1. Localized climate forecasting system: Seasonal Climate and weather prediction for farm level decision-making R.Rengalakshmi M.S.Swaminathan Research Foundation, Chennai, India

2. Goals and objectives to create an access and enhance farmer’s capacity to use location specific seasonal climate and weather predictions to improve their livelihoods. Objectives To study the seasonal climate variations and chronicle the farmer’s traditional knowledge and coping strategies. To evolve methodology for downscaling with appropriate institutional linkages and converting the generic data to location specific seasonal climate and weather forecast. To translate seasonal climate and weather forecast information into appropriate farmer friendly versions for its practical use in crop management. Approach: Multi stakeholder participatory approach.

3. Study Area : Kannivadi region, Reddiarchatram Block, Dindigul Dt, Tamil Nadu Semi arid belt with rainfed crops like millets, grain legumes, cotton Horticultural crops: vegetables Average Annual rainfall: less than 750- 800 mm October- December is a main season for rainfed crops Soil: Vertisol and alfisols in equal proportion Primary livelihood: Agriculture for > 80 per cent of the households and 50 % of them are small and marginal farmers

4. Partners at the grass root level Farmers Association: Reddiarchatram Seed Growers Association, Kannivadi Village Knowledge centers – three centers and farmers Institutions International Research Institute for Climate Prediction Indian Institute for Tropical Meteorology National Center for Medium Range Weather Forecast Tamil Nadu Agricultural University

5. Expected outcome Replicable framework for farmer friendly localized forecasting system for agricultural decision-making at the village level. To understand men and women farmers perspectives and indigenous knowledge on weather prediction indicators

6. participatory appraisals, survey and FGD Various meteorological, physical & biological indicators, proverbs, reliability and related decisions Different temporal scale, reliability Social stratification of knowledge Coping strategies Example…. If rain set during June-July - lablab, sorghum, redgram, groundnut, vegetable cowpea If it is late by 15 days – cowpea, fodder sorghum If it is late further by 15 days - green gram and blackgram If it delays further by 15 day – Minormillets/short duration sorghum Chronicling traditional knowledge

7. Need Assessment and Potential Response strategies Above normalBelow normalExpected month Land preparation (summer ploughing) and strengthening the bunds Cropping system with high value crops with longer duration and high water requiring crop Increase the area under cropping Purchase of high yielding varieties/hybrid seeds from market Purchase of organic manures and fertilizers · Ploughing before sowing · Cropping system with low value drought tolerant crops and mixed cropping system with short duration crops · Decrease the area under cropping and invest on allied enterprises · Using local seeds stored from the previous season · Manage with available manures Planning for seasonal out migration to nearby cities for non-agricultural work (mostly men) June August August September September September

8. Medium range weather forecast Institutional linkages with NCMRWF and receiving Medium Range weather forecast since November 2002 Established B type observatory managed by Farmers Association with the technical support of TNAU, Coimbatore Trained in observatory management, communication, and converting generic to locale specific information Seasonal Climate Forecast developed with the technical input of IRI, IITM and TNAU Creating Access to seasonal climate and weather forecast: Institutional networks

9. Communication and Dissemination: RSGA : Nodal point to receive Generic information (forecast) Value addition (Generic to locale specific (farmer friendly versions) and advisories) Dissemination to farmers through knowledge centers, bulletin boards, Posters, and local newspapers

10. Farmers expressed the complexities to take decisions in the farm instead could be useful to prepare themselves against anomalies in the future Many expressed that it helps to take alternate livelihood decisions Traditional practice follows dynamic strategies based on the event of the rainfall it means they need forecast with reduced lead time Also expressed that probability mode – doesn't provide confidence to the farmers instead it indicates uncertainty

11. Decentralized forecasting system Training and capacity building Participatory research Enabling access through modern ICT Bridging the Knowledge systems

12. Concluding remarks Two years experience indicate that learning takes time (observation over time/seasons) and related to familiarity Understanding the traditional knowledge systems to introduce new technology Participatory dialogue between the holders of the two different knowledge systems

13. Acknowledgements Dr.James Hansen, IRICP, New York START, Washington DC David and Lucile Packard Foundation, Dr.Gadgil, IIS, Bangalore Dr.Selvaraju, TNAU, Coimbatore Dr.Rathore and Dr.K.K.Singh, NCMRWF Scientists from IITM, Pune Local Men and women farmers and agrl.labourers Farmers Association and VKC animators