EPA Project Number: 0210826.014

Land Use Implications of Biofuels:

GTAP Approach

Final Report

January 2010

Prepared for

U.S. Environmental Protection Agency

Office of Transportation and Air Quality

1200 Pennsylvania Avenue, NW

Washington, DC 20460.

Prepared by

Dileep K. Birur

Robert H. Beach

Environmental, Technology and Energy Economics Program

Global Climate Change and Environmental Sciences Unit

RTI International

3040 Cornwallis Road, P.O. Box 12194

Research Triangle Park, NC 27709-2194.
i

Table of Contents

 Page

Table of Contents................................................................................................................. i

List of Tables ...................................................................................................................... ii

List of Figures....................................................................................................................iii

1. Overview..................................................................................................................... 1

2. Model Background...................................................................................................... 1

2.1. Structure of GTAP Model....................................................................................... 2

2.2. Aggregation of Sectors and Regions....................................................................... 4

2.3. Land Endowment in the GTAP-BIO Model........................................................... 6

2.4. Land Supply in the GTAP-BIO Model................................................................... 8

3. Key Parameter Assumptions and Qualifications ........................................................ 8

3.1 Sensitivity of Key Parameters................................................................................. 9

3.2 Qualifications........................................................................................................ 10

4. Experimental Design................................................................................................. 10

5. Results from Corn-Ethanol Experiment.................................................................... 12

6. Results from Soybean-Biodiesel Experiment ........................................................... 15

7. Conclusions............................................................................................................... 18

References......................................................................................................................... 19

Appendix A: Aggregation Tables..................................................................................... 45

Appendix B: Land Supply ................................................................................................ 48
ii

List of Tables

Table Page

Table 1. Variation in key parameters driving the general equilibrium results on biofuels..... 22

Table 2. The U.S. biofuel scenarios implemented in the GTAP model.................................. 23

Table 3. Change in agricultural output due to an increase in corn-ethanol production in the 

U.S. .......................................................................................................................... 24

Table 4. Change in commodity price due to an increase in corn-ethanol production in the 

U.S. .......................................................................................................................... 25

Table 5. Impact of an increase in U.S. corn-ethanol production on bilateral trade (Change in 

import volume)......................................................................................................... 26

Table 6. Change in land use and land cover due to an increase in corn-ethanol production in 

the U.S...................................................................................................................... 27

Table 7. SSA estimates of change in harvested area of crops due to an increase in cornethanol production in the U.S. ................................................................................. 28

Table 8. SSA estimates of change in land cover due to an increase in corn-ethanol production 

in the U.S. ................................................................................................................ 29

Table 9. Change in agricultural output due to an increase in biodiesel production in the U.S.

.................................................................................................................................. 30

Table 10. Change in commodity price due to an increase in biodiesel production in the U.S.

.................................................................................................................................. 31

Table 11. Impact of an increase in U.S. biodiesel production on bilateral trade (Ch. in import 

volume) .................................................................................................................... 32

Table 12. Change in land use and land cover due to an increase in biodiesel production in the 

U.S. (million acres).................................................................................................. 33

Table 13. SSA estimates of change in harvested area of crops due to an increase in biodiesel 

production in the US (million acres)........................................................................ 34

Table 14. SSA estimates of change in land cover due to an increase in biodiesel production in 

the US (million acres). ............................................................................................. 36

Table A1. Aggregation of sectors in the GTAP-BIO-1 Model............................................... 45

Table A2. Aggregation of Regions in the GTAP-BIO-1 model. ............................................ 47
iii

List of Figures

Figure Page

Figure 1. An illustrative overview of the GTAP model........................................................... 3

Figure 2a. Crop cover change due to an increase in U.S. corn-ethanol production (million 

acres) ...................................................................................................................... 37

Figure 2b. Pasture cover change due to an increase in U.S. corn-ethanol production (million 

acres) ...................................................................................................................... 38

Figure 2c. Forest cover change due to an in crease in U.S. corn-ethanol production (million 

acres) ...................................................................................................................... 39

Figure 3. Crop cover change due to an increase in U.S. corn-ethanol production (Ha/billion 

BTU)....................................................................................................................... 40

Figure 4a. Crop cover change due to an increase in U.S. biodiesel production (million acres)

................................................................................................................................ 41

Figure 4b. Pasture cover change due to an increase in U.S. biodiesel production (million 

acres) ...................................................................................................................... 42

Figure 4c. Forest cover change due to an increase in U.S. biodiesel production (million acres)

................................................................................................................................ 43

Figure 5. Crop cover change due to an increase in U.S. biodiesel production (Ha/billion 

BTU)....................................................................................................................... 44

Figure B1. Land supply in the GTAP-BIO models. ............................................................... 48
1

Land Use Implications of Biofuels: GTAP Approach

1. Overview

The U.S. Environmental Protection Agency (EPA) has undertaken a life cycle assessment 

(LCA) focused on greenhouse gas (GHG) emissions associated with different types of renewable 

fuels as required by the Energy Independence and Security Act of 2007 (EISA). One important 

component of this LCA is a careful accounting of GHG emissions resulting from land use 

change. The purpose of this study is to examine the global land use implications of biofuels 

production in a general equilibrium framework. The Global Trade Analysis Project (GTAP) 

model (Hertel ed. 1997), a multi-region multi-sector computable general equilibrium model, is 

widely used for global-scale assessment of economic policies. The use of the GTAP data base 

and model has been increasing with the growing research interests in international trade policies, 

energy policies, climate change, etc. The GTAP framework ensures complete accounting of the 

impacts of these topical policy issues. With the ease of access to the standard GTAP model, 

which is publicly available, there are numerous GTAP-based models in use around the world. 

However, the GTAP Center has a mechanism to control the quality of these applications through 

a peer-review process. In recent years, a version of the model was developed to explicitly

account for substitution between energy commodities (Burniaux and Truong, 2002). Lee et al. 

(2005) developed another version of the model which explicitly accounts for global competition 

for land across alternative uses in agro-ecological zones (AEZs). More recently Birur et al. 

(2008) developed the GTAP-BIO model by combining the above two frameworks and 

introducing biofuels as substitutes for petroleum products. This study covers biofuels that are 

produced from agricultural feedstocks such as corn ethanol, sugarcane ethanol, and oilseed-based 

biodiesel. More recently, the California Air Resource Board (CARB) has used the GTAP-BIO 

model for its rulemaking on a Low Carbon Fuel Standard for biofuels (CARB, 2009). The 

modeling approach used for this analysis is discussed in detail in the next section.

2. Model Background

With the growing research interest in biofuels, numerous recent studies on the economics 

of biofuels have employed cost-accounting procedures and/or partial equilibrium (PE) 

frameworks (Walsh et al. 2003; Msangi et al. 2006; Fargione et al. 2008; Searchinger et al. 
2

2008). However, these approaches do not capture the economy-wide implications of a policy. 

Also, PE approaches fail to address the linkages between factor income and expenditures

resulting from a policy. In contrast, the general equilibrium (GE) models can be used to check 

theoretical and accounting consistency (e.g., market clearing conditions are satisfied in input and 

output markets, consumers are on their budget constraints, macro-economic accounting balances 

as indicated by balance of payments conditions). With the strengthening linkage between 

agriculture and energy markets, biofuels production could impact food security, international 

trade, the environment, and natural resources. The emerging linkage is a demand-pull from the 

energy sector to agriculture, which will potentially increase agricultural commodity demand due 

to the biofuel policies and high oil prices. Such a linkage can be fully captured in a GE model 

such as GTAP. Literature on economic modeling of biofuels suggest that for analyzing the longterm consequences of biofuels, the GTAP model is a suitable economic tool to link energy and 

crop demand (Kløverpris et al. 2008; Rajagopal and Zilberman, 2007). Several studies have 

adopted the GTAP data base and/or GTAP model-based approaches for studying the economywide implications of biofuel policies in the major biofuel producing regions such as the EU and 

the U.S. (Banse et al. 2007; Eickhout et al. 2008; Birur et al. 2009; Hertel et al. 2010). The 

findings of these studies offer varied perspectives, with differences in results due primarily to 

differences in behavioral parameter assumptions and methodological approach. We will revisit 

these issues in Section 3.

2.1. Structure of GTAP Model

The GTAP model is a multi-commodity, multi-regional computable general equilibrium 

model developed by Hertel (ed., 1997). The model has been documented in the ‘GTAP Book’

(Hertel ed., 1997) with a detailed discussion on the underlying theory and derivation of the 

behavioral equations involved in the model. The standard GTAP model basically assumes 

perfect competition in all markets with Walrasian adjustment1. As represented in Figure 1 

(Hertel, Tyner, and Birur, 2010), the regional household collects all the income in its region and 

spends it over three expenditure types – private household (consumer), government, and savings, 

over a Cobb-Douglas utility function. A representative firm maximizes profits in nested Constant 

Elasticity of Substitution (CES) functions in a perfectly competitive market for each 

 

1 Walras’ Law states that if (n-1) markets are in equilibrium, then the nth market has to be in equilibrium.
3

industry/sector in each region and pays income to the regional household for utilizing the 

endowment commodities (land, labor, capital, and natural resources).

Furthermore, firms sell the final goods consequently produced by combining the 

endowments with the intermediate inputs to the private households and the government, and the 

investment goods to the regional household. In an open economy, firms also export the tradable 

commodities and import the intermediate inputs from other regions (rest of the world), but the 

model follows an Armington assumption, which accounts for product heterogeneity. The land 

endowment is imperfectly mobile, while labor and capital are perfectly mobile within a region 

but imperfectly mobile across regions. Government spending is modeled by using a CobbDouglas sub-utility function, which maintains constant expenditure shares across all the sectors. 

Rest of World

Savings

Net 

investments

Global Savings

Regional Household

Producer

Government Private

Household

Imports (intermediates) Exports

Govt. 

Expenditure

Import 

Tax

Pvt. 

Expenditure

Export 

Tax

Exp. on 

Imports 

Exp. on 

Imports 

Dom. Dom. purchases purchases

Taxes Taxes Taxes

Endowments:

(Land-AEZs, 

Labor, Capital)

(commodities Purchases

including biofuels)

Figure 1. An illustrative overview of the GTAP model

Source: Hertel, Tyner, and Birur (2010).
4

Private household consumption is modeled by adopting a non-homothetic Constant 

Difference of Elasticity (CDE) implicit expenditure function, which allows for differences in 

income elasticities across commodities. Taxes (and subsidies) go as net tax revenues (subsidy 

expenditures) to the regional household from private household, government, and the firms. As 

shown in Figure 1, the rest of the world gets revenues by exporting to private households, firms 

and government. These revenues are spent on export taxes and import tariffs, which eventually 

go to the regional household.

The standard closure of the GTAP model allows for equilibrium in all the markets, all 

firms earn zero economic profits, the regional household is on its budget constraint, global 

investment equals global savings, and the sum of global exports and imports is zero. The global 

trade balance condition determines the world price of a given commodity. The Cobb-Douglas 

utility function of the regional household allows for maintaining constant budget shares.

