Global income and production impacts of using GM crop technology 1996–2014

ABSTRACT

This paper provides an economic assessment of the value of using genetically modified (GM) crop technology in agriculture at the farm level. It follows and updates earlier annual studies which examined economic impacts on yields, key costs of production, direct farm income and effects, and impacts on the production base of the 4 main crops of soybeans, corn, cotton and canola. The commercialisation of GM crops has continued to occur at a rapid rate since the mid 1990s, with important changes in both the overall level of adoption and impact occurring in 2014. This annual updated analysis shows that there continues to be very significant net economic benefits at the farm level amounting to $17.7 billion in 2014 and $150.3 billion for the 19-year period 1996–2014 (in nominal terms). These economic gains have been divided roughly 50% each to farmers in developed and developing countries. About 65% of the gains have derived from yield and production gains with the remaining 35% coming from cost savings. The technology has also made important contributions to increasing global production levels of the 4 main crops, having, for example, added 158 million tonnes and 322 million tonnes respectively, to the global production of soybeans and maize since the introduction of the technology in the mid 1990s.

KEYWORDS:

• cost
• genetically modified crops
• income
• production
• yield

INTRODUCTION

2014 was the nineteenth year of widespread cultivation of crops containing genetically modified (GM) traits, with the global planted area of GM-traited crops having reached over 175 million hectares.

During this nearly 20-year period, there have been many papers assessing the farm level economic impacts associated with the adoption of this technology. The authors of this paper have, since 2005, engaged in an annual exercise to aggregate and update the sum of these various studies, and where possible and appropriate, to supplement this with new analysis. The aim of this has been to provide an up to date and as accurate as possible assessment of some of the key economic impacts associated with the global adoption of crops containing GM traits. It is also hoped the analysis continues to contribute to greater understanding of the impact of this technology and to facilitate more informed decision-making, especially in countries where crop biotechnology is currently not permitted.

This study updates the findings of earlier analysis into the global economic impact of GM crops since their commercial introduction in 1996 by integrating data and analysis for 2014. Previous analysis by the current authors has been published in various journals, including AgbioForum 12 (Brookes and Barfoot 2009) (2), 184–208, the International Journal of Biotechnology (Brookes and Barfoot 2011), vol 12, 1/2, 1–49 and GM Crops 3:4, 265–272 (Brookes and Barfoot 2012), GM Crops 4:1, 1–10 (Brookes and Barfoot 2013, GM Crops 5:1, 65–75 (Brookes and Barfoot 2014) and GM Crops 6: 13–46 (Brookes and Barfoot 2015). The methodology and analytical procedures in this present discussion are unchanged to allow a direct comparison of the new with earlier data. Readers should however, note that some data presented in this paper are not directly comparable with data presented in previous analysis because the current paper takes into account the availability of new data and analysis (including revisions to data for earlier years).

In order to save readers of this paper the chore of consulting the past papers for details of the methodology and arguments, these are 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 quantifies the (net) production impact of the technology. The authors recognize that an economic assessment could examine a broader range of potential impacts (e.g., on labor usage, households, local communities and economies).

However, these are not included because undertaking such an exercise would add considerably to the length of the paper and an economic assessment of wider economic impacts would probably merit a separate assessment in its own right.

RESULTS AND DISCUSSION

HT Crops

The main impact of GM HT (largely tolerant to the broad spectrum herbicide glyphosate) technology has been to provide more cost effective (less expensive) and easier weed control for farmers. Nevertheless, some users of this technology have also derived higher yields from better weed control (relative to weed control obtained from conventional technology). The magnitude of these impacts varies by country and year, and is mainly due to prevailing costs of different herbicides used in GM HT systems versus conventional alternatives, the mix and amount of herbicides applied, the cost farmers pay for accessing the GM HT technology and levels of weed problems. The following important factors affecting the level of cost savings achieved in recent years should be noted:

• The mix and amount of herbicides used on GM HT crops and conventional crops are affected by price and availability of herbicides. Herbicides used include both ‘older’ products that are no longer protected by patents and newer ‘patent-protected’ chemistry, with availability affected by commerical decisions of suppliers to market or withdraw prooducts from markets and regulation (eg, changes to approval processes). Prices also vary by year and country. For example, in 2008–2009, the average cost associated with the use of GM HT technology globally increased signficantly relative to earlier years because of the increase in the global price of glyphosate relative to changes in the price of other herbicides commonly used on conventional crops. This abated in 2010 with a decline in the price of glyphosate back to previous historic trend levels;

• The amount farmers pay for use of the technology varies by country. Pricing of technology (all forms of seed and crop protection technology, not just GM technology) varies according to the level of benefit that farmers are likely to derive from it. In addition, it is influenced by intellectual property rights (patent protection, plant breeders' rights and rules relating to use of farm-saved seed). In countries with weaker intellectual property rights, the cost of the technology tends to be lower than in countries where there are stronger rights. This is examined further in c) below;

