## Technical Report

An Analysis of EPA Biofuel Scenarios

with the CARD International Models

By CARD Staff

November, 20082 1. Introduction

United States Environmental Protection Agen cy (EPA) contracted with the Center for Agricultural and Rural Developm ent (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 ofmulti-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 c ountries 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 base d on the baseline projec tions. The individual commodity models or the full set of models have been employed fo r 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 us ed 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, tec hnical, and economic relationships among key variables within a particular commodity and acro ss commodities. They ar e based on historical data analysis, current academic research, and a reliance on accepted economic, agronomic, and biological relationships in agri cultural production and markets. The links between the U.S. and international models are made through comm odity prices and net trade equations.

In general, for each commodity sector, the economic relationship that quantity supplied equals quantity demanded is achieved through a ma rket-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 c ountries/regions. The models solve for representative world prices by equating excess supply and demand across c ountries. Using price transmission equations, the domestic price for ea ch country is linked with the representative world price through exchange rate s 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 specificati ons 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 Orga nization (FAO) of the United Nations (FAOSTAT Online); x and the Production, Supply and Di stribution View (PS&D) of the U.S. Department of Agriculture (USDA). xi Macroeconomic data were gathered from various sources, including the International Monetary F und (IMF) and Global Insight. Additional details on exchange rate estimates, international popul ation growth, international GDP growth, and other factors for key countries can be found a t: http://www.fapri.iastate.edu/outlook2008/.

2. 1 Structure of the Domestic Model

The U.S. (domestic) model is part of a cust omized 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 cons uming 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 de mand decisions of the economic agents. The models assume that exis ting 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 plan ted acreage, domestic feed, food and industrial uses, trade, and ending stocks in marketing years. The model so lves for the set of prices that brings annual4 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 re gion. 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 equa tions 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 im pacts 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 proce sses, 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, ther e 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, oxygenati on requirements and blend manda tes 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 no t been utilized since the EPA volume requirements have been utilized in the model. These predeter mined biofuel requirements have been used to estimate feedstocks going into ethanol productio n and are incorporated into the crop demand module to determine the response of the U.S. ag ricultural sector to th e biofuel requirements.

2.2 Structure of the International Model

The CARD international modul e is based on multi-market, pa rtial-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 demand5 for feed in livestock and dairy sectors, competition for land in production, and consumer substitution possibilities for close substitutes su ch 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, Im ports, 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 indivi dual sector models, we have

outlined the structure of the International Grai ns 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. Th e data sources included in the model are the USDA-Foreign Agricultural Se rvice Production, Supply, and Dist ribution (PS&D) data set, xii the International Monetary Funds' (IMF) Internationa l Financial Statistics (IFS) macroeconomic data set, USDA Attaché Reports, xiii and other sources for commodity prices.

The Grains Model interacts with the Da iry and Livestock Models that provide

information on feed demand in th e countries, and with Oilseeds and Rice Models that provide information on the relative profitability and ar ea harvested for the competing crops. Each country sub-Model consists of at least one co mmodity 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, th e following identity is satisfied for each country/region and the world: Beginning Stock + Production + Imports = Ending Stock + Consumption + Exports. Production is divided in to yield and area equations, while consumption6 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 sa tisfied with one of th e 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 th e 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 pe riod. 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 ma rketing-year differences by including a residual that is equal to world exports minus world im ports, which ensures that world demand equals world supply.

Like all the other international models, the In ternational Livestock a nd 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 c onsidered as given; parameters in the Model are either directly estimated, surveyed from the lit erature, or obtained from consensus of expert opinion; and country sources and destin ations of trade are not specified.