2.2. Aggregation of Sectors and Regions

The data base used in the GTAP-BIO model versions is based on the GTAP version 6 

data base (GTAP v6) (Dimaranan, 2006 Ed.), which pertains to the global economy for 2001. 

The original data base has 57 sectors and 87 regions of the globe, but does not include any 

sectors explicitly breaking out biofuels. To create the data base used for the GTAP-BIO model, 

Taheripour et al. (2007) split out three biofuels sectors from existing sectors in the standard 

GTAP v6 data base: the grain based ‘ethanol-1’ sector from the food products sector (ofd) 

receiving inputs from the cereal grains sector (gro); the sugar-based ‘ethanol-2’ sector out of the 

chemicals sector (crp) with inputs from sugar-cane-beet (c_b) sector; and the biodiesel sector 

was created from the vegetable oils and fats (vol) sector which gets input from the oil-seeds (osd) 

sector. The sales of biofuels are channeled through household as well as intermediate demand. 

Taheripour et al. (2009) further modify the data base to incorporate byproducts of biofuels such 

as dried distillers grains with solubles (DDGS) and vegetable oil cake (soybean meal in the U.S. 

and rapeseed cake in the EU), and then augment feed demand in the livestock sector by allowing 

for substitution between biofuel byproducts and other animal feed. 
5

For the corn ethanol production scenario, we utilize the version of the model used for the 

California LCFS (referred to in this report as GTAP-BIO-1)

2

. However, due to limitations on 

how the oilseed byproducts were treated in this version of the model, we have used a different 

version of the model for the biodiesel scenario. Additional details on this version of the model 

are documented in Taheripour et al. (2009, in press) (referred to in this report as GTAP-BIO-2). 

This version of the model was not available for the corn ethanol scenario, but offers advantages 

for the analysis of biodiesel expansion. In the GTAP-BIO-1 model, biodiesel is produced from 

oilseeds along with the production of by-products. Since biodiesel can also be produced from 

processed vegetable oil, Taheripour et al. (2009) split the vegetable oil sector into refined and 

crude-vegetable oil sectors and allow for biodiesel to be produced from crude-vegetable oil 

rather than directly from oilseeds. Unlike GTAP-BIO-1 where byproducts (oil-meal/cake) are 

produced along with the production of biodiesel, biodiesel is produced from crude-vegetable oil 

as the main input in the GTAP-BIO-2 version. The crude-vegetable oil is produced as a joint 

product along with oil-meal/cake from the oilseeds sector. This modification is expected to 

result in a better assessment of the impacts of expanded U.S. renewable fuels production, 

particularly the production of biodiesel from soybean oil. Because the oil content in soybeans is 

only about 18% and much of the remainder is soybean-meal, which is used in animal feed, large 

production of biodiesel using soybean oil as the feedstock is expected to significantly impact 

feed markets. Therefore, we utilize the GTAP-BIO-2 version of the model to analyze potential 

impacts of increasing annual U.S. biodiesel production by 1 billion gallons. 

After incorporating the biofuel and byproducts sectors, the data base is aggregated to 

permit focus on the sectors and regions of particular interest. In the GTAP-BIO-1 version of the 

model used in this report for analyses of ethanol expansion, the data base is aggregated into 20 

economic sectors and 18 regions (Table A1 and Table A2, respectively in Appendix-A). The 

sector aggregation was chosen such that we could focus on the linkages among feedstocks, 

biofuels, energy commodities, and other important sectors. The regions are aggregated such that 

each continent is broadly divided into three categories: major energy consuming countries, major 

energy exporting countries, and all remaining countries in the continent. However, in the GTAPBIO-2 model, the ‘other grains’ sector has been kept disaggregated as ‘paddy-rice’ and ‘wheat’. 

 

2 This analysis relies on the version of the model CARB used for the corn ethanol assessment conducted in February 

2009.
6

The ‘livestock’ sector is also kept disaggregated as ‘ruminants’, ‘non-ruminants’, and ‘dairyfarms’. This helps in keeping track of substitution of byproducts for traditional feed across 

alternative livestock uses. Regional aggregation is similar to that of the GTAP-BIO-1 version 

but has 19 regions in aggregate. The next section focuses on incorporating disaggregated land 

endowment across agro-ecological zones.

2.3. Land Endowment in the GTAP-BIO Model

In the standard GTAP model, land is regarded as a sluggish endowment which can be reallocated, based on relative land rents. However, not all crops are taken up in all parts of a 

country due to constraints on their adaptability. In the GTAP-BIO model versions, land is 

treated as the typical non-tradable endowment but is also further classified based on agroecological zones (AEZs) in each region. As defined by FAO and IIASA (2000), AEZs are the 

classification of a parcel of land based on soil and climate characteristics such as moisture, 

temperature, soil type, etc. Lee et al. (2005) first classify the AEZs based on “length of growing 

period” (LGP) data provided by FAO and IIASA (2000). The LGP refers to the duration when 

soil moisture and temperature are conducive to plant growth in a given year. Land is classified 

into six categories of LGP, each comprising 60 days of sufficient temperature and moisture to 

grow crops. Following Ramankutty and Foley (1999), Lee et al. (2005) overlay six categories of 

LGP on three climate zones of the world, classified as tropical, temperate, and boreal zones 

based on absolute minimum temperature and growing degree days. The resulting 18 AEZ 

categories, which account for heterogeneity of land in each region, offer better characterization 

of the competition for land across alternative uses. The global land use data base used in the 

GTAP model is compiled using three land use data sets: (i) the land cover data from Ramankutty

et al. (2005) which distinguishes forest, pastureland, and cropland cover types, (ii) data on 

harvested land acreage and production from Monfreda et al. (2008), and (iii) a data base that

maps forestry activity into the 18 AEZs as documented in Sohngen et al. (2009). Lee et al. 

(2009) utilizes these land use components and disaggregates land rents in the GTAP data base on 

the basis of prices and yields. Detailed discussion on this aspect is given in the volume edited by

Hertel, Rose, and Tol (2009).

Since the GTAP data base corresponds to economic flows, the land endowment is 

observed as rents in the land-using sectors in a given region. Lee et al. (2009) share out land 
7

rents for cropland, pasture-cover and forest-cover based on the yearly economic activity in a 

given AEZ. They determine cropland rents based on value of crops produced in a given AEZ 

using 0.5ogrid level data on harvested crop area and per hectare yield data offered by Monfreda 

et al. (2008). Due to this approach, the land rents can be treated as indicators of productivity in 

each AEZ. The temperate AEZs with a longer length of growing period (LGP) are found to have 

the highest land rents worldwide, followed by the tropical AEZs, and then boreal zones. Also, 

the land rents are largest in those AEZs where high value crops such as vegetables, fruits and 

nuts, and irrigated crops such as paddy-rice and sugarcane are grown. 

For determining land rents for the livestock sector, which includes the economic 

activities of ruminants (cattle, sheep and goats), dairy production, wool, and non-ruminants (pigs 

and poultry), Lee et al. (2009) draw on the direct competition between these sectors with the 

grazing land. For this purpose, they utilize land cover (pasture cover) data provided by

Ramankutty et al. (2005) which explicitly shows the availability of pasture/grazing land in each 

AEZ for all the regions in the world. For computing livestock sectors’ land rent, ideally it is

computed based on revenues resulting from growing forage crops. But the major constraint to 

this approach is there is no explicit ‘forage crop’ sector in the GTAP data base that would 

indicate the crop yield and hence the revenues. Therefore, Lee et al. (2009) utilized the data on 

average coarse grain yield in each AEZ and multiplied it by the pasture land cover hectares. Due 

to this approach, the aggregated land rent of livestock sectors is smaller than the aggregated land 

rents of agricultural crops. For example, in the U.S. the average cropland cash rent is $70/acre 

and that of pastureland is only $9/acre, clearly indicating the marginality of pasture land. 

Similarly, Lee et al. (2009) compute the AEZ level forest cover land rent by using 

information on timberland land rent and timberland area offered by Sohngen et al. (2009). The 

land rents for the forestry sector in the GTAP data base are distinguished as ‘natural resource’ 

rents. Lee et al. split this ‘natural resource’ sector rents based on the AEZ level forest rent 

shares. For computing the AEZ level forest rent shares, those authors exclude the inaccessible 

forest area as these forests do not generate any rents in principle. The resulting per hectare land 

rents indicate that forest land rents are slightly smaller than that of aggregated pasture sector 

rents indicating their marginality. 
8

2.4. Land Supply in the GTAP-BIO Model

Since the land endowment is split into AEZs, Lee et al. (2005) made an assumption that 

the land is mobile across uses within an AEZ, but immobile across the 18 AEZs as the crops 

grown are climate and soil specific. In line with Hertel et al. (2009), in the GTAP-BIO model

versions, the land mobility is effectively restricted across alternative uses within a given AEZ, by 

using a constant elasticity of transformation (CET) function. In principle, the land-owners 

maximize total returns to land by optimal mix among alternative uses, in two tiers (Figure B1 in 

Appendix-B). In the first stage, the land-owner makes the optimal allocation of a given parcel of 

land under crops, pasture or commercial forest, while the choice of crops is made in the second 

stage (five categories of crops: cereal grains, oilseeds, sugar-crops, other grains (paddy-rice and 

wheat), and other agriculture). The ease with which the land-AEZi is transformed across 

different crops is governed by the elasticity of transformation, ETRAE-2 (assumed to have a 

base value of -0.5) and across different land covers by ETRAE-1 (assumed to have a base value 

of -0.2). As the value of these parameters rise in absolute terms, the degree of sluggishness 

diminishes, possibly driving the land rents across alternative uses together. In other words, 

ETRAE-2 influences the extent of change in cropping pattern and ETRAE-1 influences the 

extent of land conversion.

For instance, if we boost corn-ethanol production in the U.S., the resulting rise in corn 

price is shared among all the factors of production. As a result, the land rents attract more land 

into cereal grain production, which is taken from alternative uses. The land supply function is 

nested in the value added nest of the firms’ production structure which follows constant elasticity

of substitution (CES) form. The land supply structure is discussed in detail in Birur et al. (2008) 

and the role land transformation elasticities in a biofuel policy experiment is examined in Hertel 

et al. (2010). 

3. Key Parameter Assumptions and Qualifications

In addition to land transformation elasticities discussed in the previous section, there are 

other key parameters that drive the land use change results due to biofuels production (Table 1). 

Yield elasticity (YDE_Target) is a parameter which determines the change in yield of a crop in 

response to change in price of that crop. With the increment in biofuels production, the supply

of feedstock has to come either from the diversion of land under other uses or increased yields 
9

and/or expansion of land area under the feedstock crop. Keeney and Hertel (2008) examine the 

issue of crop yield response in greater detail. The GTAP-BIO model versions use a long-run 

elasticity of yield response to price of 0.25 as recommended by those authors and is calibrated to 

reach this targeted yield response by adjusting the elasticity of substitution in crop production. 

