farms are provided and evaluated.
5.3.1 Pesticide Index
It can be difficult to compare the environmental damages due to pesticides caused
by different crop production and management system, since there are a variety of
pesticides available in the market. For the purpose of the study, a pesticide index was
constructed which considered the degree of toxicity of each pesticide used by producers.
Toxicity values were assigned to each pesticide for runoff, in sediment, and leaching
from table 5.7. The specific values are described in Table 5.8a and 5.8b. The idea of
these value assignment was taken from Teague and Mapp (1994).
In calculating the pesticide index, the toxicity value for each pesticide was
multiplied by the amount of pesticide leaving the farm due to runoff, leaching, or binding
to the sediment and summed across all the pesticides. For pesticide runoff and pesticide
in sediment, the toxicity to fish LC50 is used as the medium of determining the value
assigned, while the mammalian toxicity, Oral LD0 (Legal Dose 50), is used as the
79
Table 5.7 Pesticide Characteristics
Trade Name of
Pesticide
Atrazine
Banvel
Basagran
Bayleton
clopyralid
diazinon
dual
goal
hoelon
karmex
MCPA
nortron
ridomil
roundup
rovral
sencor
sevin
slug-bait
tilt
treflan
2-4, D
Type of
Pesticide
Herbicide
Herbicide
Herbicide
Fungicide
Herbicide
Insecticide
Herbicide
Herbicide
Herbicide
Herbicide
Herbicide
Herbicide
Fungicide
Herbicide
Fungicide
Herbicide
Insecticide
Moliuscide
Fungicide
Fungicide
Herbicide
Oral LD50
Fish LC50 (ppm)
(mg/kg) rat
trout
1,780
2,620
2,063
1,000
>5,000
300-400
2,780
>5,000
512
3,400
1,160
6.400
5,189
5,000
> 4,400
1,100-2,300
246-283
630
1517
> 10,000
699
(ST)1°
35 (ST)1
>100 (PNT)1
17.4 (ST)1
toxic (HT)1
-
0.2-0.4 (HT)'
toxic (MT)1
3.5 (MT)1
117 (PNT)
15 (ST)1
(PNT)1
(PNT)1
6.7 (MT)1
64 (ST)1
28 (MT)'
(PNT)'
(PNT)'
(MT)'
377
Source: Farm Chemicals Handbook. 1993. Willoughby, Ohio: Meister Publishing
Company.
medium of determination for the pesticide leached below the root zone. Oral LD50 is
defined as the amount of chemical that kills 5 0/ of the sampled pests, while LC50 is
defined as the median iethal concentration of the chemical in air or water (Faim
PNT - practically non toxic; ST - slightly toxic; MT - moderately toxic; HT- highly
toxic
The data were not provided in the source indicated. For the purpose of the study, the
toxicity values of the pesticides were considered as PNT.
80
Chemicals Handbook. 1993). Oral LD50 of Karmex, for example. is 3,400 mg/kg, while
its LC50 is 3.Spprn. This indicates that 50% of the sampled pest, say rats, would die if
3,400 mg/kg of Karmex were fed to them, while 50% of sampled fish, say trout, would
die if 3.5 ppm of Karmex were introduced in its resident waters.
Table 5.8a Toxicity Value Assigned for Pesticide Runoff and
Pesticide Remaining in the Sediment
Oral LD50
Runoff
Sediment
below 1,000 mg/kg
5
5
1,000 - 2,000 mg/kg
3
3
above 2,000 mg/kg
I
Table 5.8b Toxicity Value Assigned for Pesticide Leaching
Fish LC50
Leaching
below I ppm, HT
5
I-30ppm,MT
3
above 30, ST, PNT
I
Specifically, Pesticide Index (P1) for each farm is calculated as follows:
Plrunoff
==5xR5±3xR3-- I xRl
PT sediment
= 5 x S5 + 3 x R3 + I x S5
P1 leaching
= 5 x L5 + 3 x L3 -r 1 x Li
81
where Ri, R3, and R5 represent the total amounts of applied pesticides with runoff
toxicity values 1, 3, 5, respectively, while Si, S3, and S5 represent the amounts of
applied pesticides with sediment toxicity values 1, 3, 5, respectively. Similarly, LI, L3,
and L5 are the pesticide amounts with leaching toxicity values 1,3, and 5 respectively.
5.3.2 Pre-Compliance Situations
The results of EPIC simulation runs of pre.-compliance farming practice for five
rotations of the representative HEL farms are described in Table 5.9 and constitute the
basis of comparison. Overall, the continuous wheat rotation of Farm A causes the largest
soil erosion and deposits the most nitrogen, phosphorus, and pesticides in the sediment.
The wheat-corn-bean rotation of Farm D and the wheat-perennial ryegrass seed rotation
of Farm E cause the most nitrate leaching. The wheat-corn-bean rotation, in addition,
has the highest value for the amount of pesticide leached below the root zone. The
wheat-fescue seed rotation of Fa
C is the second largest contributor to erosion and
associated nutrients and pesticides in the sediment. The wheat-annual ryegrass seed
rotation of Farm B has relatively small erosion mainly because the farm is located on the
8% slope instead of 16% slope as in case of most other farms. Runoffs of nitrogen and
phosphorus are small in amount, hut the slightly high amount of nitrogen runoff is
observed for Farm B, compared to other farms.
Erosion and other environmental outputs and yields differ among soils and
degrees of slope even for the same rotation. Erosion is lower by five tons per acre for
Nekia soil than for Helmick soil on the same slope producing continuous wheat.
Table 5.9 Per Acre Crop Yields and Environmental Outputs for Pre-Compliance Situation
Rotation
soiU2/slope USLE13
NSE13(lb) NRU13(lb) NLE°(lb) HSE'3(lb) FtRU°(Ib) PRU'3
PLE13
PSE13
Yield]13
WW
farm A
wood/16%
12.15
59.25
2.12
53.56
7.56
0.71
1348
0.02
239
92.67
helm/16%
neki /16%
16.63
59.52
3.06
28.34
7.66
0.96
2017.5
305
51.2
11.5
75.74
1.65
43.95
9.6
0.51
1527.9
007
006
250
57.97
WA
woodl8%
3.12
16.22
3.63
76.79
2.14
farmB
0.61
496.01
0.21
66
95.93
1.161
helrn/8%
4.06
15.33
5.84
59.66
2.03
0.82
714.4
1.45
69
50.43
0.858
0.751
Yie1d213
Y1e1d313
Yield4'3
stei /8%
3.81
16.56
3.07
86.77
2.21
070
610.61
0.12
66
44.77
WF
wood/16%
6.16
30.54
2.19
55.36
393
farmC
0.59
673.11
0.03
104
97.04
1.047
helni/16%
8.7
31.95
3.71
43.93
4.17
0,86
1044.4
0.19
145
59.49
0.737
stei /16%
8.05
32.99
2,43
71.26
4.32
0.81
1072
1.92
189
50.87
0.562
cheh /16%
8.61
32.49
3.38
82.07
4.3
1.33
1528.8
39.73
207
32.86
0.293
wood/8%
2.65
12.47
0.14
104.38
1.91
0.22
1.02
29.76
62
95.91
6.62
10.51
helm/16%
will /8%
jory /16%
neki /8
jory /8
neki /8
neki /16
13.21
46.46
0.31
65.71
7.32
0.88
22.87
30.13
185
42.14
4.2
7.31
2.94
13.87
0.05
129.43
2.11
0.11
1.42
9.04
65
95.17
6.14
7.91
12.97
63.9
0.48
63.18
9.63
1.32
43.08
7.82
259
55.2
3.3
5.74
3.5
22.44
0.09
117.05
3.19
0.26
2.51
46.06
63
53.29
5.96
5.74
2.51
14.57
2.85
66.43
1.86
0.41
941.41
0
40
84.49
0.685
2.31
16.62
2.22
112.38
2.19
0.3
907.94
0.02
64
73.15
0.623
45
37.33
2.56
100.37
4.74
0.35
969.16
0.81
141
73.07
0.617
WCB
lanD
WR
faninE
12
Woodbum: wood; Flelmick: helm; Nekia: rieki; Steiwer: stci; Chehulpuni cheh; Willarnctt: will
USLE.
erosion (ton), NSE: organic nitrogen in sediment; NRU: nitrate runofi NLE: nitrate leaching; HSE: phosphorus in sediment; HRU: phosphorus
runoff, PSE: pesticide in sediment; PRU: pesticide runoff; and PLE: pesticide leaching; YieldI-4: Wheat (bushel), Grass Seed (ton), Bean (ton), and
Corn (ton), fespeclively
83
Chehulpum soil in the wheat-fescue rotation discharges much more pesticide from the
farm than other soils in the rotation. The large amount of nitrate leaching as well as
pesticide leaching for the soil is probably caused by its shallow root depth. In case of the
wheat-corn-bean rotation, leaching is greater for soils with less slope than those with
higher slopes. Runoff and chemicals in sediment, on the other hand, are less for the
former soils. Nekia with 8% slope has erosion and associated nutrients in sediment at
half the amount that Nekia with 16% slope has in wheat-pereimial Iyegrass rotation, but
more nitrate is leached from the soil with a 8% slope. Yields, especially for wheat, differ
significantly among soils as predicted.
The same soils farmed with different rotations also produce different quantities of
erosion and other pollutants. Helmick with a 16 % slope has erosion rates of 16.63, 8.70,
and 13.21 tons per acre for continuous wheat, wheat-fescue, and wheat-corn-bean
rotation, respectively, while the the amounts of nitrate leaching of these three rotations
are 28.34, 43.93, and 65.71 lbs per acre. Pesticide leaching rates for these rotations also
valy. Nekia with a 1 6°/a slope in continuous wheat rotation has the erosion of 11.45 tons
and 43.95 lbs of nitrate leaching. The same soil with same slope has only 5.45 tons of
erosion, but has 100.37 lbs of nitrate leaching in the rotation of wheat and perennial
ryegrass seed.
5.3.3 Potential Farming Adjustments
The pre-compliance results suggest that a site specific soil type, slope, and
rotation generates significantly different amounts of various environmental pollutants
84
and yields. Difference in farm characteristics cause alternative farming operations on
HEL farms to react uniquely to the emission of environmental outputs and yields. The
effects of potential farming adjustments described in Table 56a through 5.6e on different
environmental services are evaluated for each farm in the next section.
5.3.3.1 Case of Continuous Wheat Rotation in Farm A
Reducing the frequency and intensity of soil disturbance decreases the amount of
soil erosion and associated nitrogen, phosphorus. and pesticides in the sediment.
Changing the tillage practice from conventional (moldboard plow and 3 passes of disc) to
reduced tillage (chisel plow and I pass of disc) cuts erosion by 45-60%, while changing
from the conventional practice to no-tiliage (no use of plow and disc) reduces erosion by
over 80% for all soils in Continuous winter wheat rotation (Table C.1 .1 through C.1 .3).
There is a slight reduction in nitrate leaching rates as well, when fewer tillage
operations are used for most soils. The amount of pesticide leached from the Farm A is
very small and is not a large contributor to water pollution.
In addition to the change in tillage practice, early planting also reduces soil
erosion drastically on the land in a continuous wheat rotation. When wheat is planted on
September 20th instead of the more common date, October 15th (for the Willamette
Valley), erosion is decreased by more than a half for all the soils. This is primarily due
to the greater ground cover which protects soil from eroding during the critical rainy
winter months. This practice is as effective in reducing erosion as reducing the number
of tillage operations. When reduced tillage is used together with early planting, the
erosion rate is even lower: about 85°/c reduction in case of reduced tillage with a
September 20 planting for all soils, and 95% reduction in case of no-till with September
20 planting.
Early planting affects the production of other environmental products as well.
The nitrate leaching decreases when the planting is moved to an earlier date. The
reduction in nitrate leached varies among soils from 1 S°/o to 50% when wheat is planted
on September 20th instead of October 15th. There are in addition slight reductions in
pesticide and nutrients runoff
5.3.3.2 Case of Wheat-Annual Ryegrass Rotation in Farm B
Different straw management methods for annual ryegrass affects the soil loss
from the field, especially between open field burning and no burning (flail) methods
(Table C.2. I to C.2.3). The nutrients and pesticides in the sediment decrease, but there is
little effect from different straw management methods on other environmental outputs.
The erosion rate slightly increases when wheat in the rotation is tilled less frequently and
intensively when the annual ryegrass is produced with the burning methods. The rate
decreases, however, when the annual ryegrass is baled.
The results of different rotation for Farm B are provided in Table C.3.l to C.3.3.
The erosion rate for rotation with established fescue seeds is lower than the rate for the
annual ryegrass-winter wheat rotation. The perennial grass seeds are better suppliers of
86
winter cover. The leaching rate, however, increases when moving from more erosive
rotation to a less erosive one, especially when perennial grass is burned.
5.3.3.3 Case of Wheat-Fescue Rotation in Farm C
Erosion is slightly higher for the land which is burned (Table C.4. 1 to C.4.4),
This is true even when the no till practice for wheat is used in combination with burning
of fescue straw. Conventional or reduced tillage for wheat and fescue is used in
combination with haling straw. The difference in erosion is largest in the case when no
till winter wheat is used for both field burning and baling of fescue. When straw is baled
instead of burned, a large reduction in nitrate leaching is observed, especially for welldrained soils.
Woodburn, Helmick, and Jory soils enjoy a substantial reduction in leaching, up
to 82% less of the open burning practice. Since larger amounts of pesticides are applied
to field when baling is used, there is more off-field movement of pesticides.
5.3.3.4 Case of Wheat-Corn-Beans Rotation in Farm D
Only a slight change in soil loss and the amount of environmental outputs
produced is observed among the various alternative operations (Table C5. 1 to C.5.5).
This can be attributed to the fact that the farming operations for corn and bean are not
changed.
87
5.3.3.5 Case of Wheat-Perennial Ryegrass Rotation in Farm E
Erosion and associated nutrient losses decrease as grass straw is baled instead of
burned as was the case with Farm C (Table C.6. I to C.6.3). Reduction is
larger for soils
with greater slopes. A large reduction in nitrate leaching is observed when straw is
baled; over 80% reduction for Jory soil, while 60% reduction for Nekia soil. This is
important since leaching seems to be a large problem for Farm E (Section 5.3 3). As
described earlier, because more pesticide is necessary when the field is not sterilized
through burning, burning always reduces the amount of pesticides leaving the farm
5.4 GAMS Niaximization Result
It is assumed in the following analysis that producers want to maximize their
income subject to the production and environmental constraints they face. Each
representative farm is constrained by the soils it possesses and the production techniques
that are available. In this section, the performance of conservation compliance and
alternative policies is evaluated by using linear programming optimization method.
5.4.1 The Effects of Compliance Provision
The optimal farming operations and corresponding environmental outputs for preand post-compliance situations are obtained by maximizing net return for each farm. Net
return here is defined as the gross income minus risk and management returns (RMR)
and all production costs except land rent. It is thus pure land rent as described earlier in
Section 45.2. Land rent estimates obtained from Pease (1995) for each soil type on the
specified study location are pre-compliance net returns and are described in Table 5-10.
All production costs are obtained from enterprise budgets. The percentage of RMR of
pre-compliance or unconstrained situations for each farm is calculated and specified in
Table 5.11. Table 5. 12a and 5. 1 2h describe the optimal field operations and level of
environmental products and net return for pre- and post-compliance cases, respectively.
Table 5.10 Land Rent Estimates for Representative HIEL Farms
Farm
Rent Estimates
A
43
nI-)
C
32
D
69
E
51
Table 5.11 Percentage of RM Returns with repsect to Gross Income
Farm
RM Returns (%)
A
-2.9
B
20.3
C
8.1
Li
-..
E
3.5
89
5.4.1.1 Profit Maximizing Behavior of Farm A
Farm A has a pre-compliance land rent of 43 dollars per acre (Table 5-12a).
