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

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

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

EVIDENCE OF VALUE

The Cost of Climate Risk: Ex-post Impacts of Climate Shocks 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 CRISIS HARDSHIP

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 Agric. Syst. 101:

The Cost of Climate Risk: Ex-Ante Cost of Moving Target Plants (m -2 ) Fertilizer (kg N ha -1 ) Yield (Mg ha -1 ) N effic. (kg grain kg -1 N) Net income (KSH ha -1 ) Perfect information (Optimized by year) ,919 Climatology (Optimized for all years) ,586 Difference ,333 Percent difference10.3%10.7%24.5%15.5%40.7%

The Cost of Climate Risk: Ex-Ante Cost of Risk Aversion 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 CRISIS HARDSHIP FORFEITED OPPORTUNITY

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

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 J. Appli. Meteorol. Climatol. 47:

Empirical Evidence of Demand and Value Burkina Faso (Roncoli et al Climatic Change 92: ) –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, PNAS 102: ) –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

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

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

The Salience Challenge: Representative Forecast Products

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

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

SALIENCE

Meeting the Salience Challenge: Downscaling in Space Correlation of observed (85 stations) vs. predicted rainfall in Ceará, NE Brazil, as a function of spatial scale. Gong, Barnston, Ward, J. Climate 16: Correlation Scale

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

Podor StLou Matam Louga Lingu Bakel Thies Dakar Bambe Diour Mbour Fatic Kaola Tamba Nioro Velin Zigui

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

Meeting the Salience Challenge: Predicting Agricultural Impacts 1982 Queensland, Australia wheat yield forecast. Hansen et al., Agric. For. Meteorol. 127:77-92 climatology only + GCM forecast Forecast date Grain yield (Mg ha -1 ) Traditional sorghum, Dori, Burkina Faso. Mishra et al., Agric. For. Meteorol. 148: Correlations of Jun-Sep rainfall, and observed, de-trended wheat yields with May GCM output, prior to planting, Qld., Australia. Hansen et al., Agric. For. Meteorol. 127: km Correlation < 0.34 (n.s.) > 0.65 Rain Yield 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

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

Meeting the Salience Challenge: A Minimum Information Package for Farmers? 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 Downscaled Oct-Dec rainfall total & frequency forecast, Katumani, Kenya, presented to farmers Aug 2004.

UNDERSTANDING

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

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

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

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

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

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

LEGITIMACY

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.

REINVIGORATING SEASONAL FORECASTS FOR AGRICULTURE

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

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

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

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

Theme 4:...Managing Current Climate Risk Rural climate services Seasonal climate prediction Livelihood diversification Financial risk transfer CRM through food delivery, trade, crisis response Most effective design, delivery mechanism for rural climate products, services for local-scale risk management? Institutional arrangements, policy interventions needed? 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? How to target and implement to reduce vulnerability to climate shocks and alleviate climate risk-related rural livelihood constraints? Marcus Prior, WFP Options for managing climate impacts through climate-informed grain reserves, trade, distribution, food crisis response; and how to best implement?