Volume 12 // Number 2 // Article 4 Back Issues | Index Global Impact of Biotech Crops: Income and Production Effects 1996-2007 Graham Brookes and Peter Barfoot PG Economics, Ltd., Dorchester, UK This article updates the assessment of the impact of commercialized agricultural biotechnology on global agriculture from an economic perspective. It examines specific global economic impacts on farm income, indirect (non-pecuniary) farm-level income effects and impacts on the production base of the four main crops—soybeans, corn, cotton, and canola. The analysis shows that there have been substantial net economic benefits at the farm level, amounting to $10.1 billion in 2007 and $44.1 billion for the 12-year period (in nominal terms). The non-pecuniary benefits associated with the use of the technology have also had a positive impact on adoption (in the US accounting for the equivalent of 25% of the total direct farm income benefit). Biotech crops have also made important contributions to increasing global production levels of the four main crops—adding, for example, 68 million tonnes and 62 million tonnes respectively to global production of soybeans and corn. Key words: yield, cost, income, non-pecuniary benefit, production, biotech crops. Introduction This article presents the findings of research on the global economic impact of GM crops since their commercial introduction in 1996. It updates part of the findings of earlier analysis presented by the authors in AgBioForum 8(2&3), 9(3), and 11(1).1 The analysis concentrates on farm income effects because this is a primary driver of adoption amongst farmers (both large commercial and small-scale subsistence). It also considers more indirect farm income or non-pecuniary benefits, and quantifies the (net) production impact of the technology. Methodology The report is based largely on extensive analysis of existing farm-level impact data for biotech crops. While primary data for impacts of commercial cultivation were not available for every crop, in every year, and for each country, a substantial body of representative research and analysis is available, and this has been used as the basis for the analysis presented. Since the economic performance and impact of this technology at the farm level varies widely—both between, and within regions/countries (as applies to any technology used in agriculture)—the measurement of performance and impact is considered on a case-by-case basis in terms of crop and trait combinations. The analysis presented is based on the average performance and impact recorded in different crops by the studies reviewed; the average performance is the most common way in which the identified literature has reported impact. Where several pieces of relevant research (e.g., on the impact of using a GM trait on the yield of a crop in one country in a particular year) have been identified, the findings used have been based largely on the average of these findings. This approach may both overstate and understate the real impact of GM technology for some trait, crop, and country combinations, especially in cases where the technology has provided yield enhancements. However, since impact data for every trait, crop, location, and year is not available, the authors have had to extrapolate available impact data from identified studies to years for which no data are available. Therefore, the authors acknowledge that this represents a weakness in the research. To reduce the possibilities of over/understating impact, the analysis: directly applies impacts identified from the literature to the years that have been studied. As a result, the impacts used vary in many cases according to the findings of literature covering different years. 2 Hence, the analysis takes into account variation in the impact of the technology on yield according to its effectiveness in dealing with (annual) fluctuations in pest and weed infestation levels as identified by research;

Hence, the analysis takes into account variation in the impact of the technology on yield according to its effectiveness in dealing with (annual) fluctuations in pest and weed infestation levels as identified by research; uses current farm-level crop prices and bases any yield impacts on (adjusted—see below) current average yields. In this way some degree of dynamics has been introduced into the analysis that would otherwise be missing if constant prices and average yields indentified in year-specific studies had been used;

includes some changes and updates to the impact assumptions identified in the literature based on consultation with local sources (analysts, industry representatives) so as to better reflect prevailing/changing conditions (e.g., pest and weed pressure, cost of technology);

includes some sensitivity analysis in which the impacts based on average performance are supplemented by a range incorporating ‘below average’ and ‘above average’ performance assumptions (see Appendix 2 for details); and

adjusts downward the average base yield (in cases where GM technology has been identified as having delivered yield improvements) on which the yield enhancement has been applied. In this way, the impact on total production is not overstated. Detailed examples of how the methodology has been applied to the calculation of the 2007 year results are presented in Detailed examples of how the methodology has been applied to the calculation of the 2007 year results are presented in Appendix 1 Appendix 2 also provides details of the impacts and assumptions applied and their sources. Other aspects of the methodology used to estimate the impact on direct farm income are as follows. Impact is quantified at the trait and crop level, including where stacked traits are available to farmers. Where stacked traits have been used, the individual trait components were analyzed separately to ensure estimates of all traits were calculated.

All values presented are nominal for the year shown and the base currency used is the US dollar. All financial impacts in other currencies have been converted to US dollars at prevailing annual average exchange rates for each year.

