United States 

Department of 

Agriculture

Economic

Research

Service

Economic

Research

Report

Number 86

November 2009

Ethanol and a Changing 

Agricultural Landscape

Scott A. Malcolm

Marcel Aillery

Marca Weinberg
www.ers.usda.gov

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Malcolm, Scott A.

 Ethanol and a changing agricultural landscape.

 (Economic research report (United States. Dept. of Agriculture. 

 Economic Research Service); no. 86)

 1. Biomass energy industries—United States.

 2. Ethanol fuel industry—United States.

 3. Land use—Environmental aspects—United States.

 4. Animal industry—United States.

 I. Aillery, Marcel P.

 II. Weinberg, Marca. 

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

 II. Title.

HD9502.5.B543

Photo credits: ARS Image Gallery for corn and lab; Natural Resource 

Conservation Service for farm.

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Recommended citation format for this publication:

Malcolm, Scott A., M. Aillery, and M. Weinberg. Ethanol and a Changing 

Agricultural Landscape, Economic Research Report 86, U.S. Dept. of 

Agriculture, Economic Research Service. Nov. 2009.
United States

Department

of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

Abstract

The Energy Independence and Security Act (EISA) of 2007 established specifi c targets 

for the production of biofuel in the United States. Until advanced technologies become 

commercially viable, meeting these targets will increase demand for traditional agricultural commodities used to produce ethanol, resulting in land-use, production, and price 

changes throughout the farm sector. This report summarizes the estimated effects of 

meeting the EISA targets for 2015 on regional agricultural production and the environment. Meeting EISA targets for ethanol production is estimated to expand U.S. cropped 

acreage by nearly 5 million acres by 2015, an increase of 1.6 percent over what would 

otherwise be expected. Much of the growth comes from corn acreage, which increases by 

3.5 percent over baseline projections. Water quality and soil carbon will also be affected, 

in some cases by greater percentages than suggested by changes in the amount of cropped 

land. The economic and environmental implications of displacing a portion of corn 

ethanol production with ethanol produced from crop residues are also estimated. 

Keywords: Biofuels, corn ethanol, regional crop mix, regional environmental 

effects, water quality, water use, cellulosic ethanol, crop residues, livestock, Regional 

Environment and Agriculture Programming (REAP) Model, renewable fuel standard

Acknowledgments

The authors gratefully acknowledge the assistance of Noel Gollehon (USDA Natural 

Resources Conservation Service) and Paul Heisey, Vince Breneman, and David Nulph 

(Economic Research Service) for their contributions to the report. We also thank Don 

Blayney, Ken Mathews, and Paul Westcott (Economic Research Service), Peter Nagelhout 

(U.S. Environmental Protection Agency), Hosein Shapouri (USDA Offi ce of the Chief 

Economist), Anthony Turhollow (Oak Ridge National Laboratory), and Chuck Zelek (Natural 

Resources Conservation Service) for their comments on earlier drafts. We thank Angela 

Anderson for her thoughtful editing which helped to improve the report, and Wynnice PointerNapper for graphic layout and design.

Scott A. Malcolm, smalcolm@ers.usda.gov

Marcel Aillery, maillery@ers.usda.gov

Marca Weinberg, weinberg@ers.usda.gov

Ethanol and a Changing 

Agricultural Landscape

Economic

Research

Report

Number 86

November 2009
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Contents

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

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

 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

 Beyond the Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Exploring the Links: Implications of Increased Biofuel 

 Demand on the Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

 Increased Demand For Biofuel Feedstocks Motivates 

 Adjustments Within Crop Markets... . . . . . . . . . . . . . . . . . . . . . . . . . . 5

 ...As Well as Adjustments Between Crop and Livestock Markets . . . . . . 7

 Changes in the Crop and Livestock Sectors Infl uence, 

 and Are Infl uenced by, Land-Use Choices . . . . . . . . . . . . . . . . . . . . . . 7

 Land Allocation Decisions Affect Changes in Practices 

 and Input Use.... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

 ...Which Contribute to Changes in Environmental Quality . . . . . . . . . . 9

Assessing Changes to the Agricultural Landscape . . . . . . . . . . . . . . . . . 11

 Conventional Ethanol Production and Agriculture . . . . . . . . . . . . . . . . . 12

 The Introduction of Cellulosic Ethanol . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Expanding Corn Acreage Drives Market 

 and Environmental Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

 Crop Producers See Higher Returns . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

 Production Intensifi es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

 Water Quality Measures More Sensitive Than Change in Acreage . . . . 26

Biofuel Impacts on the Livestock Sector and Implications 

 for the Environment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

 Structural Change in the U.S. Livestock Sector . . . . . . . . . . . . . . . . . . 35

 Biofuel Demand Increases Costs for Livestock Producers . . . . . . . . . . . 35

 Higher Feed Costs May Reduce Livestock Inventories. . . . . . . . . . . . . . 37

 Biofuel Expansion Could Reduce Environmental Effects of Manure . . 38

 Implications For Expanding Ethanol Plant Capacity . . . . . . . . . . . . . . . 40

Crop Residues and the Introduction of Cellulosic Ethanol . . . . . . . . . . 43

 Effect of Crop Residue Feedstocks on Environmental 

 Resources Is Mixed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Research and Policy Options To Mitigate the Impact 

 of Biofuel Feedstock Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

 Increased Productivity Can Reduce Pressure on Land Resources . . . . . 48

 Improved Assessment Capabilities Needed . . . . . . . . . . . . . . . . . . . . . . 49

 Conservation Programs Can Reduce Potential 

 Environmental Effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Appendix: The Modeling Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
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Summary

U.S. policy to expand the production of biofuel for domestic energy use has 

signifi cant implications for agriculture and resource use. While ongoing 

research and development investment may radically alter the way biofuel is 

produced in the future, for now, corn-based ethanol continues to account for 

most biofuel production. As corn ethanol production increases, so does the 

production of corn. The effect on agricultural commodity markets has been 

national, but commodity production adjustments, and resulting environmental 

consequences, vary across regions. Changes in the crop sector have also 

affected the cost of feed for livestock producers. As the Nation demands more 

biofuel production, and markets for new biofuel feedstocks, such as crop residues, emerge, the agricultural landscape will be further transformed. 

What Is the Issue?

The Energy Independence and Security Act of 2007 (EISA) specifi es a 

minimum total amount of U.S. biofuel production through 2022, and also sets 

target levels for fuels produced from specifi c feedstock categories. Together 

with volatile energy prices, this and earlier Federal legislation supporting 

biofuel processing have increased demand for biofuels and the agricultural 

feedstocks used to produce them. Greater demand for biofuel increases pressure on the agricultural land base as more land is put into production and/or 

more inputs, such as fertilizer, water, and pesticides, are applied to cropland. 

Rising demand for corn, the principal biofuel feedstock in the United States, 

changes the profi tability of growing corn and other “energy crops”. Farmers 

respond by changing their planting decisions, which alter crop mix, land 

use, and use of inputs, such as fertilizer, which then infl uence water quality, 

soil erosion, and other environmental indicators. The environmental consequences of shifts in agricultural production vary by region. 

This report also looks at the economic and environmental implications should 

crop residues, such as corn stover and wheat straw, become commercially 

viable as biofuel feedstocks. Widespread harvesting of crop residues as an 

alternative biofuel feedstock has implications for input use, nutrient runoff, 

erosion control, and soil productivity.

What Did the Study Find?

Land for new biofuel feedstock production comes from two main sources: 

acreage not currently in production and acreage shifted from other crops. 

The amount of additional land and displaced crops associated with increased 

biofuel production differs by region. If the RFS targets are met, total cropland 

is projected to increase by 1.6 percent over baseline conditions by 2015, with 

corn acreage expanding by 3.5 percent and accounting for most of the cropland increase. While corn acreage expands in every region, traditional corngrowing areas would likely see the largest increases—up 8.6 percent in the 

Northern Plains, 1.7 percent in the Corn Belt, and 2.8 percent in Lake States. 

Prices are expected to increase slightly for most crops compared with the 

baseline, although the price increase could be reduced if corn yields increase 

at a faster rate than expected.
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Corn is a heavy user of nitrogen fertilizer. Given the RFS targets, the 

resulting increase in fertilizer use and shift from corn-soybean rotations 

to continuous corn production leads to deterioration of key environmental 

performance measures. Nitrogen losses to surface water and groundwater 

increase by 1.7 and 2.8 percent, respectively, while soil runoff increases 

by 1.6 percent from the baseline. Differences in geography, soil type, and 

prevailing agricultural production activities lead to considerable variation in 

environmental effects among regions. The increases in leaching to groundwater are greatest in the Lake States and Southeast, while increases in runoff 

to surface water are greatest in the Corn Belt and Northern Plains.

As energy feedstocks that are also used as animal feed move more toward 

biofuel use, higher costs of animal feed reduce returns to animal production. 

Production of livestock declines slightly by 2015 relative to the baseline—0.6 

percent for farm-fed cattle and 0.5 percent for poultry—which may result in 

reduced manure nutrient runoff and leaching in some areas. 

Technical advances in biofuel production may soon allow other plant material 

to be used as energy feedstock. One of the most readily available sources of 

“cellulosic” feedstock is crop residues. Increased use of residue could reduce 

demand for corn, reducing requirements for most agricultural inputs. But 

replacing corn-based ethanol with biofuel created from crop residues could 

have mixed results on environmental quality. Removal of large amounts of 

crop residues requires replacement of nutrients through increased application 

of fertilizer and increases runoff and soil erosion. Replacing 3 billion gallons 

of corn ethanol with crop residue ethanol could increase nitrogen runoff and 

leaching in the Corn Belt, although reduced corn plantings in other regions 

cause these measures to decline in much of the United States. 

How Was the Study Conducted?

A regionalized agricultural sector mathematical programming model with 

linked environmental process models was used to simultaneously estimate 

profi t-maximizing decisions on land use, livestock production, crop mix, 

crop rotations, tillage practices, and fertilizer application rates. In essence, 

we compare the market equilibrium prior to EISA’s passage with the market 

equilibrium expected if the new RFS production targets are met in 2015, the 

year that the corn-ethanol target peaks. The environmental impacts of land 

use and agronomic practices were estimated by applying coeffi cients derived 

from a crop biophysical simulation model that incorporates soil, weather, 

and management information to estimate crop yields, erosion, and chemical 

(pesticide and fertilizer) discharges to the environment under various crop 

rotation and soil management regimes. Changes to U.S. agriculture and environmental outputs from meeting EISA’s biofuel production targets for 2015 

were evaluated against a baseline case that refl ects 2007 U.S. Department of 

Agriculture (USDA) projections for biofuel demand in 2015 (developed just 

prior to EISA’s passage). 
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Introduction

The prospect of expanded biofuel use to meet U.S domestic energy needs has 

ushered in a new era of promise and uncertainty for U.S agriculture. Volatile 

energy markets, U.S. dependence on oil imports, greenhouse gas emissions 

from fossil-based fuels, and the search for new uses for agricultural commodities have combined to stimulate the development of agriculturally based 

biofuels. Broad support for biofuel development is refl ected in an aggressive 

Government response in the form of ethanol production mandates, processing 

cost subsidies, and fuel-blending requirements. Meanwhile, biofuel-related 

research and development is underway in both the public and private sectors. 

The agriculture community has responded, in turn, with signifi cant shifts of 

cropland resources to energy crops—primarily corn for ethanol production—

to meet increasing biofuel demands. Market adjustments from corn ethanol 

expansion have reverberated through the fi eld crop and livestock sectors, 

raising concerns for farm income, Government payments, and food prices. 

Natural resource concerns have also arisen with changes in land use and 

management as producers adapt to market shifts.

While ethanol can be produced from a variety of crops, corn has served as 

the predominant feedstock over the initial history of U.S. domestic biofuel 

production. Conversion of corn-based ethanol is a proven technology, and a 

production and distribution system has already evolved to service the national 

corn grain market. Other technologies that convert cellulosic biomass to 

ethanol are expected to come online at a commercial scale, but only after 

technological and economic hurdles are overcome. Until recently, ethanol was 

a small market for corn producers. The share of domestic corn production 

supplying the ethanol market, however, has grown substantially—from 7.5 

percent (705 million bushels) in 2001 to 23.2 percent (3,049 million bushels) 

in 2008 (USDA-WASDE, 2009). To meet the growing demand for corn-based 

ethanol, U.S. cropland planted to corn increased to 93.5 million acres in 2007 

(USDA-WASDE, 2009), the highest level since 1944, before declining to 86 

million acres in 2008. 

Recent Federal legislation marks a signifi cant long-term commitment to 

expand agricultural biofuels. The renewable fuel standard (RFS), passed as 

part of the Energy Independence and Security Act of 2007 (EISA), specifi es 

not just the total level of biofuels to be used until 2022, but also target levels 

for fuels produced from major feedstock categories. Corn-based ethanol 

production will likely remain a major component of the biofuel portfolio 

for most of EISA’s lifespan. Corn-based ethanol production will continue to 

increase, with as much as 15 billion gallons counting toward the 2015 RFS 

(more than 60 percent above the 2008 production level of 9 billion gallons), 

and hold at that level for the remainder of the act’s duration, unless replaced 

with ethanol from advanced feedstocks, such as crop residues, forest residue, 

or dedicated energy crops (cellulosic feedstocks). To meet the RFS, roughly 

35 percent of domestic corn production will be needed to produce the 15 

billion gallons of ethanol. The share of U.S. ethanol production supplied 

by cellulosic feedstocks will increase as new conversion technologies come 

online, with the RFS for cellulosic and other advanced biofuels set to reach 

20 billion gallons by 2022.
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While increased demand for corn and other commodities has expanded the 

market for agricultural products, growth in biofuel feedstock production puts 

more pressure on the agricultural land base and raises costs for crop and 

livestock producers as well as consumers. At the same time, rapid expansion 

in agricultural land devoted to biofuels has raised environmental concerns. 

Biofuels have been seen as an environmentally preferred alternative to fossilbased fuels and, in the energy use sector, this is well supported. Broader 

effects, however, include expansion in cultivated land with accompanying 

shifts in regional cropping patterns, production practices, and input use that 

affect the environment in various ways outside of the energy sector. The 

expansion of corn acreage is a particular agri-environmental concern, due to 

the high fertilizer use associated with corn production and its effects on other 

product markets. 

The effect of national biofuel targets will vary across regions, depending on 

the distribution of crop expansion and related adjustments in the agricultural 

sector. Environmental outcomes, in turn, will refl ect regional differences in 

climate, soil, and predominant practices. Environmental consequences may 

be driven by an increase in the number of acres planted in some regions, 

whereas changes to crop mix or management regime may be the main driver 

for others. Future demand for cellulosic feedstocks, including crop residues 

and new dedicated energy crops, will further transform the agricultural landscape as regional crop distribution changes and production practices adapt. 

While signifi cant expansion of U.S. biofuel production undoubtedly has broad 

implications for the agricultural sector, our understanding of the nature and 

magnitude of these effects remains incomplete. Agricultural sector adjustments refl ect the complexity of linkages across supply and demand markets 

for farm commodities. Commodity market adjustments, in turn, will drive 

changes in resource use and environmental quality that are both complex and 

uncertain. As we implement ambitious targets for biofuel production, a better 

understanding of potential market, resource, and environmental outcomes can 

help inform policy to support agriculture’s expanding role in energy. 

Research Objectives

To gain insight into the complex set of market and environmental interactions associated with increasing demand for biofuel feedstocks in the United 

States, we report on an agricultural sector-level analysis of producer adaptations under new Federal policy directives for biofuels. The analysis draws on 

an Economic Research Service (ERS) modeling framework of the U.S. farm 

sector to examine adjustments in prices and production and resulting changes 

in resource use and environmental indicators. The analysis is intended to 

address the following questions:

• How will increased demand for biofuel feedstocks affect regional crop 

patterns and livestock production?

• What are the likely environmental impacts and how will they be distributed regionally?

• Will recent changes in acreage, agricultural markets, and environmental 

indicators persist as corn-based ethanol production levels off and cellulosic ethanol production expands?
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• How sensitive are reported changes in the agricultural system to key 

production factors, such as input costs and crop yields, and the adoption 

of cellulosic conversion technology?

The scenarios (described in this section, “Assessing Changes to the 

Agricultural Landscape”) for the quantitative analysis represent economic 

(welfare) maximizing solutions to meet biofuel targets established by the 

2007 EISA. Results describe sectoral adjustments and costs, location/mix of 

different crops (“Expanding Corn Acreage Drives Market and Environmental 

Outcomes”) and livestock (“Biofuel Impacts on the Livestock Sector and 

Implications for the Environment”), and changes in environmental indicators under Federal mandates, relative to a scenario that replicates the offi cial 

2008 USDA baseline projections for 2015. Alternative scenarios demonstrate the potential effects of investments in research and development that 

enhance biofuel feedstock productivity and conversion effi ciency, as well 

as changes in input costs. The emergence of cellulosic ethanol production 

is examined based on the role crop residues might play as new technologies 

develop (“Crop Residues and the Introduction of Cellulosic Ethanol”). The 

geographic scope of the study is national, but agricultural production is inherently regional. Thus, the study addresses agricultural production and cost and 

the associated resource impacts at a regional level. 

