February 2011

United States 

Department 

of Agriculture

Economic 

Research 

Service

Measuring the Indirect Land-Use

Change Associated With Increased

Biofuel Feedstock Production

A Review of Modeling Efforts

Report to Congress
Elizabeth Marshall

Margriet Caswell

Scott Malcolm

Mesbah Motamed

Jim Hrubovcak

Carol Jones

Cynthia Nickerson

This repost was prepared by

the USDA Economic Research Service and 

Office of the Chief Economist, 

with contributions from:
Measuring the Indirect Land-Use

Change Associated With Increased

Biofuel Feedstock Production

A Review of Modeling Efforts

February 2011

Report to Congress

Abstract

Acknowledgments

The House Report 111-181 accompanying H.R. 2997, the 2010 Agriculture, Rural Development, Food and Drug Administration, and Related Agencies Appropriations Bill, requested the 

USDA’s Economic Research Service (ERS) in conjunction with the Office of the Chief Economist, to conduct a study of land-use changes for renewable fuels and feedstocks used to produce 

them. This report summarizes the current state of knowledge of the drivers of land-use change 

and describes the analytic methods used to estimate the impact of biofuel feedstock production 

on land use. The models used to assess policy impacts have incorporated some of the major 

uncertainties inherent in making projections of future conditions, but some uncertainties will 

continue to exist. The larger the impact of domestic biofuels feedstock production on commodity 

prices and the availability of exports, the larger the international land-use effects are likely to be. 

The amount of pressure placed on land internationally will depend in part on how much of the 

land needed for biofuel production is met through an expansion of agricultural land in the 

United States. If crop yield per acre increases through more intensive management or new crop 

varieties, then less land is needed to grow a particular amount of that crop.

We received helpful comments and advice from Harry Baumes, Jim Duffield, and Hosein 

Shapouri from the USDA Office of Energy Policy and New Uses; Sally Thompson, Mary 

Bohman, Keith Fuglie, and Erik Dohlman from the USDA Economic Research Service; and 

anonymous reviewers from the U.S. Environmental Protection Agency, Office of Management 

and Budget, Department of Energy, Department of Commerce, and Council Environmental 

Quality.

United States 

Department 

of Agriculture

Page 1 

Measuring the Indirect Land-Use Change Associated With

Increased Biofuel Feedstock Production: 

A Review of Modeling Efforts

Contents

Executive Summary .......................................................................................................... 2

I. Introduction ..................................................................................................................... 3

What is the issue?............................................................................................................ 3

This report ....................................................................................................................... 4

II. Biofuels, GHG Emissions, and Land-Use Change ........................................................ 6

What is the current state and future growth of biofuels production? .............................. 6

Cellulosic biofuels .......................................................................................................... 8

Greenhouse gas emissions .............................................................................................. 9

Text Box 1: Life-Cycle Assessments ................................................................. 10

Defining the types of land-use change arising from biofuels production ..................... 11

Drivers of land-use change and implications for the agricultural sector ...................... 12

Global perspective ........................................................................................................ 14

III. Methodologies for Estimating the Future Land-Use Change Associated With Biofuels 

Production ......................................................................................................................... 15

Text Box 2: Model Frameworks ........................................................................ 16

IV. Sources of Uncertainty in Model Methodologies and Assumptions .......................... 20

Uncertainty associated with the demand for land for feedstock production ................. 20

Text Box 3: Scientific Advances, Economic Factors, and Biological Limits 

Determine Crop Yields Over Very Long Periods of Time ................................ 23

Uncertainty associated with the supply of land in the United States ............................ 24

Uncertainty associated with the responses of domestic and international markets to 

feedstock production ..................................................................................................... 25

Uncertainty associated with the magnitude of land-use change in those countries 

responding to international price signals ...................................................................... 26

Other uncertainties in modeling indirect land-use change ............................................ 27

V. Modeling Efforts and Results to Date .......................................................................... 28

A chronology of indirect land-use change estimates .................................................... 30

Regulatory estimation efforts ........................................................................................ 33

Continuing research ...................................................................................................... 39

VI. Summary and Future Research Needs ........................................................................ 44

References ......................................................................................................................... 47

Glossary of Acronyms ...................................................................................................... 52
Page 2 

Measuring the Indirect Land-Use Change Associated With

Increased Biofuel Feedstock Production:

A Review of Modeling Efforts

Executive Summary

In recent years, concerns have been raised about potential domestic and international 

land-use changes that might be associated with scaling up biofuel feedstock production. 

Increased competition for productive land and the resulting shifts in land use to produce 

food, feed, fiber, and fuel have potential impacts on greenhouse gas emissions, 

biodiversity, and water quality. The House Report 111-181 accompanying H.R. 2997, 

the 2010 Agriculture, Rural Development, Food and Drug Administration, and Related 

Agencies Appropriations Bill, requested the USDA’s Economic Research Service (ERS)

in conjunction with the Office of the Chief Economist, to conduct a study of land-use 

changes for renewable fuels and feedstocks used to produce them. This report is a 

response to that request, and it summarizes the current state of knowledge of the drivers 

of land-use change and the role of biofuel production in affecting land-use change. The 

analytical frameworks that have been used to address land-use impacts of increased 

biofuels production are presented, and the estimation task is explored in more detail. The 

objective was to survey the literature in a neutral, objective way. There was no intention 

to suggest that USDA does or does not agree with any assumptions or model results. 

In the United States, the most advanced efforts to integrate consideration of land-use 

related impacts into policy have been associated with the Energy Independence and 

Security Act (EISA) of 2007 and policies such as the California Low Carbon Fuel 

Standard (LCFS). The analyses used for these policies require making projections about 

future values of domestic and international crop supply and demand, population growth, 

economic conditions, and land-use policies. Each element is a driver of land-use change, 

and the future value for them is uncertain. Uncertainty is an unavoidable aspect of 

policy-impact modeling that makes projections into the future. This report explores how 

some of the major uncertainties have been incorporated within models of land-use 

change, and how the estimates have changed over time. The publishing of research 

results has stimulated discussion of basic assumptions, parameters, and increased model 

transparency. 

Continued research and modeling efforts will narrow the bands of uncertainty associated 

with projections of land-use change and domestic policy. New models, model 

refinements, and improved data will all help increase the precision with which input 

parameters are estimated and behavioral relationships are represented. Still, the 

successful integration of science and policy must come with the recognition that future 

projections will always carry some degree of uncertainty suggesting that it will need to be 

accommodated in policy design.
Page 3 

Highlights of the study: 

• The larger the impact of domestic biofuels feedstock production on 

commodity prices and the availability of exports, the larger the international 

land-use repercussions are likely to be. Given the size of U.S. agriculture’s 

influence on international markets, producers and consumers overseas regularly 

react to market or policy-related events in the United States. Price signals are the 

critical link between the behavior of the domestic agricultural sector and the 

induced land-use response of other countries. 

• The amount of pressure placed on land internationally will depend in part on 

how much of the land needed for biofuel production is met through an 

expansion of agricultural land in the United States. The allocation of current 

cropland among different crops is based on expected returns in the next growing 

season. Land conversion between broad land-use categories can be both costly 

and irreversible and is therefore driven by longer-term economic factors.

• If crop yield per acre increases through more intensive management or new 

crop varieties, then less land is needed to grow a particular amount of that 

crop. Estimates of the land required for feedstock production or the crops that are 

displaced are highly sensitive to estimates of future crop yields on both existing 

and converted land. 

I. Introduction

What is the issue?

In recent years, concerns have been raised about the magnitude of land-use change that 

could be generated by scaling up biofuel feedstock production. The reliance on use of the 

U.S. agricultural land base to provide fuel as well as food, feed, and fiber will lead to 

increased competition for productive agricultural land, shifts in land use among different 

crops, and, in some cases, conversion of land from other uses into agricultural 

production. 

Land-use change in this case refers to the conversion of land from some other use for the 

production of biofuel feedstock or for some portion of the production displaced by 

expanding biofuel feedstock production. The concern first arose during life-cycle 

analyses (LCA) of the greenhouse gas (GHG) emissions due to increased production of 

biofuels. Early LCA research for GHG accounting emphasized the importance of 

considering the underlying land-use changes that might be associated with increased 

feedstock production. Furthermore, because the climate change impact of GHG

emissions depends on the aggregate of emissions worldwide, not the location of the 

emissions, land-related emissions around the world must be accounted for if they are 

induced by biofuels production. Initial estimates of potential carbon release from global 

land-use changes suggested that so much carbon dioxide would be released under some 

land conversion scenarios that biofuel feedstocks would have to be produced on that land 

for hundreds of years as a way to compensate (Searchinger et al., 2008a; Fargione et al., 

2008). These findings drew attention to the methods used to generate such numbers, and 

propelled the land-use issue into the forefront of the biofuels debate. 
Page 4 

Economists have a long history of policy analysis in agriculture, though most land-use 

analyses are limited in scope to the impact of various domestic or international trade 

policies or technologies on existing domestic cropland resources. In contrast, studies 

attempting to quantify the broader land-use implications of biofuels production and 

biofuels policy, including competition among land-use sectors and market-induced 

repercussions worldwide, have appeared only in the last 2-3 years. To date, the most 

advanced efforts to integrate consideration of land-use-related GHG emissions into policy 

have been associated with California’s Low Carbon Fuel Standard (LCFS), along with 

various efforts by member nations of the European Union (EU) to establish sustainability 

standards for biofuels, including the U.K.’s Renewable Transport Fuel Obligation 

(RTFO), and analyses associated with the U.S. Energy Independence and Security Act 

(EISA) of 2007.1 

The regulatory requirements for GHG effects stipulated by the LCFS and EISA have 

greatly accelerated the development of tools necessary to perform life-cycle analyses of 

the GHG effects of biofuel production and to address questions related to land-use 

change induced by biofuels production. Such analyses require making projections about 

future values of domestic and international crop supply and demand, however, as well as 

broader assumptions regarding projected economic and population growth and 

development behavior. Analyses involving expectations about the future are inherently 

uncertain. Analytical methods exist for dealing with uncertainty in decisionmaking, but it 

is critical to understand the source of uncertainties in quantitative analysis to best 

determine how to manage them. 

This report

During the fiscal year 2010 appropriations process, the House Appropriations Committee 

directed “the Secretary of Agriculture through the Department of Agriculture's Economic 

Research Service, in conjunction with the Office of the Chief Economist, to do an 

independent study of significant indirect land-use changes for renewable fuels and the 

feedstocks used to produce them.”2

Modeling the future land-use implications of major changes in global market conditions 

is fraught with many uncertainties, but the assumptions that are used to address these 

uncertainties significantly affect the results with respect to estimating the environmental 

impacts associated with changes. In accounting for GHG emissions, for instance, the 

results differ greatly depending on whether virgin forest or pasture is converted to 

 This study was conducted in response to that request. 

It summarizes the current state of knowledge of the drivers of land-use change and the 

role of biofuel production in affecting land-use change. It also details the analytical 

frameworks used to address land-use impacts of increased biofuels production, and the 

modeling procedures. The objective was to survey the literature in a neutral, objective 

way. There was no intention to suggest that USDA does or does not agree with any 

assumptions or model results. 

 

1

EISA section 201 amends section 211(o)(1) of the Clean Air Act to provide a new definition of “lifecycle 

greenhouse gas emissions” that includes “direct emissions and significant indirect emissions such as 

significant emissions from land use changes. 

2

 Full text: http://thomas.loc.gov/cgi-bin/cpquery/T?&report=hr181&dbname=111&). Passed 

June 18, 2009. 
Page 5 

increase the availability of cropland. Associated impacts on biodiversity and 

environmental quality from changes in nutrient, pesticide, and tillage use also will depend 

on the type of land that is converted. 

Scientific methods for estimating the impacts of land conversion are improving to the 

point that estimates can be made if the type and location of the land-use change is known. 

Because the underlying stressor–the land-use change itself–is common across 

environmental dimensions; however, this study focuses on efforts to estimate the extent 

and location of land-use changes, not on efforts to estimate the resulting GHG or other 

environmental impacts of those changes. 

Many studies of land-use change have been published in the past few years. The analytic 

approaches used in these studies have been expanded and refined over time, and the 

underlying assumptions used are now more clearly understood than in earlier studies. 

Some of the uncertainties associated with predicting indirect land-use change have been 

narrowed, while others are being examined to identify critical data gaps that are impeding 

development of more precise estimates. 

Although most of the studies that are cited in the following review address the indirect 

land-use effects of corn-based ethanol production, the principles of land-use change 

remain the same for advanced biofuels production from other feedstocks. Corn ethanol 

results are used to illustrate the dynamics of land-use change because corn is currently the 

major biofuel feedstock in the U.S., the parameters of corn production are known, and 

models of global trade in corn are well established. Soybean use for biodiesel has also 

been studied, but the amount of soybean crop diverted to fuel production has been 

relatively small compared with that of corn. Recent breakthroughs in cellulosic 

conversion technologies promise a wider range of feedstocks for ethanol production and 

the potential for production of “drop in” fuels that would substitute directly for gasoline. 

In addition, bioenergy feedstocks may become important inputs in the generation of heat 

and electricity with minimal need for conversion to liquid fuel. 3

 While the relative 

importance of particular drivers may change depending on feedstock, the underlying 

drivers of land-use change that are presented in this report will hold for any bioenergy 

feedstock (or any activity) that competes for agricultural land. 

 

3 For a more thorough discussion of increased biofuel production, see the Biomass Research and 

Development Board (2008) report, Increasing Feedstock Production for Biofuels: Economic Drivers, 

Environmental Implications, and the Role of Research. USDA’s OCE and ERS (along with other USDA 

Agencies, EPA, and DOE) participated in this report.
Page 6 

II. Biofuels, GHG Emissions, and Land-Use Change

What is the current state and future growth of biofuels production? 

In 1925, Henry Ford told a reporter “The fuel of the future is going to come from fruit 

like that sumac out by the road, or from apples, weeds, sawdust—almost anything. There 

is fuel in every bit of vegetable matter that can be fermented. There’s enough alcohol in 

one year’s yield of an acre of potatoes to drive the machinery necessary to cultivate the 

fields for a hundred years.” Ford initially designed his Model T to run on a mixture of 

alcohol and gasoline, but the advent of inexpensive petroleum products, together with 

Prohibition’s strict limitations on ethanol production, redirected development of the 

automobile industry towards hydrocarbon, rather than carbohydrate, combustion. Despite 

a transitory resurgence during WWII, when oil was scarce, the use of ethanol as a motor 

fuel remained in the shadows until the 1970s. 

In the last 30 years, however, several factors have reinvigorated interest in ethanol:

• The Arab oil embargo, which highlighted the Nation’s economic vulnerability to 

oil imports from unstable regimes and the need to diversify U.S. energy sources. 

