United States 

Department of 

Agriculture

Economic

Research

Service

Marc Ribaudo, Jorge Delgado, LeRoy Hansen, Michael Livingston, 

Roberto Mosheim, and James Williamson

Nitrogen in Agricultural 

Systems: Implications for 

Conservation Policy

Economic

Research 

Report

Number 127

September 2011
www.ers.usda.gov

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policies, see: 

www.ers.usda.gov/briefing/agchemicals/

www.ers.usda.gov/briefing/conservationpolicy/

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Recommended citation format for this publication:

Ribaudo, Marc, Jorge Delgado, LeRoy Hansen, Michael Livingston, Roberto 

Mosheim, and James Williamson. Nitrogen In Agricultural Systems: 

Implications For Conservation Policy. ERR-127. U.S. Dept. of Agriculture, 

Econ. Res. Serv. September 2011.

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A Report from the Economic Research Service

Abstract

Nitrogen is an important agricultural input that is critical for crop production. However, the 

introduction of large amounts of nitrogen into the environment has a number of undesirable impacts on water, terrestrial, and atmospheric resources. This report explores the use 

of nitrogen in U.S. agriculture and assesses changes in nutrient management by farmers 

that may improve nitrogen use effi ciency. It also reviews a number of policy approaches for 

improving nitrogen management and identifi es issues affecting their potential performance. 

Findings reveal that about two-thirds of U.S. cropland is not meeting three criteria for good 

nitrogen management. Several policy approaches, including fi nancial incentives, nitrogen 

management as a condition of farm program eligibility, and regulation, could induce farmers 

to improve their nitrogen management and reduce nitrogen losses to the environment.

Keywords

Reactive nitrogen, nitrogen management, fertilizer, water quality, greenhouse gas, economic 

incentives, conservation policy, regulation

Acknowledgments

This report benefi ted from the insightful comments provided by Keith Fuglie, Marca 

Weinberg, and Mary Bohman of USDA’s Economic Research Service, Ralph Heimlich of 

Agricultural Conservation Economics, Roberta Parry of the U.S. Environmental Protection 

Agency, Jerry Hatfi eld of USDA’s Agricultural Research Service, USDA’s Natural Resources 

Conservation Service and an anonymous reviewer. Thanks also to John Weber for excellent 

editorial assistance and to Curtia Taylor for the design and layout.

Marc Ribaudo, mribaudo@ers.usda.gov

Jorge Delgado

LeRoy Hansen

Michael Livingston

Roberto Mosheim

James Williamson

Nitrogen in Agricultural 

Systems: Implications for 

Conservation Policy

Economic

Research

Report

Number 127

September 2011
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Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

Chapter 1

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

Chapter 2

Environmental Implications of Nitrogen and Goals 

for Agricultural Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Chapter 3

State of Nitrogen Management on Cropland . . . . . . . . . . . . . . . . . . . . . . . . . 9

Chapter 4

Policy Instruments for Nitrogen Reduction . . . . . . . . . . . . . . . . . . . . . . . . . 25

Chapter 5

Implications for Nitrogen Management Policies . . . . . . . . . . . . . . . . . . . . 46

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Appendix 1

Estimating Water Treatment Costs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Appendix 2

Using NLEAP To Model Nitrogen Loses . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Appendix 3

Estimating Changes in Nitrogen Fertilizer Application Rate . . . . . . . . . . . 72

Appendix 4

Comparing Costs of Farms Using Different 

Nutrient Management Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Appendix 5

Estimating Wetland Restoration Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
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Summary

What Is the Issue?

Nitrogen is an agricultural input that is critical for crop production. Humaninduced production and release of reactive nitrogen has greatly affected the 

Earth’s natural balance of nitrogen, contributing to changes in ecosystems, 

both benefi cial and harmful, including increased agricultural productivity 

in nitrogen-limited areas, ozone-induced injury to crops and forests, overenrichment of aquatic ecosystems, biodiversity losses, visibility-impairing 

haze, and global climate change. Incentives for encouraging farmers to adopt 

improved nitrogen management can take many forms, from purely voluntary 

to regulatory. Designing a cost-effective policy requires that factors infl uencing fertilizer use be fully understood. Also, an understanding of how 

farmers are likely to respond to different incentives may help policymakers 

assess potential environmental tradeoffs driven by nitrogen’s ability to 

change forms and cycle through different environmental media. 

What Did the Study Find?

• Emission of reactive nitrogen to the environment can be reduced by 

matching nitrogen applications more closely with the needs of growing 

crops. This can be achieved by adopting three “best management practices” (BMPs): 

• Rate: Applying an amount of nitrogen at a rate that accounts for all 

other sources of nitrogen, carryover from previous crops, irrigation 

water, and atmospheric deposits.

• Timing: Applying nitrogen as close to the time that the crop needs it 

as is practical (as opposed to the season before the crop is planted).

• Method: Injecting or incorporating the nutrients into the soil to reduce 

runoff and losses to the atmosphere.

• Among all U.S. fi eld crops planted in 2006 that received nitrogen fertilizers, 35 percent are estimated to have met all three of the nutrient BMPs. 

For the remaining cropland, improvements in management are needed to 

increase nitrogen use effi ciency (i.e., reduce the amount of nitrogen available for loss to the environment). 

• Corn is the most intensive user of nitrogen fertilizer, on a per acre basis 

and in total use. Fertilizer applied to corn is least likely to be applied in 

accordance with all three BMPs.

• Incentives for improving nitrogen use effi ciency by adopting the rate, 

timing, and method BMPs can come from policy or market forces:

• Government programs that provide fi nancial assistance for adopting 

BMPs can be effective if they encourage the participation of farmers 

with land most in need of improvement and if the farmers choose the 

most cost-effective practices. Data suggest that the amount of cropland needing improvement would require a substantial increase in the 

current Federal budget devoted to nutrient management practices.
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• Including nitrogen management in compliance provisions for receiving 

Federal farm payments could encourage farmers to adopt more 

effective management practices. In 2005, producers of U.S. corn 

received Government payments that were much higher than the cost 

of improving nitrogen management. The strength of this incentive, 

however, has declined in recent years because of increases in crop 

prices and a decline in direct commodity payments.

• Emissions markets, such as water quality trading and greenhouse gas 

cap-and-trade, could provide fi nancial incentives to farmers to adopt 

improved nitrogen management and produce nitrogen credits that can 

be sold in these markets. The effectiveness of such markets would 

depend on market design, including rules defi ning who can participate 

and what needs to be done to produce credits. 

• Onfi eld improvements to nitrogen use effi ciency could be supplemented 

with off-fi eld practices, such as wetlands restoration and vegetative fi lter 

strips that can fi lter and trap reactive nitrogen that leaves the fi eld through 

surface runoff and groundwater fl ow. Of the two practices, restored 

wetlands can be more cost effective at removing nitrogen and provide 

additional environmental benefi ts, but they are limited to areas with suitable soils and hydrology. Vegetative fi lters can be employed more widely 

across the landscape but are not effective when existing tile drains bypass 

the fi lters.

• Policies for increasing nitrogen use effi ciency should recognize the potential environmental tradeoffs when addressing particular issues related to 

reactive nitrogen. Focusing strictly on one issue, such as nitrate leaching, 

could lead to increased emissions of other nitrogen compounds, such as 

nitrous oxide, even when total reactive nitrogen emissions are reduced. 

How Was the Study Conducted?

ERS researchers used an extensive literature review, modeling, and data 

from USDA’s Agricultural Resource Management Survey (ARMS) of major 

fi eld crops. ARMS data provided information on nitrogen use, defi ned 

by the rate, method, and timing application criteria. This, in turn, helped 

researchers determine the types of management improvements needed the 

most. 

The following market forces and policy instruments were evaluated to 

measure their infl uence on nitrogen management: nitrogen fertilizer taxes, 

Federal fi nancial assistance offered to farmers to adopt practices that improve 

nitrogen use effi ciency or fi lter and trap nitrogen runoff, emissions markets 

such as water quality trading and greenhouse gas cap-and-trade, compliance 

with nitrogen BMPs as a condition for receiving farm program benefi ts, and 

regulation.

Because reactive nitrogen is mobile and able to transform into different 

compounds, researchers used a fi eld-level nitrogen loss simulator developed 

by USDA’s Agricultural Research Service to track how improving nitrogen 

use effi ciency by meeting all three BMPs affects emissions of different reactive nitrogen compounds. These interactions were taken into account when 

evaluating alternative policy options. 
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Glossary

ARMS – Agricultural Resource Management Survey

BMP – Best management practice

CEAP – Conservation Effects Assessment Program

EQIP – Environmental Quality Incentives Program

NUE – Nitrogen use effi ciency

N – Nitrogen

N2 – Gaseous nitrogen

NO3 – Nitrate

NOx – Nitrogen oxides

N2O – Nitrous oxide

NH3 – Ammonia

Nr – Reactive nitrogen

NRCS – Natural Resources Conservation Service (USDA)

VFS – Vegetative fi lter strip
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Chapter 1

Introduction

Most of the cropping systems in the world are naturally defi cient in 

nitrogen, making nitrogen inputs necessary to produce the crop yields 

needed to support human populations. Gaseous nitrogen (N2) is abundant 

in the atmosphere, but it cannot be used by living organisms unless it is 

fi rst converted into useable forms. Leguminous plants and soil microorganisms contribute signifi cant amounts of nitrogen used by crops, but yields 

necessary to support growing populations need more nitrogen than can be 

provided by natural means. 

The Haber-Bosch process converts “unreactive” gaseous nitrogen from the 

atmosphere into a biologically useable “reactive” form. The development of 

the process in the early 1900s led to the massive production of relatively inexpensive nitrogen fertilizer that boosted crop yields (Follett et al., 2010). The 

increasing use of reactive nitrogen in agriculture also increased the potential 

for nitrogen to be lost to the environment as ammonia (NH3), ammonium 

(NH4), nitrogen oxides (NOx), nitrous oxide (N2O), and nitrate (NO3); 

these compounds are all reactive forms of nitrogen (Galloway et al., 2003). 

Excessive amounts of reactive nitrogen inputs can lead to imbalances in the 

natural movement of nitrogen among atmospheric, terrestrial, and aquatic 

nitrogen pools, leading to disruptions in ecosystem function and the supply of 

valuable ecosystem services. 

Reactive nitrogen directly affects species composition, diversity, dynamics, 

and the functioning of terrestrial, freshwater, and marine ecosystems (Matson 

et al., 1997; Vitousek et al., 1997). Human-induced increases in reactive 

nitrogen emissions to the environment may contribute to the following 

harmful changes to ecosystems:

• Ozone-induced injury to crop, forest, and natural ecosystems

• Acidifi cation and eutrophication (nutrient enrichment) effects on forests, 

soils, and freshwater aquatic ecosystems

• Eutrophication and hypoxia (oxygen depletion) in coastal and lake 

ecosystems

• Harmful algae blooms

• Biodiversity losses in terrestrial and aquatic ecosystems

• Regional haze

• Depletion of stratospheric ozone

• Global climate change

• Nitrate contamination of drinking water aquifers

A variety of steps can be taken to reduce the relatively large share of nitrogen 

that is lost from agricultural systems. Improved management of nitrogen 

fertilizers, animal manure, and other agricultural inputs can improve overall 

nitrogen use effi ciency (NUE) and reduce the loss of reactive nitrogen to the 

environment while maintaining crop yields. 
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Incentives for encouraging farmers to adopt improved nitrogen management can take many forms, from purely voluntary to regulatory. Designing 

a cost-effective policy requires that factors infl uencing fertilizer use be fully 

understood. Also, an understanding of how farmers are likely to respond to 

different incentives may help policymakers assess potential environmental 

tradeoffs driven by nitrogen’s ability to change forms and cycle through 

different environmental media.

This report takes a broad view of several questions related to nitrogen 

management: (1) Why is nitrogen management so important? (2) How 

many acres of cropland are not using nitrogen best management practices 

(BMP)? and (3) What are the strengths and weaknesses of alternative policy 

approaches for improving nitrogen management on those acres? 

Ideally, alternative policies would be assessed on the basis of the cost of 

achieving a particular level of NUE across U.S. crop production. However, 

physio-economic models that would allow for this type of assessment are not 

available on a national scale. Instead, this analysis uses survey data to help 

identify the number of acres of cropland that would benefi t from improved 

management and to assess the characteristics of each alternative policy 

approach. Policy approaches are assessed in terms of factors consistent with 

cost effectiveness, including fl exibility, ability to target, crop acres covered, 

and implementation costs. These factors are assessed through original 

research and an extensive review of the literature. 
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Chapter 2

Environmental Implications of Nitrogen and 

Goals for Agricultural Management

Agriculture is the predominant source of reactive nitrogen emissions into 

the environment. In the United States, agriculture contributes 73 percent 

of nitrous oxide emissions (EPA, 2010a), 84 percent of ammonia emissions 

(EPA, 2010a), and 54 percent of nitrate emissions (Smith et al., 1997). Most 

losses from cropland are attributable to runoff, ammonia volatilization, 

nitrifi cation and denitrifi cation, and nitrate leaching (see box, “Pathways for 

Nitrogen Losses”). 

Nitrogen’s impacts on water resources (Dubrovsksy et al., 2010; Bricker et 

al., 2007; Rabalais et al., 2002a, b), atmosphere (Cowling et al., 2002; Follett 

et al., 2010), and terrestrial resources (Galloway et al., 2008) are extensive. 

Estimates of the economic value of these damages are lacking. Crutchfi eld et 

Pathways for Nitrogen Losses

Soil erosion - Nitrogen can be lost from the soil surface when attached to soil 

particles that are carried off the fi eld by wind or water. Although wind and water 

erosion can be observed across all regions, wind erosion is more prevalent in 

dry regions and water erosion in humid regions. Overall, little nitrogen is lost 

through erosion when basic conservation practices are in place (Iowa Soybean 

Association, 2008b).

Runoff - Surface runoff of dissolved nitrogen (generally in the form of nitrate) 

is only a concern when fertilizer and or manure are applied on the surface and 

rain moves the nitrogen before it enters the soil (Legg and Meisinger, 1982; Iowa 

Soybean Association, 2008b).

Ammonia volatilization - Signifi cant amounts of nitrogen can be lost to the atmosphere as ammonia (NH3) if animal manure or urea is surface applied and 

not immediately incorporated into the soil (Hutchinson et al., 1982; Fox et al., 

1996; Freney et al., 1981; Sharpe and Harper, 1995; Peoples et al., 1995). Additionally, warm weather conditions can accelerate the conversion of manure and 

other susceptible inorganic nitrogen fertilizers to ammonia gas. 

Denitrifi cation and nitrifi cation - When oxygen levels in the soil are low, some 

microorganisms known as denitrifi ers will convert NO3 to nitrogen (N2) and 

nitrous oxide (N2O), both of which are gases lost to the atmosphere (Mosier and 

Klemedtsson, 1994). Nitrogen gas is not an environmental issue, but N2O is a 

powerful greenhouse gas. Denitrifi cation usually occurs when nitrate is present 

in the soil, soil moisture is high or there is standing water, and soils are warm. 

NOx and N2O gases can also be produced through a process called nitrifi cation. 

Leaching - Leaching occurs when there is suffi cient rain and/or irrigation to 

move easily dissolvable nitrate through the soil profi le (Randall et al., 2008). 

The nitrate eventually ends up in underground aquifers or in surface water via 

tile drains and groundwater fl ow. Tile drains may be a chief passageway by 

which nitrogen moves from crop soils to surface water (Turner and Rabalais, 

2003; Randall et al., 2008; Randall et al., 2010). 
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al. (1995) estimate that consumers in four U.S. rural areas would be willing 

to pay between $73 million and $780 million per year (in 1995 dollars) for 

reduced chemical concentrations (including nitrate) in groundwater tapped 

by private wells. Dodds et al. (2009) estimate that consumers spend over 

$800 million each year on bottled water due to nutrient-related taste and odor 

problems. 

Using data from water treatment plants, ERS estimates the cost of removing 

nitrate from U.S. drinking water supplies is over $4.8 billion per year (see 

app. 1). Based on the contribution of nitrate loadings from agriculture (Smith 

et al., 1997), agriculture’s share of these costs is estimated at about $1.7 

billion per year. Most costs are borne by the large utilities, due to the volume 

of water treated. ERS fi ndings indicate that reducing nitrate concentrations in 

source waters by 1 percent would reduce water treatment costs in the United 

States by over $120 million per year.

Managing Nitrogen for Agriculture 

and the Environment

USDA’s Natural Resources Conservation Service (NRCS) defi nes nutrient 

management as managing the amount, source, placement, form, and timing 

of the application of plant nutrients to the soil (USDA, NRCS, 2006). 

Optimizing nitrogen management both economically and environmentally 

requires farmers to perform a juggling act: Applying too much nitrogen cuts 

into fi nancial returns and increases the likelihood of nitrogen escaping into 

the environment; applying too little increases the risk of reduced yields and 

lost income.

Crop production is characterized by uncertain and stochastic, or random, 

weather and soil conditions that affect crop yields and nitrogen loss. To maintain viable operations, farmers may manage temporal variability in weather 

and soil nitrogen by overapplying nitrogen to protect against downside risk 

(i.e., use an “insurance” nitrogen application rate) (Sheriff, 2005; Babcock, 

1992; Babcock and Blackmer, 1992). Additionally, farmers may take a 

“safety net” approach to maximize economic returns by setting an optimistic 

yield goal for a given fi eld based on an optimum weather year to ensure that 

the needed amount of nitrogen for maximum yields is available (Schepers et 

al., 1986; Bock and Hergert, 1991). Thus, during the years in which weather 

is not optimal for maximizing yields, nitrogen will be overapplied from an 

agronomic standpoint. Almost by defi nition, optimal conditions are infrequent, so farmers overfertilize crops in most years. 

The following hypothetical example helps illustrate the reasoning behind 

a farmer’s decision to apply a certain amount of fertilizer. Assume that a 

farmer applies 179 pounds of nitrogen (N) per acre to his or her cornfi eld. 

Under ideal conditions, the farmer might produce 170 bushels of corn per 

acre. In most years, however, conditions are not ideal and production averages 

148 bushels per acre. This yield requires only 165 pounds of N per acre, but 

at this level, the farmer will miss out on an extra 22 bushels in the event of 

ideal weather conditions. Assuming a fertilizer price of $0.50 per pound of 

N, the extra N applied in an average year costs $7 per acre. Assuming a corn 

price of $4.50 per bushel, the benefi t from having enough nitrogen available 

to take advantage of optimal conditions would be $99 per acre. In most years, 
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however, the extra fertilizer is not used by the crop and is available to leave 

the fi eld and affect environmental quality.

Defi nitions of Nitrogen Use Effi ciency 

Researchers calculate nitrogen use effi ciency to assess the effectiveness of 

nitrogen management. The NUE of a cropping system is the proportion of 

all nitrogen inputs that are removed in harvested crop biomass, contained 

in recycled crop residues, and incorporated in soil organic and inorganic 

nitrogen pools (Cassman et al., 2002) (fi g. 2.1). Nitrogen not recovered in 

these nitrogen sinks is lost to the environment. Increases in NUE reduce 

the share of nitrogen left in the soil and available for loss to water or the 

atmosphere. Increased NUE is treated as a goal of environmental policy 

throughout this report.

Recommended Input Rate and Nitrogen Credits 

The nitrogen application rate has a major effect on NUE (Bock and Hergert, 

1991; Meisinger et al., 2008; Freney et al., 1995; Power et al., 2001). Nitrogen 

losses have been shown to increase rapidly when N inputs exceed assimilation capacity (Vanotti and Bundy, 1994; Schlegel et al., 1996; Dobermann et 

al., 2006; Bock and Hergert, 1991). Reducing application rates reduces the 

losses of all forms of reactive nitrogen.

Figure 2.1

Nitrogen balance and nitrogen use efficiency

Nitrogen balance consists of N inputs of fertilizer and manure/legume N (NF) and 

miscellaneous atmospheric deposition (NMISC); outputs of crop harvested N (NCH), N 

leaching (NL), erosion (NE), and gaseous losses (NG); and internal N pools of crop residue 

N (NCR), soil organic N (NSON), soil inorganic N (NSIN), and net N mineralization (NMIN). 

Nitrogen use efficiency is the proportion of all N inputs (NF and NMISC) that are removed in 

harvested crop biomass (NCH), contained in recycled crop residues (NCR), and incorporated into soil organic matter (NSON) and inorganic N (NSIN) pools. The remainder is what 

is lost to the atmosphere through gaseous emissions (NG), leaching (NL), and erosion 

(NE). The goal of nitrogen management is to reduce these losses through reductions in 

fertilizer inputs and through soil, water, fertilizer, and crop management that affects the 

cycling of nitrogen in the soil.

Source: USDA, Economic Research Service using data from Meisinger et al., 2008.

NG

NL

NF

NMISC NF

NCH

NE

NCR

NSON

NSIN NMIN
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The effectiveness of nitrogen management may be raised by accounting for 

all nitrogen sources when determining a nitrogen fertilizer application rate. 

Depending on the region, such sources may include inorganic nitrogen levels 

in the root zone, soil organic content, previous crop (e.g., leguminous crop), 

manure applications, irrigation water, and atmospheric deposition (Cassman 

et al., 2002; Meisinger et al., 2008; Iowa Soybean Association, 2008a). 

Method/Placement

The goal of appropriate method and placement of fertilizer is to provide 

nutrients to plants for rapid uptake and to reduce the potential for losses 

to the environment. Studies have shown that NUE can be doubled under 

some conditions by placing fertilizers in the soil rather than “broadcasting” 

them on the surface (Malhi and Nyborg, 1991; Power et al., 2001). Liquid or 

gaseous forms of nitrogen can be injected directly into the soil with specialized equipment that is consistent with low-till systems. Solid forms can be 

broadcast on the surface and immediately incorporated into the soil with 

tillage equipment. Such placement reduces the risks of losses to the atmosphere and through surface runoff. The method of application can also reduce 

losses of nitrogen stemming from ammonia volatization (Meisinger and 

Randall, 1991; Peoples et al., 1995; Fox et al., 1996; Freney et al., 1981). 

