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Johansson, Robert C.

Regional environment and agriculture programming model.

(Technical bulletin (United States. Dept. of Agriculture) ; no. 1916)

 1. Agriculture—Environmental aspects—United States—Mathematical models.

 2. Agriculture—Economic aspects—United States—Mathematical models.

 3. Agriculture and state—United States—Computer simulation.

 4. Environmental policy—United States—Computer simulation.

 5. Programming (Mathematics) 

 6. Spatial analysis (Statistics) 

 I. Peters, Mark.

 II. House, Robert.

 III. United States. Dept. of Agriculture. Economic Research Service.

 IV. Title. 

HD1750

Photo credit: USDA/NRCS Photo Center.
United States

Department

of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

Regional Environment and 

Agriculture Programming 

Model 

Robert Johansson, Mark Peters, 

and Robert House 

Technical 

Bulletin 

Number 1916 

March 2007 

Abstract 

This bulletin presents the Regional Environment and Agriculture Programming Model 

(REAP), which was formerly known as USMP (U.S. Mathematical Programming 

Regional Agriculture Sector Model). This bulletin is a reference document for analysts 

and model users. It includes an outline of the objectives of REAP, describes the 

methodology used to achieve these objectives, and provides details on how REAP 

works. This bulletin provides the theoretical and modeling system specification, 

descriptions of the data used, and a guide for setting up and running model simulations. 

REAP is designed for spatial analyses of U.S. agricultural and environmental policies. 

REAP has been applied to soil conservation and environmental policy design, water 

quality, environmental credit trading, irrigation policy, climate change mitigation 

policy, trade and the environment, livestock waste management, wetlands policy, new 

or alternative fuels from agriculture products, crop and animal disease, and regional 

effects of trade agreements. 

Keywords: Agriculture, environment, policy, mathematical programming, agricultural 

sector model. 

Acknowledgments 

The model and this documentation have undergone numerous changes and innovations 

over its current lifespan, more than 20 years now. We thank the many researchers who 

provided their expertise in developing the potential of this simulation model. These 

researchers include Howard McDowell, Terry Hickenbotham, Terry Disney, Ralph 

Heimlich, Vince Breneman, Jonathan Kaplan, Paul Faeth, Mindy Selman, Jimmy 

Williams, and many others at the U.S. Department of Agriculture and the World 

Resources Institute. We would also like to acknowledge Marca Weinberg, Kitty Smith, 

and Mary Bohman, USDA, Economic Research Service, for providing administrative 

support throughout the development of this technical documentation project. We also 

thank David Abler, Agapi Somwaru, Kazim Konyar, and an anonymous reviewer who 

provided excellent suggestions and edits throughout this project. And finally, thanks to 

Sharon Lee and Wynnice Pointer-Napper, USDA, Economic Research Service for 

editorial and design assistance. 
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About the Authors

Robert Johansson is an economist at the U.S. Office of Management and

Budget, and Mark Peters is an economist with the National Resources

Conservation Service, U.S. Department of Agriculture (USDA); both were

with the Economic Research Service, USDA, earlier. Robert House, an

economist retired from the Economic Research Service, USDA, is an

agricultural policy consultant with Environmental Defense. Contributors to

this bulletin are Scott Malcolm, economist with the Economic Research

Service, USDA, Suzie Greenhalgh and Elizabeth Marshall, economists with

the World Resources Institute, and John Westra, an assistant professor of

agricultural economics at the Louisiana State University, Agricultural

Center.

Contact information:

Scott Malcolm, 202-694-5517, smalcolm@ers.usda.gov

Recommended citation format for this publication:

Johansson, Robert, Mark Peters, and Robert House.

Regional Environment and Agriculture Programming

Model, TB-1916. U.S. Dept. of Agriculture, Econ.

Res. Serv. March 2007.
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Contents 

Summary.................................................................................................. iv 

Introduction.............................................................................................. 1

 Economic Modeling................................................................................ 2 

 Foundations ............................................................................................ 3 

 Environmental Modeling ........................................................................ 6 

Model Environment ................................................................................. 7

 Indexes (GAMS SETS)........................................................................... 8 

 Exogenous Variables (GAMS Parameters)........................................... 25 

 Endogenous Variables (GAMS Variables) ........................................... 37 

 Equations .............................................................................................. 41 

 Solving the Model................................................................................. 51 

Model Data.............................................................................................. 55

 Baseline Data ........................................................................................ 55 

 Crop Production Enterprises ................................................................. 57 

 Data Reconciliation............................................................................... 61 

References............................................................................................... 92

Appendix A. Installing and Using REAP............................................ 96

Appendix B. Environmental Parameters............................................ 98

Appendix C. Accessing REAP and Contributing 

 to Model Design................................................................................. 111 
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Summary 

Development of the U.S. Mathematical Programming Regional Agricultural 

Sector Model (USMP) began in 1985 to augment economic and 

environmental policy analysis at the U.S. Department of Agriculture’s 

Economic Research Service. Analysts needed a way to represent the 

interactions among product prices, choice of production practices, and 

demand for crop and livestock products when analyzing the potential effects 

of policies designed to address environmental issues associated with 

agriculture. The effects of environmental and energy policies were so 

widespread and the interaction among the various commodities so complex 

that it was impossible for analysts, using the available analytical tools and 

research results to project the ultimate effect of specified policies on 

agricultural producers or even to determine whether the policies would 

achieve their desired goals. This bulletin presents the current version of the 

USMP model—now the Regional Environment and Agriculture 

Programming Model (REAP)—its theoretical and modeling system 

specification, descriptions of the data used, and a guide for setting up and 

running model simulations. 

What Are the Issues? 

Many agricultural policy issues stem from agricultural production and its 

interface with the environment. Modeling efforts are important for 

informing policymakers on how these issues might influence the 

heterogeneous set of farms, farmers, and environmental resources that 

characterize U.S. agricultural production. Agricultural policy issues 

analyzed using REAP include soil conservation and environmental policy 

design, water quality, environmental credit trading, irrigation policy, climate 

change mitigation policy, trade and the environment, livestock waste 

management, wetlands policy, new or alternative fuels from agriculture 

products, crop and animal disease, and regional effects of trade agreements. 

What Does the Model Do? 

REAP is designed for general-purpose economic, environmental, 

technological, and policy analysis of the U.S. agriculture sector. REAP 

facilitates scenario—or “what if”—analyses by showing how changes in 

technology, commodity supply or demand, or farm, resource, 

environmental, or trade policy could affect a host of performance indicators 

important to decisionmakers and stakeholders. Analysts perform “what if” 

analyses by solving for a baseline, or status quo, economic equilibrium, then 

imposing specific policy, technology, trade, or other changes on the system 

and solving REAP again to compute a new economic equilibrium consistent 

with the scenario changes. Performance indicators include regional values 

for land use, input use, crop and animal production and prices, farm income, 

government expenditures, farm program participation, and environmental 

emissions such as erosion, nutrient and pesticide loadings, and greenhouse 
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gases. The scenarios analyzed do not predict a dated forecast or projection, 

but rather present the likely effect of proposed changes in policies, 

regulations, and markets on the agriculture sector's performance, holding 

constant all other conditions affecting the sector. 

REAP is a price-endogenous mathematical programming model. As such, it 

incorporates the assumptions of neoclassical economics, supplemented by 

the best available estimated behavioral and biophysical relationships (e.g., 

for agricultural commodity supply and demand or nitrogen run off). Many 

regularly updated data sets—production practices surveys, multiyear 

baselines, macroeconomic trend projections, and regional resource and land 

databases—are applied to construct and update REAP. To generate a 

baseline scenario, disaggregated regional data are used to map the baseline 

data projections into REAP’s smaller units of analysis. The relationships 

between production practices and environmental performance indicators 

represented in the model are derived by using biophysical models. 

How Does the Model Work? 

• REAP cropping enterprises, or activities that include rotation, tillage, 

and fertilizer choices, are linked to the Environmental Policy Integrated 

Climate Model (EPIC), a biophysical model of crop production. In 

addition to the effect of production practices on yields, EPIC is used to 

compute environmental indicators such as nitrogen loss and greenhouse 

gas emissions per acre for each REAP crop system, thereby augmenting 

economic analysis of “what if” scenarios with their environmental 

effects as well. 

• Land use, crop mix, multiyear crop rotations, tillage practices, and 

nitrogen fertilizer application rates are all endogenously determined in 

REAP’s 45 production regions. Scenario analysis explores the response 

of all these variables to “what if” changes in policy incentives, 

regulations, market conditions, technology, and so forth. 

• Crop and livestock primary and secondary products are all integral parts 

of the model and interact in the solution process. Cattle, poultry, and 

swine feed rations are formed from activities that process crops into 

protein, energy, and trace elements necessary for the respective animal 

diets. Policy and market shocks that directly affect either the crop or 

livestock industry ultimately result in a market equilibrium that reflects 

the repercussions for agricultural industries and markets. 

• REAP provides comparative static analysis from any base year in the 

historical/baseline data, which is approximately 1988-2015. REAP is 

typically calibrated to a current or future year selected from the 10-year 

USDA baseline. For example, REAP is to be calibrated to the 2010 

baseline for scenario analysis of changes introduced in 2010. Near-term 

analyses of policy, market, or technology shocks reflect short- or 

medium-term sector responses; long-term analyses reflect longer run 

adjustments. 
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• The explicit linkages in REAP between production activities and 

environmental emissions indicators can be exploited to extend analysis 

to alternative environmental policy scenarios. For example, REAP was 

extended in 1999 to provide analysis of the effects of the Kyoto Protocol 

on U.S. agriculture. REAP has also been extended by the World 

Resources Institute to examine excess fertilizer nutrient (phosphorus) 

pollution in the Great Lakes, hypoxia, climate change, and 

point/nonpoint emissions trading. 

• Data used are readily available. Most core model data are prepared and 

regularly updated by agencies of the U.S. Department of Agriculture. 

REAP applies USDA and ERS data and estimates to agriculture sector 

analysis. This includes ERS cost of production data, USDA acreage and 

production data, baseline data, and changes to commodity program 

policy instruments (e.g., fixed and counter-cyclical payments, target 

prices, loan rates, loan deficiency payments, and domestic 

agrienvironmental programs). 
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Introduction 

This bulletin describes the Regional Environment and Agriculture Programming Model (REAP), which 

was formerly known as USMP, or the U.S. Mathematical Programming Regional Agriculture Sector 

Model (box 1). This bulletin is a reference document for analysts and model users. 1 In this bulletin, we 

outline the objectives of REAP, describe the methodology used to achieve these objectives and provide 

details on how REAP works. 

Many agricultural policy issues stem from agricultural production and its interface with the environment. 

REAP is a powerful tool for analyzing the effects of policy on both agricultural structure and the 

environment. This model has been applied to: 

• soil conservation and environmental policy design (Cattaneo et al., 2005; Claassen et al., 2001); 

• water quality (Doering et al., 1998; Ribaudo et al., 2001; Peters et al., 1997; Greenhalgh and Faeth, 

2001; Greenhalgh and Sauer, 2003); 

• environmental credit trading (Ribaudo et al., 2005); 

• irrigation policy (Horner et al., 1990 ); 

• climate change mitigation policy (Peters et al., 2001; Faeth and Greenhalgh, 2000; Faeth and 

Greenhalgh, 2002; Lewandrowski et al., 2004); 

• trade and the environment (Johansson et al., 2005; Cooper et al., 2005); 

• livestock waste management (Ribaudo et al., 2003; Aillery et al., 2005; Johansson and Kaplan, 2004; 

Kaplan et al., 2004); 

• wetlands policy 

• new or alternative fuels from agriculture products (Marshall and Greenhalgh, 2006; House et al., 

1993); 

• crop and animal disease (Livingston et al., 2004; Disney and Peters, 2003); and 

• regional effects of trade agreements (Burfisher et al., 1992). 

REAP combines information on agricultural commodity supply and use relationships with policy 

instruments and environmental parameters. The model simulates how changes in government agricultural 

or environmental policy could result in changes to production practices and the effects of those changes on 

commodity markets, net returns, and the agriculture sector’s environmental performance. 

The model includes the major commodity crops, a number of livestock enterprises, and a variety of 

different processing technologies used to produce retail products from agricultural inputs. The data used to 

drive REAP are drawn from a number of national databases: the USDA production practices survey, the 

USDA multiyear baseline, and the National Resources Inventory. 

REAP divides the United States into production regions, derived from the intersection of the USDA Farm 

Production Regions, Land Resource Regions, and soil erodibility classification. For each of those regions, 

land use, crop mix, multiyear crop rotations, tillage practices, and nitrogen fertilizer application rates are 

all endogenously determined by REAP’s constrained optimization process. The biophysical effects of 

those rotations and tillage practices are then estimated by using a crop biophysical simulation model called 

the Environmental Policy Integrated Climate (EPIC) model. 

1 REAP retains basic historical policy mechanisms (i.e., no-longer-used government commodity or conservation programs which 

may be switched off or on) to facilitate analysis if a variant of an old program returns to current policy. However, completely

rebasing REAP to the policy and market conditions of a historical year (e.g., for historical counterfactual analysis) might require 

substantial effort.
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Changes in policy, demand, or production/processing technology can, therefore, be imposed upon the 

model and the results examined to determine their effects on the following: 

• regional supply of crops and livestock; 

• commodity prices; 

• crop management behavior and use of production inputs; 

• farm income; and 

• environmental indicators such as nutrient and pesticide runoff, soil loss, greenhouse gas emissions, soil 

carbon fluxes, and energy use. 

Due to the highly aggregated nature of the model and the coarseness of the estimation, REAP results are 

generally used to evaluate the relative effects of various policy options and not to predict absolute changes 

in production or environmental parameters. 

Economic Modeling 

The REAP model is a comparative-static, regional, mathematical programming model of U.S. agriculture. 

The model is written and maintained in GAMS (General Algebraic Modeling System). REAP seeks to 

determine the set of prices and quantities that establish equilibrium in several related markets by 

maximizing net social benefit. The model takes as its data the technological coefficients on production 

activities, levels of fixed resources, demand relationships for final products, and supply relationships for 

purchased inputs and generates a solution that gives the equilibrium prices and quantities of final goods, 

the pattern of use of the factors of production, prices for purchased inputs, and imputed prices for owned 

resources and production activities. The equilibrium established by the model is partial because consumer 

income and the prices of commodities produced outside the sector are held fixed. In specifying this model, 

we assume that the sector is composed of many competitive agents none of whom can, through their 

individual actions, influence prices. 

The constrained optimization estimates profit-maximizing levels of factor inputs, environmental 

emissions, crop and livestock production, processed agricultural products, commodity and processed 

product prices, and final demand sectors, including domestic use, exports, and government and 

commercial stocks (fig. 1). Geographic coverage for crop production encompasses 90 regions determined 

by the intersection of the 10 USDA Farm Production Regions, 25 USDA Land Resource Regions, and soil 

erodibility classification (fig. 2). Geographic coverage for livestock production encompasses 10 regions 

based on the 10 USDA Farm Production Regions. Twenty-three inputs and their costs (e.g., land, nitrogen 

fertilizer, energy, and labor) are represented, as well as 44 agricultural commodities (e.g., hogs for 

slaughter and corn) and processed products (e.g., soybean meal, retail cuts of pork, and ethanol) (table 1). 

Crop production activities in each region are differentiated by crop, rotation, and tillage practice. Each 

production activity contains information on input use, output, and environmental indicators. Production, 

land use, land use management (crop mix, rotations, and tillage practices), and nitrogen fertilizer 

application rates are endogenously determined. Allocation of cropland to crop rotations and associated 

tillage practices is represented with constant elasticity of transformation functions. The transformation 

function determines the rate at which production practices can be substituted for each other. Regional 

supplies of crop-specific acreage are represented with positive mathematical programming (PMP) cost 

functions, while the availability of cropland is represented with simple kinked supply functions. In this 

framework, cropland is simultaneously allocated to specific crops, specific crop rotations, and specific 

tillage practices. A constant elasticity of transformation (CET) function, which controls the allocation of 

land to tillage practices, is defined for each crop rotation. These tillage transformation functions are then 

nested within the CET transformation functions that control the allocation of land to crop rotations. The 

parameters of the CET and PMP functions are specified so that model supply response at the national level 

is consistent with supply response in the USDA's Food and Agriculture Policy Simulator (FAPSIM)(Price, 
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2004). FAPSIM is an econometrically estimated national-level dynamic simulation model of the U.S. 

agriculture sector. The nitrogen fertilizer application rate for each production activity is linked to yield by 

using a nitrogen yield response function. 

Livestock production in each region is represented within an activity analysis framework. Production 

practices are differentiated by livestock type and type of operation. Livestock production activities 

incorporate yields and input use (including feed nutrient requirements and input costs), and they generate 

manure and its associated nutrient composition per unit of production activity. Species-specific PMP cost 

functions are defined for each region. Regional supplies of pasture are represented with simple kinked 

supply functions derived from pasture supply elasticities. 

Major government agricultural programs, including income and price support and the Conservation 

Reserve Program (CRP), are also represented within REAP. Conservation compliance restrictions on use 

of highly erodible land (HEL) can also be incorporated into an equilibrium solution. For many 

environmental policy analyses, conservation compliance is particularly important as it limits expansion of 

production onto HEL by requiring producers to forgo fixed, counter-cyclical, and CRP payments if they 

choose to bring new HEL into production. 

Processing of primary crop and livestock products is represented at the national level. Processing activities 

represented for crops include conversion of crops into livestock feed, crushing of soybeans into oil and 

meal, and conversion of corn into ethanol and associated byproducts (corn gluten feed, corn gluten meal, 

corn oil, and distiller’s dried grains). Livestock and crop production are connected through the competition 

for land and conversion of crops into livestock feed. The conversion of crops into livestock feed is 

represented with a feed mix model that converts crops into their nutritional components, e.g., 

metabolizable energy and protein, and uses them to produce feed rations for beef and dairy cattle, hogs, 

and poultry. Rations are differentiated by the proportions of crops, soybean meal, and corn byproducts they 

contain. Rations defined are based on historical ranges of crop and meal proportions and substitution of 

corn byproducts for feed grain and soybean meal into those rations. Dairy products are processed into fresh 

milk, cheese, butter, and evaporated dry milk. 

On the demand side, domestic use, trade, ending stocks and price levels for crop and livestock 

commodities, and processed or retail products are determined endogenously. Trade is represented with 

export demand and import supply functions. Hence, trade volumes respond to changes in the endogenously 

determined prices. 

Foundations 

REAP is a nonlinear, price-endogenous, mathematical programming model of the U.S. agricultural sector. 

Mathematical programming models have been widely used to model the interaction between agriculture 

production and the environment at the farm, watershed, and sector level. 

Samuelson (1952) was the first to demonstrate that the spatial equilibrium problem could be cast and 

solved as a constrained maximization problem. Takayama and Judge (1971) demonstrated how linear 

supply and demand equations could be incorporated and solved as a quadratic programming model. 

McCarl and Spreen (1980) discussed the properties of price equilibrium models that could be formulated 

with implicit supply relationships. They demonstrated that a sectoral-level analysis of the type being 

considered here may be effectively conducted by using a price-endogenous, mathematical programming 

model. 

Several characteristics of programming models are useful in the analysis of the interaction between 

agricultural production and the environment. First, the structure of these types of models is well suited for 

imposing resource and policy constraints. The explicit representation of production activities permits the 

analyst to identify resource use and environmental emissions associated with production and to place 

constraints on their use. Second, the use of fixed-proportion production technology used in most 
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programming models has had intrinsic appeal (Howitt, 1995). Third, the representation of production 

activities is consistent with the manner in which production systems are represented within biophysical 

simulation models. Fourth, it is relatively easy to introduce new or alternative production activities. Fifth, 

programming models can be constructed from limited historical data, permitting full use of available 

information. The availability of time series covering the economic and environmental variables of interest 

is minimal; usually, information is only available to generate one observation per production system. This 

is not to imply that the data requirements of programming models are trivial. The data requirements for 

such models are extensive, and the time and manpower needed can be overwhelming (McCarl and Spreen, 

1980). Finally, programming models permit detailed analysis of the effects of policy changes across 

commodities, regions, and production systems. 

Despite their appeal, the extension of programming models beyond farm or regional analysis to sector 

analysis has been limited by their inability to replicate observed patterns of production. This is often the 

result of the overspecialization problem (Howitt, 1995; McCarl and Spreen, 1980; Preckel et al., 2002). 

Overspecialization occurs in activity analysis models because the marginal rate of transformation among 

production activities is constant. This means that the rate at which the inputs can be switched from the 

production of one good to another does not change. Because the marginal rate of transformation among 

production activities remains the same regardless of the quantity of inputs already devoted to the 

production of a particular good, programming models will allocate all inputs to the production activity 

with the highest net return unless constrained by resource availability. 

Before the development of currently available modeling constructs, activity models relied on fixed 

technologies that necessitated the use of arbitrary upper and lower bounds on the activities to avoid 

overspecialization. Unfortunately, flexibility constraints, particularly at the sector level, contained little 

technological or economic information. As a result, the model response was being controlled by 

constraints that did not reflect limitations imposed by technology or economic behavior. Many solutions to 

the overspecialization or the calibration problem have been suggested, ranging from greater spatial 

disaggregation to making commodity prices endogenous, to incorporating risk-averse behavior, to 

specifying multicommodity demand functions, to using linear combinations of historical distribution of 

production activities, to specifying cost functions for each production activity. Only the specification of 

cost functions has proven very satisfactory. 

Positive mathematical programming (PMP), formally articulated by Howitt (1995), is the most popular 

approach used for representing these production activity cost functions. In the PMP methodology, cropspecific cost functions are used to eliminate the need for flexibility constraints. At the sector level, 

commodity supply elasticities can be used to derive the PMP functions so that supply response reflects the 

historical data. Cross-supply effects, other than those caused by allocation constraints placed on inputs 

such as land, labor, and water, can be implicitly included in the parameters of production or cost functions 

(Paris and Howitt, 1998). 

REAP uses an approach developed for calibrating and specifying programming models. It permits the 

degree of spatial and production disaggregation required for environmental analysis but eliminates the 

need to use flexibility constraints. The approach extends the PMP formulation by nesting sets of nonlinear 

transformation functions under the PMP formulation. The use of transformation functions differ from 

flexibility constraints in that, unlike flexibility constraints, they represent constraints imposed by our 

assumptions about the production technology. 

This approach builds on the foundations laid by both positive mathematical programming and computable 

general equilibrium (CGE) modeling. The approach is similar to the technique used by Dervis et al. (1982) 

to specify country-specific export demand functions in CGE models in that it uses a functional form—a 

constant elasticity of transformation function—that can be specified by using prices, quantities, average 

costs, and an assumed elasticity of substitution. The approach also borrows from PMP in that it uses 

shadow prices from calibration constraints to obtain the difference between average and marginal returns 

needed to specify transformation function parameters. 
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In REAP, we assume that producers determine the crop they desire to produce, the rotation they will use to 

produce it, and the tillage practice they will employ. In REAP, PMP functions are used to represent the 

positively sloping marginal cost curves for the land allocation decision at the crop level. The functions are 

specified so that the resulting supply functions are consistent with supply response elasticities derived 

from the FAPSIM model. We then nest two sets of transformation functions under the crop-level PMP 

functions. The first set of CET functions allocates cropland to various crop rotations. The second set of 

CET functions allocates rotation acreage to the available tillage practices. This formulation results in a 

smooth response of acreage planted to changes in relative returns among production enterprises, in 

accordance with our neoclassical economic behavioral expectation of profit maximization. By using this 

approach, we avoid the problems of overspecialization and corner solutions that result from using linear 

activity analysis formulation. 

Figure 3 illustrates the overspecialization problem. The transformation curve, CET, represents the 

maximum amount of a corn soybean rotation (RCB) attainable given the amount of continuous corn 

rotation (RCCC). The shape of the CET curve determines the rate at which RCB acres can be transformed 

into RCCC acres. The shape is drawn as it is represented in REAP and indicates that the marginal rate of 

transformation between RCB and RCCC is declining. When the elasticity of transformation, σ, equals 

zero, the CET curve takes the familiar corner shape associated with fixed-resource transformation. This 

indicates that the amount of cropland that can be converted into RCCC and RCB is fixed. When σ= ∞, the 

transformation curve becomes a straight line, indicating that the activities are perfectly substitutable. This 

is the shape of the transformation curve in activity analysis and linear programming models. 

The terms of trade, R1

 and R2, show the rate at which RCB can be exchanged for RCCC given total 

expenditures and prices. The slope of the terms-of-trade line—or relative price ratio—is given by Prccc/Prcb. 

As Prccc increases relative to Prcb, the slope of the terms-of-trade line will increase, causing it to become 

steeper. This implies that as the relative price of RCB to RCCC falls, the amount of land devoted to RCB 

used will fall and the amount of land devoted to RCCC will increase. 

In figure 3, equilibrium occurs at the point where the CET curve is tangent to the terms-of-trade line. This 

is also the point where the marginal rate of transformation equals the terms-of-trade. When the expenditure 

line is R1

, equilibrium occurs at Q1

RCB and Q1RCCC. As RCB becomes relatively less profitable than RCCC at 

Prccc, the revenue line shifts to R2

, the use of RCB decreases, and RCCC increases until a new equilibrium is 

established at Q2

RCB and Q2RCCC.

When σ = 0, the amount of each production activity used will remain unchanged no matter how the terms 

of trade change. When σ = ∞ , as it is in linear programming models, only one of the production activities 

will be used, since the expenditure line becomes tangent to the transformation curve only at a boundary 

point. This also implies that the amount of a production activity used will only change when the terms of 

trade change sufficiently to make it more profitable to use the other production activity. When this occurs, 

the original production activity will no longer be used, since production shifts completely to the new 

activity. This is what occurs when the terms-of-trade line shifts from R1

 to R2. In figure 3, when the terms 

of trade are at R1

, all land is devoted to RCB. If the terms of trade change to R2, indicating that returns to 

RCCC are now greater than returns to RCB, all the land is allocated to RCCC. 

To prevent this type of overspecialization, some models rely on flexibility constraints to limit movement 

along the transformation curve. This is depicted in figure 4, where the dark grey shaded block depicts the 

upper bound placed on RCB use by the flexibility constraint. The heavy straight black line represents the 

linear transformation curve. However, when the terms of trade change from R1

 to R2, all land will be 

shifted to RCCC, and RCB use will go to zero. It is possible to prevent this from happening by using a 

flexibility constraint to place an upper bound on RCCC use as well. This is shown by the light grey shaded 

box in figure 4. The problem is how to identify where to place those constraints. Even with an upper bound 

to restrict movement of land to RCCC, notice that any change will still occur as a corner solution. Because 

flexibility constraints have no economic or technological justification, you will still have constraints that 
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contain no relevant information determining the solution. By using the CET function approach, we avoid 

this problem. 

Environmental Modeling 

Unique features of REAP are its explicit environmental modeling of agricultural activities and calibration 

to observed environmental data. Environmental effects of crop production activities are obtained from 

simulations of the production activities using the Environmental Policy Integrated Climate (EPIC) model. 

EPIC uses information on soils, weather, and management practices, including specific fertilizer rates, and 

produces information on crop yields, erosion, and chemical losses to the environment. EPIC has been 

continuously updated since the early 1980s by a team of researchers from the U.S. Department of 

Agriculture’s Agricultural Research Service, Natural Resources Conservation Service (formerly the Soil 

Conservation Service), and Economic Research Service (ERS), as well as scientists at the Texas 

Agricultural Experiment Station. EPIC is a field-scale model that uses a daily time step to calculate the 

fate of various environmental parameters under different tillage, crop rotation, soil management, and 

weather scenarios. Although originally developed specifically to analyze the extent and costs of soil 

erosion, the model has been expanded over the years to simulate and provide information on hydrology, 

erosion, nutrient cycling, pesticide fate, soil temperature, and crop growth and yield. In addition to the 

effect of production practices on yields, EPIC is used to estimate the effect of production practices on 

yield, as well as to compute environmental indicators such as nitrogen loss and greenhouse gas emissions 

per acre for each REAP crop system. 

Management practices and initial fertilizer application rates are consistent with agronomic practices for the 

45 regions as reported in USDA's Cropping Practices Survey. Yield and environmental indicators are 

estimated by running each of the cropping systems represented in REAP through EPIC. The set of 

environmental indicators represented includes soil erosion (water and wind), losses of nitrogen and 

phosphorus to ground and surface water, nitrogen runoff damage to coastal waters, and erosion damage 

and losses of nitrogen to the atmosphere through volatilization and denitrification, carbon sequestered by 

soils, greenhouse gas emissions associated with machinery use and fertilizer production, and pesticides lost 

to ground and surface waters. Livestock waste and associated nutrient emissions are derived from formulas 

used in Kellogg et al. (2000). These formulas are adjusted to account for differences in timeframes 

represented by REAP production activities and production activities in the report. 

Onsite phosphorus and nitrogen runoff estimated by EPIC are calibrated to in-stream measurements made 

by the U.S. Geological Survey (Smith et al., 1997). The transport coefficients for phosphorus and nitrogen 

are used to estimate the quantity of sheet and rill erosion and pesticides that also run off into surrounding 

water bodies. Pesticide leaching and runoff are measured by the active ingredient quantity and then 

normalized to reflect toxicity and half-life (Barnard et al., 1997). 

Estimates of offsite damages are derived from sediment and nitrogen damage indexes developed by USDA 

(Claassen et al., 2001). Amenities included in the indexes are municipal water use, industrial uses, 

irrigation ditch maintenance, road ditch maintenance, water storage, flooding, and soil productivity, as 

well as fresh-water-based recreation, navigation, estuary-based boating, swimming, and recreation. These 

do not reflect all amenities affected by sediment and nitrogen runoff, so the offsite damage estimates 

should be viewed as a lower bound. Table 2 lists the environmental parameters REAP estimates in each 

model run. 
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Model Environment 

The REAP model is written and maintained in GAMS modeling language (see, for example, GAMS 

Development Corporation, 2006) and employs a nonlinear solver.2

 GAMS permits the compact 

representation of programming model by using concise algebraic statements that are easily read by model 

users. The equations that compose REAP are written in terms of “defined parameters”. This means that 

REAP's equations are written in symbolic terms and the values of data objects represented by these 

symbols (defined parameters) determine the specific form of REAP that is used in a model run. 

Whenever REAP is run, GAMS generates a model based on whatever data exist in various REAP data 

objects. For example, the form and existence of domestic demand functions for commodities depend on 

the values of several coefficients in a demand parameter table, called DEMSUP (table 3). DEMSUP contains 

price, quantity, and either demand or supply elasticities for all final commodities represented in REAP. 

The existence of a price-quantity-elasticity combination for a commodity in DEMSUP will allow REAP to 

generate a supply or demand curve for that commodity in the market for which the price-quantity-elasticity 

combination exists. If there is only a price-quantity combination with no elasticity, then the price for that 

commodity in that market is kept constant. 

In this section, we describe the major components of REAP using verbal, algebraic, and actual REAP 

model tables, as well as lists, equations, and statements. 3 We presume that readers have some familiarity 

with GAMS code conventions. We use several typographic conventions to differentiate elements of REAP 

presented in this bulletin. GAMS keywords appear in uppercase: EQUATION, for example. All GAMS 

identifiers (names of SETS, PARAMETERS, VARIABLES, EQUATIONS, and so on) and labels (names 

of SET elements) used in REAP are in a block font. Portions exceeding one line are separate text blocks as 

shown in example 1. 

Example 1—REAP code Fragment (%%%%% indicates that portions of the list have been left out. This is used 

when the bulk of the list can be mentally filled in.)

$STITLE REAP MODEL REGIONAL PRODUCTION ACTIVITY SUBSETS 

 SET BC(B) CROP ENTERPRISES / 

 

2

 Note the solver(s) must be purchased separately from the GAMS modeling language. Nonlinear solvers such as CONOPT (ARKI 

Consulting and Development, 2006) or MINOS (Stanford Business Software, Inc., 2006) have successfully been used but their 

mention here should not be construed as a USDA recommendation over others that maybe suitable. 

3

 This represents the basic formulation for REAP. The formulation can and has been adjusted to represent different types of 

government policies, such as income support programs coupled to production, environmental credit trading, and agrienvironmental

compliance requirements. The formulation can also be easily adjusted to include the introduction of alternative production 

activities, such as the introduction of sustainable cropping systems, genetically modified crops and carbon sequestering activities. 

The means of accessing these modules and using them for analyses are described in Appendix B. 
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 CORN, SORGHUM, BARLEY, OATS, WHEAT, RICE, SOYBEANS 

 SUNFLOWER, COTTON, SILAGE, HAY, SUGBEET, SUGCANE 

 MILKWEED, MEADOWFM, KENAF, GUYAULE, OTHERC 

 RCCC, RBBB, RWWW, RSSS, RTTT, RRRR, 

 RLLL, RHHH, RGGG 

 RCB, RCBW, RCBWO, RCBWL, RCBWH, RCBWG, 

 %%%% 

 /; 

 

In the REAP program, we first define the sets, then declare the various data objects (parameters, tables, 

and scalars) and assign data. Explanatory text is used in the declaration of sets, parameters, variables, and 

equations. All information needed to understand REAP is in the GAMS list file. As a result, the GAMS 

program represents a portion of the model documentation. All equations and data transformations are 

written in algebraic form. This procedure not only permits data to be entered in the form in which they are 

in the originating sources, but also makes the transformation of these data and the derivation of model 

parameters transparent to model users. Many secondary components of REAP are not presented—for 

example, calculations that convert published data into the model components. Full details of these 

secondary components are in the REAP code files. 

Indexes (GAMS SETS) 

SETS in the GAMS programming language serve as the model’s building blocks—in other words the 

indexes represent the heart of the model. The indexes define the dimensions of the model with respect to 

commodities produced, inputs and production systems represented, regional sources of supply, and enduse markets. The parameters, variables, and equations defining REAP are all indexed by elements of 

REAP sets. The number of sets used in defining parameters, variables, and equations, along with the 

number of elements in the sets, determine the dimensions of the model. For example, commodity balance 

equations are defined over the set P; if set P is defined to contain only the elements CORN and SOYBEANS, 

then REAP would generate two commodity balance equations—one for corn and one for soybeans. In 

addition, SETS facilitate model formulation by permitting parameter calculations, solution algorithms, and 

more efficient reporting of results. 

In the GAMS language, set declaration consists of a SET name and description followed by a list of the set 

elements. The first text on each row is the ELEMENT; everything following the next space is a description 

field. Comment lines (beginning with an asterisk, *) add documentation and aids in reading the model. The 

description field of set elements (and other GAMS objects) is also used in formatting and reporting model 

output. 

Production Activities 

The code below lists a portion of SET B—the primary production enterprises in REAP. Two features of 

B’s elements are presented in the description field: “DESCRIPTION” clarifies what the element is, 

beyond GAMS’s 10 character limit on element names. PIGFIN, for example, is FEEDER PIG FINISHING. 

“UNIT ON WHICH NORMALIZED” indicates the unit level of the activity for which inputs and outputs 
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will be specified. For example, the unit level of a CORN enterprise is 1 acre. Yield and inputs used to 

produce corn will be specified on a per-acre basis. 

b regional production activity, b = 1,...,B 

SET B REGIONAL ENTERPRISE PRODUCTION PROCESS (ACTIVITY) SUPERSET / 

* --------------------------------------------------------------------------------- 

* UNIT ON WHICH 

* NAME DESCRIPTION NORMALIZED 

* ---------- --------------------------------------------------- ------------------ 

* ---- TRADITIONAL FIELD CROPS 

 CORN CORN ACRE PLANTED 

 SORGHUM SORGHUM ACRE PLANTED 

 BARLEY BARLEY ACRE PLANTED 

 OATS OATS ACRE PLANTED 

 WHEAT WHEAT ACRE PLANTED 

 RICE RICE ACRE PLANTED 

 SOYBEANS SOYBEANS ACRE PLANTED 

 COTTON COTTON ACRE PLANTED 

 SILAGE SILAGE ACRE PLANTED 

 HAY HAY ACRE PLANTED 

* ---- ROTATIONS 

 RCCC CONTINUOUS CORN ACRE PLANTED 

 RSSS CONTINUOUS SORGHUM ACRE PLANTED 

 RLLL CONTINUOUS BARLEY ACRE PLANTED 

 ROOO CONTINUOUS OATS ACRE PLANTED 

 RWWW CONTINUOUS WHEAT ACRE PLANTED 

 RTTT CONTINUOUS COTTON ACRE PLANTED 

 RRRR CONTINUOUS RICE ACRE PLANTED 

 RBBB CONTINUOUS SOYBEANS ACRE PLANTED 

 RHHH CONTINUOUS HAY ACRE PLANTED 

 RGGG CONTINUOUS SILAGE ACRE PLANTED 

 RCB CORN SOYBEANS ACRE PLANTED 

 RCBW CORN SOYBEANS WHEAT ACRE PLANTED 

 RCBWO CORN SOYBEANS WHEAT OATS ACRE PLANTED 

... 

... 

* ---- PRIMARY LIVESTOCK ENTERPRISES 

 DAIRY DAIRY DAIRY COW 

 FAROFIN FARROW TO FINISH HOGS HOG SLAUGHTER 10 CWT 

 FEEDRPIG FEEDER PIG PRODUCTION PIG PRODUCTION 10 CWT 

 PIGFIN FEEDER PIG FINISHING HOG SLAUGHTER 10 CWT 

 BFCOWEN BEEF COW ENTERPRISE BEEF COW 

* (COW-CALF, 17 WESTERN STATES) 

 BFCOWCF BEEF COW-CALF HERD BEEF COW 

* (COW-CALF, 31 EASTERN STATES) 

 FEEDLOT FARMER CATTLE FEEDING FED SLAUGHTER CWT 

 CFEEDLOT COMMERCIAL FEEDLOT FED SLAUGHTER CWT 

 STOCKER STOCKER (BEEF CALF TO YEARLING) BEEF YEARLING CWT 

 OTHRLVSTK OTHER LIVESTOCK (SHEEP AND HORSES) GCAU 

 EGGS EGG PRODUCTION DOZEN EGGS 

 BROILERS BROILER PRODUCTION (CARCASS) LBS BROILER CARCASS 

 TURKEY TURKEY PRODUCTION (CARCASS) LBS TURKEY CARCASS 

 OTHERL LIVESTOCK NOT OTHERWISE SPECIFIED DEPENDENT ON REPORT 

 TOTAL TOTAL USED FOR REPORTING PURPOSES DEPENDENT ON REPORT 

 OTHER ENTERPRISES NOT OTHERWISE SPECIFIED DEPENDENT ON REPORT 

* ----------------------------------------------------------------------------------------- 

 ALIAS(B,BA); ALIAS (B,BA1),(B,BA2);; 
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Subheadings such as “Traditional Field Crops” and “Primary Livestock Enterprises” divide set B elements 

into similar groups. Enterprise set B includes primary field crops such as CORN and SOYBEANS, livestock 

such as DAIRY, and several OTHER and control TOTAL categories that are used for summary reporting. 

Following the definition of set B, the ALIAS command is used to create sets BA, BA1, and BA2, which 

contain all the same elements as set B. The ALIAS command gives another name to a previously declared 

set. Alias sets are used in a GAMS statement or equation when subsequent code involves interactions of 

elements within the same set. 

Set B is further subdivided into subsets of production activities for crops and livestock. Set BC

encompasses crop production activities, while BL includes livestock production activities. 

Set bc(b) crop production activity, bc ⊂ b

SET BC(B) CROP ENTERPRISES / 

 CORN, SORGHUM, BARLEY, OATS, WHEAT, RICE, SOYBEANS 

 COTTON, SILAGE, HAY 

 RCCC, RBBB, RWWW, RSSS, RTTT, RRRR, ROOO 

 RLLL, RHHH, RGGG 

 RCB, RCBW, RCBWO, RCBWL, RCBWH, RCBWG, RCBWX, RCBH 

 ....

 /; 

Set bl(b) livestock production activity, bl ⊂ p

SET BL(B) REGIONAL LIVESTOCK PRODUCTION PROCESSES / 

 DAIRY 

 FAROFIN, FEEDRPIG, PIGFIN 

 BFCOWEN, BFCOWCF 

 FEEDLOT, CFEEDLOT 

 STOCKER, OTHRLVSTK 

 EGGS, BROILERS, TURKEY 

 /; 

Processing Activities 

Set C describes processing activities, which are specified at the national level. Numerous processing 

activities in REAP perform widely different functions; their common characteristic is that they are 

specified only at the national level. This implies, for instance, that all finished hogs are sent to a central 

processing plant for processing, and then the resulting meat is sent out to domestic and export markets; the 

processing sector does not distinguish its inputs or outputs on the basis of the regions in which those inputs 

were produced. 

Set c national processing activity, c = 1,...,C 

SET C NATIONAL LEVEL PROCESSING PROCESSES (ACTIVITIES) / 

* -------------------------------------------------------------------------------------- 

* UNIT ON WHICH 

* NAME DESCRIPTION NORMALIZED 

* ---------- --------------------------------------------------- ----------------------- 

* LIVE ANIMALS TO MEAT (RETAIL WEIGHT) 

 HOGTOPORK SLAUGHTER HOGS CWT PORK 

 SOWTOPORK SLAUGHTER CULL SOWS CWT PORK 

 FSLATOFBEF SLAUGHTER FED BEEF FROM FARMER CATTLE FEEDLOTS CWT FED BEEF 

 FSCFTOFB SLAUGHTER FED BEEF FROM COMMERCIAL FEEDLOT CWT FED BEEF 
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 DCOWNFBF SLAUGHTER CULLED DAIRY COW CWT NONFED BEEF 

... 

... 

* DAIRY PRODUCTS 

 FLUIDMLK FLUID MILK PROCESSING CWT WHOLE FARM MILK 

 MFGMILK MANFACUTURED (MFG) MILK PROCESSING CWT WHOLE FARM MILK 

 BUTTER BUTTER AND NONFAT DRY MILK PROCESSING CWT MANUFACTURING MILK 

 AMCHEESE AMERICAN CHEESE PROCESSING CWT MANUFACTURING MILK 

 OTCHEESE OTHER CHEESE PROCESSING CWT MANUFACTURING MILK 

 ICECREAM ICE CREAM PROCESSING CWT MANUFACTURING MILK 

 EVDRYMLK " EVAPORATED, DRY, AND CONDENSED MILK" CWT MANUFACTURING MILK 

* OILSEED CRUSH AND HIGH PROTEIN FEED ACTIVITIES 

 SOYCRUSH1 SOYBEAN CRUSHING BU SOYBEANS 

... 

... 

 BNMLTOHIPR SOYBEAN MEAL PROCESSING FOR HIPROTEIN FEED CWT BEANMEAL 

 OOSMTOHIPR OTHER OIL SEED MEAL PROCESSING FOR HIPROTEIN FEED CWT BEANMEAL 

 ANPRTOHIPR ANIMAL PROTEIN TANKAGE FOR HIPROFEED CONVERSION CWT BEANMEAL 

* FEED MIX AND PROTEIN SUPPLEMENT PRODUCTION 

 GRAIN1 GRAIN FEED MIX 1 FOR CATTLE FEED CWT GRAIN 

* (GRAIN1A TO GRAIN3 REPRESENT ALTERNATE COMBINATIONS 

* OF FEED GRAINS TO CREATE CATTLE PROTEIN AND ENERGY) 

 GRAIN1A GRAIN FEED MIX 1A FOR CATTLE FEED CWT GRAIN 

 GRAIN1B GRAIN FEED MIX 1B FOR CATTLE FEED CWT GRAIN 

... 

