## Biodiesel Production Prospects for the Next Decade

A special report by IHS Global

Insi g ht's A g riculture Grou p 

## PREPARED BY IHS Global Insight

## Biodiesel Production Prospects for the Next Decade

The purpose of this analysis is to consider the experience of the past and look at the potential for biodiesel production over the decade to come.

By:

John R. Kruse, PhD

March 11, 2011Page 1 of 20 

## Biodiesel Production Prospects for the Next Decade

Introduction The production of biodiesel in the United States has experienced considerable variation in the past few years. After a decade of steady increases in biodiesel production through 2008, weaker energy prices in 2009 and the uncertain status of the biodiesel blenders' credit in 2010 combined with arbitrage and carry over RIN credits resulted in reduced biodiesel production in 2009 and 2010. As we begin 2011, crude oil prices are edging higher with unrest in the Middle East. Carry over RIN credits appear to be minimal and the biodiesel blenders' credit has been reinstated. The biodiesel mandate has increased to 800 million gallons and 144 million gallons of the advanced mandate will likely be filled by biodiesel given the situation of strong global sugar prices. Technological improvements in recovering corn o il from distiller's grains suggest strong growth in available supplies of vegetable oil feedstock with excellent greenhouse gas emission reduction scores. In short, 2011 is setting up to potentially be a year of strong recovery for the industry. The purpose of this analysis is to consider the experience of the past and look at the potential for biodiesel production over the decade to come. As with any analysis it is critical to recognize the key factors affecting this future. Today's mandates set a floor for biodiesel production in the future, but will the industry sit at that mandated level or will it exceed mandated levels of production? The answer to this question depends on several factors including crop yield technologies, biofuels technologies, crude oil prices, policy certainty, and global economic growth. In this analysis we explore each of these driving factors utilizing the latest information on proven technology, the U.S. Department of Energy's (DOE) outlook for crude oil prices, the IHS outlook for global macro economic growth in combination with policy certainty. The study presents two possible future scenarios. The first scenario is based on a realistic view of crop yield growth, the DOE projection of crude oil prices, current mandated levels of biodiesel production, and a continuation of the current blender's credit. The second scenario is similar to the first scenario except that the current mandated level of biodiesel production is expanded by 300 million gallons per year reaching 2.5 billion gallons by 2017. 

## Scenario Assumptions

Crude Oil Prices For this scenario we used the reference case projection from the 2011 Annual Energy Outlook released by the Department of Energy , U.S. Energy Information Administration (DOE/EIA) on December 16, 2010. 1 Since the release of this data, crude oil prices have increased and in their February 8 , 2011 update DOE/EIA has increased their short term outlook for crude oil prices significantly (see Figure 1). 2 The importance of the crude oil price assumption cannot be understated because it affects the competitiveness of biofuels. Higher crude oil prices enable biofuels to be more competitive and result in greater production of biofuels assuming there are no barriers to blending. For this analysis the reference case continues to be used despite the reality of higher crude oil prices in the present.Page 2 of 20 Figure 1. Department of Energy, Crude Oil Price Projections



### U.S. Policy Assumptions For this scenario we assumed that the ethanol blend wall was not binding allowing corn ethanol production to expand and even exc eed mandated levels if the economics allowed. The ethanol to unleaded gasoline price ratio was assumed to fall below its energy value of 0.65 in order to incentivize consumption of higher ethanol blends. The ethanol tax credit was assumed to be extended at 36 cents per gallon through 2022. The ethanol import tariff was allowed to expire at the end of calendar year 2013. In the case of biodiesel, the $1 per gallon blenders’ credit was extended through 2022. In the baseline, the biodiesel mandate of 1 billion gallons was extended through 2022 and it was assumed that a minimum of 450 million gallons of the advanced mandate would be filled by biodiesel (300 million gallons of biodiesel after adjusting for RIN value). In the 2.5 billion gallon mandate scenario, the mandate increase by 300 million gallons per year beginning in calendar 2013 reac hing 2.5 billion gallons in calendar year 2017. (This mandate includes the assumed the minimum 450 million gallons used to fill the advanced mandate in the baseline.)



### Global Economic Growth Assumptions The IHS forecast for global macroeconomic growth suggests acceleration in global economic growth over the next decade. Real GDP growth is expected to average above 3.7 percent consistently through 2022. Strong growth in the emerging markets (> 5.4 percent) and developing countries (>3.8 percent) is expected to drive growth. The advanced economies find themselves on a slow er pace maintaining just 2 percent growth. As incomes grow in the developing world, consumers will continue to add protein to their diets increase direct consumption of vegetable oil as well as consumption of protein meals via meat consumption. This strong economic growth is the basis forPage 3 of 20 continued strong growth in global vegetable oil demand throughout the scenarios in this analysis.



