Technical Report 

An Analysis of EPA Biofuel Scenarios 

with the CARD International Models 

By CARD Staff 

November, 2008 
2

1. Introduction 

 United States Environmental Protection Agency (EPA) contracted with the Center for 

Agricultural and Rural Development (CARD) at Iowa State University to examine various 

biofuel scenarios using the CARD U.S. and global agricultural models.1

 This report outlines the 

models and assumptions used for the analysis, the scenarios performed, and the results derived 

from the analysis. 

2. The CARD Agricultural Models 

The CARD Agricultural Models are a set of multi-market, partial-equilibrium, and nonspatial econometric models.i

 The models cover all major temperate crops, sugar, ethanol, dairy, 

and livestock and meat products for all major producing and consuming countries and calibrated 

on the most recently available data (see Table 2 for a summary description of the model 

coverage). The models are used extensively for generating ten-year baseline projections for 

agricultural markets and for policy analysis based on the baseline projections. The individual 

commodity models or the full set of models have been employed for numerous studies, such as 

Abler et al. (2007),ii Elobeid et al.(2006),iii Fabiosa et al. (2007),iv Fabiosa et al. (2005),v and 

Tokgoz et al. (2008).vi The FAPRI models have been used by several organizations (e.g., the 

U.S. Congress)vii to develop baselines as well as for policy analysis purposes. Recently, research 

on biofuels that used the CARD model was supported by the USDA and several commodity 

groups.viii 

These models capture the biological, technical, and economic relationships among key 

variables within a particular commodity and across commodities. They are based on historical 

data analysis, current academic research, and a reliance on accepted economic, agronomic, and 

biological relationships in agricultural production and markets. The links between the U.S. and 

international models are made through commodity prices and net trade equations. 

In general, for each commodity sector, the economic relationship that quantity supplied 

equals quantity demanded is achieved through a market-clearing price for the commodity. In 

other countries domestic prices are modeled as a function of the world price using a price 

transmission equation. Since econometric models for each sector can be linked, changes in one 

commodity sector will impact the other sectors. 

The model for each commodity consists of a number of countries/regions, including a 

rest-of-the-world aggregate to close the model. The models specify behavioral equations for 

production, use, stocks, and trade between countries/regions. The models solve for 

representative world prices by equating excess supply and demand across countries. Using price 

transmission equations, the domestic price for each country is linked with the representative 

world price through exchange rates and other price policy wedges.2

 

1

 This model is called the Food and Agricultural Policy Research Institute (FAPRI) model system when the work is 

performed by Iowa State and its partner institution at the University of Missouri. When Iowa State runs the model 

on its own, especially when changes in structure, parameters, and specifications are introduced, the model system is 

called the CARD Model. 

2

 This may include border duties, export taxes, and domestic price supports. 
3

In general, data for supply and utilization of agricultural commodities were obtained from 

the F.O. Lichts Online Database;ix the Food and Agricultural Organization (FAO) of the United 

Nations (FAOSTAT Online);x

 and the Production, Supply and Distribution View (PS&D) of the 

U.S. Department of Agriculture (USDA).xi Macroeconomic data were gathered from various 

sources, including the International Monetary Fund (IMF) and Global Insight. Additional details 

on exchange rate estimates, international population growth, international GDP growth, and 

other factors for key countries can be found at: http://www.fapri.iastate.edu/outlook2008/. 

2. 1 Structure of the Domestic Model 

The U.S. (domestic) model is part of a customized version of the CARD modeling system 

(see Figure 1). This system includes models of supply and demand for all important, temperate 

agricultural products in major producing and consuming countries. The models are a set of 

multi-market, partial-equilibrium, and non-spatial models. Agricultural and trade policies are 

included in the models to the extent that they affect the supply and demand decisions of the 

economic agents. The models assume that existing domestic and trade policy variables will 

remain unchanged in the outlook period. 

Figure 1: Model Interactions 

The U.S. Crops Model is a partial-equilibrium model, which includes behavioral 

equations that determine crop planted acreage, domestic feed, food and industrial uses, trade, and 

ending stocks in marketing years. The model solves for the set of prices that brings annual 

 
4

supply and demand into balance in all markets. The U.S. Crops Model is divided into regions 

with equations for each crop grown within each region. Table 1 provides a detailed list of the 

regions and crops covered in the Model. 

 

Table 1: Regions and Crops in the U.S. Crops Model 

Regions Crops 

Corn Belt 

Central Plains 

Delta States 

Far West 

Lake States 

Northeast 

Northern Plains 

Southeast 

Southern Plains 

Corn 

Soybeans 

Wheat 

Upland Cotton 

Sorghum 

Barley 

Oats 

Rice 

Sunflowers 

Peanuts 

Canola 

Sugar beet 

Sugarcane 

For crops with by-products, behavioral equations for the by-products are also included 

(i.e., high fructose corn syrup, ethanol and corn oil from corn, and soybean meal, soybean oil and 

biodiesel from soybeans). For each commodity, a market-clearing price is achieved by equating 

quantity supplied to quantity demanded. The market linkages imply that changes in one 

commodity sector will have impacts on the other sectors. 

In the U.S. ethanol sub-model, total ethanol production is the sum of ethanol produced 

from corn through the dry- and wet-milling processes, ethanol produced from non-corn sources, 

and ethanol produced from cellulosic feedstock. Corn-based ethanol produced for each milling 

process is calculated by multiplying the ethanol yield by the quantity of corn used in the 

production of ethanol. On the demand side, there are three major components for ethanol: the 

additive, voluntary E-10, and E-85 markets. In the ethanol additive market, which is currently 

the largest component, oxygenation requirements and blend mandates determine the ethanol 

demand. Net trade is based on the relative price of U.S. and world ethanol. However, for these 

sets of runs the U.S. ethanol sub-model has not been utilized since the EPA volume requirements 

have been utilized in the model. These predetermined biofuel requirements have been used to 

estimate feedstocks going into ethanol production and are incorporated into the crop demand 

module to determine the response of the U.S. agricultural sector to the biofuel requirements. 

