1. Will Climate Forecasting and New Knowledge Tools help Resource-Poor Farmers move from poverty to prosperity? Farmers’ Participatory Approach to Manage Climate Variability V. Nageswara Rao Systems Modeling-Agro Ecosystems ICRISAT

2. Project Objectives To provide seasonal climate Prediction to farmers based on coupled atmospheric General Circulation Model (GCM) output statistical (MOS) downscaling. To provide forecast-based simulated crop management options for farmers’ choice, and evaluate the value of forecast to farmers with their participation. Capacity building of NARS collaborators on climate forecasting and crop modeling.

3. Project Background Initiated consequent to “Advanced Training Institute (ATI) on climate variability and food security” organized at Sponsored by global change SysTems for Analysis Research and Training Funded by Project duration: One year Pilot Project with anticipated institutional commitment.

4. Institutional Linkages Drs. Piara Singh, ICRISAT, India Dr. Jim Hansen, International Research Institute for Climate Prediction (IRI)., NY., USA. Dr. T. Giridhara Krishna, RARS, Nandyala, India Dr. S. K. Krishna Murthy, ARS, Anantapur, India Dr. Krishna Kumar, Indian Institute of Tropical Meteorology (IITM), Pune, India

5. Project Operationa l Area Kurnool, Anantapur are part of Scarce rainfall Zone of AP, India. Kurnool: annual rainfall 765 mm, Vertisols support LGP up to 165d, rich crop diversity. 3 villages selected Anantapur: rainfall 560 mm normal, shallow Alfisols, low LGP <140d, peanut systems are predominant. 2 villages selected.

6. The Approach IRI provide GCM output based predictors analyses ICRISAT develops seasonal climate forecast based on MOS correction with historical weather data for each location. Selection of villages and volunteer farmers, and collect soil data of farmers’ fields. Provide seasonal climate forecast, and Crop management choices thru systems simulations to interested farmers. Evaluate the use of forecast information and its value to farmers to minimize climate risks and optimize crop production.

7. Collaborative communication process: Seasonal rainfall forecast based cropping options to farmers IRI USA. RARS, ARS ANGRAU IITM India GCM output Farmers’ Cropping decisions Crop mgt. choices Predictors Data, knowledge Rainfall Prediction, Crop Modeling Probabilistic rainfall forecasts & Cropping options Communication ICRISAT Approach

8. Working with Farmers Understood their views on rainfall forecast info., cropping systems and their decision- making strategies (Jan 2003) by RRA survey. Installed automatic rainfall recorders in all 5 villages in march 2003, and manual recorders of rainfall for and operated by farmers Soil sampling of all 50 selected farmers’ fields (march ‘2003), analyzed physical and chemical properties of samples, distributed and explained results to individual farmers (may 2003).

9. Farmers’ perceptions on cropping decisions 33/41 correctly enlisted good seasons and bad season for preceding 10years, while 26/58 farmers in Kurnool recollected rainfall seasons correctly from their memories Preferred Cropping options 99 Farmers surveyed 50 Farmers selected Factors influencing decisions Decision making Family Head

10. Data acquisition on Weather Soil and Crops Nandyala station data from 1937-2001, Anantapur station data from 1962-2001, another four mandals in Kurnool and Anantapur districts since 1983. Soil profile data for Nandyala and Anantapur were fetched besides farmers’ fields data. Climate data analyses were performed at IITM, Pune in May with guidance from climate scientists of IITM and IRI to develop seasonal forecasts for 2003 season.

11. In Anantapur during JJAS and OND seasons (1937-2002) Nandyala during JJAS and OND seasons (1937- 2002) Rainfall variability

12. Correlation of Nandyala, Anantapur JJAS rainfall X SON-1 SSTs Good correlation of Indian ocean SSTs of previous SON months were established for rainfall in JJAS. Similar relationship was seen with Indian ocean SSTs for rainfall in JJAS months.