... 

 CATPRO1 LOW PROTEIN BEEF CATTLE FEED PROD (32% PROTEIN) CWT PROTSUP 

* (CATPRO2 TO CATPRO4 REPRESENT ALTERNATE COMBINATIONS 

* OF FEED GRAINS AND SOYBEAN MEAL TO CREATE CATTLE FEED) 

 CATPRO2 LOW PROTEIN BEEF CATTLE FEED PROD (32% PROTEIN) CWT PROTSUP 

... 

... 

 DAIRYSUP1 DAIRY PROTEIN SUPPLEMENT PRODUCTION (16% PROTEIN) CWT PROTSUP 

* (DAIRYSUP2 TO DAIRYSUP6 REPRESENT ALTERNATE 

* COMBINATIONS OF FEED GRAINS AND SOYBEAN MEAL TO 

* CREATE DAIRY PROTEIN AND ENERGY.) 

 DAIRYSUP2 DAIRY PROTEIN SUPPLEMENT PRODUCTION (16% PROTEIN) CWT PROTSUP 

 DAIRYSUP3 DAIRY PROTEIN SUPPLEMENT PRODUCTION (16% PROTEIN) CWT PROTSUP 

... 

... 

 LOPROSWN1 LOW PROTEIN SWINE FEED (19%-20% PROTEIN) CWT PROTSUP 

* (LOPROSWN2 REPRESENTS AN ALTERNATE COMBINATION OF 

* FEED GRAINS AND MEAL TO CREATE SWINE PROTEIN AND ENERGY) 

 LOPROSWN2 LOW PROTEIN SWINE FEED (19%-20% PROTEIN) CWT PROTSUP 

* FEED PROTEIN AND ENERGY RELATED ACTIVITIES 

 HIPROCAT CONVERSION OF BEANMEAL TO CATTLE PROTEIN AND ENERGY CWT BEANMEAL 

 HIPROSWI CONVERSION OF BEANMEAL TO SWINE PROTEIN AND ENERGY CWT BEANMEAL 

 HIPRODAI CONVERSION OF BEANMEAL TO DAIRY PROTEIN AND ENERGY CWT BEANMEAL 

 CORNSWI CORN CONVERSION TO PROTEIN AND ENERGY CWT CORN 

 SORGSWI SORGHUM CONVERSION TO SWINE PROTEIN AND ENERGY CWT CORN 
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 CNSGSWI CORN AND SORGHUM CONVERSION TO SWINE PROTEIN AND ENERGY CWT CORN 

... 

... 

* ETHANOL PROCESSING ACTIVITIES 

 ETHWMLCUR CORN WETMILLING FOR ETHANOL (CURRENT STATE OF THE ART) BU CORN 

 ETHWML95 CORN WETMILLING FOR ETHANOL (1995 STATE OF THE ART TECH) BU CORN 

 ETHWML20 CORN WETMILLING FOR ETHANOL (2000 STATE OF THE ART TECH) BU CORN 

 ETHDMLCUR CORN DRYMILLING FOR ETHANOL (CURRENT STATE OF THE ART) BU CORN 

 ETHDML95 CORN DRYMILLING FOR ETHANOL (1995 STATE OF THE ART TECH) BU CORN 

 ETHDML20 CORN DRYMILLING FOR ETHANOL (2000 STATE OF THE ART TECH) BU CORN 

... 

... 

* ---------------------------------------------------------------------------------------- 

“Live animals to meat” represents slaughter and processing activities that convert live animals to retail cut 

weight. Dairy-processing activities convert whole farm and manufacturing milk into retail products. 

Oilseed crush activities convert soybeans into soybean meal and soybean oil. High-protein feed activities 

convert various protein sources to high-protein livestock feed. Feed mix and protein supplement activities 

convert various individual feeds such as CORN and BEANMEAL into animal feed rations. Feed protein and 

energy activities convert livestock feed rations into protein and energy components in which animal 

nutrition requirements are satisfied. Ethanol-processing activities convert corn to ethanol and various 

coproducts. 

Regional Indexes 

Set U encompasses all geographic regions that can be used in REAP: Farm Production Regions, Land 

Resource Regions; and 2-, 4- and 8-digit U.S. Geological Service hydrological units (HUCS). REAP 

production activities are specified at either the Farm Production Region (mainly livestock) or Land 

Resource Region level (crops). Summary results are typically reported at the Farm Production Region 

level. HUCS are used primarily for tracking results at the watershed level after a solution has been 

obtained. As such, HUCS do not play a role in how the model is specified. HUCS could be used to specify 

the model at the watershed level if the supporting information on production activities to do so were 

available. Currently, results from model regions are distributed to the HUCS based on the share of 

cropland of each HUCS in the model region. The Land Resource Regions can be further divided into 

highly erodible land (HEL) and nonhighly erodible land (NHEL) if desired. 

u all regions 

SET U "REGIONS AND US TOTAL SUPERSET" / 

* ------------------------------------------------------- 

* ELEMENT REGION NAME 

* ------- ------------- 

* FARM PRODUCTION REGIONS 

 NT NORTHEAST 

 LA LAKE STATES 

 CB CORN BELT 

 NP NORTHERN PLAINS 

 AP APPALACHIA 

 SE SOUTHEAST 

 DL DELTA STATES 

 SP SOUTHERN PLAINS 

 MN MOUNTAIN STATES 

 PA PACIFIC STATES 

 US UNITED STATES 
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* LAND RESOURCE REGIONS 

 NTL NORTHEAST LAKE STATES FRUIT TRUCK AND DAIRY 

 NTN NORTHEAST EAST AND CENTRAL FARMING AND FOREST 

 NTR NORTHEAST NORTHEAST FORAGE AND FOREST 

 NTS NORTHEAST NORTH ATLANTIC SLOPE DIVERSIFIED FARMING 

 NTT NORTHEAST ATLANTIC AND GULF COAST LOWLAND FOREST AND CROP 

 LAF LAKE STATES NORTH GREAT PLAINS SPRING WHEAT 

 LAK LAKE STATES NORTH LAKE STATES FOREST AND RANGE 

 LAL LAKE STATES LAKE STATES FRUIT TRUCK AND DAIRY 

 LAM LAKE STATES CENTRAL FEED GRAINS AND LIVESTOCK 

 CBL CORN BELT LAKE STATES FRUIT TRUCK AND DAIRY 

 CBM CORN BELT CENTRAL FEED GRAINS AND LIVESTOCK 

 CBN CORN BELT EAST AND CENTRAL FARMING AND FOREST 

 CBO CORN BELT MISSISSIPPI DELTA COTTON AND FEED GRAINS 

 CBR CORN BELT NORTHEAST FORAGE AND FOREST 

 NPF NORTHERN PLAINS NORTH GREAT PLAINS SPRING WHEAT 

 NPG NORTHERN PLAINS WEST GREAT PLAINS RANGE AND IRRIGATED 

 NPH NORTHERN PLAINS WEST GREAT PLAINS WINTER WHEAT AND RANGE 

 NPM NORTHERN PLAINS CENTRAL FEED GRAINS AND LIVESTOCK 

 APN APPALACHIA EAST AND CENTRAL FARMING AND FOREST 

 APO APPALACHIA MISSISSIPPI DELTA COTTON AND FEED GRAINS 

 APP APPALACHIA S ATL AND GULF SLOPE CASH CROPS FORES AND LVST 

 APS APPALACHIA NORTH ATLANTIC SLOPE DIVERSIFIED FARMING 

 APT APPALACHIA ATLANTIC AND GULF COAST LOWLAND FOREST AND CROP 

 STN SOUTHEAST EAST AND CENTRAL FARMING AND FOREST 

 STP SOUTHEAST S ATL AND GULF SLOPE CASH CROPS FORES AND LVST 

 STT SOUTHEAST ATLANTIC AND GULF COAST LOWLAND FOREST AND CROP 

 DLN DELTA STATES EAST AND CENTRAL FARMING AND FOREST 

 DLO DELTA STATES MISSISSIPPI DELTA COTTON AND FEED GRAINS 

 DLP DELTA STATES S ATL AND GULF SLOPE CASH CROPS FORES AND LVST 

 DLT DELTA STATES ATLANTIC AND GULF COAST LOWLAND FOREST AND CROP 

 SPH SOUTHERN PLAINS WEST GREAT PLAINS WINTER WHEAT AND RANGE 

 SPI SOUTHERN PLAINS SW PLATEAUS AND PLAINS RANGE AND COTTON 

 SPJ SOUTHERN PLAINS SW PRAIRIES AND COTTON 

 SPM SOUTHERN PLAINS CENTRAL FEED GRAINS AND LIVESTOCK 

 SPN SOUTHERN PLAINS EAST AND CENTRAL FARMING AND FOREST 

 SPP SOUTHERN PLAINS S ATL AND GULF SLOPE CASH CROPS FOREST AND LVST 

 SPT SOUTHERN PLAINS ATLANTIC AND GULF COAST LOWLAND FOREST AND CROP 

 MNB MOUNTAIN NW WHEAT AND RANGE 

 MND MOUNTAIN WESTERN RANGE AND IRRIGATED 

 MNE MOUNTAIN ROCKY MOUNTAIN RANGE AND FOREST 

 MNF MOUNTAIN NORTH GREAT PLAINS SPRING WHEAT 

 MNG MOUNTAIN WEST GREAT PLAINS RANGE AND IRRIGATED 

 MNH MOUNTAIN WEST GREAT PLAINS WINTER WHEAT AND RANGE 

 PAA PACIFIC NW FOREST, FORAGE AND SPEC CROPS 

 PAB PACIFIC NW WHEAT AND RANGE 

 PAC PACIFIC CAL SUBTROP FRUIT TRUCK AND SPECIALTY CROPS 

 PAD PACIFIC WESTERN RANGE AND IRRIGATED 

 PAE PACIFIC ROCKY MOUNTAIN RANGE AND FOREST 

* LAND RESOURCE REGIONS HIGHLY ERODIBLE LAND 

 NTLH NORTHEAST LAKE STATES FRUIT TRUCK AND DAIRY HEL 

 NTNH NORTHEAST EAST AND CENTRAL FARMING AND FOREST HEL 

 NTRH NORTHEAST NORTHEAST FORAGE AND FOREST HEL 

 NTSH NORTHEAST NORTH ATLANTIC SLOPE DIVERSIFIED FARMING HEL 

 NTTH NORTHEAST ATLANTIC AND GULF COAST LOWLAND FOREST AND CROP HEL 

 LAFH LAKE STATES NORTH GREAT PLAINS SPRING WHEAT HEL 

 LAKH LAKE STATES NORTH LAKE STATES FOREST AND RANGE HEL 

 LALH LAKE STATES LAKE STATES FRUIT TRUCK AND DAIRY HEL 
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 LAMH LAKE STATES CENTRAL FEED GRAINS AND LIVESTOCK HEL 

 CBLH CORN BELT LAKE STATES FRUIT TRUCK AND DAIRY HEL 

 CBMH CORN BELT CENTRAL FEED GRAINS AND LIVESTOCK HEL 

 CBNH CORN BELT EAST AND CENTRAL FARMING AND FOREST HEL 

 CBOH CORN BELT MISSISSIPPI DELTA COTTON AND FEED GRAINS HEL 

 %%%%%% 

 * LAND RESOURCE REGIONS NON-HIGHLY ERODIBLE LAND 

 NTLN NORTHEAST LAKE STATES FRUIT TRUCK AND DAIRY NON-HEL 

 NTNN NORTHEAST EAST AND CENTRAL FARMING AND FOREST NON-HEL 

 NTRN NORTHEAST NORTHEAST FORAGE AND FOREST NON-HEL 

 NTSN NORTHEAST NORTH ATLANTIC SLOPE DIVERSIFIED FARMING NON-HEL 

 NTTN NORTHEAST ATLANTIC AND GULF COAST LOWLAND FOREST AND CROP NON-HEL 

 LAFN LAKE STATES NORTH GREAT PLAINS SPRING WHEAT NON-HEL 

 LAKN LAKE STATES NORTH LAKE STATES FOREST AND RANGE NON-HEL 

 LALN LAKE STATES LAKE STATES FRUIT TRUCK AND DAIRY NON-HEL 

 LAMN LAKE STATES CENTRAL FEED GRAINS AND LIVESTOCK NON-HEL 

 CBLN CORN BELT LAKE STATES FRUIT TRUCK AND DAIRY NON-HEL 

 CBMN CORN BELT CENTRAL FEED GRAINS AND LIVESTOCK NON-HEL 

 CBNN CORN BELT EAST AND CENTRAL FARMING AND FOREST NON-HEL 

 CBON CORN BELT MISSISSIPPI DELTA COTTON AND FEED GRAINS NON-HEL 

 CBRN CORN BELT NORTHEAST FORAGE AND FOREST NON-HEL 

 NPFN NORTHERN PLAINS NORTH GREAT PLAINS SPRING WHEAT NON-HEL 

 NPGN NORTHERN PLAINS WEST GREAT PLAINS RANGE AND IRRIGATED NON-HEL 

 NPHN NORTHERN PLAINS WEST GREAT PLAINS WINTER WHEAT AND RANGE NON-HEL 

 %%% 

Major region schemes used in REAP are defined as subsets of U. Set R contains only the 10 USDA Farm 

Production Regions, while RL contains only the Land Resource Regions (LRRs). The LRRs are defined 

by the intersection of the USDA’s 10 Farm Production Regions with the USDA’s 26 LRRs. Thus, RL

breaks each Farm Production Region into subregions defined by the portion of each Land Resource Region 

that it contains. 

r farm production region r ⊂ u , r = 1,...,R 

SET R(U) FARM PRODUCTION REGIONS / 

 NT,LA,CB,NP,AP,SE,DL,SP,MN,PA 

 / 

rl land resource region region, rl = 1,...,RL 

SET RL(U) LAND RESOURCE REGIONS / 

 NTL NORTHEAST LAKE STATES FRUIT TRUCK AND DAIRY 

 NTN NORTHEAST EAST AND CENTRAL FARMING AND FOREST 

 NTR NORTHEAST NORTHEAST FORAGE AND FOREST 

 NTS NORTHEAST NORTH ATLANTIC SLOPE DIVERSIFIED FARMING 

 NTT NORTHEAST ATLANTIC AND GULF COAST LOWLAND FOREST AND CROP 

 LAF LAKE STATES NORTH GREAT PLAINS SPRING WHEAT 

 LAK LAKE STATES NORTH LAKE STATES FOREST AND RANGE 

 LAL LAKE STATES LAKE STATES FRUIT TRUCK AND DAIRY 

 LAM LAKE STATES CENTRAL FEED GRAINS AND LIVESTOCK 

 CBL CORN BELT LAKE STATES FRUIT TRUCK AND DAIRY 

 CBM CORN BELT CENTRAL FEED GRAINS AND LIVESTOCK 

 CBN CORN BELT EAST AND CENTRAL FARMING AND FOREST 

 CBO CORN BELT MISSISSIPPI DELTA COTTON AND FEED GRAINS 

 CBR CORN BELT NORTHEAST FORAGE AND FOREST 

... 

... 

 / 

; 
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Set RL is defined above without differentiating between highly erodible and nonhighly erodible land. It is 

possible to define set RL so that it differentiates between highly erodible and nonhighly erodible land or so 

that it includes all three land types. The definition used depends on the type of analysis being run. For ease 

of exposition, we use RL as defined above in the rest of this section. 

Mappings are multidimensional GAMS sets that relate elements of one set to elements of another set. Set 

ER2RR, for example, relates REAP Farm Production Regions to the Land Resource Regions. Mapping sets 

in REAP are generally named mnemonically to suggest linking the first set to, or “2,” the second. Set 

ER2RR, which maps Land Resource Regions into Farm Production Regions, is shown below. 

SET ER2RR(U,UR) FARM PRODUCTION REGION TO FARM RESOURCE PRODUCTION REGION MAP / 

 (NTL, NTN, NTR, NTS, NTT) .NT 

 (LAF, LAK, LAL, LAM) .LA 

 (CBL, CBM, CBN, CBO, CBR) .CB 

 (NPF, NPG, NPH, NPM) .NP 

 (APN, APO, APP, APS, APT) .AP 

 (STN, STP, STT) .SE 

 (DLN, DLO, DLP, DLT) .DL 

 (SPD, SPH, SPI, SPJ, SPM, SPN, SPP, SPT).SP 

 (MNB, MND, MNE, MNF, MNG, MNH) .MN 

 (PAA, PAB, PAC, PAD, PAE) .PA 

 /; 

This mapping allows you to define operations to be applied over only those members of U that map to a 

specific region UR. Other regional mappings relating HUCS to each other and to Farm Resource Regions 

are in the REAP listing files. There are also mappings that separate Mississippi Basin HUCS from nonMississippi Basin HUCS. 

Government Program Indexes 

Set G depicts government program attributes of REAP production enterprises. For example, NP portrays 

“nonparticipant” or “normal” activities, depending on the context where it appears. 

Set g government program attribute category, g = 1,...,G 

SET G GOVERNMENT PROGRAM ATTIBUTE CATEGORY / 

 NP NONPARTICIPANT OR NORMAL 

 P1 PARTICIPANT LEVEL 1 

 /

A production activity for a crop such as CORN—for which a government program exists—can be either 

“participating in government programs” (P1) or “nonparticipating” (NP). An enterprise such as FEEDLOT

for which no government program exists, has only one relevant category: “normal” (NP). Since the 

changes in the commodity programs that took place with the 1996 Farm Bill, the differentiation of crop 

production activities by their participation or nonparticipation in farm programs has not been used. 

Production Types 

Two sets, Y and H, are reserved (but not currently used) to differentiate alternative production regimes and 

methods of production. All variables and parameters, where these sets appear in the declaration, are 

defined with default values. Set Y refers to regimes with PRD, representing predominant systems, and 

AL1, refers to alternative sustainable practices that have been defined for cropping systems. The default 

value for production regimes is PRD. AL1 systems are available for use in scenario analysis, but 

availability of those regimes would need to be switched on. Alternative systems denoted “AL2” have 
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not yet been defined; this set element was created as a placeholder to create flexibility for inclusion of 

additional systems in the future. 

Set y system of production, y = 1,...,Y 

SET Y SYSTEM OF PRODUCTION 

 PRD PREDOMINANT 

 AL1 ALTERNATE 1 

 AL2 ALTERNATE 2 

 / 

Set H denotes the use of irrigation for production activities. Crop enterprises might be either D (dry) or I

(irrigated) if irrigation is modeled for a crop. Its default value is A.

Set h method of production, h = 1,...,H 

SET H METHOD OF PRODUCTION / 

 D DRYLAND 

 I IRRIGATED 

 A ALL OR NORMAL 

 /

Element A applies to those production activities in REAP not differentiated by irrigation method. Element 

A also applies to all livestock enterprises. Although REAP permits the specification of separate 

dry/irrigated crop production activities, in its current formulation it does not do so. Thus, H is set at A for 

“all crop production activities.” This does not mean that the crop production activities specified with an A

represent an average of irrigated and dryland practices. Usually, the production activity represented with 

an A will be either irrigated or dryland, depending on whether dryland or irrigated production for this crop 

predominates in a Farm Resource Production Region. The exception to this is for production of cotton in 

the Southern Plains, where the division between irrigated and dryland acreage is split fairly evenly. In this 

case the production activities used in the model represent a weighted average of irrigated and dryland 

production activities. 

Set T covers tillage practice alternatives. All crop production activities are defined with one of the five 

tillage practices represented. ATL is used as the default setting for all livestock production activities, and 

those crop production activities, with no tillage-specific information. 

Set t tillage practice, t = 1,...,T 

SET T STRATA OF PRODUCTION / 

 CNV CONVENTIONAL WITHOUT MOLDBOARD 

 MLD CONVENTIONAL WITH MOLDBOARD 

 MCH MULCH TILLAGE 

 NTL NO TILL 

 RDG RIDGE TILLAGE 

 ATL ALL TILLAGE TYPES 

 / 

Set FT specifies the fertilizer application rates available for a crop production activity. The element 1 

represents the initial or base fertilizer application rate, while the remaining elements represent reduced 

application rates. Currently, REAP includes only the option of reducing nitrogen fertilizer application rates 

to 60 percent of the base rate. 

Set ft fertilizer application rate, ft = 1,...,FT 

SET ft FT FERTLIZER APPLICATION RATE LEVELS / 

1 BASE APPLICATION RATE (100 PERCENT) 

9 NINETY PERCENT OF BASE APPLICATION RATE 

8 EIGHTY PERCENT OF BASE APPLICATION RATE 

7 SEVENTY PERCENT OF BASE APPLICATION RATE 
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6 SIXTY PERCENT OF BASE APPLICATION RATE 

 Market Types 

 Set M includes all product supply and use market categories modeled in REAP. 

m supply and use market category, m = 1,...,M 

SET M PRODUCT SUPPLY AND USE MARKET CATEGORIES / 

* ---------------------------------------------- 

* CATEGORY DESCRIPTION 

* ---------------------------------------------- 

* SUPPLY CATEGORIES 

 STB TOTAL BEGINNING STOCKS (COMPOSED OF SGB & SCB) 

 SGB BEGINNING GOVERNMENT STOCKS (RHS VARIABLE) 

 SCB BEGINNING COMMERCIAL STOCKS (RHS VARIABLE) 

 PRDN DOMESTIC PRODUCTION (EXPLICIT SUPPLY) 

 IMP IMPORTS (EXPLICIT SUPPLY) 

 RESS RESIDUAL SUPPLY (USED IN CALIBRATION) 

* USE CATEGORIES 

 DOM DOMESTIC CONSUMPTION (EXPLICIT DEMAND THAT EXCLUDES PRPC) 

 PRPC PRODUCTION & PROCESSING USE (IMPLICIT DERIVED DEMAND) 

 EXP EXPORTS (EXPLICIT DEMAND) EXCLUDING EEP 

 EEP EXPORTS UNDER EXPORT ENHANCEMENT PROGRAM (EEP) (EXPLICIT DEMAND) 

 STE TOTAL ENDING STOCKS (COMPOSED OF SGE & SCE) 

 SGE ENDING GOVERNMENT STOCKS 

 SCE ENDING COMMERCIAL STOCKS (EXPLICIT DEMAND) 

 RESD RESIDUAL USE (USED IN CALIBRATION) 

* OTHER GOVERNMENT STOCK ACTION CATEGORIES 

 SGA GOVERNMENT STOCK ACCUMULATIONS (INCREASE OVER PERIOD) 

 SGO GOVERNMENT STOCK CARRYOVER FROM PREVIOUS PERIOD 

 SGR GOVERNMENT STOCK RELEASE TO THE COMMERCIAL MARKET (DECREASE OVER PERIOD) 

 SGV GOVERNMENT NET STOCK REMOVALS FROM THE COMMERCIAL MARKET (PURCHASES) 

 SGD GOVERNMENT STOCK DOMESTIC DONATIONS (FOOD DISTRIBUTION PROGRAMS) 

 SGX GOVERNMENT STOCK EXPORT DONATIONS (CCC EXPORTS) 

 / 

Beginning stocks, STB, are comprised of government and commercial stocks, SGB and SCB. PRDN refers to 

production, and IMP refers to imports. A residual supply category, RESD, is used in model calibration to 

account for cases where baseline total supply and use fail to balance precisely. Domestic use of products 

includes final domestic demand, DOM, intermediate input or production and processing use, PRPC, and 

ending stocks, STE (comprised of commercial and government ending stocks, SCE and SGE). Export use 

includes both commercial and export enhancement program exports, EXP and EEP. Certain other stock 

categories account for government stock transactions for selected commodities. These include stock 

accumulation, carryover, release to commercial markets, and net stock removals from commercial markets 

(SGA, SGO, SGR, and SGV), as well as government stock domestic and foreign donations (SGD and SGX). 

Inputs and Outputs 

Set IO contains the all the items that appear in REAP production activities as either inputs or outputs. 

These are commodities, production inputs, and miscellaneous coefficients that describe production 

activities. 
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io input or output of a production activity, io = 1,..., IO 

SET IO PRODUCTION INPUT-OUTPUT ITEMS / 

* -------------------------------------------------------------------------------------- 

* PRODUCTION SOLUTION 

* COMMODITY DESCRIPTION ACTIVITY UNITS REPORT UNITS 

* ----------- -------------------------------------------- -------------- -------------- 

* PRIMARY (FARM PRODUCED) COMMODITIES, CROPS 

 CORN CORN BU MILLION BU 

 SORGHUM SORGHUM BU MILLION BU 

 BARLEY BARLEY BU MILLION BU 

 OATS OATS BU MILLION BU 

 WHEAT WHEAT BU MILLION BU 

 RICE RICE CWT MILLION CWT 

 SOYBEANS SOYBEANS BU MILLION BU 

 COTTON COTTON BALE MILLION BALES 

 PCORN GOVT PROGRAM CORN BU MILLION BU 

 PSORGHUM GOVT PROGRAM SORGHUM BU MILLION BU 

 PBARLEY GOVT PROGRAM BARLEY BU MILLION BU 

 POATS GOVT PROGRAM OATS BU MILLION BU 

 PWHEAT GOVT PROGRAM WHEAT BU MILLION BU 

 PRICE GOVT PROGRAM RICE CWT MILLION CWT 

 PSOYBEANS GOVT PROGRAM SOYBEANS BU MILLION BU 

 PCOTTON GOVT PROGRAM COTTON BALE MILLION BALES 

 SILAGE SILAGE TON MILLION TONS 

 HAY HAY TON MILLION TONS 

 LCORN LONG TERM CORN BU MILLION BU 

 LSORGHUM LONG TERM SORGHUM BU MILLION BU 

 LBARLEY LONG TERM BARLEY BU MILLION BU 

 LOATS LONG TERM OATS BU MILLION BU 

 LWHEAT LONG TERM WHEAT BU MILLION BU 

 LRICE LONG TERM RICE CWT MILLION CWT 

 LSOYBEANS LONG TERM SOYBEANS BU MILLION BU 

 LCOTTON LONG TERM COTTON BALE MILLION BALES 

 LSILAGE LONG TERM SILAGE TON MILLION TONS 

 LHAY LONG TERM HAY TON MILLION TONS 

 

* ACREAGE SHARE INDICATORS (FOR ROTATION BUDGETS) 

 SCORN SHARE OF ACTIVITY DEVOTED TO CORN PROP NA 

 SSORGHUM SHARE OF ACTIVITY DEVOTED TO SORGHUM PROP NA 

 SBARLEY SHARE OF ACTIVITY DEVOTED TO BARLEY PROP NA 

 SOATS SHARE OF ACTIVITY DEVOTED TO OATS PROP NA 

 SWHEAT SHARE OF ACTIVITY DEVOTED TO WHEAT PROP NA 

 SRICE SHARE OF ACTIVITY DEVOTED TO RICE PROP NA 

 SSOYBEANS SHARE OF ACTIVITY DEVOTED TO SOYBEANS PROP NA 

 SCOTTON SHARE OF ACTIVITY DEVOTED TO COTTON PROP NA 

 SSILAGE SHARE OF ACTIVITY DEVOTED TO SILAGE PROP NA 

 SHAY SHARE OF ACTIVITY DEVOTED TO HAY PROP NA 

* PRIMARY (FARM PRODUCED) COMMODITIES, LIVESTOCK PRODUCTS 

 CLDARYCF CULL DAIRY CALVES FOR VEAL HEAD MILLION HEAD 

 CLDARYCW CULL DAIRY COWS FOR SLAUGHTER HEAD MILLION HEAD 

 MILK WHOLE FARM MILK CWT MILLION CWT 

 FEEDERPIG FEEDER PIGS CWT LW MILLION CWT 

 CULLSOW CULL SOWS FOR SLAUGHTER CWT LW MILLION CWT 
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 HOGSLAUGH SLAUGHTER HOGS CWT LW MILLION CWT 

 LIVCALF BEEF FEEDER CALVES CWT LW MILLION CWT 

 BFYRLINGS BEEF FEEDER YEARLINGS CWT LW MILLION CWT 

 CALFSLA CULL BEEF CALVES FOR SLAUGHTER CWT LW MILLION CWT 

 CLBFCOW CULL BEEF COWS FOR SLAUGHTER CWT LW MILLION CWT 

 CLBULLSTAG CULL BULLS & STAGS FOR SLAUGHTER CWT LW MILLION CWT 

 NONFDSL NONFED BEEF FOR SLAUGHTER CWT LW MILLION CWT 

 FEDSLA FED BEEF FOR SLAUGHTER CWT LW MILLION CWT 

 FEDSLACF FED SLAUGHTER COMM FEEDLOT CWT LW MILLION CWT 

 OTHRLVSTK OTHER LIVESTOCK GCAU MILLION GCAU 

* SECONDARY (PROCESSED AND/OR CONVERSION) PRODUCTS 

 BEANMEAL SOYBEAN MEAL CWT MILLION CWT 

 BEANOIL SOYBEAN OIL CWT MILLION CWT 

 OOSMEAL OTHER OILSEED MEAL CWT MILLION CWT 

 ANPROTEIN ANIMAL PROTEIN (TANKAGE) BEANMEAL EQUIVALENT CWT MILLION CWT 

 HIPROFEED HIGH PROTEIN FEED BEANMEAL EQUIVALENT CWT MILLION CWT 

* ANIMAL PRODUCTS 

 FEDBEEF FED BEEF (RETAIL WEIGHT) CWT MILLION CWT 

 NONFDBEEF NONFED BEEF (RETAIL WEIGHT) CWT MILLION CWT 

 VEAL VEAL (RETAIL WEIGHT) CWT MILLION CWT 

 PORK PORK (RETAIL WEIGHT) CWT MILLION CWT 

 FLUIDMLK FLUID MILK-CREAM (MILK EQUIVALENT) LBS MILLION LBS 

 MFGMILK MANUFACTURING MILK CWT MILLION CWT 

 BUTTER BUTTER (PRODUCT WEIGHT) LBS MILLION LBS 

 AMCHEESE AMERICAN CHEESE (PRODUCT WEIGHT) LBS MILLION LBS 

 OTCHEESE OTHER CHEESE (PRODUCT WEIGHT) LBS MILLION LBS 

 ICECREAM ICE CREAM (PRODUCT WEIGHT) LBS MILLION LBS 

 NFDMILK NONFAT DRY MILK (PRODUCT WEIGHT) LBS MILLION LBS 

 EVDRYMLK EVAPORATED, DRY, AND CONDENSED MILK (PRD. WEIGHT) LBS MILLION LBS 

 MILKFAT ALTERNATIVE ACCOUNTING METHOD FOR DAIRY PRODUCTS LBS MILLION LBS 

 EGGS EGG PRODUCTION DOZEN MILLION DOZ 

 BROILERS BROILER PRODUCTION (CARCASS WEIGHT) LB MILLION LBS 

 TURKEY TURKEY PRODUCTION (CARCASS WEIGHT) LB MILLION LBS 

 PBUTTER BUTTER PURCHASED BY GOVT LBS MILLION LBS 

 PAMCHEESE AMERICAN CHEESE PURCHASED BY GOVT LBS MILLION LBS 

 PNFDMILK NONFAT DRY MILK PURCHASED BY GOVT LBS MILLION LBS 

* ETHANOL AND SWEETENER PRODUCTS AND COPRODUCTS 

 STARCH CORN STARCH CWT MILLION CWT 

 CORNOIL CORN OIL CWT MILLION CWT 

 GLUTMEAL GLUTEN MEAL (60% PROTEIN) CWT MILLION CWT 

 GLUTFEED GLUTEN FEED (21% PROTEIN) CWT MILLION CWT 

 DDG DISTILLERS DRIED GRAINS CWT MILLION CWT 

 ETHWML ETHANOL FROM WET-MILLING GAL MILLION GAL 

 ETHDML ETHANOL FROM DRY-MILLING GAL MILLION GAL 

 ETHANOL ETHANOL GAL MILLION GAL 

* PROTEIN, ENERGY ANIMAL NUTRITION COMPONENTS 

 CATPROT CATTLE CRUDE PROTEIN AVAILABLE LB MILLION LBS 

 SWIPROT SWINE CRUDE PROTEIN AVAILABLE LB MILLION LBS 

 DAIPROT DAIRY CRUDE PROTEIN AVAILABLE LB MILLION LBS 

 CATENER CATTLE METABOLIZABLE ENERGY AVAILABLE MCAL MILLION MCAL 

 SWIENER SWINE METABOLIZABLE ENERGY AVAILABLE MCAL MILLION MCAL 

 DAIENER DAIRY METABOLIZABLE ENERGY AVAILABLE MCAL MILLION MCAL 

 SWILINO SWINE LINOLEIC ACID AVAILABLE LB MILLION LBS 
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 SWILYSI SWINE LYSINE AVAILABLE LB MILLION LBS 

 EGGPROT EGG PROD CRUDE PROTEIN AVAILABLE LB MILLION LBS 

 EGGENER EGG PROD METABOLIZABLE ENERGY AVAILABLE MCAL MILLION LBS 

 BROPROT BROILER PROD CRUDE PROTEIN AVAILABLE LB MILLION LBS 

 BROENER BROILER PROD METABOLIZABLE ENERGY AVAILABLE MCAL MILLION LBS 

 TRKPROT TURKEY PROD CRUDE PROTEIN AVAILABLE LB MILLION LBS 

 TRKENER TURKEY PROD METABOLIZABLE ENERGY AVAILABLE MCAL MILLION LBS 

* LIVESTOCK MANURE 

 MANAU MANURE EXCRETED TON MILLION TONS 

 prenit NITROGEN IN EXCRETED MANURE ALL LIVESTOCK OPERATIONS LB MILLION LBS 

 MANNIT NITROGEN AVAILABLE IN AFTER HANDLING LB MILLION LBS 

 prephs PHOSPHOROUS IN EXCRETED LB MILLION LBS 

 MANPHS PHOSPHOROUS AVAILABLE AFTER HANDLING LB MILLION LBS 

* -------------------------------------------------------------------------------------- 

$STITLE REAP MODEL'S INPUTS AND OUTPUTS: INPUTS 

* -------------------------------------------------------------------------------------- 

* PRODUCTION SOLUTION 

* INPUT DESCRIPTION ACTIVITY UNITS REPORT UITS 

* ---------------- ----------------------------------------- -------------- ------------ 

* -------------------------------------------------------------------------------------- 

* INPUTS 

 CROPLAND LAND (CROP) ACRE MILLION ACRES 

 PASTURE LAND (PASTURE) ACRE MILLION ACRES 

 AUM LAND (ANIMAL UNIT MONTHS) AUM MILLION AUMS 

 WATER IRRIGATION WATER (GROUND) ACRE FT MILLION ACFT 

 NITROGEN NITROGEN FERTILIZER US$ MILLION US$ 

 PHOSPHAT POTASSIUM FERTILIZER US$ MILLION US$ 

 POTASH POTASH FERTILIZER US$ MILLION US$ 

 LIME LIME US$ MILLION US$ 

 OVARCOST OTHER VARIABLE COSTS US$ MILLION US$ 

 PUBGRAZG PUBLIC GRAZING LAND AUMS MILLION AUMS 

 CUSTOM CUSTOM FARMING OPERATIONS US$ MILLION US$ 

 CHEMICAL CHEMICALS US$ MILLION US$ 

 SEEDCOST SEED COST US$ MILLION US$ 

 OPERCAP INTEREST ON OPERATING CAPITAL US$ MILLION US$ 

 REPAIRS MACHINERY & EQUIPMENT REPAIR US$ MILLION US$ 

 VET+MED VETERINARY & MEDICAL COST US$ MILLION US$ 

 MKT+STO MARKETING AND STORAGE US$ MILLION US$ 

 INS+FEES INSURANCE AND FEES US$ MILLION US$ 

 OWNRSHIP CASH OWNERSHIP COSTS US$ MILLION US$ 

 OWNNONC NONCASH OWNERSHIP COSTS US$ MILLION US$ 

 MANAGEMT MANAGEMENT COSTS US$ MILLION US$ 

 ESTMGMT OTHER EST. MANAGEMENT COSTS US$ MILLION US$ 

 OVERHEAD GENERAL FARM OVERHEAD US$ MILLION US$ 

 VARCNCSH VARIABLE NONCASH COSTS US$ MILLION US$ 

 PURWATER IRRIGATION WATER PURCHASED US$ MILLION US$ 

 TOTIRAPP IRRIGATION WATER APPLICATION ACRE FT MILLION ACFT 

 ENERGY ENERGY COSTS US$ MILLION US$ 

 IRENERGY IRRIGATION PUMPING COSTS US$ MILLION US$ 

 LANDTAX LAND TAXES US$ MILLION US$ 

 LANDRENT LAND RENT US$ MILLION US$ 

 CONSVCOP CONSERVING USE PROD. COST US$ MILLION US$ 

 DIVPMT ACREAGE DIVRSN PMTS (NEG COST) US$ MILLION US$ 

 LABOR LABOR (FAMILY AND HIRED) US$ MILLION US$ 

 IRRLABOR IRRIGATION LABOR (FAM & HRD) US$ MILLION US$ 

 MISCCOST MISCELLANEOUS PRODUCTION COSTS US$ MILLION US$ 
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 PROCCOST PROCESSING COSTS US$ MILLION US$ 

 INGRED INGREDIENTS OTHER THAN CORN US$ MILLION US$ 

 MANAG "MANAGEMENT, ADMINISTRATION, INSURANCE AND TAXES US$ MILLION US$" 

 OPERAT LABOR AND MAINTENANCE US$ MILLION US$ 

 KIA INCREMENTAL ADDITIONS TO WET MILLING US$ MILLION US$ 

 KAD ADAPTION OF ABANDONED CAPACITY US$ MILLION US$ 

 KNP BUILDING NEW PLANT US$ MILLION US$ 

* LVSK COP UPDATE REVISION TO THIS FILE 2/11-92) 

 AI ARTIFICIAL INSEMINATION US$ MILLION US$ 

 BY-PRODZ BY-PRODUCTS (Z) US$ MILLION US$ 

 DHIA DAIRY HERD IMPROVEMENT ASSOCIATION FEES US$ MILLION US$ 

 DAIRASSE DAIRY ASSESSMENT US$ MILLION US$ 

 DAIRYSUP DAIRY SUPPLIES US$ MILLION US$ 

 FUEL+ELE FUEL LUBE AND ELECTRICITY US$ MILLION US$ 

 HAUL LIVESTOCK HAULING US$ MILLION US$ 

 MILKHAUL MILK HAULING AND MARKETING US$ MILLION US$ 

 TAX+INSU TAXES AND INSURANCE US$ MILLION US$ 

 BEDDING BEDDING US$ MILLION US$ 

 FEEDMIX CUSTOM FEED MIXING US$ MILLION US$ 

 MANURE MANURE CREDIT US$ MILLION US$ 

 HAYLAGEZ HAYLAGE ($) US$ MILLION US$ 

 MANAGEM HIRED MANAGEMENT US$ MILLION US$ 

 MISCELLA MISCELLANEOUS US$ MILLION US$ 

 PUBGRAZE PUBLIC GRAZING US$ MILLION US$ 

 MINERALZ SALT AND MINERALS ($) US$ MILLION US$ 

 CROPRESI CROP RESIDUE US$ MILLION US$ 

 INTEREST INTEREST US$ MILLION US$ 

*ECONOMIC COST ITEM DESCRIPTION 

 CAPIREPL CAPITAL REPLACEMENT COST US$ MILLION US$ 

 OPERCAPC OPERATING CAPITAL COST US$ MILLION US$ 

 OTHECAP OTHER NONLAND CAPITAL COST US$ MILLION US$ 

 LANDCOST LAND OPPORTUNITY COST US$ MILLION US$ 

 LABUNPDZ COST OF UNPAID LABOR US$ MILLION US$ 

 TECONCOS TOTAL ECONOMIC COST US$ MILLION US$ 

 RESIDUAL RESIDUAL RETURN TO MANAGEMENT AND RISK US$ MILLION US$ 

*BUDGETED ENDOGENOUS DESCRIPTION 

 CONCZ BUDGETED COST OF CONCENTRATES US$ MILLION US$ 

 HAYZ BUDGETED COST OF HAY ($) US$ MILLION US$ 

 SILAGEZ BUDGETED COST OF SILAGE ($) US$ MILLION US$ 

 GRAINZ BUDGETED COST OF GRAIN ($) US$ MILLION US$ 

 PROTSUPZ BUDGTD COST OF PROT SUPPLEMENT US$ MILLION US$ 

*QUANTITIES DESCRIPTION 

 GRAIN GRAIN (COP BUDGET UNITS) CWT MILLION CWT 

 GRAINCORN GRAIN CORN COMPONENT IN CORN EQ UNITS CWT MILLION CWT 

 GRAINSBM GRAIN BEANMEAL COMPONENT IN CORN EQ UNITS CWT MILLION CWT 

 CONC CONCENTRATES (DAIRY) CWT MILLION CWT 

 PROTSUP PROTEIN SUPPLEMENT CWT MILLION CWT 

 LABUNPDH HOURS OF UNPAID LABOR HR MILLION HRS 

* REAP MODEL'S INPUTS AND OUTPUTS: ENVIRONMENTAL SET IO ELEMENTS 

 EMENERGY EMBODIED ENERGY UNITS MILLION UNITS 

 SOILDEP SOIL DEPRECIATION ALLOWANCE US$ MILLION US$ 

 EROSION SOIL LOSS FROM WATER EROSION TONS MILLION TONS 

 ERSNCOST OFF-SITE SOIL EROSION COST US$ MILLION US$ 

 WINDERSN SOIL LOSS FROM WIND EROSION TONS MILLION TONS 
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 NUSE NITROGEN FERTILIZER USE LBS MILLION LBS 

