United States

Department

of Agriculture

Economic

Research

Service

Marcel Aillery, Noel Gollehon, and Vince Breneman

Electronic

Report

Technical Documentation

of the Regional Manure 

Management Model for the 

Chesapeake Bay Watershed
www.ers.usda.gov

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Aillery, Marcel P.

Technical documentation of the Regional Manure 

Management Model for the Chesapeake Bay watershed.

(Technical bulletin (United States. Dept. of Agriculture.); 

no. 1913)

1. Livestock-Manure-Handling-Chesapeake Bay Region 

(Md. And Va.)-Costs-Mathematical models.

I. Gollehon, Noel R. (Noel Ray), 1954- 

II. Breneman, Vince.

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

Service.

IV. Title.

HE5621.372.A123
United States

Department

of Agriculture

www.ers.usda.gov

Electronic Report from the Economic Research Service

Technical

Bulletin

Number 1913

Technical Documentation of

the Regional Manure

Management Model for the

Chesapeake Bay Watershed

Marcel Aillery, Noel Gollehon, and Vince Breneman

March 2005

Abstract

The Regional Manure Management Model, developed for the ERS project on

“Manure Management for Improved Water Quality,” is used to evaluate the

cost and feasibility of manure land application as a manure management

strategy at the regional level. This model is a nonlinear mathematical

programming model of animal manure-nutrient production and distribution

applied to the Chesapeake Bay watershed. The model is designed to assess

regional costs of manure management, transport, and land application in the

Chesapeake Bay watershed, given the existing structure of the animal industry

and manure-storage technologies currently in use. Manure-nutrient production

is allocated within the basin to minimize costs to the animal sector, subject to

land availability and policy provisions. A defining feature of the modeling

system involves the integration—within an optimization framework of spatial

data from a Geographic Information System and farm-level data from the

1997 Agricultural Census—aggregated to the county level. The framework

captures important spatial interactions involving animal concentrations and

land available for manure spreading that can significantly affect manure land

application costs faced by animal producers. 

Keywords: Technical documentation, regional analysis, Chesapeake Bay,

animal waste, manure management, nutrient management plan, manure land

application, manure transport, cost minimization, optimization model.
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Acknowledgments

We would like to thank Utpal Vasavada, Margriet Caswell, Marca Weinberg,

and Marc Ribaudo for providing administrative support throughout development of the regional modeling analysis under the Manure and Water Quality

Research Project. Valuable assistance in data development and document

review was provided by Jean Agapoff, Shawn Bucholtz, Glenn Carpenter,

Lee Christensen, Robert Johannson, Robert Kellogg, Daniel Meyer, Dave

Moffitt, Jim Pease, and Arthur Stoecker. Finally, we appreciate the editorial

assistance of Carol Jones, Sharon Lee, and Tom McDonald in preparation of

the technical documentation, and we also thank Wynnice Pointer-Napper for

the design and layout of the report.

Cover Photo: NASA - Goddard Space Flight Center Scientific 

Visualization Studio.
Contents

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

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

Overview of Modeling Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Model Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

Model Spatial Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

Model Variables and Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Model Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

Manure-Nutrient Production and Use . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

Technology Use and Input Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

Distance Functions for Manure Hauling . . . . . . . . . . . . . . . . . . . . . . . . .14

Model Equation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21

Output Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26

Conclusion and Future Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34

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Summary

Regional concentrations of farms raising livestock and poultry in confinement and their potential adverse effects on water quality and public health

have prompted Federal regulations and guidelines on animal waste management. The primary emphasis of recent policies is to limit the application of

manure to land at rates no greater than those at which crops can extract

specified nutrients from the manure. Limiting the rate of manure application

will limit potential nutrient runoff to surface water. However, many confined

animal operations have insufficient land on the farm to land-apply all the

manure produced without exceeding nutrient requirements of the crops

grown. Some of the manure, therefore, must be moved off the farm to be

spread on other land in the area. In regions with many confined animals,

manure-hauling distances (and the resultant costs) are determined largely by

the spatial distribution of land area available for manure application relative

to the location of animal operations requiring additional land. 

What Is the Issue?

An accurate assessment of the costs of manure hauling and land application

argues for a regional perspective that accounts for the acreage needed (in

addition to land on the animal farm itself) and that considers spatial interactions across animal operations and agricultural land resources. A regional

analysis—in contrast to farm-level or national sector assessments—can be

more readily designed to capture the effect of competition for limited land

resources under alternative policy settings. As part of a broader ERS assessment of the costs of manure management, a regional modeling framework

was developed to evaluate the effect of Federal guidelines for farmland

application of manure on hauling and spreading costs. Results from an

initial application of the modeling system are featured in the ERS publication Manure Management for Water Quality: Costs to Animal Feeding 

Operations of Applying Manure Nutrients to Land (AER-824, June 2003). 

This report, Technical Documentation of the Regional Manure Management

Model for the Chesapeake Bay Watershed Model, presents details of the

regional modeling system applied to production and disposal of animal

manure in the Chesapeake Bay watershed—an environmentally sensitive

area with large concentrations of confined animals. The model is used to

evaluate the feasibility of land application of manure as a regional manure

management strategy and the effect of key policy provisions and manure use

assumptions on costs to the animal sector. The report includes an overview

of the model’s scope and structure, data sources, and modeling assumptions.

How Was the Study Conducted?

The model is designed to assess regional costs of managing the manure on

the farm of origin and transporting and spreading it on area farmland, given

the existing structure of the animal industry, manure-storage technologies,

and alternative manure disposal options currently in use. The modeling

system is centered on a nonlinear mathematical programming model of

animal manure-nutrient production and distribution.

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The regional model allocates manure nutrients produced within the Chesapeake Bay basin to agricultural land for crop use to minimize hauling and

land application costs incurred by the regional animal sector, given land

availability and nutrient management policies. The model was defined at a

watershed spatial scale that includes portions of six States (Virginia, Maryland, Delaware, Pennsylvania, New York, and West Virginia) to account for

the regional distribution of crop and pasture land as well as the animal operations competing for available land resources. A watershed scale is also

appropriate for potential modeling extensions designed to assess implications of Federal manure management policies on water quality in the Chesapeake Bay. 

A defining feature of the regional modeling system involves the integration

within an optimization framework of (1) cropland coverage from a

Geographic Information System (GIS) and( 2) farm-level data from the

1997 Agricultural Census, aggregated to the county level. Counties within

the watershed serve as the primary modeling unit, providing consistency

with Census of Agriculture data and other county-level data. County-level

specification permits subregional differentiation in animal production,

nutrient uptake, waste technologies, and regulatory conditions across county

and State boundaries within the watershed. Reliance on national data series

for key model parameters (e.g., number of animals) is an important element

of the modeling framework, ensuring consistency of data within the watershed while facilitating the potential for model updates and transferability of

the model to other U.S. watersheds.

Key decision variables in the model include the quantity of manure transported by system type, the hauling distance of manure moved off the farm,

and acres used for manure spreading in receiving counties. The direction

and magnitude of manure transfers is determined by the nutrient and moisture content of the source manure, the nutrient uptake capacity of receiving

lands, and per-unit costs of manure hauling and land application. 

What Did the Study Find?

The regional modeling framework provides a unique and valuable perspective on the cost of Federal regulations and guidelines for manure land application. The integration of Census and GIS data enables the regional model

to capture important spatial interactions between animal concentrations and

land available for manure spreading. 

With a large proportion of animal producers dependent on land off the farm

for manure spreading, competition for available land resources is an important consideration in the costs of managing manure. Competition for land

and the resultant hauling requirements and costs of manure management

will depend, in turn, on the (policy-determined) manure application rate,

quantities of manure that can be used for industrial purposes, and

landowners’ willingness to accept manure on farmland. 

During the initial application of the model, a number of potential model

improvements and extensions were identified. Several priority extensions of

the model are under development for future model applications, as outlined

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in the conclusion of this report. For a review and analysis of findings from

the initial application of the model, see Manure Management for Water

Quality: Costs to Animal Feeding Operations of Applying Manure Nutrients

to Land (AER-824, June 2003).

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Introduction

This report presents a discussion of the Chesapeake Bay Regional Manure

Management Model, developed for use in the U.S. Department of Agriculture’s (USDA) Economic Research Service (ERS) project on “Manure

Management for Improved Water Quality.” The report is intended to document the modeling framework, addressing model purpose and scope, model

structure, parameter assumptions, data sources, and output generation. The

model is designed to capture the spatial relationship between manurenutrient production and land available for manure spreading, and implications for manure-hauling costs under Federal guidelines for animal waste

management. The discussion highlights the use of farm-level survey data

and cropland coverages from national databases within a regional optimization framework. Discussion of the modeling framework draws on empirical

findings and visual presentations from the ERS analysis of costs of manurenutrient management (Ribaudo et al., 2003). 

In 1999, the Environmental Protection Agency (EPA) and USDA issued

joint guidelines for regulatory and voluntary measures to protect water

quality and public health from animal-waste pollution. In 2003, EPA

published new regulations affecting an estimated 15,500 concentrated

animal-feeding operations (CAFOs) (U.S. EPA, 2003). Meanwhile, USDA

has a stated goal that all animal-feeding operations (AFOs) develop and

implement comprehensive nutrient management plans (CNMPs) to minimize potential pollutant loadings from confined animal facilities and manure

land application (USDA, NRCS, 2000). Nutrient standards that cap total

applied nutrients—including manure nutrients—based on crop need (cropbased rates) provide the basis for manure application rates under both the

USDA policies and EPA regulations. Implementation of nutrient standards

will likely impose additional manure-hauling requirements in regions with

concentrations of confined animal production. With limits on applied

manure per acre, more land is required for manure spreading than is often

available on animal-feeding operations and nearby farms, resulting in

increased competition for available acreage and greater hauling distances. 

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Technical Documentation 

of the Regional Manure

Management Model for the

Chesapeake Bay Watershed

Model version used for the ERS Project

“Manure Management for Improved 

Water Quality”

Marcel Aillery, Noel Gollehon, and Vince Breneman
As part of the Manure Management for Improved Water Quality Project at

ERS, a regional modeling framework was developed to evaluate the effect

of crop-based nutrient application rates (reflected in Federal guidelines and

regulations) on costs of manure hauling and land application. Information

developed by USDA indicates that many confined animal operations have

insufficient land on the farm to spread all of their manure at crop-based

rates (Kellogg et al., 2000). The effect of nutrient standards for land application will require that much of the manure be moved off the confined animal

farms. Where animal production is concentrated, manure-handling costs

faced by producers are determined largely by the spatial distribution of land

area available for manure application and the level of competition among

animal farms for available land; those two factors together determine the

hauling distance required to access available land. An accurate assessment

of the costs of manure hauling and land application argued for a regional

perspective that considers spatial interactions across animal operations and

agricultural land resources, and the effect of limited land resources in areas

where confined animal production is concentrated. 

The modeling framework was applied to the Chesapeake Bay watershed

(fig. 1). The Chesapeake Bay is among the largest and most biologically

rich estuaries in the world. However, excessive nutrient loads from various

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Figure 1

Chesapeake Bay watershed

Source: Ribaudo et al., 2003.

New York

Pennsylvania

West Virginia

Virginia

Maryland

Delaware

New York

Pennsylvania

West Virginia

Virginia

Maryland

Delaware
sources—including wastewater treatment plants, urban runoff, fertilizer

applications, animal waste, and atmospheric deposition—have resulted in

eutrophication and related ecological shifts that adversely affect wildlife and

aquatic resources (Preston and Brakebill, 1999). The declining health of the

Bay ecosystem in recent decades has prompted a major Federal/State initiative to reduce excessive nutrient loading to the Bay and tributary streams.

Animal agriculture is potentially a major source of nitrogen and phosphorus

loadings due to concentrations of large confined animal feeding operations

in some areas of the watershed. The Chesapeake Bay watershed encompasses several multi-county areas where the volume of manure-nutrient

production from confined animal operations exceeds the capacity of area

cropland when manure nutrients are applied at crop-based rates (Gollehon et

al., 2001). Federal guidelines and regulations using crop-based nutrient rates

are likely to have significant cost effects in areas of the Chesapeake Bay

watershed where competition exists for land on which to apply manure. 

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Overview of Modeling Framework

At the heart of the regional analysis conducted for the Manure Management

for Improved Water Quality Project is a nonlinear mathematical programming model of animal manure-nutrient production and distribution developed for the Chesapeake Bay watershed (fig. 2). The Chesapeake Bay

regional model was developed with GAMS (General Algebraic Modeling

System) version 20.7, using the MINOS solver for large non-linear applications.1 The model is designed to assess regional costs of manure management, transport, and land application in the Chesapeake Bay watershed,

given the existing structure of the animal industry and manure-storage technologies currently in use. Manure production is allocated to crop and

pasture land within the basin to minimize costs to the regional animal

sector, subject to land availability, nutrient uptake capacity, and nutrient

management policies in effect. The model is used to evaluate the cost and

feasibility of land application for manure disposal, and the effect of key

policy provisions and manure use assumptions on costs to the animal sector. 

A defining feature of the modeling system involves the integration, within

an optimization framework, of cropland coverages from the Geographic

Information System (GIS) and farm-level data from the Agricultural Census,

aggregated to the county level. The framework captures important spatial

relationships involving animal concentrations and land available for manure

spreading that can significantly affect manure land application costs faced

by animal producers. Moreover, the reliance on national data series for key

model parameters is itself an important element of the modeling framework,

ensuring consistency of data across the watershed while facilitating the

1 Model applications were solved successfully on a personal computer with a

Pentium 4 processor and 1 GB of RAM.

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Figure 2

Regional modeling system

NLCD data

spatial distribution

of cropland

Optimization Model

(GAMS)

Policy

scenarios

Calculated

manure nutrients

& manure quantities

Estimated

area-distance

functions

(GIS)

NRCS, ARMS,

universities & industry

technology & costs

Agricultural

census data

farms, animals, & land

Output

report

Simulate initial

conditions

over a range

Solution

files Maps

Source: U.S. Department of Agriculture, Economic Research Service.
potential for model updates and the potential transferability of the model to

other U.S. watersheds.

Model Scope

As with any model, its strengths and limitations are reflected in the study’s

objectives, methodology, and analytic assumptions. The following may help

to clarify the reach of the modeling framework presented here:

z The regional modeling framework provides a unique and valuable perspective on the effect of Federal regulations and guidelines for manure

management, both on and off the manure-producing farm. As a large

portion of manure transport costs are determined by conditions off the

farm, the regional model captures important spatial interactions in animal concentrations and land available for manure spreading. The effect

of spatial considerations on sector costs are not as readily addressed in

farm-level or national-sector analyses. 

z Reliance on national data series for key model parameters ensures consistency of data across States in the watershed. County-level specification permits important subregional differentiation in such cost determinants as animal production by species, nutrient uptake, waste technologies, and regulatory conditions across county and State boundaries.

However, representative costs in the model may not accurately reflect

costs faced by all animal operations in a region.

z The model assesses, in particular, the cost and feasibility of manure land

application in the Chesapeake Bay watershed. While manure land application is an important element of EPA regulations and USDA nutrient

management policies, the model is not designed to assess the full cost

effect of a specific Federal regulation or program. 

z The model focuses on those costs specific to manure hauling and land

application. Cost categories not considered in the model include manurestorage infrastructure and processing. Moreover, additional capital, labor,

and equipment costs not captured in the model may be needed to achieve

the extent of manure land application addressed in the ERS study. A

recent Natural Resources Conservation Service (NRCS) assessment suggests that these costs may be substantial (USDA, 2003).

z The model provides a static, single-year assessment of sector costs given

prevailing production conditions in the latter 1990s. The model does not

endogenously capture adjustments in animal concentrations, crop mix,

and manure-handling systems in response to manure management policies and potential attendant changes in base estimates of manure-nutrient

excess and nutrient assimilative capacity of cropland.

z Measures of manure-nutrient excess are computed from farm-level survey information, based on reported manure production and agronomic

rates of land application. Applied manure in the model reflects calculated rates under a nitrogen or phosphorus standard, as actual rates and patterns of manure land application are unavailable. Thus, the model can be

used to assess costs under alternative nutrient standard specifications.

However, since we do not have data on actual application rates in the

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1997 census base year, we cannot compare costs before and after the

imposition of standards. 

z As a cost-minimization model, the framework provides a partial analysis

of the least-cost means of manure land disposal, based on management

alternatives specified under a given scenario. The model does not assess

changes in the profitability of animal production, since output prices and

substitution possibilities are not considered.

z While land application of manure at agronomic rates is motivated by

water policy concerns, the model is not designed to assess water quality

per se. The model allocates manure across the basin, consistent with

land-based nutrient standards, but does not currently track potential

nutrient loadings to water bodies. An assessment of water-quality implications would require integration with other modeling tools that consider

nutrient fate and transport and resulting water-quality effects.