When a T-value constraint is imposed on each soil, the net return goes down to $18.31
per acre (Table 5.1 2b). About a third of the HEI. acreage uses reduced tiflage while the
other two-thirds uses no-tiflage practices. Winter Wheat is planted on September 20th in
Table 5. l2a Optimal Field Operations and Corresponding Fifteen-Year Averaged
(Per Acre) Net Return and Environmental Outputs: Before Compliance
Farm
Net Return ($)
USLE
(ton)
NRU NSE
A
43
12.92
B
35
C
NLE
(ib)
HRU
(ib)
HSE
2.12 67.56
42.20
8.60
3.70
390 16,17
77.50
32
7.72
2.64 32.20
D
69
6.80
E
51
3.12
PRU
PLE
PSE
ID
acre
672
1605.32
005
420.00
acO3
100
2.15
0.71
607.91
0.48
66.75
bcO3
100
66.17
4.21
0,84
884.98
7,88
304.86
ccO3
100
0.22 32.25
95,55
4.86
0.57
14.69
19.94
127.25
dcO3
100
237 2159
10478
2.74
0,33
926.54
021
82.85
ecO3
100
(tb)
(Ib)
(Ib)
Table 5. 12h Optimal Field Operations and Corresponding Fifteen-Year Averaged
(Per Acre) Net Return and Environmental Outputs: After Compliance
(Meeting T constraint)
Farm
A
Net Return (5)
18.31
USLE NRU NSE
(ton)
(Jb)
1.70
3.16 9.46
(Ib)
HIRU HSE
(lb)
(Jb)
NILE
(Ib)
31.96
1.22
0.76
PRU
PLE
PSE
1526.02
0.05
56.42
ID (acre)
arOl(37), anO3(30),
a.nOl(3)
B
22.54
1.25
4.12
5.67
75.13
0.75
C
1.10
578.57
0.27
17.51
bcOl(21),bn03b(79)
infeasible
D
-226.80
1.88
0.12
9.21
47.82
E
29.42
1.16
2.34 8.29
64.44
I
1.28
0.29
8.23
9.17
19.78
th11(54), c03(46)
1.06
0.58
1068.09
0.10
26.29
erll(46), enO3b(54)
90
the reduced tilled lands, while it is planted on either September 20th or October 15th in
the no-tillage lands. The erosion rate is substantially decreased from 12.92
tons per acre
to 1.70 tons per acre. The value of each ton of erosion saved, which is calculated
as the
reduction in net return divided by the reduction in soil erosion, is $2.20. Nitrate leaching
would be cut by one-third from 42.20 lbs to 31.96 lbs. All the other environmental
outputs also are reduced.
5.4.1.2 Profit Maximizing Behavior of Farm B
Pre-compliance Farm B has a land rent of $35 per acre (Table 5. 12a). The
erosion is only 3.70 tons per acre because Farm B land is less steep and produces less
erosive crops. With the compliance constraint, almost eighty percent of the farm
changes from conventional tillage practices to no-tillage practices for winter wheat, and
reduced till/flail straw practices for annual ryegrass (Table 5. 12b). The rest of the farm
is still tilled conventionally. hut winter wheat is planted early on this parcel of land. In
meeting the compliance level, the net return is reduced by 35 percent. Erosion is cut
almost to one-third of the original rate. Combined loss of nutrient runoffs and nutrients
in sediment and overall pesticide discharge decreases as well. Little change in the
amount of nitrate leaching is observed.
5.4.1.3 Profit Maximizing Behavior of Farm C
Unconstrained land rent for Farm C is $ 32 per acre (Table 5. 1 2a). It is very low
mainly because of the Chehulpum soil. Chehulpum is categorized as a Class VT soil, and
91
the Polk County land rental estimate of the soil is only 7 dollars per acre. When
producers are required to meet the compliance, the linear programming problem
becomes infeasible. This implies that it is not possible to meet the T-constraint if all the
HEL lands are kept in production. Chehulpum has a T-value of one, making it difficult
for the farm to meet its overall T-value level. It seems that the only solution to this
problem is to stop producing on the Chehulpum soil. However, this is not possible, since
all soils in the farm are assumed to be uniformly and sparsely distributed over the
production acreage in the study. A parcel of land cannot be identified as the land
containing only one soil type. This corresponds to reality. Moreover, even if it was
possible to take Chehulpum acreage out of production, the corresponding amounts of
environmental outputs depend on how the farm is managed on this land.
5.41.4 Profit Maximizing Behavior of Farm D
The wheat-corn-bean rotation of Farm D in a pre-compliance situation has an
initial net return of S 69 per acre. Approximately 6.80 tons per acre of soils leave the
HEL field, and there is 95.55 lbs per acre of nitrate leaching. A large amount of pesticide
is leached below the crop root zone in Farm D, compared to other farms (Table 5. 1 2a).
When compliance T is applied, profit becomes negative. The negative profit, of
course, implies that the farm cannot stay in production in the long run. Thus, while the
solution is feasible, the compliance T places HEL producers a severe financial burden,
and it ultimately forces them to stop producing on their HELs. This is probably the
92
situation when the Alternative Conservation System can be justifiably applied, as
indicated in the Section 4.3.2.
The effects of compliance on the environmental outputs are positive. Erosion is
reduced to less than 2 tons per acre. Thus it is less than a third of the original erosion
rate (Table 5. 12b). The reduced erosion is accompanied by large reductions in all forms
of nutrient and pesticide discharges. Winter wheat on all acres are produced using the
no-tillage system with an early planting date. Corn and beans are produced with reduced
tillage. A little over a half of the land is farmed on the cross-slope.
5.4.1.5 Profit Maximizing Behavior of Farm E
Farm E has a land rent of S 51 when it is farmed conventionally (Table 5.1 2a).
Erosion is only 3.12 tons per acre, but nitrate leaching is as high at 104.78 lbs per acre.
When the T-value constraint is imposed, the net return decreases to $29.64 per acre
(Table 5.12b). A half of the HEL fields produce wheat with reduced till and early
planting on cross slope, and ryegrass seed with a reduced till/burning method. The other
half of the fields produce wheat with no till, and ryegrass seed with reduced till/no
burning method. Erosion decreases to 1.16 tons per acre. Nitrate leaching decreases by
40%.
5.4.2
Alternative Policies
The alternative policies considered for the study are listed in Table 5-13. A
policies consist of various T-constraints, 'B' policies are pollutant reduction targets, and
93
Table 5.13 Proposed Alternative Policies
Policy Name
uncon
Description of the Constraints
unconstrained case
AO
T-values are specified for each soil ranging from 1 to 5
Al
T-values are 5 for all soils
A2
Three-Year Averaged T-values are specified for each soil
A3
Three Year Averaged T-values are 5 for all soils
A4
Five-Year Averaged T-values are specified for each soil
AS
Five-Year Averaged T-value are 5 for all soils
A6
T-value for each soil is multiplied by 1 .5
A7
T-value for each soil is multiplied by 2.0
A8
T=5 for all soils but Chehulpum and T=
Bi
target of reducing overall pesticide discharge by 25% from original
B2
target of reducing overall pesticide discharge by 50% from original
B3
target of reducing overall nitrogen discharge by 25% from original
B4
target of reducing overall nitrogen discharge by 50% from original
B5
target of reducing nitrate leaching by 25% from original
B6
target of reducing nitrate leaching by 50% from original
CI
strict or best alternative T-constraint and Policy B 1
C2
strict or best alternative T-constraint and Policy B2
C3
strict or best alternative T-constraint and Policy B3
C4
strict or best alternative T-constraint and Policy B4
C5
strict or best alternative T-constraint and Policy B5
C6
strict or best alternative T-constraint and Policy B6
for Chehulpum soil
94
combinations of T-constraints and targets are C' policies. Policy AO is the same as the
conservation compliance. Policies Al through A8 are modified T-constraints. B Policies
consist of Policies Bi andB2, which have reduction targets on pesticide discharge:
Policies B3 and B4, which have reduction targets on overall nitrogen discharge; and
Policies B5 and B6, which have reduction targets on nitrate leaching. C policies consist
of Policies Cl through C6, which have some T-constraint in addition to the
corresponding B policies of target constraints.
As indicated in Table 5-13, all the target amounts are set as percentage reduction
in pollutants of interest from the unconstrained problem. Because of the characteristics
of NPS pollution, pollutant emission rates can only be estimated by using nonpoint
production function given the type of farming operations to be used. Thus, targets on
the emission of NPS pollutants can be enforced only indirectly, by regulating the farming
operations which must be used. For this study, the EPIC program was used as a means to
measure the amounts of pollutants emitted from the farm. Targets, however, can be
difficult to enforce. The costs of this type of enforcement are not considered in this
study.
Farming operations used with alternative policies are restricted to those described
in Table 5.6a through 5.6e. Other farming operations could have been introduced,
especially when targeting improvements of other environmental qualities than soil
resource conservation. However, because of limited information on their effects,
however, they were not included in the analysis.
The next five sub-sections discuss the effects of alternative policies on changes in
farming operations, net return, and environmental outputs for each farm. Tables 5. 14a
95
through 5. 14e describe the optimal farming operations and per-acre net return and
enviroirmental outputs for each farm when alternative policies are considered. Figures
5.1 through 5.5 illustrate the cost-effectiveness of various policies in terms of erosion,
nitrogen loss due to surface runoffs, nitrate leaching, and overall pesticide discharge.
5.4.2.1 Evaluation of Alternative Policies: The Case of Fairn A
Five different A policies are introduced and evaluated for Farm A. These are
Policies Al, A2, A3, A4, and AS. Policy Al specifies that all soils have less than S tons
of per-acre erosion. Policies A2 and A3 uses three-year averaged T-values and Policies
A4 and AS uses five-year averaged T-values as the T-constraints to meet. T-values are
specified for each soil in the cases of Policies A2 and A4, while they are five for all soils
in the cases of Policies A3 and AS. According to the alternative T-value restrictions for
Farm A described in Table 5. 14a, the reduction in profits is $14.60 per acre for Policy
Al instead of S 24.69 per acre for Policy A0. Fifty-five acres of Farm A are reducedtilled while forty-five acres are no-tilled. The erosion rate of Policy Al is higher than
that of Policy A0, but is still less than one-fourth of the original erosion rate. Lower
erosion rate as well as higher net return can be obtained with the use of Policies A2
through A5 than with Policy A0 or Al. Nitrate leaching and total pesticide leaving the
field, however, are lower for Policies A0 and Al, compared to policies A2 through AS.
The cost-effectiveness of reducing erosion, nitrate leaching, combined nitrogen
runoff and nitrogen in sediment and overall pesticide discharge from the field is
illustrated in Figure 5. 1 for various alternative policies. When modified T-constraints are
96
Table 5. 14a Fifteen-Year Averaged (Per Acre) Profit, Environmental Outputs, and Field
Operations with Pre- and Post-Compliance and Alternative Policies: Case
of Farm A
Policy
Name
Net
USLE NRU NSE
Return (ton) (ib)
(ib)
NLE
HRU FISE PRU
(Ib)
(Ib)
uncon
43
12.92
2,12
67.56
42.20 8.60
6,72
1605.32
0,05
420.00 acO3 (100)
A0
18.31
1.70
3.16
9.46
31.96
1.22
0.76
1526.02
0.05
56.42
arOI(37),anO3(30),
anOl (33)
Al
28.40
2.95
2.99
16.16
36.39 2.08
0.83
1566.72
0.05
104.83
acOI(31),arl3(24),
PLE PSE
ID (acre)
(ib)
anO3(45)
A2
32.10
2.02
3.44
11.10
37.77
1.44
1,13
1546.58
0.05
79.23
A3
36.13
3.16
3.27
17.18
39.10 2.22
1.08
1565.83
0.05
119.95 arO3(20),anO3(80)
A4
35.08
2.51
3.37
13.74
38.85
1.78
1.13
1557,59
0.05
97.33
AS
36,63
3.97
3.22
18.83
39.22 2.43
1.06
1569.80
0.05
130,80 arO3(26),anO3(74)
Bi
9.61
0.89
3.49
4.95
28.98 0.64
0.82
1487.40
0.05
31.58
anOl(92),anO3(8)
B2
infeasible
B3
42.77
7.57
2.60
40.53
40.78
5.19
0.76
1617.12
0.05
271.64
acO3(6),arO3(94)
B4
35.07
2.51
3.37
13.72
38.84
1.78
1.13
1557.55
0.05
97,24
arO3(7),anO3(93)
B5
19.37
2.98
2.65
16.42
31.65
2.11
0.48
1555.58
0.05
92.59
arOl(84), arO3(16)
B6
infeasible
CI
9.61
3.49
4.95
28.98 0.64
0,82
1487.40
0.05
31,58
anOl(92),anO3(8)
C2
infeasible
C3
35,07
2,51
3.37
13.72
38.84
1.78
1.13
1557.55
0.05
97.24
arO3(7),anO3(93)
C4
35,07
2.51
3.37
13.72
38.84
1.78
1.13
1557.55
0.05
97,24
arO3(7),anO3(93)
Cs
19.00
2.50
2,74
13.87
31.65
1.78
0.53
1549.66
0.05
76,36
arOl(82), arO3(7),
anO3(I 1)
C6
infeasible
0.89
anOl(9), anO3(91)
arO3(7),anO3(93)
0
5
0
Figure 3. I
LI)
Q
0
ft
If)
LI\-'eness
LI)
LY
LI)
ft
ol -theiriialive Po1iies on larm \
llVFIa;di\e Ii licies
LI)
II)
I
LU
C)
F---
U
1
FOsiOfl ( t011 ae
P 1 )ischaie
0 leaching (lb ae)
N RLUIOI1(Ib ie)
I
98
compared with strict T-value restriction, the reduction of erosion is most cost-effective
when Policies A3, A4, and AS are used. Policy A0 is almost three times less cost
effective than the above three modified T-constraints. The abatement cost of nitrate
leaching is similar across all T-constraints, while the cost of reducing overall pesticide
discharge is lowest in the case of Policy AS. Since A2 through A5 also cost-effectively
reduce nitrogen runoff, they seem to be better policies than Policies A0 and Al.
All the environmental pollutants are reduced when B policies are used (except for
B2 and B6 which are infeasible). Field operations with feasible pollutant reduction
targets mostly consist of reduced or no tillage. All acreage with reduction targets of
overall nitrogen discharge (Policies B3 and B4) have October 15 as the planting date.
Most acreage with the feasible pesticide and leaching targets (Policies BI and B5,
respectively) have September 20 as the planting date. No cross slope farming is
performed with the application of any B policy. Policies B3 and B4 are more costeffective than Policy A0 in reducing the emission rates of all types of pollutants. This is
because the reduction in net income is substantially larger when strict T-value is used.
When targets are combined with a T-constraint as in C policies, erosion and
pesticide discharge are less cost-effectively reduced than single targets of B policies.
More acreage is farmed with a no-tillage system. The effects on runoff are similar for B
and C policies
Policy B3, 25% reduction target on overall nitrogen discharge, is the most-cost
effective approach in reducing all pollutants overall. Ninety-four percent of this HEL
farm uses reduced tillage. Moving from conventional to reduced tillage can be done with
99
little income sacrifice, and it greatly reduces the amounts of environmental pollutants
emitted.
5.4.2.2 Evaluation of Alternative Policies: The Case of Farm B
Only one modified T-constraint, Policy Al, is considered in the case of Farm B.
The relaxed T-constrajnt is not very different from the strict T-constraint in terms of its
effects on environmental outputs and net return. The slight increase in net return results
in increased erosion when changing from Policy AU to Al. Policy AU is slightly more
cost-effective in reducing the amounts of pesticide discharge and nitrate leaching than
Policy Al (Figure 5-2).
Meeting pesticide targets is infeasible, but other target policies, B3 through B6,
give feasible solutions. Producers use conventional tillage practice on some acreage for
feasible B policies, while on the remaining acreage, they change their wheat-annual
ryegrass rotation to a rotation of four-year tall fescue followed by one-year winter wheat.
Because of this rotation change, pesticide discharge and nitrogen surface runoff rates
increase from the original unconstrained levels.
Feasible C policies introduce farming operations of no-tillage for wheat, and
reduced tillage and no field burning for ryegrass seed. C policies are more cost-effective
than feasible B policies, but less effective than Policy AU in most cases. The increases in
the amounts of nitrogen runoff and pesticide discharge with C policies also are less than
B policies.
100
Table 5. 14b Fifteen-Year Averaged (Per Acre) Profit, Environmental Outputs, and Field
Operations with Pre- and Post-Compliance and Alternative Policies: Case of
Farm B
Policy
Name
Net
[ISLE NRU NSE
Return (ton)
(Ib)
(Ib)
uncon
35
3.70
3.90
A0
22,54
1.25
Al
28.45
2.49
131
infeasible
B2
infeasible
B3
22.15
2.17
B4
12.65
B5
NLE
T{RU
PLE PSE
ID (acre)
(th)
ElSE
(Ib)
PRU
(th)
16.17
77.50 2.15
0.71
607.91
0.48
66.75
bcO3(00)
4.12
5.67
75.13
0.75
1.10
570.57 0.27
17.51
bcOl(21),bn03b(79)
3.98
10.99
76.81
1.46
0.93
593.23
0.38
44.79
bcO3(57),bnOb3(43)
11.28 8.90
53.00
1.30
21.08
720.28
1.45
23.86
bcOl(73),crOlb(27)
1.57
22.96 4.83
21.01
0.97
54.94 926.15
3.33
23,46
bcOl(28),cr0lb(72)
29.38
3.60
20.47 12,63
58,13
1.77
17.11
748.87
1.53
73.16
bcO3(74),ccO3(26)
B6
23.77
3,52
37.05 9.09
38.75
1.39
33.52
889.84
2.59
79,56
bcO3(49),ccO3(51)
Cl
infeasible
C2
infeasib[e
C3
19.49
1.38
10,27 5.49
57,41
0.84
18,67
692.35
1.27
19,57
bcOl(25),bn03b(52),
cr0 lb(23)
C4
12.65
1,57
22,96 4.83
21.01
0,97
54.94
926,65
3.33
23.46
bcOl(28),cr0lb(72)
C5
21.47
1.86
18.83 5,52
58.13
0.82
15.56
712.61
1.26
35.14
bcOi(25),bnO3b(52),
ccO3(23)
C6
20.24
2.45
34.37 5.36
38.75
0.86
34.43
862.04
2.39
52.11
bcOl(29),bn03b(22),
ceO 1(1 6),ccO3(33)
i(J
10
60
70
j
ligui'L i2 ( stE1
tivns ol f\I1cia1i\; Pohies on laim fl
Iturnat ic ItI!Ci('
i
1n;k:
N leaching ( Ibac)
P 1 )ischaoe
\J l.tui,11 (II) ac)
I iiioii (
102
Policy A0 is the most cost-effective policy in reducing erosion, nitrogen loss to
surface runoff, and overall pesticide discharge. Nitrate leaching is, however, more costeffectively reduced by the any feasible B or C policy. Thus, the most cost-effective
approach in reducing overall environmental damage is undetermined in the case of Farm
B and depends on how much weight is placed on improving each environmental service.