• Increased demand for gasoline oxygenates, which were mandated by the Clean 

Air Act to decrease smog-related vehicular emissions, and the discovery of 

adverse health and environmental impacts associated with other 

performance-related fuel additives such as lead, benzene, and MTBE.4

• The ability of ethanol production to stimulate agricultural markets, offering a 

potential market-based alternative to farm-support programs. 

• The concern that GHG emissions from fossil fuel combustion are a key causal 

factor for climate change.

• The improved cost-effectiveness of ethanol production. 

Moreover, the evolution of technology to produce biofuel from cellulose, which is in the 

demonstration stage and appears to be on the cusp of commercialization, has the potential 

to expand the spectrum of biomass raw materials that can be converted to biofuels. 

To catalyze expansion of renewable fuels markets in the United States, Congress 

included in the Energy Policy Act of 2005 (EPAct) a Federal Renewable Fuel Standard 

(RFS) that mandated increased blending of renewable fuels into the U.S. fuel supply. In 

response to both increased interest in biofuels and increased concern about the potential 

GHG emissions associated with the production and use of conventional biofuels, the RFS 

was expanded and modified in 2007 to include a breakdown of fuel types, together with 

threshold levels of GHG emission reductions that the fuel types must meet to be 

considered qualifying renewable fuels under RFS II (Figure 1). 

 

4 Section 1504 of the Energy Policy Act of 2005 eliminated the requirement that reformulated gasoline 

(RFG) contain minimum levels of oxygenates. Current air quality requirements can usually be met without 

the use of oxygenates, and the associated demand for RFG oxygenates is much smaller than it was prior to 

the EPAct.
Page 7 

The sequential blending mandates, in conjunction with a tax credit that has been available 

to blenders who blend renewable fuels into gasoline or diesel, has been successful at 

accelerating development of the ethanol industry within the United States. Figure 2 

shows the growth in U.S. corn and other starch-based ethanol production since 1992 as 

well as the forecast for growth of total ethanol production, from both starch and cellulose, 

to 2035 based on the latest long-term forecast from the Energy Information 

Administration (Annual Energy Outlook (AEO) 2010). The U.S. Department of Energy 

(DOE)-Energy Information Administration (EIA) projection of a brief plateau at roughly 

15 billion gallons over the next decade reflects the limits placed on the volume of 

non-advanced ethanol that may qualify for credits under the RFS in the EISA; production 

levels eventually rise again due to mandated minimum levels of cellulosic biofuel under 

the RFS and projected improvements in the profitability of cellulosic ethanol.5

Figure 1: Renewable Fuel Standard requirements in the United States (billions of 

ethanol-equivalent gallons). Source: Renewable Fuels Association and Energy 

Information Administration. 

 

5

Ethanol industry advocates have argued that regulatory limitations on the amount of ethanol that can be

blended into gasoline effectively limits the amount of ethanol that can be sold in low-level ethanol blends 

to 12 billion gallons per year (or roughly 10% of U.S. gasoline consumption by volume). This “blend wall” 

for low-level ethanol blends may create an obstacle to absorption of the regulated levels of ethanol by the 

market. On October 13, 2010, EPA issued a partial waiver to allow fuel and fuel additive manufacturers to 

introduce gasoline that contains greater than 10 volume percent (vol%) ethanol and up to 15 vol% ethanol 

(E15) for use in certain motor vehicles. However, extensive market penetration of E15 will require changes 

to state laws, recommendations from vehicle manufacturers and adoption by fuel distributors.

0

5

10

15

20

25

30

35

40

20062007200820092010201120122013201420152016201720182019202020212022

Billions of Ethanol-Equivalent Gallons

RFS II: Total renewable 

fuel requirement

RFS II: advanced biofuel 

(includes cellulosic)

RFS II: conventional 

biofuel

RFS I: conventional biofuel

RFS II: biomass based 

biodiesel

RFS II: cellulosic biofuel
Page 8 

Figure 2: Historical and projected ethanol production in the U.S. (billion gallons). 

This graph illustrates only domestic ethanol production from starch and cellulose; it 

does not include projections of other biomass-based non-ethanol fuels, such as 

biodiesel. Source: Renewable Fuels Association and Energy Information 

Administration.

To meet demand for ethanol use, corn production in the United States is expected to 

increase from 12.9 billion bushels in 2009/10 to 14.0 billion bushels in 2015/16, with 

acreage planted for corn increasing from 86.4 million acres to 89.5 million acres. Over 

that period, yields for corn are anticipated to increase by about 5 percent, moving from 

162.9 to 170.4 bushels per acre (USDA, Office of Chief Economist and World 

Agricultural Outlook Board, 2010). 

Cellulosic biofuels 

The majority of ethanol investment continues to be targeted at proven 

technologies—generating ethanol from corn by converting starch, a simple sugar, to 

alcohol. However, the congressionally mandated RFS II objectives call for 16 billion 

gallons of cellulosic biofuels to be produced annually by 2022. Cellulosic biofuels are 

based on the conversion of cellulose, a complex sugar, to alcohol. Cellulosic 

technologies allow the conversion of biomass feedstocks such as stalks, leaves, grasses, 

and even trees into ethanol. AEO 2010 projects that the cellulosic biofuels requirements 

under RFS II will be met by cellulosic ethanol as well as by a portfolio of new

biomass-to-liquids fuels such as Fischer-Tropsch liquids, renewable (or “green”) diesels, 

and pyrolysis oils (AEO 2010). 

Cellulosic conversion technologies for the production of ethanol offer significant benefits 

over grain-based production of ethanol, including a higher ethanol yield per acre from a 

diverse array of feedstocks, the use of perennial feedstocks that require less intensive 

management than annual grains, and a significant reduction in the demand for fossil fuels 

0

5

10

15

20

25

30

35

40

Billion Gallons

AEO 2010 Reference Case

AEO 2010 High Oil Price Case
Page 9 

during processing.6

high-demand areas, lessening the need to transport ethanol from the Midwest 

 Due to the variety of potential feedstocks for cellulosic processes, it 

could also be possible to locate cellulosic ethanol processing plants closer to 

corn-growing regions to the more populated coasts. The potential benefits of cellulosic 

processing are increases in land-use efficiency, reduced carbon intensity per unit of 

energy content, gains in cost-effectiveness, and greater flexibility in finding feedstock 

production pathways with a smaller environmental footprint. 

Obstacles to commercial deployment of cellulosic ethanol remain, such as high 

production cost and the need for separate pipelines and distribution systems, though 

technological breakthroughs have been reported. The EPA’s final rule for the RFS II

adjusted the 2010 cellulosic biofuel standard down from 100 million ethanol-equivalent 

gallons to 6.5 million ethanol-equivalent gallons. 

In the face of ongoing obstacles, uncertainties remain regarding the speed with which 

cellulosic ethanol production will be able to scale up and the types of feedstocks that will 

be available for use. The potential environmental and GHG emissions impact of 

cellulosic biofuels will vary depending on the choice of feedstock. While the option of 

using “non-food” feedstocks has been touted as one of the advantages of cellulosic 

ethanol production, in fact only the use of waste streams as a biomass source truly 

eliminates competition with food production. Corn ethanol competes with food and feed 

supply chains for agricultural output, and cellulosic ethanol from many biomass sources 

will continue to compete with food and feed supply chains for agricultural inputs, most 

notably land. Therefore, while greater potential per-acre ethanol yields from biomass 

could keep land demand down relative to a similar scale of corn ethanol production, the 

dynamics of the land-use impacts addressed in this report will continue to hold at some 

scale should large tracts of land be converted to dedicated cellulosic energy crops, such 

as perennial grasses or short rotation woody crops. 

Greenhouse gas emissions

Demand for biofuels stems from a desire to decrease use of fossil fuels, which would 

lower GHG emissions as well as increase energy security for the United States. Biofuel 

use recycles CO2 (a major GHG) unlike the combustion of fossil fuels, which adds CO2 to 

the atmosphere. However, the GHG footprints of different biofuel feedstocks are 

complex.

7

 The GHG requirement associated with the RFS II set in motion an extensive 

effort to establish a regulatory LCA methodology that would determine which fuels and 

production processes satisfy the GHG threshold requirements. 

 

6 Most ethanol processing plants currently combust coal or natural gas to generate power. Cellulosic 

biorefineries can generate their own power by burning non-fermentable lignin byproducts from their 

biomass feedstock, considerably reducing their dependence on fossil fuels. 

7 For a summary of the energy-equivalent reductions of different feedstocks compared with fossil fuels, see 

Biomass Research and Development Board, 2008, pp. 81-83.
Page 10

Text Box 1: Life-Cycle Assessments

A life-cycle assessment (LCA) is a quantitative method of calculating the GHG balance of 

a biofuel (or other product) pathway, measured from production of raw materials through 

processing and transportation to end use. The recent expansion in the use of LCA for 

policy-based analysis, versus its conventional use as a product-based analysis, has 

prompted researchers to differentiate between “attributional” LCA analysis and 

“consequential” LCA analysis.

Attributional LCA analysis: Attributional LCA (aLCA) has historically evaluated the 

environmental impact of a product through the quantification of input and output flows of 

the production system based on average data and fixing the system boundaries to those 

flows connected to the product under study. The conventional use of LCA—providing 

information on the impacts of the “average” unit of product over its production 

pathway—has been used to compare products to one another and to identify opportunities 

within the production system to reduce the impacts of producing that product (Brander 

et al., 2008). The research questions addressed by attributional LCA analyses generally do 

not require that indirect production impacts be included within the scope of analysis. 

Consequential LCA analysis: Consequential LCA (cLCA) evaluates the aggregate 

environmental impact of a change in the level of output of a product. A cLCA approach is 

appropriate in the case of policy analysis such as that performed for the RFS II, where the 

question of interest is the GHG impact of scaling up biofuels production: “cLCA models 

the causal relationships originating from the decision to change the output of the product, 

and therefore seeks to inform policy makers on the broader impacts of policies which are 

intended to change levels of production” (Brander et al., 2008). 

The relevant scope of a cLCA analysis is therefore broader than that found in aLCA; 

whereas aLCA performs its analysis taking the scale of the existing production system as 

fixed, cLCA is specifically interested in looking at the impacts of changing the aggregate 

scale of production. Such impacts include effects both inside and outside the life cycle of 

the product itself. Production processes are linked through competition for inputs and 

infrastructure, for instance, so changes in the scale of production of one good will induce 

changes in the production of other goods that compete for common inputs. Indirect 

impacts therefore become a key factor in consequential LCA analysis. 

While the aLCA approach is generally static and based on fixed relationships between 

inputs and outputs at a given point of time, a cLCA approach looks at the impacts of 

changing relationships, allowing for ripple effects across sectors. Expanding the scope of 

life-cycle analysis to include market effects requires the integration of complicated 

economic models to represent relevant relationships between demand for inputs, prices, 

elasticities, and supply chains for products and co-products. For that reason, some 

researchers caution that the results of consequential life-cycle analysis may be less precise 

than those of attributional life-cycle analysis (Brander et al, 2008), while others argue that, 

despite the additional uncertainty, the results are more comprehensive and complete 

(Schmidt, 2008). 
Page 11

Defining the types of land-use change arising from biofuels production

Increased demand for land to produce crops for biofuel feedstocks involves two 

interrelated types of land-use change—“direct” and “indirect.” Direct land-use change

refers to the conversion of land from some other use directly into biofuel feedstock 

production. Estimating the magnitude of direct land-use change in the country or region

producing biofuel is a matter of projecting where feedstocks of different types are likely 

to be grown and what pre-existing land use or uses are likely to be replaced (e.g., pasture 

land, existing crop land, forest land or idle or degraded cropland). 

Land uses displaced by feedstock production may move outside the country or region 

where they originated. However, Indirect land-use change refers to the conversion of 

land to produce some portion of the production displaced by expanding biofuel feedstock 

production. Because the aggregate demand for goods that require land for production 

increases as a result of increased demand for biofuel feedstocks, that initial, direct change 

in land use for feedstock production could trigger a cascade of off-site, induced land-use 

conversions elsewhere as land uses are redistributed to satisfy demand. Prices are the 

mediating factor between the action of increased feedstock production for biofuels and 

the effect of distant land-use change. 

Indirect land-use changes can occur domestically or globally. For instance, increased

corn production in the Midwest to supply ethanol production could induce the withdrawal 

of land from the Conservation Reserve Program (CRP) to grow wheat that has been 

displaced by the expansion of corn production for feedstocks. The conversion of CRP 

land to wheat is considered a domestic indirect land-use change associated with the initial 

increase in demand for corn for ethanol production. Sometimes, the trail from cause to 

effect is quite long, with many steps along the way. In the ethanol example, increased 

corn demand reduced soybean planting in the U.S., which reduced soy exports, which 

increased world soybean prices, which increased soybean planting in Brazil on land 

previously used to pasture beef, which increased beef prices, which increased incentives 

to clear Brazilian forests to increase pasture (Zilberman et al., 2010). The LCA 

accounting for GHG emissions is affected by the selected scope of the analysis. Changes 

in output in markets other than biofuel feedstock and ethanol production will occur as a 

result of price changes induced by increased biofuel production, and those changes in 

output may also have emissions implications. 

Although researchers agree upon the basic concepts of direct and indirect land-use 

change, common usage of the terms has added confusion to the debate. “Indirect” and 

“international” are often used interchangeably. As noted earlier, the global land-use 

changes that are induced by U.S. biofuel feedstock production may be significant, but 

there are many drivers of international land-use change that will affect conditions abroad 

regardless of domestic biofuels policies. 
Page 12

Drivers of land-use change and implications for the agricultural sector

Increased biofuel production has had and will have effects on land use in the U.S. and the 

rest of the world as will any change that increases competition for agricultural land. 

Estimating the magnitude and location of that impact, however, is extremely challenging. 

In particular, estimating the indirect land-use changes that are attributable to biofuels 

production or policy is subject to a great deal of uncertainty. The extent of indirect 

land-use change is a market-mediated response that plays out internationally through 

global markets via changes in relative land values and commodity prices. The extent to 

which displaced production is replaced will depend on elasticities of supply and 

demand,

8

land-use management, development, trade, and other countries’ biofuels policies – each 

of which affects the competitive advantage of different lands in different uses.

 and where that production will migrate is subject to constraints imposed by 

national and international policies related to trade, energy, land use, forest management, 

etc. (Marshall, 2009). Furthermore, the difficulty of attributing off-site land-use changes 

to changes in feedstock production is exacerbated by the effects of simultaneous drivers 

of land-use change such as population change, income change and the associated changes 

in demand for goods such as meat and housing space, and diverse policies related to 

 

Landowners will allocate land among competing uses based on the expected net benefits 

of those uses, and those benefits will vary for each use depending on land quality and 

location. A landowner seeking to maximize profits will allocate a land parcel to the use 

that yields the highest expected economic return after the costs of conversion, which can 

include changes in machinery investments and management practices. Relative expected 

returns change with market conditions (commodity prices, production costs, population 

growth, consumer tastes, international trade, and other factors affecting the demand for 

land in different uses), technological advancements, and weather. The level of 

uncertainty surrounding future conditions will affect a landowner’s assessment of 

expected benefits and costs.

