Developing Guidelines and Methodologies for Socio-Economic Assessment of LMOs

Developing Guidelines and Methodologies for Socio-Economic Assessment of LMOs Cooperating Center ICAR-National Academy of Agriculture Research Management (NAARM), Hyderabad PI: Dr. K Srinivas, Principal Secintist, ICAR-NAARM Final Project Presentation-26 th May 2016, India Habitat Centre, New Delhi

Introduction Indian experiences with respect to living modified organisms (LMOs) Bt Cotton Experience (Assumed to be non food) GM or LMOs can contribute significantly to Indian food security and poverty reduction subject to the conditions Widespread public reservations Socio-economic research role in designing a mechanism which would reflect the acceptability levels of farmers who are the major stakeholder Crop Maize Brinjal Traits Herbicide tolerance Insect resistance

Framework for socio-economic assessment Assessment Ex-ante Ex-post Micro Adoption Review, Perception frequency, Logit/probit, Simulation Impact Scenario analysis, Simulation, Ex-ante economic surplus analysis Logit/probit, tobit, heckman, double hurdle Randomized Control Trail (RCT), PSM (Propensity Score Matching), DD (Double Difference), Instrumental variable (IV) Macro Adoption Systematic review, Simulation Systematic review Impact Systematic review, Economic surplus using model using DREAM model Systematic review

Prioritizing methods Ex-ante macro level study on adoption. Who are the adopters and whether they are willing to adopt. Ex-ante macro level assessment of impact. Macro level impact studies using regression, DREAM or systematic reviews Ex-ante micro level study on adoption. A perception study to understand how different stakeholders perceive the technology, Logit/Probit model Ex-post micro level impact assessment using Randomized Control Trial, Propensity Score Matching (cross-sectional data), Double Differece & Instrumental Variable, panel data regressions (Panel data).

Methodology (Micro level-ex ante Adoption) Multi-stage sampling 1 2 3 4 5 District: Nalgonda Purposively 2 clusters for Brinjal & Maize Blocks: Miriyalaguda and Aleru Purposively where brinjal and maize are grown Villages: 10 villages (5 in each cluster) Randomly Farmers: 250 farmers Randomly selected 125 farmersin each cluster Data Analysis: Averages, Freqeuncey, Probit analysis, Scenario analysis

Results & Discussion General information about farmers in Nalgonda district clusters Source: Survey data, 2016 Percent Particulars Brinjal Maize Age (Average) 41.1 42.2 Education level of Farmer Illiterate 38.4 20.0 primary education 24.8 42.4 middle school education 11.2 3.2 secondary education 19.2 21.6 vocational training/education 2.4 4.8 Graduation and above 4 8 Primary source of income Agriculture-related activities 98.4 99.2 Other businesses 1.6 0.8 Service (private) 0 0 Service (govt.) 0 0 Average age of farmers growing brinjal and maize was 41.1 and 42 respectively literacy rate of maize farmers were higher compared to the brinjal farmers 98.4% (brinjal) and 99.2% (maize) were dependent on agriculture

Results & Discussion Source: Survey data, 2016 Percent Household Members Employed in Agriculture 100 90 80 70 60 50 40 30 20 10 0 Own Farm (Full Time) Own Farm (Part Time) Other Farm (Full time) Other Farm (Part time) Own Farm (Full Time) Own Farm (Part Time) Other Farm (Full time) Other Farm (Part time) The share of male and female is higher in case of full time work in their own farm in both crops Females are also involved full time in other farms Children are also involved part time in their own farm in maize farms Brinjal Male Female Children Maize

