## Technical Report

An Analysis of EPA Renew able Fuel Scenarios

with the FAPRI-CARD In ternational Models

By CARD Staff

December, 20092 1. Introduction

United States Environmental Protection Agen cy (EPA) contracted with the Center for Agricultural and Rural Devel opment (CARD) at Iowa State University of the Food and Agricultural Policy Research Institute (FAPRI) to examine various renewable fuel scenarios using the FAPRI-CARD U.S. and global agricultural models. 1 This report outlines the models and assumptions used for the analysis, the scenar ios performed, and the results derived from the analysis.

2. The FAPRI-CARD Agricultural Models

The FAPRI-CARD Agricultural Models are a set of multi-market, partial-equilibrium, and non-spatial 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 extensiv ely for generating ten-year baseline projections for agricultural markets and for policy analysis ba sed on the baseline proj ections in the last 25 years. The individual commodity models or the full set of m odels 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 Tokgoz et al. (2008), vi Hayes et al. (2009), vii Fabiosa et al. (2009) viii , and Fabiosa et al. (2009). ix The FAPRI models have been used by several organizations (e.g., the U.S. Congress) x to develop baselines as well as for polic y analysis purposes. In particular, it was used to examine world market impacts of alternative proposals during the Uruguay Round Agreement on Agriculture, several Farm Bill rounds, and the current WTO Doha Round. Recently, research on renewable fuels that us ed the FAPRI-CARD model was supported by the USDA and several commodity groups. xi

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 many 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.

1 The Food and Agricultural Policy Research Institute (F APRI) is a unique, dual-university research program, established in 1984 by a grant from the U.S. Congress. The Center for Agricultural and Rural Development (CARD) at Iowa State University develops the international side of the models, and the Center for National Food and Agricultural Policy (CNFAP) at the University of Missouri-Columbia develops the U.S. domestic component. 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 Un iversity 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 FAPRI-CARD Model.3 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

In general, data for supply and utilization of agricultural commodities were obtained from the F.O. Lichts Online Database; xii the Food and Agricultural Orga nization (FAO) of the United Nations (FAOSTAT Online); xiii and the Production, Supply and Distribution View (PS&D) of the U.S. Department of Agriculture (USDA). xiv Macroeconomic data were gathered from various sources, including the International Monetary Fund (IMF) and IHS Global Insight. Additional details on exchange rate estimates, international population growth, international GDP growth, and other factors fo r key countries can be found at: http://www.fapri.iastate.edu/outlook/2009/.

2. 1 Structure of the Domestic Model

The U.S. (domestic) model is part of a customized version of the FAPRI-CARD modeling system (see Figure 1). This system includes models of supply and demand for all important, temperate agricultura l products in major producing and consuming countries. The models are a set of multi-market, partial-equilibrium, econometric, and non-spatial models. Agricultural and trade policies are included in the m odels 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.

2 This may include border duties, export taxes, and domestic price supports.4 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. Figure 2 s hows the basic structure of a crop model. The model solves for the set of prices that brings annual supply and demand into balance in all markets. The U.S. Crops Model is divided in to 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 Sugarcane5 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, and soybean meal, soybean oil and biodiesel from soybeans). For each commodity, a market-c learing price is achie ved by equating quantity supplied to quantity demanded. The market lin kages 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 proce sses, ethanol produced from non-corn sources, and ethanol produced from cellulosic feedstock. Ethanol production is based on production capacity and capacity utilization which are both driven net revenue from ethanol production. The amount of corn that is requ ired for the level of ethanol pr oduction is derived by dividing the ethanol production with the ethanol yield. 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 com ponent, oxygenation requirements and blend mandates determine the ethanol demand. Net trade is based on the rela tive 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 im posed exogenously in the model. These predetermined renewable fuel requirements have been used to estimate feedstocks going into ethanol production and are incor porated into the crop demand m odule to determine the response of the U.S. agricultural sector to the renewable fuel requirements.

In the interest of evaluating the agriculture se ctor impacts of individual fuel requirements, certain adjustments to the FAPRI-CARD modeli ng framework were undertaken. For example, we decoupled the ethanol sub-model from fully interacting with the rest of the FAPRI-CARD models. This enabled the observa tion of agriculture sector impact s of the change in a specific fuel volume, ceteris paribus (i.e., without allowi ng market impacts to affect changes in fuel volumes). Market outcomes would be different if the ethanol sub-model is not decoupled. For example, in the Control Case the U.S. ethanol se ctor still has excess profit in 2022. If arbitrage was allowed in the model this would have pr ovided enough incentive for the sector to expand production, strengthening demand for mo re corn feedstock and resulti ng to higher prices of corn. Holding all else constant, prices of other crops are expected to increase as their supplies tighten with corn becoming more competitive in attracting the limited resource – land. Likewise, livestock prices are expected to follow as feed ing becomes more expensiv e. Mixed influences make it difficult to predict likely outcomes in the world ethanol market. On the one hand, like the other crops, sugar feedstock for ethanol will be more expensive as land is bid-away in favor of corn, but import demand in the U.S. may declin e as more ethanol is domestically produced, on the other.

2.2 Structure of the International Model

The FAPRI-CARD international module is ba sed on multi-market, partial-equilibrium, econometric, non-spatial models. The models c over all major temperate crops, sugar, ethanol, dairy, and livestock and meat products for al l 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 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.6 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, as well as the international Cotton, Dairy, Livestock, Oilseeds, Rice, and Suga r Models. The data sources included in the model are the USDA-Foreign Agricultural Service PSD data set, xv the International Monetary Funds' (IMF) International Financ ial Statistics (IFS) macroec onomic data set, USDA Attaché Reports, xvi and other sources for commodity prices.

The Grains Model interacts with the Da iry and Livestock Models that provide information on animal production that are primar y determinants in the feed demand in the countries, and with Oilseeds and Rice Models that provide information on the relative profitability and area harvested fo r the competing crops. Each count ry sub-Model consists of at least one commodity depending on the relative im portance of the commodity and the relative importance of the country in the world markets as a supplier or a buyer.

Figure 2 shows the basic structure of a crops model. 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 a product of yield and area equations, while cons umption is divided into feed and non-feed7 demand (which can be further disaggregated in to food and industrial use). 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 transmi ssion equation, and the identity is satisfied with one of the variables set as the resi dual (usually the net trade). In other cases, prices are solved to satisfy the identity.

Figure 2: Basic Crop Sector Model Structure

International Prices

Beginning

Stocks

Yield

Area

Domestic Supply Price Transmission

Feed Demand

Food Demand

Ending Stocks Other Demand

Domestic Demand Net Exports

Exports Imports Trade Policy Content

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.8 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. Figure 3 provides a schematic description of the stock of live animals (e.g., breeding inventory) as well as their changes (i.e., flow of animals, e.g., number slaughtered). Figure 4 pr ovides the supply and use of meat products. Both figures 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 releva nt prices, income, and population, while the supply side is driven by relevant prices, cost of pr oduction, and technology. When it is not a residual, trade is expressed as a function of the do mestic 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. The entire Model solves with a market-clearing world price that balances world trade and equates supply and de mand in individual countries.9 Figure 3: Cattle Production and Use

Figure 4: Beef Production and Consumption10 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.