### Crop Yield technology Many studies including USDA's annual forecast use linear trends to project future crop yield growth. Technology companies such as Monsanto, Dupont, and Sygenta have argued that genetic engineering offers the opportunity to accelerate crop yields beyond trend levels. Recent measured yield performance backs up those claims. In 2010, Monsanto yield data from over 30,000 field comparisons across the US showed an average yield advantage of 3.8 bushels per acre for Roundup Ready 2 TM versus their intial Roundup Ready TM with an average yield of 54 bushels per acre. Average US soybean yields in 2010 were 43.5 bushels per acre. 3 Other research from Farmer’s Independent Research of Seed Technologies suggests a yield advantage of 4.5 bushels per acre for Roundup Ready 2 TM versus initial Roundup Ready TM varieties. Of the 77.4 million acres planted in 2010 only about 6 million acres were planted to Roundup Ready 2 TM soybeans (7.7 percent) In 2011, the number of soybean acres planted to Roundup Ready 2 TM soybeans is expected to reach the mid-teens of millions of acres. Soybean yield technologies in the pipeline with greater than 50 percent probability of being released include high yield traits offe ring 7 percent yield improvement, other herbicide tolerant soybeans, second generation insect protection, soybean fungal resistance, and aphid resistance. Based on the demonstrated performance of technologies already available in 2010 we have assumed a 10 percent step in soybean yi elds above trend yield growth levels used by USDA and others. This 10 percent step is based on taking 10 percent of the average yield over the 2006 through 2008 period and incrementally phasing this in over the 2010 thorough 2016 period. This amounts to adding 1 bushel per acre above trend yields in 2011 and reaches 4.1 bushels above trend 2016 (see Figure 2). Note that after 2016 the yield growth returns to trend levels. Soybean yield assumptions only assume technology advancements that are now comme rcially available and increasingly adopted by U.S. producers.Page 4 of 20 Figure 2. Comparison of Realistic Soybean Yield Growth with Simple Trend Growth in the United States.

Since yield technologies tend to disseminate quickly to Brazil and Argentina, we further assume that the 10 percent step in yields extends to these countries in the planting season following US adoption.

In the case of corn, yield growth is also expected to exceed trend growth levels. Two technologies are driving this accelerated growth. The first is stacking multiple traits to enable the corn plant more resistance to various types of insect pressure in addition to herbicide tolerance. In addition, yield gains have been experienced by building the required refuge into the seed mix with a bag of seed instead of planting specific acres to corn without insect resistant traits. In 2010, Monsanto’s VT Triple Pro TM corn hybrids demonstrated an 8.8 bushel per acre yiel d advantage over competitors based on data from 9,900 strip trials. 3 Monsanto also notes regional difference in yield improvements from refuge reduction. In the Corn Belt, yield improvements ranged from 1.5 to 3 bushels per acre while in the southern states, yield improvements ranges from 3 to 6 bushels per acre from refuge reduction. In its final stage of development, drought tolerant corn is also emerging. This technology is more difficult to quantify in its impact because it affects the distribution of possible corn yields given precipitation variation. There were minimal areas of drought in the US over 2008 – 2010 period in order to fully test the yield advantage offered by the technology. Corn yield technologies in the pipeline with greater than 50 percent probability of being released include second generation drought tolerance, better nitrogen utilization, high yield traits, roundup ready 2 corn, generation 3 corn rootworm and corn borer traits, and other herbicide tolerant corn. 4 Based on the demonstrated performance of technologies already available in 2010 we have assumed a 7.5 percent step in corn yields above trend yield growth levels used byPage 5 of 20 USDA and others. While this may seem smaller than soybeans, corn is already starting from a very high yield level (US corn yields were 152.8 bushels per acre in 2010). This 7.5 percent step is based on taking 7.5 percent of the average yield over the 2006 through 2008 period and phasing it in incrementally over the 2010 through 2017 periods as farmers adopt the technology. With an initial increase 3.2 bushels per acre above trend yield growth in 2011. This increase reaches 12.2 bushels per acre by 2017. It is important to note that we did not extend this increase in corn yields to South America at this time. Issues with intellectual property protection in Argentina regarding corn technologies and other factors suggested we not make this assumption at this time.

Figure 3. Comparison of Realistic Corn Yield Growth with Simple Trend Growth in the United States

Canola yields have also experienced a jump with improvements in genetics. Yield trials in non-traditional growing areas such as Kansas averaged over 2000 pounds per acre according to Michael Stamm, canola breeder at Kansas State University. The past two years of US average canola yields have also born out this shift. In this analysis it was assumed that canola yields would trend off of the yield levels experienced the past two years representing a step up in canola yields (see Figure 4).Page 6 of 20 Figure 4. Comparison of Realistic Canola Yield Growth with Simple Trend Growth in the United States



### Biofuels Technology For these scenarios, the most important technology development is the yield improvements in de-oiling distiller’s grains . Demonstrated technology available today suggests corn oil yields per bushel of corn processed from distillers’ grains have reached levels as high as 1.3 pounds per bushel approaching fractionation yields of 1.5 pounds per bushel. This corn oil is an ideal feedstock for biodiesel production. In these scenarios we have assumed that corn oil yields from de-oiled distillers’ grains per bushel of corn processed will increase from 0.5 pounds in 2010 to 0.6 pounds by 2015 and 1 pound by 2020. By 2016, half of all ethanol plants are assumed to be de-oiling distillers’ grains and by 2022, nearly 70 percent of all ethanol plants are assumed to have this capability. Given the quality of the corn oil pr ocessed from distillers’ grains it is assumed that it will all be used in biodiesel production.