Results & Discussion Source: Survey data, 2016 Farm related characteristics of brinjal and maize farmer in district Percent Particulars Brinjal Maize Landholding Marginal (Below 1 Ha) 47.5 27.2 Small (1-2 Ha) 22.1 36.8 semi-medium (2-4 ha) 21.3 32.0 medium (4-10 ha) 6.6 4.0 Large (more than 10Ha) 2.5 0.0 Soil Type Black 1.6 4.8 Red 96.0 87.2 Both (Red & Black) 1.6 1.6 Other 0.8 6.4 Nature of Land Holding Leased-in 8.0 2.4 Leased-out 4.8 1.6 Owned 96.0 96.8 Irrigation Irrigated land share 68.2 73.7 Majority of brinjal farmers were marginal land holders Maize farmers were small semi-medium land holders Red soil is the predominant soil type Most of the farmers owns the land 68.2% of brinjal and 73.7% of maize farm lands are irrigated

Results & Discussion Cropping details of farmers in selected clusters Source: Survey data, 2016 Particulars Recommended practices (%Farmer) Average total land holding (ha) Average area under crop cultivation (Ha) Average price (Rs/Q) Average Productivity/Yield (Q/Ha) Brinjal Maize 2013-14 2014-15 2013-14 2014-15 100.00 100.00 96.00 97.60 1.75 1.75 1.71 1.71 0.24 0.23 0.98 1.03 1088.76 1094.17 1021.54 1029.5 449.5 375.4 45.5 33.26 Most farmers stated that they follow the recommended practices Brinjal is cultivated on 15% (0.24ha) of land holding and maize is cultivated on 60% of land holding drought had reduced the production in both the crops there were no significant improvement in prices

Results & Discussion Major constraints in production reported by farmer Source: Survey data, 2016 Percent 100 90 80 70 60 50 40 30 20 10 0 2013-14 2014-15 2013-14 2014-15 Insect attack (22.4%) was recorded as major constraint in case of brinjal followed by drought maize insect and weeds were the major constraints in 2013-14 and in 2014-15 it was drought (73.6%) Brinjal Maize Drought Diseases Insect Weed Rain No Response

Results & Discussion Note: 1 Average yield multiplied by average price, 2 Total value total variable cost, 3 Summation of cost saved by the preferred trait, 4 Total variable cost cost saved Cost and benefit analysis of brinjal and maize in selected clusters Source: Survey data, 2016 Brinjal Maize Particular Rs/Ha Share (%) Rs/Ha Share (%) Seed/Seedling 16437 15.81 3536 12.21 Fertilizer 6436 6.19 4984 17.21 Insecticide 17612 16.94 1402 4.84 Herbicide 0 0.00 1070 3.69 land preparation 11656 11.21 4494 15.51 Planting 9108 8.76 3696 12.76 Irrigation 3669 3.53 453 1.56 Labour for Fertilizer Application 6071 5.84 1862 6.43 Labour for Insecticide Application 5704 5.49 817 2.82 Labour for Herbicide Application 0 0.00 705 2.43 Weeding 2070 14.29 2353 8.12 Harvesting 7707 7.41 2167 7.48 Post harvesting & Transportation 4718 4.54 1427 4.93 Total Variable cost 91,188 28,966 Total Value 1 4,08,750 40,800 Return to fixed farm resources 2 3,17,562 11,834 Cost saved 3 23,316 4,128 Total Variable cost with preferred trait 4 67,872 24,838

Results & Discussion Source: Survey data, 2016 Percent 100 90 80 70 60 50 40 30 20 Farmers willingness to pay for desired trait Willingness to pay is higher in case of brinjal farmer s as they anticipates improvement in yield (more number of non-infested fruits due to less fruit borer infestation) as well as reduction in cost (reduced insecticide) application 10 0 Brinjal Maize more than 50% more than 25% more than 10% Less than 10% Maize farmers they anticipate only reduction in cost of herbicide application