5.4.2.3 Evaluation of Alternative Policies: The Case of Farm C
Two modified T-constraints, Al and A8, are considered on Farm C. Policy A8
implies that T is infinite for unproductive Chehulpum soil, and it is five for other soils in
rotation. The net return is $16.94 per acre. or a 47% reduction from the original practice
with Policy Al (Table 5.14c). All the HEL fields produce no-tilled wheat. Tall fescue
straw only on a third of the field is burned. Erosion and surface nitrogen loss (nitrogen
runoff and nitrogen in sediment) are reduced by more than half the original levels, and
nitrate leaching is reduced by 30%. Some increases in pesticide leaching and runoff,
however, are observed.
For Policy A8, net return of $ 19.45 is obtained with substantially reduced erosion
of 3.51 tons per acre. Other environmental pollutants are reduced except for pesticide
leaching and runoff Wheat is planted on September 20th with no-tillage; tall fescue
seed is produced with reduced tillage and burning, or with no tillage and straw baling.
Erosion is slightly more cost-effectively abated than by Policy Al, but the abatement
costs of all other pollutants are similar for Policies Al and A8.
103
Table 5. 14c Fifteen-Year Averaged (Per Acre) Profit, Environmental Outputs, and Field
Operations with Pre- and Post-Compliance and Alternative Policies: Case of
Farm C
Policy
Name
Net
USLE NRU NSE
Return (ton) (Ib)
(Ib)
uncon
32
7.72
NLE
(ib)
HRU HSE PRU
(Ib)
(Ib)
PLE PSE
ID (acre)
2.64
32.20
66.17
4.21
0.84
88498
7.88
304.86 ccO3(l00)
A0
infeasible
Al
16.94
3.06
2.75
14,00
46.65
1.82
0.93
1033.56
9.36
244.36 cnOl(33),cn0lb(67)
A8
19.45
3.51
2.77
15.94
49.50
2.07
0.90
1002.07
9.65
246.72
cn0lb(54), crOl(46)
B!
infeasible
B2
infeasible
B3
24.51
4.75
2.60
20.42
52.74
2.65
0.78
966.10
8.36
255.63
ccOl(64),cn0lb(36)
B4
5.26
1.86
2.63
8.624
39.26
1.13
0.96
1105.67
8.36
274.73
cnOl(36),cnIl(64)
B5
22.05
4.46
2.62
19.34
49.63
2.51
0.80
1005.75
9.00
254.33
ccOl(48),cnOlb(52)
B6
infeasible
Cl
infeasible
C2
infeasible
C3
16.94
3.06
2.75
14.00
46.65
1.82
0.93
1033.56
9.36
244.36 cnOl(33),cn0lb(67)
C4
5.26
1.86
2.63
8.624
39.26
1.13
0.96
1105.67
8.36
274,73
CS
infeasible
C6
16.94 13.06
2.75
14.00 146.65
1.82
0.93
1033.56
9.36
244.36 cnOl(33),cn0lh(67)
cnOl(36),cnll(64)
11)
-!(:1 (1
'1I N 3
( ': J[) I 1OU I I N
U/tiJ) [IOSOI [
(
)
1t1
ItO
a
I
!U°d \!IIIlJJr\
0/
O9ei
I
() s(t\c ItJ' ) :c JI1!1
p
4
105
Policies BI, B2, and B6 give infeasible solutions. The effects of feasible targets,
B3, B4, and B5, and feasible C Policies of C3, C4, and C5 on the emission rates of
various pollutants are similar to the two modified T-constraints considered above. In
order to meet targets, producers are required to move the planting date of winter wheat
from October 15 to September 20, and they produce some fescue seeds without burning
their straw. Except for pesticide discharge, all the environmental pollutants are costeffectively reduced. The introduction of baling fescue straw instead of field burning
increases the pesticide application rate resulting in an increased pesticide discharge.
5.4.2.4 Evaluation of Alternative Policies: The Case of Farm D
Since farm profit decreases substantially when Policy A0 is used, three alternative
T-values are introduced: Policies Al, A6, and A7. Policy A6 uses the T-value of the
original value multiplied by 1.5 for all soils, while Policy A7 uses the T-value of the
original value multiplied by 2 for all soils. The use of Policy Al does not cause
producers to adjust their farming operations from Policy A0, but Policy A6 or A7 makes
producers change their farming operations. Profit from applying A6 or A7 is higher than
A0. The higher allowable erosion rate of T-value, however, leads to higher rates of other
environmental pollutant emissions. There are large reductions in abatement costs when
moving from strict T-constraint to a more relaxed T-constraint for erosion and pesticide
discharge (Figure 5-4). Little change is observed in abatement cost for the nitrogen
surface off-field movement.
106
Table 5. 14d Fifteen-Year Averaged (Per Acre) Profit, Environmental Outputs, and Field
Operations with Pre- and Post-Compliance and Alternative Policies: Case of
Farm D
Policy
Name
Net
Return
USLE NRU NSE
NLE
HRU HSE PRU
(ton)
(Ib)
(ib)
(Ib)
(Ib)
(Ib)
uncon
69
680
022
3225
95.55
486
057
A0
-22680 1.88
0.12
9.24
47.82
1.28
Al
-226.80 1,88
0.12
9,24
47,82
A6
-65.73
2,83
0.18
13.81
A7
52.70
3,76
0.22
BI
65.06
5.68
B2
56.01
B3
-7.70
B4
-188,12 2,11
PLE
PSE
lID (acre)
14.69
19.94
127.25
dcO3(l00)
0.29
8.23
9.17
19,78
dnhl(54),dnO2(46)
1.28
0.29
8.23
9.17
19.78
dnll(54),dnO2(46)
71.73
1.93
0.44
12.34
13.75
29.67
dnll(81),dnO2(19)
18.68
88.63
2.73
0,57
15.79
17.82
48.21
drOl(9),drll(91)
0.22
27,54
90,92 4.07
0.57
15.02
19.93
86.45
dcOl(69),dcO3(31)
4,28
0.22
21.77
88,52 3.05
0.56
15.44
17.60
47.89
dnOI(83),dnfl(17)
3.16
0.21
15.47
80.34
2.16
0,49
13.82
15.41
33.24
dnll(91),dnO2(9)
10.31
53,56
1.44
0.33
9.211
10.27
22,16
dNll(61),dnO2(39)
L
B5
-54,57
3,95
0.18
19,60
71.66 2.88
0.47
12.68
14.97
51.34
drOI(81),dnO2(19)
B6
-219.36 2.63
0.12
13,07
47.78
1,92
0.31
8.46
9.98
34.23
drOl(54),dnO2(46)
CI
52.70
3.76
0.22
18,68
88.63
2.73
0.57
15.79
17,82
48.21
drOI(9),drIl(91)
C2
52.49
3.74
0.22
18.66
88,62 2,72
0.57
15.77
17.77
47.40
drOl(2),drll(90),
dnOl(8)
I 1OSIOfl (ton k)
t N Runoli (Iba)
N kahing (lb ae)
P 1)ishane
LC)
IL)
.
If)
C)
ft
ft
F-
It(rnattvc tuIicies
Figwe 5.4 (ost-Li1elivencss ot
lema1iv 1>OIIL ies on Farm 1)
IL)
108
Policy B I seems to be the most cost-effective policy overall. This policy has a
target of 25% reduction of overall pesticide discharge from the unconstrained problem.
Both of the target policies of nitrate leaching (Policies B5 and B6) are effective in
reducing leaching, but are not strongly effective in reducing other environmental
pollutants. The only C policies considered are Cl and C2 since the other four targets
face large negative net returns. Policies B 1 and B2 are more cost-effective in reducing
most environmental pollutants than Cl and C2.
5.4.2.5 Evaluation of Alternative Policies: The Case of Farm E
Policy Al eliminates most of the income sacrifice that producers are forced to
face with Policy A0. With almost no change in net return, erosion is two-thirds that of
the pre-compliance situation. Policy Al is more cost-effective than Policy A0 in
reducing all environmental pollutants.
With B policies, most acreage of Farm E is reduced-tilled. In the case of
pesticide discharge targets of Policies B I and B2, net returns are negative. When C
policies are used, some acreage is produced with no tillage operation. Most nitrogen
discharge and leaching targets cost-effectively reduce overall environmental pollutants
except for overall pesticide discharge. When ryegrass straw is baled, the pesticide
application rate increases. Overall, Policy Al seems to be more cost-effective in
reducing all environmental pollutants than alternative policies.
109
Table 5. 14e Fifteen-Year Averaged (Per Acre) Profit, Environmental Outputs, and Field
Operations with Pre- and Post-Compliance and Alternative Policies: Case of
Farm E
Name
Net
Return
USLE NRU NSE
(ton) (Ib) (ib)
NLE
(Ib)
uncon
51
3.12
2.37
21.59
104.7 2.74
0.33
926.54
0.21
82.85
ecO3(100)
A0
29.64
1.16
2.34
8.29
64.44
1.06
0.58
1068,09
0.10
26.29
erOl(46),en03b(54)
Al
50.88
2.12
2.44
15.09
97.44
1.91
0.28
906.90
0.02
36.61
ecO3(4),erOl(96)
BI
-35,96
1,65
2,33
11,74
8016 1.48
0.32
726.00
0.03
30.93
erO2(17),enOl(83)
B2
-165.80 1.10
1.49
7.83
53.44 0,99
0.21
484.00
0,02
20.62 erO2(45),enOl(55)
B3
42,80
1,75
2.35
12,47
81.68
1.58
0.36
975.53
0,01
31.23
erOI(76),erOlb(24)
B4
28,25
1.16
2J6
827
53.90
1.06
0.50
1100.77
0.01
25.35
erOl(34),er0lb(66)
B5
41.38
2.00
2.28
1406
78.59
1,78
0.39
1008.11
0.08
45.36
ecO3b(32),erOI(68)
B6
28,00
1,88
2.05
13.02
52.35
165
0.55
1151.93
0.18
61.15
ecO3b(77),erOl(23)
Cl
-73.60
0.93
2.00
6.75
61.01
0.86
0.38
737.98
0,21
19.01
erO2(23),enll(56),
en0lb(21)
C2
-16801 0.87
1.50
6.27
53.81
0.79
0.21
487.69
0.02
16.95
erO2(45),enOl(8),
en! 1(47)
C3
29.64
1.16
2.34
8.29
64.44
1,06
0,58
1068.09
0.10
26.29 erOl(46),eno3b(54)
C4
27.49
1.17
2.26
8,28
53.79
1.06
0.62
1113.80
0,09
27.96 erOl(30),er0lb(24),
en03b(46)
CS
29.64
1.16
2.34
8.29
64.44
1.06
0.58
1068.09
0.10
26.29
erOl(46),en03b(54)
C6
26.90
1.13
2.23
8,06
52.35
103
0.60
1117.47
0.07
27.06
erOI(29),er0lb(35),
en03b(36)
Policy
HRU HSE PRU
(Ib)
PLE PSE
ID (acre)
(ib)
L1051011 ( tol ac)
uiioll (lb ac )
N (caching (Ui ac
P 1 )ischare
i
E
\Il (rElative IOIICIeS
Figure 5.5 ( sl-1iiectiveiiess ol \ltrnaiive Policies on 1aim F
I
111
6 SUMMARY
This chapter summarizes the results of the study and reviews some of the
limitations of the study. The first section discusses the study results. The next section
provides limitations and extensions of the study.
6.1 Conclusions
This study evaluated the effects of conservation compliance and alternative
policies on five representative HEL farms in terms of farms net return, farming
operations, and environmental output using biosimulation and linear programming
models.
The conclusions of the study are:
To comply with the conservation plans, some producers have changed their
farming operations dramatically, while others have made few or no changes. The main
farming adjustments are crop rotation, tillage operations, and planting date. Some
producers has incorporated cross slope farming as well.
Conservation compliance is found to promote effective erosion control, and in
most cases, it promotes a reduction in other environmental pollutants at the same time,
especially nutrient runoffs and nutrients in the sediments. These environmental benefits
comes, however, at the expense of reduced producers net returns. Often, the use of
modified T-values in place of strict T values reduces producers cost of compliance.
112
Applying these modified T-constraints can reduce the erosion and other pollutants more
cost-effectively.
The use of Alternative Conservation System (ACS) can be justified on most farms
since the net returns of the representative farms are very low or negative making it
impossible to meet strict T-constraint. On highly erodible lands, there are many soils
that are not very productive and have very shallow root depth, such as Chehulpum soil.
When these soils are in rotation, it is difficult to meet strict T-constraints profitably. The
use of ACS, however, comes at the expense of higher pollution rate.
Policy effectiveness depends on the characteristics of each farm. Results
indicate that the policy that is optimal on one farm is not always the optimal policy for
another farm. For Farm A, Policy B4 is the optimal policy, while Al is the best policy
overall for Farm E.
The optimal policy may be difficult to identify even within a single farm. There
are some policies which work well in reducing only particular environmental pollutants.
In the case of Farm B, erosion, nitrogen surface loss and pesticide discharge are most
cost-effectively reduced using Policy AO, while for reducing nitrate leaching, any B or C
policy is better than Policy AO.
There are tradeoffs among the environmental outputs. When a reduction of
one environmental output is targeted, the policy employed sometimes reduces other
environmental outputs but it often increases them. Tradeoffs can also be seen in the
form of change in the degree of the cost-effectiveness in reducing environmental
113
pollutants. A policy which targets the reduction of one pollutant often lessens the costeffectiveness of reducing other pollutants.
These above findings imply that it is not easy to create a regulation that satisfies
everyone in society. Policy-makers have to compromise on different environmental
outputs across different farms to maximize social well-being. Conservation compliance
may be a step in the right direction, but some modifications of existing policies and
additional policies can make it work better for more farms. Policy makers need to
choose between targeting a specific farm with special characteristics and reducing
specific environmental outputs at the expense of others. After all, it is inevitable that
they lay everything on one scale and decide on how much value they put on each
category. Careful monitoring is also necessary to assure that the goals of society are
met.
6.2 Limitation
There were several limitations which must be noted in terms of data and models
used for the study. Some stem from the EPIC program and some from the Linear
programming model.
6.2.1 EPIC
There were many difficulties in obtaining EPIC parameters. Because the EPIC
program provides detailed soil information for only a limited number of soils, the use of
114
Soils-5 database which covers a wider ranges of soil was necessary. Although Soils-S
database contains just enough soil parameters necessaiy to run the EPIC program, several
important parameters were missing from the database. The exclusion of these
parameters required the EPIC program to generate these parameters internally, which at
times gave unreasonable yields which had to be adjusted.
For the management data, crop budgets for several different years (from 1990 to
1995) were used. Although the crop budgets were updated to reflect the current prices of
pesticides and fertilizers, current market value of labor and machinery was not taken into
account in the calculation of input costs.
In the study, USLE was used to determine the amount of soil loss from the farm.
However, it is possible that a more accurate estimate of the amount of soil loss could
have been obtained with the use of MUSLE, that accounts for the single storm estimate,
or RUSLE, which is the most recent soil loss equation.
EPIC also does not adequately consider the effect of pests on yields. Yield
estimates were artificially reduced when winter wheat was planted earlier than October
15th to better reflect reality. Although the adjustments were made in consultation with
Oregon State agronomists, it is difficult to determine to what extent this adjustment is
reasonable and fairly representative of reality.
115
6.2.2 Linear Programming Model
The linear programming model, like all models, is only a representation of the
real world. Various assumptions were made for the purpose of the study. Among them
were:
I) That all the acreage had to be in production. When the maximization problem does
not meet this constraint, the problem became infeasible. The model does not go further
than this.
A limited set of tillage operations were included as activities. Only certain
combinations of tillage practices, mostly adjustments made by producers who responded
to the questionnaires, were considered for the study. Other types of farming operations
are possible but not incorporated in the study.
The pesticide index used in the study was based on limited information. The toxicity
scale was arbitraiy and could be modified as more useful information is made available.
