1. Farmers Preference of Conservation Agricultural Practices, Case Study: Tentuli Village, India C. Chan-Halbrendt (Presenter) Dept. of Natural Resources and Environmental Management University of Hawaii at Manoa, Hawaii, USA Second International Conservation Agriculture Workshop and Conference in Southeast Asia: July 7, 2011 NREM & SMARTS

2. The Study Area: District of Kendujhar, Odisha, India Figure 1: Location map of the Kendujhar district of Odisha, India State of Odisha Goal of the study: To promote dialogue and implementation among farmers who are interested in sustainable and effective crop production through a Conservation Agriculture Production Systems (CAPS) approach. Kendujhar

3. Objectives of the Project:  To enhance the livelihood and food security of small-holder farmers through: 1.Determining viable CAPS programs  Conduct village baseline survey to understand current farming practices  Design and implement experimental plots to evaluate the performances of the likely CAPS programs 2.Initiating workshops to support farmers’ knowledge and understanding of these CAP/Non-CAP systems and their potential performances  Compare/Contrast the programs with 4 criteria based on experimental plot results (Yield, Profit, Labor Saving, Soil Environmental benefit) 3.Determine Farmers Preference of the experimental CAPS program  Analytical Hierarchy Process - a multi-factor decision making tool 4.Provide future insight and feedback to the design and development of on- farm trials

4. 1. CAPS/Non-CAPS Program Selected CAPS Non-Cap: Maize Plow* CAP 1: Maize Dibble* CAP 2: Maize/Cowpea Intercrop Plow** CAP 3: Maize/Cowpea Intercrop Dibble** Table 1: Representative maize and maize/cowpea CAP/Non-CAP programs Kendujhar, 2010. *For maize only plot size 0.125 ha **For maize/cowpea intercrop plot size is 0.06 ha for maize and 0.06 ha for cowpea Selected CAPS: Non-CAP Conventional Tillage (Plow) CAPS Minimum Tillage (Dibble) Intercropping**

5. Table 2: CAPS Program Outcomes/Outputs ProgramProfitLabor Saving YieldSoil Environ. Benefit NO CAPS Maize Plow CAPS 1 Maize Minimum Tillage CAPS 2 Maize/Cowpea Intercrop Plow CAPS 3 Maize/Cowpea Minimum Tillage Contrast CAPS with the representative farm model with conventional plowing (control) using the following key agronomic and economic indicators: Profit, Labor Saving, Yield, and Soil Environmental Benefit Determined based on experimental farm plot results. Ranking from one being the worst to 4 the best 2. Compare/Contrast Systems Program Performances

6. 3. Solicit Farmers Preferences for CAPS Program

7. Analytic Hierarchy Approach (AHP) Multi-criteria decision-making technique which decomposes a complex problem into a hierarchy There were 18 farmers participated - 10 males and 8 females in this exercise

8. How does AHP work? 1.Define your Goal 2.Select your criteria/objectives 3.Layout your program alternatives 4.Put them in a hierarchy

9. Analytical Hierarchy Process (Thomas Saaty, 1992) Solicit Farmers Preference for CAPS Improved Income Profit Labor Saving Yield Soil Environmental Benefit GOAL: (Level 1) OBJECTIVES: (Level 2) NO CAPS (Maize Plow) CAPS 1 (Maize Minimum Tillage) CAPS 2 (Maize/Cowpea Intercrop Plow) CAPS 3 (Maize/Cowpea Intercrop Minimum Tillage) PROGRAMS: (Level 3) 2010 Baseline Survey data, focus groups, local experts Braunschweig, 2001; Nyende & Delve 2004; Sumpsi et al. 1997; Waddington & Karingi 2001 2010 Baseline survey data and agronomists

10. Improved Income Profit Labor Saving Yield Soil Environmental Benefit GOAL: (Level 1) OBJECTIVES: (Level 2) NO CAPS (Maize Plow) CAPS 1 (Maize Minimum Tillage) CAPS 2 (Maize/Cowpea Intercrop Plow) CAPS 3 (Maize/Cowpea Intercrop Minimum Tillage) PROGRAMS: (Level 3) Figure 3: AHP Diagram of Farmers Preference for CAPS Farmers Preference for CAPS Solicitation Step 1: Weighting the objectives and weighting the programs with each objectives For each level, pairwise importance comparisons were made with each of the higher level factor(s).

11. Pairwise Importance Comparison 5 Point Scale

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13. How to solve for the eigenvector? And the overall preferences? 1.Squared the pairwise matrices 2.Summed each of the rows and normalized 3.Multiply the weight of each objective with the rankings of the programs w.r.t. the same objective to get the overall preferences

14. Comparison Matrix Level 1 Improved Income YieldProfitLabor Saving Soil Environment al Benefit Yield1a1a1 a2a2 a3a3 Profit1/a 1 1a4a4 a5a5 Labor Saving 1/a 2 1/a 4 1a6a6 Soil Environme ntal Benefit 1/a 3 1/a 5 1/a 6 1

16. Evaluate each program with each objective Make pairwise comparisons for each program with respect to each of the objective (1 comparison matrix for each criteria/objective, 4 total) ProfitNON-CAPCAP 1CAP 2CAP 3Squared, sum row and normalized Non-CAP1a1a1 a2a2 a3a3 (0.101) CAP 11/a 1 1a4a4 a5a5 (0.089) CAP 21/a 2 1/a 4 1a6a6 (0.412) CAP 31/a 3 1/a 5 1/a 6 1(0.398) Table 3 : Preference Matrix to derive priority for the programs w.r.t. Profit

17. Synthesis of overall farmers’ preference (weight) represented for each program with respect to each objective

18. Objective 3: Farmers Preference for CAPS Solicitation Synthesize all local priorities throughout the hierarchy Table 4: The resulting set of weights for each level of hierarchy as determined by the matrices ObjectivesWeight Profit0.274 Labor Saving0.184 Yield0.329 Environmental Benefit 0.213 ProgramProfitLabor Saving YieldSoil Environmental Benefit Non-CAP0.1010.2190.1420.084 CAP 10.0890.2330.0800.208 CAP 20.4120.2850.5040.201 CAP 30.3980.2630.2740.507

19. Farmers Preference for CAPS Solicitation Non-CAP CAP 1 CAP 2 CAP 3 The composite scores of farmers preference of CAP/Non-CAP systems Figure 5: Synthesized results of farmers preference for CAPS with respect to the goal of improved income

20. Conclusions Most important criteria is yield in terms of improved income of choosing any CAPS In determining farmer adoption of new programs, it clearly goes beyond agronomic evaluation - profit was important along with soil environmental benefit. Using AHP to understand how farmers make decisions in adoption can help researcher design programs that ensure addressing farmers most important criteria and at the same time identify the areas of knowledge that requires educating and training for optimal understanding

21. Mahalo! ACKNOWLEDGEMENTS: SMARTS Team: Dr. Catherine Chan-Halbrendt, Jacqueline Halbrendt, Dr. Chittaranjan Ray, Dr. Travis Idol, Dr. Carl Evensen Funding Agencies/Organizations: SANREM-CRSP, US AID In-Country Counterparts: Orissa University of Agriculture and Technology, Dr. Pravat Roul, Dr. Mishra, Dr. Dash, Soumya Dwibedy, Aliza Pradhan NREM & SMARTS