Applying Climate Information to Enhance the Resilience of Farming System Exposed to Climatic Risk in South and Southeast Asia

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Austin Walton
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1 Applying Climate Information to Enhance the Resilience of Farming System Exposed to Climatic Risk in South and Southeast Asia IRI Queensland Dept. Primary Industries (Australia) Indian Institute of Tropical Meteorology Indian Institute of Science Tamil Nadu Agricultural University (India) Bogor Agricultural University (Indonesia) Pakistan Agricultural Research Council

2 Goal and Objectives PDemonstrate & deliver benefits from climate forecasts for agricultural decision makers. Plot course for large-scale, sustained operational support of seasonal climate prediction within India, Pakistan and Indonesia < Evaluate and tailor seasonal climate forecasts to the needs of decision makers in a participating manner. < Demonstrate effective use, and evaluate benefits, of climate prediction information for improved decisionmaking by agricultural stakeholders at pilot sites < Enhance sustained operational support of agricultural use of seasonal climate prediction in each host country.

3 Key Outcomes Project-Wide PProject scientists trained PSeasonal forecast development < Downscaled statistical forecasts < MOS correction of GCM forecasts < Dynamic downscaling experiments < Impacts of different types of El Niño events PViet Nam policy outreach workshop

4 Key Outcomes India PCropping system simulation capacity building PEx-ante and ex-post evaluation of management responses to forecasts PParticipatory evaluation of groundnut varieties PEvaluation of farmer understanding and diffusion of climate application knowledge P1200 Tamil Nadu state agricultural extension workers trained in forecast applications PInstitutional consortium, strategic planning workshop and concept note for an expanded program throughout semi-arid India.

5 Key Outcomes Indonesia PThe Indramayu Climate Field School extended to Bandung District PAnalysis of ENSO Influence on Floods and Drought, Bandung PEconomics of Forecast Response, Bandung. Results show that farmers could have saved $235 ha-1, or $11.2 million across the district, by not planting a second rice crop in El Niño years. Replacing rice with soybean would further increase benefits.

6 Key Outcomes Pakistan PModel-based analysis of double cropping to enhance income and resilience PFarmer participatory trials of double cropping PLectures on project activities and simulation results for policy makers, crop scientists and extension workers

7 Exploring Predictability Tamil Nadu Subdivision Rain, Jul-Sep 400 r = observed predicted Year

8 Exploring Predictability Salem Station Rainfall, Jul-Sep Jul-Sep rain (mm) observed r = 0.62 predicted Year

9 Exploring Predictability Salem Simulated Groundnut Yield Grain yield (Mg/ha) r = 0.55 observed predicted Year

10 Avinashi, Tamil Nadu Farmers requested information about rainfall variability and its implications for what to plant and how to control pests and diseases. We heard this consistently except from "progressive" farmers.

11 Avinashi, Tamil Nadu Our meeting host grows both cotton and groundnut/sorghum to reduce income risk.

12 Avinashi, Tamil Nadu Rainfall and Crop Calendar

13 Avinashi, Tamil Nadu Optimal Land Allocation PModel: max E{U(W F )} = 1/n 3 U(W 0 + A i ), A i = 3 (x j Y ij p j - c j ) -C fixed PSubject to constraints: Ax # b total land area x $ 0 non-negativity PConstant relative risk aversion R r : U(W) = W 1-R r / (1 - R r )

14 Avinashi, Tamil Nadu Approach PSimulated crop yields with historic weather data ( ) PCalculated gross margins (B) for each year PRisk-based optimization of land allocation PMay SOI phases (Roger Stone) POptimal strategies: < Fixed solved for all years < Flexible solved for each phase

15 Avinashi, Tamil Nadu Forecasts influence optimal land allocation. April-May SOI phase neutral rising falling high low all risk-neutral (R r = 0.0) 0% 20% 40% 60% 80% 100% moderately risk-averse (R r = 2.0) 0% 20% 40% 60% 80% 100% cotton groundnut-sorghum

16 Avinashi, Tamil Nadu Tailoring land allocation to weak forecasts can increase mean income up to about 8% 8% Value, % of mean 6% 4% 2% Relative risk aversion (Rr)

17 Avinashi, Tamil Nadu Enhancements PMore realistic whole-farm economic model < Focus groups to understand decision options < Survey and monitoring to quantify resources PMore skillful forecast system (r = 0.12) FARM ENTERPRISE FARM RESOURCES MANURES SEEDS PRODUCE HOUSEHOLD FAMILY LABOUR OFF-FARM EPMLOYEMENT INFORMATION ANNUAL CROP PRODUCTION SYSTEMS PERRENIAL TREE SYSTEMS ANIMALS RAINFED IRRIGATED COCONUT FIRE AND TIMBER BULLOCK / COW BUFFALLOW GOAT SHEEP COTTON SORGHUM PEANUT HORSEGRAM COTTON PEANUT

Building socio-biophysical resilience: learning from smallholder farming community for adaptation CLIMATE VARIABILITY AND AGRICULTURE. R.

Building socio-biophysical resilience: learning from smallholder farming community for adaptation CLIMATE VARIABILITY AND AGRICULTURE R. Selvaraju Tamil Nadu Agricultural University, Coimbatore 641 003,