2.4 New Yield Specification

Three specific aspects of the yield specifica tion are new in the current version of the FAPRI-CARD models, includ ing the parameter associated with the trend, sensitivity of yield response to price and cost cha nges, and yield impact of exte nsification. Supporting equation and tables are given in Annex 1.The yield of crop i is expressed as a functi on of trend, ratio of total revenue to variable cost in period t , moving average of the ratio of total revenue to variable cost, and total crop area planted. The first two e xplanatory variables capture the effect of intensification, and the last explanatory variable captures the effect of extensification on yield from the additional new land brought into producti on. The elasticities related to intensification in the case of corn in the U.S. model (specifica lly, the corn belt region) is 0.013 in the short-run and 0.074 in the long-run. The el asticity to additional land brought into production is -0.023.

The same structure of the yield equations in the U.S. model is used for all the other countries covered by the models. Since we are c onstrained by data limitations to estimate the parameters, we use the U.S. parameters as th e base values and calib rate country-specific parameters based on the following assumptions. Firs t, we consider the response of yield to price in the short-run as primarily an allocative adjustme nt and we assume that it is the same across all countries. Second, the response of yield to long- run price trend involves some adjustment in technology. As such, we assume that a country w hose actual yield is far from its yield frontier (or potential) has more opportuni ties to adjust its technology in response to long-term price changes. For example, Brazil’s corn yield of 2. 91 is far from the U.S. yield of 8.55 (which we consider as the frontier for Brazil) so we use th e ratio of the two yield at 2.95 as the adjustment factor to calibrate the price res ponse of corn yield in Brazil to ch anges in long term price trends, giving an elasticity of 0.181 for Br azil. For the yield drag due to the increasing use of marginal lands we use the relative proporti on of available land as basis of establishing a calibration factor. For example, Brazil’s permanent meadow and past ure, which we assume as potential source of new land for crop production is 0.92 of total availa ble land compared to 0.75 that of the U.S. The ratio of the two is 0.81. We use this to calibrate the extensif ication elasticity of -0.23 in the U.S. to only -0.018 in Brazil. The same procedure is applied to the rest of the countries covered by the FAPRI-CARD model. 3

2.5 New Brazil Model

A new Brazil model was developed by CARD in 2009 and integrated into the FAPRI system. 4 Because of Brazil’s importance in world agriculture we provide here an extended

3 In a limited exception, the elasticity suggested by this method is only partially implemented when it results to unrealistic time paths of endogenous variables of interest.

4 The CARD and ICONE (Institute for International Trade Negotiations) collaborated in the development of the new Brazil model, including data collection, model specification, and parameter calibration. The model was integrated to the FAPRI-CARD models and used by CARD for the EPA analysis.11 overview of salient features of the new model. Detailed equations representing new specification features in the Brazil model are given in Annex 2.

Due to its size and geographical locati on, Brazil encompasses widely varying ecosystems, ranging from grassland and crops associ ated with temperate climates in the South to tropical forests in the North and semiarid areas in the Northeast. The different regions also present enormous developmental di sparities in terms of infrastructure, logistics, and strategies available to increase produc tion. Thus, while rapid expa nsion of production of some commodities may only be achieved by taking area away from other agricultural activities in land constrained regions, increases in area used by all activities may be observed in other parts of the country; which points to distinct dynamics in the competition for land across space. Therefore, it is becoming increasingly important to recogni ze the spatial dimensi on of the agricultural expansion as its impacts are likely to be depe ndent on the way in which it occurs as well as on the resources of the location in which production takes place.

We developed a spatially disaggregated partial equilibrium model of Brazilian agricultural production cons tructed at the regional level, inco rporating major crops, biofuels and livestock interacting and competi ng for agricultural resources, in particular land. Outputs from the model include projections of production and utilization variables, and the amount of land allocated to the activities considered. On the cr ops side, we consider co rn (1st and 2nd crop), the soybeans complex (including soybean meal, soybean oil, and biodi esel), the sugarcane complex (including sugar and ethanol), rice, cotton, a nd dry beans (multiple cropping depending on the region). The modeled animal products are beef , pork, poultry, and dairy. In terms of land allocation, and as will be discussed with more de tail in the next section, the area used by a given activity depends on its expected real returns in co mparison to expected returns of activities that compete for the resource. The strategy followed to model livestock activities closely follows the one utilized in the international livestock model of FAPRI, with the additio nal layer of explicitly modeling land used by beef and dairy production. Since not all the regions considered are equally suited for different activities, the compe tition for land is contingent on the location. As such not all activities compete with each other with the same intensity in all regions.

Through the use of spatially disaggregated in formation on historical production activities and natural resource availability, th e model is able to determine the relative profitability of different activities at the local level, which as mentioned will dr ive regional supply curves for relevant commodities and their associated land use. For this modeling effort Brazil is divided into 6 regions; South, South east, Central-West Cerrados, No rth-Northeast Cerrados, Amazon Biome, and Northeast Coast (see figure 5). The m odel is able to capture the regional differences in terms of capabilities and consequences of the expansion. As such, the impacts of land use changes derived from increasing demand for ag ricultural products can be more precisely established. A general overview of th e structure of the model follows.

a. General Model Structure

Since a large goal of the model is to be ab le to project land us e (and changes) at a regional level, production functions are modeled at that spatial scale. However, the demand side of the model is built at th e aggregate (country) level.12 FIGURE 5. Regions in the FAPRI-CARD Brazil Model

The degree of details included in this note varies widely across commodities. More detailed information and formulas are included for the crops sector, as the land use projections is where the largest departures from other models in the system are.

i. Crops

As already described in the previous section, crops curren tly included in the Brazilian regional model are: corn, soybeans, cotton, rice, dr y beans, wheat, barley, and sugarcane. These crops are further separated according whether they are competing for land with each other. Crops that do not compete for land include (a ) a second crop of corn (commonly known as “safrinha” or little season) from the main crop, (b) a second crop of dry beans, and (c) winter crops such as wheat and barley from the other crops.

Prices drive production and cons umption equations in the model. The endogenous prices in this sector of the model are those of: corn, soybeans, soybean meal, soybean oil, cotton, rice, dry beans, wheat, barley, sugar, and ethanol. Pr ices for livestock and dairy products drive the livestock section. A solution for the model is a se t of prices such that supply equals demand in all markets.13 The supply of a crop comes from two main sources, namely production and beginning stocks. Beginning stocks are not explicitly modeled, but derived from the ending stocks of the previous period. Ending stocks are modeled at the “country level” as a demand component. Crop production of a region is the product of the area planted and crop yield. Yields for each crop and region is projected including a time trend, returns to the crops (intensification), and area used for production of major comp eting activities (extensification). The area of activities (crops and pasture) competing for land in space and time is projected following a two step approach. The first step determines the total amount of land to be used by these activities combined. The second step shares that area out to the different activities. An agriculture-wide e xpected returns function is a determining factor in the first st ep. The second step of this procedure follows closely the method published by Holt (1999) 5 consisting of sharing that area out to the different activities, based on own returns, and returns of competing activities.

ii. Livestock

As in the crops section, whereas the supply side of the livesto ck model is divided into 6 regions, the demand is modeled at an aggregate level. The produc ts modeled are broilers, dairy, pork, and beef. The structure of the supply side of livestock depends on the product considered. For the case of poultry, production is modeled dir ectly. For these produc ts, output levels are projected based on their regiona l prices and costs of productio n (mostly feed costs).

