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Dynamism of Agricultural Risk

Dynamism of Agricultural Risk. Drs. D. R. Reddy, Amor Ines, Sheshagiri Rao. Overview. Decision Making Under Uncertainty Risk Aversion and Optimization Example of optimizing maize production in Kenya Analyzing climate risks and risk management approaches at community/village level

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Dynamism of Agricultural Risk

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  1. Dynamism of Agricultural Risk Drs. D. R. Reddy, Amor Ines, Sheshagiri Rao

  2. Overview • Decision Making Under Uncertainty Risk Aversion and Optimization Example of optimizing maize production in Kenya • Analyzing climate risks and risk management approaches at community/village level Example 1: Srirangapura Village, Mahabubnagar Example 2: groundnut in Anantapur • Using climate information to manage crop mixes: examples from Mahabubnagar

  3. Decision Making Under Uncertainty Amor Ines

  4. Risks Probability density Climatic outcome (e.g., rainfall)

  5. Risk Aversion

  6. Ex-Ante Impacts: Risk Aversion

  7. Ex-Ante Impacts: Risk Aversion

  8. Ex-Ante Impacts: Risk Aversion

  9. Ex-Ante Impacts: Risk Aversion

  10. Risk Aversion

  11. Risk Aversion

  12. Risk Aversion

  13. Risk Aversion

  14. Optimization Poorly-behaved response surfaces Computationally-intensive Robust methods: Simulated annealing Genetic algorithms Compromise: grid search

  15. Value for maize management, Kenya Decisions that are optimal on average are usually far from optimal. Skillful forecasts can inform management that is closer to optimal for given weather conditions. average weather 1995 (dry) 1994 (wet) yield income optimal N

  16. a c e b d f

  17. CLIMATE RISK- SEMI ARID VILLAGE AT MEHABUBNAGAR • CROP (Specific) - RAINFED MAIZE, RAINFED Bt.COTTON • LIVESTOCK (Specific) - SHEEP • RISK MGT AT • FAMILY LEVEL – LIVELIHOOD PERSPECTIVE • COMMUNITY LEVEL • GOVERNMENT AND BANK • VARIABILITY OF RISK • AT FARM SCALE – IN TIME AND SPACE • LIVELIHOOD OPTIONS • COMBINATION OF ENTERPRISES

  18. Analyzing climate risks and risk management approaches at community/village level Sheshagiri Rao

  19. CROP YIELD SCENERIO at study village

  20. Cotton

  21. Cotton

  22. Mango

  23. Sheep – One of the highest district level Population in the nation (AP has the highest amongst states)

  24. Sheep

  25. CROP -TOTAL CLIMATE RISK COMPONENT • From end to end- Land preparation, crop sowing • TO Harvest and post harvest operations • Consider both • Direct impact- by moisture stress, water logging and on Crop physiology • Indirect impact – by triggering rapid increase of pests, diseases and vector populations that are already endemic. • In any particular year a particular combination of such ‘adverse events’ would occur • It is possible to construct simple models for such climate impact by using • Existing literature • Expert knowledge of farmers, field researchers

  26. NOTE • All further slides refer to Rainfed groundnut at Anantpur • These are illustrative of methodology • similar questions (to the ones mentioned here) were asked by farmers in the study village.

  27. Plot level = Profit / loss is rain+ many others05 =43 cm, 06=32cm, 07=52cm, 08=57cm

  28. At plot level- Yield variation and rain- relationship is much weaker than EXPECTED05 =43 cm, 06=32cm, 07=52cm, 08=57cm

  29. Anantpur District average groundnut yield- (1975-1995) - Avg rain-47 cm Cost of Cultivation

  30. TOTAL CLIMATE RISK FOR GROUNDNUT CROP

  31. Simple model for Rainfed Groundnut At Anantpur- an example

  32. Climate – Direct impact

  33. Climate- indirect impact

  34. Validation of Model Prediction and Field data

  35. 6 villages in Anantpur region • Located in 3 separate Mandals, distributed in an area of about 4000 sq km • Data from Marginal and small farmers, Vulnerable sections to climate risk • Sample of 20-40% of the total families in the community • Family wise data collection from 2005 to 2008

  36. Community level Livelihood options at 3 villages of Anantpur

  37. Family wise Annual income distribution- 6 villages

  38. Family wise Cattle population in 6 villages.

  39. Family wise sheep and Goat income- 6 villages

  40. How much credit?

  41. Reasons for first debt ? – crop (during bad year), bore wells, sheep are the big reasons

  42. Debt trap (3rd Default) - Reasons

  43. Govt. programs as a safety net

  44. CPRs as the safety net

  45. Highest number of animals not with the largest of farms

  46. Mid size farms have the largest credit

  47. Using climate information to manage crop mixes Dr. Reddy

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