• Where GM HT crops (tolerant to glyphosate) have been widely grown, some incidence of weed resistance to glyphosate has occurred and resistance has become a major concern in some regions. This has been attributed to how glyphosate was used; because of its broad-spectrum post-emergence activity, it was often used as the sole method of weed control. This approach to weed control put tremendous selection pressure on weeds and as a result contributed to the evolution of weed populations predominated by resistant individual weeds. It should, however, be noted that there are hundreds of resistant weed species confirmed in the International Survey of Herbicide Resistant Weeds (www.weedscience.com). Worldwide, there are 32 weed species that are currently (accessed January 2016) resistant to glyphosate, compared to 158 weed species resistant to ALS herbicides (eg, chlorimuron ethyl commonly used in conventional soybean crops) and 73 weed species resistant to photosystem II inhibitor herbicides (eg, atriazine commonly used in corn production). In addition, it should be noted that the adoption of GM HT technology has played a major role in facilitating the adoption of no and reduced tillage production techniques in North and South America. This has also probably contributed to the emergence of weeds resistant to herbicides like glyphosate and to weed shifts toward those weed species that are not well controlled by glyphosate. As a result, growers of GM HT crops are increasingly being advised to be more proactive and include other herbicides (with different and complementary modes of action) in combination with glyphosate in their weed management systems, even where instances of weed resistance to glyphosate have not been found.. This change in weed management emphasis also reflects the broader agenda of developing strategies across all forms of cropping systems to minimise and slow down the potential for weeds developing resistance to existing technology solutionsNorsworthyJ et al., 2012. At the macro level, these changes have influenced the mix, total amount, cost and overall profile of herbicides applied to GM HT crops. Relative to the conventional alternative, however, the economic impact of the GM HT crop use has continued to offer important advantages for most users. It should also be noted that many of the herbicides used in conventional production systems had significant resistance issues themselves in the mid 1990s. This was one of the reasons why glyphosate tolerant soybeans were rapidly adopted, as glyphosate provided good control of these weeds. If the GM HT technology was no longer delivering net economic benefits, it is likely that farmers around the world would have significantly reduced their adoption of this technology in favor of conventional alternatives. The fact that GM HT global crop adoption levels have not fallen in recent years suggests that farmers must be continuing to derive important economic benefits from using the technology.

These points are further illustrated in the analysis below.

GM HT Soybeans

The average impacts on farm level profitability from using this technology are summarized in Table 1. The main farm level gain experienced has been a reduction in the cost of production, mainly through reduced expenditure on weed control (herbicides). Not surprisingly, where yield gains have occurred from improvements in the level of weed control, the average farm income gain has tended to be higher, in countries such as Romania, Mexico and Bolivia. A second generation of GM HT soybeans became available to commercial soybean growers in the US and Canada in 2009. This technology offered the same tolerance to glyphosate as the first generation (and the same cost saving) but with higher yielding potential. The realization of this potential is shown in the higher average farm income benefits (Table 1).

TABLE 1. GM soybeans: summary of average farm level economic impacts 1996–2014 ($/hectare)

Country	Cost of technology	Average farm income benefit (after deduction of cost of technology)	Aggregate income benefit (million $)	Type of benefit	References
1^st generation GM HT soybeans
Romania (to 2006 only)	50–60	104	44.6	Small cost savings of about $9/ha, balance due to yield gains of +13% to +31%	Brookes (2005) Monsanto Romania (2007)
Argentina	2–4	22 plus second crop benefits of 255	16,435.6	Cost savings plus second crop gains	Qaim and Traxler (2005) Trigo and CAP (2006) and updated from 2008 to reflect herbicide usage and price changes
Brazil	11–25	33	6,317.2	Cost savings	Parana Department of Agriculture (2004) Galveo (2010, 2012, 2013, 2014 and updated to reflect herbicide usage and price changes
US	15–53	35	12,935.0	Cost savings	Marra et al (2002) Carpenter and Gianessi (2002) Sankala and Blumenthal (2003, 2005) Johnson and Strom (2008) And updated to reflect herbicide price and common product usage
Canada	20–40	20	165.7	Cost savings	George Morris Center (2004) and updated to reflect herbicide price and common product usage
Paraguay	4–10	16 plus second crop benefits of 251	1,029.2	Cost savings	Based on Argentina as no country-specific analysis identified. Impacts confirmed by industry sources and herbicide costs and usage updated 2009 onwards from herbicide survey data (AMIS Global)
Uruguay	2–4	17	143.2	Cost savings	Based on Argentina as no country-specific analysis identified. Impacts confirmed by industry sources and herbicide costs and usage updated 2009 onwards from herbicide survey data (AMIS Global)
South Africa	2–30	5	18.1	Cost savings	As there are no published studies available, based on data from industry sources and herbicide costs and usage updated 2009 onwards from herbicide survey data (AMIS Global)
Mexico	20–45	45	6.1	Cost savings plus yield gain in range of +2% to +13%	Monsanto annual monitoring reports submitted to Ministry of Agriculture and personal communications
Bolivia	3–4	90	636.0	Cost savings plus yield gain of +15%	Fernandez W et al (2009)
2nd^t generation GM HT soybeans
US and Canada	50–65	137 (US) 126 (Can)	8,912.9	Cost savings as first generation plus yield gains in range of +5% to +11%	As first generation GM HT soybeans plus annual farm level survey data from Monsanto USA
Intacta soybeans
Brazil	51–56	134	1,100.9	Herbicide cost saving as 1^st generation plus insecticide saving $19/ha and yield gain +9% to +10%	Monsanto Brazil pre commercial trials and post marketing farm survey monitoring, MB Agro (2013)
Argentina	51–56	48	33.5	Herbicide cost saving as 1^st generation plus insecticide saving $21/ha and yield gain +8% to +9%	Monsanto Argentina pre commercial trials and post market monitoring survey
Paraguay	51–56	107	26.3	Herbicide cost saving as 1^st generation plus insecticide saving $33/ha and yield gain +12% to +13%	Monsanto Paraguay pre commercial trials and post market monitoring survey
Uruguay	51–56	44	14.1	Herbicide cost saving as 1^st generation plus insecticide saving $19/ha and yield gain +8% to +9%	Monsanto Uruguay pre commercial trials and post market monitoring survey

Notes:

¹ Romania stopped growing GM HT soybeans in 2007 after joining the European Union, where the trait is not approved for planting.

² The range in values for cost of technology relates to annual changes in the average cost paid by farmers. It varies for reasons such as the price of the technology set by seed companies, exchange rates, average seed rates and values identified in different studies.

³ Intacta soybeans (HT and IR) first grown commercially in 2013.

⁴ For additional details of how impacts have been estimated, see examples in Appendix 1.