116
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123
APPENDIX A
124
Crop Field Operations
Winter Wheat - Conventional Tillage Practice
Operation
Disc (3 times)
Moldboard Plow
DrilllHarrow
NPK
Cukipack
Dixon Harrow
Herbicide
Karmex
Fertilize
Nitrogen
Herbicide
Hoelon
Herbicide
Sencor
Herbicide
Treflan
Fertilize
Nitrogen
Fungicide
Bayleton
Combine
Haul Grain
Miscellaneous
Operating Capital Interest
Fixed Cost
Total
200 lbs
2.0 lbs
35 lbs
2.5 Pt
4.0 bz
1.0 Pt
I 0 lbs
4.0 oz
(100 acres, S/acre)
Date
Variable Seed
9/1
9/20
10/IS
16.89
8.95
10/20
10/25
10(30
2/15
2/20
3/25
3/25
4/
4/25
8/1
8/1
5.34
2.19
2 10
Pesticide
Fertilizer2
Total
16.89
8.95
12,50
5.00
5.00
5.00
2.50
2,50
5.00
5,00
1741
22.00
39,84
2,19
2.10
12.40
7.40
8.75
13.75
27,50
20.50
4.37
4.20
32,50
15.50
1 87
1.70
9.00
14.00
17.41
1.44
1.44
17 37
17.37
20,60
38.47
20.60
38 47
160.76
12,50
35,47
58.25
266,98
These budgets were made in different years. Since labor and machinery costs are not updated, some
limitations to the analysis may apply when different rotations are evaluated for the total cost of production.
2
This budget was constructed for the case of the predicted wheat yield of 100 bushels per acre. Fertilizer
application was reduced by half for some unproductive soils.
125
Winter Wheat - Reduced Tillage Practice
(100 acres, S/acre)
Operation
Date
Disc (1 times)
Chisel Plow
Drill/Harrow
NPK
Cultipack
DixonHarrow
Herbicide
Karniex
Fertilize
Nitrogen
Herbicide
Hoelon
Herbicide
Sencor
Herbicide
Treflan
Fertilize
Nitrogen
Fungicide
Bayleton
Combine
Haul Grain
Miscellaneous
Operating Capital Interest
Fixed Cost
9/1
9/20
200 lbs
2.0 lbs
35 lbs
2.5 Pt
4.0 oz
1,0 pt
110 lbs
4.Ooz
l0I5
Van.
5.63
8,59
5,34
4/1
2.19
2.10
5.00
5.00
5.00
2.50
2.50
5.00
4/25
5.00
10/20
10/25
10/30
2/15
2/20
3/25
3/25
8,' 1
8(1
Total
Seed
12.50
8.75
15.50
1.87
1.70
27,50
9.00
17.37
20,60
46.47
17.37
20,60
46.47
12.50
Date
Total
2.0 lbs
35 lbs
2.5 Pt
4,0 oz
1.0 Pt
L101bs
4.0 oz
2/iS
2/20
3/25
3/25
4/ 1
4/25
8.59
39 84
2.19
2.10
12.40
13,75
20.50
4,37
4,20
32.50
14.00
17.41
1 44
Operation
9/20
10/IS
10/20
10/25
10/30
Totai
17,41
1.44
(100 acres, S/acre)
1.5 Pt
22.00
7.40
157.14
200 lbs
Fertilizer2
5.63
Winter Wheat - No Till Practice
Herbicide
Roundup
Drill/Harrow
NPK
Cultipack
Dixon Harrow
Herbicide
Karmex
Fertilize
Nitrogen
Herbicide
Hoelon
Herbicide
Sencor
Herbicide
Treflan
Fertilize
Nitrogen
Fungicide
Bayleton
Combine
Haul Grain
Miscellaneous
Operating Capital Interest
Fixed Cost
Pesticide
Van,
5,00
5,34
2,19
2.10
5.00
5.00
5.00
2.50
2.50
5.00
5.00
Seed
35.47
58.25
Pesticide
Fertilizer2
8.25
12.50
262.86
Total
13.25
22.00
7.40
38.94
2.19
2,10
8.75
12.40
13.75
27.50
20,50
4,37
4.20
32.50
15.50
1,87
1.70
9.00
14,00
si 1
17,41
8/
1.44
17.37
17.37
20.60
46.47
20,60
46.47
1
147.92
17.41
1.44
12.50
43.47
58.25
262.14
126
Annual Ryegrass - Conventional Till / Chop and Plow Straw
(750 acres, S/acre)
Operation
Date
Van.
Seed
Flail
Plow
8/6
3.23
8/10
Harrow
Harrow & Roller (2 times)
Land Level (0.2 times)
8110
6.97
3.00
7.07
Lime
(0.15 times)
Ditching
Plant
15-15-15
Herbicide
Nortron
Fertilize
34-0-7
Herbicide
2-4, D
Herbicide
Banvel
Windrowing (3 times)
Combine
Haul Seed
Custom Seed
Seed Assessment
Miscellaneous
Operating Capital Interest
Fixed Cost
Total
140 lbs
0.166 gal
0.206 gal
0.083 gal
0.041 gal
8/10
8/10
8/10
8/15
9/15
10/1
4/1
4/1
4/1
7/4
7/18
7/19
7/19
7/21
Pesticide
Fertilizer
Total
3.23
6,97
3.00
7.07
1.03
1.03
12.00
3.64
3.89
12.00
4.25
14.30
27.39
1. 11
1.67
40.34
0.56
0.55
6.78
11.76
1,67
43,75
2.98
23.32
10.57
79.37
224,92
1.17
3.36
3.64
22.44
28.50
42.01
1.73
3,91
6.78
11,76
1.67
43.75
2.98
23.32
10,57
79,37
4,25
31.92
54.64
315,73
127
Annual Ryegrass - Open Field Burn I No Till Seeding
Operation
Flail
Plow
Date
(0.2 times)
(0.2 times)
Harrow (0.2 times)
Harrow& Roller (0.2 times)
Land Level (0.2 times)
Lime
(0.1 times)
Ditching (0.2 times)
Plant
15-15-15
Herbicide
Nortron
Fertilize
34-0-7
Herbicide
2-4, D
Herbicide
Banvel
Wmdrowing (3 times)
Combine
Haul Seed
Custom Seed
Seed Assessment
Field Burning
Miscellaneous
Operating Capital Interest
Fixed Cost
Total
140 lbs
0.166 gal
0.206 gal
0.083 gal
0.041 gal
8/ 6
8/10
8/10
8/10
8/10
8/10
8/15
9/15
10/ 1
4/1
4/1
4/1
7/4
7/18
7/19
7/19
7/21
7/21
(750 acres, s/acre)
Var.
Seed
Pesticide
Fertilizer
Total
0.65
1.39
0.65
060
0.60
1,41
1.03
1.41
1,03
8.00
0.73
8.00
0,73
3.89
0.56
1,39
4,25
14.30
22.44
40.34
14.26
42.01
0.86
13.70
1.67
0,28
0.28
6.78
0,58
1,64
1,92
6,78
11.76
11,76
1.67
1.67
43.75
2.98
10.80
23.32
43.75
2.98
10.80
23,32
7.12
58.90
7. 12
58.90
188.57
4.25
15.92
54.64
263.38
128
Tall Fescue Seed Establishment - Conventional (500 acres, S/acre)
Operation
Moldboard Plow
Lime
Disc
Harrow/Roll (3 times)
Herbicide
Roundup
Herbicide
Roundup
Plant
Fertilize
40-0-0-6
Herbicide
CurtailM
Flail Chop (2 times)
Miscellaneous
Operating Capital Interest
Fixed Cost
2.0 tons
0.25 gal
0.25 gal
Date
Van.
9/ 5
5.76
60.00
5.55
6,06
9/10
9/15
9/20
11/1
3/1
3/5
0.05 ton
0.25 gal
3/10
3/15
7/1
Total
1.52
1.52
19.18
1.75
1.52
Seed
Pesticide
Fertilizer
5.76
60,00
5,55
6.06
13.52
13.52
21.68
12.00
12.00
2.50
11.52
9.50
6,06
58 32
25,60
51.37
244.21
Total
13,27
11,02
6.06
58,32
25.60
5 1,37
2.50
33.50
11.52
Pesticide
Fertilizer
291,73
Tall Fescue Seed Establishment - Reduced tillage (500 acres, S/acre)
Operation
Date
Van.
Field Cultivator
Lime
2.0 tons
Disc
Harrow/Roll (3 times)
Herbicide
Roundup 0.25 gal
Herbicide
Roundup 0.25 gal
Plant
Fertilize
40-0-0-6 0,05 ton
Herbicide
Curtail M 0.25 gal
Flail Chop (2 times)
Miscellaneous
Operating Capital Interest
9/ 5
5.76
60.00
5.76
60.00
5,55
5,55
6,06
13.52
13.52
21.68
13.27
11.02
9/10
9/15
9/20
11/
1
3/
3/5
3/10
3/15
7/
1
Seed
6,06
1,52
1.52
19,18
1.75
1.52
12.00
12.00
2.50
11.52
9,50
6.06
58.32
25,60
6.06
58,32
25.60
FixedCost
Total
Total
5 1,37
244.2]
2.50
33.50
11.52
291.73
129
Tall Fescue Seed Production - Open Field Bum (500 acres, S/acre)
Operation
Date
Herbicide
Karmex 2.0 lbs
Herbicide
Goal
0.078 gal
Fertilize
14-14-14 0.125 tons
Fertilize
33-0-0-12 0.160 tons
Herbicide
2-4, D 0.187 gal
Herbicide
Banvel 0.041 gal
Rust Spray
Tilt
0.6 gsl
Swath
Combine
Haul Seed
Clean/Bag
Burn
Miscellaneous
Operating Capital Interest
Fixed Cost
9/ 1
9/ I
Van.
0,38
0.38
9/ 5
1.52
3/1
3.50
0.76
0.76
3.02
3/10
3/10
3/15
7/
7/10
7/15
7/20
8/15
1
Seed
Pesticide
Fertilizer
Total
23,84
9.46
6.32
25.36
8,70
5.20
30.41
2,65
2.54
20.40
3.30
23.42
14.00
22,06
14.00
22.06
1.50
128,0
10.90
1.50
128.0
10,90
28,47
18.94
31.63
28.47
18,94
31.63
Total
265.82
33,91
3.41
0.00
39.85
54.25
359.92
Pesticide
Fertihzer
Total
Tall Fescue Seed Production - Bale Straw (500 acres, S/acre)
Operation
Herbicide
Herbicide
Fertilize
Fertilize
Herbicide
Herbicide
Rust Spray
Swath
Combine
Haul Seed
Clean/Bag
Bale
Chain Harrow
Date
Karmex
Goal
14-14-14
33-0-0-12
2-4, D
Banvel
Tilt
Flail
4.0 lbs
0.156 gal
0.125 tons
0.160 tons
0.374 gal
0.082 gal
0.12 gal
9/ 1
9/
1
Van.
0.76
0.76
9/ 5
1.52
3/1
3,50
3/10
3(10
3/15
7/ 1
7/10
7/15
7/20
1.52
1.52
6.05
14.00
8/5
8/ 6
8/10
Seed
17.40
10.40
23.84
30.41
5,30
5.08
40.80
22.06
1.50
128.0
0
1.10
Miscellaneous
Operating Capital Interest
Fixed Cost
3.03
28.47
18.94
31.63
Total
264.36
18.16
11.52
25.36
33,91
6,82
6.60
46,85
14,00
22,06
1.50
128.0
0
1,10
3.03
28.47
18.94
31.63
0.00
78.98
54.25
397,59
130
Perermial Ryegrass Seed Establishment - Conventional (500 acres, S/acre)
Operation
Disc (2 times)
Rip
Disc
Moldboard Plow
Harrow/Roll
Lime
2.0 tons
Harrow/Roll
Plant
Charcoal/Fertilize
Herbicide
Roundup 0.19 gal
Herbicide
Karmex
3.0 lbs
Herbicide
Nortron
0.25 gal
Slug Control
Slug-Bait 0.1 acre
Fertilize
33-0-0-12 0.1 tons
Fertilize
Urea
0.08 tons
Boarder Spray
Roundup 0.5 acre
Broadleaf Conrol MCPA
0.l9gal
Broadleaf Conrol Banvel
0.06 gal
Rogue Weed
Roundup 0.083 gal
Rust Spray
Tilt
0.12 gal
Swath
Combine
Haul Seed
Clean/Bag
Burn
Miscellaneous
Operating Capital Interest
Fixed Cost
Total
Date
Van.
7/15
7/20
7/25
8/ 1
8(10
8/20
8/30
5,55
4,71
2.77
5,76
1.97
60.00
1.97
10/ 1
10/20
4.64
2.02
2.52
5.75
11/ 1
11/15
11(21
3/1
3/15
'
1.84
1,75
Pesticide
3/21
1.01
1.01
4/ 2
22.00
6,05
14.00
22.06
Fertilizer
Total
5,55
4.71
2.77
5,76
1,97
60.00
15,00
69.75
9.62
13,05
41.25
1,62
19.01
21,20
1,75
4/ 1
4/ 1
6(15
7/ 1
7/10
7/15
7/20
8/ 1
Seed
2.50
2.66
4,92
1,97
89.39
13.52
15,57
47.00
3.46
20.76
22.95
2.50
3,67
5.93
3.98
40.80
25.98
46.85
14.00
22.06
1.50
128,0
10,90
3.32
128.0
10,90
3.32
49.02
49.02
149.33
149.33
511.20
1,50
15.00 120.40
109.96
756.56
131
Perennial Ryegrass Seed Establishment
Operation
Disc (2 times)
Rip
Disc
Field Cultivator
Harrow/Roll
Lime
2.0 tons
Harrow/Roll
Plant
Charcoal/Fertilize
Herbicide
Roundup 0.19 gal
Herbicide.
Karmex
3.0 lbs
Herbicide
Nortron
0.25 gal
Slug Control
Slug-Bait 0.1 acre
Fertilize
33-0-0-12 0.1 tons
Fertilize
Urea
0.08 tons
Boarder Spray
Roundup 0.5 acre
Broadleaf Conrol MCPA
0.19 gal
Broadleaf Conrol Banvel
0.06 gal
Rogue Weed
Roundup 0.083 gal
Rust Spray
Tilt
0.12 gal
Swath
Combine
Haul Seed
Clean/Bag
Miscellaneous
Operating Capital Interest
Fixed Cost
Total
-
Reduced Tillage (500 acres, $/acre)
Date
Van.
7/15
7/20
7/25
4,7!
8/ 1
8/10
8/20
8/30
10/ 1
10/20
10/20
li/iS
11/21
3/
1
3/15
3/21
4/
2.77
5.76
1.97
60.00
1.97
4.64
2.02
2.52
5.75
1.84
4/2
22.00
6/iS
6.05
14.00
22.06
7/10
7/15
7/20
Fertilizer
1.97
15,00
69.75
9.62
13.05
41,25
1.62
19.01
21.20
2.50
2.66
4.92
3.98
40.80
60.00
1.97
89.39
13,52
15.57
47.00
3.46
20.76
22.95
2.50
3.67
5.93
25.98
46.85
14.00
22.06
1.50
1.50
128.0
'128.0
3.32
49.02
149.33
500.30
Total
5.55
4.71
2.77
5.76
1.75
1.75
4/ 1
7/1
Pesticide
5.55
1.01
1.01
1
Seed
3.32
49.02
149.33
15.00
120.40
109.96
745.66
132
PeremiiaJ Ryegrass Seed Production - Open Field Bum (500 acres, $/acre)
Operation
Fertilize
10-20-20
Herbicide
Goal
Herbicide
Karmex
Slug Control
Slug-Bait
Fertilize
33-0-0-12
Fertilize
Urea
Boarder Spray
Roundup
Broadleaf Conrol MCPA
Broadleaf Conrol Banvel
Rogue Weed
Roundup
Rust Spray
Tilt
Swath
Combine
Haul Seed
Clean/Bag
Burn
Miscellaneous
Operating Capital Interest
Fixed Cost
Total
Date
0.113 tons
0.05 gal
10/15
0.75 Pt
10/ 'i
0.05 acre
11/21
0.1 tons
3/ 1
0.08 ton
3/15
0.25 acre
3/21
0.095 gal
4/ 1
0.03 gal
4/ 1
0.041 gal
4/ 2
0.06 gal
6/
7/
1
7/10
7/15
7/20
8/ 1
Van.
10/
Seed
1
0.38
0.38
0.55
Pesticide
1.75
Fertilizer
24.27
3.26
3.45
0.54
1.75
1.75
19,01
1.25
1.33
26.02
3.64
3.83
1.09
21.20
0.51
Total
20.76
22.95
1.25
1.83
0.50
2.46
2.96
11.00
1.99
12.99
3.02
14,00
20.40
23,42
14.00
22,06
22.06
ISO
1,50
128.0
10.90
128.0
10.90
58.32
30.95
58.32
30.95
66.06
353.39
66.06
34.68
64.48
452.55
133
Perennial Ryegrass Seed Production - Bale (500 acres, $/acre)
Operation
Fertilize
Herbicide
Herbicide
SlugControl
Fertilize
Fertilize
Boarder Spray
Date
10-20-20
Goal
0.113 tons
0.1 gal
Karmex
1.5 Pt
Slug-Bait 0.1 acre
33-0-0-12 0.1 tons
Urea
0.08 tons
Roundup 0.5 acre
BroadieafConrol MCPA
0.19 gal
Broadleaf Conrol Banvel
0.06 gal
Rogue Weed
Roundup 0.083 gal
Rust Spray
Tilt
0.12 gal
Swath
Combine
Haul Seed
Clean/Bag
Bale
Chain Harrow
Flail
Miscellaneous
Operating Capital Interest
Fixed Cost
Total
10/ I
10/15
10/
11/21
'1
3/
1
3115
Van.