Drivers of land-use change in the United States can be roughly categorized into those that 

may encourage either expansion or contraction of cropland acreage depending on market 

conditions, those that primarily encourage expansion, and those that primarily induce 

contraction of cropland acreage. 

Examples of land-use change drivers that can induce expansion or contraction of 

cropland acreage:

• Crop prices can influence the amount of land planted to various crops because 

they affect the relative profitability of crops and farm income. 

 

8 Economists define elasticity as the responsiveness of the quantity demanded (supplied) of a good or

service to a change in its price. More precisely, it gives the percentage change in quantity demanded

(supplied) in response to a 1-percent change in price (holding constant all the other determinants of demand

(supply). When demand (supply) is elastic (greater than one), demand (supply) is very sensitive to price. 

The fewer substitutes that exist for a good, the lower the price elasticity will be (the less responsive demand 

will be to a price change). Time is also a consideration in determining both consumer and producer price 

responsiveness for many items. The longer people have to make adjustments, the more adjustments they 

will make.
Page 13

• Changes in input costs can also affect profitability. For example, use of land for 

cropping often requires application of fertilizers. Fluctuations in fertilizer costs 

can change the returns to cropping relative to other land uses. They can also 

affect decisions to grow particular crops because some crops require greater 

application of agricultural chemicals relative to other crops. 

• Technological change, such as the introduction of yield-increasing crop varieties 

can increase the net benefits on a given piece of land and may reduce the demand 

for expansion of acreage. Some innovations, such as drought resistant seeds or 

pressurized irrigation systems, can induce expansion onto lands with lower 

quality soils. 

Examples of land-use change drivers that primarily encourage expansion include: 

• Agricultural policies that increase expected returns and reduce the inherent 

risk associated with agricultural production can increase the relative benefits 

of that land use. Such policies include commodity support programs and the 

Federal crop insurance program. 

• Energy policies, such as the Renewable Fuel Standard, that stimulate 

demand for existing commodities or create a market for new agricultural 

products, such as perennial energy crops, can change the relative benefits of land 

use.

Examples of land-use change drivers primarily inducing contraction in cropland acreage: 

• Urbanization and pressure for commercial, residential, and industrial 

development can increase the demand for land and reduce the relative benefits 

of keeping land in agricultural uses. 

• Conservation policies that mitigate or reverse the environmental impacts of 

conversion can increase the benefits of retiring land from agricultural 

production. Since 1985, the CRP has been the largest driver of cropland 

changes (Lubowski et al., 2006). 

• Conservation policies that protect vulnerable natural resources from 

conversion to cropland or higher intensity uses can offer benefits to farmers. 

Such policies include the Grassland Reserve Program and the Wetland Reserve 

Program. 

Because future patterns of land use will depend on complex interactions among all of 

these forces, projecting future landscapes, and the degree to which they will be impacted 

by changes in a single driver, is very difficult. Broadening and increasing the portfolio of 

food, feed, fiber, and energy products from domestic agriculture will increase the amount 

of land required to produce those products and change domestic patterns of production. 

The response in terms of domestic land use will be sensitive to the wide array of factors 

described above. 
Page 14

Global perspective

U.S. agriculture is an integral part of global commodity markets, and changes in domestic 

production will impact how global demands for food, feed, and fiber are met. If 

competition for U.S. land reduces the availability of commodities for export, for instance, 

global markets will create the incentive, through higher prices, for those commodities to 

be produced elsewhere. Estimating the patterns of land-use change that will enable that 

increased production internationally is the crux of the indirect land-use change issue. 

Because agricultural expansion is one of the key drivers of land-use change and 

deforestation in many developing countries, there is little question that some international 

land-use change will result from domestic shifts in production. The debate intensifies, 

however, around the question of extent and location of those changes. Policies within a 

country that affect migration (population shifts) and associated land-use change can 

include road construction, colonization policies, agricultural subsidies, and tax incentives 

(Angelsen and Kaimowitz, 1999). While estimating future land-use change in a single, 

relatively data-rich country such as the United States is difficult, estimating waves of 

land-use change that propagate internationally through complex global commodity 

markets is even more challenging. 
Page 15

III. Methodologies for Estimating the Future Land-Use 

Change Associated With Biofuels Production 

As discussed previously, land-use change is influenced by a number of factors, and the 

interactions between these factors are complex. Because experiments cannot be carried 

out to determine the consequences of policy or market changes, alternative means must 

be used. Mathematical models that attempt to estimate and test the interrelationships 

between factors are often used to quantify these relationships. 

A quantitative economic model is a mathematical representation of how agents in a 

system behave under a set of hypothesized relationships informed by both theory and 

empirical evidence. A model serves as a proxy for what one cannot actually observe. In 

agricultural sector models that attempt to model land-use change, the agents are often 

producers of agricultural commodities and livestock, biofuel producers, and Government 

policy makers. A model is used to indicate, numerically, how the aggregate behavior of 

the agents will change if some facet of the production environment changes, such as 

through an increase in biofuel production mandates.

Because one cannot have perfect foresight about variables related to future weather, 

policy, and demand conditions, or many other factors that govern land-use decisions, the 

output of a model should not be taken as an immutable prediction of how the future will 

unfold. To isolate the effects of the variable of interest—in this case biofuel 

production—from the many other potential sources of uncertainty when projecting future 

land-change, modeling efforts usually measure the land-use impacts attributed to biofuel 

feedstock production as the difference between two future modeled, or projected, 

scenarios. Between those scenarios—a baseline projection and a “scaled up biofuels” 

projection—the only variable that differs is the change in biofuel production volume. 

The resulting difference in land use is therefore attributed to the biofuel policy mandating 

increased production. For example, the USDA World Agricultural Outlook Board 

coordinates a multiagency process that projects agricultural supply and demand 10 years 

out based on explicit assumptions about world markets, yields, and agricultural trade and 

environmental policies. This baseline is used in several studies as a projection against 

which to assess policy scenarios. 

Because the two future scenarios share a set of common assumptions about exogenous 

future land-use drivers such as GDP and weather pattern, the estimate of land-use change 

derived this way does not reflect additional uncertainty surrounding what weather

patterns are likely to be. Changes to these common data or assumptions, however, will 

change the resulting estimates, and, depending on the sensitivity of the model to those 

elements, may have a significant influence on the output. An important component of 

such analyses are therefore “sensitivity analyses” to determine whether the land-use 

change estimates respond significantly as input parameters and data are changed. These 

analytic frameworks can be used to assess the sensitivity of results to assumptions such as 

yield growth and future prices. It is important for any modeling study to be transparent 

about the assumptions used and the degree of confidence in the accuracy of input data. 
Page 16

Text Box 2: Model Frameworks

The first step in assessing quantitative and qualitative land-use impacts associated with a 

policy that will affect agriculture and forestry production decisions is choosing an 

analytical framework. There are several types of quantitative models that are used, and 

none can claim to be the ideal tool for assessing land-use change; each type of model has 

advantages and limitations.

Partial Equilibrium Models

Partial equilibrium (PE) models feature a detailed representation of agricultural (and/or 

forestry) production for a country or region. They typically utilize observed data to 

determine the amount of inputs required to produce a unit of a given product. The 

representation of production and land use can be highly detailed, allowing for variations 

in crop rotation, tillage, fertilizer application, and other variations in farm-level 

production decisions. Such models are “partial” in the sense that they focus on a subset 

of economic sectors and do not link explicitly to other sectors of the economy. 

Depending on the ways in which crop, livestock and forestry sectors are modeled, 

substitution between inputs may or may not be allowed. Partial equilibrium models can 

track movement of and competition for inputs such as labor, water, energy and fertilizer 

within the modeled sector(s), but economy-wide competition is beyond the scope of 

most PE models. Export and import of agricultural products and inputs are usually 

modeled in a relatively simple manner. 

Partial equilibrium models that have been used to assess land-use change in agriculture 

include the Regional Environmental and Agricultural Production model (REAP), 

maintained by the USDA’s ERS, and the Forestry and Agricultural Sector Optimization 

Model (FASOM), at Texas A&M University. Both of these models focus on domestic 

crop production. The Food and Agricultural Policy Research Institute (FAPRI) 

maintains a family of econometric agricultural models that are coordinated to produce 

domestic and international agricultural production projections. The FAPRI models 

address only the agricultural sector and in aggregate can be described as a non-spatial, 

partial equilibrium agricultural sector model that covers both domestic and international 

production. 

General Equilibrium Models

Computable general equilibrium (CGE) models are a class of economic models that use 

observed economic data to estimate how an economy might react to changes in policy, 

technology, or other external factors. CGE models attempt to portray an entire economic 

system (national or global) by accounting for interactions between all productive sectors, 

labor, flow of goods and capital between sectors (or countries), and government policies. 

The tradeoff for the expansion of modeling scope is often a loss in modeling detail for 

particular sectors such as agriculture. Furthermore, most existing global and regional 
Page 17

A robust projection of the domestic and international land-use implications of biofuel 

feedstock production requires an integrated modeling system that is capable of providing 

answers to a lengthy list of complex and interrelated questions: 

• How much feedstock will be required to meet projected biofuel demand? 

• How much land will be required to produce that much feedstock? 

• Where will land for feedstock production come from? 

o What will be the methods and costs of feedstock production? 

o What are the available sources of land, beyond existing cropland, for 

feedstock production? 

o How competitive will feedstock production be with existing land uses 

such as other crops, pasture, and forestry? 

CGE frameworks are not structured to model land-use alternatives and the associated 

emissions sources and mitigation opportunities. This work has been hindered by a lack 

of data – specifically, consistent global land resource and non-CO2 GHG emissions 

databases linked to underlying economic activity and GHG emissions and sequestration 

drivers (Hertel et al. 2009). Examples of CGE models used to evaluate land-use change 

impacts in agriculture are the Global Trade Analysis Project (GTAP), housed at Purdue 

University, and the Integrated Model to Assess the Global Environment (IMAGE) of the 

Netherlands Environmental Assessment Agency. 

While CGE models feature a full accounting of the economic flows between production 

sectors, they have not been used widely to model land-use change at the regional level. 

The recent research emphasis on the importance of land and land constraints in GHG 

analysis of policy, products, and trade is changing that. Recent modifications to the 

GTAP model have included creation of a land-use module that allows GTAP-Bio to 

represent the global competition for land among land-use sectors (Golub et al., 2009). 

Dynamic vs. Static Models 

A static model addresses the impacts on production for a single year. While there are 

dynamic elements to agricultural production that are a function of the previous year’s 

crop, such as commodity storage and crop yields, these elements can often be handled 

adequately in a static model through the use of averages or capitalized values. Dynamic 

optimization models explicitly model the evolution of the production environment over a 

range of time and can attempt to optimize over a multi-year pathway.1 Dynamic models, 

for instance, are frequently used to model the forestry sector, with its long rotation times 

and fractional annual harvest. 

Static models generally solve much faster than dynamic models and are useful in 

simulating a large number of policy shocks as uncertain inputs are varied. Dynamic 

models can be much more computation-intensive and are more sophisticated in analyzing 

transition effects and the effects on later periods of a decision made today under different 

states of the world. 
Page 18

o What additional obstacles exist to farmer adoption of feedstock 

production? 

o What are the environmental implications of changing land uses? 

• What will be the impacts of changing patterns of domestic production on 

international commodity prices?

• Which countries will respond to changing international prices with changes in 

agricultural production patterns? 

• What lands within those countries will be affected by changing patterns of 

production? If output is increased, what types of land will be converted into 

production? 

No single model currently exists that can answer such a broad list of questions for 

feedstock production in the United States. Analyses at this scale generally tie together 

models that can answer the domestic questions related to production and macroeconomic 

impacts with other models that evaluate how international markets respond to regional 

changes in production and macroeconomic impacts. While such economic models have 

been under development for decades, the introduction of an explicit land-use component 

into the economic framework is a relatively recent addition. The link to land use in these 

models has been complicated by a lack of sufficient, consistent, and comparable data 

regarding existing land uses and historical patterns of land-use change for the United 

States and worldwide. 

The sophisticated integration of models required to estimate biofuels-related land-use 

change highlights the complexity of the forecasting task. Models capturing the land-use 

change dynamics described earlier must make projections about future values of 

parameters ranging from farm production practices for crops that are not yet grown 

commercially, to expected growth in existing crop productivity, to projected 

responsiveness of world markets to changes in crop prices under specific global 

economic growth scenarios, and to enforcement of land-use policies in other countries. 

Future values for parameters such as these are not yet known, so judgments and 

assumptions must be made as to the likely values these uncertain data will take. Each 

assumption, whether made explicitly or implicitly in the structure and data of the model, 

will influence the outcome to some degree, though the extent to which they influence 

land use results varies, with some parameters generating significantly more variability 

across their plausible ranges than others. 

There is a long history of research on analytical methods for exploring and illustrating 

uncertainty in analytical contexts such as this one; model-based analysis is particularly 

well-suited to indepth exploration of how outcomes vary as the range of possible input 

values and value combinations is explored. The results of such analyses are used to 

refine outcome estimates over time in multiple ways:

• Identifying the scope of analysis that encompasses and elaborates on the most 

significant variables in generating outcomes and outcome variability;

• Reducing uncertainty surrounding those variables where uncertainty is a function 

of missing or coarse information; and 
Page 19

• Managing or bounding the uncertainty around those variables that are inherently 

uncertain because they are unobservable, such as future projections. 

