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The Production and Price Impact of Biotech Corn, Canola, and Soybean Crops
PG Economics
University of Tennessee
International Food Policy Research Institute (IFPRI)
Centre for Agricultural and Rural Development (CARD), Iowa State University
Biotech crops have now been grown commercially on a substantial global scale since 1996. This article examines the production effects of the technology and impacts on cereal and oilseed markets through the use of agricultural commodity models. It analyses the impacts on global production, consumption, trade, and prices in the soybean, canola, and corn sectors. The analysis suggests that world prices of corn, soybeans, and canola would probably be, respectively, 5.8%, 9.6%, and 3.8% higher, on average, than 2007 baseline levels if this technology was no longer available to farmers. Prices of key derivatives of soybeans (meal and oil) would also be between 5% and 9% higher, with rapeseed meal and oil prices being about 4% higher than baseline levels. World prices of related cereals and oilseeds would also be expected to be higher by 3% to 4%.
Key words: biotech crops, prices, yield, soybeans, corn, canola, partial-equilibrium model, price effects.
The effect of no longer using the current widely used biotech traits in the corn, soybean, and canola sectors would probably impact negatively on both the global supply and utilization of these crops, their derivatives and related markets for grain and oilseeds. The modelling suggests that average global yields would fall for corn, soybeans, and canola and despite some likely ‘compensatory’ additional plantings of these three crops, there would be a net fall in global production of the three crops of 14 million tonnes. Global trade and consumption of these crops/derivatives would also be expected to fall. The production and consumption of other grains such as wheat, barley, and sorghum and oilseeds—notably sunflower—would also be affected. Overall, net production of grains and oilseeds (and derivatives) would fall by 17.7 million tonnes, and global consumption would fall by 15.4 million tonnes. The cost of consumption would also increase by $20 billion (3.6%) relative to the total cost of consumption of the (higher) biotech-inclusive level of world consumption. The impacts identified in this analysis are, however, probably conservative, reflecting the limitations of the methodology used. In particular, the limited research conducted to date into the impact of the cost-reducing effect of biotechnology (notably in herbicide-tolerant [HT] soybeans) on prices suggests that the price effects identified in this article represent only part of the total price impact of the technology.

Introduction

Biotechnology crop traits have been grown on a widespread commercial global basis since 1996, and in 2008, the global cultivation area of biotech crops reached 125 million hectares, a 74-fold increase from the 1996 level. The number of countries adopting biotech crop cultivation has also increased from six in 1996 to 25 in 2008, with the United States leading the way in the utilization of biotechnology in crop production. The rapid growth of biotech crop hectares between 1996 and 2008 has made this the most rapidly adopted crop technology in agriculture over this period (James, 2008).

Currently, the biotech crop hectares are primarily utilized for soybeans, corn, cotton, and canola. The technology used thus far has been agronomic, cost-saving technology delivering herbicide tolerance in all four of these crops and insect resistance in the crops of corn and cotton. This technology has provided farmers with productivity improvements through a combination of yield improvement and cost reductions. As such, the technology is likely to have had an impact on the prices of soybeans, corn, cotton, and canola (and their derivatives) both in the countries where farmers have used biotech traits and in the global market.

Assessing the impact of the biotechnology applications on the prices of soybeans, corn, cotton, and canola (and their derivatives) is challenging since current and past prices reflect a multitude of factors—of which the introduction and adoption of new, cost-saving technologies is one. This means that disaggregating the effect of different variables on prices is far from easy. Previous studies have contributed to the literature by evaluating the impacts of biotechnology application for field crops on national/regional economies and farmers’ welfare (e.g., Anderson, Valenzuela, & Jackson, 2008; Martin & Hyde, 2001; Sobolevsky, Moschini, & Lapan, 2005). However, most of these studies primarily focused on a single crop, such as soybeans, corn, or cotton. Thus, the impact analysis of biotechnology adoption did not capture the responsiveness of the production of other crops. Furthermore, since the application of biotechnology usually occurs in various field crops, the joint impacts of biotechnology adoption on local and global agricultural markets need to be further explored.

Realizing the surging significance of biotechnology application in the US and global crop markets, this article summarizes the productivity impacts of biotech crops1 (on production) and presents the findings of analysis that has sought to quantify the impact of the use of biotech traits on usage and the prices of corn, soybeans, and canola and their main derivatives.2

Methodology

The approach used to estimate the impacts of biotech crops on usage, trade, and prices of the three crops and their derivatives has been to draw on part of a broad modelling system of the world agricultural economy comprised of US and international multi-market, partial-equilibrium models of production, use, and trade in key agricultural commodities.3 The models cover major temperate crops, sugar, ethanol and biodiesel, dairy, and livestock and meat products for all major producing and consuming countries and calibrated on most recently available data. Extensive market linkages exist in these models, reflecting derived demand for feed in livestock and dairy sectors, competition for land in production, and consumer substitution possibilities for close substitutes such as vegetable oils and meat types. The models capture the biological, technical, and economic relationships among key variables within a particular commodity and across commodities. They are based on historical data analysis, current academic research, and a reliance on accepted economic, agronomic, and biological relationships in agricultural production and markets. A link is made through prices and net trade equations between the US and international models. The models are used to establish 10-year commodity projections for a baseline and for policy analysis and are used extensively for the market outlook and policy analysis.

In general, for each commodity sector, the economic relationship that supply equals demand is maintained by determining a market-clearing price for the commodity. In countries where domestic prices are not solved endogenously, these prices are modelled as a function of the world price using a price transmission equation. Since the models for each sector can be linked, changes in one commodity sector will impact other sectors. For this particular study, the US Crops, International Grains, International Oilseed, International Sugar, and International Bio-fuels models were used.

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 (1)

Production is divided into yield and area equations, while consumption is divided into feed and non-feed demand. The models include behavioral equations for area harvested, yield, crop production on the supply side, and per-capita consumption and ending stocks on the demand side. Equilibrium prices, quantities, and net trade are determined by equating excess supply and excess demand across countries and regions.

More specifically, in terms of acreage, harvested area is expressed as a function of own and competing crop prices in real terms as well as lagged harvested area and prices. Prices enter area functions either as part of real gross returns per unit of land (price multiplied by yield) or merely as prices, depending on the particular commodity model. The US model, because of extensive data availability, is divided into nine regions. The planted area for each crop within each region depends on expected net returns—which include real, variable production expenses per unit of land—for the crop and competing crops.

To satisfy the identity in Equation 1, two different methods are used. In most of the countries, domestic price is modelled as a function of the world price with a price transmission equation, and the identity is satisfied with one of the variables set as the residual. In other cases, prices are solved to satisfy the identity.

Agricultural and trade policies in each country are included in the models to the extent that they affect the supply and demand decisions of the economic agents. The models assume that the existing agricultural and trade policy variables will remain unchanged in the outlook period. Macroeconomic variables, such as GDP, population, and exchange rates, are exogenous variables that drive the projections of the model. All models are calibrated on 2007/08 marketing year data for crops; 10-year annual projections for supply and utilization of commodities and prices for the US and the world are generated for the period between 2008 and 2017. Elasticity values for supply and demand responses are based on econometric analysis and on consensus estimates. Elasticity parameters estimates and policy variables are available at Iowa State University’s Food and Agricultural Policy Research Institute (FAPRI) website.4

Data for commodity supply and utilization are obtained from the F.O. Lichts online database, the Food and Agriculture Organization (FAO) of the United Nations (FAOSTAT Online, 2006), the Production, Supply, and Distribution View (PS&D) of the US Department of Agriculture (USDA), the European Commission Directorate General for Energy and Transport, the ANFAVEA (2005), and UNICA (2006). Supply and utilization data include production, consumption, net trade, and stocks. The macroeconomic data are gathered from the International Monetary Fund and Global Insight.

The empirical analysis relies on these agricultural commodity models of the main regions of the world (e.g., North and South America, the EU-27, etc.) to estimate the impact on national, regional, and world markets and prices for cereals and oilseeds. These models have been developed to allow for forward-looking projections (over a 10-year period) to be made relating to production, use, trade, and prices of key commodities. The models are not directly able to estimate the impact of the technology on past prices (of corn, soybeans, and canola and their key derivatives). One advantage of these models is that it is possible to establish a baseline and then remove the impact of biotechnology on yields. This allows the isolation of the impact on prices and usage due to biotech crops and not due to other factors such as macroeconomic and weather variables. However, the models do not allow for estimating the impact on crop prices arising from changes to the cost base of crop production (a major impact of HT technology). Some (limited) economic analysis has been previously undertaken to estimate the impact of biotechnology-induced cost-of-production changes, notably on the global prices of soybeans. Moschini, Lapan, and Sobolevsky (2000) estimated that by 2000 the influence of biotech soybean technology on world prices of soybeans had been between -0.5% and -1%, and that as adoption levels increased this could be expected to increase up to -6% (if all global production was biotech). Qaim and Traxler (2002, 2005) estimated the impact of GM HT soybean technology adoption on global soybean prices to have been -1.9% by 2001. Based on this analysis, they estimated that by 2005 it was likely that the world price of soybeans may have been lower by between 2% and 6% than it might otherwise have been in the absence of biotechnology. This benefit will have been dissipated through the post-farm gate supply chain, with some of the gains having been passed onto consumers in the form of lower real prices. We, therefore, acknowledge the failure to include the potential impact of biotechnology on costs of production and prices as a limitation of the research, which potentially underestimates the impact of the technology on prices. In addition, the analysis uses 2007 as the baseline against which the analysis is run. This assumes that the level of biotech trait adoption in 2007 represents the ‘counterfactual situation.’ In doing so, it fails to take into account likely trends in biotech trait adoption post 2007 and hence, this additional weakness of the analysis probably contributes further to understating the price effect of biotechnology. Despite these methodological weaknesses, the approach used in this article provides a useful tool for assessing the impact of biotech traits on the prices of corn, canola, soybeans, and derivatives of these crops on global markets.

Yield and production change assumptions for the impact of biotech crops were used as bases for analysis in the models by projecting forward a ‘what if’ scenario in which the currently used biotech traits were no longer available. The yield and production change assumptions used were those identified in the published work of Brookes and Barfoot (2008).5 For example, insect-resistant (IR) corn technology in the United States has delivered an average 5% improvement in corn yields. The Brookes and Barfoot analysis is itself based on a literature review of impacts of biotechnology traits globally since 1996, and details of the specific country and trait-specific studies used can be found in the references section of this article. To analyze the impact of this yield improvement, first a baseline is established (starting in 2008, and for the next 10 years covered by the model projections) with the trend growth rate of yield. Then a scenario is run where the yields were effectively lower than the baseline level (starting in 2008 and ending in 2017). The baseline represents the current status quo (technology used) and the scenario implies that the technology is no longer available. The difference between the baseline and scenario represents the impact of the technology (or more literally the impact of no longer using the technology).

The models effectively assume the decreases in average crop (e.g., corn) yield in the countries using GM technology as a ‘shock’ change to the various regional parts of the models. This then calculates revised yield values, changes in production and consumption, changes in stocks, changes in imports and exports, and changes in areas allocated to other crops. ‘Knock-on’ effects6 on the price of each crop (corn, soybeans, and canola) plus effects on other crop (e.g., wheat, barley, sunflower) were also derived, both at a regional and a world level. Knock-on effects on derivatives of corn, soybeans, and canola are also derived.

Production and Yield Assumptions

The production and yield change assumptions used in this analysis derive from the work of Brookes and Barfoot (2008), which itself draws on numerous crop and country-level impact studies. The next section (Production and Yield Impacts of Biotech Crops) provides a summary of this data, and the assumptions used for the analysis are presented in the following section (Conversion of Production and Yield Impacts into Useable Assumptions).