Finally, this analysis focuses on potential implications in 2015, the year 

the RFS target for corn-based ethanol production peaks but before largescale cellulosic ethanol comes online. Beyond this time, other types of 

feedstocks—including starches (other than corn) and sugar-based ethanol, 

dedicated energy crops (e.g., switchgrass or short-rotation woody crops), 

other crop residues (e.g., rice straw), urban wastes, and emerging options 

like algae—may gain or lose prominence. Many technological and economic 

factors will infl uence future biofuel markets, and future analysis incorporating new information from biological, physical, and economic research will 

be necessary to keep pace with these emerging technologies.

Beyond the Scope

In spite of the careful modeling used to examine the primary research questions, several issues were not addressed by the analysis, and many important 

factors on a cross-sector or global scale were beyond the study scope. For 

example, the study does not analyze:

• Energy market implications for biofuel demand. High energy prices, 

relative to the cost of producing biofuels, could induce a level of biofuel 

production and, thus, feedstock demand, that exceeds the mandates. 

Whether biofuel demand would increase with higher energy prices 

depends on how biofuels interact with other liquid fuels (e.g., as a substitute or an additive used in fi xed proportions) and on the difference 

between biofuel demand and the biofuel mandate. 

• Comprehensive assessment of sustainability. Carbon emissions and 

other environmental indicators are examined only within the context of 

agricultural production. A comprehensive sustainability analysis would 

require an assessment of environmental, economic, and social sustainability indicators throughout the biofuel production stream, including 
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lifecycle analyses of carbon and other greenhouse gas (GHG) emissions. 

The U.S. Environmental Protection Agency (EPA), however, is responsible for implementing the RFS under EISA and is currently developing 

policies that support a comprehensive analysis of environmental impacts, 

including effects on air and water quality.

• Transportation and infrastructure. An effi cient biomass supply depends 

on an infrastructure that ensures economically viable handling and 

delivery of feedstocks from farm to plant. Other determining factors 

include regional biofuel feedstock demand, local resource endowments 

(land and water), and a capital infrastructure with storage facilities, roads, 

rails, and barges for feedstocks and pipelines for liquid fuels. The model 

only solves for feedstock production, however, as opposed to ethanol 

production, and assumes that infrastructure is in place to produce and 

deliver the targeted volume of ethanol. 

• Food prices. Food prices will adjust as demand for biofuel feedstocks 

reverberates through the market. Increases in global and domestic 

food prices in early 2008 garnered substantial attention. Expanded use 

of corn for ethanol is part of the story, but other factors have placed 

upward pressure on food prices, including a decline in the U.S. dollar, 

high input prices and agricultural production costs, adverse weather 

conditions in 2006 and 2007 that affected global production levels, 

and a hold on commodity exports to allow some countries to mitigate 

food price infl ation. Interactions between crop price changes (see the 

section, “Expanding Corn Acreage Drives Market and Environmental 

Outcomes”) and food prices are not examined empirically. Omitting 

food price effects from the analysis is not an indication that the issue is 

not important, but rather that an issue this complex is best undertaken by 

experts in the fi eld using models explicitly designed for that purpose.

• Global production and land use. Though crop, biofuel, and energy 

production operate within global markets, the focus here is on U.S. 

domestic production. Global land-use implications and how land-use 

changes affect international feedstock markets are critical drivers in global 

policy initiatives. While global land use and market effects may infl uence 

the domestic market for biofuel feedstocks, tracking these effects is outside 

the scope of this report. The section on research and policy options delves 

into concerns about indirect land-use change, with a focus on developing 

estimates that demonstrate how such a change may occur. 
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Exploring the Links: Implications 

of Increased Biofuel Demand 

on the Environment

The impact of biofuel demand on the agricultural landscape will ultimately 

be determined by individual producer decisions and shaped by a complex and 

dynamic set of price incentives, resource constraints, and market forces that 

will be felt throughout the agricultural sector. Fundamental economic principles provide a context for understanding market responses; an increase in the 

demand for corn ethanol will lead to higher corn prices and an increased corn 

supply. The processes behind this seemingly simple relationship are complex, 

and its implications for a sector as interlinked as U.S. agriculture are often 

uncertain. Figure 1 provides a stylized illustration of linkages involving 

product markets, their effect on land and resource management decisions, and 

implications for environmental quality. 

Increased Demand For Biofuel Feedstocks Motivates 

Adjustments Within Crop Markets...

Increased demand for biofuel feedstocks reverberates through the agricultural 

sector as biofuel crops compete against nonbiofuel crops for land and other 

inputs (fi g. 1). The ultimate mix of crop prices and production levels depends 

on an equilibration of the aggregate response to the joint set of prices and 

demand shift relationships. 

If the demand for corn for ethanol outstrips increases in corn supply, 

constrained availability will lead to higher prices for alternative uses, such as 

processed foods and livestock feed. Higher corn prices, in turn, change incentives to produce and purchase other crops through shifts in both demand and 

supply. Higher corn prices make other corn substitute crops more attractive 

to consumers (e.g., sugar for corn fructose in processed foods, sorghum for 

corn grain in livestock feed). Higher corn prices in the domestic market will 

also dampen demand for corn abroad. Simultaneously, farmers may respond 

to higher corn prices by producing corn on fi elds otherwise used to produce 

alternative crops, such as soybeans or wheat. Increasing demand for corn 

substitutes, coupled with a potential reduction in supply due to competition 

for land, results in price increases for noncorn crops. These price increases 

could, in turn, motivate farmers to moderate the shift toward corn.

As cellulosic ethanol production comes online, residues produced as a 

byproduct of crop production, such as corn stover and wheat straw, will 

also increase in value. Although residues left on the fi eld reduce loss of soil, 

nutrients, moisture, and carbon, and thus have value to farmers, residues are 

currently a “free good” produced in quantities that exceed their economic 

value. Increased values for residues used in ethanol production would feed 

back into crop production decisions by increasing the total value of the 

crop—farmers would receive revenues for both the crop (e.g., corn) and 

the residue (e.g., stover)—although revenues for the primary crop may be 

reduced as crop feedstock demand declines.
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Figure 1

Increased biofuel demand affects land use, land management, and the environment through a series 

of complex agricultural interactions

Feedstock

Other 

crops

Corn for

feed 

Fertilizer

application

Cultivated cropland

Water use Other inputs

Hay and pasture lands

Air quality/GHGs Water quality Wildlife habitat

Livestock

production

Land use:

Land

management: 

Tillage

method 

Increased biofuel

demand

Feed

Manure

fertilizer 

DDG production/value

Crop 

residues

Environmental

indicators: 

Market

interactions:

Crop

production 

DDG = Distiller’s dried grains.

GHG = Greenhouse gas.

Source: USDA, Economic Research Service.
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...As Well as Adjustments Between Crop 

and Livestock Markets

Livestock farmers’ response to changing feed prices loops back to the crop 

sector along several pathways. In general, livestock producers will respond to 

an increase in feed prices by reducing animal production, which could then 

lessen the upward pressure on feed prices. Different animal species, however, 

have different feed requirements, as well as differing relationships between 

total feed costs and bottom-line profi ts. Thus, a hog farmer may respond 

differently than a dairy farmer, and they both may respond differently than a 

poultry producer. This relationship is further complicated by the availability 

of distiller’s grains, a byproduct of the ethanol distilling process that can be 

fed in limited quantities depending on the animal. Distiller’s grains become 

more plentiful as corn ethanol production expands, providing additional 

revenue for ethanol processors (see box, “Distiller’s Grains: An Important 

Coproduct of Corn Ethanol”). If distiller’s grains can be worked into feed 

rations, producers can moderate the impact of increased feed prices (Stillman 

et al., 2009).

Livestock and crop production are also linked through animal waste. 

Environmental concerns about waste production from large livestock operations, and subsequent rules governing the disposal of manure and other 

wastes to protect water quality, have increased the demand for cropland 

where livestock producers can spread their manure (so that manure nutrients can be used by crops rather than becoming runoff into water bodies). 

Likewise, higher fertilizer prices (associated with volatile energy prices) 

increase demand by crop producers for manure as a relatively cheap source 

of nutrients.

Changes in the Crop and Livestock Sectors Infl uence, 

and Are Infl uenced by, Land-Use Choices 

The rapid expansion of biofuel feedstock production has placed signifi cant 

demands on the agricultural land base, raising concerns about resource 

sustainability and environmental quality. While individual agricultural 

markets (e.g., corn) have proven responsive to new sources of demand, 

substantial increases in the production of one crop generally come at the 

expense of another. Growers may switch rotation patterns annually, growing 

corn 2 or more years in a row on a given fi eld rather than alternating crops, 

such as between corn and soybeans. 

Higher prices for corn and other crops may provide incentives to expand 

the cropland base. That land, however, would have to come from other land 

uses. One source of new land for crop and feedstock production is cropland pasture. Another potential source is idled cropland, including acreage 

enrolled in the Conservation Reserve Program (CRP), which may be available for production as multiyear contracts expire. While bringing more land 

into production can reduce upward pressure on commodity and feedstock 

prices, land that is not currently cultivated for crops may be less productive 

than other land in the region and be less likely to generate a commensurate 

production increase. Converting CRP land, cropland pasture, and other lands 
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to more intensive uses could also reduce wildlife habitat, while increasing 

delivery of sediment, nutrients, and pesticides to local water bodies.

Land Allocation Decisions Affect Changes 

in Practices and Input Use...

Higher commodity prices due to biofuel demand may also result in farmlevel resource management changes. How production regimes respond will 

depend on the crop produced, fi eld characteristics, local resource conditions, 

and producer incentives to reduce environmental impacts. Several important 

resource management decisions involve tillage systems, applied nutrients and 

pesticides, and irrigation use.

Tillage and residue management. Tillage of cropland soils is used for a 

variety of benefi cial purposes: soil loosening/aeration, incorporation of nutrients and plant residues, weed control, seedbed preparation, and soil and water 

conservation. Field tillage, however, can have adverse impacts on soil quality 

(e.g., structure, depth, soil organic carbon, and moisture/nutrient retention) 

and soil erosion rates. Tillage management systems vary in their frequency 

and intensity. A key factor used to assess the soil erosion potential of a tillage 

Distiller’s grains are a primary coproduct in corn ethanol production. During 

the dry milling process, starches from corn are converted to ethanol through 

fermentation and distillation. The resulting residual product—distiller’s grains—

comprised of protein, minerals, fat, and fi ber, has increasingly been used as a 

high-quality animal feed supplement. Distiller’s grains can best be used as a feed 

for ruminant animals, such as beef cattle and dairy cows. Monogastric animals, 

such as hogs and poultry, have a limited ability to digest distiller’s grains, but 

distiller’s grains are increasingly being used in rations for those species as well. 

Two forms of distiller’s grains may be derived from ethanol processing. Distiller’s 

wet grains (DWG), with a moisture content in excess of 50 percent, are used 

primarily as feed supplements for beef and dairy cattle. Due to the water weight of 

the product, wet distiller’s grains generally serve local markets near the processing 

facility. Distiller’s dry grains (DDG), often blended with liquid residues (solubles), 

are dried to a moisture content of roughly 10 to 15 percent. Drying increases 

production costs, but increases the shelf life of the material and reduces the costs 

of hauling. Distiller’s dry grains with solubles (DDGS) may also be modifi ed 

for use in swine and poultry feed, in addition to beef and dairy. Distiller’s dry 

grains are often shipped signifi cant distances from the processing facility, serving 

broader regional and export markets. 

Distiller’s grains account for roughly 20 percent of returns to ethanol production 

(Dhuyvetter et al., 2005). While the concentration of biofuel processing capacity 

in the upper Midwest is attributable primarily to the availability of lower cost corn, 

proximity to confi ned livestock operations, which can absorb distiller’s grains, is 

likely a contributing factor. Livestock producers, in turn, have recognized the 

value of distiller’s grains, particularly DWG used in beef cattle rations. While 

marketing challenges remain due to variability in nutrient composition, product 

storage and transport, and food safety concerns, animal feed rations are being 

modifi ed to make use of this resource.

Distiller’s Grains: An Important Coproduct of Corn Ethanol
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regime is the amount of crop residue left on the fi eld after planting. Tillage 

systems are classifi ed according to residue cover (Sandretto and Payne, 2006): 

• Conservation tillage (>30 percent residue remaining)

 No-till 

 Ridge-till

 Mulch-till

• Reduced-till (15-30 percent residue remaining)

• Conventional or intensive-till (<15 percent residue remaining) 

Once cellulosic technologies become commercially viable, residue removal 

for cellulosic biofuels may further affect environmental outcomes. As cellulosic ethanol conversion plants come online, crop residues already widely 

available—including corn stover and wheat straw—may play a major role in 

this transition. Beyond their value as an ethanol feedstock, however, crop residues left on the fi eld help maintain soil nutrients, erosion, and carbon levels. 

Therefore, an important policy consideration in implementing residue-based 

cellulosic conversion is the amount of crop residue that can be harvested, 

while also maintaining soil quality and soil productivity as determined by 

tillage regime and other factors.

Nutrient and chemical use. Increasing biofuels’ demand for feedstocks may 

result in increased use of nutrients and pesticides as cultivated land and corn 

acreage expand. Average rates of nitrogen use in U.S. corn production (138 

pounds/acre) (USDA-NASS, 2006), for example, are greater than application 

rates for crops that may be displaced—including soybeans and wheat (16 and 

66 pounds/acre, respectively) (USDA-NASS, 2007). Pesticide use also tends 

to be relatively intensive in U.S. corn production. Higher prices for corn and 

other crops may also increase the intensity of chemical input use.

Water use. Expansion of biofuel feedstock production has the potential to 

increase water demand due to ethanol processing requirements and, more 

signifi cantly, changes in irrigation. Potential demand for irrigation water will 

depend on changes in total acreage irrigated, the crops being irrigated, and 

water applied per acre. In the long term, cellulosic biofuel production is likely 

to expand, although little is known about water requirements for cellulosic 

production on more marginal lands. While expansion of ethanol production 

may occur in areas not traditionally irrigated, large new surface water and 

groundwater withdrawals may be limited by physical supply availability, 

legal constraints, and economic considerations. The net effect of biofuels on 

agricultural water withdrawals is uncertain and may vary both regionally and 

over time, depending on future spatial patterns of biofuel processing facilities, primary feedstock sources, consumptive requirements of displaced irrigated crops, and local water institutions.

...Which Contribute to Changes 

in Environmental Quality 

Environmental outcomes will refl ect the interaction of changing land 

use, crop choice, and resource management decisions made for complex 
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biophysical systems across a diverse landscape. Moreover, the potential environmental consequences of biofuel production will change rapidly as technologies evolve and the scale and distribution of production shifts. This report 

addresses three important measures of environmental sustainability: soil 

erosion, nitrogen loss, and GHG emissions.

Soil erosion. Soil erosion on agricultural lands is a signifi cant national 

concern. In 2003, the latest year for which data are available, approximately 

971 million tons of cropland soil were lost to water (sheet and rill) erosion, 

with an additional 776 million tons lost to wind erosion. (USDA-NRCS, 

2003). Deterioration of the soil structure due to erosion can reduce the longterm productivity of cropland soils. Cropland soil erosion can also cause 

signifi cant environmental damage, including water-quality degradation (e.g., 

turbidity and sedimentation) and air-quality impairment (e.g., suspended 

particulates). Fields vulnerable to erosion refl ect the inherent erodibility 

of the fi eld (e.g., soil characteristics and slope), tillage and erosion control 

practices (e.g., cover crops and residue management), and the amount and 

intensity of rainfall and wind. The potential for biofuel demand to expand 

production of row crops, such as corn, on erodible croplands has raised 

concerns over soil loss.

Nutrient loss. Nutrient loss from applied chemical fertilizers and animal 

manure on cropland—primarily nitrogen and phosphorus—is a major 

source of water-quality impairment. Primary pathways for nutrient transport 

include fi eld runoff of nitrogen and phosphorus to surface water and nitrogen 

leaching below the crop root zone to groundwater. Runoff and leaching of 

agricultural pesticides, herbicides, and other applied chemicals contribute 

to further water-quality degradation. The potential for agricultural pollutant 

nutrient loads and other applied chemicals is highly site-specifi c and depends 

on several factors, such as crop type and land cover, soil and topography, 

management regimes for nutrients and pesticides, irrigation use, and proximity to water. Increased fertilizer and pesticide runoff in the Mississippi 

River Basin, where much of the ethanol feedstock production is concentrated, 

has raised concerns about worsening hypoxia (oxygen deprivation) in the 

Gulf of Mexico (National Research Council, 2008).

GHG emissions. Expanding domestic production and the use of biofuels 

can both enhance energy security and lower GHG emissions due to reduced 

fossil fuel combustion. The various phases of corn feedstock and ethanol 

production, however, also contribute to GHG emissions, including carbon 

dioxide (CO2), nitrous oxide (N2O), and methane (CH4). Corn is an energyintensive crop due to high energy and fertilizer requirements. At the same 

time, conversion of idled cropland and pastureland to crop production 

releases stored carbon once the vegetation is removed and the soil disturbed. 