2.2 Structure of the International Model 

The CARD international module is based on multi-market, partial-equilibrium models. 

The models are econometric and simulation models covering all major temperate crops, sugar, 

ethanol, dairy, and livestock and meat products for all major producing and consuming countries 

and calibrated on most recently available data (see Table 2 for a detailed list of commodity and 

country coverage). In these models, extensive market linkages exist, reflecting derived demand 
5

for feed in livestock and dairy sectors, competition for land in production, and consumer 

substitution possibilities for close substitutes such as vegetable oils and meat types. 

Table 2: Model Description 

Exogenous Variables Population, GDP, GDP Deflators, Exchange Rates, Policy Variables 

Endogenous Variables Production, Consumption, Exports, Imports, Stocks, World and Domestic 

Prices 

Commodity Coverage Grains: Corn, Wheat, Sorghum, Barley, Oats, and Other Grains 

Oilseeds: Soybeans, Rapeseed, Sunflower, Palm, and Other Oilseeds 

Livestock: Beef, Pork, Poultry, and Other Livestock Products 

Dairy: Milk, Cheese, Butter, and Other Dairy Products 

Sugar 

Biofuels: Ethanol, Biodiesel 

Major Countries/Regions North America: United States, Canada, Mexico 

South America: Argentina, Brazil, etc. 

Asia: China, Japan, India, Malaysia, etc. 

Africa: South Africa, Egypt, Nigeria, etc. 

Europe: European Union, Russia, Ukraine, etc. 

Oceania: Australia, New Zealand 

Output by Commodity and 

Country 

World Prices, Domestic Prices, Production, Consumption, Net Trade, 

Stocks, Area Harvested, Yield 

2.3 Grains and Livestock Descriptions 

To provide more detail on the internal dynamics of the individual sector models, we have 

outlined the structure of the International Grains Model and the International Livestock and 

Poultry Model below. The International Grains Model is a non-spatial, multi-market model that 

covers several countries/regions and includes a rest-of-the world aggregate. It has links to other 

models in the modeling framework, such as the U.S. Crops Model, international Cotton, Dairy, 

Livestock, Oilseeds, Rice, and Sugar Models. The data sources included in the model are the 

USDA-Foreign Agricultural Service Production, Supply, and Distribution (PS&D) data set,xii the 

International Monetary Funds' (IMF) International Financial Statistics (IFS) macroeconomic data 

set, USDA Attaché Reports,xiii and other sources for commodity prices. 

The Grains Model interacts with the Dairy and Livestock Models that provide 

information on feed demand in the countries, and with Oilseeds and Rice Models that provide 

information on the relative profitability and area harvested for the competing crops. Each 

country sub-Model consists of at least one commodity depending on the relative importance of 

the commodity and the relative importance of the country in the world markets as a supplier or a 

buyer. 

In terms of the structure of the Models, the following identity is satisfied for each 

country/region and the world: Beginning Stock + Production + Imports = Ending Stock + 

Consumption + Exports. Production is divided into yield and area equations, while consumption 
6

is divided into feed and non-feed demand. To satisfy the identity, two different methods are 

used. In most of the countries, domestic price is modeled as a function of the world price with a 

price transmission equation, and the identity is satisfied with one of the variables set as the 

residual. In other cases, prices are solved to satisfy the identity. 

Agricultural and trade policies in each country are included in the model to the extent that 

they affect the supply and demand decisions of the economic agents. Examples of these include 

taxes on exports and imports, tariffs, tariff rate quotas, export subsidies, intervention prices, and 

set-aside rates. 

The International Grains Model assumes that the existing agricultural and trade policy 

variables will remain unchanged in the outlook period. Macroeconomic variables, such as GDP, 

population, and exchange rates, are exogenous variables that drive the projections of the Model. 

The Model also includes an adjustment for marketing-year differences by including a residual 

that is equal to world exports minus world imports, which ensures that world demand equals 

world supply. 

Like all the other international models, the International Livestock and Poultry Model is a 

partial equilibrium, econometric, non-spatial policy model. That is, all other sectors of the 

economy outside of the relevant commodities are considered as given; parameters in the Model 

are either directly estimated, surveyed from the literature, or obtained from consensus of expert 

opinion; and country sources and destinations of trade are not specified. 

The structure of the model considers carefully the biological processes involved in 

livestock and meat production and richly captures the investment, production, and consumption 

decisions of significant economic agents. Both Figure 2 and Figure 3 provide a sample 

schematic description of the model structure for cattle and beef. They show the relationship of 

the biological processes and economic decisions as well as the relationship of stock and flow 

variables in the evolution of animal and meat production and consumption. In general, the 

demand side is driven by relevant prices, income, and population, while the supply side is driven 

by relevant prices, cost of production, and technology. When it is not a residual, trade is 

expressed as a function of the domestic price relative to the world price. 