Production and Yield Impacts of Biotech Traits

IR Corn Impacts

Two biotech IR traits have been commercially used to target the common corn-boring pests—European corn borer or ECB (Ostrinia nubilalis) and Mediterranean stem borer or MSB (Sesamia nonagroides)—and corn rootworm pests (Diabrotica). These are major pests of corn crops in many parts of the world and significantly reduce yield and crop quality, unless crop-protection practices are employed.

The two biotech IR corn traits have delivered positive yield impacts in all user countries when compared to average yields derived from crops using conventional technology (mostly application of insecticides and seed treatments) for control of corn-boring and rootworm pests.

The yield impact varies from an average of about +5% in North America to +24% in the Philippines (Table 1). In terms of additional production, on an area basis, this is in a range of +0.31 tonnes/ha to +0.72 tonnes/ha.

Table 1. Corn: Yield and production impact of IR traits, 1996-2006.
Cumulative total corn area (ha)15 Cumulative trait area (ha) % of crop to trait16 Average trait impact on yield %17 Average yield impact (tonnes/ha) Additional production from trait (tonnes)
US corn-borer resistant 351,842,503 81,016,473 23% +5.0% +0.45 36,078,447
US corn-rootworm resistant As above 6,596,520 1.9% +5.0% +0.45 3,130,130
Canada corn-borer resistant 13,269,070 4,239,214 31.9% +5.0% +0.38 1,628,075
Canada corn-rootworm resistant As above 35,317 0.3% +5.0% +0.38 14,537
Argentina corn-borer resistant 23,951,406 10,024,000 41.9% +7.6% +0.49 4,862,787
Philippines corn-borer resistant 10,082,808 247,698 2.5% +24.1% +0.52 127,920
South Africa corn-borer resistant 21,909,720 2,392,000 10.9% +14.5% +0.43 1,034,735
Uruguay corn-borer resistant 184,000 100,000 54.3% +6.1% +0.31 30,559
Spain corn-borer resistant 4,013,343 303,656 7.6% +7.6% +0.72 218,132
Cumulative totals 425,252,850 104,954,778 24.7 +5.7% +0.45 47,125,322
2006 41,751,216 20,640,503 49% +6.7% +0.47 9,734,898
a For consistency purposes, the total areas presented refer only to the years in which the IR traits were used by farmers—from 1996 in the US and Canada, from 1998 in Spain and Argentina, from 2000 in South Africa, from 2003 in the Philippines, and from 2004 in Uruguay. Corn rootworm-resistant corn has also been available to US farmers from 2003 and to Canadian farmers from 2004.
b From year of first commercial planting to 2006.
c Average of impact over years of use, as used by Brookes and Barfoot (2008).

Average yield and production impact across the total area planted to biotech IR corn traits over the 11-year period has been +5.7% (+0.45 tonnes/ha). This has added 47 million tonnes to total corn production in the countries using the technology.

In 2006, the technology delivered an average of 0.47 tonnes/ha in extra production, which was equal to an extra 9.7 million tonnes of corn production (Table 1).

HT Soybeans

Weeds have traditionally been a significant problem for soybean farmers, causing important yield losses (from weed competition for light, nutrients, and water). Most weeds in soybean crops have been reasonably well controlled, based on application of a mix of herbicides.

Although the primary impact of biotech HT technology has been to provide more cost effective (less expensive) and easier weed control versus improving yields from better weed control (relative to weed control obtained from conventional technology), improved weed control has, nevertheless occurred, delivering higher yields. Specifically, HT soybeans in Romania improved the average yield by over 30% (Figure 1).

Figure 1. Herbicide-tolerant crops: Yield and production impact of biotechnology 1996-2006 by country.

Biotech HT soybeans have also facilitated the adoption of no-tillage production systems, thus shortening the production cycle. This advantage enables many farmers in South America to plant a crop of soybeans immediately after a wheat crop in the same growing season. This second crop, additional to traditional soybean production, has added 53.1 million tonnes to soybean production in Argentina and Paraguay between 1996 and 2006. In 2006, the second-crop soybean production in these countries was 11.6 million tonnes (Table 2).

Table 2. Second crop soybean production facilitated by biotech HT technology in South America 1996-2006 (million tonnes).
Country Year first commercial use of HT soybean technology Second-crop soybean production from date of first commercial use to 2006
Argentina 1996 50.9
Paraguay 1999 2.2
Total 53.1

HT Canola

Weeds represent a significant problem for canola growers because they contribute to reduced yield and impair quality by contamination (e.g., with wild mustard seeds). Conventional canola weed control is based on a mix of herbicides, and it has provided reasonable levels of control, although some resistant weeds have developed (e.g., to the herbicide trifluralin). Canola is also sensitive to herbicide carryover from (herbicide) treatments in preceding crops, which can affect yield.

The main impact of biotech HT canola technology—used widely by canola farmers in Canada and the United States—has been to provide more cost-effective (less expensive) and easier weed control, coupled with higher yields. The higher yields have arisen mainly from more effective levels of weed control than were previously possible using conventional technology. Some farmers have also obtained yield gains from biotech-derived improvements in the yield potential of some HT canola seed.

The average yield impacts have been about +6% (+0.1 tonnes/ha) in the United States and about +10% (+0.15 tonnes/ha) in Canada (Figure 1). Over the 1996-2006 period, the additional North American canola production arising from the use of biotech HT technology was 3.2 million tonnes.

HT Corn

Weeds have also been a significant problem for corn farmers, causing important yield losses. Most weeds in these crops have been reasonably controlled based on application of a mix of herbicides.

The HT technology used in corn has mainly provided more cost-effective (less expensive) and easier weed control rather than improving yields from better weed control (relative to weed control levels obtained from conventional technology).

Improved weed control from use of the HT technology has, nevertheless, delivered higher yields in some regions (Figure 1). For example, in Argentina, where HT corn was first used commercially in 2005, the average yield effect has been +9%, adding +0.36 tonnes/ha to production. Similarly in the Philippines, (first used commercially in 2006), early adopters are finding an average of +15% to yields (+0.72 tonnes/ha).

Conversion of Production and Yield Impacts into Usable Assumptions

To provide suitable assumptions for input into the agricultural commodity models, the production and yield impacts summarized in the above section (Production and Yield Impacts of Biotech Traits) were converted into national-level yield equivalents. These are presented in Table 3. These yield change assumptions were then introduced into the models to identify impacts of withdrawing the (bio) technology from production systems and hence indirectly identify the impact of the biotech traits to date. The results are presented next.

Table 3. Yield impact assumptions: To lower average yields for countries/crops assuming no biotech used from 2008 onwards.
Crop/country Average yield/production effect on biotech area 2006 % of crop to trait (2006) Yield impact of technology related to average yield on total crop if no longer used
Corn
US +5% 49% -2.45%
Canada +5% 50% -2.45%
Argentina +7.6% 73% -5.55%
Philippines +24.1% 4% -0.97%
South Africa +14.5% 35% -5.1%
EU-27 +6.1% (Spain) 15% of Spain, 3.3 % of EU-27 area -0.2% on EU-27 average yield
Soybeans
EU-27 +31% (Romania) 26% of EU-27 area -8.1%
Paraguay +7.5% second crop 7.5% -7.5%
Argentina +20% second crop 20% -20%
Canola
US canola 6% 98% -5.9%
Canada canola +3.7% 84% -3.1%

Impact of Biotech Traits on Prices, Production, Consumption, and Trade in the Cereals and Oilseeds Sectors

World Level

Prices

The running of the agricultural commodity models under the ‘no biotech traits’ scenario suggests that the impact that these productivity-enhancing biotech traits in corn, soybeans, and canola have had on world prices of both these crops/derivatives and other cereals and oilseeds is significant. We consider the no-biotech scenario as a deviation from the 2007 baseline. In the scenario, the yield shocks are fully implemented from 2008 through 2017. We report the average of these annual changes for the years 2008-2010 as a summary indicator of the short term impacts. The scenario run shows that if these traits were no longer used in global agriculture, the loss of the yield and production-enhancing capabilities of the technology would result in world prices of corn, soybeans, and canola increasing by +5.8%, +9.6%, and +3.8%, respectively (Figure 2). There would also be knock-on effects on the prices of derivatives (e.g., a +9% increase in the world price of soymeal and a +5% increase in the price of soy oil) and other cereals and oilseeds (e.g., increases in prices of +2.7% to +4.2% of wheat, barley, and sorghum). In response to the decline in yields of corn, soybean, and canola, the production of these crops decline and their prices increase. This leads to area reallocation away from wheat, thus increasing its price—though less of an increase relative to corn, soybean, and canola prices. Given the limitations of the analysis (in not including an examination of the impact of the cost-reducing impact of the technology), these estimates of the impact on crop prices are probably understated. Additional information is presented in Appendices B and C to help readers follow how the summary values presented in this section were derived.

Figure 2. Increase in world commodity prices if biotech traits are no longer used.

In monetary ($ terms), Figure 3 shows the impacts of these price increases relative to the average 2007/08 world price levels.7

Figure 3. Increase in world commodity prices if biotech traits are no longer used ($/tonne).

Relating these price changes to global consumption, this is equivalent to adding $25 billion (+4.5%) to the total cost of consumption of these crops/derivatives in 2007/08 (Table 4). The sectors most affected would be the corn- and soybean/derivative-using sectors, although there would also be a significant knock-on effect in the wheat sector.

Table 4.Global consumption of key commodities/derivatives 2007-08 and impact of price changes.
  Consumption
(million tonnes)
Cost of consumption
($ billion)
Additional cost of consumption if biotech traits no longer available
($ billion)
Corn 776.80 169.3 9.82
Wheat 618.10 194.1 5.24
Barley 136.30 33.0 1.09
Sorghum 63.28 18.9 0.79
Soymeal 157.09 49.3 4.39
Soy oil 37.40 43.1 2.24
Canola meal 27.12 8.1 0.32
Canola oil 18.34 25.9 0.72
Sunflower meal 10.43 2.0 0.07
Sunflower oil 9.41 15.4 0.26
Total 1,854.00 559.1 24.94
Sources: Baseline data from USDA Market & Trade reports. Prices based on import/export levels using mainstream ports of trade (USDA). These consumption figures (see Appendix C) differ marginally from the consumption values used in the model baseline presented in Appendix B because they are based on more recent (updated) values to those originally input into the models.

In terms of income, it is important to recognize that the productivity-enhancing technology has already had an impact on producer (farmer) incomes. The downward world price effects of the technology identified above represent a loss to farmer incomes but a gain to consumers. The negative price effects at the producer level have, though, been more than offset by the direct income gains associated with adoption of the technology for those farmers who have used biotech traits. The direct farm-income gain identified from adoption of biotech traits over the period 1996-2006 was $33.8 billion (Brookes & Barfoot, 2008); this income gain was calculated net (inclusive) of the price effects identified above by using current farm-level prices for each crop, country, and year. In contrast, those farmers who have chosen to not adopt the technology or been denied access to the technology (e.g., on political or regulatory grounds) have experienced the negative price effect but not gained from the yield gains and cost savings associated with using the technology.

Production, Trade and Consumption Impacts

The effect of no longer using the current biotech traits in the corn, soybean, and canola sectors will have an impact on both the supply and utilization of these crops, their derivatives, and related markets for grain and oilseeds.

By taking away the positive yield and production impacts of the technology from the areas planted to these traits, the negative impacts would be felt most in the current-user (technology) countries (see Production and Yield Assumptions section). At the global level, the model analysis suggests that the negative impacts on the yields of the three crops are equal to an average reduction of 1.5%, 4.3%, and 0.65%, respectively, for corn, soybeans, and canola (Table 5).