Consequently, a given quantity of biofuel that displaces the use of fossil fuel 

may result in only a modest reduction or possibly an increase in net GHG 

emissions. Commodity price changes due to biofuel policies, and the resulting 

indirect effects on domestic and international land use and land management, 

are an important consideration in assessing the net effect of GHG emissions. 
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Assessing Changes to the 

Agricultural Landscape

We employed a national agricultural sector model with regional specifi city 

in production and resource allocation to evaluate changes in the U.S. farm 

sector in response to accelerated biofuel demand. Regional differentiation in 

production and resource endowments allows us to examine adjustments in 

key environmental indicators across regions and determine whether changes 

are the result of adjustments in planted acreage, cropping patterns, input use, 

management practices, such as tillage, or a combination of factors.

The Regional Environment and Agriculture Programming (REAP) model is 

a quantitative, economics-based model used to evaluate market and policy 

impacts on U.S. agriculture. A comprehensive description of REAP is 

provided in Appendix A. Production quantities, prices, and land allocations 

were determined by the model, subject to baseline assumptions on yields, 

technology, costs, and targets for corn- and cellulosic-based ethanol (see box, 

“Key Assumptions in REAP Analysis”). In addition to determining market 

and production outcomes, REAP simulates resource and environmental 

consequences of Government policies on the agricultural sector. REAP 

computes several environmental indicators to assess the impact of biomass 

production scenarios. Indicators examined in this study include nutrient and 

pesticide runoff and leaching, soil erosion, and GHG emissions. 

• All demands for crop commodities are national, while demands for 

livestock production are regional. Transportation and marketing costs are 

not considered.

• Acres of individual crops in multicrop rotations are allocated proportionally; 

thus, for 1 million acres in a two-crop rotation, 500,000 acres are allocated to 

each crop.

• Crop yields are fi xed at average values per region and do not adjust for priceinduced effects.

• Crop production and the Conservation Reserve Program (CRP) compete for 

land based on an upward-sloping supply function. 

• Demands for fi nal agricultural products (crops, livestock, and processed 

goods) are modeled using linear functions.

• Yields for all crops are calibrated to the USDA baseline for 2015, which 

includes yield growth for all crops from the present to 2015. Corn yields in 

the “high corn-productivity” scenario increase an additional 1 bushel/year. 

Yield increases assume no corresponding increase in inputs.

• The corn-based ethanol target of 15 billion gallons (RFS15) is fi xed in all 

scenarios except the baseline, which assumes 13.3 billion gallons, and in the 

cellulosic scenario, where corn-based ethanol is allowed to vary.

• The 1-billion-gallon biodiesel standard is met entirely from soybeans.

Key Assumption in REAP Analysis
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Production decision variables—including land use, crop mix, crop rotations, 

tillage practices, and fertilizer application rates—and livestock production 

were determined simultaneously in the model. The environmental impacts 

of land use and agronomic practices were estimated, in turn, by applying 

coeffi cients derived from the Environmental Policy Integrated Climate 

(EPIC) model—a crop biophysical simulation model. EPIC incorporates 

soil, weather, and management information to estimate crop yields, erosion, 

and chemical (pesticide and fertilizer) discharges to the environment. EPIC 

calculates crop yields and input use under different tillage, crop rotation, soil 

management, and weather scenarios at a daily time step.

The USDA’s agricultural projections (referred to as the USDA baseline) 

refl ect 10-year projections for U.S. agriculture and provide a useful benchmark to evaluate changes to the agricultural sector associated with alternative 

policy scenarios (see box, “Choosing a Baseline”). The 2008 USDA baseline, 

incorporated into the REAP model, provides projections for the agricultural 

sector through 20171 (USDA, 2008). Projections cover agricultural 

commodity markets, agricultural trade, and economic indicators, such as 

farm income and food prices. 

The 2008 USDA baseline projects national biofuel production at 13.3 billion 

gallons of conventional ethanol and 700 million gallons of biodiesel in 

2015. The 2008 USDA baseline, developed before passage of EISA, refl ects 

production and market conditions envisioned over the projection period in 

the absence of a revised RFS. Thus, the baseline depicts a possible outcome 

that follows a “business as usual” biofuel policy without accelerated Federal 

mandates. The 2008 USDA baseline scenario for 2015 will be denoted “baseline” in the following analysis.

Figure 2 shows the evolution of projected biofuel production under the 2008 

USDA baseline and with the new RFS. Point 1 represents the 15-billion 

gallon RFS scenarios and 77 percent of the total ethanol production for 2015. 

Point 2 is the total RFS for 2015 of 20.5 billion gallons, including 15 billion 

gallons of conventional ethanol, 3 billion gallons of cellulosic biofuel, 1.5 

billion gallons of noncellulosic advanced biofuel, and 1 billion gallons of 

biomass-based diesel.

Conventional Ethanol Production and Agriculture

The baseline projections for commodity markets and biofuel demands are 

based on assumptions—supported by USDA empirical analysis—of macroeconomic conditions, policy, weather, and international developments. The 

2008 USDA baseline projections assume that there are no signifi cant shocks 

due to abnormal weather, outbreaks of plant or animal diseases, or other 

factors affecting global supply and demand. The Farm Security and Rural 

Investment Act of 2002, the Energy Policy Act of 2005, and the Agricultural 

Reconciliation Act of 2005 are assumed to remain in effect through the 

projection period. The projections represent a scenario for anticipated 

changes in the agricultural sector over the next decade. As such, the projections provide a point of departure for discussion of alternative farm sector 

outcomes that could result under different assumptions. 

1The USDA publishes 10-year 

projections, which in 2008 went out to 

2017; 2015 is the year we chose from 

the baseline for our analysis.
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To assess how production patterns and environmental indicators may 

change relative to projected baseline outcomes, the model was run using 

the 15-billion-gallon RFS level for 2015 (RFS15). The year 2015 represents 

the fi rst year that the RFS permits as much as 15 billion gallons of cornbased ethanol to count toward the overall mandate. After 2015, the cornbased biofuel target remains at 15 billion gallons, while the target level for 

advanced biofuel production continues to increase. Since increases in crop 

productivity are anticipated to continue, 2015 might be the year of greatest 

potential impact from corn-based ethanol. The cellulosic biofuel target for 

2015 is 3 billion gallons, a level that may be satisfi ed by crop residue and 

other available cellulosic feedstocks without displacing existing crop acreage 

with signifi cant plantings of new dedicated energy crops. While some 

planting of dedicated energy crops may occur on cropland or pastureland by 

2015, REAP does not take this into account.

Additional scenarios assess the robustness of model results relative to key 

factors that infl uence production decisions, including higher projected corn 

yield (HCY), higher ethanol conversion rates (HCE), and higher input costs 

(HIC). The fi rst two scenarios refl ect areas of ongoing research and development, and the third recognizes the importance of agricultural production 

Consequences of a policy on crop and livestock markets and environmental 

outcomes are evaluated relative to a point of reference or baseline. Two common 

choices for baselines are a previous “historical reference” and a projected 

“business as usual” situation. A “point in time” baseline would compare absolute 

changes in parameters of interest between the 2 different years. “Historical 

reference” baselines measure the combined effects of all policies on agriculture 

over the timespan. A “business as usual” baseline assesses the impacts of a policy 

over and above what may have happened in absence of the policy. We employed 

USDA agricultural projections—a “business as usual” baseline—to assess the 

differential effects of increased biofuel production beyond the current trend.

Choosing a Baseline

Figure 2

Ethanol production projections, 2008-22

Billion gallons

RFS = Renewale fuel standard.

Source: USDA, Economic Research Service.

RFS nonadvanced

2008 USDA baseline

2008 2010 2012 2014 2016 2018 2020 2022

0

5

10

15

20

25

30

35

40

Total RFS

1

2
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costs that are driven mainly by volatile energy prices. For simplicity, each 

scenario assumes a fi xed demand of 15 billion gallons of corn-based ethanol 

and 1 billion gallons of biodiesel supplied by soybeans. While we understand 

that scenario-induced changes in relative prices and feedstock demands may 

alter ethanol demand, the modeling framework precludes a full assessment 

of U.S. energy market adjustments. For each case, we make a comparison 

between the scenario, which includes the 15-billion-gallon ethanol demand, 

with the baseline. The conditions described in the scenarios could apply to 

the baseline in the absence of higher biofuel demand. The purpose, however, 

is to measure, relative to the baseline, the scenarios’ infl uence on the response 

to higher ethanol demand, regardless of whether those responses might be 

mitigated by the scenarios. Moreover, increased demand for ethanol, and 

associated price increases, may induce innovation (by making it more profitable) in yields and conversion rates. Likewise, increased crop production 

may increase the demand for key inputs, thus raising their prices. We cannot 

assess the extent to which biofuel policies may lead to scenario shifts, but we 

do fi nd the analysis of the shift implications informative. 

Research to improve corn productivity is underway in both the private and 

public sectors. Technological advances in corn productivity that increase 

yield growth beyond historic trends would allow corn feedstock production 

to meet Federal targets on fewer acres, effectively freeing up land for other 

crops. Average annual growth in corn yield over 1960-2007 has been about 

1.9 bushels per harvested acre (1.9 percent per year). The USDA baseline 

assumes that average corn yield will increase 9 percent from 2008 to 2015. 

The “high corn-productivity” growth scenario (RFS15+HCY) raises this 

fi gure by 50 percent, which leads to a 13.5-percent increase in average annual 

yield from 2008 to 2015 (see box, “Prospective Growth in U.S. Corn Yield”). 

The corn-yield increase is applied uniformly to corn production across all 

regions, rotations, and tillage. Yields for other crops remain at the levels 

assumed in the USDA baseline. 

Increasing corn supply through higher yield productivity is one way to reduce 

competition for land due to corn feedstock production. Another strategy is 

to reduce the demand for corn feedstocks by improving the effi ciency of the 

ethanol conversion process. Corn-based ethanol production has increased 

from 2.4 gallons per bushel in the 1980s to 2.8 gallons (nondenatured, before 

the addition of gasoline) per bushel in today’s state-of-the-art facilities. Our 

analysis assumes an average conversion rate of 2.8 gallons per bushel through 

2015. Technological advances that improve the effi ciency of starch collection and fermentation could increase this to a theoretical maximum level of 3 

(nondenatured) gallons per bushel. The conversion rate realized depends on 

the starch content of the corn, which may vary considerably by genetic trait, 

from region to region, from harvest to harvest, by the fraction of starch that 

is extracted, and by the ability of the fermentation process to utilize the available starch (McAloon et al., 2000). Corn varieties bred for highly fermentable 

starch content could be employed on a wider scale, but this study does not 

assume any differentiation in the corn supply. More effi cient conversion of 

corn would require less corn to produce the same amount of ethanol, thereby 

reducing the amount of land planted to corn. To measure the impact on 

outcomes of a high ethanol conversion effi ciency scenario (RFS15+HCE), we 

apply a value of 2.9 gallons per bushel. This value indicates an improvement 

in the industrywide conversion rate that still accounts for variation between 
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highly effi cient plants and plants operating at less effi cient levels. The key 

difference between the high corn-productivity growth scenario and the high 

ethanol-conversion scenario is that the former shifts the supply of corn and 

the latter shifts the demand for corn.

Higher energy prices raise the cost of producing and delivering feedstocks 

because fuel and electricity for planting, harvesting, tillage, drying, and irrigation often account for a substantial share of farm operating costs. Expenses 

from indirect energy use, such as energy for fertilizer production, also 

contribute to operating expenses (e.g., natural gas accounts for a large share of 

nitrogen fertilizer costs) for some crops. The high input-cost scenario refl ects 

the possibility that the relative cost of energy-intensive inputs to crop production will be higher than that assumed by the baseline. As the energy-dependent 

component of production costs varies by crops, region, and management practices, the change in relative crop returns will depend on the energy intensity 

of the crop. Variable costs for each production activity may be broken down 

into nonenergy (e.g., labor and overhead) and energy-dependent (e.g., fuel and 

fertilizer) categories. Refl ecting the high energy-cost scenario of the Energy 

Information Agency projections, the ‘high input-cost’ scenario (RFS15+HIC) 

incorporates a 50-percent premium to energy-dependent costs. High energy 

costs would also affect the market for biofuels. Since we model a fi xed ethanol 

demand, however, market-based effects, such as increased demand for biofuels 

if oil prices were signifi cantly higher, are not captured.

The Introduction of Cellulosic Ethanol

Emerging biofuel production technologies that derive fermentable material 

from plant cellulosic matter, such as crop residues, forest residues, and grasses, 

will develop new markets for agricultural products. Whether these products can 

compete with traditional crops for land and other resources will depend on the 

type and location of cellulosic feedstock sources. As new technologies emerge, 

corn may remain the predominant feedstock for ethanol production in the near 

future. Different cellulosic feedstocks, however, will compete to supply the 

growing demand for cellulosic-based ethanol. These new sources, while critical 

for the long-term growth and sustainability of biofuel production, are not yet 

commercially viable and may play a minor role in the initial wave of cellulosic 

ethanol production. 

This analysis looks at projected effects on the agricultural sector where 

crop residues serve as the primary cellulosic feedstock. Although signifi -

cant markets for residues do not currently exist, crop residues, such as corn 

stover and wheat straw, are widely available. Crop residues, however, play an 

important role in restricting nutrient loss and erosion and maintaining soil 

carbon levels. The amount of residue that can be harvested and still maintain soil productivity, which depends on tillage regime and other factors, is 

an important policy concern (USDA-NRCS, 2006). Switchgrass and other 

perennial grasses represent high-yield feedstock alternatives to crop residues. 

While they show promise in fi eld trials, these grasses are not yet grown on 

a commercial scale, with minor exceptions, and issues of seed availability, 

production systems and farmer adoption, storage and transport logistics, and 

market institutions will need to be resolved before large-scale production 

becomes viable. Short-rotation woody crops, such as willow and poplar, are 
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Following the introduction of commercial corn hybrids in the 

1930s, U.S. corn yields have trended upward dramatically.1

For a long time, increased yields were also supported by 

increasing input use, such as chemical fertilizers. But since 

about 1980, corn yields have continued to increase even as 

fertilizer application rates leveled off or declined.2 

Extrapolating past yield trends may help to forecast crop 

yield growth, but trends differ based on starting point and 

are not necessarily linear over time. Tannura et al. (2008) 

note a particular shift in trend growth rates for corn yields 

(Illinois): from about 1 bushel per year during 1940-1959 

to 1.7 bushels from 1960 onward. They ascribe this yield 

acceleration to widespread adoption of fertilizer and 

herbicides, while others (Sleper and Poehlman, 2006) cite 

adoption of single cross corn hybrids. Tannura et al. conclude 

that increases in trend yield growth of up to 70-75 percent 

(e.g., from 2 to 3.5 bushels per year) could be consistent with 

historical experience, but increases of 6 bushels or more per 

year, necessary to reach the widely publicized goal of 300 

bushels of corn by 2030, would be unprecedented.

Results from the National Corn Growers Association’s Corn 

Yield Contest have also been used as a proxy for potential 

corn yields. Documented yields of 360-370 bushels per acre,3

however, (Elmore and Abendroth, 2007) are contingent on 

optimal growing conditions and particular management 

strategies. For example, the highest yields are often obtained 

in the irrigated classes, where moisture can be more 

carefully regulated than under dry land farming conditions. 

The level of inputs and time spent managing contest plots 

may be far above the economic optimum in a commercial 

situation. Thus, contest yields are more of an indication of 

yield potential than of a likely national average across a wide 

range of conditions. Top yields for both State or nationwide 

irrigated classes have fl uctuated widely around a constant 

mean for the past 20 years or more (Duvick and Cassman, 

1999; Elmore and Abendroth, 2007). 

Other factors may work against high aggregate growth rates 

for corn yields. While higher corn prices may encourage 

expanded irrigation and fertilizer input use, corn area 

expansion into less productive areas could pose a downward 

Prospective Growth in U.S. Corn Yield

1Yield trends are presented in terms of yields per planted acre—in this case, corn planted for all purposes—consistent with the presentation in the modeling exercises. The REAP model used here analyzes planted area rather than harvested area because the objective is to 

model producer expectations when they plant. Much of our discussion will be in terms of yields per harvested acre, which is what the 

literature on yield trends usually considers.

2Silage acreage is excluded from the denominator in both the yield and fertilizer application rate calculations to make the yield denominator 

consistent with the denominator used in the available fertilizer series.

3These represent exceptional yields from the Yield Contest, and while it is physically possible to achieve considerably higher yields, to do so 

would be uneconomical on a national scale.

Historical and projected U.S. corn yield growth, 1940-2016

Bushels per planted acre1

1Denominator includes silage acreage.

Source: USDA, National Agricultural Statistics Service model projections.

1930 40 50 60 70 80 90 2000 10 20

0

20

40

60

80

100

120

140

160

180

Historical

50% increase

Baseline

Corn yield and fertilizer applied to corn, United States, 

1964-2007

Pounds/planted acre1

1Excluding silage acreage.

Source: USDA, National Agricultural Statistics Service.

Total nutrients

Corn yield

1964 68 72 76 80 84 88 92 96 2000 04

50

100

150

200

250

300

50

70

90

110

130

150

170

Bushels/planted acre1

Nitrogen

Continued on page 17
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another feedstock option. These fast-growing trees, which produce suffi cient 

biomass for harvest in a few years rather than the decades common in traditional forestry, are also not currently grown on a large scale. Forest-based 

residues from logging, timberland clearing, and fi re control could also be 

another biomass source. While it is impossible to forecast the supply of each 

feedstock with certainty, it is reasonable to assume that crop residues will 

factor prominently in the early phases of cellulosic ethanol production.