Model Spatial Scale

The modeling analysis is defined at a watershed spatial scale. The basin

encompasses approximately 64,000 square miles over portions of six

States—Virginia, Maryland, Delaware, Pennsylvania, New York, and West

Virginia. A watershed-wide scale was important to account for regional

distribution of crop and pasture land and animal operations competing for

available land resources. The watershed scale is also appropriate for future

modeling extensions that would address implications of Federal manure

management policies on water quality in the Chesapeake Bay. While typically run at the full watershed scale, the model may be customized to run at

a smaller county-aggregate scale useful for model development and/or

analysis of local issues.2

The county serves as the primary modeling unit for the regional model. The

county-level specification provides consistency with Census of Agriculture

data and other data available at a county level. At the same time, the county

scale permits differentiation in animal production, nutrient uptake, and

waste technologies across county and State boundaries within the watershed. Subregional variation in regulatory conditions may also be incorporated, where regulations are specified at a State or county level.

The full basin model includes 160 non-municipality counties with farmland

in the Chesapeake Bay watershed, each potentially representing a source

and a destination county. Manure is produced in a source county and landapplied (or otherwise disposed of) in a destination county. Sink counties, or

destination counties with cropland wholly outside the basin area, serve as

potential receiving areas for manure exported from the watershed. The full

watershed model also includes 55 sink counties that are non-municipality

counties within 60 kilometers (37 miles) of cropland in the Chesapeake Bay

watershed, measured from the edge of the source county’s cropland base. Of

the 160 basin counties in the model, 52 are edge counties containing a share

of cropland acreage outside the watershed. In edge counties, manurenutrient use is apportioned by share of cropland within the basin to more

accurately account for effects at a watershed scale.3 Appendix 1 provides a

list of basin and sink counties included in the model. 

2 The user specifies the set of States

and/or counties to be included in a

given model run. 

3 The share of cropland within the

Chesapeake Bay watershed was calculated within the Geographic

Information System, using an overlay

of the watershed boundary over the

U.S. Geological Survey National Land

Cover Dataset.

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Counties are further disaggregated according to a 12-square kilometer grid

system. The sub-county grids are used to spatially assign land available for

manure application within a given county (hereafter termed “spreadable”

land) to match observed cropland and pasture land coverages. The subcounty grids are also used to assign location of animal operations (discussed

under “Model Data: Distance Functions for Manure Hauling”). While

manure flows are aggregated at the county level, transport costs are calculated based on manure quantities and hauling distance from a specific

county grid point. There are 1,857 sub-county grid areas with animal farms

included in the Chesapeake Bay regional model. 

Model Variables and Activities

Key decision variables in the model include the quantity of manure transported by system type, the hauling distance of manure moved off the farm,

and acres used for manure spreading in receiving counties. The model allocates manure across the basin to minimize the regional cost of manure

hauling and land application. The direction and magnitude of manure transfers are shaped by the nutrient and moisture content of the source manure,

the nutrient uptake capacity of receiving lands, and per-unit costs of manure

hauling and land application. In addition, policy provisions for nutrient standards, as well as assumptions on manure use for industrial purposes and

landowner willingness to accept manure on cropland, have an important

bearing on regional manure allocations and sector costs. (See Appendix 2

for a listing of model variables.)

Off-farm manure transfers, including within-county and out-of-county destinations, represent the primary activities in the model. Potential county-tocounty transfers were developed based on an assumed maximum radial

distance of 60 kilometers (km), or 37 miles, measured from the outer edge

of the source county’s cropland base. For the 10 percent of modeled counties with the largest manure surplus to available land base, a maximum

radial distance of 150 km (93 miles) was assumed. This combination of

distances provided the model with a 150-km distance in the cases where

spreadable land area is most limiting and long-distance transport might be

needed, while avoiding unneeded transfer possibilities in most counties

where 60 km is adequate. Even with adjustments in maximum transport

distance by county to reduce transfer options, there are still 4,060 countylevel transfer possibilities in the full watershed model, including withincounty and out-of-county transfer combinations.

County-level manure flows represent off-farm manure transfers from all

confined animal farms. Manure transfers are disaggregated by sub-county

source grid, manure system type, and distance interval to more accurately

assess manure-hauling costs. The full model includes over 300,000 transfer

alternatives.4 The maximum set of potential county-grid transfer alternatives

generated through the automated GIS procedure was filtered to exclude

county combinations with little or no probability of occurrence.5 The

filtering process slightly reduced the dimensionality of the model, which

helped to reduce model convergence time.

4 There are roughly 372,000 variables

and 288,000 equations in the full

model specification.

5 Filtering criteria for manure transfers

excluded: (1) source counties with

zero manure surplus countywide, (2)

destination sink counties with zero

excess land capacity (after accounting

for within-county manure surplus),

and (3) county-to-county combinations

involving source counties with

extremely low manure surplus per

cropland area within-county (<0.01

ton/ac) and more than a limited hauling distance (>5 linear km) to access

out-of-county lands; source counties

with very low manure surplus per

cropland area (>0.01 and <0.1 ton/ac)

and long hauling distances (>20 km);

destinations counties with extremely

high manure surplus per cropland area

(>0.4 ton/ac) and very long hauling

distances (>60 km); destination counties with high manure surplus per

cropland area (>0.25 ton/ac) and

extremely long hauling distances

(>120 km), and destination counties

with limited cropland area (< 15,000

acres) and extremely long hauling distances (>120 km). Source-county grid

transfers were limited to those grids

with AFOs. Maximum hauling distances were also applied on lagoon

and slurry manure with high moisture

content (10 and 50 miles, respectively), limiting potential county-tocounty options for these systems.

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Model Data

The two primary data sources for the model include the 1997 Census of

Agriculture and the 1994 National Land Cover Dataset. The 1997 Census of

Agriculture was administered by the National Agricultural Statistics Service

(NASS), USDA. Primary data processing for this analysis was conducted by

the Economic Research Service (ERS), USDA, and Natural Resources

Conservation Service (NRCS), USDA. The resulting database provided base

model data on animal farms, numbers of animals (used to estimate manure

production), and cropped area and production (used to estimate the land

assimilative capacity of manure nutrients). The 1994 National Land Cover

Dataset, developed by the U.S. Geological Survey (USGS), was used to

establish the spatial pattern of land available for manure spreading. The

resulting land coverage was used as a basis for developing distance functions for manure hauling and simulating the spatial distribution of animal

operations. In addition to the two primary data sources, technology and cost

coefficients applicable to the Chesapeake Bay watershed were obtained

from various sources, including the Costs Associated with Development and

Implementation of Comprehensive Nutrient Management Plans prepared by

NRCS (USDA, NRCS, 2003), the Agricultural Resource Management

Survey (ARMS) data developed by NASS and ERS (USDA, 2002 and

2000), and additional data obtained from published literature and subject

matter specialists within the Government and universities.

Manure-Nutrient Production and Use

Farm-level data collected for the 1997 Census of Agriculture were used to

estimate county-level measures of animal operations and animal-units, total

manure production, surplus recoverable manure (in excess of source-farm

crop need), manure-nutrient content, and potential assimilative capacity of

the land for applied manure nutrients (USDA, NASS, 1999). Farm-level

measures were computed from the Agricultural Census and other technical

data. Results from the farm-level calculations were then aggregated to the

county level and combined with data from various other sources for analytic

and modeling purposes.6 Census data coefficients are computed following

procedures in Gollehon et al. (2001) and Kellogg et al. (2000). 

Animal operations. The analysis focuses on confined animal species since

they represent the primary source of excess manure nutrients produced on

farms with confined animals. Animal species types considered in the

analysis include: feedlot beef, dairy, swine, and poultry (chicken and

turkey). Numbers of confined animals and numbers of farms with confined

animals—or Animal Feeding Operations (AFOs)—were obtained by county

from the Census of Agriculture. This subset of animal farms does not represent the total production of manure nutrients, but rather the nutrient production for those operations for which State and Federal animal-waste disposal

policies are most relevant. 

Manure-nutrient production. Production of primary manure nutrients—

nitrogen and phosphorus—is estimated based on census-derived animal

numbers and coefficients of manure production by animal type. Computation of manure nutrients followed a three-step process. First, animal

6 Our analysis meets all respondent confidentiality requirements of the published Census of Agriculture values.

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numbers were converted to an average number of annual animal-units7 (AU)

from reported end-of-year inventory and annual sales data. Second, quantities of manure were computed using coefficients of manure production by

animal type and the number of AU. Data development on manure production was geared primarily to AFOs operating above a minimum scale to

reflect commercial operations.8 Third, the recoverable portion of the manure

nutrients per ton of manure was computed by animal type after adjusting for

losses during collection, transfer, and storage. Recoverable manure nutrients

represent that portion of manure that can be collected and applied to land

net of storage and handling losses at the source site. Nutrient content of

recoverable manure reflects a composite nutrient composition of manure

produced by county, based on county-level distributions of animal species

from the Census of Agriculture. (See Kellogg et al. (2000) for details of the

estimation process for manure-nutrient production and loss coefficients.)9

Nutrient assimilative capacity. Farmland assimilative capacity for nutrients

is estimated across farm types (i.e., non-animal farms, confined animal

farms, and non-confined animal farms) based on acreage and reported yields

for major field crops and pasture, aggregated to the county level. Farmland

acreage available for manure spreading is calculated based on acreage in 24

major field crops and permanent pasture from the Census of Agriculture.10

Crop and pasture land acreage in out-of-basin sink counties is assumed

available for manure from the watershed, after adjusting for application of

locally produced manure within the sink county. See Kellogg et al. (2000)

for details of the estimation process for manure-nutrient uptake coefficients.

Manure-nutrient excess. Manure-nutrient excess refers to the quantity of

manure that cannot be spread at crop-based agronomic rates on the source

animal farm and thus must be hauled off the farm for land application.

Manure-nutrient excess is computed under both a nitrogen-based (N) standard and a phosphorus (P) standard. These standards differ by the nutrient

that determines the per-acre crop application rate, with a P standard generally allowing less manure per acre. Onfarm manure-nutrient excess is estimated by applying farm-level census measures of manure-nutrient

production relative to the farm’s potential to use nutrients for crop production. Excess recoverable manure nutrients are calculated as those that

exceed the onfarm assimilative capacity of confined feeding operations,

based on the amount of land controlled by farms with confined animals.11

County surplus manure to be hauled off-farm is calculated for each nutrient

standard based on an aggregation of farm-level manure-nutrient excess

across animal farms.

Land base for surplus manure. The farmland base potentially available for

surplus manure is defined to include all cropland and pasture land in 24

major crops on non-animal farms and some portion of acreage in those

crops on both confined and nonconfined animal operations. Acreage in

nonconfined operations was adjusted for nonrecoverable manure-N available

on the farm. Acreage in confined animal operations is from farms with

surplus capacity to absorb off-farm manure nutrients, accounting for their

own crop nutrient needs.

7 Annual animal-units reflect a biologically based definition of an AU of

1,000 pounds of live animal weight for

feedlot beef, dairy, swine, and poultry,

using average animal weights. 

8 Operations were included if: (1) animals generated more than $2,000 in

sales on the farm, or (2) at least three

AU were reported on the farm.

Confined animals and their minimum

scales were: feedlot beef (15 head),

dairy (20 head), swine (50 head for

slaughter), and poultry (100 head of

broilers or 50 head of layers or

turkeys). Of particular note, these data

do not include estimates of the recoverable portion of manure from cattle,

other than fattened cattle and milk

cows (bulls, beef cows, dairy and beef

replacement heifers, calves less than

500 pounds, and calves greater than

500 pounds not in a feedlot). If cattle

other than fattened cattle and milk

cows were included in the analysis,

farm numbers would double, the number of AU would increase by only 6

percent, and recoverable manure nitrogen would increase by about 5 percent. 

9 Adjustments in base manure-nutrient

composition measures to reflect

changes in animal mix, feed mix,

genetic stock, and nutrient losses may

be incorporated into the model through

a series of factor adjustments for nitrogen and phosphorus. 

10 Adjustments in the composite

uptake rate by county to reflect

changes in crop mix and/or crop yield

may be incorporated into the model

through a series of factor adjustments

applied by farm type for crop and pasture land.

11 We recognize this calculation

process has the potential to overstate

excess manure nutrients since some

manure is moved off many production

farms. However, total excess nutrients

on confined livestock farms were more

likely to be understated since neither

commercial fertilizer applications nor

atmospheric deposition of nutrients

were considered in this analysis. Most

crop farms without animals, and many

farms with animals, use chemical fertilizers because they are less bulky,

easier to apply, and have a more predictable nutrient content than manure.

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The model assumes that all acreage on confined animal operations is available for manure spreading. In the case of non-animal farms and nonconfined animal farms, a given percentage of total farmland base is assumed

available for spreading, reflecting assumptions on the willingness of

landowners to accept manure. Landowners may be reluctant to accept

manure for various reasons. These factors include uncertainty about manurenutrient content and availability, high transportation and handling costs relative to commercial fertilizer, soil compaction from spreading equipment,

dispersion of weed seeds, concerns for added regulatory oversight, and

public perception regarding odor and pathogen issues (Risse et al., 2001).

While little data exist on levels of landowner willingness to accept manure

on their fields, findings from this empirical study suggest that this is an

important determinant of costs facing animal producers. Adjustments to

reflect willingness to accept manure, specified separately for cropland and

pasture land, are used to reduce the model land base effectively available for

manure spreading.12

Manure application rates. Application rates for manure applied off-farm are

computed for each within-county and out-of-county transfer based on

average nutrient content of manure from the source county and average peracre nutrient uptake on farmland in the destination county, adjusted for

nutrient standard requirements and field losses. Average manure-nutrient

composition by county is derived from animal mix data from the Agricultural Census and coefficients on nutrient production per AU (Kellogg et al.,

2000). Average per-acre nutrient uptake rate by county is derived from cropping pattern and yield data from the Agricultural Census. Application rates

and total quantity of manure that can be applied are tallied separately for

confined animal farms to reflect the cropping patterns and yields specific to

farms with confined animals.13

Manure application rates in the model vary with the nutrient standard in

effect. Under an N standard, manure is applied based on crop nitrogen needs

over the growing season; under a P standard, manure applications are based

on crop phosphorus needs. Manure applied according to a P standard is

generally applied at a lower per-acre rate than under an N standard,

implying more land is needed for a given quantity of manure.14 Reduced

application rates under a P standard reflect the ratio of N and P requirements of most crops relative to the N and P ratio typical of most manure.

The model user may specify the share of acres required to meet a given

nutrient standard if values are known, with variable shares permitted across

county subregions and crop and pasture land categories.15

Manure application rates are further adjusted to reflect the level of application loss. An estimated 30 percent of manure-N applied is not available to

the crop due to unavoidable losses of nitrogen, primarily from volatization

of ammonia. Applied manure under the N standard allows for sufficient

manure-N to meet both full crop needs for nitrogen, plus the 30-percent

field loss (Kellogg et al., 2000). An additional loss adjustment factor reflects

the extent of manure incorporation—the base N loss factor is adjusted

downward by 5 percent for fields with soil incorporation and 30 percent for

fields without incorporation (Fleming et al., 1998). 

12 In order to bound potential cost estimates, the ERS study fixed levels of

willingness to accept manure over a

range from 10 percent to 100 percent

of crop and pasture land on non-animal farms and nonconfined animal

farms (Ribaudo et al., 2003). 

13 Manure application rates are automatically modified to reflect adjustments in

manure nutrient content (due to changes

in feed supplements or animal mix) and

nutrient uptake rates (due to changes in

cropping patterns or yields).

14 Under a multi-year P standard,

applied manure per acre is equivalent to

that under an N standard, with treated

acres rotated over a multi-year sequence

to fully use excess stored manure-P,

thus minimizing application costs. 

15 Since reliable data on the share of

land requiring the more stringent phosphorus standard are not available at a

watershed scale, separate model scenarios were specified in the ERS study

as if all acres would apply manure

according to either an annual N or P

standard, thus bracketing the full range

of possible cost effects. 

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Technology Use and Input Costs 

Technology use and input cost data supplemented available production data

from the Agricultural Census in assessing costs of production adjustments

within the animal sector. Data categories involve nutrient management

plans; manure storage and handling systems; commercial fertilizer offsets;

industrial uses of manure; and feed supplements.

Nutrient management plans. Implementation of nutrient management plans

is recommended under USDA guidelines for all confined animal operations

and required under new Federal regulations for CAFOs. NRCS was the

primary source of cost data for nutrient management planning (USDA,

NRCS, 2003). Cost components for manure management addressed in the

study include plan development, manure testing, and soil testing. Costs for

plan development and manure testing are applied to the source county; soil

testing costs are applied to the destination county. Plan development cost

($400/confined animal farm) was calculated as an annualized cost of developing the nutrient management elements of a CNMP based on an average of

45 hours per farm and $45/hour. Manure testing cost ($200/farm) reflects

collection ($10) and analysis ($40) four times annually, applied over the

number of confined animal farms in the watershed. Annual soil testing cost

($0.40/acre receiving manure) is based on $20/sample with 10 acres per soil

test, or $2/acre, and one soil test every 5 years. Nutrient management plan

costs not specifically related to manure land application, such as recordkeeping and visual inspection, are not addressed here. Costs associated with

training and certification for manure application and calibration of the

manure spreader were assumed to be incorporated within reported application costs per ton of manure hauled.

