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

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

3. 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

4. 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.

5. y=3.33+1.34(1-exp(-0.133x)) R 2 = 0.400 Nonlinear Regression: The Mitscherlitch function

6. 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)ha300033003000 3300 Seed (‘Katumani composite B’)kg130140832 * PD a 896 * PD a Fertilizer (calcium ammonium nitrate)kg3035115.4 * N b Fertilizer (di-ammonium phosphate)kg3635211.8 * N c 205.9 * N c Hired laborday150 150 (30 + 3.24 PD + 18.7 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) +

7. 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 28.239.50.6610.014 October 37.842.20.4270.103 November 63.672.40.369-0.459 December 51.661.80.381-0.129 Makindu October-December 45.357.20.573-0.030 October 44.646.30.2980.006 November 105.3110.30.191-0.432 December 57.377.60.606-0.084

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

9. 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 manage- ment forecasts weather environ- ment climato -logy