 NSOLN NITROGEN LOSS IN SOLUTION (SURFACE RUNOFF) LBS MILLION LBS 

 NSEDMNT NITROGEN LOSS WITH SEDIMENTS LBS MILLION LBS 

 NLEACH NITROGEN LEACHING POTENTIAL LBS MILLION LBS 

 NDENITE NITROGEN LOSS BY DENITRIFICATION LBS MILLION LBS 

 NVOL NITROGEN VOLATALIZATION LBS MILLION LBS 

 NLOSS TOTAL NITROGEN LOSS TO THE ENVIRONMENT LBS MILLION LBS 

 NFLUX NITROGEN FLUX TONS MILLION TONS 

 NFLUXVAL NITROGEN FLUX VALUE US$ MILLION US$ 

 NCRED NITROGEN CREDIT LBS MILLION LBS 

 XN EXCESS NITROGEN BALANCE LBS MILLION LBS 

 PSOLN PHOSPHORUS LOSS IN SOLUTION (SURFACE RUNOFF) LBS MILLION LBS 

 PSEDMNT PHOSPHORUS LOSS WITH SEDIMENTS LBS MILLION LBS 

 PLEACH PHOSPHORUS LEACHED LBS MILLION LBS 

 PLOSS TOTAL PHOSPHORUS LOSS TO THE ENVIRONMENT LBS MILLION LBS 

 CFLUX CARBON FLUX TONS MILLION TONS 

 CFLUXVAL CARBON FLUX VALUE US$ MILLION US$ 

* EXTENDED ENVIRONMENTAL INDICATORS 

 IRGA IRRIGATION WATER APPLIED 

* LIME LIME ADDED TO SOIL 

 MUST 

 PRCP PRECIPITATION 

 SRUNOFF 

 SSFN 

* NITROGEN AND PHOSPHOROUS BALANCE DETAIL 

 N-NVOL NITROGEN LOSS BY VOLITILIZATION LBS MILLION LBS 

 N-BTN NITORGEN BEGINNING TOTAL IN SOIL LBS MILLION LBS 

 N-NDENITE NITROGEN LOSS BY DENITRIFICATION LBS MILLION LBS 

 N-FNH3 NITROGEN FERTILIZER ANHYDROUS AMMONIA LBS MILLION LBS 

 N-FNO3 NITROGEN FERTLIZER NITRATE LBS MILLION LBS 

 N-FTN NITROGEN TOTAL FERTILIZER USE LBS MILLION LBS 

 N-FX NITROGEN FIXED LBS MILLION LBS 

 N-NBAL NITROGEN BALANCE LBS MILLION LBS 

 N-NLEACH NITROGEN LEACHING POTENTIAL LBS MILLION LBS 

 N-RN NITROGEN CONTAIEND IN RAINFALL LBS MILLION LBS 

 N-SSFN NITORGEN LOSS IN SUBSURFACE FLOW LBS MILLION LBS 

 N-TFO NITROGEN FERTLIZER ORGANIC LBS MILLION LBS 

 N-YLN NITROGEN CONTAINED IN CROP YIELD LBS MILLION LBS 

 N-NSOLN NITROGEN LOSS IN SOLUTION (SURFACE RUNOFF LBS MILLION LBS 

 N-NSEDMT NITROGEN LOSS WITH SEDIMENT LBS MILLION LBS 

 P-BTP PHOSPHOROUS BEGINNING TOTAL IN SOIL LBS MILLION LBS 

 P-FTP PHOSPHOROUS TOTAL FERTILIZER APPLIED LBS MILLION LBS 

 P-PBAL PHOSPHOROUS BALANCE LBS MILLION LBS 

 P-PLAB PHOSPHOROUS LABILE LBS MILLION LBS 

 P-PLEACH PHOSPHOROUS LEACHING POTENTIAL LBS MILLION LBS 

 P-PSOLN PHOSPHORUS LOSS IN SOLUTION (SURFACE RUNNOFF) LBS MILLION LBS 

 P-YLP PHOSPHOROUS CONTAIED IN CROP YIELD LBS MILLION LBS 

 P-PSEDMT PHOSPHOROUS LOSS WITH SEDIMENTS LBS MILLION LBS 

* PESTICIDE APPLICATION COMPONENTS 

 PAPL PESTICIDE APPLIED LBS MILLION LBS 

 PDGF PESTICIDE APPLIED FOLIAR LBS MILLION LBS 

 PDGS PESTICIDE APPLIED SOIL LBS MILLION LBS 

 PLCH PESTICIDE LEACH POTENTIAL LBS MILLION LBS 

 PSED PESTICIDE LOSS WITH SEDIMENT LBS MILLION LBS 

 PSRO PESTICIDE LOSS WITH RUNOFF LBS MILLION LBS 

 PSSF PESTICIDE LOSS IN SUBSURFACE FLOW LBS MILLION LBS 
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 PTAI PESTICIDE APPLIED ACTIVE INGEDIENT LBS MILLION LBS 

 PIAI PESTICIDE APPLIED INSECTICIDE ACTIVE INGREDIENT LBS MILLION LBS 

 PHAI PESTICIDE APPLIED HERBICIDE ACTIVE INGREDIENT LBS MILLION LBS 

 PFAI PESTICIDE APPLIED FUNGICIDE ACTIVE INGREDIENT LBS MILLION LBS 

 POAI PESTICIDE APPLIED OTHER ACTIVE INGREDIENT LBS MILLION LBS 

* PESTICIDE APPLICATION BY CROP AND PESTICIDE TYPE LBS MILLION LBS 

* TOTAL INSECTICIDE HERBICIDE FUNGICIDE OTHER 

 PTCORN, PICORN , PHCORN , PFCORN , POCORN 

 PTSORGHUM , PISORGHUM , PHSORGHUM , PFSORGHUM , POSORGHUM 

 PTBARLEY , PIBARLEY , PHBARLEY , PFBARLEY , POBARLEY 

 PTOATS , PIOATS , PHOATS , PFOATS , POOATS 

 PTWHEAT , PIWHEAT , PHWHEAT , PFWHEAT , POWHEAT 

 PTRICE , PIRICE , PHRICE , PFRICE , PORICE 

 PTSOYBEANS , PISOYBEANS , PHSOYBEANS , PFSOYBEANS , POSOYBEANS 

 PTCOTTON , PICOTTON , PHCOTTON , PFCOTTON , POCOTTON 

 PTHAY , PIHAY , PHHAY , PFHAY , POHAY 

 PTSILAGE , PISILAGE , PHSILAGE , PFSILAGE , POSILAGE 

Output Subsets 

Final products, or outputs of production or processing, are defined as a subset P of set IO. Set P is further 

disaggregated into crops, livestock, and processed product subsets: PC (crop products), PL (livestock 

products), and PX (processed products). 

p commodity, p ⊂ io, p = 1,...,P 

SET P(IO) COMMODITIES-OUTPUTS / 

 CORN, SORGHUM, BARLEY, OATS 

 WHEAT, RICE, SOYBEANS, COTTON 

 PCORN, PSORGHUM, PBARLEY, POATS 

 PWHEAT, PRICE, PSOYBEANS, PCOTTON 

 SILAGE, HAY 

 CLDARYCF, CLDARYCW, MILK 

 FEEDERPIG, CULLSOW, HOGSLAUGH 

 LIVCALF, BFYRLINGS, CALFSLA, CLBFCOW, CLBULLSTAG 

 NONFDSL, FEDSLA, FEDSLACF, OTHRLVSTK 

 BEANMEAL, BEANOIL, OOSMEAL, ANPROTEIN, HIPROFEED 

 STARCH, CORNOIL, GLUTMEAL, GLUTFEED 

 CATPROT, SWIPROT, DAIPROT 

 CATENER, SWIENER, DAIENER 

 SWILINO, SWILYSI 

 EGGPROT, EGGENER, BROPROT 

 BROENER, TRKPROT, TRKENER 

 DDG, 

 ETHSOA, ETHWML, ETHDML, ETHANOL 

* 

pc crop commodity, pc ⊂ p 

SET PC(IO) CROP PRODUCTS / 

 CORN, SORGHUM, BARLEY, OATS 

 WHEAT, RICE, SOYBEANS, COTTON, 

 SILAGE, HAY 

 / 
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pl livestock commodity, pl ⊂ p

SET PL(IO) LIVESTOCK PRODUCTS / 

 CLDARYCF, CLDARYCW, MILK 

 CULLSOW, FEEDERPIG, HOGSLAUGH 

 LIVCALF, BFYRLINGS, CALFSLA, CLBFCOW, CLBULLSTAG 

 NONFDSL, FEDSLA, FEDSLACF 

 OTHRLVSTK 

px processed commodity, px ⊂ p

PX(IO) PROCESSED PRODUCTS / 

 BEANMEAL, BEANOIL, OOSMEAL, ANPROTEIN, HIPROFEED 

 STARCH, CORNOIL, GLUTMEAL, GLUTFEED 

 DDG, 

 ETHSOA, ETHWML, ETHDML, ETHANOL 

* 

 CATPROT, SWIPROT, DAIPROT 

 CATENER, SWIENER, DAIENER 

 SWILINO, SWILYSI 

 EGGPROT, EGGENER, BROPROT 

 BROENER, TRKPROT, TRKENER 

 FEDBEEF, NONFDBEEF, VEAL, PORK 

 FLUIDMLK, MFGMILK, BUTTER, AMCHEESE, OTCHEESE, ICECREAM 

 NFDMILK, EVDRYMLK, EGGS, BROILERS, TURKEY 

Input Subsets 

Inputs are defined as a subset I of set IO. Set I is further disaggregated into national and regional input 

subsets: IN and IR. National inputs are specified with a single fixed price in any area of the U.S. Regional 

inputs specify a relationship between price and quantity used by Farm Production Region: 

I(IO) INPUTS EXCLUSIVE OF MODEL-ENDOGENOUS PRODUCTS / 

 CROPLAND, PASTURE, AUM, WATER 

 NITROGEN, PHOSPHAT, POTASH 

 LIME, OVARCOST, PUBGRAZG, CUSTOM, CHEMICAL, SEEDCOST 

 OPERCAP, REPAIRS, VET+MED, MKT+STO, INS+FEES, OWNRSHIP 

 IRENERGY 

 MANAGEMT, ESTMGMT, OVERHEAD, VARCNCSH, PURWATER, TOTIRAPP 

 ENERGY, LANDTAX, LANDRENT, CONSVCOP, DIVPMT, LABOR 

 MISCCOST, PROCCOST 

* LVSK ADDITION CASH COSTS 

 AI, BY-PRODZ, DHIA, DAIRASSE, DAIRYSUP, FUEL+ELE 

 HAUL, MILKHAUL, TAX+INSU, BEDDING, FEEDMIX, MANURE 

 HAYLAGEZ, MANAGEM, MISCELLA, PUBGRAZE, MINERALZ, CROPRESI 

 INTEREST 

* LVSK ADDITION ECONOMIC COSTS 

 CAPIREPL, OPERCAPC, OTHECAP, LANDCOST, LABUNPDZ 

 TECONCOS, RESIDUAL 

 / 

IR(IO) REGIONAL INPUTS / 

 CROPLAND, PASTURE, AUM, WATER, TOTAL 

 / 

IN(IO) NATIONAL INPUTS / 
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 NITROGEN, PHOSPHAT, POTASH 

 LIME, OVARCOST, PUBGRAZG, CUSTOM, CHEMICAL, SEEDCOST 

 OPERCAP, REPAIRS, VET+MED, MKT+STO, INS+FEES, OWNRSHIP 

 IRENERGY 

 MANAGEMT, ESTMGMT, OVERHEAD, VARCNCSH, PURWATER, TOTIRAPP 

 ENERGY, LANDTAX, LANDRENT, CONSVCOP, DIVPMT, LABOR 

 MISCCOST, PROCCOST, 

* ETHANOL REVISION 

 INGRED, MANAG, OPERAT, KIA, KAD, KNP 

* LVSK ADDITION CASH COSTS 

 AI, BY-PRODZ, DHIA, DAIRASSE, DAIRYSUP, FUEL+ELE 

 HAUL, MILKHAUL, TAX+INSU, BEDDING, FEEDMIX, MANURE 

 HAYLAGEZ, MANAGEM, MISCELLA, PUBGRAZE, MINERALZ, CROPRESI 

 INTEREST 

* LVSK ADDITION ECONOMIC COSTS 

 CAPIREPL, OPERCAPC, OTHECAP, LANDCOST, LABUNPDZ 

 TECONCOS, RESIDUAL 

 /

Exogenous Variables (GAMS Parameters) 

Since REAP’s formulation is parameter-driven, it is necessary to be familiar with its key data parameters; 

i.e., exogenous variables. In GAMS, data are stored in objects called “PARAMETERS.” SCALARS are 

PARAMETERS with a single dimension. TABLES are PARAMETERS with 2 to 10 dimensions. GAMS 

parameters with several dimensions are shown in this bulletin and in GAMS, input or output code as twodimensional tables. Indexes for additional dimensions (that is, beyond two) appear either in the table row 

stub or column heading, depending on what most clearly illustrates the data in question. Here, we present 

only the minimum information necessary to understand the REAP equations. We list and discuss 

definitions and at least part of the contents for the most important REAP PARAMETERS. Where the 

PARAMETER contains a large amount of data, we present only a fragment or two of the data. REAP 

model PARAMETERS that contain raw input data or are used in intermediate calculations are not 

presented or discussed here (but are present in the REAP source and listings). 

Production Activity Data 

REAP crop and livestock production activity coefficients reside in table PP. Production activity 

coefficients represent the quantity of outputs produced or inputs used per unit of each production activity. 

The production activity data to produce the coefficients come from the ERS Farm Costs and Returns 

Survey data, the USDA baseline, the agricultural census, and other sources. 

PP is indexed over seven dimensions: input-output item, enterprise, government program category, method 

of production (not active), system of production (not active), tillage type, and region. The rows of the PP

crops fragment refer to input-output items, and columns refer to the other indexes. 
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Example 2—PP(IO,B,G,H,Y,T,RL,R) Crop Fragment 

PARAMETER PP(IO,B,G,H,Y,T,RL,R) ENTERPRISE TECHNICAL COEFFICIENTS 

 RCCC RCB RCB RCBW RCBW 

 NP NP NP NP NP 

 A A A A A 

 PRD PRD PRD PRD PRD 

 CNV CNV MCH CNV NLL 

 CBM CBM CBM LAL APP 

 CB CB CB LA AP 

 CORN 132.582 136.690 136.470 129.979 89.953 

 WHEAT 43.092 51.948 

 SOYBEANS 47.318 47.171 51.219 19.759 

 LCORN 132.960 137.099 137.189 131.408 90.014 

 LWHEAT 42.957 51.967 

 LSOYBEANS 47.649 47.459 51.227 19.766 

 SCORN 1.000 0.500 0.500 0.333 0.500 

 SWHEAT 0.333 0.500 

 SSOYBEANS 0.500 0.500 0.333 0.500 

 CROPLAND 1.000 1.000 1.000 1.000 1.000 

 NITROGEN 41.480 20.360 19.980 17.560 31.940 

 PHOSPHAT 13.620 13.670 13.670 13.590 9.490 

 POTASH 8.740 5.110 5.110 2.200 8.940 

 LIME 1.040 0.060 0.060 0.090 

 CHEMICAL 16.110 10.670 15.680 9.300 17.770 

 SEEDCOST 22.900 18.110 18.110 19.700 24.010 

 OPERCAP 2.200 1.520 1.570 1.480 2.000 

 REPAIRS 8.680 7.720 7.150 8.950 9.440 

 INS+FEES 19.750 18.920 18.920 17.590 11.970 

 OWNRSHIP 14.200 12.980 12.170 13.020 16.540 

 OVERHEAD 12.440 14.560 14.560 19.370 19.980 

 ENERGY 5.580 4.790 4.270 6.270 4.860 

 LANDRENT 90.350 90.350 90.350 57.290 46.390 

 LABOR 5.180 4.460 4.000 5.510 5.660 

 EMENERGY 47.330 25.585 25.957 21.966 39.317 

 SOILDEP -0.304 -0.507 -0.587 -0.339 -0.042 

 EROSION 3.925 4.587 3.325 2.411 0.529 

 WINDERSN 0.081 0.049 0.025 0.016 

 NSOLN 3.000 

 NSEDMNT 12.202 14.487 11.198 3.766 0.457 

 NLEACH 11.000 11.000 10.000 6.000 13.000 

 NDENITE 52.328 53.500 52.647 29.389 10.441 

 NLOSS 75.529 78.988 73.845 42.155 23.898 

 PSOLN 1.000 1.000 1.000 3.000 

 PSEDMNT 1.744 2.043 1.552 0.534 0.064 

 PLEACH 1.872 1.833 1.830 1.518 1.586 

 PLOSS 4.616 4.876 4.382 5.052 1.650 

 CFLUX -3.448 -3.314 -3.024 -2.380 2.060 

 SUSTOTAL 1.000 1.000 1.000 0.999 1.500 

 SUSFLEX 1.000 1.000 1.000 0.999 1.000 

In example 2 above, the label for column 1 refers to a continuous corn, nonprogram, normal, predominant 

production system, using conventional tillage in the Land Resource Region M portion of the Corn Belt 

Farm Production Region (CBM). The other four columns present activities that differ from the first by 

crop, rotation, tillage practice, and region. The column lists the input-output coefficients for each activity. 

The first index of PP is IO, input-output items that appear as row stubs in the listing. Set IO includes the 

subsets P (products), IN (national inputs), IR (regional inputs), and environmental indicators. 
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In the crops PP fragment, corn yield per planted acre is 132.582 bushels in the Corn Belt region. The share 

coefficient SCORN is 1.000, indicating that corn’s share of this production activity is 100 percent. This 

happens for all continuous crop enterprises or production activities. If this were a multiplecrop rotation, 

then the value of SCORN would be less than 1. If it were a two-crop rotation as represented in the second 

column, then SCORN would be 0.500, indicating the proportion of corn yield in the PP table for this 

activity that would be attributed to this rotation. The NITROGEN coefficient indicates that nitrogen fertilizer 

used costs $41.480 per acre. The EROSION coefficient indicates that soil loss from water erosion averages 

3.925 tons per acre on an annual basis. Likewise, the NLOSS coefficient indicates that nitrogen loss to 

water and the atmosphere for this system averages 75.529 pounds per acre annually. 

Dairy, feeder pig, and beef cow enterprises are abstracted in the PP fragment in example 3. Although the 

dimensions of the PP table are the same for livestock production activities as for the crop production 

activities, several of the production strata sets are not relevant and are set to the same value for all 

livestock production activities. These include sets G, H, Y, and T. 

Example 3—PP(IO,B,G,H,Y,T,R,R) Livestock Fragment 

TABLE PP(IO,B,G,H,Y,T,R,R) ENTERPRISE TECHNICAL COEFFICIENTS 

 DAIRY FEEDRPIG BFCOWEN 

 NP NP NP 

 A A A 

 PRD PRD PRD 

 ALT ALT ALT 

 NT CB CB 

 NT CB CB 

 SILAGE -7.250 -0.380 

 HAY -2.626 -1.090 

 CLDARYCF 0.328 

 CLDARYCW 0.189 

 MILK 216.495 

 FEEDERPIG 7.970 

 CULLSOW 2.030 

 LIVCALF 1.866 

 BFYRLINGS 2.063 

 CLBFCOW 0.624 

 CATPROT -117.789 

 SWIPROT -855.389 

 DAIPROT -950.019 

 CATENER -521.485 

 SWIENER -7675.991 

 DAIENER -9869.950 

 SWILINO -89.487 

 SWILYSI -41.152 

 PASTURE 0.414 

 REPAIRS 59.160 49.800 21.790 

 VET+MED 36.780 17.000 7.400 

 MKT+STO 17.900 5.780 

 OVERHEAD 157.520 70.900 56.410 

 LABOR 160.130 26.100 13.850 

 AI 26.710 

 BY-PRODZ 11.450 

 DHIA 11.520 

 DAIRYSUP 31.520 

 FUEL+ELE 40.920 76.900 15.420 

 HAUL 1.800 2.600 1.790 

 MILKHAUL 121.680 

 TAX+INSU 60.810 19.000 26.860 
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 BEDDING 4.700 

 FEEDMIX 10.700 0.630 

 MANURE -1.900 

 PUBGRAZE 0.950 

 MINERALZ 2.050 

 CROPRESI 0.220 

 INTEREST 147.420 59.900 41.170 

 CAPIREPL 270.960 120.100 61.280 

 OPERCAPC 12.860 12.100 7.110 

 OTHECAP 119.480 35.500 33.170 

 LANDCOST 40.510 4.700 84.380 

 LABUNPDZ 114.600 195.900 79.860 

 TECONCOS 2056.650 1060.400 525.830 

 RESIDUAL 214.810 -444.600 -141.040 

 CONCZ 562.430 

 HAYZ 109.190 26.920 

 SILAGEZ 99.190 6.250 

 GRAINZ 186.300 8.770 

 PROTSUPZ 211.500 29.540 

 GRAIN -43.980 -1.860 

 CONC -72.721 

 PROTSUP -12.060 -2.280

In example 3, the label for column 1 refers to dairy production activity, nonprogram, normal, predominant 

system in the Northeast Farm Production Region. The other two columns present activities for hogs and 

beef. Since the livestock production activities are disaggregated only to the Farm Production Region, the 

Land Resource Region index is set to be the same as used for the Farm Production Region. Set T is set at 

ALT since tillage systems do not apply to livestock production activities. The column lists the input-output 

coefficients for each activity and uses the same signing conventions as used for crops. The first index of PP

is IO, input-output items, which appear as row stubs in the listing. 

In the livestock PP fragment in example 3, milk production per cow in the Northeast is 216.495 cwt. per 

cow per year. Fuel and electricity use total $40.92 per cow, while each cow requires 950.019 pounds of 

protein and 9,869.950 million calories of energy from feed per year. 

Processing Activity Data 

Processing activity coefficients reside in the table PPC. Four general types of processing activities are 

represented: livestock slaughter, dairy product conversion, feed ration mixing, and corn and oilseed 

crushing. Coefficients for these activities come from various Situation and Outlook reports, National 

Academy of Science publications or are derived from the baseline data or agriculture census data. 

References for these sources can be found in the model code. 

Example 4—Processing activity livestock slaughter fragment 

PARAMETER PPC(P,C) PROCESSING ACTIVITIES (CONT.) 

 HOGTOPORK FSLATOFBEF FSCFTOFB DCOWNFBF BCOWNFBF NFSLATONFB CLBLTONFBF 

CLDARYCW -0.200 

CULLSOW 

HOGSLAUGH -1.432 

CLBFCOW -2.406 

CLBULLSTAG -2.406 

NONFDSL -2.406 

FEDSLA -2.247 

FEDSLACF -2.288 

FEDBEEF 1.000 1.000 
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NONFDBEEF 1.000 1.000 1.000 1.000 

PORK 1.000 

In example 4, the column headings list the types of slaughter activities, and the row labels give the inputs 

and outputs. Examples 5 to 7 follow a similar format, with column headings listing the processing 

activities and the row labels giving the inputs and outputs. 

Example 5 shows dairy-processing activities found in parameter PPC. Fluid milk (FLUIDMLK) is converted 

directly to fluid milk. In contrast, manufactured milk (MFGMILK) is converted to butter (BUTTER), American 

cheese (AMCHEESE), other cheese (OTCHEESE), ice cream (ICECREAM), and evaporated dry milk (EVDRYMLK). 

 Example 5—Processing activity milk processing fragment 

PARAMETER PPC(P,C) PROCESSING ACTIVITIES (CONT.) 

 + FLUIDMLK MFGMILK BUTTER AMCHEESE OTCHEESE ICECREAM EVDRYMLK 

FLUIDMLK 102.000 

MFGMILK 1.000 -1.000 -1.000 -1.000 -1.000 -1.000 

BUTTER 4.805 

NFDMILK 3.105 

AMCHEESE 10.825 

OTCHEESE 18.253 

ICECREAM 8.430 

EVDRYMLK 25.288 

MILK -1.000 -1.000 

Example 6 shows the feed ration processing activities for fed cattle. The rations use crops as input and 

produce protein (CATPROT) and energy (CATENER). Similar types of rations are specified for dairy, hogs, and 

poultry. 

Example 6—Processing activity feed mix fragment 

PARAMETER PPC(P,C) PROCESSING ACTIVITIES (CONT.) 

 + GRAIN1 GRAIN2 GRAIN3 GRAIN1A GRAIN1B GRAIN1C GRAIN1D 

CORN -1.231 -1.364 -1.366 -1.231 -1.364 -1.366 -1.350 

SORGHUM -0.234 -0.163 -0.225 -0.399 -0.326 -0.321 

BARLEY -0.096 -0.060 -0.069 -0.128 -0.099 -0.092 -0.024 

OATS -0.262 -0.183 -0.203 -0.087 -0.031 -0.030 -0.004 

WHEAT -0.082 -0.095 -0.018 -0.200 

CATPROT 9.519 9.438 9.222 9.150 9.075 9.017 8.580 

CATENER 126.559 127.519 127.141 126.907 127.965 127.330 115.105 

Example 7 shows the processing activities for soybeans and ethanol. The soybean-processing activity 

(SOYCRUSH1) converts soybeans into bean meal (BEANMEAL) and oil (BEANOIL). The ethanol-processing 

activities take corn and convert it into ethanol and its byproducts—corn starch (STARCH), corn gluten meal 

(GLUTMEAL), corn gluten feed (GLUTFEED), and distiller’s dried grains (DDG). Ethanol-processing 

activities include wet milling (ETHWMLCUR, ETHWML95) and dry milling (ETHDMLCUR, ETHDML95). 

Example 7—Processing Activity corn-soybean crushing 

PARAMETER PPC(P,C) PROCESSING ACTIVITIES (CONT.) 

 + SOYCRUSH1 ETHWMLCUR ETHWML95 ETHDMLCUR ETHDML95 ETHSOA 

SOYBEANS -1.000 

CORN -1.000 -1.000 -1.000 -1.000 

ETHSOA 2.500 2.500 2.600 2.600 -1.000 

BEANMEAL 0.477 
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BEANOIL 0.113 0.020 0.020 

STARCH 0.315 0.315 

GLUTMEAL 0.026 0.026 

GLUTFEED 0.135 0.135 

DDG 0.175 0.175 

ETHANOL 1.000 

Processing and Production Activity Costs 

wc cost of processing activity c 

SCR(C,*) PROCESSING ACTIVITY COST-RETURNS SUMMARY TABLE 

Input costs are not explicitly represented for most of the model’s processing activities. Processing activity 

cost (example 8) is determined as value added in production or net return to production. Net return for 

production is determined as the difference between revenue at base prices received for all outputs from the 

processing activity minus the value of intermediate inputs at base prices used by the processing activity. In 

a few instances, primarily ethanol processing, the processing costs are explicitly represented in the 

production activity. These costs are included in the calculation of net returns (value added) for these 

processing activities. The formula for calculating processing cost is shown here. 

SCR(C, "COST") = SUM(IN, PPC(IN,C)* INPUTN(IN,"PBASE")) 

 -SUM(P, PPC(P,C) * DEMSUP(P,"DOM","PBASE") 

 $(PPC(P,C) LT 0)); 

* ETHANOL CALCULATION 

 SCR("ETHWMLCUR ","COST") = SCR("ETHWMLCUR ","COST") - PPC("KIA","ETHWMLCUR "); 

 SCR("ETHWML95 ","COST") = SCR("ETHWML95 ","COST") - PPC("KAD","ETHWML95 "); 

 SCR("ETHWML20 ","COST") = SCR("ETHWML20 ","COST") - PPC("KNP","ETHWML20 "); 

* ETHANOL REVISION END 

 SCR(C, "REVENUE") = SUM(P, PPC(P,C) * DEMSUP(P,"DOM","PBASE") 

 $(PPC(P,C) GT 0)); 

 SCR(C, "NETRETURN") = SCR(C,"REVENUE") - SCR(C,"COST"); 

Example 8 – Processing activity net return fragment 

SCR(C, " NETRETURN ") PROCESSING ACTIVITY COST-RETURNS SUMMARY TABLE 

-------------------------------------------------- 

Processing activity Net Return 

 ($/unit) 

-------------------------------------------------- 

HOGTOPORK 217.621 

SOWTOPORK 204.828 

FSLATOFBEF 187.379 

FSCFTOFB 184.170 

DCOWNFBF 161.051 

BCOWNFBF 141.291 

NFSLATONFB 141.291 

CLBLTONFBF 141.291 

DCLFVEAL 415.507 

FLUIDMLK -0.481 

MFGMILK -2.158 

BUTTER -3.422 

AMCHEESE 2.375 

OTCHEESE 16.158 

ICECREAM -0.311 

EVDRYMLK 3.327 

SOYCRUSH1 1.338 

ETHWMLCUR 0.925 

ETHWML95 0.845 
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ETHDMLCUR 0.962 

ETHDML95 0.892 

--------------------------------------------------

wvc

b,g,h,t,,rl,r variable cost of production activity b,g,h,t in region rl,r

Production activity costs are reported in PCR(B,G,H,Y,T,U,UR, “VCOST”). Costs are calculated for 

each production activity at base period prices. The calculations for production activity costs can be found 

in file A1A0C.GMS in the root model directory. Cropping activity VCOST is determined by multiplying the 

inputs from the crop production budgets in PP by the input prices in parameter INPUTN and summing up 

over the inputs contained in set INVC. Since the input items contained in the production activity budgets 

represent expenditures and are already expressed in terms of dollar value, the prices in INPUTN are set at 

one. VCOST is adjusted by adding a credit equal to the rental rate for cropland in the base period. This 

credit is added to ensure that net returns for production activities are positive, a requirement for CET 

parameters to be calculated. Differences among regions in cropland costs will still affect any changes in 

cropland use from the base since any expansion or reduction in cropland use will cause crop price to 

change. 

Crops: PCR(B,G, H,Y,T,U,UR, "VCOST") $ XCROPP(B,G,H,Y,T,U,UR) 

* ADD CREDIT FOR REGIONAL LAND PRICES FOR ENVIRONMENTAL VERSION 

 =- (PP("CROPLAND",B,G, H,Y,T,U,UR) * INPUTR(UR,"CROPLAND","PBASE") 

 $ ((INPUTR(UR,"CROPLAND","PBASE") GT 0) AND (INPUTR(UR,"CROPLAND","PFXP") GE 0))) 

 + SUM(INVC, PP(INVC,B,G, H,Y,T,U,UR) * INPUTN(INVC,"PBASE")); 

Livestock: PCR(B,G, H,Y,T,U,UR, "VCOST") $ XLVSTP(B,G,H,Y,T,U,UR) 

 = SUM(INVC, PP(INVC,B,G,H,Y,T,U,UR) * INPUTN(INVC,"PBASE")); 

Similar calculations are done for livestock production activities. Examples of the results of the 

calculations are shown in example 9 below. 

Example 9—PCR(B,G,H,Y,T,U,UR, ‘VCOST’) cost of production fragment 

-------------------------------------------------------------------------------------- 

 CB.CB CBLN.CB CBMN.CB CBNN.CB CBON.CB CBRN.CB 

-------------------------------------------------------------------------------------- 

RCCC .NP.CNV 167.854 167.934 169.187 167.854 168.015 

RCCC .NP.MLD 161.600 161.680 162.261 162.331 

RCCC .NP.MCH 163.688 163.748 163.768 163.798 

RCCC .NP.NLL 151.929 151.989 152.520 152.570 

RTTT .NP.CNV 283.074 

RBBB .NP.CNV 113.542 113.482 

RBBB .NP.NLL 145.134 

RHHH .NP.MLD 92.001 95.709 93.584 92.001 

RGGG .NP.CNV 165.180 

RGGG .NP.MCH 162.021 

RCB .NP.CNV 137.960 137.970 137.970 138.561 

RCB .NP.MLD 151.580 151.590 152.782 152.191 

RCB .NP.MCH 146.925 146.935 146.925 147.526 

RCB .NP.NLL 142.277 142.287 142.878 142.878 

RCBW .NP.CNV 114.043 114.043 114.043 

RCBW .NP.MLD 125.153 125.153 

RCBW .NP.MCH 129.446 129.446 

RCBW .NP.NLL 122.435 122.435 

RCBWH .NP.CNV 115.718 115.738 117.371 

%%%%%% 

wn

b,g,t,ft,rl,r nitrogen fertilizer cost for production activity b,g,t,ft in region rl,r

PCRNIT(B,G,H,Y,T,FT,U,UR,"NCOST") NITROGEN FERTILIZER COSTS PER UNIT ACTIVITY 
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Nitrogen fertilizer costs (example 10) for rotations and tillage practice pairings are the same across all 

Land Resource Regions within a Farm Production Region. This is because fertilizer use by rotation and 

tillage practice was derived at the Farm Production Region level. 

Example 10—Nitrogen fertilizer costs fragment 

PCRNIT(B,G,H,Y,T,FT,U,UR,"NCOST") NITROGEN FERTILIZER COSTS PER UNIT ACTIVITY 

 CBLN.CB CBMN.CB CBNN.CB CBON.CB CBRN.CB 

RCCC .NP.A.PRD.CNV.1 41.480 41.480 41.480 41.480 41.480 

RCCC .NP.A.PRD.CNV.9 37.332 37.332 37.332 37.332 37.332 

RCCC .NP.A.PRD.CNV.8 33.184 33.184 33.184 33.184 33.184 

RCCC .NP.A.PRD.CNV.7 29.036 29.036 29.036 29.036 29.036 

RCCC .NP.A.PRD.CNV.6 24.888 24.888 24.888 24.888 24.888 

RCCC .NP.A.PRD.MLD.1 42.500 42.500 42.500 42.500 

RCCC .NP.A.PRD.MLD.9 38.250 38.250 38.250 38.250 

RCCC .NP.A.PRD.MLD.8 34.000 34.000 34.000 34.000 

RCCC .NP.A.PRD.MLD.7 29.750 29.750 29.750 29.750 

RCCC .NP.A.PRD.MLD.6 25.500 25.500 25.500 25.500 

RCCC .NP.A.PRD.MCH.1 42.750 42.750 42.750 42.750 

RCCC .NP.A.PRD.MCH.9 38.475 38.475 38.475 38.475 

RCCC .NP.A.PRD.MCH.8 34.200 34.200 34.200 34.200 

RCCC .NP.A.PRD.MCH.7 29.925 29.925 29.925 29.925 

RCCC .NP.A.PRD.MCH.6 25.650 25.650 25.650 25.650 

RCCC .NP.A.PRD.NLL.1 34.350 34.350 34.350 34.350 

RCCC .NP.A.PRD.NLL.9 30.915 30.915 30.915 30.915 

RCCC .NP.A.PRD.NLL.8 27.480 27.480 27.480 27.480 

RCCC .NP.A.PRD.NLL.7 24.045 24.045 24.045 24.045 

RCCC .NP.A.PRD.NLL.6 20.610 20.610 20.610 20.610 

wσ

b,g,t,ft,rl,r risk premium charged for nitrogen fertilizer use in production activity b,g,t,ft in region rl,r 

PCRNIT(B,G,H,Y,T,FT,U,UR,"RSKADJ") NITROGEN RISK ADJUSTMENT PER UNIT ACTIVITY 

The risk premium (example 11) represents the amount producers would need to receive to make them 

indifferent between using the reduced rate of fertilizer application and the base rate of fertilization. The 

risk premium represents producers’ perceptions about having sufficient fertilizer available for meeting 

crop needs in order to achieve yield targets. The risk premium associated with reduced nitrogen fertilizer 

use varies across rotation/tillage management pairings even for pairings in the same Farm Production 

Region. This is because the yield response curve for nitrogen fertilizer varies across all regions. 

Example 11—Nitrogen fertilizer risk premium fragment 

PCRNIT(B,G,H,Y,T,FT,U,UR,"RSKADJ") RISK ADJUSTMENT COST PER UNIT ACTIVITY 

 CBLN.CB CBMN.CB CBNN.CB CBON.CB CBRN.CB 

RCCC .NP.A.PRD.CNV.9 2.863 6.470 8.695 4.609 

RCCC .NP.A.PRD.CNV.8 5.216 11.790 15.844 8.398 

RCCC .NP.A.PRD.CNV.7 12.204 27.583 37.069 19.648 

RCCC .NP.A.PRD.CNV.6 30.016 67.844 91.175 48.327 

RCCC .NP.A.PRD.MLD.9 6.434 8.110 9.946 7.563 

RCCC .NP.A.PRD.MLD.8 11.724 14.778 18.123 13.780 

RCCC .NP.A.PRD.MLD.7 27.430 34.576 42.402 32.241 

RCCC .NP.A.PRD.MLD.6 67.467 85.043 104.291 79.299 

RCCC .NP.A.PRD.MCH.9 4.064 7.652 10.597 6.923 

RCCC .NP.A.PRD.MCH.8 7.405 13.943 19.309 12.614 

RCCC .NP.A.PRD.MCH.7 17.324 32.621 45.176 29.513 

RCCC .NP.A.PRD.MCH.6 42.610 80.236 111.114 72.590 

crp wir,r CRP rental rate for regional input ir in region r 

ACRESDY("TOTAL","HST","CRPR","A","2005",R) CROP PLANTINGS AND ACREAGE BASE 
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CRP rental rates (example 12) are fixed in the formulation of the model because rental rates are set by the 

government, based on prevailing local market rental rates. 

Example 12—CRP rental rate 

ACRESDY("TOTAL","HST","CRPR","A","2005",R) CROP PLANTINGS AND ACREAGE BASE 

NT 72.826 

LA 71.833 

CB 90.115 

NP 46.411 

AP 60.787 

SE 44.317 

DL 40.497 

SP 35.082 

MN 35.358 

PA 43.843 

Demand and Supply Function Data 

Commodity demand and supply relationships are incorporated explicitly and implicitly in REAP. The 

parameters for the explicitly defined demand and supply equations are derived from supply and demand 

elasticities and base year prices and quantities. This information is contained in the DEMSUP parameter (see 

table 3). The prices and quantities contained in DEMSUP are updated automatically to the baseline year 

selected for the analysis. The absence of an elasticity indicates that no explicit supply or demand curve is 

specified for that particular commodity in that particular market—i.e., the price remains constant. The 

absence of a price indicates that the value or price of the commodity in that market is determined 

implicitly. A positive sign on elasticity indicates that it is a supply, elasticity, and a negative sign indicates 

it is a demand elasticity. MIN and MAX indicate lower and upper bounds on quantity to be imposed in that 

market. Sources of demand and supply elasticities in DEMSUP are also in the REAP calibration run listing 

or in the A1A0A.gms file. 

The formulas used to derive the commodity demand and supply function parameters are provided here. 

βm,p slope for commodity p demand or supply equation in market m

BETA(P,M) DEMAND AND SUPPLY FUNCTION SLOPES BY MARKET 

βm,p = (Pm,p /Z m,p) * (1/em,p) such em,p ≠ 0 ∀ p =1,K,P; m =1,K,M;

BETA(P,M) = (DEMSUP(P,M,"PBASE") / DEMSUP(P,M,"QBASE") 

/ DEMSUP(P,M,"ELAS")) $ DEMSUP(P,M,"ELAS"); 

αm,p intercept on commodity p demand or supply equation in market m

ALPHA(P,M) DEMAND AND SUPPLY FUNCTION INTERCEPTS BY MARKET 

αm,p = Pm,p - βm,p * Zm,p

ALPHA(P,M) = (DEMSUP(P,M,"PBASE") - BETA(P,M) * DEMSUP(P,M,"QBASE")) 

where Pm,p and Zm,p represent base year price and quantity, respectively, for commodity p in market m and 

ep,m equals the price elasticity for commodity p in market m. 

The parameters on the input supply equations are also derived from supply and demand elasticities and 

base year prices and quantities. This information is contained in the INPUTR parameter (table 4). The 

prices reported in this table are not updated automatically but on a periodic basis. Quantity information is 
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not from an outside data source but is derived from baseline information on crop and livestock production. 

This information is updated automatically to the baseline year. The formulas used to derive the input 

supply functions are provided here. 

βir,r slope for regional input ir supply equation in farm production region r

BETAI(R,IR) INPUT SUPPLY FUNCTION SLOPES 

βir,r = (wr,ir /VI r,ir) * (1/er,ir) such that er,ir > 0 ∀ r =1,K,R; ir =1,K,IR;

BETAI(R,IR) = (INPUTR(R,IR,"PBASE") / INPUTR(R,IR,"QBASE") 

/ INPUTR(R,IR, "ELAS")) $ INPUTR(R,IR, "ELAS"); 

αir,r intercept on regional input ir supply equation in farm production region r

ALPHAI(R,IR) INPUT SUPPLY FUNCTION INTERCEPTS 

αr,ir = wr,ir - βir,r * VIr,ir

ALPHAI(R,IR) = (INPUTR(R,IR,"PBASE")-BETAI( R,IR) * INPUTR(R,IR,"QBASE"))$ INPUTR(R,IR,"QBASE"); 

where wr,ir and VIr,ir represent base year price and quantity, respectively, for variable input ir in region r,

and er,ir equals the price elasticity of supply for variable input ir in region r. 

The parameters on CRP land supply equations are also derived from cropland supply elasticities. The 

derivations, however, depend on base year CRP rental rates and CRP enrollment acreage. Information 

about CRP rental rates and enrollment is contained in ACRESDY (see example 12). 

βcrp

ir,r slope for regional CRP input ir supply equation in farm production region r

BETAC( IR,YR,UR) PMP CRP FUNCTION SLOPES BY PROCESS AND REGION 

αcrp

ir,r intercept on regional CRP input ir supply equation in farm production region r

ALPHAC(IR,YR,UR) PMP CRP FUNCTION INTERCEPTS BY PROCESS AND REGION 

CRP supply parameters are set at zero since CRP acreage is fixed in the base version of the model. CRP 

can be made endogenous by specifying values for these parameters and setting the CRP rental rate 

parameter to zero. National CRP enrollment is updated by the baseline data. CRP rental rates and 

distributions of enrollment acreage are updated periodically. 

CET Parameters 

The parameters for the CET allocation functions are derived from an elasticity of transformation and 

information on the value of the production activities. The transformation elasticities are given, while the 

value of the production activities is determined from shadow prices obtained by solving REAP, with 

constraints imposed on allocation of production activities associated with the level of CET function being 

derived. The elasticities of transformation are fixed for both the rotation CET function and the tillage CET 

function. 

σ b,rl Elasticity of transformation. 

SIGMA CET FUNCTION ROTATION ACREAGE ELASTICITY OF SUBSTITUTION 

SIGMAT CET FUNCTION TILLAGE ACREAGE ELASTICITY OF SUBSTITUTION 

SIGMAT(BA,U) $RACD(BA,U) 

 = -10; 

SIGMA(B,U) = -2 $SUM(BA, BSBROT(B,BA,U)); 

ρ b,rl CET substitution parameter for crop or rotation b acres in farm resource region rl 

RHOT CET FUNCTION TILLAGE SUBSTITUTION PARAMETER 

RHO CET FUNCTION ROTATION SUBSTITUTION PARAMETER 
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b,rl b,rl b,rl ρ = (1−σ )/σ where σ b,rl is the elasticity of transformation among rotations used to produce 

crop b or among tillage practices used by rotation b in farm resource production region rl.

RHOT(BA,U) $SIGMAT(BA,U) 

= (1-SIGMAT(BA,U))/SIGMAT(BA,U); 

RHO(BA,U) $SIGMA(BA,U) 

 ο = (1-SIGMA(BA,U))/SIGMA(BA,U); 

δ b,t,rl CET allocation parameter for t tillage acres planted to rotation b acres in farm resource production 

region rl 

DELTAT CET FUNCTION TILLAGE DISTRIBUTION PARAMETER 

b B t ta T rl RL

NR X

NR X

b rl

b rl

ghyr

b g h y ta rl r

ta

b ta rl

ghyr

b t rl b g h y t rl r

b t rl , 1, , ; , 1, , ; 1, , * )

*( )

,

,

1

, , , , , , , ,

1

, , , , , , , ,

, , = + = K = K = K

+

∑ ∑∑∑∑

∑∑∑∑

ρ

ρ

δ

DELTAT(BA,T2,U)$(XACTD(BA,T2,U) > 0) 

 = (MPRICET(BA,T2,U) 

 * SUM(XCROPP(BA,G,H,Y,T2,U,UR), XACT.L(XCROPP))**(1+RHOT(BA,U))) 

 / SUM(T2A, MPRICET(BA,T2A,U) 

 * SUM(XCROPP(BA,G,H,Y,T2A,U,UR), XACT.L(XCROPP))**(1+RHOT(BA,U))); 

where NRb,t,rl equals net return per acre to rotation b under tillage practice t in region rl. NRb,rl is derived 

from the shadow value of constraints placed on the allocation of production activities in each rl region. In 

the model code, $(XACTD(BA,T2,U) > 0) is used to ensure that DELTAT is calculated only for systems with 

positive acreages. (MPRICET(BA,T2,U) represents the marginal price on production practices constrained to 

their base levels (XACT.L(B,G,H,Y,T,RL,R). 