Table 5.Potential change to global production base if biotech traits are no longer used.
Area change
(million ha)
Yield
(tonnes/ha)
Production
(million tonnes)
Corn +0.48 (+0.3%) -0.08 (-1.5%) -9.48 (-1.2%)
Soybeans +2.27 (+2.5%) -0.11 (-4.3%) -4.36 (-2%)
Canola +0.11 (+0.4%) -0.01 (-0.65%) -0.14 (-0.3%)
Soymeal n/a n/a -2.69 (-1.7%)
Soy oil n/a n/a -0.67 (-1.8%)
Canola/rape meal n/a n/a -0.03 (-0.1%)
Canola/rape oil n/a n/a -0.04 (-0.2%)
Notes: n/a = not applicable. Baseline for these changes are 2007/08 values. These are marginally different to the model baseline values presented in Appendix B.

The dynamic effect on subsequent plantings and the production base would result in a projected increase in the total area planted to these three crops of just under 3 million hectares, although this ‘compensatory’ additional planting would not offset the yield-reduction effects of no longer using biotech traits, resulting in a net fall in global production of the three crops of 14 million tonnes. In respect of the key oilseed derivatives of meal and oil, the reduction in the supply of the base seed (soybeans and rapeseed) would result in knock-on falls in global production of soymeal (1.7%), soy oil (1.8%), rapemeal (0.1%), and rape oil (0.2%). The total reduction in supply of these crops and key derivatives of meal and oil is projected to be 17.4 million tonnes.

The change in the supply availability of these three crops and the resulting upward effect on prices is forecast to lead to falls in global trade of these crops/derivatives. The modelling suggests that world trade in these crops/derivatives would fall by about 6.6 million tonnes, of which the main changes would be decreased trade volumes of 3.2 million tonnes, 1.65 million tonnes, and 1.24 million tonnes for corn, soymeal, and soybeans, respectively.

The model also predicts annual decreases in global consumption of these commodities and derivatives of 14.25 million tonnes. The main decreases in consumption would be for corn (8.07 million tonnes: a 0.98% decrease), soymeal (2.67 million tonnes: 1.7% decrease), and soy oil (0.64 million tonnes: a 1.7% decrease). Change in global consumption of canola/rapeseed derivatives would be marginal.

The analysis also identifies impacts on related grain and oilseed sectors. In addition to the impact on prices (see IR Corn Impacts section), the production and consumption of grains such as wheat, barley, sorghum, and oilseeds, notably sunflower, would be affected (Table 6). The global production of wheat is projected to fall by 0.1%, while the production of sorghum would increase by 0.5%. The decline in wheat production is due to area reallocation away from wheat towards crops such as corn, soybean, and canola, which experienced price increases after a yield decline when biotechnology was no longer available. This is in part due to the impact of looking only at the yield impacts of biotech crops, but not at the lower production cost advantages brought about by biotech. In relation to global consumption, this is projected to fall for wheat but increase for barley, sorghum, sunflower meal, and oil.

Table 6.Potential global changes to other grains and oilseeds if biotech traits are no longer used.
Production
(million tonnes)
Consumption
(million tonnes)
Wheat -0.61 (-0.1%) 0.09 (0.01%)
Barley Nil +0.10 (+0.07%)
Sorghum +0.32 (+0.5%) +0.36 (+0.57%)
Sunflower meal Nil +0.02 (+0.2%)
Sunflower oil Nil +0.02 (+0.2%)

Taking both the impacts on the three directly affected sectors of corn, soybeans, canola, and related grains and oilseeds, the net impacts of existing biotech traits (if no longer used in global agriculture) are an additional 2.64 million hectares of land being brought into grain and oilseed production. Despite this increase in total planted area, net production of these grains and oilseeds (excluding derivatives) would fall by 14.3 million tonnes. Inclusive of the main oilseed derivatives (including sunflower), net production is forecast to fall by 17.7 million tonnes. World trade in these commodities and derivatives would also fall (by 6.6 million tonnes) and global consumption of these grains and oilseed derivatives is forecast to fall by 15.4 million tonnes. Lastly, the model estimates that the cost of global consumption of these crops and derivatives would increase by $20 billion (3.6%) relative to the total cost of consumption of the (higher) biotech-inclusive level of world consumption. In unit terms, the average cost of consumption would increase by about 4.6% from an average of $301/tonne to $315/tonne.

Country Level

This section discusses the impact at the global level on specific countries and regions of the world of biotech traits no longer being available.

US

If existing biotech traits were no longer available to farmers globally (including US farmers), the impact in the affected US cropping sectors would be significant (Table 7). The model analysis points to production of US corn and canola falling by 3% (10.8 million tonnes) and 5.7% (50,000 tonnes), respectively, mainly due to reduced yields (loss of yield-enhancing nature of the biotech traits). Soybean production, however, would potentially increase by 2.4 million tonnes due to increased plantings of soybeans (the yield losses to corn improving the relative competitive position of soybeans at the farm level).

Table 7. Potential change to the US production base if biotech traits are no longer used (% change).
  Area Average yield Production Net trade (net exports)
Corn -0.8% -2.5% -3% -10%
Soybeans +3.6% 0% +3.4% +14%
Canola +0.2% -5.9% -5.7% -10%

Trade effects would be similar to the production impacts, with decreases in the volumes of exported corn and canola of about 10%. Soybean exports, however, would potentially increase significantly due to the additional production. The model also forecasts knock-on effects in other sectors; plantings of wheat and sorghum would be expected to fall, resulting in decreased production of these crops (0.6% for wheat and 0.5% for sorghum). In contrast, plantings and production of barley are expected to increase by 1.1%. Lastly, domestic US consumption of corn, soybeans, and canola is expected to fall by 2%, 0.5%, and 2%, respectively (caused by the higher price; see Prices section).

Argentina

The effect of no longer using biotech traits globally in the Argentine corn and soybean sectors is summarized in Table 8. Production of corn is forecast to fall by 3.1% (about 0.7 million tonnes) due to reduced yields (loss of yield-enhancing nature of the biotech traits). Output of soybeans is predicted to fall more significantly because of the negative effect on second-crop soybeans, which accounted for 20%-plus of the total Argentine soybean crop in 2006 (GM HT technology having contributed to shortening the production cycle for soybeans allowing many farmers to plant a crop of soybeans after wheat in the same season). As such, no longer having access to this technology would potentially threaten plantings of second-crop soy, resulting in a significant fall in total soybean production (equal to almost 9 million tonnes).

Table 8. Potential change to the Argentine production base if biotech traits are no longer used (% change).
Area Average yield Production Net trade
(net exports)
Corn +1.6% -4.6% -3.1% -3.9%
Soybeans -18.5% (inclusive of loss of 2ndcrop soy) -0% -18.8% -81%
Soymeal n/a n/a -7% -7%
Soy oil n/a n/a -7% -8%
Note: n/a = not applicable. The model results presented in Appendix B differ from the changes presented in this table because the model inputs the loss of second-crop soybeans as a yield decrease. The effects presented in this table therefore adjust the negative yield effect used in the modelling to an area change which is projected to be a 1.5% increase in first-crop soybean plantings, relative to a 20% decrease in second-crop soybeans.

The declines in production of soybeans and corn would have an important negative impact on the wider Argentine economy. Domestic consumption of both corn and soybeans is forecast to fall by about 1% and 7%, respectively (due to reduced availability and higher prices). More importantly, the reduced levels of production would result in decreased volumes available for export, especially in the soybean and derivative sectors. Given that soybean exports have contributed and will continue to contribute tax revenues to the Argentine Exchequer, this would result in important cuts in government tax revenues. Lastly, the modelling results suggest that production of other cereals, notably wheat and barley, would potentially increase by over 1% due to increased plantings of these crops.

Canada

The estimated impact of no longer making available the existing biotech traits in the global corn, soybean, and canola markets on the relevant Canadian cropping sectors is summarized in Table 9. Production of corn and canola is forecast to fall by more than 2% (0.3 million tonnes for corn and 0.3 million tonnes of canola) due to reduced yields (loss of yield-enhancing nature of the biotech traits). Soybean production, however, would likely increase (by more than 2%) because of increased plantings (as in the United States, the yield losses to corn improving the relative competitive position of soybeans at the farm level). The model predicts that domestic consumption and use of all three commodities and derivatives would fall (by more than 4% for both soybeans and canola and by about 1% for corn) due to higher prices (see World Level section). Canada, a net importer of corn, increases its net imports because of the decline in production. Exports of soybeans, however, would potentially increase as decreased domestic consumption results in additional volumes becoming available for export. In contrast, exports of canola and derivatives would be expected to fall—exports being a major outlet for Canadian canola relative to domestic consumption; hence, any additional supplies available for export from reduced domestic consumption would be more than offset by the fall in production associated with the withdrawal of biotech traits. The changes in biotech crops also impact the other crop markets. With the increase in corn prices, wheat area in Canada declines as area shifts away from wheat to corn. This increases wheat prices and thus domestic use of wheat declines. Net exports of wheat in Canada increase since domestic use declines more than domestic supply because of the relatively larger decline in stocks of wheat.

Table 9. Potential change to the Canadian production base if biotech traits are no longer used (% change).
Area Average yield Production Net trade (net exports)
Corn +0.4% -2.5% -2.1% +5.6%
Soybeans +2.2% 0% +2.2% +8.8%
Canola +0.2% -3.1% -2.9% -1.5%
Soymeal n/a n/a -1.8% -3.3%
Soy oil n/a n/a -1.8% -3.3%
Canola/rape meal n/a n/a -5.3% -6.8%
Canola/rape oil n/a n/a -5.3% -6.8%
Wheat -0.14% 0% -0.14% 0.13%
Note: n/a = not applicable

South Africa and the Philippines—Corn Sector

Both these countries currently use biotech IR technology in their corn sectors. Consequently, if this technology was no longer available to these and all farmers globally, there would be important negative impacts for those farmers who currently use the technology. At the national level in South Africa, average corn yields would be expected to fall by more than 5%, resulting in a net 5.5% reduction in total corn production.8 In the Philippines, where adoption of biotech IR corn traits is more recent—and hence less widespread than in South Africa (5% of total crop compared to 63% of the total corn crop in South Africa)—the national-level impacts are an average decrease in corn yield of 1% and production falling by about 0.5%.9

The modelling results suggest that domestic consumption of corn is also expected to fall by more than 1.5% in both countries (due to higher prices of corn). In terms of net trade, imports in the Philippines would increase by about 0.1 million tonnes (50%), while in South Africa, exports (of corn) would fall by nearly 30% (about 0.45 million tonnes).

The European Union

There were two biotech traits in use commercially in EU-27 countries of relevance during the 1998-2006 period: IR corn in several member states and HT soybeans in Romania. The modelling analysis identifies negative impacts of no longer using these technologies (both in the EU and globally).10 Average EU-27 corn yields and production would be expected to fall marginally (by 0.2%),11 while both consumption and net trade (imports) of corn would fall by 0.3% and 1.2%, respectively (negative effect of higher world prices for corn). Average soybean yields across the EU would also be expected to fall by -3.2%, and production would be lower by -1.3% due to the negative effect on yields and production of soybeans in the important EU soybean-producing country of Romania. This reduced supply of domestic soybeans is forecast to result in reductions in the EU production of soymeal and soy oil (by 1.1%). Usage of soymeal and soy oil is also forecast to fall by 2.6% and 1.4%, respectively (due to higher world prices).

Conclusions

This study quantified, through the use of agricultural commodity models, the impact of biotech traits on production, usage, trade, and prices in the corn, soybean, and canola sectors. The previous analysis (Brookes & Barfoot, 2008) estimated that biotech crops, through the two main traits of insect resistance and herbicide tolerance have, during the 1996-2006 period, added 53.3 million tonnes and 47.1 million tonnes, respectively, to global production of soybeans and corn. The technology has also contributed an extra 3.2 million tonnes of canola.