Results & Discussion Cost and benefit SCENARIO analysis of brinjal and maize Crops Brinjal Maize Benefit scenarios Rs/ha Ratio Rs/ha Ratio Present Scenario Benefit with preferred trait LMO 1 3,40,878 1.073 15,962 1.350 Scenario 1 With 10% increased seed cost 3,22,797 1.016 12,072 1.020 Scenario 2 With 25% increased seed cost 3,20,332 1.009 11,542 0.975 Scenario 3 With 50% increased seed cost 3,16,223 0.996 10,658 0.901 Scenario 4 With 5% increase in yield with 10% increase seed cost 3,22,047 1.014 14,112 1.193 Scenario 5 With 10% increase in yield with 10% increase seed cost 3,47,547 1.094 16,152 1.365 Scenario 6 With 5% increase in yield with 25% increase seed cost 3,19,582 1.006 13,582 1.148 Scenario 7 With 10% increase in yield with 25% increase seed cost 3,45,082 1.087 15,622 1.320 Scenario 8 With 5% increase in yield with 50% increase seed cost 3,154,73 0.993 12,698 1.073 Scenario 9 With 10% increase in yield with 50% increase seed cost 3,40,973 1.074 14,738 1.245 1 Total value total variable cost with preferred trait. The ratio is calculated by dividing the respective scenario benefit with current benefit Source: calculated by Author based on Survey data, 2016

Results & Discussion Source: Survey data, 2016 Percent 100 90 80 70 60 50 40 30 20 10 0 Saved Seed OPV Hybrid HYV Brinjal Source of seed preferred by farmers Unknown (Seedling) Unknown seed GM Saved Seed OPV Hybrid Maize HYV Saved Seed Pvt/Dealers Govt. Org. NGO GM Brinjal farmers buy hybrids seedlings and also unknown seeds from private dealers. Hybrid brinjal seeds @ of Rs. 200 per 100 gms Cost of 100 seedlings is Rs 130 and unknown seeds is Rs 275/ 100gms Maize farmers grow mainly hybrids 75% of them get seeds from private dealers and 25% of them get seeds from government sources at an average cost of Rs. 150/kg

Results & Discussion Pesticides Trait specific use of chemicals Pesticides Brinjal Maize Percentage Frequency Corazen 40.0 50 Messile 9.6 12 Caldon 6.4 8 Carbaryl 5.6 7 Copperoxyc hloride 4.8 6 Fipronil 4.8 6 Monocroto phos 4.8 6 Pesticide Percentage Frequency Carbofuran 3g 59.2 74 Coragen 6.4 8 Phorate 4.0 5 Carbofuran 10g 3.2 4 Carbofuran 4g 2.4 3 Dithane M-45 2.4 3 Fipronil 4G 1.6 2 Carbofuran 1.6 2 Emamectin 1.6 2 Benzoate Gullikalu10g 1.6 2 Fipronil 4G 0.8 1

Results & Discussion Trait specific use of chemicals Pesticides Farmers in in brinjal cluster do not use any herbicides for weed control. In maize Atrazine (85.6%) and Paraquat (52.8%) are commonly used.

Results & Discussion Health problems due to pesticides and herbicides faced by farmers Farmers proficiency in using pesticides and herbicides 100 100 90 90 Source: Survey data, 2016 Percent 80 70 60 50 40 30 20 10 0 80 70 60 50 40 30 20 10 0 Received Training Cover nose Use Gloves and mouth Cover Limbs Use Goggles Wash Hands after applying chemicals Brinjal Maize Brinjal Maize

Results & Discussion Awareness about GM crops among farmers Percent Openion about GM crops Harmful to human Harmful to livestock adopt GM Crops 92.893.6 93.695.2 Source: Survey data, 2016 Particulars Brinjal Maize Aware 4.0 3.2 Not Aware 96.0 96.8 GM crop known Cotton Cotton GM Crop Grown 4.0 0.0 6.45.6 58.4 40 76 0.8 0.81.6 3.21.6 22.4 3.2 3.21.6 Yes No No response Yes No No response Brinjal Maize