In the case of other commodities, such as beef, dairy, and pork, both the stocks of animals and production levels consistent with these stoc ks are modeled. Milk production levels are calculated by multiplying the number of milking cows and milk yield per animal. A slightly more involved structure is used fo r both beef and pork projections.

The stocks of cattle and hogs are mainly driven by the modeled stocks of cows and sows. Given these stocks, and the proj ected birth rates, the crop size (calves and piglets) can be obtained. Adult animals not part of the breeding he rd are allocated to an "other" (cattle or hogs) category. The supplies of beef and pork meat are calculated by multiplying the number of animals slaughtered (given by st ocks and slaughter rates) by th e average slaughter weight.

The modeling of the stock of beef cows in the Brazil model warrants some more explanation, as it differs from the structure used in other countries. The evolution of the beef cattle herd is (in the Brazil mode l) linked to the area of pasturel and available, as beef production is the largest user of pasture. As mentioned, this is a departur e from the other models in the system which do not model pastureland directly. A link between pasture avai lability and the size of the cattle herd is introduced by modeling the stocking rate ( number of cows per hectare of pasture) directly. Drivers of th is stocking rate include returns to beef production, and the lagged stocking rate. Pastureland area varies by region as each region has its own land limit and has unique elasticity of total land to the sector-wide aggregate revenue . Moreover, the set of other enterprises that compete for land and their el asticities vary by region. Both factors drive differential evolution of pastur e intensification across Brazil.

5 Holt, M. T. 1999. "A Linear Approximate Acreage Allocation Model." Journal of Agricultural and Resource Economics 24 (2): 383-397.14 3. Description of Scenario Simulations for EPA

Nine scenarios were run for the EPA analys is. The first established a reference case based on the AEO 2007 projected renewable fuel volumes (labeled “Reference Case”), which includes 12.3 billion gallons (Bgal) of corn et hanol, 0.4 Bgal of biodi esel, and 0.6 Bgal of imported sugarcane ethanol by 2022. In order to match the AEO renewable fuel projections, the ethanol demand module was turned off, and th e specific volume mandates were imposed on the FAPRI-CARD models. The renewabl e fuel 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 renewable fuel requirements. In addition, the dry and wet mill yiel ds 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 re quirements and the dry and wet mill yields.

The second scenario (labeled “Control Case”) incorporates the rene wable fuels that EPA assumed would contribute to meet the EISA requirements, which include 15 Bgal of corn ethanol, 1 .7 Bgal of biodiesel, and 2.24 Bgal of imported ethanol by 2022. 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 2.7 Bgal of corn ethanol (labeled “Corn Only Case”), the biodiesel case analyzed only the increa se of 0.8 Bgal of biodiesel (labeled “Biodiesel Only Case”). Table 4 outlin es the ethanol and biodiesel volumes for these four scenarios. The fifth scenario was the im ported ethanol case, which analyzed only an increase of 1.6 Bgal of imported ethanol (labeled "Import Only"). 6

The FAPRI-CARD models do not include ce llulosic ethanol sources; however we attempted to approximate the impact of an increase in switchgra ss ethanol by modeling a scenario that displaces area for agricultural crops 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. The number of acres dive rted from agricultural crops modeled in the U.S. Crops Model was determined from the output of the FASOM model. 7

6 The Import Only Case has the Control Case volumes with Reference Case volumes for imported ethanol.

7 Beach, Robert; McCarl, Bruce, U.S. Agricultural and Forestry Impacts of the Energy Independence and Security Act: FASOM Results and Model Description , RTI International, January, 2010.15 Table 4: Renewable Fuel Specific Mandates

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Reference Case

Ethanol Dry Mill Corn Ethanol 9.33 9.50 9.61 9.71 9.78 9.87 9.92 10.05 10.21 10.39 10.62 10.84 11.04
Wet Mill Corn Ethanol 1.16 1.19 1.20 1.21 1.22 1.23 1.24 1.25 1.28 1.30 1.21 1.23 1.25 Imported Ethanol 0.29 0.16 0.18 0.19 0.20 0.39 0.51 0.53 0.54 0.58 0.60 0.63 0.64 Cellulosic Ethanol (Offline) 0.12 0.19 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25
Total Ethanol 10.91 11.03 11.24 11.37 11.45 11.74 11.92 12.08 12.28 12.52 12.67 12.94 13.18
Biodiesel Soybean Oil Biodiesel 0.08 0.09 0.09 0.09 0.10 0.10 0.10 0.10 0.11 0.11 0.11 0.12 0.12
Yellow Grease Biodiesel 0.24 0.24 0.24 0.24 0.14 0.15 0.25 0.25 0.26 0.26 0.26 0.26 0.27
Total Biodiesel 0.32 0.33 0.33 0.33 0.23 0.25 0.35 0.36 0.36 0.37 0.37 0.38 0.39 Control Case

Ethanol Dry Mill Corn Ethanol 10.02 10.80 11.51 12.09 12.74 13.42 13.61 13.61 13.61 13.61 13.61 13.61 13.61
Wet Mill Corn Ethanol 1.22 1.28 1.31 1.33 1.35 1.37 1.39 1.39 1.39 1.39 1.39 1.39 1.39 Imported Ethanol 0.29 0.16 0.18 0.19 0.20 0.39 0.63 1.07 1.51 1.96 1.88 1.81 2.24 Cellulosic Ethanol (Offline) 0.03 0.08 0.15 0.31 0.54 0.92 1.31 1.69 2.15 2.61 3.23 4.15 4.92
Total Ethanol 11.56 12.31 13.16 13.92 14.82 16.10 16.93 17.76 18.67 19.57 20.11 20.96 22.16
Biodiesel Soybean Oil Biodiesel 0.42 0.44 0.52 0.60 0.62 0.66 0.66 0.66 0.66 0.66 0.66 0.66 0.66
Corn Oil Biodiesel 0.08 0.14 0.22 0.30 0.39 0.49 0.51 0.54 0.57 0.60 0.62 0.65 0.68 Fats, Oils, Greases Biodiesel 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 Cellulosic Diesel (Offline) 0.04 0.10 0.20 0.41 0.71 1.22 1.73 2.24 2.85 3.46 4.28 5.50 6.52 Algae/Cam (Offline) 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.02 0.03 0.04 0.06 0.08 0.10
Total Biodiesel 0.91 1.07 1.32 1.69 2.10 2.75 3.29 3.84 4.49 5.14 6.00 7.27 8.34 Corn Only Case

Ethanol 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Dry Mill Corn Ethanol 8.92 9.27 9.56 9.67 9.74 9.83 9.90 10.02 10.21 10.40 10.53 10.76 10.97
Wet Mill Corn Ethanol 1.22 1.28 1.31 1.33 1.35 1.37 1.39 1.39 1.39 1.39 1.39 1.39 1.39 Imported Ethanol 0.29 0.16 0.18 0.19 0.20 0.39 0.63 1.07 1.51 1.96 1.88 1.81 2.24
Total Ethanol 10.44 10.70 11.05 11.19 11.29 11.59 11.91 12.48 13.11 13.75 13.81 13.97 14.61
Biodiesel Fats, Oils, Greases Biodiesel 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38
Corn Oil Biodiesel 0.07 0.12 0.18 0.24 0.30 0.36 0.38 0.41 0.44 0.46 0.49 0.52 0.56 Soybean Oil Biodiesel 0.42 0.44 0.52 0.60 0.62 0.66 0.66 0.66 0.66 0.66 0.66 0.66 0.66
Total Biodiesel 0.86 0.95 1.08 1.22 1.30 1.40 1.42 1.45 1.48 1.50 1.53 1.56 1.60 Biodiesel Only Case