International trade in commodities also determines the land use change in a given region. The 

trade elasticities (ESUBD) in the GTAP model determine the substitutability of imports among 

all the exporting regions. In other words, as the domestic price of a commodity changes in a 

given region, ESUBD determines the extent to which an importer will substitute to an alternate 

exporter who can supply at a lower price.

In addition to key parameters listed in Table 1, CARB (2009) assumes there is an effective 

cropland adjustment (ETA). As a result, crop yields in the newly converted land are lower than 

the yields obtained on regular cropland already in production. In the CARB analysis, it is 

assumed that for every one acre of additional cropland, two acres of other land covers are to be 

converted (ETA=1/2 = 0.5). More recently, Taheripour et al. (2009) assumed the value of ETA 

as 0.66 implying that for every two acres of cropland, three acres of other land are to be 

converted to grow crops. We follow the latter to keep our comparisons consistent. 

3.1 Sensitivity of Key Parameters

Since the parameters discussed above are the key inputs that drive the general 

equilibrium results, there are uncertainties associated with these parameter values. For exploring 

this uncertainty, we undertake systematic sensitivity analysis (SSA) in which the model is resolved for different draws from the underlying parameter distributions presented in Table 1. 

Monte Carlo analysis is the standard approach to this problem. However, it is impractical for 

large scale models. Therefore, Arndt (1996) recommends a systemic sensitivity analysis (SSA) 

with Gaussian Quadrature (GQ) numerical integration technique, which gives robust results with 

only few draws from the distribution of random variables. Pearson and Arndt (2000) employ this 

technique in the GTAP framework using Stroud Quadrature, which requires the model to solve 

only 2N times where N is the number of varying parameters/variables.

Hertel et al. (2010) follow the approach given by Pearson and Arndt (2000) and use 

symmetric triangular distributions to approximate the underlying distribution of the key

parameters, which permits us to characterize the parameter distribution through the mean and 
10

lower end point. The mean parameter values and end points obtained from the literature are as 

given in Table 1. The last column of the table shows the amount of variation which is the amount 

that the parameter is displaced from its central point to its endpoint. In the triangular 

distribution, the amount of variation is equivalent to the standard deviation multiplied by the 

square root of six. In our biofuel policy scenarios we simultaneously vary all the parameters 

listed in Table 1 and the model solves 84 times holding the solution at each point to calculate 

mean and standard deviation of the model variables. The results reported in this study are the 

mean values and confidence intervals computed based on SSA.

3.2 Qualifications

 Both the versions of the GTAP-BIO model include only first-generation biofuels. Due to 

the absence of nascent feedstock sectors in the GTAP data base, second generation 

biofuels are not included in this study.

 Since this study is based on the static GTAP model, we cannot capture dynamic market 

interactions.

 Conversion of land from pasture or forest covers involve accessibility cost which we 

have not captured in this model.

 The current structure of the GTAP-BIO model versions classifies shrubland, savanna 

grassland, built-up land, and other land as “unmanaged” and treats it as exogenously 

fixed in the model. The “managed” categories refer to cropland, pasture, and forest. This 

would rule out any possibility of conversion of “unmanaged” land categories.

 The scenarios included in this analysis are only a rough approximation of the renewable 

fuels standard required by EISA, because we have only analyzed the impact of increasing 

one renewable fuel volume, rather than analyzing the impact of the entire RFS2 and then 

backing out the impact of one fuel at a time. Furthermore, the corn ethanol scenarios do 

not get close to the projected volume of 15 billion gallons.

4. Experimental Design

Before implementing the biofuel scenarios in the GTAP-BIO model versions, we first 

perform historical analysis as offered by Birur et al. (2008) which validates the model as well as 

projects the biofuel economy for 2006. For implementing the historical analysis those authors 
11

consider three key factors that were responsible for the biofuel boom in the U.S. and EU during 

the 2001-2006 period. 

1. A major factor is the rise in annual average real price of crude oil from $25.29/barrel in 

2001 to $59.69/barrel in 2006. The 136% rise in crude oil price escalated gasoline prices, 

which further influences the price of biofuels.

2. Another factor that was responsible for the ethanol boom, particularly in the U.S., was the 

phase out of methyl tertiary-butyl ether (MTBE), a petroleum derived additive used as 

octane enhancer in the oil industry. As MTBE was phased out, it was replaced by

ethanol, the other recognized additive at a rate of about 49.24% during the five year 

period.

3. Other factors that encouraged the biofuels industry to expand rapidly were the subsidies 

and tax credits offered by governments. The U.S. has subsidized biofuels for three 

decades. Several member states in the European Union also announced tax credits to the 

biofuel industry during the 2001-2006 period. The ad valorem equivalent of subsidies 

and tax credits to biofuels industry in these two regions were also considered for the 

historical validation experiment.

After implementing the historical experiment involving the key biofuel drivers listed 

above, we start from the ex-post 2006 biofuel economy and implement the corn-ethanol and 

biodiesel scenarios separately. As mentioned earlier, for the corn ethanol analysis we use the

GTAP-BIO-1 model and for biodiesel, we use the GTAP-BIO-2 version. In both versions of the 

model the resulting simulated 2006 biofuel economy baseline has 4.252 billion gallons of cornethanol, 0.140 bg of biodiesel, and 0.303 bg of imported sugar-ethanol in the U.S. As 

summarized in Table 2, we conducted two analyses based on increases in biofuels volumes 

specified by EPA. In the corn-ethanol scenario, we increase the quantity of corn-ethanol 

production by 2 bg (47% increase) in the U.S. while keeping production of all other biofuels at 

2006 level. Similarly, we implement the biodiesel scenario by increasing quantity of biodiesel 

production in the U.S. by 1 bg (714% increase) while all other biofuels production remain 

constant.
12

5. Results from Corn-Ethanol Experiment

Impact on Production: In this section, we discuss the impact of increasing U.S. corn 

ethanol production by 2 billion gallons per year on some of the important variables related to 

biofuel economy. As ethanol production is exogenously increased in the U.S., as expected, 

production of cereal grains in the U.S. rises by 7 million tonnes so as to meet the new demand 

for feedstock from the ethanol industry (Table 3). Most of this rise is attributed to corn as 87% 

of cereal grains area and 94% of cereal grains revenue sales constitute corn in the U.S. during 

2006. Model simulations for the baseline experiment indicate that about 18% of the corn 

produced in the U.S. would be used in the U.S. ethanol industry in 2006, with the share 

increasing to 25.3% of U.S. corn production in the post corn-ethanol scenario. As seen from 

Table 3, due to competition for land, production slightly declined in all other crop sectors such as 

oilseeds (-0.76 mt), other grains (-0.96 mt), sugar-crops (-0.13 mt), and other agriculture (-1.83 

mt) due to the rise in corn production. As the domestic use of corn increases in the U.S., export 

sales decline slightly. The model predicted U.S. export sales of cereal grains decline from 26.3% 

of U.S. corn production in 2006 to 25.1% in the ex-post scenario. The rest of the world responds 

to this smaller decline in export supply as it leads to a slight rise in commodity prices in the 

international market. Except for sugar-crops, all the other agricultural sectors in the rest of the 

world indicate a gain in production. Production of sugar-crops in Brazil decline by 0.66 mt 

mainly because Brazil ends up exporting more oilseeds to partially offset the decrease in U.S. 

oilseeds exports to the rest of the world. In all, the 2 bg corn-ethanol production in the U.S. 

leads to a permanent rise in cereal grains production by 8 mt globally. 

Impact on Commodity Prices: Table 4 reports the simulated percent changes in market 

price in important regions and for the world price in agricultural and energy sectors. It is 

important to note that, since we have included only corn-ethanol shock in this exercise and not 

all the exogenous variables that affect the global economy, our model predicted prices apply only 

to the post-2006 biofuel economy. The market price for cereal grains in the U.S. rose by 1.95% 

over the baseline where the sectoral prices are denoted as numeraire and equal to 1. The change 

in price of all other agricultural sectors was positive but relatively small. 
13

Impact on Bilateral Trade: The corn-ethanol impact in the domestic market that we 

discussed above could have repercussions around the world through trade linkages. Table 5 

depicts bilateral trade in some of the key commodities such as cereal grains, oilseeds and crude 

oil. Interestingly, only the U.S. shows a modest decline in exports of cereal grains by about $143 

million. The main regions that registered a decline in imports were Japan, Latin-America, and 

Middle-Eastern countries (these regions are folded into Rest of the World to keep the table 

concise). The oilseeds sector is an important competitor for cereal grains particularly in the U.S. 

Even the modest decline in oilseeds production in the U.S. resulted in a decline in exports by

$113 million, mainly to the European Union, China, and other Asian regions. This trend is 

partially offset by an increase in Brazilian oilseeds exports by $21 million. Other agricultural 

commodities did not register any significant change in trade patterns. 

Impact on Land-Use and Land-Cover: As we discussed above, the corn-ethanol 

experiment boosts demand for ethanol feedstocks and due to competition for limited land 

resources, the price of other crops also rise. These higher crop prices transmit across borders, 

implying higher profitability and hence a shift in cropping pattern towards the crop in greater 

demand. The corn-ethanol driven change in land use and land cover across all the regions are 

presented in Table 6. As seen from the table, harvested area under cereal grains in the U.S. 

expanded by 2.22 million acres, part of it came at the cost of other major land using sectors such 

as other grains (paddy-rice and wheat: -0.98 mill. acres) and oilseeds (-0.64 mill. acres). The

demand for corn spilled over to the rest of the world, resulting in a small positive change in 

acreage under cereal grains in most of the regions, adding up to 2.66 million acres globally. 

Some of the decline in oilseeds acreage in the U.S. was offset by Canada, Sub Saharan Africa, 

and Brazil due to which oilseeds acreage declined only by -0.09 million acres, globally. 

However, not much response was reported from the rest of the world to mitigate the reduced 

acreage under other grains in the U.S. which lead to global reduction of -0.92 million acres under 

paddy-rice and wheat. After accounting for all the cropping pattern adjustments, about 1.39 

million acres of additional cropland were required to meet the demand. 

As discussed earlier, in the GTAP-BIO model, expansion in crop cover is allowed to 

come from pastureland and accessible forest covers. The model predicted expansion in cropland 

in the U.S. (0.58 mill. acres) comes from decline in accessible forests (-0.22 mill. acres) and 

pasture lands (-0.36 mill. acres). The only other regions which showed significant expansion in 
14

cropland were Sub Saharan Africa (0.20 mill. acres), Canada (0.15 mill. acres), and the EU (0.12 

mill. acres). While much of the cropland expansion in Africa came from a decline in pasture 

cover (-0.24 mill. acres), in Canada and EU it was due to a decline in forest cover by 0.10 and 

0.07 million acres, respectively. The forest is mainly sold to the lumber sector (for example, in 

the GTAP data base 90% of forest sector sales are made to the lumber sector in the U.S. and the 

corresponding percentage for Canada is 70%), therefore most of the other regions did not report 

much change in forest cover. Instead, most of the cropland expansion came from a decline in 

pasture cover (Table 6). On the global scale, the 1.39 million acres of additional cropland came 

from a decline in pasture and forest cover by 1.16 and 0.23 million acres, respectively. 