Factors influencing adoption: Empirical evidences through probit analysis Brinjal Maize Coef. Std. Err. P>z Coef. Std. Err. P>z Age 0.413 0.158 0.009*** 0.217 0.119 0.068* Age square -0.004 0.002 0.011** -0.002 0.001 0.078* Household size 0.941 0.524 0.073* -0.006 0.118 0.957 Farmers education -0.016 0.339 0.964-0.899 0.369 0.015* * Household education -0.265 0.404 0.511-0.105 0.421 0.803 Seed -0.400 0.326 0.220 Pesticide cost 0.000 0.000 0.187 0.000 0.000 0.084* Herbicide cost 0.000 0.000 0.426 Total land holding -3.011 1.512 0.046* Area under crop 1.439 1.171 0.219 Share of cultivated crop -0.006 0.007 0.354 Irrigated area (%) 0.010 0.005 0.051* -0.003 0.005 0.609 Irrigation source 0.536 0.309 0.084* 0.114 0.295 0.700 Extension contact 0.163 0.393 0.678 Number of obs 101 124 LR chi2(11) 21.57 23.160 Prob > chi 2 0.0279** 0.017** Pseudo R 2 0.1601 0.169 Log likelihood -56.58-57.031

Summary and Conclusion In Brinjal, the major insect is fruit borer. The loss due to this insect is as high as 60%. Insecticides are to be applied at proper time (Flowering stage). Lack of awareness and timely availability of proper insecticide is the major constraints. Crops with high resistant to such insect attack are needed. Maize is infested with different weeds causing very high losses. Weed management is very difficult in early stages of crop and also labour availability is constrained. Farmers revealed that the weeding is either very difficult or very costly. Weed tolerant maize hybrids are required. GMOs can be one such alternative.

Summary and Conclusion Farmers really wanted and alternative crop varieties (HYV, Hybrid, GM etc) in different crops which can increase the profitability of the farming. These can be done either by reducing the cost of cultivation or by increasing the yield. Farmers opinion study showed that they are ready to adopt new technologies that would enhance profitability and reduce labour requirement. Farmers also are in the opinion that they would adopt new varieties (GMOs, LMOs) when government takes all environmental safety precautions.

Photo credit: Rajvardhan, ICAR-NAARM Thank you

Supplementary slides

Results & Discussion Factors influencing selection of seed sources among farmers Percent 100 90 80 70 60 50 40 30 20 10 0 Good Price Good Quality Brinjal Source: Survey data, 2016 Closeness to Home Maize Trust on Seed Source brinjal good quality of seed followed by trust on source, closeness to home were the major factors Closeness to home -because many of them buy seedlings instead of seed maize price of seed followed by quality of seed brinjal and maize quality of seed is a major issue most of them reported that they are receiving spurious seeds

Adoption Review Level Author Year Journal Crop Region Method Sampling framework Sample Analytic al tools Data 1 Micro & Macro 2 Micro & Macro Ex-Post Wang et al 2015 AgBioForum Cotton Cotton productio n zones na na FGD Panel Lalitha & 2015 AgBioForum Cotton Gujarat Dealers 82 Viswanathan 3 Micro Ex-ante Kolady & Lesser 4 Micro Ex-post Kiresur & Ichnagi 2006 AgBioForum Egg plant (brinjal) 2011 Agricultural Economics Review Maharast hra 5 Micro Ex-post Mal et al 2012 AgBioForum Cotton Haryana & Punjab 6 Micro Ex-post Pandey & Dash 2013 International Conference* 7 Macro Ex-ante Smyth et al 2013 Plant Biotechnology Journal 8 Macro Ex-post Scandizzo & Sarastano Farm level Random at subregionsal level Cotton Karnataka Farm level Multi-Stage sampling Cotton Maharast hra Farm level Banana Macro data na 2010 AgBioForum GM crops 13 States Macro data: State elevel Radom sampling 290 Bivariate probit model 60 Logit 200 Double hurdle Cross section al Cross section al Farm level 100 Tobit Cross section al 9 Macro Ex-post Kumar & 2014 Current Biotica Cotton India Review na na Swamy 10 Macro Huseing et al 2016 Journal of Agricultural and Food Chemistry GM crops Global Review na na 117 OLS Panel