Table 1. Distillers’ Grains De-Oiling Assumptions

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Percent of Ethanol Production with De-Oiling Capacity 33% 37% 40% 43% 47% 50% 53% 57% 60% 63% 67% 70% Yield (Lbs Per Bu of Corn Used in Ethanol Production) 0.53 0.55 0.58 0.60 0.60 0.68 0.76 0.84 0.92 1.00 1.00 1.00 Share of corn oil from DDG's used for biodiesel production 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%Page 7 of 20 

## Scenario Definitions and Methodology The intention of the scenarios in this analysis is to investigate the economically feasible production level for U.S. biodiesel production. As discussed, technology, crude oil prices, and government policy play critical roles in determining this level of production. We constructed two scenarios in this analysis. The first scenario makes no changes to current U.S. policy on biodiesel and investigates the opportunity for biodiesel production given current global economic growth and crude oil price projections. As we have seen in the past two years, crude oil price changes and policy uncertainty have led to considerable variability for the biodiesel sector. The second scenario looks at the impact of imposing a mandate to support biodiesel production at the baseline level. This provides the certainty necessary for the industry to move forward.



### Baseline Assumptions

x Realistic yield growth for corn and soybeans based on proven performance.

x Expansion in corn oil yields from distiller’s grains reaching 1 pound per bushel of corn process in 2020.

x Corn ethanol is not constrained by the blend wall. A tax credit of 36 cents is extended through 2022 and the ethanol import tariff expires at the end of 2013.

x Extension of $1 biodiesel blender’s credit through 2022.

x EU biofuels targets are only 57 percent met.

x Brazil meets its 4 percent blending m andate as supported by government auction purchases.

x Argentina meets its 5 percent blending mandate.

x Biodiesel fills at least 450 million gallons of the advanced mandate (300 million gallons of biodiesel) above the 1.5 billion gallons from the biodiesel mandate (RIN equivalent).

x Biodiesel mandate remains at 1 billion gallons


### 2.5 Billion Gallon Mandate By 2017 Scenario Assumptions (Change from Baseline Assumptions)

x Biodiesel mandate increases by 300 million gallons beginning in calendar 2013 reaching 2.5 billion gallons in calendar year 2017. The new mandate would include the 450 million gallons of the advanced mandate (300 million gallons of biodiesel) assumed in the baseline


### Analysis Methodology This analysis uses a system of global par tial equilibrium models similar to those operated by FAO, OECD, USDA, and FAPRI. For this analysis the US crops and International Oilseed modules were used. These models are maintained in Microsoft Excel TM which facilitates transparency. The analysis results represent a deterministic solution based on the assumptions outlined above.Page 8 of 20 The core agricultural modeling system used by the Agriculture group at IHS Global Insight is a set of structural partial equilibrium econometric models. The word partial refers to the fact that these models consider only the agriculture sector and not their impacts on the whole economy. Computabl e general equilibrium (CGE) models capture the whole economy but are usually not detailed enough to be useful for analysis of a particular sector such as agriculture. Partial equilibrium models require that all sources of supply and demand are included so our models use catch-all regions for the countries we do not explicitly break out. The models solve for a "global" price by equating global supply and demand. This global price is usually the price at the port in a count ry that exports a significant portion of world trade in the commodity and has relatively few trade barriers. This country is referred to as the residual supplier. All other prices are linked to this world price adjusting for trade policies, exchange rates and any relevant processing margins. The process of simultaneously solving for prices is initiated by initially assuming a level of exports for the residual supplying country which allows the model to solve for the "global" price. This global price is then translated through tariffs, exchange rates, processing margins, etc. to determine the local price in a particular country. Based on their local price, each country can then determine its domestic supply and demand. The difference between the supply and demand becomes the net trade position for the country. This net trade position is summed across all the countries to create a new export level for the residual supplier replacing the initial assumption. Of course this generates a new "global" price and restarts the cycle which continues until global supply and demand equilibrium is reached. The following diagram may be useful in better understanding the process:

Assume an export path for the Residual Supplier

Residual Supplier

Country Model Residual Supplier Farm Price World Price

Recursive Country Model

Net trade positions are summed across countries Net exports needed become the Residual Supplier's export path