Ethanol 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Dry Mill Corn Ethanol 10.02 10.80 11.51 12.09 12.74 13.42 13.61 13.61 13.61 13.61 13.61 13.61 13.61
Wet Mill Corn Ethanol 1.22 1.28 1.31 1.33 1.35 1.37 1.39 1.39 1.39 1.39 1.39 1.39 1.39 Imported Ethanol 0.29 0.16 0.18 0.19 0.20 0.39 0.63 1.07 1.51 1.96 1.88 1.81 2.24
Total Ethanol 11.53 12.23 13.01 13.61 14.29 15.17 15.63 16.07 16.51 16.96 16.88 16.81 17.24
Biodiesel Fats, Oils, & Greases Biodiesel 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38
Corn Oil Biodiesel 0.08 0.14 0.22 0.30 0.39 0.49 0.51 0.54 0.57 0.60 0.62 0.65 0.68 Soybean Oil Biodiesel 0.082 0.086 0.089 0.092 0.095 0.098 0.101 0.104 0.107 0.110 0.113 0.117 0.120
Total Biodiesel 0.54 0.61 0.69 0.77 0.86 0.96 0.99 1.02 1.06 1.09 1.12 1.15 1.1816 Finally, three more scenarios were run as a sensitivity st udy, with higher yield assumptions for corn and soybeans in the U. S. and the top five international producers (Argentina, Brazil, China, the EU and Mexico). The high-yield assumptions were implemented in the Control Case, the Corn Only Case and th e Biodiesel Only Case (labeled “Control Case with High Yield”, “Corn Only Case with High Yi eld”, and “Biodiesel On ly with High Yield”, respectively). Corn yields were assumed to be 27% higher and soybeans 30% higher by 2022 relative to the standard Control Case. The higher yield is achieve d with a linear improvement in technology. As Table 5 shows, the higher corn and soybean yields were implemented by region in the U.S. and Brazil.

Table 5: Corn and Soybean Yiel d Increases in High-Yield Scenarios

Corn Corn Soybeans Soybeans Control Case High Yield Control Case High Yield
United States Corn Belt bu/ac 196.67 249.71 50.64 66.01 Central Plains bu/ac 168.35 213.75 48.93 63.78 Delta States bu/ac 162.56 206.41 41.67 54.32 Far West bu/ac 204.03 259.06 0.00 Lake States bu/ac 185.09 235.01 45.04 58.71 Northeast bu/ac 143.54 182.25 41.18 53.68 Northern Plains bu/ac 157.52 200.00 37.51 48.90 Southeast bu/ac 151.97 192.96 37.74 49.19 Southern Plains bu/ac 150.80 191.47 25.61 33.38
Brazil First crop corn Region 1 mt/ha 5.83 7.40 2.74 3.58 Region 2 mt/ha 6.41 8.14 3.32 4.33 Region 3 mt/ha 6.57 8.34 3.17 4.14 Region 4 mt/ha 2.61 3.32 3.56 4.64 Region 5 mt/ha 1.10 1.40 Region 6 mt/ha 2.20 2.79 3.53 4.60 Brazil mt/ha 4.66 5.92 3.11 4.05 Corn Safrinha Region 1 mt/ha 5.21 6.61 Region 2 mt/ha 3.83 4.86 Region 3 mt/ha 5.07 6.44 Region 4 mt/ha 5.03 6.38 Region 5 mt/ha Region 6 mt/ha 1.63 2.06 Brazil mt/ha 4.82 6.12 Argentina mt/ha 8.40 10.67 3.10 4.04 China mt/ha 6.42 8.15 2.01 2.62
EU mt/ha 6.94 8.81
Mexico mt/ha 3.50 4.45

4. Assumptions and Model Adjustments

The FAPRI-CARD models were customized to simulate EISA according to EPA’s specifications. Customized scenarios were de veloped by making certain model adjustments and adding assumptions about renewable fuel pr oduction, which are described below.17 4.1 Model Extension

As mentioned above, one of the major mode l adjustments was to extend the projection period of the models. The FAPRI-CARD models we re originally built to project out over a tenyear time horizon. For this pr oject, the models were extended to generate projections until the 2022 calendar and marketing years when all the Energy Independence and Security Act (EISA) RFS2 levels are reached. This necessitated extend ing all the behavioral equations in the models, as well as the exogenous variab les including macroeconomic pr ojections and all the policy variables. Also, the trend was extended whic h affected some variab les reflecting technical relationships such as crop yields. Calibration and solution of the models remain the same.

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 US DA, IHS Global Insight, F.O. Licht, and the Renewable Fuels Association (RFA). For the Reference Case, renewable fuel volumes were calibrated to the U.S. DOE’s Annual Ener gy Outlook for 2007 (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 (AEO 2007) xvii projects reneweable fuel supplies and renewable fuel usage throughout the projection period. Energy prices, such as crude oil prices, gasoline prices and renewable fuel prices were calibrated to AEO 2009 Reference case prices, April Release.

4.3 Policy Assumptions

The FAPRI-CARD models include agricultu ral and energy policies such as trade agreements, tariffs, and subsidies. Generally the FAPRI-CARD models ex tend provisions in the 2002 Farm Bill through the end of the period of analysis.

Bioenergy mandates continue to be key driv ers in the current market outlook. Major energy policies included in the baseline are the U.S. Energy In dependence and Security Act (EISA) of 2007, the 2003 Renewable Fuels Directiv e of the EU, fuel mandates and regulations for Argentina and Brazil, Indonesia’s newly impl emented biodiesel consumption mandate, and producer incentives in Canada. Specifically, in the modeling conducted for EPA, agricultural and energy policies in place as of January 2009, we re maintained through 2022. Policies set to sunset, such as the $0.54/gallon U.S. ethanol impo rt tariff, the $0.45/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 co nsistently been renewed in the past. The European Union moved from the indicative ta rgets proposed under directive 2003/30/EC toward a mandatory biofuel usage of 10% of the energy us ed for transport by 2020. In the Control Case, total EU biofuels consumption from first genera tion feedstock reaches 4% of transport fuels in 2022.18 4.4 Yields of Key Crops

As described in section 2.4, the trend parame ters in the yield equation were updated with recent data up to 2008 in most commodities. This trend parameter captures the growth in yield under average weather conditions. The models in th is analysis also include price-induced yield changes from intensification of agricultural practic es and some yield drag impact when marginal land is brought into production. Three new scen arios run as a sensitiv ity analysis assumed significant yield improvements in so me crops for several countries.

4.5 Key Commodity By-Products

To be consistent with other areas of th e EPA rulemaking, the average dry mill ethanol yield was set to 2.71 gallons of pure ethanol pe r bushel and the average wet mill ethanol yield was set to 2.50 gallons. The models also assu med that the distillers grains not consumed domestically in the U.S. would be exported to the international markets. The amount of the Distillers Grains (DG) that would be expor ted changed depending on each scenario.

