Models for Managing Climate Risk: Predicting Agricultural Impacts and Assessing Responses with Input from James Hansen Agriculture Systems, IRI.

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Presentation transcript:

Models for Managing Climate Risk: Predicting Agricultural Impacts and Assessing Responses with Input from James Hansen Agriculture Systems, IRI

Managing The Full Range of Variability FOREFITED OPPORTUNITY CRISIS HARDSHIP common assumption of a static policy

Climate-based food and forage production forecasting Climate-informed management of trade & strategic reserves for food security, price stabilization Climate-informed food crises response Climate (forecast, monitoring, historic) information, education, advisory service to farmers Fostering resilience within farming systems (e.g., diversification) Supporting adaptive management Financial risk management services (e.g., index insurance) Strategic planning and investment under changing climatic baseline (e.g., breeding, land use planning) A Few Examples of Climate Risk Management for Agriculture

Eastern Equatorial Africa (Kenya) Crops respond not to mean conditions but to dynamic interactions: –Soil water balance –Phenology Crop response to 3-month rainfall amount: nonlinear and non-monotonic.

y= (1-exp(-0.133x)) R 2 = Nonlinear Regression: The Mitscherlitch function

Table 1. Prices and costs used for enterprise budgets. Price (KSH unit -1 )Total cost (KSH ha -1 ) ResourceUnitMachakosMakinduMachakos Makindu Tillage (animal)ha4000 Tillage (tractor)ha Seed (‘Katumani composite B’)kg * PD a 896 * PD a Fertilizer (calcium ammonium nitrate)kg * N b Fertilizer (di-ammonium phosphate)kg * N c * N c Hired laborday ( PD Y) d Maize grainkg16.67 a PD (plants m-2) * price (KSH (kg seed)-1) * 10,000 (m2 ha-1) / 2500 (plants (kg seed)-1) b application rate (kg N ha-1) * price (KSH (kg CAN)-1) / 0.26 (kg N (kg CAN)-1) c application rate (kg N ha-1) * price (KSH (kg DAP)-1) / 0.17 (kg N (kg DAP)-1) d price (KSH day-1) * (60 days (weeding) +

Table 2. Rainfall prediction skills (Seasonal and monthly rainfall correlations between observed rainfall and Obs_SST & P_SST based rainfall forecasts) PeriodRMSE (mm)Correlation (r) KatumaniObs_SSTP_SSTObs_SSTP_SST October-December October November December Makindu October-December October November December

Correlation between observed weather vs observed and persisted SST based crop yields estimated at Katumani and Makindu.

Assessing Responses, Benefits Ex-ante impact evaluation –Confidence and credibility –Targeting Value of information: Expected outcome of best response to new information minus expected outcome of best response to prior information: value utility returns management forecasts weather environ- ment climato -logy