The estimated impact of these additional volumes of production on markets and prices in the cereals and oilseeds sectors has been significant. Our modelling analysis of the potential impact of no longer using these traits in world agriculture shows that the world prices of these commodities, their key derivatives, and related cereal and oilseed crops would be significantly affected. World prices of corn, soybeans, and canola would probably be respectively 5.8%, 9.6%, and 3.8% higher than the baseline 2007 levels (when the technology was available for the analysis purposes). Prices of key derivatives of soybeans (meal and oil) would also be between 5% (oil) and 9% (meal) higher than the baseline levels, with rapeseed meal and oil prices being about 4% higher than baseline levels. World prices of related cereals and oilseeds would also be expected to rise by 3-4%.

The effect of no longer using the current biotech traits in the corn, soybean, and canola sectors would also impact both the supply and utilization of these crops, their derivatives, and related markets for grain and oilseeds. Average global yields are estimated to fall by 1.5%, 4.3%, and 0.65% for corn, soybeans, and canola, respectively. While there is likely to be some ‘compensatory’ additional plantings (of just under 3 million hectares) of these three crops, this would not offset the yield-reduction effects of no longer using biotech traits, thus resulting in a net fall in global production of the three crops of 14 million tonnes. The modelling also suggests that a fall in the supply availability of these three crops and the resulting upward effect on prices would lead to a projected decrease in global trade of these crops/derivatives of 6.6 million tonnes, a 1.4% decrease in corn usage and a 1.7% decrease in usage of soymeal and soy oil (changes in global consumption of canola/rapeseed derivatives would be marginal).

The production and consumption of grains such as wheat, barley, and sorghum and oilseeds, notably sunflower, would also be affected (e.g., the global production and consumption of wheat would fall by 0.1% and 0.01%, respectively).

Overall, the net impacts of existing biotech traits (if no longer used) in global agriculture are that an additional 2.64 million hectares of land would probably be brought into grain and oilseed production. Despite this, net production of grains and oilseeds (including derivatives) would potentially fall by 17.7 million tonnes12 and global consumption would potentially fall by 15.4 million tonnes. The cost of consumption would also increase by $20 billion (3.6%) relative to the total cost of consumption of the (higher) biotech-inclusive level of world consumption. In unit terms, the net cost of consumption would increase by about 4.6%.

The impacts identified in this analysis are probably conservative, reflecting the limitations of the methodology used to estimate the productivity-enhancing effects of biotech traits so far used in global agriculture. In particular, the limited research conducted to date into the impact of the cost-reducing effect of biotechnology (notably in HT soybeans) on prices and the assumption of using 2007 levels of biotech adoption as the ‘counterfactual’ position suggests that the price effects identified in this article represent only part of the total price impact of the technology. Subsequent research might usefully extend this analysis to incorporate consideration of the cost-reducing effect of the technology (especially HT technology), a more dynamic counterfactual position, and to examination of the cotton sector.

Endnotes

1 Drawing primarily on work by one of the authors, Brookes and Barfoot (2008). A more detailed paper is also available on http://www.pgeconomics.co.uk/pdf/globalimpactstudyjune2008pgeconomics.pdf.

2 The impact of biotech traits in the cotton sector is not included in the analysis.

3 More details about the modelling structure are presented in Appendix A.

4 http://www.fapri.iastate.edu/tools/

5 Also available at http://www.pgeconomics.co.uk. The specific yield impacts used derive from Appendix 2 of the AgBioForum article (2008).

6 Indirect effects on the prices of derivatives as a result of changes in the price of the base commodities (e.g., a change in the price of soybeans affecting the price of soymeal). Also, the effect on prices arising from changes in production levels.

7 The impacts presented in Appendix B show the price increases relative to the baseline price levels (average of 2008 through 2010) and are therefore marginally different from the changes presented in Figure 3, which relate to actual 2007/08 average prices. Appendix C summarizes the 2007/08 data used as the base for this figure.

8 Area planted is projected to fall by 0.5%.

9 Area planted is projected to increase by 0.7%.

10 The removal of access to this technology has, in fact, occurred in relation to herbicide tolerant soybeans in Romania, which joined the EU in 2007, and hence, had to adopt EU regulations relating to biotechnology—the planting of biotech herbicide tolerant soybeans is currently not permitted in the EU-27.

11 Readers should note that biotech IR corn was planted on about 0.1 million hectares in the EU-27 in 2007, equal to 1.3% of total EU-27 corn planting.

12 Sum of Tables 5 and 6.

13 http://www.fapri.iastate.edu/models/

14 http://www.fapri.iastate.edu/tools/

References

Anderson, K., Valenzuela, E., & Jackson, L. (2008). Recent and prospective adoption of genetically modified cotton: A global computable general equilibrium analysis of economic impacts. Economic Development and Culture Change, 56(2), 265-296.

Brookes, G., & Barfoot, P. (2008). GM crops: Global socio-economic and environmental impacts 1996-2006. AgBioForum, 11(1), 21-38. Available on the World Wide Web: http://www.agbioforum.org.

Elobeid, A., Tokgoz, S., Hayes, D.J., Babcock, B.A., & Hart, C.E. (2007). The long-run impact of corn-based ethanol on the grain, oilseed, and livestock sectors with implications for biotech crops. AgBioForum, 10(1), 11-18.

Fabiosa, J., Beghin, J., De Cara, S., Fang, C., Isik, M., Matthey, H., et al. (2005). The Doha Round of the WTO and agricultural markets liberalization: Impacts on developing economies. Review of Agricultural Economics, 27(3), 317-335.

Fabiosa, J.F., Beghin, J.C., Dong, F., Elobeid, A., Fuller, F., Matthey, H., et al. (2007). The impact of the European enlargement and CAP reforms on agricultural markets. Much ado about nothing? Journal of International Agricultural Trade and Development, 3(1), 57-70.

James, C. (2008). Global status of commercialized biotech/GM crops 2008 (ISAAA Brief 39). Ithaca, NY: International Service for the Acquisition of Agri-biotech Applications (ISAAA).

Martin, M., & Hyde, J. (2001). Economic considerations for the adoption of transgenic crops: The case of Bt corn. Journal of Nematology, 33(4), 173-177.

Moschini, G., Lapan, H., & Sobolevsky, A. (2000). Roundup Ready soybeans and welfare effects in the soybean complex. Agribusiness, 16(1), 33-55.

Qaim, M., & Traxler, G. (2002, July). Roundup Ready soybeans in Argentina: Farm level, environmental and welfare effects. Paper presented at the 6th International Consortium on Agricultural Biotechnology Research (ICABR) Conference, Ravello, Italy.

Qaim, M., & Traxler, G. (2005). Roundup Ready soybeans in Argentina: Farm level & aggregate welfare effects. Agricultural Economics, 32(1), 73-86.

Sobolevsky, A., Moschini, G., & Lapan, H. (2005). Genetically modified crops and product differentiation: Trade and welfare effects in the soybean complex. American Journal of Agricultural Economics, 87(3), 621-644.

Tokgoz, S., Elobeid, A., Fabiosa, J., Hayes, D.J., Babcock, B.A., Yu, T., et al. (2008). Bottlenecks, drought, and oil price spikes: Impact on US ethanol and agricultural sectors. Review of Agricultural Economics, 30(4), 604-622.

Appendix A: Agricultural Modelling System—Methodological Details

General Description of the Modelling System

This study uses part of a broad modelling system of world agricultural economy comprised of US and international multi-market, partial-equilibrium models. The models are econometric and simulation models covering all major temperate crops, sugar, ethanol and bio-diesel, dairy, and livestock and meat products for all major producing and consuming countries and calibrated on most recently available data. A Rest-of-the-World aggregate is included to close the models. Table A1 presents a detailed list of commodity and country coverage. Extensive market linkages exist in these models, reflecting derived demand for feed in livestock and dairy sectors, competition for land in production, and consumer substitution possibilities for close substitutes such as vegetable oils and meat types.

Table A1. Model inputs and output.
Commodities Major countries/regions Exogenous inputs Historical data (inputs) Output by commodity and country
Grains
Corn
Wheat
Sorghum
Barley
Oilseeds
Soybeans
Rapeseed
Sunflower
Sugar
Biofuels
Ethanol
Biodiesel
North America: United States, Canada, Mexico
South America: Brazil, Argentina, Colombia, etc.
Asia: China, Japan, India, Indonesia, Malaysia, etc.
Africa: South Africa, Egypt, etc.
European Union
Oceania: Australia
Middle East: Iran, Saudi Arabia, etc.
Rest of the World
Population
GDP
GDP deflator
Exchange rate
Population
Policy variables
Production
Consumption
Exports
Imports
Ending stocks
Domestic prices
World prices
World prices
Domestic prices
Production
Consumption
Net trade
Stocks
Area harvested
Yield

The models capture the biological, technical, and economic relationships among key variables within a particular commodity and across commodities. They are based on historical data analysis, current academic research, and a reliance on accepted economic, agronomic, and biological relationships in agricultural production and markets. A link is made through prices and net trade equations between the US and international models. The models are used to establish commodity projections for a baseline and for policy analysis, and are used extensively for the market outlook and policy analysis. This set of agricultural models have been used in a number of studies including Elobeid et al. (2007), Fabiosa et al. (2005, 2007), and Tokgoz et al. (2008).

In general, for each commodity sector, the economic relationship that supply equals demand is maintained by determining a market-clearing price for the commodity. In countries where domestic prices are not solved endogenously, these prices are modelled 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 other sectors. A detailed description of the models is available on Iowa State University’s FAPRI website.13 Figure A1 provides a diagram of the overall modelling system. For this particular study, the US Crops, International Grains, International Oilseed, International Sugar, and International Bio-fuels models were used.

Figure A1. Model interactions: Trade, prices and physical flows.

More specifically in terms of the structure of the models, the following identity is satisfied for each country/region and the world:

Beginning Stock + Production + Imports = Ending Stock + Consumption + Exports

Production is divided into yield and area equations, while consumption is divided into feed and non-feed demand. The models include behavioral equations for area harvested, yield, crop production on the supply side, and per-capita consumption and ending stocks on the demand side. Equilibrium prices, quantities, and net trade are determined by equating excess supply and excess demand across countries and regions. To satisfy the identity in Equation 1, two different methods are used. In most of the countries, domestic price is modelled as a function of the world price with a price transmission equation, and the identity is satisfied with one of the variables set as the residual. In other cases, prices are solved to satisfy the identity.

Agricultural and trade policies in each country are included in the models to the extent that they affect the supply and demand decisions of the economic agents. Examples of these include taxes on exports and imports, tariffs, tariff rate quotas, export subsidies, intervention prices, and set-aside rates. The models assume that the existing agricultural and trade policy variables will remain unchanged in the outlook period. Macroeconomic variables, such as GDP, population, and exchange rates, are exogenous variables that drive the projections of the model. The models also include an adjustment for marketing-year differences by including a residual that is equal to world exports minus world imports, which ensures that world demand equals world supply.

All models are calibrated on 2007/08 marketing year data for crops and 2007 calendar year data for livestock and biofuels, and 10-year projections for supply and utilization of commodities and prices are generated for the period between 2008 and 2017. The models also adjust for marketing-year differences by including a residual that is equal to world exports minus world imports, which ensures that world demand equals world supply. Elasticity values for supply and demand responses are based on econometric analysis and on consensus estimates. Elasticity parameters estimates and policy variables are available in Iowa State University’s FAPRI’s Elasticity Database.14

Data for commodity supply and utilization are obtained from the F.O. Lichts online database, the Food and Agriculture Organization (FAO) of the United Nations (FAOSTAT Online, 2006), the Production, Supply and Distribution View (PS&D) of the US Department of Agriculture (USDA), the European Commission Directorate General for Energy and Transport, the ANFAVEA (2005), and UNICA (2006). Supply and utilization data include production, consumption, net trade, and stocks. The macroeconomic data are gathered from the International Monetary Fund and Global Insight.