Use of DG in animal feed is highest in the b eef cattle sector (62 to 65% share), followed by the dairy sector (11 to 13%), and then pork (1 2 to 13%) and poultry (10 to 13%) 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 highest in dairy cattle at 64 to 93%, followed by beef cattle at 53 to 79%, then pork at 28 to 43%, and poultry at 22 to 37%.

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. 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 studies, including Noll xviii for poultry, Shurson xix and Thaler xx for swine, Trenkle xxi and Tjardes and Wright xxii for cattle, and Schingoethe xxiii for dairy. These studie s establish inclusion rates that avoid compromising animal production perfor mance (i.e., reduced daily weight gain), as well as final product quality (i.e., meat quality). Beef cattle feed ration has the highest inclusion19 rate at 41 to 50%. The maximu m inclusion rate for dairy foll ows at 27 to 30%, for hogs 22 to 25%, and for poultry 22 to 25%.

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, cor n, and soymeal) consider ations. As described in the previous paragraphs, there are currently se veral challenges associated with the quality and transport of DGs that prevent higher inclusion ra tes in animal feed. However, as DG markets mature, increases in efficiency, quality, and trans port of DGs are likely to improve. With recent feeding trials showing improvement in feed e fficiency in the use of DGS, for the analysis conducted for EPA, it assumed that DG efficiency gains will eventually reach the potential identified in these trials. Specifically, we assu me feed efficiency gains when DG is used in ruminants but none when used in monogastric anim als. Cost minimization of feed ration with DG suggests that DG can displace corn as a s ource of energy from 1.028 in 2009 and increasing to 1.196 by 2022 in beef cattle, from 0.564 and increas ing to 0.731 in dairy cattle, 0.89 in swine, and 0.79 in poultry. Displacement of soymeal as a source of protein in creases from 0.488 in 2009 to 0.633 by 2022 in dairy cattle, 0.110 for pork, and 0.21 for poultry.

Furthermore, we assumed that the proporti on of dry mills (in terms of volume) that extract corn oil from the DGs increases from 10 to 70%, and the proportion of dry mills that adopt fractionation increases from 3 to 20%, such that by 2022 only 10% of the dry mills do not produce corn oil as a by-product, based on the EP A’s engineering analysis conducted as part of the RFS2..

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. The new Brazil model allows direct inclusion of limits on land. The rest of the countries do not incorporate limits on la nd availability, but th e inclusion of crossprice elasticities allows total crop area to respond to changes in a consistent manner. Countries respond to increases in crop prices by increa sing their supply through area expansion and by decreasing their domestic demand. Which crop ar ea increases is determined by the relative prices of each crop to competing crops whic h are country specific. FAPRI-CARD does not explicitly model land co nversion costs, however the internat ional production supply curves used by the FAPRI-CARD model implic itly take into account conve rsion costs. FAPRI-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.20 5. Summary of Results

In this section, some of the key results of the alternative renewable fuel scenarios are presented. The key results for this analysis include change in crop acreage, crop production, livestock production, international trad e patterns, and commodity prices.

5.1 Crop Acreage Changes

Table 6 shows crop area changes for the 2022 cr op year as we move from each individual case to the Control Case, which represents high er renewable fuel volumes as outlined in the EISA RFS2 requirements. This means that the ta ble shows percentage cha nge as we move from Reference Case to the Control Case, Corn Only Case to the Control Case, etc. The only exception is with respect to Switchgrass Only Case where we move from Control Case to Switchgrass Only case with its higher renewable fuel volume. As such, Switchgrass Only Case results are discussed separate from all the other cases.

Under the Control Case, corn acreage for the 2022 crop year is 80.9 million acres in the U.S., 8.4 million acres in Argentina, 36.7 million acres in Brazil, and 79.5 million acres in China. In general, corn area increases for all these c ountries as we move to Control Case from the Reference Case, the Corn Only Case, and the Impor t Only Case. The exception is that corn area in Brazil declines in the Import Only Case as sugar becomes more comp etitive with the higher import demand of sugar ethanol in the U.S. Also, crop area declines slightly for U.S., Argentina, and Brazil in the Biodiesel Only Case. As we move from the Control Case to the Switchgrass Only Case, area increases for Argentina, Brazil, and China as land for crop production decreases in the U.S. to plant for more switchgrass.

Corn area declines for the U.S., Argentina, Brazil, and China from the standard Control Case to the Control High Yield Ca se (table 6A). There is the sa me pattern of higher corn area in all countries of interest as we move from the Corn Only High Yield Case to the Control High Yield Case, and lower corn area when we move from the Biodiesel Only High Yield Case to the Control High Yield Case.21 Table 6. Changes in Crop Acreage

Table 6A. Changes in Crop Acreage with High Yield Control Reference Corn Only Biodiesel Only Import Only Switchgrass Only

Case Case Case Case Case Case

Corn (000 acres) (percent) (percent) (percent) (percent) (percent) United States 80,892 2.24 1.87 -0.25 0.17 -1.72
A rgentina 8,379 1.25 1.07 -0.13 0.07 0.51 Brazil 36,717 2.72 3. 63 -0.26 -1.21 1.42 China 79,469 0.55 0.46 -0.05 0.03 0.26

Soybeans United States 77,425 -1.0 9 -1.08 0.42 -0.06 -4.53
A rgentina 52,737 -0.21 -0.17 0.11 -0.09 0.06 Brazil 70,225 1.09 0. 12 1.92 0.04 3.07

Wheat United States 48,060 -0.45 -0.36 -0.19 -0.04 -3.33
A rgentina 13,615 -0.23 -0.17 -0.08 -0.01 0.07
A ustralia 33,203 0.00 0.02 -0.01 -0.01 0.05 Canada 22,046 -0.15 -0.04 -0.14 -0.03 0.11 Russia 65,648 0.01 0. 00 0.02 0.01 0.11

Sugarcane
A ustralia 1,011 0.53 0.05 0.02 0.46 0.04 Brazil 28,213 3.62 -0.11 -0.03 3.77 -0.07 India 12,825 0.12 -0.03 -0.02 0.16 -0.06 Thailand 2,718 0.02 -0.05 0.00 0.08 -0.02
Note: These are percentage changes calculated based on moving from each case to the Control Case except the Swichgrass Only Cas e.

Control Corn Only Biodiesel Only

Case Case Case

Corn (000 acres) (percent) (percent) United States 65,737 2.42 -0.07
A rgentina 7,980 1.33 -0.05 Brazil 31,007 3.44 -0.06 China 76,897 0.55 -0.01

Soybeans United States 76,648 -1.09 0.34
A rgentina 50,895 -0.15 0.09 Brazil 36,112 0.77 3.79

Wheat United States 51,611 -0.30 -0.24
A rgentina 13,827 -0.25 -0.12
A ustralia 33,108 0.00 -0.02 Canada 22,772 -0.13 -0.20 Russia 65,305 0.01 0.02

Sugarcane
A ustralia 1,007 0.04 0.02 Brazil 28,265 -0.07 -0.04 India 12,952 -0.03 -0.01 Thailand 2,736 -0.05 0.00
Note: These are percentage changes calculated based on moving from each case to the Control Case except the Switchgrass Only Ca se.22 Soybean plantings for the 2022 crop year under the Control Case are 77.4 million acres in the U.S., 52.7 million acres in Argentina and 70.2 million acres in Brazil. Soybean area increases for Brazil in the Reference, Corn Onl y, and Import Only Cases. In contrast, soybean area decreases in Argentina for all these cases. S oybean area increases for all three countries in the Biodiesel Only Case. As in the case for corn acreage, the impact of the Import Only Case is lower relative to the other cases. As we move from the Control Case to the Switchgrass Only Case, soybean area increases in both Argentina and Brazil bu t decreases in the U.S.

