Seasonal to Inter-Annual Climate Forecasts and their Applications in Agriculture

1. Seasonal to Inter-Annual Climate Forecasts and their Applications in Agriculture James Hansen International Workshop on Addressing the Livelihood Crisis of Farmers: Weather and Climate Services Belo Horizonte, Brazil, 13 July 2010

2. Introduction • Basis for seasonal, interannual prediction • Relevance for farmer livelihoods • Underexploited opportunity or underappreciated constraints? El Niño neutral La Niña

3. Overview • Value of seasonal forecasts for agriculture • Challenges to achieving potential value • Enhancing salience • Enhancing understanding • Enhancing legitimacy • Re-invigorating seasonal forecasts for agriculture

4. Evidence of Value

5. The Cost of Climate Risk:Ex-post Impacts of Climate Shocks HARDSHIP CRISIS • Loss of life, assets, infrastructure • Persistent impacts of coping responses: • Reduce consumption • Overexploit resources • Liquidate productive assets • Default on loans • Withdraw children • from school • Abandonment

6. The Cost of Climate Risk:Ex-Ante Cost of Moving Target • Katumani, Kenya • Simulated maize yields: • Observed weather • 11 N fertilizer rates • 4 planting densities • Enterprise budget • Optimal management • Fixed • By year • Hansen, Mishra, Rao, Indeje, Ngugi. 2009. Agric. Syst. 101:80-90.

7. The Cost of Climate Risk:Ex-Ante Cost of Moving Target

8. The Cost of Climate Risk: Ex-Ante Cost of Risk Aversion FORFEITED OPPORTUNITY HARDSHIP CRISIS • Risk aversion effect • Low-risk crops, varieties • Under-use of inputs • Shift household labor • Non-productive precautionary assets • Poor adoption of • innovation • Also affects markets • Cost of uncertainty is large, inequitable

9. Model-Based Ex-Ante Valuation value utility weather net income forecasts climatology environment management Expected outcome of best response to new information minus expected outcome of best response to prior information:

10. Model-Based Ex-Ante Valuation • Reviewed 58 estimates from 33 papers • Most focused on rainfed agronomic crops • Highest values estimated for horticultural crops Meza, Hansen, Osgood. 2008. J. Appli. Meteorol. Climatol. 47:1269-1286.

11. Empirical Evidence of Demand and Value • Burkina Faso (Roncoli et al. 2009. Climatic Change 92:433-460) • Most workshop participants (91%) and non-participants (78%) changed management in response to forecast • Participants disseminated to 2/3 of non-participants • Zimbabwe (Patt, Suarez, Gwata, 2005. PNAS 102: 12623-12628) • Of the 75% who received forecasts, 57% changed management resulting in yield increases • Workshop participants 5 X more likely to respond • Successes within reported failures • Evidence of latent demand

12. Challenges to Achieving Potential Value • Do poor smallholder farmers lack the capacity to change management in response? • Will climate forecasts that could be wrong expose farmers to unacceptable risk? • Can farmers understand and deal with the complexities of probabilistic forecasts? • Communication challenges: • Salience

13. The Salience Challenge: Farmers’ Forecast Information Needs • Local spatial scale • Temporal scale – “Weather-within-climate” • Agricultural impacts and management implications • Transparent presentation of forecast accuracy

14. The Salience Challenge:Representative Forecast Products

15. Challenges to Achieving Potential Value • Do poor smallholder farmers lack the capacity to change management in response? • Will climate forecasts that could be wrong expose farmers to unacceptable risk? • Can farmers understand and deal with the complexities of probabilistic forecasts? • Communication challenges: • Salience • Legitimacy

16. The Legitimacy Challenge: Illustrated by the RCOFs “users” “…a hub for activation and coordination of regional climate forecasting and applications activities into informal networks” climate community, COFs applications • The RCOF purpose, design, process • Credibility, legitimacy, salience • Illustrative of broader challenge

17. Salience

18. Meeting the Salience Challenge:Downscaling in Space 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.

19. Meeting the Salience Challenge:“Weather Within Climate” • Seasonal total = frequency × mean intensity • Frequency more spatially coherent, predictable • Dry, wet spell length distributions • Timing of season onset, length

20. Meeting the Salience Challenge:Predicting Agricultural Impacts Observed soybean yields (GA yield trials) vs. seasonal rainfall, temperature, simulated yields