Sensitivity of Impact on Land: As discussed previously, since the key parameters listed 

in Table 1 drive the general equilibrium results on impacts of biofuel production, we also report 

the results from a systematic sensitivity analysis on change in harvested acreage (Table 7) and 

land cover (Table 8). As depicted in Table 7, the mean value of change in cereal grains acreage 

was 2.17 million acres which ranged between 1.66 and 2.69 million acres in the U.S., when all 

the key parameters are varied simultaneously. The model reported no significant variation in the 

range of cereal grains acreage in the rest of the regions and globally the cereals grains acreage 

ranged between 2.25 and 3.0 million acres. Global oilseeds acreage indicated a positive upper 

bound, suggesting that the decline in U.S. oilseeds acreage (-0.46 mill. acres) due to corn-ethanol 

production may be completely offset by other regions leading to a marginal increase in global 

oilseeds acreage (0.05 mill. acres). However, the other land using sectors did not indicate 

significant variation in their harvested acreage. 

Sensitivity on expansion of cropland to other land cover is presented in Table 8. The 

SSA results on crop cover indicate that U.S. corn-ethanol production may require as low as 0.98 

million acres of additional cropland and the same could be 1.78 million acres on the upper scale 

subjected to variability in parameter values. It is clear from the table that although most of the 

additional cropland came from pasture cover (-1.15 mill. acres), the SSA results indicate a range 

of pasture cover change between -1.50 and -0.83 million acres. However, all the significant 

variation in the change in forest cover was observed in Canada and EU regions. For a better 

understanding of these results, changes in cropland, pasture cover, and forest cover with error 

bars are depicted in Figures 2a, 2b, and 2c, respectively. The SSA exercise suggests that much 

of the variation in the land cover was reported in the U.S. and not in the rest of the regions. 
15

Hence the theory that variation in land transformation elasticities drive the impact on land use 

though the role of trade elasticities is not undermined. These sensitivity results prove useful 

especially when accounting for land conversion due to biofuels production. 

Apart from the sensitivity of the key parameters, the magnitude of the shock and the type 

of biofuel also drives the general equilibrium results. For relative comparison purpose, we have 

reported land required for the production of corn-ethanol normalized on an energy basis

3

. As 

seen from Figure 3, production of one billion BTUs of corn-ethanol in the U.S. requires about 

3.70 hectares of cropland globally, which ranges from 1.93 to 5.42 hectares subjected to 

sensitivity of the key parameters.

6. Results from Soybean-Biodiesel Experiment

Impact on Production: As mentioned in the experimental design section, we implemented

a 1bg biodiesel production scenario starting from the 2006 biofuel economy. The results on 

changes in agricultural output across the regions due to biodiesel production are presented in 

Table 9. The demand for oilseeds feedstock resulted in the production of 3.61 million tonnes in 

the U.S. which came at the expense of a reduction in all other agricultural sectors such as cereal 

grains (-1.82 mt), wheat (-0.55 mt), and other agriculture (-2.60 mt). Unlike the corn-ethanol 

case, a strong spillover effect on the rest of the world was observed in the biodiesel scenario. 

Soybeans are the major feedstock used for production of biodiesel in the U.S., but they 

contain only about 18% oil. Thus, expanded biodiesel production in this experiment results in a 

very large increase in demand for the soybean feedstock. Note that, as USDA reports, nearly 

57% of the global oilseed production basket constitutes soybean, followed by rapeseed (12%), 

cottonseed (11%), peanuts (10%) and the remaining 10% include palm-kernel, copra, sunflower, 

etc. This clearly indicates the extent of the impact that could result due to soybean derived 

biodiesel production in the U.S. The impact of biodiesel production on the rest of the world is 

such that all of the regions indicate a positive change in the production of oilseeds resulting in 

9.46 million tonnes of global production. Malaysia and Indonesia end up producing 2.26 mt of 

oilseeds basically to meet the demand for refined vegetable oil from EU and other Asian regions. 

Global production of cereal grains and wheat drop by 2.20 mt and 1.38 mt, respectively. 

However, production of paddy-rice, the other staple crop, does not get affected much.

 

3 Considered low-heating value of ethanol = 76,000 Btu/gallon.
16

Impact on Commodity Prices: Changes in regional market prices and world prices in the 

agricultural and energy sectors due to biodiesel production are presented in Table 10. Keeping in 

mind that our simulation experiment included only a biodiesel shock and not any attempt to 

project the global economy in time, the model predicted a wide range of implications on sectoral 

prices, particularly on the feed industry. As seen from the table, although the market price of 

oilseeds in the U.S. increased by only 3.58%, the price of crude-vegetable oil, which is the major 

component of biodiesel in the model, rose by 111% in the U.S. market. Since a large portion of 

soybean constitutes oil-meal/cake (VOBP sector), the production of which is complementary to 

biodiesel, expanded biodiesel production resulted in a 51% drop in its market price. Since oilmeal is mainly used by the livestock industry as animal feed, it substitutes for processed-feed 

leading to a drop in the latter’s market price by 10% in the U.S. Though there is a mixed 

spillover of market price to other regions, the world price of oilseeds rises by 1.44% and that of 

crude vegetable oil by 17%. 

Impact on Bilateral Trade: The changes in market prices due to biodiesel production 

discussed above have implications on bilateral trade. Table 11 depicts change in bilateral trade

volume in selected commodities after implementing the biodiesel scenario. As seen from the 

middle panel of the table, exports of oilseeds from the U.S. fall by $208 million and this deficit is 

adequately met in the international market by Brazil ($163 million) and Canada ($101 million). 

Overall, the world trade in oilseeds rises by $269 million. Though U.S. cereal grains production 

decreases due to biodiesel production, exports of cereal grains fall only $17 million. 

Impact on Land-Use and Land-Cover: As the U.S. biodiesel production escalates 

demand for oilseeds, the changing crop prices drive land use changes in all the regions. Table 12 

depicts change in harvested area across the six crop categories and change in land cover across 

the three cover types. In these simulations, 1 bg of increased biodiesel production required a 

permanent shift in oilseed acreage by 4.18 mill. acres in the U.S. and 11 mill. acres globally. 

The cropping pattern changes in the U.S. were larger than that of any other regions. The acreage 

under coarse grains, wheat, and other agriculture fell by 1.73, 1.09, and 1.29 million acres, 

respectively in the U.S. As reported by USDA acreage data, nearly 80% of the oilseeds area 

planted in the U.S. constitutes soybeans acreage, followed by cottonseed which forms about 

15%. Therefore, any change in the oilseeds sector in the U.S. may be treated as soybeans. Due 

to this model predicted pattern essentially no additional cropland was required in the U.S. to 
17

meet the demand for oilseed. Oilseed acreage increased mainly in the EU (1.27 mill. acres), 

followed by Canada, Brazil and other South American regions. Globally, harvested acreage 

under cereal grains, wheat, other agriculture, and paddy-rice, fell by 2.70, 2.23, 2.32, and 0.56 

million acres, respectively.

After adjusting cropping pattern across all the regions, the model predicted an additional 

3.01 million acres of cropland to meet the U.S. oilseeds demand. Globally, this additional 

cropland mainly came from pasture cover (-2.24 mill. acres) and accessible forests (-0.77 mill. 

acres). However, due to the complementary nature of feed and pasture in the livestock industry, 

the model predicted an increase in pasture-cover by 0.72 million acres which mainly came from 

accessible forests (-0.69 mill. acres) in the U.S. In contrast, most other regions indicated an 

expansion in cropland that mainly came from the decline in pasture cover. 

Sensitivity of Impact on Land: As depicted in Table 13, the SSA results on harvested 

acreage due to the biodiesel experiment indicates a range of land use change particularly in the 

oilseeds and wheat sectors in the U.S. While the mean estimate of oilseeds acreage in the U.S. is 

4.10 mill. acres, it ranged from 3.06 to 5.14 mill. acres subjected to variation in the key 

parameters listed in Table 1. Though the change in paddy-rice acreage was only -0.54 mill. 

acres on the global scale, much of this change came from Malaysia and Indonesia with a range 

of -0.29 to -0.18 mill. acres. These countries also reported an increase in oilseeds acreage with a 

range of 0.34 to 0.56 mill. acres. This increase may be due to the fact that some of the reduction 

in U.S. oilseeds exports is offset by Malaysia and Indonesia. The major competing crop for 

soybean acreage in the U.S. is corn. The decline in area under cereal grains ranged between -

2.05 and -1.34 mill. acres in the U.S.. Since we kept the sugar-ethanol constant at the 2006 level, 

no significant change in sugar-crops acreage was predicted by the model. The Africa region also 

reported a range in the increase in oilseeds acreage (0.72 to 1.12 mill. acres) though it did not 

come at the cost of a large decline other crop acres.

Given the range of harvested acreage adjustments, the mean cropland requirement in the 

U.S. was -0.01 mill. acres with a range of -0.14 to 0.13 million acres (Table 14). This indicates 

that though U.S. cropland acreage actually went down in the biodiesel experiment, subjected to 

sensitivity of the key parameters, it could even demand additional cropland to meet the demand 

for oilseeds. It is clear from Table 14 that pasture-cover change in all regions was sensitive to 
18

variation in the key parameters. A large increase in forest cover change was predicted only in 

the U.S., EU, and Canada. The model predicted oilseeds acreage in Brazil ranged from 0.61 to 

1.01 mill. acres, most of this came from the decline in pasture-cover without any large impact on 

forest cover. It is interesting to note that China, Eastern Europe, and some of the South 

American regions indicated positive change in forest cover. However, these regions reported a 

decline in pasture cover due to U.S. biodiesel production. These variations are depicted as error 

bars in Figure 4a, 4b, and 4c, corresponding to land cover change in crops, pasture, and forest, 

respectively. 

The SSA exercise on the biodiesel experiment suggests that subjected to variation in the 

parameters there is a range in the requirement of additional cropland across the regions including 

the U.S. Figure 5 depicts the hectares required to produce one billion BTUs of biodiesel 

normalized on an energy basis4. As shown in the figure, about 11 hectares of cropland is 

required to produce 1 billion BTU of biodiesel. Most of this land comes from regions other than 

the U.S. However the error bars indicate that that results are sensitive to the parameters, so that 

the total hectares required could even range between 3.6 to 18.4 hectares per billion BTU of 

biodiesel.

