1. Using multiple objective linear programming and economic surplus analysis to predict the economic impact of CA adoption: A case study in Odisha, India. Reed B.F., 1 Chan-Halbrendt C., 1 Halbrendt J., 1 Roul P. 2 1 Dept. of Natural Resources and Environmental Management, University of Hawaii, USA 2 Orissa University of Agriculture and Technology, Bhubaneswar, India 1

2. Introduction 2 Conservation Agriculture (CA) increased yields and biodiversity, decreased downstream pollution, erosion and sedimentation Adopted on over 100 million ha worldwide (Pretty et al., 2007) Adoption remains low among rural smallholders in many less developed regions due in part to -Unclear benefits at the farm level -Lack of economic motivation to provide incentives at institutional or regional level (Pannell et al., 2006; Wall, 2007)

3. 3 Farm Scale Careful analysis of farmer preferences and on-farm benefits of CA practices Institutional or Regional Scale Economic impact of adoption; implications for development, food security, poverty alleviation A Powerful Tool for Prediction and Promotion of CA Adoption

4. Objective 4 Using a case study, demonstrate how this two- pronged approach may be used to determine: 1.Which CA practices have the highest probability of long-term adoption given farmer preferences. 2.The economic impact adoption of such practices would have at the state level.

5. Case Study 5 Reed et al. (2013) entitled, “Economic analysis of conservation agriculture production system adoption in Odisha, India: a multi-scale approach.” Part of a USAID-funded project to develop and evaluate conservation agriculture production systems (CAPS) for tribal farmers in Nepal and India. CAPS: systems that incorporate CA strategies with regional farming practices for the purpose of encouraging CA adoption (SANREM, 2012).

6. Study Site: Odisha, India 6 High poverty – nearly 50% headcount poverty rate (approx. 16.3 million ppl; Haan, 2011) Agriculture: contributes 20% to state economy, employs 60% of population. -Production characterized by smallholder farming (over 50% < 2ha, avg. 1.25 ha) Village of Tentuli near Kendujhar used for development of CAPS and farmer preferences (Lai et al., 2012).

7. 7 12 production systems x 3 objectives = 36 coefficients. Production systems are traditional maize-based systems integrated with one or more CA practices, -legume intercropping -crop rotation -residue management (cover cropping) Method 1: Define Coefficients

8. 8 Objectives identified by literature review, farmer focus groups and expert opinion. -Maximize Profit: crop yield x (market price - fertilizer and herbicide costs). -Maximize Soil Quality: evenly-weighted index of change in soil N and OC at depths of 0-5 and 5-10 cm for each production system -Minimize Labor Costs: hourly wage rates for 2010-2011 x average time requirements

9. Method 1: Define Coefficients 9 Production Systems Codes MCMuMCHMCN (FP)MRMuMRHMRNICMuICHICNIRMuIRHIRN Profit (INR/m2) Maize4.585 3.784 3.907 4.937 Cowpea1.181 1.120 Mustard5.9685.1367.3787.089 Horse Gram 2.969 2.873 4.968 3.746 Non-Labor Costs0.336 Profit (INR/m2)10.2167.2174.2488.5846.3213.44812.1309.7204.75112.8099.4665.720 Soil Quality OC and N Index0.3660.0810.4820.4830.3060.8200.4120.0680.6760.5120.6360.874 Labor Cost (INR/m2) 1st season5.959 3.326 6.501 3.395 2nd season1.0381.075 1.0381.075 1.0381.075 1.0381.075 Total6.9977.0355.9594.3644.4013.3267.5397.5776.5014.4324.4703.395

10. Method 2: MOLP Model 10 Multi-Objective Linear Programming Model: Using the coefficients, determine what combination of CAPS maximizes profit, soil quality and labor cost, given weighted preferences for each. Results given in % of CAPS to implement per land using and are thus scalable to farm size.

11. Method 2: MOLP Model 11 MINIMAX model calculates optimal solution for each objective (weighted by preference)then determines the mix of production systems that minimizes the total deviation from all optimal solutions. Model results: the optimal solution is to plant system IRMu on 63% of farmland and system IRN on remaining 37% land. MCMuMCHMCN (FP)MRMuMRHMRNICMuICHICNIRMuIRHIRN Profit10.2167.2174.2488.5846.3213.44812.1309.7204.75112.8099.4665.720 Soil Quality0.3660.0810.4820.4830.3060.8200.4120.0680.6760.5120.6360.874 Labor Cost6.9977.0355.9594.3644.4013.3267.5397.5776.5014.4324.4703.395 % of field to plant0% 63%0%37%

12. Method 3: Economic Surplus 12 Economic Surplus selected for its ability to account for the net benefits of both producers and consumers, which together, reflects the overall change in economic welfare resulting from the introduction of new technologies (Laxmi et al., 2007; Shiferaw, 2005). Demand and supply elasticity coefficients used from past studies (Kumar et al., 2011; Mythili, 2008) Production numbers published by Odisha’s Directorate of Agriculture (NIAM, 2011).