GM HT soybeans have also facilitated the adoption of no tillage production systems, shortening the production cycle. This advantage has enabled 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 considerably to farm incomes and to the volumes of soybean production in countries such as Argentina and Paraguay.

Overall, in 2014, GM HT technology in soybeans (excluding second generation ‘Intacta’ soybeans: see below) has boosted farm incomes by $5.2 billion, and since 1996 has delivered $46.6 billion of extra farm income. Of the total cumulative farm income gains from using GM HT soybeans, $13.3 billion (29%) has been due to yield gains/second crop benefits and the balance, 71%, has been due to cost savings.

GM HT and IR (Intacta) Soybeans

This combination of GM herbicide tolerance (to glyphosate) and insect resistance in soybeans was first grown commercially in 2013, in South America. In the first 2 years, the technology was used on approximately 9.6 million hectares and contributed an additional $1.17 billion to farm income of soybean farmers in Argentina, Brazil, Paraguay and Uruguay, through a combination of cost savings (decreased expenditure on herbicides and insecticides) and higher yields (see Table 1).

GM HT Maize

The adoption of GM HT maize has mainly resulted in lower costs of production, although yield gains from improved weed control have arisen in Argentina, Brazil and the Philippines (Table 2).

TABLE 2. GM HT maize: summary of average farm level economic impacts 1996–2014 ($/hectare)

Country	Cost of technology	Average farm income benefit (after deduction of cost of technology)	Aggregate income benefit (million $)	Type of benefit	References
US	15–30	26	6,106.1	Cost savings	Carpenter and Gianessi (2002) Sankala and Blumenthal (2003, 2005) Johnson and Strom (2008) Also updated annually to reflect herbicide price and common product usage
Canada	17–35	14	137.3	Cost savings	Monsanto Canada (personal communications) and updated annually since 2008 to reflect changes in herbicide prices and usage
Argentina	16–33	79	1,243.0	Cost savings plus yield gains over 10% and higher in some regions	Personal communication from Monsanto Argentina, Grupo CEO and updated since 2008 to reflect changes in herbicide prices and usage
South Africa	10–18	5	48.3	Cost savings	Personal communication from Monsanto South Africa and updated since 2008 to reflect changes in herbicide prices and usage
Brazil	16–32	53	1,368.3	Cost savings plus yield gains of +1% to +7%	Galveo (2010, 2012, 2013, 2014)
Colombia	22–24	16	3.8	Cost savings	Mendez et al (2011)
Philippines	24–47	34	141.6	Cost savings plus yield gains of +5% to +15%	Gonsales (2009) Monsanto Philippines (personal communications) Updated since 2010 to reflect changes in herbicide prices and usage
Paraguay	16–17	1	0.9	Cost saving	Personal communication from Monsanto Paraguay and AMIS Global – annually updated to reflect changes in herbicide prices and usage
Uruguay	9–17	3	1.2	Cost saving	Personal communication from Monsanto Uruguay and AMIS Global - updated annually to reflect changes in herbicide prices and usage

1. The range in values for cost of technology relates to annual changes in the average cost paid by farmers. It varies for reasons such as the price of the technology set by seed companies, exchange rates, average seed rates and values identified in different studies.

2. For additional details of how impacts have been estimated, see examples in Appendix 1.

In 2014, the total global farm income gain from using this technology was $1.6 billion with the cumulative gain over the period 1996–2014 being $9.05 billion. Within this, $2.81 billion (31%) was due to yield gains and the rest derived from lower costs of production.

GM HT Cotton

The use of GM HT cotton delivered a net farm income gain of about $146.5 million in 2014. In the 1996–2014 period, the total farm income benefit was $1.65 billion. As with other GM HT traits, these farm income gains have mainly arisen from cost savings (77% of the total gains), although there have been some yield gains in Argentina, Brazil, Mexico and Colombia (Table 3).

TABLE 3. GM HT cotton summary of average farm level economic impacts 1996–2014 ($/hectare)

Country	Cost of technology	Average farm income benefit (after deduction of cost of technology)	Aggregate income benefit (million $)	Type of benefit	References
US	13–82	21	1,074.1	Cost savings	Carpenter and Gianessi (2002) Sankala and Blumenthal (2003, 2005) Johnson and Strom (2008) Also updated to reflect herbicide price and common product usage
South Africa	15–32	35	4.2	Cost savings	Personal communication from Monsanto South Africa and updated since 2008 to reflect changes in herbicide prices and usage
Australia	32–82	28	91.5	Cost savings	Doyle et al (2003) Monsanto Australia (personal communications) and updated to reflect changes in herbicide usage and prices
Argentina	12–30	40	145.0	Cost savings and yield gain of +9%	Personal communication from Monsanto Argentina, Grupo CEO and updated since 2008 to reflect changes in herbicide prices and usage
Brazil	33–52	76	133.2	Cost savings plus yield gains of +1.6% to +4%	Galveo (2010, 2012, 2013, 2014)
Mexico	29–79	227	183.2	Cost savings plus yield gains of +3% to +18%	Monsanto Mexico annual monitoring reports submitted to the Ministry of Agriculture and personal communications
Colombia	96–187	97	23.0	Cost savings plus yield gains of +4%	Monsanto Colombia annual personal communications

1. The range in values for cost of technology relates to annual changes in the average cost paid by farmers. It varies for reasons such as the price of the technology set by seed companies, exchange rates, average seed rates, the nature and effectiveness of the technology (eg, second generation ‘Flex’ cotton offered more flexible and cost effective weed control than the earlier first generation of HT technology) and values identified in different studies.

2. For additional details of how impacts have been estimated, see examples in Appendix 1.

Other HT Crops

GM HT canola (tolerant to glyphosate or glufosinate) has been grown in Canada, the US, and more recently Australia, while GM HT sugar beet is grown in the US and Canada. The farm income impacts associated with the adoption of these technologies are summarised in Table 4. In both cases, the main farm income benefit has derived from yield gains. In 2014, the total global income gain from the adoption of GM HT technology in canola and sugar beet was $662 million and cumulatively since 1996, it was $5.22 billion.