The impact of fertilizer placement on nitrous oxide emissions is less certain. 

Liu et al. (2006) found that injection of liquid urea ammonium nitrate at 

deeper levels resulted in 40-70 percent lower N2O emissions than the rate 

associated with shallow injection or surface application. Some studies, 

however, have reported that incorporation into the soil increases N2O emissions (Flessa and Beese, 2000; Wulf et al., 2002; Drury, 2006). Injection or 

incorporation could also increase nitrate leaching, especially where soils are 

coarse textured (Abt Associates, 2000). 

Timing

The research on improving NUE in crop production emphasizes the need 

for greater synchronization between crop nitrogen demand and the supply 

of nitrogen from all sources throughout the growing season (Doerge et al., 

1991; Cassman et al., 2002; Meisinger and Delgado, 2002). Balancing supply 

and demand implies maintaining low levels of inorganic nitrogen in the soil 

when there is little plant growth and providing suffi cient inorganic nitrogen 

fertilizer during periods of rapid plant growth (Doerge et al., 1991; Alva et 

al., 2005). For example, the corn plant’s need for nitrogen is not very high 

until about 4 weeks after it emerges from the ground, which typically falls in 

June through July in the major corn-producing States (Baker, 2001). Ideally, 

to ensure that growing crops have adequate N and to minimize losses from 

the soil, a farmer could split nitrogen applications or “spoon feed” nitrogen 

when using center-pivot sprinkler irrigation systems from June through JulyAugust, using information from soil tests and/or advanced remote sensing 

techniques (Bausch and Delgado, 2003). Though splitting nitrogen applications is seen as an effective way to increase NUE and reduce nitrogen losses 

to the environment, several factors must fi rst be considered: workload, 

seasonal fertilizer price differences, the risk associated with not being able 

to apply at the right time, application costs, the possibility of compacting the 

soil, and possible damage to growing crops (Doerge et al. 1991; Westermann 
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and Kleinkopf, 1985; Westermann et al., 1988; Alva et al., 2005; Delgado and 

Bausch, 2005). 

Form

NUE is also infl uenced by the form of nitrogen fertilizer (Raun and Schepers, 

2008; Freney et al., 1995). Some of the more widely used nitrogen fertilizer 

forms include anhydrous ammonia (gas), urea (solid), UAN (liquid), and 

manure (solid). These forms vary in how quickly they can be transformed 

into nitrate, which is what crops actually use. The closer in time the fertilizer 

is applied to when the crop needs it, the faster it needs to be transformed into 

nitrate. A mismatch of fertilizer form with appropriate timing can lead to 

large environmental losses and poor yields. 

Manure Effects

Manure is an important source of N, but it poses challenging management 

problems (Eghball et al., 2002; Kirchmann and Bergstrom, 2001; Davis et 

al., 2002). The nitrogen content of manure depends on the animal type and 

the method of manure storage (Davis et al., 2002; Eghball et al., 2002), and 

nitrogen content may be inconsistent from batch to batch (Davis et al., 2002). 

Manure is more diffi cult to handle than inorganic nitrogen fertilizers, and, 

if in solid form, is diffi cult to apply uniformly. Most of the nitrogen content 

of manure is in the organic form and has to be mineralized before crops can 

use it. Since the transformation process depends on manure type, soil, and 

weather conditions, it is more diffi cult to control soil nitrate levels relative 

to crop needs when manure is applied than when other forms are applied 

(Eghball et al., 2002; Power et al., 2001). Consequently, controlling environmental losses from manured fi elds is more diffi cult than from fi elds using 

commercial fertilizer. 

Off-Site Practices That Capture Nitrogen

Off-fi eld conservation measures can be used in conjunction with onfi eld 

nitrogen management to either capture reactive nitrogen in biomass or convert 

it to inert N2 through denitrifi cation. Examples of off-site practices include 

vegetative buffers or fi lters and restored and constructed wetlands (Hefting 

et al., 2003; Jacobs and Gilliam, 1985; Lowrance et al., 1984). Buffers and 

wetlands reduce nitrogen loads to water through plant uptake, microbial 

immobilization and denitrifi cation, soil storage, and groundwater mixing 

(Pionke and Lowrance, 1991; Lowrance et al., 1997; Hey et al., 2005; Mayer 

et al., 2005). 

Buffers can remove nitrogen from both surface fl ow and groundwater (Mayer 

et al., 2005; Angier et al., 2001; Randall et al., 2008; Mitsch and Day, 2006). 

The effectiveness of vegetative buffers depends on the size of the buffer, 

the density of vegetation, and hydrologic conditions within the buffer zone 

(Dosskey et al., 2005; 2007). Based on a wide range of studies, Mayer et 

al. (2005) estimate that buffers can remove about 74 percent of the nitrogen 

passing through the buffer root zone. However, in many areas of the country 

where tile drains are used to control the water table, especially in the Corn 

Belt, subsurface fl ows pass below the root zone and are not fi ltered by vegetative buffers.
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Restored wetlands have been shown to be effective at reducing the transfer of 

nitrogen from agricultural land to water bodies (Jansson et al., 1994) and have 

been proposed as a technique to remove reactive nitrogen from the environment (Hey et al., 2005; Mitsch and Day, 2006). Wetland vegetation uptakes 

nitrogen, and wet soils enhance denitrifi cation. The effectiveness of wetlands 

as a fi lter of reactive nitrogen depends on their size, seasonal weather conditions, and hydrologic characteristics. Wetlands also provide a host of other 

ecosystem services that are valued by society, such as wildlife habitat and 

carbon sequestration.
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Chapter 3

State of Nitrogen Management 

on Cropland

Nitrogen Management on U.S. Cropland

Data on the nutrient management practices of U.S. producers of barley, corn, 

cotton, oats, peanuts, sorghum, soybeans, and wheat (table 3.1) are derived 

from USDA’s Agricultural Resource Management Survey (ARMS) (see 

box, “Agricultural Resource Management Survey”). The basic practices for 

improving NUE are agronomic application rate, appropriate timing of applications, and proper placement (USDA, NRCS, 2006). For the purposes of this 

analysis, these practices are defi ned as follows:

• Rate. Applying no more nitrogen (commercial and manure) than 40 

percent more than that removed with the crop at harvest, based on the 

stated yield goal, including any carryover from the previous crop. This 

approach is consistent with a more traditional approach for estimating 

N rate recommendations (Millar et al., 2010) and is also the criterion 

used by NRCS in its assessment of conservation practices in the Upper 

Mississippi Basin (USDA, NRCS, 2010). Crop uptake coeffi cients are 

from NRCS (Lander et al., 1998, table 3.1). This agronomic rate accounts 

for unavoidable environmental losses that prevent some of the nitrogen 

that is applied from actually reaching crops. 

Table 3.1

Crops, ARMS Phase II reference years, States surveyed, commodities, and nitrogen uptake 

per unit of crop yield

Crop

Reference 

year States surveyed Commodity

Lbs N 

per unit Unit

Barley 2003 CA, ID, MN, MT, ND, PA, SD, UT, WA, WI, WY grain 0.9 bushel

Corn 2005 CO, GA, IL, IN, IA, KS, KY, MI, MN, MO, NE, NY, NC, 

ND, OH, PA, SD, TX, WI

grain 0.8 bushel

silage 7.09 ton

Cotton 2003 AL, AZ, AR, CA, GA, LA, MS, MO, NC, SC, TN, TX lint plus seed 15.19 bale

Oats 2005 IL, IA, KS, MI, MN, NE, NY, ND, PA, SD, TX, WI grain 0.59 bushel

Peanuts 2004 AL, FL, GA, NC, TX nuts with 

pods 0.04 pound

Sorghum 2003 CO, KS, MO, NE, OK, SD, TX grain 0.98 bushel

14.76 ton

Soybeans 2006 AR, IL, IN, IA, KS, KY, LA, MI, MN, MS, MO, NE, NC, 

ND, OH, SD, TN, VA, WI beans 3.55 bushel

Wheat

 Winter

 Other spring

 Durum

2004 CO, ID, IL, KS, MI, MN, MO, MT, NE, ND, OH, OK, OR, 

SD, TX, WA

grain 1.13 bushel

grain 1.39 bushel

grain 1.29 bushel

Notes: N = nitrogen. ARMS = USDA’s Agricultural Resource Management Survey. The nitrogen uptake coeffi cients are from Lander et al. 

(1998). The coeffi cients for soft (1.02 lbs/bushel) and hard (1.23 lbs/bushel) winter wheat were averaged because the type of winter wheat 

produced was not available. To download estimates based on these data, or to learn more about the surveys, go to www.ers.usda.gov/data/

arms/beta.htm. 

Source: USDA, Economic Research Service using data from USDA’s Agricultural Resource Management Survey (2003-06) and Lander et al. 

(1998).
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• Timing. Not applying nitrogen in the fall for a crop planted in the spring.

• Method. Injecting (placing fertilizer directly into the soil) or incorporating (applying to the surface and then discing the fertilizer into the soil) 

nitrogen rather than broadcasting on the surface without incorporation.

Form also plays a role in nitrogen management for improving NUE. 

However, available data do not allow for an assessment of the form of 

nitrogen fertilizer applied. 

In this report, we evaluate nitrogen management only during the survey year 

covered by ARMS data. The loss of nitrogen to the environment in a particular year is mostly a function of current and not past management decisions. 

However, current management decisions have to account for past management, such as planting of a legume. The amount of commercial nitrogen 

applied is readily available from the ARMS responses; however, the amount 

of manure nitrogen must be estimated. We base these estimates on the quantity of raw manure applied, the form of the manure (liquid or solid), and the 

animal source of the manure. We also note whether the previous crop was a 

legume so as to account for the potential carryover of nitrogen.

A farm can fall into one of eight nitrogen management categories, defi ned by 

the three management decisions in a particular year: 

1. All of the criteria are followed.

2. The rate and timing criteria are followed.

3. The rate and method criteria are followed.

4. The timing and method criteria are followed.

5. Only the rate criterion is followed.

6. Only the timing criterion is followed.

7. Only the method criterion is followed.

8. None of the criteria are followed.

Agricultural Resource Management Survey

USDA’s Agricultural Resource Management Survey (ARMS) is an annual survey 

of farm and ranch operators administered by ERS and the National Agricultural 

Statistics Service (NASS). ARMS gathers data on fi eld-level production practices, farm business accounts, and farm households. ARMS is a multiple-phase 

survey. In the fall, NASS interviews producers of major commodities, such as 

feed grains, food grains, or cotton, to collect information about production practices and land use on select fi elds. In the spring, NASS re-interviews farmers 

who successfully completed the fall survey. Spring data collection focuses on 

the structural and economic characteristics of the farm business and farm operator household. This approach helps link commodity production activities and 

conservation practices with the farm business and operator household.

Each phase of ARMS contains multiple versions of the survey questionnaire. The 

commonality of questions across versions provides one facet of data integration. 

In the fall data collection, the target commodity distinguishes questionnaires. 
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How Many Acres Treated With Nitrogen Met the Criteria for Best 

Management Practices?

Because the crops covered in the analysis were surveyed in different years, 

we specify a reference year, 2006, to examine the extent to which best 

nitrogen management practices are being followed. Weights are calibrated 

so that the weighted sums of acres planted by the surveyed crop producers 

match USDA’s published estimates of planted acres for 2006 (USDA, NASS, 

2008). This provides reasonable baseline estimates under the assumption that 

the percentages of planted and treated acres and application rates by management category were stable between the survey reference years (see table 3.1) 

and 2006. We maintain this assumption throughout the analysis.

Sixty-nine percent of the 242 million acres planted to barley, corn, cotton, 

oats, peanuts, sorghum, soybeans, and wheat in 2006 were estimated to be 

treated with commercial and/or manure nitrogen (table 3.2). Corn accounted 

for an estimated 45 percent of the 167 million crop acres treated with 

nitrogen and 65 percent of the 8.7 million tons of nitrogen applied to these 

crops during 2006. 

The application rate criterion was not met on over 53 million acres treated 

with nitrogen (32 percent). Cotton had the highest percentage of treated acres 

not meeting the rate criterion (47 percent), followed by corn (35 percent). 

However, corn accounted for 50 percent of all treated crop acres not meeting 

the rate criterion. 

The timing criterion was not met on over 40 million treated acres (24 

percent). About 34 percent of treated corn acres received commercial and/or 

manure nitrogen in the fall. These corn acres account for over 64 percent of 

all treated crop acres not meeting the timing criterion. 

Table 3.2

Planted and nitrogen-treated acres, nitrogen applied, and the shares of treated acres and applied nitrogen 

that did not meet the rate, timing, or method criteria, by crop, 2006

Total Did not meet rate Did not meet timing Did not meet method

Planted 

acres

Treated 

acres Tons N

Treated 

acres Tons N

Treated 

acres Tons N

Treated 

Crop acres Tons N

Thousands Percent

Barley 3,452 3,176 98 14 23 20 20 25 25

Corn 78,327 76,052 5,799 35 46 34 26 37 20

Cotton 15,274 12,566 591 47 61 18 11 32 24

Oats 4,168 2,748 93 33 49 28 32 42 41

Peanuts 1,243 737 14 1 7 16 11 39 29

Sorghum 6,522 5,370 220 24 31 16 16 27 21

Soybeans 75,522 16,827 248 3 31 28 56 45 43

Wheat 57,344 49,808 1,766 34 50 11 12 37 32

Total 241,852 167,285 8,829 32 47 24 23 37 24

Notes: N = nitrogen. These estimates are based on weighted sums, where the weights were calibrated so that the sums of planted acres for 

each crop based on the survey data match published estimates of planted acres for 2006 (USDA, 2008).

Source: USDA, Economic Research Service using data from USDA’s Agricultural Resource Management Survey (2003-06), Phase II. See table 

3.1 for details.
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Nitrogen was not incorporated/injected on over 61 million treated crop 

acres (37 percent). These acres received 24 percent of all applied nitrogen. 

Soybeans (45 percent) had the highest percentage of acres not meeting the 

method criterion. However, corn accounted for about 46 percent of all treated 

acres not meeting the method criterion. 

Corn acres make up nearly half of all acres that are in need of some type of 

improvement in nitrogen management, in that at least one of the three criteria 

is not met (fi g. 3.1). Any policy aimed at improving nitrogen use effi ciency 

would have to consider the factors driving management decisions in corn 

production.

From a regional standpoint, the Corn Belt and Northern Plains dominate in 

terms of cropland not meeting the management criteria (fi gs. 3.2, 3.3). Not 

coincidentally, these are the primary corn-growing areas in the United States. 

However, in terms of nitrogen application in excess of the criterion rate, the 

Corn Belt and Lake States receive the greatest amounts of excess nitrogen 

(fi g. 3.4). 

As described in the previous chapter, NUE is highest when all three management criteria are met. Table 3.3 shows the percentage of treated acres in each 

nitrogen management category, as well as the degree to which excess nitrogen 

is applied in relation to the rate criterion. About 35 percent (58 million 

acres) of the treated acreage meet all three criteria. Corn has the smallest 

percentage of treated acres meeting all three criteria (30.4 percent). Because 

of the large amount of cropland planted to corn, this represents about half of 

all crop acres needing improvement in nitrogen management (rate, timing, 

or method). Only 4.2 percent of all treated acres do not meet any of the three 

criteria. 

About 47 percent of all treated crop acres meet the method and timing 

criteria. Most of the acres exceeding the rate criterion do so by less than 50 

Figure 3.1

Acres treated with commercial and/or manure nitrogen not using nitrogen best 

management practices, 2006

Source: USDA, Economic Research Service using data from USDA’s Agricultural Resource Management Survey (2003-06), 

Phase II. See table 3.1 for details.

Million treated acres

Barley Corn Cotton Oats Peanuts Sorghum Soybeans Wheat

0

10

20

30

40

50

60
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percent. For example, about 14 percent of corn acres receive applications of 

10 percent or less over the criterion rate. Reducing application rates on these 

acres so that the rate criterion is met would mean that nearly 80 percent of 

all corn acres would meet the rate criterion and that 35 percent of corn acres 

would meet all three criteria. 

Source: USDA, Economic Research Service using data from USDA’s Agricultural Resource Management Survey (2003-06), Phase II. 

See table 3.1 for details.

Million treated acres

Figure 3.2

Acres treated with commercial and/or manure nitrogen not using nitrogen best management 

practices, by region, 2006

0

5

10

15

20

25

30

35

40

Appalachia Corn

Belt

Delta Lake

States

Mountain Northeast Northern

Plains 

Pacific Southeast Southern

Plains 

Source: USDA, Economic Research Service.

Figure 3.3

USDA farm production regions

Pacific Lake

Delta

Corn Belt

Mountain

Southeast

Northeast

Appalachia

Northern

Plains

Southern

Plains
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It should be noted that our fi ndings differ somewhat from those reported by 

USDA’s Conservation Effects Assessment Project (CEAP) assessment of 

the Upper Mississippi River Basin (USDA, 2010). The CEAP study reports 

smaller percentages of cropland meeting the nitrogen management criteria. 

The ERS study, however, examines nitrogen management for only the survey 

year. The CEAP analysis looks at nutrient management practices over an 

entire crop rotation, which may run from 2 to 5 years (see box, “CEAP 

Analysis of Nitrogen Management in the Upper Mississippi River Basin”). 

All three criteria had to be met in each year of the rotation for CEAP to 

consider the cropping system as having met the nitrogen management goal. 

The CEAP approach is stricter than that used by ERS.

Manure Use

Previous research has indicated that farms with animals tend to overapply 

nutrients to crops, primarily because of the large amount of manure produced 

on the farm needing disposal (Ribaudo et al., 2003; Gollehon et al., 2001). 

ARMS data provide additional evidence that manure use is associated with 

overapplication of nutrients. About 10 percent of crop acres treated with 

nitrogen (treated acres) received manure. Ninety-three percent of treated 

acres receiving manure did not meet all three criteria, compared with 62 

percent of treated acres not receiving manure (table 3.4). Most of the cropland 

receiving manure was used to grow corn (72 percent). Over 95 percent of the 

corn acres receiving manure did not meet all three criteria, compared with 65 

percent for corn acres not receiving manure. 

1,000 tons excess nitrogen

Figure 3.4

Total nitrogen applications above criterion rate by region, 2006

Appalachia Corn

Belt

Delta Lake

States

Mountain Northeast Northern

Plains 

Pacific Southeast Southern

Plains 

Note: Criterion rate defined as nitrogen removed at harvest plus 40 percent.

Source: USDA, Economic Research Service using data from USDA’s Agricultural Resource Management Survey (2003-06), 

Phase II. See table 3.1 for details.

36

298

1

185

7

44

84

1 5 18

0

50

100

150

200

250

300

350
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Table 3.3

Percent treated acres by management category, crop, and degree of excess application, 2006

Rate criterion status

Timing or method criteria met

Timing and method Timing Method Neither

Percent of treated acres

At or less than criterion rate

Barley 52.0 16.4 13.0 4.3

Corn 30.4 15.0 12.0 6.2

Cotton 32.9 11.6 6.5 2.3

Oats 33.8 13.5 8.0 11.0

Peanuts 53.5 29.7 7.0 8.7

Sorghum 44.5 18.4 9.5 3.3

Soybeans 43.0 27.7 9.8 16.1

Wheat 36.8 22.2 5.2 1.7

Total 34.8 18.3 9.2 5.5

0 -10% over rate

Barley 1.8 1.1 0.7 0.1

Corn 4.6 2.0 4.2 3.3

Cotton 6.6 3.5 3.7 0.7

Oats 1.2 1.2 0.0 0.1

Peanuts 0.3 0.0 0.0 0.0

Sorghum 3.7 0.3 1.1 0.1

Soybeans 0.0 0.1 0.1 0.0

Wheat 4.4 2.5 0.8 0.1

Total 4.1 2.0 2.5 1.6

10-50% over rate

Barley 3.9 1.7 1.0 0.9

Corn 4.6 5.0 2.3 2.8

Cotton 10.4 7.2 1.8 0.8

Oats 6.4 2.3 0.5 1.6

Peanuts 0.1 0.0 0.0 0.0

Sorghum 6.6 1.9 1.7 0.3

Soybeans 0.0 0.2 1.1 0.6

Wheat 8.9 5.3 2.6 0.1

Total 5.9 4.5 2.1 1.5

50-100% over rate

Barley 1.1 0.3 0.1 0.2

Corn 0.7 1.0 0.4 1.1

Cotton 3.3 4.0 1.3 0.5

Oats 3.6 1.9 0.4 1.3

Peanuts 0.0 0.4 0.0 0.0

Sorghum 1.7 0.3 0.0 0.0

Soybeans 0.1 0.1 0.1 0.2

Wheat 3.9 3.1 0.1 0.0

Total 1.9 1.7 0.3 0.6

-- continued
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Other Considerations

The environmental impacts of low nitrogen use effi ciency on the environment 

can be affected by different land management practices, such as the presence of underground tile drains and the use of fi lter strips or riparian buffers. 

Tile drainage plays a role in nitrogen losses from fi elds (David et al., 2010). 

Tile drainage lowers the water table, enabling fi elds that would otherwise be 

wet part of the year to be intensively cropped. These drained soils tend to be 

highly productive. Tiles, however, provide a rapid conduit for soluble nitrate, 

effectively bypassing any attenuation that may occur in the soil. ARMS 

data indicate that nearly 26 percent of treated cropland is tiled, most of this 

in corn production (table 3.5). Of particular interest is the degree to which 

nitrogen management on this vulnerable cropland is not using nitrogen BMP. 

ARMS data indicate that about 71 percent of tiled acres do not meet all three 

nitrogen management criteria. Most of these acres are in corn production. 

Much of the tile-drained cropland is located in the Mississippi River Basin, 

which has implications for hypoxia in the Gulf of Mexico.