13. Weak correlation of Nandyala, Anantapur OND rainfall X SST of JJA-1 or MAM

14. ENSO Phase Analyses 1950-2002 Smith and Reynolds (2003) Extended Reconstructed SSTs of (1971-2000) 3.4 region (El Nino 16, La Nina 15, Neutral 22)

15. ENSO Phase Analyses Smith and Reynolds (2003) Extended Reconstructed SSTs of (1971-2000) 3.4 region (El Nino 16, La Nina 15, Neutral 22)

16. JJAS and OND rainfall in response in ENSO phases in Nandyal and Anantapur Nandyala rainfall from 1937-2002 and Anantapur rainfall from 1963-2002

17. Statistical Downscaling of GCM OS correction Seasonal rainfall forecasting Cross correlated Analysis of GCM (ECHAM,NCEP and CCM3) precipitation predictors time series were provided by IRI Spatial domain associated with the predictors are two boxes 66E to 90E, 5N to 30N and 100E to 130E, 15S to 15N. We developed seasonal rainfall forecasts as well as monthly totals with leave-1-out Cross Validated regression skill.

18. Cross validated Model Output Statistical correction- Nandyala. ECHAM 4.1GCM precipitation fields were used

19. Cross validated Model Output Statistical correction-Anantapur ECHAM 4.1GCM precipitation fields were used

20. Seasonal rainfall Forecast communication Vorvakallu, 11Jun 03 Nusikottalu, 12 Jun 03 West Narasapur, 12 Jun ‘03

21. ENSO effect on Cropping - Kurnool

22. ENSO Effect on Cropping Anantapur La Niña favorable for peanut and medium duration pigeonpea intercrop. El Niño seasons yields of both crops are around all seasons median.

23. Discussion on cropping options Water Balance calculations as in Thornthwaite and Mather (1955)

24. Discussion on cropping options Water Balance calculations as in Thornthwaite and Mather (1955)

25. Modeled Cropping Options Farmers’ Decisions -Kurnool Adoptive farmers Double cropping as sequentially system, Sunflower+Chickpea, Mungbean+Sunflower, Mungbean+chickpea; sowing condition: June-July rain >120 mm, sowing 10-25 June Double cropping as intercropping system: Peanut/pigeonpea, Foxtail millet/Pigeonpea; sowing condition: June-July rain >120 mm, sowing 25 June - 05 July. Years: 84, 89,91,92,96,2000 Risk averse farmers Single rainy season Sunflower, or post-rainy system of Rabi Sorghum (10-25 September) or Chickpea (25 Sep-5 November) No additional fertilizer inputs Adoptive farmers’ on decisions with forecast, what if observed rainfall

26. Intercropping of peanut/Medium duration pigeonpea; 25 June to15 July (16/38 yrs) Intercropping peanut/short duration pigeonpea; 15 July-05 August (31/38 yrs) Sole peanut for the short cropping season. End of August (30/38 yrs) 7 yrs Crop failure was unavoidable Modeled Cropping Options Farmers’ Decisions – Anantapur

27. Seasonal forecast X Observed rainfall, Nandyala Majority farmers (33/50) have chosen double cropping based on seasonal forecasting instead of single crop and /or post rainy season crop Others expressed less confidence and wish to verify climate forecast that season.

28. Seasonal forecast X Observed rainfall, Anantapur Sowings of peanut systems were done in August. 12 Farmers took decisions based on crop options as climate forecast failed. Rainfall was insufficient and occurred in small amounts in June and negligible in July.

29. Forecast based Cropping Decisions X Gross Profit Margins Kurnool

30. Forecast based Cropping Decisions X Yield Gains

31. Farmers’ Evaluation Meet

32. Conclusions Prediction skill is available for this scarce rainfall zone as seen thru ENSO and GCM prediction downscaling techniques. Improvements to Predictability can be contemplated with institutionalizations. Farmers understand the uncertainty of seasonal prediction, but could not follow probabilities however, preferred monthly estimates than seasonal rainfall estimates for decision-making. Farmers are response to useful prediction skill as there is good diversity of cropping systems in scarce rainfall zone. Continues exposure of farmers to seasonal predictions may help farmers’ confidence on forecasting based decision-making

33. Forecast Application Specialist’s interviews with Farmers Climate risk is more important than price risk Reliable forecast of JJA followed by SOND would be enough for double cropping and intercropping decisions. Time series graphs are simply useful than probabilities Most farmers wanted to evaluate climate applications for for 2-3 years for its value while some differed.

34. Capacity Building with NARS Provided training to three of NARs Scientists thru START project. Mr. Sree Ramulu, on climate forecasting (IITM), Pune Dr. Giridhara Krishna and Dr. Krishna Murthy and Mr. Srinivas in crop modeling (ICRISAT, Hyderabad) during 2003. Three NCMRWF scientists got training in crop modeling.

35. Thanks WMO ATI Participants