Exchange Rates

Trade Barriers

Local country's government policy Residual Supplier government policy 

### Global Price Determination

Local Market Price Import Price subject to quotas

Simultaneous Country Model

Net Import (might be negative) Net Import (might be negative) Price Transmission No Price TransmissionPage 9 of 20 Note that the diagram also includes a provision for those countries which operate with quota systems such that their internal prices have no relationship with the prices in the residual supplying country. This can be an issue for countries with binding tariff rate quotas. If this situation occurs for a particular country, prices in that country are solved for simultaneously given the domestic supply, demand, and exogenous trade (set at the binding tariff rate quota.) The IHS Global Insight agricultural modeling system consists of structural partial equilibrium models designed to replicate the structure of the market place and the manner in which data are collected on the market. The structure mimics a supply and utilization table with explicit econometric equations for each component of supply and demand. Typically supply is broken down in to acreage, yield, production, imports, and beginning stocks. Demand is broken down into mill use, feed use, food use, industrial use, and ending stocks or some combination depending on the commodity. Each commodity is interrelated with other commodities that serve as compliments, substitutes, and/or inputs. The country model flow diagram below represents a typical country showing the interrelationships among the macroeconomic variables, crops, livestock and government policy. The top half of the diagram with the light red boxes refers to the meat sector, while the bottom half of the diagram with the light green boxes refers to the crops sector. The blue boxes refer to the key assumptions that drive the model. All forecasts are conditional upon the assumptions of income growth, population growth, trade and domestic support polices, input prices, and technology (i.e. yield growth).

Per Capita Meat Consumptio n

Per Capita Crop Consumptio n Per Capita Income Population

Feed Demand

Food Demand

Industrial Demand

Seed Demand

Exports

Ending Stocks Ending Stocks

Exports Domestic Meat Consumption

Meat Prices Feed

Meat Production Breeding Animals

Market Animals

Beginning Stocks Imports

Crop Prices Production

Beginning Stocks Imports

Area

Yield Government Policy Livestock Cost of Production

Crop Cost of Productions

Assumptions
Meat Variables Crop Variables 

# Country Model Flow Diagram

Vet/Med

Electricity

Labor

Fuel

Interest

Land

Seed

Fertilizer

Chemicals Crude Oil PricePage 10 of 20 Due to the complexity of the agricultural modeling system, the system is broken down into major components. The current IHS Global Insight system is outlined in the diagram below. Due to the number of commodities and countries included in our system, the models are broken up into specific parts that all interact with one another but help make the process more manageable.

At present, the models reside mostly in Microsoft Excel TM for ease of use and transparency for the model user. IHS Global Insight also utilizes SAS TM for some of the models, particularly those targeted for stochastic analysis. The estimated equations are derived using ordinary least squares for the most part. More advanced techniques such as two-stage and three-stage least squares are occasionally employed, but issues such as simultaneous equation bias are very small if the ordinary least squares equations fit well. Synthetic equations are sometimes derived based on elasticities from other studies and are often useful where there are limited observations because of structural change in a market. The US biodiesel module of the US Agricultural model is set up as a partial equilibrium model with supply and demand equations and an endogenous biodiesel price. Central to the model's operation is the biodiesel processing margin which is calculated as the value of the biodiesel and glycerin produced less the cost of feedstocks and other variable inputs. The supply side of the model is made up capacity and capacity utilization equations. Capacity is function of margins realized over the past 4 years with most of the impact from margin two and three years prior to the current period. This

Copyright © 2009 IHS Global Insight. All Rights Reserved. 

# Agricultural Models – A n Integrated Approach

Int’l Feed Grains Corn including ethanol Sorghum Barley Other Coarse Grains

Int’l Oilseeds Soybeans, meal, oil Rapeseed, meal, oil Sunflower, meal, oil Palm oil Biodiesel Int’l Fibers
Cotton Int’l Food Grains
Wheat Rice

Int’l Livestock
Cattle Inventory & Beef Swine Inventory & Pork Poultry Inventory & Broilers Dairy Inventory US Crops
Corn Sorghum Barley Oats Wheat Rice Cotton Soybeans, meal, oil Sunflowers, meal, oil Rapeseed, meal, oil Peanuts, meal, oil Cottonseed, meal, oil Ethanol Biodiesel Hay Sugar US Livestock Cattle Inventory & Beef Swine Inventory & Pork Poultry Inventory & Broilers Dairy Cow Inventory Dairy Products US Agricultural Inputs Ag prices paid indices Cost of production Fertilizer Macroeconomic Inputs
Prices paid indices, GDP, CPI, Population, and Exchange Rates

Agricultural Policy

Domestic Policy

Trade Policy

Int’l Sugar Sugarcane Sugar Beets Centrifugal Sugar Global Calorie Consumption Model
>70% of total global food calories explained through integration with existing modelsPage 11 of 20 reflects the time lag in building a biodiesel plant. The capacity utilization equation estimates how much of current capacity is being utilized. It is highly sensitive to current margins with the industry. Practically, since the biodiesel industry has considerable capacity, the capacity utilization equation is the most important in the near term. By multiplying the capacity and capacity utilization equations the model estimates the amount of biodiesel produced. The demand for biodiesel is based on ratio of biodiesel price and the #2 diesel prices adjusted for the tax credit as well as biodiesel mandates. If biodiesel prices are above their energy content value, demand is determined by the mandate. Demand also has a small driver fo r use of biodiesel as an additive for its lubricity properties. Biodiesel prices are found through the balancing of supply and demand within the model.