δ b,rl CET allocation parameter for crop b acres in farm resource region rl 

DELTA CET FUNCTION ROTATION DISTRIBUTION PARAMETER 

b ba B rl RL

NR X

NR X

ba rl

b rl

ba

ba rl

ba

ba rl

b

b rl b rl

b rl , , 1, , ; 1, , *( )

*( )

,

,

1

, ,

1

, ,

, = + = K = K

+

∑ ∑

∑

ρ

ρ

δ

MPRICER(BA,U) = CETT.M(BA,U); 

MPRICEPX(B,BA,U) = BSBROT(B,BA,U)*CETT.M(BA,U); 

BBASACS(B,BA,RL) $CAC(RL) 

 = SUM(XCROPP(BA,G,H,Y,T2,RL,UR), XSB7(B,XCROPP) * XACT.L(XCROPP)) 

 / CAC(RL); 

MPRICEP(B,RL) = SUM(BA$RCROP4(BA,RL), MPRICEPX(B,BA,RL) * BBASACS(B,BA,RL)); 

LAMDA(BA,RL) $RACD2(BA,RL) 

 = SUM(B$BSBAS(B,BA,RL), BSBROT(B,BA,RL) * MPRICEP(B,RL) * 

ACLRRL(B,RL,"CK4")**(1+RHO(B,RL)) * RAC.L(BA,RL)**(-RHO(B,RL) - 1) ); 

LAMINV(BA,U) $LAMDA(BA,U) 

 = LAMDA(BA,U)**(-1); 

DELTA(BA,U) $SUM(B, LAMINV(B,U)* MPRICER(B,U)) 

 = (LAMINV(BA,U) * MPRICER(BA,U)) / SUM(B, LAMINV(B,U)* MPRICER(B,U)); 
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where NRb,rl equals net return per acre to rotation b in region rl. NRb,rl is derived from the shadow value of 

constraints placed on the allocation of rotation acreage in each rl region. In the model code, the dollar 

control variables ($RACD2(BA,RL), BSBAS(B,BA,RL), $LAMDA(BA,U) & $SUM(B, LAMINV(B,U)* MPRICER(B,U))) 

are used to ensure that the calculations are performed only for those activities with nonzero values for the 

control variable. MPRICEP(B,RL) represents the average net returns to crop b planted in farm resource region 

rl , and CETT.M(BA,U) is the shadow price of the tillage function and represents net returns to those 

rotations. 

αb,rl CET scale parameter for crop or rotation b acres in farm resource region rl 

AT CET FUNCTION TILLAGE SHIFT PARAMETER 

A CET FUNCTION ROTATION SHIFT PARAMETER 

Tillage and Rotation Strata 

αb,rl =∑ ∑ ∑∑∑∑ ∑∑∑∑ − −

g ta b h y r

b ta rl b g h y ta rl r

hytr

b rl b rl

b g h y t rl r X g X , ,, , , , , ,

1/

, , , , , , , , /( ) ρ ρ δ

AT(BA,U) $RACD(BA,U) 

 = SUM(XCROPP(BA,G,H,Y,T2,U,UR), XACT.L(XCROPP)) 

 /(SUM(T2A, DELTAT(BA,T2A,U) * SUM(XCROPP(BA,G,H,Y,T2A,U,UR), XACT.L(XCROPP)) 

 **(-RHOT(BA,U)))**(-1/RHOT(BA,U))); 

αb,rl = b ba rl

b ba

b rl b rl b ba rl ba rl X s RAC s b rl b rl, ,

1/

, /( , , , , ) where , ,

∑ ∑ −ρ − ρ δ is the crop enterprise b share of one unit 

of rotation ba acres in region rl . 

A(B,U) $ACLRRL(B,U,"CK4") = ACLRRL(B,U,"CK4") 

 /(SUM(BA, DELTA(BA,U) * BSBROT(B,BA,U) * RAC.L(BA,U)**(-RHO(B,U))) 

 **(-1/RHO(B,U))); 

The scale parameters for both the tillage and rotation CET functions are obtained directly from the relevant 

CET function once the substitution ( ρ b,rl ) and allocation parameters (δ b,rl ) have been obtained. 

PMP Cost Parameters 

The crop and livestock PMP functions used differ from each other by the level in the model at which they 

are specified. The crop PMP functions are part of a nested system of CET functions that determine the 

substitution behavior among crop rotations and tillage practices. The CET functions aggregate individual 

production activities that differ by crops produced, rotations used, and tillage practices employed into a 

crop production index ACLRR that is used in the PMP function for crops. The PMP function for livestock, 

in contrast, is specified for each livestock production activity represented in the model. The formulas for 

deriving the parameters for the PMP functions for crops and livestock are shown here. 

βb,rl slope for crop production activity b supply equation in farm resource region rl

BETA3(B,U) PMP AGGREGATE ACREAGE COST FUNCTION SLOPES LRR LEVEL CET FORMULATION 

αb,rl intercept on crop production activity b supply equation in farm resource region rl

ALPHA3(B,U) PMP AGGREGATE ACREAGE COST FUNCTION INTERCEPTS LRR LEVEL CET FORMULATION 

SCALAR ACCFAF AGGR CROP AC COST FUNCTION ACREAGE FACTOR 

βb,rl = ∑

p∈p2b

'dom',p b,rl ((P /X ) * (1/ep

) *( X b,rl / ∑

ba

ba rl X ) , * YLD ) p,rl such that 
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ep,,rl > 0 ∀p, p ∈ pc,b∈bc, p =1,K,P; b =1,...,BC;rl =1,K,RL;

BETA3( B,RL) $YCROP3(B,RL) 

= PES(B,"BPLNTP")* ACCFAF * (SUM(RLA, ACLRR.L(B,RLA)) 

/ACLRR.L(B,RL)) {WGHT LRR RESP BY CROP LRR ACRES} 

z SUM(P$P2B(P,B), YLDTPCLR(P,"CK5",RL)); 

αb,rl = wb,rl - βb,rl * X b,rl , b =1,K,B; rl =1,K,RL;

ALPHA3(B,RL) $YCROP3(B,RL) 

= CETR.M(B,RL) - BETA3(B,RL) * ACLRR.L(B,RL); 

βb,g,h,r slope for livestock production activity b supply equation in farm production region r

PARAMETERS BETAP( B,G,H,UR) PMP COST FUNCTION SLOPES BY PROCESS AND REGION 

αb,g,h,rl intercept on livestock production activity b supply equation in farm production region r

 ALPHAP(B,G,H,UR) PMP COST FUNCTION INTERCEPTS BY PROCESS AND REGION 

PARAMETER PMPLCL(B,*,U) OPTIMAL AGGREGATE LIVESTOCK PRODUCTION LEVELS 

 PMPLCM(B,*,U) SHADOW PRICES ON OPTIMAL LVSK PRODUCTION LEVELS 

SCALAR ALCFAF AGGREGATE LIVESTOCK PRODUCTION COST FUNCTION FLEXIBILITY FACTOR 

βb,g,h,r = ( ∑

p∈p b

Pdom p

2

' ', /∑∑∑∑∑

g h y t rl

Xb,g,h, y,t,rl,r ) * (1/ep) 

• ( ∑ ∑∑∑∑∑∑

ba g h y t rl ra ∈xlvstp

Xba,g,h, y,t,rl,ra / 

∑ ∑∑∑∑∑

b g h y t rl ∈xlvstp

Xb,g,h, y,t,rl,r ) 

ο * YLDp,rl )

BETAP( B,"NP","A",R) $(PMPLCL(B,"CAL",R) GT 0) {SLOPE WRT PRODVAL BY REGN} 

• = SUM(P$PESL(B,P,"BPRDNP"), PESL(B,P,"BPRDNP") 

• ALCFAF * (PMPLCL(B,"CAL","US") / PMPLCL(B,"CAL",R)) 

• *(SUM(XLVSTP(B,"NP",H,Y,T,U,R), PP(P,XLVSTP)) <<SIMPLE AVG YIELD 

• /SUM(XLVSTP(B,"NP",H,Y,T,U,R), 1$PP(P,XLVSTP)))); 

αb,g,h,rl = wb,rl - βb,rl * X b,rl , b =1,K,B; rl =1,K,RL;

ALPHAP(B,"NP","A",R) $ (PMPLCL(B,"CAL",R) GT 0) 

• = PMPLCM(B,"CAL",R) - BETAP(B,"NP","A",R) * PMPLCL(B,"CAL",R); 

where P’dom’,p is the price of commodity p in the domestic market (‘dom’), ep equals the supply elasticity for 

commodity p ∈pl or pc (see tables 5 and 6), p2b maps p commodities to the b commodity production 

activities that the p commodities come from, YLDp,u represents the yield of commodity p in region r or rl,

and wb,rl represents the price or net returns per unit of production activities b in region r or rl. The 

parameters ACCFAF and ALCFAF are scaling factors for the slopes. These factors can be used to adjust the 

slope of the supply functions if desired. 

Endogenous Variables (GAMS Variables)

The POSITIVE VARIABLE or VARIABLE statement declares endogenous variables used in the REAP 

formulation. Each block of variables is indexed over one or more sets. For example, DOMESUSE(P)
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establishes a block of domestic demand variables over the set P of products; XACT(B,G,H,Y,T,RL,R)

establishes production activity variables over the index space of sets B,G,H,Y,T,RL,R--that is, 

enterprise, government program category, method of production (not used), system of production (not 

used), tillage type, Land Resource Region and Farm Production Region. Activities are designated 

production activities if indexed over enterprise, geographic area, program category, and so on, or 

processing activities if indexed only over enterprises (and therefore formulated as a national-level 

input/output process, rather than differentiated by region). Variables in REAP represent commodity 

supply and demand levels, production and processing activity levels, variable input levels, and 

government programs. 

Commodity Demand and Supply Variables 

Commodity demand and supply variables in REAP are represented both explicitly and implicitly. Explicit 

variables are DOMESUSE(P), EXPORTUSE(P), EEPUSE(P), STKUSEC(P), STKUSEG(P), and RESIDUSE(P). 

Other supply and demand variables are represented implicitly in the model's formulation but do not exist 

as a specific variable. For example, livestock feed use of corn is determined through the accumulation of 

enterprises producing various livestock types in various regions across the country by using various feed 

rations and nutrient combinations that react to conditions in livestock and feed markets. Although no 

explicit variable exists, the amount of corn used to feed livestock could be calculated from the levels of 

these other enterprises. 

DOMESUSE(P) represents primarily seed and industrial uses for each commodity. (EXPORTUSE(P), 

EEPUSE(P)) represent quantity of commodity exported, and (STKUSEC(P), STKUSEG(P)) represent 

commercial and government acquired stocks, respectively. Residual supply or use (RESIDUSE(P), 

RESIDSUP(P)) is specified during model calibration for commodities for which their baseline supply and 

use fails to balance precisely. Commodity use categories such as government stock accumulation, 

carryover, release, net removals, and domestic and foreign donations are used in presolution and 

postsolution calculations but are not endogenous model variables. 

Commodities for which explicit supply and demand functions exist are shown in table 7. Commodity supply and 

demand variables are declared separately for each of the m markets represented in set M. 

Zp,m Commodity supply or demand for commodity p in market m

* COMMODITY DEMAND AND SUPPLY 

DOMESUSE(P) DOMESTIC DEMAND 

EXPORTUSE(P) EXPORT DEMAND EXCLUDING EEP 

EEPUSE(P) EXPORT DEMAND -- EEP ONLY 

IMPORTSUP(P) IMPORT SUPPLY 

PRDNSUP(P) AGGREGATE PRODUCTION FUNCTION SUPPLY 

STKUSEC(P) COMMERCIAL STOCK DEMAND 

STKUSEG(P) GOVERNMENT STOCK DEMAND 

RESIDUSE(P) RESIDUAL DEMAND 

RESIDSUP(P) RESIDUAL SUPPLY 

Variables YACT(C) represent processing activities in REAP. Example processing activities include 

SOYCRUSH, which converts soybeans into soybean meal and soybean oil, and DAIRYSUP5, which 

converts a specific mix of feed grains and soybean meal into the protein and energy nutrients available for 

dairy cattle. Processing activity variables are indexed only over C, indicating that they are only specified at 

the national level. 

Yc

 Quantity processing activity c

YACT(C) PROCESSING ACTIVITY LEVELS 
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Regional Input Supply and Use Variables 

REAP designates inputs as either regional or national. Inputs are modeled regionally if we can specify a 

reliable relationship between price and quantity used and model region. Examples are CROPLAND, 

PASTURE, AUM, and WATER. 

National inputs are those specified with a single fixed price in any area of the United States in which they 

are used. Examples include LIME and CHEMICALS, which are specified in dollar units, and their prices are 

always in dollars. LIME and CHEMICALS are specified in dollars (instead of tons or another unit) in the 

ERS cost of production budget source data. Modeling input use in physical units (instead of value) and 

actual market prices is always preferred, but often not feasible. For example, LIME prices vary greatly even 

within one region, and there exist so many CHEMICAL types and compositions that one price would not be 

accurate. In these cases, the most accurate accounting of input cost for a production enterprise is cost per 

acre for the specified inputs. 

National inputs require no model variable in REAP’s formulation—their supply functions are implicit. By 

holding the prices of these inputs fixed, we are assuming that they have perfectly elastic supply curves. 

Because the cost per unit of using these inputs does not change, we do not explicitly represent them in the 

objective function. Rather, we calculate the total cost per unit for the production activity and include that 

in the objective function. Most input use takes place in the production and processing activities discussed 

below. 

Two types of regional input variables are distinguished in REAP. Those variables with fixed prices (e.g., 

WATER) are tallied in INPUTRFSUP(R,IR). Input prices that vary with quantity supplied (e.g., CROPLAND, 

PASTURE, and AUM (animal unit months)) are tallied in two variables: the price-sensitive supply in 

INPUTRSUP(R,IR), and any optional quantity available in INPTRSUPFP(R,IR). The supply of each is 

represented with a kinked supply function. INPUTRSUP(R,IR) represents the portion of the regional input 

that is available at a fixed price, and INPTRSUPFP(R,IR) represents the portion beyond that, which is 

available at increasing prices. 

CRPLND(R,IR,YR) represents the amount of cropland enrolled in the Conservation Reserve Program (CRP) 

in the base year (YR). This variable can be fixed or allowed to vary, with fixed default setting, meaning 

that the amount of land enrolled in CRP is not allowed to change in response to any scenario that may be 

run. 

VIir,r Variable supply of input ir in farm production region r

INPUTRSUP( R,IR) REGIONAL PRICE SENSITIVE INPUTS 

FIir,r Fixed supply of input ir in region farm production region r

INPTRSUPFP(R,IR) REGIONAL NON-PRICE SENSITIVE PRICE SENSITIVE INPUTS 

CRACir,r

4

 Acres of input ir in farm production region r placed in the Conservation Reserve Program (CRP) 

CRPLND(R,IR,YR) CROP LAND ENROLLED IN THE CRP IN THE YEAR DESIGNATED 

 

4

 CRPLND indexed by ‘YR’ to indicate what year CRP is for 
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Production Activity Variables 

Production activities combine inputs to produce a product. These variables are differentiated by enterprise, 

geographic area, program category, method, and other indexes. Crop and livestock production are tallied in 

variable XACT(B,G,H,Y,T,U,UR), which are indexed over enterprise or production activity, government 

program category, method of production (not used), system of production (not used), tillage practice, 

subregion, and region. In addition, SXACT(B, T,FT, U, UR) represents the proportion of acreage XACT that 

uses fertilizer application rate FT. SXACT ranges in value from 0 to 1, with the sum of SXACT over 

fertilizer application rates, FT, equal to 1. 

Xb,g,h,y,,t,rl,r

5 Quantity of production activity b in government program g, using method h, in system y

utilizing tillage practice t in land resource region rl in farm production region r

XACT(B,G,H,Y,T,U,UR) PRODUCTION ACTIVITY LEVELS 

Sb,t,ft,rl,r Share of fertilizer rate ft used in crop rotation b using tillage practice t in land resource region rl in 

farm production region r

SXACT(B,T,FT,U,UR) ALTERNATE NITROGEN ENTERPRISES SHARE OF CROPPING SYSTEM ACRES 

Cropland Allocation Variables 

The cropland allocation variables are part of the nesting structure for the CET functions that allocate 

cropland to rotations and tillage practices. ACLRR(B,U) is defined over crop (corn, soybeans, wheat, 

etc.) and Land Resource Regions, representing the total amount of acres planted to a crop across 

all rotations that produce that crop. RAC(B,U) is defined over crop rotations and Land Resource 

Regions and represents the total amount of acres planted to a particular rotation across all tillage practices. 

RACb,rl Quantity of rotation acres b ∈bc used in land resource region rl 

RAC(B,U) ROTATION ACREAGE LEVEL--ROT:LRR:NPR 

Xb,rl Quantity of crop acres b ∈bc used in land resource region rl

ACLRR(B,U) AGGREGATE CROP ACREAGE PLANTED LRR LEVEL CET FORM 

Objective Function Variable 

CPS is a scalar variable that represents the value of the programming problem’s objective function. CPS

measures the sum of consumer and producer surplus, minus or plus any social costs/payoffs associated 

with system behavior, such as environmental emissions. 

 

5

 This variable includes both livestock and crop production activities that were separated out in the presentation of the model given 

in Chapter 2 the Model Environment section of this report. 
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CPS Objective function value (net social payoff) 

CPS CONSUMER & PRODUCER SURPLUS (DOL) ;

Equations 

EQUATION definition statements define how a GAMS model is generated—that is, what rows and what 

columns are generated to pass to the solver for execution. 

Objective Function 

The objective function represents net social benefit, or consumer plus producer surplus (CPS). Net social 

benefit equals the sum of the areas under the crop demand functions plus government payments, such as 

Conservation Reserve Program (CRP) rental payments, minus the areas under the supply functions for the 

quasi-fixed regional inputs, crop-specific PMP cost functions, CRP land supply functions, and production 

costs. The objective function is written as: 

[ ] ( )

2

, , , , ,

crp

,

( , , , , , , ) ( , , , , , , )

, , , , , , , , , ( , , , , , , )

2

, , ,

2

( , , , , , , ) , , , ( , , , , , , ) , ,

2

2

2

1

2

1

2

1 Max CPS

ir r

ir r ir r

crp

ir r ir r

crp

ir r ir r

ir r

ir r

vc

xlvstp xlvstp b g h y t r r xlvstp

vc

xcropp xcropp b g h y t rl r xcropp

b g h y t ft rl r

n

b,g,h y t,ft,rl,r ft b,t,ft,rl,r xcropp b g h y t rl r xcropp

b rl

b rl bc

b,rl b rl

b rl bc

b rl

xlvstp xlvstp b g h y t r r xlvstp b g h r xlvstp b g h y t r r b,g h r

ir,r

r ir

ir,r

r i

ir,r ir,r ir,r

r ir

ir,r

c

c

m,p c

m p

m,p m,p

m p

m,p

w CRAC CRAC CRAC

X w X w

X S w w

X X

X X

VI VI w FI

Z Z w Y

∑∑ ∑∑ ∑∑

∑ ∑

∑ ∑

∑∑ ∑∑

∑ ∑

∑∑ ∑∑ ∑∑

∑∑ ∑∑ ∑

+ − −

− −

− +

− −

− −

− − −

= + −

∈ ∈

α β

α β

α β

α β

α β

σ

The first terms in the objective function, 2

2

1

m,p

m p

m,p m,p

m p

∑∑α m,pZ + ∑∑β Z , represent the sum of the area 

under market demand and supply curves. The parameters for these curves are derived from the demand or 

supply for each commodity p in each market m in the base year ( 0 Zm,p ), the commodity price in the base 

year ( 0 Pp ), and the price elasticity of demand or supply ( m, p ε ). The formula for deriving the slope 

parameter is βm,p= ( 0 Pp / 0 Zm,p ) * (1/ m, p ε ). The intercept is then obtained from the equation αm,p = 0 Pp - βm,p

* 0 Zm,p . 

The third term, c

c

∑wcY , is the sum of production costs incurred by intermediate product processing 

activities. These are costs for labor and inputs separate from the cost of primary products used by the 

activities. For many of the processing activities, these costs are zero because the activities are assumed 

only to transform the initial product into another form. In other cases, such as ethanol production, costs of 

processing are explicitly represented. 
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The fourth and fifth terms, ∑∑ − ∑∑

r i

ir,r ir,r ir,r

r ir

ir,rVI VI 2

2

1

α β , represent the sum of areas under the 

quasi-fixed regional input supply curves. The parameters for these curves are derived from the supply of 

each input ir in each Farm Production Region r in the base year ( 0 VIir,r ), the input price in each region in 

the base year ( 0 wir,r ), and the price elasticity of supply ( ir,r ε ). The slopes for these equations are then 

obtained from βir,r = ( 0 wir,r / 0 VIir,r ) * (1/ ir,r ε ), and the intercepts are obtained from αir,r = 0 wir,r - βir,r * 

0 VIir,r . 

The sixth and seventh terms, 2

( , , , , , , ) , , , ( , , , , , , ) , , xlvstp xlvstp b g h y t r r xlvstp b g h r xlvstp b g h y t r r ∑ αb,g h r X −∑ β X , represent the 

sum of areas under the PMP supply functions for livestock production activities. The parameters for the 

PMP functions are derived from the supply of each livestock commodity pl in each Farm Production 

Region r in the base year ( 0 Qpl,r ), the net return to the livestock production activity in the base year ( 0 Rbl,r ), 

and the price elasticity of supply ( pl ε ). Livestock production is represented only at the Farm Production 

Region (r) level. Net returns per production activity are obtained from shadow prices on calibration 

constraints. 

The slopes for the livestock PMP functions are then obtained from βb,g,h,r =

( 0 Rbl,r / 0 Xxlvstp )*(1/ pl ε )*( 0 Xxlvstp / ∑

b∈xlvstp

Xxlvstp

0 )* YLDp,r ), where YLDp,r = 0 Qpl,r / 0 Xxlvstp . The intercepts for 

the PMP functions are then obtained from αb,g,h,r = 0 Rbl,r - βb,g,h,r * 0 Xxlvstp .

The eighth and ninth terms in the function, 2

, , b,rl

b rl bc

b,rl b rl

b rl bc

∑∑ b rl X ∑∑ X ∈ ∈

α − β , are the sum of the areas 

under the PMP supply functions for crops. The parameters for these PMP functions are derived from the 

supply of crop acreage bc in each Land Resource Region rl in the base year ( 0 Xbc,rl ), the net return to crop 

bc in the Land Resource Region rl in the base year ( 0 Rbc,rl ), and the price elasticity of supply for crops 

( pc ε ). Crop production is represented at the Land Resource Region rl level and is therefore more 

disaggregated than the level at which livestock production is represented. Net returns per production 

activity are obtained from shadow prices on calibration constraints. 

The slopes for the crop PMP functions are derived from βb,r= ∑

p∈p b

p b,rl P /X

2

0 0 (( ) * (1/ pc ε ) *( 0 Xbc,rl / 

∑

ba

Xbc,rl) 0 * YLD ) p,rl , where p∈p2b maps livestock product p to livestock production activity b and 

YLDp,rl equals average yield for crop p in Land Resource Region rl. The intercept is then obtained from 

αb,rl 

= 0 Rbc,rl - βb,rl * 0 Xbc,rl . 

The 10th term, [ ( )] ( , , , , , , ) ∑ ∑ , , + , , , , , , , xcropp b g h y t rl r b g h y t ft rl r

n

b,g,h y t,ft,rl,r ft

Xxcropp Sb,t,ft,rl,r w wσ , is the sum of the 

fertilizer costs for crop production activities, where Sb,t,ft,rl,r is the convexity variable that indicates the 

proportion of a particular fertilizer application activity ft used by crop production activity 

Xxcropp(b,g,h,y,t,rl,r) . The expression contained within the parentheses represents the cost of each of the 

fertilizer activities ft associated with the crop production activity Xxcropp(b,g,h,y,t,rl,r) , where 
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n wb,g,h, y,t,ft,rl,r represents fertilizer cost and σwb,g,h, y,t, ft,rl,r represents the risk premium associated with using a 

particular fertilizer application rate. 

The 11th term in the objective function, vc

xcropp xcropp b g h y t rl r ∑ Xxcroppw

( , , , , , , ) , is the sum of the production costs, 

excluding fertilizer, for the primary crop production activities, where vc wxcropp represents the sum of all the 

cost of the inputs, excluding quasi-fixed and fertilizer inputs used by the production activity. The implicit 

assumption underlying this specification is that the prices of these inputs are constant. 

Similarly, the 12th term, vc

xlvstp xlvstp b g h y t r r ∑ Xxlvstpw

( , , , , , , ) , represents the total costs of the primary livestock 

production activities. 

The 13th term, ir r

ir r

wir rCRAC ,

crp ∑∑ , , is the sum of rental payments for CRP land, while the final two terms, 

2

, , , , 2

1

ir r

ir r ir r

crp

ir r ir r

crp ∑∑ ∑∑ αir rCRAC − β CRAC represent the sum of the areas under the CRP supply 

functions. The parameters of the CRP supply functions are derived from the supply of each input ir placed 

in the CRP in each Farm Production Region r in the base year ( 0 CRACir,r ), the net return to CRP activities 

in each region in the base year ( 0 Rir,r ), and the price elasticity of input supply ( ir,r ε ). Slopes and intercepts 

are obtained by using the similar formulas to the formulas used to derive them for the other supply 

functions. In GAMS code, the objective function is depicted as:

UOBJ.. CPS =E= SUM(P, ALPHA(P,"DOM") * DOMESUSE(P) 

 + 0.5 * BETA( P,"DOM") * SQR(DOMESUSE(P))) 

 + SUM(P$PRES(P), ALPHA(P,"RESD") * RESIDUSE(P)) 

 - SUM(P$PRES(P), ALPHA(P,"RESS") * RESIDSUP(P)) 

 + SUM(P, ALPHA(P,"EXP") * EXPORTUSE(P) 

 + 0.5 * BETA( P,"EXP") * SQR(EXPORTUSE(P))) 

* NOTE: ALPHA(P,"EEP") WAS INCREASED ABOVE BY AVE BNS LVL 

 + SUM(P, ALPHA(P,"EEP") * EEPUSE(P) 

 + 0.5 * BETA( P,"EEP") * SQR(EEPUSE(P))) 

 + SUM(P, ALPHA(P,"SCE") * STKUSEC(P) 

 + 0.5 * BETA( P,"SCE") * SQR(STKUSEC(P))) 

 + SUM(P, ALPHA(P,"SGE") * STKUSEG(P) 

 + 0.5 * BETA( P,"SGE") * SQR(STKUSEG(P))) 

* << + SUM(P, ALPHA(P,"SGA") * STKACCG(P)) 

 - SUM(P, (ALPHA(P,"IMP") * IMPORTSUP(P)) $ (ALPHA(P,"IMP") GT 0) 

 + 0.5 * BETA( P,"IMP") * SQR(MAX(0, (IMPORTSUP(P) - DIF(P,"IMP"))))) 

 - SUM(P, (ALPHA(P,"PRDN") * PRDNSUP(P)) $ (ALPHA(P,"PRDN") GT 0) 

 + 0.5 * BETA( P,"PRDN") * SQR(MAX(0, (PRDNSUP(P) - DIF(P,"PRDN"))))) 

 - SUM(C, YACT(C) * (PPC("PROCCOST",C) + SCR(C,"NET RETURN"))) 

 - SUM((R,IR), 

 INPTRSUPFP(R,IR) * PMINI(R,IR) $ PMINI(R,IR)) 

 - SUM((R,IR), ALPHAI(R,IR) * INPUTRSUP(R,IR) 

 + 0.5 * BETAI( R,IR) * SQR(INPUTRSUP(R,IR))) 

 - SUM((R,IR), 

 INPUTRFSUP(R,IR) * INPUTR(R,IR,"PFXP")) 

 - SUM((B,H,R), 

 ( ALPHAP(B,"NP",H,R) * SUM(XLVSTP(B,"NP",H,Y,T,U,R), XACT(XLVSTP)) 

 + 0.5 * BETAP( B,"NP",H,R)* SQR(SUM(XLVSTP(B,"NP",H,Y,T,U,R), XACT(XLVSTP))) 

 ) $ (XI4(B,"NP",H,R) GT 0) 

 )$RTYPE 
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 - ( SUM(XCROPP(BC,G,H,Y,T2,RL,R), XACT(XCROPP) 

 * SUM(FT, SXACT(BC,T2,FT,RL,R) * 

 ( PCRNIT(BC,G,H,Y,T2,FT,RL,R, "NCOST") 

* +PCRNIT(BC,G,H,Y,T2,FT,RL,R, "GRNPMT") 

 +PCRNIT(BC,G,H,Y,T2,FT,RL,R, "RSKADJ") 

 ) 

 ) 

 ) 

 ) $((ENVIR=1)$(VNITF=1)) 

 - ( SUM(XALL, XACT(XALL) * PCR(XALL, "VCOST")) ) $(ENVIR=1) 

 - ( SUM(XALL, XACT(XALL) * PCR(XALL, "NICOST")) )$(STDCROP=1) 

 - SUM(XALNDC, XACT(XALNDC) * PCR(XALNDC,"NET RETURN")) $(ENVIR=1) 

 - SUM(XALNDC, XACT(XALNDC) * PCR(XALNDC,"NET RETURN")) $(STDCROP=1) 

 - SUM(XALBLV, XACT(XALBLV) * PCR(XALBLV,"NET RETURN")) $(ENVIR=1) 

 - SUM(XALBLV, XACT(XALBLV) * PCR(XALBLV,"NET RETURN")) $(STDCROP=1) 

$BATINCLUDE MODULE.CTL ACPROG apACUOBJ.GMS 

* 

*$BATINCLUDE MODULE.CTL VNITF vnUOBJ.GMS 

 - SUM(YCROP(B,H,R), 

 ALPHAX(B,H,R) * PMPAC(B,H,R) 

 + 0.5 * SUM((BA)$YCROPX(B,H,R,BA), 

 PMPAC(B,H,R) * BETAX(B,H,R,BA) * PMPAC(BA,H,R)) 

 )$RTYPE 

 - SUM(YCROP(B,H,R), 

 ( ALPHAA(YCROP) * PMPAC(YCROP) 

 + 0.5 * BETAA(YCROP) * SQR(PMPAC(YCROP))) 

 )$RTYPE 

 - SUM(YCROP2(B,H,RL,R), 

 ( ALPHA2(YCROP2) * PMPAC2(YCROP2) 

 + 0.5 * BETA2(YCROP2) * SQR(PMPAC2(YCROP2))) 

 )$RTYPE 

 - SUM((B,RL)$SACPLRR(B,RL), 

 ( ALPHA3(B,RL) * ACLRR(B,RL))$ (ALPHA3(B,RL) GT 0) 

 + 0.5 * BETA3(B,RL) * SQR(MAX(0, (ACLRR(B,RL) - PMPDIF(B,RL)))) 

 )$RTYPE 

 - SUM(XCROPP(B,G,H,Y,T,U,R), 

 ( ALPHAZ(XCROPP) * XACT(XCROPP) 

 + 0.5 * BETAZ(XCROPP) * SQR(XACT(XCROPP))) 

 )$RTYPE 

$BATINCLUDE MODULE.CTL ACPROG apPBUOBJ.GMS 

$BATINCLUDE MODULE.CTL ACPROG apDPUOBJ.GMS 

 + SUM(XCROPP( B,G,H,Y,T,U,R), XACT(XCROPP) * PCR(XCROPP,"MKRETADJ")) 

$BATINCLUDE MODULE.CTL ACPROG apMKUOBJ.GMS 

* - 0.5 * CAR * SUM(R$R10(R), SUM(BA$VENT(BA,R), SUM(B$VENT(B,R), 

* XACT(B,R) * VCV(B,BA,R)) * XACT(BA,R))) 

 + SUM((R,IR), 

 CRPLND(R,IR,"%1") * ACRESDY("TOTAL","HST","CRPR","A","%1",R) 

 $ACRESDY("TOTAL","HST","CRPR","A","%1",R) 

 + ( ALPHAC(IR,"%1",R) * CRPLND(R,IR,"%1") 

 + 0.5 * BETAC( IR,"%1",R) * SQR(CRPLND(R,IR,"%1")) 

 ) $ ACRESDY("TOTAL","HST","CRPR","A","%1",R) 

 ) 
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 - SUM(RL$GPPBASE(RL), (PFCPR(RL) + CRPPR(RL)) * GPPAC(RL)) 

 ; 

CPS (consumer and producer surplus) is defined to be the sum of the areas under domestic, export, and 

commercial stock demand functions, minus the sum of areas under import, regional (variable and fixed 

price) input, national input, and other cost supply functions, plus the expected value of deficiency 

payments. The area under domestic demand functions, for example, is computed by the terms: SUM(P, 

ALPHA(P,"DOM")*DOMESUSE(P) + 0.5*BETA(P,"DOM")*SQR(DOMESUSE(P))). In simpler 

algebra, this is: (intercept * price + 0.5 * slope * (price*price)). Some of the expressions in UOBJ are 

complicated by the DIF and DIFI terms and formulation, which are necessary to exclude negative surplus 

values. The GAMS code used to represent the objective function differs from the algebraic formulation 

mainly in that it includes portions of code that permit alternative representations of the supply response for 

crop production and make supply of CRP land and the CRP rental rate exogenous (i.e., fixed). Control 

variables are used to determine which parts of the objective function are active. For example, the control 

variables ENVIR, STDCROP, and VNITF are used to control the supply response formulation for crops. 

When STDCROP = 1 and ENVIR = 0, then crop production in REAP is represented with single 

production activities for each crop down to the Farm Production Region level. This formulation of the 

model uses the standard PMP functions to represent crop acreage response. When STDCROP = 0 and 

ENVIR = 1,then crop production in REAP is represented with multiple rotations and tillage practices for 

a single crop down to the Land Resource Region. This formulation uses the nested set of CET functions in 

combination with the standard PMP crop function to represent crop acreage response. If VNITF = 1, then 

nitrogen fertilizer application rates are determined endogenously, whereas when VNITF = 0, then 

nitrogen fertilizer application rates per production activity are fixed. Permitting variable nitrogen fertilizer 

application rates per production activity is only available when ENVIR = 1. 

Commodity Balance 

The commodity balance constraints require that the supply of a commodity from all its sources is greater 

than or equal to the demand for it in all its uses. This ensures that no more of a commodity is consumed 

than is available for consumption. In equilibrium, this constraint will be binding, or the product will not be 

produced at all. Sources of P include the amount produced from all production activities B producing P

across all government programs, G; production methods, H; system types, Y; tillage practices, T; in regions 

RL, and R or from all processing activities C or unspecified domestic source plus the amount supplied by 

imports and from beginning stocks. Uses of a commodity P include domestic use (seed and industrial 

uses), commercial and government stocks, and exports. This is represented algebraically by the constraint: 

0 1,..., ;

( )

, ( , , , , , , )

, , , ( , , , , , , )

Z Z , p P

pp X ppc Y

s pp S X

m S

m,p

m S

m,p

c

xlvstp p c c xlvstp b g h y t r r p,xlvstp

ft

b,t,ft,rl, r xcropp

n

p,b,g h y t,ft,rl,r xcropp b g h y t rl r p,xcropp

+ − ≥ =

+ +

∑ ∑

∑ ∑

∑ ∑

∈ ∉

where ∑ ∑

ft

b,t,ft,rl,r xcropp

n

p,b,g h y t,ft,rl,r xcropp b g h y t rl r s p,xcropp ( pp , , , S )X

( , , , , , , ) represents the amount of commodity p

produced by all primary crop production activities, xlvstp xlvstp b g h y t r r ∑ ppp,xlvstpX

( , , , , , , ) represents the net 

amount of p produced by all livestock production activities, ∑c

ppcp,cYc represents the net amount of p

produced (or used) by the processing activities, ∑

m∈S

Zm,p is the amount of commodity p supplied from 
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supply markets (import and beginning stock markets) m , and ∑

m∈D

Zm,p represents the amount of 

commodity p used in demand markets (domestic, export, and ending stocks). In GAMS code, this is 

depicted as: 

PRODBAL(P).. (SUM(XCROPP, XACT(XCROPP) * PP(P,XCROPP) * XSP7(P,XCROPP)) )$(VNITF=0) {Fix Fert } 

 + ( SUM(XCROPP(B,G,H,Y,T,RL,R), XACT(XCROPP) * SUM(FT, SXACT(B,T,FT,RL,R) 

 * PNIT(P,B,G,H,Y,T,FT,RL,R))* XSP7(P,XCROPP)) $(VNITF=1) {Var Fert } 

 + SUM(XLVSTP, XACT(XLVSTP) * PP(P,XLVSTP)) {LVSTK} 

* + SUM(XFRUTP, XACT(XFRUTP) * PP(P,XFRUTP)) {FRUIT} 

 + SUM(XOLU, XACT(XOLU ) * PP(P,XOLU )) {OLUSE} 

$BATINCLUDE MODULE.CTL ACPROG apPRDBAL.GMS 

 + SUM(C, YACT(C) * PPC(P,C)) {PROCESSING} 

 + IMPORTSUP(P) $ PI(P) 

 + PRDNSUP(P) $ PF(P) 

 - DOMESUSE(P) $ PD(P) 

 - EXPORTUSE(P) $ PE(P) 

 - EEPUSE(P) $ PEEP(P) 

 - STKUSEC(P) $ PSCE(P) 

 - STKUSEG(P) $ PSGE(P) 

 - RESIDUSE(P) $ PRES(P) 

 + RESIDSUP(P) $ PRES(P) 

 =G= 

 - STKCOMB(P) 

 - STKGOVB(P) 

 + STKGOVD(P) 

 + STKGOVX(P) 

 + RESIDUAL(P) <<ZERO IN CALIBRATE, FIXED IN VERIFY AND BEYOND RUNS 

 ; 

Fertilizer Application Convexity Constraints 

The fertilizer application constraints permit the relationship between yield and fertilizer application rates 

per unit per production activities to be approximated by a small set of discreet fertilizer application 

activities. This set of convexity constraints allows fertilizer application rates per unit of a production 

activity to vary independently of the application rate used for other production activities. The set can be 

easily extended to cover other inputs if desired. 

The constraint on fertilizer application rates is represented algebraically by: 

∑ − = = = = = ft

Sb t ft rl r 1 0, b 1,...,B; t 1,...,T; rl 1,...,RL; r 1,...,R; , , , ,

where 0 ≤ Sb,t, ft,rl,r •≤ 1, and∑

ft

Sb,t, ft,rl,r represents the sum of the proportions of a fertilizer application rate 

used per cropping system b,t and must equal one. In GAMS code, this is written: 

CNVXBAL(B,T2,RL,R)$CNVX(B,T2,RL,R).. 

 SUM(HCROPPFT(B,T2,FT,RL,R), SXACT(B,T2,FT,RL,R)) 

 =E= 1; 
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Input Supply Balance 

The supplies of all inputs, except quasi-fixed inputs (cropland and pasture), are assumed to be perfectly 

elastic. This means that there is no need to explicitly represent supply balance for these inputs since it is 

assumed that there will always be sufficient supply to meet demand. 

The supplies of quasi-fixed inputs are divided into two separate pools: livestock (pasture and AUMs) and 

crop (cropland). The supply of livestock land is specified by using a simple linear inverse supply function 

in each of the 10 Farm Production Regions. AUM is used in the Pacific, Mountain, and the Northern and 

Southern Plains regions to represent the carrying capacity of the land. 

The pool of cropland in each Farm Production Region is further split into crop, rotation, and tillagespecific pools for each of the 45 Land Resource Regions by soil erosion category (HEL/NHEL). Cropland 

supply is represented with a simple inverse supply function for each Farm Production Region. Allocation 

of land to crops is represented with a system of simple linear, PMP calibrated, supply functions. In each 

Land Resource Region, the distribution of crop-specific land to rotations and tillage type is represented 

with a set of nested constant elasticity of transformation functions. 

In essence, the structure of the model assumes that farmers engage in a multistage decision process 

whereby they first determine the amount of land to allocate to crops and livestock. In the next stage, 

farmers determine how much livestock land to allocate to each species and how much of the cropland to 

each crop. Then, for each crop, farmers decide how much land to allocate to each rotation, and they 

determine the tillage practice they will use for each rotation. 

Regional Crop Rotation Acres Balance 

The regional crop rotation acres balance ensures that land allocated to a particular crop rotation b is equal 

to the use of land by all the tillage practice activities t associated with that rotation in region rl. The 

balance is represented by the function: 

( ) ( ) 0, 1,..., ; 1,..., ; ,

1

( , , , , , , ) , , , , , X xcropp RACb rl b B rl RL

xcropp b g h y t r rl b rl b t rl b rl b rl − = = = ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

− −

∑ ρ ρ α δ

where 

b,rl b,rl b,rl ρ = (1−σ )/σ

b rl

b rl

xcropp

ta

xcropp

xcropp xcropp

b t rl R X

R X

,

,

0 0 1

0 0 1

, ,

*( )

* ( )

ρ

ρ

δ +

+

∑=

and 

αb,rl = b rl b rl

ta

Xxcropp b ta rl Xxcropp

, 0 , 1/

, ,

0 /( * ) ρ ρ δ − −

∑

The function is nonlinear, implying that the marginal rate of transformation between land used in one 

tillage activity of a particular type of rotation and land used for other tillage practices used with the same 

rotation is declining. The parameters for these equations are derived from the quantity of each crop 

production activity in the base year 0 Xxcropp , the net return to each production activity, 0 Rxcropp , and an 

elasticity of transformation σ b,rl for each crop rotation b in each Land Resource Region rl. Net returns per 
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crop production activity are obtained from shadow prices on calibration constraints. In GAMS code, this 

can be written as: 

CETT(BA,RL) $RCROP4(BA,RL).. 

 AT(BA,RL) * SUM(T2$DELTAT(BA,T2,RL), DELTAT(BA,T2,RL) 

 * SUM(XCROPP(BA,G,H,Y,T2,RL,UR)$ROTSHR(BA,T2,RL), 

 XACT(BA,G,H,Y,T2,RL,UR))$(XACTD(BA,T2,RL) > 0) 

 **(-RHOT(BA,RL)) )**(-1/RHOT(BA,RL)) 

$BATINCLUDE MODULE.CTL ACPROG apCET1.GMS 

 =E= RAC(BA,RL); 

Dollar control statements are used to make sure that these equations are generated only for those rotations 

with positive acreage in a region and include only those production activities with a positive amount of 

acreage. 