Land management practices can mitigate nitrogen losses from fi elds. The use 

of fi lter strips or riparian buffers can reduce the amount of nitrogen lost to 

surface water bodies. Less than 10 percent of treated crop acres not meeting 

the rate, timing, or method criteria have fi lter strips that could reduce losses 

in runoff and subsurface fl ows (table 3.6). For corn, about 11 percent of acres 

not using nitrogen BMPs have fi lter strips that could mitigate losses to water, 

but signifi cant improvements could still be made. Filter strips, however, 

do not address atmospheric losses and may not be effective if not sited or 

managed appropriately. In addition, buffers would be ineffective on the 26 

percent of treated cropland that is tile drained.

Table 3.3

Percent treated acres by management category, crop, and degree of excess application, 

2006 (continued)

Rate criterion status

Timing or method criteria met

Timing and method Timing Method Neither

Percent of treated acres

Greater than 100% over rate

Barley 0.2 0.0 0.1 0.2

Corn 0.6 0.3 1.2 0.8

Cotton 1.5 0.9 0.2 0.2

Oats 2.2 4.7 1.3 4.3

Peanuts 0.3 0.0 0.0 0.0

Sorghum 4.3 2.0 0.1 0.0

Soybeans 0.4 0.0 0.0 0.0

Wheat 0.2 1.7 0.1 0.1

Total 0.7 0.9 0.6 0.5

Total not meeting rate criterion 12.6 9.1 5.5 4.2

Notes: Figures in bold meet the rate criterion. See the notes to table 3.2. 

Source: USDA, Economic Research Service using data from USDA’s Agricultural Resource Management Survey (2003-06), Phase II. See

table 3.1 for details.
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Nitrogen Management on U.S. Corn

A high percentage of crop acres meet at least some of the nitrogen management criteria (see table 3.3). Corn, however, meets all three criteria least 

often. Corn is the most intensive user of nitrogen and the most widely planted 

crop. Improvements in rate, timing, and/or application method are needed 

on 70 percent of corn acres to improve NUE. In addition, growth in corn 

demand due to the biofuels mandate suggests that corn acreage may increase 

CEAP Analysis of Nitrogen Management in the 

Upper Mississippi River Basin

Our assessment of nitrogen management on cropland using data from USDA’s 

Agricultural Resource Management Survey (ARMS) has some similarities 

with the assessment of nutrient management on cropland in the Upper Mississippi River Basin (UMRB) conducted by the Conservation Effects Assessment 

Project (CEAP). The two studies also have some important differences. CEAP 

was initiated by USDA’s Natural Resources Conservation Service, Agricultural 

Research Service, and Cooperative State Research, Education, and Extension 

Service (recently renamed the National Institute of Food and Agriculture). The 

goal of CEAP is to estimate conservation benefi ts from conservation investments and to provide research and an assessment on how to best use conservation practices in managing agricultural landscapes to protect and enhance 

environmental quality. The assessment of cultivated cropland in the UMRB is 

the fi rst of a series of studies that will cover major crop-producing areas of the 

United States. Findings from the UMRB study are available at www.nrcs.usda.

gov/technical/nri/ceap/umrb/index.html.

Both analyses assess baseline nitrogen management on cropland according to 

three criteria: rate, timing, and method. The defi nitions we used for each are 

based on those used in the CEAP analysis. Both studies used a survey to collect data on nutrient management practices. The major difference between our 

analysis using ARMS data and the CEAP analysis is how the criteria were applied. ARMS collects information about cropping practices during a single 

crop year. Our analysis, therefore, based the assessment of nitrogen management on practices used to produce the crop sampled by the survey. The CEAP 

analysis focused on cropping systems, which could be up to 5 years in length 

and contain several different crops. Data were collected on production practices 

used each year of the crop rotation. CEAP used these data to evaluate the entire 

rotation, not just the crop grown during the year the survey was conducted. If 

the rate, timing, or method criteria were not met during any year of the rotation, 

then that sample point was identifi ed as not meeting the nitrogen management 

criteria. This approach is more stringent than the one used in our analysis. For 

example, assume corn and soybeans were on a 2-year rotation and that corn was 

grown during the year the ARMS and CEAP surveys were conducted. In our 

analysis, if the nitrogen application rate on corn met the rate criterion, then that 

corn sample was identifi ed as such. In the CEAP study, the nitrogen application 

rate on both the corn and the previous year’s soybean crops were assessed. If 

the application rate on corn met the rate criterion but excess nitrogen was applied to soybeans, then the rotation was identifi ed as not meeting the criterion. 

This leads to the CEAP assessment reporting a smaller percentage of crop acres 

meeting the rate criterion than we would report. Overall, the CEAP analysis 

reports fewer crop acres meeting the rate, timing, and method criteria than does 

the ERS report.
18

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in the future, along with the intensity of corn production. Together, these 

factors could increase reactive nitrogen emissions to the environment unless 

nitrogen use effi ciency is improved.

An examination of an additional year of survey data collected during the 

2001 growing season and disaggregated regionally helps determine if 

management has undergone recent changes and if such changes vary by 

region. The share of corn acres not meeting the rate criterion declined 

from 41 to 35 percent between 2001 and 2005 (table 3.7). This fi nding is in 

Table 3.4

Percent treated crop acres receiving commercial or manure nitrogen that did not meet the rate, timing, 

and method criteria, by crop, 2006 

Crop

Planted

acres

Treated

acres

Acres treated

with commercial N only

Acres treated with 

commercial and manure N

Acres treated with 

manure N only

Thousands

Percent of 

all treated 

acres

Percent 

vulnerable1

Percent of 

all treated 

acres

Percent 

vulnerable

Percent of 

all treated 

acres

Percent 

vulnerable

Barley 3,452 3,176 94 45 4 96289

Corn 78,327 76,052 84 65 14 96 2 91

Cotton 15,274 12,566 96 67 3 85 1 29

Oats 4,168 2,748 78 59 9 88 13 92

Peanuts 1,243 737 93 46 5 52 2 41

Sorghum 6,522 5,370 98 55 1 98149

Soybeans 75,522 16,827 85 51 2 100 13 91

Wheat 57,344 49,808 99 63 1 92 0 28

Total 241,852 167,285 90 62 7 96 3 86

1Vulnerable acres are those not meeting the rate, timing, and method criteria.

Notes: N = nitrogen. See notes to table 3.2. These estimates were weighted by the total amount of nitrogen applied by management category.

Source: USDA, Economic Research Service using data from USDA’s Agricultural Resource Management Survey (2003-06), Phase II. See table 

3.1 for details.

Table 3.5

Nitrogen-treated acres with tile drainage that did not meet the rate, timing, or method criteria by crop, 2006 

Treated acres

Crop Total With tile drains

Tile-drained acres that do not meet the 

rate, timing, or method criteria

Thousands Percent

Barley 3,176 42 43

Corn 76,052 34,738 70

Cotton 12,566 583 71

Oats 2,748 216 66

Peanuts 737 40 44

Sorghum 5,370 46 43

Soybeans 16,827 5,690 69

Wheat 49,808 1,644 94

Total 167,285 43,000 71

Notes: See notes to table 3.2. 

Source: USDA, Economic Research Service using data from USDA’s Agricultural Resource Management Survey (2003-06), Phase II. See table 

3.1 for details. 
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agreement with those of other reports on improving nitrogen use effi ciency 

based on steady application rates and increased corn yields (Turner et al., 

2007). Improvements in rate were seen in all regions except Appalachia and 

the Southeast. Notable improvements were seen in the Corn Belt, Lake States, 

and Northeast. Timing and method, however, did not show similar improvements in the more recent data. For most regions, the percentage of corn acres 

not meeting these two criteria increased. 

Changing Management May Result in Environmental Tradeoffs

Changing management practices may improve nitrogen use effi ciency, but 

the environmental outcomes may not always be desirable. We use the new 

Nitrogen Loss and Environmental Assessment Package with GIS (Geographic 

Information System) capabilities (NLEAP-GIS) model to assess how changes 

in nitrogen management practices on corn affect the losses of nitrate (to 

water), nitrous oxide (to air), and ammonia (to air) (Shaffer et al., 2010; 

Delgado et al., 2010a). Of particular interest is the extent to which tradeoffs 

in environmental outcomes might occur as overall nitrogen use effi ciency is 

improved. See appendix 2 for more details on NLEAP.

Because NLEAP is a fi eld-level model, we selected eight different soils in 

four States (Arkansas, Ohio, Pennsylvania, and Virginia) to assess changes in 

nitrogen emissions to the environment from management changes in nonirrigated corn production.1 Four of the soils are type A or B soils (well drained), 

and four are type D soils (relatively poorly drained). For each soil, we 

examined two rotations (corn-corn and corn-soybeans), two tillage practices 

(conventional and no-till), and two sources of nitrogen (inorganic fertilizer 

and inorganic fertilizer + animal manure). The slopes for these soils were 0 

to 6 percent, with low erosion potential.

For each soil/rotation/tillage/nitrogen-source combination, eight different 

scenarios were modeled with NLEAP, each representing one of the combi1These four States were selected 

because they present a wide variation 

in growing conditions and because the 

data necessary for running NLEAP 

were already developed.

Table 3.6

Nitrogen-treated acres not meeting the rate, timing, or method criteria 

that have fi lter strips, by crop, 2006 

Crop

Number of acres not 

meeting rate, timing 

or method criteria

No. of acres not meeting 

rate, timing, or method 

criteria with fi lter strips

% of acres with 

fi lter strips not 

meeting criteria

Barley 1,523 68 4

Corn 52,910 5,909 11

Cotton 8,432 397 5

Oats 1,818 99 5

Peanuts 343 42 12

Sorghum 2,983 64 2

Soybean 9,600 475 5

Wheat 31,475 2,530 8

Total 109,084 9,584 9

Notes: See notes to table 3.2.

Source: USDA, Economic Research Service using data from USDA’s Agricultural Resource 

Management Survey (2003-06), Phase II. See table 3.1 for details.
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nations of nitrogen management criteria outlined in table 3.4. Therefore, 64 

different scenarios were modeled for each soil. 

A recommended application rate was specifi ed for each soil/cropping system 

combination, based on local agronomic recommendations, as described by 

Espinoza and Ross (2008) for Arkansas, Alley et al. (2009) for Virginia, 

Beegle and Durst (2003) for Pennsylvania, and Vitosh et al. (1995) for Ohio. 

For the purposes of this analysis, overapplication was set at 75 percent more 

than the recommended rate (at the upper end of overapplication found in the 

ARMS data and reported in table 3.3). For example, if the recommended rate 

was 132 pounds of N per acre, the overapplication scenario used 231 pounds 

(see app. 2).

The modeled policy goal is that all three nitrogen management criteria be 

met. For demonstration purposes, we used the NLEAP results to assess the 

potential emissions tradeoffs when method, timing, timing and method, or 

rate BMPs are adopted by corn farmers. For example, to evaluate the tradeoff when timing is improved (rate and method criteria are already met), we 

compare the NLEAP results for the rate and method BMPs with the results 

for the rate, timing, and method BMPs. Each cropping system is evaluated 

separately. Because of the volume of results for the eight soils modeled, we 

present only those from the two soils in Ohio (tables 3.8a-d). Results for the 

other States are similar, in terms of the direction of changes. 

All the scenarios show the expected changes in total nitrogen losses, with 

reductions indicating improvements in NUE. The NLEAP results were 

consistent with the expectation that nitrogen emissions are minimized when 

all three criteria are met. Since nitrogen cycles through different forms and 

ecosystems, the long-term environmental benefi ts of reducing total nitrogen 

Table 3.7

Nitrogen-treated acres and the shares that did not meet the rate, timing, or method criteria for corn, 

2001 and 2005

Region Treated acres Did not meet rate Did not meet timing Did not meet method

2001 2005 2001 2005 2001 2005 2001 2005

Thousands Percent of treated acres

Appalachia 1,925 2,118 52 66 12 16 56 78

Corn Belt 35,087 39,145 46 38 41 41 39 34

Lake States 12,965 13,958 46 34 37 41 36 30

Mountain 1,243 1,018 18 14 9 20 33 50

Northeast 2,696 2,477 42 32 39 40 54 53

Northern Plains 16,962 18,293 27 28 10 15 36 45

Southeast 280 286 39 50 27 29 41 55

Southern Plains 1,708 2,109 31 32 45 38 33 18

Total 72,868 79,404 41 35 32 34 38 37

Notes: In both years, corn producers were surveyed in Colorado (Mountain); Kansas, Nebraska, North Dakota, and South Dakota (Northern 

Plains); Texas (Southern Plains); Michigan, Minnesota, and Wisconsin (Lake States); Illinois, Indiana, Iowa, Missouri, and Ohio (Corn Belt); New 

York and Pennsylvania (Northeast); Kentucky and North Carolina (Appalachia); and Georgia (Southeast). These estimates are based on weighted sums, with the weights recalibrated so that the weighted sums of planted acres for each crop based on the survey data match estimates for 

2001 and 2005 (USDA, 2008).

Source: USDA, Economic Research Service using data from USDA’s 2001 and 2005 Agricultural Resource Management Survey, Phase II.
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are clear. However, some of the tradeoffs between different forms of nitrogen 

could pose environmental problems. Adopting injection/incorporation always 

increases nitrate leaching, sometimes substantially (more than doubling 

leaching in some cases). Similarly, shifting applications from fall to spring 

(without changing application rate) reduces nitrate losses and total nitrogen 

losses but increases N2O emissions as applications are shifted to generally 

warmer, wetter conditions, which is consistent with the fi ndings of Delgado 

et al. (1996), Rochette et al. (2004), and Hernandez-Ramirez et al. (2009). 

Because of concerns over greenhouse gas (GHG) emissions, this outcome 

would have to be carefully considered when making recommendations to 

improve nitrogen use effi ciency. 

Adopting both method and timing again produces mixed results. NH3

emissions are always reduced. Leaching is generally reduced, but in some 

cases where manure is used, it may increase. N2O emissions almost always 

increase, from 5 to 50 percent, depending on the situation. In agreement with 

basic principles of nitrogen management, only reducing the application rate 

guarantees that losses of all three forms of reactive nitrogen are reduced 

(Mosier et al., 2002; Meisinger and Delgado, 2002). Based on these fi ndings, 

a recommendation could be that in areas where leaching to drinking water 

sources is a concern, improvements in nitrogen use effi ciency could focus on 

application rate reductions or improvements in timing. 

Table 3.8a

Changes in nitrogen losses resulting from improvements in nitrogen management, NLEAP estimates - 

Ohio - Type A soil - conventional tillage

Management 

improvement

Without manure

Criterion rate=132 pounds N per acre

With manure

Criterion rate=198 pounds N per acre*

Total NO3

5 N2O6 NH36 Total NO3 N2O NH3

Pounds of N per acre

Continuous corn

Method1 -32.8 7.0 -1.7 -38.1 -17.0 24.6 -1.2 -40.4

Timing2 -16.6 -17.4 0.8 + -16.6 -17.6 1.0 +

Method+timing3 -33.0 -9.1 0.4 -23.7 -18.6 11.4 0.8 -30.8

Rate4 -69.3 -50.6 -0.9 -17.7 -105.1 -81.0 -1.3 -22.9

Criterion rate=102 pounds N per acre Criterion rate=153 pounds per acre*

Corn-soybean

Method1 -16.6 0.4 -0.8 -16.2 -14.7 3.8 -0.4 -18.1

Timing2 -5.7 -6.0 0.3 + -5.2 -5.6 0.4 +

Method+timing3 -13.1 -4.2 0.1 -9.0 -13.8 0.5 0.3 -14.6

Rate4 -15.7 -8.6 -0.4 -6.8 -37.2 -26.0 -0.6 -10.6

Note:*Manure is applied every other year. Criterion rate is met on average over 2-year period. + indicates a positive but very small change. 

N = nitrogen. NO3 = nitrogen trioxide. N2O = nitrous oxide. NH3 = ammonia.

1Timing and rate best management practices (BMP) are already in place.

2Method and rate BMPs are already in place.

3Rate BMP is already in place.

4No BMPs are in place.

5Nitrate loss to water (primarily through leaching but often ends up in surface water).

6Ammonia and nitrous oxide loss to atmosphere.

Source: USDA, Economic Research Service.
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Summary

The survey data indicate that in 2006, all of the nitrogen management criteria 

were met on an estimated 35 percent of the crop acres treated with commercial and/or manure nitrogen.2 In addition, a high percentage of treated acres 

met at least some of the nitrogen management criteria. Among all crops, corn 

met the criteria the least, and corn accounts for 50 percent of the treated acres 

upon which one or more improvements to management could be made to 

improve nitrogen use effi ciency. Improvements in rate, timing, and/or method 

might be needed on 67 percent of corn acres. 

NLEAP-GIS simulation results reported in the literature show that changing 

timing or method of application could potentially increase the loss of one 

type of nitrogen compound, even if total nitrogen emissions decline and NUE 

increases. NLEAP modeling indicates that only reducing application rates 

ensures that all nitrogen emissions decrease, in agreement with established 

principles of nitrogen management.

2Recall that this adoption rate is 

higher than that reported by the USDANRCS CEAP analysis, which considers 

adoption over multiyear rotations (see 

box on page 17).

Table 3.8b

Changes in nitrogen losses resulting from improvements in nitrogen management, NLEAP 

estimates – Ohio – Type A soil - no-till

Management 

improvement

Without manure

Criterion rate=116 pounds N per acre

With manure

Criterion rate=174 pounds N per acre*

Total NO3

5 N2O6 NH36 Total NO3 N2O NH3

Pounds of N per acre

Continuous corn

Method1 -29.6 5.6 -1.1 -34.1 -15.6 23.5 -0.3 -38.8

Timing2 -27.5 -28.6 1.1 + -16.2 -17.3 1.1 +

Method+timing3 -40.6 -20.8 1.1 -20.9 -27.3 0.2 1.3 -28.8

Rate4 -53.7 -37.3 -0.6 -15.8 -85.0 -63.8 -0.8 -20.3

Criterion rate=86 pounds N per acre Criterion rate=129 pounds N per acre*

Corn-soybean

Method1 -14.0 0.7 -0.8 -13.9 -12.7 4.7 -0.1 -17.3

Timing2 -9.9 -10.3 0.4 + -8.6 -9.0 0.4 +

Method+timing3 -14.9 -7.6 0.3 -7.6 -15.1 -2.2 0.5 -13.4

Rate4 -15.5 -9.5 -0.3 -5.7 -28.2 -18.7 -0.4 -9.1

Note:*Manure is applied every other year. Criterion rate is met on average over 2-year period. + indicates a positive but very small change. 

N = nitrogen. NO3 = nitrogen trioxide. N2O = nitrous oxide. NH3 = ammonia.

1Timing and rate best management practices (BMP) are already in place.

2Method and rate BMPs are already in place.

3Rate BMP is already in place.

4No BMPs are in place.

5Nitrate loss to water (primarily through leaching but often ends up in surface water).

6Ammonia and nitrous oxide loss to atmosphere.

Source: USDA, Economic Research Service.
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Table 3.8c

Changes in reactive nitrogen losses resulting from improvements in nitrogen management, NLEAP 

estimates – Ohio - Type D soil - conventional till

Management 

improvement

Without manure

Criterion rate=132 pounds N per acre

With manure

Criterion rate=198 pounds N per acre*

Total NO3

5 N2O6 NH36 Total NO3 N2O NH3

Pounds of N per acre

Continuous corn

Method -28.3 0.7 -5.0 -24.0 -20.0 12.9 -3.1 -29.8

Timing -8.1 -9.4 1.3 + -12.1 -13.5 1.4 +

Method+timing -20.2 -7.6 1.2 -13.8 -17.2 4.6 1.7 -23.5

Rate -56.3 -44.1 -1.8 -10.4 -91.3 -70.9 -3.0 -17.4

Criterion rate=102 pounds N per acre Criterion rate=153 pounds N per acre*

Corn-soybean

Method -14.7 0 -4.1 -10.6 -16.2 1.3 -2.2 -15.3

Timing -1.9 -2.5 0.6 + -2.7 -3.3 0.6 +

Method+timing -6.8 -2.1 0.5 -5.2 -12.5 -0.4 0.8 -12.9

Rate -9.3 -4.7 -0.7 -3.9 -27.8 -17.1 -1.4 -9.3

Note:*Manure is applied every other year. Criterion rate is met on average over 2-year period. + indicates a positive but very small change. 

N = nitrogen. NO3 = nitrogen trioxide. N2O = nitrous oxide. NH3 = ammonia.

1Timing and rate best management practices (BMP) are already in place.

2Method and rate BMPs are already in place.

3Rate BMP is already in place.

4No BMPs are in place.

5Nitrate loss to water (primarily through leaching but often ends up in surface water).

6Ammonia and nitrous oxide loss to atmosphere.

Source: USDA, Economic Research Service.
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Table 3.8d

Changes in reactive nitrogen losses resulting from improvements in nitrogen management, NLEAP 

estimates –Ohio - Type D soil - no-till

Management 

improvement

Without manure

Criterion rate=116 pounds N per acre

With manure

Criterion rate=174 pounds N per acre*

Total NO3

5 N2O6 NH36 Total NO3 N2O NH3

Pounds of N per acre

Continuous corn

Method -35.4 0.7 -1.4 -34.4 -25.8 13.6 -0.3 -39.1

Timing -21.4 -22.0 0.6 + -11.1 -11.8 0.7 +

Method+timing -38.8 -18.3 0.6 -21.1 -32.2 -4.1 1.2 -29.3

Rate -37.3 -20.4 -1.0 -15.9 -66.3 -44.2 -1.8 -10.4

Criterion rate=86 pounds N per acre Criterion rate=129 pounds N per acre*

Corn-soybean

Method -14.5 0.3 -0.8 -14.0 -16.0 1.6 0 -17.6

Timing -7.2 -7.4 0.2 + -6.2 -6.5 0.3 +

Method+timing -13.3 -5.9 0.2 -7.6 -16.7 -3.7 0.6 -13.6

Rate -10.1 -4.0 -0.4 -5.7 -20.4 -10.5 -0.7 -9.2

Note:*Manure is applied every other year. Criterion rate is met on average over 2-year period. + indicates a positive but very small change. 