21. Meeting the Salience Challenge:Predicting Agricultural Impacts Rain + GCM forecast climatology only Traditional sorghum, Dori, Burkina Faso. Mishra et al., 2008. Agric. For. Meteorol. 148:1798-1814. Grain yield (Mg ha-1) 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 1982 Queensland, Australia wheat yield forecast. Hansen et al., 2004. Agric. For. Meteorol. 127:77-92 Forecast date 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 • Improves accuracy = reduces uncertainty • Benefit greatest early in growing season • Before planting, forecasts potentially more accurate for yield than for seasonal rainfall • Have developed & evaluated a suite of methods

22. Meeting the Salience Challenge:Predicting Agricultural Impacts growing season EVENT Uncertainty (e.g., RMSEP) seasonal forecast marketing anthesis planting harvest Time of year Food security early warning, planning Farmer advisories Insurance evaluation, payout Input supply management Insurance contract design Trade planning, strategic imports APPLICATION Risk analysis

23. Meeting the Salience Challenge:A Minimum Information Package for Farmers? Downscaled Oct-Dec rainfall total & frequency forecast, Katumani, Kenya, presented to farmers Aug 2004. • Downscaled to local station • Convey uncertainty in probabilistic terms • Historic variability context • …paired with historic model performance • “Weather within climate” • Packaged with training, group interaction

24. understanding

25. Enhancing Understanding:A Workshop-Based Process • Relate measurements to farmers’ experience

26. Enhancing Understanding:A Workshop-Based Process • Relate measurements to farmers’ experience • Convert series to relative frequency, then probability Years with at least this much rain Oct-Dec rainfall (mm)

27. Enhancing Understanding:A Workshop-Based Process ? • Relate measurements to farmers’ experience • Convert series to relative frequency, then probability • Explanation & repetition

28. Enhancing Understanding:A Workshop-Based Process • 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

29. Enhancing Understanding:A Workshop-Based Process • 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

30. Enhancing Understanding:A Workshop-Based Process • 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 • Exploit co-learning in a group process • Accelerated experience through decision games • Build on indigenous indicators, culturally-relevant analogies of decisions under uncertainty

31. legitimacy

32. Improving Institutional Support • Mainstream climate information services into agricultural development strategy. • Foster capacity for agriculture to use and effectively demand relevant climate information. • Give agriculture greater ownership and effective voice in climate information products and services. • Target & coordinate an expanded set of applications. • Realign and resource NMS as providers of services for development, participants in development process. • Treat meteorological data as a free public good and a resource for sustainable development.

33. Reinvigorating seasonal forecasts for agriculture

34. WCC3 and GFCS • Strengthen the production, availability, delivery and application of science-based climate prediction and services • Advance understanding and management of climate risks and opportunities • Improve climate information • Meet climate-related information needs of users • Promote effective routine use of climate information

35. ClimDev-Africa • Joint program of AU, AfDB, UN-ECA • Overcome lack of climate information, analysis, options for decision-makers at all levels • Institutional capacity to generate, disseminate useful information (beginning with RCCs) • Capacity of end-users to mainstream climate into development • Implement adaptation and mitigation programs that incorporate climate-related information • Response to gap analysis

36. CCAFS • Co-proposed by CGIAR & ESSP • Overcome threats to food security, livelihoods, environment posed by a changing climate: • Close critical knowledge gaps • Develop & evaluate adaptation options • Enable stakeholders to monitor, assess, adjust

37. Research Themes • Diagnosing vulnerability and analyzing opportunities • Unlocking the potential of macro-level policies • Linking knowledge to action • Adaptation pathways based on managing current climate risk • Adaptation pathways under progressive climate change • Poverty alleviation through climate mitigation

38. Theme 4: ...Managing Current Climate Risk Options for managing climate impacts through climate-informed grain reserves, trade, distribution, food crisis response; and how to best implement? Most effective design, delivery mechanism for rural climate products, services for local-scale risk management? Institutional arrangements, policy interventions needed? How to target and implement to reduce vulnerability to climate shocks and alleviate climate risk-related rural livelihood constraints? How and when can seasonal prediction support adoption of innovation, better proactive coping strategies, market opportunities linked to climate variations? Options for diversification at field, farm, market scales to reduce food insecurity and livelihood risk? Optimal portfolio for given context? Marcus Prior, WFP • Rural climate services • Seasonal climate prediction • Livelihood diversification • Financial risk transfer • CRM through food delivery, trade, crisis response

39. Thank You