The analysis focuses on changes in farm income in each year arising from impact of GM technology on yields, key costs of production (notably seed cost and crop protection expenditure, but also impact on costs such as fuel and labor),3 crop quality (e.g., improvements in quality arising from less pest damage or lower levels of weed impurities, which result in price premia being obtained from buyers), and the scope for facilitating the planting of a second crop in a season (e.g., second crop soybeans in Argentina following wheat that would, in the absence of the GM herbicide-tolerant [HT] seed, probably not have been planted). Thus, the farm income effect measured is essentially a gross margin impact (impact on gross revenue less variable costs of production) rather than a full net cost of production assessment. Through the inclusion of yield impacts and the application of actual (average) farm prices for each year, the analysis also indirectly takes into account the possible impact of biotech crop adoption on global crop supply and world prices. The article also examines some of the more intangible (more difficult to quantify) economic impacts of GM technology. The literature in this area is much more limited and, in terms of aiming to quantify these impacts, largely restricted to the US-specific studies. The findings of this research are summarized4 and extrapolated to the cumulative biotech crop planted areas in the United States in the 1996-2007 period. The article also examines some of the more intangible (more difficult to quantify) economic impacts of GM technology. The literature in this area is much more limited and, in terms of aiming to quantify these impacts, largely restricted to the US-specific studies. The findings of this research are summarizedand extrapolated to the cumulative biotech crop planted areas in the United States in the 1996-2007 period. Lastly, the article includes estimates of the production impacts of GM technology at the crop level. These have been aggregated to provide the reader with a global perspective of the broader production impact of the technology. These impacts derive from the yield impacts (where identified), but also from the facilitation of additional cropping within a season (notably in relation to soybeans in South America). Details of how these values were calculated (for 2007) are shown in Appendix 1. Results GM technology has had a significant positive impact on farm income derived from a combination of enhanced productivity and efficiency gains (Table 1). In 2007, the direct global farm income benefit from biotech crops was $10.1 billion. This is equivalent to having added 4.4% to the value of global production of the four main crops of soybeans, maize, canola, and cotton. Since 1996, farm incomes have increased by $44.1 billion. Table 1. Global farm income benefits from growing biotech crops, 1996-2007 (US $ million). Trait 2007 increase in farm income 1996-2007 increase in farm income 2007 farm income benefit as % of total value of production of these crops in biotech adopting countries 2007 farm income benefit as % of total value of global production of crop GM HT soybeans 3,935.5 21,814.1 7.2 6.4 GM HT maize 442.3 1,507.6 0.7 0.4 GM HT cotton 24.5 848.2 0.1 0.1 GM HT canola 345.6 1,438.6 7.65 1.4 GM IR maize 2,075.3 5,673.6 3.2 1.9 GM IR cotton 3,204.0 12,576.2 16.5 10.2 Others 54.4 208.8 n/a n/a Totals 10,081.6 44,067.1 6.9 4.4 Global farm income benefits from growing biotech crops, 1996-2007 (US $ million). The largest gains in farm income have arisen in the soybean sector, largely from cost savings. The $3.9 billion additional income generated by GM HT soybeans in 2007 has been equivalent to adding 7.2% to the value of the crop in biotech-growing countries, or adding the equivalent of 6.4% to the $60 billion value of the global soybean crop in 2007. These economic benefits should, however be placed within the context of a significant increase in the level of soybean production in the main biotech-adopting countries. Since 1996, the soybean area in the leading soybean-producing countries—United States, Brazil, and Argentina—increased by 58%. Substantial gains also have arisen in the cotton sector through a combination of higher yields and lower costs. In 2007, cotton farm income levels in the biotech-adopting countries increased by $3.2 billion, and since 1996, the sector has benefited from an additional $12.6 billion. The 2007 income gains are equivalent to adding 16.5% to the value of the cotton crop in these countries, or 10.2% to the $27.5 billion value of total global cotton production. This is a substantial increase in value-added terms for two new cotton seed technologies. Significant increases to farm incomes have also occurred in the maize and canola sectors. The combination of GM insect-resistant (GM IR) and GM HT technology in maize has boosted farm incomes by $7.2 billion since 1996. In the North American canola sector, an additional $1.4 billion has been generated. Table 2 summarizes farm income impacts in key biotech-adopting countries. This highlights the important farm income benefit arising from GM HT soybeans in South America (Argentina, Brazil, Paraguay, and Uruguay), GM IR cotton in China and India, and a range of GM cultivars in the United States. It also illustrates the growing level of farm income benefits being obtained in South Africa, the Philippines, and Mexico. Table 2. GM crop farm income benefits in selected countries, 1996-2007 ($ million). GM HT soybeans GM HT maize GM HT cotton GM HT canola GM IR maize GM IR cotton Total US 10,422 1,402.9 804 149.2 4,778.9 2,232.7 19,789.7 Argentina 7,815 46 28.6 n/a 226.8 67.9 8,184.3 Brazil 2,868 n/a n/a n/a n/a 65.5 2,933.5 Paraguay 459 n/a n/a n/a n/a n/a 459 Canada 103.5 42 n/a 1,289 208.5 n/a 1,643 South Africa 3.8 5.2 0.2 n/a 354.9 19.3 383.4 China n/a n/a n/a n/a n/a 6,740.8 6,740.8 India n/a n/a n/a n/a n/a 3,181.0 3,181.0 Australia n/a n/a 5.2 n/a n/a 190.6 195.8 Mexico 8.8 n/a 10.3 n/a n/a 65.9 85 Philippines n/a 11.4 n/a n/a 33.2 n/a 44.6 Romania 92.7 n/a n/a n/a n/a n/a 92.7 Uruguay 42.4 n/a n/a n/a 2.7 n/a 45.1 Spain n/a n/a n/a n/a 60.0 n/a 60 Other EU n/a n/a n/a n/a 8.6 n/a 8.6 Colombia n/a n/a n/a n/a n/a 12.6 12.6 GM crop farm income benefits in selected countries, 1996-2007 ($ million). In terms of the division of the economic benefits obtained by farmers in developing countries relative to farmers in developed countries, Table 3 shows that in 2007, 58% of the farm income benefits were earned by developing-country farmers. The vast majority of these income gains for developing-country farmers have been from GM IR cotton and GM HT soybeans.5 Over the twelve years—1996-2007—the cumulative farm income gain derived by developing country farmers was $22.1 billion (50.1% of the total). Table 3. GM crop farm income benefits in developing versus developed countries, 2007 ($ million). Developed Developing GM HT soybeans 1,375.1 2,560.5 GM IR maize 1,773.4 301.9 GM HT maize 401.6 40.8 GM IR cotton 285.8 2,918.1 GM HT cotton 16.3 8.2 GM HT canola 345.6 0 GM virus-resistant papaya and squash 54.4 0 Total 4,252.2 5,829.5 GM crop farm income benefits in developing versus developed countries, 2007 ($ million). Examining the cost farmers pay for accessing GM technology, Table 4 shows that across the four main biotech crops, the total cost in 2007 was equal to 24% of the total technology gains (inclusive of farm income gains plus the cost of the technology payable to the seed supply chain).6 Table 4. Cost of accessing GM technology relative to the total farm income benefits, 2007 ($ million). Cost of technology: All farmers Farm income gain: All farmers Total benefit of technology to farmers and seed supply chain Cost of technology: Developing countries Farm income gain: Developing countries Total benefit of technology to farmers and seed supply chain: Developing countries GM HT soybeans 930.8 3,935.5 4,866.3 326 2,560.5 2,886.5 GM IR maize 714.3 2,075.3 2,789.6 79.1 301.9 381 GM HT maize 530.8 442.3 973.1 20.2 40.8 61 GM IR cotton 670.4 3,204.0 3,874.4 535.1 2,918.1 3,453.2 GM HT cotton 226.4 24.5 250.9 8.5 8.2 16.7 GM HT canola 102.2 345.6 447.8 n/a n/a n/a Total 3,174.9 10,027.2 13,202.1 968.9 5,829.5 6,798.4 Cost of accessing GM technology relative to the total farm income benefits, 2007 ($ million). For farmers in developing countries the total cost was equal to 14% of total technology gains, while for farmers in developed countries the cost was 34% of the total technology gains. While circumstances vary between countries, the higher share of total technology gains accounted for by farm income gains in developing countries relative to the farm income share in developed countries reflects factors such as weaker provision and enforcement of intellectual property rights in developing countries and the higher average level of farm income gain on a per-hectare basis derived by developing country farmers relative to developed country farmers. As indicated in the methodology section, the analysis presented above is largely based on estimates of average impact in all years. Recognizing that pest and weed pressure varies by region and year, additional sensitivity analysis was conducted for the crop/trait combinations where yield impacts were identified in the literature. This sensitivity analysis (see Appendix 2 for details) was undertaken for two levels of impact assumption: one in which all yield effects in all years were assumed to be ‘lower than average’ (levels of impact that reflected yield impacts in years of low pest/weed pressure), and one in which all yield effects in all years were assumed to be ‘higher than average’ (levels of impact that reflected yield impacts in years of high pest/weed pressure). The results of this analysis suggest a range of positive direct farm income gains in 2007 of +$8.5 to +$12.9 billion and, over the 1996-2007 period, a range of +$38.2 to +$52.2 billion (Table 5). This range is broadly within 85% to 120% of the main estimates of farm income presented above. Table 5. Direct farm income benefits 1996-2007 under different impact assumptions ($ million). Crop Consistent below average pest/weed pressure Average pest/weed pressure (main study analysis) Consistent above average pest/weed pressure Soybeans 21.796.0 21,814.1 21,829.0 Corn 4,571.0 7,181.2 12,152.0 Cotton 10,920 13,424.4 15,962.0 Canola 818.7 1,438.6 2.013.0 Others 101.4 208.8 224.3 Total 38,207.1 44,067.1 52,180.3 Direct farm income benefits 1996-2007 under different impact assumptions ($ million). Indirect (Non-Pecuniary) Farm-Level Impacts In addition to the tangible and quantifiable impacts on farm profitability presented above, there are other important, more intangible (difficult to quantify) impacts of an economic nature. Many of the studies7 of the impact of biotech crops have identified the following reasons as being important influences for adoption of the technology. Herbicide Tolerant Crops HT crops allow for increased management flexibility and convenience that comes from a combination of the ease of use associated with broad-spectrum, post-emergent herbicides like glyphosate and the increased/longer time window for spraying. This not only frees up management time for other farming activities but also allows additional scope for undertaking off-farm, income-earning activities.

In a conventional crop, post-emergent weed control relies on herbicide applications before the weeds and crop are well established. As a result, the crop may suffer ‘knock-back’ to its growth from the effects of the herbicide. In the GM HT crop, this problem is avoided because the crop is both tolerant to the herbicide and spraying can occur at a later stage when the crop is better able to withstand any possible “knock-back” effects.

These crops facilitate the adoption of conservation or no-tillage systems. This provides for additional cost savings such as reduced labor and fuel costs associated with plowing, additional moisture retention, and reductions in soil erosion levels.

Improved weed control has contributed to reduced harvesting costs—cleaner crops have resulted in reduced times for harvesting. It has also improved harvest quality and led to higher levels of quality price bonuses in some regions and years (e.g., HT soybeans and HT canola in the early years of adoption, respectively, in Romania and Canada).

Elimination of potential damage caused by soil-incorporated residual herbicides in follow-on crops and less need to apply herbicides in a follow-on crop because of the improved levels of weed control;

HT crops also contribute to a general improvement in human safety (as manifest in greater peace of mind about own and worker safety) from reduced exposure to herbicides and a switch to more environmentally benign products. Insect Resistant Crops IR crops offer benefits in the areas of production risk management and insurance. The technology takes away much of the worry of significant pest damage occurring and is, therefore, highly valued. Although not applicable in 2007 (piloted in 2008 and likely to be more widely operational from 2009), US farmers using stacked corn traits (containing IR and HT traits) are being offered discounts on crop insurance premiums equal to $7.41/hectare.

These crops have a ‘convenience’ benefit derived from having to devote less time to crop walking and/or applying insecticides.

IR crops offer savings in energy use—mainly associated with less use of aerial spraying and less tillage.

Planting IR crops can produce savings in machinery use (for spraying and possibly reduced harvesting times).

IR crops produce a higher quality of crop. There is a growing body of research evidence relating to the superior quality of GM IR corn relative to conventional and organic corn from the perspective of having lower levels of mycotoxins. Evidence from Europe (as summarized in Brookes [2008]) has shown a consistent pattern in which GM IR corn exhibits significantly reduced levels of mycotoxins compared to conventional and organic alternatives. In terms of revenue from sales of corn, however, no premia for delivering product with lower levels of mycotoxins have been reported to date; however, where the adoption of the technology has resulted in reduced frequency of crops failing to meet maximum permissible fumonisin levels in grain maize (e.g., in Spain), this delivers an important economic gain to farmers selling their grain to the food-using sector. In one study (Yorobe, 2004), GM IR corn farmers in the Philippines have also been reported to have obtained price premia of 10% relative to conventional corn because of better quality, less damage to cobs, and lower levels of impurities.

They also offer improved health and safety for farmers and farm workers—from reduced handling and use of pesticides, especially in developing countries where many apply pesticides with little or no use of protective clothing and equipment.