Manure storage and handling systems. Manure production levels from the

Agricultural Census were apportioned by manure storage and handling

systems by county (table 1). Three representative manure system categories

were defined in the study—lagoon systems (open, uncovered storage),

slurry systems, and dry systems (primarily poultry litter in the Chesapeake

Bay watershed). Allocation of manure production by storage/handling

system was necessary to capture important cost differences across manurehauling modes, hauling weight, and application. 

Allocation of manure production by manure-storage system category was

computed based on AUs by species as a share of total confined AUs, system

shares by animal species, and manure generation per AU. Animal-units by

species as a share of total confined AUs were obtained from the information

developed from the Agricultural Census. Information on manure system

shares for hog and dairy operations was obtained from Agricultural

Resource Management Survey (ARMS) data (USDA, ERS, 2000). Manure

shares for lagoon, slurry, and dry systems by animal species were based on

animal operations with a single system. Hog values were based on reported

values for Virginia, the sole State in the Chesapeake Bay watershed represented in the 1998 ARMS hog survey. Dairy values were reported for

Virginia, New York, and Pennsylvania in the 2000 ARMS dairy survey, with

estimates for Delaware and Maryland based on Pennsylvania. Beef cattle

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estimates were assumed equivalent to dairy estimates by State. Poultry

production is assumed to use dry litter systems.

Manure-hauling weights are based on wet tons of manure, which contain moisture and bedding content that vary by manure system and species type (USDA,

NRCS, 1999; and Barker et al., 2001). Wet manure weights are estimated from

a dry manure weight (theoretic zero-moisture weight), adjusted by moisture

and bedding material. Dry manure estimates per AU in dry tons per species

type are: dairy, 2.156; feedlot beef, 1.143; swine, 1.2635; and poultry, 3.0

(Kellogg et al., 2000). Estimates of moisture content by system type are as

follows: lagoon, 99 percent (all species); slurry, 90 percent (all species); dry,

30 percent (poultry); and dry, 50 percent (non-poultry). Manure bedding as a

percentage of dry manure tonnage, by species, are: dairy, 30 percent; poultry,

10 percent; and feedlot beef and hogs, 0.16

Model costs for manure hauling and application are presented in table 1.

Hauling and application charges were based on published literature (Pease

et al., 2001; and Fleming et al., 1998), supplemented with data from NRCS

(USDA, NRCS, 2003). Charges reflect a base rate per wet ton (manure

loading/unloading and application) and cost per ton-mile (manure hauling).

Charges are specified by storage/hauling mode and distance interval to

reflect substantial differences in per-unit costs. (Application costs, expressed

on a per-ton basis, are separated out for reporting purposes).

Maximum hauling distances for lagoon and slurry waste were fixed at 10 and

50 road miles, respectively; hauling distance for dry litter system waste was

bounded by maximum transport distances in the model. Hauling costs were

based on a round trip distance, with no backhauling. All manure-hauling costs

are applied to the source county, although the model provides flexibility in

assigning a share of costs across source and destination counties. 

Manure incorporation costs—not reflected in application costs above—

assume a cost of $6.00 per acre (Iowa State Farm Survey, 2001), with an

16 While moisture content varies with

the manure system type, manure-nutrient content per dry ton of manure is

based on a composite across species

by county and is not varied by manure

system type. 

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Table 1—Manure-hauling and application costs by system type 

System Distance Base Application Distance

type interval Hauling mode charge1 cost only charge

Miles ————— $ per ton ———— $ per mile

Lagoon Onfarm Pump/spray field 1.25 0.375 0.25

0.5-2.0 Truck mounted liquid sprayer 2.00 0.600 0.30

2.0-10.0 Truck mounted liquid sprayer 2.00 0.600 0.30

Slurry Onfarm Tractor/spreader (honey wagon) 2.00 0.600 0.30

0.5-2.0 Truck mounted liquid sprayer 2.00 0.600 0.30

2.0-10.0 Tanker truck 2.00 0.600 0.30

>10.0 Tanker truck 2.00 0.600 0.30

Dry Onfarm Spreader truck 6.00 1.400 0.50

0.5-2.0 Spreader truck 6.00 1.400 0.50

2.0-10.0 Truck 10.00 3.700 0.11

>10.0 Truck 10.00 3.700 0.11

1 Includes cost of manure hauling/unloading and land application (without incorporation).

Sources: NRCS, 2003; Fleming et al., 1998; Pease et al., 2001; and Borton et al., 1995.
estimated 40 percent of acres using incorporation based on information from

the ARMS hog and dairy surveys.

Commercial fertilizer. The calculation of savings from fertilizer offsets

assumes that organic nutrients from manure replaces chemical fertilizer on a

1:1 basis. Calculation further assumes that only the manure nutrients beneficially used in crop production are valued. Thus, excess P applied under a

nitrogen standard is not considered in calculation of fertilizer savings, i.e.,

no benefit was given for manure nutrients in excess of crop needs. Moreover, savings do not consider the additional benefits of manure as a soil

amendment (organic matter and soil tilth).

Chemical fertilizer cost savings are based on reported 1997 prices by

USDA’s National Agricultural Statistics Service (NASS), based on representative fertilizer products for the Northeastern U.S. (USDA, NASS, 2001).

Nitrogen price reflects the U.S. average price ($160/ton) for a nitrogen solution of 30 percent N, or a price per active ingredient of $0.27/lb. N. (The

30-percent nitrogen solution is selected as a representative form of N

because it was the lowest priced form of N with adequate use for NASS to

record prices for both regions—Northeast and Southeast—encompassing

area within the Chesapeake Bay watershed.) Phosphorus price reflects the

price per ton of triple superphosphate (45 percent P), averaged across the

Northeast and Southeast ($267/ton), or an active ingredient price of

$0.30/lb. P. Cost-savings for reduced fertilizer application costs (under an N

standard) of $5/acre were from Fleming (1998). 

Industrial uses of manure. Primary industrial uses of manure include use as

an input source for power generation and as a direct ingredient in

composted fertilizer products, primarily for specialty uses (i.e., residential,

nursery, and golf courses). Industrial uses of manure lessen the aggregate

cost of manure land application in the basin through reductions in both the

amount of manure requiring application on crop and pasture land and the

need for long distance hauls in areas where animal production is concentrated. Information on manure use in existing applications was obtained by

processing facility via personal contact with extension agents and industry

representatives. 

Manure used in industrial uses is represented in the model as an exogenous

reduction in the total supply of poultry litter manure to be land-applied.

Manure tonnage in industrial uses, expressed in wet tons (with bedding and

moisture included), is converted to dry-ton equivalents for consistency with

modeling units for manure nutrients. Reductions in dry manure tonnage

requiring land application are then apportioned across counties in the

vicinity of a given processing facility, based on the relative proximity and

volume of manure surplus by county. 

Two alternative industrial use scenarios were developed for the recent ERS

study, representing a near-term (2002-04) and mid-term (within 5-year) time

frame (table 2). An estimated 200,000 tons of poultry litter would be

diverted to industrial alternatives in the near-term, increasing to 376,000

tons within 5 years. Estimates represent approximately 0.30 and 0.65

percent of the total manure produced in the region, respectively. Near-term

estimates include existing composting facilities and two new large-scale

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plants. Future estimates reflect projected growth in composting operations,

full use of existing plants’ capacity, and the completion of industrial uses

currently in the planning or construction stage. (For more information on

industrial use scenarios, see Ribaudo et al., 2003.)

Feed supplements. Phytase has been used as a feed supplement for swine

and poultry to increase phosphorus use in feed rations and thus reduce phosphorus content of excreted manure. The model assumes a 30-percent reduction in phosphorus content per ton of dry manure with use of phytase as a

feed supplement (Council for Agricultural Science and Technology, 2002).

Use of phytase is thus represented as an adjustment in the phosphorus

content of the county-composite manure, based on manure-P generated by

species type, the mix of AUs in the county, and the share of AUs by species

receiving phytase feed supplements. The baseline model condition assumes

that phytase was not used (consistent with production conditions in the 1997

Agricultural Census survey year); additional model runs were generated to

reflect full phytase adoption on hog and poultry operations. 

Distance Functions for Manure Hauling

Hauling distances for off-farm manure spreading are assessed based on

area-to-distance functions derived from county land use patterns. These

functions are a central component of the optimization model—linking the

area needed for manure spreading in a destination county with average

transport distance required to access the area from a given source county.

By incorporating spatial relationships involving animal operations and

spreadable land area, area-to-distance functions are intended to capture the

inherent competition for land that exists among producers required to move

surplus manure off the farm. Figure 3 shows a stylized area-to-distance relationship for manure hauling.

Competition for spreadable land is, in part, a function of the spatial pattern

of spreadable area. Where farmland is scattered, a higher slope of the area14

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Table 2—Estimated county-level quantities of poultry litter (wet tons)

used for industrial purposes—1997, 2002-04, and 2005+

State/county Base case Near-term Mid-term

(1997) (2002-04) (2005+)

Delaware:

Kent 0 0 10,000

Sussex 0 72,500 118,500

Maryland:

Caroline 0 5,000 5,000

Dorchester 0 3,500 5,500

Somerset 0 0 28,700

Wicomico 0 45,000 61,000

Worchester 0 13,000 75,000

Virginia:

Accomack 0 0 12,300

Rockingham 0 60,000 60,000

Total 199,000 376,000

Source: U.S. Department of Agriculture, Economic Research Service.
to-distance relationship reflects relatively long average hauls within the

destination county to access a given acreage. Where farmland distribution is

more dense, a reduced slope reflects comparatively shorter hauls to access a

given acreage. The degree of competition also depends on the number, size,

and proximity of confined animal operations, both within and out-of-county.

Where land is limited, greater concentrations of animal production will

increase competition for spreadable acreage, resulting in longer hauling

distances to access available land and greater potential for out-of-county

manure exports. 

GIS estimation of area-to-distance functions involved a series of procedures.

First, the spatial coverage for spreadable land was developed for the Chesapeake Bay watershed (CBW) study area. Second, the location of animal

feeding operations was assigned within CBW basin counties. Third, area-todistance relationships were calculated for within-county transfers. Fourth,

distant intercepts and area-to-distance relationships were calculated for outof-county transfers. Fifth, the slope of linearized area-to-distance functions

were estimated for direct use in the model. Finally, area-to-distance relationships were adjusted to reflect adjustments in landowner willingness to

accept manure. 

Spreadable land coverage. The modeling system uses the National Land

Cover Dataset (NLCD) developed by the U.S. Geological Survey (Homer et

al., 2000; and USGS, 2004) to assess the spatial pattern of land available for

manure application. This dataset is based on 1992 Landsat thematic mapper

imagery at 30-meter resolution, classified into 21 land use categories. By

combining the crop and pasture land categories, we can assemble a spatial

data set of potentially spreadable land in all counties of the study region,

both within the Chesapeake Bay watershed and adjacent counties within a

60-km reach of the watershed boundary. Figure 4 shows the spatial distribution of crop and pasture land in a portion of northwestern Virginia. 

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Figure 3

Stylized area-to-distance relationship for manure hauling

Average hauling distance

Computed

relationship

Amax

Spreadable land area

D

Notes: Amax based on Census of Agriculture data. The computed relationship was 

based on GIS procedures applied to National Land Cover Dataset and Census of 

Agriculture data.

Source: U.S. Department of Agriculture, Economic Research Service.
Location of animal operations. The degree of competition for spreadable

land is influenced by the number, location, and size of confined animal

operations. While the number and average size of animal feeding operations

can be obtained from the census at a county level, the specific locations of

operations within a county were unavailable. (The census does not collect

precise locational information, and the data are not generally available at a

regional scale from other sources.) Therefore, animal operations in the

Chesapeake Bay watershed had to be locationally assigned by county within

the GIS. For purposes of this analysis, animal operations were randomly

assigned within crop and pasture land portions of each county, using a 30-

meter grid overlay of the county. Manure production and manure by system

shares are applied uniformly across animal farms by grid location in the

model. Figure 5 shows the assignment of farm operations with confined

animals over cropland areas in northwestern Virginia. 

The random assignment of animal operations in the GIS may yield somewhat conservative estimates of actual hauling distances. While the majority

of animal operations tend to be located in proximity to crop and pasture

land, some operations may be separated from arable land suitable for

manure spreading since production is not as sensitive to soil conditions.

Moreover, the spatial concentrations of manure production within a

county—reflecting the presence of larger CAFO operations and observed

clustering of animal operations—will tend to increase competition for adja16

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Figure 4

Crop and pasture land distribution in northwest Virginia

Augusta

Louisa

Albemarle

Rockingham

Nelson

C

Page

Hanover

Orange

Culpeper

Madison

Spotsylvania

Fluvanna

Powhatan

Goochland

C bl d

Greene

pp

K

Rich

Staunton

Harrisonburg

Charlottesville

Waynesboro

a Vista

Augusta

Louisa

Albemarle

Rockingham

Nelson

C

Page

Hanover

Orange

Culpeper

Madison

Spotsylvania

Fluvanna

Powhatan

Goochland

C bl d

Greene

pp

K

Rich

Staunton

Harrisonburg

Charlottesville

Waynesboro

a Vista

Note: Grey shading indicates crop and pasture land.

Source: U.S.Department of Agriculture, Economic Research Service, based on 

U.S. Geological Survey National Land Cover Dataset.
cent land resources. Nonetheless, the random assignment procedure was

regarded as reasonable at a watershed scale, given limitations of the data.

Within-county area-to-distance relationships. We then used the GIS to

compute area-to-distance relationships for within-county manure transfers

for each within-basin county in the model. Area-to-distance functions for incounty manure transfers represent the average hauling distance from animal

farms in a given county to spreadable land within the same county. With

limited amounts of surplus manure, spreadable land is relatively accessible

and hauling distances are generally short. As manure-spreading requirements increase, animal operations must compete increasingly for the same

acreage—reducing accessibility and increasing the average hauling distance

needed to access available acreage.17

Area-to-distance relationships for within-county transfers were computed

for each basin county in the model by incrementally increasing, through a

series of expanding 30-meter concentric bands, the search for farmland in

the same source county around each of the assigned animal operations. The

change in aggregate spreadable area—excluding non-farmland and farmland

previously claimed by a competing operation in closer proximity—is measured for each additional distance increment. Thus, the area-to-distance relationship reflects the average distance that must be traveled across all

confined animal operations to access a given level of spreadable acreage,

accounting for competition among animal producers within the county. The

relationship between the spreadable acreage requirement and average

distance hauled is upward sloping and fairly linear along much of the

observed range (computed line in figure 6). 

17 The actual area of available spreadable acreage used for manure application in a given county is determined by

the optimization model, reflecting

manure flows within and across counties that minimize aggregate hauling

and application costs in the basin.

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Figure 5

Assignment of animal operations

Note: 1 dot = 1 animal feeding operation. This map illustrates the spatial assignment

of animal feeding operations within crop and pasture land area, by county and grid.

Source: U.S. Department of Agriculture, Economic Research Service, based on 

U.S. Geological Survey National Land Cover Dataset.
Out-of-county area-to-distance relationships. Out-of-county relationships

represent manure-hauling distances from confined animal operations within

a source county to spreadable acreage in other destination counties. Unique

out-of-county relationships were generated for all county-to-county combinations within an assumed 60-km linear transport radius. The transport

radius for the 16 counties (10 percent of all basin counties) with the highest

concentrations of surplus manure relative to spreadable land was expanded

to 150 km (93 linear miles), reflecting the greater hauling distances that are

likely to be required from areas where animal production is concentrated.

A three-stage process was used to generate the area-to-distance relationships

for out-of-county transfers (nonlinear curve shown in figure 7). First, to

reduce the number of possible source-county grid alternatives, animal farms

were aggregated (binned) using a 12-km grid overlay across the entire area.

Although the binning procedure reduces the precision of travel distances for

out-of-county functions, the procedure was necessary to ensure tractability

for model optimization. Second, for each 12 km grid with animal operations, distance was measured from the grid centroid to the closest edge of

spreadable area in the destination county; this distance represents the intercept term of the functional relationship. Third, the area-to-distance relationship within the destination county was computed in a fashion similar to that

for in-county transfers. Thus, the area-to-distance relationship represents

average hauling distance to access a given spreadable area within the destination county but measured from the direction of the source county. 

Estimating linearized area-to-distance functions. For use in the regional

model, area-to-distance relationships estimated from the GIS were

linearized by truncating the upper and lower tails of the distribution (10

percent of acreage, respectively) and fitting a linear function to the midrange observations (80 percent) (linear portions of figures 6 and 7). The use

of linear representations reflects the significantly reduced computer memory

requirements relative to non-linear functions for the area-to-distance relationship, and the fact that observed relationships are very nearly linear over

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Figure 6

Within-county area-to-distance relationship

Average hauling distance

Computed relationship

Linearized function

Amax

Spreadable land area

D

A

Used onfarm Hauled off-farm

d

Source: U.S. Department of Agriculture, Economic Research Service.
the relevant mid range. Regression coefficients for the linearized area-todistance functions were incorporated as parameters in the regional model.