The structure of the model considers carefu lly the biological processes involved in livestock and meat production and richly captures the invest ment, production, and consumption decisions of significant economic agents. Bo th 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 econom ic decisions as well as the re lationship of stock and flow

variables in the evolu tion of animal and meat production an d consumption. In general, the demand side is driven by relevant prices, inco me, and population, while the supply side is driven by relevant prices, cost of pr oduction, and technology. When it is not a residua l, 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. Anim al inventory is an example of a stock variable, while animals slaughtered is a fl ow variable. Second, in the sp ecification 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 variable s are generally the only ones with behavioral specification representing economic decisions of si gnificant 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 determ ination in individual country sub- models is specified as either price transmission from the world price or market clearing when th ere 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.7 The Model strives to capture policy instruments that influence the incentives faced by economic agents. This includes domestic policie s (for example, price support) and border policies (for example, duties, ta riff 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 Use8 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 co rn ethanol, 0.4 Bgal of biodies el, 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 volum e mandates were imposed on the CARD models. The biofuel requirements were specifically impo sed in the U.S. Crops Model’s ethanol module

with the connections to the rest of the model ma intained 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 fu el ethanol was then deri ved 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 impo rted ethanol. A series of additional fuel specific volumes were also run, which looked at th e incremental change due to the increase in a single renewable fuel, holding all other renewabl e fuels at the Control Case levels. A corn ethanol case analyzed only the in crease of 2.6 Bgal of corn ethanol (labeled “Corn Only Case”), a low corn ethanol case analyzed only the incr ease of 6.3 Bgal of corn ethanol (labeled “Low Corn Case”), 3 the biodiesel case analyzed only the incr ease 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 et hanol sources; however we attempted to approximate the impact of an increase in sw itchgrass 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 scen ario 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 bil lion gallons of ethanol.

Table 3: Biofuel-Speci fic 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

Ethanol 4 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 simula te EISA according to EPA’s specifications. Customized scenarios were developed by ma king certain model adjustments and adding assumptions about renewable fuel produc tion, which are described below.

4.1 Model Extension

As mentioned above, one of the major mode l 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 ex tended 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 technica l 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 da ta 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, unde r the baseline, the U.S. DOE’s Annual Energy Outlook for 2007 (AEO 2007) xiv projects biofuel supplies a nd biofuel usage throughout the projection period. Energy prices, such as crude oil prices, gaso line prices and biofuel prices were calibrated to AEO 2008 Reference case prices.

4.3 Policy Assumptions

The CARD models include agri cultural 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 impo rt 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 acreag e 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 po int, 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 undert aken for this analysis, seve ral 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, intern ational corn yields were assu med 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 yiel ds are calculated as a ratio of crop production over area harvested,

data for which is obtained from USDA Fo reign 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 in crease over time by (e.g., 1.14% on average annually in the world over the projection peri od), 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 produ ce the same yields as land already in crop production (i.e., cropland extensification). Th erefore, 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), whic h is consistent with the US DA baseline projections. International corn yields increa sing 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 in crease), and Brazilian soybean yields are approximately 50 bu/acre in 2022 (a 1.1% annual in crease). Soybean hi storical yields are calculated by dividing soybean pr oduction 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 th e EPA rulemaking, the CARD model was modified to limit average dry mill ethanol yields to 2.71 ga llons 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 assume d 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 woul d 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%), a nd then pork (9%) and poultr y (9%) sectors. The

differential use of DG in animal feed is a func tion 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 disc ourage full adoption by animal producers. This is particularly true to non-ru minant animals such as swine and poultry where specific amino acid requirement are critical be cause 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 m onogastric animals which have to use DG in dry form. For these reasons, adoption rate is highes t 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 acid s 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 se rious 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 an imal feed ration has been examined in many studie, including Noll xvii for poultry, Shurson xviii and Thaler xix for swine, Trenkle xx and Tjardes and Wright xxi for cattle, and Schingoethe xxii for dairy. These studies es tablish 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 cat tle feed ration has the hi ghest inclusion rate at

