Applications of crop simulation in agricultural extension and research in Kenya

High weather variability in Kenya's semi-arid regions causes marginal rainfed maize yields most of the time. Traditional agricultural research has proved inadequate in bringing about beneficial improvements in crop yields for resource-poor farmers. Simulation modeling, when tested for the region, has provided an appropriate tool for technology in the marginal environments where growing numbers of people are migrating. The crop-simulation model CMKEN, a locally adapted version of CERES-Maize, was shown to simulate yields reasonably close to the experimental results at several sites where plant populations, cultivars, sowing dates, and nitrogen fertilizer rates were variables. For the applications, simulations were made using approximately 30 years of weather data to establish probabilities of outcomes for different combinations of management variables. The results demonstrated that timely sowing after the onset of the rainy season, a plant population of 3-4 plants/m(2) with about 40 kg/ha N fertilizer and use of a locally, adapted cultivar could increase productivity about twofold, on average, when compared to more traditional low-input farming. The simulations were used to evaluate farmers' decisions with respect to management options and the inherent economic implications. This has enabled farmers to make choices compatible with their socio-economic circumstances. Adaptive research work with models is being undertaken to enhance adoption of new technologies, especially for resource-poor farmers.