## Scenario Results The baseline in this analysis is the most important result because it shows what the biodiesel industry can produce given current policy. In discussing the results, graphs will be presented with both the baseline and 2.5 billion gallon mandate by 2017 scenario. Essentially the only difference in the scenarios occurs in marketing year 2013 and 2014 when the mandate is very slightly stronger than what the biodiesel industry would produce. While the scenario mandated level could be fine-tuned to exactly the possible level of production, policy tends to be set in round increments. A few references are made to the 2.5 billion gallon mandate by 2017 scenario in the following text, but the primary focus of the discussion is on the baseline. The baseline results for biodiesel and its implications for the US oilseed sector are presented in Tables 2 through 4. The baseline shows that given the assumptions, it is possible to reach 3.3 billion gallons of biodiesel by 2022 an increase of 2.5 billion gallons from projected 2010/11 marketing year levels. The feed stocks come from a variety sources in addition to soybean oil (see Figure 5). Nearly 80 percent of the increase is supply by soybean oil (31%) corn oil (22%) and palm fatty acid distillate (26%) Animal fats, yellow grease, and other grease supply another 16 percent of the total with the remaining 3 percent coming from rapeseed and palm oil. U.S. demand for biodiesel moves off of the mandate levels including the advanced mandate in marketing year 2013/14 when crude oil prices reach 86 dollars per barrel. As demand for biodiesel exceeds mandate leve ls, the price of biodiesel converges towards its energy value relative to #2 diesel. The biodiesel to diesel wholesale price ratio falls to 93 percent by 2022 compared with a historical average over the calendar year 2008 to 2009 period of 120 percent. Biodiesel plant returns over variable costs with soybean oil as a feedstock hover in the 22 cents per gallon range consistent with a healthy industry just covering its fixed costs. (see Table 3). Soybean oil exports fall in the U.S. as exports increase from Argentina and Brazil with stronger than linear trend yield growth. Price pressure on soybean oil prices eases in 2011/12 as global supplies are replenished. However, soybean oil prices do not return to 2009/10 levels as strong global demand supports prices (see Figure 6). Note that Figure 6 also show slightly stronger soybean oil prices under 2.5 billion gallon mandate by 2017 scenario.Page 12 of 20 Figure 5. Biodiesel Production by Feedstock Type

Figure 6. U.S. Soybean Oil Price, Decatur, IllinoisPage 13 of 20 In contrast, soybean prices do ease throughout the projection period despite increased crush demand (see Figure 7). Falling meal pr ices and a weaker soybean to corn price ratio (soybean yield growth outpaces corn yield growth) are primarily responsible for weaker soybean prices. Soybean acreage average 77.2 million acres over the 2011/12 – 2022/23 period, with slightly stronger acreage at the beginning of the period and weaker acreage at the end of the period. Soybean exports remain relatively steady during the forecast ranging from a low of 1.43 billion bushels to a high of 1.6 billion bushels in 2022 (see Figure 8).

Figure 7. US Soybean Prices

Figure 8. US Soybean ExportsPage 14 of 20 Table 6 presents the global vegetable oil food demand projected with the baseline scenario. Global demand for vegetable oils continues to grow rapidly through the projection period as developing economies respond to include growth and demand more protein in their diets. These demands are met primarily through yield growth in the United States, Brazil, and Argentina.


## Conclusions By using demonstrated yield technologies availa ble to farmers today in combination with consistent biofuels policies and the DOE/EIS crude oil price forecast, biodiesel production can reach 3.3 billion gallons by 2022. This can be reached with growing global food demand for vegetable oils without dramatic increases in vegetable oil prices. A modest expansion of the biodiesel mandate to 2.5 billion gallons by 2017 would provide support for what the industry can deliv er given known feedstocks. Biodiesel’s classification as an advanced biofuel makes it an attractive option for reducing greenhouse gases.Page 15 of 20 Table 2. US Biodiesel Supply and Demand, Baseline

Marketing Year Beginning October 1 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18 18/19 19/20 20/21 21/22 22/23

Production Capacity, Marketing Year Average 2240 2792 2792 2856 2900 2949 3055 3170 3270 3342 3359 3345 3326 3302 3277 3242