The specification of the Model is based on five principles. The first is a clear 

differentiation of stock and flow variables. Animal inventory is an example of a stock variable, 

while animals slaughtered is a flow variable. Second, in the specification of the model, except 

for the breeding herd, all stock variables are derived in an accounting identity from the changes 

in the flow variables. Third, flow variables are generally the only ones with behavioral 

specification representing economic decisions of significant agents in the sector. Fourth, flow 

variables are specified in terms of rates rather than levels. Rates normally give a more stable 

behavior to the model compared with levels and lend easily to comparison across countries and 

time periods. Finally, price determination in individual country sub-models is specified as either 

price transmission from the world price or market clearing when there are significant restrictions 

in trade flow (such as the case of the quantitative quota of broilers in Russia). The entire Model 

solves with a market-clearing world price that balances world trade and equates supply and 

demand in individual countries. 
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The Model strives to capture policy instruments that influence the incentives faced by 

economic agents. This includes domestic policies (for example, price support) and border 

policies (for example, duties, tariff rate quotas, export subsidies). Other policies that are difficult 

to represent quantitatively, such as environmental regulations, are accounted for exogenously.

Figure 2: Cattle Production and Use 
8

Figure 3: Beef Production and Consumption 

3. Description of Scenario Simulations for EPA 

 Six scenarios were run for the EPA analysis. The first established a reference case based 

on the AEO 2007 projected renewable fuel volumes (labeled “Reference Case”), which includes 

12.4 billion gallons (Bgal) of corn ethanol, 0.4 Bgal of biodiesel, and 0.6 Bgal of imported 

sugarcane ethanol by 2022. In order to match the AEO biofuel projections, the ethanol demand 

module was turned off, and the specific volume mandates were imposed on the CARD models. 

The biofuel requirements were specifically imposed in the U.S. Crops Model’s ethanol module 

with the connections to the rest of the model maintained such that the rest of the Model was 

allowed to respond to the U.S biofuel requirements. In addition, the dry and wet mill yields were 

capped at the levels provided by EPA (i.e., 2.71 and 2.5 gallons of ethanol per bushel of corn, 

respectively). The amount of corn used for fuel ethanol was then derived based on the volumes 

requirements and the dry and wet mill yields. 

The second scenario (labeled “Control Case”) incorporates the renewable fuels EPA 

assumed would contribute to meet the EISA requirements, which includes 15 Bgal of corn 

ethanol, 1 Bgal of biodiesel, and 3.1 Bgal of imported ethanol. A series of additional fuel 

specific volumes were also run, which looked at the incremental change due to the increase in a 

single renewable fuel, holding all other renewable fuels at the Control Case levels. A corn 

ethanol case analyzed only the increase of 2.6 Bgal of corn ethanol (labeled “Corn Only Case”), 

a low corn ethanol case analyzed only the increase of 6.3 Bgal of corn ethanol (labeled “Low 

Corn Case”),3

 the biodiesel case analyzed only the increase of 0.7 Bgal of biodiesel (labeled 

 

3

 This “Low Corn” scenario analyzes a larger change (8.7 Bgal to 15 Bgal) in corn ethanol volume to test how 

dependent results are on volumes considered. 
9

“Biodiesel Only Case”), and the imported ethanol case analyzed only an increase of 2.5 Bgal of 

imported ethanol (labeled "Import Only"). 

The CARD models do not include cellulosic ethanol sources; however we attempted to 

approximate the impact of an increase in switchgrass ethanol by modeling a scenario that 

allowed an additional 7.4 million acres of CRP land to be used for switchgrass ethanol (labeled 

“Switchgrass Only Case”), and compared it to a scenario that did not allow these acres to be used 

for switchgrass production. Based on EPA estimates we inferred that 7.4 million acres of CRP 

land would produce 46.3 million tons of switchgrass feedstock, at 6.2 tons per acre, which would 

be converted into 3.2 billion gallons of ethanol. 

Table 3: Biofuel-Specific Volume Requirements 

 

Reference 

Case 

2022 

Control 

Case 

2022 

Corn Only 

Case 

2022 

Low Corn 

Case 

2022 

Biodiesel 

Only Case 

2022 

Imports 

Only Case 

2022 

Switchgrass 

Only Case 

 (billion gallons) 

Corn-Grain 

Based 

Ethanol 

12.3 15 12.4 8.7 15 15 15 

Cellulosic 

Ethanol4

NA NA NA NA NA NA 3.2 

Imported 

Ethanol 

0.6 3.1 3.1 3.1 3.1 0.6 3.1 

Biodiesel 0.4 0.98 0.98 0.98 0.7 0.98 0.98 

NA- Not Applicable 

4. Assumptions and Model Adjustments 

 The CARD models were customized to simulate EISA according to EPA’s specifications. 

Customized scenarios were developed by making certain model adjustments and adding 

assumptions about renewable fuel production, which are described below. 

4.1 Model Extension 

As mentioned above, one of the major model adjustments was to extend the projection 

period of the models. The CARD models were originally built to project out over a ten-year time 

horizon. For this project, the models were extended to generate projections until the 2022 

calendar and marketing years when all the EISA RFS levels are reached. This necessitated 

extending all the behavioral equations in the models, as well as the exogenous variables 

including macroeconomic projections and all the policy variables. Also, the trend was extended 

which affected some variables reflecting technical relationships such as crop yields. Calibration 

and solution of the models remain the same. 

 

4

 Cellulosic ethanol is not explicitly modeled in the CARD modeling system, and therefore does not include the 

various feedstocks for cellulosic ethanol. 
10

4.2 Model Calibration and Data Sources 

The models are typically calibrated to match data from a variety of sources, such as the 

United States Department of Agriculture USDA, Global Insight, F.O. Licht, and the Renewable 

Fuels Association (RFA). For the Reference Case, biofuel volumes were calibrated to AEO 

2007 levels. 

Data for 2006 were matched up with United States Departments of Energy (U.S. DOE) 

and Agriculture sources. For these runs, under the baseline, the U.S. DOE’s Annual Energy 

Outlook for 2007 (AEO 2007)xiv projects biofuel supplies and biofuel usage throughout the 

projection period. Energy prices, such as crude oil prices, gasoline prices and biofuel prices 

were calibrated to AEO 2008 Reference case prices. 