These include a unique set of slope coefficients for each within-county and

out-of-county function, as well as individual distance intercept terms by

source-county grid for each out-of-county function.

The developed slope and distance intercept terms are then applied to the

spreadable acres obtained from the 1997 Census of Agriculture. The slope

represents the average transport distance to use all the spreadable crop and

pasture land, accounting for competition from neighboring farms that also

require land to spread manure. For example, let Amax represent the

maximum spreadable area in figure 6. If the area needed for manure land

application by producers in the county for a given nutrient standard is A,

then distance D would be the average distance traveled to access A acres.

(In figure 6, acreage includes both on-farm acres and off-farm acres.) Figure

7 has a similar interpretation, except that a distance intercept accounts for

the transport distance from the manure source to the edge of potentially

spreadable land in receiving counties. Thus, the total distance to access land

area A in figure 7 is represented by distance from the origin to D2, with the

origin to D1, representing the distance to a receiving county and D1 to D2

the distance within the receiving county. 

Adjustment for landowner willingness to accept manure. Area-to-distance

functions derived from the GIS assume full acceptance of manure on all of

the spreadable land base. Restrictions on availability of spreadable land due

to the unwillingness of some landowners to accept manure is captured in the

model through automated adjustments in both: (1) the quantity of spreadable

acreage, and (2) the slope of area-to-distance functions, or hauling distance

required to access a given spreadable area. Figure 8 shows the effect of a

stylized reduction in available spreadable area on manure-hauling distance.

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Figure 7

Out-of-county area-to-distance relationship

Average hauling distance

Amax

Spreadable land area

D2

A

D1

Distance within

destination county

Distance from

source county to

edge of cropland

in destination

county

Source: U.S. Department of Agriculture, Economic Research Service.
As spreadable area is reduced (to 60 percent of the maximum land base),

producers must haul a greater distance on average (D3 to D4) to access the

same amount of spreadable area A.

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Figure 8

Shift in area-to-distance function with changes in landowner 

willingness to accept manure (WTAM) 

Average hauling distance

Amax:

WTAM=60%

Spreadable land area

D4

A

D3 Distance within

destination county

Distance from

source county to

edge of cropland

in destination

county

Dmax

Source: U.S. Department of Agriculture, Economic Research Service.

Amax:

WTAM=100%
Model Equation System

The following section presents a review of the modeling equation system,

with model variables defined by equation. Model equations include: (1) an

objection function that minimizes sector costs relating to manure hauling

and land application, (2) balance equations that track stocks and flows of

manure and manure nutrients, (3) constraints on land availability, distribution of confined animal farms (manure sources), and manure-nutrient application, and (4) cost accounting equations. Appendix 3 provides a listing of

equation variables used below, with the names of corresponding GAMS

model variables (Appendix 2) and model report variables (Appendix 4). 

The regional optimization model minimizes the net cost of applying manure

in the Chesapeake Bay watershed, subject to total manure produced, crop

and pasture land available for manure spreading (onfarm and off-farm), and

share of manure diverted to non-land-based uses. Net costs are defined as

costs associated with manure land application, plus a penalty cost for

manure that cannot be land-applied within the basin, less savings on

reduced commercial fertilizer. The model allocates manure production in

basin counties (ct) to spreadable land in destination counties (ct2), both

within and outside the source county, to minimize the objective function

expression (OBJ):

Costs include manure-hauling and application costs (HAC), manure-incorporation costs (INC), and nutrient management plan charges for source

(NM1) and destination (NM2) counties (see equations 17 and 18 for cost

items included). A penalty cost applied to manure levels’ exceeding land

application capacity in the basin (ELA) ensures that all manure is landapplied subject to available land (see equation 8 for calculation of surplus

manure). However, the penalty cost is removed from the actual total cost

value reported in the model solution report. Aggregate costs are further

adjusted to reflect savings from reduced purchase and application costs for

chemical fertilizers (FS). Net cost reported in the model solution is defined

as total cost, net of savings from chemical fertilizer use.

In general, wet manure quantities are used to assess manure hauling and

application costs, while manure-nutrient content and uptake determine the

volume and direction of manure flows. Primary manure transfer equations

are as follows:

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= ∑ ∑ + + + + − ct ct ct,ct ct ct ct ct ct 2

2 2 2 2 (1) OBJ [HAC INC NM1 NM2 ELA FS ].

∑ ≤

= ∑ ∑ ∑

∑ ≤

= + −

ds ct gr ct sy ds ct ct gr sy

ds ct gr ct sy ds gr sy

ct ct ct

ct ct

ct,ct ct ct N ct ct ct P ct

Amax

, , 2, , , ,

ct,ct2 , , 2, ,

ct,ct2 2 2

, 2

2 , 2, * 2 , 2, * 2

(5) M_TRN M_PRD *SH_M

(4) M_TRAN M_TRN

(3) AC_SPR * WTAM

 * AC_SPR

(2) M_TRAN ((M_AP *SH_N ) (M_AP *(1 SH_N )))
(where N* represents a nitrogen standard and P* represents a phosphorus

standard, gr is county grid location, sy is manure system (lagoon, slurry, and

dry), and ds is hauling distance interval in miles. Onfarm hauling distance is

fixed based on average onfarm distance by county computed from the

Census. Off-farm hauling distance is an endogenously derived, continuous

variable falling within one of three distance intervals (0.5-2 miles, 2-10

miles, and more than 10 miles), with per-unit hauling cost dependent on the

distance interval. 

Manure transfers (M_TRAN) refer to manure hauled off the source farm

and land-applied within or outside the county. In equation 2, dry manure

tons by manure transfer is defined as the product of manure application rate

(M_AP) by manure transfer—weighted by acreage shares under an N standard (SH_N) and P standard (1- SH_N)—and receiving acres (AC_SPR) in

the destination county. Adjustments in applied manure per acre provide the

link between restrictions on manure-nutrient use and manure transfers in 

the model.

Equation 3 restricts applied manure from all potential source counties to

total spreadable acreage (Amax) in the destination county, adjusted for

assumptions on land operator willingness to accept manure (WTAM). Equation 4 sets aggregate county-level manure transfers (M_TRAN) equal to the

sum of manure transfers by source-county grid location (gr), system type

(sy), and distance interval (ds). Equation 5 restricts source-county manure

transfers by grid (gr) and system type (sy), based on the share (SH_M) of

total manure production (M_PRD) across system type and grid, based on

assignment procedures followed in the GIS.

Equations 6 through 8 balance manure production, onfarm surplus manure,

manure transferred off-farm, and quantity of manure exceeding land application capacity in the basin.

Equation 6 sets county surplus manure to be moved off the farm (M_SRP)

equal to manure production (M_PROD) less that used onfarm (M_ONFRM)

in the source county. Equation 7 fixes manure use (M_USE) as onfarm

manure use plus that quantity obtained from all off-farm sources

(M_TRAN) in the destination county. Equation 8 sets the manure that

exceeds land application capacity (M_ELA) within the assumed transport

radius of a source county equal to the manure surplus in the source county

less the sum of industrial uses (M_IND) and the sum of manure transfers

out-of-county. Quantities of M_ELA manure are minimized in the model

through the use of a penalty cost parameter that assigns a high cost to

manure not land-applied in the basin. 

Hauling distances for off-farm transfers are computed based on equations

9–11.

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= − ∑ − ∑

= + ∑

= −

2 t , 2

2 2 , 2

(8) M_ELA M_SRP M_IND M_TRAN

(7) M_USE M_ONFRM M_TRAN

(6) M_SRP M_PROD M_ONFRM

ct ct,ctsy c ct ct sy

ct ct ct ct ct

ct ct ct
In equation 9, average hauling distance (DS) from source county (ct) and

grid (gr) is calculated as a function of spreadable acres in the destination

county (ct2). Off-farm hauling distance by manure transfer is computed

based on acreage using manure from the source county (AC_SPR)—above a

fixed acreage for onfarm manure use on confined animal farms

(AC_ONF)—in the destination county. Intercept α and slope coefficient are

β estimated from the GIS-derived linear regressions for within-county and

out-of-county transfers.18 The intercept term, representing linear hauling

distance from the source farm for out-of-county transfers, is adjusted (δ1 )

for selected county-to-county transfers to reflect significant natural barriers

(e.g., large bodies of water). In addition, a circuity parameter (δ2 ) is used to

convert linear distance to road miles.19 Thus, equation 9 establishes the key

linkage in the model involving: (1) acreage accessed for manure spreading

and (2) average hauling distance within and between counties, with values

of each derived endogenously across county-transfer combinations.

In equation 10, average hauling distance (DS) from source-county grid to a

given destination county represents a weighted average of hauling distances

(DST) by manure-system type (sy) and distance interval (ds). This equation

effectively integrates per-unit manure-hauling costs within area-to-distance

relationships from the GIS, linking: (1) average hauling distance by county

transfer with (2) individual hauls from source-county grid points. Minimum

(D_MN) and maximum (D_MX) distance is specified by distance interval in

equation 11, used in assessing per-unit costs.

Stocks and flows of manure nutrients (np)—nitrogen n or phosphorus p—

are tied to manure quantities as follows:

Total excess manure nutrients (NP_EXC) are obtained from farm-level

census data on manure production and onfarm assimilative capacity, aggregated to the county level. Equation 12 calculates manure surplus (M_SRP)

based on pounds of excess N or P (np), depending on the nutrient standard

in effect (N* or P*), and county-average nutrient content in lbs. per dry ton

of manure (NP_M). In equation 13, onfarm manure nutrients (NP_ONF)

reflect the quantity (M_ONFRM) and composition (NP_M) of manure

produced and used on confined animal feeding operations. In equation 14,

manure-nutrient flows (NP_TRN) are tied to manure transfers off the farm. 

18 For in-county manure transfers, the

intercept term of the area-to-distance

relationship is set to zero.

19 A fixed circuity parameter of 1.2

reflects an average of State-level

parameters reported for the

Chesapeake Bay watershed region

(U.S. Department of Commerce,

1978).

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ds ct,gr,ct ,sy,ds ds

ct gr ct sy ds ct gr ct sy ds

sy ds ct,gr,ct ct,gr,ct

ct ct,ct ct

ct,gr ct ct gr ct ct ct ct ct ct

(11) D_MN DST D_MX

(10) DS * M_TRN (DST * M_TRN )

 AC_SPR )) ] * 

(9) DS [( * ) ( *(AC_ONF

2

2 2 , , 2, , , , 2, ,

2

2

2

, 2

1

, 2 , , 2 , 2

≤ ≤

= ∑ ∑

∑

= + +

δ

α δ β

ct,ct ,np ct ct ct np

ct ,np ct ct np ct ct

ct ct,np ct np

2 , 2 ,

2 2 , 2

,

(14) NP_TRN M_TRAN * NP_M

(13) NP_ONF M_ONFRM * NP_M where

(12) M_SRP NP_EXC / NP_M

=

= =

=
In equation 15, manure-hauling and application costs (HAC) are computed

for onfarm and off-farm transfers. Costs reflect loading, unloading, and

application costs per ton hauled (C1), hauling cost per ton-mile (C2),

distance hauled (DST), and dry tons of manure hauled (M_TRN), adjusted

for moisture content (MS) and bedding (BED). Hauling and application

costs vary across animal-waste systems due to differences in manure moisture content and equipment use. The model simulates a stepwise cost function for manure-hauling/application cost, with cost coefficients defined by

system type (lagoon, slurry, and dry) and distance interval hauled. Costs of

manure incorporation into the soil (INC) are computed in equation 16 based

on per-acre charge (C3), total onfarm and off-farm acres using manure,

share of acres in cropland (SH_C) (as manure is not generally incorporated

on pasture land), and share of manured cropland using incorporation (SH_I). 

Selected nutrient management plan costs related to land application are

identified for manure source farms and receiving farms. Equation 17

computes source-county costs (NM1), based on representative costs for

manure testing (M_TST) and plan development costs (C_NMP), summed

across confined animal-feeding operations (AFOs) in the source county.

Equation 18 computes destination county costs (NM2) for soil testing, based

on representative costs (S_TST) per acre of land receiving manure. 

Calculation procedures for fertilizer cost savings vary, depending on the

nutrient standard in effect. In equation 19, savings calculated under an N

standard (FS_N) include reduced chemical fertilizer purchases and reduced

chemical application costs. Savings from reduced fertilizer purchases are

computed based on the price (PR) of nitrogen (N) and phosphorus (P), and

the quantity of manure nutrients used by crops. Nitrogen savings reflects the

full application of manure-N, both onfarm (N_ONF) and off-farm

(N_TRN), as all manure-N is assumed to be beneficially used in crop

production since producers are assumed to meet nutrient management

guidelines. Phosphorus savings reflects use of manure-P onfarm (P_ONF)

and off-farm (P_TRN)—adjusted to capture that portion of P (P_PCT) that

is beneficially used by the crop (or the ratio of applied manure at an annual

P standard to applied manure under an N standard). Savings from reduced

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(16) INC ( C3*SH_I *( AC_ONF AC_SPR )*SH_C )

 *(M_TRN / (1 (MS BED ) ]

(15) HAC [ C1 * (C2 * DST ) ]

2 2 2 , 2 2

, , 2, ,

2 , , , , 2, ,

ct ct ct ct ct ct ct

ct gr ct sy ds sy sy

ds sy ds sy ds ct gr ct sy ds gr sy

ct,ct

= + ∑

− +

= ∑ ∑ ∑

(18) NM2 S_TST * ( AC_ONF AC_SPR )

(17) NM1 ( M_TST C_NMP ) * AFO

2 2 ct,ct 2 ct ct ct

ct ct

= + ∑

= +

 ( C_AP *(AC_ONF AC_ ))

 (PR * (P_ONF P_TRN )* P_PCT )

(19) FS_N ( PR * (N_ONF N_TRN ))

2 , 2

, 2, , 2, 2

2 , 2, , 2,

ct ct ct ct

ct ct P ct ct P ct ct P

ct ct N ct ct N ct ct N

+ + ∑ SPR

+ + ∑

= + ∑
chemical application costs reflects the per acre cost of chemical application

(C_AP) and total acres receiving manure under an N standard.20

In equation 20, savings calculated under an annual P standard (FS_P) reflect

the value of only the manure-nutrient offset. There are no savings in chemical application costs (chemical fertilizer application is still required), as

manure-N is insufficient to meet full crop needs. In contrast to the N standard where some portion of applied manure-P cannot be used by the crop,

all applied manure nutrients are beneficially used under a P standard.21

Equation 21 computes an acreage-weighted fertilizer cost savings (FS),

based on the share of acres under an N standard and the more stringent

(annual or multi-year) P standard. 

20 It is assumed that chemical nutrients

are applied at crop-based rates, that

manure nutrients directly offset nutrients obtained from chemical fertilizers, and that per acre field application

costs are fixed regardless of the level

of applied chemical fertilizer.

21 The model can be easily modified to

consider a multiple-year application of

manure-P, in cases where soil-P levels

allow for heavier manure applications.

Under a multiyear P application,

manure treatments are rotated over the

farm acreage. Producers are permitted

to apply multiple years’ manure quantities at one time (up to the N standard

level) on a given field where nutrients

can be used in subsequent years of the

multiyear rotation. Savings reflect the

full value of the manure nutrients, as

all applied manure nutrients are fully

used by the crop. Savings also reflect a

partial reduction in chemical field

application costs, based on the share

of acres treated annually within the

multiyear rotation (equivalent to

P_PCT). For the multiyear P case,

equation 20 would be modified to

include the additional cost savings:

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+

+ ∑

(C_ AP * (AC_ ONF

AC_ SPR ) * P_ PCT )

ct

ct,ct ct ct

2

2 2

 (PR * (P_ONF P_TRN ))

(20) FS_P ( PR * (N_ONF N_TRN ))

, 2, , 2,

2 , 2, , 2,

ct ct P ct P ct ct P

ct ct N ct ct N ct ct N

+ + ∑

= + ∑

(21) FS ( FS_N *SH_N ) (FS_P *(1 SH_N )) ct 2 = ct 2 ct 2 + ct 2 − ct 2
Output Generation

For a given model solution, the optimization model generates a series of

output variables in a standard GAMS output file. Customized computer

programs are used to extract and organize selected output variables and

associated values for display and analysis. 

Appendix 4 includes a list of report variables from a standard output file.

Report variables include those endogenously derived within the model, plus

additional variables computed from the solution results. Report variables are

organized under costs, acres, manure quantities, manure nutrients, and

hauling distance.

Model reporting variables are reported at various spatial scales—county

grid, county aggregate, and full basin levels. Model solution values for edge

counties, or those that straddle the watershed boundary, are apportioned by

share of farmland within the watershed to more accurately account for

manure disposition at the basin level. Consequently, aggregate values may

be reported only for the full modeled area and watershed area (without

sinks). Aggregate costs are also reported with and without adjustments for

chemical fertilizer savings. 