TABLE 4. Other GM HT crops summary of average farm level economic impacts 1996–2014 ($/hectare)

Country	Cost of technology	Average farm income benefit (after deduction of cost of technology)	Aggregate income benefit (million $)	Type of benefit	References
GM HT canola
US	12–33	51	311.4	Mostly yield gains of +1% to +12% (especially Invigor canola)	Sankala and Blumenthal (2003, 2005) Johnson and Strom (2008) And updated to reflect herbicide price and common product usage
Canada	15–32	55	4,492.8	Mostly yield gains of +3% to +12% (especially Invigor canola)	Canola Council (2001) Gusta et al (2009) and updated to reflect herbicide price changes and seed variety trial data (on yields)
Australia	12–41	54	55.8	Mostly yield gains of +12% to +22% (where replacing triazine tolerant canola) but no yield gain relative to other non GM (herbicide tolerant canola)	Monsanto Australia (2009), Fischler and Tozer (2009) and Hudson (2013)
GM HT sugar beet
US and Canada	130–151	116	356.6	Mostly yield gains of +3% to +13%	Kniss (2010) Khan (2008) Jon-Joseph and Sprague (2010) Annual updates of herbicide price and usage data

Notes:

1. In Australia, one of the most popular type of production has been canola tolerant to the triazine group of herbicides (tolerance derived from non GM techniques). It is relative to this form of canola that the main farm income benefits of GM HT (to glyphosate) canola has occurred.

2. InVigor’ hybrid vigour canola (tolerant to the herbicide glufosinate) is higher yielding than conventional or other GM HT canola and derives this additional vigour from GM techniques.

3. The range in values for cost of technology relates to annual changes in the average cost paid by farmers. It varies for reasons such as the price of the technology set by seed companies, exchange rates, average seed rates and values identified in different studies.

4. For additional details of how impacts have been estimated, see examples in Appendix 1.

GM IR Crops

The main way in which these technologies have impacted on farm incomes has been through lowering the levels of pest damage and hence delivering higher yields (Table 5).

TABLE 5. Average (%) yield gains GM IR cotton and maize 1996–2014

	Maize insect resistance to corn boring pests	Maize insect resistance to rootworm pests	Cotton insect r esistance	References
US	7.0	5.0	9.9	Carpenter and Gianessi (2002) Marra et al (2002) Sankala and Blumenthal (2003, 2005) Hutchison et al (2010) Rice (2004) Mullins and Hudson (2004)
China	N/a	N/a	10.0	Pray et al (2002) Monsanto China (personal communications)
South Africa	11.3	N/a	24.0	Gouse et al (2005, 2006a, 2006b) Van der Wald (2010) Ismael et al (2002) Kirsten et al (2002) James (2003)
Honduras	23.8	N/a	N/a	Falk Zepeda et al (2009, 2012)
Mexico	N/a	N/a	11.0	Traxler and Godoy-Avila (2004) Monsanto Mexico annual cotton monitoring reports
Argentina	6.1	N/a	30.0	Trigo (2002) Trigo and Cap (2006) Qaim and De Janvry (2002, 2005) Elena (2006)
Philippines	18.3	N/a	N/a	Gonsales (2009) Yorobe (2004) Ramon (2005)
Spain	10.9	N/a	N/a	Brookes (2003, 2008) Gomez-Barbero, Barbel, & Rodriguez-Cerezo (2008) Riesgo et al (2012)
Uruguay	5.6	N/a	N/a	As Argentina (no country-specific studies available and industry sources estimate similar impacts as in Argentina)
India	N/a	N/a	32.0	Bennett et al (2004) IMRB (2006, 2007) Herring and Rao (2012)
Colombia	21.7	N/a	18.0	Mendez et al (2011) Zambrano (2009)
Canada	7.0	5.0	N/a	As US (no country-specific studies available and industry sources estimate similar impacts as in the US)
Burkina Faso	N/a	N/a	18.0	Vitale J et al (2008) Vitale (2010)
Brazil	12.1	N/a	0.5	Galveo (2009, 2010, 2012, 2013, 2014) Monsanto Brazil (2008)
Pakistan	N/a	N/a	21.0	Nazli et al (2010), Kouser and Qaim (2013)
Myanmar	N/a	N/a	30.4.0	USDA (2011)
Australia	N/a	N/a	Nil	Doyle (2005) James (2002) CSIRO (2005) Fitt (2001)
Paraguay	5.5	N/a	Not available	As Argentina (no country-specific studies available and industry sources estimate similar impacts as in Argentina)

Note: N/a = not applicable.

The greatest improvement in yields has occurred in developing countries, where conventional methods of pest control have been least effective (eg, reasons such as less well developed extension and advisory services, lack of access to finance to fund use of crop protection application equipment and products), with any cost savings associated with reduced insecticide use being mostly found in developed countries. These effects can be seen in the level of farm income gains that have arisen from the adoption of these technologies, as shown in Table 6.