Regional Crop Acreage Balance 

The regional crop acreage balance constraint ensures that supply of land allocated to crop b in Land 

Resource Region rl is equal to the land used by the crop rotations ba to produce that crop. This balance is 

represented by the function: 

0, , 1,..., ; 1,..., ; ,

1

, , , , ,

, , s RAC Xb rl b bc b B rl RL

ba

b rl ba rl b ba rl ba rl

b rl b rl ⎟ − = ∈ = = ⎠

⎞ ⎜

⎝

⎛ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

−

−

∑ ρ ρ α δ

where 

b ba B rl RL

R RAC X s

R RAC X s

b rl b ba rl

b

ba rl b

ba

ba rl

b rl b ba rl

b

ba rl ba rl b

ba rl b rl b rl ba rl

b rl b rl b rl

, , 1, , ; 1, , * *( ) * )

* *( ) * )

1

, ,

( 1/ 1)

,

1 0

,

0

,

1

, ,

( 1/ 1)

,

1 0

,

0

,

, , , ,

, , ,

= = K = K − − − − +

− − − − +

∑ ∑

∑

ρ ρ ρ

ρ ρ ρ

λ

λ

δ

αb,rl =∑ ∑ − −

b ba

b rl b rl b ba rl ba rl

b rl b rl X s RAC , , 1/

, , , , , /( ) ρ ρ δ

b ba rl s , , is the crop enterprise b share of one unit of rotation ba acres in region rl. 

The function is nonlinear and implies that there is a declining rate of transformation between land used in 

one crop rotation and land used to produce the same crop as part of another rotation. The parameters for 

these equations are derived from the quantity of each rotation acre supplied in the base year 0 RACba,rl , the 

net return to each crop rotation activity, 0 Rba,rl , the weighted sum of crop b 

acreages,

( 1/ 1)

,

, , ( ) − − −

∑ b rl b rl

b rl

b

bX ρ ρ λ , and an elasticity of transformation r σ b,rl for each crop b in each Land 

Resource Region rl. Net returns per crop rotation activity are obtained from shadow prices on the model’s 

calibration constraints. The transformation elasticities used in the calculation of the parameters of these 

functions are derived by using an iterative procedure that selects the set of transformation elasticities that 

generate the same crop supply response as obtained from FAPSIM (Price, 2004). In GAMS code, this is 

written as: 

CETR(B,RL) $ACLRRL(B,RL,"CK4").. 

 A(B,RL)*SUM(BA$DELTA(BA,RL), DELTA(BA,RL) * BSBROT(B,BA,RL) 

 * RAC(BA,RL)**(-RHO(B,RL)) 

 )**(-1/RHO(B,RL)) 

 =E= ACLRR(B,RL); 
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Again, the dollar control statements are used to restrict the equations generated and rotations represented in 

the equations to those for which positive crop and rotation acreage exists. 

Regional Input Balance 

The regional input balance equations ensure that no more of a quasi-fixed input ir is used in region r than 

can be supplied and list all sources of supply and all sources of use for any such input (production activity, 

government program category, method of production, system of production, strata of production, and 

region). This includes all cropland put into the Conservation Reserve Program. 

0; 1,..., ; 1,..., ; , ,

( , , , , , , ) , ( , , , , , , ) ,

CRAC VI ir I r R

pp X pp X

ir r ir r

xlvstp b g h y t r r ir xlvstp xlvstp xcropp b g h y t rl r ir xcropp xcropp

+ − ≤ = =

∑ +∑

In GAMS code this is written as: 

INPUTRBALF(R,IR)$(INPUTR(R,IR,"QBASE") OR INPUTR(R,IR,"PFXP")) 

 .. SUM(XCROPP(B,G,H,Y,T,U,R), XACTS(XCROPP) * PP(IR,XCROPP)) {PIP+NPR} 

 + SUM(XLVSTP(B,G,H,Y,T,U,R), XACT(XLVSTP) * PP(IR,XLVSTP)) {LIVESTK} 

 + SUM(XFRUTP(B,G,H,Y,T,U,R), XACT(XFRUTP) * PP(IR,XFRUTP)) {FRUT+VEG} 

$BATINCLUDE MODULE.CTL ACPROG apINPBAL.GMS 

 + CRPLND(R,IR,"%1") {+ CRP} 

 $ (ACRESDY("TOTAL","HST","CRP","A","%1",R) GT 0) 

 =L= INPUTRSUP( R,IR) $ MFI( R,IR) {CONVERTED TO MFI QUAL} 

 +(INPTRSUPFP(R,IR) $ MFI( R,IR)) $ PMINI(R,IR) {CONVERTED TO MFI QUAL} 

 + INPUTRFSUP(R,IR) $ ((INPUTR(R,IR,"PFXP") GT 0) AND 

 (INPUTR(R,IR,"ELAS") LE 0)) 

 ; 

In REAP, the nonlinear regional input supply curves are represented in two linear segments. The first 

portion—over which input price is constant—is represented by INPTRSUPFP(R,IR). After input use 

exceeds INPTRSUPFP(R,IR), input supply is represented with an upward sloping linear curve. 

Nonnegativity Constraints 

Zm.p , Yc , VIir,r , FIir,r , Xb,rl , RACb,rl, Xb,g,h,y,t,rl, r , Sb,t,ft, rl, r , CRACir,,r ≥ 0 

Nonnegativity constraints in GAMS are implied when the POSITIVE VARIABLE command is used when 

the variables are declared. 

Bounds and Starting Values 

Variable bounds are specified by placing an .UP, .LO, or .FX after the primary variable name. For 

example, INPUTRUSE.UP(R, IR) is used to specify upper bounds placed upon regional input activity 

levels—this bounds regional input supply. Input supply functions may be bounded with limits on the 

physical availability of the resource. Input supply and commodity demand functions are generally bounded 

with arbitrary limits, not meant to restrict the model solution, but to improve optimizer efficiency by 

restricting the domain over which the optimizer must search. 
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 INPUTRUSE.UP(R,IR)$INPUTR(R,IR,"QBASE") = INPUTR(R,IR,"QBASE")*3; 

 INPUTRUSE.UP(R,IR)$SFI(R,IR) = INPUTRUSE.UP(R,IR) / SFI(R,IR); 

 

 DOMESUSE.UP(P) = 2*DEMSUP(P,"DOM","QBASE")$DEMSUP(P,"DOM","QBASE"); 

 DOMESUSE.UP(P)$SF(P,"DOM") = DOMESUSE.UP(P) / SF(P,"DOM"); 

 EXPORTUSE.UP(P) = 2*DEMSUP(P,"EXP","QBASE")$DEMSUP(P,"EXP","QBASE"); 

 EXPORTUSE.UP(P)$SF(P,"EXP") = EXPORTUSE.UP(P) / SF(P,"EXP"); 

 STOCKUSE.UP(P) = 2*DEMSUP(P,"STKE","QBASE")$DEMSUP(P,"STKE","QBASE"); 

 STOCKUSE.UP(P)$SF(P,"STKE") = STOCKUSE.UP(P) / SF(P,"STKE"); 

Starting values are specified by placing .L after the primary variable name. XACT.L(B,R), for example, 

specifies initial values for primary commodity production activities. The base activity level values, 

PP("BASELEVL",B,R), are specified as initial values to give the optimizer a good starting point; the 

REAP optimization is begun from the initial values rather than, say, a previous saved basis. 

The remaining starting values are specified as part of the PMP formulation. A calibration run is first 

performed with the PMP type activities locked onto the PMP base activity levels. The XACT.FX(B,R)

specification is used to fix the PMP activities at the desired levels: 

 XACT.L(B,R) = PP("BASELEVL",B,R); 

 XACT.FX(B,R)$PMP(B,R) = PMP(B,R); 

Two additional sets of variables and equations are used in REAP to ensure that the CET functions for the 

tillage and rotation strata are always defined when evaluated by the solver. (This is needed because many 

other solvers evaluate exponents by transforming them into logarithms; consequently, if a value of zero is 

returned, the model will stop and give an error message indicating that an illegal operation was 

performed.) 

EQUATIONS CETTEVAL CET TILLAGE PRACTIVE EVALUATION DOMAIN CONSTRAINT 

POSITIVE VARIABLE CETTEVALV(B,T,U) CET TILLAGE PRACTICE DOMAIN VARIABLE 

CETTEVAL(BA,T2,U) $ROTSHR(BA,T2,U).. 

 SUM(XCROPP(BA,G,H,Y ,T2,U,UR), XACT(XCROPP)) 

 =E= CETTEVALV(BA,T2,U); 

CETTEVALV.L(BA,T2,U) $XACTD(BA,T2,U) 

 = XACTD(BA,T2,U); 

CETTEVALV.UP(BA,T2,U) $XACTD(BA,T2,U) 

 = CETTEVALV.L(BA,T2,U) * 100; 

CETTEVALV.LO(BA,T2,U) $XACTD(BA,T2,U) 

 = CETTEVALV.L(BA,T2,U) * .01; 

EQUATIONS CETREVAL CET ROTATION EVALUATION DOMAIN CONSTRAINT 

POSITIVE VARIABLE CETREVALV(BA,U) CET ROTATION DOMAIN VARIABLE 

 

CETREVAL(BA,RL) $RAC.L(BA,RL).. 

 RAC(BA,RL) =E= CETREVALV(BA,RL); 

CETREVALV.L(BA,RL) $RAC.L(BA,RL) 

 = RAC.L(BA,RL); 

CETREVALV.UP(BA,RL) $CETREVALV.L(BA,RL) 

 = CETREVALV.L(BA,RL) * 10; 

CETREVALV.LO(BA,RL) $CETREVALV.L(BA,RL) 

 = CETREVALV.L(BA,RL) * .01; 
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Solving the Model 

MODEL Statement Specification 

The MODEL statement is used to define a GAMS model as some combination of the equations that have 

been declared. REAP1 is the first calibration run model, consisting of all equation blocks initially specified 

for the first calibration run: the objective function, the product balance equations, the regional variableprice input balance, the regional fixed-input balance, the supply response equations, and the government 

program equations (plus several optional dry/irrigation and acreage limit controls not used in current 

REAP formulations). REAPS8 is the final, validated REAP formulation, after calibration of all supply 

response and constant elasticity share functions. The BATINCLUDE statements shown below allow 

equations to be added to the model definition for REAPS8 if options for including those modules 

have been turned on. In the MODEL statement for REAPS8 the variable nitrogen application rate 

module has been turned on (VNITF = 1), causing the convexity constraint equations to be added 

to the model definition for REAPS8.

$STITLE REAP MODEL SPECIFICATION: ROWS AND OBJECTIVE FUNCTION, CALIBRATION RUN 

* ------------------------------------------------------------------------------ 

* DECLARE THE MODEL EQUATIONS 

 EQUATIONS 

 OBJECTIVE FUNCTION 

 UOBJ REAP OBJECTIVE FUNCTION 

* COMMODITY AND INPUT BALANCE 

 PRODBAL COMMODITY PRODUCT BALANCE EQUATION 

 INPUTRBALF REGIONAL INPUT BALANCE EQUATION 

 INPUTRFBAL REGIONAL FIX PRICE INPUT BALANCE EQUATION 

 CETT CONSTANT ELASTICITY OF TRANSFORMATION AMONG TILLAGE TYPES 

 CETR CONSTANT ELASTICITY OF TRANSFORMATION AMONG ROTATIONS 

 CNVXBAL VARIABLE N FERT APPLICATION CONVEXITY CONSTRAINTS 

 GPPBASEAC GOVERNMENT PROGRAM PAYMENT BASE SODBUSTER PROVISIONS 

 MODELS 

 REAP1 FLEXIBLE MODEL FORUMLATION / 

 UOBJ, PRODBAL, INPUTRBALF, INPUTRFBAL, GPCON 

 ACEQ2 

 /; 

 REAP8 STRATA FIXED MODEL FORUMLATION / 

 UOBJ, PRODBAL, INPUTRBALF 

 CETR, CETREVAL, CETT, CETTEVAL 

BATINCLUDE D:\REAPGAMS\A1A0\CET\MODULE.CTL 

BATINCLUDE D:\REAPGAMS\A1A0\CET\MODULE.CTL 

BATINCLUDE D:\REAPGAMS\A1A0\NITR\VNMODEL1.GMS 

 *<vnMODEL1.GMS> 

 *bVNITF 

 CNVXBAL 

 *eVNITF 

 

 /; 

SOLVE Statement Specification 

The SOLVE statement calls for solution of a particular model. The following SOLVE statement asks for 

solution of model REAPS8 by maximizing variable CPS: 

SOLVE REAPS8 USING DNLP MAXIMIZING CPS. 
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The SOLVE statement essentially causes GAMS to generate the model in a form in which it can be passed 

to and solved by an optimizer or other solution procedure. Discontinuous nonlinear programming (DNLP) 

is the solution method specified. REAP requires DNLP because of the two expressions in the objective 

function that contain the MAX function, for example: (MAX(0,(IMPORTSUP(P)-DIF(P,"IMP")))). This 

formulation causes a discontinuity in the objective function, at which point the function gradients for these 

variables are be undefined. 

Model Solution 

Some useful solution and diagnostic information is printed into the LST output file for every model run, 

indicating first whether an optimal solution was found. 

 S O L V E S U M M A R Y 

MODEL REAP8 OBJECTIVE CPS 

 TYPE DNLP DIRECTION MAXIMIZE 

 SOLVER MINOS5 FROM LINE 58062 

 **** SOLVER STATUS 1 NORMAL COMPLETION 

   MODEL STATUS 2 LOCALLY OPTIMAL 

 ** ** OBJECTIVE VALUE 769542.4849 

 RESOURCE USAGE, LIMIT 1656.960 10000.000 

 ITERATION COUNT, LIMIT 3500 20000 

 EVALUATION ERRORS 0 0 

M I N O S 5.3 (NOV 1990) VER: 225-386-02 

 = = = = = 

B. A. MURTAGH, UNIVERSITY OF NEW SOUTH WALES 

 AND 

 P. E. GILL, W. MURRAY, M. A. SAUNDERS AND M. H. WRIGHT 

 SYSTEMS OPTIMIZATION LABORATORY, STANFORD UNIVERSITY. 

 OPTIONS FILE 

 ------------ 

 BEGIN GAMS/MINOS OPTIONS 

 * MINOS5.OP6 USED IN A0C2 DIRECTORY FOR VFY RUN AFTER CTF 

 HESSIAN DIMENSION 1050 

 SUPERBASICS LIMIT 1050 

 COMPLETION PARTIAL 

 LOG FREQUENCY 20 

 SOLUTION NO 

 PRINT LEVEL 1 

 ROW TOLERANCE 1.0E-7 

 MINOR ITERATIONS 1000 

 MAJOR ITERATIONS 40 

 MAJOR DAMPING PARAMETER .2 

 MINOR DAMPING PARAMETER .2 

 PENALTY PARAMETER .5 

 END GAMS/MINOS OPTIONS 

WORK SPACE ALLOCATED -- 7.52 MB 

EXIT -- OPTIMAL SOLUTION FOUND 

MAJOR ITNS, LIMIT 13 40 

 FUNOBJ, FUNCON CALLS 5294 5302 

 SUPERBASICS 580 

 INTERPRETER USAGE 346.59 

 NORM RG / NORM PI 1.969E-07 
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 NO. OF ITERATIONS 3500 OBJECTIVE VALUE 7.6954248487E+05 

 NO. OF MAJOR ITERATIONS 13 LINEAR OBJECTIVE -4.9270982959E+04 

 PENALTY PARAMETER 0.000000 NONLINEAR OBJECTIVE 8.1881346783E+05 

 NO. OF CALLS TO FUNOBJ 5294 NO. OF CALLS TO FUNCON 5302 

 NO. OF SUPERBASICS 580 NORM OF REDUCED GRADIENT 5.363E-04 

 NO. OF BASIC NONLINEARS 1086 NORM RG / NORM PI 2.119E-07 

 NO. OF DEGENERATE STEPS 0 PERCENTAGE 0.00 

 NORM OF X 4.174E+02 NORM OF PI 2.531E+03 

 NORM OF X (UNSCALED) 1.084E+03 NORM OF PI (UNSCALED) 2.724E+03 

 CONSTRAINT VIOLATION 7.906E-12 NORMALIZED 7.288E-15 

STATUS OPTIMAL SOLN ITERATION 3500 SUPERBASICS 580 

 SOLUTION FILE SAVED ON FILE 20 

 MAJOR ITNS, LIMIT 13 40 

 FUNOBJ, FUNCON CALLS 5294 5302 

 SUPERBASICS 580 

 INTERPRETER USAGE 346.59 

 NORM RG / NORM PI 1.969E-07 

Output Reports 

In the LST output file generated, GAMS will report the solution and marginal values for model variables 

and equations. In addition, two standard sets of reports are generated by REAP. The first report, called 

“A1A0RPT00.GMS,” is found in the A1A0LIB directory (table 8). This bulletin is generated 

automatically every time REAP solves successfully and is found at the end of the GAMS listing. 

A1A0RPT00 calculates such things as changes in acreage planted, commodity supply and uses, 

commodity prices, farm income, and environmental indicators. The tables in A1A0RPT00 are in standard 

GAMS format and are used primarily in the evaluation of model results. 

In addition, a second report of about 40 to 60 pages of model results can be generated for distribution. This 

summary report is generated by running ARPT20.GMS, that is located in the AREPORT directory (table 

9). The report includes explanations of commodity, input, and environmental indicators, plus tables 

reporting supply and use, acreage, income, other economic indicators, and physical and economic 

environmental indicators. Detailed tables focusing on additional topics are often produced for specific 

scenario analysis. A fragment of the output generated by ARPT20 for a carbon sequestration analysis is 

shown in example 13. This fragment lists some of the tables that are generated by ARPT20. An example of 

the tables from this report is shown in table 9. 

Example 13—ARPT20 fragment 

 REAP REGIONAL AGRICULTURAL MODEL -- CARBON SEQUESTRATION ALTERNATIVES PAGE 2 

 SCP1100S 2010 0201bsl AER SCP RUNS ALL C=0 P=100 D=0 FP=1 NDISC=1 A1A093V TCM15bV 03/04/02 

 CONTENTS 

 TABLE PAGE 

 -------------------------------------------------------- ---- 

 PRIMARY COMMODITY AND PRODUCT LISTS 2 

 SECONDARY (PROCESSED) PRODUCTS LISTS 4 

 PRODUCTION INPUTS 5 

 ENVIRONMENTAL INDICATORS 6 

 ALTERNATIVE CROP ROTATIONS 7 

 TABLE 1: GOVERNMENT PROGRAM ASSUMPTIONS 8 

 TABLE 2: CROP PRODUCT SUPPLY AND USE 9 

 TABLE 3: PROCESSED PRODUCT SUPPLY AND USE 10 

 TABLE 4: LIVESTOCK PRODUCT SUPPLY AND USE 12 

 TABLE 5: TOTAL ACRES PLANTED BY REGION 13 

 TABLE 6: CROP PLANTINGS AND ACREAGE BASE USE 14 
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 TABLE 7: INCOME ACCOUNTS BY REGION 17 

 TABLE 8: PRODUCTION FLEXIBILTY CONTRACT PAYMENTS BY REGION 18 

 TABLE 8A: CARBON PAYMENTS BY CROP AND REGION 29 

 TABLE 8B: COST SHARE PAYMENTS BY CROP AND REGION 20 

 TABLE 9: ENTERPRISE INCOME ACCOUNTS BY REGION 22 

 TABLE 10: CROP PRODUCTION BY REGION 27 

 TABLE 11: LIVESTOCK PRODUCTION BY REGION 28 

 TABLE 12: CARBON & OTHER ENVIRONMENTAL INDICATORS BY REGION 29 

 TABLE 12A GREENHOUSE GAS INDICATORS BY REGION 31 

 TABLE 12B NITROGEN BALANCE INDICATORS BY REGION 32 

 TABLE 12C PHOSPHOROUS BALANCE INDICATORS BY REGION 34 

 TABLE 12D PHYSICAL ENVIRONMENTAL INDICATORS BY REGION 35 

 TABLE 13: ECONOMIC ENVIRONMENTAL CHARACTERISTICS BY REGION 37 

 TABLE 14: ALTERNATIVE TILLAGE ACREAGE BY REGION 38 

 TABLE 15: ALTERNATIVE CROP ROTATION ACREAGES BY REGION 39 

 TABLE 16: ALTERNATIVE CROP ROTATION ACREAGES BY REGION XX 

 TABLE 17: ENERGY COMPOSITION AND COST ITEMS BY REGION 51 

 TABLE 18: CARBON EMITTED BY REGION 53 

 TABLE 19: ACRES PLANTED IN CARBON SEQUESTRATION PROGRAMS 55 

 -------------------------------------------------------------- 
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Model Data 

In the REAP model system, we link supply and use projections to regional land use production practices 

and their corresponding environmental loadings. To establish the linkage, we pooled information from 

several sources of data, including the baseline data, crop production enterprise database, the National 

Resources Inventory, and the Agricultural Resource Management Survey (ARMS). The data sources, their 

use, and the procedures used to integrate them into REAP are outlined below. 

Baseline Data 

The baseline data provides the market prices and quantities used in setting up the base solution. The 

baseline data provides projections of agricultural commodity production, prices, trade, and farm income 

over a 10-year time period. Commodities covered include corn, sorghum, barley, oats, wheat, rice, cotton, 

soybean and soybean products, milk, beef, pork, chicken, and turkey. Projections contained in the 

baseline assume the continuation of current farm policies and specific conditions for the economy, 

weather, and global situation. For more detailed information on the baseline see the ERS Briefing Room

(www.ers.usda.gov/Briefing/Baseline/). 

The information from the baseline is brought into REAP through files generated by a relational database. 

We use the database to import the baseline spreadsheets; select the relevant price, supply and use, and 

policy variable data; and organize it so that it is consistent with REAP naming conventions and GAMS 

coding requirements. With the database, we generate two files to be called into REAP, one containing the 

baseline data for crops (bucammyy.gms) and one containing the baseline data for livestock 

(bulimmyy.gms). Both files are located in the model library directory (A1A0LIB). Mmyy represents the 

month and year that the baseline data was published. Baseline files are updated to the most recent baseline 

data; periodically and current files exist intermittently for baseline data going back to 1992.6

 

REAP calls in the most recent baseline files available by default. The selection of the baseline files, 

however, is hard coded into the file BGSET.GMS. The baseline call needs to be updated when new 

baseline files become available. The file BGSET.GMS is in the base model directory. 

To change the baseline files being used, all you need to do is to change the name of the baseline files being 

called in BGSET.GMS. Make sure the desired year exists in the library directory to avoid errors. An 

example of changing the baseline files used is demonstrated in example 14. 

Example 14—Changing from 2001 baseline data to the 2003 baseline 

In this example, we are changing the baseline files being used from those containing data from the 2001 

baseline data to those containing data from the 2003 baseline data. In this example, the call for baseline 

files contained in bgset.gms changes from: 

 

6

 One needs to be aware that current formulation of REAP, particularly with respect to its representation of farm programs, is not 

necessarily consistent with the historical baseline files. REAP can be changed fairly easily to accommodate farm programs as they 

existed in these historical files if so desired. 
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$STITLE SETUP REAP TO MATCH SELECTED BASELINE YEAR YB--calls USDA baseline file 

* FILE: <BGSET.GMS> THIS IS CALLED FROM REAP RUN SETUP FILE: E.G. A1A0A.GMS 

* GET BASELINE LIVESTOCK DATA TABLES 

$BATINCLUDE MODULE.CTL LIB1 BULI0201.GMS %1 %2 %3 

* GET BASELINE CROP DATA TABLES 

$BATINCLUDE MODULE.CTL LIB1 BUCA0201.GMS %1 %2 %3 

to 

$STITLE SETUP REAP TO MATCH SELECTED BASELINE YEAR YB--calls USDA baseline file 

* FILE: <BGSET.GMS> THIS IS CALLED FROM REAP RUN SETUP FILE: E.G. A1A0A.GMS 

. 

* GET BASELINE LIVESTOCK DATA TABLES 

$BATINCLUDE MODULE.CTL LIB1 BULI0203.GMS %1 %2 %3 

. 

* GET BASELINE CROP DATA TABLES 

$BATINCLUDE MODULE.CTL LIB1 BUCA0203.GMS %1 %2 %3 

In addition, the first time a new baseline file is run, a new table specifying the parameter BSAC will be 

needed to be changed in the ACRGLY.TBL file in the ENVIR sub-directory. The new BSAC table is 

created the first time REAP is run with the new baseline file. A code at the bottom of the bucammyy.gms 

file that automatically aborts the model run when a new baseline file is detected and prints the new BSAC 

table to use in the listing file. 

* FILE <BUCA0201.GMS> <LIBRARY FILE> CALLED FROM BGSET.GMS 

. 

. 

. 

ABORT$(ABS(BLCROPT - BSACT) > 0.0001) "**** MUST UPDATE BSAC IN ACRGLY.TBL", BLCROPT, BSACT, 

BLCROPA; 

To update the BSAC table in the ACRGLY.TBL file, copy the BSAC table from the listing over the 

existing BSAC table in ACRGLY.TBL. 

*FILE<ACRGLY.TBL> AUGMENTED BY CROPSHR, ROTSHR TABLES AND DERIVATIVES 

* ENVIR FORMULATION 

. 

TABLE BSAC(B,YR) BASELINE ACRES PLANTED 0201 BASELINE 

 + 2005 2006 2007 2008 2009 2010 

CORN 80.000 80.000 80.500 80.500 81.000 81.000 

SORGHUM 9.500 9.600 9.700 9.800 9.900 10.000 

Becomes 

TABLE BSAC(B,YR) BASELINE ACRES PLANTED 0203 BASELINE 

 + 2007 2008 2009 2010 2011 2012 

CORN 80.000 80.000 80.500 80.500 81.000 81.000 

SORGHUM 9.500 9.600 9.700 9.800 9.900 10.000 

If there have been no changes in government programs that would change policy variable names or the 

manner in which the programs affect producer decisions, then the changes needed to include the new 
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baseline file is complete. However, when the farm programs represented in the baseline data change, then 

REAP will need to be reformulated to account for the new programs. Because government farm policy 

changed from 2001 to 2003, updating REAP to the 2003 baseline required some slight adjustments, 

primarily in naming conventions for government programs and in the way government program payments 

and expenditures are calculated in reports. 

The major change to farm policy in the 2002 Farm Bill was in the manner in which decoupled income 

support payments were structured. Prior to the 2002 Farm Bill, decoupled income support payments were 

fixed in the 1996 Farm Act and called “Production Flexibility Contract (PFC) payments.” With the 2002 

Farm Bill, we divide the uncoupled income support payments into two parts: a direct payment and countercyclical payment. The direct payment was setup similar to the PFC payment. All that was required to 

incorporate the direct payment into REAP was to add a label for it and substitute this label for PFC in the 

calculations of government program payments and expenditures. The counter-cyclical payment, however, 

depends on the difference between the target price and market price or loan rate (whichever is higher), and 

so required that some additional calculations be added to the code in addition to adding a label for it to the 

model. Even though the counter cyclical payment was tied to the market price, it was not tied to 

production since the payment was based on base acreage and program yields that are derived from 

historical production rather than current production. Actual production of crop was not required to receive 

the payment. Because both programs were decoupled from production, we assumed that they had no effect 

on production decisions and could be treated as exogenous variables in the model formulation. 

The changes to the model formulation required when the 1996 Farm Bill was enacted, in contrast, were 

much more extensive. Prior to the 1996 Farm Bill income support payments were tied to both prices and 

production. The area planted to program crops was limited by base acreage constraints and annual acreage 

reduction programs. In addition, farmers had limited flexibility to plant oilseeds on a portion of their base 

acreage. 

Crop Production Enterprises 

The crop production enterprise database provides data needed to construct crop production activities for 

REAP and provides the field operation data used to run the EPIC simulations used to generate the 

environmental indicator coefficients for the cropping systems used in REAP. The database was developed 

by using information contained in ARMS and literature reviews for each of the 10 Farm Production 

Regions. 

The database includes two types of cropping systems. “Predominant systems” are those systems currently 

in widespread use. Predominant system coefficients are based upon ARMS7

 estimates, with input costs 

updated to reflect current input prices and ERS Cost of Production estimates. “Alternative systems” are 

newer systems that may prove to be less intensive over time in terms of soil erosion and chemical use. 

There are 623 predominant systems that include rotations of up to 4 crops differentiated by up to 5 tillage 

practices. The alternative systems include innovative and experimental rotations. These rotations are 

designed to be less detrimental to the environment. The rotations include using legume crops as a 

 

7

 The information used in the current database came from the CPS, which is now part of ARMS. See the ERS Briefing Room. 
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substitute for nitrogen fertilizer and using crop rotation sequences to break up pest life cycles in place of 

chemical pesticides. 

Predominant Systems 

The 1992 National Resources Inventory (NRI) and the 1992 Cropping Practices Survey (CPS)8 were used 

to define the 62 crop rotations commonly used throughout the United States and the tillage practices 

commonly associated with them. Rotations were defined based on the number of different REAP crops 

contained in the cropping history. Records in NRI were divided into regions by overlaying the 26 Land 

Resource Regions onto the 10 Farm Production Regions. The records were then subdivided again based on 

whether they were on HEL or NHEL soils. Acreage for each rotation was then recorded. 

Tillage practices associated with the rotations and acreage devoted to them were derived from the CPS. 

Crop rotations as identified through the NRI were used to group the CPS records. ERS definitions 

pertaining to tillage practices were used to assign tillage practices and acreage associated with each 

rotation. 

All information used to construct predominant systems was obtained from ARMS, primarily the Phase II 

field practices survey. The Phase II was previously known as the CPS. Because the information used to 

produce the production systems came from the CPS before its name was changed, we will refer to it as the 

CPS for the remainder of this section. To create the management files to run EPIC simulations for the 

predominant systems (and obtain the physical effects of these systems—yields and environmental effects) 

required obtaining all the management information needed to mimic the complete production cycle of any 

crop in a rotation. This included information on all field operations from pre-planting to post-harvesting 

(i.e., what occurred, when it occurred, with what type of equipment, and how frequently) and input levels 

(i.e., seeding rates, fertilization and liming rates, and pesticide applications) for each crop within a 

production activity sorted by rotation, tillage practice, and model region. 

Data to create the crop production enterprise database for the production of corn, soybeans, wheat, cotton, 

and rice came from the 1992 CPS. The data used for sorghum came from the 1991 CPS. Information on 

field operations for silage, which was not included in the CPS, was obtained by assuming that silage was 

corn being harvested for silage. The exception to this was in the Southern Plains, where it was assumed 

that silage was sorghum being harvested for silage. Accordingly, we assumed that a silage system was 

managed identically to its “twin” corn or sorghum system, except that silage systems were harvested for 

silage at the appropriate time. Analogously, EPIC management files for barley and oats were assumed to 

be similar to wheat managed in a comparable wheat system, with minor modifications (e.g., to ensure that 

any herbicide applications derived from a wheat system to be used on a comparable barley system were 

not toxic to barley). 

Finally, it was assumed that hay was grown according to established practices for alfalfa in that region. In 

most regions, it was assumed that alfalfa was established in the summer and fall after the preceding crop 

 

8

 Not to be confused with the objective function variable CPS. 
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had been harvested and grown for 4 years, with cuttings two or three times each year. Where alfalfa was 

not suited to the growing conditions of a particular region, clover or hairy vetch was substituted. 

Machinery implement compliments used in each rotation-tillage system were based on the size 

(horsepower) of the tractor used. The CPS was used to determine the average size (horsepower) of the 

tractor used in each FPR. A representative implement complement (machinery and equipment) for each 

FPR was constructed by using the average tractor size in that FPR and information obtained from the 

publication Minnesota Farm Machinery Economic Cost Estimates for 1992 (Fuller et al., 1992) and staff 

of the Rodale Institute Research Center. 

Planting information for each crop in a rotation-tillage system was derived from information in the CPS on 

average seeding rates and planting dates. The type of tillage practice under consideration determined the 

type of planter for the particular crop. Fertilizer regimes for each crop in a rotation-tillage system were 

derived from the fertilizer information contained in the CPS. The mean number of fertilizer applications as 

reported in the CPS for a crop (rounding up or down to an integer, according to convention, but not 

rounding down to zero) was used to determine the number of fertilizer applications. The mean of CPS for 

total quantity of nitrogen (N), phosphate (P), and potash (K) were used to determine how many pounds per 

acre of elemental N, P, and K to apply to each system. Likewise, liming information from the CPS was 

used to determine lime applications. Also, the most frequently occurring month(s), in the CPS data, were 

used to set fertilization date(s). 

The CPS did not provide necessary information about adjusting the amount of N, P, and K to apply to 

reflect credits due to manure applications. Yields of some crops in the base systems may have been 

affected by this omission. The CPS did not contain sufficient information to identify the type of fertilizer 

used. This is because fertilizer applications can be reported in the survey by type of fertilizer and quantity 

or by amount of elemental form (N, P, and K) applied or both. As a result, the quantity of each type of 

fertilizer applied was set so as to equal the quantity of elemental nutrient (N, P, and K) as reported in 

the CPS. 

The appropriate pesticide schedule for a crop (again, by FPR, specific to each crop within a rotation-tillage 

system) was determined from the mean number of pesticide applications for a particular crop and quantity 

of active ingredients in the pesticide. To determine which pesticides to apply in the production system, the 

CPS data was used to determine the first two or three active ingredients in a product mix. The most 

frequently applied pesticides (active ingredients) for that rotation-tillage system were used as much as 

possible. Potential complications occurred whenever the number of pesticide applications was greater than 

one. It was difficult to associate specific pesticides with a particular pesticide application. 

Take, for example, a rotation-tillage system where an average of two pesticide applications were reported 

for a particular crop. The first most frequently occurring pesticide is a herbicide, but this herbicide is a 

substitute for the second most commonly applied pesticide for the crop. It is conceivable that only one 

application of a herbicide was being applied for this type of system, but some producers used the first 

herbicide while others used the second herbicide rather than two applications of herbicide. Also, again 

suppose that for a rotation-tillage system, there were two pesticide applications reported, and this time, 

both a herbicide and an insecticide were used. It is difficult to detect from the CPS data whether producers 

sprayed their fields with a herbicide only once, sprayed their fields a second time with an insecticide, or 

sprayed their fields twice by using both a herbicide and an insecticide. The typical or representative 

pesticide application regime for a given system was based on the most frequent pesticide application and 

the average number of pesticide applications for that crop as observed in the CPS. Information from the 

CPS was used to determine the application rate of each pesticide (active ingredient) applied in the system 

and the month(s) of which the most frequent specific pesticide application(s). 

Lastly, information from the CPS was used to determine how many field or tillage operations (other than 

planting, fertilizing, or spraying) occurred for a crop (again, by FPR, specific to each crop within a 

rotation-tillage system). The mean number of machinery operations reported in the tillage table in the CPS 

data for that crop (rounding up or down to an integer, according to convention) was used. Further, 
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information in the CPS was used to determine the most frequently occurring time(s) of field (tillage) 

operations. 

Harvest information from the CPS was not used in the analysis. In EPIC simulations, crops were allowed 

to reach physiological maturity before being killed. Further, crops harvested for grain were allowed to dry 

in the fields to a specific moisture content, avoiding storage and drying issues and costs. 

Alternative Systems 

Regional agronomists were asked to complete surveys for alternative systems as part of a study undertaken 

by the World Resources Institute.9

 The agronomists were asked to complete surveys only for those systems 

for which there was documented information on all aspects of the systems (i.e., management practices, 

input levels, and yields). These sources included records of field trials at experiment stations, records 

from onfarm producer trials, and results of studies published in university experiment station bulletins or 

peer-reviewed journals. All management information from the completed surveys was used to create the 

EPIC input files for simulating the biophysical effects (yields and environmental indicators) of the 

systems. These simulated effects were used in the analysis, but no actual effects were recorded on the 

survey instrument. 

Despite the fact that this database is the most extensive single source of alternative production systems, the 

coverage is not uniformly comprehensive or representative of all alternatives in each FPR. The unevenness 

in coverage results from one or more of the following: (1) producers may not have been using many 

alternative systems in that region when the surveys were completed; (2) producers may have been using 

alternative systems in that region, but any scientifically gathered or published data for alternative systems 

in that region may have been limited; and (3) there may have been scientifically gathered or published data 

for the alternative systems in that region. In general, FPRs, with many alternative systems, have more 

comprehensive coverage of alternative systems than FPRs with fewer systems. 

Crop Production Enterprise Budgets 

REAP’s production enterprise budgets contain economic items typical of the cost of production budgets 

(e.g., nitrogen cost, chemical cost, and energy cost) and physical measures of output and input use (e.g., 

yields, cropland, and irrigation water). They also contain a set of environmental coefficients10 (e.g., erosion 

and nitrogen leaching) corresponding to the specific rotation and tillage cropping practices (e.g., soil 

erosion and nitrogen leaching). 

Economic coefficients in the budgets were initially developed by using methods similar to those used in 

the USDA/Natural Resources Conservation Service's crop budget generator. Ten agronomists in different 

 

9

 See Faeth (1995) for details. 

10 Environmental indicators are typically reported in physical units. A few such as soil depreciation allowance, soil erosion off-site 

costs, carbon flux value, and nitrogen flux value are reported in monetary terms. 
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regions across the United States contributed to the effort. Scientists at RIRC developed a customized 

budget generator and data-processing procedures to combine the information into budgets and consolidate 

the budgets into databases suitable for managing the large volume of information.11

 

ERS analysts updated the initial set of crop budgets in several ways. Cost of production estimates are 

updated to USDA baseline projections of variable cost for the year in which a model scenario is based. The 

estimates are also updated to be consistent with recent ERS crop cost of production estimates.12 In 

addition, the environmental coefficients for each cropping system derived by way of the EPIC biophysical 

model have been recalculated as part of several update and calibration processes discussed below (see 

Appendix B). 

Livestock Budgets 

Budgets for beef, dairy, and swine operations are obtained from cost of production estimates published 

by ERS.13 Poultry, slaughtering, and feed ration generation activities are for the most part represented as 

value added activities with constant returns to scale. Poultry activities have been slightly modified to 

include energy costs. Energy cost for poultry was obtained from various ERS Situation and Outlook 

reports. Manure production and nutrient content of animal manure are based on estimates in 

Kellogg et al. (2000). 

Data Reconciliation 

We combine information from four databases to derive the base solution in REAP: land use and tillage 

practices, crop production enterprise, baseline data, and crop yield. The process of combining the 

information in these databases to obtain a base solution can be thought of as economic calibration and 

biophysical calibration. Economic calibration is the process of adjusting parameters on behavioral 

relationships and the production enterprise budgets so that the base solution replicates price, supply, and 

use as reported in the baseline data. Biophysical calibration is the process of adjusting various parameters 

in the EPIC model and environmental indicator coefficients in the crop enterprise budgets to be consistent 

with available information on crop yields, soil erosion, and assumptions about producer behavior, with 

respect to application of nitrogen fertilizer. 

 

11 See Faeth (1995) for details. 

12 These costs are updated periodically by using ARMS data. Future costs of production will be econometrically estimated from 

farm surveys (see USDA, ERS, 2006). 

13 These costs are updated periodically using ARMS data. Future costs of production will be econometrically estimated from farm 

surveys (see USDA-ERS, 2006). 
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Economic Calibration 

Economic calibration is to USDA’s agricultural baseline for a historical or projected year, including 

agricultural commodity policies affecting prices and production of crops and livestock, and environmental 

policies affecting resource use, such as the Conservation Reserve Program (USDA, 2006). National 

acreage for 10 major field crops is specified by the baseline. Regional acreage allocation is calibrated to 

crop rotations (up to 4 years). REAP applies several routines to set up a base solution from which to work. 

The routines enable REAP to replicate production and price estimates as represented in the baseline data 

for any given year in a manner consistent with available information on the distribution of production 

activities across the United States. The routines are used to distribute crop and livestock production 

estimates contained in the USDA baseline to the model regions, adjust crop yields, and update costs of 

production estimates for production activities. Data sources used during this process include the baseline 

data, the National Resources Inventory (NRI), ARMS, and production and yield estimates from USDA’s 

National Agricultural Statistics Service (NASS). 

To account for data reporting inconsistencies across the REAP data sources, several reconciliation 

procedures were followed. These procedures are either designed to reconcile baseline projections with 

historical distribution of production activity or, for the environmental indicators such as soil erosion, 

reconcile simulation results with levels estimated directly from the NRI. 

Information on production for livestock and crop production from NASS estimates are used to apportion 

the baseline data projections to model the Farm Production Regions. Each region’s share of crop 

production as reported by NASS is applied to the national estimate provide in the baseline projection to 

allocate crop production among the 10 Farm Production Regions. The regional allocations are then used to 

specify parameters in cost functions so that REAP validates to the regional production estimates. 

Crop Acreage Reconciliation 

Crop acreage allocation is further extended by distributing crop acreage in each Farm Production Region 

obtained from the general procedure used to assign crop acreage to the Farm Resource Regions among the 

various crop production enterprise systems in each region. REAP uses three databases to reconcile 

distribution of crop acreage by rotation and tillage practice, with the national crop acreage estimate from 

the baseline data projections for any given year. The crop rotation and tillage information is combined with 

the baseline production data by using a matrix balancing routine to allocate crop acreage in each model 

region/practice strata as determined by share information from NRI and USDA CPS regional data. 