Map presentations, developed in ARC-View, are generated from output solution variables reported at a county level. Maps are particularly useful in

highlighting spatial values across the basin under various policy and

resource assumptions. Figure 9 presents a sample map for the Chesapeake

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Figure 9

Net manure exports in the Chesapeake Bay watershed

assuming landowner willingness to accept manure of 60 percent

-60 to -40

-40 to -20

35 to 140

5 to 35

-5 to 5

-20 to -5

140 to 355

Imports

Exports

Inter-county manure movement

1,000 dry tons of net transfer

Source: U.S. Department of Agriculture, Economic Research Service.
Bay watershed, indicating net manure exports under a P standard, given a

landowner-willingness-to-accept-manure level of 60 percent. Figure 10 presents a multi-sequence map set that captures the effect of alternative levels of

landowner willingness to accept manure on county-level exports and

imports under an N standard. Figure 10 shows that as the willingness to

accept manure declines, the model transports manure increasingly longer

distances and that more counties become net manure importers to access

adequate land at the basin level to meet an N standard. 

Maps may also be used to isolate key information for a single county. Figure

11 shows manure transfers from a single source county (Rockingham County,

VA) to destination counties across the basin under a P standard. As manure

transfers are estimated simultaneously across all counties, the direction and

volume of manure flows that minimize aggregate costs to the basin necessarily reflect the effect of competing manure sources in neighboring counties.

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Figure 10

Effect of landowner willingness to accept manure (WTAM) on the spatial distribution of manure transfers 

in the Chesapeake Bay watershed, based on 1997 animal production and an N standard

WTAM=100%

WTAM=40% WTAM=20%

WTAM=80% WTAM=60%

-50 to -35

-35 to -20

20 to 75

5 to 20

-5 to 5

-20 to -5

75 to 305

Imports

Exports

Inter-county manure movement

1,000 dry tons of net transfer

Source: U.S. Department of Agriculture, Economic Research Service.
Output data at the aggregate basin level may best be characterized in graphical form. In figure 12, a pie chart shows the share of manure produced in

the Chesapeake Bay watershed, by disposition of use. In figure 13, a

segmented bar graph is used to depict the disposition of manure under alternative levels of willingness to accept, given a P standard. Figure 14 presents

a combination bar-line graph showing both manure management costs and

manure quantities exceeding land application levels across alternative willingness to accept levels under a P standard. (For more discussion about

these outputs, see Ribaudo et al., 2003.)

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Figure 11

Manure transfers from Rockingham County, Virginia, 

with a P standard and 60-percent willingness to accept manure

0 - 2

2 - 8

30 - 65

18 - 30

8 - 18

1,000 dry tons

Source: U.S. Department of Agriculture, Economic Research Service.

Figure 12

Disposition of manure in the Chesapeake Bay watershed under both 

N and P standards with a willingness to accept manure of 70 percent

Source: U.S. Department of Agriculture, Economic Research Service.

N standard P standard

On-farm use Within-county use Out-of-county use
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Figure 13

Effect of landowner willingness to accept manure

on manure disposition in the Chesapeake Bay watershed

Source: Ribaudo et al., 2003.

Percent of manure

N standard

Willingness to accept manure (percent)

0

10

20

30

40

50

60

70

80

90

100

10 20 30 40 50 60 70 80 90 100

Within-county use

Out-of-county use Excess of land applications

On farm use

Figure 14

Effect of landowner willingness to accept manure on land 

application costs and manure exceeding land application capacity

in the Chesapeake Bay watershed: P standard

Source: Ribaudo et al., 2003.

10 20 30 40 50 60 70 80 90 100

0

500

1,000

1,500

2,000

2,500

0

20

40

60

80

100

120

140

160

180

Manure not land applied (1,000 tons)(bars) Cost ($ mil.)(lines)

Dashed line represents land application costs

where all manure cannot be land-applied

Manure quantities exceeding 

land application levels

Net cost

Total cost

Willingness to accept manure (percent)
Conclusion and Future Extensions

The Chesapeake Bay Regional Model, developed under the ERS Manure

Management for Improved Water Quality Project, provides a useful framework to assess the importance of spatial factors—spreadable land base,

animal concentrations, and manure-storage technologies—on potential

manure management costs facing animal producers. Indeed, findings from

application of the model suggest that spatial factors underlying competition

for spreadable land in the Chesapeake Bay watershed are an important

consideration in accurately assessing the feasibility and costs of new manure

management guidelines to the animal sector. The modeling framework is

also notable for its integration of farm-level survey data from the Agricultural Census, aggregated to the county level, with geo-specific data from the

National Land Cover Dataset. The resulting database—developed from

public secondary data sources—captures important intra-regional variation

in key cost determinants for animal waste management. Moreover, the

reliance on national data series ensures data consistency across a watershed

scale, while facilitating data update and model transferability to other watersheds. Results from an initial application of the modeling system are

featured in a recent ERS publication (Ribaudo et al., 2003). 

While the Chesapeake Bay regional modeling system was successfully

applied as a part of the ERS’s water quality analysis, the developers noted

potential model improvements and extensions as limitations were revealed

during the initial application. Many of these improvements and extensions

are being incorporated for subsequent applications of the regional modeling

framework. Areas of model development (not in priority order) currently

identified include:

z Inclusion of air emissions by manure-handling technology; 

z Developing a procedure to respecify the spatial grid sizes used for aggregating (binning) of animal operations to achieve more efficient and rapid

model convergence; 

z Incorporating model differentiation in manure-nutrient production and

related management requirements across CAFOs and non-CAFO animal

farms; 

z Developing the data and model capacity to adjust the available land base

for manure spreading to reflect stream buffer guidelines, use of municipal sewage byproducts, and agricultural land conversion;

z Developing consistent regional data on public cost-sharing for manurehauling and other manure management options, the use of backhauling,

and local manure-pricing practices. These factors can adjust the effective

hauling costs faced by animal producers as well as the distributional pattern of manure management costs; 

z Incorporating onfarm drying technologies as a manure management

alternative;

z Developing a process to estimate within the model the cost of reducing

the stock of animals as a means of addressing manure that cannot be

land-applied within the basin;

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z Developing a process to integrate our model findings on the spatial distribution of land-applied manure with other models designed to address

nutrient transport and water quality.

Implementation of improvements and extensions to the Chesapeake Bay

regional model will depend on research priorities, resources allocated to the

project, and availability of supporting data. Cooperation with regional subjectarea experts remains an important consideration, as many of these data items

are local in nature and not readily available through national data series. 

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References

Barker, James C., Joseph P. Zublena, and F.R. Walls. 2001. “Animal and

Poultry Manure Production and Characterization,” North Carolina State

University, Cooperative Extension Service, Raleigh, NC.

www.bae.ncsu.edu/people/faculty/barker/a&pmp&c/table_of_content.htm

Borton, L.R., C.A. Rotz, H.L. Person, T.M. Harrigan, and W.G. Bickert.

1995. “Simulation to Evaluate Dairy Manure Systems,” Applied

Engineering in Agriculture. Vol. 11, No. 2, pp. 301-310.

Council for Agricultural Science and Technology. July 2002. Animal Diet

Modification to decrease the Potential for Nitrogen and Phosphorus

Pollution. Issue Paper 21.

Fleming, R., B. Babcock, and E. Wang. 1998. “Resource or Waste? The

Economics of Swine Manure Storage and Management.” Review of

Agricultural Economics, Vol. 20, No. 1, pp. 96-113.

Gollehon, N., M. Caswell, M. Ribaudo, R. Kellogg, C. Lander, and D.

Letson. June 2001. Confined Animal Production and Manure Nutrients.

Agriculture Information Bulletin No. 771. U.S. Department of

Agriculture, Economic Research Service.

Homer, Collin, Chengquan Huang, Limin Yang, and Bruce Wylie. 2000.

Development of a Circa 2000 Landcover Database for the United States.

U.S. Department of the Interior, U.S. Geological Survey, EROS Data

Center, Sioux Falls, SD. Available at:

http://landcover.usgs.gov/pdf/asprs_final.pdf

Kellogg, Robert, Charles Lander, David Moffitt, and Noel Gollehon.

October 2000. Manure Nutrients Relative to the Capacity of Cropland

and Pastureland to Assimilate Nutrients: Spatial and Temporal Trend for

the United States. U.S. Department of Agriculture, Natural Resources

Conservation Service. 

Pease, James, Beth Ann Pelletier, and David Kenyon. January 29-31, 2001.

“Poultry Litter Transport Alternatives for Land Application in Virginia.”

Southern Agricultural Economics Association, 2001 Annual Meeting, Ft.

Worth Texas.

Preston, S.D., and J.W. Brakebill. 1999. Application of Spatially Referenced

Regression Modeling for the Evaluation of Total Nitrogen Loading in the

Chesapeake Bay Watershed. USGS Water-Resources Investigations

Report 99-4054, U.S. Geological Survey, Reston, VA.

Ribaudo, Marc, Noel Gollehon, Marcel Aillery, Jonathan Kaplan, Robert

Johansson, Jean Agapoff, Lee Christensen, Vince Breneman, and Mark

Peters. June 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. 

Risse, L.M., M.L. Cabrerea, A.J. Franzluebbers, J.W. Gaskin, J.E. Gilley, R.

Killorn, D.E. Radcliffe, W.E. Tollner, and J. Zhang. 2001. “Land

Application of Manure for Beneficial Reuse.” National Center for

Manure and Animal Waste Management white paper.

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U.S. Department of Agriculture, Economic Research Service. 2002. “briefing room: farm income and costs, commodity costs and returns.”

Available at: http://www.ers.usda.gov/briefing/farmincome/costsandreturns.htm.

U.S. Department of Agriculture, Economic Research Service. 2000. Data

from the 1998 Agricultural Resource Management Survey, Hogs

Production Practices and Costs and Returns Report, unpublished.

U.S. Department of Agriculture, National Agricultural Statistics Service.

1999. 1997 Census of Agriculture. AC97-A-51. Volume 1, Geographic

Area Series.

U.S. Department of Agriculture, Natural Resources Conservation Service.

June 2003. Costs Associated With Development and Implementation of

Comprehensive Nutrient Management Plans, Part I—Nutrient

Management, Land Treatment, Manure and Wastewater Handling and

Storage, and Recordkeeping.

U.S. Department of Agriculture, Natural Resources Conservation Service.

December 2000. Comprehensive Nutrient Management Planning

Technical Guidance.

U.S. Department of Commerce, Bureau of the Census. 1978. “Description

and Technical Documentation of the PICADAD File.” U.S. Government

Printing Office.

U.S. Environmental Protection Agency. February 12, 2003. “National

Pollutant Discharge Elimination System Permit Regulation and Effluent

Limitation Guidelines and Standards for Concentrated Animal Feeding

Operations (CAFOs); Final Rule.” 40 CFR Parts 9, 122, 123, and 412.

Federal Register. Vol. 68, No. 29. pp. 7,176-7,274. 

U.S. Geological Survey. 2004. 1994 National Land Cover Data. 

http://landcover.usgs.gov/index.asp.

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Appendix Tables

34

Regional Manure Management Model for the Chesapeake Bay Watershed / TB-1913

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Appendix 1—Basin and sink counties included in the Chesapeake Bay regional model

Model FIPS State County Edge County

Basin counties:

C001 10001 DE KENT y

C002 10003 DE NEW CASTLE y

C003 10005 DE SUSSEX y

C004 24001 MD ALLEGANY

C005 24003 MD ANNE ARUNDEL

C006 24005 MD BALTIMORE

C007 24009 MD CALVERT

C008 24011 MD CAROLINE 

C009 24013 MD CARROLL

C010 24015 MD CECIL y

C011 24017 MD CHARLES

C012 24019 MD DORCHESTER

C013 24021 MD FREDERICK

C014 24023 MD GARRETT y

C015 24025 MD HARFORD

C016 24027 MD HOWARD

C017 24029 MD KENT

C018 24031 MD MONTGOMERY

C019 24033 MD PRINCE GEORGE'S 

C020 24035 MD QUEEN ANNE'S

C021 24037 MD ST. MARY'S

C022 24039 MD SOMERSET

C023 24041 MD TALBOT

C024 24043 MD WASHINGTON

C025 24045 MD WICOMICO

C026 24047 MD WORCESTER y

C027 36003 NY ALLEGANY y

C028 36007 NY BROOME y

C029 36015 NY CHEMUNG y

C030 36017 NY CHENANGO

C031 36023 NY CORTLAND y

C032 36025 NY DELAWARE y

C033 36043 NY HERKIMER y

C034 36053 NY MADISON y

C035 36067 NY ONONDAGA y

C036 36077 NY OTSEGO y

C037 36095 NY SCHOHARIE y

C038 36097 NY SCHUYLER y

C039 36101 NY STEUBEN y

C040 36107 NY TIOGA

C041 36109 NY TOMPKINS y

C042 42001 PA ADAMS

C043 42009 PA BEDFORD

C044 42011 PA BERKS y

C045 42013 PA BLAIR

C046 42015 PA BRADFORD

C047 42021 PA CAMBRIA y

C048 42023 PA CAMERON

C049 42027 PA CENTRE

C050 42029 PA CHESTER y
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Appendix 1—Basin and sink counties included in the Chesapeake Bay regional model—Continued

Model FIPS State County Edge County

C051 42033 PA CLEARFIELD y

C052 42035 PA CLINTON

C053 42037 PA COLUMBIA

C054 42041 PA CUMBERLAND

C055 42043 PA DAUPHIN

C056 42047 PA ELK y

C057 42055 PA FRANKLIN

C058 42057 PA FULTON

C059 42061 PA HUNTINGDON

C060 42063 PA INDIANA y

C061 42067 PA JUNIATA

C062 42069 PA LACKAWANNA y

C063 42071 PA LANCASTER

C064 42075 PA LEBANON y

C065 42079 PA LUZERNE y

C066 42081 PA LYCOMING 

C067 42087 PA MIFFLIN

C068 42093 PA MONTOUR

C069 42097 PA NORTHUMBERLAND

C070 42099 PA PERRY

C071 42105 PA POTTER y

C072 42107 PA SCHUYLKILL y

C073 42109 PA SNYDER

C074 42111 PA SOMERSET y

C075 42113 PA SULLIVAN

C076 42115 PA SUSQUEHANNA

C077 42117 PA TIOGA

C078 42119 PA UNION

C079 42127 PA WAYNE y

C080 42131 PA WYOMING

C081 42133 PA YORK

C082 51001 VA ACCOMACK y

C083 51003 VA ALBEMARLE

C084 51005 VA ALLEGHANY

C085 51007 VA AMELIA

C086 51009 VA AMHERST

C087 51011 VA APPOMATTOX y

C088 51015 VA AUGUSTA

C089 51017 VA BATH

C090 51019 VA BEDFORD y

C091 51023 VA BOTETOURT y

C092 51029 VA BUCKINGHAM

C093 51031 VA CAMPBELL y

C094 51033 VA CAROLINE

C095 51036 VA CHARLES CITY

C096 51041 VA CHESTERFIELD

C097 51043 VA CLARKE

C098 51045 VA CRAIG y

C099 51047 VA CULPEPER

C100 51049 VA CUMBERLAND

C101 51053 VA DINWIDDIE y

C102 51057 VA ESSEX

C103 51059 VA FAIRFAX

C104 51061 VA FAUQUIER

C105 51065 VA FLUVANNA
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Appendix 1—Basin and sink counties included in the Chesapeake Bay regional model—Continued

Model FIPS State County Edge County

C106 51069 VA FREDERICK

C107 51071 VA GILES y

C108 51073 VA GLOUCESTER

C109 51075 VA GOOCHLAND

C110 51079 VA GREENE

C111 51085 VA HANOVER

C112 51087 VA HENRICO

C113 51091 VA HIGHLAND

C114 51093 VA ISLE OF WIGHT y

C115 51095 VA JAMES CITY

C116 51097 VA KING AND QUEEN

C117 51099 VA KING GEORGE

C118 51101 VA KING WILLIAM

C119 51103 VA LANCASTER

C120 51107 VA LOUDOUN

C121 51109 VA LOUISA

C122 51113 VA MADISON

C123 51115 VA MATHEWS

C124 51119 VA MIDDLESEX

C125 51121 VA MONTGOMERY y

C126 51125 VA NELSON

C127 51127 VA NEW KENT

C128 51131 VA NORTHAMPTON y

C129 51133 VA NORTHUMBERLAND

C130 51135 VA NOTTOWAY y

C131 51137 VA ORANGE

C132 51139 VA PAGE

C133 51145 VA POWHATAN

C134 51147 VA PRINCE EDWARD y

C135 51149 VA PRINCE GEORGE y

C136 51153 VA PRINCE WILLIAM

C137 51157 VA RAPPAHANNOCK

C138 51159 VA RICHMOND

C139 51161 VA ROANOKE y

C140 51163 VA ROCKBRIDGE

C141 51165 VA ROCKINGHAM

C142 51171 VA SHENANDOAH

C143 51177 VA SPOTSYLVANIA

C144 51179 VA STAFFORD 

C145 51181 VA SURRY y

C146 51187 VA WARREN

C147 51193 VA WESTMORELAND

C148 51199 VA YORK

C149 51550 VA CHESAPEAKE y

C150 51800 VA SUFFOLK y

C151 51810 VA VIRGINIA BEACH y

C152 54003 WV BERKELEY

C153 54023 WV GRANT

C154 54027 WV HAMPSHIRE

C155 54031 WV HARDY

C156 54037 WV JEFFERSON

C157 54057 WV MINERAL

C158 54063 WV MONROE

C159 54065 WV MORGAN

C160 54071 WV PENDLETON
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Appendix 1—Basin and sink counties included in the Chesapeake Bay regional model—Continued