Shorter growing seasons (e.g., for some cotton growers in India) allow some farmers to plant a second crop in the same season.8 Also, some Indian cotton growers have reported benefits for bee keepers, as fewer bees are now lost to insecticide spraying. Some of the economic impact studies have attempted to quantify some of these benefits. For example, Qaim and Traxler ( Some of the economic impact studies have attempted to quantify some of these benefits. For example, Qaim and Traxler ( 2002 ) quantified some of these in Argentina—a $3.65/hectare saving (-7.8%) in labor costs and a $6.82/ha (-28%) saving in machinery/fuel costs associated with the adoption of GM HT soybeans. Where identified, these cost savings have been included in the analysis presented above. Nevertheless, it is important to recognize that these largely intangible benefits are considered by many farmers as a primary reason for adoption of GM technology, and in some cases farmers have been willing to adopt for these reasons alone, even when the measurable impacts on yield and direct costs of production suggest marginal or no direct economic gain. Since the early 2000s, a number of farmer-survey-based studies in the United States have also attempted to better quantify these non-pecuniary benefits. These studies have usually employed contingent valuation techniques9 to obtain farmer valuations of non-pecuniary benefits. A 2002 survey of 600 US corn farmers explored opinions and valuations of the then new IR corn trait resistant to corn rootworm, which was introduced in the following year (2003). Respondents were asked to value any potential time and equipment savings, additional farmer and worker safety, additional environmental benefits, and production risk management benefits (from more consistent control of rootworm) that they thought might arise from use of the technology relative to existing corn rootworm control methods. The production risk management benefit was mostly highly valued by farmers, followed by operator/worker safety and environmental gains. The average value of all the non-pecuniary benefits was $17.89/hectare for likely adopters, $9.54/hectare for unlikely adopters, and an overall average of $16.33/hectare across all farmers surveyed.

A 2002 survey of 610 US soybean farmers sought farmers’ views on the benefits associated with their use (since 1996) of GM HT soybeans. Respondents were asked to value additional farmer and worker safety, the environmental impact of the technology and the additional convenience and flexibility the technology provided for weed control relative to the conventional alternatives. All of these benefits were valued by the soybean farmers, with convenience given the highest value. Overall, the average benefit attributed to these three categories of non-pecuniary benefits was $27/hectare (58% of which came from the convenience benefit).

