2009 Articles

Potential value of GCM-based seasonal rainfall forecasts for maize management in semi-arid Kenya

Hansen, James W.; Mishra, Ashok; Rao, K.P.C.; Ngugi, Robinson Kinuthia

We estimate the potential value of general circulation model (GCM)-based seasonal precipitation forecasts for maize planting and fertilizer management decisions at two semi-arid locations (Katumani and Makindu) in Southern Kenya. Analyses combine downscaled rainfall forecasts, crop yield simulation, stochastic enterprise budgeting and identification of profit-maximizing fertilizer N rates and stand densities. October–February rainfall predictions were downscaled from a GCM, run with both observed and forecast sea surface temperature boundary conditions – representing upper and lower bounds of predictability – and stochastically disaggregated into daily crop model inputs. Simulated interactive effects of rainfall, N supply and stand density on yield and profit are consistent with literature. Perfect foreknowledge of daily weather for the growing season would be worth an estimated 15–30% of the average gross value of production and 24–69% of average gross margin, depending on location and on whether household labor is included in cost calculations. GCM predictions based on observed sea surface temperatures increased average gross margins 24% at Katumani and 9% at Makindu when labor cost was included. At the lead time used, forecasts using forecast sea surface temperatures are not skillful and showed nearzero value. Forecast value was much more sensitive to grain price than to input costs. Stochastic dominance analysis shows that farmers at any level of risk aversion would prefer the forecast-based management strategy over management optimized for climatology under the study’s assumptions, despite high probability (25% at Katumani, 34% at Makindu) of lower returns in individual years. Results contribute to knowledge of seasonal forecast value in a relatively high-risk, high-predictability context; utility and value of forecasts derived from a GCM; and risk implications of smallholder farmers responding to forecasts.