Model FIPS State County Edge County

Sink counties:

C167 34011 NJ CUMBERLAND

C168 34015 NJ GLOUCESTER

C169 34033 NJ SALEM

C170 36001 NY ALBANY

C173 36011 NY CAYUGA

C177 36035 NY FULTON

C178 36039 NY GREENE

C180 36051 NY LIVINGSTON

C182 36055 NY MONROE

C183 36057 NY MONTGOMERY

C184 36065 NY ONEIDA

C186 36069 NY ONTARIO

C188 36093 NY SCHENECTADY

C191 36099 NY SENECA

C193 36105 NY SULLIVAN

C195 36121 NY WYOMING

C196 36123 NY YATES

C197 42005 PA ARMSTRONG

C200 42025 PA CARBON

C202 42031 PA CLARION

C204 42045 PA DELAWARE

C206 42051 PA FAYETTE

C207 42053 PA FOREST

C209 42065 PA JEFFERSON

C212 42077 PA LEHIGH

C214 42083 PA MCKEAN

C215 42089 PA MONROE

C216 42091 PA MONTGOMERY

C217 42095 PA NORTHAMPTON

C218 42103 PA PIKE

C223 42129 PA WESTMORELAND

C227 51021 VA BLAND

C229 51025 VA BRUNSWICK

C231 51037 VA CHARLOTTE

C234 51063 VA FLOYD

C235 51067 VA FRANKLIN

C237 51081 VA GREENSVILLE

C238 51083 VA HALIFAX

C240 51111 VA LUNENBURG

C241 51117 VA MECKLENBURG

C245 51143 VA PITTSYLVANIA

C248 51155 VA PULASKI

C250 51175 VA SOUTHAMPTON

C252 51183 VA SUSSEX

C256 54001 WV BARBOUR

C257 54025 WV GREENBRIER

C258 54055 WV MERCER

C260 54075 WV POCAHONTAS

C261 54077 WV PRESTON

C262 54083 WV RANDOLPH

C263 54089 WV SUMMERS

C264 54091 WV TAYLOR

C265 54093 WV TUCKER

C266 54097 WV UPSHUR

C267 54101 WV WEBSTER

Note: y denotes counties with a portion of cropland area outside the basin.
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Appendix 2—Model variables

TOTCOST TOTAL COST - MANURE USE AND DISPOSAL ($MIL)

D_IN(CT,CT2) AVE DIST FOR M IN-CNTY HAUL - WITHOUT INTERCEPT (KMS)

D_EX(CT,CT2) AVE DIST FOR M EXPT-CT HAUL - WITHOUT INTERCEPT (KMS)

DIST_IN(CT,CT2) AVE DIST FOR M IN-CNTY HAUL - WITH INTERCEPT (KMS)

DIST_EX(CT,GR,CT2) AVE DIST FOR M EXPT-CT HAUL - WITH GRID INTERCEPT (KMS)

DST_IN(CT,CT2,SYS,DST) AVE DIST HAULED - FROM AC CALC - IN-COUNTY (KMS)

DST_EX(CT,GR,CT2,SYS,DST) AVE DIST HAULED - FROM AC CALC - EXPORTS (KMS)

HCST_ON(CT,CT2,SYS) HAULING & APPL COST - ONFARM - NON-EXCESS ($MIL)

HCST_IN(CT,CT2,SYS,DST) HAULING & APPL COST - IN-COUNTY ($MIL)

HCST_EX(CT,GR,CT2,SYS,DST) HAULING & APPL COST - EXPORTS ($MIL)

HC_ON(CT,CT2) HAULING & APPL COST - ONFARM - NON-EXCESS ($MIL)

HC_IN(CT,CT2) HAULING & APPL COST - IN-COUNTY ($MIL)

HC_EX(CT,GR,CT2) HAULING & APPL COST - EXPORTS ($MIL)

HC1(CT) HAULING & APPL COST BY SOURCE CNTY - IN-COUNTY ($MIL)

HC2(CT) HAULING & APPL COST BY SOURCE CNTY - EXPORTS ($MIL)

HCOST(CT) TOTAL MANURE HAULING & APPL COST BY SOURCE COUNTY ($MIL)

HCST TOTAL MANURE HAULING & APPL COST ACROSS SOURCE CNTS ($MIL)

HLC1(CT2) HAULING & APPL COST BY DESTINATION CNTY - IN-COUNTY ($MIL)

HLC2(CT2) HAULING & APPL COST BY DESTINATION CNTY - EXPORTS ($MIL)

HLCOST(CT2) TOTAL MANURE HAULING & APPL COST BY DEST COUNTY ($MIL)

HLCST TOTAL MANURE HAULING & APPL COST ACROSS DEST CNTS ($MIL)

COST_SHR(CT) COST-SHARE PAYMENTS - MANURE HAULING ($MIL)

CST_SHR COST-SHARE PAYMENTS - MANURE HAULING ($MIL)

HAULCST TOTAL MANURE HAULING & APPL COST ($MIL)

M_PURCH(CT2) MANURE PURCHASE COST - BY DESTINATION COUNTY ($MIL)

M_REVN(CT) MANURE REVENUES - BY SOURCE COUNTY ($MIL)

M_PRCH MANURE PURCHASE COST ($MIL)

M_REV MANURE REVENUES ($MIL)

STORCOST(CT) MANURE STORAGE COST BY COUNTY ($MIL)

STORCST MANURE STORAGE COST ($MIL)

INCORP(CT2) MANURE INCORPORATION COST ($MIL)

INCRP MANURE INCORPORATION COST ($MIL)

NMP_SRCE(CT NUTRIENT MNGMT PLAN CHARGE - SOURCE COUNTIES ($MIL)

NMP_SRC NUTRIENT MNGMT PLAN CHARGE - SOURCE COUNTIES ($MIL)

NMP_DEST(CT2) NUTRIENT MNGMT PLAN CHARGE - DESTINATION COUNTIES ($MIL)

NMP_DST NUTRIENT MNGMT PLAN CHARGE - DESTINATION COUNTIES ($MIL)

CHMSAV_N(CT2) SAVINGS IN CHEMICAL APPLICATIONS - N STANDARD ($MIL)

CHMSAV_P(CT2) SAVINGS IN CHEMICAL APPLICATIONS - P STANDARD ($MIL)

CHMSAV SAVINGS IN CHEMICAL NUTRIENT APPLICATIONS ($MIL)

AU_RED(CT) REDUCTION IN ANIMAL-UNITS (AU)

AU_VAL(CT) VALUE OF A REDUCTION IN ANIMAL-UNITS ($MIL)

AU_VL VALUE OF A REDUCTION IN ANIMAL-UNITS ($MIL)

AC_SPR1(CT,CT2) SPREAD ACS FOR EXCESS M - INTRA-COUNTY (1000 ACS)

AC_SPR2(CT,CT2) SPREAD ACS FOR EXCESS M - INTER-COUNTY EXPORTS (1000 ACS)

AC_SPR(CT2) TOTAL SPREAD ACS FOR EXCESS M - BY DEST COUNTY (1000 ACS)

AC_ON_SY(CT,CT2,SYS) ONFARM SPREAD ACS - BY SYSTEM (1000 ACS)
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Appendix 2—Model variables—Continued

M_PROD(CT) TOTAL MANURE PRODUCTION - REG AND NON-REG (1000 TONS)

M_ONFRM_R(CT,CT2) MANURE PRODUCED & USED ONFARM ON REGULATED AFOS (1000 TONS)

M_TRAN(CT,CT2) EXCESS MANURE TRANSFERS BY COUNTY (1000 TONS)

M_TRAN1(CT) EXCESS MANURE TRANSFERRED - BY SOURCE COUNTY (1000 TONS)

M_TRAN2(CT2) EXCESS MANURE TRANSFERRED - BY DESTINATION COUNTY (1000 TONS)

M_TRANCT(CT2) EXCESS MANURE TRANSFERRED WITHIN COUNTY (1000 TONS)

M_EXPRT(CT) EXCESS MANURE EXPORTED - BY SOURCE COUNTY (1000 TONS)

M_IMPRT(CT2) EXCESS MANURE IMPORTED - BY DESTINATION COUNTY (1000 TONS)

M_SINK(CT2) EXCESS MANURE EXPORTED TO SINK (1000 TONS)

M_STOR(CT) EXCESS MANURE STORAGE - NOT LAND APPLIED (1000 TONS)

M_USE(CT2) MANURE USE ON ALL FARMLAND - ONFARM AND EXCESS (1000 TONS)

M_TRAN_G(CT,GR,CT2) EXCESS M TRANSFERS BY COUNTY AND GRID (1000 TONS)

M_TRN_G(CT,GR) EXCESS M TRANSFERS BY COUNTY AND GRID (1000 TONS)

M_PRD_SY(CT,SYS) MANURE PRODUCTION BY WASTE SYSTEM (1000 TONS)

M_REG_SY(CT,SYS) REGULATED M BY WASTE SYSTEM (1000 TONS)

M_SY(CT,SYS) REGULATED EXCESS M HAULED & USED BY SYSTEM (1000 TONS)

M_ONF_SY(CT,CT2,SYS) NON-EXCESS M USED ON REGULATED FARMS - BY TECHNOLOGY (1000 TONS)

M_SYS_IN(CT,CT2,SYS) EXCESS M TRANSFER - INCOUNTY - BY SYSTEM (1000 TONS)

M_SYS_G(CT,GR,CT2,SYS) EXCESS M TRANSFER - EXPORTS - BY SYSTEM (1000 TONS)

M_SYS_EX(CT,CT2,SYS) EXCESS M TRANSFER - EXPORTS - BY SYSTEM (1000 TONS)

M_SYS2(CT,CT2,SYS,DST) M TRANSFER - BY WASTE TECH AND DST SEG (1000 TONS)

M_SYS2_G(CT,GR,CT2,SYS,DST) M TRANSFER - BY TECHNOLOGY AND DST SEG (1000 TONS)

M_STR_SY(CT,SYS) MANURE STORAGE - BY WASTE-SYSTEM TECHNOLOGY (1000 TONS)

N_PROD(CT) TOTAL MANURE-N PRODUCTION (MIL LBS)

N_TRAN(CT,CT2) EXCESS MANURE-N TRANSFERS BY COUNTY (MIL LBS)

N_TRAN1(CT) EXCESS MANURE-N USED - BY SOURCE COUNTY (MIL LBS)

N_TRAN2(CT2) EXCESS MANURE-N USED - BY DESTINATION COUNTY (MIL LBS)

N_M_ONF(CT2) MANURE-N USED ON ANIMAL OPERATIONS (MIL LBS)

P_PROD(CT) TOTAL MANURE-P PRODUCTION (MIL LBS)

P_TRAN(CT,CT2) EXCESS MANURE-P TRANSFERS BY COUNTY (MIL LBS)

P_TRAN1(CT) EXCESS MANURE-P USED - BY SOURCE COUNTY (MIL LBS)

P_TRAN2(CT2) EXCESS MANURE-P USED - BY DESTINATION COUNTY (MIL LBS)

P_M_ONF(CT2) MANURE-P USED ON ANIMAL OPERATIONS (MIL LBS)
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Appendix 3—Variables used in equation listing (text), with names of corresponding GAMS model variables

(Appendix 2) and model report variables (Appendix 4)1

Item Description GAMS model GAMS model

linkage—data linkage—

parameter or aggregate

model variable output variable

(See Appendix 2) (See Appendix 4)

Subscripts:

ct Counties in the watershed that produce manure CT CT

ct2 Counties that receive manure CT2 CT2

sy Manure system (lagoon, slurry, and dry) SYS SYS

ds Hauling distance interval (onfarm and 0.5-2 miles, 

2-10 miles, >10 miles for off-farm) DST DST

gr County grid location GR GR

N* Nitrogen standard in effect -- --

P* Phosphorus standard in effect -- --

np Nutrient under consideration -- --

Variables:

OBJ Objective function value TOTCOST --

HAC Manure hauling and application costs HCOST HAULCSTB

INC Manure incorporation costs INCORP INCRPB

NM1 Source-county nutrient management plan costs NMP_SRCE NMP_SRCB

NM2 Destination county nutrient management plan costs MVP_DEST NMP_DSTB

ELA Penalty cost applied to manure levels exceeding land 

application capacity STORCOST STORCSTB

FS Savings from reduced purchase and application costs CHMSAV_N CHMSVB_N

for chemical fertilizers CHMSAV_P CHMSVB_P

M_TRAN Dry manure tons transferred from sources M_TRAN M_TRAN1B

to destinations M_TRAN_G M_TRAN2B

M_AP Manure application rate M_APPL AVEAPPL

SH_N Share of acreage under an N standard N_SHR_C --

N_SHR_P

AC_SPR Acres receiving manure in the destination county AC_SPR AC_SPRB

Amax Total acreage potentially available in the destination 

county ACSPR_MX --

WTAM Land operator willingness to accept manure ACCEPT_C --

ACCEPT_P

M_TRN Manure transfers by source-county grid location, M_SYS2 M_TRAN1B

system type and distance interval M_SYS2_G M_TRAN2B

M_PROD Total manure production in each source county M_PROD M_PRODB

SH_M Share of manure produced in each source-county 

grid location by system type M_SYS2_G --

M_SRP Surplus manure to be moved off the farm M_EXC M_EXCB

M_ONFRM Manure used on the farm of production M_ONFRM M_ONFRMB

M_USE On-farm manure use plus manure obtained from 

all off-farm sources in the destination county M_USE M_USEB

M_ELA Manure produced that exceeds land application capacity M_STOR M_STORB

M_IND Manure used in industrial uses M_OTHUSE M_INDSTB
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Appendix 3—Variables used in equation listing (text), with names of corresponding GAMS model variables

(Appendix 2) and model report variables (Appendix 4)1—Continued

Item Description GAMS model GAMS model

linkage—data linkage—

parameter or aggregate

model variable output variable

(See Appendix 2) (See Appendix 4)

Variables:—continued

M_ELA Manure produced that exceeds land application capacity M_STOR M_STORB

M_IND Manure used in industrial uses M_OTHUSE M_INDSTB

DS Average hauling distance DIST_IN AVDS_INM 

DIST_EX AVDS_EXM

α Intercept of the estimated area to distance relationship GRD_DIST --

δ1 Adjustment to selected county-to-county transfers 

to reflect significant natural barriers (e.g., large bodies 

of water) BARRIER --

β Slope of the estimated area to distance relationship D_COEF1 --

D_COEF2

AC_ONF Acreage on manure source farms used to apply onfarm produced manure AC_ONFRM AC_ONFMB

δ2 Circuity parameter used to convert linear distance 

to road miles CRC_FCT --

DST Hauling distance by manure system and distance DST_IN --

interval DST_EX

D_MN Minimum distance for distance interval ds, MNHAUL_S --

based on system type and technology MNHAUL_M

MNHAUL_L

D_MX Maximum distance for distance interval ds, 

based on system type and technology MXHAUL_S --

MXHAUL_M

NP_EXC Total excess manure nutrients in the county N_EXC N_EXCB 

P_EXC P_EXCB

NP_M County-average nutrient content per dry ton of manure N_TON_M --

P_TON_M

NP_ONF Onfarm manure nutrients N_M_ONF N_M_ONFB

P_M_ONF P_M_ONFB

NP_TRN Manure nutrients transfers off the farm N_TRAN --

P_TRAN

C1 Manure loading, unloading, and application costs 

per ton hauled CST_TN HC_WTN

C2 Manure hauling cost per ton-mile CST_ML --

MS Manure moisture content MOIST --

BED Manure bedding content BED_SYS --

C3 Per-acre charge for manure incorporation AP_CST --

SH_I Share of manured cropland using incorporation INCRP_SHR --

SH_C Cropland acres share CRPL_SHR --

FS_N Savings in commercial fertilizer purchases and

application under an N standard CHMSAV_N CHMSVB_N

PR Commercial fertilizer price N_PRICE --

P_PRICE
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Appendix 3—Variables used in equation listing (text), with names of corresponding GAMS model variables

(Appendix 2) and model report variables (Appendix 4)1—Continued

Item Description GAMS model GAMS model

linkage—data linkage—

parameter or aggregate

model variable output variable

(See Appendix 2) (See Appendix 4)

