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.

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).

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).

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.

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.

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

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.

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).

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.

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).

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).

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.

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 6^th 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.

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.

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

Appendix C

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Table 1
Figure 1
Table 2
Table 3
Figure 2
Figure 3
Table 4
Table 5
Table 6
Table 7
Table 8
Table 9
Endnotes
References
Appendix A
Table A1
Figure A1
Appendix B
Appendix C

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