GM Seeds Adoption and Environmental Sustainability in Brazil

1. GM Seeds Adoption and Environmental Sustainability in Brazil Renato Seixas – UC Berkeley/ARE José Maria Silveira – IE/UNICAMP David Zilberman – UC Berkeley/ARE June 18th 2013

2. Introduction and Outline • GM seeds have been considered a major technological innovation in agricultural systems in the past decades • Its relevance can be measured by the spam of controversial issues that have been raised since their introduction • Related issues: IPR, productivity effects , economic returns, consumer safety, environmental and health effects and distribution of surplus (Qaim, 2009) • Environmental front: positive effects have been argued based on findings about pesticide use and agricultural practices • Insect Resistant (IR) cotton has been found to reduce the use of insecticides and thererfore positive environmental and health safety effects • Qaim & Zilberman (2003), Qaim & de Janvry (2005), Huang et all (2002) • Herbicide Tolerant (HT) soybeans: substitution for less toxic herbicides and non-till practices • Qaim & Traxller (2005), Ferandez-Cornejo et all (2002), Brooks & Barfoot (2012)

3. Introduction and Outline • Project looks at the effect of GM biotechnolgy on environmental impact due to pesticides (herbicides and insecticides) • Heuristic model shows how different biotechnology traits (IR or HT) affects the use of pesticides • Use a farm level dataset to estimate reduced form models that documents effects for two different crops: cotton and soybeans • Environmental impact measure accounts for toxicity and exposure risk of pesticides to different components of agricultural systems • Identification strategy: intrafarm variation in pesticide use for different seed traits (conventional x GM) • Main results: • IR trait: reduction of environmental impact due to less use of insecticides • HT trait: low substitution betwen herbicides increases environmental impact

4. The Model • Model of profit maximizing competitive farm shows how GM seeds affect optimal choices of pesticides (Ameden, Qaim & Zilberman, 2005) • Damage control framework: total output modeled as interaciton between a regular production function and damage abatment function (Lichtenberg & Zilberman, 1986) • Comparative statics results show two different effects for each trait • IR trait works as a substitute for insecticies: reduction of optimal level relative to conventional • HT trait lowers knock-back effect on crops that result as side-effects of herbicides: increases optimal level of herbicides • Environmental impact is differentiated by technology: • IR: reduction in environmental impact • HT: uncertain enviromental impact due to scale and substitution effercts

5. Dataset and Empirical Strategy • Survey on costs, revenues, production and biotech adoption that has been conducted by a private consulting firm among 1143 farmers distributed over 10 States for harvest seasons 2008-2011 • Data on pesticide use collected for seasons 2009-2011 (839 farms) • Information is disagregated by farm/crop/trait (field) • Crops: cotton, maize and soybean • Traits: HT (soybean) and IR (cotton and maize) • Farmer participation in each round is voluntary and attrition rates are very high (not a conventional panel) • Information on different fields for the same farm allows using intrafarm variation between fields with conventional and GM seeds • Holds constant all farm-specific characteristics (management, prices of inputs and outputs, location, weather) • Main caveats: • Systematic differences on land quality across fields within the farm • Only considers adopters (self-selection)  

6. Dataset and Empirical Strategy

7. Dataset and Empirical Strategy

8. Dataset and Empirical Strategy

9. Dataset and Empirical Strategy

10. Dataset and Empirical Strategy: cotton Renato Seixas Development Lunch

11. Dataset and Empirical Strategy: soybeans Renato Seixas Development Lunch

12. Dataset and Empirical Strategy • Environmental Impact Quotient (Kovach, Petzoldt, Degnil & Tette, 1992) • Model designed by scientists from the IPM program at Cornell University (NY) to calculate the environmental impact of commom pesticides: acaricides, fungicides, herbicides and insecticides • General principle: environmental impact for each active ingredient is given by interaction of toxicity and exposure risk to farmworker, consumer and ecological system • EIQ field use rating: EIQ index calculated for a given weight (eg. kg) of individual active ingredients, multiplied by percent content of active ingredient in formulae, dose (kg/ha) required to provide control and number of applications and summed over different products • Final number measures intense usage of pesticides in a given pest management strategy

13. Results: Cotton IR

14. Results: Cotton IR IN: -20.6% TT: -8.5% IN: -21% TT: -11.4% Median EIQ: 32.07 Renato Seixas Development Lunch

15. Results: Soybean HT

16. Results: Soybean HT HE: 56.3% TT: 30% HE: 44.7% TT: 17% Median EIQ: 19.5 Glyphosate: 15.33

17. Results: Soybean HT • Twelvefold increase in toxicity classes 3 and 4 relative to decrease in classes 1 and 2 • Substitution effect among toxicity classes is very weak • Scale effect is not so big compared to other countries

18. Wrapping Up • Cotton crops : evidence that IR trait decreases quantities of Insecticides used (no change in other pesticides) and environmental impact • Result is expected given the predictions of the theoretical model • It is still usefull since it shows that EIQ index gives plausible results • Soybean crops: evidence that HT trait increases the environmental impact of herbicides • Intensification of use of less toxic herbicides (toxicity classes III and IV) • No corresponding decrease in more toxic herbicides increases in environmental impact • Result contributes to uncover environmental effec that is hidden by qualitative effect • Environmental policy makers might consider this new evidence when formulating incentives for biotechnology adoption

19. THANK YOU!