For wheat, plantings for 2022 under Control Ca se are 48.1 million acres in the U.S., 13.6 million acres in Argentina, 33.2 million acres in Australia, 22.0 million acres in Canada, and 65.6 million acres in Russia. Wheat area declines fo r most of the countries in almost all of the Cases. The exceptions are Australia and Russia for the Reference and Corn Only; and Russia for Biodiesel Case and Import Only Case. As we move from the Control Case to the Switchgrass Only Case, wheat area increases in Argentina, Australia, Canada, and Russia, but decreases in the U.S.

Australia is projected to have 1.0 million acre s devoted to sugarcane production by 2022. Brazil is projected to have 28.3 million acres; Indi a is projected to have 12.9 million acres; and Thailand is projected to have 2.7 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 3% in both the Reference and Import Only Cases.

The direction of impact for soybeans, wheat , and sugarcane in the High Yield sensitivity scenarios is the same as that of the standard scen arios with slight differences in magnitudes in the Corn Only and Biodiesel 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 7. U.S. corn exports are lower in the Reference and Corn Only Cases, whereas, they are higher in the Biodiesel Only, and Import Only Cases. A similar pattern holds for U.S. sorghum net exports. In all the cases, U.S. wheat net exports de cline with the largest impact seen between the Control Case and the Re ference Case. Soybean net exports decreases in all cases. The impact on soybean oil net trade is the largest with a decline of 26% in the Biodiesel Only Case and followed by the Referen ce Case. On the other hand, soybean meal net exports increase by more than 3% in the Referenc e and Corn Only Cases. Trade in all selected commodity of interest decline as we move from the Control Case to the Switchgrass Only Case.

In general, a similar pattern of trade changes is shown in the High Yi eld sensitivity cases, where U.S. net trade for commodities of interest declined with the exception of soybean meal and soybean oil in the Corn Only Case (table 7). In the Biodiesel Only Case, the decline in net trade now includes corn and sorghum as well.23 Table 7. U.S. Trade Patterns

Table 7A. U.S. Trade Patterns with High Yield

5.3 Ethanol Trade Changes

Projected ethanol trade and the changes under the scenarios for the year 2022 are shown in Table 8. 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 renewable fuel requirements have rather different impacts for ethanol trade. The largest impacts can be seen in the Reference and Import Only Ca ses, where U.S. net imports increase by nearly 236%. Brazil increases its net exports by 37.8% to meet the increased import demand from the U.S. in both cases. The impact of the Corn Only and Biodiesel Only, and Switchgrass Only Cases are negligible for all c ountries since they are driven mostly by second round impacts to changes in feedstock prices. For almost all cases, ne t imports of ethanol dec line for the rest of the countries, especially in the Reference and Import Only Cases.

With no change in the U.S. ethanol net imports for both the Corn Only and Biodiesel Only High Yield Cases, the impacts shown in table 8A are second round impacts, responding to changes in feedstock prices. As in the earlier scenarios, in general, net imports of ethanol decline. Control Reference Corn Only Biodiesel Only Import Only Switchgrass Only Case Case Case Case Case Case

(TMT) (percent) (percent) (percent) (percent) (percent) Corn 55,034 -15.83 -15.04 0.03 1.33 -8.23 Sorghum 5,589 -1.70 -1.15 0.04 0.01 -6.09 Wheat 26,300 -1.53 -1.22 -0.19 -0.11 -5.88 Soybeans 28,091 -2.99 -2.29 -0.43 -0.11 -11.36 Soybean Meal 15,270 3.63 3.59 -0.34 -0.05 -2.95 Soybean Oil 4,051 -6.27 4.12 -26.28 -0.05 -2.82 Note: These are percentage changes calculated based on moving from each case to the Control Case except the Switchgrass Only Ca se.

Control Corn Only Biodiesel Only

Case Case Case

(TMT) (percent) (percent) Corn 43,046 -16.32 -0.82 Sorghum 7,429 -2.01 -0.29 Wheat 32,356 -0.96 -0.32 Soybeans 46,889 -1.93 -0.41 Soybean Meal 17,972 3.37 -2.15 Soybean Oil 5,156 3.78 -21.87 Note: These are percentage changes calculated based on moving from each case to the Control Case except the Switchgrass Only Ca se.24 Table 8. Ethanol Trade

Table 8A. Ethanol Trade with High Yield

5.4 Vegetable Oil Trade Changes

The projected vegetable oil trade figures and changes are shown in Table 9. 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 a nd Brazil fill the gap, while China, EU, and India reduce their net imports in both the Reference and the Biodiesel Only Cases. The opposite happens in the Corn Only Case where U.S. net exports increase, displacing exports from Argentina and Brazil, while China, EU, and India absorbs some of the increase in net exports. 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 Import Only Cases, while U.S. imports decline including in the Corn Only Case. The impact on sunflower and palm oil trade is very small in a ll cases with the exception of the Biodiesel Only Case where the EU’s net imports of sunflower oil decreased. As we move from the Control Case to the Switchgrass Only Case, U.S. exports of of soybean oil declines. The exports from Argentina and Brazil increase, while the im ports to China, EU, and India decline.

The impact in the High Yield Cases in table 9A follow exactly the same pattern as in the standard cases. Control Reference Corn Only Biodiesel Only Import Only Switchgrass Only Case Case Case Case Case Case

(TMT) (percent) (percent) (percent) (percent) (percent) U.S. -2,146 235.50 0. 00 0.00 235.50 0.00 Brazil 5,331 37.79 0. 02 0.00 37.77 0.02 China -142 -5.28 0.54 -0.03 -5.82 0.46
EU -714 -2.30 -0.03 -0.01 -2.24 -0.02
India -190 -7.38 -0.13 -0.06 -7.20 -0.20 Japan -580 -0.54 -0.01 0.00 -0.52 -0.02 South Korea -377 -0.43 -0.01 0.00 -0.42 -0.01
Note: These are percentage changes calculated based on moving from each case to the Control Case except the Switchgrass Only Ca se.

