1. ISP Meeting, Ouagadougou, 23 Oct 2012 Making Climate Information More Relevant to Smallholder Farmers James Hansen, CCAFS Theme 2 Leader IRI, Columbia University, New York

2. Prerequisites to benefitting from an information service Credibility Salience Legitimacy Access Understanding Capacity to respond WG 1 WG 3 WG 2,4 WG 5 } Information product } Information service Delivery system } Users

3. Salience: What kind of information do farmers need? Types of climate information: –Historic observations –Monitored –Predictive, all lead times ≤ ~20 years Some generalizations: –Downscaled, locally-relevant –Tailored to types & timing of decisions –“Value-added” climate information: impacts on agriculture, advisories –Capacity to understand and act on complex information

4. Anecdote 1: RCOFs for farmer decision-making? “Weather-within-climate” Probabilistic information needed for risk management Capacity development through training, dialog with trusted advisors (http://www.wmo.int/pages/prog/wcp/wcasp/clips/outlooks/climate_forecasts.html)http://www.wmo.int/pages/prog/wcp/wcasp/clips/outlooks/climate_forecasts.html climate community “users” applications “…a hub for activation and coordination of regional climate forecasting and applications activities into informal networks” Basher et al. (Ed) (2001). Coping with Climate: A Way Forward. Summary and Proposals for Action. Palisades, New York: IRI. Owned, designed, convened by providers Spatial scale Seasonal rainfall total Probabilistic: Tercile format, often lost before reaching users Capacity development through stakeholder meeting participation

5. Anecdote 2: Early doubts about value of seasonal forecasts to farmers Error accumulates from: –SSTs to regional rainfall –Regional to local rainfall –Local rainfall to crop yield Therefore prediction of climate impacts on farms is not feasible. Given the inherent uncertainty, poor farmers can’t bear the risk of a wrong forecast. Barrett, 1998. AJAE 80:1109-12

6. Depends on time horizon of decision Generalizations about increasing lead time: –Decisions more context- and farmer-specific –Information becomes more uncertain, hence more complex –Therefore the scope of services needed increases “Weather-within-climate:” –Timing of season onset, length –Seasonal total = frequency × intensity. Frequency more predictable. –Dry, wet spell length distributions Elements of salience: Time scale HOURS DAYS WEEKS MONTHS YEARS DECADES … WEATHERCLIMATE Tillage Sowing Irrigation Crop protection Harvest Changing farming or livelihood system Major capital investment Migration Family succession Land allocation Crop selection Household labor allocation, seasonal migration Technology selection Financing for inputs Contract farming

7. Elements of salience: Spatial scale Correlation of observed (85 stations) vs. predicted rainfall in Ceará, NE Brazil, as a function of spatial scale. Gong, Barnston, Ward, 2003. J. Climate 16:3059-71. Correlation Scale

8. Elements of salience: Communicating uncertainty Relate measurements to farmers’ experience

9. Elements of salience: : Communicating uncertainty Relate measurements to farmers’ experience Convert series to relative frequency, then probability Oct-Dec rainfall (mm) Years with at least this much rain

10. Elements of salience: Communicating uncertainty Relate measurements to farmers’ experience Convert series to relative frequency, then probability Explanation & repetition ?

11. Elements of salience: Communicating uncertainty Relate measurements to farmers’ experience Convert series to relative frequency, then probability Explanation & repetition Compare with e.g., El Niño years to convey forecast as a shifted distribution

12. Elements of salience: Communicating uncertainty Relate measurements to farmers’ experience Convert series to relative frequency, then probability Explanation & repetition Compare with e.g., El Niño years to convey forecast as a shifted distribution Explore management implications

13. Elements of salience: Translation to impacts on agriculture Example: Integrate seasonal forecasts into yield predictions Reduces uncertainty, more early in growing season Before planting, forecasts potentially more accurate for crop yield than for seasonal rainfall Traditional sorghum, Dori, Burkina Faso. Mishra et al., 2008. Agric. For. Meteorol. 148:1798- 1814. Correlations of Jun-Sep rainfall, and observed, de-trended wheat yields with May GCM output, prior to planting, Qld., Australia. Hansen et al., 2004. Agric. For. Meteorol. 127:77-92 2000200400 km Correlation < 0.34 (n.s.) 0.34 - 0.45 0.45 - 0.50 0.50 - 0.55 0.55 - 0.60 0.60 - 0.65 > 0.65 Rain Yield

14. Elements of salience: Translation to management guidance At weather time scale, broadly-relevant advisories for time-sensitive decisions (sowing, irrigation, pest and disease control) At climate time scale, caution about top-down recommendations: –Decisions more farmer- specific –Uncertainty is greater Combine sources of expertise Involve trusted advisors Dialog with experts Farmer-to-farmer discussion

15. Institutional arrangements for salience? Limitations of supply-driven climate services Expanding the boundary to agricultural research and development Expanding the boundaries to give farmers a voice CLIMATE SERVICE NMS (climate) User (farmer) INFORMATION CLIMATE SERVICE NMS (climate) User (farmer) VALUE-ADDED INFORMATION NARES (agriculture) PARTNERSHIP CLIMATE SERVICE NMS (climate) Co- owner(far mer) NARES (agriculture) PARTNERSHIP

16. Salience and historic data Local decision-making depends on local information. Many promising opportunities to adapt to climate variability and change depend on historic data, are constrained by gaps. In Africa, feasible to blend station and satellite rainfall data => complete 30-year, 5-10 km grid daily record. Extending to other agriculturally-important variables. Meteorological data policy – Is it time to consider change? STATIONBLENDEDSATELLITE