4/
I
4/2
3/15
7/ 1
7/10
7/15
7/20
7/15
7/21
7/21
Pesticide
1.75
6,525
6.90
1.10
1,75
1.75
1.08
1.01
1.01
22.00
6.05
14.00
22.06
Fertilizer
24.27
0.76
0.76
3/21
4/ 1
Seed
19.01
21.20
2.50
2.66
4.92
3.98
40.80
26.02
7.29
7.66
2.18
20.76
22.95
2.50
3.67
5.93
25.98
46.85
14.00
22.06
1.50
1.50
128.0
128.0
0.00
0.00
1.0!
3,03
58.32
30.95
66.06
1.10
3.03
58 32
30.95
66.06
362.96
Total
69.37
64.48
496.81
134
Sweet Corns (175 Irrigated Acres, $ /acre)
Operation
Herbicide
Chisel Plow
Disc
Drag/Roll
Herbicide
Herbicide
Plant
Fertilize
Fertilize UREA
Date
Roundup
Dual
Atrazine
2.Oqt
1.5 qt
UREA
108 lbs
Phosphorus 134 lbs
Potash
36 lbs
Sulfur
26 lbs
326 lbs
Irrigation
Herbicide
Atrazine
Custom Topping
Custom Picking
Haul
Lime
Disc
Tillage
Plant Cover Crop
Miscellaneous
Operating Capital Interest
Fixed Cost
Total
1.5 pt
2 in Sx
2.0 qt
2/25
2/26
2/27
2/28
3/ 2
3/ 2
4/25
4/25
4/25
4/25
4/25
4/26
4/30
5/
1
5/ 2
7.88
12,28
36,60
42.62
36.51
[54
Total
9.42
8,59
4.89
4.56
35.44
32.75
9,60
2.58
72,50
3,21
11117
Fertilizer
3.02
0.65
11/20
11/15
Pesticide
14.31
19,50
11/l7
1
9/ 1
Seed
1.54
8.59
4.89
4,56
2.69
2.68
6.02
7,00
56.43
57.94
26.25
3.77
9.96
8/
0,625 tons
Van.
13,20
14.31
19.50
3.02
0.65
39,09
72.50
14.74
7.00
56.43
57.94
26.25
3,77
9,96
12.50
15.71
23.77
23.77
23.31
162.48
23.31
481.71
162, 48
49,10
63.43
73.99
668.23
135
Bush Beans (100 Irngated Acres, $ /acre)
Operation
Herbicide
Chisel Plow
Disc
Roto-Till
Level Field
Herbicide
Herbicide
Fertilize
Date
Roundup
1.5 pt
Dual
2.0 qt
Trefian
1.5 Pt
UREA
109 lbs
Phosphorus 128 lbs
Potash
43 lbs
Sulfur
33 lbs
Plant
RoIl
Herbicide
Basagran
Fertilize
UREA
Bloom Spray
Rovral
Bloom Spray
Sevin
Bloom Spray
Diazinon
Spot Spray
Roundup
Irrigation
CustomPicking
Haul
Lime
Disc
Plant Cover Crop
Miscellaneous
Operating Capital Interest
Fixed Cost
Total
1,5 Pt
65 lbs
1.0 lbs
1.25 lbs
0.75 lbs
0,050 ApI
1.5 in 6x
3/
1
3/ 2
3/ 3
3/ 4
3/ 5
3/ 2
3/ 2
4/ 3
4/ 3
4/ 3
4/ 3
5/ 1
5/ 2
5/3
Van,
Seed
1.54
8 59
3.26
Pesticide
7.88
4.20
2.36
2.65
2,65
14.44
17,70
1.61
1.61
1.61
3.61
0.82
128,0
19.38
1.54
2,73
12.94
6/ 1
6/ 1
6/ 3
0.77
0,77
6/ 4
6/ 5
0.62
93.25
21,00
6.38
3.94
0.26
7/ 1
7/
8/
152.9
37.18
8/ 2
4.89
6/
1
1
9/1
8,61
1.61
19.31
5.22
2.43
153.40
2.83
14.48
11.34
21.77
7,15
5,55
0,88
10,50
4.89
12,50
15,71
37,56
33.76
207 73
629.56
8,65
16.06
93,25
152,90
37,18
10.50
3,21
Total
9.42
8.59
3,26
4,20
2,36
35,40
32.75
6,00
1.62
6.02
2.83
Fertilizer
37.56
33.76
207.73
140.50 110.53
45.18
925,77
1.,
Li
APPENDIX B
137
THE HEL CONSERVATION PLAN
911194
I. How many acres of cropland did you operate during the 1993 crop year?
Acres
2. How many acres of your total cropland are considered Highly Erodible Land (HEL) for the 1993 crop
year?
Acres
In which crop yeas did you first sign a HEL conservation compliance plan with the Soil Conservation
Services (SCS)?
Year
How many acres of the HEL, if any, are also in the Conservation Reserve Program (CRP) for the 1993
crop year? (if 0, please write it in.)
Acres
4a. (IF GREATER THAN1 0,) during which sign-up period did you enroll in the CRP?
How many acres are there in your largest HEL tract?
Acres
NOTE: When answering the rest of the questions, please refer exclusively to the operations on your
largest HEL tract specified in Q5.
138
FOR YOUR LARGEST HEL TRACT.
6a What was the rotation on your Highly Erodible Land (HEL) tract just before you first signed the HEL
agreement? (for example, tail fescue-4 years-9/l 5 and wheat-2 years- 10/I or continuous wheat)
CROP
YEARS IN ROTATION
PLANTING DATE
6b. What is the current or planned rotation on your HEL tract? (for example,continuous pasture)
CROP
YEARS IN ROTATION
PLANTING DATE
7a. What were your tillage practices for each crop listed in the rotation on your HEL tract just before you
first signed HEL agreement? (for example.wheat-moldboard plow, harrow, disk)
CROP
TIILLAGE PRACTICES
139
FOR YOUR LARGEST HEL TRACT..
7b. What are your tillage practices for each crop hsted in the current or planned rotation on your HIEL tract?
(for example, wheat: chisel plow, disk, harrow)
CROP
1ILLAGE PRACTICES
8, Did you set up terraces on your Highly Erodibie Land (HEL) acres as a result of your conservation
compliance plan? (Circle One Number)
NO (SkiptoQ.9)
YES
L> 8a. What was your initial cost for establishing terraces?
S
/acre
8b. Are there yearly maintenance costs for the terraces?
ircle One Numher)
NO (SkiptoQ,9)
YES
-> Please estimate the annual maintenance costs.
S
/acre
9. Did you set up crop-slope farming on your Highly Erodible Land (HEL) tract as a result of your
conservation compliance plan? (Circle One Number)
1. NO (SkiptoQ.lO)
2. YES
9a Please estimate the additional yearly costs per acre to farm the slope.
S
/acre
140
FOR YOUR LARGEST HEL TRACT.
10. Did you mai(e any other changes in your farming practices on your Highly Erodible Land (HEL) acres as
a result of your conservation compliance plan?
(Circle One Number)
1. NO (SkiptoQ.lI)
2. YES
1 Qa. Please specif the adjustment made and estimate the additional costs per acre required because
of the adjustment?
ADJUSTMENT
COST
/acre
S
/acre
11. Was it necessary to make changes m your equipment Inventory to implement your conservation plan?
(Circle One Number)
I. NO (SkiptoQ12)
2. YES
>
1 Ia. Please list equipment purchased and the price paid, and explain the reasons for the purchase.
EQUIPMENT
PRICE PAID
REASONS
S
12. Did you change the chemical fertilizer application rates on the crops listed in your current rotation as
the result of the conservation plan? (Circle One Number)
I. NO (SkiptoQ13)
2. YES
12a. Please indicate crop, name of fertilizer, the change in amount of fertilizer, and increase or
decrease.
CROP
FER l'ILIZER
AMOUNT
(Please specifj u
INCREASE/DECREASE
(Please circle)
/acre
/acre
I
I
D
D
141
FOR YOUR LARGEST REL TRACT..
13. Did you use fertilizing methods other than applying chemical fertilizer as a result of the conservation
plan? (for example, organic fertilizer) (Circle One Number)
NO (Skip to Q.14)
YES
13a. Please specify the name of the crop and fertilizing methods.
CROP
FERTILIZING METHODS
14. Did you change the pesticide application rates on the crops listed in your current rotation as the result of
the conservation plan? (Circle One Number)
I. NO (SkiptoQJ5)
2. YES
->
decrease.
14a. Please indicate crop, name of pesticide, the change in amount of pesticide, and increase or
CROP
PESTICIDE
AMOUNT
(Please specify unit)
INCREASE/DECREASE
(Please circle)
/acre
I
D
acre
I
D
15. Did you use pest control practices other than applying pesticide as a result of the conservation plan?
(Circle One Number)
NO (SkiptoQ.16)
YES
L> I 5a. Please speci
CROP
the name of the crop and pest conol practices.
PEST CONTROL PRACTICES
142
FOR YOUR LARGEST HEL TRACT...
Did you change the number or type of livestock? (Circle One Number)
I. NO (SkiptoQ.17)
2. YES
L>
16a. Please specde the type, number, and increase or decrease.
TYPE
NUMBER
INCREASE/DECREASE
(Please circle)
ID
ID
Was there a change in the yield of your crops due to the HEL adjustments you have made?
('Circle One Number)
1. NO (SkiptoQ.18)
2. YES
17a, Please speci' the crop, change in yield, and increase or decrease.
CROP
YIELD
(Please specif, unit)
INCREASE/DECREASE
(Please circle)
/acre
I
/acre
ID
D
18. Is there anything that you would like to say about the HEL conservation compliance program or the
Conservation Reserve Program?
THANK YOU FOR YOUR HELP!
143
APPENDIX C
144
EPIC Runs Results
Table C. 1.1 Fifteen -Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations: Rotaion 1 (Continuous Winter Wheat) on
Farm A
Woodburn 16%
ID
USLE2
(ton)
NSE
NRU
NLE
HSE
HRU
(Ib)
(Ib)
(Jb)
(Ib)
(Ib)
acOl
422
2156
205
45.7
2.73
ad!
3.17
16.37
2.05
46.0
acO2
8.45
41.83
2.06
acl2
6.35
31.75
acO3
12.15
acl3
PRU
PLE
PSE
Yield
(bu)
024
1320.64
0.02
53
78.64
2.07
0.23
1320.64
0.02
40
78.67
52.5
5.31
0.49
1350.99
0.02
148
84.85
2.07
53,1
4.03
0.36
1357.00
0.02
114
84.89
59.25
2,12
53,5
7.56
0.71
1348,00
0.02
239
92.67
9.13
45.01
2.18
53,3
5,73
0,65
1368,02
0.02
180
92.73
arOl
1.56
8,39
2.67
44.2
1.08
0.37
1299.96
0.02
21
77.30
arIl
1.17
6.36
2,67
44.4
0.82
0,37
1302,96
0.02
16
77.31
arO2
3.82
19.86
2,50
52.8
2,53
0,46
1328.97
0.02
87
83,30
arl2
2.87
15,07
2,50
53.1
1.92
0,46
1331.97
0.02
66
83.32
arO3
6.16
31.07
2.78
53,7
3.97
0.75
1375.03
0.02
149
90.93
arl3
4.63
23.59
2.78
53.2
3.01
0.74
1378.04
0.02
115
90,97
anOl
0.67
3.49
3,80
43.4
0.46
0.71
1189.95
0,02
12
73,59
anIl
0.50
2.65
3,80
43.5
0.35
0,71
1192.95
0,02
10
73,60
anO2
1.20
6.22
3.50
52.8
0.81
0.83
1265.99
0.02
25
79.22
anI2
0:90
4,72
3,50
52.9
0.61
0.83
1266.59
0.02
19
79.23
anO3
1.72
8.81
3.64
53,5
1.15
1,11
1283.97
0.02
42
86,53
anl3
1.29
6.67
3,64
53.7
0.87
1,11
1283.99
0.02
32
86.54
See Table 5.6a on page 73 for the farming operations corresponding to each ID.
2
See foonote 13 on page 82 for the description of notations.
145
Table C. 1.2 Fifteen -Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations: Rotaion 1 (Continuous Winter Wheat) on
Farm A
Helmick 16%
ff
USLE
(ton)
NSE
(ib)
NRU
NLE
HSE
HRU
(1b)
(Ib)
(Ib)
(Ib)
acOl
707
2620
293
13.9
3.33
acIl
5.29
19.74
2.91
13.7
acO2
12.5449
45.33
3,04
acI2
9.39
34.12
acO3
16.634
acl3
PRU
PLE
PSE
Yield
(bu)
0.34
1996.3
0.06
92
49.26
2.51
0.33
1982.3
0.06
68
49,54
22.6
5.79
0,55
1974.5
0.07
213
49.01
3.05
22.1
4.35
0.52
1986.5
0.06
159
49.54
59,52
3,06
28.3
7.66
0.96
2017.5
0.07
305
51.20
12.4550
44.82
3.08
27.7
5,76
0.89
2032.5
0.07
229
51,64
arOl
3.41
12.78
3.90
12.9
1.65
0.65
1937.4
0.05
47
48,95
arlI
2.55
9.61
3.89
12,8
1.24
0,55
1940.4
0.05
33
49.09
arO2
6.86
25,68
3.84
19.5
3.29
0.67
2012.5
0.05
147
48.98
arl2
5.14
19.34
3.75
19,3
2,48
0,66
1963.5
0.06
109
49,09
arO3
9.76
35.92
3,75
25,8
4.63
0.98
2028,5
0.06
219
51.11
arO3
7.30
27,02
3,69
23.7
3.48
0.97
2034.5
0.06
167
51,55
anOl
1.13
4.39
4.10
11.3
0,58
1.05
1878.3
0.06
23
48,53
anIl
0.84
3,31
4.10
11,3
0,44
1.04
1878.3
0.06
18
48.53
anO2
2.16
8,29
4.00
18,6
1,08
1,10
1933.3
0,06
51
47,09
anl2
1.61
6.24
4,00
18.5
0,82
1.10
1936.3
0.06
38
47,21
anO3
3.00
11,35
4.94
22.3
1,49
1.35
1971.3
0.06
85
49,81
anl3
2.24
8.54
4.93
22.4
1.12
1,35
1974.3
0,06
63
49,96
146
Table C. 1.3 Fifteen -Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations: Rotaion 1 (Continuous Winter Wheat) on
Farm A
Nekia 16%
USLE2
(ton)
NSE
NRU
NLE
IISE
HRU
(Ib)
(Ib)
(Ib)
(Ib)
(lb)
acOl
4.45
30.68
1,51
33.8
3.87
aclIl
3.35
23.27
1.50
33.8
acO2
8.21
55,03
1.59
acl2
6.17
41,74
acO3
11.5
acl3
PRU
PLE
PSE
Yield
(bu)
0.22
1524.10
0.06
63
53,16
2.93
0.22
1530 10
0.06
50
53,29
40,3
6.95
0.30
1532.68
0.06
163
54.75
1.58
40.3
5,27
0.29
1538.68
0.05
124
55.02
75.74
1.65
43.9
9.60
0.51
1527,89
0.06
250
57.97
8.60
57.39
1.65
43.7
7.27
.0,48
1536.89
0.06
191
58.39
arOl
1.90
13,65
2.02
31.1
1.75
0.37
1468,58
0.06
30
52.59
arlI
1.43
10.34
2.02
31.1
1.32
0.37
1467.68
0.06
23
52.65
arO2
4.26
29,59
1.96
38,7
3.77
0.43
1523.93
0.05
103
54.22
arl2
3.20
22,42
1,96
38.6
2.86
0,42
1526.93
0.05
81
54.37
arO3
6.52
44.23
1.99
42,0
5.66
0.67
1533.93
0.05
175
57.51
arl3
4.90
33.52
1,98
42.0
4.29
0.66
1536.93
0.05
131
57,75
anOl
0.67
4.84
3,05
28,9
0.62
0,72
1439.90
0.06
16
50.90
anlI
0.51
3.66
3,05
28.9
0.47
0.72
1429.90
0.06
11
50.93
anO2
1.31
9.32
2,77
36.7
1.20
0,79
1441.93
0.06
34
52.74
anl2
0.98
7.06
2.77
36.7
0.91
0,79
1441,93
0.06
26
52.68
anO3
1.92
13.42
2.57
39.4
1,74
1.08
1477,95
0.06
54
56.05
anl3
1.44
10.15
2.57
39.4
1,32
1,08
1477,95
0,06
41
56.14
147
Table C.2. 1 Fifteen - Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations of Rotation 2 (2 year annual ryegrass and
year winter wheat) on Farm B
Soil Type: Woodburn 8 %
ID3
USLE2
(ton)
NSE
NRU
NLE
HSE
HRU
(ib)
(Ib)
(Ib)
(Ib)
(ib)
bcOl
2.05
1095
3.99
73.54
144
bell
1.55
8.36
3.99
73.60
bcO3
3.12
16.22
3.63
bcl3
2.35
12.38
brOl
2.09
brIl
PRU
PLE
PSE
Yield
(bu)
Yield
(ton)
0.54
484.75
0.36
21
80.91
1.162
1.10
0.54
484.75
0.36
18
80.92
1,162
76.79
2.14
0.61
496.01
0.21
66
95.93
1.161
3.64
76.89
1.63
0.60
499.01
0.16
51
95.94
1.161
11.34
8.36
73.10
1.50
0.57
484.73
0.36
21
79.29
1.162
1.57
8.65
4.06
73.16
1,15
0.56
48473
0.31
18
79.30
1.162
brO3
3.17
16.69
3.73
76.32
2.21
0.64
495.99
0.30
66
94.00
1.161
brl3
2.39
12.74
3,73
76.42
1.69
0.63
498.99
0.23
51
94,02
1.161
bnOl
2.17
11.76
4,06
72.94
1,55
0,55
484.73
0.36
24
75,25
1.162
bnll
1.63
8.98
3.64
73.00
1,18
0,54
484,73
0,36
18
75.25
1.162
bnO3
3.29
17.28
3,74
76.16
2.28
0,59
495.99
0.30
66
89.21
1.161
bnl3
2.48
13.19
3,74
76.27
1,74
0,58
499.00
0.23
54
89.22
1.161
bc0lb
1.10
5.95
3,07
74.30
0.77
0.29
498,22
1.67
18
80,95
1.159
bcllb
0.82
4.53
3.07
74,34
0.59
0.29
447.08
0.99
3
80.96
1,159
bc03b
2.01
10.59
3.09
77,11
1.39
0.55
451.21
0.16
15
95.93
1.158
bcl3b
1.51
10.59
3.09
77,17
1,05
0.54
495.5
0.09
42
95,94
1.158
brOib
0.44
2.49
3.51
71.92
0.33
0.69
481.43
1.62
6
79.40
1.150
brllb
0.33
1.89
3.51
71.95
0.25
0.69
481.43
1.64
6
79.41
1.150
bro3b
1.01
5.48
3.46
75.39
0.73
1.01
477.51
0.34
33
93.99
1.150
brl3b
0.76
4.16
3.46
75.44
0.55
1.01
477.51
0.18
24
94.00
1.150
bn0lb
0.33
1.84
3.82
71.42
0.24
0.80
447.08
1.18
3
75,48
1.150
bnllb
0.24
1.40
3.82
71.45
0.18
0.80
447.08
0.99
3
75.48
1,150
bnO3b
0.62
3.31
4.03
74.83
0.44
1.25
451.21
0.16
15
89.25
1.150
bnl3b
0.46
2,52
4.03
74.87
0.33
1,25
454,21
0.16
12
89.26
1.150
See Table 5.6b on page 74 for the farming operations corresponding to each ID.
