The report is based 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.
As 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 being 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 largely based on the average of these findings.
This approach may both, overstate or 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, as impact data for every trait, crop, location and year data are 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 of 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.b 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 dynamic has been introduced into the analysis that would, otherwise, be missing if constant prices and average yields identified 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 Supplemental Materials, Appendix 2 for details);
Adjusts downwards 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 2010 year results are presented in Supplemental Materials, Appendix 1. Appendix 2 (also in Supplemental Materials) 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 laborc), 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 (GM 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 paper 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 summarizedd and extrapolated to the cumulative biotech crop planted areas in the US over the period 1996–2010.
Lastly, the paper 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 2010) are shown in Appendix 1 (Supplemental Materials).
GM technology has had a significant positive impact on farm income derived from a combination of enhanced productivity and efficiency gains (Table 1). In 2010, the direct global farm income benefit from biotech crops was $14 billion. This is equivalent to having added 4.3% to the value of global production of the four main crops of soybeans, maize, canola and cotton. Since 1996, farm incomes have increased by $78.4 billion.
Table 1. Global farm income benefits from growing biotech crops 1996–2010: USD million(s)
||Increase in farm income 2010
||Increase in farm income 1996–2010
||Farm income benefit in 2010 as % of total value of production of these crops in biotech adopting countries
||Farm income benefit in 2010 as % of total value of global production of crop
|GM herbicide tolerant soybeans
|GM herbicide tolerant maize
|GM herbicide tolerant cotton
|GM herbicide tolerant canola
|GM insect resistant maize
|GM insect resistant cotton
All values are nominal. Others = Virus resistant papaya and squash and herbicide tolerant sugar beet. Totals for the value shares exclude “other crops” (i.e., relate to the 4 main crops of soybeans, maize, canola and cotton). Farm income calculations are net farm income changes after inclusion of impacts on yield, crop quality and key variable costs of production (e.g., payment of seed premia, impact on crop protection expenditure).
The largest gains in farm income in 2010 have arisen in the cotton sector, largely from yield gains. The $5 billion additional income generated by GM insect resistant (GM IR) cotton in 2010 has been equivalent to adding 14% to the value of the crop in the biotech growing countries, or adding the equivalent of 11.9% to the $42 billion value of the global cotton crop in 2010.
Substantial gains have also arisen in the maize sector through a combination of higher yields and lower costs. In 2010, maize farm income levels in the biotech adopting countries increased by almost $5 billion and since 1996, the sector has benefited from an additional $21.6 billion. The 2010 income gains are equivalent to adding 6% to the value of the maize crop in these countries, or 3.5% to the $139 billion value of total global maize production. This is a substantial increase in value added terms for two new maize seed technologies.
Significant increases to farm incomes have also resulted in the soybean and canola sectors. The GM HT technology in soybeans has boosted farm incomes by $3.3 billion in 2010, and since 1996 has delivered over $28 billion of extra farm income (the highest cumulative increase in farm income of the biotech traits). In the canola sector (largely North American) an additional $2.7 billion has been generated (1996–2010).
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, Bolivia, Brazil, Paraguay and Uruguay), GM IR cotton in China and India and a range of GM cultivars in the US. It also illustrates the growing level of farm income benefits being obtained in South Africa, the Philippines, Mexico and Colombia.
Table 2. GM crop farm income benefits 1996–2010 selected countries: USD million(s)
||GM HT soybeans
||GM HT maize
||GM HT cotton
||GM HT canola
All values are nominal. Farm income calculations are net farm income changes after inclusion of impacts on yield, crop quality and key variable costs of production (e.g., payment of seed premia, impact on crop protection expenditure). N/a = not applicable. US total figure also includes $296.4 million for other crops/traits (not included in the table). Also not included in the table is $4.3 million extra farm income from GM HT sugar beet in Canada.
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 2010, 54.8% 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.e Over the 15 years, 1996–2010, the cumulative farm income gain derived by developing country farmers was 50% ($39.24 billion).
Table 3. GM crop farm income benefits 2010: developing vs. developed countries: USD million(s)
|GM HT soybeans
|GM IR maize
|GM HT maize
|GM IR cotton
|GM HT cotton
|GM HT canola
|GM virus resistant papaya and squash and GM HT sugar beet
Developing countries = all countries in South America, Mexico, Honduras, Burkino Faso, India, China, the Philippines and South Africa.
Examining the cost farmers pay for accessing GM technology, Table 4 shows that across the four main biotech crops, the total cost in 2010 was equal to 28% of the total technology gains (inclusive of farm income gains plus cost of the technology payable to the seed supply chainf).
Table 4. Cost of accessing GM technology [USD million(s)] relative to the total farm income benefits 2010
||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
|GM IR maize
|GM HT maize
|GM IR cotton
|GM HT cotton
|GM HT canola
N/A = not applicable. Cost of accessing technology based on the seed premia paid by farmers for using GM technology relative to its conventional equivalents.
For farmers in developing countries the total cost was equal to 17% of total technology gains, while for farmers in developed countries the cost was 37% 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 Sup. Material, 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” (level of impact that largely 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” (level of impact that largely 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 2010 of +$12 billion to +$18.5 billion and over the 1996–2010 period, a range of +$68.5 billion to +$93.1 billion (Table 5). This range is broadly within 87% to 119% of the main estimates of farm income presented above.
Table 5. Direct farm income benefits 1996–2010 under different impact assumptions [USD million(s)]
||Consistent below average pest/weed pressure
||Average pest/weed pressure (main study analysis)
||Consistent above average pest/weed pressure
No significant change to soybean production under all three scenarios as almost all gains due to cost savings and second crop facilitation.
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