A 2003 survey of nearly 300 farmers of GM HT crops (soybeans, corn, and cotton) asked respondents to value additional farmer and worker safety, the environmental impact of the technology, and the additional convenience and flexibility the technology provided for weed control relative to the conventional alternatives. Results obtained were similar to those in the 2002 soybean farmer survey referred to above. In terms of valuations, the average benefit attributed to these three categories of non-pecuniary benefits were, respectively, $32/hectare for HT corn farmers, $35.70/hectare for HT soybean farmers, and $39.40/hectare for HT cotton farmers. The values for non-pecuniary benefits identified in these surveys are, however, usually subject to bias due to factors such as the hypothetical nature of the contingent valuation technique, the framing of questions, and what is referred to as part-whole bias.10 Marra and Piggott (11 the sum of the values given by respondents to each separate non-pecuniary benefit and identified revised average (median) values for the non-pecuniary benefits in each survey ( The values for non-pecuniary benefits identified in these surveys are, however, usually subject to bias due to factors such as the hypothetical nature of the contingent valuation technique, the framing of questions, and what is referred to as part-whole bias.Marra and Piggott ( 2006 ) examined bias (notably part-whole bias) in the three surveys referred to above and found most respondents tended to overstate the value of parts by more than 60% compared with the separately stated total values for all non-pecuniary benefits. They subsequently rescaledthe sum of the values given by respondents to each separate non-pecuniary benefit and identified revised average (median) values for the non-pecuniary benefits in each survey ( Table 6 ). This suggests that US farmers who make widespread use of biotech HT traits value the non-pecuniary benefits of the technology at between $12.35/hectare and $24.71/hectare, with cotton farmers valuing the non-pecuniary aspects highest and corn farmers having the lowest valuation. In terms of attributes most valued, convenience is perceived to provide between 50% and 66% of the total non-pecuniary benefit of the HT technology. It is also interesting to note that the most recent survey of cotton farmers using HT (flex) technology have valued this technology as delivering an additional $12/hectare in terms of benefit from extra convenience relative to the first generation of biotech HT cotton technology. Corn producers value the non-pecuniary benefits of the IR (rootworm resistance) technology at about $7.40/hectare, of which the risk reduction component accounted for the largest single share (about a third). Table 6. Re-scaled values of non-pecuniary benefits. Survey Median value ($/hectare) 2002 IR (to rootworm) corn growers survey 7.41 2002 soybean (HT) farmers survey 12.35 2003 HT cropping survey (corn, cotton & soybeans)—North Carolina 24.71 2006 HT (flex) cotton survey 12.35 (relative to first generation HT cotton) Re-scaled values of non-pecuniary benefits. Aggregating the Impact to US Crops 1996-2007 The approach used to estimate the non-pecuniary benefits derived by US farmers from biotech crops over the period 1996-2007 has been to draw on the re-scaled values identifed by Marra and Piggot (2006, 2007, Table 6) and to apply these to the biotech-crop planted areas during this 12-year period. Figure 1 summarizes the values for non-pecuniary benefits derived from biotech crops in the United States (1996-2007) and shows an estimated (nominal value) benefit of $792 million in 2007 and a cumulative total benefit (1996-2007) of $5.11 billion. Relative to the value of direct farm income benefits presented above, the non-pecuniary benefits were equal to 21% of the total direct income benefits in 2007 and 25% of the total cumulative (1996-2007) direct farm income. This highlights the important contribution this category of benefit has had on biotech trait adoption levels in the United States, especially where the direct farm income benefits have been identfied to be relatively small (e.g., HT cotton). Figure 1. Non-pecuniary benefits derived by US farmers by trait, 1996-2007 ($ million). Estimating the Impact in Other Countries It is evident from the literature review that GM technology-using farmers in other countries also value the technology for a variety of non-pecuniary/intangible reasons. The most appropriate methodology for identifying these non-pecuniary benefit valuations in other countries would be to repeat the type of US farmer surveys in other countries. Unfortunately, the authors are not aware of any such studies undertaken to date. Production Effects of the Technology Based on the yield assumptions used in the direct farm income benefit calculations presented above (see Appendix 1) and taking into account the second soybean crop facilitation in South America, biotech crops have added important volumes to global production of corn, cotton, canola, and soybeans since 1996 (Table 7). Table 7. Additional crop production arising from positive yield effects of biotech crops. 1996-2007 additional production (million tonnes) 2007 additional production (million tonnes) Soybeans 67.80 14.46 Corn 62.42 15.08 Cotton 6.85 2.01 Canola 4.44 0.54 Additional crop production arising from positive yield effects of biotech crops. The biotech IR traits—used in the corn and cotton sectors—have accounted for 99% of the additional corn production and almost all of the additional cotton production. Positive yield impacts from the use of this technology have occurred in all user countries (except GM IR cotton in Australia)12 when compared to average yields derived from crops using conventional technology (such as application of insecticides and seed treatments). Since, 1996 the average yield impact across the total area planted to these traits over the 12 year period has been +6.1% for corn traits and +13.4% for cotton traits (Figure 2). Figure 2. Average yield impact of biotech IR traits by country and trait, 1996-2007. 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 in some countries. Specifically, HT soybeans in Romania improved the average yield by more than 30%, and biotech HT corn in Argentina and the Philippines delivered yield improvements of +9% and +15%, respectively. Biotech HT soybeans have also facilitated the adoption of no-tillage production systems, 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 67.5 million tonnes to soybean production in Argentina and Paraguay between 1996 and 2006—accounting for 99% of the total biotech-related additional soybean production. Using the same sensitivity analysis as applied to the farm income estimates presented above to the production impacts (one scenario of consistent lower-than-average pest/weed pressure and one of consistent higher-than-average pest/weed pressure), Table 8 shows the range of production impacts. Table 8. Additional crop production arising from positive yield effects of biotech crops under different pest/weed pressure assumptions and impacts of the technology, 1996-2007 (million tonnes). Crop Consistent below average pest/weed pressure Average pest/weed pressure (main study analysis) Consistent above average pest/weed pressure Corn 46.0 62.42 109.5 Cotton 4.61 6.86 9.03 Canola 2.09 4.44 6.26 Additional crop production arising from positive yield effects of biotech crops under different pest/weed pressure assumptions and impacts of the technology, 1996-2007 (million tonnes). Concluding Comments This study quantified the cumulative global impact of GM technology between 1996 and 2007 on farm income and production. The analysis shows that there have been substantial direct economic benefits at the farm level, amounting to a cumulative total of $44.1 billion; half of this has been derived by farmers in developing countries. Important non-pecuniary benefits have also been derived by many farmers, which in the case of US farmers added a further $5.1 billion to the farm income benefits derived from the technology. GM technology has also resulted in additional production of important crops, equal to an extra 68 million tonnes of soybeans and 62 million tonnes of corn (1996-2007). The impacts identified are based on estimates of average impact, reflecting the limitations of the methodologies used and the limited availability of relevant data. Applying alternative assumptions that reflect the extremes of low weed and pest pressure in all years and high weed and pest pressure in all years suggests that the impact on farm income probably falls within a range of -15% to +20% around the cumulative estimate of $44.1 billion referred to above. Subsequent research at the trait-and country-level might usefully extend this analysis to incorporate more sophisticated consideration of dynamic economic impacts and broader (outside the United States) examination of the less tangible (non-pecuniary) economic impacts. Endnotes 1 Readers should note that some data presented in this article are not directly comparable with data presented in the previous three articles because the current articles takes into account the availability of new data and analysis (including revisions to data for earlier years). 2 Examples where such data is available include the impact of GM insect-resistant cotton: in India, see Ismael, Kambhampati, and Morse (2004) and IMRB (2006, 2007); in Mexico, see Traxler, Godoy-Avilla, Falck-Zepeda, and Espinoza-Arellano (2001) and Monsanto Mexico (2005, 2007); and in the US, see Sankula and Blumenthal (2003, 2006) and Mullins and Hudson (2004). 3 Impacts on these categories of cost are, however, more limited than the impacts on seed and crop protection costs because only a few of the papers reviewed have included consideration of such costs in their analyses. Therefore, in most cases the analysis relates to impact of crop protection and seed cost only. 4 Notably relating to the US—Marra and Piggott (2006). 5 The authors acknowledge that the classification of different countries into developing or developed country status affects the distribution of benefits between these two categories of country. The definition used in this article is consistent with the definition used by James (2007). 6 The cost of the technology accrues to the seed supply chain, including sellers of seed to farmers, seed multipliers, plant breeders, distributors, and the GM technology providers. 7 For example, relating to HT soybeans, USDA (1999), Gianessi and Carpenter (1999), and Qaim and Traxler (2002); relating to IR maize, Rice (2004) and Brookes (2008); relating to IR cotton, Ismael, Bennett, Morse, and Buthelezi (2002) and Pray et al. (2002). 8 Notably maize in India. 9 Survey-based method of obtaining valuations of non-market goods that aim to identify willingness to pay for specific goods (e.g., environmental goods, peace of mind, etc.) or willingness to pay to avoid something being lost. 10 In the case of non-pecuniary benefits, the sum of values given by farmers to individual categories of benefit is greater than their stated total value of all non-pecuniary benefits (farmers being asked to value each type of benefit separately in addition to separately valuing total non-pecuniary benefits). 11 See Marra and Piggott (2006). 12 This reflects the levels of Heliothis pest control previously obtained with intensive insecticide use. The main benefit and reason for adoption of this technology in Australia has arisen from significant cost savings (on insecticides) and the associated environmental gains from reduced insecticide use. References Asia-Pacific Consortium on Agricultural Biotechnology (APCoAB). (2006). Bt cotton in India: A status report. New Delhi, India: ICRASTAT. Bennett, R.M., Ismael, Y., Kambhampati, U., & Morse, S. (2004). Economic impact of genetically modified cotton in India. AgBioForum, 7(3), 96-100. Available on the World Wide Web: http://www.agbioforum.org. Brookes, G. (2003, July). The farm level impact of using Bt maize in Spain. Paper presented at the 6th International Consortium on Agricultural Biotechnology Research (ICABR) Conference, Ravello, Italy. Available on the World Wide Web: http://www.pgeconomics.co.uk/pdf/bt_maize_in_spain.pdf. Brookes, G. (2005). The farm-level impact of herbicide-tolerant soybeans in Romania. AgBioForum, 8(4), 235-241. Available on the World Wide Web: http://www.agbioforum.org. Brookes, G. (2008). The benefits of adopting GM insect resistant (Bt) maize in the EU: First results from 1998-2006. International Journal of Biotechnology, 134(3-4). Canola Council of Canada. (2001). An agronomic & economic assessment of transgenic canola. Winnipeg: Author. Available on the World Wide Web: http://www.canola-council.org/research_transgenic.aspx. Carpenter, J., & Gianessi, L. (2002). Agricultural biotechnology: Updated benefit estimates. Washington, DC: National Center for Food and Agriculture Policy (NCFAP). Available on the World Wide Web: http://www.ncfap.org/reports/biotech/updatedbenefits.pdf. Commonwealth Scientific and Industrial Research Organisation (CSIRO). (2005). The cotton consultants Australia 2005 Bollgard II comparison report. Collingwood, Australia: Author. Doyle, B. (2005). The performance of Ingard and Bollgard II cotton in Australia during the 2002/2003 and 2003/2004 seasons. Armidale, Australia: University of New England. Doyle, B., et al. (2003). The performance of roundup ready cotton 2001-2002 in the Australian cotton sector. Armidale, Australia: University of New England. Elena, M.G. (2001). Economic advantages of transgenic cotton in Argentina [Ventajas económicas del algodón transgénico en Argentina] (Working paper). Instituto Nacional de Tecnologia Agropecuaria (INTA), Estacion Experimental Saenz Pena-Chaco. As cited in Trigo, E.J., & Cap, E.J. (2006, December). Ten years of genetically modified crops in Argentine agriculture. Interno, Argentina: ArgenBio. Fitt, G. (2001). Deployment and impact of transgenic Bt cotton in Australia. As cited in James, C. (2002). Global review of commercialised transgenic crops: 2001 feature: Bt cotton (ISAAA No. 26). George Morris Centre. (2004). Economic & environmental impacts of the commercial cultivation of glyphosate tolerant soybeans in Ontario (unpublished report). Guelph, Ontario: Author. Gianessi, L., & Carpenter, J. (1999). Agricultural biotechnology: Insect control benefits. Washington, DC: NCFAP. Available on the World Wide Web: http://www.ncfap.org/reports/biotech/insectcontrolbenefits.pdf. Gomez-Barbero, M., & Rodriguez-Cerezo, E. (2006, July). The adoption of GM insect-resistant Bt maize in Spain: An empirical approach. Paper presented at the 10th International Consortium on Agricultural Biotechnology Research (ICABR) conference, Ravello, Italy. Gonsalves, D. (2005). Harnessing the benefits of biotechnology: The case of Bt corn in the Philippines. Laguna, Philippines: Strive Foundation. Gouse, M., Piesse, J., & Thirtle, C. (2006). Output & labour effect of GM maize and minimum tillage in a communal area of Kwazulu-Natal. Journal of Development Perspectives, 2(2), 71-86. Gouse, M., Pray, C., Kirsten, J.F., & Schimmelpfennig, D. (2005). A GM subsistence crop in Africa: The case of Bt white maize in South Africa. International Journal of Biotechnology, 7(1/2/3), 84-94. Gouse, M., Pray, C., Schimmelpfennig, D., & Kirsten, J. (2006). Three seasons of subsistence insect-resistant maize in South Africa: Have smallholders benefited? AgBioForum, 9(1), 15-22. Available on the World Wide Web: http://www.agbioforum.org. Gusta, M., et al (2008). Economic producer benefits from GMHT canola production, University of Saskatchewan, College of Biotechnology (draft paper). IMRB International. (2006). Socio-economic benefits of Bollgard and product satisfaction (in India). Mumbai, India: Author. IMRB International. (2007). Socio-economic benefits of Bollgard and product satisfaction in India. Mumbai, India: Author. Ismael, Y., Bennett, R., Morse, S., & Buthelezi, T.J. (2002, July). Bt Cotton and pesticides. A case study of smallholder farmers in Makhathini Flats South Africa. Paper presented at the International ICABR Conference, Ravello, Italy. James, C. (2002). Global review of commercialized transgenic crops 2001: Feature Bt cotton (ISAAA Issue Brief No. 26). Ithaca, NY: International Service for the Acquisition of Agri-Biotech Applications (ISAAA). James, C. (2003). Global review of commercialized transgenic crops 2002: Feature Bt maize (ISAAA Issue Brief No. 29). Ithaca, NY: ISAAA. James, C. (2006). Global status of transgenic crops, various global review briefs from 1996 to 2006. Ithaca, NY: ISAAA. James, C. (2007). Global status of commercialized biotech/GM crops: 2006 (ISAAA Issue Brief No. 35). Ithaca, NY: ISAAA. Johnson, S., & Strom, S. (2007, February). Quantification of the impacts on US agriculture of biotechnology-derived crops planted in 2006. Washington, DC: National Center for Food and Agricultural Policy (NCFAP). Kirsten, J., Gouse, M., & Jenkins, L. (2002, July). Bt cotton in South Africa: Adoption and the impact on farm incomes amongst small-scale and large-scale farmers. Paper presented at the 6th International ICABR Conference, Ravello, Italy. Marra, M., Pardey, P., & Alston, J. (2002). The payoffs of agricultural biotechnology: An assessment of the evidence. Washington, DC: International Food Policy Research Institute (IFPRI). Marra, M., & Piggott, N. (2006). The value of non pecuniary characteristics of crop biotechnologies: A new look at the evidence. In Just, J. Alston, & D. Zilberman (Eds.), Regulating agricultural biotechnology: Economics and Policy, pp. 145-178. New York: Springer-Verlag. Marra, M., & Piggott, N. (2007). The net gains to cotton farmers of a national refuge plan for Bollgard II cotton. AgBioForum, 10(1), 1-10. Available on the World Wide Web: http://www.agbioforum.org. Monsanto Brazil. (2008). Farm survey of conventional and Bt cotton growers in Brazil 2007 (unpublished). Monsanto Comercial Mexico. (2005). Official report to Mexican Ministry of Agriculture (unpublished). Monsanto Comercial Mexico. (2007). Official report to Mexican Ministry of Agriculture of the 2006 crop (unpublished). Morse, S., Bennett, R., & Ismael, Y. (2004). Why Bt cotton pays for small-scale producers in South Africa. Nature Biotechnology, 22(4), 379-380. Mullins, W., & Hudson, J. (2004, Jan.). Bollgard II versus Bollgard sister line economic comparisons, 2004. Paper presented at the Beltwide cotton conferences, San Antonio, TX. Parana Department of Agriculture. (2004). Cost of production comparison: Biotech and conventional soybeans (USDA GAIN Report BR4629). Washington, DC: Author. Available on the World Wide Web: http://www.fas.usad.gov/gainfiles/200411/146118108.pdf. Pray, C., Huang, J., Hu, R., & Rozelle, S. (2002). Five years of Bt cotton in China: The benefits continue. The Plant Journal, 31(4), 423-430. Qaim, M., & De Janvry, A. (2002, July). Bt cotton in Argentina: Analysing adoption and farmers’ willingness to pay. Paper presented at the American Agricultural Economics Association annual meeting, Long Beach, CA. Qaim, M., & De Janvry, A. (2005). Bt cotton and pesticide use in Argentina: Economic and environmental effects. Environment and Development Economics, 10, 179-200. Available on the World Wide Web: http://are.berkeley.edu/~sadoulet/papers/Argentina_EDE.pdf. Qaim, M., & Traxler, G. (2002, July). Roundup Ready soybeans in Argentina: Farm level, environmental, and welfare effects. Paper presented at the 6th International 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. Ramon, G. (2005). Acceptability survey on the 80-20 bag in a bag insect resistance management strategy for Bt corn. Quezon City, Philppines: Biotechnology Coalition of the Philippines. Rice, M.E. (2004). Transgenic rootworm corn: Assessing potential agronomic, economic, and environmental benefits. Plant Health Progress. Available on the World Wide Web: http://www.plantmanagementnetwork.org/pub/php/review/