148
Table C.2.2 Fifteen - Year Averaged Environmental and Crop Outputs per acre) for
Different Farming Operations of Rotation 2 (2 year annual ryegrass and I
year winter wheat) on Farm B
Soil Type: Helmick 8 %
ID3
PRJ
USLE2
(ton)
NSE
NRU
HRU
(Ib)
NLE
(lb)
HSE
(ib)
(Ib)
(Ib)
bcOl
2.83
11.0
6.11
53.29
1.45
0.87
688.84
073
bcll
2.12
8.34
6.37
78.15
L10
0,97
688.85
bcO3
4.06
15.3
5.84
59.66
2.03
0.82
bcI3
3.05
11.6
5.83
59.57
1.54
brOl
2,85
fl.2
6.22
52.88
brIl
2.15
8.54
6.21
brO3
4.09
15.6
brl3
3.08
bnOl
PLE
PSE
Yield
(bu)
Yield
27
4982
0.851
0.71
18
49,89
0.853
714.40
1.45
69
50,43
0.858
0.81
717.40
1.46
54
50,55
0.861
1.48
0.86
688,81
0.70
27
48.82
0.851
52.84
1.12
0.85
691.82
0.72
18
48.89
0.853
6.10
58.80
2.07
0.79
721.32
1.32
69
49.59
0.861
11.8
5.98
59.14
1.57
0.78
717.38
1.45
54
49.53
0.861
2.91
11.5
6.25
52.72
1.51
0,84
688.81
0.74
27
46.33
0,851
bnll
2.19
8.72
6.25
52.67
1.14
0.84
691.81
0.71
21
46,39
0,853
bnO3
4.17
15.9
6.14
58.65
2.10
0.73
721.32
1.37
72
47.05
0.860
bnl3
3.13
12.0
6.02
58.98
1.59
0.72
718.38
1.42
54
47.00
0.860
bc0lb
2.03
7.89
4.61
54.65
1.03
0.45
692.13
1.55
27
50.08
0.855
bcllb
1.52
5.96
4.61
54.59
0.78
0.45
692.13
1.55
18
50.14
0.857
bc03b
3.12
11.7
4.93
61.06
1.54
0.73
714.65
1.45
63
50.50
0.858
bcl3b
2.33
8.86
4.93
60.96
1.17
0,72
717.65
1.30
51
50.61
0.860
brOib
0.89
3.62
5.28
53.29
0.48
0.84
668.35
0.64
12
48.90
0.850
brllb
0.67
2.73
5.28
53.25
0,35
0.84
671.35
0.63
9
48.93
0.851
brO3b
1.66
6.46
5.46
58.34
0.86
1.20
710.76
0.63
42
50.05
0.852
brl3b
1,24
4.88
5.46
58.30
0.65
1.20
710.76
1.50
33
50.13
0,853
bnOlb
0.65
2.66
5.57
53.00
0.35
0.95
653.29
0.65
6
46.46
0.851
bnllb
0.49
2.01
5.57
52.97
0.27
0.95
653.29
0.64
3
46.49
0.851
bn03b
1.04
4.11
5.94
57.98
0.55
1.39
691.66
0.68
18
47.55
0.847
bnl3b
0.78
3.10
5.93
57.95
0.41
1.39
691.66
0.68
12
47.61
0,848
(ton)
149
Table C.2.3 Fifteen - Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations of Rotation 2 (2 year annual ryegrass and 1
year winter wheat) on Farm B
Soil Type: Steiwer S%
ID3
USLE2
NSE
NRU
NLE
HSE
PLE
PSE
(Ib)
(lb)
(Ib)
(Ib)
EIRU
(ib)
PRU
(ton)
Yield
(bu)
Yield
(ton)
bcOI
2.61
1L7
3.42
81.19
1.56
0.60
605.38
0.10
24
43.20
0.778
bcll
1.96
8.95
3.42
81.14
1.19
0.60
605.3.8
0.10
21
43.26
0,780
bcO3
3.81
16.5
3.07
86.77
2.21
0.70
610.61
0.12
66
44.77
0.751
bcl3
2.87
12.6
3,06
86.72
1.69
0.69
613.59
0.12
48
44,89
0.753
brOl
2.62
11.9
3.48
80.71
1.60
0.60
605.36
0.109
24
42.35
0.779
brIl
1,97
9.10
3,47
80.66
1.22
0.60
605.36
0.10
18
42.40
0.780
brO3
3.84
17.0
3,14
86.31
2.28
0.72
610.57
0.13
66
43.89
0.751
brI3
2.89
12.9
3,13
86.24
1.73
0.71
613.58
0.12
48
44.00
0.753
bnOI
2.67
12.3
3,51
80,49
1.65
0.59
605.35
0.10
24
40,19
0,779
bnIl
2,01
9,37
3.50
80.44
1.25
0,59
605.35
0,10
21
40.25
0.780
bnO3
3.90
17.4
3,18
86.09
2.34
0,70
610.56
0,13
66
41,65
0.751
bnl3
2.94
13.2
3.17
86.02
1.78
0.68
613.56
0.12
48
41,76
0.753
bc0lb
1.92
8.72,
2.47
84.82
1,15
0,37
612.77
0,09
24
43,31
0.761
bcllb
1.44
6.66
2.47
84,79
0.88
0.37
612,77
0.09
21
43,37
0,762
bc03b
3,06
13.2
2.55
87.90
1.75
0,62
606,94
0,11
57
44,99
0,740
bcl3b
2.31
10.1
2,55
87.89
[.35
0.62
609,92
0.11
45
45,09
0.742
brOib
0.82
3.91
2.99
81.20
0.52
0,67
595,00
0,09
12
42,76
0.776
brllb
0.61
2.97
2,99
81.18
0.39
0.67
595.00
0.09
12
42.79
0,777
brO3b
1.44
6.58
2.87
86,11
0.89
1.01
608.22
0.11
36
44.34
0.752
brl3b
1.08
4.98
2.87
86.07
0,67
1.00
608.21
0.11
24
44,40
0.752
bnOlb
0.62
2.99
3,12
80.44
0.40
0.75
575.89
0,10
6
40.94
0.777
bnllb
0,46
2,27
3.12
80,43
0.30
0,75
575.89
0,10
3
40.97
0,777
bnO3b
0.97
4.55
3.17
85,39
0.61
1,13
562.04
0.11
15
42.32
0756
bnl3b
0.72
345
3.17
85.36
0.46
1.1.3
571.04
0,11
12
42,37
0.757
150
Table C.3. 1 Fifteen - Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations of Rotation 3 (4 year tall fescue and 1 year
winter wheat) on Farm B
Soil Type: Woodburn 8%
ID4
USLE2
NSE
NRU
NLE
PSE
(Ib)
(Ib)
HRU
(lb)
PLE
(ib)
HSE
(ib)
PRU
(ton)
Yield
(bu)
Yield
(ton)
ccOi
2.04
10,71
1.91
57.76
0.37
0.82
593.28
0.05
34
93.89
1Q49
ccli
1.53
8.24
1.98
57.61
L37
0.40
589.19
0.05
22
93.90
1.048
ccO3
2.65
13.71
1.93
65.55
1.76
0.52
690.86
0.19
25
97.16
1.048
ccl3
1.99
10.45
1.93
65.69
1.34
0.51
694.78
0.22
18
97.19
1.048
cr01
1.49
8.10
2.21
60.28
1.03
0.56
589.32
0.05
29
92.03
1.043
cr11
1.12
6.19
2.21
60.30
0.79
0.56
589.32
0.05
25
92.04
1.043
cr03
2.16
11.34
2.29
67.95
1.45
0.63
694.83
0.21
12
95.22
1.042
cr13
1.62
8.68
2.29
67.98
1.11
0.63
694.83
0.22
10
95.25
1.042
cnOl
1.45
7.77
2.23
60.19
0.99
0.60
619.48
0.05
48
87.33
1.043
cn!1
1.09
5.94
2.23
60.21
0.76
0.60
623.86
0.05
27
87.34
1.043
cnO3
2.21
11.60
2.32
67.98
1.48
0.64
691.75
0.21
11
90.24
1.042
cnl3
1.66
8.88
2.32
67.99
1.13
0.63
692.75
0.19
7
90.36
1.042
cc0lb
1.63
8.39
1.56
18.76
1.07
0.52
605.91
0.04
53
92.76
1.075
ccl lb
1.22
6,39
1.59
18.60
0,82
0.53
608.91
0.04
35
92.80
1.074
cc03b
2.29
11.60
1.56
24.80
1,49
0.57
709.25
0.21
44
97.12
1.073
ccl3b
1.72
8.82
1,56
24.80
1.13
0.56
709.25
0.21
34
97.15
1.073
crOib
0.74
4.03
1.78
18.25
0.53
0.68
605.88
0.04
49
90.92
1.062
crllb
0.56
3.07
1.79
18.25
0.40
0.68
608.88
0.04
35
90,93
1.062
cr03b
1.26
6.69
1.80
24.39
0.87
0.74
722.53
0.19
28
95.29
1.061
crl3b
0.94
5.10
1.80
24.40
0.67
0.74
725.52
0.19
17
95,31
1.061
cnOlb
0.53
2.91
1.84
18.24
0.38
0.77
609.93
0.06
55
86.28
1.063
cnllb
0.39
2.21
1.85
18.24
0.29
0.77
609.93
0.06
45
86.28
1,063
cn03b
0.90
4.82
1,86
24.29
0.63
0.85
699.26
0.21
21
90.59
1.061
cnl3b
0.68
3.66
1.86
24.29
0.48
0.84
702.26
0,20
15
90.60
1.062
.
See Table 5.6c on page 75 for the farming operations corresponding to each ID.
