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Climate and Agricultural Risk

Climate and Agricultural Risk. Drs. Reddy, Amor Ines, Sheshagiri Rao. Overview. I. Drivers of agriculture risk (climate and non-climate) II. Analyzing variability at different spatial and temporal scales Yield variability and spatial scales Rainfall variability across time

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Climate and Agricultural Risk

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  1. Climate and Agricultural Risk Drs. Reddy, Amor Ines, Sheshagiri Rao

  2. Overview I. Drivers of agriculture risk (climate and non-climate) II. Analyzing variability at different spatial and temporal scales • Yield variability and spatial scales • Rainfall variability across time III. Analyzing roles of climate and non-climate factors in yield variability • Using de-trending to separate low-frequency and high frequency influences on crop yield variability • Yield Analysis: Mahabubnagar case

  3. Overview contd… IV. Implications of variability for decision making • Decisions are dynamic • Limitations of using average values V. Identifying various levels of spatial analysis • Options for decision making on climate risk and opportunities • Time horizons in decision making • Role of different decision makers VI. “Good” and “bad” years • What are good and bad years? • Methods for analyses: Z-score approach and Percentile Threshold approach VII. Weather Manager: Tool for analyzing weather data

  4. I. Drivers of Agricultural Risk and Across Scales • Climate (temperature/rainfall extremes) • Prices (of seeds/inputs, mandi prices) • Institutions (banks and access to credit, community support groups, etc) • Policies (subsidies, government relief programs, water/land access rights, etc)

  5. II. Analyzing Variability Across Scales • Yield Variability and Spatial Scales • Rainfall Variability across Time

  6. Scale and Yield Variability Variability of groundnut yields at multiple scales, residuals about smoothed trend.

  7. Scale and Yield Variability Variability of groundnut yields at multiple scales, residuals about smoothed trend.

  8. Scale and Yield Variability Variability of groundnut yields at multiple scales, residuals about smoothed trend.

  9. Scale and Yield Variability Variability of groundnut yields at multiple scales, residuals about smoothed trend.

  10. Scale and Yield Variability Variability of groundnut yields at multiple scales, residuals about smoothed trend.

  11. Spatial (Rainfall) Variability Dependable rainfall (mm) in different regions of Andhra Pradesh

  12. Temporal (Rainfall) Variability Annual rainfall (mm) trend in Andhra Pradesh Mean Rainfall Trend line

  13. Temporal (Rainfall) Variability Rainfall deviation (%) over Andhra Pradesh

  14. III. Climate variability (and de-trending) Analyzing roles of climate and non-climate factors in yield variability • Using de-trending to separate low-frequency and high frequency influences on crop yield variability • Yield Analysis: Mahabubnagar case

  15. Climate variability and de-trending Impact of the deficits of the monsoon rainfall significant despite the technology inputs

  16. Yield Analysis – Mahabubnagar Example III. Climate variability and de-trending

  17. Yield reconstruction using three datasets Kg/ha Year

  18. Yield reconstruction and de-trending Kg/ha Year

  19. Yield residuals (=Yobs/Ytrend-1) Kg/ha Year

  20. Yield reconstruction and de-trending A low-pass Fourier-based smoother is used Kg/ha Year

  21. Yield residuals (=Yobs/Ytrend-1) Residuals

  22. Yield residuals (=Yobs/Ytrend-1) Residuals

  23. IV. Implications of Variability for Decision Making • Decisions are dynamic • Limitations of using Average Values

  24. Station Rainfall Variability Months

  25. Average Monthly Rainfall Months

  26. Seasonal Rainfall Variability (JAS)

  27. Exeedence Probability of Rainfall JJA JAS Rainfall amount, mm

  28. V. Levels of Spatial Analysis • Spatial levels decision making • Options for decision making on climate risk and opportunities • Time horizons in decision making • Role of different decision makers

  29. FOREFITED OPPORTUNITY HARDSHIP CRISIS Managing the Full Range of Variability

  30. Levels of Spatial Analysis

  31. Diversification and RiskLow Correlation + Diversification = Reduced Risk A & B independent random normal Ct = 0.5 At + 0.5 Bt SDA = 1.03, SDB = 0.96, SDC = 0.51

  32. Diversification and RiskMore can be better!

  33. Avinashi, TN Optimal crop mix: groundnut-sorghum cotton Maximize CEincome Diversification and Risk

  34. Levels of Spatial Analysis Crop mixes with Negative correlation in yield – Non overlapping critical periods

  35. Levels of Spatial Analysis Family wise Cattle population in 6 villages.

  36. Family wise sheep and Goat income- 6 villages Levels of Spatial Analysis

  37. Common Property Resources, Safety Net

  38. Highest number of animals not with the largest of farms Levels of Spatial Analysis

  39. VI. “Good” and “Bad” years • What are good and bad years? • Two methods for analysis • Percentile Threshold Approach • Z-score Approach

  40. Reality on the ground Examples from Mahabubnagar illustrating multiple factors that determine good and bad years Higher night temperature (4.5oC) from Nov. –Dec, 1997 resulted in severe outbreak of Helicoverpa Higher sun shine hours (3-4 hrs over normal) during Jan, Feb and March, 1998-98 enhanced the yield level of rice and groundnut and pesticide usage has come down

  41. Z-score (Residuals) Z=(x-mean)/sd

  42. Residuals Probability X

  43. Seasonal Rainfall-JAS: ENSO States mm Years

  44. VII. Weather Manager • Tool for Analyzing Weather Data

  45. WeatherManager

  46. WeatherManager

  47. WeatherManager

  48. WeatherManager

  49. WeatherManager

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