Appendix B. Scenario Results

Table B1. Wheat prices.
08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18
US FOB Gulf (US dollars per metric ton)
Baseline 251.95 252.04 258.65 257.80 261.80 264.06 266.98 270.41 272.93 273.75
Scenario 1 255.89 260.37 267.10 264.47 268.57 271.17 273.74 276.99 279.76 280.78
% change 1.56% 3.31% 3.27% 2.58% 2.59% 2.69% 2.53% 2.43% 2.50% 2.57%
Canadian Wheat Board
Baseline 262.60 262.06 267.48 266.15 269.33 270.37 271.87 274.00 275.66 276.48
Scenario 1 265.99 269.20 274.65 271.77 275.07 276.40 277.61 279.59 281.47 282.47
% change 1.29% 2.73% 2.68% 2.11% 2.13% 2.23% 2.11% 2.04% 2.11% 2.16%
AWB limited export quote
Baseline 252.70 251.43 257.05 256.47 259.85 261.86 264.39 267.37 269.58 270.34
Scenario 1 256.04 258.60 264.41 262.32 265.75 268.04 270.28 273.11 275.53 276.45
% change 1.32% 2.85% 2.86% 2.28% 2.27% 2.36% 2.23% 2.15% 2.21% 2.26%
European Union market
Baseline 270.66 252.49 241.79 237.26 231.78 230.18 231.70 233.38 235.10 236.16
Scenario 1 274.11 255.21 244.21 239.81 234.39 232.74 234.34 236.12 237.94 239.14
% change 1.27% 1.08% 1.00% 1.08% 1.13% 1.11% 1.14% 1.17% 1.21% 1.26%

Table B2. World wheat supply and utilization.
08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18
Area harvested (Thousand hectares)
Baseline 222,149 221,970 219,530 220,580 220,862 220,987 221,245 221,363 221,426 221,668
Scenario 1 222,096 221,555 219,352 220,685 220,838 220,943 221,229 221,338 221,386 221,626
% change -0.02% -0.19% -0.08% 0.05% -0.01% -0.02% -0.01% -0.01% -0.02% -0.02%
Yield (Metric tons per hectare)
Baseline 2.92 2.93 2.96 2.98 3.00 3.03 3.05 3.07 3.10 3.12
Scenario 1 2.92 2.93 2.96 2.98 3.00 3.03 3.05 3.07 3.10 3.12
% change -0.02% 0.00% 0.01% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Production (Thousand metric tons)
Baseline 648,567 650,692 649,049 657,034 662,973 668,541 674,503 680,056 685,459 691,360
Scenario 1 648,294 649,468 648,582 657,345 662,873 668,398 674,438 679,951 685,304 691,199
% change -0.04% -0.19% -0.07% 0.05% -0.02% -0.02% -0.01% -0.02% -0.02% -0.02%
Beginning stocks
Baseline 111,043 128,080 133,956 134,678 136,261 137,314 138,218 138,988 139,655 140,416
Scenario 1 111,043 127,138 131,963 132,452 134,419 135,564 136,444 137,304 138,047 138,804
% change 0.00% -0.74% -1.49% -1.65% -1.35% -1.27% -1.28% -1.21% -1.15% -1.15%
Domestic supply
Baseline 759,610 778,772 783,005 791,712 799,235 805,854 812,720 819,044 825,114 831,777
Scenario 1 759,337 776,605 780,545 789,797 797,292 803,962 810,882 817,254 823,350 830,003
% change -0.04% -0.28% -0.31% -0.24% -0.24% -0.23% -0.23% -0.22% -0.21% -0.21%
Feed use
Baseline 106,204 110,104 110,389 111,272 112,283 112,932 113,533 114,211 114,658 115,137
Scenario 1 106,652 110,543 110,836 111,712 112,657 113,336 113,921 114,568 115,024 115,514
% change 0.42% 0.40% 0.41% 0.40% 0.33% 0.36% 0.34% 0.31% 0.32% 0.33%
Food and other
Baseline 525,325 534,712 537,938 544,178 549,639 554,705 560,199 565,178 570,040 575,047
Scenario 1 525,547 534,099 537,258 543,666 549,071 554,181 559,657 564,640 569,522 574,524
% change 0.04% -0.11% -0.13% -0.09% -0.10% -0.09% -0.10% -0.10% -0.09% -0.09%
Ending stocks
Baseline 128,080 133,956 134,678 136,261 137,314 138,218 138,988 139,655 140,416 141,593
Scenario 1 127,138 131,963 132,452 134,419 135,564 136,444 137,304 138,047 138,804 139,965
% change -0.74% -1.49% -1.65% -1.35% -1.27% -1.28% -1.21% -1.15% -1.15% -1.15%
Domestic use
Baseline 759,610 778,772 783,005 791,712 799,235 805,854 812,720 819,044 825,114 831,777
Scenario 1 759,337 776,605 780,545 789,797 797,292 803,962 810,882 817,254 823,350 830,003
% change -0.04% -0.28% -0.31% -0.24% -0.24% -0.23% -0.23% -0.22% -0.21% -0.21%
Trade *
Baseline 89,343 94,120 94,202 95,988 98,715 100,937 103,167 105,148 106,888 108,747
Scenario 1 89,429 94,198 94,095 95,910 98,588 100,845 103,045 105,056 106,839 108,694
% change 0.10% 0.08% -0.11% -0.08% -0.13% -0.09% -0.12% -0.09% -0.05% -0.05%
Stocks-to-use ratio (Percent)
Baseline 20.28 20.77 20.77 20.79 20.74 20.70 20.63 20.56 20.51 20.52
Scenario 1 20.11 20.47 20.44 20.51 20.49 20.44 20.38 20.32 20.28 20.28
% change -0.84% -1.46% -1.62% -1.34% -1.25% -1.27% -1.19% -1.13% -1.13% -1.13%
* Excludes international trade

Table B3. Coarse grain prices.
08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18
Corn (FOB Gulf) (US dollars per metric ton)
Baseline 196 216 209 209 215 215 217 221 221 220
Scenario 1 206 229 222 219 226 226 227 231 231 231
% change 4.97% 6.32% 6.08% 4.89% 4.80% 5.17% 4.73% 4.51% 4.78% 4.94%
Sorghum (FOB Gulf)
Baseline 175 191 183 184 189 188 191 194 195 195
Scenario 1 181 199 192 191 196 195 197 201 202 202
% change 3.64% 4.60% 4.49% 3.50% 3.56% 3.87% 3.47% 3.36% 3.61% 3.71%
Barley (Canada feed)
Baseline 146 153 153 154 158 161 164 169 172 175
Scenario 1 149 159 159 159 162 166 169 173 177 180
% change 2.15% 3.87% 3.89% 3.21% 2.96% 3.14% 2.95% 2.71% 2.78% 2.85%
Corn (EU)
Baseline 259.24 234.42 224.72 221.50 217.38 215.39 216.36 217.33 217.60 217.06
Scenario 1 264.28 238.93 228.88 225.46 221.36 219.47 220.41 221.42 221.88 221.53
% change 1.94% 1.93% 1.85% 1.79% 1.83% 1.89% 1.87% 1.88% 1.97% 2.06%
Barley (EU)
Baseline 244.80 225.86 217.26 213.89 209.27 207.91 209.25 210.64 211.87 212.54
Scenario 1 247.67 228.19 219.26 216.01 211.43 210.04 211.45 212.90 214.21 215.00
% change 1.17% 1.03% 0.92% 0.99% 1.03% 1.03% 1.05% 1.07% 1.11% 1.16%

Table B4. World corn supply and utilization.
08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18
Area harvested (Thousand hectares)
Baseline 160,424 161,061 166,781 168,047 167,954 170,035 170,820 171,280 172,286 172,931
Scenario 1 160,599 161,436 167,628 169,176 168,638 170,345 171,256 171,616 172,407 173,089
% change 0.11% 0.23% 0.51% 0.67% 0.41% 0.18% 0.26% 0.20% 0.07% 0.09%
Yield (Metric tons per hectare)
Baseline 4.96 5.03 5.14 5.25 5.31 5.39 5.47 5.53 5.60 5.67
Scenario 1 4.90 4.95 5.05 5.17 5.22 5.29 5.37 5.43 5.50 5.56
% change -1.18% -1.60% -1.72% -1.61% -1.60% -1.76% -1.78% -1.75% -1.82% -1.84%
Production (Thousand metric tons)
Baseline 795,217 810,266 856,591 882,789 891,255 915,958 934,479 947,376 964,302 980,380
Scenario 1 786,714 799,131 846,138 874,440 880,580 901,471 920,223 932,587 947,439 963,237
% change -1.07% -1.37% -1.22% -0.95% -1.20% -1.58% -1.53% -1.56% -1.75% -1.75%
Beginning stocks
Baseline 102,533 103,581 97,074 101,584 106,107 103,897 105,121 106,391 105,725 106,354
Scenario 1 102,533 100,234 91,708 95,717 101,391 99,763 100,321 101,790 101,531 101,839
% change 0.00% -3.23% -5.53% -5.78% -4.44% -3.98% -4.57% -4.33% -3.97% -4.24%
Domestic supply
Baseline 897,750 913,848 953,665 984,374 997,362 1,019,854 1,039,600 1,053,768 1,070,027 1,086,734
Scenario 1 889,248 899,365 937,846 970,158 981,971 1,001,234 1,020,544 1,034,377 1,048,969 1,065,076
% change -0.95% -1.58% -1.66% -1.44% -1.54% -1.83% -1.83% -1.84% -1.97% -1.99%
Feed use
Baseline 490,514 486,098 497,113 506,626 509,382 517,178 523,330 527,204 532,514 538,892
Scenario 1 487,048 480,003 490,879 501,903 504,689 511,585 518,109 522,215 527,057 533,278
% change -0.71% -1.25% -1.25% -0.93% -0.92% -1.08% -1.00% -0.95% -1.02% -1.04%
Food and other
Baseline 303,655 330,676 354,968 371,640 384,084 397,555 409,878 420,839 431,159 439,609
Scenario 1 301,966 327,653 351,250 366,864 377,519 389,329 400,645 410,631 420,073 428,408
% change -0.56% -0.91% -1.05% -1.29% -1.71% -2.07% -2.25% -2.43% -2.57% -2.55%
Ending stocks
Baseline 103,581 97,074 101,584 106,107 103,897 105,121 106,391 105,725 106,354 108,233
Scenario 1 100,234 91,708 95,717 101,391 99,763 100,321 101,790 101,531 101,839 103,390
% change -3.23% -5.53% -5.78% -4.44% -3.98% -4.57% -4.33% -3.97% -4.24% -4.47%
Domestic use
Baseline 897,750 913,848 953,665 984,374 997,362 1,019,854 1,039,600 1,053,768 1,070,027 1,086,734
Scenario 1 889,248 899,365 937,846 970,158 981,971 1,001,234 1,020,544 1,034,377 1,048,969 1,065,076
% change -0.95% -1.58% -1.66% -1.44% -1.54% -1.83% -1.83% -1.84% -1.97% -1.99%
Trade *
Baseline 85,330 82,314 83,886 86,491 87,216 89,114 91,056 92,342 94,072 96,335
Scenario 1 83,408 79,105 80,681 83,874 84,859 86,613 88,685 90,151 91,852 94,045
% change -2.25% -3.90% -3.82% -3.03% -2.70% -2.81% -2.60% -2.37% -2.36% -2.38%
Stocks-to-use ratio (Percent)
Baseline 13.04 11.89 11.92 12.08 11.63 11.49 11.40 11.15 11.04 11.06
Scenario 1 12.70 11.35 11.37 11.67 11.31 11.14 11.08 10.88 10.75 10.75
% change -2.60% -4.46% -4.66% -3.40% -2.75% -3.10% -2.82% -2.40% -2.57% -2.80%
* Excludes intraregional trade