TABLE 6. GM IR crops: average farm income benefit 1996–2014 ($/hectare)

Country	GM IR maize: cost of technology	GM IR maize (income benefit after deduction of cost of technology)	Aggregate income benefit GM IR maize (million $)	GM IR cotton: cost of technology	GM IR cotton (income benefit after deduction of cost of technology)	Aggregate income benefit GM IR cotton (million $)
US	17–32 IRCB, 22–42 IR CRW	81 IRCB, 80 IR CRW	32,198.3	26–58	110	4,750.1
Canada	17–25 IRCB, 22–42 IR CRW	77 IRCB 94 IR CRW	1,229.5	N/a	N/a	N/a
Argentina	15–33	20	678.3	21–86	248	803.0
Philippines	30–47	99	418.3	N/a	N/a	N/a
South Africa	8–17	91	1,711.9	14–50	154	30.9
Spain	17–51	212	231.7	N/a	N/a	N/a
Uruguay	15–33	29	24.8	N/a	N/a	N/a
Honduras	100	59	9.6	N/a	N/a	N/a
Colombia	43–49	254	82.5	50–175	67	19.0
Brazil	44–69	86	4,787.1	31–52	31	72.7
China	N/a	N/a	N/a	38–60	347	17,537.6
Australia	N/a	N/a	N/a	85–299	216	801.7
Mexico	N/a	N/a	N/a	48–75	204	194.3
India	N/a	N/a	N/a	13–54	227	18,268.4
Burkina Faso	N/a	N/a	N/a	51–54	100	177.6
Myanmar	N/a	N/a	N/a	17–20	103	185.0
Pakistan	N/a	N/a	N/a	4–15	128	1,954.0
Paraguay	19–20	12	13.1	N/a	N/a	N/a
Average across all user countries		78			220

Notes:

1. GM IR maize all are IRCB unless stated (IRCB = insect resistance to corn boring pests), IRCRW = insect resistance to corn rootworm.

2. The range in values for cost of technology relates to annual changes in the average cost paid by farmers. It varies for reasons such as the price of the technology set by seed companies, the nature and effectiveness of the technology (eg, second generation ‘Bollgard’ cotton offered protection against a wider range of pests than the earlier first generation of ‘Bollgard’ technology), exchange rates, average seed rates and values identified in different studies.

3. Average across all countries is a weighted average based on areas planted in each user country.

4. n/a = not applicable.

At the aggregate level, the global farm income gains from using GM IR maize and cotton in 2014 were $5.4 billion and $3.94 billion respectively. Cumulatively since 1996, the gains have been $41.5 billion for GM IR maize and $44.8 billion for GM IR cotton.

Aggregated (Global Level) Impacts

GM crop technology has had a significant positive impact on global farm income, which amounted to $17.74 billion in 2014. This is equivalent to having added 7.2% to the value of global production of the 4 main crops of soybeans, maize, canola and cotton. Since 1996, farm incomes have increased by $150.3 billion.

At the country level, US farmers have been the largest beneficiaries of higher incomes, realizing over $66.1 billion in extra income between 1996 and 2014. This is not surprising given that US farmers were first to make widespread use of GM crop technology and for several years the GM adoption levels in all 4 US crops have been in excess of 80%. Important farm income benefits ($34.5 billion) have occurred in South America (Argentina, Bolivia, Brazil, Colombia, Paraguay and Uruguay), mostly from GM technology in soybeans and maize. GM IR cotton has also been responsible for an additional $35.8 billion additional income for cotton farmers in China and India.

In 2014, 46.5% of the farm income benefits were earned by farmers in developing countries. The vast majority of these gains have been from GM IR cotton and GM HT soybeans. Over the 19 years 1996–2014, the cumulative farm income gain derived by developing country farmers was $76.2 billion, equal to 50.7% of the total farm income during this period.

The cost to farmers for accessing GM technology, across the 4 main crops, in 2014, was equal to 28% of the total value of technology gains. This is defined as the farm income gains referred to above plus the cost of the technology payable to the seed supply chain. Readers should note that 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.

In developing countries, the total cost was equal to 23% of total technology gains compared with 32% in developed countries. While circumstances vary between countries, the higher share of total technology gains accounted for by farm income in developing countries relative to 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 per hectare derived by farmers in developing countries compared to those in developed countries.

Sixty-five per cent of the total income gain over the 19-year period derives from higher yields and second crop soybean gains with 35% from lower costs (mostly on insecticides and herbicides). In terms of the 2 main trait types, insect resistance and herbicide tolerance have accounted for 58% and 42% respectively of the total income gain. The balance of the income gain arising from yield/production gains relative to cost savings is changing as second generation GM crops are increasingly adopted. Thus in 2014 the split of total income gain came 85% from yield/production gains and 15% from cost savings.

Crop Production Effects

Based on the yield impacts used in the direct farm income benefit calculations above and taking account of the second soybean crop facilitation in South America, GM 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 GM crops

	1996–2014 additional production (million tonnes)	2014 additional production (million tonnes)
Soybeans	158.4	20.25
Corn	321.80	50.10
Cotton	24.7	2.90
Canola	9.2	1.17
Sugar beet	0.9	0.15

Note: Sugar beet, US and Canada only (from 2008).

The GM IR traits, used in maize and cotton, have accounted for 94.9% of the additional maize production and 99.2% of the additional cotton production. Positive yield impacts from the use of this technology have occurred in all user countries, except for GM IR cotton in Australia where the levels of Heliothis sp (boll and bud worm pests) pest control previously obtained with intensive insecticide use were very good. The main benefit and reason for adoption of this technology in Australia has arisen from significant cost savings and the associated environmental gains from reduced insecticide use, when compared to 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 19 years since 1996 has been +13.1% for maize and +17.3% for cotton.

As indicated earlier, the primary impact of GM HT technology has been to provide more cost effective (less expensive) and easier weed control, as opposed to improving yields, the improved weed control has, nevertheless, delivered higher yields in some countries. The main source of additional production from this technology has been via the facilitation of no tillage production systems, shortening the production cycle and how it has enabled 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 135.7 million tonnes to soybean production in Argentina and Paraguay between 1996 and 2014 (accounting for 85.7% of the total GM HT-related additional soybean production). Intacta soybeans added a further 2.56 million tonnes since 2013.

CONCLUDING COMMENTS

The use of crop biotechnology, by 18 million farmers in 2014, has delivered important economic benefits over the 19-year period to 2014. The GM IR traits have mostly delivered higher incomes through improved yields in all countries. Many farmers, especially in developed countries, have also benefited from lower costs of production (less expenditure on insecticides). The GM HT technology-driven farm income gains have mostly arisen from reduced costs of production, notably on weed control. In South America, the technology has also facilitated the move away from conventional to low/no-tillage production systems and, by effectively shortening the production cycle for soybeans, enabled many farmers to plant a second crop of soybeans after wheat in the same season. In addition, second generation GM HT soybeans, now widely used in North America, are delivering higher yields, as are the new ‘stacked’ traited HT and IR soybeans being used in South America since 2013.