The total ethanol RFS for 2015 is 19.5 billion gallons—with at least 4.5 

billion gallons of advanced biofuel (3 billion gallons is from cellulosic feedstock) and the remainder from conventional sources. Actual feedstock shares, 

however, may vary depending on the relative costs and production capacity 

of conventional and cellulosic sources. The discrepancy in shares may be 

especially true if cost-effective cellulosic conversion technology advances at 

a rapid pace. In this case, cellulosic ethanol might substitute for corn ethanol, 

reducing demand for corn. To measure the effects of an increasing share of 

cellulosic ethanol in the analysis, crop residue-based ethanol production is 

varied from 3 billion gallons to 6 billion gallons, with corn-based ethanol 

making up the difference (i.e., ranging from 15 billion gallons down to 12 

billion gallons). The remaining 1.5 billion gallons of advanced biofuel is 

assumed to be derived from nonagricultural sources like urban waste. This 

scenario is meant to illustrate the possible tradeoffs between producing 

ethanol from corn or from cellulosic residue. The scenario is not meant as 

a projection or a likely outcome. It does, however, represent some of the 

issues that may emerge as production of cellulosic ethanol, and consequently 

demand for cellulosic feedstocks, increases. Table 1 summarizes the key 

parameter values for the baseline and increased ethanol production scenarios.

drag on yield increases. The REAP model includes regionspecifi c yields that aggregate up to the national average. For 

the national average to reach projected levels, some regions 

(e.g., Corn Belt States, such as Iowa and Illinois) would have 

to eclipse national-average baseline yields to offset lower 

yields in other corn-producing areas. 

The baseline model used in the agricultural projections 

includes a jump in yields from 2007 to 2008, primarily to put 

yields back on the apparent trend line, and then an increase 

of about 2 bushels per year in yields per harvested acre. This 

results in an aggregate national corn yield per harvested acre 

of about 171 bushels annually by 2015, which is equivalent 

to the reported fi gure of yields per planted acre of about 156 

bushels per acre per year. Two bushels per year is slightly 

higher than the long-term yield trend, but is consistent with a 

recent slight acceleration in yield growth.

The “50-percent yield increase” scenario results in a yield of 

about 175 bushels per harvested acre in 2015. This is similar 

to the “increased yield” scenario presented by the National 

Corn Growers Association (2009) and would be equivalent to 

about 161 bushels per planted acre in 2015. It would require 

an increase in yields per harvested acre of over 3 bushels 

per year, a more than 50-percent acceleration in trend yield 

growth if the base is 2.0 bushels per year (the assumption of 

the baseline model), or a more than two-thirds acceleration 

if the base is 1.85 bushels per year (our linear estimate based 

on aggregate data for 1960-2007). Such an acceleration could 

occur as currently available biotechnologies, such as stacked 

traits, or other imminent technologies are applied. Most of 

the yield growth would result from investments in research 

that have already been made, not in investments to be made 

over the next 10 years.

Continued from page 16
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Table 1

Key parameters for 2015 ethanol production scenarios

 RFS 

 RFS 15 billion gallons RFS RFS

 15 billion gallons + 15 billion gallons 15 billion gallons

 RFS + High conversion + +

 15 billion gallons High corn yield effi ciency High input cost Cellulosics

Item Baseline (RFS15) (RFS15+HCY) (RFS15+HCE) (RFS15+HIC) (RFS15+CELL)

Corn ethanol 

(billion gallons) 13.3 15.0 15.0 15.0 15.0 12-15.0

Biodiesel 

(billion gallons) 0.7 1.0 1.0 1.0 1.0 1.0

Corn yield 

(bushels per planted acre) 156 156 161 156 156 156

Ethanol conversion 

(gallons per bushel) 2.8 2.8 2.8 2.9 2.8 2.8

Input cost multiplier 1 1 1 1 1.1-1.8 1

Cellulosic 

(billion gallons) 0 0 0 0 0 3.-6.

RFS=renewable fuel standard.

Note: Highlighted cells indicate parameters that differ from baseline parameter values. Biodiesel production is held at 1 billion gallons 

in all scenarios.

Source: Economic Research Service calculations based on Regional Environment and Agriculture Programming (REAP) model data.
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Expanding Corn Acreage Drives Market 

and Environmental Outcomes

Greater U.S. demand for corn and the need for greater domestic production will 

create a new set of conditions under which farmers make planting, input use, 

and management decisions. Increased land planted to corn will mean either less 

land available for other crops or new land coming into production, affecting the 

economics of all crops. Shifts in the relative returns of different crops will cause 

changes in crop mix at the national level and across regions. Demand for all 

agricultural commodities will need to adjust to the new price signals, leading to 

changes in consumption. Environmental effects will vary across the landscape, 

refl ecting regional differences in cropping activity and production practices and 

their interaction with soil and water resources on and off the farm.

An increase to 15 billion gallons of U.S. ethanol demand over the 13.3 billion 

gallons assumed in the baseline (year 2015) increases demand for corn, 

raising prices and stimulating an increase in land planted to corn. While 

some soybean acreage is displaced by corn, higher biodiesel demand leads to 

a net increase in acres planted to soybeans. As acres for corn crops expand, 

acres planted to other crops decrease. Much of the additional planted acres 

are offset by a reduction in land enrolled in the CRP. Domestic corn price 

increases also lead to a reduction in corn exports, which reduces pressure 

on the land base somewhat. Every region shows an increase in corn acreage, 

with the majority of additional acres concentrated in the traditional corn 

growing regions of the Corn Belt, Northern Plains, and Lake States.

Crop production involves various practices and inputs—soil cultivation; 

application of fertilizer, pesticides, and other chemicals; and irrigation—

all of which can affect resource use and environmental quality. Increased 

acreage due to additional biofuel feedstock demand affects land management, 

with implications for soil productivity, water quality and quantity, air quality, 

and GHG emissions. Because regions differ in how crop production translates 

to environmental outcomes, the effect on environmental indicators (e.g., soil 

erosion, nutrient runoff, and GHG emissions) varies considerably by region. 

In some regions, the changes in environmental measures are greater than the 

change in acreage, indicating an intensifi cation of input use and, possibly, 

expanded production on marginal lands. 

Table 2 represents the magnitude and direction of the changes in market, land 

use, and environmental indicators for each of the RFS15 scenarios, relative to 

the baseline scenario. The arrow’s direction denotes the direction of change; 

an up-arrow means an increase in the level of the indicator over the baseline 

and a down-arrow means a decrease in the level of the indicator below the 

baseline. The number of arrows denotes the magnitude of change relative to 

the baseline scenario, where a single arrow represents a percent change close 

to the change in acreage from the baseline to the reference. In all cases, with 

the exception of the high input-cost scenario (RFS15+HIC), acreage increases 

over the baseline. Under the RFS15 scenario, price, production, and returns 

to crop farmers all rise, while livestock returns decline. An improvement in 

corn yield (RFS15+HCY) is accompanied by a reduction in corn price and an 

increase in production, which benefi ts livestock producers through interactions with the feed market. Reduction of corn demand via improved ethanol 
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conversion mitigates price and production effects, but outcomes for resource 

and environmental quality are generally similar to the RFS15 case. High 

input costs (RFS15+HIC) lead to a large decrease in acres planted and an 

increase in price and returns to crop farmers. In all scenarios except the high 

input-cost scenario, environmental indicators worsen relative to the baseline 

due to increased acres planted and changes in cropland management. In the 

high input-cost case, the large decline in acres planted contributes to proportionally large reductions in environmental effects. 

Table 2

Change in market and environmental indicators from USDA baseline, 2015

Indicator

RFS 

15-billion gallons 

(RFS15)

+ High 

corn yield 

(HCY)

+ High 

conversion 

effi ciency 

(HCE)

+ High 

input cost 

(HIC)

Market:

Corn price qq qq q qqq

Corn production qq qqq q q

Returns:

Corn qqq qq qq qqq

Other crops qq qq qq qq

Livestock q q q

Cropland:

Total acres in production qqq qqq

Corn qq q qq q

Continuous corn qqq q qq q

Highly erodible cropland in production qqq qqq

Tillage:

Conventional qqq qqq

Conservation qq qq qq qqq

No-till qq qq qq qqq

Soil erosion:

Soil loss qqq qqq

Nutrient loss:

Surface runoff – in sediment qqq qqq

Surface runoff – in solution qqq qqq

Groundwater leaching qq q qq qqq

Pesticide loss:

Surface runoff – in sediment qq q qq qq

Surface runoff – in solution qqq qq

Groundwater leaching qq q qq qq

Greenhouse gas:

Greenhouse gas emissions qq q q qqq

RFS=Renewable fuel standard.

Notes: Up-arrow indicates increases in 2015 relative to baseline projections; down-arrow indicates decreases. The number of arrows denotes 

relative magnitude of change: one arrow, less than 2 percent; two arrows, 2 percent to 4 percent; three arrows, greater than 4 percent.

Source: Economic Research Service calculations based on Regional Environment and Agriculture Programming (REAP) model data.
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Crop Producers See Higher Returns

Cultivated U.S. cropland area is projected to expand in response to Federal 

biofuel targets as increased demand for corn drives prices higher across grain 

commodities. According to model projections, U.S. cropland in production 

for 2015 increases slightly over the 2008 USDA baseline, from 313.8 to 318.7 

million acres (1.6 percent), with some growth in nearly all regions (table 3). 

Acreage adjustments lead to changes in production that refl ect regional differences in yield (tables 4 and 5). The prices of corn and soybeans are higher 

relative to the baseline, while other crop prices hold steady (table 6). Crop 

producers realize higher returns as the increases in price and production 

over the baseline are greater than the increase in production costs necessary 

to achieve higher production levels (table 7). Corn producers see a greater 

increase in returns compared with producers of other crops. 

Higher feed prices, however, lead to lower returns to the livestock sector and a 

small contraction in animal inventories. The high corn-yield and high ethanolconversion rate scenarios also show higher returns to corn producers relative 

to the baseline. In the high ethanol-conversion scenario, the price of corn 

increases slightly because the higher demand for ethanol keeps corn production above the baseline. The price of corn declines in the high corn yield case, 

but production increases keep returns higher than the baseline scenario. Corn 

production increases in all regions, with greater increases where corn is most 

profi table. The high input-cost scenario raises returns to corn farmers due to the 

increase in price and production. Other agricultural producers see a reduction 

in net income because, even as prices rise, production is reduced by a larger 

percentage, lowering revenue compared with the baseline. 

Land allocations shift considerably across regions, refl ecting regional 

comparative advantage in fi rst-generation biofuel feedstock production. 

Cropland acreage in production expands in all regions. The largest absolute 

acreage gains occur in the Northern Plains (up 2 million acres to 67.2 million 

Table 3

Regional acreage planted, by scenario, 2015

 RFS + High + High + High

 15-billion corn conversion input

 gallons yield effi ciency cost

Region Baseline (RFS15) (HCY) (HCE) (HIC)

 Million acres

Northeast 14.7 14.8 14.8 14.8 14.1

Lake States 39.4 39.9 39.7 39.8 37.3

Corn Belt 101.2 102.3 101.9 102.1 100.8

Northern Plains 65.2 67.2 65.8 66.7 57.1

Appalachian 18.1 18.5 18.4 18.4 17.5

Southeast 5.8 6.1 6.0 6.0 5.4

Delta 14.5 15.0 15.0 15.0 14.0

Southern Plains 25.7 25.8 25.7 25.8 20.1

Mountain 21.3 21.3 21.3 21.3 18.5

Pacifi c 7.7 7.8 7.8 7.8 6.1

United States 313.8 318.7 316.2 317.7 291.0

RFS=Renewable fuel standard.

Note: Totals may not sum due to rounding.

Source: Economic Research Service calculations based on Regional Environment and 

Agriculture Programming (REAP) model data.
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acres), Corn Belt (up 1.1 million acres to 102.3 million acres), and Lake 

States (up 490,000 acres to 39.9 million acres), accounting for almost threequarters of the additional acres in crop cultivation. Higher corn yield and 

higher ethanol conversion both put downward pressure on cultivated acreage, 

but additional acres are still needed to achieve the higher biofuel production 

level. In contrast, high input costs dramatically reduce the number of acres in 

production to 291 million acres (down 22.8 million acres from baseline) as 

profi t margins decline in many regions despite higher commodity prices.

Expansion in U.S corn acreage drives both increased cropland in production and regional crop pattern shifts. Corn acreage increases by 3.25 million 

acres over the baseline (3.5 percent), with substantial increases occurring 

in all U.S. regions (table 8). The greatest absolute increase occurs in the 

Northern Plains, with an additional 1.5 million acres in 2015 (8.6 percent). 

The Corn Belt adds 770,000 acres, although the gain over baseline levels (1.7 

Table 4

Acreage planted to major crops, by scenario, 2015

 RFS + High + High + High

 15-billion corn conversion input

 gallons yield effi ciency cost

Ctop Baseline (RFS15) (HCY) (HCE) (HIC)

 Million acres

Corn 91.5 94.7 92.9 93.7 92.1

Sorghum 5.8 5.7 5.0 5.7 3.7

Barley 3.5 3.5 3.5 3.5 3.0

Oats 3.8 3.8 3.8 3.8 3.5

Wheat 56.0 56.0 56.0 56.0 49.5

Rice 3.2 3.1 3.1 3.1 2.6

Soybeans 68.0 69.9 69.9 69.9 68.5

Cotton 12.1 12.1 12.1 12.1 10.0

Silage and hay 69.9 69.9 70.0 69.9 58.1

Total 313.8 318.7 316.2 317.7 291.0

RFS=Renewable fuel standard. Note: Totals may not sum due to rounding.

Source: Economic Research Service calculations based on Regional Environment and 

Agriculture Programming (REAP) model data.

Table 5

Commodity production, by scenario, 2015

 RFS + High + High + High

 15-billion corn conversion input

 gallons yield effi ciency cost

Crop Baseline (RFS15) (HCY) (HCE) (HIC)

Production —— Percent change from baseline ——

Corn (million bushels) 14,240 2.7 5.3 1.9 0.7

Sorghum (million bushels) 340 -2.0 -14.2 -1.6 -35.6

Barley (million bushels) 210 0.5 0.5 0.5 -9.4

Oats (million bushels) 249 -0.5 0.1 -0.2 -4.7

Wheat (million bushels) 2,125 -0.3 0.0 -0.2 -9.8

Rice (million cwt) 240 -2.3 -2.3 -2.3 -18.1

Soybeans (million bushels) 3,035 2.0 2.1 2.0 0.7

Cotton (million bales) 22 -0.2 -0.1 -0.2 -9.1

RFS=Renewable fuel standard.

Cwt = hundredweight.

Source: Economic Research Service calculations based on Regional Environment and 

Agriculture Programming (REAP) model data.
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Table 6

Commodity prices, by scenario, 2015

 RFS + High + High +High

 15-billion corn conversion input

 gallons yield effi ciency cost

Crop Baseline (RFS15) (HCY) (HCE) (HIC)

 Price —— Percent change from baseline ——

Corn ($/bushel) 3.55 2.2 -3.0 1.6 6.8

Sorghum ($/bushel) 3.30 0.3 -0.8 0.2 5.3

Barley ($/bushel) 3.85 -0.3 -0.3 -0.3 5.8

Oats ($/bushel) 2.25 1.0 -0.2 0.5 10.1

Wheat ($/bushel) 4.55 0.1 0.0 0.1 5.0

Rice ($/cwt) 12.12 0.1 0.1 0.1 1.0

Soybeans ($/bushel) 8.90 2.7 2.6 2.7 3.5

Cotton ($/pound) 0.56 0.1 0.1 0.1 3.9

RFS=Renewable fuel standard.

Cwt = hundredweight.

Source: Economic Research Service calculations based on Regional Environment and 

Agriculture Programming (REAP) model data.

Table 7

Net returns to agricultural production, by scenario, 2015

 RFS + High + High + High

 15-billion corn conversion input

 gallons yield effi ciency cost

Comnodity Baseline (RFS15) (HCY) (HCE) (HIC)

 Net returns

 ($ billion) —— Percent change from baseline ——

Corn 31.5 5.4 2.4 3.8 6.4

Other crops 23.7 2.8 3.3 3.1 -4.0

Livestock 33.6 -0.3 0.5 -0.2 -1.3

RFS=Renewable fuel standard.

Source: Economic Research Service calculations based on Regional Environment and 

Agriculture Programming (REAP) model data.

Table 8

Regional acreage planted to corn, by scenario, 2015

 RFS + High + High + High

 15-billion corn conversion input

 gallons yield effi ciency cost

Region Baseline (RFS15) (HCY) (HCE) (HIC)

 Million acres

Northeast 3.9 4.0 3.9 4.0 3.9

Lake States 14.7 15.1 14.8 14.9 14.4

Corn Belt 45.2 46.0 45.5 45.7 46.0

Northern Plains 17.8 19.3 18.4 18.8 17.9

Appalachian 4.6 4.7 4.7 4.7 4.6

Southeast 1.8 1.9 1.9 1.9 1.7

Delta 0.7 0.8 0.7 0.7 0.8

Southern Plains 1.2 1.3 1.2 1.2 1.2

Mountain 1.3 1.4 1.4 1.4 1.3

Pacifi c 0.3 0.4 0.3 0.3 0.3

United States 91.5 94.7 92.9 93.7 92.1

RFS=Renewable fuel standard.