The evolution of the indirect land-use change modeling efforts has made progress along 

each of these pathways. Before describing the status and evolution of that analytical 

effort, the following section briefly reviews the major sources of uncertainty in this 

analytical context.
Page 20

IV. Sources of Uncertainty in Model Methodologies and 

Assumptions 

The sources of uncertainty in modeling the indirect land-use change associated with 

biofuels production have been roughly grouped into five categories:

1. The uncertainty associated with the demand for land for feedstock production; 

2. The uncertainty associated with the supply of land in the United States; 

3. The uncertainty associated with responses of domestic and international markets 

to feedstock production; 

4. The uncertainty associated with the magnitude of land-use changes in those 

countries responding to international price signals; and 

5. Other uncertainties in modeling indirect land-use change. 

Assumptions made about the uncertain future not only affect the model results directly

but also determine the baseline from which change is measured. Although a few models 

measure change from current conditions (attributing the total change to biofuel feedstock 

production), most project the likely conditions at some future date with and without the 

activity or policy of interest. The difference between the baseline and scenario analysis is 

meant to capture the policy effect. For example, the USDA World Agricultural Outlook 

Board coordinates a multiagency process that projects agricultural supply and demand 

10 years out based on explicit assumptions about world markets, yields, and agricultural 

trade and environmental policies. This baseline is used in several studies to assess policy 

scenarios and the sensitivities of the results to the underlying assumptions embedded in 

the model. 

The sections that follow explore some of the assumptions that are significant in 

generating variability around estimates of land-use change associated with future biofuel 

feedstock production. 

Uncertainty associated with the demand for land for feedstock production

As demand for land for biofuel production rises, competition for that land will intensify

and the effects of indirect land conversion will likely grow as well. Several factors 

influence direct feedstock demand for land, including feedstock yield assumptions and 

substitutability of biofuel coproducts for other products that require land as an input. 

Any bioenergy feedstock that has the potential to be grown on agricultural land can be 

considered a competitor for that land. The type of land on which it will be grown and the 

current use that it will displace will depend on the resource requirements of the feedstock 

and the relative profitability of that feedstock compared to the current use. One of the 

most important determinants of profitability is the yield that can be produced per acre. 

Yields: A crop variety has a “yield potential” that represents the expected yield if all 

conditions are perfect. The yield potential can be changed through research (or 

mutation). Actual yields are an outcome of environmental conditions and the choices 

made by the producer about the use of agricultural practices and inputs such as tillage, 
Page 21

irrigation, and fertilizer. If crop yield per acre increases through more intensive 

management or new crop varieties, then less land is needed to grow a particular amount 

of that crop. This is true for bioenergy feedstocks and any crops that they displace. 

Estimates of the land required for feedstock production are therefore highly sensitive to 

estimates of future crop yields on both existing and newly converted land. 

Estimating future crop yields on newly converted lands is complicated by uncertainty 

about the productivity of land that has not yet been converted. Unfortunately, there is 

little empirical evidence to guide modelers in selecting the appropriate value for 

estimating the productivity of converted land. In most regions, existing crops are already 

on the most productive agriculture land, so yields on newly converted lands would likely 

be lower than on existing cropland.9 New crop varieties or more intensive input use may 

mitigate such yield losses. The expected productivity of new land in agriculture is a 

major determinant of how much new land will be required to accommodate increased 

demand for biofuel feedstock or any use that competes for land. 

The uncertainty about yield on land converted today to a new use is compounded when 

yields are projected over time. The yield potential for most crops has grown over time 

due to public and private investments in plant breeding, biotechnology, and other crop 

improvements (Figure 3). More intensive use of existing technologies and the adoption 

of new technologies also can enhance productivity. Yields, for instance, can be increased 

in response to higher prices by more intensive use of inputs such as fertilizer (Keeney and 

Hertel, 2009). Greater input use or more intensive management, however, may have 

environmental consequences. Increased nitrogen application may result in increased 

direct N2O emissions, and more intensive farming practices may result in increased 

erosion and decreased soil carbon sequestration. If one of the underlying motivations for 

concern about indirect land use is concern about GHG emissions, then such potential 

tradeoffs between use of land and use of other inputs to increase production must be 

acknowledged and incorporated into a comprehensive analysis. This concern extends to 

cellulosic feedstocks; although cellulosic feedstocks such as perennial grasses can be 

grown with fewer inputs than a crop such as corn, yields can be improved through the use 

of fertilizer, and added nutrients will be applied if increased revenues outweigh the costs. 

Pest-resistant and herbicide-tolerant biotechnology-derived varieties of corn, cotton, and 

soybeans have been adopted extensively by farmers in the United States, but acceptance 

has not been as widespread in other parts of the world. Current biotechnology research is 

focused on traits such as drought tolerance that would increase yield potential for many 

crops. Biotechnology research will be employed in the development of bioenergy 

feedstocks as well as traditional agricultural commodities. Whether the recent trend in 

yield growth will continue unchanged, increase, or decrease is a matter of agronomic 

limits and research investments (Heisey, 2009). Estimates of the rate of yield growth 

may also differ across crops and locations. Productivity performance across countries 

and regions has not been uniform (Fuglie, 2010). 

 

9 There may be regions where newly converted cropland is as productive as existing cropland (e.g., parts of 

Brazil), but those cases may be the exception. Furthermore, converted areas may be more productive in the 

short run than in the long run. Estimates of land-use demand will greatly benefit from improved data on 

the productivity of converted land for cropland worldwide.
Page 22

Figure 3: Yield increases for corn over time in the United States. Source: USDA, 

Office of the Chief Economist and World Agricultural Outlook Board, 2010. 

0

20

40

60

80

100

120

140

160

180

0

20

40

60

80

100

120

140

160

Bushels per Acre

Million Acres

Planted acres

Yield
Page 23

Coproducts from biofuels production may mitigate land-use pressures: Because 

ethanol diverts corn from the livestock sector, it is often perceived to be a competitor, 

rather than a supplier, in the livestock feed market. Ethanol dry mill production produces 

a coproduct called distillers’ dried grains (DDGs), which can substitute for corn as feed, 

thereby reducing the amount of corn required by the livestock sector. The greater the 

Text Box 3: Scientific Advances, Economic Factors, and Biological Limits 

Determine Crop Yields Over Very Long Periods of Time

The very long-run trajectory of yields for a major field crop like corn usually begins 

with a phase in which yields are flat. Yields then increase over a long time with the 

application of science to agriculture. Finally, yields might reach some theoretical 

maximum based on the plant’s capacity to capture available sunlight, efficiently 

convert it to biomass, and partition that biomass into grain. The resulting simple 

S-shaped graph may conceal a number of important factors. First, flat yields may not 

always signal periods of technological stagnation. Mechanical technology may have 

lowered the labor inputs into land preparation, cultivation, or harvesting. Different 

crop varieties may have maintained yields that would have otherwise deteriorated 

because of pests and diseases, or allowed expansion of commercial crop production 

into different growing zones. Second, the yield trajectory over time and maximum 

attainable yield nationwide will differ from results on small-scale experimental plots. 

It takes time for new corn technology to move from scientific plots to commercial 

application by farmers, and additional time may be required to adapt corn technology 

to less favorable growing regions. Furthermore, maximum yields on experimental 

plots will differ from yields in farmers’ fields because a greater number of detrimental 

factors can be controlled on small experimental plots, and farmers must consider 

economic costs and benefits, which are less relevant in trials aimed at maximizing 

yields. 
Page 24

substitutability between DDGs and corn, the fewer total cropland acres will be needed to 

supply both the ethanol and livestock sectors. 

Dietary considerations place constraints on the amount of DDGs that can be used in 

livestock diets. Furthermore, marketing challenges remain due to variability in nutrient 

composition, issues related to product storage and transport, and food safety concerns 

(Malcolm et al., 2009). Nevertheless, research currently being conducted on how animal 

feed rations can be modified to make use of DDGs will lead to an improved 

understanding of how and to what extent DDGs can substitute for feed products, thereby 

offsetting some of the increased land demand necessary for corn production. 

Uncertainty associated with the supply of land in the United States

The amount of pressure placed on land internationally will depend in part on how much 

of the land needed for biofuel production is met through an expansion of agricultural land 

in the United States. Agricultural production models for the United States have a long 

history of estimating the movement of land allocation among different crops on existing 

cropland based on changes in expected return. Land conversion between broad land-use 

categories, however, such as from forest to pastureland or cropland, can be both costly 

and irreversible and is therefore driven by longer-term economic factors. For example, 

Midwest farmers can readily move cropland between corn and soybeans when the 

relative profitability of those crops change. In contrast, the conversion of forest acreage 

to cropland represents a long-term change with high transition costs: such a decision must 

consider the relative profitability between agricultural and forestry commodities for many 

years into the future. 

 

The uncertainty of the future values of prices and costs will affect an owner’s expectation 

of long-term profits from any alternative. In addition, the partial irreversibility of some 

land-use changes gives rise to option values that increase the incentive to keep land in its 

current use (Stavins, 1999).10 Using an option model, Song, Zhao, and Swinton (2009) 

found that landowners would be more reluctant to convert land from annual to perennial 

crops. The irreversibility of some land-use changes coupled with uncertainty about 

future economic returns can act as a brake on land conversion. Farmers hesitate to 

convert land because they value the “option” or “flexibility” for future land-use decisions 

that is preserved when land is maintained in its current use. Isik and Yang (2004) also 

found that such option values play a significant role in farmer decisions to retire land and 

that they reduce the probability of farmer participation in land retirement programs. 

Other factors may significantly affect land conversion decisions in a particular area or 

country, such as national or regional conservation and preservation policies and 

programs. 

 

10 Option value is the gain from being able to learn about future benefits that would be precluded by the 

conversion of land to an irreversible or partially irreversible use–the gain from retaining the option to 

continue with the current use and/or change uses in the future (Fisher and Hanemann, 1990).
Page 25

Uncertainty associated with the responses of domestic and international markets to 

feedstock production

If biofuel feedstock production disrupts domestic production of other crops through 

competition for land or other inputs, market prices of existing crops will be affected, 

which will send signals internationally that may result in changes to production patterns 

abroad. Increased commodity prices in 2007 sparked a debate about the effects of cornbased ethanol production on consumer food prices. The “food versus fuel” discussion 

demonstrated the lack of consensus on the extent to which use of a portion of the 

Nation’s corn for ethanol production impacts the prices of finished food products, as well 

as the prices and export availability of the ethanol feedstock and other commodities 

(Trostle, 2008; Leibtag, 2008). This price signal, however, is the critical link between the 

behavior of the domestic agricultural sector and the induced land-use response projected 

for other agricultural exporters. 

Given the size of U.S. agriculture’s influence on international markets, producers and 

consumers overseas regularly react to market or policy-related events in the United 

States. Domestic adjustments in production and crop mix may change incentives for 

foreign producers to supply their own markets or the world market, by bringing new land 

into agricultural production. The larger the impact of domestic biofuels production on 

price and availability of exports (of both the feedstock and, through land competition, all 

other commodities), the larger the international land-use repercussions of that domestic 

biofuel production are likely to be. The strength of the signal sent to markets around the 

world is therefore highly dependent on the domestic land supply issue already described. 

The nature and magnitude of the international market response to increased prices hinges 

on both the production and consumption response. The consumption response is 

reflected in estimates of the price elasticity of demand for agricultural commodities. This 

variable captures what happens to world demand for food and feed crops, including crops 

fed to animals for meat/dairy production, when their prices increase on the world market. 

Assumptions of inelastic demand, where food and feed consumptions are relatively 

unresponsive to price, lead to larger estimates of land use impact than when it is assumed 

that food consumption will decline sharply as food and feed prices rise; a relatively more 

elastic demand for agricultural commodity good production means that in aggregate, fuel 

production on agricultural land “crowds out” food production to a certain extent. There 

are consumer welfare implications associated with the increased prices and reduced 

consumption; Roberts and Schelenker (2010) econometrically estimate commodity 

demand elasticity and the consumer welfare losses associated with reduced consumption 

due to biofuels-driven price increases.

Predictions of international production responses to domestic market changes often rely 

on one of two assumptions concerning the economic mechanism by which goods are 

traded between countries. The first assumption asserts that trade generally occurs in 

response to a single world price that results from the production and consumption 

decisions of individuals around the world. Thus, after controlling for transportation costs 

and border-related barriers, only those producers that can profitably sell at the world price 

will participate in the world market, irrespective of their location or their past 

relationships with buyers. 
Page 26

The second assumption, first proposed by Armington (1969), contends that, in addition to 

prices, bilateral relationships between traders do matter and that consumers distinguish 

from which country their goods originate. Under the Armington assumption, an analyst 

must rely on a quantitative estimate of the strength of the bilateral trade relationship. 

Though models may be limited to a single assumption about trade structure, the two 

frameworks are not mutually exclusive; a realistic trade response may be best represented 

using an Armington assumption in the short term, but allowing for evolution toward a 

“single world price” structure in the long term.

Uncertainty associated with the magnitude of land-use change in those countries 

responding to international price signals 

Each country participating in the global market has its own characteristics with respect to 

current land use, land quality, resource availability, legal framework, demographics, 

infrastructure, international relations and economic conditions. All of these factors must 

be considered when analyzing the effects of changes in agricultural prices on the 

allocation of land in a particular country. 

Using trade analysis to estimate the responses of international markets in such cases 

generally involves predicting changes in exports and imports of specific countries or 

regions under the new production scenario. The next step in tracking indirect land-use 

change is to associate those changes in production with the underlying land-use changes 

in each country that would be required to meet new export and import patterns. This 

requires a calculation of how much land would be required to meet the changing 

export/import patterns and a projection of where that land would come from. The 

amount of land converted into agriculture, and its original coverage (e.g., forests, pasture, 

or idle land), can critically determine the one-time release of carbon emissions

attributable to land-conversion impacts. The challenge, therefore, is identifying where 

land conversion will occur, and which type of land will get converted. 

All of the land-use drivers described here are relevant in the international context, but in 

varying degrees. As in the U.S., land prices, development pressure, access to markets 

and transportation costs, and opportunity costs associated with alternative uses of land

need to be included in the model, along with information on the effectiveness of national, 

regional and local land-use policies such as protected areas and conversion set asides. 

Lack of sufficient data makes it especially difficult to model or project future land-use 

change in foreign countries. Researchers must therefore make a number of simplifying 

assumptions about how land-use change within the country is likely to occur in the future 

under business as usual (the baseline) as well as how that baseline scenario will change 

under the biofuels feedstock production scenario. Cross-country assumptions that U.S. 

biofuel policies will have the same type of impact on land use within each country may 

result in misleading conclusions because each country has a unique set of resource 

endowments, institutions, trade relationships, and economic drivers (Angelsen and 

Kaimowitz, 1999). 
Page 27

Other uncertainties in modeling indirect land-use change

Many forces of varying magnitude will affect future conditions that, in turn, will affect 

the extent of indirect land-use change. For example, extreme weather events, natural 

disasters, political instability or conflict, and technological breakthroughs all have the 

potential to alter global supply and demand for agricultural products. One of the biggest 

determinants for biofuel feedstock production is the energy market, which includes the 

supply and demand for fossil fuel as well as all renewable energy sources. 