4.3 Policy Assumptions 

The CARD models include agricultural and energy policies such as trade agreements, 

tariffs, and subsidies. Generally the CARD models extend provisions in the 2002 Farm Bill 

through the end of the period of analysis. In the modeling conducted for EPA, agricultural and 

energy policies in place as of January 2008, were maintained through 2022. Policies set to 

sunset, such as the $0.54/gallon U.S. ethanol import tariff, the $0.51/gallon ethanol blenders tax 

credit, and the $1.00/gallon tax credit for biodiesel, were assumed to be extended over the full 

projection period, since these incentives have consistently been renewed in the past. 

One adjustment that was made to the policy assumptions for the EPA modeling was 

related to the release of acreage from the Conservation Reserve Program (CRP). Typically the 

CARD models allow acreage to be released from the program based on economic conditions and 

current CRP funding levels. However, in all of the scenarios for EPA except the switchgrass 

scenario, CRP was allowed to reenter production from current projected levels until 32 million 

acres remained in the program. After that point, it was assumed payments would increase to 

maintain the remaining CRP acres, which is consistent with the 2008 Farm Bill. 

4.4 Yields of Key Crops

For all of the scenarios undertaken for this analysis, several key assumptions about crop 

yields were made. U.S. corn yields were assumed to increase to approximately 180 bushels/acre 

(bu/acre) in 2022 (a 1.3% annual increase), which is consistent with the USDA baseline 

projections. In addition, international corn yields were assumed to increase over time. For 

example, corn yields in Argentina are assumed to be approximately 140 bu/acre in 2022 (a 0.8% 

annual increase). Corn yields in Brazil are assumed to be 68 bu/acre in 2022 (a 1.1% annual 

increase). The historical yields are calculated as a ratio of crop production over area harvested, 

data for which is obtained from USDA Foreign Agricultural Service PSD database.xv The yield 

projections are based on yield equations in each commodity’s model. Yield equations employ a 

time trend to capture yield growth and assume average weather conditions. 

 Although corn yields are assumed to increase over time by (e.g., 1.14% on average 

annually in the world over the projection period), the model does not currently include price-
11

induced yield changes from intensification of agricultural practices as a result of the 

implementation of the renewable fuels policy. On the other hand, CARD also assumed that 

marginal land brought into production would produce the same yields as land already in crop 

production (i.e., cropland extensification). Therefore, implicitly, CARD assumes that priceinduced yield changes offset declines in yields from the production of new lands. In other words, 

intensification and extensification effects from the biofuels policy offset each other. 

U.S. national average soybean yields were assumed to be approximately 50 bu/acre in 

2022 (a 1.0% annual increase), which is consistent with the USDA baseline projections. 

International corn yields increasing over time, however the models do not assume any change in 

yields due to the renewable fuels policy. For example, in Argentina, soybean yields are 

approximately 48 bu/acre in 2022 (a 0.7% annual increase), and Brazilian soybean yields are 

approximately 50 bu/acre in 2022 (a 1.1% annual increase). Soybean historical yields are 

calculated by dividing soybean production by area harvested. This data is obtained from USDA 

Foreign Agricultural Service PSD database.xvi As is the case for all crops, soybean yield 

projections are based on trend yields. 

4.5 Key Commodity By-Products 

 To be consistent with other areas of the EPA rulemaking, the CARD model was modified 

to limit average dry mill ethanol yields to 2.71 gallons of pure ethanol per bushel. The default 

CARD assumption that 2.5 gallons of ethanol would be produced per bushel of corn using wet 

milling was not altered. The models also assumed that the distillers grains not consumed 

domestically in the U.S. would be exported to the international markets. The amount of the 

distillers grains that would be exported changed depending on each scenario. 

Use of Distillers Grains (DG) in animal feed is highest in the beef cattle sector (61% 

share), followed by the dairy sector (21%), and then pork (9%) and poultry (9%) sectors. The 

differential use of DG in animal feed is a function of the adoption rate of DG by livestock and 

poultry producers and the inclusion rate of DG in their feed rations. A major concern of 

livestock and poultry producers in the adoption of DG as a feed ingredient is on its quality 

including stability of its nutritional content, storability, and ease of transport. In particular, the 

variability of the nutritional content of DG products may discourage full adoption by animal 

producers. This is particularly true to non-ruminant animals such as swine and poultry where 

specific amino acid requirement are critical because these animals are unable to metabolize on 

their own some amino acid in adequate amounts. Ruminant animal feed ration, on the other 

hand, can use DG in wet form, which bypasses the cost of drying, thereby, enabling this sector to 

be competitive in attracting DG compared to monogastric animals which have to use DG in dry 

form. For these reasons, adoption rate is highest in beef cattle at 54 to 80%, followed by dairy 

cattle at 69 to 88%, then pork at 21 to 23%, and poultry at 19 to 22%. 

There are other considerations in the use of DG in feed ration such as the issue of 

storability. Because DGs contain unstable fatty acids it is subject to rancidity in a short span of 

time. Furthermore, mycotoxins from molds, if present in the feedstock corn can spillover into 

the co-products, and can have serious adverse repercussions on animal production performance. 
12

These technical factors can potentially discourage the use of DG as a feed ingredient, even if 

economic considerations appear favorable. 

The inclusion of DG in the animal feed ration has been researched extensively. The 

maximum allowable inclusion rate of DG in animal feed ration has been examined in many 

studie, including Nollxvii for poultry, Shursonxviii and Thalerxix for swine, Trenklexx and Tjardes 

and Wrightxxi for cattle, and Schingoethexxii for dairy. These studies establish inclusion rates that 

avoid compromising animal production performance (i.e., reduced daily weight gain), as well as 

final product quality (i.e., meat quality). Beef cattle feed ration has the highest inclusion rate at 

41 to 50%. The maximum inclusion rate for dairy follows at 26 to 30%, for hogs 20%, and for 

poultry 20%. 