In relation to HT crops, over reliance on the use of glyphosate and the lack of crop and herbicide rotation by some farmers, in some regions, has contributed to the development of weed resistance. In order to address this problem and maintain good levels of weed control, farmers have increasingly adopted a mix of reactive and proactive weed management strategies incorporating a mix of herbicides and other HT crops (in other words using other herbicides with glyphosate rather than solely relying on glyphosate or using HT crops which are tolerant to other herbicides, such as glufosinate). This has added cost to the GM HT production systems compared to several years ago, although relative to the conventional alternative, the GM HT technology continues to offer important economic benefits in 2014.

Overall, there is a considerable body of evidence, in peer reviewed literature, and summarized in this paper, that quantifies the positive economic impacts of crop biotechnology. The analysis in this paper therefore provides insights into the reasons why so many farmers around the world have adopted and continue to use the technology. Readers are encouraged to read the peer reviewed papers cited, and the many others who have published on this subject (and listed in the references below) and to draw their own conclusions.

METHODOLOGY

The report is based on extensive analysis of existing farm level impact data for GM crops, much of which can be found in peer reviewed literature. 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. In addition, the authors have undertaken their own analysis of the impact of some trait-crop combinations in some countries (notably GM herbicide tolerant (HT) traits in North and South America) based on herbicide usage and cost data.

As indicated in earlier papers, the economic impact of this technology at the farm level varies widely, both between and within regions/countries. Therefore, the measurement of impact is considered on a case by case basis in terms of crop and trait combinations and is based on the average performance and impact recorded in different crops by the studies reviewed. Where more than one piece of relevant research (eg, on the impact of using a GM trait on the yield of a crop in one country in a particular year) has been identified, the findings used in this analysis reflect the authors assessment of which research is most likely to be reasonably representative of impact in the country in that year. For example, there are many papers on the impact of GM insect resistant (IR) cotton in India. Few of these are reasonably representative of cotton growing across the country, with many papers based on small scale, local and unrepresentative samples of cotton farmers. Only the reasonably representative research has been drawn on for use in this paper – readers should consult the references to this paper to identify the sources used.

This approach may still both, overstate, or understate, the 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 is not available, the authors have had to extrapolate available impact data from identified studies to years for which no data are available. In addition, if the only studies available took place several years ago, there is a risk that basing current assessments on comparisons from several years ago may not adequately reflect the nature of currently available alternative (non GM seed or crop protection) technology. The authors acknowledge that these factors represent potential methodological weaknesses. To reduce the possibilities of over/understating impact due to these factors, 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. Examples where such data is available include the impact of GM insect resistant (IR) cotton: in India (see Bennett R et al (2004), IMRB (2006) and IMRB (2007)), in Mexico (see Traxler and Godoy-Avila, 2004) and Monsanto Mexico annual monitoring reports submitted to the Ministry of Agriculture in Mexico) and in the US (see Sankala & Blumenthal, 2003 and 2005; Mullins & Hudson, 2004; Rice, 2004). 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;

• Uses current farm level crop prices and bases any yield impacts on (adjusted – see below) current average yields. In this way a 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;

• It includes some changes and updates to the impact assumptions identified in the literature based on new papers, annual consultation with local sources (analysts, industry representatives, databases of crop protection usage and prices) and some ‘own analysis’ of changes in crop protection usage and prices;

• 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 2014 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:

• Where stacked traits have been used, the individual trait components were analyzed separately to ensure estimates of all traits were calculated. This is possible because the non stacked seed has been (and in many cases continues to be) available and used by farmers and there are studies that have assessed trait-specific impacts;

• 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 (source: United States Department of Agriculture Economics Research Service);

• 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. Inclusion of these costs is, 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 analysis. In most cases the analysis relates to impact of crop protection and seed cost only, crop quality (eg, 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 (eg, second crop soybeans in Argentina following wheat that would, in the absence of the 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 GM crop adoption on global crop supply and world prices.

The paper also 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 and the facilitation of additional cropping within a season (notably in relation to soybeans in South America). Details of how these values were calculated (for 2014) are shown in Appendix 1.

DISCLOSURE OF POTENTIAL CONFLICTS OF INTEREST

No potential conflicts of interest were disclosed.

Funding

The authors acknowledge that funding toward the researching of this paper was provided by Monsanto. The material presented in this paper is, however, the independent views of the authors—it is a standard condition for all work undertaken by PG Economics that all reports are independently and objectively compiled without influence from funding sponsors.

Appendix 1:

Details of Methodology as Applied to 2014 Farm Income Calculations

GM IR corn (targeting corn boring pests) 2014

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	26,916	+7	10.16	162	−27.5	−25.5	+89.6	+2,628,908	+26,691
Canada	1,031	+7	8.84	167	−19.0	−16.9	+86.4	+89,088	+638
Argentina	4,399	+5.5	5.41	119	−15.5	−15.5	+20	+87,792	+1,309
Philippines	602	+18	2.86	288	−45.1	−30.4	+117.7	+70,854	+310
South Africa	2,653	+10.6	3.39	229	−10.4	−1.47	+80.7	+214,237	+953
Spain	132	+12.6	10.29	207	−46.2	−37.9	+198	+26,040	+170
Uruguay	76	+5.5	5.48	173	−15.5	−15.5	+36.8	+2,807	+23
Honduras	29	+24	3.58	157	−100	−100.0	+34.7	+1,007	+24.9
Portugal	8.5	+12.5	7.32	224	−46	−46	+158.3	+1,352	+8
Czech Republic	1.7	+10	8.45	205	−46	−23.9	+150.4	+264	+2
Brazil	11,910	+11.1	4.985	191	−67.6	−50.9	+54.72	+651,698	+7,146
Colombia	67	+22	3.54	334	−44.4	+5.4	+265.7	+17,752	+52
Paraguay	500	+5.5	4.41	119	−19.92	−19.92	+9.69	+4,846	+121