2004/rootworm/. Sankula, S., & Blumenthal, E. (2003). Impacts on US agriculture of biotechnology-derived crops planted in 2003: An update of eleven case studies. Washington, DC: NCFAP. Available on the World Wide Web: http://www.ncfap.org. Sankula, S., & Blumenthal, E. (2006). Impacts on US agriculture of biotechnology-derived crops planted in 2005: An update of eleven case studies. Washington, DC: NCFAP. Available on the World Wide Web: http://www.ncfap.org. Traxler, G., Godoy-Avilla, S., Falck-Zepeda, J., & Espinoza-Arellano, J.J. (2001, June). Transgenic cotton in Mexico: Economic and environmental impacts. Paper presented at the 5th International Conference on Biotechnology, Science and Modern Agriculture: A new industry at the dawn of the century, Ravello, Italy. Trigo, E., Chudnovsky, D., Cap, E., & Lopez, A. (2002). Genetically modified crops in Argentine agriculture: An open ended story. Buenos Aires, Argentina: Libros del Zorzal. Trigo, E., & Cap, E. (2006, December). Ten years of GM crops in Argentine agriculture. Buenos Aires: Argentine Council for Information and Development of Biotechnology (ArgenBio). Yorobe, J. (2004, October). Economics impact of Bt corn in the Philippines. Paper presented to the 45th PAEDA Convention, Querzon City, Philippines.

Appendix 1: Details of Methodology as Applied to 2007 Farm Income Calculations Table A1. GM IR corn (targeting corn boring pests), 2007. Country Area of trait ('000 ha) Yield assumption % change Base yield (tonnes/ha) Farm level price ($/tonne) Cost of technology ($/ha) Impact on costs, net of cost of technology ($/ha) Change in farm income ($/ha) Change in farm income at national level

('000 $) Production impact ('000 tonnes) United States 18,561 +5 9.25 135.4 -17.3 -1.42 61.22 +1,136,212 +8,584.4 Canada 831 +5 8.29 165.44 -19.3 +1.68 +70.26 +58,382 +344.4 Argentina 2,509 +5.5 6.8 113.0 -19.9 -19.9 +22.41 +56,220 +938.4 Philippines 194 +24.15 2.52 215.12 -36.2 -22.14 +108.78 +21,091 +118 South Africa 1,234 +15 4.0 304.47 -16.19 -2.29 +180.39 +222,601 +740.4 Spain 75.1 +10 9.34 283.77 -47.75 +9.55 +274.59 +20,634 +70.2 Uruguay 105 +5.5 5.61 125 -19.9 -19.9 +18.63 +1,956.6 +32.4 France 22.1 +10 9.4 256.48 -54.57 +13.64 +254.73 +5,638.5 +20.8 Germany 2.7 +4 9.09 285.13 -54.57 +13.64 +117.32 +315 +1 Portugal 4.3 +12.5 5.51 278.31 -47.75 -47.75 +143.95 +613.6 +2.9 Czech Republic 5 +10 5.75 294.68 -47.75 -23.19 +146.25 +713.2 +2.9 Slovakia 0.9 +12.3 4.28 285.13 -47.75 -47.75 +102.35 +97.1 +0.5 Poland 0.3 +12.5 5.28 259.21 -47.75 -47.75 +123.33 +40 +0.2 Romania 0.3 +7.1 3.50 315.14 -43.66 -43.66 +34.66 +12 +0.1 GM IR corn (targeting corn boring pests), 2007. Table A2. GM IR corn (targeting corn rootworm), 2007. Country Area of trait ('000 ha) Yield assumption % change Base yield (tonnes/ha) Farm level price ($/tonne) Cost of technology ($/ha) Impact on costs, net of cost of technology ($/ha) Change in farm income ($/ha) Change in farm income at national level

('000 $) Production impact ('000 tonnes) US 8,417.6 +5 9.25 135.4 -35 +2.47 +65.10 +547,991 +3,893.2 Canada 39.3 +5 8.29 165.44 -35 +2.47 +71.04 +2,788.7 +16.3 GM IR corn (targeting corn rootworm), 2007. Table A3. GM IR cotton, 2007. Country Area of trait ('000 ha) Yield assumption % change Base yield (tonnes/ha) Farm level price ($/tonne) Cost of technology ($/ha) Impact on costs, net of cost of technology ($/ha) Change in farm income ($/ha) Change in farm income at national level

('000 $) Production impact ('000 tonnes) US 2,585.2 +10 0.93 1,202 -46.95 -5.77 +106.02 +274,078 +240.4 China 3,800 +10 1.18 807.4 -48.07 +152.48 +248.08 +942,695 +449.9 South Africa 9.9 +24 0.692 1,172.0 -49.43 -31.23 +163.42 +1,617.8 +1.6 Australia 55.3 0 1.91 1,458 -251.3 +212.0 +212.09 +11,734.3 0 Mexico 60.0 +9.28 1.18 1088.7 -70.41 +20.49 +139.71 +8,382.1 +6.6 Argentina 162.3 +30 0.418 1,455 -37.85 -21.17 +161.31 +26,180.8 +20.3 India 5,868 +50 0.43 1,536.9 -55.29 -8.86 +321.57 +1,886,986 +1,261.6 Colombia 20.0 +9.28 0.95 1,900 -70.41 +20.49 +187.99 +3,749.8 +1.8 Brazil 358 +6.23 1.32 1,316.6 -43.94 +71.21 +135.54 +48,524 +29.4 GM IR cotton, 2007. Table A4. GM HT soybeans, 2007 (excluding second crop soybeans—see separate table). Country Area of trait ('000 ha) Yield assumption % change Base yield (tonnes/ha) Farm level price ($/tonne) Cost of technology ($/ha) Impact on costs, net of cost of technology

($/ha) Change in farm income ($/ha) Change in farm income at national level

('000 $) Production impact ('000 tonnes) US 23,433.5 0 2.77 331 -24.71 +57.96 +57.96 +1,358,206.4 0 Canada 688 0 2.3 395 -37.47 +24.52 +24.52 +16,871.2 0 Argentina 16,419.5 0 2.83 221.7 -2.5 +26.11 +29.23 +480,012.1 0 Brazil 13,562.5 0 2.85 282.4 -18.77 +57.2 +61.2 +830,022.6 0 Paraguay 2,600 0 2.41 261.3 -9.64 +18.97 +22.11 +57,476.6 0 South Africa 144 0 1.12 356.6 -27.94 +5.01 +5.01 +722.1 0 Uruguay 443.5 0 2.19 256.1 -2.5 +26.11 +28.9 +12,819.2 0 Mexico 5 +9.1 1.48 360 -34.5 +120 +168.48 +842 +0.7 Romania 0 n/a n/a n/a n/a n/a n/a n/a n/a GM HT soybeans, 2007 (excluding second crop soybeans—see separate table). Table A5. GM HT corn, 2007. Country Area of trait ('000 ha) Yield assumption % change Base yield (tonnes/ha) Farm level price ($/tonne) Cost of technology ($/ha) Impact on costs, net of cost of technology ($/ha) Change in farm income ($/ha) Change in farm income at national level

('000 $) Production impact ('000 tonnes) US 19,697.3 0% 9.48 135 -24.71 +19.89 +19.89 +391,779.1 0 Canada 751 0% 8.51 165.44 -31.8 +13.01 +13.01 +9,771.3 0 Argentina 369 +3% corn belt