Supply (Million Gallons) Production 716 544 435 821 1,239 1,340 1,500 1,810 2,179 2,529 2,741 2,997 3,135 3,227 3,304 3,322 Soybean Oil 433 276 224 395 605 624 685 828 1,005 1,135 1,187 1,261 1,270 1,218 1,236 1,179 Corn Oil 0 0 87 129 149 185 212 240 266 328 393 459 526 602 637 683 Canola Oil 84 50 35 54 66 68 75 90 109 124 129 137 138 133 135 128 Other Feed Stocks 199 218 88 244 419 463 528 652 799 942 1,032 1,139 1,200 1,275 1,297 1,332 Inedible tallow 21 23 7 25 83 142 200 259 317 317 317 317 317 317 317 317 Yellow grease 15 19 36 40 48 56 64 71 79 79 79 79 79 79 79 79 Other grease 34 25 22 30 38 46 54 62 70 70 70 70 70 70 70 70 Lard 31 18 17 25 26 28 29 30 31 33 34 35 35 35 35 35 Palm Oil 0 0 0 0 4 7 11 15 19 23 27 30 34 38 41 45 Other 99 132 7 124 220 185 170 215 283 421 506 608 665 736 755 786

Domestic Disappearance 352 338 295 781 1,199 1,300 1,460 1,770 2,139 2,489 2,701 2,957 3,095 3,187 3,264 3,283

Net Exports 363 206 139 40 40 40 40 40 40 40 40 40 40 40 40 40

Total Disappearance 716 544 435 821 1,239 1,340 1,500 1,810 2,179 2,529 2,741 2,997 3,135 3,227 3,304 3,323

Crude Oil Prices (Dollars Per Barrel) Refiners Acquisition 99.61 70.90 70.22 80.12 83.17 86.08 90.10 93.96 98.08 102.92 108.02 113.03 118.03 122.93 127.58 130.02

Diesel Fuel Prices (Dollars Per Gallon) #2 Diesel Refiner Sales 3.13 1.73 2.12 2.57 2.71 2.81 2.94 3.06 3.19 3.35 3.52 3.68 3.84 3.99 4.14 4.21 #2 Diesel Retail 3.88 2.52 2.89 3.26 3.40 3.51 3.66 3.79 3.94 4.11 4.29 4.45 4.62 4.77 4.92 4.99

Biodiesel Prices (Dollars Per Gallon) Biodiesel Plant 4.49 3.13 3.25 4.26 4.04 4.03 3.89 3.98 4.08 4.20 4.33 4.45 4.59 4.73 4.86 4.93

Price Ratio (Adjusted for Tax Credit) Biodiesel/Diesel Wholesale Price Ratio 111.4% 123.3% 106.3% 126.9% 112.4% 108.0% 98.5% 97.5% 96.4% 95.3% 94.7% 94.0% 93.6% 93.4% 93.3% 93.3%Page 16 of 20 Table 3. US Biodiesel Processing Margins - Baseline

Marketing Year Beginning October 1 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18 18/19 19/20 20/21 21/22 22/23 Soybean Oil Feedstock Product Conversions Biodiesel (Lbs RDB Soy Oil Per Gal Biodiesel) 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 Glycerin (Lbs Glycerin Per Gal Biodiesel) 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9

Gross Revenue (Dollars Per Gallon of Biodiesel) Biodiesel 4.49 3.13 3.25 4.26 4.04 4.03 3.89 3.98 4.08 4.20 4.33 4.45 4.59 4.73 4.86 4.93 Glycerin 0.05 0.05 0.04 0.08 0.08 0.08 0.08 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 Total Gross Revenue 4.54 3.18 3.29 4.33 4.13 4.11 3.97 4.06 4.15 4.26 4.40 4.52 4.66 4.80 4.93 5.00

Variable Expenses (Dollars Per Gallon of Biodiesel) Soybean Oil 4.26 2.63 2.94 3.93 3.60 3.59 3.46 3.52 3.60 3.72 3.86 3.98 4.10 4.27 4.38 4.44 Other Operating Costs 0.30 0.31 0.31 0.31 0.31 0.31 0.32 0.32 0.32 0.33 0.33 0.33 0.33 0.34 0.34 0.34 Total Variable Expenses 4.56 2.94 3.25 4.24 3.91 3.90 3.78 3.84 3.92 4.04 4.19 4.31 4.43 4.61 4.72 4.78

Net Operating Margin -0.03 0.24 0.04 0.09 0.22 0.21 0.19 0.22 0.22 0.22 0.21 0.21 0.23 0.19 0.21 0.22Page 17 of 20 Table 4. US Soybean Meal and Oil Supply, Demand, and Prices - Baseline

Marketing Year Beginning October 1 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18 18/19 19/20 20/21 21/22 22/23

Soybean Oil Prices (Cents Per Pound) Decatur, Crude 52.03 32.16 35.95 48.00 43.91 43.80 42.28 42.94 43.96 45.41 47.15 48.59 50.07 52.14 53.48 54.19

Crushing Yield (Lbs Oil Per Lb Soybeans) Actual 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19