Note: Totals may not sum due to rounding.

Source: Economic Research Service calculations based on Regional Environment and 

Agriculture Programming (REAP) model data.
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percent) is comparatively small. In regions where corn is less predominant, 

corn acreage expansion is modest. In response to rising biodiesel demand, 

the Northern Plains and Delta regions add over 500,000 acres of soybeans as 

production shifts from the Corn Belt region. Total acreage in wheat does not 

change nationally. The Northern Plains region loses 180,000 acres of wheat in 

response to corn and soybean expansion, but makes up these losses by small 

gains in other regions.

Shifts in corn yield may have implications for acreages of other crops as well. 

In our analysis, the effect of corn yield on soybean area is mixed in terms of 

regional shifts and fairly small in magnitude. Corn acreage expansion appears 

more important than corn yield in infl uencing soybean acreage. Increases in 

projected corn yield, however, cause some signifi cant shifts in wheat acreage, 

with large decreases in the Southeast and Northeast and comparatively large 

increases in the Appalachian and Lake States regions. Observed volatility 

outside major grain-producing areas may refl ect a higher degree of crop 

switching due to shifts in relative crop returns.

Higher commodity prices will motivate producers to convert land currently 

idled through the CRP to crop production as contracts expire. The 2008 Farm 

Act lowered the amount of acreage that can be enrolled nationally in the 

CRP, from 39.2 million to 32 million acres. The 2008 USDA baseline, which 

was fi nalized prior to passage of the 2008 Farm Act, projected enrollment at 

35.4 million acres in 2015, which is slightly above current enrollment. In our 

model, CRP enrollment declines from the baseline level of 35.4 million acres 

to 32.3 million acres under the RFS15 scenario. CRP acreage shifts west, 

with reduced acreage enrollment in the Corn Belt and Plains regions and a 

modest enrollment increase in the Mountain States. Results suggest that much 

of the additional acreage in production under the RFS15 scenario will come 

from CRP land as contracts expire, although not all of that land will be used 

to produce biofuel feedstock.

Production Intensifi es

Expansion in U.S. corn acreage is attributable, in part, to an increase in 

continuous corn production—planting corn on the same fi eld for at least 

3 consecutive years—effectively reversing a trend that sees different 

crops planted on a given fi eld in alternate years (e.g., corn in rotation with 

soybeans). Rotating crops can reduce soil loss and enhance soil productivity, 

while reducing the need for pesticides and fertilizers. Continuous planting 

accounts for about 30 percent of the corn produced under the baseline. 

Continuous planting, however, represents half of the corn acreage under the 

15-billion-gallon biofuel scenario. Expansion of continuous corn production 

occurs in all regions (fi g. 3), but is especially strong in the Northern Plains, 

where 56 percent of the additional corn acres are planted in continuous rotations. In the high input-cost scenario, continuous corn acres increase in the 

Corn Belt and Lake States, but decrease in the Northern Plains.

The shift to more intensive farming practices with continuous corn is mitigated somewhat by a shift from conventional soil tillage to conservation tillage 

methods. Tillage choice—the method used to prepare the soil for planting—is 

an important determinant of soil erosion control, as well as the fuel, machinery, 

and labor resources needed to manage the fi eld. Conventional tillage, which 
25

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accounts for 72 percent of all cropland under the baseline, remains the predominant system under the RFS15 scenario. No-till, which minimizes disturbance 

of the fi eld, accounts for 11 percent of cropland. No-till acres increase in the 

Corn Belt and Northern Plains, but decrease in the Lake States (fi g. 4). Of 

the 4.9 million additional acres under the RFS15 scenario, 23 percent are in 

no-till acres and 51 percent are conventionally tilled acres. The higher cornyield growth scenario, while requiring fewer acres in corn than in the RFS15 

scenario, shows that a third of the additional acres that are needed relative to 

the baseline go into no-till systems and only 20 percent into conventional tillage 

systems. Land that would have been planted to corn is planted to other crops 

where no-till is a profi table option. Even more interestingly, the high input-cost 

scenario, with a large overall decline in total crop acres, shows an increase in 

crops planted under no-till as producers respond to lower fuel and equipment 

costs associated with no-till systems. 

The REAP model shows that U.S. nitrogen fertilizer use in 2015 will increase 

by 2 percent over the baseline. This change is greater than the 1.6 percent 

increase in planted acres, indicating more intensive farming with increased 

corn production and higher corn prices. Regional differences in crops planted 

and heterogeneity in soil and climate characteristics lead to regional variation in 

the change in fertilizer use. Nitrogen use in the Northern Plains increases by 3.6 

percent from the baseline, compared with 0.8 percent in the Southern Plains. 

On a per-acre basis, modest increases in fertilizer applications are exhibited in 

the major crop-producing regions (fi g. 5). A large shift into soybeans planted 

in rotation with other crops in the Delta leads to a large decline in fertilizer 

intensity. Increasing yield growth for corn—a fertilizer-intensive crop—reduces 

the need for fertilizer per unit of yield. It should be noted, however, that the 

increase in corn yield assumes no corresponding increase in nitrogen uptake. 

Any increases in nitrogen uptake required to support higher yields would 

counter the fertilizer adjustments reported here. High input costs signifi cantly 

reduce the amount of fertilizer used nationally (down 6.5 percent from the 

baseline), but do not reduce the intensity of fertilizer use. The acreage reduction 

Figure 3

Change in continuous corn acres, by scenario, 2015

Million acres

RFS = Renewable fuel standard.

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.

Change from baseline

Lake States Corn Belt Northern Plains

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1.0 Total RFS High corn yield High ethanol conversion High input costs
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under the high input-cost scenario (down 7.3 percent) more than offsets the 

reduction in fertilizer, resulting in increased fertilizer intensity in regions where 

corn production increases over the baseline. In regions where corn production 

declines, fertilizer intensity declines as well.

Water Quality Measures More Sensitive 

Than Change in Acreage

Ramping up corn production to satisfy biofuel production targets raises 

concerns about potential environmental impacts. Environmental effects 

refl ect a shift in cropping patterns toward corn production, overall production intensifi cation (as refl ected by increased continuous corn and fertilizer 

use), and expansion of cropland acreage under production. Expanded use of 

Figure 4

Change in no-till acres from baseline, by scenario, 2015

Million acres

RFS = Renewable fuel standard.

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.

Change from baseline

Total RFS High corn yield High ethanol conversion High input costs

Lake States Corn Belt Northern Plains Other regions

-0.5

0

0.5

1.0

1.5

2.0

2.5

Figure 5

Change in nitrogen applied per acre, by scenario, 2015

Change from baseline

RFS = Renewable fuel standard.

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.

Total RFS High corn yield High ethanol conversion High input costs

Lake States Corn Belt Northern Plains Other regions

-8.0

-6.0

-4.0

-2.0

0

2.0

4.0

6.0

8.0
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conservation tillage systems may mitigate potential environmental impacts 

in some areas. Water quality impacts examined here include nitrogen runoff 

to surface water, nitrogen impacts on coastal estuaries, and nitrogen leaching 

to groundwater. While this report focuses primarily on potential effects of 

biofuels on water quality, water demands for irrigated feedstock production 

and ethanol processing will compete for limited water supplies in some areas 

(see box, “Water Use Impacts From Ethanol Production”). 

Nutrient Loss

Nitrogen leaching to groundwater in 2015 is estimated to increase by 

roughly 23,000 tons (2.8 percent) over baseline levels, refl ecting the 

expansion of cropland acreage and increase in nitrogen application rates. 

This increase is substantially higher than the 1.6-percent increase in total 

acreage, driven mainly by large increases in the Lake States, Appalachian, 

and Southern regions. Figure 6 demonstrates the change in nitrogen 

leaching due to increased acreage planted and the amount attributed to 

shifts in cropland allocation and management that affect nutrient runoff. 

The size of the circle represents the absolute change in the indicator for a 

given region, demonstrating how much a region contributes to the national 

increase. The position of the region on the chart, relative to the diagonal 

line, indicates how much of the change in nitrogen leaching is caused by 

increases in total acreage and how much is infl uenced by changes in crop 

mix and management. A region positioned on or near the diagonal line, 

as seen for the Corn Belt and Appalachian regions, generally means that 

the increase in nitrogen leaching is driven by increased acreage, since the 

change in both acreage and leaching are equal along the line. A position 

Figure 6

Change in nitrogen leached to groundwater, by region, 2015

Change in indicator from baseline

Notes: Size of circle represents the absolute change in the indicator, demonstrating how much 

a region contributes to the national increase. The circle’s position, relative to the diagonal line, 

indicates how much of the change in nitrogen leaching is caused by increases in total acreage 

and how much is influenced by changes in crop mix and management.

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.

Change in acreage from baseline

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

-1.0 0.0 1.0 2.0 3.0 4.0 5.0

Northeast

Pacific

Corn Belt

Southeast

Appalachian Northern Plains

Dalta

Mountain

Southern Plains

Lake States
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below the diagonal line, such as that of the Delta, indicates that the increase 

in acres is accompanied by shifts in crop mix and management that cause 

the increase in nitrogen leaching to be less than the change in acreage. A 

position above the diagonal line, such as for the Lake States and Southeast, 

means that the increase in acreage is accompanied by shifts in management that drive an increase in nitrogen leaching in excess of the increase 

in acreage. In some cases, conversion of marginal cropland acreage may 

explain high relative shifts in nitrogen leaching. 

Nitrogen runoff to surface water increases by roughly 29,000 tons (1.7 

percent) over baseline levels. Higher nitrogen loads refl ect an expansion 

in U.S. cropland acreage in production, as well as higher nitrogen application rates per acre due to increased corn production. The change in nitrogen 

loads over the baseline, however, varies considerably by region (fi g. 7). 

Nitrogen runoff from the Northern Plains increases by 3.1 percent, while 

nitrogen runoff from the Corn Belt—accounting for roughly 44 percent 

of U.S. nitrogen loadings to surface water from fi eld crop production in 

2006—increases by 1.3 percent. Increased productivity growth implies that 

the same amount of corn can be produced with less fertilizer per acre and 

with less land. Thus, much of the surface-water impact from meeting the 

increased biofuel demand is mitigated with high corn yield growth (scenario 

HCY). The increase over the baseline is limited to 13,600 tons (compared 

with 29,000 tons), mainly due to a large reduction of runoff in the Southern 

Plains under the HCY scenario compared with the baseline.

Nutrient loads, which refl ect both the amount of nutrient fi eld runoff and 

spatial proximity to coastal waters, have implications for water quality in 

Figure 7

Change in nitrogen runoff to surface water, by region, 2015

Change in indicator from baseline

Notes: Size of circle represents the absolute change in the indicator, demonstrating how much 

a region contributes to the national increase. The circle’s position, relative to the diagonal line, 

indicates how much of the change in nitrogen leaching is caused by increases in total acreage 

and how much is influenced by changes in crop mix and management.

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.

Northeast

Pacific

Corn Belt

Southeast

Appalachian

Northern Plains

Dalta

Mountain

Southern Plains

Change in acreage from baseline

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

-1.0 0.0 1.0 2.0 3.0 4.0 5.0

Lake States
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U.S. estuaries. Nationally, runoff to estuaries increases by 1.8 percent from 

the baseline. The largest contributor is the Corn Belt, with the Northeast, 

Lake States, Appalachian, and Delta regions also increasing runoff to estuaries (fi g. 8). The increase in the Southern Plains, though small, is high 

in percentage terms. This indicates that further acreage expansion in the 

region may result in greater nutrient loads to the Gulf of Mexico. High corn 

yield growth (HCY) reduces the overall impact, as well as the amount of 

regional variation. Under this scenario, runoff from the Delta decreases 

only slightly with respect to the RFS15 scenario as a result the region’s 

small dependence on corn and smaller-than-average acreage reduction.

Soil Erosion

Soil erosion effects include both sheet (rainfall) and wind erosion. Soil erosion 

potential is affected by soil structure, slope of the fi eld, and surface vegetation, 

among other factors. Crop residue management and vegetative cover crops 

can help minimize potential soil erosion by exposing less soil to wind and 

by reducing the impact of rainfall. The national increase in sheet erosion (1.7 

percent from the baseline) in the RFS15 scenario mirrors the national increase 

in planted acreage (1.6 percent), with similar variation among regions (fi g. 9). 

Sheet erosion levels higher than the national average occur in the Northern 

Plains, Appalachian, Delta, and Southeast regions. Most soil erosion occurs 

in the Corn Belt, with smaller amounts in the Northern Plains, Southeast, and 

Delta regions. For the most part, soil loss is driven by increases in planted 

acreage, which may be offset by shifts to conservation tillage.

Wind erosion increases 0.7 percent from the baseline in the RFS15 scenario, 

less than the national change in cropped land. Almost all the change occurs 

Figure 8

Change in nitrogen runoff to estuaries, by region, 2015

Change in indicator from baseline

Notes: Size of circle represents the absolute change in the indicator, demonstrating how much 

a region contributes to the national increase. The circle’s position, relative to the diagonal line, 

indicates how much of the change in nitrogen leaching is caused by increases in total acreage 

and how much is influenced by changes in crop mix and management.

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.

Change in acreage from baseline

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

-1.0 0.0 1.0 2.0 3.0 4.0 5.0

Northeast

Pacific

Corn Belt

Appalachian Southeast

Northern Plains

Dalta

Mountain

Southern Plains

Lake States
30

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in the Northern Plains and Corn Belt (fi g. 10). In both regions, expanded 

acreage accounts for much of the change, with shifts to no-till reducing 

the effect of expanded acreage in the Northern Plains. The region most 

susceptible to wind erosion, the Southern Plains, does not see much change 

in acreage, so the impact on wind erosion is small. The high input-costs 

scenario reduces wind erosion by 20 percent.

GHG Emissions

GHGs most closely associated with crop production include carbon dioxide 

(CO2) and nitrous oxide (NOx). These gases are produced by farm machinery 

and fertilizer inputs, as well as by processes that naturally occur in the 

soil. Converting land to crop production will reduce carbon sequestered 

in biomass as vegetation is removed, and soil disturbance will change soil 

carbon levels. Soil management also infl uences GHG emissions associated with energy use. For example, fuel-associated emissions decline with 

conversion from conventional tillage to reduced till or no-till. Increasing crop 

acreage leads to a small increase in GHG emissions from land-use change, 

which is partially offset by changes in land management (table 9). The EPA 

estimates that total U.S. GHG emissions for 2007 included 7.15 billion tons of 

CO2 equivalent, 6 percent of which came from agriculture (EPA, 2009). The 

EPA further estimates that total annual carbon sequestration in U.S. cropland 

for 2007 was 19.7 million tons of CO2 equivalent. If emission rates remain 

constant through 2015, the estimated 2.9-million-ton increase in emissions—

3.8-million-ton decrease in sequestration due to the increase in capital, 

partially offset by 0.9-million-ton increase in sequestration due to increased 

no-till—would represent a 0.17-percent increase in U.S. emissions and a 

15-percent reduction in agricultural sequestration. These emission changes 

Figure 9

Change in sheet erosion, by region, 2015

Change in indicator from baseline

Notes: Size of circle represents the absolute change in the indicator, demonstrating how much 

a region contributes to the national increase. The circle’s position, relative to the diagonal line, 

indicates how much of the change in erosion is caused by crop mix and management.

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.

Change in acreage from baseline

-0.5

0.0 

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

-1.0 0.0 1.0 2.0 3.0 4.0 5.0

Northeast

Pacific

Corn Belt

Southeast

Appalachian

Northern Plains

Dalta

Mountain

Southern

 Plains

Lake States
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are due to changes in agricultural land use and do not include the lifecycle 

emissions of the biofuel produced or offsets from less petroleum use.

Because of differences in inputs between crops, there will be some net 

change in GHG emissions not captured in this analysis. Net agricultural GHG 

emissions can be reduced in two ways: (1) through changes in farm operations that reduce direct and indirect emissions, such as lower energy use in 

fi eld practices or reduced fertilizer use, and (2) through changes in tillage and 

other land-use practices that sequester carbon, such as creating grasslands 

and planting trees.

Figure 10

Change in wind erosion, by region, 2015

Change in indicator from baseline

Notes: Size of circle represents the absolute change in the indicator, demonstrating how much 

a region contributes to the national increase. The circle’s position, relative to the diagonal line, 

indicates how much of the change in erosion is caused by crop mix and management.

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.

Change in acreage from baseline

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

-1.0 0.0 1.0 2.0 3.0 4.0 5.0

Corn Belt

Southeast

Northern Plains

Mountain

Southern

 Plains

Lake States

Table 9

Change from baseline in emissions due to land 

and tillage changes, 2015

 RFS + High + High + High

 15-billion corn conversion input

 gallons yield effi ciency cost

Item (RFS15) (HCY) (HCE) (HIC)

 Million metric tons of CO2 equivalent

Change in land use 3.8 2.1 3.1 -14.8

Change in tillage system -0.9 -0.8 -0.8 -2.8

RFS = Renewable fuel standard.