+22% marginal regions 7.68 corn belt

4.31 marginal areas 113 -19.9 0 +26.1 corn belt

+107.43 marginal regions +27,637.1 +244.1 South Africa 453 0% 4.29 304.47 -17.19 +6.02 +6.02 +2,725.8 0 Philippines 191.3 +15 2.52 215.12 -26.69 -26.69 +54.47 +10,419.2 +72.2 GM HT corn, 2007. Table A6. GM HT cotton, 2007. Country Area of trait ('000 ha) Yield assumption % change Base yield (tonnes/ha) Farm level price ($/tonne) Cost of technology ($/ha) Impact on costs, net of cost of technology ($/ha) Change in farm income ($/ha) Change in farm income at national level

('000 $) Production impact ('000 tonnes) US 3,067.1 0 0.985 1,202 -70.35 +5.2 +5.2 +15,949 0 South Africa 9.7 0 0.8 1,172 -23.6 -22.9 -0.72 -7 0 Australia 50.5 0 1.91 1,458 -42.71 +7.54 +7.54 +380.4 0 Argentina 124 Farm saved seed area 0%

Certified seed area +17.4% 0.453 1,455 -39.86 certified seed, -8 farm saved seed -17.67 certified seed, +14.19 farm saved seed +99.57 certified seed, +14.19 farm saved seed -3,876.5 +2.0 Mexico 50 +3.6 1.208 1,089 -66.4 +39.67 +87.02 +4,350.8 +2.2 GM HT cotton, 2007. Table A7. GM HT canola, 2007. Country Area of trait ('000 ha) Yield assumption % change Base yield (tonnes/ha) Farm level price ($/tonne) Cost of technology ($/ha) Impact on costs, net of cost of technology ($/ha) Change in farm income ($/ha) Change in farm income at national level

('000 $) Production impact ('000 tonnes) US glyphosate tolerant 271.9 +4 1.65 359.36 -12.36 +27.73 +51.45 +13,990.1 +5.2 US glufosinate tolerant 182.9 +10 1.65 359.36 -12.36 +22.28 +81.57 +14,918.4 +8.7 Canada glyphosate tolerant 2,840.9 +4 1.41 508.27 -34.01 +6.82 +35.49 +100,823.3 +160.2 Canada glufosinate tolerant 2,588.4 +10 1.41 508.27 0 +11.72 +83.38 +215,830.1 +365.0 GM HT canola, 2007. Table A8. GM virus-resistant crops. Country Area of trait (ha) Yield assumption % change Base yield (tonnes/ha) Farm level price ($/tonne) Cost of technology ($/ha) Impact on costs, net of cost of technology ($/ha) Change in farm income ($/ha) Change in farm income at national level

('000 $) Production impact ('000 tonnes) US papaya 778 +15 22.86 864.36 -148 -148 +2,816.1 +2,190 +2.7 US squash 3002 +100 31.4 566.90 -398 -398 +17,402.9 +52,252.3 +94.3 GM virus-resistant crops. Second Soybean Crop Benefits: Argentina An additional farm income benefit that many Argentine soybean growers have derived comes from the additional scope for second cropping of soybeans. This has arisen because of the simplicity, ease, and weed management flexibility provided by the (GM) technology which has been an important factor facilitating the use of no- and reduced-tillage production systems. In turn, the adoption of low/no-tillage production systems has reduced the time required for harvesting and drilling subsequent crops and, hence, has enabled many Argentine farmers to cultivate two crops (wheat followed by soybeans) in one season. As such, the proportion of soybean production in Argentina using no- or low-tillage methods has increased from 34% in 1996 to 90% by 2005. Also, 30% of the total Argentine soybean crop was second crop in 2007, compared to 8% in 1996. Based on the additional gross margin income derived from second crop soybeans (see Table A9), this has contributed a further boost to national soybean farm income of $1.1 billion in 2007 and $4.4 billion cumulatively since 1996. Table A9. Farm-level income impact of using GM HT soybeans in Argentina, 1996-2007 (2): Second crop soybeans. Year Second crop area (million ha) Average gross margin/ha for second crop soybeans ($/ha) Increase in income linked to GM HT system (million $) 1996 0.45 128.78 Negligible 1997 0.65 127.20 25.4 1998 0.8 125.24 43.8 1999 1.4 122.76 116.6 2000 1.6 125.38 144.2 2001 2.4 124.00 272.8 2002 2.7 143.32 372.6 2003 2.8 151.33 416.1 2004 3.0 226.04 678.1 2005 2.3 228.99 526.7 2006 3.2 218.40 698.9 2007 4.94 229.36 1,133.6 Farm-level income impact of using GM HT soybeans in Argentina, 1996-2007 (2): Second crop soybeans. Base Yields Used Where GM Technology Delivers a Positive Yield Gain In order to avoid over-stating the positive yield effect of GM technology (where studies have identified such an impact) when applied at a national level, average (national level) yields used have been adjusted downwards (see example in Table A10). Production levels based on these adjusted levels were then cross checked with total production values based on reported average yields across the total crop. Table A10. Example: GM IR cotton (2007). Country Average yield across all forms of production (t/ha) Total cotton area ('000 ha) Total production ('000 tonnes) GM IR area ('000 ha) Conventional area ('000 ha) Assumed yield effect of GM IR technology Adjusted base yield for conventional cotton (t/ha) GM IR production ('000 tonnes) Conventional production ('000 tonnes) United States 0.985 4,381.6 4,315.9 2,585.2 1,796.5 +10% 0.93 2,644.7 1,670.7 China 1.257 6,200.0 7,793,4 3,800.0 2,400.0 +10% 1.184 4,949.1 2.841.6 Example: GM IR cotton (2007). Appendix 2: Impacts, Assumptions, Rationale and Sources for All Trait/Country Combinations Table A11. IR corn (resistant to corn-boring pests). Country Yield impact assumption used Rationale Yield references Sensitivity analysis applied to yield assumptions Cost of technology data/assumptions Cost savings (excluding impact of seed premium) assumptions Cost references GM IR corn resistant to corn boring pests US & Canada +5% all years Broad average of impact identified from several studies/papers Carpenter and Gianessi (2002) found yield impacts of +9.4% in 1997, +3% in 1998, +2.5% in 1999

Marra et al. (2002) average impact of +5.04% 1997-2000 based a review of five studies, James (2003) average impact of +5.2% 1996-2002, Sankula and Blumenthal (2003, 2006) range of +3.1% to +9.9%

Canada—no studies identified—as US—impacts qualitatively confirmed by industry sources (personal communication, 2005, 2007). +3% to +9% $25 in 1996 & 1997

$20 in 1998 & 1999

$22 in 2000-2004

$17 in 2005 and onwards $15.50 all years to 2004

$15.90 2005 onwards The same reference sources as yield were used. Industry sources also confirmed costs of technology and estimated cost-saving values for Canada. Argentina +9% all years to 2004, +5.5% 2005 onwards Average of reported impacts in first seven years, later revised downwards for more recent years to reflect professional opinion James (2003) cites two unpublished industry survey reports; one for 1996-1999 showing an average yield gain of +10% and one for 2000-2003 showing a yield gain of +8%. Trigo, Chudnovsky, Cap, and Lopez (2002), Trigo and Cap (2006) +10%, Trigo (personal communication, 2007, 2008) estimates average yield impact since 2005 to be lower at between +5% and +6%. +5% all years to +9% all years Same as US to 2005 then 60 Pesos 2006 onwards None, as maize crops not traditionally treated with insecticides for corn boring pest damage Cost of technology drawn from Trigo et al. (2002) and Trigo and Cap (2006), i.e., costed/priced at same level as US (Trigo, personal communication, 2007, 2008). Philippines +24.6% all years Average of three studies used all years Gonsalves (2005) found average yield impact of +23% dry season crops and +20% wet season crops;

Yorobe (2004) +38% dry season crops and +35% wet season crops; Ramon (2005) found +15.3% dry season crops and +13.3% wet season crops. +14% to +34% all years 1,673 Pesos all years 651 Pesos all years Based on Gonsalves (2005)—the only source to break down these costs. For 2006 and 2007, this level of cost and average cost savings were confirmed by industry sources. South Africa +11% 2000 & 2001

+32% 2002

+16% 2003

+5% 2004

+15% 2005 onwards Reported average impacts used for years available (2000-2004), 2005 onwards based on average of other years Gouse, Pray, Kirsten, and Schimmelpfennig (2005), Gouse, Piesse, and Thirtle (2006), and Gouse, Pray, Schimmelpfennig, and Kirsten (2006) reported yield impacts as shown (range of +11% to +32%). +5% to +32% all years 84 Rand 2000 & 2001

90 Rand 2002

94 Rand 2004 & 2005

113 Rand 2006 onwards 97 Rand all years Based on the same papers as used for yield, plus confirmation in 2006 and 2007 that these are representative values from industry sources. Spain +6.3% 1998-2004