In the U.S., livestock and poultry industry corn is the main source of energy and soymeal 

is the main source of protein. DG can be used as a substitute for corn and soymeal. The use of 

DG in feed ration is determined by both technical (energy and protein content of DG relative to 

corn and soymeal) and economic (price of DG, corn, and soymeal) considerations. Cost 

minimization of feed ration with DG suggests that DG can displace corn as a source of energy by 

close to a one-for-one basis with the highest displacement in ruminant animals - beef and dairy 

cattle at 0.97, followed by pork at 0.89, and then poultry at 0.79. Displacement of soymeal as a 

source of protein is 0.03 for beef and dairy cattle, 0.11 for pork, and 0.21 for poultry. Even if 

DGs are relatively high in crude protein compared to corn, the protein is low in the limiting 

amino acid lysine, when compared to soymeal. 

4.6 Crop Production Patterns 

Changes in international production capacity and, in turn, international land use changes 

are based on the response of the international crop models to commodity price changes. Land 

use changes depend not only on own price (i.e., gross returns) elasticities but also on cross-price 

elasticities. This set up allows country models to respond to relative price (i.e., gross return) 

changes in each crop separately. Although CARD models do not incorporate limits on land 

availability, the inclusion of cross-price elasticities allows total crop area to respond to changes 

in a consistent manner. Countries respond to increases in crop prices by increasing their supply 

through area expansion and by decreasing their domestic demand. Which crop area increases is 

determined by the relative prices of each crop to competing crops which are country specific. 

CARD does not explicitly model land conversion costs, however the international production 

supply curves used by the CARD model implicitly take into account conversion costs. CARD's 

supply curves are based on historical responses to price changes, which take into account the 

conversion costs of land, based on expected future returns associated with land conversion. 

Additional details on exchange rate estimates, international population growth, and international 

GDP growth for key countries can be found at: http://www.fapri.iastate.edu/outlook2008/. 

5. Summary of Results 

In this section, some of the key results of the alternative biofuel scenarios are presented. 

The key results for this analysis include change in crop acreage, crop production, livestock 

production, international trade patterns, and commodity prices. 
13

5.1 Crop Acreage Changes 

Table 4 shows crop area changes for the 2022 crop year as we move from each individual 

case to the Control Case, which represents renewable fuel volumes as outlined in the EISA RFS 

requirements. This means that the table shows percentage change as we move from Reference 

Case to the Control Case, Corn Only Case to the Control Case, etc. 

Under the Control Case, corn acreage for the 2022 crop year is 90 million acres in the 

U.S., 9.1 million acres in Argentina, 36.1 million acres in Brazil, and 70.4 million acres in China. 

Corn area increases for all these countries as we move to Control Case from the Reference Case, 

the Corn Only Case, the Import Only Case, and the Low Corn Case. Crop area declines slightly 

for U.S., Argentina, and Brazil in the Biodiesel Only Case. Corn area decreases for the 

Switchgrass Only Case in Argentina, Brazil, and China, whereas it increases in the U.S. 

Table 4: Changes in Crop Acreage

 Control Reference Corn Only Biodiesel Only Import Only Switchgrass Only Low Corn

 Case (000 acres) Case Case Case Case Case Case

CORN 

United States 89,981 4.20% 4.17% -0.23% 0.34% 0.25% 10.04%

Argentina 9,062 1.79% 1.77% -0.05% 0.13% -0.12% 5.06%

Brazil 36,154 1.22% 1.48% -0.17% 0.11% -0.07% 4.37%

China 70,415 1.03% 0.89% 0.02% 0.08% -0.10% 2.43%

SOYBEANS 

United States 69,092 -1.29% -2.29% 0.77% -0.27% 0.75% -4.79%

Argentina 49,641 0.32% 0.01% 0.15% -0.01% -0.05% -0.12%

Brazil 76,043 0.95% 0.26% 0.32% 0.01% -0.15% 0.37%

WHEAT 

United States 46,768 -1.44% -0.87% -0.19% -0.13% 1.71% -2.20%

Argentina 16,149 -0.27% -0.17% -0.07% 0.06% -0.08% -0.46%

Australia 33,085 -0.07% 0.08% 0.01% -0.15% -0.04% 0.17%

Canada 22,580 -0.11% -0.07% -0.07% 0.10% -0.12% -0.18%

Russia 59,444 0.23% 0.13% 0.02% 0.05% -0.08% 0.32%

SUGARCANE 

Australia 1,139 0.14% 0.07% 0.02% 0.02% -0.02% 0.15%

Brazil 23,777 8.39% -0.13% -0.06% 8.71% 0.04% -0.27%

India 12,648 -0.08% -0.08% 0.00% -0.01% 0.01% -0.17%

Thailand 2,788 -0.08% -0.09% 0.00% 0.00% 0.01% -0.19%

Note: These are percentage changes are calculated based on moving from each case to the Control Case

Soybean plantings for the 2022 crop year under the Control Case are 69 million acres in 

the U.S., 49.6 million acres in Argentina and 70 million acres in Brazil. Soybean area increases 

for Argentina and Brazil in the Reference, Corn Only, and Biodiesel Cases. As in the case for 

corn acreage, the impact of the Import Only Case is lower relative to the other cases. Soybean 

area decreases in Argentina for the Low Corn Case, whereas it increases in Brazil. For the 

Switchgrass Only Case, soybean area declines in both Argentina and Brazil. 
14

For wheat, plantings for 2022 under Control Case are 16.1 million acres in Argentina, 

33.1 million acres in Australia, 22.6 million acres in Canada, and 59.4 million acres in Russia. 