13. Method 3: Economic Surplus 13 Change in consumer surplus = P 0 abP 1, change in producer surplus=P 1 bcd, change in total economic surplus = P 0 abP 1 +P 1 bcd = I 0 abI 1

14. Results 14 A production system using reduced tillage, maize/cowpea intercropping in the first season with a mustard cover crop on approximately 60% of farmer land with 40% fallow provides the optimum benefit at the farm level, given stated preferences of farmers and on-station trial plot data. Comparison of Farmer Practice and Model Output ObjectivesFPModelDifference% change Profit (INR/m2)4.24845710.202045.95140.14% Soil Quality*0.4819380.6449510.1633.82% Labor Cost5.9594734.050729-1.91-32.03%

15. Results 15 According to economic surplus analysis, adoption of model results among smallholders (farm size <2 ha) at 1%, 3% and 5% would produce: NET CHANGE IN ECONOMIC SURPLUS in USD 1% $19,812,669.06 3% $59,783,614.41 5%$100,215,369.39

16. Discussion 16 Significant incentive exists at the farm and state levels for adoption of CAPS among smallholders in Odisha It is predicted that the production system (reduced tillage, maize intercrop with a 60/40 mustard/fallow cover crop combination will provide the best economic returns for farmers This system has high likelihood of adoption given farmer preferences The findings of this analysis could be strengthened with the multi-year results of on-station trial plots (trial plot data for one year only).

17. Discussion 17 This provides ample motivation for more research into CAPS adoption in Odisha, and can serve as a guideline analysis by which to compare current policy and make future policy decisions. Results could lead to the provision of improved seeds or subsidies for the purchase of CA machinery and improved legume seeds, or payments to offset risks for early adopters (Wall, 2007).

18. Conclusion 18 This study provides a two-pronged approach for estimating the on- and off-farm benefits of CA adoption. The authors of this study argue that without an understanding of farmer profitability and preference, adoption efforts may be less efficient. Likewise, if the economic impact of adoption at the state or province level is unknown, decision-makers and institutional leaders may lack the incentive to facilitate adoption and could even hinder the technology transfer process.

19. Thank you. 19

20. References 20 Haan, A.D. (2011). “Rescuing exclusion from the poverty debate: group disparities and social transformation in India.” International Institute of Social Studies Working Paper No. 517. The Netherlands: Institute of Social Studies. Lai C., C. Chan-Halbrendt, J. Halbrendt, D. Naik, and C. Ray (2012). “Farmers Preference of Conservation Agricultural Practices in Kendujhar, Odisha using Analytical Hierarchy Process.” 22nd Annual International Food and Agribusiness Management Association World Forum and Symposium, Shanghai, China, June 11-14, 2012. Laxmi, V., O. Erenstein, and R. K. Gupta. (2007). "CIMMYT. Assessing the impact of natural resource management research: The case of zero tillage in India’s rice–wheat systems." The Impact of Natural Resource Management Research: Studies from the CGIAR. CAB International, Wallingford, UK 68-90. Reed, B.F, C. Chan-Halbrendt, B. B. Tamang and N. Chaudhary (2013). “Economic analysis of conservation agriculture production system adoption in Odisha, India using a multi- objective linear programming model and economic surplus analysis.” Unpublished Results.

21. References 21 Mythili, G. (2008). “Acreage and Yield Response for Major Crops in the Pre- and Post- Reform Periods in India: A Dynamic Panel Data Approach.” Published by IGIRD-ERS/USDA Project: Agricultural Markets and Policy, Mumbai, India. Shiferaw, B, H.A. Freeman and S.M. Swinton (eds) (2005). Natural Resource Management in Agriculture: Methods for Assessing Economic and Environmental Impacts. CAB International, Oxfordshire, UK. pp155-174. Kumar P., A. Kumar, S. Parappurathu and S.S. Raju (2011). “Estimation of Demand Elasticity for Food Commodities in India.” Agricultural Economics Research Review 24: 1-14. NIAM (2011). “A study on agricultural marketing system in Odisha.” Published by the National Institute of Agricultural Marketing, Jaipur, Rajasthan, India. SANREM (2012). Conservation Agriculture Production Systems (CAPS). Last updated: January 2011. Available online at: http://www.oired.vt.edu/sanremcrsp/professionals/research-themes/caps/. http://www.oired.vt.edu/sanremcrsp/professionals/research-themes/caps/