+10% 2005 onwards Impact based on author's own detailed, representative analysis for period 1998-2002 then updated to reflect improved technology based on industry analysis Brookes (2003) identified an average of +6.3% using the Bt 176 trait mainly used in the period 1998-2004 (range +1% to +40% for the period 1998-2002). From 2005, 10% used based on Brookes (2008), which derived from industry (unpublished sources) commercial-scale trials and monitoring of impact of the newer, dominant trait Mon 810 in the period 2003-2007. Gomez-Barbero and Rodriguez-Cerezo (2006) reported an average impact of +5% for Bt 176 used in 2002-2004. +3% to +15% all years 30 Euros 1998 & 1999

28 Euros 2000

18.5 Euros 2001-2005

35 Euros 2006 onwards 42 Euros all years Based on Brookes (2003), the only source to break down these costs. The more recent cost of technology costs derive from industry sources (reflecting the use of Mon 810 technology). Industry sources also confirm value for insecticide cost savings as being representative. Other EU France +10%, Germany +4%, Portugal +12.5%, Czech Republic +10%, Slovakia +12.3%, Poland +12.5%, Romania +7.1% Impacts based on average of available impact data in each country. Based on Brookes (2008), which drew on a number of sources. For France, four sources with average yield impacts of +5% to +17%; for Germany the sole source had average annual impacts of +3.5% and +9.5% over a two year period; for Czech Republic, three studies identified average impacts in 2005 of an average of 10% and a range of +5% to +20%; for Portugal, commercial trial and plot monitoring reported +12% in 2005 and between +8% and +17% in 2006; in Slovakia based on trials for 2003-2007 and 2006/07 plantings with yield gains averaging between +10% and +14.7%; in Poland based on variety trial tests 2005 and commercial trials 2006 which had a range of +2% to +26%; Romania based on estimated impact by industry sources for the 2007 year. Not applied in context of total study due to very small scale of production (i.e., would produce an insignificant impact range in the context of the whole study). France and Germany, 40 Euros; Portugal, Czech, Slovak Republics, and Poland, 35 Euros; Romania, 32 Euros France and Germany, 50 Euros; Portugal, Slovakia, Poland and Romania, 0; Czech Republic, 18 Euros Data derived from the same sources referred to for yield. Uruguay Same as Argentina Same as Argentina No country-specific studies identified, so impact analysis from nearest country of relevance (Argentina) applied. Same as Argentina: +5% to +9% Same as Argentina Same as Argentina Same as Argentina GM IR corn (resistant to corn rootworm) US & Canada +5% all years Based on the impact used by the references cited. Sankula and Blumenthal (2003, 2006) used +5% in analysis, citing this as conservative, themselves having cited impacts of +12%-+19% in 2005 in Iowa, +26% in Illinois in 2005, and +4%-+8% in Illinois in 2004. Johnson and Strom (2007) used the same basis as Sankula and Blumenthal

Rice (2004) range of +1.4% to +4.5% (based on trials)

Canada—no studies identified—as US—impacts qualitatively confirmed by industry sources (personal communication, 2005, 2007). +3% to +9% $42 in 2003 and 2004

$35 in 2005 onwards $32 2003

$37 2004 onwards Data derived from Sankula and Blumenthal (2006) and Johnson and Strom (2007).

Canada—no studies identified—as US—impacts qualitatively confirmed by industry sources (personal communication, 2005, 2007). IR cotton US +9% 1996-2002

+11% 2003 & 2004

+10% 2005 onwards Based on the (conservative) impact used by the references cited Sankula and Blumenthal (2003, 2006) drew on earlier work from Carpenter and Gianessi (2002) in which they estimated the average yield benefit in the 1996-2000 period was +9%. Marra et al. (2002) examined the findings of over 40 state-specific studies covering the period 1996 up to 2000, the approximate average yield impact was +11%. The lower of these two values was used for the period to 2002. The higher values applied from 2003 reflect values used by Sankula and Blumenthal (2006) and Johnson and Strom (2007) that take into account the increasing use of Bollgard II technology, and draws on work by Mullins and Hudson (2004) that identified a yield gain of +12% relative to conventional cotton. The values applied 2005 onwards were adjusted downwards to reflect the fact that some of the GM IR cotton area has still been planted to Bollgard I. +5% to +15% $58.27 1996-2002

$68.32 2003 & 2004

$49.60 2005 onwards $63.26 1996-2002

$74.10 2003-2005

$41.18 2006 onwards Data derived from the same sources referred to for yield. China +8% 1997-2001

+10% 2002 onwards Average of studies used to 2001. Increase to 10% on basis of industry assessments of impact and reporting of unpublished work by Schuchan. Pray, Huang, Hu, and Rozelle (2002) surveyed farm level impact for the years 1999-2001 and identified yield impacts of +5.8% in 1999, +8% in 2000, and +10.9% in 2001

Monsanto China (personal communication, 2007, 2008) +6% to +12% $46.30 all years to 2005

366 Yuan 2006 onwards $261 2000

$438 2001

average of these used all other years to 2004

1,530 Yuan 2005 onwards Data derived from the same sources referred to for yield. Australia None Studies have usually identified no significant average yield gain. Fitt (2001)

Doyle (2005)

James (2002)

Commonwealth Scientific and Industrial Research Organisation (CSIRO, 2005) None applied Aus $245 1996 & 1997

Aus $155 1998

Aus $138 1999

Aus $155 2000-2001

Aus $167 2002

Aus $190 2003

Aus $250 2004

Aus $300 2005 onwards Aus $151 1996

Aus $157 1997

Aus $188 1998

Aus $172 1999

Aus $267 2000-2002

Aus $598 2003

Aus $509 2004

Aus $553 2005 onwards Data derived from the same sources referred to for yield (Fitt, 2001) covering earlier years of adoption, then CSIRO for later years. For 2006 and 2007 cost of technology values confirmed by personal communication from Monsanto Australia. Argentina +30% all years More conservative of the two pieces of research used Qaim and De Janvry (2002, 2005) analysis based on farm level analysis in 1999/00 and 2000/01 +35% yield gain, Trigo and Cap (2006) used an average gain of +30% based on work by Elena (2001). +25% to +35% $86 all years to 2004

116 Pesos 2005 onwards 51 Pesos all years Data derived from the same sources referred to for yield. Cost of technology in 2006 and 2007 also confirmed from industry sources. South Africa +24% all years Lower end of estimates applied Ismael et al. (2002) identified yield gain of +24% for the years 1998/99 & 1999/2000. Kirsten, Gouse, and Jenkins (2002) for 2000/01 season found a range of +14% (dry crops/large farms) to +49% (small farmers).

James (2002) also cited a range of impact between +27% and +48% during the years 1999-2001. +15% to +40% 149 Rand all years to 2005

345 Rand 2006 onwards 127 Rand all years Data derived from the same sources referred to for yield. Values for cost of technology and cost of insecticide cost savings also provided/confirmed from industry sources. Mexico +37% 1996

+3% 1997

+20% 1998

+27% 1999

+17% 2000

+9% 2001

+6.7% 2002

+6.4% 2003

+7.6% 2004

+9.25% 2005

+9% 2006

+9.28 2007 Recorded yield impact data used as available for almost all years The yield impact data for 1997 and 1998 is drawn from the findings of farm level survey work by Traxler et al. (2001). For all other years the data is based on the commercial crop monitoring reports required to be submitted to the Mexican government (Monsanto Mexico, 2005, 2007). As data from this source was not available for 2007, the yield applied in 2007 is the average for the period 2000-2006. None applied as almost all years are crop-specific estimates 540 Pesos all years to 2005

760 Pesos 2006 onwards 985 Pesos 1996 and 1999 onwards

$121 1997

$94 1998 Data derived from the same sources referred to for yield India +45% 2002

+63% 2003

+54% 2004

+64% 2005

+50% 2006 & 2007 Recorded yield impact used for almost all years Yield impact data 2002 and 2003 is drawn from Bennett et al. (2004), for 2004 the average of 2002 and 2003 was used. 2005 and 2006 are derived from IMRB (2006, 2007). 2007 impact data based on lower end of range of impacts identified in previous three years (2007 being a year of similar pest pressure to 2006—lower than average). 45% to 65% all years 2,636 Rupees 2002

2,512 Rupees 2003

2,521 Rupees 2004

2,307 Rupees 2005

2,211 Rupees 2006 & 2007 2,032 Rupees 2002

1,767 Rupees 2003

1,900 Rupees 2004

1,362 Rupees 2005

2,308 Rupees 2006

1,857 Rupees 2007 Data derived from the same sources referred to for yield. 2007 cost of technology confirmed from industry sources and cost savings for 2007 taken as average of past three years Brazil +6.23% The only data source identified (unpublished farm survey data—Monsanto Brazil, 2008) has been used covering the 2006 season. This has also been used for 2007. +4% to +8% all years $40 2006 onwards 141 Real Data derived from the same source referred to for yield. GM HT soybeans US 0% Not relevant Not relevant Not relevant $14.82 1996-2002

$17.30 2003

$19.77 2004

$24.71 2005 onwards $25.20 1996-97

$33.90 1998-2002

$78.50 2003

$60.10 2004

$69.40 2005

$81.70 2006 onwards Marra, Pardey, and Alston (2002)

Gianessi and Carpenter (1999)

Carpenter and Gianessi (2002)