7. Conclusions

This study illustrates the global impact of expanded production of corn-ethanol and biodiesel 

in the U.S. in a general equilibrium framework, which simulates the changes in markets that 

would result from expanded demand while ensuring that all factor and commodity markets in all 

the regions are in equilibrium. Keeping the relative treatment of biodiesel byproducts in mind, 

we used two different versions of the GTAP-BIO model to predict the implications due to the 

production of 2 billion gallons of corn ethanol and 1 billion gallon of biodiesel. We found that 

the corn-ethanol experiment requires 1.39 million acres of additional cropland or 3.70 ha per 

billion BTU of ethanol, whereas the biodiesel experiment revealed that about 3.21 million acres 

of additional cropland is required to meet the oilseeds demand or 11 ha per billion BTU of 

biodiesel. The systematic sensitivity analysis showed that the model results are sensitive to 

variation in the key parameter values. 

 

4 Considered low-heating value of biodiesel = 118,296 Btu/gallon.
19

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22

Table 1. Variation in key parameters driving the general equilibrium results on biofuels.

Parameters Lower 

bound Mean

Upper 

bound

Standard 

Deviation

Amount of 

Variation: 

SD*(6^0.5)

1.

Yield elasticity

(YDE_Target) 0 0.25 0.5 0.102 0.25

2.

Elasticity of 

transformation 

across land cover 

types (ETRAE-1)

-0.04 -0.2 -0.36 0.065 0.16

3.

Elasticity. of 

transformation 

across cropland 

(ETRAE-2)

-0.1 -0.5 -1 0.163 0.40

4. Armington CES elasticity of substitution for domestic and imported (ESUBD):

a. Coarse Grains 0.75 1.30 1.85 0.225 0.55

b. Other Grains 2.66 4.75 6.84 0.853 2.09

c. Oilseeds 2.05 2.45 2.85 0.163 0.40

d. Sugarcane 1.70 2.70 3.70 0.408 1.00

e. Other Agri 1.57 1.89 2.21 0.129 0.32

b1. Paddy Rice 3.05 5.05 7.05 0.816 2.00

b2. Wheat 2.35 4.45 6.55 0.857 2.10

Source: Adapted from Hertel, Tyner, and Birur (2010).
23

Table 2. The U.S. biofuel scenarios implemented in the GTAP model.

Billion gallons

Corn-Ethanol Biodiesel Sugar-Ethanol

2006 baseline 4.252 0.140 0.303

Corn-ethanol Experiment: +2.00

(47.04%) 0.140 0.303

Biodiesel Experiment: 4.252 +1.00

(714.29%)

0.303
24

Table 3. Change in agricultural output due to an increase in corn-ethanol production in the U.S.

(million tonnes)

Regions Cereal 

grains Oilseeds Other grains

Sugar 

crops

Other 

Agri

U.S.A. 7.24 -0.76 -0.96 -0.13 -1.83

Canada 0.08 0.06 0.00 0.00 0.02

EU-27 0.09 0.03 0.07 0.02 0.18

Brazil 0.09 0.12 0.00 -0.66 0.01

Japan 0.00 0.00 0.01 0.00 0.00

China-Hong Kong 0.18 0.07 0.07 0.01 0.02

India 0.00 0.01 0.04 0.02 0.02

Latin American EEx. 0.15 0.11 0.00 -0.04 -0.12

Rest of Latin Am. 0.02 0.02 0.01 -0.04 -0.01

EE & FSU EEx. 0.03 0.01 0.02 0.01 0.06

Rest of Europe 0.04 0.01 0.04 0.00 0.02

Middle Eastern N 

Africa EEx. 0.04 0.00 0.05 0.00 -0.06

Sub Saharan EEx. -0.01 0.06 0.02 0.00 0.07

Rest of North Africa & 

SSA 0.03 0.00 0.00 0.01 0.00

South Asian EEx. 0.01 0.18 0.02 0.00 0.00

Rest of High Income 

Asia 0.00 0.00 0.00 0.00 0.00

Rest of Southeast & 

South Asia 0.01 0.03 0.03 0.01 0.00

Oceania countries 0.04 0.01 0.00 0.01 0.02

Global Total 8.05 -0.02 -0.57 -0.79 -1.58
25

Table 4. Change in commodity price due to an increase in corn-ethanol production in the U.S.

(percent change)

Sectors Market Price World Price USA Canada EU-27 Brazil

Cereal Grains 1.95 0.21 0.08 0.17 0.48

Other Grains 0.43 0.17 0.08 0.13 0.07

Oilseeds 0.80 0.25 0.12 0.16 0.25

Sugarcane 1.08 0.24 0.06 0.07 0.12

Other Agri 0.35 0.12 0.04 0.07 0.08

Proc Livestock 0.11 0.05 0.00 0.01 0.03

Ethanol1 0.98 -3.42 -0.04 0.01 0.80

DDGS -0.22 12.95 0.41 0.17 0.09

Biodiesel 0.53 0.11 0.10 0.06 0.21

Oil_Cake 0.06 0.05 0.01 0.03 0.03

Ethanol2 -0.03 -0.09 -0.08 0.00 -1.18

Coal 0.06 0.04 0.05 0.04 0.03

Oil -0.87 -0.75 -0.66 -0.64 -0.70

Gas -0.04 -0.06 -0.04 -0.08 -0.05

Oil_Pcts -0.64 -0.61 -0.58 -0.53 -0.58

Electricity -0.02 -0.02 -0.02 -0.05 -0.06
26

Table 5. Impact of an increase in U.S. corn-ethanol production on bilateral trade (Change in 

import volume)

 ($ millions)

Exporters: (post-2006)

Coarse Grains Oilseeds

US EU RoW Total 

Exports US EU RoW Total Exports

1 US 0 -4 -139 -143 0 -34 -79 -113

2 Canada -3 0 2 -1 1 0 9 10

3 EU-27 -1 0 12 11 0 0 2 2

4 Brazil 0 0 7 7 0 8 12 21

5 Japan 0 0 0 0 0 0 0 0

6 China-Hong Kong 0 0 9 9 0 1 5 6

7 India 0 0 0 0 1 1 3 4

8 Latin American Energy Exporters 0 -1 10 9 0 0 9 9

9

Rest of Latin America & 

Caribbean -1 0 2 1 0 0 3 4

10 EE & FSU Energy Exp 0 0 4 5 0 3 1 4

11 Rest of Europe 0 0 1 1 0 0 0 1

12 Middle Eastern N Africa energy 

exporters 0 0 1 1 0 1 0 1

13 Sub Saharan Energy exporters 0 0 1 1 0 1 3 4

14 Rest of North Africa & SSA 0 0 4 4 0 0 0 0

15 South Asian Energy exporters 0 0 0 0 0 0 1 1

16 Rest of High Income Asia 0 0 0 0 0 0 0 0

17 Rest of Southeast & South Asia 0 0 1 0 0 0 1 1

18 Oceania countries 0 0 5 5 0 0 2 2

Total -5 -5 -81 -90 3 -19 -29 -44

Note: Change in volume of exports of coarse grains, and oilseeds from all the 18 regions to the US, EU, and Rest of 

the World were evaluated at initial market prices. 
27

Table 6. Change in land use and land cover due to an increase in corn-ethanol production in the U.S.

(million acres)

Regions Change in harvested area Change in land cover

CrGrains Oilseeds Sugar crops OthGrains OthAgri Cropland Forest Pasture

U.S.A. 2.22 -0.64 -0.01 -0.98 -0.02 0.58 -0.22 -0.36

Canada 0.05 0.10 0.00 0.00 0.00 0.15 -0.10 -0.05

EU-27 0.04 0.03 0.00 0.03 0.02 0.12 -0.07 -0.04

Brazil 0.05 0.08 -0.04 0.00 -0.02 0.06 0.00 -0.07

Japan 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

China-Hong Kong 0.06 0.06 0.00 0.00 -0.09 0.02 0.01 -0.03

India 0.00 0.01 0.00 0.00 -0.01 0.00 0.00 0.00

Latin American EEx. 0.07 0.06 -0.01 -0.02 -0.05 0.06 0.00 -0.06

Rest of Latin Am. 0.02 0.01 -0.01 0.00 -0.01 0.02 0.02 -0.04

EE & FSU EEx. 0.03 0.03 0.00 0.01 0.01 0.09 0.09 -0.17

Rest of Europe 0.03 0.01 0.00 0.01 0.00 0.03 -0.02 -0.02

Middle Eastern N Africa EEx. 0.03 0.01 0.00 0.01 -0.03 0.02 0.00 -0.02

Sub Saharan EEx. -0.01 0.11 0.00 0.04 0.06 0.20 0.04 -0.24

Rest of North Africa & SSA 0.03 0.01 0.00 0.00 -0.01 0.03 0.00 -0.02

South Asian EEx. 0.00 0.03 0.00 0.00 -0.03 -0.01 0.01 -0.01

Rest of High Income Asia 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Rest of Southeast & South Asia 0.00 0.01 0.00 0.00 -0.01 0.00 0.00 0.00

Oceania countries 0.04 0.01 0.00 -0.02 0.00 0.03 0.00 -0.03

Global 2.66 -0.09 -0.06 -0.92 -0.19 1.39 -0.23 -1.16
28

Table 7. SSA estimates of change in harvested area of crops due to an increase in corn-ethanol production in the U.S.

(million acres)

Regions

CrGrains Oilseeds Sugar crops OthGrains Other Agri

Mean

Confidence 

Interval (95%) Mean

Confidence 

Interval (95%) Mean

Confidence 

Interval (95%) Mean

Confidence 

Interval (95%) Mean

Confidence 

Interval (95%)

Lower Upper Lower Upper Lower Upper Lower Upper Lower Upper

U.S.A. 2.17 1.66 2.69 -0.62 -0.79 -0.46 -0.01 -0.01 -0.01 -0.95 -1.37 -0.53 -0.03 -0.08 0.02

Canada 0.05 0.01 0.09 0.09 0.05 0.14 0.00 0.00 0.00 0.00 -0.04 0.04 0.00 -0.02 0.02

EU-27 0.04 -0.01 0.09 0.03 0.00 0.05 0.00 0.00 0.00 0.02 -0.01 0.05 0.02 0.01 0.03

Brazil 0.05 0.02 0.08 0.08 0.04 0.12 -0.03 -0.04 -0.03 0.00 -0.01 0.00 -0.02 -0.04 -0.01

Japan 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

China-Hong Kong 0.06 0.03 0.10 0.05 0.02 0.08 0.00 0.00 0.00 0.00 0.00 0.00 -0.09 -0.12 -0.06

India 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.01 -0.01 -0.01 0.00

Latin American EEx. 0.07 0.02 0.12 0.06 0.03 0.10 -0.01 -0.01 0.00 -0.02 -0.04 0.00 -0.04 -0.06 -0.03

Rest of Latin Am. 0.02 0.01 0.04 0.01 0.00 0.01 -0.01 -0.01 0.00 0.00 -0.01 0.01 -0.01 -0.02 0.00

EE & FSU EEx. 0.03 0.02 0.05 0.03 0.02 0.04 0.00 0.00 0.00 0.01 -0.01 0.03 0.01 0.00 0.02

Rest of Europe 0.03 0.02 0.04 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.00 -0.01 0.01

Middle Eastern N Africa 

EEx. 0.04 0.02 0.05 0.01 0.00 0.01 0.00 0.00 0.00 0.01 0.00 0.02 -0.03 -0.05 -0.02

Sub Saharan EEx. -0.01 -0.03 0.01 0.11 0.07 0.14 0.00 0.00 0.00 0.04 0.02 0.05 0.06 0.03 0.09

Rest of North Africa & 

SSA 0.03 0.01 0.04 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 -0.01 0.00

South Asian EEx. 0.00 0.00 0.01 0.02 0.01 0.04 0.00 0.00 0.00 0.00 0.00 0.00 -0.03 -0.04 -0.02

Rest of High Income Asia 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Rest of Southeast & South 

Asia 0.00 0.00 0.01 0.01 0.01 0.02 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 -0.02 -0.01

Oceania countries 0.04 0.02 0.06 0.01 0.01 0.02 0.00 0.00 0.00 -0.02 -0.05 0.01 0.00 -0.01 0.00

Global 2.63 2.25 3.00 -0.09 -0.23 0.05 -0.06 -0.07 -0.05 -0.90 -1.19 -0.61 -0.19 -0.29 -0.10
29

Table 8. SSA estimates of change in land cover due to an increase in corn-ethanol production in the U.S.