Notes:

1. Impact on costs net of cost of technology = cost savings from reductions in pesticide costs, labor use, fuel use etc from which the additional cost (premium) of the technology has been deducted. For example (above) US cost savings from reduced expenditure on insecticides = +$15.88/ha, limited to an area equivalent to 10% of the total crop area (the area historically treated with insecticides for corn boring pests). This converted to an average insecticide cost saving equivalent per hectare of GM IR crop of =$1.99/ha. After deduction of the cost of technology which is shown as a negative ‘in farm income terms’ (−$27.5/ha) is deducted to leave a net impact on costs of −$25.5 (ie, a negative sign for impact on costs = an incease in costs so that the cost of the trait is greater than the savings on insecticide expenditure).

2. There are no Canadian-specific studies available, hence application of US study findings to the Canadian context (US being the nearest country for which relevant data is available).

GM IR corn (targeting corn rootworm) 2014

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	18,672	+5	10.16	162	−27.49	−4.89	+77.31	+1,443,680	+9,487
Canada	734	+5	8.84	167	−27	+2.0	+75.81	+55,623	+324

Note:

1. There are no Canadian-specific studies available, hence application of US study findings to the Canadian context (US being the nearest country for which relevant data is available)

GM IR cotton 2014

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	3,113	+10	0.865	1,699	−49.92	−17.61	+129.23	+402,595	+269
China	4,092	+10	1.358	2,144	−59.70	+28.20	+319.34	+1,306,753	+556
South Africa	15	+24	0.322	1,259	−31.79	−20.09	+77.23	+1,192	+1
Australia	195	Zero	2.44	2,025	−270.5	+228.3	+228.3	+44,719	Zero
Mexico	100	+15.8	1.51	1,757	−64.41	−40.71	+378.28	+37,778	+24
Argentina	362	+30	0.35	2,401	−21.25	−32.36	+316.88	+114,804	+42
India	11,684	+24	0.414	1,161	−13.12	+17.31	+137.27	+1,604,055	+1,161
Colombia	29	+10	0.861	1,670	−157.2	−79.92	+66.46	+1,904	+2
Brazil	330	+2.3	1.49	2,053	−40.29	+18.4	+91.3	+30,136	+12
Burkina Faso	454	+18.15	0.395	1,259	−53.48	−0.9	+89.38	+40,591	+33
Pakistan	2,625	+22	1.14	430	−4.01	+6.06	+113.86	+298,949	+658
Myanmar	218	+30	0.97	430	−20	−9.93	+115.15	+36,618	+93

Note: Price is for lint, except in Myanmar and Pakistan which is for seed.

GM HT soybeans 2014 (Excluding second crop soybeans – see separate table)

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 1^st generation	10,375	Nil	3.19	459	−43.53	+15.91	+15.91	+165,067	Nil
US 2^nd generation	21,044	+9	3.0	459	−52.76	+7.09	+131.1	+2,758,824	+5,682
Canada 1^st generation	127	Nil	2.71	406	−23.79	+18.16	+18.16	+2,305	Nil
Canada 2^nd generation	1,214	+9	2.58	406	−40.55	+1.41	+95.64	+116,113	+282
Argentina	19,047	Nil	2.7	246	−2.5	+22.96	+22.96	+436,419	Nil
Brazil	23,977	Nil	3.0	460	−11.05	+30.23	+30.23	+724,876	Nil
Paraguay	3,230	Nil	2.58	326	−4.4	+11.51	+11.51	+37,177	Nil
South Africa	618	Nil	1.4	461	−1.38	+7.94	+7.94	+4,906	Nil
Uruguay	1,070	Nil	2.33	289	−2.5	+15.14	+15.14	+16,194	Nil
Mexico	18	−2.1	1.96	453	−45.2	+18.8	+0.08	+1,464	−1
Bolivia	1,001	+15	2.05	390	−3.32	+5.96	+101.01	+107,313	+327

Note:

1. Price discount for GM soybeans relative to non GM soybeans in Bolivia of 2.7% - price for non GM soybeans was $399/tonne - price shown above is discounted

GM IR/HT (Intacta) soybeans 2014

Table

Country	Area of trait (000′ ha)	Yield assumption % change	Base yield sucrose(tonnes/ha)	Farm level price: $/tonne)	Cost of tech ($/ha)	Impact on costs, net of cost of tech ($/ha)	Change in farm income ($/ha)	Change in farm income at national level (‘000 $)	Production impact (‘000 tonnes)
Brazil	5,870	+9.42	2.95	460.1	−50.98	−7.29	+135.05	+792,770	+1,630
Argentina	634	+7.8	2.69	246.2	−50.98	+5.03	+46.68	+29,595	+133
Paraguay	200	+11.9	2.56	326.4	−50.98	−1.96	+101.48	+20,295	+61
Uruguay	250	+7.8	2.99	289.05	−50.98	+14.34	+43.22	+16,805	+50

GM HT corn 2014

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	29,944	Nil	10.73	162	−28.32	+36.17	+36.17	+1,083,083	Nil
Canada	1,184	Nil	9.36	167	−31.28	+23.53	+23.53	+27,860	Nil
Argentina: as single trait	401	+3% con belt, +22% marginal areas	6.08 corn belt, 3.75 marginal areas	119	−8.9	+6.71	+21.74 corn belt, +98.34 marginal areas	+29,823	+227
Argentina: as stacked trait	3,401	+10.25	5.41	119	−18.9	−3.32	+62.8	+213,577	+1,886
South Africa	1,990	Nil	3.7	229	−11.06	+12.36	+12.36	+24,602	Nil
Philippines	688	+5	2.86	288	−45.05	−14.21	+26.92	+18,530	+98
Colombia	55	Zero	3.65	334	−21.65	+15.34	+15.34	+841	Nil
Brazil	7,980	+3	4.99	191	−15.67	−3.48	+25.15	+200,785	+1,298
Uruguay	67	Nil	5.76	173	−8.92	+6.71	+6.71	+467	Nil
Paraguay	500	Nil	4.53	119	−16.47	+1.02	+1.02	+511	Nil

Notes:

1. Where no positive yield effect due to this technology is applied, the base yields shown are the indicative average yields for the crops and differ (are higher) than those used for the GM IR base yield analysis, which have been adjusted downwards to reflect the impact of the yield enhancing technology (see below).

