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ISP Meeting, Ouagadougou, 23 Oct 2012

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. Prerequisites to benefitting from an information service. }. Credibility Salience Legitimacy Access

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ISP Meeting, Ouagadougou, 23 Oct 2012

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  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 } Information product Information service WG 1 WG 3 WG 2,4 WG 5 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. “users” “…a hub for activation and coordination of regional climate forecasting and applications activities into informal networks” climate community applications Anecdote 1: RCOFs for farmer decision-making? • Owned, designed, convened by providers • Spatial scale • Seasonal rainfall total • Probabilistic: Tercile format, often lost before reaching users • Capacity development through stakeholder meeting participation “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) Basher et al. (Ed) (2001). Coping with Climate: A Way Forward. Summary and Proposals for Action. Palisades, New York: IRI.

  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. Elements of salience: Time scale • 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 • Tillage • Sowing • Irrigation • Crop protection • Harvest • Land allocation • Crop selection • Household labor allocation, seasonal migration • Technology selection • Financing for inputs • Contract farming • Changing farming or livelihood system • Major capital investment • Migration • Family succession WEATHER CLIMATE HOURS DAYS WEEKS MONTHS YEARS DECADES …

  7. Elements of salience: Spatial scale ? Correlation 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.

  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 Years with at least this much rain Oct-Dec rainfall (mm)

  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. Rain Traditional sorghum, Dori, Burkina Faso. Mishra et al., 2008. Agric. For. Meteorol. 148:1798-1814. C o r r e l a t i o n Yield < 0 . 3 4 ( n . s . ) 0 . 3 4 - 0 . 4 5 0 . 4 5 - 0 . 5 0 0 . 5 0 - 0 . 5 5 0 . 5 5 - 0 . 6 0 0 . 6 0 - 0 . 6 5 > 0 . 6 5 2 0 0 0 2 0 0 4 0 0 k m 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 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

  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 NMS (climate) NMS (climate) NARES (agriculture) User (farmer) User (farmer) VALUE-ADDED INFORMATION PARTNERSHIP INFORMATION CLIMATE SERVICE CLIMATE SERVICE Co-owner(farmer) PARTNERSHIP NMS (climate) NARES (agriculture) CLIMATE SERVICE

  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? STATION BLENDED SATELLITE

  17. Thank You

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