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

In the U.S., livestock and poultry industry co rn 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 technica l (energy and protein co ntent 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 di splacement in ruminant animals - beef and dairy cattle at 0.97, followed by pork at 0.89, and then pou ltry 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 a nd, in turn, international land use changes are based on the response of th e international crop models to commodity price changes. Land use changes depend not only on own price (i.e., gross returns) elas ticities 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 CA RD models do not incorporate limits on land availability, the inclusion of cr oss-price elasticities allows to tal crop area to respond to changes in a consistent manner. Countries respond to incr eases in crop prices by increasing their supply through area expansion and by decreasing their do mestic 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 conversio n 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 futu re returns associated with land conversion. Additional details on exchange rate estimates, in ternational population gr owth, 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 ke y results of the alternative bi ofuel scenarios are presented. The key results for this analysis include cha nge in crop acreage, crop production, livestock production, international trade pa tterns, and commodity prices.13 5.1 Crop Acreage Changes

Table 4 shows crop area changes for the 2022 cr op year as we move from each individual case to the Control Case, which represents renewa ble 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 th e Low Corn Case. Crop area declines slightly for U.S., Argentina, and Brazil in the Biodies el Only Case. Corn area decreases for the Switchgrass Only Case in Argentina, Brazil, a nd China, whereas it increases in the U.S.

Table 4: Changes in Crop Acreage

Control Reference Corn Only Biodiesel On ly 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 milli on 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 th e other cases. Soybean area decreases in Argentina for the Low Corn Ca se, whereas it increases in Brazil. For the Switchgrass Only Case, soybean area dec lines in both Argentina and Brazil.14 For wheat, plantings for 2022 under Control Ca se 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 wh ere wheat area increases.

Australia is projected to have 1.1 million acre s devoted to sugarcane production by 2022. Brazil is projected to have 23.8 million acres; Indi a 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 fo r Import Only and Switchgrass Only Cases, U.S. corn exports are projected to be lower when we move from the indivi dual 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 Cont rol Case and the Reference Case. Soybean net exports increase in the Biodiesel Only and Switc hgrass 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 On ly 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 Brazi l 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 im port demand from the U.S. For almost all cases, net imports of ethanol decline fo r the rest of the countries. The impact of the Switchgrass Only Case is ne gligible for all countries.15 Table 6: Ethanol Trade

Control Reference Corn Only Biodiesel On ly 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 c ountries: 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 majo r 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 dec line. 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 On ly 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 case s except the Switchgrass Case Only, the cost of producing livestock and dairy pr oducts also increases. As seen in Table 8, given biofuel target higher than the Control Case, livestock and dairy pr ices 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 increas e in feedstocks. In contrast, the Import Only Case gives lower rate of increase in the prices of feed grains result ing in lower rate of increase in the cost of livestock and poultry pr oduction, and directly tr anslating to lower ra te of increases in meat and dairy prices.17 Table 8: Impact on Livestock and Dairy Prices

Control Reference Corn Only Biodiesel On ly 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 unde r the scenarios. The commodity prices increase in most of the scenarios and the impact s are largest between the Reference Case and the Control Case, and in the Low Corn Case. Et hanol prices show a 61% increase between the

Reference Case and the Control Case and 57% in crease in the Import Only Case. World prices increase significantly in the Low Corn Ca se, with corn prices going up by 16%. In the Switchgrass Only Case, all commodity prices declin e, albeit to a lesser degree when compared to

most of the other cases.

Table 9: World Price Impacts

Control Case Reference Corn Only Biodiese l 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, “Pol icy and Forecasting Models for the Ch inese, 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 Agri cultural Markets. Much Ado about Nothing?” Journal of International Agricultura l 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 Willo tt, 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, “B ottlenecks, 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 Manufactur ers Association/Food Products Association, National Cattlemen’s Beef Association, National Chicken Council, National Pork Producers, Coun cil, National Oilseed Proce ssors 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 Agricu lture Foreign Agricu ltural Service. http://ffas.usda.gov/scriptsw/attacherep/default.asp19 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 Scien ce, 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 Co rn 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

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