Wheat area declines for most of the countries in the Reference, Corn Only, Switchgrass Only, 

Low Corn, and Biodiesel Cases. The exceptions are Australia and Russia for the Corn Only, 

Low Corn, and Biodiesel Cases where wheat area increases. 

Australia is projected to have 1.1 million acres devoted to sugarcane production by 2022. 

Brazil is projected to have 23.8 million acres; India is projected to have 12.6 million acres; and 

Thailand is projected to have 2.8 million acres. The changes in the sugarcane area are small in 

all cases, except in the case of Brazil, where sugarcane area increases by over 8% in both the 

Reference and Import Only Cases. 

5.2 U.S. Trade Changes 

The projected amounts and changes in selected U.S. commodity trade for the year 2022 

are given in Table 5. In all the cases, except for Import Only and Switchgrass Only Cases, U.S. 

corn exports are projected to be lower when we move from the individual case to the Control 

Case. In all the cases except for Switchgrass Only, U.S. wheat and sorghum net exports decline 

with the largest impact seen between the Control Case and the Reference Case. Soybean net 

exports increase in the Biodiesel Only and Switchgrass Only Cases and decline in all the other 

cases. The impact on soybean oil net trade is the largest with a decline of 27% in the Reference 

Case, 20% in the Low Corn Case, and 18% in the Biodiesel Only Case. 

Table 5: U.S. Trade Patterns

 Control Reference Corn Only Biodiesel Only Import Only Switchgrass Only Low Corn

 Case (TMT) Case Case Case Case Case Case

CORN 88,713 -7.63% -8.01% -0.28% 1.29% 0.84% -18.77%

SORGHUM 4,314 -23.48% -2.85% -0.23% -21.78% 2.53% -8.35%

WHEAT 24,188 -3.64% -2.93% -0.32% -0.01% 3.65% -6.86%

SOYBEANS 21,664 -5.11% -5.73% 0.83% -0.69% 2.11% -11.46%

SOYBEAN MEAL 11,639 -0.15% 0.23% 0.52% -0.41% 0.68% 1.20%

SOYBEAN OIL 3,566 -26.90% 6.90% -18.31% -0.44% 0.77% 19.83%

Note: These are percentage changes are calculated based on moving from each case to the Control Case

5.3 Ethanol Trade Changes 

Projected ethanol trade and the changes under the scenarios for the year 2022 are shown 

in Table 6. Negative values in the first column of the table indicate net imports. As can be seen 

for the countries listed, only Brazil is projected to export ethanol while the others import. The 

biofuel requirements have rather different impacts for ethanol trade. The largest impacts can be 

seen in the Reference and Import Only Cases, where U.S. net imports increase by nearly 400%. 

Brazil increases its net exports by 66% to meet the increased import demand from the U.S. For 

almost all cases, net imports of ethanol decline for the rest of the countries. The impact of the 

Switchgrass Only Case is negligible for all countries. 
15

Table 6: Ethanol Trade 

 Control Reference Corn Only Biodiesel Only Import Only Switchgrass Only Low Corn

 Case (Mill. Gal.) Case Case Case Case Case Case

U.S. -3,140 388.03% 0.00% 0.00% 388.03% 0.00% 0.00%

Brazil 5,422 65.88% 0.01% -0.04% 65.95% 0.00% 0.10%

China -64 -60.25% 5.85% -0.92% -61.00% -0.43% 18.63%

EU -507 -7.37% -0.17% -0.08% -7.15% 0.02% -0.30%

India -388 -12.74% -0.50% -0.15% -12.19% 0.09% -0.95%

Japan -365 -2.61% -0.09% -0.03% -2.49% 0.02% -0.17%

South Korea -175 -2.92% -0.10% -0.03% -2.79% 0.02% -0.19%

Note: These are percentage changes are calculated based on moving from each case to the Control Case

5.4 Vegetable Oil Trade Changes 

The projected vegetable oil trade figures and changes are shown in Table 7. Soybean oil 

exports are dominated by three countries: Argentina, Brazil, and the U.S. With the decline in 

U.S. net exports of soybean oil, Argentina and Brazil fill the gap in both the Reference Case and 

the Biodiesel Only Case. In the Low Corn Case, the U.S. soybean oil exports increase by almost 

20%. For rapeseed/canola oil, Canada is the major exporter and it roughly maintains its export 

levels under the scenarios. The EU increases its import under the Reference, Biodiesel Only, and 

Switchgrass Only Cases, while U.S. imports decline. The impact on sunflower and palm oil 