N = nitrogen. NO3 = nitrogen trioxide. N2O = nitrous oxide. NH3 = ammonia.

1Timing and rate best management practices (BMPs) are already in place.

2Method and rate BMPs are already in place.

3Rate BMP is already in place.

4No BMPs are in place.

5Nitrate loss to water (primarily through leaching but often ends up in surface water).

6Ammonia and nitrous oxide loss to atmosphere.

Source: USDA, Economic Research Service.
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Chapter 4

Policy Instruments for Nitrogen Reduction

Based on ARMS data, 65 percent of surveyed cropland, or 109 million acres, 

is in need of improved nitrogen management. Given nitrogen’s effects on the 

environment, improving nitrogen management on vulnerable lands is a policy 

goal, both nationally and regionally. 

Farmers adjust the management of their crops for a variety of reasons. 

Economic factors, such as input or output price changes, may lead to more 

(or less) careful use of nitrogen inputs. Farmers may also have to consider 

various policy-based incentives for adopting practices that improve nitrogen 

management. Over the years, policy instruments have been employed to 

improve the management of agricultural inputs and resources. USDA conservation programs rely primarily on subsidies for management practices and 

education. USDA also employs compliance mechanisms to protect wetlands 

and highly erodible soils. The U.S. Environmental Protection Agency (EPA) 

is using regulations to address nutrient management on certain confi ned 

animal feeding operations. A few States have used nitrogen fertilizer taxes 

to raise revenue for nutrient management programs. Such policy approaches 

may have a role to play in increasing the number of crop acres that meet the 

three nitrogen management criteria described earlier. 

Provide Information (Education)

A lack of knowledge about their performance may be preventing farmers 

from using the most effi cient nutrient management practices. Education is 

used to provide producers with information on how to farm more effi ciently. 

Its success depends on alternative practices being more profi table to farmers 

than current practices (Ribaudo and Horan, 1999). Two practices that can 

lead to more effi cient fertilizer use are soil testing and tissue testing. These 

tests provide information that reduces some of the uncertainty surrounding 

nutrient availability and enables producers to apply fertilizer at rates more 

consistent with plant needs and high nitrogen use effi ciency. 

ERS research supports previous fi ndings that nitrogen testing is having 

the desired effect on nitrogen application rates for certain nitrogen users. 

Data from the 2001 and 2005 ARMS indicate that about 21 percent of corn 

farmers used a soil or tissue test as a basis for their level of nitrogen application (table 4.1). Farmers who used commercial nitrogen followed the recommendations closely. In our sample, their mean application rate of nitrogen was 

136 lbs per acre, and the mean recommended rate based on a nitrogen soil 

test was 137 lbs per acre (table 4.2).

Compliance with the soil test, however, was much worse for farmers who 

used both manure and commercial fertilizer. In their case, the recommended 

nitrogen application rate was 123 pounds per acre. And while farmers applied 

only 85 pounds per acre of commercial fertilizer, total nitrogen application 

rates were 175 pounds per acre when manure was added. 

We compared nitrogen application rates of those farmers who use soil N and 

tissue tests with those who do not using regression analysis that accounts 
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for a number of production, land, and operator characteristics (see app. 3). 

Findings show that soil nitrogen testing has a statistically signifi cant impact 

on nitrogen application rates. In the case of farmers who use commercial 

nitrogen exclusively, those who tested the soil applied 73.9 pounds per acre 

less than those who did not, all else equal. Other studies have found soil tests 

to be of similar effectiveness (Wu and Babcock, 1998; Musser et al., 1995). 

An information-based approach can meet nitrogen effi ciency goals only if 

the information provided leads to increased profi ts for farmers (Ribaudo 

and Horan, 1999). As long as there are expectations that more effi cient 

nitrogen management leads to increased risk or higher costs, then nitrogen 

management goals are unlikely to be met with information alone. However, 

information has proven valuable in support of other policy goals. Education 

can reduce the cost of adopting nitrogen BMPs required by regulation or 

funded through fi nancial incentives. For example, Bosch et al. (1995) found 

that education affected the outcomes associated with a regulation requiring 

nitrogen testing in Nebraska. Producers did not use the information provided 

by testing unless they received education assistance.

Financial Incentives

Financial assistance is an important tool used in many USDA conservation 

programs to promote the adoption of BMPs. Program effectiveness depends 

on how farmers respond to the incentive being offered. When a farmer 

accepts a payment in return for adopting a management practice, he or she 

is signaling that the payment at least represents the economic cost of implementing the practice, sometimes referred to as the willingness-to-accept. 

Generally, only the producer knows the true cost. This makes it diffi cult for 

program managers to fi nd the minimum payment rate that entices enough 

producers into the program to achieve the particular environmental goal at 

least cost. 

Table 4.1

Factors infl uencing farmers’ nitrogen fertilizer application decision

Application used 2001 2005

Percent of farmers

Soil or tissue test 18.8 27.0*

Crop consultant recommendation 13.0 17.6*

Fertilizer dealer recommendation 28.7 41.2*

Extension service recommendation 3.2 4.6*

Cost of nitrogen and/or expected commodity price 11.4 17.3*

Routine practice 70.9 71.7*

Number

Observations 1,646 1,344

*Statistically different from 2001 at the 1-percent level, based on pairwise two-tailed delete-agroup Jackknife t-statistics (Dubman, 2000)

Source: USDA, Economic Research Service using data from USDA’s 2001 and 2005 

Agricultural Resource Management Survey, Phase II, Cost of Production Practices and Costs 

Report.
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USDA’s NRCS supports management practices that specifi cally address fertilizer application rate, timing, or method in their standards. The Environmental 

Quality Incentives Program (EQIP) is the largest USDA program that 

provides producers with technical and fi nancial assistance for implementing 

and managing BMPs on working farmland. Management practices supported 

by EQIP that can infl uence nitrogen use effi ciency include nutrient management and waste utilization (for manure). Implementing a nutrient management plan directly affects measures of stewardship. Nutrient management 

planning addresses the amount, source, placement, form, and timing of the 

application of plant nutrients and soil amendments (USDA, NRCS, 2006). 

Further, the practice requires the application rate be based on an assessment 

of plant-available nitrogen developed through Land Grant University soil 

and tissue tests or recognized industry practices. Waste utilization guidelines 

specify that rates of application must be compatible with the soil’s ability to 

absorb and hold the waste, and methods of incorporation are prescribed for 

liquid manure forms to prevent nutrients from rising to the surface.

Data from EQIP contracts in force for year 2008 show that participating 

farmers accepted an average payment of $8.88 per acre for adopting nutrient 

management (table 4.3). A higher per acre payment induced farmers to adopt 

a waste utilization practice ($14.75). Relatively few corn farm operations have 

livestock or a direct source of manure (organic) fertilizer, and, as reported 

later, the practice can be more costly to farmers than using commercial (inorganic) fertilizer.

A focus on the Corn Belt reveals variation in the accepted payments for 

the two practices (table 4.3). The variation may stem from cost differences 

within the region that are driven by local conditions, which, in turn, infl uence 

the State-level payment rate for the practice. To examine how management 

practices can affect a farm’s cost of operations, we estimate a cost function 

using a generalized linear regression model estimated with 2001 ARMS data 

(see app. 4).3 Model results show that several conservation practices have 

3Because we are comparing 2001 

costs with 2008 payments, we infl ate 

2001 costs using the U.S. Bureau of 

Labor Statistics’ Consumer Price Index.

Table 4.2

Infl uence of soil/tissue nitrogen testing on fertilizer application rates for corn, with and without 

manure use, 2001 and 2005

For farmers using a soil test Required nitrogen 

based on expected yield1

Soil test 

recommended nitrogen 

Commercial 

nitrogen applied

Total nitrogen applied 

(commercial + manure)

Pounds of nitrogen per acre

Commercial nitrogen with 

manure

Observations = 154

152 123 85† 175†

Commercial nitrogen without manure

Observations = 645

165 137 136 136

1Based on nitrogen removed in expected harvest plus 40 percent to account for unavoidable nitrogen losses.

†Means are statistically different from the recommended nitrogen amount at the 1-percent level, based on pairwise two-tailed delete-a-group 

Jackknife t-statistics (Dubman, 2000). 

Source: USDA, Economic Research Service using data from USDA’s 2001 and 2005 Agricultural Resource Management Survey, Phase II, Cost 

of Production Practices and Costs Report.
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little effect, on average, on the cost of operation relative to other methods of 

management. For example, the difference in operation costs for farms using 

nutrient management and for farms not using these practices is not statistically signifi cant.

Based on results from our cost analysis, we also fi nd that using manure as a 

nitrogen source costs roughly $26.84 more per acre than using only commercial fertilizer. However, we observe a national average per acre EQIP payment 

for the waste utilization of $14.75, and only two States in the Corn Belt 

(Illinois and Indiana) have payment levels that approach the estimated cost 

fi gure. The results suggest that the EQIP rate is insuffi cient to entice farmers 

who are not using manure to begin doing so in an environmentally sensitive manner. However, farms with livestock or poultry need to dispose of the 

waste. Therefore, rather than be a practice by choice, waste utilization may be 

a practice that complements the necessary disposal of manure, and a payment 

that covers increased production costs may not be a necessary condition for 

the willingness to adopt the practice.

Not all farmers require a cost share to adopt conservation practices. Cooper 

and Keim (1996) use farmer surveys to conclude that 12 to 20 percent of 

farmers may be willing to adopt practices such as split fertilizer applications 

and nutrient testing without fi nancial assistance but do not do so because they 

lack information or are uncertain about the practices’ economic performance. 

However, they also fi nd that the adoption rate would not increase beyond 30 

percent unless subsidy rates were substantially increased. A farmer’s perception of the effectiveness of a practice can also infl uence the decision to adopt. 

Evidence from Lichtenberg and Lessley (1992) suggests that farmers may 

need more than a cost share to overcome perceptions of conservation practices and the state of environmental quality off-site.

In some cases, farmers are willing to adopt conservation practices that 

reduce profi ts if they believe that others will benefi t from the subsequent 

change in environmental quality (Bishop et al., 2010; Chouinard et al., 2008). 

For example, based on survey responses from the State of Washington, 

Chouinard et al. (2008) conclude that farmers would be willing to forgo up 

Table 4.3

Per acre average EQIP payments for conservation practices, 2008

Corn Belt

Practice All States Illinois Indiana Iowa Missouri Ohio

Dollars per acre

Nutrient management1 8.88 9.75 7.47 6.12 13.90 10.91

Waste utilization2 14.75 25.95 25.84 10.90 5.83

1Nutrient management planning addresses the amount, source, placement, form, and timing of the application of plant nutrients and soil 

amendments.

2Waste utilization guidelines specify that rates of application must be compatible with the soil’s ability to absorb and hold the animal waste, and 

methods of incorporation are prescribed for liquid manure forms to prevent nutrients from rising to the surface.

Notes: Blank cells indicate no contracts for such practice in that State. 

Source: USDA, Economic Research Service using contract data from USDA’s Environmental Quality Incentives Program, fi scal years 

1997-2008, payments made in fi scal year 2008.
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to $4.52 (median value estimate) in per acre annual profi ts to implement soilconserving stewardship practices. 

The scope of a program’s coverage is an important consideration for policymakers and program managers evaluating the adequacy of the fi nancial 

incentives offered to program participants. In 2008, the fi nancial incentives 

from EQIP encouraged farmers to enroll 4 million acres in the program’s 

nutrient management practice. However, because participation in the program 

is voluntary, it is not known if the cropland most in need of treatment was 

enrolled. 

We can use the data from EQIP and table 3.3 to estimate the cost to improve 

nitrogen use effi ciency on those acres needing additional treatment. About 35 

percent of all crop acres meet all three criteria, which means that over 108 

million acres of cropland are not using nitrogen BMPs. Applying the average 

payment rate for nutrient management ($8.88 per acre) to all acres needing 

improved management implies annual EQIP payments of $959 million. 

However, the fi ndings from Cooper and Keim (1996) suggest that higher 

rates would be needed to entice a sizable percentage of farmers to voluntarily 

enroll in a program. Assuming a payment rate 50 percent higher results in 

program expenditures of $1.4 billion. This is roughly the current annual 

budget for EQIP. 

Given the potential cost of treating the entire 108 million acres of cropland 

not using nitrogen BMPs, which groups might be most important to address 

fi rst? We previously reported that manure users generally apply much more 

total nitrogen to the fi eld than farmers who exclusively apply commercial 

nitrogen. Providing fi nancial assistance for nutrient management on the 7.7 

million acres that received manure and failed to meet the rate criterion would 

cost between $68.4 and $103 million per year. 

Off-Site Filtering for Reducing Nitrogen 

Losses From Fields

Similar to its efforts aimed at improving nitrogen use effi ciency on working 

lands, the Government can provide fi nancial incentives for installing management practices that capture nitrogen after it leaves a fi eld, primarily nitrogen 

in water. This analysis estimates and evaluates the cost effectiveness of 

two such measures, wetlands restoration and vegetative fi lter strips (VFS), 

assuming that funding is targeted to areas where nitrogen removal is likely to 

be most effective. 

The Costs of Nitrogen Capture by Restoring Wetlands

Our analysis of wetlands restoration focuses on the Glaciated Interior Plains 

(GIP), where models of wetlands nitrogen removal have been developed. The 

GIP includes major parts or all of Ohio, Minnesota, Wisconsin, Michigan, 

Iowa, Illinois, and Indiana—major corn-producing States. This area is also an 

important source of nitrogen that reaches the Gulf of Mexico and contributes 

to the hypoxic zone (Goolsby et al., 2001; Robertson et al., 2009). Wetlands 

in other parts of the United States can also reduce nitrogen loadings. But, 

because of regional differences in ecosystems, we do not extrapolate our fi ndings to other areas.
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Wetlands once made up a large portion of land on the GIP (fi g. 4.1). Water 

tables were lowered to facilitate crop production by installing underground 

tile and surface drainage systems. Such drainage systems become conduits 

for the rapid movement of nitrate from fi elds to water resources.

The costs of creating wetlands vary widely as do nitrogen removal rates on 

wetlands. Costs are driven by the cost of the land and the cost of restoring 

wetland ecosystems. Nitrogen removal depends on the rate of nitrogen infl ow, 

nitrogen concentration, seasonal variations in fl ow, wetland size, and other 

factors. 

We use the USDA Wetland Reserve Program (WRP) contract data for the 

GIP to estimate multinomial land and restoration cost functions (see app. 

5). With these functions, we generate county-level cost estimates throughout 

the GIP. The objectives of the WRP are to enhance, restore, and preserve 

wetlands. As of October 1, 2009, the WRP enrolled 2.18 million acres, with 

wetlands in every State. Along with the land and restoration cost variables, 

the WRP contract data contain information on the size and the county location of each contract. The land (wetland easement) cost variable represents 

the difference between the agricultural value of the land and the value of 

the land with a wetland easement. The easement requires that the landowner 

maintain the health of the ecosystem. Data for other variables in our analysis 

come from the NASS agricultural census. Across the counties within the GIP, 

wetland easement costs range from $1,490 to $3,030 per acre, as generated 

by our estimated land cost function. Expected wetland restoration costs range 

from $506 to $602 per acre. Annualizing over perpetuity with a discount rate 

of 5 percent, we estimate that the median annual expected cost of restoring 

and preserving wetlands is $153 per acre per year (table 4.4). Because 

marginal costs are less than average costs, one can expect average per acre 

Source: USDA, Economic Research Service using data from the 1997 

National Resources Inventory.

Figure 4.1

Historical wetlands converted to cropland, by county, 1997

Former wetland acres

< 500

500-1,500

> 1,500
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costs to be lower for larger wetlands and potentially more cost effective as a 

nitrogen fi lter, all other things being equal.

Wetlands remove most nitrogen through denitrifi cation (Crumpton et al., 

2008), which converts nitrate to nitrous oxide (N2O). However, there is a 

general belief, supported by a limited number of studies, that N2O releases 

are a very small portion of nitrogen removal, even in wetlands with elevated 

nitrogen loadings (EPA, 2010b). Researchers estimate that N2O accounts 

for between 0.13 and 0.30 percent of total annual wetland nitrogen loss 

(Hernandez and Mitsch, 2006; Crumpton et al., 2008). The reported rates of 

N2O releases by wetlands are similar to estimated releases on cropland in the 

Midwest, so restoring wetlands is likely to have no net effect on N2O emissions (Crumpton et al., 2008). 

Crumpton et al. estimate that nitrogen loads to surface water could be 

reduced by 30 percent (~500 million pounds) in the Upper Mississippi and 

Ohio River basins with the addition of 0.5 to 1.1 million acres of strategically 

placed wetlands, for an average per acre reduction of 450 to 1,000 pounds 

per year. These removal rates assume an optimal placement of the restored 

wetlands—areas with a high water fl ow with high nitrogen concentrations. 

Mitch et al. (1999) estimate that wetlands in the Midwest remove 142 to 

214 pounds per acre of nitrogen per year. The researchers assume that the 

wetlands are well constructed and placed, but their estimates are based on 

a wide range of nitrogen concentrations and hydrologic fl ows. Each study 

includes multiple wetlands and a variety of fl ow conditions and nitrogen 

concentrations. 

The unit cost of nitrogen removal by wetlands, based on nitrogen removal 

rates of 450 to 1,000 pounds per acre per year reported by Crumpton et al. 

(2008), is $0.08 to $0.34 per pound (table 4.4). Based on the removal rates of 

142 to 214 pounds per acre per year reported by Mitch et al. (1999), unit cost 

ranges from $0.71 to $1.08 per pound. 

The Costs of Nitrogen Capture Using Vegetative Filter Strips

Vegetative fi lter strips present another off-fi eld option for capturing and 

removing nitrogen from runoff and subsurface waters. The cost of a VFS 

tends to be lower than the cost of wetlands restoration. The VFS cost has two 

components: the opportunity cost of holding the land out of production and 

the cost of establishing cover (e.g., grasses, trees, or both). Cropland rental 

Table 4.4

Costs of nitrogen removal by wetlands 

Wetland cost

N removal rate = 

142 lbs/ac

N removal rate = 

214 lbs/ac

N removal rate = 

450 lbs/acre

N removal rate = 

1,000 lbs/acre

$/acre $/lb of N removed by wetland

Marginal cost 77 0.54 0.36 0.17 0.08

Average cost 153 1.08 0.71 0.34 0.15

Note: Because marginal costs are less than average costs, per acre costs would be lower for larger wetlands. N = nitrogen.

Source: USDA, Economic Research Service using data from Mitsch et al., 1999 (142 and 214 pounds per acre) and Crumpton et al., 

2008 (450 and 1,000 pounds per acre).
32

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rates are an economic measure of the opportunity cost of taking cropland 

out of production. We assume that average cropland rental rates are equal to 

the economic return to land converted to a VFS. Based on the distribution 

of corn acreage reported in the 2005 ARMS and county-level rental data 

provided by NASS, the annual opportunity cost of converting corn cropland 

into a VFS is estimated at $94 per acre. 

We assume that the cost of establishing vegetative cover is about the same as 

establishing cover on land retired in USDA’s Conservation Reserve Program 

(CRP). CRP data do not specify cost by cover type, but data do provide 

insights on the range of costs. Across the 25th, 50th, and 75th percentiles, 

cover costs are $16, $35, and $60 per acre. Because establishing forest cover 

is more costly, the lower percentile costs likely refl ect the cost of establishing 

grasses. 

The cover cost is a one-time investment. We annualized this cost by assuming 

that it is to last for the foreseeable future and a 5-percent discount rate. 

Together, the land and cover cost would total approximately $95 to $97 per 

acre per year, with the higher estimate more likely representative of the use of 

forest cover. 

Mitch et al. (1999) tabulate several plot studies with a focus on the quantity 

of nitrogen removed across varying sizes of fi lter strips and levels of nitrogen 

infl ow. They apply their fi ndings to nitrogen runoff rates typical of those in 

corn-producing areas and estimate that properly designed forested riparian 

VFS will remove approximately 17.8 to 53.0 pounds of nitrogen per acre with 

strips ranging in width from 10 to 50 feet (Mitch et al., 1999, pg. 47). 

At an annual nitrogen runoff removal rate of 17.8 to 53.0 lbs per acre and a 

forested VFS cost of $97 per acre, VFS nitrogen removal costs are estimated 

to range from $1.83 to $5.45 per pound of nitrogen. The cost estimate is a 

weighted average across the corn-producing areas of the GIP. 

Results suggest that, within the GIP, wetlands can be much more cost effective at removing nitrogen than VFS, primarily because of their substantial 

nitrogen removal rates. Within corn-producing regions, especially in areas 

where fi elds are tile drained, water moves quickly through and passes under 

root zones, rendering VFS ineffective. On the other hand, VFS can be established in many landscape settings where wetlands cannot. 

The wide range in nitrogen removal rates by wetlands refl ects, at least in part, 

the advantage of targeting wetlands to areas where they are likely to be more 

effective—areas where wetlands capture large quantities of water with high 

nitrogen concentration rates. But even the low nitrogen removal rates of 142 

to 214 pounds per acre reported by Mitch et al. are three or more times the 

removal rates of VFS. Additionally, the rich wetland ecosystems have the 

potential of providing a greater array of environmental services than those 

delivered by VFS. 

Participation in Emissions Trading Programs

An alternative to publicly provided fi nancial incentives for adoption of 

conservation practices is for private markets to pay farmers to adopt 
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management practices that produce ecosystem services valued by consumers 

(the public). Emissions trading uses markets to effi ciently achieve pollution 

targets. The development of markets for ecosystem services is characterized 

by uncertainties about whether viable markets for public goods can exist, 

but the EPA and USDA are promoting emissions trading markets for water 

quality and greenhouse gases as a way of reducing the costs of meeting environmental goals. Agriculture has a potential role to play in both markets. 