Sankula and Blumenthal (2003, 2006)

Johnson and Strom (2007) Canada 0% Not relevant Not relevant Not relevant Can $32 1997-2002

Can $48 2003

Can $45 2004 & 2005

Can $41 2006 onwards Range of Can $66-89 1997-2007 converted to US $ at prevailing exchange rate George Morris Centre (2004) Argentina 0% but second crop benefits Not relevant except 2nd crop—see separate table Not relevant Not relevant $3-$4 all years to 2001

$1.20 2002-2005 (reflecting all use of farm saved seed)

$2.50 2006 onwards (Monsanto royalty rate) $24-$30: varies each year according to exchange rate Qaim and Traxler (2002, 2005), Trigo and Cap (2006). Brazil 0% Not relevant Not relevant Not relevant Same as Argentina to 2002 (illegal plantings)

$9 2003

$15 2004

$16 2005

$19.80 2006

$18.80 2007 $88 in 2004 applied to all other years at prevailing exchange rate Data from the Parana Department of Agriculture (2004). Also agreed royalty rates from 2004. Paraguay 0% but second crop benefits Not relevant except 2nd crop Not relevant Not relevant Same as Argentina to 2004

2005 $4.86

2006 $3.09

2007 $9.64 Same as Argentina Same as Argentina: no country-specific analysis identified. Impacts confirmed from industry sources (personal communication, 2006, 2008). South Africa 0% Not relevant Not relevant Not relevant 170 Rand all years to 2005

195 Rand 2006 onwards 230 Rand each year converted to US $ at prevailing exchange rate No studies identified—based on Monsanto South Africa (personal communication, 2005, 2007, 2008). Uruguay 0% Not relevant Not relevant Not relevant Same as Argentina Same as Argentina Same as Argentina: no country-specific analysis identified. Impacts confirmed from industry sources (personal communication, 2006, 2008). Mexico +9.1% Recorded yield impact from studies From Monsanto (2007) unpublished study—the only identified data None applied—small scale plantings $34.50 all years $154.50 No studies identified based on Monsanto (2007) and updated by personal communication (2008). Romania +31% Based on only available study covering 1999-2003 (note not grown in 2007). For previous year—based on Brookes (2005)—the only published source identified +20% to +40% $160 1999-2000

$148 2001

$135 2002

$130 2003 & 2004

$121 2005

$100 2006

Not permitted for use in EU 2007

All years includes 4 liters of herbicide $150-$192 1999-2006 depending on Euro to $ exchange rate

2007 not applicable—trait not permitted for growing in EU Brookes (2005) GM HT corn US 0% Not relevant Not relevant Not relevant $14.80 all years to 2004

$17.30 2005

$24.71 2006 onwards $39.90 all years to 2003

$40.55 2004

$40.75 2005

$44.60 2006 onwards Carpenter and Gianessi (2002)

Sankula and Blumenthal (2003, 2006)

Johnson and Strom (2007)—these are the only available studies breaking down impact into disaggregated parts. Canada 0% Not relevant Not relevant Not relevant Can $27 1999-2005

Can $35 2006 onwards Can $48.75 all years No studies identified—based on personal communications with industry sources, including Monsanto Canada. Argentina +3% corn belt

+22% marginal areas Based on only available analysis—Corn Belt = 70% of plantings, marginal areas 30%—industry analysis (note no significant plantings until 2006) No studies identified—based on personal communications with industry sources in 2007 and 2008 Monsanto Argentina and Grupo CEO (personal communication, 2007, 2008). +1% to +5% corn belt, +15% to +30% marginal areas 61 Pesos all years 61 Pesos all years No studies identified—based on Monsanto Argentina and Grupo CEO (personal communication, 2007, 2008). South Africa 0% Not relevant Not relevant Not relevant 80 Rand 2003-2005

120 Rand 2006 onwards 162 Rand all years No studies identified—based on Monsanto South Africa (personal communication, 2005, 2007, 2008). Philippines +15% Based on only available analysis—industry analysis +10% to +20% all years 1,232 Pesos all years Not known so conservative assumption of zero used No studies identified—based on Monsanto Philippines (personal communication, 2007, 2008). GM HT cotton US 0% Not relevant Not relevant Not relevant $12.85 1996-2000

$21.32 2001-2003

$34.55 2004

$68.22 2005

$70.35 2006 onwards $34.12 1996-2000

$66.59 2001-2003

$83.35 2004

$71.12 2005

$75.55 2006 onwards Carpenter and Gianessi (2002)

Sankula and Blumenthal (2003, 2006)

Johnson and Strom (2007)—these are the only available studies breaking down impact into disaggregated parts. Australia 0% Not relevant Not relevant Not relevant Aus $50 all years Aus $60 all years Doyle et al. (2003)

Monsanto Australia (personal communication, 2005, 2007, 2008) South Africa 0% Not relevant Not relevant Not relevant 133 Rand 2001-2004

101 Rand 2005

165 Rand 2006 onwards 160 Rand all years No studies identified—based on Monsanto South Africa (personal communication, 2005, 2007, 2008) Argentina 0% on area using farm saved seed, +17.4% on area using certified seed Based on only available data—company monitoring of commercial plots. No studies identified—based on personal communications with Grupo CEO and Monsanto Argentina (2007, 2008). +10% to +20% on certified seed area 122 Pesos all years 68 Pesos all years No studies identified—based on personal communications with Grupo CEO and Monsanto Argentina (2007, 2008). Mexico +3.6% Based on only available data—company monitoring of commercial plots. Same as source for cost data 0% to +5% all years 720 Pesos all years 1,150 Pesos all years No studies identified—based on personal communications with Monsanto Mexico (2007). GM HT canola US +6% all years to 2004. Post 2004, based on Canada—see below Based on the only identified impact analysis—post 2004 based on Canadian impacts as same alternative (conventional HT) technology to Canada available. Same as source for cost data +3% to +9% all years $29.50 1999-2001

$33 2002-2004

$12 2005 onwards for glyphosate tolerant

$17.30 all years for glufosinate tolerant to 2004

$12 from 2005 Glyphosate tolerant

$60.75 1999-2001

$67 2002 and 2003

$69 2004

$49 2005

$40 2006 onwards

Glufosinate tolerant

$44.89 all years to 2003

$44 2004

$40 2005

$435 2006 onwards Sankula and Blumenthal (2003, 2006)

Johnson and Strom (2007)

These are the only studies identified that examine GM HT canola in the US. Canada +10.7% all years to 2004. After 2004, based on differences between average annual variety trial results for Clearfields (non-GM HT varieties) and GM alternatives. GM alternatives differentiated into glyphosate tolerant and glufosinate tolerant. This resulted in—for GM glyphosate tolerant varieties—no yield difference for 2004 and 2005 and +4% for 2006 and 2007. For GM glufosinate tolerant varieties, the yield differences were +12% in 2004, +19% in 2005, and +10% for 2006 and 2007. Same as source for cost data +4% to +12% all years Can $44.63 all years to 2003

2004 onwards based on difference seed premium and technology fee relative to Clearfields HT canola; $0 for GM glufosinate tolerance and Can $37 for glyphosate tolerance Glyphosate tolerant

Can $39 all years to 2003

Can $40 2004 onwards

Glufosinate tolerant

Can $39 all years to 2003

Can $10 2004 onwards Based on Canola Council of Canada (2001) to 2003, then adjusted to reflect main current non GM (HT) alternative of 'Clearfields'—data derived from personal communications with the Canola Council of Canada (2008) and Gusta et al. (2008). GM VR crops US Papaya Between +15% and +50% 1999-2007—relative to base yield of 22.86 t/ha Based on average yield in three years before first use. Draws on only published source disaggregating to this aspect of impact. +15% all years to +50% all years $0 1999 to 2003

$42 2004

$148 2005 onwards None—no effective conventional method of protection. Sankula and Blumenthal (2003, 2006)

Johnson and Strom (2007) Squash +100% on area planted Assumes virus otherwise destroys crop on planted area. Draws on only published source disaggregating to this aspect of impact. +50% all years $398 all years None—no effective conventional method of treatment. Sankula and Blumenthal (2003, 2006)

Johnson and Strom (2007) IR corn (resistant to corn-boring pests). Readers should note that the assumptions are drawn from the references cited, supplemented and updated by industry sources (where the authors have not been able to identify specific studies). This has been particularly of relevance for some of the HT traits more recently adopted in several developing countries. Accordingly, the authors are grateful to industry sources who have provided information on impact, notably on cost of the technology and impact on costs of crop protection. While this information is not derived from detailed studies, the authors are confident that it is reasonably representative of average impacts; in fact, in a number of cases, information provided from industry sources via personal communications has suggested levels of average impact that are lower than those identified in independent studies. Where this has occurred, the more conservative (industry source) data has been used. Suggested citation: Brookes, G., & Barfoot, P. (2009). Global impact of biotech crops: Income and production effects 1996-2007. AgBioForum, 12(2), 184-208. Available on the World Wide Web: Brookes, G., & Barfoot, P. (2009). Global impact of biotech crops: Income and production effects 1996-2007.(2), 184-208. Available on the World Wide Web: http://www.agbioforum.org