Localized climate forecasting system: Seasonal Climate and weather prediction for

farm level decision-making

R.Rengalakshmi

M.S.Swaminathan Research Foundation, Chennai, India

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.

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

Partners at the grass root level

Farmers Association: Reddiarchatram Seed Growers Association, KannivadiVillage 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

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

participatory appraisals, survey and FGD

• Various meteorological, physical & biological indicators, proverbs, reliability and related decisions • Different temporal scale, reliability • Social stratification of knowledgeCoping 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

Need Assessment and Potential Response strategies Above normal Below normal Expected 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

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 Forecastdeveloped with the technical input of IRI, IITM and TNAU

Creating Access to seasonal climate and weather forecast:Institutional networks

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

• 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

Decentralized forecasting system

• Training and capacity building

• Participatory research

• Enabling access through modern ICT

• Bridging the Knowledge systems

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

Acknowledgements

Dr.James Hansen, IRICP, New York START, Washington DCDavid and Lucile Packard Foundation,Dr.Gadgil, IIS, BangaloreDr.Selvaraju, TNAU, CoimbatoreDr.Rathore and Dr.K.K.Singh, NCMRWFScientists from IITM, PuneLocal Men and women farmers and agrl.labourersFarmers Association and VKC animators