Source: Economic Research Service calculations based on Regional Environment and 

Agriculture Programming (REAP) model data.
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With projected expansion of the U.S. biofuels industry, 

increasing attention has focused on the implications for 

water demand: 

• How much water will be required to support expansion 

in biofuels? 

• How will emerging demands affect the allocation and 

sustainability of water supplies? 

• To what extent will limited water supplies inhibit expansion of feedstock production? 

Most U.S. corn acreage is not irrigated. Irrigation is essential 

for corn production in much of the Western United States, 

where crop water requirements generally exceed natural 

soil moisture reserves. In the more humid Eastern States, 

supplemental irrigation may be applied to minimize the risk 

of production shortfalls due to below-normal precipitation. 

In 2002, 9.7 million acres of corn were irrigated nationally, 

accounting for about 14 percent of U.S. corn acres. Of the 

major corn-producing regions, irrigated corn production 

accounted for 55 percent of the irrigated land in the Northern 

Plains, 38 percent in the Corn Belt, and 14 percent in the 

Southern Plains (USDA-NASS, 2004a). 

While it is unclear how much corn ethanol feedstock is 

produced under irrigation, the distribution of ethanol 

processing plants provides some indication (see map). 

Approximately 15 percent of current ethanol capacity is 

located in counties where more than half of the corn is irrigated. When considering both current and projected plant 

capacities, 20 percent of projected ethanol capacity is in 

counties where more than half of the corn is irrigated. The 

reliance on irrigation will refl ect, in part, the share of feedstock supplied by locally grown corn and the share of local 

production that is irrigated. 

Demand for Corn-Based Ethanol May Drive 

Changes in Agricultural Water Demand

The demand for biofuel feedstocks, and resulting increases in 

the price of corn and other grain commodities, may increase 

demand for agricultural water where irrigation expansion 

is feasible. The potential effect on water withdrawals will 

Irrigated corn share and existing ethanol plant locations

Irrigated corn acres \ Total corn acres

0 - 0.1

0.11 - 0.32

0.33 - 0.58

0.59 - 0.84

0.85 - 1

Existing plants

Source: ERS calculations based on 2003 Census data and 2007 Renewable Fuels Association data.

Water Use Impacts From Ethanol Production

Continued on page 33
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depend on changes in total irrigated acres, acreage by irrigated crop, and applied water per crop acre. The net effect 

on agricultural water withdrawals is uncertain and may vary 

both regionally and over time, depending on the distribution 

of biofuel processing facilities, feedstock sources, and institutions that govern water allocation.

In the short run, potential changes in water demand for feedstock production would largely be driven by an expansion of 

irrigated corn. In 2003, average applied water on irrigated 

corn acreage ranged from more than 20 inches in the West 

to less than 6 inches in the humid Eastern States (USDA, 

2004a). Expressed in terms of average applied water per 

bushel of corn, more than 4,000 gallons of irrigation water 

were used per bushel produced in Mountain and Pacifi c 

regions, compared with 2,000-3,000 gallons in the Plains 

States and less than 1,000 gallons in the Eastern States. 

Assuming 2.7 gallons of ethanol can be produced per bushel 

of corn, irrigation water use per gallon of ethanol ranges 

from less than 400 gallons of applied water in the East to 

more than 1,500 gallons applied in the West. Estimates of 

water use per bushel of corn differ substantially across States 

and years due to variation in corn yields, natural precipitation, and other local production conditions. 

The effect of a regional shift to irrigated corn on aggregate water demand depends, in part, on the water requirements of the crops displaced by corn. Available data 

suggest that increased corn acreage has displaced acreage 

in soybean production. Other potential sources of land for 

corn expansion include acreage in cotton and pasture, as 

well as fallow land on acreage in expiring Conservation 

Reserve Program (CRP) contracts (Westcott, 2007). In 

the Plains States, where much of the expansion in biofuel 

processing capacity is projected to occur, a shift from irrigated soybean or cotton production would likely result in a 

net increase in water use. Alternatively, a shift from more 

water-intensive crops, such as alfalfa hay or sugarbeets, 

could reduce water use in some areas. In the Southeast 

and Delta regions, where applied water in irrigated corn 

production is low relative to other irrigated crops, pressure on regional water supplies may be reduced. 

Regional water demand could increase with expansion of 

irrigated acreage, through both reductions in fallowed land 

and conversion of nonirrigated cropland and pasture land. 

The effect of ethanol demand on the price of corn, and 

indirect price impacts on the profi tability of other fi eldcrop 

commodities, generally increases the demand for irrigated 

cropland and associated water. ERS model results indicating 

an expansion of cropland in the Plains States and other 

regions (see, table 3) suggest a potential for increased reliance on irrigation. 

While corn is likely the primary feedstock source for ethanol 

production over the foreseeable future, other irrigated fi eldcrops—including sorghum, small grains, and sugarbeets—

may become increasingly important in noncorn-producing 

areas. Sorghum and small grains are generally less waterconsumptive than corn, while sugarbeets generally use more 

water. In the longer term, cellulosic biofuel production is slated 

to expand. Although relatively little is known about water 

requirements for cellulosic crops produced on a commercial 

scale, native grasses (e.g., switchgrass) may use less water while 

trees (e.g., poplar) and other dedicated biomass crops may 

have greater water requirements. Potential irrigation demands 

will depend on the regional location of production, the reliance on marginal cropland soils, plant breeding advances, and 

evolving production technologies (NRC, 2007). 

Water is an essential input in ethanol processing. Consumptive 

water use at ethanol processing facilities results largely from 

Water for corn production is higher than many 

crops, but lower than those that may be displaced, 

Plains States, 2003

Source: ERS calculations based on 2003 Census of Agriculture, 

Farm and Ranch Irrigation Survey (FRIS) data.

Barley

Other small grains

Vegetables

Dry beans

Wheat

Soybeans

Cotton

Sorghum

Other hay

Pasture

Peanuts

Potatoes

Corn

Alfalfa

Corn silage

Sugarbeets

Orchards

Rice

0 5 10 15 20 25 30

Irrigation application (inches)

Continued on from 32

Continued on page 34
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evaporation losses from cooling towers and evaporators 

during ethanol distillation following fermentation. Current 

estimates of consumptive water use from these facilities 

are approximately 4 gallons of water per gallon of ethanol 

produced. Thus, a plant producing 100 million gallons of 

ethanol per year would require approximately 400 million 

gallons of water per year. Total water withdrawals used in 

ethanol processing, however, are small relative to potential irrigation demands for feedstock production. Based on 

applied water for irrigated corn in Nebraska (USDA-NASS, 

2004a), an estimated 780 gallons of irrigation water are 

used per gallon of ethanol produced—or roughly 200 times 

more water than is typically used for ethanol processing 

(NRC, 2007). But as water use for ethanol processing is 

concentrated in a smaller area, effects may be substantial 

at a local level. 

Scarce Water Resources May Face 

Additional Pressure

Potential increases in water demand for energy crop production have raised concerns over the allocation and long-term 

sustainability of groundwater and surface water resources. 

Much of the current irrigated corn production in the Great 

Plains region relies on groundwater withdrawals from 

the Ogallala Aquifer. Groundwater withdrawals for agricultural and municipal uses have generally exceeded the 

natural recharge process of the aquifer, resulting in water 

table declination exceeding 100 feet over large portions of 

the region. Expansion of ethanol processing capacity in the 

Plains region, and increased irrigated feedstock production 

to supply these facilities, could accelerate withdrawals in 

some areas (Roberts et al., 2007). Moreover, as contracts 

expire for acreage idled under the CRP, some marginal 

cropland with potentially higher water requirements could 

return to irrigated production, placing additional pressures on groundwater resources. While corn production in 

the Corn Belt is largely rainfed, groundwater overdraft is a 

concern in areas of the upper Midwest where much of the 

current ethanol processing capacity is concentrated (NRC, 

2007). In western irrigated regions that rely on surface-water 

withdrawals, emerging water demands for urban and environmental uses compete increasingly for agricultural water 

supplies. Expanding energy crop production has the potential 

to increase pressure on available water resources.

Under high prices for biofuel feedstocks, irrigation could 

potentially make corn economically profi table in some 

noncorn-producing areas. While biofuel feedstock production could conceivably expand in areas not traditionally 

irrigated, signifi cant increases in surface and groundwater 

withdrawals may be limited by physical water-supply 

availability, storage and conveyance infrastructure, legal 

constraints, and economic considerations. Irrigation expansion often requires major capital investment in water-supply 

development, as well as assurances that potential economic 

gains (due to higher yields and reduced yield variability) 

will cover increased capital and operating costs. It is unclear 

whether biofuel markets would provide suffi cient incentives 

for irrigation expansion in areas where irrigated fi eldcrop 

production is not currently established. 

At the national level, agricultural production to meet the 

demand for biofuels is not likely to greatly alter aggregate 

water withdrawals. Competing demands for limited water 

resources would restrict increases in the share of water allocated to the expansion of irrigated agriculture. While development of the biofuels industry has had a generally marginal 

impact on water supplies at the regional and local levels, 

future expansion and diversifi cation of feedstock production 

could increase or decrease pressures where water resources 

are under stress, depending on shifts in cropping allocations. 

As biofuel production expands to meet national goals, the 

long-term sustainability of surface and groundwater resources 

used for feedstock production and ethanol processing may 

require policy attention. 

Continued on from 33
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Biofuel Impacts on the Livestock Sector 

and Implications for the Environment

While increasing biofuel demand has contributed to greater returns to U.S. 

crop production, the livestock industry faces higher feed costs. Greater 

concentrations in livestock production have increased both cost effi ciencies and the relative importance of purchased feed. But biofuels also offer 

opportunities for livestock producers, as the sector adapts to changing market 

conditions and technologies. Shifts in animal production due to biofuel 

expansion could have positive implications for environmental quality.

Structural Change in the U.S. Livestock Sector 

The U.S. animal sector has undergone signifi cant structural change in recent 

decades. The number of farms with animals has declined, while farms 

accounting for most production are much larger. Production is generally more 

specialized, with farms usually raising a single species of animal. Operators 

often specialize in a specifi c stage of animal production, which is then 

linked to other stages of production and processing through formal contracts. 

Greater concentration of the industry has largely been driven by fi nancial 

considerations. Larger operations are able to realize lower costs and higher 

returns, while tighter coordination among fi rms at different processing stages 

can reduce fi nancial risks (MacDonald and McBride, 2009). 

More animals on larger, specialized farms has been accompanied by regional 

shifts in livestock and poultry concentrations (Norton, 1998; Herath et al., 

2005). Broiler production has increased in the Southeast and Delta regions. 

Swine production has shifted east and west from traditional hog-producing 

areas of the Midwest. Dairy production has expanded from the Midwest 

to the Western States, while the fed-cattle industry has consolidated in the 

Plains and Southwest. Factors motivating regional production shifts include 

lower costs for land and labor, availability of livestock support infrastructures, increased acceptance of contract production, and a strong local business environment (Herath et al., 2005).

Larger operations tend to rely more heavily on purchased feed than do 

smaller livestock operations. With greater concentrations in the livestock and 

poultry sector, the industry has shifted from reliance on grass forage to a 

grain-based diet under confi ned production conditions. The role of purchased 

feed has also increased as farm grain and forage production for animal 

feeding has declined and demand for feed supplements had expanded. While 

the animal industry has shifted location in recent decades, producers remain 

largely dependent on Midwestern feed grain supplies—primarily corn—

which accounts for more than 90 percent of feedgrain use in the United States 

(USDA-ERS, 2008b). 

Biofuel Demand Increases Costs 

for Livestock Producers

Feed grain costs increase with greater biofuel demand. As described in 

the section “Expanding Corn Acreage Drives Market and Environmental 
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Outcomes,” corn prices are predicted to increase by 2.2 percent due to the 

1.7-billion-gallon increase in corn-based ethanol necessary to meet the 

15-billion-gallon target (see table 5). Prices for soybean oil—another important input in animal feed rations—also increase. Soybean prices increase by 

2.7 percent due to higher demand for soybean oil in biodiesel production and 

reduced soybean acreage due to competition with corn. Higher animal feed 

costs can reduce returns to animal production, as feed costs generally account 

for more than half of total variable expenditures in a given year (Becker, 

2008). Reduced carryover stocks due to biofuel demand may also increase 

the variability of feed grain prices (Lawrence et al., 2008). Results suggest 

a reduction in average aggregate returns to livestock production of about 

0.3 percent as biofuel production increases (see table 7). This effect occurs 

despite slight increases in livestock prices. 

The use of corn ethanol coproducts, such as distiller’s grains, in animal feed 

rations may offset higher feed grain costs (see box, “Prospective Growth in 

U.S. Corn Yield” p. 6). According to a 2006 survey of animal feeding operations in the Midwest and Plains States, approximately 14 percent of beef, 

dairy, and hog operations used biofuel coproducts, predominantly in the form 

of distiller’s dried grains with solubles (DDGS) (USDA-NASS, 2007). Beef 

cattle and dairy account for a large share of consumption, although incorporation in swine and poultry diets is increasing. 

Production of distiller’s grains under the USDA baseline is projected to 

reach 41.5 million tons in 2015. Higher ethanol production under the RFS15 

scenario would expand production of DDGS by an additional 12 percent, with 

the bulk of this increase consumed by the domestic cattle feeding sector (fi g. 

11). The increased demand for distiller’s grains, associated with increased 

corn and soybean prices, results in higher prices for distiller’s grains, despite 

increases from expanded ethanol production. As production of distiller’s 

grains tracks closely with corn ethanol targets, changes in corn yields and 

input cost assumptions do not affect coproduct production appreciably. 

Figure 11

Changes in price and selected uses of distiller’s grains under 

alternative scenarios, 2015

RFS = Renewable fuel standard.

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.

Percentage change from baseline

RFS 15

Higher input costs

Increased ethanol conversion

Increased corn productivity

Consumption 

by beef cattle

Consumption 

by hogs

Price

-5 0 5 10 15 20 25
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Increases in ethanol conversion effi ciency, however, could reduce the amount 

of distiller’s grains available to livestock producers.

Productivity growth in corn yields would increase corn production and 

lower prices, offsetting the predicted effects of increased biofuel production. As a result, distiller’s grains are priced 1.2 percent lower than the baseline scenario. On the other hand, combining increased biofuel production 

with improved ethanol conversion effi ciencies leaves distiller’s grain prices 

unchanged relative to the RFS15 scenario, but the reduced demand for cornbased ethanol leaves more corn available for feed and reduces the amount 

of distiller’s grains fed to beef cattle by about a third. Higher corn productivity and increased consumption of distiller’s grains by beef cattle displaces 

consumption in the hog sector; in contrast, increased conversion effi ciency 

has a negligible effect on the hog sector. Higher ethanol conversion effi -

ciency will lower production of distiller’s grains since more of the starch is 

converted to ethanol and less is available for coproducts.

Higher Feed Costs May Reduce 

Livestock Inventories

In the short term, increased feed costs may result in fi nancial losses to the 

U.S. livestock sector. As markets adjust over time, higher feed costs and 

reduced returns lead to decreases in production by livestock producers, 

raising prices for livestock products. In evaluating the increase in corn 

ethanol demand from 13.3 to 15 billion gallons in 2015 under the revised 

RFS, ERS projects that higher feed prices would lead to a slight contraction in livestock inventories (table 10). Animal inventories decline across all 

species, but declines are small, generally less than 0.5 percent.

A 50-percent increase in projected corn yield growth that occurs concurrently with biofuel expansion would offset the effect of the higher corn-ethanol 

target on livestock production levels due to feedgrain price reductions relative 

to biofuel expansion and absent increased yield growth. In contrast, higher 

Table 10

Livestock and poultry inventories under alternative scenarios, 2015

 RFS + High + High + High

 15-billion corn conversion input

 gallons yield effi ciency cost

Inventories Baseline (RFS15) (HCY) (HCE) (HIC)

 —— Percent change from baseline ——

Dairy (million cows) 9.0 -0.10 0.13 -0.07 -0.42

Hogs (billion pounds) 34.3 -0.14 0.33 -0.11 -1.72

Beef cow: 

 Grazing (million cows) 29.3 -0.50 0.52 -0.36 -2.20

 Farm fed (million cwt) 57.2 -0.62 0.80 -0.44 -2.87

 Feedlot (million cwt) 300.2 -0.38 0.39 -0.28 -2.00

 Stockers (million cwt) 114.7 -0.38 0.38 -0.28 -2.03

Poultry (million pounds) 38,771 -0.50 0.67 -0.36 -1.53

RFS = Renewable fuel standard.

Cwt = Hundredweight.

Source: Economic Research Service calculations based on Regional Environment and 

Agriculture Programming (REAP) model data.
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ethanol conversion effi ciencies have a small effect on feedgrain costs and livestock inventories. An increase in projected energy costs that raises the cost of 

feed grains and other livestock expenses, without a commensurate increase in 

productivity, results in further contraction of livestock production levels. 

Small aggregate shifts in the livestock sector belie regional shifts in production in a few cases. Results suggest that small regional shifts would occur in 

the cattle and dairy industry. The size of dairy herds, for example, is fairly 

stable across much of the Western United States, while generally declining 

in the Corn Belt, Southeast, and Delta regions (table 11). Regional changes 

associated with RFS15 are small (less than 0.5 percent), but larger in many 

cases than the 0.1 percent aggregate decline for the United States as a whole. 