(million acres)

Regions

Crop Cover Forest Cover Pasture Cover

Mean

Confidence 

Interval (95%) Mean

Confidence 

Interval (95%) Mean

Confidence 

Interval (95%)

Lower Upper Lower Upper Lower Upper

U.S.A. 0.57 0.35 0.78 -0.21 -0.28 -0.14 -0.36 -0.51 -0.20

Canada 0.15 0.06 0.23 -0.10 -0.15 -0.04 -0.05 -0.08 -0.02

EU-27 0.12 0.05 0.18 -0.08 -0.12 -0.03 -0.04 -0.06 -0.02

Brazil 0.06 0.03 0.10 0.00 -0.01 0.01 -0.06 -0.09 -0.04

Japan 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

China-Hong Kong 0.02 0.01 0.04 0.01 0.00 0.02 -0.03 -0.06 -0.01

India 0.00 -0.01 0.01 0.00 -0.01 0.01 0.00 0.00 0.00

Latin American EEx. 0.06 0.03 0.09 0.00 0.00 0.01 -0.06 -0.10 -0.03

Rest of Latin Am. 0.02 0.01 0.03 0.02 0.01 0.03 -0.04 -0.06 -0.02

EE & FSU EEx. 0.09 0.04 0.13 0.08 0.03 0.13 -0.17 -0.24 -0.10

Rest of Europe 0.03 0.02 0.05 -0.02 -0.02 -0.01 -0.02 -0.03 -0.01

Middle Eastern N Africa EEx. 0.02 0.01 0.03 0.00 0.00 0.00 -0.02 -0.03 -0.01

Sub Saharan EEx. 0.20 0.12 0.27 0.04 0.01 0.06 -0.24 -0.32 -0.16

Rest of North Africa & SSA 0.03 0.01 0.04 0.00 -0.01 0.00 -0.02 -0.04 -0.01

South Asian EEx. -0.01 -0.01 0.00 0.01 0.00 0.02 -0.01 -0.01 0.00

Rest of High Income Asia 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Rest of Southeast & South Asia 0.00 0.00 0.01 0.00 0.00 0.00 0.00 -0.01 0.00

Oceania countries 0.03 0.01 0.05 0.00 0.00 0.01 -0.03 -0.05 -0.01

Global 1.38 0.98 1.78 -0.24 -0.36 -0.11 -1.15 -1.50 -0.83
30

Table 9. Change in agricultural output due to an increase in biodiesel production in the U.S.

(million tonnes)

Cereal 

Grains Oilseeds PaddyRice Wheat Sugarcrops

Other 

Agri

U.S.A. -1.82 3.61 -0.08 -0.55 -0.05 -2.60

EU-27 0.31 0.75 0.00 -0.52 0.04 -2.17

Brazil -0.12 0.59 -0.01 -0.01 -0.38 -0.30

Canada -0.18 0.35 0.00 -0.13 0.00 -0.64

Japan 0.00 0.00 0.00 0.00 0.00 0.01

China, Hong Kong -0.18 0.24 -0.07 -0.01 -0.01 0.04

India -0.02 0.11 -0.03 -0.04 -0.06 0.11

Caribbean and Central 

America -0.05 0.13 0.01 0.00 0.07 0.01

South and Rest of 

America -0.06 0.70 0.02 -0.05 0.14 -0.23

East Asia 0.00 0.00 0.00 0.00 0.00 0.01

Malaysia, Indonesia -0.04 2.26 -0.03 0.00 -0.02 -0.02

Rest of Southeast Asia -0.05 0.16 0.02 0.00 0.00 0.06

Rest of South Asia 0.00 0.01 0.00 -0.01 -0.01 0.02

Russia -0.01 0.04 0.00 -0.01 0.00 0.01

Other Eastern Europe & 

FSU 0.00 0.04 0.00 0.00 0.00 0.10

Rest of Europe 0.00 0.00 0.00 0.00 0.00 0.03

Middle East & North 

Africa 0.00 0.05 -0.01 -0.02 0.02 0.23

SSA & Rest of Africa 0.01 0.36 -0.01 0.00 0.17 0.29

Oceania countries 0.01 0.07 0.00 -0.02 -0.01 0.25

Global Total -2.20 9.46 -0.15 -1.38 -0.10 -4.80
31

Table 10. Change in commodity price due to an increase in biodiesel production in the U.S.

(percent change)

Sectors Market Price World Price USA EU-27 Brazil Canada

Paddy_Rice 0.35 0.20 0.58 -0.02 0.14

Wheat -0.09 0.31 0.33 0.10 0.15

CrGrains 0.27 0.51 0.44 0.11 0.22

Oilseeds 3.58 1.34 1.02 1.23 1.44

Sugar_Crop 0.60 0.41 0.48 0.34 0.28

OthAgri 0.43 0.36 0.47 0.21 0.23

Proc_Dairy -0.88 0.00 0.09 -0.23 -0.25

Proc_Rum -1.18 -0.05 0.11 -0.33 -0.44

proc_NonRum -1.29 -0.13 0.07 -0.29 -0.36

Proc_Feed -10.44 -0.70 0.15 -1.91 -2.71

Ethanol1 -0.94 -0.04 0.08 0.00 -0.88

DDGS 0.21 0.79 0.79 0.15 0.28

Cveg_Oil1 110.89 6.98 2.28 16.42 16.53

VOBP -50.89 -12.02 -0.24 -15.11 -22.07

Biodiesel 56.44 5.44 0.40 2.77 28.57

Ethanol2 0.02 -0.02 0.13 -0.01 -0.69

Coal -0.13 -0.02 -0.03 -0.04 -0.05

Oil -0.35 -0.20 -0.18 -0.27 -0.23

Gas -0.03 -0.02 -0.02 -0.05 -0.02

Oil_Pcts -0.24 -0.16 -0.14 -0.19 -0.17

Electricity -0.04 -0.02 -0.01 -0.03 -0.03
32

Table 11. Impact of an increase in U.S. biodiesel production on bilateral trade (Ch. in import 

volume)

 ($ millions)

Exporters: (post-2006)

Cereal Grains Oilseeds

US EU RoW Total 

Exports US EU RoW Total Exports

1 U.S.A. 0 1 -18 -17 0 -74 -134 -208

2 EU-27 -1 0 -7 -8 0 0 4 4

3 Brazil 0 1 -3 -2 0 101 62 163

4 Canada -1 0 0 0 21 7 73 101

5 Japan 0 0 0 0 0 0 0 1

6 China, Hong Kong 0 0 2 3 1 12 30 43

7 India 0 0 0 0 5 4 13 22

8 Caribbean and Central America 0 0 0 0 4 0 8 13

9 South and Rest of America -1 2 -3 -2 3 10 36 49

10 East Asia 0 0 0 0 0 0 2 2

11 Malaysia, Indonesia 0 0 0 0 0 0 0 -1

12 Rest of Southeast Asia 0 0 1 1 1 0 5 6

13 Rest of South Asia 0 0 0 0 0 0 2 2

14 Russia 0 1 0 1 0 4 0 4

15 Oth Eastern Europe & FSU 0 1 1 2 2 18 2 22

16 Rest of Europe 0 0 0 0 0 0 0 0

17 Middle East & North Africa 0 0 0 0 1 3 1 5

18 SSA & Rest of Africa 0 0 0 0 2 7 12 21

19 Oceania countries 0 0 0 0 7 3 10 20

Total -3 7 -25 -21 47 96 126 269

Note: Change in volume of exports of coarse grains and oilseeds from all the 18 regions to the US, EU, and Rest of 

the World were evaluated at initial market prices.
33

Table 12. Change in land use and land cover due to an increase in biodiesel production in the U.S. (million acres).

Change in harvested area Change in land cover

Paddy Rice Wheat CrGrains Oilseeds Sugar crops OthAgri Cropland Forest Pasture

U.S.A. -0.07 -1.09 -1.73 4.18 -0.03 -1.29 -0.03 -0.69 0.72

EU-27 0.00 -0.48 0.11 1.27 0.00 -0.33 0.57 -0.35 -0.22

Brazil -0.02 -0.03 -0.16 0.80 -0.03 -0.14 0.41 -0.08 -0.34

Canada 0.00 -0.16 -0.12 0.92 0.00 -0.09 0.55 -0.08 -0.47

Japan 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

China, Hong Kong -0.10 -0.05 -0.16 0.42 0.00 -0.08 0.03 0.12 -0.15

India -0.04 -0.05 -0.06 0.30 0.00 0.02 0.16 -0.09 -0.07

Caribbean and Central 

America 0.01 0.00 -0.04 0.09 0.01 0.01 0.08 0.17 -0.25

South and Rest of America -0.01 -0.19 -0.15 0.80 -0.01 -0.27 0.18 0.12 -0.29

East Asia 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 -0.01

Malaysia, Indonesia -0.24 0.00 -0.08 0.46 -0.01 -0.11 0.02 -0.01 0.00

Rest of Southeast Asia -0.06 0.00 -0.08 0.16 0.00 -0.01 0.00 0.01 -0.01

Rest of South Asia 0.00 -0.01 0.00 0.02 0.00 0.01 0.02 -0.01 -0.02

Russia 0.00 -0.05 -0.05 0.15 0.00 -0.06 -0.01 0.16 -0.15

Oth Eastern Europe & FSU 0.00 -0.01 0.00 0.13 0.00 0.01 0.12 0.01 -0.12

Rest of Europe 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.01

Middle East & North Africa 0.00 -0.05 -0.02 0.16 0.00 0.02 0.11 0.00 -0.11

SSA & Rest of Africa -0.04 -0.01 -0.18 0.91 0.01 -0.06 0.63 -0.09 -0.53

Oceania countries 0.00 -0.03 0.02 0.11 0.00 0.05 0.16 0.04 -0.19

Global -0.56 -2.23 -2.70 10.88 -0.07 -2.32 3.01 -0.77 -2.24
34

Table 13. SSA estimates of change in harvested area of crops due to an increase in biodiesel production in the US (million acres).