Water Quality Trading Program

The promise of emissions trading, along with the real-world success of air 

emissions trading, has led to the creation of water quality trading markets in 

a number of impaired watersheds. Under the Clean Water Act, point sources 

(e.g., factories, sewage treatment plants) were initially regulated through 

a nontradable permit system. A permit specifi es how much of a particular 

pollutant the permit holder can discharge. Traditionally, permit holders were 

required to meet their permit obligations through their own effl uent reductions. EPA policy guidelines on water quality trading now allow point sources 

to meet their Water Quality Based Effl uent Limitation requirements through 

discharge reductions from other sources under certain conditions, including 

agricultural nonpoint sources (EPA, 2004). The guidelines encourage 

States to consider agriculture as a source of offsets in water quality trading 

programs, and a number of States are either implementing or considering 

water quality trading programs that allow point/nonpoint source trading. 

There appears to be many opportunities for point/nonpoint trading programs 

to be established. Almost 7,000 water bodies impaired by nutrients (pollutants produced by both point and nonpoint sources) have been listed under 

Section 303(d) of the Clean Water Act (EPA, 2009). To date, over 4,000 Total 

Maximum Daily Loads (TMDLs) have been developed to address 5,000 

of these impaired waters. The presence of a TMDL is a basic requirement 

for a trading program, as it creates the demand for credits (Ribaudo et al., 

2008). Agriculture is a major source of nutrients in most of the watersheds 

containing impaired waters (Ribaudo and Nickerson, 2009). The marginal 

cost of reducing nitrogen loss from cropland is generally less than the 

marginal cost of reducing nitrogen discharges from point sources (primarily 

sewage treatment plants) (Camacho, 1992; Shortle, 1990). 

Forty water quality trading programs have been created in the United States 

since 1990 (Breetz et al., 2004). Fifteen include production agriculture as a 

potential source of credits for regulated point sources, most often for nutrients 

(nitrogen and phosphorus). However, point/nonpoint trading has not been 

very successful, at least in terms of the participation of potential traders and 

the number of trades between regulated sources and farms (Breetz et al., 

2004). 

Regulators designing point/nonpoint trading markets must contend with 

uncertainty about sources and levels of emissions, the effectiveness of best 

management practices, the water quality impacts of emissions from different 

sources, and farmer willingness to participate in a market driven by regulation (on point sources) (Hoag and Hughes-Popp, 1997; King, 2005; King and 

Kuch, 2003; Woodward and Kaiser, 2002; Ribaudo and Gottlieb, 2011; Horan 

and Shortle, 2011). The failure of current programs to perform as advertised 

can largely be attributed to failures of market design and program rules to 
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adequately address these issues, or the high transactions from incorporating 

uncertainties into market design. 

One issue that has particular relevance for addressing nitrogen pollution 

is the baseline used for calculating credits. The EPA defi nes a baseline 

participation requirement as the pollutant control requirements that apply to 

a seller in the absence of trading (EPA, 2007). EPA suggests that practices 

generally accepted as good management defi ne a baseline for agriculture, 

under an assumption that all farms would eventually adopt these practices 

voluntarily. Some practices that States have used in trading programs to 

defi ne a baseline include the use of fi lter strips or a nutrient management 

plan (Wisconsin DNR, 2002; Pennsylvania DEP, 2008). However, the issue 

is that our survey data indicate that very few crop acres would meet these 

baseline requirements as the percentages of cropland with fi lter strips or 

nutrient management plans are only 6.8 and 5.0, respectively, meaning that 

most crop acres would not be able to participate in a trading program until 

the baseline requirements were met. If the incentives from a credit market 

are insuffi cient to induce farms that have not already voluntarily adopted the 

minimum set of practices to incur the cost of meeting the baseline requirement, then these farms will continue unabated discharge. This entry cost 

would therefore potentially limit participation and adversely affect the effi -

ciency of the market (Ribaudo and Gottlieb, 2011; Ghosh et al., 2011). 

Greenhouse Gas Mitigation

Another emissions market that might infl uence nitrogen management decisions in agriculture is an offset market for mitigating emissions of CO2 and 

other greenhouse gases, such as nitrous oxide (N2O). Nitrous oxide is a 

powerful greenhouse gas (310 times the global warming potential of CO2

over 100 years) and can be emitted from fi elds receiving nitrogen fertilizer 

(see chapter 2). A trading program for nitrous oxide emissions would have 

many of the same design and implementation issues of point/nonpoint trading 

for water quality. One would expect that the use of models for predicting 

reductions, based on fi eld and management characteristics, would fi gure 

heavily in any trading program.

We use NLEAP results and ARMS cost data to determine changes farmers 

might make given the opportunity to participate in an offset market for N2O 

reductions by producing credits and likely environmental tradeoffs. These 

analyses were conducted across different management scenarios and general 

hydrologic soils (e.g., well-drained soils with a large leaching potential versus 

poorly drained soils with a low leaching potential) from selected counties in 

Virginia, Ohio, Pennsylvania, and Arkansas. 

For each soil, we identifi ed the changes a farmer might make in nitrogen 

management practices to produce N2O reductions (offset credits) at the lowest 

cost while meeting a requirement that total nitrogen emissions (the sum of 

NO3, N2O, and NH3 losses) not increase. In other words, trading rules do not 

permit a management change that reduces N2O but increases total nitrogen 

emissions. Changes in cost are defi ned as the difference in average variable 

costs (chemicals, fuel, and electricity) and value of lost production (changes 

in yields). We assumed farmers would maintain the same basic cropping 

system and alter timing, method, or application rate only. A description of 
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NLEAP and the cost model and assumptions are presented in appendices 2 

and 3. 

Table 4.5 summarizes the nitrogen management systems that farmers evaluated in the model would adopt to produce credits at the lowest cost, given 

baseline practices. For example, of the 64 farm types not meeting any of the 

criteria prior to a market (“None” in the baseline criteria column), 17 would 

reduce the application rate to the criterion rate, 10 would reduce the rate and 

inject/incorporate nitrogen, 1 would reduce the rate and apply nitrogen in the 

spring, and 36 would adopt all three management choices. The choice depends 

on the soil type, climate, rotation, tillage practice, and nitrogen source. 

The results highlight the importance of meeting the application rate criterion 

for reducing both N2O and total reactive nitrogen. For all farms not meeting 

the rate criterion, reducing application rate either alone or in combination 

with another practices was selected to reduce N2O. Method or timing was 

never the sole practice adopted by farms to reduce N2O emissions. Model 

results also indicate that 148 of the 512 farming systems will not be able to 

reduce N2O emissions by meeting the rate, timing, or method criteria. For 

example, none of the 64 farm types meeting the rate and method criteria at 

the start of a market can reduce N2O emissions by also meeting the timing 

criterion. 

Table 4.6 provides more detail for one soil in Ohio. It shows the reduction in 

N2O that would be generated for each decision a farmer in a particular baseline situation could make and credit revenue earned assuming a carbon price 

of $15 per ton of CO2 equivalent.4 The range of N2O reductions presented 

here is similar to that found for the other soils modeled with NLEAP.

4Based on EPA analysis of the 

American Clean Energy and Security 

Act of 2009, H.R. 2454.

Table 4.5

Least-cost N management systems in corn production for reducing N2O emissions for 512 model 

farms, assuming a credit price of $15 per ton of CO2 equivalent, based on NLEAP modeling

Criteria1 met after changing 

management Method Rate Timing

Rate and 

method

Rate and 

timing

Timing and 

method

Rate, 

timing, and 

method

Total 

model 

farms

Number of model farms

Criteria1 met in baseline

None 17 10 1 36 64

Method 16 17 3 28 64

Rate 19 42 3 64

Timing 63 1 64

Rate and method 64 64

Rate and timing 3 23 1 37 64

Timing and method 31 33 64

Rate, timing, and method 64 64

1Criteria are appropriate rate, timing, and method of nitrogen application (see chapter 3).

Note: N = nitrogen. NLEAP = Nitrogen Leaching Environmental Analysis Project. N2O = nitrous oxide. CO2 = carbon dioxide. A total of 512 

cropping systems are evaluated with NLEAP, 128 each in Arkansas, Ohio, Pennsylvania, and Virginia. Each defi nes a soil type (A or D), a rotation (continuous corn, corn soybeans), tillage practice (conventional, no-till), nutrient source (inorganic, manure+inorganic), timing of application 

(before planting, at/after planting), method (inject/incorporate, broadcast) and application rate (meet criterion, 75% over criterion). 

Source: USDA, Economic Research Service.
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Table 4.6

How a corn farmer may change N management in a market for nitrous 

oxide (N2O) greenhouse gas emissions with credit payments of $15/

ton of carbon dioxide equivalent, for a model Ohio farm on Ottoke soil 

Baseline practice Practices after 

N2O credit offered N2O reduction Credit revenue

Pounds per acre Dollars per acre

CC-CON-MF

M RTM 0.9 2.09

RM No change 0.0 0.0

R RM 0.3 0.70

RTM No change 0.0 0.0

RT RM 3.4 7.90

TM RT 3.0 6.98

T RT 4.4 10.23

NONE RTM 0.8 1.86

CC-CON-OF

M RTM 0.3 0.70

RM No change 0 0

R RM 0.6 1.40

RTM No change 0 0

RT RTM 2.7 6.28

TM RT 0.9 2.09

T RT 3.1 7.21

NONE RTM 0.8 1.86

CC-NT-MF

M RTM 0.2 0.46

RM No change 0 0

R No change 0 0

RTM No change 0 0

RT RTM 0.5 1.16

TM RT 3.3 7.67

T RT 2.8 6.51

NONE RM 0.9 2.09

CC-NT-OF

M R 1.1 2.58

RM No change 0 0

R RM 0.2 0.46

RTM No change 0 0

RT RTM 1.7 3.95

TM RT 1.4 3.26

T RT 2.8 6.51

NONE R 0.9 2.09

-- continued
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Table 4.6

How a corn farmer may change N management in a market for nitrous 

oxide (N2O) greenhouse gas emissions with credit payments of $15/

ton of carbon dioxide equivalent, for a model Ohio farm on Ottoke soil 

-- continued

Baseline practice Practices after 

N2O credit offered N2O reduction Credit revenue

Pounds per acre Dollars per acre

CS-CON-MF

M RTM 0.6 1.40

RM No change 0 0

R RM 0.2 0.46

RTM No change 0 0

RT RM 1.3 3.02

TM RT 1.6 3.72

T RT 1.7 3.95

NONE RTM 0.2 0.46

CS-CON-OF

M RTM 0.2 0.46

RM No change 0 0

R RM 0.3 0.70

RTM No change 0 0

RT RTM 1.2 2.79

TM RTM 1.1 2.56

T RT 1.2 2.79

NONE RTM 0.5 1.16

CS-NT-MF

M RT 0.2 0.46

RM No change 0 0

R No change 0 0

RTM No change 0 0

RT RM 0.8 1.86

TM RT 1.4 3.26

T RT 1.4 3.26

NONE RM 0.5 1.16

CS-NT-OF

M R 0.2 0.46

RM No change 0 0

R RM 0.2 0.46

RTM No change 0 0

RT RTM 1.3 3.02

TM RTM 1.1 2.56

T RT 1.4 3.26

NONE R 0.5 1.16

Note: N = nitrogen. CC = continuous corn, CS = corn-soybeans, CON = conventional till, 

NT = no-till, MF = manure+inorganic N, OF = inorganic N, M = N incorporate d/injected, 

R = N rate is less than 40% more than N removed at harvest, T = spring application.

Source: USDA, Economic Research Service.
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Even though our sample of cropping conditions is very small, we believe 

we can still make some inferences from the results. We found that if the 

baseline system is not meeting the application rate criterion, application 

rate will be reduced to produce credits, either alone or in combination 

with timing or method; reducing the application rate is generally the most 

cost-effective means of reducing N2O emissions. Adopting method and/

or timing BMPs alone cannot reduce N2O emissions or can do so only by 

reducing overall nitrogen use effi ciency, which is not permitted under our 

simulated market rules.

Farms already meeting both the rate and method criteria will only be able to 

reduce N2O emissions by reducing their application rate below recommended 

rates. The NLEAP modeling indicates only small reductions in N2O when the 

application rate is reduced to a level below the criterion rate. This is consistent with fi eld studies that indicate a nonlinear relationship between excessive 

N application rates and N2O emissions (Jarecki et al., 2009; McSwiney and 

Robertson, 2005). Excessive nitrogen inputs accelerate the rate of N2O emissions. For example, reducing the application rate from the criterion rate to 25 

percent below the recommended rate only reduces N2O by between .2 and 1.3 

pounds per acre for the Class A (well-drained) soil in Ohio, depending on the 

cropping system. Assuming a credit rate of $15 per ton of CO2 equivalent, 

this translates into a payment of between $0.46 and $3.02 per acre. These 

rates are insuffi cient to cover the 10-percent reduction in corn yields that 

we assume would occur for such a reduction in N (Bock and Hergert, 1991). 

Even for smaller N reductions, it is unlikely that revenue from GHG credits 

would be suffi cient to cover the increased risk from cutting N application 

rates to something close to plant uptake. However, higher offset prices could 

increase the incentive to cut application rates to reduce N2O emissions, even 

when yields might be affected.

When we apply these results to the survey results summarized in table 3.3, 

we conclude that farmers with treated corn acres meeting the rate, timing, 

and method criteria or the rate and method criteria (about 42 percent of all 

corn acres) will not likely participate in a GHG cap-and-trade program that 

would allow farmers to sell offsets from N2O reductions. These farms cannot 

make any management changes to reduce N2O without reducing overall 

nitrogen use effi ciency, which would violate a market rule. The treatment of 

such “good stewards” in an emissions trading program is an important policy 

issue.

The potential revenue from GHG credits produced by reducing N2O appears 

to be quite small. In the Ohio example, only a few situations are capable of 

producing credit revenue of over $5 per acre, assuming a credit price of $15 

per ton of CO2 equivalent (and the results are similar for the other States 

studied). These rates are less than the rates farmers could receive for nutrient 

management from EQIP, which is a measure of farmers’ willingness to accept 

payment for the practice (table 4.3). In general, farms overapplying nitrogen 

and broadcasting fertilizer can produce the largest reductions in N2O. 

However, only 8.3 percent of corn acres fall in this category (see table 3.3). 

While we found that changes in operating costs after changing management 

are near 0 or even negative in most cases, we did not consider short-term 

adjustment costs, changes in risk, or the administrative costs of participating 

in an offset program. In the case of farms that also have animals, we did not 
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consider the cost of moving manure produced on the farm to more acres (to 

reduce application rates), or of moving excess manure off the farm entirely 

(Ribaudo et al., 2003)—all of which would reduce farmer participation below 

the rates estimated here.

One issue of concern is the possibility that reducing N2O could increase 

nitrate losses to water. As described in chapters 2 and 3, changes in management could change conditions in the soil so that gaseous forms, such as N2O, 

are converted to highly soluble nitrate (NO3). It might seem that allowing 

only management changes that do not increase total losses of nitrogen 

would prevent this, but we found otherwise. In 25 percent of the cases 

where management changes were made to reduce N2O, NO3 losses to water 

increased, even though total nitrogen emissions fell. This occurred almost 

exclusively when the rate criterion was already being met and injection/incorporation was adopted as an additional practice. While overall N2O and total 

nitrogen losses decreased, water quality worsened. Such an outcome would 

be a concern in regions trying to address water quality problems, such as the 

Corn Belt, where corn production is the major source of nitrogen contributing to hypoxia in the Gulf of Mexico. Including these factors in the analysis 

would likely further reduce the net value to society of producing GHG offsets 

through N2O emissions reductions.

Response to Price Changes, and What It 

Means for an Input Tax

Input prices can infl uence a farmer’s planning. For example, low fertilizer 

prices can lead to “insurance” applications of fertilizer that reduce overall 

nitrogen use effi ciency. Increases in fertilizer prices relative to other input and 

output prices through the use of an input tax would likely decrease fertilizer 

use and reduce the number of acres receiving excessive rates. Several States 

have levied fertilizer taxes in the past but only at low levels that had little 

impact on use. 

The effectiveness of an input tax in reducing excessive application rates 

would depend largely on the responsiveness of farmers to changes in nitrogen 

prices. Data from studies spanning several decades reveal that responses to 

a price change (known as the price elasticity) can vary widely, depending on 

the data source and time period covered, the type of econometric methods 

used to analyze the data, the number of crops covered, and the type of crop 

to which the nitrogen fertilizer is applied. While no true consensus exists, 

study fi ndings generally show that nitrogen demand was relatively insensitive to price. Burrell (1989) provides a convenient summary of 14 empirical 

demand studies through the 1980s. Of those 14 studies, only 4 report elasticities greater than unity. Estimates were generally in the range of -0.20 to -0.70, 

implying that a 10-percent increase in the price of fertilizer reduced demand 

by 2 to 7 percent (see, for example, Griliches (1958); Carman (1979); Ray 

(1982); and Shumway (1983)). 

Denbaly and Vroomen (1993) use cointegrated and error-corrected models 

with time series data from 1964 to 1989 to estimate short- and longrun 

Marshallian elasticities. They report a shortrun Marshallian elasticity of -0.21 

and a longrun elasticity of -0.41. Hansen (2004) estimates nitrogen fertilizer 
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demand of farmers in Denmark using an unbalanced panel spanning 1982-91. 

He concludes that nitrogen demand is similarly insensitive to own-price, with 

an elasticity of -0.45.

Not all studies found the price elasticity of demand for nitrogen fertilizer to 

be inelastic. Carman (1979) examines the nitrogen demand in 11 Western 

States and fi nds signifi cant State-level variation in elasticities. Statistically 

signifi cant elasticity estimates in Carman’s study range from -0.55 to as large 

as -1.84. His study shows that demand can vary signifi cantly even within 

a region. Roberts and Heady (1982) also use annual time-series data from 

the United States, but spanning 1952-76, and fi nd price elastic demand for 

nitrogen applied to corn (-1.148). In a study of aggregate fertilizer, Weaver 

(1983) investigates the demand in just two States, North Dakota and South 

Dakota, and fi nds fertilizer demand to be highly elastic, ranging from -1.377 

to -2.156. 

Some evidence suggests that farmers may be becoming more sensitive 

to changes in fertilizer prices. Using 2001 and 2005 fi eld-level data from 

ARMS, we estimate a demand elasticity of nitrogen fertilizer of -1.38 for 

farmers who applied commercial nitrogen fertilizer to corn (app. 3). Stated 

another way, if the price of nitrogen fertilizer was to rise by 10 percent, 

farmers would reduce the amount applied by 13.8 percent. At the mean 

amount of commercial nitrogen, such a change in price would result in a 

decrease of 18.2 lbs of fertilizer per acre.5

Manure can also be used as a source of nitrogen nutrients, usually in conjunction with commercial nitrogen fertilizer. In the ARMS sample, slightly 

less than a quarter of corn farmers applied manure to the fi eld, and all of 

them did so in conjunction with commercial nitrogen. When the analysis 

is expanded to include these farmers, we fi nd a demand elasticity of -0.67; 

that is, for every 10-percent increase in the price of commercial nitrogen 

fertilizer, farmers reduce their use of nitrogen (organic and inorganic) by 

about 7 percent. The results are driven by farmers who use both manure and 

commercial nitrogen; we fi nd they are relatively less sensitive to the price of 

commercial nitrogen fertilizer than farmers who apply commercial nitrogen 

exclusively, which is consistent with the idea that manure and inorganic forms 

of nutrients are imperfect substitutes. Also, manure management decisions on 

farms with animals might be driven less by nitrogen prices than by the need 

to dispose of manure (Ribaudo et al., 2003).

The estimates of price elasticity can be used to provide a rough estimate of 

the tax that would be needed to reduce application rates so that more acres 

meet the rate criterion. Figure 4.2 displays the distribution of the nitrogen 

application rates that represent the criterion rate described in chapter 3. In 

the case of farmers who used commercial nitrogen exclusively, we have estimated an average criterion application rate at 170.8 lbs per acre for production year 2005. Thirty-fi ve percent of the 76 million corn acres treated with 

nitrogen exceeded their criterion rate (26.7 million acres), and farmers who 

exceeded their criterion rate had a mean rate of 185.5 pounds per acre. From 

the distribution depicted in fi gure 4.3, the concentration of farmers near 

zero indicates that most of the farmers who applied nitrogen at rates above 

the criterion rate are situated near the threshold (also seen in table 3.3). In 

5The mean commercial nitrogen 

application rate in our sample was 

129.72 lbs per acre.
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1

Criterion rate defined as nitrogen removed at harvest plus 40 percent, based on the 

farmer-stated yield goal.

Note: The kernel density, represented by the smooth line, is an estimate of the continuous 

density using an Epanechnikov kernel. 

Source: USDA, Economic Research Service using USDA’s 2005 Agricultural Resource 

Management Survey.

Figure 4.2

Distribution of criterion rates1 for corn, based on reported expected 

yield, 2005

Percent

Pounds per acre

5

10

15

20

25

0 50 100 150 200 300

0

250

1

Criterion rate defined as nitrogen removed at harvest plus 40 percent, based on the 

farmer-stated yield goal.

Note: The kernel density, represented by the smooth line, is an estimate of the continuous 

density using an Epanechnikov kernel. 

Source: USDA, Economic Research Service using USDA’s 2005 Agricultural Resource 

Management Survey.

Figure 4.3

Distribution of nitrogen fertilizer applied to corn that exceeded 

the criterion rate,1 2005

Percent

Pounds per acre exceeding criterion rate

0

10

20

30

5

15

25

0 100 200 300 400 50 150 250 350 
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fact, 50 percent of farmers who exceeded the criterion rate exceeded it by 19 

pounds per acre or less. 

Table 4.7 provides a summary of the input tax needed to reduce the excess 

use of nitrogen by farmers who exceed their criterion rate, evaluated for 

differing levels of demand elasticity. From the table it is evident that the more 

elastic the demand, the less the price must change to reduce excessive application rates. A highly inelastic demand for nitrogen, for example -0.20, would 

require more than a 50-percent increase in the price to achieve a 50-percent 

reduction in excess application. To achieve a reduction of 75 percent, the price 

would have to more than double. 