As with other variables, the largest changes would occur if increased biofuel 

demand coincides with higher energy-related input costs. Contraction in 

the feedlot-cattle sector mostly affects the Northern and Southern Plains. 

Regional shifts are also predicted for beef stocker production, with some 

growth in the Corn Belt and some contraction in the Southern Plains.

Biofuel Expansion Could Reduce 

Environmental Effects of Manure 

Structural change in the U.S. animal sector has raised environmental 

concerns regarding animal waste. Larger operations concentrate animals in a 

limited area, and manure nutrients applied to land in excess of onfarm crop 

requirements contribute to air and water pollution. Under the Federal Clean 

Water Act, the EPA regulates animal waste on concentrated animal feeding 

operations (CAFOs), which generally include the largest operations. A CAFO 

rule was established in 2003 (and revised in 2008) that requires Nutrient 

Management Plans as a permit requirement for CAFOs that fall under the 

National Pollution Discharge Elimination System (NPDES) program (EPA, 

Table 11

Regional shifts in dairy cows, 2015

 RFS + High + High + High

 15-billion corn conversion input

 gallons yield effi ciency cost

Region Baseline (RFS15) (HCY) (HCE) (HIC)

 —— Percent change from baseline ——

Northeast 1.7 -0.1 0.1 0.0 -0.2

Lake States 2.1 0.0 0.0 0.0 -0.2

Corn Belt 0.9 -0.5 0.6 -0.3 -1.8

Northern Plains 0.3 0.0 0.0 0.0 -0.2

Appalachian 0.7 -0.1 0.2 -0.1 -0.5

Southeast 0.3 -0.4 0.6 -0.3 -1.1

Delta 0.2 -0.4 0.6 -0.3 -1.1

Southern Plains 0.4 -0.1 0.2 -0.1 -0.5

Mountain States 0.8 -0.1 0.2 -0.1 -0.5

Pacifi c 1.7 0.1 -0.1 0.1 0.0

United States 9.0 -0.1 0.1 -0.1 -0.4

RFS = Renewable fuel standard.

Note: Totals may not sum due to rounding.

Source: Economic Research Service calculations based on Regional Environment and 

Agriculture Programming (REAP) model data.
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2008). Nutrient Management Plans restrict the rate of land-applied manure 

to the nutrient assimilative capacity of crops produced. States have also 

extended manure nutrient standards, in some cases, to operations not subject 

to Federal regulations. For livestock operations with a limited land base, 

much of the manure must be moved off the farm—often over long distances 

at considerable hauling cost—to land suffi cient for manure application 

(Ribaudo et al., 2003). 

Expanding biofuel production may result in an indirect, if small, improvement in water quality due to reduced manure nutrient runoff and leaching. 

This effect is primarily due to reducing the volume of manure produced by 

reducing animal inventories. Findings suggest that expanding ethanol production from 13.3 to 15 billion gallons in 2015 (RFS15) would reduce recoverable manure production in confi ned animal operations by roughly 0.2 percent. 

The largest adjustments occur in the cattle and poultry sectors. 

Reducing manure production translates to reductions of 7.9 million pounds 

of manure nitrogen and 4.5 million pounds of manure phosphorus. Total 

recoverable manure nutrients decline across all U.S. regions under the RFS15 

target because potential reductions in recoverable manure are closely correlated with changes in animal inventories. Those changes, however, are all 

less than 1.0 percent. The most signifi cant reductions occur in the Delta, 

Southeast, and Southern Plains, where manure nitrogen (fi g. 12) and phosphorus decline from 0.7 percent to 0.9 percent. Declines in other regions 

range from 0.2 percent to 0.6 percent. Changing relative species’ composition 

alters nitrogen and phosphorus availability. 

Figure 12

Changes from baseline in available nitrogen from manure, by region and scenario, 2015

Percent change in available nitrogen

RFS = Renewable fuel standard.

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.

Northeast

Lake States

Corn Belt

Northern Plains

Appalachia

Southeast

Delta

Southern Plains

Mountain States

Pacific

United States

-4

-3

-2

-1

0

1

2

RFS 15 High corn yield High ethanol conversion High input costs
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Projected manure nutrient levels are affected by modeling assumptions that 

infl uence corn production and feed costs. Declines in manure nitrogen will 

be lessened if increased biofuel demand is coupled with increased ethanol 

conversion rates (HCE), but will be even larger if input costs increase 

substantially (HIC). Notably, an increase in projected corn yields that lowers 

animal feed costs (HCY) will raise levels of recoverable manure nutrients, 

with the Delta and Southeast seeing the biggest increases. 

Biofuel expansion may benefi t confi ned feeding operations through increased 

demand for manure nutrients. Cropland expansion, increased cropland allocations to corn, and higher yields increase potential nutrient uptake. As a result, 

animal producers may have greater access to farm fi elds for manure spreading 

to comply with nutrient management provisions. Potential savings in hauling 

costs will depend on the willingness of local landowners to accept manure 

(Ribaudo et al., 2003). Water quality benefi ts will refl ect the susceptibility of 

local water bodies to nutrient runoff and leaching, as well as the manure regime 

applied and the realized offsets in applied chemical fertilizers.

Animal production is also an increasing concern for air quality and GHG 

emissions (Aillery et al., 2005). Recommended guidelines for incorporating applied manure on corn feedstock acreage can limit emissions of 

volatilized ammonia and nitrogen. Methane gas from animal manure, 

collected from livestock holding areas and waste lagoons, has been used 

to generate electricity for confi ned livestock facilities. Recent interest 

has focused on methane as a supplemental power source for ethanol 

processing. Colocation of ethanol processing facilities with large feedlots 

and dairy operations can provide a signifi cant share of an ethanol plant’s 

energy needs (Hart and Carriquiry, 2007). Methane capture for power 

generation can also generate additional revenue for livestock producers 

through sale of carbon offsets in the emerging market for GHG emission 

reductions. Methane captured and fi ltered could also be integrated with 

the natural gas distribution system.

Implications For Expanding 

Ethanol Plant Capacity

Linkages between U.S. biofuel and livestock sectors will grow over time 

as competitiveness intensifi es in the biofuels industry and markets expand 

for ethanol coproducts in animal feed. Ethanol producers may rely more on 

fi nancial arrangements with livestock producers to capture cost effi ciency 

gains as the industry evolves, further transforming the agricultural landscape. 

With projected increases in energy costs, ethanol plant locations become 

more dependent on coproduct markets (Dhuyvetter et al., 2005). Higher 

costs for drying (typically with natural gas) provide an economic incentive 

to market distiller’s grains in their wet form, while higher fuel costs increase 

the cost of shipment. Increased drying and transportation costs favor closer 

proximity of ethanol plants to animal concentrations to utilize wet distiller’s 

grains. Rising costs may also encourage placement of ethanol plants near 

confi ned animal operations for methane power generation.

By placing ethanol production facilities away from farmer-owned cooperatives, integrated facilities become less spatially tied to corn-producing areas. 
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At the same time, the shift to larger capacity plants increases the need for 

access to reliable distiller’s grains markets (Dhuyvetter et al., 2005). 

Expanding ethanol processing in the Southern High Plains,2 in proximity to 

large cattle feedlot operations, indicates the growing importance of coproducts in plant siting decisions. Distiller’s wet grains produced and fed to 

feedlot cattle can provide a reliable market outlet, as well as energy cost 

savings. A shift in the concentration of ethanol processing capacity from the 

Midwest will depend, in part, on the relative cost savings in local markets for 

distiller’s grains versus the additional cost of feedstock transport from major 

corn-producing regions (Hart and Carriquiry, 2007). 

Expanding the ethanol industry, in turn, may infl uence long-term trends in 

the livestock industry. Access to distiller’s wet grains could spur increased 

concentrations of beef and dairy herds near ethanol processing facilities. 

Spreading manure on energy feedstock crops and potential use of animal 

waste for onsite power generation provide additional incentives for herd 

expansion near processing facilities. Ethanol’s reliance on corn as the primary 

feedstock may adjust relative regional cost advantages in livestock production, potentially slowing or reversing the recent shift in animal concentrations 

from the Midwest.

In fact, current and planned ethanol production capacities appear to correlate 

strongly with the presence of livestock and, in particular, with livestock’s 

capacity for distiller’s grain consumption (fi g. 13). Planned ethanol expansion 

into areas of Arizona and the Southern Plains, with high concentrations of 

confi ned livestock that can consume distiller’s grains, is particularly striking. 

Certainly some outliers exist — strong DDG demand appears in Southern 

California without substantial ethanol capacity, while ethanol capacity in the 

Southeast and Eastern Corn Belt does not coincide with substantial DDG 

demand. The relationship could also be spurious, however, with livestock 

operations and ethanol plants independently located near corn production. It 

is clear that both the livestock and ethanol sectors are in fl ux. More research 

is needed to improve understanding of cause-and-effect linkages, if any, in 

livestock and ethanol location decisions, but also on likely implications for 

resource conditions.

2Represents an area where ethanol 

production is expanding, but does 

not exactly correspond to regions 

cited previously; a smaller part of the 

Southern Plains (the panhandle of 

Texas and western Oklahoma).
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Figure 13

Ethanol production capacity and corn acreage, 2002

Source: ERS calculations based on Renewable Fuels Association data, USDA’s 2002 Agricultural Census, 

and Dhuyvetter, Kastens, and Boland (2005).

Current capacity

Mil. gallons/year

0 - 18

19 - 46

47 - 80

81 - 160

161 - 400

With planned expansions

Mil. gallons/year

0 - 27

28 - 58

59 - 86

87 - 215

216 - 478

Irrigated corn acres / Total corn acres

0 - 0.1

0.11 - 0.32

0.33 - 0.58

0.59 - 0.84

0.85 - 1

No data
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Crop Residues and the Introduction 

of Cellulosic Ethanol

Field residues from agricultural crop production represent a potential source 

of cellulosic feedstock to meet growing ethanol demand. Crop residues may 

be a particularly important feedstock in the coming years, as technologies 

and commercial markets continue to develop for cellulosic feedstocks. Crop 

residues may also provide an additional revenue source for crop farmers, 

although returns will depend on interactions in crop and residue markets. 

In this section, we evaluate potential sector impacts of crop residue harvest 

to meet policy targets for ethanol produced from cellulosic feedstocks and 

implications for resource use and environmental quality.

Crops that could provide above-ground crop residue for cellulosic ethanol 

production include corn, wheat, soybeans, barley, and oats. The quantity 

of residue produced per unit of yield varies by crop (table 12). The amount 

of sustainable residue that can be recovered from a fi eld for a given crop 

is determined by harvest technology, soil nutrients, water availability, and 

erosion potential, among other factors. Crop residues provide soil nutrients 

and organic matter, but also prevent soil erosion and retain moisture. In this 

analysis, we assume that 50 percent of crop residues left on the fi elds can 

be harvested from fi elds that use no-till systems, 30 percent of the residues 

can be harvested from fi elds that use reduced tillage systems, and 10 percent 

of the residues can be harvested from fi elds that use conventional systems 

without adversely affecting soil productivity. These fi gures are intended only 

as a starting point and represent one possible residue collection scenario; 

future research will refi ne these values. Ongoing research analyzes how 

much residue can be harvested while maintaining soil productivity and crop 

yield, and removal rates used in this report may be higher than optimal given 

soil organic carbon requirements (Wilhelm et al., 2007). Because of erosion 

considerations, this analysis assumes that residue harvest is not permitted on 

land classifi ed as highly erodible.

Residue harvest costs vary by region, crop, and the amount collected. In 

addition to the direct cost of collection, handling, and storage, harvest costs 

may also include the foregone value of nutrients, soil, and future yield lost. 

Typical nutrient content for crop residues are about 17 pounds of nitrogen 

and 4 pounds of phosphate per ton of corn residue and 11 pounds of nitrogen 

and 3 pounds of phosphate per ton of wheat residue (Wortmann et al., 2008). 

Table 12

Residue-to-grain ratio for selected residue-producing crops 

 Residue-to-grain ratio

Crop (dry weight)

Corn 1.0

Soybeans 1.5

Wheat 1.3

Oats 1.4

Barley 1.5

Sorghum 1.0

Source: Graham et al., 2007.
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Wortmann et al. place the value of nutrients lost per ton of corn residue at 

$17.93. Graham et al. (2007) provide a set of curves that estimate the collection cost as a function of stover collected per acre and collection method, 

including the cost of nutrient replacement (given as $6.50 per ton). For this 

analysis, we simplify the process by imposing a representative $40/ton cost of 

residue harvest across regions and crops. This value represents the midpoint 

of the curves in Graham et al., adjusted by the higher replacement cost of the 

Wortmann et al. analysis.

Effect of Crop Residue Feedstocks 

on Environmental Resources Is Mixed

To measure the effects of an increasing share of cellulosic ethanol in the 

analysis, crop residue-based ethanol production ranges from 3 billion gallons 

to 6 billion gallons, with corn-based ethanol making up the difference (i.e., 

15 billion gallons down to 12 billion gallons). This scenario represents the 

possibility for accelerated development of cellulosic ethanol conversion technology, but keeps total ethanol demand constant. Below 6 billion gallons of 

cellulosic ethanol production, crop residue availability (after accounting for 

rate of removal by tillage system) exceeds demand by ethanol producers in 

most regions. At around 6 billion gallons of cellulosic production, all available crop residues are used in some regions, increasing the economic value 

of residue and causing primary crop production to increase, which puts 

downward pressure on the price of the primary crop. The higher price of 

crop residue would, in turn, provide production incentives for other sources 

of cellulosic feedstock that would also compete for land with the residueproducing crops. At 3 billion gallons of cellulosic production, the Northern 

Plains manufactures the most.

Crop prices relative to production levels, when 3 billion gallons of cellulosic ethanol replace corn ethanol in 2015 (i.e., corn ethanol production is 

reduced from 15 billion gallons to 12 billion gallons), are shown in fi gure 

14. Prices for major crops vary considerably over the range of cellulosic 

production due to the different levels of assumed corn-based ethanol across 

the cellulosic scenarios. Corn shows the largest decline in price, dropping 

4.2 percent as cellulosic production increases from 3 to 6 billion gallons, 

with steady price declines over the entire range. Less corn for ethanol and 

lower corn prices lead to more corn used for food and feed. In contrast, 

wheat prices decline only slightly over the whole range. The fraction of the 

corn crop used for ethanol declines from 37 percent to 31 percent as corn 

used for ethanol falls from 15 billion gallons to 12 billion gallons. The 

amount of corn used for food, feed, and exports increases from 9.3 billion 

bushels to 9.7 billion bushels.

Once the RFS15 target of 15 billion gallons of corn ethanol and 3 billion 

gallons of cellulosic ethanol is reached, total land planted to major crops will 

be 318.7 million acres. As crop residue-based cellulosic ethanol is substituted for increasing amounts of corn ethanol, less total land is planted to 

traditional crops. The decline in traditional planted crops is dominated by a 

large reduction in corn acreage, along with small reductions in soybeans and 

hay. Other crops show a slight increase in acreage as land is freed from corn 

production. The rate that acreage reduces as cellulosic production increases 
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and corn ethanol production declines is about 1.7 million acres per billiongallon reduction in corn ethanol. Acreage response varies across regions. 

Total planted acres decline in all major corn producing areas, as illustrated 

in fi gure 15, but at different rates. This decline is mainly driven by a greater 

reduction in corn acres in the Northern Plains, compared with other regions, 

as crop-residue demand increases. As crop residues gain economic value, the 

Northern Plains adds wheat acres, contributing to an increase in total cropped 

acreage in the region. Once over the 3 to 6 billion-gallons range in cellulosic 

production (and corresponding decrease in corn ethanol production), corn 

acres decrease by 5.8 percent, from 94.7 to 89.2 million acres. Reduced corn 

Figure 14

Crop prices relative to 3 billion gallons of cellulosic ethanol production

Price index (3bg=100)

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.

Cellulosic production (billion gallons)

95.5

96

96.5

97

97.5

98

98.5

99

99.5

100

100.5

3 4 4.5 5 5.5 6 3.5

Corn

Sorghum

Barley

Oats

Wheat

Rice

Soybeans

Figure 15

Total acreage relative to 3 billion gallons of cellulosic 

ethanol production

Total acre index (3bg=100)

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.

Cellulosic production (billion gallons)

Lake States

Corn Belt

Northern Plains

95.5

96

96.5

97

97.5

98

98.5

99

99.5

100

100.5

3 4 4.5 5 5.5 6 3.5
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area in the major corn-producing regions of the Corn Belt, Lake States, and 

Northern Plains drives the decline in overall acreage.

Although total acreage is reduced at higher levels of cellulosic ethanol 

production, aggregate environmental effects may not be reduced when additional fertilizer is applied to replace the nutrients removed with the harvested 

residue. We examined the response of four critical environmental measures: 

• Nitrogen leaching to groundwater; 

• Nitrogen runoff to estuaries; 

• Nitrogen runoff to surface water; and 

• Soil erosion. 

