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Extended range forecast system and climate risk management in Agriculture

Extended range forecast system and climate risk management in Agriculture. By Kripan Ghosh India Meteorological Department, Pune, India James Hansen, Amor Ines and Andrew Robertson International Research Institute for Climate and Society, New York Makarand A. Kulkarni and Palash Sinha

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Extended range forecast system and climate risk management in Agriculture

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  1. Extended range forecast system and climate risk management in Agriculture By Kripan Ghosh India Meteorological Department, Pune, India James Hansen, Amor Ines and Andrew Robertson International Research Institute for Climate and Society, New York Makarand A. Kulkarni and Palash Sinha Indian Institute of Science, Delhi, India

  2. Objectives To study the predictability of rainfall and yield of maize. To evaluate different methods for incorporating climate information for crop yield prediction at different lead times. To study the impact on maize at Telengana during Rainy season under optimal management strategy.

  3. Climate predictors Downscaled rainfall forecast Crop model (observed weather) Stochastic Disaggregator NHMM Statistical yield model Crop model (hindcast weather) Predicted Crop yield

  4. Diagnostics using historical data

  5. Correlation between observed and simulated frequency of rainfall by Disaggregator during rainy season for Mehabubnagar

  6. Performance of Disaggregator and NHMM in simulating maize yield at Mehabubnagar

  7. Application of seasonal climate forecast

  8. Domain used for MOS approaches Predictors considered for study P1: Precipitation of D1 P2: Vertically integrated all liquid water content of D2 P3: Specific humidity at 850 hPa of D2 P4: Specific humidity at 850 hPa of D3 P5: Zonal wind at 850 hPa of D3 P6: Meridional wind at 850 hPa of D3 P7: Meridional wind at 200 hPa of D2

  9. Performance of downscaled seasonal rainfall forecast at Mehbubnagar

  10. Performance of Disaggregator and NHMM in generation of daily weather sequence for the season based on seasonal rainfall forecast Based on regression with Predictor 2 June start July start August start

  11. Simulated maize yield for Mehabubnagar using generated daily weather sequence of rainfall forecast based on Predictor 2 r=0.18 r=0.22 June start DISAGGREGATOR r=0.26 NHMM r=0.07 July start r=0.73 r=0.70 August start

  12. Simulated maize yield for 2002 at Mehabubnagar by Probabilistic Regression Method using generated daily weather sequence of rainfall forecast based on Predictor 2 r=0.49 r=0.68 r=0.33

  13. Influence of method on predictability of maize yield

  14. Optimization of cultivation practices

  15. Optimal management strategy under climatology • Cultivars: Varying crop duration (early, normal and late maturing). • Nitrogen fertilization strategy: different application rates (30, 60, 90 and 120 kg/ha). • Planting density (4, 6, 8, 10 and 12 / sq. m.). • Response farming based on optimal management (different sets of management practices) under following 3 scenarios: • Early onset of monsoon. • Average / typical onset of monsoon. • Late onset of monsoon.

  16. Best variety and management practices of maize for maximization of profit (averaged over all the years)

  17. Best variety and management practices of maize for maximization of profit under early sowing

  18. Performance of maize under different N doses atNormalsowing condition

  19. Performance of maize under different N doses atLatesowing condition

  20. Conclusion • Disaggregator showed very marginal advantage over NHMM in generating daily weather sequences but predicted maize yield more consistently when observed weather data have been used. • The skill of generation of weather sequences at seasonal scale by Disaggregator and NHMM showed gradual improvement with the advancement of lead time. • Predictability of maize yield gradually improved from beginning of June with the progression of the season. • During June and July probablistic regression method predicted maize yield better than Disaggregator and NHMM when Predictor 2 was directly used. • During August all the models predicted maize yield reasonably well with marginal difference among them. • Maximum profit of maize may be achieved when the crop is sown between 14th June to 10th July. However, longer duration variety than the existing one may give higher profit when sown with lower density with higher nitrogen dose during above mentioned period.

  21. I am extremely thankful to Dr. James Hansen, Dr. Amor Ines, Dr. Andrew Robertson, Dr. Shiv Someswar and Dr. Michale Tippett for their constant guidance and support. • Sincere thanks are also for Ms. Esther Conrad, Ms. Ann Binder, Ms. Althea Murolio, Ms. Sara Barone, Mr. Michale Davin and all the scietists of IRI to extend constant help. • My special thanks are due for Dr. Paul Block, Dr. Amor Ines and Ms. Ashley Curtis for all the local supports.

  22. Thanks and hope to meet once again…..

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