Some uncertainties in modeling indirect land-use changes that will not be explicitly 

addressed in this report are associated with global energy markets and the interactions 

between energy demand and population growth, economic growth, and income 

generation more broadly. Biofuel feedstock production depends on the demand for 

renewable energy. Higher gasoline prices in recent years decreased the U.S. public’s 

demand for energy in general and increased its demand for alternative energy sources. 

As was observed after the “oil crisis” of 1973, however, the public interest in energy 

efficiency and alternative energy can fade quickly when fossil fuel prices fall. 

The long-term viability of renewable energy markets will depend on renewable energy 

being cost competitive with fossil fuels. Large investments are being made to lower 

biofuel production costs. However, it has been noted that relatively low energy prices 

can have the unintended effect of increasing energy use (the “rebound effect”) and 

potentially increasing GHG emissions (Alfredsson, 2004; Beckman et al., forthcoming 

2011). 

Assumptions about future energy prices, including the impacts of such prices on 

economic growth and income generation, are therefore critical variables within the 

models that are used to estimate indirect land-use change. A related issue will be the 

direct competition in the provision of biofuels if domestic demand sufficiently increases.

It is predicted that countries such as Brazil and Indonesia are likely to convert more land 

to biofuel production to supply U.S. renewable fuel needs (Fargione et al., 2008). 

Another uncertainty associated with modeling energy markets that include both fossil and 

renewable fuels is predicting technological breakthroughs that can affect either supply or 

demand for one or more energy sources. Large investments are currently being made to 

improve cellulosic conversion so as to widen the range of bioenergy feedstocks that can 

profitably be used as fuel. However, research is also underway to develop technologies 

to process coal, tar sands, and oil shale with reduced emissions. Hydrogen power may be 

on the horizon, which, depending on the price, would affect demand for both bioenergy 

crops and fossil fuels. On the other hand, improvements in feedstock to fuel conversion 

efficiencies would increase the relative advantages of biomass-based fuels. Since such 

technological breakthroughs cannot be predicted with certainty, many models include 

assumptions about innovation rates that lower production costs over time. As with the 

crop yield increases described earlier, innovation depends on research investments that 

may or may not pay off. No assumption about a steady rate of technological 

improvement can capture the impacts of a “game-changing” breakthrough.
Page 28

V. Modeling Efforts and Results to Date

Interest in measuring the scale of indirect land-use change attributable to biofuel 

production largely arose from concern about the potential GHG implications of such 

conversions. In January 2008, Dr. Alex Farrell, then director of the Transportation 

Sustainability Research Center at UC Berkeley, presented to the California Air Resources 

Board (CARB) Low Carbon Fuel Standard (LCFS) Program an illustration of potential 

indirect GHG impacts from biofuels production in the U.S. that would arise from indirect 

land-use change in other countries. That analysis, which was the first to attract major 

interest in the subject, presented what Farrell described as “crude upper limit estimates” 

on the GHG emissions associated with land-use change for biofuels production (Farrell 

and O’Hare, 2008). To get his extreme “worst case” scenarios, he made the assumption 

that an acre of bioenergy feedstock production will result in an acre of land conversion 

and coupled it with the assumption that the land lost will come from the highest carbon 

land use available for conversion. This resulted in estimates that an acre of corn 

production for ethanol in the United States would ultimately lead to an acre of tropical 

rainforest conversion, at conversion costs that go as high as 826 gCO2e/MJ of energy 

from corn ethanol.11 Less carbon-intensive scenarios where the analysis assumed that 

lower-carbon temperate grasslands, rather than tropical forests, are converted for corn 

production (while maintaining the 1:1 acreage conversion assumption) result in 

significantly lower estimates of 140 gCO2e/MJ.

The evolution of estimation procedures since then has largely focused on identifying and 

improving the precision with which key variables–such as anticipated future crop yields 

and source, carbon-intensity, and productivity of new land brought into production 

internationally–can be represented in modeling efforts. In particular, the integration of 

land use into traditional economic models of production and trade, and the accompanying 

capacity to estimate the indirect land-use impacts of domestic production, has rapidly 

increased in sophistication in response to CARB and EPA’s regulatory requirement to 

measure the GHG impact of biofuel production. Table 1 presents the results from several 

recent modeling efforts that estimate the effects of ethanol production on global land use. 

These studies attempt to quantify the market response in the United States and in other 

countries to increases in commodity prices due to increases in biofuel production. These 

studies also quantify the GHG emissions from these market responses and attribute these 

emissions to biofuel production. The table is not meant to be comprehensive but shows a 

selected range of estimates. Other models, such as MIT’s Emissions Prediction and 

Policy Analysis model, have also been used to examine indirect land-use change impacts 

(Gurgel et al., 2007; Melillo et al., 2009). 

 

11 Assuming that the non-land-use-related carbon savings from corn ethanol is a 20 percent reduction from 

gasoline (19 gCO2/MJ), this conversion cost would require approximately 866 years of corn ethanol

production to pay back. This number, though spectacular, is meant only to illustrate the rough magnitude 

of potential impact, as it is based on a set of unlikely “worst case” assumptions. 
Page 29

Table 1: Estimates of land-use change and emissions related to land-use change 

associated with biofuel production.

Study Modeling 

framework

Increase in 

ethanol 

production 

Change in 

Global Land 

Use

Change in 

Global Land 

Use

CO2 equivalent 

emissions

Billion 

gallons per 

year

Million acres Million acres 

per bil. gal 

per year

Grams CO2e per

MJ of ethanol per 

year 1/

Searchinger et al.,

2008a 2/ 

FAPRI/

CARD

14.8 26.73 1.81 104

Fabiosa et al., 2009 

3/ 

FAPRI/

CARD

1.174 1.923 1.638 na

California (CARB) 

2009 

GTAP 13.25 9.62 0.726 30

EPA proposed rule 

for RFS II

(corn ethanol 4/)

FASOM/

FAPRI/

GREET

2.6 

(12.4 to 15.0)

(p. 422, RIA)

4.4

(table 2.9-3, 

RIA)

1.692 60.4

EPA Final Rule for 

RFS II

(Corn Ethanol 4/)

FASOM/

FAPRI/

GREET

2.7 

(12.3 to 15.0) 

(p. 311, RIA)

1.95

(table 2.4-29, 

RIA)

.722 30.3

Hertel et al., 2010 GTAP-BIO 15 9.4 .628 27

Tyner et al. 2010 5/ GTAP

(scenario 3)

 2001 to 2006 3.085 1.155 .374 17.3

 2006 to 7 BG 2.145 .577 .269 12.9

 7 to 9 BG 2 .581 .291 13.4

 9 to 11 BG 2 .607 .304 13.6

 11 to 13 BG 2 .655 .327 14.3

 13 to 15 BG 2 .684 .342 14.5

 2001 to 15 BG 13.23 4.258 .322 14.5

EU JRC-IE (Edwards 

et al., 2010) 6/

LEITAP 4.08 151.11

AGLINK 2.41 89.32

GTAP .78 28.83

(62)

IMPACT .51 17.5

Na=not applicable.

1/ The carbon content of fuels, or the aggregate LCA emissions associated with production, including 

land-use change impacts, is often expressed relative to energy content so the carbon to energy ratio 

(gCO2e/MJ) can be compared across fuels. A wide variety of methods and assumptions are used by the 

studies in converting land-use change impacts into associated estimates of carbon emissions; an explanation 

of such methods is beyond the scope of this report but critical to a full understanding of the carbon intensity 

estimates and why they differ among studies.

2/ Searchinger et al. reported their results in terms of a 55.92-billion-liter increase in ethanol production, 

which resulted in a 10.8-million-hectare change in global land use. 

3/ Based on a 10-percent increase in U.S. ethanol use using 10-year averages of U.S. ethanol use and world 

crop area taken from the 2007 FAPRI baseline. Impact multiplier of 0.009 taken from Fabiosa et al.

(2009), Table 2.

4/ Figures refer to international indirect land-use change only.
Page 30

5/ Conversion to megajoules (MJ) of ethanol assumes each gallon of ethanol contains 76,330 Btu’s of 

energy and each Btu is equal to 0.00105 MJ. Tyner et al (2010) derive results separately for multiple 

categories of scale of production, or billion gallons (BG) per year. 

6/ The reported emissions figures associated with estimated land-use change are based on a central carbon 

stock estimate of 40 tC/ha of conversion and a 20-year carbon payback horizon. The JRC-IE report 

illustrates error bars around that estimate based on a range of 10-95 tC/ha. In the case of the GTAP model

results, the JRC-IE calculated an additional emissions estimate based on a table of regional emissions 

factors that more finely reflects projections of different types of regional land conversion (with varying 

carbon stocks); the resulting emissions estimate is shown in parentheses.

While it is conceptually useful to distinguish between indirect and direct land use impacts 

in discussing the impacts of biofuel policy, modeling efforts often present an aggregate 

land-use impact estimate and do not attempt to distinguish between the two. Modeling 

frameworks may not be spatially explicit enough to directly associate increased 

commodity production with specific parcels of land, so they are unable to specify 

whether converted land goes to feedstock production (direct impact) or to other crops 

displaced by feedstocks (indirect impact). As specified under EISA, both types of 

impacts are critical components in determining the full land-use impact of a biofuel 

policy, so the aggregate estimate is an appropriate indicator for a comprehensive analysis 

of the land-use impacts.

 

The aggregate land-use impact estimates shown in Table 1 vary for a number of reasons. 

Some of the estimates are derived using modeling structures with different ways of 

representing relationships and different boundaries of analysis. Furthermore, these 

models are often based on, or incorporate, differences in assumptions about the many 

variables described earlier that can affect land-use estimates. Even where a path of 

values over time for a particular variable has a relatively narrow band of uncertainty, 

researchers may make different assumptions about the year in which the estimates are 

derived. The year of comparison is important in determining what technologies are 

assumed to be in place, such as those governing crop yields and ethanol yields per unit of 

feedstock. The section that follows describes in more detail the specific difference 

among the research efforts illustrated and how they lead to varying estimates of land-use 

impact.

A chronology of indirect land-use change estimates

In the February 2008 issue of Science, Searchinger et al. (2008a) published an influential 

early study of the effects of biofuel production on indirect GHG emissions. That study 

built on the Farrell et al. (2008) premise but used a more analytically rigorous estimation 

method to determine the land-use impacts of using corn for ethanol in the United States. 

The researchers used a worldwide agricultural trade model to explore how aggregate corn 

acreage diverted to ethanol feedstock production in the United States would translate into 

increased land use in the United States and elsewhere. Using a multi-market, 

multi-commodity international model of agricultural markets called the FAPRI (Food and 

Agricultural Policy Research Institute) model, Searchinger et al. assessed the land-use 

change and GHG implications of increasing corn ethanol production in the United States 

by 14.8 billion gallons. They projected that an additional 26.7 million acres of land 

would be brought into crop production worldwide (1.8 million acres per billion gallons of 

ethanol). The impact of domestic ethanol production was transmitted into global markets 
Page 31

largely through price and export impacts on corn, wheat, and soybeans; prices were 

increased by 40 percent, 20 percent and 17 percent, respectively, while exports were 

estimated to decline by 62 percent, 31 percent, and 28 percent.

12

As with all such estimates, Searchinger’s (2008b) land-use change estimates are highly 

sensitive to the set of underlying assumptions used, including: 

• Historical patterns of land conversion are used to estimate land conversion 

probabilities internationally (Searchinger used data collected at the Woods Hole 

Research Center estimating the proportion of newly converted cropland coming 

from different forest and grassland pools in different regions of the world). 

• The analysis assumed that yields would continue to rise as they have in the past, 

but no additional price-induced yield increases were considered. (It was assumed 

that the impact of such increases would be cancelled out by greater use of 

lower-productivity marginal lands in production). 

• The analysis employs a partial-equilibrium, one-world price model to generate 

projections on worldwide indirect land-use change. From their model, significant, 

market-driven, acreage responses emerge in China and India. 

When the researchers change assumptions about land productivity and conversion 

efficiency, the estimated magnitude of land conversion and the resulting GHG pay-back 

period, are significantly reduced. The authors, however, also argue that their land 

conversion estimates may be low, due to an assumption that conversion of grassland has 

no further indirect impact (as grazing cattle are pushed into forest, for instance) 

(Searchinger, 2008b). 

The increased concern about indirect land-use issues engendered by the initial studies by 

Farrell and O’Hare (2008) and Searchinger et al. (2008a) led researchers to use refined or 

adjusted estimation procedures to address some of the criticisms of that original analysis 

and provide additional perspective on the complexity of the issue. Gibbs et al. (2008) use 

a spatially explicit data set of crop locations and yields to explore in more detail the 

influence of changing crop yields and advances in conversion technology on 

biofuels-related land conversion. Although they demonstrate that land demand can be 

substantially reduced with yield and technology improvements, the authors echo 

Searchinger’s finding that when biofuel production triggers conversion of tropical forests, 

the estimated payback times for GHG emissions remain on the order of 30-300 years. 

In a subsequent analysis of the degree to which ethanol production in the United States 

would drive agricultural land-use change in other countries, Keeney and Hertel (2009) 

also focus on the sensitivity of yield-gain assumptions, but they add a consideration of 

bilateral trade patterns into their estimates of global supply response. This analysis uses a 

modified version of GTAP—a CGE model that considers production, consumption and 

trade of goods and services by region and globally across multiple sectors. Over the past 

 

12 Searchinger et al. (2008a) calculated that the land-use change associated with corn-based ethanol has a 

carbon impact in the range of 103 gCO2eq/MJ. These emissions alone were larger than the estimated 

emissions from gasoline (92 gCO2eq/MJ). In contrast, when emissions from land-use change were not 

included in the estimate of GHG content, corn-starch based ethanol reduced GHG emissions by 20 percent 

compared to gasoline.
Page 32

several years, a team of researchers at Purdue University has refined and updated the 

GTAP model on an ongoing basis to support analysis of land-use change in response to 

biofuels policy. To capture the competition for land between land-use sectors, the GTAP 

model was augmented with a land-use module (GTAP-AEZ) that models the expansion 

of cropland and its distribution among different agricultural activities based on the price 

elasticity of yield and the ratio of productivities of marginal and average lands (Tyner et 

al., 2009). Other model modifications provided further refinement of the energy sector, 

including the three major biofuels (corn ethanol, sugarcane ethanol and biodiesel) and 

energy sector demand and supply elasticities that have been re-estimated and calibrated to 

2006 data (Beckman and Hertel, 2009) 

Keeney and Hertel (2009) conclude that assumptions about the responsiveness of U.S. 

yields to price are critical in determining the magnitude of acreage conversion in the rest 

of the world (ROW); when yields do not respond to price (as assumed by Searchinger), 

ROW acreage conversions are much higher. Using a range of yield elasticities that they 

describe as “plausible” based on past work and current agricultural conditions, they find 

that after 5 years, nearly 30 percent of the increased corn output can be met through yield 

increases rather than through acreage conversions with indirect repercussions. This study 

and several that followed have therefore concluded that yield-increasing technology plays 

a key role as a land substitute in analyses of the land demands of ethanol expansion. 