Responses in individual region, tillage practice, rotation, and other strata follow nested adjustment 

functions, which are part of the calibration, and sum to aggregate response. The matrix balancing routine 

used to achieve this reconciliation is in \REAPgams\a1a0\envir\acrgly.tbl. A fragment of code using this 

routine is shown in Example 15—Crop acreage matrix balancing routine 

* I------------------------------------------I 

* I QUADRATIC CONSTRAINED MATRIX BALANCING I 

* I------------------------------------------I 

 PARAMETER XAC0(BA,U,UR) BASE PRODUCTION SYSTEM ACREAGE BY MODEL REGION (FROM NRI) 

 SRL0(B, U,UR) BASE CROP ACREAGE BY MODEL REGION (FROM NRI) 

 SR0( B, U ) BASE CROP ACREAGE BY FARM REGION (FROM NASS) 

 DRL0(BA,U ) BASE PRODUCTION SYSTEM ACREAGE BY FARM REGION (FROM NRI) 

 SXRL(B,BA,U,UR) CROP ACREAGE SHARE PER UNIT OF PRODUCTION SYSTEM BY SYSTEM BY MODEL REGION 

 ARI( B, U ) WEIGHT ON CROP ACREAGE BY FARM REGION DEVNS OF COMPUTED VS NASS 

 ARIF WEIGHT ADJUSTER ON ACREAGE BY FARM REGION DEVNS OF COMPUTED VS NASS 

 BRLJ(BA, UR) WEIGHT ON PRODUCTION SYSTEM ACREAGE BY FARM REGION DEVNS OF COMPUTED VS NRI 

 GRLI(BA,U,UR) WEIGHT ON PRODUCTION SYSTEM ACREAGE BY MODEL REGION DEVNS OF COMPUTED VS NRI 
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 RSR(B, *,UR) REPORT OF CROP ACREAGE BY FARM REGION 

 RDRL(BA,*,UR) REPORT OF PRODUCTION SYSTEM ACREAGE BY FARM REGION 

 RXAC(BA,*,U,UR) REPORT OF PRODUCTION SYSTEM ACREAGE BY MODEL REGION 

* DRL0(BA,U,UR) ROTATION ACREAGE OBTAINED (FROM NRI) 

* BRLI(BA,U,UR) WEIGHT PLACED ON MODEL REGION CROP ACREAGE DEVIATIONS FROM NRI ACREAGE 

 ; 

 OPTION XAC0:3:1:2, SRL0:3:1:2, SXRL:3:2:2, GRLI:3:1:2; 

 OPTION RSR:3:2:1, RDRL:3:2:1, RXAC:3:2:2; 

XAC0(BA,RL,R) = SUM(T2, ROTAC(BA,T2,RL)$ER2RR(RL,R)); 

 DRL0(BA,R) = SUM(RL, XAC0(BA,RL,R)); 

 SR0(B,R) = ACRG92(B,R); 

 SXRL(B,BA,RL,R) $SUM(T2A, XSB7(B,BA,"NP","A","PRD",T2A,RL,R)) 

 = SUM(T2, XSB7(B,BA,"NP","A","PRD",T2,RL,R)) 

 / SUM(T2A, 1$XSB7(B,BA,"NP","A","PRD",T2A,RL,R)) ; 

 ARIF = 1; 

* DEVIATION WEIGHT CALCULATIONS 

* WEIGHTS: INVERSES IMPLIES CHI-SQUARE MINIMAND FOR CONSTRAINED MATRIX 

PROBLEM 

* EQUAL TO 1 IMPLIES CONSTRAINED LEAST SQUARES 

 ARI(B,R) $SR0(B,R) 

 = 1 / SR0(B,R); 

 BRLJ(BA,R) $DRL0(BA,R) 

 = 1 / DRL0(BA,R); 

 GRLI(BA,RL,R) $XAC0(BA,RL,R) 

 = 1 / XAC0(BA,RL,R); 

 DISPLAY XAC0, DRL0, SR0, SXRL, ARI, BRLJ, GRLI; 

 POSITIVE VARIABLES 

 XAC(BA,RL,R) ACREAGE OF PRODUCTION SYSTEM BY MODEL PRODUCTION REGION 

 SR(B,R) ACREAGE OF CROP PLANTED IN FARM PRODUCTION REGION 

 DRL(BA,R) ACREAGE OF CROP SYSTEM ACREAGE BY FARM PRODUCTION REGION 

 ; 

 VARIABLE DEVFN QUADRATIC CONSTRAINED MATRIX DEVIATION FUNCTION 

 ; 

 EQUATIONS OBJCHIS CHI-SQUARE OBJECTIVE FUNCTION 

* OBJCLSQ CONSTRAINED LEAST-SQUARES OBJECTIVE FUNCTION 

 SCROP(B,R) CROP ACREAGE BALANCE BY FARM REGION 

 NCROP(B) NATIONAL BASELINE CROP ACREAGE BALANCE REQUIREMENT 

 DSYSTEM(BA,R) PRODUCTION SYSTEM BALANCE BY FARM REGION 

 XACP(BA,RL,R) PRODUCTION SYSTEM BY MODEL REGION POSITIVE CONSTRAINT 

 {REQUIRE ALL SYSTEMS > 0 

 ACREAGE} 

 ; 

 OBJCHIS.. DEVFN =E= SUM((B, R)$SR0(B,R), ARIF * ARI(B, R) * SQR(SR( B, R) - SR0(B, R))) 

 + SUM((BA,R)$DRL0(BA,R), BRLJ(BA,R) * SQR(DRL(BA,R) - DRL0(BA,R))) 

 + SUM((BA,RL,R)$XAC0(BA,RL,R), GRLI(BA,RL,R) * SQR(XAC(BA,RL,R)- XAC0(BA,RL,R))); 

SCROP(B,R)$SR0(B,R).. 

 SR(B,R)$SR0(B,R) =E= SUM((BA,RL)$XAC0(BA,RL,R), XAC(BA,RL,R) * SXRL 

( B,BA,RL,R)); 

 NCROP(B)$BSAC(B,"%1").. 

 BSAC(B,"%1") =E= SUM(R$SR0(B,R), SR( B, R)); 

 DSYSTEM(BA,R)$DRL0(BA,R).. 

 DRL(BA,R)$DRL0(BA,R) =E= SUM(RL$XAC0(BA,RL,R), XAC(BA,RL,R)); 
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 XACP(BA,RL,R)$XAC0(BA,RL,R).. 

 XAC(BA,RL,R)$XAC0(BA,RL,R) =G= MIN(.05, .05 * XAC0(BA,RL,R)); 

MODEL QCM_CHIS2 RECONCILE CROP AND ROTATION SYSTEM ACREAGE BY FARM PRODUCTION REGION / 

 OBJCHIS, SCROP, NCROP, DSYSTEM, XACP /; 

 OPTION LIMROW = 10; 

 OPTION LIMCOL = 10; 

 SOLVE QCM_CHIS2 USING NLP MINIMIZING DEVFN; 

 RSR(B,"RCHI-2", R) = SR.L(B,R); 

 RDRL(BA,"RCHI-2", R) = DRL.L(BA,R); 

 RXAC(BA,"RCHI-2",RL,R) = XAC.L(BA,RL,R); 

DISPLAY "QUADRATIC CONSTRAINED MATRIX ADJUSTMENTS", RSR, RDRL, RXAC; 

Cost of Production Reconciliation 

Crop and livestock production activity costs can be updated to the most recently available cost of 

production estimates obtained from the ARMS survey if desired. Costs can be updated by using a set of 

matrix balancing routines that adjust individual line item budget expenditures in the REAP budgets to 

ensure that average costs as reported for these items by crop and region by ERS match REAP estimates. 

The routines for updating are in \REAPgams\a1a0lib\bucaxx96.gms for crops and 

\REAPgams\a1a0lib\bulixx96.gms for livestock production activities. 

Demand and Supply Response Calibration 

Demand and supply parameters are calibrated to replicate demand and supply response embodied in 

FAPSIM supply and demand effect multipliers. The multipliers represent the supply and demand response 

obtained when a single commodity is subjected to a supply or demand shock over a 10-year period. All 

other prices and quantities are allowed to vary in response to the shock. The multipliers are similar to 

elasticities except that they rely on the total derivative, not the partial derivative. Most supply and demand 

elasticities reported in the literature are derived by using the partial derivative, where all other prices and 

quantities are held constant. Export demand and import supply parameters were derived from elasticities 

contained in the Partial Equilibrium Agricultural Trade Simulator (PEATSim) model (Abler, 2006). 

On the supply side, several sets of parameters need to be calibrated, including the elasticities of 

transformation used in specifying the CET functions for crop rotations and tillage practices. These 

elasticities were calculated by using a search algorithm that loops through a range of possible values and 

calculates the change in production with respect to a price shock for each commodity for each set of 

elasticity values. The set of elasticity values that comes closest in replicating the FAPSIM supply 

multipliers are used in the model formulation. Currently, the transformation elasticities are set at -2. The 

results from the search algorithm indicate that crop supply response is sensitive to the magnitude of the 

transformation elasticity on the CET function used to control substitution among crop rotations. Crop 

supply response was largely unaffected by the magnitude of the transformation elasticity used on the CET 

functions that control substitution among the tillage practices. 

Biophysical Calibration 

Biophysical calibration is accomplished by running regional specification of soil type, weather conditions, 

crop systems, and management practices through EPIC biophysical simulations. A representative soil was 

selected for each region from the NRI and SOILS5 databases by using a multidimensional measure of 

similarity to regional average Universal Soil Loss Equation (USLE) variables capturing slope, 

hydrological, and erodability characteristics (Wischmeier and Smith, 1978). Representative weather 
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conditions are estimated from distributional information (mean and variance) on temperature and rainfall 

from the National Oceanic and Atmospheric Administration (NOAA) combined with information on 

location of crop production. Average crop yields are estimated for the model regions from county yield 

data for the 10 crops. 

Each crop system is specified as a sequence of crops, with dated field operations including planting, 

cultivation, fertilizer and pesticide application, and harvesting. Biophysical model biological parameters 

are validated in each production region so that simulated yields calibrate to regional yields and to ensure 

the yield-nitrogen response is consistent with observed nitrogen application rates and rational economic 

behavior on the part of producers. 

Once the biophysical model has been calibrated, the environmental loading coefficients used by REAP for 

each combination of crop, rotation, tillage, drainage (tile drained or not), and region are derived. To more 

closely replicate the long-term effects of a particular set of practices, EPIC is first used to simulate the 

evolution of soil condition over 60 years of weather conditions for each set of practices. After that period 

of priming the soil, the soil condition is fixed, and the coefficients for nitrogen leaching, soil erosion, and 

so forth are calculated. These parameters represent the average annual results over a number of years; the 

specific number varies, depending on the number of crops in rotation and the number of years in the 

rotation, but is usually equal to 7 years’ worth of results per crop. 

Onsite phosphorus and nitrogen runoff estimated by using EPIC are calibrated to in-stream measurements 

made by the U.S. Geological Survey (Smith et al., 1997). The transport coefficients for phosphorus and 

nitrogen are used to estimate the quantity of sheet and rill erosion and pesticides that also run off into 

surrounding water bodies. Pesticide leaching and runoff are measured by the quantity of the active 

ingredient and then normalized to reflect toxicity and half-life (Barnard et al., 1997). 

An interface called I_EPIC is used to manage the multiple EPIC runs necessary for calibration and 

generation of REAP environmental parameters. I_EPIC was developed and is maintained by Iowa State 

University. In order to increase the transparency of input, I_EPIC uses Microsoft Access databases as its 

input files rather than the more cryptic text-based input files used directly by EPIC. When input 

modifications are required, these databases can easily be opened and edited, then reloaded into I_EPIC. 

I_EPIC generates the text-based input files required by EPIC, and then translates EPIC’s output into preformatted output tables in the same Microsoft Access database. 

EPIC requires extremely detailed information about soil conditions, other site conditions (such as slope 

and weather), and sequence and timing of field operations in order to run. Creation of the data-rich input 

databases used by I_EPIC is therefore the most time-intensive step in the process of calibrating the model 

and generating the environmental effect parameters. As mentioned earlier, soil and weather information 

were originally compiled from external data sources, while field operations were compiled by USDA and 

WRI from existing data. 

I_EPIC supports several versions of EPIC, including version 0509, which was used to generate the most 

recent set of environmental parameters used by REAP. More information on EPIC is at 

http://www.brc.tamus.edu/epic/. More information on I_EPIC is at 

http://www.public.iastate.edu/~elvis/i_epic_main.html. 

Crop Yield Reconciliation 

Crop production in REAP is calibrated to baseline data projections by adjusting crop yields in the crop 

production enterprise systems. This is accomplished by distributing projected crop production to the Farm 

Resource Region by using each region's share of crop production as derived from crop production 

information reported by NASS. The regional crop production results for each crop are then compared with 

each simulated yield for a Farm Resource Region to form a crop-specific index in each Farm Resource 

Region, which is then used to adjust yields in each production enterprise system so that simulated 
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production matches projected production for each crop in each Farm Resource Region. This routine is in 

a1a0\bgcaxa.gms. 

Environmental Indicator Reconciliation 

The erosion estimates in the budgets are calibrated to meet erosion information obtained from the NRI by 

obtaining the soils erosion estimate by REAP model region from the 1992 NRI. The erosion coefficients in 

the REAP budgets are then used to calculate REAP estimates of cropland erosion in each region. An 

erosion index for adjusting REAP erosion coefficients are constructed by dividing the NRI estimates by 

the REAP soil erosion estimate for each model region. A similar routine is used to calibrate wind erosion. 

The code for this is in a1a0\envir\evactga.gms. 
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The U.S. Mathematical Programming Regional Agricultural Sector Model (USMP) 

(now Regional Environment and Agriculture Programming Model (REAP), an 

agriculture sector analysis model, grew out of innovations in optimization, economic 

modeling, and computing in the 1970s and 1980s. Following Duloy and Norton’s (1973 

and 1975) application of separable programming, the Economic Research Service 

(ERS) began sector model efforts in 1981 to support analysis of 1981 and 1985 farm 

legislation. Robert House built the initial USMP sector model based on the Duloy and 

Norton model. In 1982, House reformulated USMP as a nonlinear system enabling 

direct solution of USMP’s quadratic demand and Positive Math Programming (PMP) 

supply system, based on Howitt’s (1995) PMP methodology. 

From the mid-1980s to late 1980s, crop commodity programs and endogenous acreage 

program participation were added to the model. The addition of the Conservation 

Reserve Program followed, along with incorporation of and calibration to the USDA 

multiyear baselines. In these years, USMP contributed to ERS food and agricultural 

public and staff analyses, including the 1981 and 1985 Farm Bills. 

Terry Hickenbotham collaborated on USMP enhancements and analyses in the 1980s, 

including GATT trade liberalization and analyses of the new triple base provisions of 

the 1990 Farm Bill. In 1991, W. Terry Disney joined the USMP effort. Disney rebuilt 

the livestock sector, and Mark Peters developed comprehensive feed grain and wet/dry 

milling components that supported 1990’s ethanol analyses. A joint project with the 

World Resources Institute (WRI) and the Rodale Institute (including Paul Faeth and 

John Westra of WRI, and Kim Kroll and Jim Reynolds of the Rodale Institute) was 

initiated to add environmental indicators and alternative tillage and rotation cropping 

systems to the model, facilitating conservation and environmental policy analysis. 

House and Peters enhanced these by integrating constant-elasticity crop supply to manage the 

numerous regional, tillage, fertilizer application, and multiyear rotation alternatives. House and 

Peters integrated calibrated estimates of crop yield and alternative nitrogen application rates and 

other physical indicators by using the EPIC crop biophysical model. Howard McDowell 

enhanced the environmental indicator measures and analysis and linked GIS information with 

USMP analyses. 

After 2000, Keith Paustian and Mark Sperow of the Natural Resource Ecology Laboratory of 

Colorado State University helped to develop carbon sequestration implications of changes in U.S. 

crop and forestry land uses; Robert Johansson and Jonathan Kaplan of ERS helped to incorporate 

the environmental implications of animal production; Suzie Greenhalgh, Mindy Selman, and 

Elizabeth Marshall of WRI helped to develop watershed applications and biofuel components for 

the model. 

In 2006, the model was renamed “REAP” to reflect the importance of the regionspecific interaction between the environmental and economic components of the model, 

which are currently being revised by researchers at WRI (Elizabeth Marshall, Suzie 

Greenhalgh, and Mindy Selman), ERS (Scott Malcolm), and the Louisiana State 

University Agricultural Center (John Westra). 

Box 1—History of Model’s Development 
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Table 1 

REAP Inputs and Outputs

Regional National Livestock Crops Processed

cropland nitrogen fertilizer fed beef for slaughter corn soybean meal

pasture land potassium fertilizer nonfed beef for slaughter soybeans soybean oil

potash fertilizer beef calves for slaughter sorghum livestock feed mixes

lime beef feeder yearlings barley dairy feed supplements

other variable costs beef feeder calves oats swine feed supplements

public grazing land cull beef cows wheat fed beef

custom farming operations cull dairy cows cotton nonfed beef

chemicals cull dairy calves rice veal

seed milk silage pork

interest on operating capital hogs for slaughter hay broilers

machinery and equipment repair cull sows for slaughter turkeys

veterinary and medical costs feeder pigs eggs

marketing and storage other livestock butter

ownership costs american cheese

labor and management costs other cheese

land taxes and rent ice cream

general farm overhead nonfat dry milk

irrigation water application manufacturing milk

energy costs ethanol

insurance corn syrup

Inputs Outputs
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Table 2 

List of tables contained in ENVACTGA

*---------------------------------------------------------------* 

* PARAMETER DEFINITIONS FOR ENVACTGB.GMS * 

*---------------------------------------------------------------* 

PARAMETER BTIL(B,T,R,CAS) SUM OF TILLAGE ACREAGE BY MDR; 

PARAMETER BTIL_N(B,T,CAS) SUM OF TILACRES NATIONALLY; 

PARAMETER TIL_CPSR(T,R,CAS) REG. SUM OF TIL ACRES LIKE CROPPING PRACTICES SURVEY; 

PARAMETER TIL_CPSN(T,CAS) NAT. SUM OF TIL ACRES LIKE CROPPING PRACTICES SURVEY; 

PARAMETER TCROP(RL,R,CAS) TOTAL PROGRAM AND FLEX AND ARP ACREAGE, BY MDR; 

PARAMETER TCROP_P(R,CAS) TOTAL PROGRAM AND FLEX AND ARP ACREAGE, BY FPR; 

PARAMETER SACPP1(B,G,H,Y,T,U,UR) PP ACTIVITY SHARE OF TOTAL CROP ACRES BY MDR; 

PARAMETER CHGCROPAC(B,G,H,Y,T,U,UR,*) PP CHANGE IN CROP ACRES PLANTED; 

PARAMETER RCHGCROPAC(U,UR,*) REGIONAL CHANGE IN CROP ACRES PLANTED; 

PARAMETER ACRES059(RL,R) 0-92 AND 50-92 ACREAGE WEIGHTED BY MDR CROP ACREAGE; 

PARAMETER OLUADJ(RL,R,OTHRLAND,CAS) OTHER LAND USE - ADJUSTED TO CURRENT YEAR; 

PARAMETER OLUTD(RL,R,CAS) DIFFERENCE BETWEEN ACTUAL AND CALCULATED LANDUSE TOTALS; 

PARAMETER PR_ER_RTS(RL,PRLAND) PASTURE AND RANGE WATER EROSION RATES; 

PARAMETER PR_WR_RTS(RL,PRLAND) PASTURE AND RANGE WIND EROSION RATES; 

PARAMETER CL_ERSN(RL,R,CAS) CROP LAND SOIL EROSION TOTAL IN TONS MDR; 

PARAMETER ARP_ERSN(RL,R,CAS) ARP EROSION TOTAL IN TONS BY MDR; 

PARAMETER DIV_ERSN(RL,R) 50-92 AND 0-92 EROSION BY MDR; 

PARAMETER CRP_ERSN_L(RL,R) CONS. RESERVE PROGRAM SOIL EROSION TOTAL IN TONS BY PRODUCTION REGION; 

PARAMETER DIV_WERSN(RL,R) 50-92 AND 0-92 WIND EROSION BY MDR; 

PARAMETER CRP_WRSN_L(RL,R) CONS. RESERVE PROGRAM WIND EROSION TOTAL IN TONS BY PRODUCTION REGION; 

PARAMETER PR_EROSN(PRLAND,RL,R,CAS) PASTURE AND RANGE LAND SOIL EROSION TOTAL IN TONS BY LAND RESOURCE 

REGION; 

PARAMETER TEROSN_L(RL,R,CAS) TOTAL SOIL EROSION IN TONS BY LR IN MILLIONS OF TONS; 

PARAMETER TEROSN_P(R,CAS) TOTAL SOIL EROSION IN TONS BY PRODUCTION REGION IN MILLIONS OF TONS; 

PARAMETER TEROSN_N(CAS) TOTAL NATIONAL SOIL EROSION IN TONS IN MILLIONS OF TONS; 

PARAMETER SEROSN_L(RL,R,CAS) SHEET AND RILL SOIL EROSION IN TONS BY LR IN MILLIONS OF TONS; 

PARAMETER SEROSN_P(R,CAS) SHEET AND RILL SOIL EROSION IN TONS BY PRODUCTION REGION IN MILLIONS OF TONS; 

PARAMETER SEROSN_N(CAS) SHEET AND RILL NATIONAL SOIL EROSION IN TONS IN MILLIONS OF TONS; 

PARAMETER WINDERSN_L(RL,R,CAS) TOTAL WIND EROSION IN TONS BY LR IN MILLIONS OF TONS; 

PARAMETER WINDERSN_P(R,CAS) TOTAL WIND EROSION IN TONS BY PRODUCTION REGION IN MILLIONS OF TONS; 

PARAMETER WINDERSN_N(CAS) TOTAL NATIONAL WIND EROSION IN TONS IN MILLIONS OF TONS; 

PARAMETER AVGCLERSN(RL,R,CAS) AVERAGE CROPLAND (WATER) EROSION RATE IN TONS PER ACRE; 

PARAMETER AVGCLECST(RL,R,CAS) AVERAGE CROPLAND (WATER) EROSION COST IN DOLLARS PER ACRE; 

PARAMETER ERSNCST_L(RL,R,CAS) OFF-SITE SOIL EROSION DAMAGES BY RESOURCE REGION IN MILLIONS OF DOLLARS; 

PARAMETER ERSNCST_P(R,CAS) OFF-SITE SOIL EROSION DAMAGES BY PRODUCTION REGION IN MILLIONS OF DOLLARS; 

PARAMETER ERSNCST_N(CAS) TOTAL NATIONAL OFF-SITE SOIL EROSION DAMAGES IN BILLIONS OF DOLLARS; 

PARAMETER EMENERGY_L(RL,R,CAS) EMBODIED ENERGY USE BY MDR IN MILLIONS OF BARRELS DIESEL EQUIV; 

PARAMETER EMENERGY_P(R,CAS) EMBODIED ENERGY USE BY PRODUCTION REGION IN MILLIONS OF BARRELS DIESEL 

EQUIV; 

PARAMETER EMENERGY_N(CAS) NATIONAL EMBODIED ENERGY USE IN MILLIONS OF BARRELS DIESEL FUEL EQUIV; 

PARAMETER EMCARBON_L(RL,R,CAS) EMBODIED CARBON IN PRODUCTION ACTIVITY INPUTS BY LR IN MILLIONS OF METRIC 

TONS; 

PARAMETER EMCARBON_P(R,CAS) EMBODIED CARBON IN PRODUCTION ACTIVITY INPUTS BY FPR IN MILLIONS OF METRIC 

TONS; 

PARAMETER EMCARBON_N(CAS) EMBODIED CARBON IN PRODUCTION ACTIVITY INPUTS NATIONAL TOTOAL IN MILLIONS 

METRIC OF TONS; 

PARAMETER PR_C_RTS(U,UR,PRLAND) PASTURE AND RANGE CARBON SEQ RATES; 

PARAMETER CRP_CRBN(U,UR) LAND CARBON SEQ TOTAL IN METRIC TONS; 

PARAMETER ARP_CRBN(U,UR) ARP CARBON SEQ TOTAL IN METRIC TONS BY MDR; 

PARAMETER DIV_CRBN(U,UR) 50-92 AND 0-92 CARBON SEQUESTERED BY MDR; 

PARAMETER PPCFLUX(B,G,H,Y,T,U,UR) C SEQ ESTIMATE FOR PRODUCTION ACTIVITIES BASED OFF OF ARS TOFROM 

ESTIMATES; 

PARAMETER RCFLUX(U,UR) CARBON SEQUESTERED BY REGION; 

PARAMETER RACRES(U,UR) CROPLAND ACRES BY REGION; 

PARAMETER RAVGCFLUX(U,UR) AVERAGE CARBON SEQUESTERED ON CROPLAND BY REGION; 

PARAMETER PMTBASE(B,G,H,Y,T,U,UR) CARBON PAYMENT BASE; 

PARAMETER CRP_NLOS_L(RL,R) CONS. RESERVE PROGRAM TOTAL NITORGEN LOSSS IN TONS BY RESOURCE REGION; 
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PARAMETER GHG_L(RL,R,CAS) GREENHOUSE GAS EMISSIONS BY MDR IN MM TONS C EQUIV; 

PARAMETER GHG_P(R,CAS) GREENHOUSE GAS EMISSIONS BY PR IN MM TONS C EQUIV; 

PARAMETER GHG_N(CAS) TOTAL GREENHOUSE GAS EMSSIONS IN MM OF TONS C EQUIV; 

PARAMETER NLOSS_L(RL,R,CAS) NITRATE LOSSES BY MDR IN MILLION TONS; 

PARAMETER NLOSS_P(R,CAS) NITRATE LOSSES BY PRODUCTION REGION IN MILLION TONS; 

PARAMETER NLOSS_N(CAS) NATIONAL NITRATE LOSSES IN MILLION TONS; 

PARAMETER ANLOSS(RL,R,CAS) AVERAGE N LOSS IN POUNDS PER ACRE FOR CROPPED ACRES; 

PARAMETER PNLOSS_L(RL,R,CAS) PERCENT N LOSSES PER ACRE FOR CROPPED ACRES; 

PARAMETER NLEACH_L(RL,R,CAS) TOTAL NITROGEN LEACHED TO GROUNDWATER BY MDR; 

PARAMETER NLEACH_P( R,CAS) TOTAL NITROGEN LEACHED TO GROUNDWATER BY PR; 

PARAMETER NLEACH_N( CAS) TOTAL NITROGEN LEACHED TO GROUNDWATER; 

PARAMETER ANLEACH(RL,R,CAS) AVERAGE N LEACHED TO GROUNDWATER BY MDR; 

PARAMETER CRP_NLCH_L(RL,R) CONS. RESERVE PROGRAM NITORGEN LEACHE DTO GROUNDWATER IN TONS BY REOURCE 

REGION; 

PARAMETER NSOLN_L(RL,R,CAS) TOTAL NITROGEN LOST IN SOLUTION BY MDR; 

PARAMETER NSOLN_P( R,CAS) TOTAL NITROGEN LOST IN SOLUTION BY PR; 

PARAMETER NSOLN_N( CAS) TOTAL NITROGEN LOST IN SOLUTION; 

PARAMETER ANSOLN(RL,R,CAS) AVERAGE N LOST IN SOLUTION BY MDR; 

PARAMETER CRP_NSOL_L(RL,R) CONS. RESERVE PROGRAM TOTAL NITORGEN LOST IN SOLUTION IN TONS BY RESOURCE 

REGION; 

PARAMETER NSEDMNT_L(RL,R,CAS) TOTAL NITROGEN LOST IN SEDIMENT BY MDR; 

PARAMETER NSEDMNT_P( R,CAS) TOTAL NITROGEN LOST IN SEDIMENT BY PR; 

PARAMETER NSEDMNT_N( CAS) TOTAL NITROGEN LOST IN SEDIMENT; 

PARAMETER ANSEDMNT(RL,R,CAS) AVERAGE N LOST IN SEDIMENT BY MDR; 

PARAMETER CRP_NSDM_L(RL,R) CONS. RESERVE PROGRAM TOTAL NITORGEN LOST IN SEDIMENT IN TONS BY RESOURCE 

REGION; 

PARAMETER NDENITE_L(RL,R,CAS) TOTAL NITROGEN LOST IN ATMOSPHERE BY MDR; 

PARAMETER NDENITE_P( R,CAS) TOTAL NITROGEN LOST IN ATMOSPHERE BY PR; 

PARAMETER NDENITE_N( CAS) TOTAL NITROGEN LOST IN ATMOSPHERE; 

PARAMETER ANDENITE(RL,R,CAS) AVERAGE N LOST IN ATMOSPHERE BY MDR; 

PARAMETER CRP_NDNI_L(RL,R) CONS. RESERVE PROGRAM TOTAL NITROGEN LOST IN ATMOSPHERE IN TONS BY RESOURCE 

REGION; 

PARAMETER NVOL_L(RL,R,CAS) TOTAL NITROGEN LOST IN ATMOSPHERE BY MDR; 

PARAMETER NVOL_P( R,CAS) TOTAL NITROGEN LOST IN ATMOSPHERE BY PR; 

PARAMETER NVOL_N( CAS) TOTAL NITROGEN LOST IN ATMOSPHERE; 

PARAMETER ANVOL(RL,R,CAS) AVERAGE N LOST IN ATMOSPHERE BY MDR; 

PARAMETER CRP_NVOL_L(RL,R) CONS. RESERVE PROGRAM TOTAL NIOROGEN LOST IN ATMOSPHERE IN TONS BY RESOURCE 

REGION; 

PARAMETER NLOSSGS_N(CAS) NATIONAL NITRATE LOSSES TO GROUND AND SURFACE WATER IN MILLION TONS; 

PARAMETER NLOSSGS_L(RL,R,CAS) NITRATE LOSSES TO GROUND AND SURFACE WATER BY MDR IN MILLION TONS; 

PARAMETER NLOSSGS_P(R,CAS) NITRATE LOSSES TO GROUND AND SURFACE WATER BY PRODUCTION REGION IN MILLION 

TONS; 

PARAMETER NWATCST_L(RL,R,CAS) OFF-SITE VALUE OF REDUCTION IN NITROGEN LOSS TO WATER BY RESOURCE REGION IN 

MILLIONS OF DOLLARS; 

PARAMETER NWATCST_P(R,CAS) OFF-SITE VALUE OF REDUCTION IN NITROGEN LOSS TO WATER BY PRODUCTION REGION IN 

MILLIONS OF DOLLARS; 

PARAMETER NWATCST_N(CAS) OFF-SITE VALUE OF REDUCTION IN NITROGEN LOSS TO WATER IN MILLIONS OF DOLLARS; 

*NOTE MAY WANT TO PUT IN AN AVERAGE HERE 

PARAMETER PLOSS_L(RL,R,CAS) PHOSPHATE LOSSES BY MDR IN MILLION TONS; 

PARAMETER PLOSS_P(R,CAS) PHOSPHATE LOSSES BY PRODUCTION REGION IN MILLION TONS; 

PARAMETER PLOSS_N(CAS) NATIONAL NITRATE LOSSES IN MILLION TONS; 

PARAMETER APLOSS(RL,R,CAS) AVERAGE N LOSS IN POUNDS PER ACRE FOR CROPPED ACRES; 

PARAMETER PLEACH_L(RL,R,CAS) TOTAL PHOSPHATE LEACHED TO GROUNDWATER BY MDR; 

PARAMETER PLEACH_P( R,CAS) TOTAL PHOSPHATE LEACHED TO GROUNDWATER BY PR; 

PARAMETER PLEACH_N( CAS) TOTAL PHOSPHATE LEACHED TO GROUNDWATER; 

PARAMETER APLEACH(RL,R,CAS) AVERAGE P LEACHED TO GROUNDWATER BY MDR; 

PARAMETER PSOLN_L(RL,R,CAS) TOTAL PHOSPHATE LOST IN SOLUTION BY MDR; 

PARAMETER PSOLN_P( R,CAS) TOTAL PHOSPHATE LOST IN SOLUTION BY PR; 

PARAMETER PSOLN_N( CAS) TOTAL PHOSPHATE LOST IN SOLUTION; 

PARAMETER APSOLN(RL,R,CAS) AVERAGE P LOST IN SOLUTION BY MDR; 

PARAMETER PSEDMNT_L(RL,R,CAS) TOTAL PHOSPHATE LOST IN SEDIMENT BY MDR; 

PARAMETER PSEDMNT_P( R,CAS) TOTAL PHOSPHATE LOST IN SEDIMENT BY PR; 

PARAMETER PSEDMNT_N( CAS) TOTAL PHOSPHATE LOST IN SEDIMENT; 

PARAMETER APSEDMNT(RL,R,CAS) AVERAGE P LOST IN SEDIMENT BY MDR; 
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PARAMETER NUSED_L(RL,R,CAS) NITRATE USED BY MDR IN MILLION TONS; 

PARAMETER NUSED_P(R,CAS) NITRATE USED BY PRODUCTION REGION IN MILLION TONS; 

PARAMETER NUSED_N(CAS) NATIONAL NITRATE USED IN MILLION TONS; 

PARAMETER ANUSE_L(RL,R,CAS) AVERAGE N USE POUNDS PER ACRE MDR CROPPED ACRES; 

PARAMETER ANUSE_P(R,CAS) AVERAGE N USE POUNDS PER ACRE FPR CROPPED ACRES; 

PARAMETER PUSED_L(RL,R,CAS) PHOSPHATE USED BY MDR IN MILLION TONS; 

PARAMETER PUSED_P(R,CAS) PHOSPHATE USED BY PRODUCTION REGION IN MILLION TONS; 

PARAMETER PUSED_N(CAS) NATIONAL PHOSPHATE USED IN MILLION TONS; 

PARAMETER APUSE_L(RL,R,CAS) AVERAGE P USE POUNDS PER ACRE MDR CROPPED ACRES; 

PARAMETER APUSE_P(R,CAS) AVERAGE P USE POUNDS PER ACRE FPR CROPPED ACRES; 

PARAMETER KUSED_L(RL,R,CAS) POTASH USED BY MDR IN MILLION TONS; 

PARAMETER KUSED_P(R,CAS) POTASH USED BY PRODUCTION REGION IN MILLION TONS; 

PARAMETER KUSED_N(CAS) NATIONAL POTASH USED IN MILLION TONS; 

PARAMETER AKUSE_L(RL,R,CAS) AVERAGE K USE POUNDS PER ACRE MDR CROPPED ACRES; 

PARAMETER AKUSE_P(R,CAS) AVERAGE K USE POUNDS PER ACRE FPR CROPPED ACRES; 

*PARAMETER PKLOSS_L(RL,R,CAS) PERCENT K LOSSES PER ACRE FOR CROPPED ACRES; 

PARAMETER R_SDA(R,CAS) regional soil depreciation allowances ; 

PARAMETER N_SDA(CAS) national soil depreciation allowance ; 

PARAMETER ROTACRES_L(B,RL,R,CAS) SUM OF ROTATION ACREAGE BY MDR; 

PARAMETER ROTACRES_P(B,R,CAS) SUM OF ROTACRES BY PRODUCTION REGION; 

PARAMETER ROTACRES_N(B,CAS) SUM OF ROTACRES NATIONALLY; 

PARAMETER ROTACRET_L(B,T,RL,R,CAS) ROT_TIL ACREAGE BY MDR; 

PARAMETER ROTACRET_P(B,T,R,CAS) ROT_TIL ACRES BY PRODUCTION REGION; 

PARAMETER ROTACRET_N(B,T,CAS) ROT_TIL ACRES NATIONALLY; 

PARAMETER ROTACRE_P(T,R,CAS) SUM OF ROT_TIL ACRES BY PRODUCTION REGION; 

PARAMETER ROTACRE_N(T,CAS) SUM OF ROT_TIL ACRES NATIONALLY; 

PARAMETER ALTACRES_L(Y,RL,R,CAS) SUM OF ACREAGE BY MDR AND ALTERNATIVE; 

PARAMETER ALTACRES_P(Y,R,CAS) SUM OF ACRES BY PRODUCTION REGION AND ALTERNATIVE; 

PARAMETER ALTACRES_N(Y,CAS) SUM OF ACRES NATIONALLY AND BY ALTERNATIVE; 

PARAMETER CHEMCOSTP(R,CAS) TOTAL CHEMICAL COSTS BY PR; 

PARAMETER CHEMCOSTN(CAS) TOTAL NATIONAL CHEMICAL COSTS; 

PARAMETER NITCOSTP(R,CAS) TOTAL NITROGEN COSTS BY PR; 

PARAMETER NITCOSTN(CAS) TOTAL NATIONAL NITROGEN COSTS; 

PARAMETER PHOSCOSTP(R,CAS) TOTAL PHOSPHATE COSTS BY PR; 

PARAMETER PHOSCOSTN(CAS) TOTAL NATIONAL PHOSPHATE COSTS; 

PARAMETER POTCOSTP(R,CAS) TOTAL POTASH COSTS BY PR; 

PARAMETER POTCOSTN(CAS) TOTAL POTASH CHEMICAL COSTS; 

**NITROGEN 

*INPUTS 

PARAMETER BTN_L(RL,R,CAS) BEGINNING TOTAL NITROGEN BY MDR; 

PARAMETER BTN_P( R,CAS) BEGINNING TOTAL NITROGEN BY PR; 

PARAMETER BTN_N( CAS) BEGINNING TOTAL NITROGEN; 

PARAMETER BTN_LA(RL,R,CAS) BEGINNING TOTAL NITROGEN PER ACRE BY MDR; 

PARAMETER BTN_PA(R,CAS) BEGINNING TOTAL NITROGEN PER ACRE BY FPR; 

PARAMETER FNH3_L(RL,R,CAS) AMMONIA FERT BY MDR IN MILLION TONS; 

PARAMETER FNH3_P(R,CAS) AMMONIA FERT BY PRODUCTION REGION IN MILLION TONS; 

PARAMETER FNH3_N(CAS) NATIONAL AMMONIA FERT IN MILLION TONS; 

PARAMETER FNH3_LA(RL,R,CAS) AMMONIA FERT PER ACRE BY MDR; 

PARAMETER FNH3_PA(R,CAS) AMMONIA FERT PER ACRE BY FPR; 

PARAMETER FNO3_L(RL,R,CAS) NITRATE FERT BY MDR IN MILLION TONS; 

PARAMETER FNO3_P(R,CAS) NITRATE FERT BY PRODUCTION REGION IN MILLION TONS; 

PARAMETER FNO3_N(CAS) NATIONAL NITRATE FERT IN MILLION TONS; 

PARAMETER FNO3_LA(RL,R,CAS) NITRATE FERT PER ACRE BY MDR; 

PARAMETER FNO3_PA(R,CAS) NITRATE FERT PER ACRE BY FPR; 

PARAMETER FNTOT_L(RL,R,CAS) TOTAL N FERT BY MDR IN MILLION TONS; 

PARAMETER FNTOT_P(R,CAS) TOTAL N FERT BY PRODUCTION REGION IN MILLION TONS; 

PARAMETER FNTOT_N(CAS) NATIONAL TOTAL N FERT IN MILLION TONS; 

PARAMETER FNTOT_LA(RL,R,CAS) TOTAL N FERT PER ACRE BY MDR; 

PARAMETER FNTOT_PA(R,CAS) TOTAL N FERT PER ACRE BY FPR; 
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PARAMETER FX_L(RL,R,CAS) NITROGEN FIXED BY MDR IN MILLION TONS; 

PARAMETER FX_P(R,CAS) NITROGEN FIXED BY PRODUCTION REGION IN MILLION TONS; 

PARAMETER FX_N(CAS) NATIONAL NITROGEN FIXED IN MILLION TONS; 

PARAMETER FX_LA(RL,R,CAS) NITROGEN FIXED PER ACRE BY MDR; 

PARAMETER FX_PA(R,CAS) NITROGEN FIXED PER ACRE BY FPR; 

PARAMETER RN_L(RL,R,CAS) NITROGEN IN RAIN BY MDR; 

PARAMETER RN_P( R,CAS) NITROGEN IN RAIN BY PR; 

PARAMETER RN_N( CAS) NITROGEN IN RAIN; 

PARAMETER RN_LA(RL,R,CAS) NITROGEN IN RAIN PER ACRE BY MDR; 

PARAMETER RN_PA(R,CAS) NITROGEN IN RAIN PER ACRE BY FPR; 

*OUTPUTS 

PARAMETER YLN_L(RL,R,CAS) NITROGEN IN CROP YIELD BY MDR; 

PARAMETER YLN_P( R,CAS) NITROGEN IN CROP YIELD BY PR; 

PARAMETER YLN_N( CAS) NITROGEN IN CROP YIELD; 

PARAMETER YLN_LA(RL,R,CAS) NITROGEN IN CROP YIELD PER ACRE BY MDR; 

PARAMETER YLN_PA(R,CAS) NITROGEN IN CROP YIELD PER ACRE BY FPR; 

PARAMETER TFO_L(RL,R,CAS) NITROGEN IN CROP RESIDUE BY MDR; 

PARAMETER TFO_P( R,CAS) NITROGEN IN CROP RESIDUE BY PR; 

PARAMETER TFO_N( CAS) NITROGEN IN CROP RESIDUE; 

PARAMETER TFO_LA(RL,R,CAS) NITROGEN IN CROP RESIDUE PER ACRE BY MDR; 

PARAMETER TFO_PA(R,CAS) NITROGEN IN CROP RESIDUE PER ACRE BY FPR; 

PARAMETER PRKN_L(RL,R,CAS) NITROGEN LEACHED BY MDR; 

PARAMETER PRKN_P( R,CAS) NITROGEN LEACHED BY PR; 

PARAMETER PRKN_N( CAS) NITROGEN LEACHED; 

PARAMETER PRKN_LA(RL,R,CAS) NITROGEN LEACHED PER ACRE BY MDR; 

PARAMETER PRKN_PA(R,CAS) NITROGEN LEACHED PER ACRE BY FPR; 

PARAMETER YNO3_L(RL,R,CAS) NITRATE IN SOLUTION BY MDR; 

PARAMETER YNO3_P( R,CAS) NITRATE IN SOLUTION BY PR; 

PARAMETER YNO3_N( CAS) NITRATE IN SOLUTION; 

PARAMETER YNO3_LA(RL,R,CAS) NITRATE IN SOLUTION PER ACRE BY MDR; 

PARAMETER YNO3_PA(R,CAS) NITRATE IN SOLUTION PER ACRE BY FPR; 

PARAMETER YON_L(RL,R,CAS) ORGANIC NITROGEN IN SEDIMENT BY MDR; 

PARAMETER YON_P( R,CAS) ORGANIC NITROGEN IN SEDIMENT BY PR; 

PARAMETER YON_N( CAS) ORGANIC NITROGEN IN SEDIMENT; 

PARAMETER YON_LA(RL,R,CAS) ORGANIC NITROGEN IN SEDIMENT PER ACRE BY MDR; 

PARAMETER YON_PA(R,CAS) ORGANIC NITROGEN IN SEDIMENT PER ACRE BY FPR; 

PARAMETER SSFN_L(RL,R,CAS) NITRATE IN SUBSURFACE FLOW BY MDR; 

PARAMETER SSFN_P( R,CAS) NITRATE IN SUBSURFACE FLOW BY PR; 

PARAMETER SSFN_N( CAS) NITRATE IN SUBSURFACE FLOW; 

PARAMETER SSFN_LA(RL,R,CAS) NITRATE IN SUBSURFACE FLOW PER ACRE BY MDR; 

PARAMETER SSFN_PA(R,CAS) NITRATE IN SUBSURFACE FLOW PER ACRE BY FPR; 

PARAMETER DN_L(RL,R,CAS) DENITRIFICATION BY MDR; 

PARAMETER DN_P( R,CAS) DENITRIFICATION BY PR; 

PARAMETER DN_N( CAS) DENITRIFICATION NATIONAL; 

PARAMETER DN_LA(RL,R,CAS) DENITRIFICATION PER ACRE BY MDR; 

PARAMETER DN_PA(R,CAS) DENITRIFICATION PER ACRE BY FPR; 

PARAMETER AVOL_L(RL,R,CAS) NITROGEN VOLITILIZATION BY MDR; 

PARAMETER AVOL_P( R,CAS) NITROGEN VOLITILIZATION BY PR; 

PARAMETER AVOL_N( CAS) NITROGEN VOLITILIZATION; 

PARAMETER AVOL_LA(RL,R,CAS) NITROGEN VOLITILIZATION PER ACRE BY MDR; 

PARAMETER AVOL_PA(R,CAS) NITROGEN VOLITILIZATION PER ACRE BY FPR; 

PARAMETER AIRN_L(RL,R,CAS) NITROGEN LOST TO ATMOSPHERE BY MDR; 

PARAMETER AIRN_P( R,CAS) NITROGEN LOST TO ATMOSPHERE BY PR; 

PARAMETER AIRN_N( CAS) NITROGEN LOST TO ATMOSPHERE; 