Supply (M illion P ounds) Beginning Stocks 3,085 2,485 2,742 3,358 2,640 2,207 2,256 2,375 2,365 2,347 2,303 2,248 2,213 2,159 2,094 2,056 Production 20,580 18,746 19,615 18,966 19,469 20,585 21,530 22,433 23,075 23,756 24,005 24,216 24,457 24,333 24,716 24,868 Imports 65 90 105 105 105 105 105 105 105 105 105 105 105 105 105 105 Total Supply 23,730 21,321 22,462 22,429 22,213 22,898 23,891 24,914 25,545 26,209 26,413 26,569 26,775 26,597 26,915 27,029

Domestic Disappearance Food Use 15,089 14,316 14,022 14,200 14,103 14,205 14,541 14,621 14,683 14,692 14,644 14,633 14,644 14,592 14,604 14,641 Biodiesel Use 3,245 2,069 1,682 2,962 4,538 4,683 5,135 6,209 7,537 8,514 8,900 9,461 9,528 9,136 9,268 8,845 Total Domestic Disappearance 18,335 16,385 15,704 17,162 18,641 18,889 19,675 20,829 22,220 23,206 23,544 24,094 24,171 23,727 23,872 23,486

Exports 2,911 2,193 3,400 2,733 1,365 1,753 1,840 1,720 978 699 622 262 445 776 987 1,497

Total Disappearance 21,246 18,578 19,104 19,895 20,006 20,642 21,516 22,549 23,198 23,906 24,166 24,356 24,616 24,503 24,859 24,983

Ending Stocks 2,485 2,742 3,358 2,640 2,207 2,256 2,375 2,365 2,347 2,303 2,248 2,213 2,159 2,094 2,056 2,046

Soybean Gross Crushing Margin ($ Per Bushel) 1.13 0.80 1.15 0.82 0.85 1.21 1.22 1.22 1.16 1.13 1.18 1.24 1.32 1.21 1.41 1.40Page 18 of 20 Table 5. US Soybean Supply, Demand, and Prices - Baseline

Marketing Year Beginning September 1 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18 18/19 19/20 20/21 21/22 22/23

Prices (Dollars Per Bushel) Marketing Year Average Price 10.10 9.97 9.59 11.83 10.81 10.18 9.86 9.46 9.24 9.23 9.19 9.14 9.07 9.07 9.00 9.00 Central IL Processor Price 12.32 10.26 9.94 12.42 11.43 10.82 10.51 10.12 9.90 9.90 9.86 9.81 9.74 9.73 9.67 9.67

Soybean to Corn Price Ratio 2.40 2.46 2.59 2.28 2.31 2.24 2.19 2.22 2.21 2.20 2.20 2.16 2.15 2.14 2.13 2.12

Acreage (Million Acres) Planted Area 64.7 75.7 77.5 77.7 78.5 79.0 77.8 76.9 76.2 75.7 75.7 76.4 77.5 77.0 78.0 77.9 Harvested Area 64.1 74.7 76.4 76.8 77.5 77.9 76.8 75.9 75.2 74.8 74.8 75.5 76.5 76.0 77.1 77.0 Harvested Area % of Planted 99% 99% 99% 99% 99% 99% 99% 99% 99% 99% 99% 99% 99% 99% 99% 99%

Yield (Bushels Per Acre) 41.7 39.7 44.0 43.9 44.2 45.3 46.5 47.7 48.5 49.3 49.7 50.1 50.5 50.9 51.2 51.6

Supply (Million Bushels) 1.13 0.80 1.15 0.82 0.85 1.21 1.22 1.22 1.16 1.13 1.18 1.24 1.32 1.21 1.41 1.40 Beginning Stocks 574 205 138 151 161 168 180 185 196 201 203 201 198 205 200 205 Production 2,677 2,967 3,359 3,375 3,426 3,531 3,572 3,620 3,650 3,688 3,716 3,780 3,864 3,869 3,949 3,977 Imports 10 13 15 15 15 15 15 15 15 15 15 15 15 15 15 15 Total Supply 3,261 3,185 3,512 3,541 3,602 3,713 3,767 3,820 3,860 3,904 3,934 3,996 4,076 4,089 4,165 4,197

Domestic Disappearance Crush 1,803 1,662 1,752 1,665 1,739 1,839 1,923 2,004 2,061 2,122 2,144 2,163 2,185 2,174 2,208 2,221 Seed & Residual 94 102 108 118 126 127 131 135 138 142 146 151 155 159 163 166 Total Domestic Disappearance 1,897 1,764 1,860 1,783 1,865 1,966 2,054 2,139 2,200 2,264 2,290 2,315 2,340 2,332 2,371 2,388

Exports 1,159 1,283 1,501 1,594 1,569 1,568 1,528 1,486 1,459 1,438 1,443 1,483 1,531 1,556 1,589 1,604

Total Disappearance 3,056 3,047 3,361 3,377 3,434 3,534 3,582 3,624 3,659 3,702 3,733 3,798 3,871 3,889 3,960 3,992

Ending Stocks 205 138 151 161 168 180 185 196 201 203 201 198 205 200 205 205Page 19 of 20 Table 6. World Vegetable Oil Food Use* - Baseline