Assumptions about how/whether yields will continue to increase, and the role of 

biotechnology in boosting that increase, will strongly influence any estimation of future 

land conversion. 

Keeney and Hertel (2009) also find that a consideration of bilateral trade patterns is 

critical in predicting patterns of acreage conversion. In a departure from the “one world 

price” philosophy of global response, they theorize that countries with a well-developed 

historical trading relationship with the U.S. are more likely to be affected, and to 

experience a market response, when prices and U.S. exports change (Hertel et al., 2010). 

Unlike the Searchinger results, which linked cropland conversion in the United States to 

acreage responses in Brazil, China, and India (as well as the United States), Keeney and 

Hertel’s results project the most dramatic international acreage responses in Canada and 

Brazil. Because land-use policies and conversion patterns vary widely from country to 

country, location of acreage response can have a very important impact on associated 

environmental impacts of interest such as GHG emissions.

In a later study of biofuels-induced land allocation using FAPRI, Fabiosa et al. (2009) 

estimated that a 1 percent increase in U.S. ethanol use would result in a 0.009 percent 

increase in world crop area. Most of the increase in world crop area would come from an 

increase in world corn area as corn producers respond to projected drops in corn exports 

and an estimated 26 percent increase in world corn price. Brazil and South Africa would 

respond the most, with multipliers of 0.031 and 0.042, respectively, followed by Mexico, 

the United States, Thailand, and Egypt. More moderate acreage responses also would 

occur among other feed grains and soybeans. Based on the 10-year averages of U.S. 

ethanol use and world crop area taken from the 2007 FAPRI international baseline, and 

using the world area impact multiplier from Fabiosa et al. (2009) (0.009), the results 

suggest a land-use impact multiplier of 1.64 million acres per 1 billion gallons of 

additional ethanol use. This figure includes both the domestic and international cropland 

expansion expected as a result of increased ethanol production. The authors, however, 
Page 33

add a caveat to their findings with the observation that data on the behavior of ethanol 

markets are limited, which makes it difficult to econometrically estimate the elasticities 

required by the biofuel market module. They also find their results are sensitive to the 

assumptions made about the ability of the livestock market to adapt to the use of DDGs in 

feed and the behavior of commodity stock adjustments in the short term. 

Regulatory estimation efforts 

The California Air Resources Board, in support of its recently adopted low carbon fuel 

standard, contracted with the Purdue research team to use the modified GTAP model 

described earlier to calculate how global patterns of land use would change globally in 

response to a 13.25 BGY increase in ethanol production, assuming a 2006 baseline for 

crop production patterns and conversion efficiency. CARB released its findings in 

March 2009; its results suggested each additional billion gallons of corn-starch-based 

ethanol would require only 726,000 acres; about 60 percent less than that suggested in 

Searchinger et al. (2008a).13 Because completely different models are used to derive the 

results, it is difficult to directly attribute differences to specific assumptions, but 

Searchinger attributes some of the gap to varying assumptions about how world food 

demand and production would respond to increased prices (Charles, 2009). 

Searchinger’s methodology assumed a modest response of food demand to world prices; 

CARB’s finding of a more extreme food response buffers the land requirement impact of 

increased biofuel production. A more elastic food demand response, while keeping land 

demand low, may also exacerbate issues related to hunger and poverty (Charles, 2009). 

In a parallel regulatory drive to quantify the GHG content of biofuels, the EPA integrated 

several models to explore the emissions from domestic and international land-use 

changes induced by increased renewable fuels consumption in the U.S. (See 

Figure 4.) For its analysis of the domestic response to biofuels production, EPA relied on 

the Forestry and Agriculture Sector Optimization Model (FASOM). FASOM estimated 

changes in domestic agricultural land use, as well as changes in domestic crop prices and 

crop export volumes. A parallel analysis used the integrated FAPRI models to project the 

responses of international agricultural markets and land use to the change in domestic 

activity. Because FAPRI does not address the locations and types of land that comes into 

production within countries, country-specific estimates of conversion types were 

extrapolated from Winrock estimates of land-use conversion between 2001 and 2004 

derived from satellite imagery (U.S.E.P.A., 2009). 

 

13 Primarily as a result of this reduced acreage, CARB estimated the GHG emissions associated with 

land-use change were 70 percent less than those estimated by Searchinger et al. The GHG emissions due to 

land-use change were reduced from 104 grams of CO2 equivalent per MJ of ethanol to 30 grams of CO2 

equivalent per MJ of ethanol.
Page 34

Figure 4: EPA system boundaries and models used. Source: Renewable Fuel 

Standard Program (RFS II) Regulatory Impact Analysis. 

The EPA analysis produced two rounds of results for the domestic and international 

land-use changes associated with domestic biofuel production, corresponding to the 

proposed and final rules. The proposed rule was released in May 2009. The GHG 

emissions associated with international indirect land-use change were found to be a 

significant component of the overall GHG content of all of the biofuels analyzed; one set 

of results for corn ethanol is shown in Figure 5. According to these preliminary 

estimates, none of the corn ethanol production pathways satisfy the requirement that 

conventional corn ethanol reduce GHG emissions by 20 percent relative to gasoline to 

qualify under the Renewable Fuel Standard.
Page 35

Figure 5: Corn ethanol life-cycle GHG results calculated for the 30-year, 0 percent

discount rate scenario. Source: EPA Draft Regulatory Impact Analysis: Changes to 

Renewable Fuel Standard Program, 

http://www.epa.gov/otaq/renewablefuels/420d09001.pdf 

Acknowledging the complexity and uncertainty inherent in deriving these numbers, EPA 

solicited public and expert feedback on the proposed rule throughout an extended 

120-day public comment period and through use of an independent peer review process. 

The specific components of the methodology targeted for peer review were: 

1. Land-use modeling, focusing on the use of satellite data to estimate land 

conversion probabilities and EPA’s proposed land conversion GHG emission 

factors;

2. Methods to account for time in weighting GHG emissions and savings; 

3. Methodology for calculating the GHG emissions from foreign crop production, 

including both the models and the data/assumptions used; and 

4. The integration of the various models required to provide overall lifecycle GHG 

estimates.

A summary and compilation of the peer review results and the extensive public 

comments are available on the EPA website.14 Table 2 gives a short description of the 

major changes between the proposed and final rules for RFS II. 

In response to public and expert input during the public comment period, and to the 

availability of more recent data than were available during the proposed rule 

development, EPA made a number of changes to the assumptions and methodology 

 

14 http://www.epa.gov/otaq/renewablefuels/420r10003.pdf 
Page 36

underlying its life-cycle analysis (U.S.E.P.A., 2009). As a result, their estimates for 

international indirect land-use change associated with domestic biofuel production 

dropped from 1.692 acres per 1000 gallons of ethanol to .722 acres per 1000 gallons of 

ethanol. 

Table 2: Updates between proposed and final rules for RFS II

Updates to domestic agricultural sector modeling

• Incorporated results from the FASOM forestry module as well as the 

cropland module

• Added new land classifications: cropland, cropland-pasture, rangeland, 

forest-pasture, forest, Conservation Reserve Program (CRP), developed land 

• Updated emissions factors for N2O and soil carbon

• Updated emissions factors for farm input production (fertilizer, etc.)

Updates to international agricultural sector modeling

• Incorporated a detailed Brazil module into the international model 

framework (including regional crop and pasture modeling)

• Added a factor representing price-induced yield changes

• Updated international agricultural GHG emission estimates

• Updated figures for Brazil’s sugarcane production

Updates to biofuel processing in both domestic and international agricultural sector 

modeling

• Built in corn fractionation pathways (with coproduct markets, etc.) 

• Adjusted DGS coproduct replacement rates to reflect more efficient use of 

DGS coproduct in livestock diets

• Added a coproduct credit for glycerin in biodiesel production

• Updated estimates of process energy use

Updates to land-use change modeling

• Used more recent and higher resolution satellite data with longer time 

coverage (2001-2007) 

• Augmented satellite data with region-specific data where available (e.g., 

data from Brazil on pasture intensification)

• Used new soil carbon data

• Used new studies on long-term forest growth rates

Petroleum baseline updates

• Updated petroleum baseline to 2005

The most significant of the EPA changes that affected the magnitude and location of 

projected indirect land-use demand, were the inclusion of induced corn yield increases in 

response to corn price increases, increased substitutability of distillers’ grains with 

existing corn and soybean meals in beef cattle and dairy cow diets (which reduces 

demand and land requirements for other meals), improved spatial and temporal resolution 

of satellite data used for investigating land conversion internationally and more detailed 

modeling of Brazil’s agricultural sector and land-use policies, and inclusion of forest and 

idle cropland as potential sources of domestic agricultural land. While the new 
Page 37

assumptions regarding DDG prospects and yield projections had a substantial impact on 

land demand, some researchers argue that it is still unclear whether the new projected 

estimates represent improvements over the estimates used in the proposed rule (Plevin, 

2010). 

The reduction in the land-use change estimates associated with the revised RFSII 

assumptions had an impact on calculations used to determine whether corn ethanol 

satisfies the 20 percent reduction in GHG emissions required under the Renewable Fuel 

Standard. The final rule results for GHG emissions from ethanol production are shown in 

Figure 6. According to the revised estimates, in 2022 both corn ethanol production from 

natural-gas-powered plants and corn ethanol production from biomass-powered plants 

will satisfy the 20 percent reduction in GHG emissions required by the RFS. Note the 

significant reduction in the contribution of GHG emissions from international land-use 

change.

Figure 6. Results for new corn ethanol plants by fuel source and life-cycle stage for 

an average 2022 plant assuming 63 percent dry and 37 percent wet DDGs (with 

fractionation). Source: Renewable Fuel Standard (RFS II) Regulatory Impact 

Analysis (Final), http://www.epa.gov/otaq/renewablefuels/420r10006.pdf 

Although EPA and CARB came up with similar aggregate estimates of the indirect 

land-use requirements associated with domestic corn ethanol production (see Table 1), 

they used very different analytical structures and modeling frameworks to derive those 

estimates. EPA’s analysis, for instance, measures the impact of biofuels policy relative to 

a “business as usual” case projected for the year 2022; parameter inputs, and estimated 

results, are assumed to reflect anticipated changes in crop yields, energy costs, and 

production plant efficiencies between now and 2022.15

 

15 The final year in which the RFS II renewable fuel volume mandates are phased in is 2022.

 The CARB (2009) analysis, on 

the other hand, looks at land-use changes relative to a 2001 baseline year, which is the 

most recent year for which a complete global land-use database was available. The 

resulting estimates of land conversion are then corrected to account for the changes in 
Page 38

agriculture observed to occur between 2001 and present. The most significant of those 

adjustments captures improved corn yields, which were observed to increase by 9.5 

percent between 2001 and 2009. The CARB analysis therefore represents an estimate of 

the biofuel impacts on land demand assuming current crop production and conversion 

technologies as a baseline, while the EPA analysis generates impact estimates assuming a 

set of anticipated future technologies as a baseline. 

In addition to overarching differences in the structure of the impact analysis and the way 

that baselines, or business-as-usual assumptions, are defined, the two regulatory efforts 

used completely different modeling frameworks to quantify the land-use changes 

associated with biofuel production. EPA used the FASOM/FAPRI combination to 

evaluate land use and GHG impacts, while CARB derived its estimates using the GTAP 

model. To explore the robustness of its land-use estimates to modeling framework, EPA 

performed a series of exploratory runs using a version of GTAP that it customized to 

mimic as closely as possible its FASOM/FAPRI scenarios. The results of that 

comparison (see Figure 7) highlight some of the differences in results generated by the

two modeling frameworks.

Figure 7. Changes in land cover from an increase in corn ethanol. Source: 

Renewable Fuel Standard (RFS II) Regulatory Impact Analysis (Final), 

http://www.epa.gov/otaq/renewablefuels/420r10006.pdf 

EPA’s GTAP estimates suggest that, worldwide, a smaller amount of land will be 

converted to cropland per billion BTUs of ethanol and a greater proportion of that will 

come from pasture rather than forest, relative to a similar scenario derived using the 

FAPRI model. EPA identifies a few important factors that contribute to the difference in 

aggregate land demand (U.S.E.P.A., 2009): 
Page 39

• GTAP reflects a more optimistic assessment of the potential for intensification of 

agriculture as a result of higher prices, so that higher prices induced by renewable 

yield result in higher yields for both corn and other crops impacted by 

competition for land. 

• GTAP is a general equilibrium model that explicitly models land demand in 

multiple sectors, so it captures the buffering impact on agricultural land demand 

that occurs when the prices of non-agricultural products rise and “push back” 

against the agricultural sector in its push to expand. 

• The GTAP version used does not include a pool of “unmanaged” land that is 

available for cropland expansion. FAPRI allows land to come from a variety of 

sources such as grassland, savanna, shrubland, and wetlands, while GTAP 

assumes that all land that is not cropland or pasture is forest. If forestland is a 

relatively high-valued land use, that assumption could constrain the expansion of 

cropland acres.

The regional distribution of new cropland demand and land-cover change diverges 

between the two models as well. In particular, while FAPRI suggests there will be 

significant land-use change in Brazil, Asia, and Eastern Europe, GTAP does not. As 

mentioned earlier, GTAP was originally designed as a trade model, and its structure 

explicitly reflects historical trade relationships. GTAP results therefore have a tendency 

to maintain existing trade patterns and impose land-use demands on countries that have 

historically been major trading partners with the United States, while FAPRI results are 

more flexible with respect to how future trade patterns will respond to changes in global 

market conditions (U.S.E.P.A., 2009). Despite these differences, however, the EPA 

assessment of the modeling comparison concluded that “the GTAP model results were 

generally consistent with our FAPRI-CARD/satellite data analysis, in particular 

supporting the significant impact on international land use” (U.S.E.P.A., 2009). 

