1. Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, INDIA Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, INDIA Who owns groundwater? Climate Information contributes to better water management Ramasamy Selvaraju

2. Smallholder farming systems 1.Smallholder farms have undergone substantial changes in the last century increasing their exposure to climate variability 2.78% of total operational holdings occupies 32% of total agricultural area 3.The number of shallow tube wells and deep tube wells has increased by about 100% over the last 10 years

3. Lorenz curves and Gini coefficients for the distributions of total farm income (FI) and agricultural income (AI) under various irrigated cropping systems

4. What needs to be done? l Climate is one of the many factors influencing agriculture l Make the farmers to understand the climatic risks / opportunities l Help to manage the system through local knowledge and scientific tools

5. To utilize the ability to predict climate variability and change on range of scales to improve decision making using climatic risk management strategies in agriculture at farm, regional and national scales for enhancing resilience and sustainability. AIM

6. Overall aim of the project To assess and manage the impact of climate variability on the irrigated crop production systems to improve smallholder food security in a highly vulnerable semi-arid India.

7. Specific objectives l To document the information on climate and its predictability, water resources and water need of the irrigated crop production systems. l To assess the impact of El-Niño Southern Oscillation (ENSO) on water availability and on crop yield through system simulation approaches. l To develop a ENSO based resource allocation and cropping decision framework for the smallholder situations. l To demonstrate the benefit of seasonal climate forecasting to the smallholding farmers, extension and PWD workers of irrigated cropping systems to manage climatic risks.

8. Diverse Cropping Systems

9. Correlation between monthly SOI values and seasonal rainfall

10. The spatial pattern of correlation coefficient between JJA SST anomalies and (a) summer and (b) winter monsoon rainfall

11. Observed and hindcast station rainfall and simulated groundnut yields based on transformed, cross-validated ECHAM predictions.

12. Spatial variation in ground water table (depth from surface in meters)

13. The average monthly rainfall and potential evapotranspiration (PET) under ENSO phases

14. Decadal water requirement of irrigated maize (120 days duration) under various ENSO phases

15. Water balance components for irrigated maize (June- September) under ENSO composites ParticularsWarmColdNeutral Effective rainfall (mm)161196198 ETcrop (mm)640562615 Total gross irrigation requirement (mm) 600450500 Irrigation efficiency (%)778382 Actual water use by crop (mm)640562615

16. Irrigation water requirement (mm) of crops conditioned on ENSO phases ParticularsWarmColdNeutral Banana141112061346 Vegetable – I511411468 Summer Maize600450500 Vegetable – II521451486 Winter Maize529455525 Total357229733325

17. Monthly irrigation requirement of crops (banana, vegetable-1, summer maize, vegetable -2 and winter maize)

18. Farm level water availability scenario under various ENSO phases

19. Area under irrigation for various crops conditioned by ENSO phases and price scenarios

20. Gross margin (Rs.) from irrigated area of a 3 ha farm under ENSO phases and produce price scenarios

21. Economic value of climate forecasts in dryland systems CropDecisionEconomic value (Rs./ha/year) NegPosFalRisNeu GroundnutStand density25460204900 N Fertiliser177911500 Crop Choice41280405900 CottonSowing window0751780580 N fertiliser60029054800 Sorghum (Rabi) Stand density430272830

22. Are simulation models useful for smallholder farmers? Management responses to seasonal climate forecasts (developing the options) Converting climate forecasts into management options to satisfy the diverse requirements of smallholder farmers Impacts conditioned on forecasts

23. Media for climate information l Climate workshops – “Farmer groups” We learned to Skip the mass media (message distortion) Use generic methods with slight modifications for different target groups

24. Can farmers understand probabilistic climate forecasts ? l Yes, but … l Tried to convert the probabilities in to deterministic forecasts(320 x 0.65 = 208 mm) l Tried to convert into subjective and convenient categories Good rainfall / Low rainfall l Seems to understand the probabilistic forecasts, but ignores the probability and remembers only the rainfall quantity Frequent contacts and gambling analogies

25. Awareness is the first step for successful implementation of climate and agriculture programmes Decision capacity of the farmers can be improved through climate education programmes at different levels Improving the knowledge and decision capacity

26. Challenges l Message distortion and artificial skill Farmer ‘feel’ that a forecast is “right” in neighboring village although there is no “right” or “wrong” in probabilistic forecasting We need to understand that climate greatly affect the performance of technology The information provider needs to understand possible consequences of options Wrong interpretation may lead to conflicts Many pseudo forecasters and forecasts without understanding of the physical mechanisms are emerging l Spatial variability in rainfall needs to be better addressed l Climate knowledge is more than just providing a forecast l How can we combine and evaluate the local indigenous knowledge with the scientific technologies? l Climate communication – Capacity building at various levels l Ownership of the climate information, options and decisions by the end users l Improving the predictability

27. Participatory farmer interactions We engaged with local farmer groups to understand their agricultural system and their needs We considered their practices and rules of thumb and considered those as part of our system analysis framework We developed options and discussed risks and opportunities as well as consequences of management alternatives through simulation modelling We encouraged farmers to make informed decision after understanding the risk and consequences We solicited feed back and responses from farmers and reconsidered options

28. How demand-driven research helps for participatory co-learnig? Focus group meetings On-farm varietal evaluation On-farm experiments on varietal response to drought New insights into system analysis Analysing the options

29. AWARENESS ON WATER RESOURCE MANAGEMENT l Public Works Department l Water Technology Centre l Geology Department l Ground Water Board l Local Political presidents l State Agricultural Extension l Farmers organizations l NGOs

30. Knowledge to Science

31. CAPACITY BUILDING ACTIVITIES National level training workshop on “Systems Approach for Climatic Risk management in Agriculture”

32. Training Curriculum system analysis and modeling assessment of impacts of climate variability and change on agricultural systems climate forecasting methods use of climate forecasts in farm decision making linking climate and bio-physical models to explore management options and outcomes management of risks in agriculture associated with drought, floods and cyclones. application of remote sensing in climate risk management

33. Publications

34. Acknowledgements