2. Argentina: single trait. In the Corn Belt it is assumed that 70% of trait plantings occur in this region and marginal regions account for the balance. In relation to stacked traits, the yield impact (+10.25%) is in addition to the yield 5.5% impact presented for the GM IR trait (above). In other words the total estimated yield impact of stacked traits is +15.75%. The cost of the technology also relates specifically to the HT part of the technology (sold within the stack).

GM HT cotton 2014

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	3,370	Nil	0.939	1,699	−74.13	+14.09	+14.09	+47,507	Nil
S Africa	15	Nil	0.4	1,259	−16.8	+34.26	+34.26	+528	Nil
Australia	210	Nil	2.44	2,443	−67.63	+26.26	+26.26	+5,599	Nil
Argentina	412	Farm saved seed area nil Certified seed area +9.3%	0.5	2,401	−11.82 certified seed,−10 farm saved seed	+5.78 certified seed,+7.6 farm saved seed	+117.21 certified seed, +7.6 farm saved seed	+16,667	+6
Mexico	160	+13.3	1.51	1,757	−54	−23.42	+329.77	+52,762	+32
Colombia	30	+4.0	0.861	1,670	−167.9	+26.37	+83.89	+2,503	+1
Brazil	380	+1.6	1.49	2,053	−40.29	+6	+55.1	+20,937	+9

Notes:

1. Where no positive yield effect due to this technology is applied, the base yields shown are the indicative average yields for the crops and differ (are higher) than those used for the GM IR base yield analysis, which have been adjusted downwards to reflect the impact of the yield enhancing technology (see below).

2. Argentina: 30% of area assumed to use certified seed with 70% farm saved seed.

GM HT canola 2014

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 glyphosate tolerant	320	+3.4	1.7	377	−17.3	−0.71	+22.52	+7,197	+19
US glufosinate tolerant	278	+11	1.7	377	−17.3	+16.4	+54.10	+15,047	+40
Canada glyphosate tolerant	3,563	+3.4	1.84	475	−33.45	−30.2	+26.42	+94,115	+223
Canada glufosinate tolerant	4,356	+11	1.84	475	Nil	+13.01	+109.00	+474,746	+881
Australia glyphosate tolerant	350	+11	1.3	409	−11.72	+1.18	+45.59	+15,958	+37

Note: Baseline (conventional) comparison in Canada with herbicide tolerant (non GM) ‘Clearfield’ varieties.

GM virus resistant crops 2014

Table

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	455	+17	22.86	1,058	−494	−494	+3,619	+1,648	+1.8
US squash	2,000	+100	18.71	655	−736	−736	+11,527	+23,054	+37

GM herbicide tolerant sugar beet 2014

Table

Country	Area of trait (000′ ha)	Yield assumption % change	Base yield sucrose(tonnes/ha)	Farm level price equivalent (sucrose: $/tonne)	Cost of tech ($/ha)	Impact on costs, net of cost of tech ($/ha)	Change in farm income ($/ha)	Change in farm income at national level (‘000 $)	Production impact (‘000 tonnes)
US	455	+3.21	9.99	345.82	−148	+6.22	+117.26	+53,327	+154
Canada	15	+3.21	9.57	345.82	−148	+6.22	+112.60	+1,689	+5

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 2 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 and has remained at over 90% since then.

Farm level income impact of using GM HT soybeans in Argentina 1996–2013 (2): Second crop soybeans

Table

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
2008	3.35	224.87	754.1
2009	3.55	207.24	736.0
2010	4.40	257.70	1,133.8
2011	4.60	257.40	1,184.0
2012	2.90	291.00	844.6
2013	3.46	289.80	1,001.6
2014	4.0	195.91	783.6

Source and notes:

1. Crop areas and gross margin data based on data supplied by Grupo CEO and the Argentine Ministry of Agriculture. No data available before 2000, hence 2001 data applied to earlier years but adjusted, based on GDP deflator rates.

2. The second cropping benefits are based on the gross margin derived from second crop soybeans multiplied by the total area of second crop soybeans (less an assumed area of second crop soybeans that equals the second crop area in 1996 – this was discontinued from 2004 because of the importance farmers attach to the GM HT system in facilitating them remaining in no tillage production systems).

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 below). Production levels based on these adjusted levels were then cross checked with total production values based on reported average yields across the total crop.

Example: GM IR cotton (2014)

Table

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)
US	0.939	3,706	3,479	3,113	227	+10%	0.865	2,962	517
China	1.484	4,400	6,530	4,092	308	+10%	1.358	6,113	417

Note: Figures subject to rounding.

 

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 herbicide tolerant traits more recently adopted in several developing countries. Accordingly, the authors are grateful to industry sources which have provided information on impact, (notably on cost of the technology and impact on costs of crop protection). While this information does not derive from detailed studies, the authors are confident that it is reasonably representative of average impacts; in a number of cases, information provided from industry sources via personal communications has suggested levels of average impact that are lower than that identified in independent studies. Where this has occurred, the more conservative (industry source) data has been used.