trade is very small in all cases. 
16

Table 7: Vegetable Oil Trade Impacts

 Control Reference Corn Only Biodiesel Only Import Only Switchgrass Only Low Corn

 Case (TMT) Case Case Case Case Case Case

Soybean Oil 

 Argentina 9,258 1.76% -0.47% 0.98% -0.01% 0.02% -1.13%

 Brazil 1,297 30.95% -3.91% 16.99% 0.34% -0.54% -8.80%

 U.S. 3,566 -26.90% 6.90% -18.31% -0.44% 0.77% 19.83%

 China -5,517 -4.16% 0.68% -2.54% -0.07% 0.10% 1.67%

 EU -1,100 -11.94% 2.91% -7.86% -0.13% 0.20% 7.45%

 India -3,143 -2.81% 0.06% -1.47% -0.06% 0.15% 0.32%

Rapeseed/Canola Oil 

 Canada 1,760 0.30% 0.06% 0.06% 0.03% -0.04% 0.16%

 EU -997 3.36% -0.52% 1.93% -0.13% 0.25% -1.26%

 U.S. -595 -9.24% -1.02% -4.46% -0.08% -0.02% -2.44%

Sunflower Oil 

 Argentina 2,043 -0.01% -0.08% 0.04% -0.01% -0.01% -0.22%

 CIS 2,383 -0.29% 0.06% -0.19% 0.00% -0.03% 0.14%

 EU -1,693 1.34% -0.19% 0.81% 0.01% 0.08% -0.39%

Palm Oil 

 Malaysia 21,862 0.44% -0.04% 0.25% 0.01% -0.02% -0.09%

 Indonesia 25,908 0.23% -0.02% 0.13% 0.01% -0.01% -0.05%

 China -12,222 0.09% -0.01% 0.05% 0.00% -0.01% -0.01%

 EU -5,178 0.76% -0.08% 0.43% 0.02% -0.04% -0.17%

 India -7,560 0.32% -0.03% 0.18% 0.01% -0.02% -0.06%

Note: These are percentage changes are calculated based on moving from each case to the Control Case

5.5 World Food Consumption Changes 

Due to the increase in crop prices in all cases except the Switchgrass Case Only, the cost 

of producing livestock and dairy products also increases. As seen in Table 8, given biofuel target 

higher than the Control Case, livestock and dairy prices increase in all cases except the Biodiesel 

Only and Switchgrass Only Cases. The largest impacts are seen between the Control Case and 

the Low Corn Case because of highest price increase in feedstocks. In contrast, the Import Only 

Case gives lower rate of increase in the prices of feed grains resulting in lower rate of increase in 

the cost of livestock and poultry production, and directly translating to lower rate of increases in 

meat and dairy prices. 
17

Table 8: Impact on Livestock and Dairy Prices 

 Control Reference Corn Only Biodiesel Only Import Only Switchgrass Only Low Corn

 Case ($ per MT) Case Case Case Case Case Case

Beef 2,237 1.42% 1.46% -0.15% 0.21% -0.28% 3.70%

Pork 1,159 2.72% 3.08% -0.40% 0.33% -0.45% 7.43%

Broiler 1,910 1.75% 2.65% -0.67% 0.34% -0.42% 6.75%

Butter 3,200 0.13% 0.15% -0.04% 0.04% -0.01% 0.36%

Cheese 4,195 0.32% 0.35% -0.09% 0.12% -0.05% 1.00%

Non-Fat Dry Milk 3,482 1.28% 1.19% -0.02% 0.12% -0.23% 2.60%

Whole Milk Powder 3,555 0.57% 0.55% -0.03% 0.07% -0.09% 1.26%

Note: These are percentage changes are calculated based on moving from each case to the Control Case

5.6 World Grain, Oilseed, and Biofuel Price Changes 

 Table 9 outlines the world price impacts under the scenarios. The commodity prices 

increase in most of the scenarios and the impacts are largest between the Reference Case and the 

Control Case, and in the Low Corn Case. Ethanol prices show a 61% increase between the 

Reference Case and the Control Case and 57% increase in the Import Only Case. World prices 

increase significantly in the Low Corn Case, with corn prices going up by 16%. In the 

Switchgrass Only Case, all commodity prices decline, albeit to a lesser degree when compared to 

most of the other cases. 

Table 9: World Price Impacts

 Control Case Reference Corn Only Biodiesel Only Import Only Switchgrass Only Low Corn

 Case ($ per MT) Case Case Case Case Case Case

CORN 197 7.51% 5.86% 0.32% 0.59% -0.80% 15.95%

SORGHUM 200 10.74% 3.92% 0.29% 5.63% -0.96% 10.24%

BARLEY 183 4.02% 3.01% 0.18% 0.46% -0.66% 7.86%

WHEAT 276 3.73% 2.53% 0.24% 0.54% -0.80% 6.57%

SOYBEANS 496 5.56% 2.44% 1.10% 0.50% -0.83% 6.12%

RAPESEED 548 2.39% 0.65% 0.67% 0.19% -0.29% 1.75%

SUNFLOWER 591 3.20% 1.31% 0.76% 0.26% -0.54% 3.43%

($ per gallon) 

ETHANOL 1.36 60.80% 1.24% 0.36% 56.50% -0.23% 2.52%

BIODIESEL 6.65 2.11% -0.15% 1.19% 0.05% -0.12% -0.34%

Note: These are percentage changes are calculated based on moving from each case to the Control Case

5.7 For Further Information 

 See data tables included in the docket. 
18

 

i Standard validation of models are conducted when they are initially developed, usually with a single country and 

commodity group coverage. Sample publications on validation includes; Fabiosa, Jacinto, Xiaoting Qi, 1998, 

“A New General Conceptual Approach to Modeling the Livestock Sector: An Application to the Japanese SwinePork Sector”, CARD Working Paper 98-WP 204 ; Barrett, C., and Jacinto F. Fabiosa, 1998, “A Policy Model for the 

Livestock and Poultry Sectors of Mexico”, CARD Working Paper, 98-TR 39; Fuller, 1997, “Policy and Projection 

Model for the Meat Sector in the People's Republic of China”, CARD Working Paper 97-TR 36; Shaw, I., J. 

Shaffer, and V. Premakumar, 1997, “Policy and Forecasting Models for the Chinese, South Korean, Australian, and 

European Union Meat Sectors”, CARD Working Paper 97-TR 35 

ii Abler, David, John C. Beghin, David Blandford, and Amani Elobeid. “Changing the U.S. Sugar Program into a 

Standard Crop Program: Consequences under NAFTA and Doha.” Review of Agricultural Economics, Volume 

30(1):82-102, 2007. 

iii Elobeid, Amani, Simla Tokgoz, Dermot J. Hayes, Bruce A. Babcock and Chad E. Hart. “The Long-Run Impact of 

Corn-Based Ethanol on the Grain, Oilseed, and Livestock Sectors with Implications for Biotech Crops.” 