Variables:—continued

N_ONF Manure-N applied on source farms N_M_ONF N_M_ONFB

N_TRN Manure-N transferred off source farms for land 

application N_TRAN N_TRAN1B

P_ONF Manure-P applied on source farms P_M_ONF P_M_ONFB

P_TRN Manure-P transferred off source farms for land 

application P_TRAN P_TRAN1B

P_PCT Portion of P beneficially used by the crop PN_RATIO2 --

C_AP Per-acre cost of applying commercial fertilizer under 

an N standard CHMAP_SV --

FS_P Savings in commercial fertilizer purchases under 

a P standard CHMSAV_P CHMSVB_P

1 GAMS is the General Algebraic Modeling System.
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Appendix 4—Model report variables

Costs:

* TOTCOST TOTAL COST - MANURE USE AND DISPOSAL ($ MIL)

TOTCOSTB TOTAL COST - MANURE USE AND DISPOSAL - CH.BASIN ($ MIL)

TOTCST2 TOTAL COST - WITH STORCST NETTED OUT ($ MIL)

TOTCST2B TOTAL COST - WITH STORCST NETTED OUT - CH.BASIN ($ MIL)

* CST_SHR COST-SHARE PAYMENTS - MANURE HAULING ($ MIL)

* HAULCST TOTAL MANURE HAULING COST ($ MIL)

HAULCSTB TOTAL MANURE HAULING & APPL COST - CH.BASIN ($ MIL)

HCOST_ON TOTAL MNR HAULING & APPL COST - ON-FARM ($ MIL)

HCST_ONB TOTAL MNR HAULING & APPL COST - ON-FARM - CH.BASIN ($ MIL)

HCOST_IN TOTAL MNR HAULING & APPL COST - OFF-FARM IN-CNTY ($ MIL)

HCST_INB TOTAL MNR HAULING & APPL COST - OFF-FARM IN-CNTY - CHB ($ MIL)

HCOST_EX TOTAL MNR HAULING & APPL COST - OFF-FARM OUT-OF-CNTY ($ MIL)

HCSTEXB1 TOTAL MNR HAULING & APPL COST - OFF-FARM OUT-OF-CNTY - SOURCE ($ MIL)

HCSTEXB2 TOTAL MNR HAULING & APPL COST - OFF-FARM OUT-OF-CNTY - DEST ($ MIL)

* STORCST MANURE STORAGE COST ($ MIL)

STORCSTB MANURE STORAGE COST - CH.BASIN ($ MIL)

* INCRP MANURE INCORPORATION COST ($ MIL)

INCRPB MANURE INCORPORATION COST - CH.BASIN ($ MIL)

* NMP_SRC NUTRIENT MNGMT PLAN CHARGE - SOURCE COUNTIES ($ MIL)

NMP_SRCB NUTRIENT MNGMT PLAN CHARGE - SOURCE COUNTIES ($ MIL)

* NMP_DST NUTRIENT MNGMT PLAN CHARGE - DESTINATION COUNTIES ($ MIL)

NMP_DSTB NUTR MNGMT PLAN CHARGE - DEST COUNTIES - CH.BASIN ($ MIL)

* CHMSAV SAVINGS IN CHEMICAL NUTRIENT APPLICATIONS ($ MIL)

CHMSV_N SAVINGS IN CHEMICAL APPLICATIONS - N-STANDARD ($ MIL)

CHMSV_P SAVINGS IN CHEMICAL APPLICATIONS - P-STANDARD ($ MIL)

CHMSVB_N SAVINGS IN CHEMICAL APPL - N-STANDARD - CH.BASIN ($ MIL)

CHMSVB_P SAVINGS IN CHEMICAL APPL - P-STANDARD - CH.BASIN ($ MIL)

MNR_TSTB TOTAL ANNUAL COST - MANURE TESTING ($ MIL)

REC_RPTB TOTAL ANN COST - RECORD KEEPING AND REPORTING ($ MIL)

VIS_INSB TOTAL ANNUAL COST - VISUAL INSPECTION ($ MIL)

CRT_APPB TOTAL COST - TRAINING & CERTIFICATION FOR M APPL ($ MIL)

NMP_DEVB TOTAL COST - DEVELOPMENT OF NMP ($ MIL)

SL_TSTB TOTAL COST - ONFARM SOIL TESTING ($ MIL)

CAL_SPRB TOTAL COST - CALIBRATION OF MANURE SPREADER ($ MIL)

Acres:

AC_SPR1_ TOTAL SPREAD ACS - EXCESS M - WITHIN COUNTY (1000 ACS)

AC_SPR1B TOTAL SPREAD ACS - EXCESS M - WITHIN COUNTY - CH.BASIN (1000 ACS)

AC_SPR2_ TOTAL SPREAD ACS - EXCESS M - OUT-OF-COUNTY (1000 ACS)

AC_SPR2B TOTAL SPREAD ACS - EXCESS M - OUT-OF-COUNTY - CH.BASIN (1000 ACS)

AC_SPR_ TOTAL SPREAD ACS - EXCESS M - BY DEST COUNTY (1000 ACS)

AC_SPRB TOTAL SPREAD ACS - EXCESS M - BY DEST COUNTY - CH.BASIN (1000 ACS)

AC_ONFM ONFARM ACRES - APPLIED MANURE (1000 ACS)

AC_ONFMB ONFARM ACRES - APPLIED MANURE - CH.BASIN (1000 ACS)

Manure quantity (dry):

M_PROD_ TOTAL MANURE PRODUCTION (1000 TONS)

M_PRODB TOTAL MANURE PRODUCTION - CH.BASIN (1000 TONS)

M_EXC_ EXCESS MANURE ON ANIMAL FARMS (1000 TONS)

M_EXCB EXCESS MANURE ON ANIMAL FARMS - CH.BASIN (1000 TONS)

M_INDUST EXCESS MANURE IN INDUSTRIAL USES (1000 TONS)

M_INDSTB EXCESS MANURE IN INDUSTRIAL USES - CH.BASIN (1000 TONS)
44

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Appendix 4—Model report variables—Continued

Manure quantity (dry):—continued

M_STOR_ TOTAL MANURE STORAGE - NONCLEARING (1000 TONS)

M_STORB TOTAL MANURE STORAGE - NONCLEARING - CH.BASIN (1000 TONS)

M_ONFRM_ MANURE PRODUCED AND USED ON OWN LVSTK FARM (1000 TONS)

M_ONFRMB MANURE PRODUCED AND USED ON OWN LVSTK FARM - CH.BASIN (1000 TONS)

M_TRAN1_ EXCESS MANURE TRANSFERRED - BY SOURCE CTY (1000 TONS)

M_TRAN1B EXCESS MANURE TRANSFERRED - BY SOURCE CTY - CH.BASIN (1000 TONS)

M_TRAN2_ EXCESS MANURE TRANSFERRED - BY DEST CTY (1000 TONS)

M_TRAN2B EXCESS MANURE TRANSFERRED - BY DEST CTY - CH.BASIN (1000 TONS)

M_TRNCT_ EXCESS MANURE TRANSFERRED WITHIN COUNTY (1000 TONS)

M_TRNCTB EXCESS MANURE TRANSFERRED WITHIN COUNTY - CH.BASIN (1000 TONS)

M_EXPRT EXCESS MANURE EXPORTED - BY SOURCE COUNTY (1000 TONS)

M_EXPRTB EXCESS MANURE EXPORTED - BY SOURCE COUNTY - CH.BASIN (1000 TONS)

M_IMPRT_ EXCESS MANURE IMPORTED - BY DEST COUNTY (1000 TONS)

M_IMPRTB EXCESS MANURE IMPORTED - BY DEST COUNTY - CH.BASIN (1000 TONS)

* M_SINK_ EXCESS MANURE EXPORTED TO SINK (1000 TONS)

* M_SINKB EXCESS MANURE EXPORTED TO SINK - CH.BASIN (1000 TONS)

M_USE_ MANURE USE ON ALL FARMLAND - ONFARM & EXCESS (1000 TONS)

M_USEB MANURE USE ON ALL FARMLAND - ONFARM & EXCESS - CH.BASIN (1000 TONS)

M_PR_SY_(SYS) MANURE PRODUCTION BY WASTE SYSTEM (1000 TONS)

M_PR_S_1 MANURE PRODUCTION FOR LAGOON WASTE SYSTEM (1000 TONS)

M_PR_S_2 MANURE PRODUCTION FOR SLURRY WASTE SYSTEM (1000 TONS)

M_PR_S_3 MANURE PRODUCTION FOR DRY WASTE SYSTEM (1000 TONS)

M_PR_SYB(SYS) MANURE PRODUCTION BY WASTE SYSTEM - CH.BASIN (1000 TONS)

M_PR_S1B MANURE PRODUCTION FOR LAGOON WASTE SYSTEM - CH.BASIN (1000 TONS)

M_PR_S2B MANURE PRODUCTION FOR SLURRY WASTE SYSTEM - CH.BASIN (1000 TONS)

M_PR_S3B MANURE PRODUCTION FOR DRY WASTE SYSTEM - CH.BASIN (1000 TONS)

M_IND_(SYS) EXCESS MANURE IN INDUSTRIAL USES BY SYSTEM (1000 TONS)

M_IND_1 EXCESS LAGOON MANURE IN INDUSTRIAL USES BY SYSTEM (1000 TONS)

M_IND_2 EXCESS SLURRY MANURE IN INDUSTRIAL USES BY SYSTEM (1000 TONS)

M_IND_3 EXCESS DRY MANURE IN INDUSTRIAL USES BY SYSTEM (1000 TONS)

M_INDB(SYS) EXCESS MANURE IN INDUSTRIAL USES BY SYSTEM - CH.BASIN (1000 TONS)

M_IND1B EXCESS LAGOON MANURE IN INDUSTRIAL USES BY SYSTEM - CH.BASIN (1000 TONS)

M_IND2B EXCESS SLURRY MANURE IN INDUSTRIAL USES BY SYSTEM - CH.BASIN (1000 TONS)

M_IND3B EXCESS DRY MANURE IN INDUSTRIAL USES BY SYSTEM - CH.BASIN (1000 TONS)

M_SY_(SYS) EXCESS MANURE TRANSFERRED BY SYSTEM (1000 TONS)

M_SY_1 EXCESS MANURE TRANSFERRED BY LAGOON SYSTEM (1000 TONS)

M_SY_2 EXCESS MANURE TRANSFERRED BY SLURRY SYSTEM (1000 TONS)

M_SY_3 EXCESS MANURE TRANSFERRED BY DRY SYSTEM (1000 TONS)

M_SYB(SYS) EXCESS MANURE TRANSFERRED BY SYSTEM - CH.BASIN (1000 TONS)

M_SY1B EXCESS MANURE TRANSFERRED BY LAGOON SYSTEM - CH.BASIN (1000 TONS)

M_SY2B EXCESS MANURE TRANSFERRED BY SLURRY SYSTEM - CH.BASIN (1000 TONS)

M_SY3B EXCESS MANURE TRANSFERRED BY DRY SYSTEM - CH.BASIN (1000 TONS)

M_ON_SY_(SYS) MANURE USED ONFARM BY SYSTEM (1000 TONS)

M_ON_S_1 MANURE USED ONFARM FROM LAGOON SYSTEM (1000 TONS)

M_ON_S_2 MANURE USED ONFARM FROM SLURRY SYSTEM (1000 TONS)

M_ON_S_3 MANURE USED ONFARM FROM DRY SYSTEM (1000 TONS)

M_ON_SYB(SYS) MANURE USED ONFARM BY SYSTEM - CH.BASIN (1000 TONS)

M_ON_S1B MANURE USED ONFARM FROM LAGOON SYSTEM - CH.BASIN (1000 TONS)

M_ON_S2B MANURE USED ONFARM FROM SLURRY SYSTEM - CH.BASIN (1000 TONS)

M_ON_S3B MANURE USED ONFARM FROM DRY SYSTEM - CH.BASIN (1000 TONS)
45

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Appendix 4—Model report variables—Continued

Manure quantity (dry):—continued

M_SY_IN_(SYS) EXCESS M TRANSFERRED IN-COUNTY BY SYSTEM (1000 TONS)

M_SY_I_1 EXCESS M TRANSFERRED IN-COUNTY LAGOON SYSTEM (1000 TONS)

M_SY_I_2 EXCESS M TRANSFERRED IN-COUNTY SLURRY SYSTEM (1000 TONS)

M_SY_I_3 EXCESS M TRANSFERRED IN-COUNTY DRY SYSTEM (1000 TONS)

M_SY_INB(SYS) EXCESS M TRANSFERRED IN-COUNTY BY SYSTEM - CH.BASIN (1000 TONS)

M_SY_I1B EXCESS M TRANSFERRED IN-COUNTY LAGOON SYSTEM - CH.BASIN (1000 TONS)

M_SY_I2B EXCESS M TRANSFERRED IN-COUNTY SLURRY SYSTEM - CH.BASIN (1000 TONS)

M_SY_I3B EXCESS M TRANSFERRED IN-COUNTY DRY SYSTEM - CH.BASIN (1000 TONS)

M_SY_EX_(SYS) EXCESS MANURE EXPORTED BY SYSTEM (1000 TONS)

M_SY_E_1 EXCESS MANURE EXPORTED BY LAGOON SYSTEM (1000 TONS)

M_SY_E_2 EXCESS MANURE EXPORTED BY SLURRY SYSTEM (1000 TONS)

M_SY_E_3 EXCESS MANURE EXPORTED BY DRY SYSTEM (1000 TONS)

M_SYEXB1(SYS) EXCESS M EXPORTED BY SYSTEM - CH.BASIN - ADJ ON CT (1000 TONS)

M_SYE1B1 EXCESS M EXPORTED BY LAGOON SYSTEM - CH.BASIN - ADJ ON CT (1000 TONS)

M_SYE2B1 EXCESS M EXPORTED BY SLURRY SYSTEM - CH.BASIN - ADJ ON CT (1000 TONS)

M_SYE3B1 EXCESS M EXPORTED BY DRY SYSTEM - CH.BASIN - ADJ ON CT (1000 TONS)

M_SYEXB2(SYS) EXCESS M EXPORTED BY SYSTEM - CH.BASIN - ADJ ON CT2 (1000 TONS)

M_SYE1B2 EXCESS M EXPORTED BY LAGOON SYSTEM - CH.BASIN - ADJ ON CT2 (1000 TONS)

M_SYE2B2 EXCESS M EXPORTED BY SLURRY SYSTEM - CH.BASIN - ADJ ON CT2 (1000 TONS)

M_SYE3B2 EXCESS M EXPORTED BY DRY SYSTEM - CH.BASIN - ADJ ON CT2 (1000 TONS)

M_STR_(SYS) EXCESS M STORED -NOT CLEARING- BY SYSTEM (1000 TONS)

M_STR_1 EXCESS M STORED -NOT CLEARING- LAGOON SYSTEM (1000 TONS)

M_STR_2 EXCESS M STORED -NOT CLEARING- SLURRY SYSTEM (1000 TONS)

M_STR_3 EXCESS M STORED -NOT CLEARING- DRY SYSTEM (1000 TONS)

M_STRB(SYS) EXCESS M STORED -NOT CLEARING- BY SYSTEM - CH.BASIN (1000 TONS)

M_STR1B EXCESS M STORED -NOT CLEARING- LAGOON SYSTEM - CH.BASIN (1000 TONS)

M_STR2B EXCESS M STORED -NOT CLEARING- SLURRY SYSTEM - CH.BASIN (1000 TONS)

M_STR3B EXCESS M STORED -NOT CLEARING- DRY SYSTEM - CH.BASIN (1000 TONS)

Manure quantity (wet):

WMPR_SY_(SYS) WET MANURE PRODUCTION BY WASTE SYSTEM (1000 TONS)

WMPR_S_1 WET MANURE PRODUCTION LAGOON WASTE SYSTEM (1000 TONS)

WMPR_S_2 WET MANURE PRODUCTION SLURRY WASTE SYSTEM (1000 TONS)

WMPR_S_3 WET MANURE PRODUCTION DRY WASTE SYSTEM (1000 TONS)

WMPR_SYB(SYS) WET MANURE PRODUCTION BY WASTE SYSTEM - CH.BASIN (1000 TONS)

WMPR_S1B WET MANURE PRODUCTION LAGOON WASTE SYSTEM - CH.BASIN (1000 TONS)

WMPR_S2B WET MANURE PRODUCTION SLURRY WASTE SYSTEM - CH.BASIN (1000 TONS)

WMPR_S3B WET MANURE PRODUCTION DRY WASTE SYSTEM - CH.BASIN (1000 TONS)

WMIND_(SYS) EXCESS WET MANURE IN INDUSTRIAL USES BY SYSTEM (1000 TONS)

WMIND_1 EXCESS WET MANURE IN INDUSTRIAL USES LAGOON SYSTEM (1000 TONS)

WMIND_2 EXCESS WET MANURE IN INDUSTRIAL USES SLURRY SYSTEM (1000 TONS)