Control Corn Only Biodiesel Only

Case Case Case

(TMT) (percent) (percent) U.S. -2,146 0.00 0.00 Brazil 5,302 0.02 0.00 China -126 0.62 0.04
EU -712 -0.03 0.00
India -192 -0.13 -0.08 Japan -581 -0.01 -0.01 South Korea -377 -0.01 0.00
Note: These are percentage changes calculated based on moving from each case to the Control Case except the Switchgrass Only Ca se.25 Table 9. Vegetable Oil Trade Impacts

Table 9A. Vegetable Oil Trade Impacts with High Yields Control Reference Corn Only Biodiesel Only Import Only Switchgrass Only

Case Case Case Case Case Case

Soybean Oil (TMT) (percent) (percent) (percent) (percent) (percent)
A rgentina 7,976 0.19 -0.23 1.24 0.00 0.11 Brazil 1,921 3.13 -2.76 27.55 -0.06 0.32 U.S. 4,051 -6.27 4.12 -26.28 -0.05 -2.82 China -4,535 -1.78 0.97 -8.38 -0.04 -0.93 EU -1,613 -1.68 0.16 -7.33 0.02 -0.82 India -2,200 -0.74 0.39 -3.76 -0.03 -0.52

Rapeseed/Canola Oil Canada 2,191 -0.49 -0.12 0.04 0.01 0.03
EU -538 41.59 -5.14 2.57 0.24 0.66 U.S. -1,752 -18.29 -0.28 -1.26 0.00 -0.08

Sunflower Oil
A rgentina 1,809 -0.20 -0.08 0.07 0.00 -0.02 Other CIS 993 -0.14 0.03 -0.67 0.00 -0.03 EU -1,613 0.20 -0.32 1.39 0.02 0.10

Palm Oil Malaysia 20,424 -0.06 -0.06 0.42 0.00 0.04 Indonesia 25,275 -0.01 - 0.03 0.23 0.00 0.02 China -10,914 -0.02 -0.02 0.19 0.00 0.02 EU -4,746 -0.08 -0.09 0.70 0.01 0.06 India -7,509 -0.03 - 0.04 0.34 0.00 0.04
Note: These are percentage changes calculated based on moving from each case to the Control Case except the Switchgrass Only Ca se.

Control Corn Only Biodiesel Only

Case Case Case

Soybean Oil (TMT) (percent) (percent)
A rgentina 8,031 -0.26 1.31 Brazil 2,203 -2.44 23.67 U.S. 5,156 3.78 -21.87 China -5,136 0.93 -7.52 EU -1,716 1.12 -6.40 India -2,532 0.37 -3.07

Rapeseed/Canola Oil Canada 2,175 -0.04 0.13
EU -438 -0.41 10.24 U.S. -1,791 -0.37 -1.25

Sunflower Oil
A rgentina 1,815 -0.03 0.10 Other CIS 1,001 0.05 -0.63 EU -1,580 -0.09 1.67

Palm Oil Malaysia 20,261 -0.03 0.44 Indonesia 25,169 -0.01 0.24 China -10,867 -0.01 0.20 EU -4,687 -0.05 0.74 India -7,456 -0.02 0.35
Note: These are percentage changes calculated based on moving from each case to the Control Case exept the Switchgrass Only Cas e.26 5.5 World Food Consumption Changes

Due to the increase in crop prices in almost all cases, the cost of producing livestock and dairy products also increases. As seen in Table 10, given the highe r renewable fuel target in the Control Case, livestock and dairy prices increase in all cases except the Biodiesel Only Case. Under the biodiesel case, feed prices become favorable as more of the by-product soybean meal is produced to meet higher biodiesel targets. As we move from the Control Case to the Switchgrass Case, feed grains and oilseed meal supplies tightens raising the price of meat and dairy products. With the Import Only Case la rgely impacting the suga r sector, th e rate of increase in the prices of feed grains is small re sulting in lower rate of increase in the cost of livestock and poultry production, and di rectly translating to lower ra te of increases in meat and dairy prices.

The direction of impacts in the High Yield sens itivity cases in table 10A is the same as the standard cases, but with slightly larger magnitudes.27 Table 10. Impact on Livestock and Dairy Prices

Table 10A. Impact on Livestock a nd Dairy Prices with High Yield

5.6 World Grain, Oilseed, and Re newable Fuel Price Changes

Table 11 outlines the world pr ice impacts under the scenarios. The commodity prices increase in most of the scenarios. The imp acts are largest in the Reference and Import Only Cases. Ethanol prices show an 8.5% increase between the Reference Case and the Control Case and 8.3% increase in the Import Only Case. Biodiesel price increases in the Biodiesel Only Case by 2.9%. All commodity prices in creases as we move from the Control Case to the Switchgrass Only Case. 8

Table 11A shows that the same pattern holds in the High Yield cases as in the standard

cases.

8 The other industrial rapeseed oil use in the U.S. is higher in the reference case relative to the control case. As a result, the projected rapeseed oil price is higher in the reference case in the last couple of year s of the projection period. Since rapeseed oil is the main feedstock used for biodiesel production in the EU, and with our world biodiesel price quoted as a Central Europe FOB pr ice, it is also higher in the reference case. Control Reference Corn Only Biodiesel Only Import Only Switchgrass Only

Case Case Case Case Case Case

($ per MT) (percent) (percent) (percent) (percent) (percent)
Bee f 2,821 0.38 0.12 -0.34 0.06 0.72 Pork 1,448 1.58 1.24 -1.19 0.15 1.30 Broile r 2,462 0.93 0.78 -1.84 0.12 1.23 Butte r 2,595 0.14 0.10 -0.11 0.01 0.17 Cheese 3,617 0.18 0.16 -0.23 0.02 0.24 Non-Fat Dry Milk 2,816 0.37 0.23 -0.28 0.02 0.46 Whole Milk Powde r 3,018 0.20 0.15 -0.22 0.02 0.20 Note: These are percentage changes calculated based on moving from each case to the Control Case except the Switchgrass Only Ca se.

Control Corn Only Biodiesel Only

Case Case Case

($ per MT) (percent) (percent)
Bee f 2,501 0.40 -0.53 Pork 1,065 1.94 -1.52 Broile r 1,822 1.58 -2.97 Butte r 2,536 0.10 -0.10 Cheese 3,459 0.23 -0.21 Non-Fat Dry Milk 2,681 0.19 -0.29 Whole Milk Powde r 2,919 0.15 -0.20
Note: These are percentage changes calculated based on moving from each case to the Control Case except the Switchgrass Only Ca se.28 Table 11. Impact on World Crops and Biofuel Prices

Table 11A. Impact on World Crops a nd Biofuel Prices with High Yield

5.7 For Further Information

See data tables included in the docket. Control Reference Corn Only Biodiesel Only Import Only Switchgrass Only

Case Case Case Case Case Case

($ per MT) (percent) (percent) (percent) (percent) (percent) Corn 204 3.07 2.32 0.06 0.27 2.36 Sorghum 208 1.69 1.24 0.18 0.21 2.68 Barley 194 1.46 1.10 0.11 0.16 1.53 Wheat 269 1.08 0.77 0.18 0.15 1.94 Soybeans 402 0.86 0.22 0.89 0.17 2.01 Rapeseed 418 -0.69 -0.71 1.10 0.14 0.85 Sunflowe r 483 0.23 0.15 1.07 0.12 1.14

($ per gallon)
Ethanol 2.15 8.54 0.11 0.05 8.32 0.22 Biodiesel 6.78 -1.34 -0.77 2.87 0.05 0.28
Note: These are percentage changes calculated based on moving from each case to the Control Case except the Switchgrass Only Ca se.