151
Table C.3.2 Fifteen - Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations of Rotation 3 (4 year tall fescue and 1 year
winter wheat) on Farm B
Soil Type: Helrnick 8%
ID4
USLE2
(ton)
NSE
NRU
NLE
HSE
HRU
(Ib)
(Ib)
(Ib)
(Ib)
(Ib)
ccOl
100
1155
365
40.05
1.49
0.62
971.50
0.22
ccli
2.24
8.79
3.65
39.99
1.13
0.62
975.50
ccO3
3.75
14.23
3.39
50.66
1.85
0.77
ccl3
2.82
10.80
3.39
50.59
1.41
cr01
2.39
9.39
4.01
44.22
cr11
1.80
7.16
4.01
cr03
3.30
12.58
cr13
2.47
cnOl
PRU
PLE
PSE
Yield
Yield
(bu)
(ton)
47
58.15
0.745
0.21
36
58,23
0.746
987.22
0.23
63
60.01
0.743
0.77
987.22
0.23
52
60.13
0.744
1.21
0.86
973.63
0.22
46
57.03
0.735
44.17
0.92
0.86
976.63
0.22
38
57,09
0,735
3.69
54.45
1,59
0.94
988.37
0.23
66
58,86
0.732
9.55
3.68
54.48
1.28
0.93
988.37
0.23
55
58.96
0.733
2.27
8.91
3.91
44.70
1.14
0.90
977.48
0.22
50
54.03
0.734
cnlI
1.70
6.78
3.91
44.66
0.87
0.89
977,48
0.22
41
54.08
0.735
cnO3
3.22
12.30
3.74
54.40
1.21
0.69
991.41
0.23
77
55.86
0.733
cnl3
2.55
9.85
3.69
54,40
1.09
3.22
991.42
0.23
62
55.95
0.732
ccOlb
2.24
8.39
2.81
10.64
1.08
0.71
1172.4
0.25
29 '
54.68
0.797
ccllb
1.68
6.36
2.81
10.62
0.82
0.71
1172.4
0.25
24
54.63
0.798
cc03b
2.41
9.34
3.74
14.58
1.21
0,94
1197,2
0.27
48
60.16
0.790
ccl3b
2.26
8.45
3.69
14.10
1.09
0.75
1200.2
0.27
37
60.17
0.793
crOib
1.19
3.53
3.06
10.21
0.46
0.61
1177.4
0.25
17
53.59
0.791
crllb
0.89
3.74
2.34
10.20
0.49
0.46
1180.4
0.25
12
53.52
0.792
cr03b
1.84
7,06
3.05
14.10
0.92
0.91
1203.2
0.27
32
58.96
0.786
crl3b
1.38
5.33
3.04
14.05
0.70
0.91
1203.2
0.27
26
59.05
0.789
cn0lb
0.92
3.74
2.34
12.57
0.49
0.82
1167.2
0.27
13
49.80
0.773
cnllb
0.69
2.84
2.34
12.56
0.37
0.82
1167.2
0.27
12
49.84
0.774
cn03b
1.41
5.57
2.23
16.53
0.74
0.83
1210.2
0.24
31
55.30
0.769
cnl3b
1.06
4.22
2.23
16.50
0.56
0.83
1213.2
0.24
21
55.38
0.769
152
Table C.3.3 Fifteen - Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations of Rotation 3 (4 year tall fescue and 1 year
winter wheat) on Farm B
Soil Type: Steiwer 8 %
ID4
USLE2
(ton)
NSE
NRU
NLE
HSE
HRU
(Ib)
(Ib)
(Ib)
(Ib)
(ib)
ccOl
2.82
1257
1.99
73.66
1.63
ccli
2.12
9.61
1.99
73.63
ccO3
3.49
15.35
2.05
cci3
2.62
11.71
cr01
2.28
cr11
P1W
PLE
PSE
Yie(d
(bu)
Yield
(ton)
0.56
998.52
9.10
65
49.87
0.580
1,24
0.56
998.61
9.09
25
49.92
0.581
79.03
2.00
0.70
1004.1
9.06
87
50.85
0.575
2.04
79.23
2.00
0.68
1001.1
9.05
65
50.99
0.575
10.39
2.24
74.01
1.34
0.70
981.74
3.39
68
49.73
0.567
1.72
7.89
2.23
74.00
1.02
0.69
984.74
3.38
52
49.78
0.567
cr03
2.96
13.27
2.33
79.99
1.73
0.80
997.20
8.85
77
49.97
0.562
cr13
2.31
10.41
2.32
79.55
1,36
0.79
998.03
8.70
69
50.01
0.562
cnOl
2.48
11.32
2.43
73.48
1.46
0.76
994.69
8.86
65
'47,28
0568
cnll
1,86
8.62
2.42
73.10
Lii
0.75
994.69
8,85
55
47.29
0.570
cnO3
3.02
13.48
2.36
79,48
1,75
0.80
998.07
8.73
99
47.43
0.561
cnl3
2.26
10.20
2.36
79.38
1.33
0.79
1001,0
8.72
75
47.50
0.563
ccOlb
2.26
9.99
1.92
47.11
1.28
0.61
1224.9
5.17
39
46.58
0.630
ccllb
1.70
7.61
1.92
47.26
0,98
0,61
1224.9
4.16
31
46,64
0,631
cc03b
2.95
12.84
1,96
52.27
1.66
0.66
1218.5
10.5
63
48.48
0,629
ccl3b
2.22
9.81
1.96
52.23
1.26
0.66
1218.4
10.5
50
48.56
0.630
crOib
1.43
6.55
2.07
46.50
0.85
0.73
1203.3
10.1
33
45.84
0.630
crllb
1.06
4.88
2.06
46.75
0,63
0.73
1203.3
10.1
24
45.87
0.631
cr031,
1.97
8.88
2.11
50.15
1.16
0.77
1220.9
4.90
34
47.86
0.623
crl3b
1.47
6.73
2.11
50.31
0.88
0.76
1220.9
4.89
37
47.93
0.624
cn0lb
1,10
5.13
2.16
46.37
0.67
0.79
1195.3
10.2
24
43.84
0.631
cnllb
0.82
3.88
2.16
46.39
0.51
0.79
1198.3
10.2
23
43.87
0.632
cnO3b
1,59
7.26
2.23
50.00
0.95
0.83
1222.4
4.90
64
45.72
0.623
cnl3b
1.19
5.50
2.23
50.08
0.72
0.83
12224
4.90
51
45.78
0.624
153
Table C.4. 1 Fifteen - Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations of Rotation 3 (4 year tall fescue and year
winter wheat) on Farm C
Soil Type: Woodburn 16 %
ID5
USLE2
NSE
NRU
HRU
(Ib)
(Ib)
NLE
(ib)
HSE
(ton)
(ib)
(Ib)
ccOl
4,75
23.86
2.17
47.88
3.05
0.45
663.47
0.03
cciTt
3.57
18.10
2.17
47.97
2.31
0.45
663.47
ccO3
6.16
30.54
2.19
55.36
3.93
0.59
ccl3
4.95
24.53
2.26
58.52
3.15
cr01
3.46
17.80
2.49
50.41
cr11
2.61
13.58
2.48
cr03
4.97
35.01
cr13
3.75
cnOl
PRU
PLE
PSE
Yield
(bu)
Yield
68
93.41
1.046
0.03
54
93.45
1.046
673.11
0.03
104
97.04
1.047
0.61
673.11
0.03
83
97.09
1.048
2.27
0.62
652.51
0.03
69
91.63
1.036
50.44
1.73
0.62
655.52
0.03
51
91.65
1.036
2.56
57.84
3.21
0.71
670.11
0.03
100
95,14
1.038
19.06
2.56
57.87
2.45
0.70
673,11
0.03
80
95.09
1.038
3.36
17,17
2.50
50.28
1,29
0,66
690.69
0.03
72
87,00
1.038
cnll
2.53
13.07
2.49
50.30
1.66
0.66
690,69
0.03
58
87.02
1.037
cnO3
5.09
25.56
2.60
57.90
3.27
0,72
671.16
0.03
121
90.25
1,038
cnl3
3.84
19,48
2,59
57,94
2.49
0,71
674.16
0.03
99
90.31
1.039
cc0lb
3.67
18.14
1.87
19.01
2.32
0.58
785,04
0,04
53
93.41
1.060
ccllb
2.76
13.78
1.87
18.94
1,76
0.58
784.04
0.04
42
93.45
1.061
cc03b
5.16
25.13
1.87
25.29
3.22
0.62
804,73
0.04
91
97.00
1.059
ccl3b
3.88
19.09
1,87
25.27
2,44
0.62
804.73
0.04
71
97,07
1,060
crOlb
1,64
8.52
2.09
8,53
1.11
0.74
778.00
0,04
42
91.63
1.050
crllb
1.23
6,48
2.08
18.52
0.84
0,74
778.00
0,04
28
91.65
1,050
crO3b
2.80
14.14
2.14
24.87
1.84
0,78
794.59
0,03
55
95.27
1.048
crl3b
2.10
10.75
2.14
24.85
1,40
0.78
794.59
0,03
42
95,30
1,049
crOib
1.20
6.25
2.14
18.52
0.82
0,82
812.12
0,04
21
87.00
1.051
cnllb
0.90
4.74
2,14
18,51
0.62
0,82
811.68
0.04
18
87.02
1.051
cn03b
1.56
8.04
2.20
24.82
1.05
0.87
781,49
0.04
49
90.59
1,049
cnl3b
1.04
5.27
1.93
21.19
0.69
0,92
784,49
0.04
41
90,57
1.049
See Table 5.6c on page 75 for the farming operations corresponding to each ID.
(ton)
154
Table C.4.2 Fifteen - Year Averaged Environmental and Crop Outputs (per
acre) for
Different Farming Operations of Rotation 3 (4 year tall fescue and 1 year
winter wheat) on Farm C
Soil Type: Helmick 16%
ID5
USLE2
(ton)
NSE
NRU
NLE
(Ib)
(Ib)
HSE.
(Ib)
HRU
(Ib)
ccOl
6.96
25.94
3.97
33.89
3.35
0.68
1029.6
0.17
ccli
5.25
19.70
3.96
33.74
2.54
0.66
1029.7
ccO3
8.70
31.95
3.71
43.93
4.17
0.86
ccI3
7.10
35.99
3.81
47.54
3.39
cr01
5.57
21.04
4.34
37.68
cr11
4.18
15.93
4.33
cr03
7.60
28.12
cr13
5.72
cnOl
PRU
PLE
PSE
Yield
(bu)
Yield
107
61 72
0735
0.17
82
61.91
0.735
1044.4
0.19
145
59.49
0.737
0.94
1047.4
0.18
107
59.72
0.735
2.71
0.92
1027.8
0.17
106
60,50
0.732
37.64
2.05
0,91
1030,8
0.16
81
60.74
0.734
4,01
47.19
3.66
1.02
1039.5
0.18
149
59.58
0.728
21.31
4,01
47.08
2.77
1.00
1042.5
0.18
115
58.84
0731
5.28
20.03
4,41
37.52
2.57
0.97
1032.8
0.17
114
56.73
0.733
cull
398
15.18
4.23
37.79
1.95
0.95
1033.6
0.17
90
56.83
0.733
cnO3
7.65
27.53
4.07
47.12
3.58
1.03
1041.6
0.19
172
55.41
0.742
cnl3
5,59
20.86
4,06
47.14
2.71
1.02
1044.6
0.18
131
55.64
0.743
cc0lb
5.10
18.58
3,55
9,84
2.40
0.86
1255.6
0.20
68
61 74
0763
ccllb
3.83
14.05
3.55
9.80
1.81
0.85
1255.6
0.19
52
61.91
0.764
ccO3b
6.94
24.99
3.47
2l.14
3.24
0,93
1278.3
0.21
110
59.52
0752
ccl3b
5.20
18,88
3.47
21.99
2.44
0.92
1278.3
0.21
81
59.79
0756
crOlb
2.60
9.85
3.84
9.63
1.29
1.05
1262.6
0.19
36
60.81
0759
crllb
1.94
7.43
3.87
9,68
0,97
1.07
1259.6
0,19
29
60.95
0.760
cr03b
4.18
15.47
3.70
21.51
2.02
1.05
1281.4
0.21
71
58.64
0.751
crl3b
3.13
'11,69
3.69
21.41
1.53
1,04
1284.4
0.21
54
58.86
0,752
cn0lb
2.01
7.84
2,70
11.36
1.03
0.95
1253.4
0.21
33
56.73
0743
cnllb
1.51
5.93
2.70
11.38
0.78
0.94
1254.4
0.21
26
56.83
0.743
cn03b
3.27
12,34
2.60
23.11
1.62
0.95
1289.4
0.18
62
55.31
0734
cnl3b
2.45
9.33
2,60
23.02
123
0.95
I2894
0 18
47
5548
0736
(1b)
(ton)
155
Table C.4.3 Fifteen - Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations of Rotation 3 (4 year tall fescue and 1 year
winter wheat) on Farm C
Soil Type: Steiwer 16 %
Yield
(bu)
Yield
145
50.58
0.560
2.34
114
50.71
0,562
1071.9
1.92
189
50.87
0.562
0.85
1076.8
1.90
146
51.02
0.553
3.04
0.80
1067.8
2.17
153
49.87
0.553
66.81
2.29
0.79
1067,8
2.15
115
49.98
0.556
2.66
70.02
3.88
0.91
1074.4
2.20
167
49.88
0.547
22.45
2.64
71.98
2.93
0.90
1080.4
2.17
130
50.05
0.552
5.06
22.28
2,75
66.94
2,88
0.84
1069.8
2.17
161
47.36
0.554
cnlI
3.81
16.93
2.74
66.57
2.19
0.83
1071.8
2.15
123
47.46
0.556
cnO3
6.98
30.10
2.66
70.35
3.93
0.91
1074.3
2.03
223
47.35
0.548
cnl3
5.34
23.20
2.70
72.13
3.03
0.91
1084.3
2.07
172
47.51
0.549
ccOlb
4.75
19.97
2.44
59.49
2.61
0.87
1337.2
3.42
90
51.12
0.588
ccllb
3.58
15.28
2.44
59.45
1,99
0.86
1337.2
3.40
67
51.23
0.588
ccO3b
6.07
25.10
2.40
63.99
3.30
1.05
1330.2
3.23
136
50.97
0.580
ccl3b
4.59
19.33
2.41
64,20
2.44
1.03
1330.2
3.20
106
51.12
0.580
crOib
3.17
13.91
2.51
47.00
1.81
0.90
1324.6
2.31
77
47.16
0,583
crllb
2.34
10.37
2.50
47.1!
1.35
0.90
1321.6
2.29
56
47.23
0.584
crO3b
4.31
18.71
2.43
52.11
2.45
0.96
1338.1
3.18
102
49.08
0.576
crl3b
3.27
14.32
2.44
52.04
1.87
0.96
1342.1
2.16
74
49.23
0,577
cnOlb
2.53
11.25
2.62
46.68
1.47
0.97
1315.6
2.30
53
45.02
0.584
cnllb
1.86
8.35
2.62
46.81
1.09
0.96
1312.6
2.29
49
45.08
0.584
cnO3b
3.57
15.56
2.49
51.99
2.04
1.02
1325.2
3.17
97
46.85
0.577
cn13b
2.67
11.76
2.49
51,93
1,54
1.01
1325.2
2.15
76
47,00
0,578
PLE
USLE2
(ton)
NSE
NRU
(ib)
NLE
HSE
RRU
(Ib)
(ib)
(Ib)
(Ib)
ceO!
6.51
27.24
227
66.87
3.54
0.65
1076.9
2.36
cell
4.91
20.96
2.27
66.71
2.72
0.63
1079.9
ccO3
8.05
32.99
2.43
71.26
4.32
0.81
ccl3
6.41
26.59
2.50
70.54
3.48
cr01
5.35
23.44
2.68
67.09
cr11
4.01
17.71
2.67
cr03
6.89
29.71
cr13
5.15
cnOl
ID5
.
PRU
'
PSE
(ton)
156
Table C.4.4 Fifteen - Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations of Rotation 3 (4 year tall fescue and 1 year
winter wheat) on Farm C
Soil Type: Chehulpum 16 %
ID5
USLE2
(ton)
NSE
NRU
HRU
(lb)
NLE
(ib)
HSE
(ib)
(Ib)
(Ib)
ccOl
7.10
27.15
3.33
76.23
3.57
1.13
1514.9
33.6
ccli
5,12
19.96
3.29
78.36
2.62
1.'lI
1540.1
ccO3
8.61
32.49
3.38
82.07
4.30
1,33
ccl3
6.18
23.75
3.13
84.82
3.14
cr01
6.26
29.61
3.66
75.32
cr11
4.69
22.59
3.63
cr03
8.10
31.27
cr13
6.70
cnOl
PRU
PLE
PSE
Yield
(bu)
Yield
199
35.42
0.293
34.8
160
35.21
0,290
1528.8
39.7
207
32.86
0.293
1.30
1519.9
33.5
179
32.82
0.293
3.85
1.22
1504.2
47.5
183
34,53
0.288
75,68
2.94
1.20
1506.2
42.3
135
34.76
0.286
3.68
81.66
4.08
1.36
1554.8
50.2
258
32.43
0.283
38.42
3.70
81.70
5.01
1.28
1552.9
46.4
183
32,33
0.285
5.87
28.37
3,75
74.78
3.68
1.19
1504.1
48.8
188
32.93
0.285
cnll
4.15
20.34
3.69
75.73
2.64
1.22
1530.2
39.9
122
33.07
0.285
cnO3
7.65
36.10
3.57
80.81
4.70
1.24
1531.2
50.4
245
30,44
0.284
cnl3
5.82
27.89
3,58
79.76
3.62
1,20
1527.2
44.9
193
30,28
0,287
cc0lb
4.78
20.89
2.91
67.23
2.44
1,05
1945.5
45.5
106
34.77
0.315
ccllb
3.82
16.93
2.90
63.18
1.95
0,99
1932.6
62.5
87
34.59
0,303
ccO3b
6.12
23.39
2.98
72.03
3.09
1.24
1962.6
46.7
144
32.34
0.306
ccl3b
4.68
18.12
2.98
73.74
2.39
1,26
1934.5
61.1
113
32.28
0.309
crOib
4.17
16.57
3.38
70.80
2.59
1.17
2056.4
46.7
107
34.46
0.308
crllb
3.12
12.45
3.41
62.25
1.96
1.14
2017.3
40.3
81
34.12
0.305
cr03b
6.24
28.81
3.21
68.29
3.74
1.19
2038.9
63.4
159
31.08
0.303
crl3b
4,69
21.97
3.25
66.32
2.85
1.18
2031.9
57.2
123
31.19
0,301
cn0lb
3.14
12.95
3.45
67.62
2.00
1.23
2025.3
41.2
68
33.42
0,311
cnllb
2.37
9.80
3.43
62.66
1.52
1.19
2022.3
41.1
65
33.18
0,308
cn03b
5.19
24.56
3.24
71.61
3.18
1.16
1995.1
54.1
153
30.25
0,305
cnl3b
3.88
18.64
3.33
72,12
2.41
1,17
1998.0
48.5
121
30.44
0.306
(ton)
157
Table C 5.1 Fifteen - Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations of Rotation 4 (1 year corn, 1 year beans and
1 year wheat) on Farm D
Soil Type: Woodburn. 8 %
1106
USLE2
(ton)
NSE
NRU
NLE
HSE
HRU
(Ib)
(Ib)
(ib)
(Ib)
(Ib)
dcOl
1.55
7.39
0.13
95.54
1.14
0.22
1.02
29,76
dcli
116
5.55
0.13
95.65
0.86
0.22
1.02
dcO3
2.65
12,4
0.14
104.3
1.90
0.22
dcl3
1.98
9.36
0.14
104.5
1.43
drOl
1.30
6.30
0.13
95,39
dill
0.98
4.73
0,13
drO3
2.21
10.5
drl3
1.66
dnOl
PRU
PLE
PSE
Yld
(bu)
Yld
Yld
(t)
(t)
20
90.5
10.51
6.62
29,75
16
90.5
10.50
6.62
1.02
29.76
62
95.9
10.51
6.62
0.22
1.02
29.75
46
95.9
10.50
6.64
0,96
0.23
1,01
29.57
17
88.7
10.50
6.62
95.48
0.72
0.23
1.01
29.55
13
88.7
10.50
6.62
0.14
104.2
1.59
0.23
1.01
29.57
53
93.9
10.51
6,62
7.93
0.14
104.4
1.19
0.23
1.01
29.55
35
94.0
10.50
6.64
1.07
5.19
0.13
95.31
0.76
0.23
1.01
26.41
8
84.2
10.50
6,62
drill
0.80
3.90
0.13
95.38
0.57
0.23
1.01
26.40
4
84.2
10.50
6.64
dnO3
1.61
7.75
0.14
104.3
1.13
0.23
1.01
26.45
35
89.1
10.50
6.64
dnl3
1.21
5.82
0.14
104.4
0,84
0,23
1.01
26,40
26
89.1
10.50
6.64
See Table 5.6d on page 76 for the farming operations corresponding to each ID.