Impact Review Level Author Year Journal Crop Region Method Sampling framework Krishna & Qaim 2007 AAEA, WAEA & CAES joint annual meeting Egg plant(brinjal) Andhra Pradesh, Karnataka, Karnataka, West Bengal Farm level Purposive & expert assessment Sample Analytical tools 360 Simulation; economic surplus data 1 Micro Exante Crosssectional 2 Micro Expost 6 Micro Expost 7 Micro Expost Bennett et al Pemsl et al Crost et al Subramani an & Qaim Kouser & Qaim Kathage & Qaim 2004 AgBioForu m 2004 Crop protection 2007 Journal of Agricultural Economics 2010 Journal of Developme nt Studies 2011 Ecological Economics Cotton Maharashtra Farm plot Random sampling at three subregions 9000 Kruskal-wallis nonparametri c test Cotton Karnataka farm level na 100 Simulation; monte carlo Maharashtra farm level Random sampling at two subregions Cotton Maharashtra Households Village census Cotton Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu 2012 PNAS Cotton Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu Farm level Farm level Multistage random sampling Multistage random sampling 718 plots 338 farmers Fixed effects model 305 Simulation; Social Accounting matrix 198 Poisson panel regression 533 Fixed effects model & Random effect model Panel 3 Micro Expost 4 Micro Expost Crosssectional panel 5 Micro Expost Crosssectional Panel Panel

Impact Review Level Author Year Journal Crop Region Method Sampling framework 8 Micro Expost Krishna & 2012 Agricultural Cotton Maharashtra, Farm level Multistage Qaim Systems Karnataka, Andhra random Pradesh, Tamil Nadu sampling 9 Macro Exante 10 Macro Expost 11 Macro Expost 12 Macro Expost 13 Macro Expost Shelton et al Brookes & Barfoot 2002 Annual review of Entomolog y 2005 AgBioForu m Cotton, Corn, Potatoes Soyabean, maize, cotton, canola, other GM crops Global Meta data Purposive review US, Argentina, Brazil, Paraguay, Canada, South Africa, China, India, Australia, Mexico, Philippines, Romania, Uruguay, Spain, Other EU, Columbia, Bolivia, Myanmar, Pakistan, Burkina Faso, Honduras Marvier 2007 Science Cotton & Maize Meta data Systematic review Finger et al 2011 Sustainabili Maize, India, China, Australia, Meta data Systematic ty Cotton USA, South Africa, review Spain, Germany, Argentina Qaim & Kouser 2013 PlosOne Cotton Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu Sample Analytical tools data 341 Fixed effect Panel (base) & random effects na na na Meta data Purposive na Tabular Na Radom sampling 42 na 721 Linear regression model 1431 Radom effect model Panel Panel

Impact Review Level Author Year Journal Crop Region Method Sampling framework Sample Analytical tools data 14 Macro Expost Klumper & 2014 PlosOne All GM crops Global Meta Data Systematic 147 na na Qaim review 15 Macro Expost Brookes & Barfoot Meta data Purposive 18 na na 16 Macro Expost 17 Macro Expost Srivastava & Kolady 2015 Soyabean, maize, cotton, canola, other GM crops 2016 Current Science Cotton US, Argentina, Brazil, Paraguay, Canada, South Africa, China, India, Australia, Mexico, Phillipines, Romania, Uruguay, Spain, Other EU, Columbia, Bolivia, Myanmar, Pakistan, Bukina Faso, Hnduras Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu, Gujarat, Madhya Pradesh, Punjab, Haryana, Rajasthan Macro data Purposive: Maize growing states 162 Panel yield model Pray et al Cotton Meta data na na Panel Panel