Table B5. World barley supply and utilization.
08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18
Area harvested (Thousand hectares)
Baseline 56,910 56,795 57,012 57,019 57,048 57,086 57,213 57,237 57,304 57,387
Scenario 1 56,895 56,761 57,024 57,044 57,071 57,093 57,225 57,255 57,316 57,397
% change -0.03% -0.06% 0.02% 0.04% 0.04% 0.01% 0.02% 0.03% 0.02% 0.02%
Yield (Metric tons per hectare)
Baseline 2.53 2.55 2.56 2.57 2.59 2.60 2.62 2.63 2.64 2.65
Scenario 1 2.53 2.55 2.56 2.57 2.59 2.60 2.62 2.63 2.64 2.65
% change -0.03% 0.05% 0.05% 0.04% 0.02% 0.03% 0.03% 0.02% 0.02% 0.02%
Production (Thousand metric tons)
Baseline 144,105 144,573 145,914 146,705 147,629 148,556 149,633 150,443 151,326 152,241
Scenario 1 144,027 144,556 146,021 146,822 147,725 148,619 149,706 150,527 151,393 152,306
% change -0.05% -0.01% 0.07% 0.08% 0.07% 0.04% 0.05% 0.06% 0.04% 0.04%
Beginning stocks
Baseline 15,413 18,066 18,557 19,015 19,259 19,355 19,455 19,562 19,605 19,710
Scenario 1 15,413 17,876 18,260 18,718 19,005 19,115 19,201 19,319 19,377 19,475
% change 0.00% -1.05% -1.60% -1.56% -1.32% -1.24% -1.30% -1.24% -1.17% -1.19%
Domestic supply
Baseline 159,518 162,639 164,471 165,720 166,888 167,912 169,088 170,005 170,931 171,951
Scenario 1 159,440 162,432 164,281 165,540 166,730 167,733 168,907 169,847 170,769 171,781
% change -0.05% -0.13% -0.12% -0.11% -0.09% -0.11% -0.11% -0.09% -0.09% -0.10%
Feed use
Baseline 97,028 98,901 99,685 100,262 100,904 101,440 102,101 102,621 103,072 103,537
Scenario 1 97,166 99,042 99,843 100,390 101,033 101,564 102,213 102,738 103,191 103,655
% change 0.14% 0.14% 0.16% 0.13% 0.13% 0.12% 0.11% 0.11% 0.12% 0.11%
Food and other
Baseline 44,424 45,181 45,772 46,198 46,629 47,017 47,425 47,778 48,149 48,524
Scenario 1 44,397 45,130 45,720 46,145 46,583 46,968 47,375 47,732 48,103 48,477
% change -0.06% -0.11% -0.11% -0.11% -0.10% -0.10% -0.11% -0.10% -0.10% -0.10%
Ending stocks
Baseline 18,066 18,557 19,015 19,259 19,355 19,455 19,562 19,605 19,710 19,890
Scenario 1 17,876 18,260 18,718 19,005 19,115 19,201 19,319 19,377 19,475 19,650
% change -1.05% -1.60% -1.56% -1.32% -1.24% -1.30% -1.24% -1.17% -1.19% -1.21%
Domestic use
Baseline 159,518 162,639 164,471 165,720 166,888 167,912 169,088 170,005 170,931 171,951
Scenario 1 159,440 162,432 164,281 165,540 166,730 167,733 168,907 169,847 170,769 171,781
% change -0.05% -0.13% -0.12% -0.11% -0.09% -0.11% -0.11% -0.09% -0.09% -0.10%
Trade *
Baseline 15,871 16,721 17,067 17,246 17,430 17,539 17,648 17,729 17,783 17,829
Scenario 1 15,918 16,786 17,110 17,270 17,454 17,565 17,669 17,749 17,804 17,850
% change 0.30% 0.39% 0.25% 0.14% 0.14% 0.15% 0.12% 0.11% 0.12% 0.11%
Stocks-to-use ratio (Percent)
Baseline 12.77 12.88 13.07 13.15 13.12 13.10 13.08 13.04 13.03 13.08
Scenario 1 12.63 12.67 12.86 12.97 12.95 12.93 12.92 12.88 12.87 12.92
% change -1.13% -1.66% -1.63% -1.37% -1.30% -1.35% -1.28% -1.21% -1.24% -1.25%
* Excludes intraregional trade

Table B6. World sorghum supply and utilization.
08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18
Area harvested (Thousand hectares)
Baseline 41,252 40,889 41,670 41,378 41,134 41,487 41,507 41,724 41,976 42,008
Scenario 1 41,265 41,116 41,983 41,694 41,366 41,732 41,796 41,984 42,233 42,296
% change 0.03% 0.56% 0.75% 0.76% 0.56% 0.59% 0.70% 0.62% 0.61% 0.69%
Yield (Metric tons per hectare)
Baseline 1.54 1.53 1.54 1.56 1.57 1.59 1.60 1.61 1.62 1.64
Scenario 1 1.54 1.53 1.54 1.56 1.57 1.59 1.60 1.61 1.63 1.64
% change 0.05% 0.03% 0.01% 0.04% 0.06% 0.04% 0.05% 0.05% 0.04% 0.05%
Production (Thousand metric tons)
Baseline 63,439 62,547 64,362 64,602 64,739 65,874 66,423 67,263 68,200 68,820
Scenario 1 63,494 62,915 64,850 65,122 65,143 66,286 66,917 67,718 68,648 69,325
% change 0.09% 0.59% 0.76% 0.81% 0.62% 0.63% 0.74% 0.68% 0.66% 0.73%
Beginning stocks
Baseline 3,972 4,372 4,013 4,174 4,257 4,229 4,308 4,320 4,304 4,334
Scenario 1 3,972 4,273 3,853 3,998 4,110 4,085 4,151 4,176 4,166 4,187
% change 0.00% -2.26% -3.99% -4.22% -3.46% -3.41% -3.64% -3.34% -3.21% -3.38%
Domestic supply
Baseline 67,411 66,919 68,376 68,776 68,997 70,103 70,731 71,583 72,505 73,154
Scenario 1 67,466 67,189 68,703 69,120 69,253 70,371 71,068 71,894 72,814 73,513
% change 0.08% 0.40% 0.48% 0.50% 0.37% 0.38% 0.48% 0.43% 0.43% 0.49%
Feed use
Baseline 26,931 26,123 26,534 26,529 26,630 26,808 26,791 26,846 26,937 26,999
Scenario 1 27,069 26,288 26,686 26,691 26,759 26,933 26,929 26,966 27,049 27,117
% change 0.51% 0.63% 0.57% 0.61% 0.48% 0.47% 0.51% 0.45% 0.42% 0.44%
Food and other
Baseline 36,108 36,783 37,668 37,989 38,138 38,987 39,620 40,432 41,234 41,774
Scenario 1 36,123 37,048 38,020 38,319 38,409 39,287 39,963 40,761 41,578 42,165
% change 0.04% 0.72% 0.94% 0.87% 0.71% 0.77% 0.87% 0.81% 0.83% 0.94%
Ending stocks
Baseline 4,372 4,013 4,174 4,257 4,229 4,308 4,320 4,304 4,334 4,381
Scenario 1 4,273 3,853 3,998 4,110 4,085 4,151 4,176 4,166 4,187 4,231
% change -2.26% -3.99% -4.22% -3.46% -3.41% -3.64% -3.34% -3.21% -3.38% -3.43%
Domestic use
Baseline 67,411 66,919 68,376 68,776 68,997 70,103 70,731 71,583 72,505 73,154
Scenario 1 67,466 67,189 68,703 69,120 69,253 70,371 71,068 71,894 72,814 73,513
% change 0.08% 0.40% 0.48% 0.50% 0.37% 0.38% 0.48% 0.43% 0.43% 0.49%
Trade *
Baseline 6,109 5,621 5,557 5,761 5,823 5,935 6,100 6,192 6,277 6,409
Scenario 1 6,094 5,600 5,441 5,721 5,817 5,918 6,075 6,178 6,255 6,371
% change -0.24% -0.38% -2.10% -0.70% -0.10% -0.29% -0.40% -0.23% -0.35% -0.59%
Stocks-to-use ratio (Percent)
Baseline 6.94 6.38 6.50 6.60 6.53 6.55 6.50 6.40 6.36 6.37
Scenario 1 6.76 6.08 6.18 6.32 6.27 6.27 6.24 6.15 6.10 6.11
% change -2.50% -4.64% -4.96% -4.19% -4.00% -4.26% -4.03% -3.85% -4.03% -4.14%
* Excludes intraregional trade

Table B7. Soybean and product prices.
08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18
Soybean prices (US dollars per metric ton)
Illinois processor
Baseline 398 378 386 399 388 395 405 406 409 412
Scenario 1 442 419 415 432 422 426 437 439 441 445
% change 11.18% 10.81% 7.37% 8.21% 8.81% 7.85% 7.83% 8.15% 7.94% 8.03%
CIF Rotterdam
Baseline 511 486 496 511 497 505 517 518 521 523
Scenario 1 567 537 531 552 540 544 557 559 561 565
% change 10.94% 10.58% 7.22% 8.04% 8.63% 7.69% 7.67% 7.98% 7.78% 7.86%
Soymeal prices
FOB Decatur 48%
Baseline 306.76 289.97 281.68 283.93 283.49 285.21 289.20 287.94 285.00 281.04
Scenario 1 336.88 317.19 303.02 307.20 308.00 309.09 313.86 313.51 310.80 307.64
% change 9.82% 9.39% 7.57% 8.20% 8.65% 8.37% 8.52% 8.88% 9.05% 9.47%
CIF Rotterdam
Baseline 402.14 380.55 369.90 372.79 372.22 374.43 379.57 377.95 374.16 369.07
Scenario 1 440.80 415.53 397.33 402.71 403.73 405.13 411.26 410.81 407.33 403.27
% change 9.61% 9.19% 7.42% 8.03% 8.47% 8.20% 8.35% 8.69% 8.86% 9.27%
Soy oil prices
FOB Decatur
Baseline 1,034 1,029 1,075 1,102 1,055 1,070 1,094 1,111 1,140 1,171
Scenario 1 1,084 1,092 1,125 1,164 1,125 1,139 1,168 1,190 1,221 1,254
% change 4.83% 6.13% 4.62% 5.60% 6.61% 6.45% 6.78% 7.16% 7.11% 7.11%
FOB Rotterdam
Baseline 1,255 1,249 1,304 1,336 1,280 1,298 1,326 1,346 1,381 1,418
Scenario 1 1,314 1,324 1,363 1,409 1,363 1,380 1,414 1,441 1,477 1,516
% change 4.73% 6.01% 4.53% 5.49% 6.47% 6.31% 6.64% 7.01% 6.96% 6.97%

Table B8. Rapeseed and product prices.
08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18
Rapeseed prices (US dollars per metric ton)
Cash Vancouver
Baseline 411.34 411.19 413.68 396.40 392.59 395.74 396.93 398.25 402.49 405.44
Scenario 1 426.09 427.14 428.47 412.66 409.56 412.84 415.11 417.33 422.15 425.98
% change 3.58% 3.88% 3.58% 4.10% 4.32% 4.32% 4.58% 4.79% 4.89% 5.06%
CIF Hamburg
Baseline 529.20 529.00 532.27 509.59 504.60 508.73 510.28 512.01 517.58 521.45
Scenario 1 548.56 549.94 551.70 530.93 526.85 531.16 534.14 537.06 543.39 548.41
% change 3.66% 3.96% 3.65% 4.19% 4.41% 4.41% 4.68% 4.89% 4.99% 5.17%
Rapeseed meal price
FOB Hamburg
Baseline 303 301 295 294 301 305 308 309 308 304
Scenario 1 316 314 305 304 311 315 318 319 318 315
% change 4.32% 4.10% 3.57% 3.49% 3.34% 3.29% 3.28% 3.28% 3.37% 3.56%
Rapeseed oil price
FOB Hamburg
Baseline 1,310 1,344 1,385 1,347 1,338 1,362 1,385 1,413 1,456 1,502
Scenario 1 1,345 1,384 1,423 1,391 1,385 1,409 1,436 1,467 1,512 1,560
% change 2.65% 2.98% 2.78% 3.22% 3.50% 3.48% 3.65% 3.81% 3.81% 3.83%