158
Table C.5.2 Fifteen - Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations of Rotation 4 (1 year corn, 1 year beans and
1 year wheat) on Farm D
Soil Type: Willamette 8 %
ID6
USLE2
(ton)
NSE
NR(J
NLE
HSE
HRU
(Ib)
(Ib)
(Ib)
(Ib)
(Ib)
dcOl
1.75
8.36
0.05
120.8
1.27
0.11
1,34
9,04
dcli
1.31
6.27
0.05
120.9
0.96
0.11
1.34
dcO3
2.94
13,8
0.05
129.4
2.11
0.11
dcl3
2.20
10.4
0.05
129.5
1.58
drOl
1.50
7.22
0.06
120.6
di-1 1
1.13
5.43
0.05
drO3
2.50
11.8
dIrl3
1.86
dm01
PSE
PRU
PLE
Yld
(bu)
Yld
Yld
(t)
(t)
17
89.95
7.89
6.14
9.03
11
89.96
7.87
6.14
1.42
9.04
65
95.17
7.91
6.14
0.11
1.42
9.03
53
95,19
7.90
6.14
1.07
0,12
1.38
8.91
11
88.13
7.87
6.14
120.6
0.81
0.11
1.38
8.91
11
88.13
7.87
6.14
0.05
129.1
1.74
0.11
0,41
8.92
56
93.27
7.89
6.14
8.87
0.05
129.2
1.30
0.11
0.41
8.91
41
93.28
7.89
6.14
1.22
5.89
0,06
120.4
0,86
0.12
0,51
5.88
8
83.64
7.87
6.14
dm11
0.91
4.42
0.06
120.4
0.64
0,12
0.51
5.87
8
83.65
7.86
6.14
dm03
1,84
8.82
0.05
129.1
1.27
0.11
0.41
5,88
38
88,51
7.89
6.14
dm13
1.38
6,62
0.05
129.1
0.95
0,11
0.40
5.88
26
88.52
7.87
6.14
159
Table C.5.3 Fifteen Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations of Rotation 4 (1 year corn, 1 year beans and
1 year wheat) on Farm D
Soil Type: Jory 16%
ID6
USLE2
(ton)
NSE
(Ib)
NRU
(lb)
NLE
HSE
(Ib)
RRU
(ib)
PRU
(ib)
dcOI
10.54
52.77
0.49
58.66
7.90
1.30
45.23
7.79
dcli
7.92
40.23
0,48
58.63
5.99
1.26
47.59
dcO3
12.97
63.90
0,47
63.18
9.63
1.32
dcl3
9.75
48.94
0,49
62,95
7.33
drOl
10,210
53.83
0.49
58.66
drli
7.63
40.27
0.48
dsO3
12.565
65.97
drl3
9.38
dnOl
PLE
Yld
Yld
Yld
(bu)
(t)
(t)
164
55.97
5.67
3.30
7.73
121
56,43
5.63
3,30
43,08
7.82
259
55.20
5.74
3.30
1.31
44.65
7.72
194
56.05
5.67
3.30
7,88
1,31
44.72
4.45
159
54.98
5.66
3.30
58,57
5.86
1.27
47.69
4.39
119
55,01
5.61
3.30
0.47
63.17
9.69
1.35
44.16
4.50
254
54.25
5.73
3.30
49.36
0.48
62.89
7.20
1.31
47.78
4.42
193
55.05
5.67
3.30
9.32
49.24
0.48
58.55
7.06
1.25
47.85
4.32
132
52.20
5.64
3.30
dnII
6.96
36.86
0.48
58.51
5.26
1.20
46.84
4,27
98
52.60
5.60
3.30
dnO3
11.107
58.42
0.47
62.96
8.37
1.27
46.32
4.35
209
51.84
5.70
3.30
dnI3
8,29
43.71
0.48
62.78
6.23
1.24
46.91
4.29
160
52.48
5.64
3.30
PSE
160
Table C.5.4 Fifteen - Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations of Rotation 4 (1 year corn, 1 year beans and
1 year wheat) on Farm D
Soil Type: Nekia 8 %
ID6
USLE2
(ton)
NSE
NRU
NLE
HSE
FLRU
(Ib)
(Ib)
(Ib)
(Ib)
(Ib)
dcOl
2.45
15.83
0.08
109.89
2.25
0.26
2.36
46.06
dcli
1.84
11.88
0.08
109.92
1.69
0.26
2.36
dcO3
3.50
22.43
0.09
117.05
3.19
0.26
dcl3
2.63
16.82
0.09
117.11
2.39
drOl
2.29
14.89
0.08
109.63
dill
1.72
11.17
0.08
drO3
3.24
20.85
drl3
2.43
driOl
PSE
PRU
PLE
Yld
(bu)
Yld
(t)
Yld
(t)
27
51.10
5.67
5.96
46,06
18
51.10
5.63
5.96
2,51
46.06
63
53.29
5.74
5.96
0.26
2,51
46,06
45
53.39
5.67
5.96
2.10
0.27
2,35
37.06
24
50.06
5.66
5.96
109,65
1.57
0,27
2,35
36.06
iS
50.13
5.61
5.96
0.09
116.77
2.94
0.27
2.50
37,06
57
52.23
5.73
5.96
15,63
0.09
116.83
2.20
0.27
2.51
37,06
42
52.32
5.67
5.96
2.19
14,25
0.08
109.51
2.00
0.27
1.35
34,05
16
47.54
5.64
5,96
drill
1.64
10,69
0.08
109.54
1.50
0,27
1.35
33.05
12
47.64
5.60
5.96
dnO3
2.74
17,70
0,09
116.84
2,46
0,27
1.50
34,06
42
49.39
5.70
5,96
dnl3
2.06
13,27
0,09
116.89
1,84
0.27
1.50
34.05
27
49.96
5.64
5,96
161
Table C.55 Fifteen - Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations of Rotation 4 (1 year corn, 1 year beans and
1 year wheat) on Farm D
Helmick 16%
6
USLE2
(ton)
NSE
NRU
HRU
(Ib)
LE
(Ib)
HSE
(Ib)
(Ib)
(ib)
dcOl
10480 45.34 0.33
61.23
5.81
0.86
dcli
7.82
27,79
0,33
61.15
4.33
0.83
dcO3
13,210
46.46
0.31
65.71
7.32
dcI3
9.87
34,75
0.31
65.59
drOl
9.96
35.57
0,33
dril
7.43
26.61
drO3
12.481
dtl3
PRU
PLE
PSE
Yld
(by)
Yld
(t)
Yld
(t)
30.13
103
4025
724
4,20
22.85
27,12
75
40.69
7.19
420
0.88
22.87
30.13
185
42.14
731
4.20
5.45
0.85
22.86
30.13
132
42.79
723
4.20
61.04
5.52
0.90
21,95
21.12
97
39.54
723
4.20
0.33
60.99
4.11
0.87
22.82
21,12
69
39.91
717
420
44.19
0,31
65.48
6.91
0.93
21.96
21,13
173
41.46
7.30
420
9.32
33.02
0.31
65,37
5.13
0.89
21,94
21.13
131
42.05
7.23
420
dnOl
9.17
32,86
0,33
60.95
4,98
0.87
22,95
18.12
82
37.62
7.21
4,20
dnll
6.85
24.58
0.33
60.89
3.71
0.84
22.93
18,12
56
37.98
7.16
420
dnO3
11.031
39.18
0.31
65.28
5.95
0.89
22.02
21.13
140
39.68
7.25
420
dnI3
8,24
29.28
0.31
65.19
4.42
0.85
22.01
18.13
99
40.14
720
4,20
162
Table C.6. 1 Fifteen - Year Averaged Environmental and Crop Outputs (per
acre) for
Different Farming Operations of Rotation 5 (4 year perennial ryegrass and
1 year winter wheat) on Farm E
Soil Type: Jory 8 %
ID
USLE2
(ton)
NSE
NRU
NLE
HSE
HRU
(Ib)
(Ib)
(Ib)
(Ib)
(Ib)
ecOl
1.06
6.06
2.39
9.54
0.78
0.75
919.89
0.01
ecu
0.79
4.58
2.38
9.53
0.59
0.75
918.89
ecO3
1.70
9.66
2.52
12.46
1.24
0.81
ecl3
1.28
7.34
2,52
12.44
0.94
cr01
0.68
3.98
2.51
9,05
en!
051
3.02
2,51
erO3
1.18
6.86
cr13
0.88
enOl
PRU
PLE
PSE
Yield
(bu)
Yield
39
80.21
0683
0.01
25
80.25
0 683
941.41
0.
40
84.45
0.680
0.81
943.40
0.
52
84.45
0.681
0.52
0.80
899.95
0.01
27
78.49
0.675
9,04
0,39
0,80
899,95
0.01
15
78,52
0675
2.63
12.98
0.89
0.89
929.47
0.
52
82.73
0672
5.20
2.63
12.97
0.68
0.89
929.47
0.
37
82.73
0.673
0.44
2.65
2.85
8.43
0.35
1.02
875.15
0.01
30
74.40
0.672
enlI
0.33
2.01
2.85
8,42
0.26
1.02
878.16
0.01
19
74.42
0.672
enO3
0.79
4.64
2.95
11.90
0.61
1,12
908.72
0.01
62
78.38
0671
enl3
0.59
3.52
2,95
11,89
0.46
1.12
909.72
0.01
42
78.45
0,671
ecOib
1.72
12.51
2.18
105.4
1.58
0.21
1232.0
0.01
28
72.52
0623
ecllb
1.30
9.57
2.18
105.4
1.21
0.21
1232.0
0.01
21
72.58
0623
ecO3b
2.32
16.62
2.22
112.3
2.19
0.30
l233.6
0.01
50
73.15
0.623
ecl3b
1,74
12.66
2.21
112.4
1.61
0.30
1235.6
0.01
39
73.24
0.623
erOib
1.55
11.46
2.31
104.0
1.45
0.26
1223.1
0.01
14
71.16
0.627
erllb
1.16
8.74
2.31
104.0
1.11
0.25
1200.1
0.01
9
71.21
0.627
erO3b
2.16
15.68
2.32
111.1
2.00
0.34
1226.6
0.01
33
71,73
0621
erl3b
1.62
11,94
2.31
111.1
1.52
0.33
1226.6
0,01
26
71.82
0.621
enOlb
1.50
11.05
2.56
104.4
1,40
0.37
1197.1
0,01
13
67.51
0.626
enlib
1.12
8.43
2.56
104,4
1.07
0.36
1200,1
0.01
9
67.51
0.626
enO3b
2.12
15.29
2.48
110.4
1.94
0.40
1214.7
0.01
27
68.06
0625
eni3b
1.59
11.65
2.48
1105
1.48
040
1214.7
0.01
20
68,14
0625
See Table 5.6e on page 77 for the farming operations corresponding to each ID.
(ton)
163
Table C.62 Fifteen - Year Averaged Environmental and Crop Outputs (per acre) for
Different Farming Operations of Rotation 5 (4 year perennial ryegrass and
1 year winter wheat) on Farm F
Soil Type: Nekia 00 0/
/0
ID7
USLE2
(ton)
NSE
NLE
HSE
HRU
(Ib)
(Ib)
(Ib)
(Ib)
(Ib)
ecOl
1.72
12.5
2.18
105.4
1.58
ecu
1.30
9.57
2.18
105.4
ecO3
2.32
16.6
2.22
ecl3
1.74
12.6
cr01
1.55
eril
RU
PRU
PLE
PSE
Yield
(bu)
Yield
(ton)
'0.21
899.55
001
40
72.52
0.623
1.21
021
899.55
0.01
33
72.58
0.623
112.3
2.19
0.30
907.94
0.02
64
73.15
0.623
2.21
112.4
1,61
0,30
910.94
0.02
48
73.24
0.623
11.4
2.31
104.0
1.45
0.26
888.55
0.01.
27
71.16
0.627
1.16
8.74
2.31
104.0
1.11
0.25
888.55
0.01
21
71.21
0.627
erO3
2.16
15.6
2.32
111.1
2.00
0.34
897.97
0.02
50
71.73
0.621
cr13
1.62
11.9
2.31
111.1
1.52
0.33
900.97
0.02
36
71.82
0.621
enOl
1.50
11.0
2.56
104.4
1.40
0,37
862.82
0.02
30
67.51
0.626
enIl
1.12
8.43
2.56
104.4
1.07
0.36
862.81
0.02
24
67,51
0.626
enO3
2.12
15.2
2.48
110.4
1.94
0.40
882.25
0.02
57
68.06
0.625
enI3
1.59
11.6
2.48
110.5
1,48
0,40
882.24
0.02
44
68.14
0.625
ecOib
0.82
5.91
1.77
37.29
0.75
0.54
1187.6
0.02
30
65.79
0.621
ecllb
0.61
4.49
1.77
37.29
0.57
0.54
1187.6
0.02
22
65.82
0.621
ec03b
1.33
9.49
1.81
43.89
1.21
0.59
1199.1
0.02
52
70.21
0.616
eèl3b
1.00
7,21
1,81
43.89
0,92
0,59
1199,1
0.02
37
70.30
0,616
erOib
0.50
3.72
1.90
35,79
0.48
0.58
1172.6
0.02
15
64.53
0.616
erllb
0.37
2.83
1,89
35.80
0.36
0.58
1172,6
0.02
12
64,55
0,616
erO3b
0.90
6.59
1.90
42.85
0.85
0.64
1187.1
0.02
34
68.85
0.614
erl3b
0.68
5.01
1.90
42,85
0.64
0,64
1187,1
0.02
24
68.91
0.614
enOib
0.31
2.34
2.16
34.08
0.30
0.73
1144.7
0.02
9
61.46
0.615
enlib
0.23
1.78
2.17
34.08
0.23
0.73
1144.7
0.02
8
61.47
0.615
enO3b
0.60
4.39
2.10
41.12
0.57
0.79
1176.2
0.02
20
65.65
0.617
enl3b
0.45
3.34
2.11
41,12
043
0,79
1176.2
0.02
16
65,70
0,617
164
Table C.6.3 Fifteen - Year Averaged Environmental and Crop Outputs (per
acre) for
Different Farming Operations of Rotation 5 (4 year perennial ryegrass and
1 year winter wheat) on Farm E
Soil Type: Nekia 16%
ID
USLE2
(ton)
NSE
NRU
NLE
HSE
HRU
(lb)
(Jb)
(ib)
(Jb)
(Ib)
ecOl
4.06
28.0
249
9486
3.53
0.24
961.76
0.01
ecu
3.05
21.3
2.49
94.93
2.69
0.23
961.76
ecO3
5.45
37.3
2,56
100.3
4.74
0.35
ecl3
4.10
28.4
2.55
100,4
3.61
cr01
3.63
25.4
2,64
93.55
erIl
2.73
19.4
2.64
erO3
5.04
35.0
erI3
3.79
enOl
PRU
PLE
PSE
Yield
(bu)
Yield
75
73 27
0.617
0.01
57
73.39
0.618
969.16
0.81
141
73.07
0.617
0.34
972.18
0.48
113
73.27
0.618
3.22
0,30
953.78
0.01
57
71 84
0.620
93,62
2.45
0.29
953.78
0.01
45
71,95
0620
2.68
99.15
4.46
0.40
962.20
0.23
107
71.64
0,616
26,6
2,67
99.24
3.39
0.38
962.20
0.23
85
71 83
0,616
3.49
24,4
2.92
93.01
3,10
0.41
927.01
1.24
60
68.16
0617
erill
2.63
18.6
2.92
93.08
2,36
0.40
930,01
1,06
48
68.26
0.618
enO3
4.93
34.0
2.87
98.72
4.32
0.46
943.44
1,26
122
67.96
0617
enl3
3.71
25.9
2,86
98,81
3,29
0.44
946.45
1.46
95
68.15
0.617
ecOlb
1.95
13.5
1.99
31.52
1.71
0.24
1276,8
0.33
79
66,40
0.616
ecllb
1.46
10.2
1.99
31.52
1.30
0.23
1276,8
0.19
56
66.49
0616
ecO3b
3.16
21.4
2.04
37.19
2,73
0,65
1288.3
0.87
121
7077
0.612
ecl3b
2,37
16.2
2.04
37,17
2.07
0.64
1291.3
0.84
89
70.96
0613
erOib
1.18
8.38
2.13
30.19
1.07
0.30
1264.8
0.01
38
65,12
0.611
erllb
0.88
6.35
2.13
30.18
0.81
0.29
1264,8
0.01
26
65.18
0611
er03b
2.13
14.8
2.15
37.19
1.90
0.70
1282.3
0.01
73
69.57
0.609
erl3b
1.60
11.2
2.15
37.26
1,44
0.70
1282,3
0.01
54
69.64
0.609
enOl
0.72
5.22
2,42
29.70
0.68
0.41
1239.9
0.89
18
62.01
0,609
enlJ
0.54
3.96
2.42
29.70
0.51
0.40
1239.9
0.48
13
62.05
0.609
enO3
1.40
9.78
2.38
35.86
1.27
0.86
1274,4
0.67
47
66.27
0.611
eril3
1,05
7.42
2.38,
35.91
0.96
0.86
1274.4
0.38
39
66.37
0.611
(ton)