Continuing research

Spurred by the regulatory modeling efforts, research on this topic has continued to 

emerge and to evolve. Hertel et al. (2010) again used the modified GTAP model to 

explore four market-mediated responses to increased domestic ethanol production: 

1. A reduction in global food consumption; 

2. An intensification of agricultural production, including increases in crop yields; 

3. Land-use changes into cropping in the United States; and

4. Land conversion in the rest of the world. 

To more explicitly illustrate the impacts of these critical factors, their analytical approach 

focused on determining the proportional influence of each of those factors on the 

resulting estimates of international land-use change. To isolate the land-use impacts of 

these individual factors, the study used an equilibrium model based on 2001 data, rather 

than a comparison against a forward projection of changes over time with multiple other 

variable forces operating on land uses simultaneously (the approach taken by EPA). 
Page 40

In exploring the sensitivity of their results to the factors just listed, Hertel et al. 

sequentially impose a series of assumptions regarding critical variables such as the 

yield elasticity (.25) and the relative productivity of new land brought into production 

(66 percent of the productivity of land currently in cropland), and calculated the impact 

of each additional factor on the land requirements associated with a 15 billion gallon per 

year increase in corn ethanol production. Figure8 illustrates the progressive effects on 

land demand of introducing market-mediated adjustment assumptions one at a time. The 

“gross” land requirement on the far left represents a case where no price or market 

responses are considered at all; producing corn for ethanol production requires an amount 

of land equal to the amount of corn required divided by the average yield. The 

researchers then sequentially added additional elements reflecting on particular aspects of 

market response and calculated the implications for land demand. 

Figure 8: Estimates of indirect land-use change evolve as the scope of 

market-mediated impacts expands. Source: Hertel et al., 2010. 

The order of the elements added into the analysis is as follows: 

1. Price response arising from the introduction of constraints on the availability of 

suitable land, including a reduction in non-food demand and an intensification of 

livestock and forestry activities.

2. The ability of coproducts to substitute for feed meals and therefore reduce 

demand and land required for production or crops for those meals. 

3. Response of world food demand to increase in prices (which is sensitive to 

assumptions made about price elasticity).

4. Inclusion of a yield elasticity that increases crop yields in response to price 

increases.

5. Assumption of a reduction in the productivity of newly converted land relative to 

existing cropland. 
Page 41

Because these factors interact in influencing land-use demand, the magnitude of impact 

for each factor is sensitive to the order in which market response elements are added into 

the analysis. Nevertheless, the figure effectively illustrates the way in which such factors 

contribute to the price responsiveness of supply and demand in the market and, taken 

together, determine the full market-mediated response to biofuels production. 

When all market-mediated effects are considered, Hertel et al. (2010) found that despite a 

consideration of price-driven crop productivity improvements, domestic average yields of 

non-coarse grains and oilseeds drop as these crops are displaced from prime acreage and 

moved onto marginal acreage with reduced productivity. In the United States, they 

estimate that two-thirds of the new marginal acreage will come from forest cover, while 

one-third will come from pastureland. Internationally, the regions where production 

responds most vigorously to drops in corn exports are regions that are either significant 

importers of corn from the United States or that compete with U.S. corn exports in other 

markets, including Latin America, the EU, and China. 

While much of the research in this field reports out a single figure for average land-use 

impact, Hertel et al. (2010) analyze in more detail how the uncertainty associated with 

their parameter assumptions impacts their estimates of expansion and contraction of 

various land uses. In the supporting documentation for their research, the authors 

identify the following variables and parameters as the most critical ones in driving their 

estimates of land-use change:

1. The response of yields to price (yield elasticity);

2. The ease with which land moves between alternative uses, including cropland, 

pasture, and forestry (elasticity of land transformation across uses); 

3. The assumed yield from marginal lands (elasticity of effective cropland with 

respect to harvested cropland); 

4. The response of other countries’ land markets to shocks in U.S. exports and prices 

(trade elasticities for crops and other food products); and 

5. The extent to which imports can substitute for one another when one commodity 

or product experiences a shock (elasticity of substitution among imports from 

different sectors).

Their analysis suggests that the most uncertain results are the forestry conversion 

estimates, particularly in areas outside of the U.S., Canada, and the EU. 

Hertel et al. (2010) also vary their parametric assumptions to compute bounding values 

for the emissions impacts associated with their land-use change analysis. The resulting 

lower and upper bounds, 15 and 90 gCO2 per MJ per year, suggest that the distribution of 

possible land-use change values is not symmetrically distributed around the average 

value of 27. The tail of the distribution of possible emissions values is much longer on 

the side of increased emissions; in other words, if actual emissions deviate from the 

estimated average on the low side, they may be somewhat lower than the projection, but 

if they deviate on the high side, they may be significantly higher than the projection. 

The authors therefore conclude that, “Better understanding of skewness and long tails in 

an estimated distribution of the ILUC value will probably imply that an optimal value for 

the index assigned to a particular biofuel will be different from a central estimator of its 

ILUC effect.”
Page 42

The most recent estimates of biofuels-related land-use change to emerge from the Purdue 

research team were released in April 2010 (with revised estimates released in July 2010). 

Tyner et al. (2010) compare the land-use implications under three different sets of 

parameter assumptions: 

1. An analysis of impacts relative to the 2001 database; 

2. An analysis of impacts relative to a 2006 database that is calculated as having 

evolved from the 2001 database in a world with regional changes to GDP, gross 

capital formation, population, biofuel production, crop yields, and forest area; and 

3. An analysis of impacts relative to the 2006 baseline but with continued growth in 

crop yield and population for ethanol amounts corresponding to production years 

beyond 2006. 

The land conversion requirements drop sequentially as the authors move through the 

scenarios, largely due to the impacts of changes in assumptions about crop yields. 

Scenario 2 reflects observed increases in crop yields over the period 2001-2006, while 

scenario 3 further assumes an annual 1 percent increase in crop yields for all crops in all 

regions beyond 2006. The reduced land requirements in scenario 3 reflect a set of 

underlying parameter assumptions that result in land-saving yield impacts dominating the 

increased land demand associated with increased food demand and a growing population. 

These results, of course, are sensitive to the underlying assumptions; there may be some 

debate, for instance, about whether it is reasonable to expect a 1 percent annual increase 

in crop yields for all crops in all regions. 

The land-use change requirements results for scenario 3 are shown in Table 1; 1000 

gallons of ethanol production requires an average of .32 acres of additional land. The 

land requirements for biofuel production are significantly lower than those found in other 

studies, including those performed using GTAP. The authors describe two recent 

modifications to the model that contribute to this reduction (Tyner et al., 2010). The first 

is the introduction of two new land categories into GTAP’s available land pools: cropland 

pasture and unused cropland, including acreage enrolled in the CRP. Land brought into 

production from these land pools is not considered “land conversion,” so using these 

lands reduces the amount of forest or grassland that must be converted. The second 

modification increased the refinement with which the productivity of marginal lands is 

considered in the model. In prior GTAP analyses, newly converted land is generally 

assumed to have 66 percent of the productivity of existing cropland. The revised model, 

however, uses a process-based biophysical simulation model to calculate a set of regional 

productivity factors at the agro-ecological zone (AEZ) level, some of which are larger 

than 66 percent. Over the three scenarios, the authors find that the fraction of land-use 

change that occurs in the United States varies between 24 and 34 percent, while the 

percentage of new cropland that comes from forest ranges between 25 and 33 percent.

In the summer of 2010, the European Commission’s Joint Research Centre published a 

series of consultation documents that had been contracted to inform decisions on how 

indirect land use effects should be handled in implementation of the EU Renewable 

Energy and Fuel Quality Directives (RED/FQD). One of those documents—“Indirect 

Land Use Change from increased biofuels demand: Comparison of models and results for 

marginal biofuels production from different feedstocks”—describes the extensive 

modeling effort conducted, some scenarios of which looked at the impacts of U.S. 
Page 43

grain-based ethanol production (Edwards et al., 2010). This effort employed a range of 

models; some of the results of the EU analysis for U.S.-based ethanol production from 

maize or coarse grains are shown in Table 1. 

An indepth description of the EU modeling effort, and an explanation of the results, can 

be found in the source document listed above. However, the report highlights the 

following reasons for the discrepancies between model results: 

• The IFRPI-IMPACT model assumes a large price-induced yield gain across 

crops, which results in relatively low area changes. 

• GTAP assumes significant contributions from price-induced yield gains as well, 

but that effect is countered by the fact that GTAP assumes lower yields for crops 

produced on newly converted production areas. 

• Three of the models employ some type of Armington trade assumptions (GTAP, 

LEITAP and AGLINK) to introduce “stickiness” into the global trade response to 

represent transport costs, import tariffs and regulations, and information flows. 

This stickiness affects the extent to which crop production can be shifted to 

developing countries, in comparison to the integrated world market assumed by 

IMPACT. 

• LEITAP does not account for byproduct effects, which increases land-use 

impacts relative to the other models.

• Significant differences exist among the models in how they calculate the area 

change required for an increase in crop production. Only GTAP, for instance, 

calculates the incremental area required based on an assumed marginal/average 

yield ratio (.66) that accounts for the fact that crop areas are expanding onto 

marginal lands with the potential for lower yields. 

• Significant differences exist in the extent to which food demand declines as 

prices rise. LEITAP assumes very little food demand response, while the landuse change impacts predicted by both IMPACT and GTAP drop by more than 

50% when their food demand response assumptions are implemented. 

The EU modeling effort illustrates the spectrum of potential sources of variability and 

uncertainty across models and parameter assumptions. While the land-use impact 

estimates derived reflect this variability, all of the estimates across models, feedstocks, 

and type of biofuel, show significant increases in land-use requirements for crops 

resulting from scaled-up biofuel demand (Edwards et al., 2010). 
Page 44

VI. Summary and Future Research Needs

The increase of biofuel production to reduce energy dependence on fossil fuels led to 

concerns about unintended consequences of that activity. In particular, life-cycle 

analyses that accounted for greenhouse gas emissions identified the importance of 

considering the underlying land-use changes that might be associated with increased 

feedstock production. Estimating these changes, however, has been a daunting task. 

Mathematical models are used to indicate numerically how the aggregate behavior will 

change due to new conditions or policies, and they serve as a proxy for what cannot be 

directly observed. However, no single model currently exists that can address all of the 

questions related to this issue. 

Uncertainty is an unavoidable aspect of policy impact modeling that makes projections 

into the future. There are many drivers of land-use change in the U.S. and internationally 

that exist regardless of biofuel policy. The assumptions embedded within the different 

models estimating land-use change will affect model results. In the past few years, the 

analytic approaches used in these studies have been expanded and the underlying 

assumptions have been made more transparent. Many of the differences in model 

estimates can be traced to differences in assumptions about: 

• Crop yields and the projected elasticity of response to demand-driven price 

increases;

• The baseline that is used from which to measure change; 

• The anticipated productivity of newly converted land and the amount of land 

required to meet increased production demands by region; 

• The structure and flexibility of trade flows; 

• The price elasticity of demand for agricultural food and feed products; 

• The scope of the life-cycle assessment–including, for instance, whether the 

livestock and forest sectors are explicitly modeled as competitors for land.

Managing uncertainty in the context of modeling indirect land-use change has involved:

• Identifying the variables that are particularly important in contributing to the 

uncertainty of estimates and improving the precision with which such variables 

are represented with the analysis (e.g., future crop yields, the productivity of 

newly converted lands, and the substitutability of DDGs in livestock diets);

• Identifying relevant relationships that require more refined analysis (such as the 

importance of trade relations in determining likely sources of increased 

agricultural production); 

• Understanding the nature of the remaining uncertainty, its effects on the 

distribution of potential outcomes, and the implications of incorporating different 

measures of that uncertainty into policy; 

• Designing policy to ensure that existing regulations evolve as the science 

becomes more sophisticated. 

Both EPA and CARB call for regular examination and updating of the indirect land-use 

impacts component of the GHG quantification methodology. EPA includes in its final 
Page 45

rule the following stipulation: “EPA will request that the National Academy of Sciences 

evaluate the approach taken in this rule, the underlying science of life-cycle assessment, 

and in particular indirect land-use change, and make recommendations for subsequent 

lifecycle GHG assessments on this subject” (EPA, 2010). The CARB resolution also 

states, "... that the Board directs the Executive Officer to convene an expert workgroup 

to assist the Board in refining and improving the land use and indirect effect analysis of 

transportation fuels and return to the Board no later than January 1, 2011 with regulatory 

amendments or recommendations, if appropriate, on approaches to address issues 

identified” (CARB, 2009). 

Establishing such mechanisms for continuous refinement is a critical element of policy 

design in this area. The science underpinning these regulations is an evolving discipline, 

and the estimates derived from it will continue to improve, as will the policy mechanisms 

established to handle ongoing uncertainty. Additional research on variables that have 

been identified as critical drivers in determining the land-use impacts of biofuels policy 

also will be instrumental in refining modeling capacity and estimates in this area. 

Research and data that can facilitate greater understanding of critical dynamics in 

land-use impact modeling includes:

• Improved data on land use and land cover change worldwide;

• Improved data on the extent and productivity of existing and potential cropland 

worldwide, including previously cleared, “degraded,” or underutilized lands;

• Greater understanding of and ability to model prospective growth in crop demand 

and supply by region worldwide. This includes refined analysis of development 

and adoption of crop productivity technologies such as biotechnology as well as 

impacts of income, population growth, and dietary transitions; 

• Improved modeling and parameter estimates around the substitutability of biofuel 

coproducts in other markets, such as livestock feed markets. 

Future research that addresses more explicitly the sources and magnitude of this 

uncertainty will be particularly useful in informing policy design. Hertel et al. (2010) set 

a precedent for this by highlighting the need for performing and reporting more extensive 

sensitivity analysis in research on land-use impacts. These authors state that a more 

explicit treatment of sensitivity and distributions of possible outcomes is also needed to 

inform a more sophisticated treatment of uncertainty within policy design itself. They 

suggest that central estimators such as average or median values may not be the most 

appropriate values to assign to land-use change or emissions estimates in cases where 

highly asymmetrical distributions of possible outcomes exist (Hertel et al. 2010). Further 

research on alternative estimators or alternative methods of capturing distributions within 

a policy indicator, together with research on how those methods correspond to particular 

attitudes toward risk and risk management, may lead to a critical development in the 

appropriate incorporation of inherently uncertain estimates into policy such as that related 

to biofuels and land-use change. 

As the science of projecting land-use change evolves, USDA has an important role to 

play both in supporting ongoing EPA efforts and in developing additional research 

capacity for exploring the critical variables determining the direct and indirect land-use 

impacts of domestic agricultural production and the way they play out through domestic 
Page 46

and global market interactions. Existing ERS research corroborates the upwards pressure 

that biofuel production puts on commodity prices and land demand (Malcolm et al. 2009) 

and the sensitivity of domestic bioenergy’s economic and welfare impacts to uncertain 

projected responses within world energy markets (Gehlar et al. 2010). 