WMIND_3 EXCESS WET MANURE IN INDUSTRIAL USES DRY SYSTEM (1000 TONS)

WMINDB(SYS) EXCESS WET MANURE IN INDUSTRIAL USES BY SYSTEM - CH.BASIN (1000 TONS)

WMIND1B EXCESS WET MANURE IN INDUSTRIAL USES LAGOON SYSTEM - CH.BASIN (1000 TONS)

WMIND2B EXCESS WET MANURE IN INDUSTRIAL USES SLURRY SYSTEM - CH.BASIN (1000 TONS)

WMIND3B EXCESS WET MANURE IN INDUSTRIAL USES DRY SYSTEM - CH.BASIN (1000 TONS)

WMSY_(SYS) EXCESS WET MANURE TRANSFERRED BY SYSTEM (1000 TONS)

WMSY_1 EXCESS WET MANURE TRANSFERRED LAGOON SYSTEM (1000 TONS)

WMSY_2 EXCESS WET MANURE TRANSFERRED SLURRY SYSTEM (1000 TONS)

WMSY_3 EXCESS WET MANURE TRANSFERRED DRY SYSTEM (1000 TONS)

WMSYB(SYS) EXCESS WET MANURE TRANSFERRED BY SYSTEM - CH.BASIN (1000 TONS)
46

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Appendix 4—Model report variables—Continued

Manure quantity (wet):—continued

WMSY1B EXCESS WET MANURE TRANSFERRED LAGOON SYSTEM - CH.BASIN (1000 TONS)

WMSY2B EXCESS WET MANURE TRANSFERRED SLURRY SYSTEM - CH.BASIN (1000 TONS)

WMSY3B EXCESS WET MANURE TRANSFERRED DRY SYSTEM - CH.BASIN (1000 TONS)

WMON_SY_(SYS) WET MANURE USED ONFARM BY SYSTEM (1000 TONS)

WMON_S_1 WET MANURE USED ONFARM LAGOON SYSTEM (1000 TONS)

WMON_S_2 WET MANURE USED ONFARM SLURRY SYSTEM (1000 TONS)

WMON_S_3 WET MANURE USED ONFARM DRY SYSTEM (1000 TONS)

WMON_SYB(SYS) WET MANURE USED ONFARM BY SYSTEM - CH.BASIN (1000 TONS)

WMON_S1B WET MANURE USED ONFARM LAGOON SYSTEM - CH.BASIN (1000 TONS)

WMON_S2B WET MANURE USED ONFARM SLURRY SYSTEM - CH.BASIN (1000 TONS)

WMON_S3B WET MANURE USED ONFARM DRY SYSTEM - CH.BASIN (1000 TONS)

WMSY_IN_(SYS) EXCESS WET M TRANSFERRED IN-COUNTY BY SYSTEM (1000 TONS)

WMSY_I_1 EXCESS WET M TRANSFERRED IN-COUNTY LAGOON SYSTEM (1000 TONS)

WMSY_I_2 EXCESS WET M TRANSFERRED IN-COUNTY SLURRY SYSTEM (1000 TONS)

WMSY_I_3 EXCESS WET M TRANSFERRED IN-COUNTY DRY SYSTEM (1000 TONS)

WMSYINB1(SYS) EXCESS WET M - INTRA-COUNTY BY SYSTEM - CH.BASIN - ADJ ON CT (1000 TONS)

WMSYI1B1 EXCESS WET M - INTRA-COUNTY LAGOON SYSTEM - CH.BASIN - ADJ ON CT (1000 TONS)

WMSYI2B1 EXCESS WET M - INTRA-COUNTY SLURRY SYSTEM - CH.BASIN - ADJ ON CT (1000 TONS)

WMSYI3B1 EXCESS WET M - INTRA-COUNTY DRY SYSTEM - CH.BASIN - ADJ ON CT (1000 TONS)

WMSYINB2(SYS) EXCESS WET M - INTRA-COUNTY BY SYSTEM - CH.BASIN - ADJ ON CT2 (1000 TONS)

WMSYI1B2 EXCESS WET M - INTRA-COUNTY LAGOON SYSTEM - CH.BASIN - ADJ ON CT2 (1000 TONS)

WMSYI2B2 EXCESS WET M - INTRA-COUNTY SLURRY SYSTEM - CH.BASIN - ADJ ON CT2 (1000 TONS)

WMSYI3B2 EXCESS WET M - INTRA-COUNTY DRY SYSTEM - CH.BASIN - ADJ ON CT2 (1000 TONS)

WMSY_EX_(SYS) EXCESS WET MANURE EXPORTED BY SYSTEM (1000 TONS)

WMSY_E_1 EXCESS WET MANURE EXPORTED LAGOON SYSTEM (1000 TONS)

WMSY_E_2 EXCESS WET MANURE EXPORTED SLURRY SYSTEM (1000 TONS)

WMSY_E_3 EXCESS WET MANURE EXPORTED DRY SYSTEM (1000 TONS)

WMSYEXB1(SYS) EXCESS WET M EXPORTED BY SYSTEM - CH.BASIN - ADJ ON CT (1000 TONS)

WMSYE1B1 EXCESS WET M EXPORTED LAGOON SYSTEM - CH.BASIN - ADJ ON CT (1000 TONS)

WMSYE2B1 EXCESS WET M EXPORTED SLURRY SYSTEM - CH.BASIN - ADJ ON CT (1000 TONS)

WMSYE3B1 EXCESS WET M EXPORTED DRY SYSTEM - CH.BASIN - ADJ ON CT (1000 TONS)

WMSYEXB2(SYS) EXCESS WET M EXPORTED BY SYSTEM - CH.BASIN - ADJ ON CT2 (1000 TONS)

WMSYE1B2 EXCESS WET M EXPORTED LAGOON SYSTEM - CH.BASIN - ADJ ON CT2 (1000 TONS)

WMSYE2B2 EXCESS WET M EXPORTED SLURRY SYSTEM - CH.BASIN - ADJ ON CT2 (1000 TONS)

WMSYE3B2 EXCESS WET M EXPORTED DRY SYSTEM - CH.BASIN - ADJ ON CT2 (1000 TONS)

WMSTR_(SYS) EXCESS WET M STORED -NOT CLEARING- BY SYSTEM (1000 TONS)

WMSTR_1 EXCESS WET M STORED -NOT CLEARING- LAGOON SYSTEM (1000 TONS)

WMSTR_2 EXCESS WET M STORED -NOT CLEARING- SLURRY SYSTEM (1000 TONS)

WMSTR_3 EXCESS WET M STORED -NOT CLEARING- DRY SYSTEM (1000 TONS)

WMSTRB(SYS) EXCESS WET M STORED -NOT CLEARING- BY SYSTEM - CH.BASIN (1000 TONS)

WMSTRB_1 EXCESS WET M STORED -NOT CLEARING- LAGOON SYSTEM - CH.BASIN (1000 TONS)

WMSTRB_2 EXCESS WET M STORED -NOT CLEARING- SLURRY SYSTEM - CH.BASIN (1000 TONS)

WMSTRB_3 EXCESS WET M STORED -NOT CLEARING- DRY SYSTEM - CH.BASIN (1000 TONS)

WMUS_SY_(SYS) TOTAL WET MANURE USED - ONFARM AND OFF-FARM - ALL SYSTEMS (1000 TONS)

WMUS_S_1 TOTAL WET MANURE USED - ONFARM AND OFF-FARM - LAGOON SYSTEM (1000 TONS)

WMUS_S_2 TOTAL WET MANURE USED - ONFARM AND OFF-FARM - SLURRY SYSTEM (1000 TONS)

WMUS_S_3 TOTAL WET MANURE USED - ONFARM AND OFF-FARM - DRY SYSTEM (1000 TONS)

WMUS_SYB(SYS) TOTAL WET MANURE USED - ONFARM AND OFF-FARM- CH.BASINALL SYSTEMS (1000 TONS)
47

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Appendix 4—Model report variables—Continued

Manure quantity (wet):—continued

WMUS_S1B TOTAL WET MANURE USED - ONFARM AND OFF-FARM- CH.BASINLAGOON SYSTEM (1000 TONS)

WMUS_S2B TOTAL WET MANURE USED - ONFARM AND OFF-FARM- CH.BASINSLURRY SYSTEM (1000 TONS)

WMUS_S3B TOTAL WET MANURE USED - ONFARM AND OFF-FARM- CH.BASINDRY SYSTEM (1000 TONS)

WMPROD_ TOTAL WET MANURE PRODUCTION (1000 TONS)

WMPRODB TOTAL WET MANURE PRODUCTION - CH.BASIN (1000 TONS)

WMINDUST EXCESS WET MANURE IN INDUSTRIAL USES (1000 TONS)

WMINDSTB EXCESS WET MANURE IN INDUSTRIAL USES - CH.BASIN (1000 TONS)

WMONFRM WET MANURE PRODUCED AND USED ON OWN LVSTK FARM (1000 TONS)

WMONFRMB WET MANURE PRODUCED AND USED ON OWN LVSTK FARM - CH.BASIN (1000 TONS)

WMTRNCT_ EXCESS WET MANURE TRANSFERRED WITHIN COUNTY (1000 TONS)

WMTRNCTB EXCESS WET MANURE TRANSFERRED WITHIN COUNTY - CH.BASIN (1000 TONS)

WMEXPRT_ EXCESS WET MANURE EXPORTED - BY SOURCE COUNTY (1000 TONS)

WMEXPRTB EXCESS WET MANURE EXPORTED - BY SOURCE COUNTY - CH.BASIN (1000 TONS)

WMIMPRT_ EXCESS WET MANURE IMPORTED - BY DEST COUNTY (1000 TONS)

WMIMPRTB EXCESS WET MANURE IMPORTED - BY DEST COUNTY - CH.BASIN (1000 TONS)

WMTRAN1_ EXCESS WET MANURE TRANSFERRED - BY SOURCE CTY (1000 TONS)

WMTRAN1B EXCESS WET MANURE TRANSFERRED - BY SOURCE CTY - CH.BASIN (1000 TONS)

WMTRAN2_ EXCESS WET MANURE TRANSFERRED - BY DEST CTY (1000 TONS)

WMTRAN2B EXCESS WET MANURE TRANSFERRED - BY DEST CTY - CH.BASIN (1000 TONS)

WMEXC_ EXCESS WET MANURE ON ANIMAL FARMS (1000 TONS)

WMEXCB EXCESS WET MANURE ON ANIMAL FARMS - CH.BASIN (1000 TONS)

WMUSE_ WET MANURE USE ON ALL FARMLAND - ONFARM & EXCESS (1000 TONS)

WMUSEB WET MANURE USE ON ALL FARMLAND - ONFARM & EXCESS - CH.BASIN (1000 TONS)

WMSTOR_ TOTAL WET MANURE STORAGE - NONCLEARING (1000 TONS)

WMSTORB TOTAL WET MANURE STORAGE - NONCLEARING - CH.BASIN (1000 TONS)

Manure nutrients:

N_PROD_ TOTAL MANURE-N PRODUCTION (MIL LBS)

N_PRODB TOTAL MANURE-N PRODUCTION - CH.BASIN (MIL LBS)

N_EXC_ EXCESS MANURE-N ON ANIMAL FARMS (MIL LBS)

N_EXCB EXCESS MANURE-N ON ANIMAL FARMS - CH.BASIN (MIL LBS)

N_TRAN1_ EXCESS MANURE-N TRANSFERRED - BY SOURCE CTY (MIL LBS)

N_TRAN1B EXCESS MANURE-N TRANSFERRED - BY SOURCE CTY - CH.BASIN (MIL LBS)

N_TRAN2_ EXCESS MANURE-N TRANSFERRED - BY DEST COUNTY (MIL LBS)

N_TRAN2B EXCESS MANURE-N TRANSFERRED - BY DEST COUNTY - CH.BASIN (MIL LBS)

N_M_ONF_ ONFARM MANURE-N USED ON ANIMAL FARMS (MIL LBS)

N_M_ONFB ONFARM MANURE-N USED ON ANIMAL FARMS - CH.BASIN (MIL LBS)

P_PROD_ TOTAL MANURE-P PRODUCTION (MIL LBS)

P_PRODB TOTAL MANURE-P PRODUCTION - CH.BASIN (MIL LBS)

P_EXC_ EXCESS MANURE-P ON ANIMAL FARMS (MIL LBS)

P_EXCB EXCESS MANURE-P ON ANIMAL FARMS - CH.BASIN (MIL LBS)

P_TRAN1_ EXCESS MANURE-P TRANSFERRED - BY SOURCE CTY (MIL LBS)

P_TRAN1B EXCESS MANURE-P TRANSFERRED - BY SOURCE CTY - CH.BASIN (MIL LBS)

P_TRAN2_ EXCESS MANURE-P TRANSFERRED - BY DEST COUNTY (MIL LBS)

P_TRAN2B EXCESS MANURE-P TRANSFERRED - BY DEST COUNTY - CH.BASIN (MIL LBS)

P_M_ONF_ ONFARM MANURE-P USED ON ANIMAL FARMS (MIL LBS)

P_M_ONFB ONFARM MANURE-P USED ON ANIMAL FARMS - CH.BASIN (MIL LBS)
48

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Appendix 4—Model report variables—Continued

Hauling distance:

AVDST_ON AVE HAULING DISTANCE - ON-FARM (KMS)

AVDST_IN AVE HAULING DISTANCE - EXCESS M - WITHIN COUNTY (KMS)

AVDST_EX AVE HAULING DISTANCE - EXCESS M - OUT-OF-COUNTY (KMS)

AVDS_ONM AVE HAULING DISTANCE - ON-FARM (MILES)

AVDS_INM AVE HAULING DISTANCE - EXCESS M - WITHIN COUNTY (MILES)

AVDS_EXM AVE HAULING DISTANCE - EXCESS M - OUT-OF-COUNTY (MILES)

Application rate:

AVAP_OFF AVE MANURE APPLICATION RATE - OFF-FARM (DRY TONS-AC)

AVAP_ON AVE MANURE APPLICATION RATE - ON-FARM (DRY TONS-AC)

AVEAPPL AVE MANURE APPLICATION RATE - ALL (DRY TONS-AC)

Hauling cost per ton:

HC_ON_TN HAULING & APPL COST PER TON HAULED - ONFARM ($-DRY TON)

HC_IN_TN HAULING & APPL COST PER TON HAULED - INCOUNTY ($-DRY TON)

HC_EX_TN HAULING & APPL COST PER TON HAULED - OUT-OF-COUNTY ($-DRY TON)

HC_TN HAULING & APPL COST PER TON HAULED - TOTAL ($-DRY TON)

TC_TN TOTAL COST PER TON HAULED ($-DRY TON)

HCON_WTN HAULING & APPL COST PER TON HAULED - ONFARM ($-WET TON)

HCIN_WTN HAULING & APPL COST PER TON HAULED - INCOUNTY ($-WET TON)

HCEX_WTN HAULING & APPL COST PER TON HAULED - OUT-OF-COUNTY ($-WET TON)

HC_WTN HAULING & APPL COST PER TON HAULED - TOTAL ($-WET TON)

TC_WTN TOTAL COST PER TON HAULED ($-WET TON)

Application costs:

APPL_ON(CT,CT2,SYS) MANURE APPLICATION COST - ONFARM ($ MIL)

APPL_IN(CT,CT2,SYS,DST) MANURE APPLICATION COST - INCOUNTY ($ MIL)

APPL_EX(CT,GR,CT2,SYS,DST) MANURE APPLICATION COST - OUT-OF-CNTY ($ MIL)

AP_ON(CT,CT2) TOTAL M APPL COST - ONFARM ($ MIL)

AP_IN(CT,CT2) TOTAL M APPL COST - INCOUNTY ($ MIL)

AP_EX(CT,GR,CT2) M APPL COST - OUT-OF-COUNTY GRID ($ MIL)

AP1(CT) APPL COST BY SOURCE CNTY - INCOUNTY ($ MIL)

AP2(CT) APPL COST BY SOURCE CNTY - EXPORTS ($ MIL)

APCOST(CT) TOTAL APPL COST BY SOURCE COUNTY ($ MIL)

APCST_ APPL COST ACROSS SOURCE COUNTIES ($ MIL)

APCSTB APPL COST ACROSS SOURCE CNTIES - CH.BASIN ($ MIL)

APL1(CT2) APPL COST BY DEST CNTY - INCOUNTY ($ MIL)

APL2(CT2) APPL COST BY DEST CNTY - EXPORTS ($ MIL)

APLCOST(CT2) TOTAL APPL COST BY DEST COUNTY ($ MIL)

APLCST_ APPL COST ACROSS DEST COUNTIES ($ MIL)

APLCSTB APPL COST ACROSS DEST COUNTIES - CH.BASIN ($ MIL)

AC_SHARE(CT2) ACREAGE MANURED AS A SHARE OF TOTAL AVAILABLE (.XX)

* Asterisks denote endogenously specified variables in the model; all others are derived from the model solution based on levels

of endogenous variables.