Control Corn Only Biodiesel Only

Case Case Case

($ per MT) (percent) (percent) Corn 114 4.23 0.60 Sorghum 145 2.38 0.44 Barley 152 1.39 0.25 Wheat 217 1.02 0.35 Soybeans 216 0.66 1.70 Rapeseed 361 0.13 2.31 Sunflowe r 380 0.64 1.59

($ per gallon)
Ethanol 2.12 0.12 0.08 Biodiesel 6.43 -0.21 3.69
Note: These are percentage changes calculated based on moving from each case to the Control Case except the Switchgrass Only C ase.29 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, “P olicy 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 Livest ock 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 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 Hayes, Dermot, Bruce Babcock, Jacinto Fabiosa, Simla Tokgoz, Amani Elobeid, Tun-Hsiang Yu, Fengxia Dong, Chad Hart, Wyatt Thompson, Seth Meyer, Edward Chavez, and Suwen Pan. “Biofuels: Potential Production Capacity, Effects on Grain and Livestock Sectors, and Implications for Food Prices and Consumers.” Journal of Agricultural and Applied Economics, 41(2) August 2009: 465-491.

viii Fabiosa, Jacinto., John C. Beghin, Fengxia Dong , Amani Elobeid, Simla Tokgoz, Tun-Hsiang Yu. “Land Allocation Effects of the Global Ethanol Surge: Predictions from the International FAPRI Model.” Land Economics

, Forthcoming

ix Fabiosa, J.F., John C. Beghin, Fengxia Dong, Amani Elobeid, Simla Tokgoz, and Tun-Hsiang Yu. 2010. “The Global Bioenergy Expansion: How Large Are the Food–Fuel Trade-Offs?” Part II – Chapter 8. pp 113-132. Natural Resource Management and Policy Series: Handbook of Bioenergy Economics and Policy, , Vol. 33 Khanna, Madhu; Scheffran, Jürgen; Zilberman, David (Eds.) 2010, XIV, 439 p. Springer, USA

x 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 A ll U.S. Program Crops,” Briefing Paper 01-BP 33

xi Tokgoz, S. A. Elobeid, J. Fabiosa, D. Hayes, B. Babcoc k, 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 Associatio n, National Cattlemen’s Beef Association,30 National Chicken Council, National Pork Producers, Coun cil, National Oilseed Processors Association, National Turkey Federation, and the North American Millers Association.

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

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

xiv United States Department of Agriculture. Foreign Agricultural Service Production, Supply, and Distribution database. http://www.fas.usda.g ov/psdonline/psdHome.aspx

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

xvii 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

xviii 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.

xix Shurson, J. n.d. “The Advantages of Using Corn Dis tillers Dried Grains with Solu bles in Swine Diets.” Fact sheets, Dept. of Animal Science, University of Minnesota. http://www.ksgrains.com/ ethanol/DDGSFacts.pdf

xx Thaler, B. 2002. “Use of Distillers Dried Grai ns 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

xxi Trenkle, A. n.d. “The Advantages of Using Corn Distille rs Grains with Solubles in Finishing Beef Cattle Diets.” Fact sheets. http://www.ksgrains.com/ethanol/DDGSFacts.pdf

xxii 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

xxiii 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 Extens ion Service, SDSU. http://agbiopubs.sdstate.edu/articles/ExEx4022.pdf

.31 Annex 1 – New yield specification

The new yield specification includes a trend, ratio of current revenue and cost, moving average of revenue and cost, and total area,

[A1] 

##   10

10 Re Re i y ave

i y ave it it t it it t it T v T v y Trend a a TCost TCost      



                   

## 

To illustratrate the methodology, we use yield and land data for the U.S. and Brazil (table A1.1) and elasticities from the U.S. model (table A1.2) to derive adjustment factors to calibrate the elasticities for Brazil. The derivation of the adjustment factors are given in table A1.3.

Table A1.1. U.S. and Braz il yield and land data

Yield specification US Brazil Corn Yield 8.55 2.91 Arable Land 175,379 58,048

Forest area, permanent meadow, Pasture 538,557 689,327 Ratio 0.75 0.92 Source: USDA PSD Database and FAO Agriculture Database

Table A1.2. Elasticity of corn yi eld in the U.S. corn belt region

Variable Short-run Long-run Price-Cost ratio 0.013 0.074 10-year moving average of price-cost ratio 0.061 Area planted to all crops -0.023 Source: FAPRI-CARD U.S. Crops Model

Table A1.3 Adjustment factor for elasticity calibration methodology

Yield specification US Elasticity Adjustment Factor Brazil Elasticity Technology Adjustment 0.061 2.95 (a) 0.181 Marginal area adjustment -0.023 0.81 (b) -0.018 (a) ratio of U.S. – Brazil yield to represent differential yield gap. (b) ratio of U.S.-BR proportion of forest plus permanent meadows plus pasture relative to total area (0.92 in Brazil and 0.75 in U.S.)32 Annex 2 – New land allocation approach in the Brazil model

Two additional features in the new Brazil model warrant more detailed description. This includes the twostage land allocation approach where land supply constraints are imposed, and the other one is the simultaneous modeling of animal numbers and pasture area to account for stocking rate.

The first stage in the land allocation approach determines the total amount of land to be used for all agricultural enterprises covered in the model based on expe cted returns and the potential availability of land using the following equation; 

#   ag T jt j j jt A Am r  ,

where ag jt A is the land that will be used for agriculture (including pastures) in region j , and year t, T j A is the

potential amount of land for agricultural activities in region j , and 

#   j jt m r is the share of that potential land that

will be actually used depending on aggregate expected returns in the region and year jt r . In the Control Case, this share ranged from 49% for the North Amazon region to 93% for the Northeast Coas t region by 2022 . These agriculture-wide expected returns evolve with time base on the following formula,

1 1 1

* I ijt ijt jt jt ag i jt ijt A r

r r A r 



  

     

#  

,

which indicates that it evolves based on a weighted average of the change in returns of the activities considered. The

variable ijt A  is defined in the next paragraph. The second stage of this approach (which follows closely the method published by Holt (1999) xxiii ) consist of sharing that area out to the different activities, based on own returns, and returns of competing activities with the
restriction that the sum of the shares needs to add to one. Hence, for these activities we have * ag ijt jt ijt A Av   , where

ijt v is the share (just mentioned) for activity i in region j and year t, with

1 1 I

ijt i v

  

#  for all j and t

.
For each crop in each region and year production is thus projected as

ˆ ˆ * ijt ijt ijt Y Ay  

, and the country level area and production of a crop i are obtained by summing across regions, that is,

6

1 it ijt j A A

  

##    and 6

1 ˆ ˆ it ijt j Y Y

  

##  . Total supply available for that crop would then be estimated as

ˆ ˆ ˆ
it it it S Y BS  

Another new feature of the Brazil model is an explicit modeling of pasture area as an integrated decision in

the overall area allocation. A link betw een pasture availability and the size of the cattle herd is introduced by

modeling the stocking rate (number of cows per hectare of pasture) directly. We model “stocking rate” of cows to

directly link the cattle inventory and pasture area needed. Let t s be the stocking rate in period t , b

t E

S

, d

t E

S

,

ot

t ES denote ending stocks of beef cows, dairy cows, and other cattle respectively, and 1 p
t A

 is pasture area. Thus

b d ot b d ot ttt
t ttt p
t BS BS BS
s sss A    33 For each region we model 

#    1 2 0 1 * b b bb t
t tt p
t ES s at s r A   

  

Where t r denotes returns. In log form



#    
1 10 1 12 ln( ) ' ln( ) ln( ) ln( ) ln ln b pp b b

t tt t t ES a A A t ES r   

      

If pasture area is reduced say because of competition from cr ops, the cattle herd will get a signal to contract (fewer

cows will result in fewer calves and less “other cattle” in the next periods). Pasture expansion will let the herd grow

quicker.