Regions

Paddy Rice Wheat CrGrains

Mean

Confidence 

Interval (95%) Mean

Confidence 

Interval (95%) Mean

Confidence 

Interval (95%)

Lower Upper Lower Upper Lower Upper

U.S.A. -0.07 -0.08 -0.05 -1.07 -1.42 -0.73 -1.69 -2.05 -1.34

EU-27 0.00 0.00 0.00 -0.47 -0.56 -0.39 0.11 0.02 0.21

Brazil -0.02 -0.03 -0.01 -0.03 -0.04 -0.03 -0.16 -0.20 -0.12

Canada 0.00 0.00 0.00 -0.15 -0.23 -0.07 -0.11 -0.15 -0.08

Japan 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

China, Hong Kong -0.10 -0.12 -0.07 -0.05 -0.07 -0.02 -0.16 -0.19 -0.12

India -0.03 -0.07 0.00 -0.05 -0.06 -0.04 -0.06 -0.07 -0.04

Caribbean and Central America 0.01 0.01 0.01 0.00 0.00 0.00 -0.04 -0.05 -0.03

South and Rest of America -0.01 -0.01 0.00 -0.18 -0.21 -0.16 -0.15 -0.17 -0.12

East Asia 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Malaysia, Indonesia -0.24 -0.29 -0.18 0.00 0.00 0.00 -0.08 -0.10 -0.06

Rest of Southeast Asia -0.06 -0.10 -0.02 0.00 0.00 0.00 -0.08 -0.10 -0.06

Rest of South Asia 0.00 -0.01 0.01 -0.01 -0.01 0.00 0.00 0.00 0.00

Russia 0.00 0.00 0.00 -0.05 -0.06 -0.03 -0.05 -0.06 -0.05

Oth Eastern Europe & FSU 0.00 0.00 0.00 -0.01 -0.06 0.04 0.00 -0.02 0.02

Rest of Europe 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Middle East & North Africa 0.00 0.00 0.00 -0.04 -0.08 -0.01 -0.02 -0.03 -0.01

SSA & Rest of Africa -0.03 -0.04 -0.03 -0.01 -0.02 0.00 -0.17 -0.26 -0.07

Oceania countries 0.00 0.00 0.00 -0.03 -0.05 -0.01 0.02 0.01 0.03

Global

-0.54 -2.16 -2.63
35

Table 13. SSA estimates of change in harvested area of crops due to an increase in biodiesel production in the US (million acres)

(Continued)

Regions

Oilseeds Sugar crops Other Agri

Mean

Confidence 

Interval (95%) Mean

Confidence 

Interval (95%) Mean

Confidence 

Interval (95%)

Lower Upper Lower Upper Lower Upper

U.S.A. 4.10 3.06 5.14 -0.03 -0.03 -0.02 -1.25 -1.55 -0.96

EU-27 1.27 1.13 1.41 0.00 -0.01 0.01 -0.32 -0.41 -0.22

Brazil 0.81 0.61 1.01 -0.02 -0.04 -0.01 -0.14 -0.18 -0.09

Canada 0.92 0.77 1.07 0.00 0.00 0.00 -0.08 -0.15 -0.01

Japan 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

China, Hong Kong 0.42 0.32 0.53 0.00 0.00 0.00 -0.08 -0.12 -0.05

India 0.30 0.22 0.37 0.00 -0.01 0.00 0.02 -0.01 0.05

Caribbean and Central America 0.09 0.08 0.11 0.01 0.00 0.01 0.01 0.00 0.03

South and Rest of America 0.79 0.67 0.91 -0.01 -0.01 0.00 -0.26 -0.32 -0.21

East Asia 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00

Malaysia, Indonesia 0.45 0.34 0.56 -0.01 -0.01 -0.01 -0.11 -0.13 -0.08

Rest of Southeast Asia 0.16 0.10 0.21 0.00 -0.01 0.00 -0.01 -0.03 0.00

Rest of South Asia 0.02 0.01 0.03 0.00 0.00 0.00 0.01 0.01 0.02

Russia 0.15 0.13 0.18 0.00 0.00 0.00 -0.06 -0.07 -0.04

Oth Eastern Europe & FSU 0.14 0.09 0.18 0.00 0.00 0.00 0.01 -0.02 0.05

Rest of Europe 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01

Middle East & North Africa 0.16 0.13 0.18 0.00 0.00 0.00 0.02 0.00 0.04

SSA & Rest of Africa 0.92 0.72 1.12 0.01 0.00 0.01 -0.04 -0.26 0.18

Oceania countries 0.11 0.10 0.13 0.00 0.00 0.00 0.06 0.01 0.10

Global 10.82 -0.07 -2.21
36

Table 14. SSA estimates of change in land cover due to an increase in biodiesel production in the US (million acres).

Regions

Crop Cover Forest Cover Pasture Cover

Mean

Confidence 

Interval (95%) Mean

Confidence 

Interval (95%) Mean

Confidence 

Interval (95%)

Lower Upper Lower Upper Lower Upper

U.S.A. -0.01 -0.14 0.13 -0.66 -0.83 -0.49 0.67 0.45 0.89

EU-27 0.59 0.28 0.91 -0.37 -0.58 -0.15 -0.23 -0.33 -0.13

Brazil 0.43 0.20 0.67 -0.09 -0.20 0.01 -0.34 -0.49 -0.19

Canada 0.57 0.28 0.86 -0.10 -0.24 0.04 -0.47 -0.68 -0.27

Japan 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00

China, Hong Kong 0.04 -0.01 0.09 0.12 0.05 0.19 -0.16 -0.26 -0.06

India 0.18 0.02 0.34 -0.10 -0.19 -0.01 -0.08 -0.15 -0.01

Caribbean and Central America 0.08 0.04 0.12 0.17 0.10 0.23 -0.25 -0.34 -0.15

South and Rest of America 0.18 0.08 0.29 0.12 0.04 0.20 -0.30 -0.48 -0.13

East Asia 0.00 0.00 0.00 0.01 0.00 0.01 -0.01 -0.01 -0.01

Malaysia, Indonesia 0.02 0.01 0.02 -0.01 -0.02 -0.01 0.00 -0.01 0.00

Rest of Southeast Asia 0.00 -0.01 0.00 0.01 0.00 0.02 -0.01 -0.02 0.00

Rest of South Asia 0.03 0.00 0.05 -0.01 -0.02 0.00 -0.02 -0.04 0.00

Russia 0.00 -0.04 0.03 0.16 0.03 0.30 -0.16 -0.28 -0.04

Oth Eastern Europe & FSU 0.14 0.01 0.26 0.00 -0.03 0.03 -0.14 -0.25 -0.03

Rest of Europe 0.00 0.00 0.01 0.00 0.00 0.00 -0.01 -0.01 0.00

Middle East & North Africa 0.12 0.04 0.19 0.00 0.00 0.00 -0.12 -0.19 -0.05

SSA & Rest of Africa 0.67 0.20 1.15 -0.11 -0.23 0.02 -0.57 -0.93 -0.21

Oceania countries 0.16 0.08 0.24 0.03 0.02 0.05 -0.20 -0.29 -0.10

Global 3.21 -0.83 -2.39
37

1.382

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

Ch in Crop Cover

Figure 2a. Crop cover change due to an increase in U.S. corn-ethanol production (million 

acres)

Note: This figure is based on the data reported in Table 5.
38

-1.146

-1.60

-1.40

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

Ch in Pasture Cover

Figure 2b. Pasture cover change due to an increase in U.S. corn-ethanol production (million 

acres)

Note: This figure is based on the data reported in Table 5.
39

-0.236

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

Ch in Forest Cover

Figure 2c. Forest cover change due to an in crease in U.S. corn-ethanol production (million 

acres)

Note: This figure is based on the data reported in Table 5.
40

3.707

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Cropcover change (Ha/bBTU)

Figure 3. Crop cover change due to an increase in U.S. corn-ethanol production (Ha/billion 

BTU)

Note: This figure is based on the data shown below.

Cropcover change (Ha/billion BTU)

Corn-ethanol scenario: Mean Lower Upper

USA 1.532 0.928 2.085

CAN 0.393 0.170 0.606

EU27 0.306 0.141 0.476

BRAZIL 0.171 0.077 0.261

JAPAN 0.007 0.003 0.012

CHIHKG 0.059 0.019 0.102

INDIA -0.003 -0.038 0.038

LAEEX 0.165 0.084 0.245

RoLAC 0.042 0.017 0.067

EEFSUEX 0.229 0.114 0.350

RoE 0.087 0.046 0.128

MEASTNAEX 0.043 0.016 0.069

SSAEX 0.538 0.332 0.729

RoAFR 0.071 0.035 0.107

SASIAEEX -0.017 -0.031 -0.001

RoHIA 0.000 0.000 0.000

RoASIA 0.004 -0.009 0.019

Oceania 0.081 0.028 0.133

GLOBAL 3.707 1.933 5.425
41

3.214

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

Ch in Crop Cover

Figure 4a. Crop cover change due to an increase in U.S. biodiesel production (million acres)

Note: This figure is based on the data reported in Table 8.
42

-2.388

-3.00

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

Ch in Pasture Cover

Figure 4b. Pasture cover change due to an increase in U.S. biodiesel production (million 

acres)

Note: This figure is based on the data reported in Table 8.
43

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

Ch in Forest Cover

Figure 4c. Forest cover change due to an increase in U.S. biodiesel production (million 

acres)

Note: This figure is based on the data reported in Table 8.
44

10.997

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

Cropcover change (Ha/bBTU)

Figure 5. Crop cover change due to an increase in U.S. biodiesel production (Ha/billion 

BTU)

Note: This figure is based on the data shown below.

