Integrating the data for 2010 into the context of earlier developments, this study updates the findings of earlier analysis into the global economic impact of genetically modified (GM) crops since their commercial introduction in 1996; earlier analysis by the current authors was published in AgbioForum 12:184–208¹ and the International Journal of Biotechnology 12:1–49.² The methodology and analytical procedures in this present discussion are unchanged to allow a direct comparison of the new with earlier data.^a

In order to save readers the chore of consulting these earlier papers for details of the methodology and arguments, the journal’s editors have agreed that these elements may be included in full in this updated paper.

The analysis concentrates on farm income effects because this is a primary driver of adoption among 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.

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:

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:

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 summarized^d 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.

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.

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).

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 chain^f).

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.

As well as 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 studies^g on the impact of biotech crops have identified the following reasons as being important influences for adoption of the technology:

Based on the yield assumptions used in the direct farm income benefit calculations presented above (see Supplemental Material, Appendix 1) and taking account of 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).

The biotech IR traits, used in the corn and cotton sectors, have accounted for 98% of the additional corn production and 99.4% 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^j) when compared with average yields derived from crops using conventional technology (such as application of insecticides and seed treatments). The average yield impact across the total area planted to these traits over the 15 y period since 1996 has been +9.6% for corn traits and +14.4% for cotton traits (Fig. 2).

Although the primary impact of biotech HT technology has been to provide more cost effective (less expensive) and easier weed control vs. 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 (e.g., HT soybeans in Romania, Bolivia and Mexico, HT corn in Argentina and the Philippines: see Supplemental Material, Appendix 2).

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 96.1 million tons to soybean production in Argentina and Paraguay between 1996 and 2010 (accounting for 98.5% 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.

This study quantified the cumulative global impact of GM technology between 1996 and 2010, on farm income and on production. It shows that there have been substantial direct economic benefits at the farm level, amounting to a cumulative total of $78.4 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 $7.6 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 97.5 million tons of soybeans and 159.4 million tons of corn (1996–2010).

The authors undertook this (updated) analysis to provide interested readers with on-going and current assessments of some of the key economic impacts associated with the global adoption of biotech crops. By doing so, it is hoped that the data and analysis presented will contribute to wider and greater understanding of the impact of this technology adoption in agriculture and facilitate more informed decision making relating to the use of the technology, especially in countries where crop biotechnology is currently not permitted.