PARAMETER AIRN_LA(RL,R,CAS) NITROGEN LOST TO ATMOSPHERE PER ACRE BY MDR; 

PARAMETER AIRN_PA(R,CAS) NITROGEN LOST TO ATMOSPHERE PER ACRE BY FPR; 

PARAMETER WATN_L(RL,R,CAS) NITROGEN LOST TO WATER BY MDR; 

PARAMETER WATN_P( R,CAS) NITROGEN LOST TO WATER BY PR; 

PARAMETER WATN_N( CAS) NITROGEN LOST TO WATER; 

PARAMETER WATN_LA(RL,R,CAS) NITROGEN LOST TO WATER PER ACRE BY MDR; 

PARAMETER WATN_PA(R,CAS) NITROGEN LOST TO WATER PER ACRE BY FPR; 

PARAMETER FTN_L(RL,R,CAS) FINAL TOTAL NITROGEN BY MDR; 

PARAMETER FTN_P( R,CAS) FINAL TOTAL NITROGEN BY PR; 

PARAMETER FTN_N( CAS) FINAL TOTAL NITROGEN; 

PARAMETER FTN_LA(RL,R,CAS) FINAL TOTAL NITROGEN PER ACRE BY MDR; 

PARAMETER FTN_PA(R,CAS) FINAL TOTAL NITROGEN PER ACRE BY FPR; 
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Table 2 

List of tables contained in ENVACTGA—continued 

*BALANCES 

PARAMETER NBAL_L(RL,R,CAS) EPIC NITROGEN BALANCE BY MDR; 

PARAMETER NBAL_P( R,CAS) EPIC NITROGEN BALANCE BY PR; 

PARAMETER NBAL_N( CAS) EPIC NIROGEN BALANCE; 

PARAMETER NBAL_LA(RL,R,CAS) EPIC NITROGEN BALANCE PER ACRE BY MDR; 

PARAMETER NBAL_PA(R,CAS) EPIC NITROGEN BALANCE PER ACRE BY FPR; 

PARAMETER XNBAL_L(RL,R,CAS) EXCESS NITROGEN BALANCE BY MDR; 

PARAMETER XNBAL_P( R,CAS) EXCESS NITROGEN BALANCE BY PR; 

PARAMETER XNBAL_N( CAS) EXCESS NIROGEN BALANCE; 

PARAMETER XNBAL_LA(RL,R,CAS) EXCESS NITROGEN BALANCE PER ACRE BY MDR; 

PARAMETER XNBAL_PA(R,CAS) EXCESS NITROGEN BALANCE PER ACRE BY FPR; 

**PHOSPHOROUS 

*INPUT 

PARAMETER BTP_L(RL,R,CAS) BEGINNING TOTAL PHOSPHOROUS BY MDR; 

PARAMETER BTP_P( R,CAS) BEGINNING TOTAL PHOSPHOROUS BY PR; 

PARAMETER BTP_N( CAS) BEGINNING TOTAL PHOSPHOROUS; 

PARAMETER BTP_LA(RL,R,CAS) BEGINNING TOTAL PHOSPHOROUS PER ACRE BY MDR; 

PARAMETER BTP_PA(R,CAS) BEGINNING TOTAL PHOSPHOROUS PER ACRE BY FPR; 

PARAMETER PLAB_L(RL,R,CAS) LABILE PHOSPHORUS BY MDR IN MILLION TONS; 

PARAMETER PLAB_P(R,CAS) LABILE PHOSPHORUS BY PRODUCTION REGION IN MILLION TONS; 

PARAMETER PLAB_N(CAS) LABILE PHOSPHORUS IN MILLION TONS; 

PARAMETER PLAB_LA(RL,R,CAS) LABILE PHOSPHORUS PER ACRE BY MDR; 

PARAMETER PLAB_PA(R,CAS) LABILE PHOSPHORUS PER ACRE BY FPR; 

*OUTPUTS 

PARAMETER YLP_L(RL,R,CAS) PHOSPHOROUS IN CROP YIELD BY MDR; 

PARAMETER YLP_P( R,CAS) PHOSPHOROUS IN CROP YIELD BY PR; 

PARAMETER YLP_N( CAS) PHOSPHOROUS IN CROP YIELD; 

PARAMETER YLP_LA(RL,R,CAS) PHOSPHOROUS IN CROP YIELD PER ACRE BY MDR; 

PARAMETER YLP_PA(R,CAS) PHOSPHOROUS IN CROP YIELD PER ACRE BY FPR; 

PARAMETER PRKP_L(RL,R,CAS) PHOSPHOROUS LEACHED BY MDR; 

PARAMETER PRKP_P( R,CAS) PHOSPHOROUS LEACHED BY PR; 

PARAMETER PRKP_N( CAS) PHOSPHOROUS LEACHED; 

PARAMETER PRKP_LA(RL,R,CAS) PHOSPHOROUS LEACHED PER ACRE BY MDR; 

PARAMETER PRKP_PA(R,CAS) PHOSPHOROUS LEACHED PER ACRE BY FPR; 

PARAMETER YAP_L(RL,R,CAS) PHOSPHOROUS IN SOLUTION BY MDR; 

PARAMETER YAP_P( R,CAS) PHOSPHOROUS IN SOLUTION BY PR; 

PARAMETER YAP_N( CAS) PHOSPHOROUS IN SOLUTION; 

PARAMETER YAP_LA(RL,R,CAS) PHOSPHOROUS IN SOLUTION PER ACRE BY MDR; 

PARAMETER YAP_PA(R,CAS) PHOSPHOROUS IN SOLUTION PER ACRE BY FPR; 

PARAMETER YP_L(RL,R,CAS) PHOSPHOROUS IN SEDIMENT BY MDR; 

PARAMETER YP_P( R,CAS) PHOSPHOROUS IN SEDIMENT BY PR; 

PARAMETER YP_N( CAS) PHOSPHOROUS IN SEDIMENT; 

PARAMETER YP_LA(RL,R,CAS) PHOSPHOROUS IN SEDIMENT PER ACRE BY MDR; 

PARAMETER YP_PA(R,CAS) PHOSPHOROUS IN SEDIMENT PER ACRE BY FPR; 

PARAMETER WATP_L(RL,R,CAS) PHOSPHOROUS LOST TO WATER BY MDR; 

PARAMETER WATP_P( R,CAS) PHOSPHOROUS LOST TO WATER BY PR; 

PARAMETER WATP_N( CAS) PHOSPHOROUS LOST TO WATER; 

PARAMETER WATP_LA(RL,R,CAS) PHOSPHOROUS LOST TO WATER PER ACRE BY MDR; 

PARAMETER WATP_PA(R,CAS) PHOSPHOROUS LOST TO WATER PER ACRE BY FPR; 

PARAMETER FTP_L(RL,R,CAS) FINAL TOTAL PHOSPHOROUS BY MDR; 

PARAMETER FTP_P( R,CAS) FINAL TOTAL PHOSPHOROUS BY PR; 

PARAMETER FTP_N( CAS) FINAL TOTAL PHOSPHOROUS; 

PARAMETER FTP_LA(RL,R,CAS) FINAL TOTAL PHOSPHOROUS PER ACRE BY MDR; 

PARAMETER FTP_PA(R,CAS) FINAL TOTAL PHOSPHOROUS PER ACRE BY FPR; 

*BALANCES 

PARAMETER PBAL_L(RL,R,CAS) EPIC PHOSPHOROUS BALANCE BY MDR; 

PARAMETER PBAL_P( R,CAS) EPIC PHOSPHOROUS BALANCE BY PR; 

PARAMETER PBAL_N( CAS) EPIC PHOSPHOROUS BALANCE; 

PARAMETER PBAL_LA(RL,R,CAS) EPIC PHOSPHOROUS BALANCE PER ACRE BY MDR; 

PARAMETER PBAL_PA(R,CAS) EPIC PHOSPHOROUS BALANCE PER ACRE BY FPR; 

PARAMETER XPLBAL_L(RL,R,CAS) EXCESS LABILE PHOSPHOROUS BALANCE BY MDR; 

PARAMETER XPLBAL_P( R,CAS) EXCESS LABILE PHOSPHOROUS BALANCE BY PR; 

PARAMETER XPLBAL_N( CAS) EXCESS LABILE PHOSPHOROUS BALANCE; 

PARAMETER XPLBAL_LA(RL,R,CAS) EXCESS LABILE PHOSPHOROUS BALANCE PER ACRE BY MDR; 

PARAMETER XPLBAL_PA(R,CAS) EXCESS LABILE PHOSPHOROUS BALANCE PER ACRE BY FPR; 
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Table 2 

List of tables contained in ENVACTGA—continued 

PARAMETER XPFBAL_L(RL,R,CAS) EXCESS FERT. PHOSPHOROUS BALANCE BY MDR; 

PARAMETER XPFBAL_P( R,CAS) EXCESS FERT. PHOSPHOROUS BALANCE BY PR; 

PARAMETER XPFBAL_N( CAS) EXCESS FERT. PHOSPHOROUS BALANCE; 

PARAMETER XPFBAL_LA(RL,R,CAS) EXCESS FERT. PHOSPHOROUS BALANCE PER ACRE BY MDR; 

PARAMETER XPFBAL_PA(R,CAS) EXCESS FERT. PHOSPHOROUS BALANCE PER ACRE BY FPR; 
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Table 3 

DEMSUP: commodity demand and supply data 

PARAMETER DEMSUP COMMODITY DEMAND AND SUPPLY DATA 

 PBASE QBASE ELAS MIN MAX 

CORN .SCB 2.600 917.000 

CORN .PRDN 2.600 11235.000 

CORN .IMP 2.600 10.000 0.201 

CORN .DOM 2.600 1715.800 -0.070 

CORN .PRPC 6904.200 

CORN .EXP 2.600 2675.000 -0.530 

CORN .SGE 1.890 

CORN .SCE 2.600 867.000 -0.800 

SORGHUM .SCB 2.350 71.000 

SORGHUM .PRDN 2.350 670.000 

SORGHUM .IMP 2.350 

SORGHUM .DOM 2.350 80.000 -0.840 

SORGHUM .PRPC 280.000 

SORGHUM .EXP 2.350 315.000 -1.170 

SORGHUM .SGE 1.660 

SORGHUM .SCE 2.350 66.000 -0.440 

BARLEY .SCB 2.400 107.000 

BARLEY .PRDN 2.400 365.000 

BARLEY .IMP 2.400 55.000 0.201 

BARLEY .DOM 2.400 172.000 -0.260 

BARLEY .PRPC 175.000 

BARLEY .EXP 2.400 14.000 -0.650 

BARLEY .EEP 0.859 56.000 -0.650 

BARLEY .SGE 1.570 

BARLEY .SCE 2.400 110.000 -0.810 

OATS .SCB 1.450 56.000 

OATS .PRDN 1.450 150.000 

OATS .IMP 1.450 125.000 0.201 

OATS .DOM 1.450 78.000 -0.100 

OATS .PRPC 195.000 

OATS .EXP 1.450 2.000 -0.650 

OATS .SGE 1.090 

OATS .SCE 1.450 56.000 -1.530 

WHEAT .SCB 3.700 591.000 

WHEAT .PRDN 3.700 2545.000 

WHEAT .IMP 3.700 115.000 0.201 

WHEAT .DOM 3.700 1131.000 -0.090 

WHEAT .PRPC 225.000 

WHEAT .EXP 3.700 491.362 -1.440 

WHEAT .EEP 2.816 833.638 -1.440 

WHEAT .SGE 2.580 

WHEAT .SCE 3.700 570.000 -0.370 

RICE .SCB 7.710 27.160 

RICE .PRDN 7.710 194.200 

RICE .IMP 7.710 13.120 0.201 

RICE .DOM 7.710 153.100 -0.330 

RICE .PRPC 0.000 

RICE .EXP 7.710 52.202 -2.410 

RICE .EEP 5.344 2.298 -2.410 

RICE .SGE 6.500 

RICE .SCE 7.710 26.880 -0.630 

SOYBEANS .SCB 6.300 225.000 

SOYBEANS .PRDN 6.300 3245.000 

SOYBEANS .IMP 6.300 10.000 0.201 

SOYBEANS .DOM 6.300 189.600 -0.380 
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Table 3 

DEMSUP: commodity demand and supply data—continued

PARAMETER DEMSUP COMMODITY DEMAND AND SUPPLY DATA 

 PBASE QBASE ELAS MIN MAX 

SOYBEANS .PRPC 1995.000 

SOYBEANS .EXP 6.300 1070.000 -0.730 

SOYBEANS .SGE 5.070 

SOYBEANS .SCE 6.300 225.400 -6.670 

COTTON .SGB 2.500 

COTTON .SCB 312.000 1.759 

COTTON .PRDN 312.000 17.500 

COTTON .IMP 312.000 0.025 0.201 

COTTON .DOM 312.000 9.300 -1.020 

COTTON .PRPC 0.005 

COTTON .EXP 312.000 8.300 -1.260 

COTTON .SGE 249.600 2.500 

COTTON .SCE 312.000 1.679 -0.890 

SILAGE .PRDN 20.000 51.771 

SILAGE .DOM 20.000 51.771 -5.000 517.713 

HAY .PRDN 59.600 160.190 

HAY .DOM 59.600 84.533 -5.000 845.330 

OTHRLVSTK .PRDN 209.720 9.000 

OTHRLVSTK .DOM 209.720 9.000 -0.201 

EGGS .SCB 0.684 5.000 

EGGS .PRDN 0.684 7586.570 

EGGS .IMP 0.684 5.000 0.201 

EGGS .DOM 0.684 7401.570 -0.720 

EGGS .EXP 0.684 190.000 -0.601 

EGGS .SCE 0.684 5.000 -0.201 

BROILERS .SCB 0.356 880.000 

BROILERS .PRDN 0.356 34942.970 

BROILERS .DOM 0.356 29246.970 -0.020 

BROILERS .EXP 0.356 5700.000 -0.601 

BROILERS .SCE 0.356 880.000 -0.201 

TURKEY .SCB 0.385 275.000 

TURKEY .PRDN 0.385 5950.010 

TURKEY .DOM 0.385 5456.010 -0.030 

TURKEY .EXP 0.385 495.000 -0.601 

TURKEY .SCE 0.385 275.000 -0.201 

FLUIDMLK .PRDN 0.135 93462.600 

FLUIDMLK .DOM 0.135 93462.600 -0.260 

MFGMILK .PRDN 11.980 884.600 

MFGMILK .DOM 11.980 

MFGMILK .PRPC 11.980 884.600 

BUTTER .SCB 1.072 58.250 

BUTTER .PRDN 1.072 1360.300 

BUTTER .IMP 1.072 6.180 0.201 

BUTTER .DOM 1.072 1349.220 -0.470 

BUTTER .EXP 1.072 0.010 -0.601 

BUTTER .SGE 1.072 

BUTTER .SCE 1.072 75.500 -0.201 

BUTTER .SGV 1.072 442.800 

BUTTER .SGD 1.072 

BUTTER .SGX 1.072 

NFDMILK .SCB 1.014 114.600 

NFDMILK .PRDN 1.014 879.000 

NFDMILK .IMP 1.014 1.150 0.201 

NFDMILK .DOM 1.014 933.740 -1.490 

NFDMILK .EXP 1.014 0.010 -0.601
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Table 3 

DEMSUP: commodity demand and supply data—continued

PARAMETER DEMSUP COMMODITY DEMAND AND SUPPLY DATA 

 PBASE QBASE ELAS MIN MAX 

NFDMILK .SGE 1.014 

NFDMILK .SCE 1.014 61.000 -0.201 

NFDMILK .SGV 1.014 279.000 

NFDMILK .SGD 1.014 

NFDMILK .SGX 1.014 

AMCHEESE .SCB 1.343 435.100 

AMCHEESE .PRDN 1.343 2776.900 

AMCHEESE .IMP 1.343 21.060 0.201 

AMCHEESE .DOM 1.343 2837.550 -0.390 

AMCHEESE .EXP 1.343 0.010 -0.601 

AMCHEESE .SGE 1.343 

AMCHEESE .SCE 1.343 395.500 -0.201 

AMCHEESE .SGV 1.343 81.600 

AMCHEESE .SGD 1.343 

AMCHEESE .SGX 1.343 

OTCHEESE .SCB 1.608 107.500 

OTCHEESE .PRDN 1.608 3229.300 

OTCHEESE .IMP 1.608 200.200 0.201 

OTCHEESE .DOM 1.608 3438.090 -0.390 

OTCHEESE .EXP 1.608 0.010 -0.601 

OTCHEESE .SCE 1.608 98.900 -0.201 

ICECREAM .PRDN 1.381 1193.100 

ICECREAM .IMP 1.381 5.710 0.201 

ICECREAM .DOM 1.381 1198.800 -0.330 

EVDRYMLK .SCB 0.615 70.000 

EVDRYMLK .PRDN 0.615 671.100 

EVDRYMLK .IMP 0.615 5.270 0.201 

EVDRYMLK .DOM 0.615 643.060 -1.490 

EVDRYMLK .EXP 0.615 0.010 -0.601 

EVDRYMLK .SCE 0.615 103.300 -0.201 

ETHSOA .PRDN 1.760 

ETHSOA .DOM 1.760 

CLDARYCF .PRDN 58.880 

CLDARYCF .DOM 58.880 

CLDARYCW .PRDN 518.400 

CLDARYCW .DOM 518.400 

MILK .PRDN 14.300 1794.900 

MILK .IMP 14.300 26.000 0.201 

MILK .DOM 14.300 20.000 20.000 20.000 

MILK .PRPC 14.300 1800.900 

FEEDERPIG .PRDN 72.042 

FEEDERPIG .DOM 72.042 

CULLSOW .PRDN 36.023 

CULLSOW .DOM 36.023 

HOGSLAUGH .PRDN 41.300 

HOGSLAUGH .IMP 41.300 2.297 0.201 

HOGSLAUGH .DOM 41.300 

HOGSLAUGH .EXP 41.300 0.557 -0.201 

LIVCALF .PRDN 99.340 

LIVCALF .IMP 99.340 4.358 0.201 

LIVCALF .DOM 99.340 

BFYRLINGS .PRDN 84.410 

BFYRLINGS .IMP 84.410 10.256 0.201 

BFYRLINGS .DOM 84.410 
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Table 3 

DEMSUP: commodity demand and supply data—continued

PARAMETER DEMSUP COMMODITY DEMAND AND SUPPLY DATA 

 PBASE QBASE ELAS MIN MAX 

BFYRLINGS .EXP 84.410 0.896 -0.201 

CALFSLA .PRDN 85.392 

CALFSLA .DOM 85.392 

CLBFCOW .PRDN 53.262 

CLBFCOW .DOM 53.262 

CLBULLSTAG.PRDN 53.262 

CLBULLSTAG.DOM 53.262 

NONFDSL .PRDN 53.262 

NONFDSL .IMP 53.262 0.420 0.201 

NONFDSL .DOM 53.262 

FEDSLA .PRDN 77.650 

FEDSLA .IMP 77.650 0.050 0.201 

FEDSLA .DOM 77.650 

FEDSLA .EXP 77.650 0.050 -0.201 

FEDSLACF .PRDN 77.650 

FEDSLACF .DOM 77.650 

BEANMEAL .SCB 9.360 5.000 

BEANMEAL .PRDN 8.140 947.000 

BEANMEAL .IMP 8.250 2.000 0.201 

BEANMEAL .DOM 8.250 

BEANMEAL .PRPC 768.000 

BEANMEAL .EXP 8.140 181.000 -1.020 

BEANMEAL .SCE 8.140 5.000 -0.210 

BEANOIL .SCB 15.400 16.150 

BEANOIL .PRDN 15.400 228.400 

BEANOIL .IMP 19.000 1.150 0.201 

BEANOIL .DOM 15.400 206.426 -0.460 

BEANOIL .PRPC -6.426 

BEANOIL .EXP 15.400 29.000 -1.340 

BEANOIL .SCE 15.400 16.700 -0.260 

OOSMEAL .PRDN 8.250 40.120 0.801 30.000 50.000 

OOSMEAL .PRPC 40.120 

ANPROTEIN .PRDN 8.250 72.280 0.801 65.000 100.000 

ANPROTEIN .PRPC 72.280 

HIPROFEED .DOM 8.250 

FEDBEEF .SCB 334.000 1.889 

FEDBEEF .PRDN 334.000 152.219 

FEDBEEF .IMP 334.000 0.010 0.201 

FEDBEEF .DOM 334.000 133.944 -0.480 

FEDBEEF .EXP 334.000 18.295 -0.601 

FEDBEEF .SCE 334.000 1.889 -0.201 

NONFDBEEF .SCB 243.820 0.630 

NONFDBEEF .PRDN 243.820 21.470 

NONFDBEEF .IMP 243.820 20.182 0.201 

NONFDBEEF .DOM 243.820 41.652 -0.480 

NONFDBEEF .SCE 243.820 0.630 -0.201 

VEAL .SCB 484.300 0.033 

VEAL .PRDN 484.300 1.527 

VEAL .IMP 484.300 0.000 0.201 

VEAL .DOM 484.300 1.527 -3.120 

VEAL .EXP 484.300 0.060 -0.601 

VEAL .SCE 484.300 0.033 -0.201 

PORK .SCB 263.000 5.225 

PORK .PRDN 263.000 189.817 
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Table 3 

DEMSUP: commodity demand and supply data—continued

PARAMETER DEMSUP COMMODITY DEMAND AND SUPPLY DATA 

 PBASE QBASE ELAS MIN MAX 

PORK .IMP 263.000 10.450 0.201 

PORK .DOM 263.000 185.780 -0.070 

PORK .EXP 263.000 14.487 -0.601 

PORK .SCE 263.000 5.225 -0.201 

CORNOIL .PRDN 20.180 

CORNOIL .DOM 20.180T 

GLUTMEAL .SGB 12.830 25.035 

GLUTMEAL .PRDN 12.829 8.997 

GLUTMEAL .IMP 12.829 0.049 0.201 0.050 

GLUTMEAL .DOM 12.829 

GLUTMEAL .PRPC 6.681 

GLUTMEAL .EXP 12.829 27.400 -2.500 90.000 

GLUTFEED .SGB 5.040 127.537 

GLUTFEED .PRDN 5.041 45.832 

GLUTFEED .IMP 5.041 0.462 0.201 0.470 

GLUTFEED .DOM 5.041 

GLUTFEED .EXP 5.041 173.828 -3.500 300.000 

DDG .SGB 6.220 7.157 

DDG .PRDN 6.218 24.483 

DDG .DOM 6.218 

DDG .PRPC 14.364 

DDG .EXP 9.370 17.276 -3.500 300.000 

ETHANOL .PRDN 1.760 1212.500 

ETHANOL .DOM 1.760 1212.500 1212.000 1212.500 

CARBON .DOM 0.010 1000.000 
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Table 4 

Regional production input supply data 

___________________________________________________________ 

 PBASE QBASE ELAS MAXIMUM 

___________________________________________________________ 

NT.CROPLAND 38.000 14.704 0.300 17.000 

NT.PASTURE 21.830 2.144 0.600 6.800 

LA.CROPLAND 51.000 39.253 0.300 45.000 

LA.PASTURE 21.410 3.858 0.600 11.800 

CB.CROPLAND 82.000 101.700 0.300 100.400 

CB.PASTURE 27.860 17.391 0.600 30.800 

CB.AUM 17.391 30.800 

NP.CROPLAND 36.000 69.603 0.300 106.900 

NP.PASTURE 10.190 5.000 

NP.AUM 132.550 132.550 

AP.CROPLAND 46.000 19.690 0.300 30.400 

AP.PASTURE 22.650 13.547 0.600 20.600 

SE.CROPLAND 43.000 8.408 0.300 20.400 

SE.PASTURE 20.300 10.807 0.600 24.600 

DL.CROPLAND 43.000 18.419 0.300 24.900 

DL.PASTURE 16.960 7.953 0.600 28.600 

SP.CROPLAND 24.000 33.162 0.300 54.600 

SP.PASTURE 7.380 5.000 

SP.AUM 216.580 216.580 

MN.CROPLAND 40.000 24.614 0.300 43.900 

MN.PASTURE 11.020 5.000 

MN.AUM 288.760 288.760 

PA.CROPLAND 94.000 8.619 0.300 25.400 

PA.PASTURE 29.360 5.000 

PA.AUM 73.000 73.000 

___________________________________________________________ 
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Table 5 

Supply elasticities for crops used in REAP 

TABLE PES(B,*) DIRECT PRICE ELASTICITY OF SUPPLY 

 PLNT HARV PRDN 

 CORN .38 .37 .33 

 SORGHUM .51 .48 .50 

 BARLEY .34 .32 .32 

 OATS .15 .16 .24 

 WHEAT .29 .29 .23 

 RICE .40 .37 .35 

 SOYBEANS .25 .25 .27 

 COTTON .56 .54 .73 

 SILAGE .2 .2 .17 

 HAY .2 .2 .17 

 ; 

 

Table 6 

Supply elasticities for livestock used in REAP 

TABLE PESL(B,P,*) PRICE ELASTICITY OF SUPPLY

 PRDN NAAF <<4/ 

 FEEDLOT .FEDSLA .32 .7271 <<1/ 

 CFEEDLOT.FEDSLA .32 .7271 

*UFEEDLOT.FEDSLA .32 .7271 

 BFCOWEN .BFYRLINGS .32 .7271 <<1/ 

 BFCOWCF .BFYRLINGS .32 .7271 

 FAROFIN .HOGSLAUGH .38 .4019 <<2/ 

 FEEDRPIG.FEEDERPIG .38 .4019 

 PIGFIN .HOGSLAUGH .38 .4019 

 DAIRY .MILK .11 .9936 <<3/ 

 EGGS .EGGS .11 1.0 <<5/ 

 BROILERS.BROILERS .10 1.0 

 TURKEY .TURKEY .10 1.0 

 ; 
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Table 7 Demand and supply function active combo map  SGB SCB PRDN IMP RESS DOM PRPC EXP EEP SGE CORN YES YES YES YES YES YES SORGHUM YES YES YES YES YES BARLEY YES YES YES YES YES YES YES OATS YES YES YES YES YES YES WHEAT YES YES YES YES YES YES YES RICE YES YES YES YES YES YES YES SOYBEANS YES YES YES YES YES YES COTTON YES YES YES YES YES YES SILAGE YES HAY YES OTHRLVSTK YES EGGS YES YES YES YES BROILERS YES YES YES TURKEY YES YES YES FLUIDMLK YES MFGMILK YES BUTTER YES YES YES YES YES NFDMILK YES YES YES YES YES AMCHEESE YES YES YES YES YES OTCHEESE YES YES YES YES ICECREAM YES YES EVDRYMLK YES YES YES YES MILK YES YES YES HOGSLAUGH YES YES LIVCALF YES BFYRLINGS YES YES 
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Table 7 Demand and supply function active combo map—continued SGB SCB PRDN IMP RESS DOM PRPC EXP EEP SGE NONFDSL YES FEDSLA YES YES BEANMEAL YES YES YES YES BEANOIL YES YES YES YES OOSMEAL YES ANPROTEIN YES FEDBEEF YES YES YES YES NONFDBEEF YES YES YES VEAL YES YES YES YES PORK YES YES YES YES GLUTMEAL YES YES YES GLUTFEED YES YES YES DDG YES YES ETHANOL YES 
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Table 8 

List of tables contained in A1A0RPT00.GMS 

$STITLE REAP REPORT MODULE: DECLARATIONS AND HST SPECS 

* ------------------------------------------------------------------------------ 

* 

* REPORT MODULE DECLARATIONS AND HST SPECS 

* 

* ------------------------------------------------------------------------------ 

PARAMETER SUPC(P,CAS,*) CROP PRODUCT SUPPLY AND USE: BASE DATA AND SOLUTION 

 SUPL(P,CAS,*) LIVESTOCK PRODUCT SUPPLY AND USE: BASE DATA AND SOLUTION 

 SUPX(P,CAS,*) PROCESSED PRODUCT SUPPLY AND USE: BASE DATA AND SOLUTION 

 XACTL(B,G,H,Y,T,U,UR,CAS) OPTIMAL PRODUCTION ACTIVITY USE LEVELS: UNSCALED 

 XFCTL(B,G,H,Y,T,U,UR,CAS) SUM OF XACTL & FACTL & AACTL 

 XACTM(B,G,H,Y,T,U,UR,CAS) SHADOW PRICES (MARGINALS) ON PRODUCTION ACTIVITIES 

 XACTSL(B,G,H,Y,T,U,UR,CAS) OPTIMAL PRODUCTION ACTIVITY SUPPLY LEVELS: UNSCALED 

 YACTL(C,CAS) OPTIMAL PROCESSING ACTIVITY LEVELS: UNSCALED 

 RIR(IR,*,*,U) REGIONAL INPUT PRICE AND USE: BASE DATA AND SOLUTION 

 PROCINC(*,*,C) REVENUE COST INCOME PER UNIT OF AN ACTIVITY - PROCESSING 

 FEEDU(FCCAT,CAS,*,IO) FEED AND COPRODUCT USE BY ANIMAL TYPE OR INDUSTRY 

 INCOME(INCITEMS,*,UR) INCOME ACCOUNTING TABLE 

 CRPVALUES(*,*,R) ENDOGENOUS AND EXOGENOUS CRP VALUES 

 EEPVALUES(P,*,*) ENDOGENOUS AND EXOGENOUS EEP VALUES 

 EXPEN(P,CAS,Q) GOVERNMENT PROGRAM EXPENDITURES 

 SRPC(P,*,U) CROP PRODUCTION BY REGION 

 SRPL(P,*,U) LIVESTOCK PRODUCTION BY REGION 

 ACPNP(P,UR) TEMPORARY ARRAY 

 PPMK(IO) COMMODITY MARKET PRICES FOR PART & NONPARTIC. 

* ------------------------------------------------------------------------------ 

* ACREAGE TABLES AND DEFICIENCY PAYMENTS, HISTORICAL DATA 

* ------------------------------------------------------------------------------ 

PARAMETER ACTOTPC( P,*,U) TOTAL ACRES PLANTED BY CROP AND REGION (MIL. AC.) 

 YIELDTPC( P,*,U) YIELD PER ACRE BY CROP AND REGION 

 PFCPMT( P,*,U) PRODUCTION FLEXIBILITY CONTRACT PAYMENTS BY CROP AND REGION 

 (MIL. DOL.) 

 ACIRRPC( P,*,U) IRRIGATED ACRES PLANTED BY CROP AND REGION (MIL. AC.) 

 ACDRYPC( P,*,U) DRYLAND ACRES PLANTED BY CROP AND REGION (MIL. AC.) 

 ACSADPC( P,*,U) ACRES SET-ASIDE AND DIVERTED BY CROP AND REGION (MIL. AC.) 

 NCHG PERCENTAGE INCREASE IN NITROGEN FERT PRICE 

 PCHG PERCENTAGE INCREASE IN PHOSPHAT FERT PRICE 

 KCHG PERCENTAGE INCREASE IN POTASH FERT PRICE 

 CSVPMT(B,G,H,Y,T,U,UR) CONSERVATION PAYMENT TO PRODUCTION ACTIVITY PER ACRE 

 GGRPMT( P,*,U) GOVERNMENT ENVIRONMENTAL PAYMENTS (MIL DOL) 

 GPR(U,CAS) TOTAL GOVERNMENT GREEN PAYMENTS RECEIVED BY FARMERS BY REGION 

 (MIL DOL) 

 DAMAGE(U) ENVIRONMENTAL DAMAGE (DOLLARS) 

 LANDR LAND RETIREMENT SWITCH 

 TEEROS TRADE AND ENVIRONMENT EROSION SCENARIOS 

 TPRAC TURN TILLAGE PRACTICE OPTION ON 
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Table 8 

List of tables contained in A1A0RPT00.GMS—continued

 LANDREDER(U) REDUCTION IN CROPLAND BY ECONOMIC REGION 

 LANDREDRR(U) REDUCTION IN CROPLAND BY RESOURCE REGION 

 NBUFFR(*) NITROGEN BUFFERRING RATE FROM RETIRED LAND (TONS PER AC) 

 LR2REAPR LAND RETIREMENT PROGRAM TO REAP ACRE RATIO 

 ETBALM(EC,U,UR,CAS) ENVIRONMENTAL TARGET BALANCE EQUATION SHADOW VALUE 

 CSTSHRPMT(P,*,*,U) COST SHARE PAYMENTS BY CROP AND REGION (MIL. DOLLARS) 

 ACTOTLR( P,*,U) TOTAL ACRES PLANTED BY CROP AND LAND RESOURCE SUB-REGION 

 (MIL. AC.) 

 YLDTPCLR( P,*,U) YIELD PER ACRE BY CROP AND LAND RESOURCE SUB-REGION 

 SRPCLR(P,*,U) CROP PRODUCTION BY LAND RESOURCE SUB-REGION 
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Table 9 ARPT20 report fragment TABLE 2 --CROP PRODUCT SUPPLY AND USE: BASE DATA AND SOLUTION ------------------------------------------------------------------------------------------------------------------- SUPPLY USE  ------------------------------------ ---------------------------------------- BEGINNING STOCKS ENDI ------------------ ------PRODUCT UNITS PRICE GOVERN- COMMER- PRODUC- IMPORTS DOMES- FEED AND EXPORTS EXPORT GOVE MENT CIAL TION TIC RESIDUAL COMMRCL ENHANCE MEN------------------------------------------------------------------------------------------------------------------- $/UNIT -- MILLION UNITS -- CORN BU BASE 2.60 0.0 917.0 11234.4 10.0 1715.8 6903.6 2675.0 0.0 0. SHOCK 3.11 0.0 917.0 10693.2 10.4 1692.4 6798.1 2398.4 0.0 0. CHANGE 0.51 0.0 0.0 -541.3 0.4 -23.4 -105.5 -276.6 0.0 0. %CHANGE 19.51 0.0 0.0 -4.8 3.9 -1.4 -1.5 -10.3 0.0 0.SORGHUM BU BASE 2.35 0.0 71.0 670.2 0.0 79.9 280.7 314.6 0.0 0. SHOCK 2.78 0.0 71.0 553.7 0.0 67.8 248.0 248.1 0.0 0. CHANGE 0.42 0.0 0.0 -116.5 0.0 -12.1 -32.7 -66.5 0.0 0. %CHANGE 18.02 0.0 0.0 -17.4 0.0 -15.2 -11.6 -21.1 0.0 0.BARLEY BU BASE 2.40 0.0 107.0 365.0 55.0 172.0 175.0 14.0 56.0 0. SHOCK 2.56 0.0 107.0 334.8 55.7 169.0 161.8 13.4 49.2 0. CHANGE 0.16 0.0 0.0 -30.2 0.7 -3.0 -13.2 -0.6 -6.8 0. %CHANGE 6.64 0.0 0.0 -8.3 1.3 -1.7 -7.6 -4.3 -12.1 0.OATS BU BASE 1.45 0.0 56.0 150.0 125.0 78.0 195.0 2.0 0.0 0. SHOCK 1.98 0.0 56.0 90.5 134.1 75.2 178.9 1.5 0.0 0. CHANGE 0.53 0.0 0.0 -59.5 9.1 -2.8 -16.1 -0.5 0.0 0. %CHANGE 36.22 0.0 0.0 -39.7 7.3 -3.6 -8.2 -23.5 0.0 0.WHEAT BU BASE 3.70 0.0 591.0 2545.3 115.0 1131.0 225.3 491.4 833.6 0. SHOCK 4.09 0.0 591.0 2269.1 117.4 1120.3 225.3 416.8 667.4 0. CHANGE 0.39 0.0 0.0 -276.2 2.4 -10.7 0.0 -74.6 -166.2 0. %CHANGE 10.54 0.0 0.0 -10.9 2.1 -0.9 0.0 -15.2 -19.9 0.RICE CWT BASE 7.71 0.0 27.2 194.2 13.1 153.1 0.0 52.2 2.3 0. SHOCK 8.70 0.0 27.2 168.0 13.5 146.6 0.0 36.1 1.3 0. CHANGE 0.99 0.0 0.0 -26.2 0.3 -6.5 0.0 -16.1 -1.0 0. %CHANGE 12.83 0.0 0.0 -13.5 2.6 -4.2 0.0 -30.9 -44.6 0.SOYBEANS BU BASE 6.30 0.0 225.0 3245.0 10.0 189.6 1995.0 1070.0 0.0 0. SHOCK 6.96 0.0 225.0 2885.9 10.2 182.1 1882.8 988.2 0.0 0. CHANGE 0.66 0.0 0.0 -359.1 0.2 -7.5 -112.2 -81.8 0.0 0. %CHANGE 10.47 0.0 0.0 -11.1 2.1 -4.0 -5.6 -7.6 0.0 0.
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Table 9 ARPT20 report fragment—continuedTABLE 2 --CROP PRODUCT SUPPLY AND USE: BASE DATA AND SOLUTION ------------------------------------------------------------------------------------------------------------------- SUPPLY USE  ------------------------------------ ---------------------------------------- BEGINNING STOCKS ENDI ------------------ ------PRODUCT UNITS PRICE GOVERN- COMMER- PRODUC- IMPORTS DOMES- FEED AND EXPORTS EXPORT GOVE MENT CIAL TION TIC RESIDUAL COMMRCL ENHANCE MEN------------------------------------------------------------------------------------------------------------------- $/UNIT -- MILLION UNITS -- COTTON BALE BASE 312.00 2.5 1.8 17.5 0.0 9.3 0.0 8.3 0.0 2. SHOCK 336.10 2.5 1.8 15.8 0.0 8.6 0.0 7.5 0.0 2. CHANGE 24.10 0.0 0.0 -1.7 0.0 -0.7 0.0 -0.8 0.0 0. %CHANGE 7.72 0.0 0.0 -9.5 1.6 -7.9 0.0 -9.7 0.0 0.SILAGE TON BASE 21.68 0.0 0.0 95.6 0.0 30.1 65.5 0.0 0.0 0. SHOCK 22.50 0.0 0.0 84.9 0.0 19.4 65.5 0.0 0.0 0. CHANGE 0.82 0.0 0.0 -10.7 0.0 -10.7 -0.1 0.0 0.0 0. %CHANGE 3.80 0.0 0.0 -11.2 0.0 -35.4 -0.1 0.0 0.0 0.HAY TON BASE 60.58 0.0 0.0 155.6 0.0 77.5 78.0 0.0 0.0 0. SHOCK 62.77 0.0 0.0 136.8 0.0 62.1 74.7 0.0 0.0 0. CHANGE 2.18 0.0 0.0 -18.8 0.0 -15.5 -3.3 0.0 0.0 0. %CHANGE 3.60 0.0 0.0 -12.1 0.0 -20.0 -4.2 0.0 0.0 0.-------------------------------------------------------------------------------------------------------------------
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References 

Abler, D. 2006. Partial Equilibrium Agricultural Trade Simulator. Online documentation available at 

http://trade.aers.psu.edu/index.cfm, (last accessed August 10). 

Aillery, M., N. Gollehon, R. Johansson, N. Key, and M. Ribaudo. 2005. Managing Manure to Improve Air 

and Water Quality. Economic Research Report No. 9, U.S. Department of Agriculture, Economic 

Research Service, (September). 

ARKI Consulting and Development. 2006. CONOPT Home Page. Online documentation available at 

http://www.conopt.com/, (last accessed September 7). 

Babcock, B.A. 1992. “The Effects of Uncertainty on Optimal Nitrogen Application,” Review of Agricultural 

Economics, Vol. 14, No. 2, pp. 271-280. 

Barnard, C., S. Daberkow, M. Padgitt, M.E. Smith, and N.D. Uri. 1997. “Alternative Measures of Pesticide 

Use,” The Science of the Total Environment, Vol. 203, pp. 229-244. 

Burfisher, M.E., R.M. House, and S.V. Langley. 1992. “Effects of a Free Trade Agreement on U.S. and 

Southern Agriculture,” Southern Journal of Agriculture Economics, (July), pp. 61-78. 

Cattaneo, A., R. Claassen, R. Johansson, and M. Weinberg. 2005. Flexible Conservation Measures on 

Working Land. Economic Research Report No. 5, U.S. Department of Agriculture, Economic Research 

Service, (June). 

Cerrato, M.E., and A.M. Blackmer. 1990. “Comparison of Models for Describing Corn Yield Response to 

Nitrogen Fertilizer,” Agronomy Journal, Vol. 82, pp. 138-143. 

Claassen, R., L. Hansen, M. Peters, V. Breneman, M. Weinberg, A. Catteneo, P. Feather, D. Gadsby, D. 

Hellerstein, J. Hopkins, P. Johnson, M. Morehart, and M. Smith. 2001. Agri-Environmental Policy at the 

Crossroads: Guideposts on a Changing Landscape. Agricultural Economic Report No. 794. U.S. 

Department of Agriculture, Economic Research Service, (January). 

Claassen R., P. Johnston, and M. Peters. 2000. "Compliance Provisions for Soil and Wetland Conservation." 

AREI, Chapter 5.3. U.S. Department of Agriculture, Economic Research Service, (October). 

Cooper, J., M. Peters, and R. Claassen. 2005. "Effects of Agri-Environmental Payment Policies on 

Agricultural Trade," Global Agricultural Policy Reform and Trade—Environmental Gains and Losses. J. 

Cooper (Ed.). Edward Elgar, London, UK. 

Cooper, J., R. Johansson, and M. Peters. 2005. "Some Domestic Environmental Effects of U.S. Agricultural 

Adjustments under Liberalized Trade: A Preliminary Analysis," Global Agricultural Policy Reform and 

Trade—Environmental Gains and Losses. J. Cooper (Ed.), Edward Elgar, London, UK. 

Dervis, K., J. de Melo, and S. Robinson. 1982. General Equilibrium Models for Development Policy. 

Cambridge and New York: Cambridge University Press. 

Disney, T., and M. Peters. 2003. “Simulation Modeling to Derive the Value-of-Information for Risky Animal 

Disease-Import Decisions,” Preventative Veterinary Medicine, Vol. 61, pp. 171-184. 

Doering, O., R. Heimlich, R.F. Kazmierczak, Jr., M.A. Peters, M. Ribaudo, and R.R. Solow. 1998. 

"Measuring the Economic Consequences of Hypoxia and Its Mitigation: Gulf Fisheries and Nutrient 

Reduction Strategies," The Effects of Hypoxia on Living Resources, with Emphasis on the Northern Gulf 

of Mexico, Nancy N. Rabalais and R. Eugene Turner (Ed.). Publisher: American Geophysical Union, 

(November). 
Regional Environment and Agriculture Programming Model / TB-1916

Economic Research Service/USDA

93

Duloy, J.H., and R.D. Norton. 1975. “Prices and Incomes in Linear Programming Models,” American Journal 

of Agricultural Economics, Vol. 57, (November), pp. 591-600. 

Duloy, J.H., and R.D. Norton. 1973. “CHAC: A Programming Model of Mexican Agriculture,” Multi-Level 

Planning: Case Studies in Mexico, L.M. Goreux and A.S. Manne, (Eds.), North-Holland Publishing 

Company, Amsterdam, pp. 373-399. 

Faeth, P., and S. Greenhalgh. 2002. “Policy Synergies Between Nutrient Over-Enrichment and Climate 

Change,” Estuaries, Vol. 25, No. 4b, pp. 869-877. 