Crop-cover change (Ha/billion BTU)

Biodiesel scenario: Mean Lower Upper

USA -0.031 -0.490 0.428

EU27 2.035 0.967 3.103

Brazil 1.484 0.686 2.282

Canada 1.955 0.958 2.952

Japan 0.008 -0.006 0.023

China-hkg 0.146 -0.030 0.322

India 0.614 0.079 1.149

C_C_Amer 0.273 0.137 0.408

S_o_Amer 0.632 0.278 0.986

E_Asia 0.003 0.001 0.005

Mala_Indo 0.061 0.037 0.084

R_SE_Asia -0.001 -0.019 0.017

R_S_Asia 0.095 0.007 0.183

Russia -0.011 -0.140 0.117

Oth_CEE_CIS 0.464 0.041 0.887

Oth_Europe 0.013 0.004 0.022

MEAS_NAfr 0.398 0.151 0.646

S_S_AFR 2.309 0.680 3.937

Oceania 0.550 0.278 0.823

GLOBAL 10.997
45

Appendix A: Aggregation Tables

Table A1. Aggregation of sectors in the GTAP-BIO-1 Model

No. Aggregated 

Sectors GTAP sectors Description

1 CrGrains gro Other Grains: maize (corn), barley, rye, oats, other cereals

2 OthGrains pdr wht Paddy Rice: rice, husked and unhusked; Wheat: wheat and meslin

3 Oilseeds osd Oil Seeds: oil seeds and oleaginous fruit; soy beans, copra

4 Sugarcane c_b Cane & Beet: sugar cane and sugar beet

5 Livestock ctl oap rmk wol 

Cattle: cattle, sheep, goats, horses, asses, mules, and hinnies; and semen thereof; Other Animal Products: swine, poultry and 

other live animals; eggs, in shell (fresh or cooked), natural honey, snails (fresh or preserved) except sea snails; frogs’ legs, 

edible products of animal origin n.e.c., hides, skins and furskins, raw , insect waxes and spermaceti, whether or not refined or 

coloured; Raw milk; Wool: wool, silk, and other raw animal materials used in textile

6 Forestry frs Forestry: forestry, logging and related service activities

7a Ethanol1 eth1 Ethanol1 (Grain based)

7b DDGS Byproduct of 

eth1 Distillers dried grains solubles

8 Ethanol2 eth2 Ethanol2 (sugarcane based)

9a Biodiesel biod Biodiesel

9b BDBP Byproduct of 

biod Oil-meal/cake

10 OthFoodPdts voln ofdn 

Vegetable Oils: crude and refined oils of soya-bean, maize (corn),olive, sesame, ground-nut, olive, sunflower-seed, safflower, 

cotton-seed, rape, colza and canola, mustard, coconut palm, palm kernel, castor, tung jojoba, babassu and linseed, perhaps 

partly or wholly hydrogenated,inter-esterified, re-esterified or elaidinised. Also margarine and similar preparations, animal or 

vegetable waxes, fats and oils and their fractions, cotton linters, oil-cake and other solid residues resulting from the extraction 

of vegetable fats or oils; flours and meals of oil seeds or oleaginous fruits, except those of mustard; degras and other residues 

resulting from the treatment of fatty substances or animal or vegetable waxes; Other Food: prepared and preserved fish or 

vegetables, fruit juices and vegetable juices, prepared and preserved fruit and nuts, all cereal flours, groats, meal and pellets of 

wheat, cereal groats, meal and pellets n.e.c., other cereal grain products (including corn flakes), other vegetable flours and 

meals, mixes and doughs for the preparation of bakers’ wares, starches and starch products; sugars and sugar syrups n.e.c., 

preparations used in animal feeding, bakery products, cocoa, chocolate and sugar confectionery, macaroni, noodles, couscous 

and similar farinaceous products, food products n.e.c.

11 ProcLivestoc cmt omt mil 

Cattle Meat: fresh or chilled meat and edible offal of cattle, sheep, goats, horses, asses, mules, and hinnies. raw fats or grease 

from any animal or bird.; Other Meat: pig meat and offal. preserves and preparations of meat, meat offal or blood, flours, 

meals and pellets of meat or inedible meat offal; greaves; Milk: dairy products.
46

Table A1. Aggregation of sectors in the GTAP-BIO-1 Model (continued)

[[

12 OthAgri v_f pfb ocr 

pcr sgr b_t 

Veg & Fruit: vegetables, fruitvegetables, fruit and nuts, potatoes, cassava, truffles,; Plant Fibres: cotton, flax, hemp, sisal and 

other raw vegetable materials used in textiles; Other Crops: live plants; cut flowers and flower buds; flower seeds and fruit seeds; 

vegetable seeds, beverage and spice crops, unmanufactured tobacco, cereal straw and husks, unprepared, whether or not chopped, 

ground, pressed or in the form of pellets; swedes, mangolds, fodder roots, hay, lucerne (alfalfa), clover, sainfoin, forage kale, 

lupines, vetches and similar forage products, whether or not in the form of pellets, plants and parts of plants used primarily in 

perfumery, in pharmacy, or for insecticidal, fungicidal or similar purposes, sugar beet seed and seeds of forage plants, other raw 

vegetable materials; Processed Rice: rice, semi- or wholly milled; Sugar; Beverages and Tobacco products.

13 OthPrimSect fsh omn Fishing: hunting, trapping and game propagation including related service activities, fishing, fish farms; service activities 

incidental to fishing; Other Mining: mining of metal ores, uranium, gems. other mining and quarrying

14 Coal coa Coal: mining and agglomeration of hard coal, lignite and peat

15 Oil oil Oil: extraction of crude petroleum and natural gas (part), service activities incidental to oil and gas extraction excluding surveying

16 Gas gas gdt Gas: extraction of crude petroleum and natural gas (part), service activities incidental to oil and gas extraction excluding 

surveying (part); Gas Distribution: distribution of gaseous fuels through mains; steam and hot water supply

17 oil_pcts p_c Petroleum & Coke: coke oven products, refined petroleum products, processing of nuclear fuel

18 electricity ely Electricity: production, collection and distribution

19 En_Int_Ind crpn i_s nfm Chemical Rubber Products: basic chemicals, other chemical products, rubber and plastics products; Iron & Steel: basic 

production and casting; Non-Ferrous Metals: production and casting of copper, aluminium, zinc, lead, gold, and silver

20 Oth_Ind_Se

tex wap lea 

lum ppp nmm 

fmp mvh otn 

ele ome omf 

wtr cns trd otp 

wtp atp cmn 

ofi isr obs ros 

osg dwe 

Textiles: textiles and man-made fibres; Wearing Apparel: Clothing, dressing and dyeing of fur; Leather: tanning and dressing of 

leather; luggage, handbags, saddlery, harness and footwear; Lumber: wood and products of wood and cork, except furniture; 

articles of straw and plaiting materials; Paper & Paper Products: includes publishing, printing and reproduction of recorded 

media; Petroleum & Coke: coke oven products, refined petroleum products, processing of nuclear fuel; Non-Metallic Minerals: 

cement, plaster, lime, gravel, concrete; Fabricated Metal Products: Sheet metal products, but not machinery and equipment; 

Motor Vehicles: cars, lorries, trailers and semi-trailers; Other Transport Equipment: Manufacture of other transport equipment;

Electronic Equipment: office, accounting and computing machinery, radio, television and communication equipment and 

apparatus; Other Machinery & Equipment: electrical machinery and apparatus n.e.c., medical, precision and optical 

instruments, watches and clocks; Other Manufacturing: includes recycling; Water: collection, purification and distribution; 

Construction: building houses factories offices and roads; Trade: all retail sales; wholesale trade and commission trade; hotels 

and restaurants; repairs of motor vehicles and personal and household goods; retail sale of automotive fuel; Other Transport: 

road, rail ; pipelines, auxiliary transport activities; travel agencies; Water transport; Air transport; Communications: post and 

telecommunications; Other Financial Intermediation: includes auxiliary activities but not insurance and pension funding (see 

next); Insurance: includes pension funding, except compulsory social security; Other Business Services: real estate, renting and 

business activities; Recreation & Other Services: recreational, cultural and sporting activities, other service activities; private 

households with employed persons (servants); Other Services (Government): public administration and defense; compulsory 

social security, education, health and social work, sewage and refuse disposal, sanitation and similar activities, activities of 

membership organizations n.e.c., extra-territorial organizations and bodies; Dwellings: ownership of dwellings (imputed rents of 

houses occupied by owners).
47

Table A2. Aggregation of Regions in the GTAP-BIO-1 model.

No. New Code New Description GTAP regions Description of GTAP Regions 

1 USA United States usa United States of America

2 CAN Canada can Canada

3 EU27 European Union 27

aut bel dnk fin fra deu 

gbr grc irl ita lux nld prt 

esp swe bgr cyp cze hun 

mlt pol rom svk svn est 

lva ltu 

Austria, Belgium, Denmark, Finland, France, Germany, United 

Kingdom, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, 

Spain, Sweden, Bulgaria, Cyprus, Czech Republic, Hungary, Malta, 

Poland, Romania, Slovakia, Slovenia, Estonia, Latvia, Lithuania

4 BRAZIL Brazil bra Brazil

5 JAPAN Japan jpn Japan

6 CHIHKG China, Hong Kong chn hkg China, Hong Kong

7 INDIA India ind India

8 LAEEX Latin American Energy 

Exporters mex col ven arg Mexico, Colombia, Venezuela, Argentina

9 RoLAC Rest of Latin America +

Caribbean

xna per xap chl ury xsm 

xca xfa xcb 

Rest of North America, Peru, Rest of Andean Pact, Chile, Uruguay, Rest 

of South America, Central America, Rest of Free Trade Area of the 

Americas, Rest of the Caribbean.

10 EEFSUEX EE & FSU Energy Exp xef rus xsu Rest of EFTA, Russian Federation, Rest of Former Soviet Union

11 RoE Rest of Europe che xer alb hrv tur Switzerland, Rest of Europe, Albania, Croatia, Turkey

12 MEASTNAEX Middle Eastern N Africa E 

Exp xme tun xnf bwa Rest of Middle East, Tunisia, Rest of North Africa, Botswana

13 SSAEX Sub Saharan Energy 

Exporters

xsc mwi moz tza zwe 

xsd mdg uga xss 

Rest of South African Customs Union, Malawi, Mozambique, Tanzania, 

Zimbabwe, Rest of Southern African Development Community, 

Madagascar, Uganda, Rest of Sub-Saharan Africa.

14 RoAFR Rest of North Africa & SSA mar zaf zmb Morocco, South Africa, Zambia

15 SASIAEEX South Asian Energy 

Exporters idn mys vnm xse Indonesia, Malaysia, Vietnam, Rest of Southeast Asia

16 RoHIA Rest of High Inc Asia kor twn Korea, Taiwan

17 RoASIA Rest of Southeast & South 

Asia

xea phl sgp tha bgd lka 

xsa 

Rest of East Asia, Philippines, Singapore, Thailand, Bangladesh, Sri 

Lanka, Rest of South Asia

18 Oceania Oceania countries aus nzl xoc Australia, New Zealand, Rest of Oceania
48

Appendix B: Land Supply

Figure B1. Land supply in the GTAP-BIO models.

Land-AEZi

Cropland Pasture

Forest

ETRAE2 =-0.5

Coarse grains

Oilseeds

Sugar-crops

Other grains

Other 

agriculture

Land Supply in Value-Added Nest of Production Structure

ETRAE1 =-0.20