AgBioForum, 9(4): 1-8, 2006. 

iv Fabiosa, J.F, J. Beghin, F. Dong, A. Elobeid, F. Fuller, H. Matthey, S. Tokgöz, and E. Wailes. “The Impact of the 

European Enlargement and CAP Reforms On Agricultural Markets. Much Ado about Nothing?” Journal of 

International Agricultural Trade and Development, Volume 3, Number 1: 57-69, 2007. 

v

 Fabiosa, Jacinto, John Beghin, Stéphane De Cara, Amani Elobeid, Cheng Fang, Murat Isik, Holger Matthey, 

Alexander Saak (Iowa State University) and Pat Westhoff, D. Scott Brown, Brian Willott, Daniel Madison, Seth 

Meyer, and John Kruse (University of Missouri-Columbia). “The Doha Round of the WTO and Agricultural 

Markets Liberalization: Impacts on Developing Economies.” Review of Agricultural Economics, Volume 27, 

Number 3, pages 317-335, 2005. 

vi Tokgoz, Simla, Amani Elobeid, Jacinto Fabiosa, Dermot J. Hayes, Bruce A. Babcock, Tun-Hsiang (Edward) Yu, 

Fengxia Dong, and Chad E. Hart, “Bottlenecks, Drought and Oil Price Spikes: Impact on U.S. Ethanol and 

Agricultural Sectors.” Review of Agricultural Economics, Volume 30(4), 2008. 

vii FAPRI Staff, 2008, FAPRI 2008 U.S. and World Agricultural Outlook, 08-FSR 1, and FAPRI Staff, 2001, 

“Impact of a 10 Percent Decrease in Planted Acreage of All U.S. Program Crops,” Briefing Paper 01-BP 33 

viii Tokgoz, S. A. Elobeid, J. Fabiosa, D. Hayes, B. Babcock, T. (Edward) Yu, F. Dong, C. Hart, and J. Beghin. 2007 

“Emerging Biofuels: Outlook of Effects on U.S. Grain, Oilseed, and Livestock Markets,” Staff Report 07-SR 101. 

This study was supported by the following organizations: National Grain and Feed Association, American Meat 

Institute, Grocery Manufacturers Association/Food Products Association, National Cattlemen’s Beef Association, 

National Chicken Council, National Pork Producers, Council, National Oilseed Processors Association, National 

Turkey Federation, and the North American Millers Association. 

ix F.O. Lichts. Online Database, Licht Interactive Data. http://www.agranet.com/portal/puboptions.jsp?Option=menu&pubId=ag072

x FAOSTAT. Database. http://faostat.fao.org/faostat/collections?subset=agriculture 

xi United States Department of Agriculture. Foreign Agricultural Service Production, Supply, and Distribution 

database. http://www.fas.usda.gov/psdonline/psdHome.aspx 

xii United States Department of Agriculture. Foreign Agricultural Service Production, Supply, and Distribution 

database. http://www.fas.usda.gov/psdonline/psdHome.aspx 

xiii United States Department of Agriculture Foreign Agricultural Service. 

http://ffas.usda.gov/scriptsw/attacherep/default.asp
19

 

xiv The reference is "Annual Energy Outlook 2007 with Projections to 2030", Energy Information Administration, 

Report #:DOE/EIA-0383(2007), February 2007 available at 

http://www.eia.doe.gov/oiaf/archive/aeo07/pdf/0383(2007).pdf 

xv United States Department of Agriculture. Foreign Agricultural Service Production, Supply, and Distribution 

database. http://www.fas.usda.gov/psdonline/psdHome.aspx 

xvi United States Department of Agriculture. Foreign Agricultural Service Production, Supply, and Distribution 

database. http://www.fas.usda.gov/psdonline/psdHome.aspx 

xvii Noll, S., V. Stangeland, G. Speers and J. Brannon, 2001. Distillers Grains in Poultry Diets. 62nd Minnesota 

Nutrition Conference and Minnesota Corn Growers Association Technical Symposium, Bloomington, MN. 

September 11-12, 2001. 

xviii Shurson, J. n.d. “The Advantages of Using Corn Distillers Dried Grains with Solubles in Swine Diets.” Fact 

sheets, Dept. of Animal Science, University of Minnesota. http://www.ksgrains.com/ethanol/DDGSFacts.pdf

xix Thaler, B. 2002. “Use of Distillers Dried Grains With Solubles (DDGS) in Swine Diets.” Extension Extra 2035, 

August 2002, College of Agriculture & Biological Sciences, South Dakota State University, USDA Cooperative 

Extension Service, SDSU, http://agbiopubs.sdstate.edu/articles/ExEx2035.pdf

xx Trenkle, A. n.d. “The Advantages of Using Corn Distillers Grains with Solubles in Finishing Beef Cattle Diets.” 

Fact sheets. http://www.ksgrains.com/ethanol/DDGSFacts.pdf

xxi Tjardes, K., and C. Wright. 2002. “Feeding Corn Distiller’s Co-Products to Beef Cattle.” Extension Extra 2036, 

August 2002, College of Agriculture & Biological Sciences, South Dakota State University, and USDA Cooperative 

Extension Service, SDSU 

xxii Schingoethe, D.J., K.F. Kalscheur, and A.D. Garcia. 2006. “Distillers Grains for Dairy Cattle.” Extension Extra

4022, Dairy Science Department, College of Agriculture & Biological Sciences, South Dakota State University, 

USDA Cooperative Extension Service, SDSU. http://agbiopubs.sdstate.edu/articles/ExEx4022.pdf.