Faeth, P., and S. Greenhalgh. 2000. A Climate and Environmental Strategy for U.S. Agriculture, World 

Resources Institute, Washington, DC. 

Faeth, P. 1995. Growing Green: Enhancing the Economic and Environmental Performance of U.S. 

Agriculture. World Resources Institute, Washington, DC. 

Fuller, E., B. Lazarus, L. Carrigan, and G. Green. 1992. Minnesota Farm Machinery Economic Cost 

Estimates for 1992. Report No. AG-FO-2308C. Minnesota Extension Service, University of Minnesota, 

St. Paul, Minnesota. 

GAMS Development Corporation. 2006. The GAMS System. Online documentation available at 

http://www.gams.com/docs/intro.htm, (last accessed August 12). 

Greenhalgh, S., and P. Faeth. 2001. “A Potential Integrated Water Quality Strategy for the Mississippi River 

Basin and the Gulf of Mexico,” The Scientific World, Vol. 1, No. S2, pp. 976-983. 

Greenhalgh, S., and A. Sauer. 2003. Awakening the Dead Zone: An Investment for Agriculture,Wwater 

Quality, and Climate Change. World Resources Institute, Washington, DC. 

Heimlich, R.E., and K.D. Wiebe, R. Claassen, and R.M. House. 1997. “Recent Evolution of Environmetal 

Policy: Lessons from Wetlands,” Journal of Soil and Water Conservation, Vol. 52, No. 3, pp. 157-161. 

Horner, G., S.A. Hatchett, R.M. House, and R.E. Howitt, 1990. “Impacts of San Joaquin Valley DrainageRelated Policies on State and National Agricultural Production,” National Impact of Drainage-Related 

Policies, University of California San Joaquin Valley Drainage Program, University of California-Davis. 

House, R., M. Peters, H. Baumes, and W.T. Disney. 1993. Ethanol and Agriculture: Effect of Increased 

Production on Crop and Livestock Sectors, Agricultural Economic Report No. 667, U.S. Department of 

Agriculture, Economic Research Service, (February). 

House, R.M., H. McDowell, M. Peters, and R. Heimlich. 1999. "Agriculture Sector Resource and 

Environmental Policy Analysis: An Economic and Biophysical Approach," Environmental Statistics: 

Analyzing Data for Environmental Policy. V. Barnett (Ed.), New York: John Wiley and Sons, 

pp. 243-261. 

Howitt, R.E. 1995. “Positive Mathematical Programming,” American Journal of Agricultural Economics, 

Vol. 77, No. 2, pp. 329-342. 

Johansson, R.C., J. Cooper, and U. Vasavada. 2005. “Greener Acres or Greener Waters? 

U.S. Adjustments to Agricultural Trade Liberalization,” Agricultural and Resource Economics Review, Vol. 

34, No. 1, pp. 42-53. 

Johansson, R.C. and J.D. Kaplan. 2004. “A Carrot and Stick Approach to Environmental 

Improvement: Marrying Agri-Environmental Payments and Water Quality Regulations,” Agriculture and 

Resource Economics Review, Vol. 31, No. 1, pp. 91-104. 
Regional Environment and Agriculture Programming Model / TB-1916

Economic Research Service/USDA

94

Kaplan, Jonathan D., Robert C. Johansson, and Mark A. Peters. 2004. "The Manure Hits the Land: Economic 

and Environmental Implications When Land Application of Nutrients is Constrained," American Journal 

of Agricultural Economics, Vol. 86, No. 3, pp. 688-700. 

Kellogg, R.L., C.H. Lander, D.C. Moffitt, and N. Gollehon. 2000. Manure Nutrients Relative to the Capacity 

of Cropland and Pastureland to Assimilate Nutrients: Spatial and Temporal Trends for the United States, 

U.S. Department of Agriculture’s Natural Resource Conservation Service and Economic Research 

Service. 

Kellogg, R.L., M. Stewart-Maizel, and D.W. Goss. 1992. Agricultural Chemical Use and Ground Water 

Quality: Are There Potential Problem Areas? U.S. Department of Agriculture’s Soil Conservation 

Service, Economic Research Service, Cooperative State Research Service, and National Center for 

Resource Innovations, (December). 

Lee, D.J. and R.E. Howitt. 1996. “Modeling Regional Agricultural Production and Salinity Control 

Alternatives for Water Quality Policy Analysis,” American Journal of Agricultural Economics, Vol. 78, 

pp. 41-53. 

Lewandrowski, J., M. Peters, C. Jones, R. House, M. Sperow, M. Eve, and K. Paustian. 2004. The Economics 

of Sequestering Carbon in the U.S. Agricultural Sector. Technical Bulletin No. 1909. U.S. Department of 

Agriculture, Economic Research Service, (April). 

Livingston, M., R. Johansson, V. Breneman, S. Daberkow, and M. Ash. 2004. “Economic Impacts of Asian 

Soybean Rust Epidemics in the United States,” Outlook, Report No. OCS-04D-02, Economic Research 

Service, U.S. Department of Agriculture, (April). 

Magleby, R., C. Sandretto, W. Crosswhite, and C.T. Osborn. 1995. Soil Erosion and Conservation in the 

United States, Agriculture Information Bulletin 718, U.S. Department of Agriculture, Economic 

Research Service, (October). 

Marshall, L., and S. Greenhalgh. 2006. “Beyond the RFS: The Environmental and Economic Impacts of 

Increased Grain Ethanol Production in the U.S.,” World Resources Institute Policy Note, available at 

http://biofuels.wri.org/, (last accessed August 10). 

McCarl, B.A., and T.H. Spreen. 1980. “Price Endogenous Mathematical Programming as a Tool for Sector 

Analysis,” American Journal of Agricultural Economics, (February), pp. 87-102. 

Paris, Quirino, and Richard E. Howitt. (1998) “An Analysis of Ill-Posed Production Problems Using 

Maximum Entropy,” American Journal of Agricultural Economics, Vol. 80, No. 1, (February), 

pp. 124-139.

Peters, M., J. Lewandrowski, R. House, and H. McDowell. 2001. "Economic Impacts of Carbon Charges on 

U.S. Agriculture," Climatic Change, Vol. 50, No. 4, pp. 445-473. 

Peters, M., H. McDowell, and R. House. 1997. “Environmental and Economic Effects of Taxing Nitrogen 

Fertilizer,” Selected Paper presented at the American Agricultural Economics Association annual 

meetings, Toronto, Canada, (July). 

Preckel, P, D. Harrington, and R. Dubman. 2002. “Primal/Dual Positive Math Programming: Illustrated 

Through an Evaluation of the Impacts of Market Resistance to Genetically Modified Grains,” American 

Journal of Agricultural Economics, Vol. 84, No. 3, pp. 679-689. 

Price, M. 2004. “Effects of U.S. Dairy Policies on Markets for Milk and Dairy Products: Appendix B—Food 

and Agricultural Policy Simulator: Model Overview,” Technical Bulletin No. 1910, U.S. Department of 

Agriculture, Economic Research Service, (May). 
Regional Environment and Agriculture Programming Model / TB-1916

Economic Research Service/USDA

95

Ribaudo, M., R. Heimlich, R. Claassen, and M. Peters. 2001. "Source Reductions vs. Interception Strategies 

for Controlling Nitrogen Loss from Cropland: The Case of Gulf Hypoxia," Ecological Economics, Vol. 

37, pp. 183-197. 

Ribaudo, M., R. Heimlich, and M. Peters. 2005. "Nitrogen Sources and Gulf Hypoxia: Potential for 

Environmental Credit Trading," Ecological Economics, Vol. 52, No. 2, pp. 159-168. 

Ribaudo, M., N. Gollehon, M. Aillery, J. Kaplan, R. Johansson, J. Agapoff, L. Christensen, V. Breneman, and 

M. Peters. 2003. Manure Management for Water Quality: Costs to Animal Feeding Operations of 

Applying Manure Nutrients to Land. Agricultural Economic Report No. 824. U.S. Department of 

Agriculture, Economic Research Service, (June). 

Samuelson, P.A. 1952. “Spatial Equilibrium and Linear Programming,” American Economic Review, Vol. 42, 

No. 3, pp. 282-303. 

Smith, R.A., G.E. Schwarz, and R.B. Alexander. 1997. “Regional Interpretation of Water-Quality Monitoring 

Data,” Water Resources Research, Vol. 33, pp. 2,781-2,798. 

Stanford Business Software, Inc. 2006. MINOS 5.5. Online documentation available at http://www.sbsi-soloptimize.com/asp/sol_product_minos.htm. 

Takayama, T., and G.G. Judge. 1971. Spatial and Temporal Price and Allocation Models. Amerstan, 

Netherlands: North-Holland Publishing Co. 

U.S. Department of Agriculture, Economic Research Service. 2006. Commodity Costs and Returns. Data 

available online at http://www.ers.usda.gov/Data/costsandreturns/, (last accessed August 9). 

U.S. Department of Agriculture, Economic Research Service. 1992. Cropping Practices Survey Data. 

U.S. Department of Agriculture, National Agricultural Statistics Service and Economic Research Service. 

1992-96. Agricultural Resource Management Survey. 

U.S. Department of Agriculture, World Agricultural Outlook Board. 2006. Agricultural Baseline Projections 

to 2015. Baseline Report No. OCE-2006-1, (February). 

U.S. Department of Agriculture, Agricultural Research Service. 1990. EPIC—Erosion Productivity-Impact 

Calculator, 1. Model Documentation, Technical Bulletin No. 1768, (September). 

U.S. Department of Agriculture, National Agricultural Statistics Service. 1993. Agricultural Prices 1992 

Summary, (July). 

Williams, J.R., and D.E. Kissel. 1991. “Water Percolation: An Indicator of Nitrogen-Leaching Potential,” 

Managing Nitrogen for Groundwater Quality and Farm Profitability. R.F. Follet, D.R. Keeney, and 

R.M. Cruse (Eds.). Soil Science Society of America, Inc. Madison, WI, pp. 59-83. 

Williams, J.R, P.T. Dyke, W.W. Fuchs, V.W. Benson, O.W. Rice, and E.D. Taylor. 1990. EPIC—

Erosion/Productivity Impact Calculator, Vols. 1 and 2. Technical Bulletin No. 1768. A.N. Sharpley and 

J.R. Williams (Eds.) U.S. Department of Agriculture, Agricultural Research Service, Temple, TX. 

Wischmeier, W.H., and D.D. Smith. 1978. Predicting Rainfall Erosion Losses—A Guide to Conservation 

Planning. Agriculture Handbook No. 537. U.S. Department of Agriculture, Agricultural Research 

Service. 
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Appendix A 

Installing and Using REAP 

Installation Instructions for Base REAP 

REAP is written in the GAMS language. The installation instructions are written with the assumption that 

GAMS-IDE has been installed and a nonlinear solver (e.g., CONOPT or MINOS) has been selected as the 

default solver. 

The files necessary to run REAP are contained in REAPv1.ZIP. This file contains several control files and 

three folders: A1A0, A1A0LIB, and AREPORT. A1A0 contains the main model files and consists of 

several subdirectories that contain files for running different versions of the model. A1AOLIB contains the 

report writing files as well as supporting data files, such as crop and livestock data baseline files. 

AREPORT contains files for generating summary reports of results. 

All files in REAPv1.ZIP need to be extracted into a directory called REAPv1 retaining the directory 

structure. 

REAP is driven by a series of control files and a master program. The master program is called 

REAPDRIVER.GMS. The control files are PARAMDEFAULT.INC, PARAMUSER.INC, 

PARAMS.INC, and OPTIONS.INC. The file REAPDRIVER.GMS calls in all the component files 

automatically. To run REAPDRIVER, open a GAMS-IDE project in the same directory (REAPv1) as the 

control files. A new project is created from the GAMS-IDE by selecting File -> Project -> New Project

and specifying the name of the project. Open REAPDRIVER.GMS (File -> Open) into the GAMS-IDE. 

Calibrating to a Baseline Year 

To calibrate to a year in the USDA baseline, first open the file PARAMDEFAULT.INC. Change the name 

of parameter “run_name” (“test_run2” in the example) to a name that reflects the nature of the run (for 

example “Cal2010”). This name will become the name of a new directory in the REAPv1 folder. All the 

REAP output will be saved in this new directory. If the directory already exists, files contained within it 

will be overwritten. You may also give the run a title. Parameter runyear must be specified. Be sure to save 

the file. 

* The run_name is the name of a directory where results 

* are written. The results include key listing files and 

* GDX files with result data. 

$setglobal run_name test_run2 

* Goes to the $title of the input file 

$setglobal run_title This is a test run 

* 

* runyear 

* 

$setglobal runyear 2010 {calibration year} 

$setglobal rym1 2009 {runyear minus 1} 

$setglobal ryp1 2011 {runyear plus 1} 

Excerpt from PARAMDEFAULT.INC 

To run the calibration, make sure REAPDRIVER.GMS is active in the project window. Press the Run 

GAMS toolbar button (or select File -> Run from the menu). A new window will open in the project that 
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will display the progress of REAP. When the runs are complete, the output (LST and GDX files) is in the 

directory specified by the run_name parameter. 

There are four steps or jobs submitted through the REAPDRIVER.GMS to obtain a base solution off of 

which scenarios can be run. The purpose of these steps are described below, followed by the steps needed 

for submitting a scenario run and generating summary reports. 

Step 1: The file A1A0A.GMS in the A1A0 directory is called “REAPDRIVER.GMS” to calibrate 

rotation acreage based on NRI data to individual crop acreage from the selected base year as reported in 

the baseline data. This file also updates cost of production estimates to the latest data contained in the 

selected year from the baseline data. 

Step 2: The file ALF4N00.GMS in the ALF subdirectory of A1A0 is called “REAPDRIVER.GMS” work 

files. The main purpose of this run is to find shadow values for calibrating the CET parameters. 

Step 3: The file CET4N00.GMS in the CET subdirectory of A1A0 is called “REAPDRIVER.GMS” to 

derive the parameters for the CET functions. The file contains two sequential solves. The first solve 

specifies the parameters for the CET functions for tillage practices. The second solve specifies the 

parameters for the rotations. 

Step 4: A1A0VC.GMS in the A1A0 directory is called “REAPDRIVER” to specify the PMP functions 

for crop and livestock production. Step 4 represents the full verify or base run from which all the shocks 

will be run. At this stage, all calibration bounds have been removed. 
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Appendix B 

Environmental Parameters 

Environmental Policy Integrated Climate Model 

Environmental indicators contained in REAP production activities were generated using the EPIC model. 

EPIC is a crop biophysical simulation model that is used to estimate the effect of management practices on 

crop yields, soil quality, and various environmental emissions at the field level. EPIC uses information on 

soils, weather, and management practices, including specific fertilizer rates, and produces information on 

crop yields, erosion, and chemical losses—including nitrogen losses—to the environment. 

Management practices used in the EPIC management files are consistent with agronomic practices for the 

45 regions as reported in the USDA ARMS, which has absorbed the CPS. 

We generated crop yields and the array of environmental indicators associated with each crop production 

activity represented in REAP by running EPIC in four sequential steps. The first step is to condition the 

soil while the next three are used to calculate short-term yield, average rate of emission, and long-term 

yield (app. table B.1). 

The first or conditioning step allows EPIC to rectify any inconsistencies in the soil profile imported from 

the SOILS5 database. This first step involves running EPIC for a period of 5 years with the soil erosion 

module turned off. The output of this step has the added advantage of making the soils profile at the 

beginning of the second step consistent with a field that has been subjected to the management practices 

being simulated. This is important because any particular soil profile used does not necessarily come from 

a field where the system being simulated has been used. 

In step two, we calculate the short-term yield. EPIC is run for a period of 7 years, again with its soil 

erosion module turned off and short-term yield calculated as average yield during this period. By turning 

the erosion off, any variation in yield will be due strictly to variation in weather. 

In step three, we calculate the environmental indicators by running EPIC for 60 years, this time with soil 

erosion turned on. Total emissions for each environmental indicator are tabulated and divided by the 

length of the simulation to obtain the annual rate of emission. Running the systems for 60 years eliminates 

any dependence of the emissions from the sequence of weather for any particular time period and provides 

a consistent base for comparing systems. By eliminating the dependence of the systems on a specific 

weather pattern, we did not need to coordinate weather patterns among the various weather sites. In this 

step, all systems are run through two full weather cycles. At the same time, each management regime is 

run through at least five full management cycles. 

In step four, we calculate the long-term yields. EPIC is run as described in step two, except that EPIC uses 

the soil profile generated from the previous 60-year simulations. The difference between shortrun and 

longrun yield represents the change in soil productivity associated with each management system. 

While EPIC was designed for use over a wide range of soil and climatic conditions, we found that crop 

yields were highly variable. For some regions and some crops, EPIC's yield projections matched USDA 

yield data, but in others, its yield projections were over or under yields reported for those areas. More 

importantly, EPIC's estimated yields alter the ranking of the regions with respect to crop yield. 

It could be argued that since yield has little effect on erosion that the errors in EPIC’s yield estimates are 

not that important. However, yield is a critical factor determining plant nutrient uptake and, consequently, 

nitrogen emissions to the environment. Yield, especially the relative yield between crops and regions are 

important for determining the economic relationships as well. We found it necessary, therefore, to calibrate 

EPIC so that its simulated yields matched the reported yields. 
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In order to calibrate EPIC, we conduct sensitivity analysis over EPIC parameters for a large number of 

production systems in a consistent fashion. A variety of factors can be used to calibrate the EPIC 

parameters; the two most common factors are crop yield and soil profile. We selected crop yield because 

data on yield is readily available down to the county level; yield affects many of the environmental 

relationships pertaining to chemical emissions of interest; and we lacked the expertise to calibrate EPIC 

parameters to changes in soil profile. 

Outside information needed to calibrate the systems includes yields, weather, growing degree days, and 

soils. Yields were obtained by aggregating USDA’s NASS county yield data up to the REAP region. 

Weather information was obtained by aggregating rainfall and temperature data for NOAA weather 

districts to REAP regions. Growing degree days were obtained from a USDA database put together by 

EPIC developers called “Potential Heat Unit.” Soils information was obtained from the SOILS5 database 

and selected by using a distance function procedure that took into account a set of soil characteristics 

related to soil quality and productivity. 

EPIC parameters used in the calibration were WA, HI, and nitrogen demand (ba1, ba2, and ba3). WA is a 

growth parameter and controls the efficiency by which the plant converts sunlight into biomass. HI is the 

harvest index and specifies the ratio of above ground to below ground biomass. Nitrogen demand 

parameters (ba1, ba2, and ba3) control plant demand for nitrogen throughout the growing season. These 

parameters affect the sensitivity of yield to available nitrogen and play a critical role in determining 

nitrogen uptake and nitrogen loss. 

The yield target used to calibrate EPIC parameters was the 7-year average yield for 1987-1995 for each 

REAP region. The 7-year average yields were calculated by aggregating NASS county yield data up to 

REAP regions. Because EPIC does not account for all factors, primarily disease and insects, which affect 

yields, EPIC should overestimate yield relative to reported yield. Consequently, we adjusted the target 

yield by setting it 10 percent above each crop's average yield. 

The EPIC parameters were calibrated in two steps. In the first step, WA and HI were adjusted up or down 

until the simulated yield came within 10 percent of the target yield. The parameters were varied in 

sequence, first WA and then HI, until the simulated yield fell within the desired range. 

In the second step, nitrogen demand was shifted up or down until the nitrogen-yield response was 

consistent with fertilizer application rates reported in the CPS and profit-maximizing behavior in an 

uncertain environment. To do this, we assumed that the nitrogen-yield relationship can be represented with 

a nondifferentiable plateau response model. The von Liebig and quadratic-plateau response models have 

this characteristic. A study by Cerrato and Blackmer (1990) found that these two functions best 

represented the nitrogen-yield response relationship based on data from experimental plots in Iowa. A 

study by Babcock (1992) demonstrated that given these types of response functions, farmers should apply 

the same amount of fertilizer every year based on yield potential. This, in turn, implies that the nitrogenyield response function obtained from an average yield over a specific time period should be discontinuous 

at the optimal fertilizer application rate. We then shifted the nitrogen demand function until the 

discontinuity in the simulated nitrogen-yield response was centered over the nitrogen application rate. 

Soil Selection 

Reliability of the EPIC simulation of crop yield and environmental indicators depends not only on the 

management practices used, but also on weather and soils. Both soils and weather play important roles in 

crop growth, nutrient uptake, erosion, chemical losses to air, run off, and leaching. Therefore, it is 

important that the soils and weather sites selected be as representative of conditions as possible in each 

REAP region. 

Representative soils were chosen for each REAP model region by using factors that reflect the 

susceptibility of the soil to erosion, surface runoff, and leaching. These soils were used in the EPIC crop 
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simulations for each region. Soils were selected for each of 45 REAP regions. Soils can be separated by 

HEL and NHEL soils, or combined (app. tables B.2 and B.3). From the NRI, a subset of polygons was 

selected on the basis of REAP crops grown. The hydrologic groups, erodibility factors, slope lengths, and 

steepness were obtained from NRI and SOILS5 data. These variables summarize the inherent 

characteristics of the soil and land pertaining to water erosion, surface runoff, and leaching. Soils for each 

REAP region were selected by considering proximity to the centroid of these variables for each region and 

by acreage. 

Soil Selection Factors 

Environmental losses of soil and chemicals in crop production can be divided into surface and subsurface 

categories. Chemicals can move from the field in solution with the runoff water and on soil particles 

eroded from the field. Chemicals can leach through the soil into subsurface flows and groundwater. 

Surface runoff and sheet and rill erosion generally increase with slope length and steepness and are further 

affected by precipitation, the soil hydrological group, inherent erodibility of the soil, and physical cropping 

system attributes. The erodibility of soil, depending in part on the proportions of silt, sand, and clay, 

increases with the value of the USLE K factor. Hydrologic groups reflect runoff potential and are 

negatively related to the infiltration and leaching potential of soils. As runoff potential declines, the rate of 

infiltration increases along with the amount of precipitation absorbed into the soil and the potential for 

chemical leaching. Thus, the slope length and steepness, USLE erodibility factor (K factor), and 

hydrological group are included in the soil selection. Background information and data conversions and 

calculations of averages and distance from the centroids for each region are summarized below. 

Representative Soil Delineation by Erodibility 

Soils are chosen to be representative of the individual REAP regions. The separation of highly erodible 

and nonhighly erodible soils is desirable for some analyses. Thus, a single soil is chosen to be 

representative of each REAP region as well as separate soils representing highly erodible and nonhighly 

erodible land. The highly erodible designation by NRCS is reserved for soil inherently susceptible to 

erosion, considering rainfall, soil characteristics, and slope length and steepness (Magleby et al., 1995). 

The choice of soils begins with choosing the relevant cropland. Crop acreage where any one of REAP 

crops was grown within 4 years was selected from the 1992 NRI. The relevant SOILS5 records were 

merged with the NRI polygons in the REAP crop acreage. From this data set, the hydrologic group, 

erodibility, slope length, and steepness were extracted for the NRI data points. These data were converted 

to variables ranging from 0 to 1, and averages were calculated for each model region for HEL, NHEL, and 

combined soils by using the NRI polygon expansion factor as a weight. Observations are limited to those 

where the slope steepness is no greater than 30 percent. Vector distances from the means of the four 

variables or the centroid were calculated as deviation scores for each NRI point. Soils in each category 

were ranked by ascending score, with lower scores being the most desirable. Among soils with similar 

scores, those associated with the greatest acreage were given preference. Representative soil parameters 

for each region are presented in tables B.1 through B.3) 

Weather Selection 

Weather sites used in the EPIC simulations were originally selected based on the proximity to the 

geographical center of the region (app. table B.4). However, selecting a representative weather site based 

on its proximity to the geographical center of a REAP region can cause the weather represented by the 

selected weather site to be inconsistent with weather conditions where a majority of the crops are grown. 

In relatively homogeneous regions—such as the Corn Belt—this does not present much of a problem, but 

in many REAP regions, weather varies considerably and poses a problem. Consequently, before calibrating 
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EPIC system parameters, we reselected weather sites that were more representative, with respect to 

average rainfall and temperature for areas within the REAP region with crop production rather than 

selecting sites based on their proximity to the center of the region. 

The average rainfall and temperature for the 7-year period (1987-95) used to calculate average crop yields 

were used as the basis for selecting representative weather sites. This information was obtained by 

aggregating NOAA weather districts into REAP regions based on their share of crop acreage in each 

REAP region. Weather sites from EPIC's database were selected based on the average rainfall and 

temperatures from the NOAA data (see table B.4 for locations of selected sites). 

After the weather site was selected for each region, the weather generator was set so that average rainfall 

and temperature generated by EPIC matched the 7-year average calculated from the NOAA data. This was 

needed because the EPIC weather generator is designed to replicate 30-year average rainfalls and 

temperatures at any specific weather site. As a result, any length of time shorter than 30 years will have 

average temperatures and rainfall that deviate from the 30-year average. The shorter the timespan selected 

and the greater the variability in weather at the site means the greater the deviations associated with any 

one draw from the weather distribution is likely to be. Since we wanted to calibrate EPIC parameters to the 

7-year average yields, we needed to set the weather generator seed so that the average temperature and 

rainfall during the simulation period were equal to the averages recorded for the period used in calculating 

crop yields. 

Erosion Calibration 

Surface Runoff, Leaching, and Lateral Surface Flow 

Surface run off is estimated by the EPIC system as a function of soil hydrological groups, slope, crop type, 

and daily rainfall (EPIC Model Documentation pages 4-5, EPIC Users Manual, table II.1). Hydrologic 

groups reflect runoff potential of the soils. The negative relationship between surface runoff and leaching 

was exploited by Williams and Kissel (1991) in the development of a leaching index by using EPIC for 

eight sites to simulate a range of soils and climates (Kellogg et al., 1992). Thus, the hydrological group 

captures the various chemical and physical soil characteristics pertinent to both surface run off and 

leaching. 

Group A—Lowest runoff potential—highest percolation or leaching potential. High infiltration rate even 

when wet. Deep, well-drained sand or gravel. High water transmission rate. 

Group B—Moderate infiltration rate—Moderately deep soils, with moderately fine-course texture. 

Moderate water transmission rate. 

Group C—Slow infiltration rate—Soils with a layer impeding downward flow, or soil with moderately 

fine to fine texture. Slow water transmission rate. 

Group D—High runoff potential—lowest percolation or leaching potential. Slow infiltration rate. Clay 

soils with high swelling potential, permanent high water table, clay layer near surface, and 

shallow soils over impervious material. Very slow water transmission rate. 

Water Erosion 

Water erosion estimation variables capture the remaining slope and soil characteristics used for selecting 

the REAP representative soils. 

The USLE equation is represented in EPIC as follows: 
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Y = (EI) (K) (CE) (PE) (LS) (ROKF) 

where: 

Y is the sediment yield 

EI is the rainfall intensity measure 

K is the soil erodibility factor 

CE is the crop management factor (tillage) 

PE is the erosion control factor 

LS is the slope length and steepness factor 

ROKF is the course fragment factor 

The REAP representative soils choice factors include slope length and steepness and directly affect the 

USLE soil erodibility factor (K). The hydrologic group reflects the course fragment factor (ROKF). The 

crop management factor is determined by the REAP crop system, and rainfall parameters are incorporated 

in the EPIC simulation for each of the 45 regions. Thus, all USLE variables are accounted for by REAP 

values other than the erosion control factor (PE). The absence of PE could result in low erosion estimates, 

where such physical controls such as terracing are needed but have not been incorporated. However, over 

the years much of such land has either been taken out of tilled crop production or has undergone treatment. 
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Appendix table B-1—Representative soils combined erodiblility class Farm Resource Region 

Erodibility Class 

Soil SOILS5 

ID 

SURFACE TEXTURE 

ACRES HYDROLOGIC 

GROUP 

K FACTOR S

L

APN HEL ZANESVILLE KY0001 SICL 165700 C 0.37 APO HEL LORING TN0011 SIL 455400 C 0.49 APP NHEL APPLING NC0032 SL 177700 B 0.24 2APT NHEL GOLDSBORO NC0041 FSL 162800 B 0.20 2CBL NHEL ELSTON IN0029 L 33000 B 0.28 3CBM NHEL GALVA IA0163 SICL 562200 B 0.32 2CBN NHEL BARDEN MO0032 SIL 49000 C 0.37 2CBO NHEL WARDELL MO0045 L 38700 C 0.37 2CBR NHEL CANFIELD OH0057 SIL 131300 C 0.37 2DLN NHEL ROXANA LA0067 SIL 36200 B 0.43 2DLO NHEL DUNDEE MS0057 VFSL 938000 C 0.37 DLP NHEL SAVANNAH MS0083 L 41100 C 0.37 2DLT NHEL MIDLAND LA0017 SICL 156300 D 0.43 LAF NHEL VALLERS MN0055 L 31900 C 0.28 LAK NHEL CHETEK WI0120 SL 111400 B 0.24 LAL NHEL KIBBIE MI0041 L 75900 B 0.28 2LAM NHEL CLARION IA0521 L 108100 B 0.28 MNB NHEL TETONIA ID0217 SIL 18700 B 0.37 5MND NHEL PARLEYS UT0062 SIL 61000 B 0.32 6
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Appendix table B-1—Representative soils combined erodiblility class— continued MNE NHEL NUNN CO0038 CL 15300 C 0.28 2MNF HEL SCOBEY MT0124 CL 792100 C 0.37 3MNG HEL NORKA CO0071 SIL 197800 B 0.32 4MNH HEL CLOVIS NM0969 FSL 107800 B 0.28 2NPF NHEL BARNES ND0119 L 3486600 B 0.28 2NPG HEL HUGGINS SD0093 SIL 6600 C 0.32 3NPH NHEL HARNEY KS0047 SIL 3101300 B 0.32 2NPM NHEL MOODY SD0343 SICL 696300 B 0.32 2NTL NHEL APPLETON NY0145 SIL 60000 C 0.32 2NTN HEL GILPIN PA0007 SIL 229100 C 0.32 NTR HEL WELLSBORO PA0027 L 134700 C 0.28 NTS HEL GLENELG MD0021 L 181900 B 0.32 2NTT NHEL SASSAFRAS MD0039 SL 243600 B 0.28 PAA NHEL WOODBURN OR0325 SIL 81600 C 0.32 PAB NHEL WALLA WALLA WA0026 SIL 349700 B 0.43 4PAC NHEL TWISSELMAN CA0699 C 122600 C 0.32 8PAD NHEL FURY ID0568 SIL 64500 C 0.32 8PAE NHEL CLAYTON WA0302 FSL 13700 B 0.28 SEN NHEL CHENNEBY AL0026 SICL 33600 C 0.32 4SEP NHEL TIFTON GA0001 SL 1249100 B 0.17 SET NHEL DOTHAN AL0010 LS 30000 B 0.15 2SPH NHEL ROWENA TX0159 CL 492900 C 0.32 2SPI HEL DUVAL TX0208 VFSL 113800 B 0.32 3
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Appendix table B-1—Representative soils combined erodiblility class— continued SPJ NHEL AUSTIN TX0144 SIC 84100 C 0.32 2SPM NHEL DENNIS OK0004 SIL 96200 C 0.43 SPT NHEL LAKE CHARLES TX0020 C 611200 D 0.32 2
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Appendix table B-2—HEL Representative soils 

Region Erodibility SOIL SOILS5 MUSYM State SCH_TOT Acres S.text H.Gr. K Fact SAPN NHEL BELKNAP IL0004 Bn KY 0.150455 131200 SIL C 0.37 20APO NHEL COLLINS MS0030 8 KY 0.035029 205100 SIL C 0.43 1APP NHEL APPLING NC0032 ApB VA 0.092878 177700 FSL B 0.24 20APS NHEL HAGERSTOWN MD0004 HcC WV 0.142632 25800 SIL C 0.32 1APT NHEL GOLDSBORO NC0041 GoA NC 0.130436 162800 FSL B 0.2 20CBL NHEL ELBURN IL0136 198 IL 0.130818 26200 SIL B 0.28 3CBM NHEL GALVA IA0163 GaA IA 0.096073 562200 SICL B 0.32 2CBN NHEL STENDAL IN0058 St OH 0.120973 65400 SIL C 0.37 20CBO NHEL COMMERCE LA0041 42 MO 0.065068 127500 SICL C 0.37 2CBR NHEL CANFIELD OH0057 CdB OH 0.038391 131300 SIL C 0.37 20DLN NHEL BARLING AR0029 2 AR 0.118307 6400 SIL C 0.37 1DLO NHEL SHARKEY LA0050 Sb MS 0.165205 1990100 SICL D 0.37 1DLP NHEL SAVANNAH MS0083 SaA MS 0.074307 41100 SIL C 0.37 20DLT NHEL CROWLEY LA0044 CrA LA 0.073238 423800 SIL D 0.49 1LAF NHEL BEARDEN ND0296 67A MN 0.089325 428700 SIL C 0.28 10LAK NHEL ROSHOLT WI0226 RoB WI 0.061142 185600 SL B 0.24 20LAL NHEL FOX WI0026 FoB MI 0.046413 176400 SL B 0.24 20LAM NHEL CLARION IA0074 ClB MN 0.057651 480500 L B 0.24 10MNB NHEL REXBURG ID0083 700A ID 0.156065 84000 SIL B 0.43 60MND NHEL WITT NM1122 R0B CO 0.167687 95000 L B 0.37 50MNE NHEL AMSTERDAM MT0499 11 MT 0.187658 85700 SIL B 0.32 2MNF NHEL WILLIAMS ND0258 69 MT 0.152982 492400 L B 0.43 2MNG NHEL WELD CO0054 WeB CO 0.178504 220100 SIL C 0.32 40
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Appendix table B-2—HEL Representative soils— continued

MNH NHEL KUMA CO0028 25 CO 0.148546 137700 SIL B 0.32 30

NPF NHEL BARNES ND0119 BhA ND 0.128394 3486600 L B 0.28 2NPG NHEL KEITH NE0049 KeC NE 0.153496 214300 L B 0.28 20NPH NHEL HARNEY KS0047 HB KS 0.100918 3101300 SIL B 0.32 2NPM NHEL NORA SD0060 NoC NE 0.164418 721900 SIL B 0.32 2NTL NHEL LIMA NY0120 Lo NY 0.179558 194700 FSL B 0.32 20NTN NHEL GILPIN PA0007 GNB PA 0.073243 28200 SIL C 0.32 20NTR NHEL MARDIN NY0060 MrB NY 0.148794 139200 SIL C 0.24 20NTS NHEL HAGERSTOWN MD0004 35B2 PA 0.146413 103900 SIL C 0.32 2PAA NHEL WOODBURN OR0325 77A OR 0.03915 81600 SIL C 0.32 3PAB NHEL WALLA WA0026 31B OR 0.069468 349700 SIL B 0.43 4PAC NHEL COLUMBIA CA0188 144 CA 0.018474 22500 FSL C 0.32 9PAD NHEL FURY ID0568 7A OR 0.055534 64500 SIL C 0.32 50PAE NHEL NARCISSE WA0103 NcA WA 0.157449 8000 SIL C 0.37 20SEN NHEL CHENNEBY AL0026 Lk AL 0.139673 33600 SICL C 0.32 4SEP NHEL TIFTON GA0001 TuB GA 0.029616 1249100 SL B 0.17 1SET NHEL GOLDSBORO NC0041 Gb SC 0.083078 178200 LS B 0.17 20SPH NHEL OLTON TX0129 OcA TX 0.037056 892300 CL C 0.32 2SPI NHEL CLAREVILLE TX0207 12B TX 0.046369 26900 CL C 0.32 20SPJ NHEL HOUSTON B. TX0093 HoB2 TX 0.14973 756500 C D 0.32 1SPM NHEL DENNIS OK0004 DtB OK 0.068226 96200 SIL C 0.43 10SPT NHEL L.CHARLES TX0020 24 TX 0.043141 611200 C D 0.32 1
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Appendix table B-3—Representative soils, NHEL 

Region Erodibility SOIL SOILS5 MUSYM State SCH_TOT Acres S.text H.Gr. K Fact SAPN HEL LOWELL KY0032 LoC KY 0.16414 218500 SIL C 0.37 1APO HEL LORING TN0011 LoC3 TN 0.088049 455400 SIL C 0.49 1APP HEL CECIL NC0018 Cf VA 0.093005 308600 FSL B 0.28 2APS HEL FREDERICK VA0059 29C2 VA 0.155668 55900 SIL B 0.32 2APT HEL MUNDEN VA0162 MuA VA 0.064154 3600 SL B 0.2 2CBL HEL HITT IL0216 506C IL 0.04111 18000 SIL B 0.32 2CBM HEL FAYETTE IA0564 163C2 IA 0.146531 694600 SIL B 0.37 1CBN HEL COSHOCTON OH0104 CnC OH 0.093539 86400 SIL C 0.37 2CBO HEL LORING TN0011 3C2 MO 0.166762 18800 SIL C 0.49 1CBR HEL WOOSTER OH0017 WsC2 OH 0.073686 104400 SIL C 0.37 2DLN HEL ENDERS AR0002 20 AR 0.096942 2500 FSL C 0.32 1DLO HEL LORING TN0011 33B MS 0.033393 284600 SIL C 0.49 1DLP HEL SAVANNAH MS0083 SaC2 MS 0.03369 53300 SIL C 0.37 1DLT HEL MIDLAND LA0017 McA LA 0.088073 1800 SICL D 0.43 1LAF HEL POPPLETON MN0131 148 MN 0.122787 20900 LFS A 0.15 1LAK HEL ROSHOLT WI0226 RrC2 WI 0.082225 34600 SL B 0.24 1LAL HEL MARLETTE MI0083 36C MI 0.072976 90600 L B 0.32 2LAM HEL VALTON WI0127 DtC2 WI 0.030537 159200 SIL B 0.32 1MNB HEL TETONIA ID0217 58 ID 0.167971 55300 SIL B 0.37 4MND HEL GLENDALE AZ0130 Gf NM 0.113567 39800 L B 0.32 7MNE HEL WINIFRED MT0135 602WM MT 0.145761 42300 C C 0.32 3MNF HEL SCOBEY MT0124 657156 MT 0.039211 792100 CL C 0.37 3MNG HEL NORKA CO0071 WBC CO 0.147358 197800 SIL B 0.32 4
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Appendix table B-3—Representative soils, NHEL— continued

MNH HEL AMARILLO TX0130 Ab NM 0.101994 246600 FSL B 0.24 3

NPF HEL WILLIAMS ND0042 341C ND 0.12297 197700 L B 0.28 3NPG HEL SAVO SD0084 86B SD 0.150627 5200 SIL C 0.32 3NPH HEL RICHFIELD KS0096 RN KS 0.057905 1580300 SIL B 0.32 2NPM HEL SHARPSBURG IA0033 ShD2 NE 0.189385 350200 SICL B 0.32 1NTL HEL HONEOYE NY0117 HnB NY 0.146097 58400 SIL B 0.32 2NTN HEL GILPIN PA0007 GgC2 PA 0.122794 229100 SIL C 0.32 1NTR HEL WELLSBORO PA0027 32C NY 0.060963 134700 L C 0.28 1NTS HEL GLENELG MD0021 GEB2 PA 0.129692 181900 SIL B 0.32 2PAA HEL MANITA OR0650 53B OR 0.094004 800 L C 0.32 4PAB HEL ATHENA OR0002 AsE2 WA 0.090662 319900 SIL B 0.37 4PAC HEL NACIMIENTO CA0045 175 CA 0.08746 72500 SICL C 0.32 1PAD HEL HOLTVILLE CA0279 110 CA 0.078941 27600 SIC C 0.32 1PAE HEL BONNER ID0232 35 WA 0.139642 10400 SIL B 0.32 4SEN HEL WYNNVILLE AL0042 Tk AL 0.157549 95500 SIL C 0.28 8SEP HEL CECIL NC0018 CeB SC 0.099457 171000 SL B 0.28 1SET HEL CARNEGIE GA0027 CoC2 GA 0.217712 4900 SL C 0.28 3SPH HEL AMARILLO TX0130 Aa TX 0.082723 1767600 FSL B 0.24 2SPI HEL KNIPPA TX0435 KnA TX 0.121015 63600 C C 0.32 3SPJ HEL AUSTIN TX0144 8 TX 0.059579 28000 SIC C 0.32 1SPM HEL DENNIS OK0004 DeC OK 0.069785 26100 SIL C 0.43 1SPT HEL VICTORIA TX0224 7A1 TX 0.117146 36000 C D 0.32 3
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Appendix table B.4—Selected weather sites and their location 

Model Region State Station Lat Long 

APN KY 09 36.75 86.20 

APP NC 10 35.73 81.38 

APS WV 03 39.27 80.35 

CBL OH 04 41.40 81.85 

CBM IL 10 40.47 87.67 

CBN IN 08 38.60 86.10 

CBO TN 11 35.62 85.20 

CBR OH 10 40.50 81.45 

DLN AR 01 34.55 92.62 

DLO MS 11 31.95 90.98 

DLP MS 02 31.57 90.43 

DLT MS 13 30.63 89.05 

LAF MN 31 48.57 95.63 

LAK WI 13 46.35 91.82 

LAL MI 24 47.17 88.50 

LAM WI 15 43.55 90.88 

MNB ID 16 42.32 111.30 

MND AZ 27 36.28 113.07 

MNE MT 32 48.40 115.53 

MNF MT 13 48.40 115.53 

MNG CO 01 40.12 103.17 

MNH CO 14 40.58 102.30 

NPF ND 15 & 03 46.40 97.23 & 46.38 

NPG NE 13 42.27 101.35 

NPH KS 01 39.47 98.83 

NPM NE 06 41.43 96.48 

NTL NY 12 41.85 73.62 

NTN PA 12 41.48 79.43 

NTR ME 01 44.32 69.80 

NTS NJ 02 40.25 74.28 

NTT DE 01 38.63 75.47 

PAA OR 05 44.57 123.28 

PAB OR 09 45.35 119.55 

PAC CA 17 36.73 119.82 

PAD OR 12 43.28 118.83 

PAE WA 06 48.53 117.87 

SPH TX 01 32.43 99.68 

SPI TX 09 25.92 97.47 

SPJ TX 66 33.60 96.65 

SPM OK 20 36.30 95.32 

SPT LA 04 29.88 93.42 

STN GA 06 34.52 83.53 

STP AL 02 33.57 86.75 

STT FL 14 30.42 81.65 
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Appendix C 

Accessing REAP and Contributing to Model Design 

Accessing the Model 

A current version of REAP is available on CD from the Economic Research Service, U.S. Department of 

Agriculture: 

Scott Malcolm 

Resource, Environmental, and Science Policy Branch 

Resources and Rural Economics Division 

Economic Research Service—U.S. Department of Agriculture 

1800 M Street NW 

Washington, DC 20036-5831 

Email: smalcolm@ers.usda.gov. 

Contributing to the Model Design 

New members of the REAP-user network will be asked to sign a Memorandum of Understanding. This 

agreement stipulates that model innovations will be well documented and shared with ERS for inclusion in 

the module library. In this way, the model will continue to expand its range of applications and will 

facilitate updating of the background data sources.