Based on our estimated elasticity of -1.38, if an input tax increased the price 

of nitrogen by 7.4 percent, 50 percent (about 13.4 million acres) of the 26.7 

million overtreated acres would then meet the rate criterion. Seventy-fi ve 

percent of heavy nitrogen users exceed the criterion rate by 43.4 pounds per 

acre or less; thus, raising the price of nitrogen by 17 percent would reduce 

cropland exceeding the criterion rate by 20 million acres. For context, 

consider the mean price of nitrogen fertilizer in 2005 was 33 cents per pound; 

therefore, a 7.4-percent change in the price equates to slightly more than 2.4 

cents per pound, and a 17-percent change equates to less than 6 cents per lb.

As a policy instrument, a tax on inputs has some desirable characteristics 

as well as some well-known drawbacks. First, a tax gives farmers fl exibility 

in how they reduce emissions. Farmers face heterogeneous costs, and a tax 

enables farmers to tailor their input responses (nitrogen abatement) accordingly (Ribaudo et al., 1999). In the case of nitrogen, an input tax directly 

affects the farmer’s decision that has the largest impact on nitrogen losses to 

the environment. It would also encourage a farmer to manage nitrogen more 

carefully, which could lead to appropriate timing and method of application. 

A tax does not require monitoring or enforcement, unlike a regulation. It 

can also be easily adjusted if policy goals are not met or exceeded. Another 

advantage of an input tax is that it raises revenue while reducing application 

rates. The revenue could be used to reduce the tax burden of crop producers 

through a system of lump-sum rebates to those producers who improve 

Table 4.7

Fertilizer price increases needed to reduce excess nitrogen†

applications by 50 percent and 75 percent

Elasticity of 

nitrogen fertilizer 

demand

Reduce excess nitrogen application by:

50 percent 75 percent

Necessary 

price change Tax

Necessary 

price change Tax

Percent Dollars Percent Dollars

-0.20 51.2 0.169 117.0 0.386

-0.50 20.5 0.068 46.0 0.154

-0.70 14.6 0.048 33.4 0.110

-1.00 10.2 0.034 23.4 0.077

-1.38 7.4 0.024 17.0 0.056

Note: † Excess nitrogen application is defi ned as rate exceeding 40 percent more than 

nitrogen removed at harvest (see chapter 3).

Source: USDA, Economic Research Service.
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nitrogen use effi ciency. Revenue can also be used to remedy damages caused 

by nitrogen losses. 

A tax on an input also has drawbacks. An input tax makes no distinction 

between whether fertilizer is in excess or not. A tax on nitrogen may also 

encourage increased use of untaxed manure, resulting in no discernable 

change in nitrogen applications where manure is readily available. 

The question of who bears the burden of the tax, also known as the incidence, 

can have notable distributional consequences. Statutorily, the incidence of 

the tax could fall on the wholesaler or retailer of nitrogen fertilizer; however, 

the true, or economic, incidence is likely to be shared with the farmer. How 

much so is an empirical question that relies on the relative sensitivity of 

farmers to the price change, as well as the elasticity of the supply of nitrogen: 

the more sensitive a farmer’s demand for nitrogen is, the less of a burden he 

or she will bear, all else equal. The supply of nitrogen fertilizer is projected 

to more than meet the demand over the near term; therefore, the standard 

assumption is that the burden of the excise tax would be considerably shifted 

to the consumer of the good, in this case the farmer (Fullerton and Metcalf, 

2002; FAO, 2008). While corn production in the United States accounts for 

39 percent of the world’s total corn production, the ability of U.S. farmers to 

pass along the cost of the tax will depend on the relative elasticities of supply 

and demand for corn (USDA, FSA, 2011). While a factor tax on nitrogen may 

improve welfare from society’s point of view, ultimately, the tax will change 

the functional distribution of income. The distributional impact may be mitigated if revenues raised by the tax are returned to the farmer in some manner, 

for example, by supporting other conservation activities.

Nitrogen Compliance

Compliance provisions require farmers to meet some minimum standard of 

environmental protection on environmentally sensitive land as a condition for 

eligibility for many Federal farm program benefi ts, including conservation 

and commodity program payments. Under current compliance requirements, 

farm program eligibility could be denied to producers who:

• Fail to implement and maintain an NRCS-approved soil conservation 

system on highly erodible land (HEL) (Conservation compliance)

• Convert HEL grasslands to crop production without applying an 

approved soil conservation system (Sodbuster)

• Convert a wetland to crop production (Swampbuster)

Evidence suggests that the current compliance provisions have contributed 

to a reduction in soil erosion and discouraged the conversions of noncropped 

HEL land and wetlands to cropland (Claassen et al., 2004). A possible extension of the provisions could include nutrient management.

Crop producers are a major source of nitrogen. Assessments of the potential 

effi cacy of compliance must consider two key questions:
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• To what extent do crop producers who have the greatest potential for 

reducing nitrogen emissions also participate in farm programs?

• Are Government payments to these producers large enough to encourage 

broad adoption of practices that improve nitrogen use effi ciency and 

reduce nitrogen emissions?

Claassen et al. (2004) estimate that 75 percent or more of cropland acres 

with medium, high, or very high potential for nitrogen leaching or runoff 

are located on farms that receive Government payments. We used data from 

the 2005 ARMS corn survey to estimate Government payments received by 

corn producers.6 We looked at all treated corn acres, as compliance provides 

an incentive both for farmers already practicing good nitrogen management 

and willing to continue and for farmers not using nitrogen BMPs and willing 

to adopt them. Over 97 percent of corn acres receive Government payments, 

averaging $51.39 per acre. This average is higher than our estimated costs 

of improving NUE or of adopting NRCS practices. Eighty-eight percent 

of treated corn acres receive Government payments in excess of $27 per 

acre per year, which is more than the average EQIP payments for nutrient 

management or waste use. (Note that for corn acres that are highly erodible 

and subject to conservation compliance, it is the sum of erosion control and 

nitrogen management costs that would be considered by the farmer.)

A drawback of compliance is that the strength of the incentive is dependent 

on the level of Government payments. Current events present a good example. 

Direct Government payments have been reduced by about 50 percent between 

2005 and 2009 due to a number of factors, including higher crop prices and 

smaller disaster payments (USDA, ERS, 2010). Assuming that average per 

acre payments to corn producers were reduced by the same percentage, the 

average estimated cost of the more expensive nitrogen management practices, such as waste utilization, would be greater than the program benefi t. 

Compliance would not be an effective tool in this case. The point is that 

program payments can vary greatly, making compliance an unpredictable 

policy instrument.

Regulation

Another policy approach for improving NUE is to legally require farms to 

adopt and implement particular management practices. Such an approach 

would be a major change in the way most of agriculture is treated under 

current environmental laws. With few exceptions, agricultural operations 

are exempt from regulation under the Clean Water Act and Clean Air Act. A 

number of arguments have been used as justifi cation. First, agriculture is so 

diverse across the United States that the conventional regulatory approach of 

applying uniform standards is impractical (Nanda, 2006). Second, due to the 

nonpoint nature of agricultural pollution, individual polluters cannot be identifi ed except at great cost. 

Regulation can conceptually be placed on a continuum between performance 

standards and design standards (Ribaudo et al., 1999). Performance standards 

directly regulate emissions. Design standards dictate how producers manage 

their operations, including practices that should not be used and/or BMPs that 

should be adopted. Because of the nonpoint nature of agricultural pollution, 

6The ARMS data do not enable us to 

identify only those program payments 

subject to compliance, but they are a 

good approximation.
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design standards are the only practical approach for addressing nitrogen 

losses. 

One approach would be to require that farmers adopt specifi c BMPs to 

improve their nitrogen use effi ciency. Generally, a practice-based regulation is 

ineffi cient because it requires producers to adopt the same practice, whether 

it is appropriate for their particular farm or not. It may be more effective to 

defi ne BMPs locally so as to allow fl exibility and to account for agriculture’s 

heterogeneous nature. For example, a nitrogen management plan is a fl exible 

practice that is based on a farmer’s resources and cropping system. However, 

farmers may fail to implement the plan properly. The effectiveness of a regulation therefore requires effective inspection and enforcement by a resource 

management agency. Implementation costs would likely be high. Several 

States, such as Nebraska and Maryland, have required farmers in particularly 

vulnerable areas to adopt specifi c nutrient management practices to protect 

ground or surface water (Ribaudo, 2009).

One of the few segments of the agricultural sector that has been subjected 

to regulatory environmental measures at the national level is animal feeding 

operations, refl ecting heightened concern over pollution from animal waste 

from the largest operations (USDA-EPA, 1999). Manure is estimated to be 

a source of about 17 percent of nitrogen entering U.S. waters (Smith et al., 

1997). Clean Water Act regulations now require that animal feeding operations designated as Concentrated Animal Feeding Operations, or CAFOs, and 

needing a National Pollutant Discharge Elimination System (NPDES) permit 

(those CAFOs that discharge or propose to discharge to surface waters), 

develop and implement a nutrient management plan to cover fi elds that 

receive manure. Such a plan, which would meet NRCS standards, sets a limit 

on the amount of nutrients that can be applied per acre of land and specifi es 

erosion control measures to prevent the loss of sediment and nutrients. Also 

under the new regulations, CAFOs that are not required to have an NPDES 

permit but that wish to claim the storm water exemption (the provision in 

the Clean Water Act that exempts fi eld practices from requiring a discharge 

permit) for runoff from fi elds must develop and implement a nutrient management plan to demonstrate that due care is being taken to minimize polluted 

runoff from fi elds receiving manure. If a waterway becomes polluted with 

animal waste from fi eld runoff and a CAFO does not have an approved 

nutrient management plan, this would be a violation of the Clean Water Act. 

This approach sets a level of expected stewardship, namely the implementation of a nutrient management plan. 

Requiring not just CAFOs but all animal feeding operations to adopt nutrient 

management plans would be costly. ERS estimates that reductions in net 

returns in the livestock and poultry sector would be about $1.4 billion per 

year, and national economic welfare for producers and consumers would 

decline almost $2 billion per year (Ribaudo et al., 2003). The benefi t would 

be improved air and water quality. Targeting the regulatory approach only 

to those operations most susceptible to pollution problems would lower the 

overall costs.
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Chapter 5

Implications for Nitrogen 

Management Policies

Nitrogen is critical for producing abundant food and generating high net 

returns to producers, yet it has wide-ranging environmental impacts across 

land, water, and the atmosphere. More careful management that reduces 

environmental losses would address a number of environmental issues, such 

as hypoxia in coastal estuaries and bays, the potential for global warming, 

and nutrient enrichment of terrestrial ecosystems. Policymakers have a 

number of tools at their disposal, each with its own strengths and weaknesses (table 5.1). No one policy approach can be considered “best,” and 

a concerted effort to address the Nation’s nitrogen problems will likely 

require a solution comprising a mix of policies. Our analysis provides some 

guidance on determining which sectors of agriculture are most in need of 

improved management, what are the potential pitfalls, and how might the 

different policies be orchestrated in an overall policy framework.

Reducing Application Rates as a Priority Policy Goal

Reducing the application of nitrogen fertilizers appears to be the most effective BMP for reducing the emission of nitrogen into the environment. Based 

on the literature, and confi rmed by our NLEAP modeling, reducing application rates is the one BMP that reduces all forms of reactive nitrogen, even 

when the timing and method of application are not ideal. Improving timing 

or method of application alone could increase one type of reactive nitrogen 

(transmitted to the atmosphere, groundwater, or surface water) while still 

reducing total nitrogen emissions. Reducing the application rate is therefore 

conducive to an ecosystem approach to management that provides protection 

to all ecosystem services and functions. Improving rate, timing, and method 

of nitrogen application would produce the greatest environmental benefi ts.

Reducing application rates that are agronomically excessive may increase 

the perceived risk of reduced yields. Farmers often use nitrogen fertilizer 

to manage the downside risk due to uncertain weather and soil nitrogen. 

Research on how farmers view risk and how they might respond to an incentive payment for reducing application rates, coupled with the use of a risk 

management instrument, could result in the development of a more effective approach for reducing nitrogen in the environment. Revenue or yield 

insurance policies could be offered to protect the income of farmers who 

adopt conservation measures that improve nitrogen use effi ciency but may 

decrease yields because of nitrogen insuffi ciency stemming from unfavorable weather conditions. Findings from other studies suggest that insurance 

will likely lead to reductions in nitrogen fertilizer applications, but by how 

much is uncertain (see Babcock and Hennessy, 1996; Mishra et al., 2005; 

Smith and Goodwin, 1996).
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Corn Is the Most Important Crop for Addressing 

Nitrogen-Related Environmental Issues 

Corn is the most widely planted crop in the United States and the most intensive user of nitrogen. In 2006, corn accounted for an estimated 65 percent 

of the total quantity of nitrogen applied to major U.S. fi eld crops. Corn also 

accounted for half of all nitrogen-treated crop acres that were not meeting the 

rate, timing, or method of application criteria used in this analysis to defi ne 

acceptable nitrogen management. Land used to grow corn accounted for the 

largest share of treated acres that had tile drainage in 2006. Although tile 

Table 5.1

Summary of policy instruments for improving nitrogen use effi ciency

Policy instrument

Characteristics Input tax Information

Financial 

incentives Compliance

Emissions 

market Regulation

Strength of 

incentive

Depends on 

level of tax and 

price elasticity of 

demand.

A farmer will 

take action only 

if management 

practice 

improves profi ts.

Depends on 

level of subsidy.

Depends 

on level of 

Government 

program 

payments 

subject to 

compliance.

Depends on 

level of demand 

from regulated 

sectors.

Strong.

Acres covered Covers all acres 

that are treated 

with commercial 

nitrogen.

No guarantee 

that acres 

in need of 

treatment will be 

addressed.

No guarantee 

that acres most 

in need of 

treatment will be 

addressed.

May not cover 

all acres.

May be limited 

by geographic 

scope of market 

and baseline 

rules.

Can cover all 

acres that use 

commercial 

nitrogen or 

animal waste.

Targets problem Directly 

addresses 

application rate, 

but not timing and 

method. Also, 

does not address 

application of 

animal waste.

Information can 

be targeted 

to specifi c 

problems.

Incentives can 

be targeted 

to specifi c 

practices 

and regions. 

However, 

important 

to consider 

potential 

environmental 

tradeoffs.

Strength of 

incentive 

may not be 

correlated with 

acres most 

in need of 

treatment.

Generally 

limited to one 

pollutant and 

not overall 

nitrogen use 

effi ciency. 

Environmental 

tradeoffs 

a potential 

problem.

Can target 

all aspects 

of nutrient 

management. 

However, 

important 

to consider 

potential 

environmental 

tradeoffs.

Flexibility Very fl exible 

– farmers can 

adjust in the most 

cost effective way.

Flexible – 

farmers act on 

information that 

is benefi cial to 

them.

Practice-based 

incentives are 

less fl exible than 

incentives on 

environmental 

performance.

Flexibility 

depends on 

how provisions 

are defi ned.

Can be fl exible, 

but depends on 

market rules.

Limited 

fl exibility, as 

regulations 

generally 

require 

specifi c 

practices.

Implementation 

costs

Easy to 

implement, 

and generates 

revenues that can 

be used to reduce 

economic impacts 

for farmers 

who make 

improvements.

Requires 

research and 

extension 

outreach.

High costs to 

taxpayers.

Enforcement 

costs may be 

high.

Transactions 

costs can be 

very high.

Enforcement 

costs can be 

high.
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drains improve yields, they also increase the amount of nitrogen that is lost 

to surface water. Tiled corn cropland not meeting all three nitrogen management criteria would be a prime target for policies for improving nitrogen use 

effi ciency.

In addition, recent demand pressures due to the biofuels mandate, as well as 

increasing international demand for feed grains, suggests that corn acreage 

and the intensity of corn production are likely to increase. Together, these 

factors increase the importance of raising the NUE in corn production in the 

United States, especially on farms that raise livestock and apply manure to 

their fi elds.

Which Policy Is Best?

This analysis provides some guidance on how different policies might 

be orchestrated in an overall policy framework. The current approach to 

improving nutrient management on cropland has relied primarily on fi nancial 

incentives and information. While years of fi nancial and technical assistance 

have resulted in some progress, operators of over 65 percent of U.S. cropland are still not implementing nitrogen BMPs. Higher payment rates would 

encourage more producers to adopt practices that improve nitrogen use effi -

ciency, but the cost to taxpayers may be substantial. The level of fi nancial 

assistance that would be required to entice all farmers with cropland acres 

needing improved management to enroll in a program would likely consume 

most of the budget for EQIP. While nitrogen management is an important 

conservation goal, EQIP and other USDA conservation programs address a 

host of other issues. Any elevation of nitrogen management as a priority for 

EQIP may result in fewer resources for other conservation issues.

Emissions markets, such as those for water quality or greenhouse gases, 

could be a source of fi nancial support for improving nitrogen use effi -

ciency. Markets for agricultural offsets shift the fi nancial burden away from 

taxpayers to regulated sectors of the economy. While emissions markets are 

receiving much interest in efforts to improve water quality and to reduce 

greenhouse gas emissions, their role in improving nutrient management on all 

acres needing improvement is probably limited. Emissions markets generally 

target particular geographic areas or particular practices, potentially limiting 

the number of acres that might be affected. Market rules designed to ensure 

the “additionality” of offsets by setting baselines consistent with a high level 

of management may limit participation by farmers not using BMPs, even 

though a market would benefi t by their participation. In addition, the nonpoint 

source nature of nitrogen emissions from agriculture greatly complicates the 

design of markets and raises transactions costs. 

If voluntary fi nancial assistance programs or emissions markets are limited 

in their ability to improve nitrogen management across all crop acres, what 

other approaches might achieve improved nitrogen use effi ciency at least 

cost? The alternative approaches all result in increased costs for farmers. In 

theory, cost-effective policy instruments target the problem, are fl exible, are 

easy to implement (low transactions costs), and limit costs to both farmers 

and Government. A tax on nitrogen fertilizer would provide an incentive to all users to manage commercial nitrogen more carefully. If farmers 

are responsive to price, then this instrument may be an effective means of 
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reducing nitrogen losses. Our assessment of farmer price responsiveness 

indicates that a relatively low tax may pay high environmental dividends. 

However, if farmers are as unresponsive to nitrogen prices as generally 

reported in the literature, a substantially higher tax would be necessary to 

obtain the same environmental benefi ts. The burden on farmers would be 

substantial. Another drawback of an input tax is that a tax would also be 

paid on applications that are not excessive. A tax only on emissions would be 

far more effi cient, but such a tax is not practical since emissions cannot be 

observed or easily measured. Finally, some means of addressing the application of animal waste would have to be found, as a fertilizer tax would likely 

encourage the substitution of manure for commercial nitrogen. 

A nutrient management plan is an inherently fl exible management practice 

that is strongly encouraged by USDA but only required for animal feeding 

operations that are designated as CAFOs. Requiring that all users of nitrogen 

inputs (commercial and manure) develop and implement a nutrient management plan would be a major change in the way the environmental performance of agriculture is managed. The costs to crop farmers of implementing 

a nutrient management plan may not be high, except for those managing 

large amounts of manure produced on the farm. However, many aspects of a 

nutrient management plan, such as application rate, are diffi cult to observe, 

making enforcement diffi cult and costly. 

Enforcement costs could also be high for a compliance approach to getting 

farmers to adopt nutrient management plans. The effectiveness of compliance 

would depend on the level of program payments received by farmers and a 

coincidence of the incentive with those crop acres most in need of improved 

management. A large share of crop acres in need of treatment receives high 

levels of Government program payments. While the incentive level in 2005 

was quite high, program payments have declined in recent years as crop 

prices have risen. Continued high prices and general concerns about Federal 

budget outlays may limit the strength of a compliance-type policy instrument 

unless it is linked to a broader suite of payments than current compliance 

requirements.

Improving nitrogen use effi ciency reduces the amount of emissions from 

cropland but does not eliminate them. In areas where even small levels of 

emissions could cause environmental problems, offsite fi ltering could supplement onfi eld management. The Government currently provides fi nancial 

incentives for creating and preserving wetlands and vegetative fi lter strips. 

Though funds are not allocated solely for nitrogen capture and removal, there 

may be reasons to do so. An economic comparison of the two types of fi lters 

suggests that wetlands can be much more cost effective at removing nitrogen 

than fi lter strips. While our analysis found that the cost of establishing a 

wetland is greater than the cost of establishing fi lter strips, annual nitrogen 

removal rates are several times greater for wetlands. Filter strips may also 

be rendered ineffective where tile drains are present, while wetlands can be 

strategically positioned in the landscape to fi lter drainage coming from tiled 

fi elds. Wetlands also produce a number of other desirable ecosystem services, 

such as wildlife habitat. Filter strips, however, can be established in landscape 

settings where wetlands cannot. The choice will depend on geography, soil, 

and hydrologic conditions. 
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While one single policy instrument does not emerge as a clearly superior approach to improving NUE across all cropland, a role can be seen 

for each. Financial assistance could be made available to those producers 

wanting to voluntarily improve nutrient management and to install vegetative fi lters or resore wetlands. Since commodity programs are important to 

farmers, compliance can provide some incentive for those receiving program 

payments. The level of incentive may vary from year to year, but it may be 

effective for some farmers. Finally, in regions where nitrogen-related pollution is of particular concern, such as the Chesapeake Bay watershed and the 

watersheds contributing nitrogen to the Gulf of Mexico, a regulatory backstop could be a measure of last resort for those unwilling to voluntarily adopt 

nitrogen BMPs. 

Information Supports All Policies

Information about the environmental and economic performance of improved 

nitrogen management practices supports all policies aimed at improving 

NUE. Reliable, timely information on soil and plant nitrogen reduces one 

source of uncertainty that tends to encourage overapplication of nitrogen. Our 

research supports previous fi ndings that testing for nitrogen available in the 

soil and contained in crops may result in lower application rates. Information 

from testing can be incorporated into an adaptive management framework, 

where a farmer evaluates his practices from the previous year (or even at 

the start of the current growing season) to assess what options may be available to improve nutrient management while sustaining yields and reducing 

nutrient losses to the environment. So, whether farmers are considering best 

nitrogen management practices due to regulation, taxes, or fi nancial incentives, information on how to conduct and interpret nitrogen tests and how 

to successfully implement new practices can reduce the overall costs and 

increase adoption rates.