Figure 16 shows how these measures change relative to the baseline target 

across the range of cellulosic ethanol demand. To measure the effect of 

changes in production practices on environmental performance, independent 

of the direct effect of fewer acres planted, we divided the environmental 

impacts by the change in total acres for the given level of cellulosic production. In general, net levels of environmental impacts increase, even after 

accounting for the reduction in planted acres. Nitrogen leached to groundwater increased steadily up to 6 billion gallons of cellulosic ethanol production. Not all regions, however, exhibit an increase in all measures, as shown 

in fi gure 17 (also adjusted for changes in total acreage). High levels of runoff 

in the Corn Belt drive an increase in national levels of nitrogen lost to surface 

water. Reductions in leaching to groundwater in all other regions offset this 

high level of leaching in the Corn Belt, largely due to fertilizer replacement 

that compensates for the nutrient value of the harvested residue.

The potential for increased soil erosion from residue harvesting is an important policy concern. Findings suggest, however, that residue harvest could be 

Figure 16

Changes in environmental indictors relative to 3 billion gallons 

of cellulosic ethanol production

Environmental indicator index (3bg=100)

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.

Cellulosic production (billion gallons)

97

98

99

100

101

102

103

Nitrogen to estuaries

Nitrogen to groundwater

Nitrogen to surface water

Soil erosion

3 4 4.5 5 5.5 6 3.5
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accompanied by changes in management regimes, contributing to a net reduction in soil erosion. As cellulosic production increases from 3 to 6 billion 

gallons and corn-based ethanol production falls from 15 to 12 billion gallons, 

acres planted to continuous corn decline, particularly in the Corn Belt, while 

the use of no-till systems expands. Use of no-till systems increases from 11 

percent to 21 percent of national cropland in production, while use of conventional tillage systems declines from 72 percent to 53 percent. Wider adoption 

of no-till systems is driven by the economic value of crop residues, more of 

which can be harvested from no-till systems.

Production of feedstocks to supply the emerging cellulosic ethanol industry 

will become increasingly important to the agricultural landscape. The abundant supply of existing crop residues will play a large role, especially as 

dedicated energy crops begin to enter the picture. Harvesting crop residues 

as an ethanol feedstock, however, is not without consequences. Nutrients and 

organic matter left in the soil would need to be replaced if the residue was 

not harvested. Application of additional fertilizer has consequences to environmental measures, although the extent to which residues may be harvested 

while minimizing environmental effects is a topic of additional research.

Figure 17

Change in nitrogen leaching to groundwater and nitrogen runoff 

to surface water from an increase of 3 to 6 billion gallons of 

cellulosic ethanol production and a reduction of corn ethanol from 

15 to 12 billion gallons, by region

Runoff to surface water (million tons)

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.

Leaching to ground water (million tons)

-40

-30

-20

-10

0

10

20

30

-40 -20 0 20 40 60 80 100 120 140

Corn Belt

Rest of

United States

Lake States

U.S. total

Northern Plains
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Research and Policy Options To Mitigate 

the Impact of Biofuel Feedstock Production

Increased agricultural feedstock production to support the biofuels industry 

will reshape the U.S. farm sector through changes in commodity markets, 

land allocations, and production systems. As this study suggests, the implications for resource use and environmental quality are potentially far reaching. 

Technological advances at different stages of the biofuel production chain can 

improve the effi ciency and cost-effectiveness of conventional and emerging 

technologies. At the same time, greater understanding of the complex 

biophysical and economic linkages may lessen the impact of unintended 

consequences on human and natural systems. Public and private research is 

underway on new technologies and the infrastructure needed to support an 

evolving biofuels sector. Research may inform policy design by improving 

our understanding of potential outcomes and tradeoffs for agriculture, energy, 

and the environment. 

Increased Productivity Can Reduce 

Pressure on Land Resources

Rising demand for corn, the primary feedstock for ethanol processing under 

conventional technology, has increased competition for land resources in 

food and feed production. Research to increase corn productivity may reduce 

pressure on cropland by increasing ethanol output per acre of corn feedstock. 

Increased productivity supports both corn producers (through higher returns 

per unit yield) and consumers (through lower grain prices). Environmental 

indicators are also enhanced once higher corn yields can be achieved without 

commensurate increases in chemical and carbon-based inputs. Rates of yield 

growth will depend on the availability of higher yielding varieties (including 

new cultivars with higher starch content), the use and sustainability of irrigation and other yield-enhancing practices, and the distribution of new corn 

acreage across regions and land-quality classes. Additionally, improved 

crop-ethanol conversion effi ciencies through updated fermentation and distillation technology or new crop varieties with higher starch content could also 

reduce cropland demand, although with differing impacts on product markets 

and resource use. Results suggest that increased productivity in the production and conversion of corn feedstock can enhance food and energy security, 

while lessening adverse effects on the environment.

Crop residues, such as corn stover and wheat straw, may serve as important 

feedstock sources to meet targets for cellulosic production. Crop residues are 

already widely available as biomass alternatives to corn feedstock, although 

signifi cant markets and processing capacity do not currently exist. Once 

cellulosic technologies develop on a commercial scale, crop residues could 

provide an additional revenue source for grain producers. Residues, however, 

play an important role in managing soil erosion, nutrient loss, soil carbon, 

and soil moisture. Thus, residues are not “free”—there are costs associated 

with harvesting—and soil productivity and environmental quality could be 

adversely impacted. The amount of residue that can be harvested while maintaining productivity—based on the erodibility of the soil and tillage regime 
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used—is an important policy concern and the focus of ongoing research 

(USDA-NRCS, 2006; Wilhelm et al., 2004).

Improved Assessment Capabilities Needed 

As U.S. biofuel demand expands, many uncertainties remain regarding the 

implications of domestic production for resource use and environmental 

quality. Production of energy feedstock crops is projected to be much larger, 

while regional feedstock sources and technologies are still evolving. Basic 

research is needed to better understand the underlying mechanisms and 

processes that drive resource and environmental outcomes, both at the fi eld 

level and at more aggregate regional scales. Linkages between agricultural 

economic sectors, the energy sector, and the broader economy are also 

complex, and the effects of market, technology, and infrastructure development are not clearly understood. Continuing research is needed to examine 

dynamic adjustments within the U.S. farm sector and their effect on ecologic 

processes and environmental outcomes across the agricultural landscape.

Improved assessment of biofuel sector impacts involves developing new 

analytic tools and supporting databases. Analysis at multiple scales—farmlevel, watershed/regional, national, and international—would address the 

range of factors and outcomes likely to shape future biofuels policy. Linkages 

across agricultural and energy models are increasingly important as energy 

markets drive returns to agricultural feedstocks, with impacts across the 

farm economy. Assessment of the full range of resource impacts will depend 

on improved integration of bio-physical and economic models that capture 

dynamic linkages across human and natural systems. Comprehensive data 

on farm-level land use and resource management decisions under various 

agronomic conditions, and their impact on ecologic processes, is critical to 

assess environmental effects. Probability distributions for key model assumptions would help address uncertainty inherent in human and natural systems. 

Monitoring is also needed to support basic conclusions regarding environmental outcomes. 

One facet of biofuels policy that has received considerable attention recently 

is the extent to which U.S biofuel policies infl uence land-use changes and 

the consequent changes in GHG emissions, both domestically and abroad. 

Accounting for international land-use change is beyond the scope of this 

study. Global land-use implications and feedbacks in international feedstock 

markets, however, remain an important unknown regarding the net benefi ts of 

biofuel policies. 

Reducing GHG emissions, relative to the fossil fuels they are meant to 

replace, is an important part of current biofuel policy and a factor in determining which biofuels will qualify toward meeting the RFS. GHG emissions 

include those caused by growing, processing, and transporting the feedstock 

and production and transport of the biofuel itself. A major component of the 

GHG calculation is the additional land used to grow the feedstock. Land-use 

changes are often categorized into direct and indirect components. Although 

the distinction between direct and indirect land-use change blurs in a biofuel 

context, it is easiest to think of direct land use as the land used to produce the 

biofuel feedstock. This effect is mainly driven by the policy target, crop yield, 

and the ethanol conversion rate. Indirect land-use change results from land 
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planted to other crops and conversion of pasture and forests that is induced—

generally by price shifts—by the biofuel policy. 

In this report, the additional 3.25 million acres of cropland devoted to corn 

production is considered direct land-use change and will factor into the direct 

GHG emissions calculation. The reduction in soybean acres for more corn 

acres could also be considered a direct land-use change. But, as illustrated 

in the section “Expanding Corn Acreage Drives Market and Environmental 

Outcomes,” those production shifts lead to price shifts that echo through the 

crop and livestock sector, changing land use as farmers adjust to take advantage of price shifts. Those adjustments would be considered indirect land-use 

change. In a global economy, price and production shifts in the United States 

can also lead to indirect land-use change internationally. Indeed, two 2008 

studies published in Science (Fargione et al., 2008; Searchinger et al., 2008) 

concluded that if GHG emissions from indirect land use changes were taken 

into account, GHG emissions from biofuel production could be far larger than 

previously estimated. 

The Fargione and Searchinger articles effectively brought the potential implications of indirect land-use change into the public spotlight. Where, how 

much, and what type of land will be introduced into agriculture is a topic of 

considerable debate. Modeling the land-use change caused by agricultural 

policy requires projections about future values of parameters that cannot be 

known with certainty. Therefore, judgments and assumptions must be made 

about the likely values for this uncertain data. The bottom line of an integrated agriculture sector and GHG lifecycle model is, to a greater or lesser 

degree, sensitive to the values chosen and to the underlying structure of the 

model. Since the future cannot be measured, assumptions must be made 

regarding many factors, such as energy prices, rate of technological change, 

GHG policy (e.g., taxes, permit trading, offset markets, etc.), and macroeconomic indicators. Each assumption, whether made explicitly or implicitly 

in the structure and data of the model, will infl uence the outcome. Table 13 

lists some modeling elements subject to uncertainty and how each element 

infl uences measurement of land-use change in agricultural sector models. 

Research leading to a better understanding and possible scientifi c consensus 

of these factors would improve analysis of climate change mitigation and 

adaptation policies.

Conservation Programs Can Reduce 

Potential Environmental Effects

Conservation programs can play an important role in mitigating the adverse 

environmental effects of biofuel feedstock production. USDA provides costsharing and technical assistance to those who adopt improved agricultural 

practices through the Environmental Quality Incentives Program (EQIP) 

and other conservation programs for working farms. Conservation practices 

widely used in fi eldcrop production, and eligible for conservation payments, 

could be applied to corn production and other potential energy crops to 

enhance environmental stewardship, such as:

• Nutrient management measures, including soil testing, application 

regimes, and fi lter strips, can mitigate potential increases in nutrient 

runoff and leaching.
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• Water-conserving technologies can enhance irrigation conveyance and 

fi eld application effi ciency, while water-supply enhancements, such as 

rainfall harvesting and wastewater reuse, can reduce demands on existing 

water resources. 

• Conservation tillage systems can reduce erosion, while enhancing soil 

carbon, nutrients, and moisture. 

Expanded use of crop residues as a biofuel feedstock would likely encourage 

conservation tillage systems, if guidelines can ensure sustainable residueremoval limits. 

USDA conservation compliance provisions, which withhold Federal farm 

payments to producers who convert highly erodible soils or wetlands for cropland use, may limit cropland expansion on environmentally sensitive lands. 

Table 13

Key uncertainties in measuring land-use change

Source of uncertainty Impact on land use Why is it uncertain? Range of values

Productivity (yield) growth of 

corn—domestic and international

As productivity increases, less 

land is required to grow the 

same amount of crop.

Yield trends change over 

time with research innovations. Actual realized yields 

will be dependent on weather 

and soil quality, and thus will 

change with the location of 

converted lands.

Some studies assume yield 

growth high enough to 

eliminate land-use change 

impacts.

Displacement of corn-based 

feed by distiller’s grains 

(DDGS)

Distiller’s grains are a coproduct 

of corn-ethanol production and 

can partially substitute for corn 

as feed, reducing the demand 

for corn that goes directly to 

livestock feed.

The amount of displaced corn 

and soybeans in animal feed 

is subject to debate; availability 

and cost are partially dependent on proximity of livestock 

to ethanol plants. An animals’ 

capacity for digesting DDGS 

varies.

Estimates range from a credit 

of 0.3 acre to 0.7 acre per 

acre of corn for biofuels.

Rate and cost of development, deployment, and conversion effi ciency of cellulosic 

biofuel production

Higher yields (gallons/acre) 

are anticipated for cellulosic 

production, which would then 

require less land to produce 

the same amount of biofuel.

Cellulosic conversion technology is not yet operated on a 

commercial scale. Technologies with the greatest conversion potential may not be fi rst 

to emerge.

Dependent on break-even 

cost of production estimates. 

Estimates range from $1 per 

gallon up to values considerably higher (not economical).

Amount of cropland shifting 

tillage practice (adopting or 

retaining conservation tillage)

Tillage affects carbon sequestration rates and GHG producing input use.Incentives to switch tillage 

(carbon taxes, fuel and input 

prices) are subject to uncertainty.

Assumptions typically based on 

current practice. Some studies 

assume all new cropland is 

conventionally tilled.

Elasticity of supply and demand for biomass cropsMore inelastic demand will 

require more land since there 

is less substitution by other 

commodities.

Imperfect knowledge of substitutes; technology changes over 

time that affect demand.

Usually not explicit in study, but 

implicit in the supply/demand 

structure of the model.

Yield and distribution of dedicated energy crops (switchgrass)Growing energy crops will 

require conversion of cropland, pasture and/or forests, 

potentially diverting crop 

production.

Yields for energy crops are 

only beginning to be established in fi eld trials—no 

production yet on a commercial scale. The land that will 

actually be used for production is unknown.

Values vary by region, but 

generally from 4-10 tons per 

acre. Fertilizer application and 

active management typically 

not factored in.
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In some cases, production on highly erodible soils may be permitted with an 

approved conservation plan that limits potential erosion through conservation tillage and other measures. Conservation compliance provisions may be 

less useful in diverting second-generation energy crops from environmentally 

sensitive land, as these crops are not currently eligible for farm payments. 

The effectiveness of conservation compliance in providing environmental 

safeguards will depend on whether cellulosic feedstock producers receive 

Government payments through other farming enterprises. 
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Appendix: The Modeling Framework

Empirical fi ndings for this study are based on the Regional Environment and 

Agriculture Programming (REAP) model (Johansson et al., 2007). REAP is a 

mathematical optimization model that quantifi es agricultural production and 

its associated environmental outcomes for 50 regions in the conterminous 

United States. The regions are defi ned by the intersection of the USDA’s 

Farm Production Regions (10 groups of States with similar agri-economic 

characteristics1) and the Natural Resources Conservation Service’s Land 

Resource Regions (defi ned by predominant soil type and geography). 

Regional production levels are determined in the model for 10 crops and 

13 livestock categories, with national production levels determined for 20 

processed products. REAP explicitly models regional differences in crop 

rotations, tillage practices, and input use, such as fertilizer and pesticides. 

Commodity prices and input use are determined endogenously. REAP 

employs detailed data (derived from USDA’s Agricultural Resource and 

Management Survey (ARMS) that is conducted by the National Agricultural 

Statistics Service and the Economic Research Service and the Environmental 

Productivity and Integrated Climate (EPIC) model) at the regional level on 

crop yields, input requirements, costs and returns, and environmental parameters to estimate long-run equilibrium outcomes. 

For the analysis in this report, the model is calibrated to prices and quantities for 2015 of the 2008 USDA baseline. Changes in agricultural production 

from this baseline can be assessed for a wide range of policy, market, or environmental shocks. The model has been widely applied to address agri-environmental issues, such as soil conservation and environmental policy design, 

environmental credit trading, climate change mitigation policy, and regional 

effects of trade agreements (Johansson et al., 2007).

REAP is implemented as a nonlinear mathematical program using the 

General Algebraic Modeling System (GAMS) programming environment. 

The goal of the model is to fi nd the competitive equilibrium (welfare-maximizing) of production levels subject to land constraints and processing and 

production balance requirements. Production activities for crops within a 

region (defi ned by crop rotation and tillage) behave according to a constant 

elasticity of transformation (CET) relationship. The CET specifi cation allows 

for a solution away from “corner points,” thus allowing the solution to refl ect 

a realistic variety of producer behavior. 

The model is calibrated to national production levels given by the USDA 

baseline and the Positive Math Programming (PMP) method. This method 

introduces the baseline levels as calibration constraints, and the resulting 

marginal costs are used to modify the objective function that adjusts for 

discrepancies between the original model output and the baseline values. 

The modifi ed model, without the calibration constraints, will solve to the 

precise levels specifi ed by the baseline. Shocks based on policy, technical, 

or environmental scenarios can be introduced as adjustments to constraints, 

modifi cations of baseline data assumptions, addition of terms to the objective 

function, or a combination of approaches. This permits the model to evaluate 

deviations from the baseline.

1There are 10 Farm Production 

Regions; each consists of 3 to 11 States.
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It should be noted that REAP holds many factors unchanged that infl uence 

planting decisions and the markets for agricultural commodities. Weather 

and pest conditions are assumed to be average for the growing season. REAP 

does, however, provide an economics-based framework for analyzing how 

agricultural markets respond to shocks to the production environment created 

by policy or technology on both the supply side and demand side.