Table B9. Sunflower seed and product prices.
08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18
(US dollars per metric ton)
CIF Lower Rhine
Baseline 601 588 596 587 577 578 580 579 579 578
Scenario 1 617 610 614 606 596 596 599 598 598 598
% change 2.59% 3.73% 3.05% 3.13% 3.30% 3.16% 3.24% 3.32% 3.31% 3.40%
CIF Rotterdam
Baseline 276 270 265 264 268 272 275 274 271 266
Scenario 1 285 280 274 272 276 280 282 282 279 275
% change 3.49% 3.76% 3.20% 3.04% 3.00% 2.93% 2.88% 2.89% 2.97% 3.13%
FOB NW Europe
Baseline 1,432 1,424 1,463 1,471 1,467 1,490 1,517 1,545 1,577 1,609
Scenario 1 1,451 1,453 1,487 1,497 1,495 1,517 1,546 1,575 1,607 1,639
% change 1.35% 2.00% 1.63% 1.78% 1.92% 1.83% 1.88% 1.93% 1.90% 1.89%

Table B10. World soybean sector supply and utilization.
08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18
Soybeans (Thousand hectares)
Area harvested
Baseline 96,946 99,931 100,256 100,770 102,729 103,231 103,792 105,049 105,939 106,803
Scenario 1 97,822 102,806 103,924 103,881 105,798 106,571 107,050 108,264 109,268 110,143
% change 0.90% 2.88% 3.66% 3.09% 2.99% 3.24% 3.14% 3.06% 3.14% 3.13%
Production (Thousand metric tons)
Baseline 242,217 252,279 255,277 258,491 266,315 270,217 274,164 280,326 285,531 290,682
Scenario 1 233,417 248,311 253,470 255,008 262,557 267,188 270,815 276,729 282,179 287,262
% change -3.63% -1.57% -0.71% -1.35% -1.41% -1.12% -1.22% -1.28% -1.17% -1.18%
Beginning stocks
Baseline 47,227 48,060 49,742 50,129 49,637 50,547 50,748 50,506 50,755 50,910
Scenario 1 47,227 45,053 46,583 47,617 46,967 47,645 47,971 47,716 47,850 48,013
% change 0.00% -6.26% -6.35% -5.01% -5.38% -5.74% -5.47% -5.52% -5.72% -5.69%
Domestic supply
Baseline 289,444 300,339 305,019 308,621 315,952 320,764 324,913 330,832 336,286 341,591
Scenario 1 280,643 293,364 300,052 302,625 309,524 314,833 318,785 324,445 330,029 335,275
% change -3.04% -2.32% -1.63% -1.94% -2.03% -1.85% -1.89% -1.93% -1.86% -1.85%
Crush
Baseline 209,533 218,369 222,558 226,309 232,128 236,595 240,822 246,127 251,150 256,042
Scenario 1 204,327 214,881 220,184 223,155 228,795 233,559 237,612 242,789 247,905 252,748
% change -2.48% -1.60% -1.07% -1.39% -1.44% -1.28% -1.33% -1.36% -1.29% -1.29%
Food use
Baseline 14,504 14,829 14,949 15,131 15,384 15,462 15,497 15,624 15,715 15,887
Scenario 1 14,182 14,484 14,723 14,900 15,112 15,218 15,257 15,366 15,468 15,637
% change -2.22% -2.33% -1.51% -1.53% -1.77% -1.58% -1.55% -1.65% -1.58% -1.57%
Other use
Baseline 16,473 16,963 16,948 17,107 17,457 17,524 17,653 17,891 18,075 18,283
Scenario 1 16,224 16,981 17,093 17,167 17,537 17,651 17,765 18,004 18,208 18,409
% change -1.51% 0.10% 0.85% 0.35% 0.45% 0.73% 0.64% 0.63% 0.74% 0.69%
Residual
Baseline 436 436 436 436 436 436 436 436 436 436
Scenario 1 436 436 436 436 436 436 436 436 436 436
% change 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Ending stocks
Baseline 48,060 49,742 50,129 49,637 50,547 50,748 50,506 50,755 50,910 50,943
Scenario 1 45,053 46,583 47,617 46,967 47,645 47,971 47,716 47,850 48,013 48,045
% change -6.26% -6.35% -5.01% -5.38% -5.74% -5.47% -5.52% -5.72% -5.69% -5.69%
Domestic use
Baseline 289,006 300,339 305,020 308,621 315,952 320,765 324,913 330,832 336,286 341,592
Scenario 1 280,222 293,364 300,053 302,625 309,525 314,834 318,786 324,446 330,030 335,276
% change -3.04% -2.32% -1.63% -1.94% -2.03% -1.85% -1.89% -1.93% -1.86% -1.85%
Trade *
Baseline 70,094 72,279 73,476 75,117 77,980 79,962 81,824 84,209 86,428 88,696
Scenario 1 68,359 71,000 73,071 74,718 77,695 80,052 82,051 84,462 86,814 89,144
% change -2.48% -1.77% -0.55% -0.53% -0.36% 0.11% 0.28% 0.30% 0.45% 0.51%
Soybean meal
Production
Baseline 165,122 172,092 175,401 178,363 182,958 186,486 189,825 194,015 197,983 201,848
Scenario 1 161,027 169,352 173,543 175,893 180,349 184,113 187,316 191,406 195,448 199,275
% change -2.48% -1.59% -1.06% -1.38% -1.43% -1.27% -1.32% -1.34% -1.28% -1.27%
Consumption
Baseline 162,560 169,579 173,013 176,107 180,656 184,203 187,570 191,700 195,656 199,516
Scenario 1 158,877 166,793 171,072 173,657 178,056 181,814 185,063 189,095 193,116 196,945
% change -2.27% -1.64% -1.12% -1.39% -1.44% -1.30% -1.34% -1.36% -1.30% -1.29%
Ending stocks
Baseline 5,768 6,069 6,243 6,286 6,374 6,445 6,487 6,588 6,702 6,822
Scenario 1 5,356 5,703 5,961 5,984 6,064 6,150 6,190 6,288 6,406 6,524
% change -7.14% -6.03% -4.52% -4.80% -4.87% -4.58% -4.58% -4.56% -4.42% -4.37%
Trade *
Baseline 56,655 60,137 61,994 62,804 64,161 65,415 66,896 68,602 70,286 71,907
Scenario 1 54,520 58,498 60,491 60,863 62,049 63,673 65,023 66,656 68,326 69,918
% change -3.77% -2.73% -2.42% -3.09% -3.29% -2.66% -2.80% -2.84% -2.79% -2.77%
Soybean oil
Production
Baseline 39,020 40,765 41,647 42,446 43,638 44,587 45,498 46,618 47,694 48,753
Scenario 1 38,034 40,098 41,182 41,827 42,979 43,977 44,848 45,940 47,027 48,071
% change -2.53% -1.64% -1.12% -1.46% -1.51% -1.37% -1.43% -1.46% -1.40% -1.40%
Consumption
Baseline 39,063 40,488 41,453 42,156 43,383 44,331 45,289 46,427 47,488 48,554
Scenario 1 38,171 39,841 40,965 41,562 42,735 43,719 44,649 45,754 46,821 47,875
% change -2.28% -1.60% -1.18% -1.41% -1.49% -1.38% -1.41% -1.45% -1.40% -1.40%
Ending stocks
Baseline 2,361 2,422 2,400 2,473 2,512 2,552 2,545 2,521 2,511 2,494
Scenario 1 2,267 2,307 2,308 2,358 2,386 2,428 2,412 2,381 2,372 2,352
% change -4.00% -4.76% -3.81% -4.67% -5.02% -4.86% -5.23% -5.53% -5.55% -5.70%
Trade *
Baseline 9,651 10,042 10,268 10,443 11,019 11,350 11,733 12,191 12,638 13,091
Scenario 1 9,458 9,708 9,938 10,027 10,525 10,814 11,153 11,581 12,011 12,456
% change -2.00% -3.33% -3.21% -3.99% -4.48% -4.72% -4.95% -5.00% -4.96% -4.85%
Per-capita consumption (Kilograms)
Baseline 5.78 5.92 6.00 6.03 6.14 6.20 6.27 6.36 6.44 6.51
Scenario 1 5.65 5.83 5.93 5.94 6.04 6.12 6.18 6.27 6.35 6.42
% change -2.28% -1.60% -1.18% -1.41% -1.49% -1.38% -1.41% -1.45% -1.40% -1.40%
* Excludes intraregional trade