Potential Tradeoffs Are an Important Consideration

Reactive nitrogen is easily converted to forms that are readily transported 

by hydrologic and atmospheric processes. Therefore, focusing strictly on one 

issue, such as nitrate leaching, could lead to increased emissions of other 

nitrogen compounds, such as nitrous oxide to the atmosphere, if nitrogen’s 

characteristics are ignored. Even when total nitrogen emissions are reduced 

by a policy, emissions of one or more nitrogen compounds might increase 

and degrade environmental quality. This effect was predicted in the case of 

the market for nitrous oxide offsets—farmers reduced total emissions but 

increased nitrogen losses to water. These tradeoffs often depend on soils 

and cropping practices, so it is diffi cult to develop general “rules of thumb,” 

other than recommending that a holistic approach to management that 

considers potential environmental tradeoffs be adopted. Reducing nitrogen 

application rates is the easiest and most effective way to reduce all forms of 

reactive nitrogen.
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Appendix 1

Estimating Water Treatment Costs

We estimated a treatment cost model with data from the 1996 American 

Water Works Association (AWWA) survey of its members. There are only 

52 usable observations for which utilities provided all required data. This 

is the last survey in which data on costs and water quality (both raw water 

coming into and fi nished water going out of the utility) were gathered at the 

same time by AWWA. We assume this sample is representative of all water 

treatment plants. The model is a variable cost function with two outputs (one 

desirable (water) and one undesirable (nitrogen)); four inputs (three variable and one fi xed); and nine factors hypothesized to infl uence production of 

drinking water (app. table 1.1). 

The bootstrap method employed uses network density as the stratum—the 

result of this stratifi cation is a more homogeneous sample and hence a smaller 

standard error.

Econometric specifi cation of simple production

 model and discussion

1 2

12 1 2

3 33

ˆ ˆˆ ln ln ln ln ln ln ˆ ˆ ˆ V w w yN K

w ww

βα α β β η ο

⎛⎞ ⎛⎞ ⎛⎞ =+ + + + + ⎜⎟ ⎜⎟ ⎜⎟ ⎝⎠ ⎝⎠ ⎝⎠

 2.01*** 0.80*** 0.03*** 0.62 *** 0.43*** 0.03**

(4.55) (81.50) (2.69) (16.82) (5.22) 2.27)

1 2 3 4 567

ˆˆ ˆ ˆ ˆˆˆ δδ δ δ δδδ ε netd public dww syssize loc loc loc + + + ++++ 234

-0.00004*** 0.14*** 0.08*** 0.05*** 0.21*** 0.22*** 0.15*** 

(1) (-94.50) (4.65) (4.96) (4.19) (5.53) (4.77) (2.93) 

Bootstrapped z in parenthesis. Signifi cance level of 0.01 and 0.05 denoted by 

*** and **, respectively.

The estimated variable cost function meets most of the theoretical regularity 

conditions (i.e., it is monotonically increasing in desirable output as well as in 

variable inputs). The only case in which the desirable theoretical properties 

of inputs are not met is in the case of capital, which, in variable cost function 

setting, should be negative. The explanation resides in overcapitalization of 

water utilities—a phenomenon widely observed for regulated utility fi rms of 

all kinds. Homogeneity in the cost function is imposed by dividing both input 

prices and variable costs by price of chemicals. Consistent with the literature 

on undesirable outputs, the presence of an undesirable byproduct in a production process, in this case nitrogen, implies a higher cost to the utility which 

it then abates either to meet regulation6 or more generally to reduce risk to 

customers.

6EPA regulates nitrate in drinking 

water (measured as nitrogen) at 10 

mg/L.
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As to the exogenous effects, network density has a negative effect on variable costs as expected. Also, larger systems have higher variable costs. Public 

utilities have higher variable costs than investor-owned utilities. This makes 

sense from the perspective that public fi rms may have agency and control 

problems relative to investor-owned enterprises. Operations that have only 

a distribution function have lower variable costs than those that have both 

waste water and distribution. All locations have higher variable costs relative 

to New England.

Derivation of shadow cost of nitrogen abatement and discussion

1 2

12 1 2 1

3 3

2 3 4 567

ˆ ˆˆˆ ˆ ˆ ln ln ln ln ˆ exp

ˆ ˆ ˆ ˆˆˆ 234

w w y N netd

V w w

public dww syssize loc loc loc

βα α β β δ ο

δ δ δ δδδ

⎛ ⎞ ⎛⎞ ⎛⎞ ++ + + + ⎜ ⎟ ⎜⎟ ⎜⎟ = ⎝⎠ ⎝⎠

⎝ ⎠ + + + +++

*w3 (2)

Appendix Table 1.1

Summary statistics and defi nitions

Defi nition (unit) variable Mean (Variance) Defi nition (unit) variable Mean (Variance)

Variable cost

(in $) VC

8,479,039

(13,477,167)

System type

(1 = Distribution and waste 

water, 0 = Otherwise) dww

0.54

(0.50)

Annual water production

(in millions of gallons) y

14,449

(23,498)

System size

(1 = if population served 

greater than 100,000, 0 = 

Otherwise) syssize

0.48

(0.50)

Annual salary

(in $) w1

$34,353

($11,538)

Consumer structure

(ratio of residential to total 

water delivered) cs

0.57

(0.23)

Nitrogen abatement

(in difference of raw-fi nished 

nitrates in water) (in mg/L) N

0.98

(4.04)

Water system location

(New England) loc1

0.19

(0.40)

Electricity price

(in $ per kilowatt hour) w2

$0.05

($0.01)

Water system location

(Northeast) loc2

0.21

(0.41)

Chemicals price

in $ per pound) w3

0.2

(0.0)

Water system location

(South) loc3

0.15

(0.36)

Capital

(residual rate of return) K

$ 145,916,037

(217,806,925)

Water system location

(Mid-west) loc4

0.21

(0.41)

Network density

(population served/length of 

distribution main) netd

1176

(5608)

Water system location

(West) loc5

0.23

(0.43)

Organizational type

(1 = public, 0 = otherwise) public

0.87

(0.34)

Source: USDA, Economic Research Service using data from 1996 American Water Works Association survey.
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2 ˆ ˆ ˆ * N

V SC V

N N

∂ α = = ∂ (3)

The shadow marginal cost of nitrogen abatement is derived in equation (3) by 

taking the derivative of (2), estimated variable cost, which in turn was 

derived by taking the exponential of (1). From equation (3), various additional derivations can be made: shadow marginal cost by millions gallons, 

Vˆ

N

∂ ∂ / y, estimated shadow total variable cost of nitrogen abatement (SVC),

Vˆ N N

∂ × ∂ , and SVC per millions of gallons of water produced ( Vˆ N N

∂ × ∂ ) 

/ y. 

The results from the above derivations were used to estimate nitrogen 

removal costs by system size (app. table 1.2).

Appendix Table 1.2

National estimates of nitrogen removal costs for community water systems, by system size

System size (SS)

[in millions of gallons per year]

(CWS population

in parenthesis)

Estimated average 

production by CWS

(millions of gallons

per year)

Estimated average cost of 

nitrogen removal

(variable cost per million 

gallons per year per CWS)

Estimated total cost of 

nitrogen abatement

(million $ per year for all 

systems)

SS > 0 and SS <= 3,300 

(42,624) 570

$34.2

[46 %]1 830

SS > 3,300 and SS <= 10,000 

(4,871) 4,797

$25.55

[41 %]1 597

SS > 10,000 (4,156) 42,485 $19.18

[31 %]1 3,386

CWS = Community Water System.

1Percent of cost attributable.

Source: USDA, Economic Research Service using data from 1996 American Water Works Association survey.
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Appendix 2

Using NLEAP To Model Nitrogen Losses

The Nitrogen Loss and Environmental Assessment Package (NLEAP) 

(Delgado et al., 2010a; Shaffer et al., 2010) can be used to assess the potential 

for management practices to increase nitrogen use effi ciency and generate 

nitrogen savings that can be traded in water and air quality markets (Delgado 

et al., 2008b; 2010a). The NLEAP model has been used extensively across 

national and international systems (Delgado et al., 2008b). 

This tool is capable of simulating the effects of management practices and 

generating reasonable assessment values that are similar to measured fi eld 

studies conducted across small-scale plots and large commercial fi eld operations (e.g., water budgets, nitrate leaching, residual soil nitrate, crop uptake, 

nitrogen dynamics, and N2O emissions; Beckie et al., 1995; Khakural and 

Robert 1993; 2001; Delgado et al., 2001; Xu et al., 1998). 

Detailed descriptions of NLEAP-GIS capabilities and limitations can be 

found in Shaffer and Delgado, 2001; Shaffer et al., 2010; Delgado and 

Shaffer, 2008; and Delgado et al., 2010a; 2010b. This improved version can 

quickly evaluate multiple long-term scenarios across a large number of soils 

and conduct assessments of the effects of BMPs on nitrogen use effi ciency 

and nitrogen losses via different pathways. The new NLEAP-GIS tool also 

has a Nitrogen Trading Tool option (with GIS capabilities) (Delgado et al., 

2008a; 2008b; 2010a; 2010b).

General assumptions

NLEAP has been tested, calibrated, and used to accurately evaluate the 

effects of management for cropping systems and risky landscape combinations across national and international agroecosystems. In order to evaluate 

these systems, users established basic assumptions to simplify the evaluation process, which is very complex due to the nature of the nitrogen cycle 

and management interactions with environmental factors (Shaffer and 

Delgado, 2001).

Yields: It is well known that yield variability can impact nitrogen use effi -

ciency (Bock and Hergert, 1991). Instead of using the maximum yields at a 

given site as traditionally done by farmers as a safety net approach to calculating nitrogen inputs (Bock and Herget, 1991), State average yields for corn 

and soybeans derived from the USDA Census of Agriculture were used for 

the NLEAP-GIS simulations.

We assumed that yields for no-till systems were 10 percent lower than those 

for conventional tillage. Since we also evaluated excessive nitrogen input 

scenarios and low nitrogen input (defi cit) scenarios, we used the corn yield 

and nitrogen input response curve from Bock and Herget (1991) to estimate 

the average yields for these scenarios. It was assumed that for the excessive 

nitrogen input rates, yields were increased by only 1 percent; however, for 

the defi cit nitrogen input scenario a 10-percent drop in average yield was 

assumed (Bock and Herget, 1991). We believe that our approach of using 

average yields to evaluate the effects of management on the nitrogen use 
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effi ciency of commercial systems is a valid approach, as reported by Shaffer 

and Delgado (2001), Delgado (2001), and Delgado et al. (2000; 2001).

Since the USDA Census of Agriculture does not report yields by soil type, 

we assumed that yields for the soil types tested were similar. However, corn 

yield can vary among soil type, with lower yields in the sandier, less fertile 

soils that have higher nitrate leaching potential than those fi ner soils with 

lower leaching potential (Khosla et al., 2002; Bausch and Delgado, 2003; 

Delgado and Bausch, 2005; Delgado et al., 2005). Nonetheless, we still 

believe that assuming average yields for a 24-year period being evaluated is 

a valid approach to assessing the trends and effects of management practices 

on these different soil types and produces results that are in agreement with 

average measured values (Delgado et al., 2001; 2008b; 2010a). If additional 

site-specifi c fi eld information for a given farm is needed, spatial soil maps for 

the given farm can be downloaded from USDA NRCS websites, and evaluations using farmers’ inputs can be conducted. 

Nitrogen Inputs and Uptake: For nitrogen rates, we used the recommended 

best management practices for site-specifi c State and/or soil as described 

by Espinoza and Ross (2008) for Arkansas; Alley et al. (2009) for Virginia; 

Beegle and Durst (2003) for Pennsylvania; and Vitosh et al. (1995) for Ohio. 

We calculated the recommended nitrogen (N) rate per bushel of corn derived 

from each State’s recommended BMPs (Espinoza and Ross, 2008; Alley et 

al., 2009; Beegle and Durst, 2003; Vitosh et al., 1995). A summary of the 

nitrogen inputs simulated is presented in appendix table 2.1.

Since nitrogen fertilizer inputs were calculated based on yield, the no-till 

systems received lower nitrogen fertilizer inputs than the conventional 

systems. However, since a similar rate of uptake per unit of bushel was used 

for both systems, the removal of nitrogen in harvested grain from the no-till 

system was also lower than the removal of nitrogen in the grain from the 

higher yield conventional system. Total nitrogen uptake by the plant was 

calculated. Initial surface residue cover was simulated at 100, 90, 40, and 30 

percent for no-till corn-corn, no-till corn-soybeans, conventional corn-corn, 

and conventional corn-soybeans, respectively.

For the manure system, manure was applied every 2 years. For the corncorn rotation, manure was applied in the fi rst year, and only fertilizer was 

applied in the second year. The manure rate was calculated for each system 

to match the fertilizer rate. However, since manures will have a large fraction of organic nitrogen that is not immediately available (Davis et al., 2002; 

Eghball et al., 2002), an additional 50 percent of the recommended rate was 

added as inorganic nitrogen fertilizer. In other words, the total nitrogen input 

during the fi rst year of corn-corn rotation was 150 percent of the total application rate of the inorganic nitrogen fertilizer scenario (app. table 2.1). The 

corn-corn rotation did not receive any manure application in the second year, 

and the corn received the same rate of nitrogen fertilizer as in the nitrogenfertilizer-only scenario. Thus, over the 2-year period, the manure scenario 

for corn-corn received an average of 25 percent more nitrogen input per year. 

The same relationships apply to the excessive and defi cit nitrogen scenarios 

(app. table 2.1). 
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For the corn-soybean rotation, there was no application of nitrogen fertilizer or manure for any of the scenarios during the soybean year (app. table 

2.1). Additionally, for this rotation, the nitrogen cycling from the leguminous 

soybean crop was credited, as is recommended for each State, so the calculated nitrogen inputs for the corn in the corn-soybean rotation was lower than 

in the corn-corn system.

The excessive nitrogen fertilizer scenarios received 75 percent higher nitrogen 

inputs than the State-recommended rate. For the defi cit nitrogen application 

scenarios, nitrogen inputs were applied at a 25-percent lower nitrogen rate 

than the best management practice scenario (app. table 2.1). 

Soil Type Physical and Chemical Information: For each State, the county’s 

soil chemical and physical information averages for the selected soils were 

downloaded. To evaluate all of the management scenarios described above, 

we selected a soil with a higher leaching potential (Hydrology A or B) and a 

soil with a lower leaching potential (Hydrology C or D). 

Long-Term Weather: Long-term USDA, Natural Resources Conservation 

Service weather databases for each county were used to conduct the 24-year 

assessment as described by Delgado et al. (2008b, 2010a) nitrogen trading 

tool evaluations. 

Other Best Management Practices Tested: For all the scenarios described 

above, we evaluated the method of application. The best management practice for method of application was incorporation of nitrogen fertilizer and/

or manure. Surface application without incorporation was found to be a 

poor management practice. We also evaluated time of application. The best 

management practice for time of application was application of manure and/

or nitrogen fertilizer before planting, closer to the time of higher demand by 

Appendix Table 2.1

Relationships used to develop yields and nitrogen (N) rates used across the study sites

Tillage Best management practice Excessive Defi ciency

Yield (bushels per acre)

Conventional x1 x*1.01 x*0.9

No-till x*0.9 x*0.9*1.01 x*0.81

N rate for fertilizer-only scenarios (lbs N per acre)

Conventional x2 z*1.75 z*0.75

No-till y3 y*1.75 y*0.75

N rate for manure with N fertilizer scenarios (lbs N per acre)

Conventional z(org) + 0.5z(fert) 1.75z(org) + z(0.875) 0.75z(org) + z(0.375)

No-till y(org) + 0.5y(fert) 1.75y(org) + y(0.875) 0.75z(org) + z(0.375)

1The x values were 131, 101, 103, and 107 corn bushels per acre for OH, VA, PA, and AR, respectively. The x values were 40, 27, 37 and 27 

soybean bushels per acre for OH, VA, PA, and AR, respectively.

2The z values were 132, 121, 100, 120, and 125 lbs of N per acre for OH, VA, PA, AR (Hydrology A) and AR (Hydrology D), respectively, for 

conventional tillage. 

3The y values were 116, 109, 90, 100, and 105 lbs of N per acre for OH, VA, PA, AR (Hydrology A) and AR (Hydrology D), respectively, for 

conventional tillage.

Source: USDA, Economic Research Service using data from USDA, Agricultural Research Service.
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the crop. The poor management scenario was application of manure and/or 

fertilizer the previous fall, when the nitrogen is more susceptible to losses. 

Long-Term Evaluations: All these scenarios were evaluated over the long 

term. To conduct the long-term evaluations, we used a 24-year period using 

long-term weather data for the given county. Similar to what was done with 

the nitrogen trading tool, the fi rst 12 years were used to run the model, and 

years 13 to 24 were used to evaluate the effect of management practices on 

nitrogen use effi ciency and on reactive losses to the environment (Delgado et 

al., 2008b, 2010a). 
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Appendix 3

Estimating Changes in Nitrogen Fertilizer 

Application Rate 

This appendix describes the econometric model used to estimate changes 

in nitrogen (N) fertilizer application rate. We estimate nitrogen application 

rates using an instrumental variables (IV) approach to overcome identifi cation issues presented by farmer heterogeneity and endogenous soil N-testing. 

Price plays an important role in the nitrogen management decision, and 

the recent price growth of nitrogen has implications for nitrogen management behavior and by extension, nitrogen use effi ciency (NUE). Notably, 

we instrument for nitrogen price using a cross-section of data by exploiting 

exogenous spatial variation between domestic ammonia production plants 

and cornfi eld locations. 

Research using observational data presents econometric challenges, and this 

is particularly true for research examining the effect of potentially endogenous variables on a study population. For example, when estimating the effect 

of N-soil tests on application rate, researchers do not know why two observationally identical farmers make different choices about testing the soil. The 

underlying problem is the concern that unobserved farmer characteristics are 

responsible for determining whether the farmer conducts a test. For example, 

a farmer who tests the soil regularly may also have unobserved preferences 

for land stewardship. If differences beyond observed fi eld, farm operation, 

and operator characteristics play a role in determining who conducts the 

test and how the test is used, then the test may be endogenous to the amount 

applied.

Nitrogen price also presents a challenge in a sample of microdata. Prices are 

likely to embody an error-in-variables problem because in the case of ARMS, 

they were created as a share variable that represents the nitrogen fertilizer’s 

relative size of the total expenditures for all fertilizer (nitrogen, phosphorus, 

and potassium). To see how this effects the estimation of nitrogen demand, 

consider that we observe nitrogen price as a function of the true, unobserved 

price plus a disturbance term, v.

(1) Price Observed Price True* N N = + v .

Because the observed price on the left-hand side of equation (1) is a function of true price and v, an ordinary least squares (OLS) model of nitrogen 

demand estimated with the observed price will include v and will cause the 

estimate to be biased and inconsistent. Specifi cally, in the classic errors-invariables example, the coeffi cient in an OLS model will be biased toward 

zero.7 Prices farmers pay may also change with their level of demand. For 

example, if farmers receive quantity discounts when purchasing nitrogen 

fertilizer and their application rate is correlated with total nitrogen demand, 

then failing to account for this also results in bias.

To overcome the problem of mismeasured nitrogen prices and endogenous 

soil testing, we employ an IV approach, which allows for the development 

of consistent and unbiased estimates. In the case of endogenous N-soil 

testing, we fi nd a set of instruments that are correlated with N-soil testing 

7See Greene (2000) for a formal 

discussion of measurement error and 

the resulting attenuation bias.
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but uncorrelated with disturbance process: average annual soil percolation 

and average annual precipitation. Because percolation facilitates nutrient 

leaching (Williams and Kissell, 1991), we expect the greater soil percolation 

to increase uncertainty about available nutrients, and, therefore, encourage 

soil testing. Higher precipitation generally reduces the ability to conduct soil 

test, therefore we expect annual average precipitation to be negatively related 

to N-soil test.

We identify the nitrogen own-price effect on demand using three sources 

of exogenous variation: distance between the fi eld and domestic ammonia 

fertilizer production; production capacity of nearby ammonia plants; and 

distance from the fi eld to New Orleans, LA, site of the majority of international ammonia importation.8 Ammonia is increasingly being imported by 

the United States, and a majority of shipments enter from the Gulf of Mexico, 

and specifi cally, New Orleans; therefore, we also include a distance-to-New 

Orleans measure. These variables are useful instruments because the distance 

between the fi eld and production capacity are arguably uncorrelated with the 

behavior of the farmer or the placement of the fi eld;9 therefore, the instruments allow one to capture the exogenous variation in price and use it to estimate application rates. 

Instrumental variables model

We use an IV model specifi ed with two endogenous variables to estimate a 

partial-equilibrium static demand model derived from profi t maximization 

theory. The model assumes producers make immediate adjustments to quantity demanded in response to changes in price, and that prices are known at 

the time of production planning. These assumptions are reasonable given the 

ability of farmers to enter into contracts that establish price for delivered corn 

and inputs to production, such as forward or marketing contracts, and other 

hedging instruments. Further, production technology is assumed known and 

fi xed. Since only two time periods separated by 4 years are used, technology 

is unlikely to change. The most likely technological change is that of seed 

technology—the use of biotech (Bt) corn; however, the model specifi cation 

controls for this. In 2001, 20 percent of corn acres were planted with Bt corn; 

in 2005, the amount was slightly greater than 30 percent.

We characterize the problem posed to the farmer as one of profi t maximization with uncertainty, as evidenced by the nitrogen overtreatment, but the 

decision of the farmer could also be conceptualized as a utility maximization 

problem. In this case, the farmer chooses a level of output that maximizes the 

farmer’s initial wealth plus expected profi t from the operation. Under utility 

maximization, a farmer considers not only expected profi t but moments of 

the profi t distribution as well, and deviations from the recommended level of 

nitrogen then depend on the farmer’s level of risk aversion. Evidence from 

fi eld trial suggests that risk-neutral farmers would be willing to overapply 

nitrogen to increase profi ts during a year of “good” growing conditions 

(Rajsic et al., 2009). On the other hand, risk-averse farmers will reduce their 

nitrogen rate to reduce profi t variance. In practice, our empirical results are 

not dependent on the conceptual framework; in both cases, nitrogen prices 

enter the profi t function, and the identifi cation strategy would not change. 