In an ongoing effort to integrate more sophisticated land allocation considerations into its 

analyses of bioenergy production, USDA’s Economic Research Service is expanding two 

in-house agricultural models to more explicitly represent the critical variables driving the 

relationship between land use and biofuels and to differentiate among potential pools of 

land for production. The Regional Environment and Agriculture Programming Model, a 

detailed domestic partial equilibrium model of cropland and livestock agriculture, is 

being expanded to include a forestry component and a more sophisticated treatment of 

competition for land and conversion among uses. The Future Agricultural Resources

Model, a global general equilibrium model that was a pioneer in the movement to 

partition land as an economic input into different land classes, is being 

retooled to accommodate a dynamic forestry sector, to explicitly track energy 

technologies and energy accounting, and to allow for cellulosic conversion technologies 

that create biofuels from feedstocks such as crop residue, switchgrass, or fast-growing 

trees. 

ERS models without explicit land-use analysis capacity are also being modified to 

support research related to trade and market response to biofuels production that can then 

be used to inform future land-use analyses. The U.S. Applied General Equilibrium 

(USAGE) Model includes more than 20 major importing and exporting trade partners and 

the data are being updated to capture the supply response of the farm sector to address 

bioenergy-related issues. The Partial Equilibrium Agricultural Trade Simulator 

(PEATSim) is being enhanced to include more domestic and international policies 

affecting major crop and oilseed markets, as well as oilseed product markets, sugar, 

livestock, and dairy. 

Continued research and modeling efforts will be required to narrow the bands of 

uncertainty associated with projections of land-use change and domestic policy. New 

models, model refinements, and improved data will all help increase the precision with 

which input parameters are estimated and behavioral relationships are represented. Still, 

the successful integration of science and policy must come with the recognition that

future projections will always carry some degree of uncertainty suggesting that policy 

design must accommodate uncertainty. As research moves forward, an explicit focus on 

the nature and structure of input and output uncertainty, and on the full distributions of 

possible outcomes and estimates that result, will facilitate improvements to policy design 

over time.
Page 47

References 

Alfredsson, E.C. 2004. “`Green’ Consumption – No Solution for Climate Change,” 

Energy, 29(4); 513-524. 

Angelsen, Arild and David Kaimowitz. 1999. “Rethinking the Causes of Deforestation: 

Lessons from Economic Models,” World Bank Research Observer, 14(1), February. 

Armington, Paul. 1969. “A Theory of Demand for Products Distinguished by Place of 

Production,” Staff Papers - International Monetary Fund, 16(1), March. 

Beckman, Jayson, and Thomas Hertel. 2009. “Why Previous Estimates of the Cost of 

Climate Mitigation Are Likely Too Low,” Purdue University, GTAP Working Paper No. 

54. 

Beckman, Jayson, Carol Jones, and Ron Sands. 2011 Forthcoming. “A Global General 

Equilibrium Analysis of Biofuel Mandates and Greenhouse Gas Emissions,” American 

Journal of Agricultural Economics, January. 

Biomass Research and Development Board. 2008. Increasing Feedstock Production for 

Biofuels: Economic Drivers, Environmental Implications, and the Role of Research, 

Report submitted by the Interagency Feedstocks Team, December.

Brander, M., R. Tipper, C. Hutchison, and G. Davis. 2008. “Consequential and 

Attributional Approaches to LCA: A Guide to Policy Makers with Specific Reference to

Greenhouse Gas LCA of Biofuels,” Technical Paper, Econometrica Press.

California Air Resources Board. 2009. “Proposed Regulation to Implement the Low 

Carbon Fuel Standard, Vol 1.” Staff Report: Initial Statement of Reasons. 374 pp. 

Available online at http://www.arb.ca.gov/regact/2009/lcfs09/lcfsisor1.pdf. 

Charles, Dan. 2009. “Corn-Based Ethanol Flunks Key Test.” Science 324: p. 587. 

Edwards, Robert, Declan Mulligan, and Luisa Marelli. 2010. “Indirect Land Use Change 

from Increased Biofuels Demand: Comparison of models and results for marginal 

biofuels production from different feedstocks.” European Commission Joint Research 

Centre Scientific and Technical Report, Institute for Energy (JRC-IE). Available online at 

http://ec.europa.eu/energy/renewables/studies/doc/land_use_change/study_4_iluc_modell

ing_comparison.pdf. 

Fabiosa, Jacinto F., John C. Beghin, Fengxia Dong, Amani Elobeid, Simla Tokgoz, and 

Tun-Hsiang Yu. 2009. “Land Allocation Effects of the Global Ethanol Surge: 

Predictions from the International FAPRI Model,” Iowa State University, CARD 

Working Paper 09-WP 488, March. 
Page 48

Fargione, Joseph, Jason Hill, David Tilman, Stephen Polasky, and Peter Hawthorne. 

2008. “Land Clearing and the Biofuel Carbon Debt,” Science, 319: 1235-1238, 29 

February.

Farrell, Alex, and Michael O’Hare. 2008. “Greenhouse gas (GHG) emissions from 

indirect land use change (ILUC).” Memorandum to John Courtis, California Air 

Resources Board. Available online at 

http://www.arb.ca.gov/fuels/lcfs/011608ucb_luc.pdf. 

Fisher, Anthony C., and W. Michael Hanemann. 1990. “Option Value: Theory and 

Measurement,” European Review of Agricultural Economics, 17: 167-180. 

Fuglie, Keith O. 2010. “Accelerated Productivity Growth Offsets Decline in Resource 

Expansion in Global Agriculture,” Amber Waves, Vol. 8, Issue 3, U.S. Department of 

Agriculture, Economic Research Service, September. 

Gehlar, Mark, Agapi Somwaru, Peter Dixon, Maureen Rimmer, and Ashley Winston. 

2010. “Economywide Implications from U.S. Bioenergy Expansion,” American 

Economic Review: Papers & Proceedings, 100: 172-177. 

Gibbs, Holly, Matt Johnston, Jonathon Foley, Tracey Holloway, Chad Monfreda, Navin 

Ramankutty and David Zaks. 2008. “Carbon Payback Times for Crop-Based Biofuel 

Expansion in the Tropics: the Effects of Changing Yield and Technology,” 

Environmental Research Letters. 3(3), July. 

Golub, Alla, Thomas W. Hertel, and Brent Sohngen. 2009. "Land Use Modeling in 

Recursively-Dynamic GTAP Framework," Chapter 10 in Economic Analysis of Land Use 

in Global Climate Change Policy, edited by Thomas W. Hertel, Steven Rose, and 

Richard S.J. Tol, Rouledge Press.

Gurgel, Angelo, John Reilly, and Sergey Paltsev. 2007. “Potential Land Use 

Implications of a Global Biofuels Industry,” Journal of Agricultural & Food Industrial 

Organization, Vol 5. 

Heisey, Paul W. 2009. “Science, Technology, and Prospects for Growth in U.S. Corn 

Yields,” Amber Waves, Vol. 7, Issue 4, U.S. Department of Agriculture, Economic 

Research Service, December.

Hertel, Thomas W., Alla A. Golub, Andrew D. Jones, Michael O’Hare, Richard J. Plevin, 

and Daniel M. Kammen. 2010. “Effects of US Maize Ethanol on Global Land Use and 

Greenhouse Gas Emissions: Estimating Market-mediated Responses,” BioScience 60(3): 

223-231, March. 

Hertel, Thomas W., Wallace E. Tyner, and Dileep K. Birur. 2010. “The Global Impacts 

of Biofuels Mandates,” The Energy Journal, 31(1): 75-100. 

Hertel, Thomas W., Steven Rose and Richard S.J. Tol. 2009. "Land Use in Computable 

General Equilibrium Models: An Overview." Chapter 1 in Economic Analysis of Land 
Page 49

Use in Global Climate Change Policy, edited by Thomas W. Hertel, Steven Rose, and 

Richard S.J. Tol, Rouledge Press. 

Isik, M. and W. Yang. 2004. “An Analysis of the Effects of Uncertainty and 

Irreversibility on Farmer Participation in the Conservation Reserve Program,” Journal of 

Agricultural and Resource Economics, 29 (2): 242-259. 

Keeney, Roman, and Thomas E. Hertel. 2009. “The Indirect Land Use Impacts of United 

States Biofuel Policies: The Importance of Acreage, Yield, and Bilateral Trade 

Responses,” American Journal of Agricultural Economics, 91(4): 895-909, November. 

Leibtag, Ephraim. 2008. “Corn Prices Near Record High, But What About Food Costs,” 

Amber Waves, Vol. 6, Issue 1, U.S. Department of Agriculture, Economic Research 

Service, February.

Lubowski, Ruben N., Marlow Vesterby, Shawn Bucholtz, Alba Baez, and Michael J. 

Roberts. 2006. Major Uses of Land in the United States, 2002. Economic Information 

Bulletin No. 14, U.S. Department of Agriculture, Economic Research Service, May.

Malcolm, Scott A., Marcel Aillery, and Marca Weinberg. 2009. Ethanol and a 

Changing Agricultural Landscape, Economic Research Report No. 86, U.S. Department 

of Agriculture, Economic Research Service, November.

Marshall, Liz. 2009. “Biofuels and the Time Value of Carbon: Recommendation for 

GHG Accounting Protocols” WRI Working Paper. World Resources Institute, 

Washington DC. April. 

Melillo, Jerry M., Angelo C. Gurgel, David W. Kicklighter, John M. Reilly, Timothy W. 

Cronin, Benjamin S. Felzer, Sergey Paltsev, C. Adam Schlosser, Andrei P. Sololov, and 

X. Wang. 2009. Unintended Environmental Consequences of a Global Biofuels 

Program, MIT Joint Program on the Science and Policy of Global Change Report No. 

168, January. 

Plevin, Richard. 2010. “Review of final RFS2 Analysis.” Comments available online at 

http://plevin.berkeley.edu/docs/Plevin-Comments-on-final-RFS2-v7.pdf. 

Roberts, Michael J. and Wolfram Schlenker, 2010. “The U.S. Biofuel Mandate and 

World Food Prices: An Econometric Analysis of the Demand and Supply of Calories.” 

Presented at the National Bureau of Economic Research conference “Agricultural 

Economics and Biofuels.” Available online at 

http://www.nber.org/confer/2010/AGs10/summary.html. 

Searchinger, Timothy, Ralph Heimlich, R.A. Houghton, Fengxia Dong, Amani Elobeid, 

Jacinto Fabiosa, Simla Tokgoz, Dermot Hayes, and Tun-Hsiang Yu. 2008a. “Use of US 

Croplands for Biofuels Increases Greenhouse Gases Through Emissions from Land-Use 

Change,” Science 319: 1238-1240. 
Page 50

Searchinger, Tim. 2008b. “Evaluating Biofuels: The Consequences of Using Land to 

Make Fuel,” The German Marshall Fund of the United States Policy Brief. Available 

online at 

http://www.gmfus.org/galleries/ct_publication_attachments/Econ_Searchinger_Biofruel.

pdf. 

Schmidt, Jannick H. 2008. “System delimitation in agricultural consequential LCA,” 

International Journal of Life Cycle Assessment 13: 350-364. 

Song, F., J. Zhao, and S. Swinton. 2009. “Switching to Perennial Crops Under 

Uncertainty and Costly Reversibility,” Michigan State University, Department of 

Agricultural, food, and Resource Economics, Staff Paper No. 2009-14, Devember. 

Stavins, R.N. 1999. “The Costs of Carbon Sequestration: A Recealed Preference 

Approach,” American Economic Review, 89 (4): 994-1009. 

Trostle, Ronald. 2008. Global Agricultural Supply and Demand: Factors Contributing to 

the Recent Increase in Food Commodity Prices, Outlook Report WRS-0801, U.S. 

Department of Agriculture, Economic Research Service, July.

Tyner, Wallace E., Farzad Taheripour, and Uris Baldos. 2009. “Land Use Change Carbon 

Emissions Due to US Ethanol Production,” Report to Argonne National Laboratory, 

January.

Tyner, Wallace E., Farzad Taheripour, Qianlai Zhuang, Dileep Birur, and Uris Baldos. 

2010. “Land Use Changes and Consequent CO2 Emissions Due to US Corn Ethanol 

Production: A Comprehensive Analysis” Department of Agricultural Economics, Purdue 

University. Final Report (revised) to Argonne National Laboratory. Available at: 

http://www.transportation.anl.gov/pdfs/MC/625.PDF. 

U.S. Department of Agriculture, Office of Chief Economist and World Agricultural 

Outlook Board. 2010. USDA Agricultural Projections to 2019, Prepared by the 

Interagency Agricultural Projections Committee. 

U.S. Department of Energy, Energy Information Administration. 2010. Annual Energy 

Outlook. May.

U.S. Environmental Protection Agency. 2009. EPA Draft Regulatory Impact Analysis: 

Changes to Renewable Fuel Standard Program. 822 pp. 

http://www.epa.gov/otaq/renewablefuels/420d09001.pdf . 

U.S. Environmental Protection Agency. 2010. EPA Final Regulatory Impact Analysis: 

Renewable Fuels Standard Program (RFS2). 1120 pp. Available online at 

http://www.epa.gov/otaq/renewablefuels/420r10006.pdf. 
Page 51

Zilberman, David, Gal Hochman, and Deepak Rajagopal. 2010. “Indirect Land Use: 

One Consideration Too Many in Biofuel Regulation,” Agricultural and Resource 

Economics Update, Giannini Foundation of Agricultural Economics, University of 

California, 13(4): 1-4, March/April. 
Page 52

Glossary of Acronyms

AEO Annual Energy Outlook 

AEZ Agro-Ecological Zone

CARB California Air Resources Board

CGE Computable General Equilibrium 

CO2 Carbon Dioxide 

CRP Conservation Reserve Program 

DDGs Distillers’ Dried Grains

DOE U.S. Department of Energy 

EIA Energy Information Administration

EISA Energy Independence and Security Act

EPA U.S. Environmental Protection Agency 

EPACT Energy Policy Act of 2005 

ERS USDA Economic Research Service

FAIR Federal Agriculture Improvement and Reform 

FARM Future Agricultural Resources Model 

FAPRI Food and Agricultural Policy Research Institute 

FASOM Forestry and Agricultural Sector Optimization Model

GHG Greenhouse Gas

GTAP Global Trade Analysis Project

ILUC Indirect Land Use Change

IMAGE Integrated Model to Assess the Global Environment 

LCA Life-Cycle Analysis

LCFS Low Carbon Fuel Standard 

MJ Megajoule 

PE Partial Equilibrium

PEATSIM Partial Equilibrium Agricultural Trade Simulator

REAP Regional Environmental and Agricultural Production 

RIA Regulatory Impact Analysis

RFS Renewable Fuel Standard

RFS II Renewable Fuel Standard - 2009 

ROW Rest of World

RTFO Renewable Transport Fuel Obligation

USAGE U.S. Applied General Equilibrium

USDA U.S. Department of Agriculture