Table B11. World rapeseed sector supply and utilization.
08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18
Rapeseed (Thousand hectares)
Area harvested
Baseline 28,729 29,388 29,904 30,355 30,612 30,935 31,345 31,743 32,145 32,587
Scenario 1 28,785 29,532 30,060 30,516 30,806 31,147 31,565 31,979 32,395 32,842
% change 0.19% 0.49% 0.52% 0.53% 0.63% 0.69% 0.70% 0.75% 0.78% 0.78%
Production (Thousand metric tons)
Baseline 50,085 51,784 53,205 54,597 55,648 56,797 58,087 59,356 60,642 62,001
Scenario 1 49,836 51,703 53,148 54,542 55,643 56,819 58,115 59,405 60,708 62,072
% change -0.50% -0.16% -0.11% -0.10% -0.01% 0.04% 0.05% 0.08% 0.11% 0.11%
Beginning stocks
Baseline 2,860 3,034 3,050 3,061 3,146 3,183 3,199 3,224 3,254 3,279
Scenario 1 2,860 2,975 2,987 3,005 3,084 3,120 3,136 3,159 3,187 3,212
% change 0.00% -1.94% -2.04% -1.85% -1.96% -2.01% -1.97% -2.04% -2.07% -2.06%
Domestic supply
Baseline 52,945 54,818 56,254 57,658 58,794 59,981 61,285 62,580 63,896 65,281
Scenario 1 52,696 54,678 56,135 57,546 58,727 59,938 61,251 62,564 63,895 65,284
% change -0.47% -0.25% -0.21% -0.19% -0.11% -0.07% -0.06% -0.03% 0.00% 0.00%
Crush
Baseline 46,056 47,742 49,123 50,373 51,428 52,616 53,943 55,300 56,690 58,164
Scenario 1 45,918 47,706 49,091 50,338 51,430 52,631 53,956 55,323 56,718 58,188
% change -0.30% -0.08% -0.07% -0.07% 0.00% 0.03% 0.02% 0.04% 0.05% 0.04%
Other use
Baseline 3,584 3,755 3,799 3,868 3,912 3,895 3,846 3,755 3,656 3,537
Scenario 1 3,532 3,714 3,768 3,853 3,906 3,901 3,864 3,783 3,694 3,585
% change -1.46% -1.08% -0.80% -0.40% -0.15% 0.14% 0.46% 0.76% 1.04% 1.35%
Residual
Baseline 271 271 271 271 271 271 271 271 271 271
Scenario 1 271 271 271 271 271 271 271 271 271 271
% change 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Ending stocks
Baseline 3,034 3,050 3,061 3,146 3,183 3,199 3,224 3,254 3,279 3,308
Scenario 1 2,975 2,987 3,005 3,084 3,120 3,136 3,159 3,187 3,212 3,239
% change -1.94% -2.04% -1.85% -1.96% -2.01% -1.97% -2.04% -2.07% -2.06% -2.07%
Domestic use
Baseline 52,945 54,818 56,254 57,658 58,794 59,981 61,285 62,580 63,896 65,281
Scenario 1 52,696 54,678 56,135 57,546 58,727 59,938 61,251 62,564 63,895 65,284
% change -0.47% -0.25% -0.21% -0.19% -0.11% -0.07% -0.06% -0.03% 0.00% 0.00%
Trade *
Baseline 7,472 8,088 8,304 8,493 8,675 8,868 9,086 9,330 9,591 9,866
Scenario 1 7,476 7,976 8,160 8,327 8,496 8,674 8,877 9,108 9,357 9,619
% change 0.05% -1.39% -1.74% -1.96% -2.06% -2.18% -2.30% -2.38% -2.44% -2.50%
Rapeseed meal
Production
Baseline 27,251 28,226 29,031 29,768 30,384 31,080 31,862 32,662 33,481 34,352
Scenario 1 27,170 28,203 29,011 29,745 30,383 31,087 31,866 32,672 33,494 34,362
% change -0.30% -0.08% -0.07% -0.08% 0.00% 0.02% 0.01% 0.03% 0.04% 0.03%
Consumption
Baseline 27,559 28,537 29,340 30,081 30,703 31,397 32,177 32,976 33,793 34,662
Scenario 1 27,489 28,513 29,317 30,058 30,702 31,402 32,182 32,985 33,806 34,672
% change -0.25% -0.08% -0.08% -0.08% 0.00% 0.02% 0.01% 0.03% 0.04% 0.03%
Ending stocks
Baseline 314 322 333 339 339 342 345 351 357 366
Scenario 1 303 312 324 330 331 334 338 343 350 358
% change -3.72% -3.34% -2.70% -2.54% -2.43% -2.34% -2.28% -2.19% -2.14% -2.13%
Trade *
Baseline 2,404 2,738 2,946 3,008 3,121 3,214 3,291 3,369 3,446 3,626
Scenario 1 2,389 2,792 2,992 3,062 3,180 3,274 3,356 3,437 3,517 3,589
% change -0.62% 1.95% 1.58% 1.82% 1.89% 1.88% 1.97% 2.02% 2.04% -1.03%
Rapeseed oil
Production
Baseline 18,068 18,760 19,321 19,821 20,250 20,731 21,265 21,809 22,367 22,957
Scenario 1 18,009 18,744 19,307 19,808 20,252 20,738 21,272 21,821 22,381 22,970
% change -0.33% -0.08% -0.07% -0.07% 0.01% 0.03% 0.03% 0.05% 0.06% 0.06%
Consumption
Baseline 18,287 19,016 19,580 20,067 20,498 20,987 21,522 22,068 22,629 23,220
Scenario 1 18,237 19,005 19,567 20,056 20,501 20,994 21,530 22,081 22,644 23,233
% change -0.27% -0.06% -0.07% -0.06% 0.02% 0.04% 0.04% 0.06% 0.06% 0.06%
Ending stocks
Baseline 427 429 428 440 451 453 453 452 448 443
Scenario 1 418 416 414 424 433 435 434 433 428 422
% change -2.18% -3.13% -3.24% -3.58% -3.86% -3.95% -4.14% -4.35% -4.49% -4.63%
Trade *
Baseline 1,591 1,631 1,708 1,759 1,830 1,909 1,981 2,045 2,101 2,158
Scenario 1 1,481 1,563 1,648 1,707 1,785 1,870 1,949 2,018 2,078 2,140
% change -6.91% -4.15% -3.53% -2.99% -2.45% -2.02% -1.64% -1.32% -1.07% -0.85%
Per-capita consumption (Kilograms)
Baseline 2.71 2.78 2.83 2.87 2.90 2.94 2.98 3.02 3.07 3.11
Scenario 1 2.70 2.78 2.83 2.87 2.90 2.94 2.98 3.02 3.07 3.12
% change -0.27% -0.06% -0.07% -0.06% 0.02% 0.04% 0.04% 0.06% 0.06% 0.06%
* Excludes intraregional trade

Table B12. World sunflower sector supply and utilization.
08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18
Sunflower seed (Thousand hectares)
Area harvested
Baseline 24,273 24,401 24,392 24,474 24,513 24,538 24,600 24,680 24,759 24,850
Scenario 1 24,261 24,392 24,404 24,490 24,537 24,571 24,633 24,717 24,800 24,891
% change -0.05% -0.04% 0.05% 0.07% 0.10% 0.13% 0.13% 0.15% 0.17% 0.16%
Production (Thousand metric tons)
Baseline 29,838 30,284 30,612 31,042 31,425 31,784 32,182 32,610 33,037 33,480
Scenario 1 29,828 30,287 30,642 31,074 31,469 31,841 32,241 32,675 33,108 33,552
% change -0.04% 0.01% 0.10% 0.10% 0.14% 0.18% 0.18% 0.20% 0.22% 0.21%
Beginning stocks
Baseline 1,884 2,032 2,089 2,105 2,136 2,179 2,198 2,215 2,238 2,258
Scenario 1 1,884 2,002 2,051 2,076 2,105 2,147 2,169 2,186 2,209 2,230
% change 0.00% -1.50% -1.81% -1.38% -1.43% -1.44% -1.29% -1.29% -1.28% -1.22%
Domestic supply
Baseline 31,722 32,316 32,701 33,147 33,561 33,962 34,380 34,825 35,275 35,737
Scenario 1 31,712 32,289 32,693 33,150 33,575 33,988 34,410 34,861 35,317 35,782
% change -0.03% -0.09% -0.02% 0.01% 0.04% 0.08% 0.09% 0.10% 0.12% 0.12%
Crush
Baseline 26,228 26,695 27,040 27,421 27,746 28,106 28,487 28,880 29,292 29,706
Scenario 1 26,276 26,738 27,084 27,480 27,817 28,181 28,567 28,967 29,381 29,797
% change 0.18% 0.16% 0.16% 0.21% 0.25% 0.27% 0.28% 0.30% 0.31% 0.31%
Other use
Baseline 3,387 3,458 3,481 3,515 3,561 3,583 3,602 3,631 3,650 3,680
Scenario 1 3,359 3,425 3,458 3,490 3,536 3,562 3,581 3,610 3,631 3,661
% change -0.84% -0.96% -0.66% -0.72% -0.72% -0.59% -0.59% -0.58% -0.53% -0.52%
Residual
Baseline 75 75 75 75 75 75 75 75 75 75
Scenario 1 75 75 75 75 75 75 75 75 75 75
% change 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Ending stocks
Baseline 2,032 2,089 2,105 2,136 2,179 2,198 2,215 2,238 2,258 2,276
Scenario 1 2,002 2,051 2,076 2,105 2,147 2,169 2,186 2,209 2,230 2,249
% change -1.50% -1.81% -1.38% -1.43% -1.44% -1.29% -1.29% -1.28% -1.22% -1.21%
Domestic use
Baseline 31,722 32,316 32,701 33,147 33,561 33,962 34,380 34,825 35,275 35,737
Scenario 1 31,712 32,289 32,693 33,150 33,575 33,988 34,410 34,861 35,317 35,782
% change -0.03% -0.09% -0.02% 0.01% 0.04% 0.08% 0.09% 0.10% 0.12% 0.12%
Trade *
Baseline 513 615 757 856 947 1,012 1,083 1,163 1,238 1,318
Scenario 1 511 577 708 811 905 972 1,043 1,125 1,200 1,280
% change -0.33% -6.18% -6.50% -5.30% -4.47% -3.98% -3.71% -3.33% -3.09% -2.94%
Sunflower meal
Production
Baseline 11,614 11,806 11,958 12,129 12,268 12,419 12,579 12,745 12,918 13,092
Scenario 1 11,636 11,824 11,978 12,155 12,300 12,454 12,616 12,785 12,959 13,134
% change 0.18% 0.16% 0.16% 0.22% 0.26% 0.28% 0.29% 0.31% 0.32% 0.32%
Consumption
Baseline 11,276 11,483 11,636 11,808 11,949 12,099 12,259 12,424 12,596 12,770
Scenario 1 11,301 11,502 11,655 11,834 11,981 12,134 12,296 12,464 12,638 12,813
% change 0.22% 0.16% 0.16% 0.22% 0.27% 0.28% 0.30% 0.32% 0.33% 0.33%
Ending stocks
Baseline 257 262 266 268 270 272 274 277 280 284
Scenario 1 253 258 263 266 267 269 271 274 278 281
% change -1.41% -1.42% -1.14% -1.05% -1.01% -0.97% -0.92% -0.88% -0.86% -0.85%
Trade *
Baseline 2,652 2,696 2,683 2,675 2,677 2,688 2,695 2,702 2,713 2,728
Scenario 1 2,655 2,699 2,685 2,677 2,680 2,691 2,698 2,705 2,716 2,731
% change 0.13% 0.09% 0.08% 0.11% 0.12% 0.11% 0.12% 0.12% 0.12% 0.12%
Sunflower Oil
Production
Baseline 10,680 10,875 11,016 11,171 11,304 11,452 11,609 11,771 11,940 12,111
Scenario 1 10,700 10,893 11,034 11,195 11,333 11,483 11,642 11,807 11,977 12,149
% change 0.18% 0.16% 0.17% 0.22% 0.26% 0.27% 0.29% 0.30% 0.31% 0.31%
Consumption
Baseline 10,294 10,518 10,673 10,823 10,950 11,105 11,262 11,424 11,594 11,766
Scenario 1 10,320 10,538 10,690 10,848 10,979 11,135 11,296 11,460 11,631 11,803
% change 0.25% 0.19% 0.15% 0.23% 0.27% 0.28% 0.30% 0.31% 0.32% 0.32%
Ending stocks
Baseline 427 443 443 449 462 467 472 477 481 484
Scenario 1 421 434 436 441 453 459 464 469 473 476
% change -1.57% -2.03% -1.59% -1.75% -1.82% -1.68% -1.70% -1.72% -1.66% -1.63%
Trade *
Baseline 3,236 3,349 3,396 3,446 3,516 3,600 3,685 3,774 3,869 3,972
Scenario 1 3,240 3,354 3,401 3,454 3,525 3,610 3,697 3,787 3,883 3,986
% change 0.13% 0.15% 0.17% 0.24% 0.26% 0.28% 0.31% 0.33% 0.34% 0.35%
Per-capita consumption (Kilograms)
Baseline 1.52 1.54 1.54 1.55 1.55 1.55 1.56 1.56 1.57 1.58
Scenario 1 1.53 1.54 1.55 1.55 1.55 1.56 1.56 1.57 1.58 1.58
% change 0.25% 0.19% 0.15% 0.23% 0.27% 0.28% 0.30% 0.31% 0.32% 0.32%
* Excludes intraregional trade

Appendix C

Baseline 2007-08 world production, consumption, and price data.
Area (million hectares) Production (million tonnes) Trade (million tonnes) Consumption (million tonnes) Price ($/tonne)
Corn 159 790 97 777 218
Soybeans 91 218 78 n/a 469
Canola 28 48 4 n/a 644
Wheat 217 611 115 618 314
Barley 57 133 18 176 242
Sunflower 22 27 1 n/a 745
Sorghum 41 63 9 63 299
Soymeal n/a 158 55 157 314
Soy oil n/a 37 10 37 1,151
Rapemeal n/a 27 4 27 298
Rape oil n/a 18 2 18 1,410
Sun meal n/a 11 3 10 191
Sun oil n/a 10 3 9 1,639
Note: All values rounded to nearest million; n/a = not applicable


Suggested citation: Brookes, G., Yu, T.H., Tokgoz, S., & Elobeid, A. (2010). The production and price impact of biotech corn, canola, and soybean crops. AgBioForum, 13(1), 25-52. Available on the World Wide Web:http://www.agbioforum.org.
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