07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18 18/19 19/20 20/21 21/22 22/23 (1000 Metric Tons) North America 11,505 10,895 10,934 11,215 11,435 11,591 11,817 11,975 12,096 12,215 12,310 12,427 12,550 12,648 12,775 12,919 Canada 790 732 689 769 779 788 794 805 813 827 841 856 870 884 898 914 Mexico 1,420 1,344 1,419 1,517 1,538 1,572 1,607 1,647 1,687 1,729 1,772 1,817 1,861 1,906 1,952 1,999 United States 9,295 8,819 8,826 8,930 9,119 9,231 9,416 9,523 9,596 9,659 9,696 9,755 9,820 9,858 9,925 10,006

Central/South America 6,702 6,717 6,867 6,815 7,151 7,307 7,455 7,594 7,724 7,828 7,932 8,035 8,148 8,266 8,373 8,496 Argentina 720 736 749 727 799 820 841 860 880 901 922 944 968 993 1,016 1,042 Brazil 3,165 3,263 3,370 3,289 3,493 3,580 3,674 3,755 3,836 3,883 3,928 3,974 4,021 4,068 4,117 4,170 Uruguay 33 34 32 31 35 37 37 38 38 39 39 40 40 41 41 42 Paraguay 37 42 42 41 44 44 46 47 48 48 49 50 51 51 52 53 Bolivia 77 77 77 82 85 88 91 93 96 98 100 103 105 107 110 112 Other Latin America 2,670 2,565 2,597 2,646 2,696 2,738 2,767 2,801 2,826 2,858 2,893 2,925 2,963 3,006 3,038 3,077

Europe 10,479 10,681 10,814 10,493 10,937 11,030 11,120 11,218 11,306 11,398 11,489 11,577 11,668 11,759 11,841 11,929 European Union 9,929 10,168 10,219 9,902 10,353 10,430 10,512 10,600 10,679 10,762 10,843 10,923 11,005 11,087 11,160 11,240 Other Europe 550 513 595 591 584 600 608 619 627 636 646 654 663 672 681 689

FSU 3,270 3,372 3,353 3,383 3,528 3,628 3,680 3,751 3,804 3,863 3,921 3,978 4,032 4,085 4,138 4,193 Russia 2,391 2,340 2,371 2,384 2,385 2,449 2,468 2,505 2,525 2,553 2,580 2,605 2,629 2,651 2,671 2,694 Ukraine 501 505 342 460 589 611 629 646 662 677 692 707 721 736 752 768 Other FSU 378 527 640 539 555 567 584 600 617 633 650 666 682 699 715 731

Africa 6,493 6,766 7,213 6,885 7,469 7,758 7,976 8,268 8,499 8,751 9,010 9,276 9,546 9,824 10,106 10,394

Middle East 3,593 3,600 3,752 4,034 4,062 4,132 4,208 4,316 4,424 4,537 4,653 4,766 4,879 4,997 5,111 5,216

Asia/Oceania 40,866 44,100 48,121 50,756 53,071 54,034 55,178 56,828 57,950 59,394 61,024 62,694 64,481 66,284 68,085 69,944 China 13,232 13,679 15,327 16,818 18,505 18,801 19,132 19,717 19,912 20,385 20,929 21,500 22,159 22,818 23,501 24,223 Indonesia 8,147 9,166 9,994 10,445 10,508 10,709 10,933 11,240 11,545 11,882 12,229 12,586 12,943 13,323 13,705 14,099 India 7,313 9,181 9,962 10,587 10,797 11,054 11,388 11,787 12,105 12,412 12,824 13,254 13,714 14,157 14,595 15,044 Japan 3,060 3,130 3,282 3,336 3,285 3,253 3,304 3,353 3,399 3,445 3,487 3,528 3,567 3,607 3,645 3,683 Malaysia 1,776 1,784 1,841 1,902 1,937 1,976 2,012 2,058 2,101 2,146 2,190 2,233 2,273 2,311 2,347 2,383 Other Asia/Oceania 6,819 6,599 7,088 7,013 7,406 7,601 7,761 8,017 8,215 8,433 8,658 8,868 9,085 9,313 9,520 9,725

World 69,582 72,161 75,669 77,794 81,661 83,531 85,355 87,602 89,168 91,006 92,990 95,016 97,191 99,339 101,500 103,752

*Includes Soybean Oil, Sunflower Oil, Rapeseed Oil, and Palm Oil.Page 20 of 20 

## References

1 U.S. Department of Energy, Energy Information Administration. December 16, 2010. Website: http://tonto.eia.doe.gov/forecasts/aeo/index.cfm

2 U.S. Department of Energy, Energy Information Administration, February 8, 2011. Website: http://tonto.eia.doe.gov/steo/

3 Presentation from Monsanto Yield Review Luncheon, November 8, 2010.

4 Presentation from Monsanto entitled, “Annual R&D Pipeline Review”, January 6, 2011.