Rather, the level of risk aversion primarily drives the differences. Some 

8Ammonia production data come 

from the North American Fertilizer 

Capacity Annual Reports issued by the 

International Fertilizer Development 

Center. We calculate the distances from 

the fi eld to ammonia production using 

the location of the plant and geocoded 

corn fi eld samples from USDA’s 

2001 and 2005 Agricultural Resource 

Management Survey. It should be 

noted that these are sample points, and 

they do not represent all corn production in the United States; however, 

when we estimate a model of nitrogen 

demand, the sample points are weighted 

to refl ect total U.S. corn production.

9To test that the instruments are 

uncorrelated with the residual component in the second stage of the IV 

model, or exogenous to the rate of 

fertilizer application, we test overidentifi cation restrictions using a Sargan test. 

The test statistic is computed as n×R2

and has a χ2(k-r) distribution, where k is 

the number of instruments and r is the 

number of endogenous variables. The 

results of the test are presented in the 

results table.
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research, however, suggests that risk-averse farmers are more responsive to 

price because of profi t risk (Just, 1975; Roosen and Hennessy 2003; Rajsic et 

al., 2009), and, if farmers are on average risk averse, our elasticity estimates 

will represent an upper bound.

Equation (2) is the outcome equation where Y represents the log transformed 

per acre rate of nitrogen applied to the fi eld of farm i in USDA production 

region r at time t. Endogenous variables, T

ˆ and P

ˆ , are estimated N-soil 

testing probability and nitrogen price from equations (3) and (4). The set of 

excluded instruments for N-soil test are represented by ZT, and the excluded 

instruments used to estimate nitrogen price are represented by ZP. The vector 

X is a set of independent variables that includes characteristics of the operator, farm operation, and the fi eld; the disturbance term is represented by ε.

Yirt Tirt Pirt irt r t irt = α + β + λ + δ +φ +υ + ε 1 1 1 1 1 1 ˆ ˆ X ,

r t irt irt T Tirt =α 2 + Xirtβ 2 + Z δ 2 +φ2 +υ2 +κ ,

r t irt irt P Pirt =α3 + Xirtβ3 + Z δ 3 +φ3 +υ3 + u .

A case can be made that countrywide trends over time affect the use of 

nitrogen. Perhaps in response to outreach efforts to reduce fertilizer runoff 

due to overuse, for example, environmental awareness campaigns that 

communicate the benefi ts of reduced nitrogen in the environment, attitudes 

about nitrogen rates have changed. We control for trends in nitrogen use that 

change over time with a time effect term, υt

. As well, use of nitrogen across 

production USDA-defi ned regions may also affect application rates, therefore 

we control for region-specifi c factors with a fi xed-effect term, φr 

.

Data

The data are cross-sectional and come from USDA’s Agricultural Resource 

Management Survey (ARMS). ARMS comprises responses to a series of interviews with farm operators designed to solicit information about production 

practices, costs of production, business fi nances, and operator and household 

characteristics. Commodity specifi c surveys are fi elded on a rotating basis, 

usually every 5 to 8 years. We focus on corn production because of its intense 

use of nitrogen, for which ARMS last fi elded surveys in 2001 and 2005.

We use data from two components of ARMS. The fi rst component is the 

Corn Production Practices and Costs Report, which surveys the farm enterprise’s costs of production and a host of production practices at the fi eld level. 

The second component is the Corn Costs and Returns Report, which collects 

indepth fi nancial information concerning the farm business and the household of the operator. The two components can be linked together to provide 

a complete view of the farm operation from the farm’s representative fi eld to 

its fi nancial statement, and we restrict the sample to farmers who completed 

both surveys.

As covariates, we include the farmer’s age, education, and income earned 

from work off the farm. We account for land quality and tenancy issues by 

including the per acre annual value of production, the per acre value of the 
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land, and acres owned by the operator. We also control for environmental 

characteristics of the fi eld, for example, whether any part of the fi eld is a classifi ed as a wetland. The presence of livestock and a nutrient management plan 

on the farm may indicate a greater reliance on manure, driven often by the 

need to dispose of manure. We account for these with dummy variables as 

well. The nutrient requirements of a current corn crop are also based, in part, 

on the plant-available nutrients existing in the soil, and past cropping practice 

can infl uence these nutrients. Therefore, we use a dummy variable to control 

for crop rotation pattern of 3-year straight corn rotation.

The timing and method of application may also be important determinants 

of application rate. A spring application is better timed to meet the plant’s 

need for nutrients and reduces the risk of loss due to environmental factors 

relative to a fall or winter application. On the other hand, farmers may opt to 

apply nitrogen in the fall, when there are fewer time demands and prices are 

often lower. In such a case, a nitrogen inhibitor is often used to further slow 

the nitrifi cation process, though average annual nitrate losses can still be 50 

percent higher under fall application than under spring application (Randall 

and Mulla, 2001). To counter this, in many cases, anhydrous ammonia is 

injected into the soil because low temperatures at this time of year slow 

the conversion of ammonia to ammonium and nitrate, reducing the loss of 

nitrogen. We control for the method of application with a dummy variable 

indicating whether the nutrient was incorporated or injected into the soil.

Technology and other management practices thought to affect nitrogen rate 

are captured by explanatory variables indicating the use of fi eld irrigation 

and biotech (Bt) corn seed. Irrigation is an important component in nitrogen 

management. Irrigation may be a necessary practice due to the climate, or 

it may be another way of more precisely controlling growing conditions. 

If water and nitrogen are complementary inputs, the presence of irrigation 

should increase the rate of nitrogen application. The use of biotech seed is 

driven by the associated cost reductions from the technology’s herbicide, pest, 

or fungus resistance. We also include a dummy variable representing whether 

the corn crop was grown for silage or corn. A full list of covariates and 

summary statistics is presented in appendix table 3.1.

Outcome Measures

We estimate the application rate for four different permutations of nitrogen 

fertilizer use. First, we estimate commercial nitrogen use by farmers who 

exclusively apply commercial nitrogen—a group that accounts for a 78 

percent of the farmers in our sample. We also examine the rate of total 

commercial nitrogen use by all farmers, regardless of whether they used 

commercial nitrogen exclusively or in conjunction with manure. The third 

measure examines the sensitivity of commercial nitrogen use by farmers 

who use manure in conjunction with commercial nitrogen—a group that 

employs an imperfect substitute for commercial nitrogen. These farmers 

make up a minority of the sample, 22 percent. Finally, we examine the effect 

of our explanatory variables on total nitrogen application rate, which includes 

commercial nitrogen and manure. It should be noted that all of the farmers 

in the sample reported at least some use of commercial nitrogen fertilizer. 

Estimates from the IV model are presented in appendix table 3.2.
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Appendix Table 3.1

Summary statistics

Variable name Description Mean

95% 

confi dence Interval

Soiltestn Nitrogen soil test 0.21 0.18 0.24

Nprice Nitrogen price 0.328 0.324 .332

Dealerrec Dealer recommendation 0.32 0.29 0.35

Consultrec Consultant recommendation 0.14 0.12 0.16

Extrec Extension agent recommendation 0.04 0.02 0.05

Routine Routine practice 0.28 0.26 0.30

op_age Operator’s age 52.73 52.11 53.36

Retired Operator is retired from farming 0.04 0.03 0.06

College Operator holds college degree 0.35 0.31 0.37

Workoff Derive income from off-farm work 0.38 0.35 0.42

Anycropins Insurance participation rate 0.659 0.62 0.70

Prodvalpa Production value per acre $4, 372.57 $337.29

Landvalpa Land value per acre $1,616.55 $709.46 $2,523.64

Ownacre Acres owned 323.37 301.10 345.63

Corn_p Corn price 1.87 1.84 1.90

CCC Straight corn rotation (3 years) 0.25 0.21 0.28

Nutrient plan Nutrient plan in place 0.076 0.063 0.088

Irrigate Irrigate the fi eld 0.063 0.0397 0.0853

Wetland Wetland on any part of the fi eld 0.03 0.02 0.04

Tenure Years farming 27.61 26.89 28.33

Spring Spring fertilizer application 0.80 0.77 0.84

Inc Incorporated fertilizer 0.75 0.73 0.78

Inhibit Fertilizer applied with inhibitor 0.07 0.05 0.09

Bt_corn Biotech corn 0.34 0.30 0.38

Yldgoal Yield goal 173.62 166.31 180.94

Silage Corn for silage 0.11 0.09 0.13

Livestock Presence of livestock on the farm 0.576 0.55 0.602

Commercial nitrogen w/o manure Commercial nitrogen users only 129.72 125.67 133.77

Total commercial nitrogen Total commercial nitrogen use 118.42 114.42 122.42

Commercial nitrogen w/ manure Commercial nitrogen use by manure users 77.23 70.60 83.87

Total commercial nitrogen and manure use 137.59 132.16 143.02

Total nitrogen observations 2,874

Source: USDA, Economic Research Service using data from USDA’s 2001 and 2005 Agricultural Resource Management Survey.
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Appendix Table 3.2

IV estimates of nitrogen application rate

Commercial 

nitrogen: 

nonmanure users S.E.

Total 

commercial 

nitrogen S.E.

Commercial 

nitrogen: only 

manure users S.E.

Total nitrogen 

(manure and 

nonmanure users) S.E.

Soiltestn -0.924** 0.290 -1.142** 0.336 0.333 0.742 -1.080** 0.308

Lognprice -1.347 0.715 -1.379* 0.630 0.531 1.408 -0.674 0.589

Dealerrec 0.131** 0.043 0.159** 0.047 0.155 0.099 0.157** 0.042

Consultrec 0.229** 0.078 0.291** 0.083 -0.004 0.171 0.303** 0.078

Extrec 0.084 0.086 0.143 0.084 0.239 0.156 0.163* 0.073

Routine -0.170** 0.065 -0.164** 0.063 -0.071 0.100 -0.136** 0.057

Op_age -0.011** 0.003 -0.008** 0.003 0.005 0.007 -0.008** 0.002

Retired 0.104 0.098 0.107 0.100 0.171 0.211 0.002 0.089

College 0.043 0.034 0.055 0.037 0.118 0.117 0.025 0.034

Workoff -0.091** 0.037 -0.0810* 0.0398 -0.124 0.094 -0.115** 0.037

Anycropins 0.061 0.054 0.1065* 0.0498 0.035 0.083 0.1203** 0.0468

Prodvalpa -7.84E-06 3.32E-05 -3.09E-05 2.50E-05 -4.38E-05 3.25E-05 4.64E-05** 1.98E-05

Landvalpa -4.93E-07 4.41E-07 -8.96E-07 7.65E-07 -5.21E-05 2.81E-05 -1.57E-06 8.10E-07

Ownacre 3.62E-05** 1.37E-05 3.92E-05** 1.48E-05 -6.32E-05 5.88E-05 2.95E-05 1.51E-05

logcorn_p 0.006 0.043 0.029 0.048 -0.032 0.112 0.034 0.045

Ccc 0.0315 0.055 0.092 0.052 0.192** 0.088 0.082 0.051

Wetland -0.081 0.118 -0.065 0.110 -0.187 0.326 -0.013 0.098

Nutrplan 0.167** 0.070 0.023 0.077 -0.280** 0.133 0.172** 0.072

Irrigate 0.527** 0.085 0.532** 0.089 -0.370 0.364 0.551** 0.084

Tenure 0.006** 0.002 0.005** 0.002 0.005 0.007 0.004 0.002

Spring 0.028** 0.041 0.013 0.048 -0.083 0.150 0.026 0.042

Inc 0.063 0.050 0.061 0.049 0.052 0.101 0.053 0.046

Inhibit 0.083 0.057 0.2239** 0.0590 0.556** 0.120 0.176** 0.056

Bt_corn 0.042 0.036 0.067 0.040 0.081 0.100 0.062 0.036

Yldgoal 0.001** 0.0002 0.0003 0.0002 -5.27E-06 2.23E-04 0.0001 0.0002

Silage -0.404** 0.093 -0.350** 0.078 -0.060 0.094 -0.098 0.076

Live -0.154** 0.046 -0.233** 0.051 -0.265 0.186 -0.142** 0.047

Observations 2253 2874 624 2874

F-Statistic 6.69

[<0.000]

11.87

[<0.000]

6.31

[<0.000]

7.82

[<0.000]

Source: USDA, Economic Research Service using data from USDA’s 2001 and 2005 Agricultural Resource Management Survey.
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Appendix 4 

Comparing Costs of Farms Using Different 

Nutrient Management Practices

The goal of this analysis was to estimate the variable production costs for 

farms using different nutrient management strategies. The results are used to 

estimate the cost of changing from a less-effi cient to a more-effi cient nutrient 

management strategy. We restricted our analysis to corn, given the large 

acreage and its intensive use of nitrogen. 

Data on corn are from USDA’s 2001 Agricultural Resource Management 

Survey (ARMS). This is the last corn survey from which fi eld-level cost of 

production data are estimated for each observation. SAS General Linear 

Model procedure (GLM) was used to estimate a model of variable production 

costs as a function of management and resource-base variables. Least squares 

means were used to compare the per acre variable production costs between 

practices directly related to nitrogen management. 

Total variable costs (TVC) were defi ned as the costs of seed, fertilizer, 

manure, pesticides, custom work, and fuel lubricants. We specifi ed a model of 

TVC as a function of the following variables: 

(1) Use of biotech or herbicide resistant corn

(2) Use of rotation with soybeans

(3) Use of nitrogen inhibitor

(4) Tillage (conventional till vs. reduced/no till)

(5) Timing (fall vs. spring application)

(6) Method (broadcast vs. inject/incorporate)

(7) Conservation cropping (contour or strip)

(8) Presence of nutrient management plan

(9) Use of variable rate technology

(10) Presence of irrigation 

(11) Presence of highly erodible soils (yes or no)

(12) Presence of tile drains

(13) Growing season (northern tier, middle tier, southern tier)

(14) Farm size (total corn acres on farm)

(15) Yield goal

An interaction term for timing and method (fall/no fall – incorporate/

broadcast) was also included. The cost model was run separately for those 

farms that do not use manure and for those farms that use both manure and 

commercial fertilizer. About 16 percent of U.S. corn acres receive manure.

Since most of the variables are class variables, we used the SAS General 

Linear Model procedure (GLM) to estimate the model. The R-Squares of the 

no-manure and manure-cost models are 0.21 and 0.16, respectively, and the 

models are signifi cant at the 1-percent level. The majority of the explanatory 
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variables are statistically signifi cant at the 5-percent level. Least-square 

means of the production costs ($/acre) under the different management 

systems are presented in app. table 4.1, along with an indication of whether 

the difference is statistically signifi cant. Of interest to this study is that the 

cost under the preferred method/timing combination (spring/incorporate) 

is signifi cantly different from the costs under the less-preferred, alternative 

combinations (at the 5- and 10-percent levels) for those farms that use only 

commercial fertilizer (84 percent of treated corn acres). No signifi cant differences in costs were found for farms that use both manure and commercial 

fertilizer. 

Part of the difference in costs observed with ARMS data is due to differences in chemical application rates. Since the NLEAP scenarios assumed the 

management changes were independent, altering rate, timing, and method in 

different combinations, we needed to separate out the nitrogen fertilizer cost 

from the total of changing management. We ran the same models, but with 

nitrogen application rate as the dependent variable. Both of the models were 

Appendix Table 4.1

Variable cost per acre of management practices

Commercial 

nitrogen only

Commercial 

nitrogen and manure

Dollars per acre Pr > t Dollars per acre Pr > t

Management choice

Continuous corn

Rotation with soybeans

131.23

124.02

.0001 165.63

158.06

.1330

Conventional tillage

Reduce/no-till

128.79

126.46

.1554 128.79

126.46

.6671

Fall/broadcast

Fall/incorporate

Spring/broadcast

Spring/incorporate

127.84

128.39

132.54

121.74

.0582

.0557

.0001

158.89

155.25

158.53

174.70

.1587

.1867

.2078

No irrigate

Irrigate

133.33

121.92

.0009 164.11

159.58

.7292

No highly erodible soil

Highly erodible soil

124.92

130.34

.0088 157.98

165.71

.2013

No nitrogen inhibitor

Nitrogen inhibitor

125.34

129.92

.0832 153.80

169.88

.0441

No conservation cropping

Conservation cropping

131.83

123.42

.0004 163.25

160.44

.6278

No nutrient plan

Nutrient plan

127.87

127.39

.8475 157.63

166.06

.1461

No variable rate 

technology

Variable rate technology

125.45

129.81

.1522 155.39

168.30

.2945

No tiles

Tiles

128.79

126.46

.2095 168.02

155.67

.0366

Source: USDA, ERS using USDA’s 2001 Agricultural Resource Management Survey.
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signifi cant, with R-squares of 0.23 and 0.24. Differences in application rates 

between the spring/inject and the other management combinations were positive (as expected) and signifi cant at the 1-percent level for farms using only 

commercial fertilizer (app. table 4.2). The difference in nitrogen fertilizer 

costs was subtracted from the cost difference derived from the cost model, 

using a nitrogen fertilizer price of $0.30/lb. The cost of adopting appropriate 

method (assuming no change in fertilizer application rate) was estimated to 

be $7.35/acre, appropriate timing was $3.01 per acre, and both appropriate 

method and timing were $1.86/acre. For farms using manure, we assumed no 

differences in costs. 

Appendix Table 4.2

Nitrogen application rates per acre by management practice

Commercial 

nitrogen only

Commercial 

nitrogen and manure

Pounds

per acre Pr > t

Pounds

per acre Pr > t

Management choice

Continuous corn

Rotation with soybeans

136

140

.1544 218

192

.0420

Conventional tillage

Reduce/no-till

137

139

.3583 202

208

.6433

Fall/broadcast

Fall/incorporate

Spring/broadcast

Spring/incorporate

143

141

140

129

.0001

.0042

.0001

191

201

220

208

.0420

.6433

.8382

No irrigate

Irrigate

129

147

.0002 210

200

.7692

No highly erodible soil

Highly erodible soil

139

137

.5955 222

188

.0353

No nitrogen inhibitor

Nitrogen inhibitor

135

141

.0017 189

221

.1354

No conservation cropping

Conservation cropping

143

133

.0004 175

235

.0001

No nutrient plan

Nutrient plan

137

139

.6002 197

213

.2950

No variable rate technology

Variable rate technology

138

138

.9899 224

186

.3175

No tiles

Tiles

139

137

.4957 214

196

.2384

Note: Parameter estimates from GLM model.

Source: USDA, Economic Research Service using data from USDA’s 2001 Agricultural 

Resource Management Survey.
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Appendix 5

Estimating Wetland Restoration Costs

The cost of restoring a wetland is the sum of the cost of the land and the cost 

of restoring the land’s water-hold capability and the wetland ecosystem. We 

generate wetland and restoration costs using cost functions that we estimated 

using available wetland cost data. Sample observations lie in the Glaciated 

Interior Plains (GIP).

The cost of the land to society is the difference in its value with and without 

the wetland. The value of agricultural land without a wetland is assumed to 

be a function of the net value of its output, but the potential for nonagricultural use can play a role. 

The USDA Wetland Reserve Program (WRP) sets wetland easement prices 

equal to the difference in land values with and without a permanent wetland 

easement. Therefore, WRP easement payments are well suited as a measure 

of land cost. 

Land cost is modeled as a function of the agricultural value and value 

squared of the land in the contract (AgrValue and AgrValuesq), contract size 

and size squared (Acres and Acressq), the potential for urban development 

(Urban), and farm size (Fsize). Because a measure of the agricultural value of 

the land is not available, we use the product of the county-average farmland 

rental rate (Rent) and contract acreage as a proxy (it represents the annual 

agricultural value of the land). 

The adjusted R-square of the estimated land cost model indicates that the 

estimated ordinary least squares model explains 90 percent of the variation in 

WRP land costs. Variables are statistically signifi cant and have the expected 

sign. With this cost function, we generate marginal and average land cost 

estimates by county throughout the GIP. To generate average cost, we divide 

total land cost (generated with our model) by the size of the contract—all cost 

estimates are based on the median-size WRP easement. Across the counties 

of the GIP, average per acre costs range from $1,490 to $3,030.

Second, we generate the marginal cost function (MCL) by differentiating the 

estimated land-cost model with respect to Acres:

MCL = 925 + 4.32*Rent + 2.39(10-6)*AgrValue*Rent - 0.127*Acres.

For average-sized contracts, county-level estimates of MCL in GIP range 

from $985 to $1,790 per acre with a median cost of $1,390.

Restoration costs are modeled as a function of the agricultural value of the 

land, the size of the contract, and other variables. The agricultural value 

is included as an explanatory variable because we believe that landowners 

would spend more to drain more productive lands and assume that restoration 

costs are positively correlated with drainage costs. 

Approximately 15 percent of the WRP contracts of the GIP report zero restoration costs. Because the dependent variable is truncated, we use the Tobit 
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procedure to estimate the restoration cost function. The Tobit procedure 

simultaneously estimates the probability that the dependent variable is nonzero and its expected size. Variables of the estimated model are statistically 

signifi cant and have the expected sign.

The estimated model is used to generate expected restoration costs. By 

dividing our model’s county-level expected cost estimates by contract size, 

we generate estimates of expected average restoration costs. Costs range from 

$506 to $602 per acre across counties.

Differentiating the estimated Tobit model with respect to the contract acres 

generates the expected marginal restoration cost function (MCR):

MCR = (Z)*(0.888*Rent -2.12* AgrValue*Rent + 167) 

where (Z) is the cumulative probability function and Z is the estimated Tobit 

equation. For average-sized contracts, estimates of MCR across counties of 

the GIP range from $101 to $210 per acre.