IJPP 2011; Vol. 2 (1): 23–32
Authors:
P.O. Odwori, Department of Agricultural Economics and Resource Management, Moi University; This e-mail address is being protected from spambots. You need JavaScript enabled to view it
M.O. Odhiambo, Department of Agricultural Economics and Resource Management, Moi University
P.M. Nyangweso, Department of Agricultural Economics and Resource Management, Moi University
M.Z. Mapelu, Department of Physics, Moi University.

Abstract

Even though forecasting of the future crop productivity and profitability with accuracy remains the desire of many farmers, few of them have, at their disposal, requisite tools for achieving their desired goal. Simulation models have been used successfully to forecast productivity of cropping systems under various weather, management and policy scenarios in the developed world. These models have helped farmers make efficient resource allocation decisions. However, in Kenya simulation models have not been used extensively and more specifically in modeling maize cropping system. The purpose of this study was to forecast productivity and profitability of maize cropping system in Uasin Gishu District, Kenya, and compare forecasted with actual results. Both time series and cross-sectional data for variables of interest were collected and complemented by a survey of 20 maize farmers who were systematically selected to verify information obtained from secondary sources. Cropping systems simulation model (CropSyst) was used to forecast maize productivity under different soil, agroecological zones and management practices while Monte Carlo simulation model was used to forecast maize profitability under alternative price scenarios. Results showed that, even though simulated yields under-estimated actual maize yield both at the district and across the four agro-ecological zones, the deviation from the actual yield was marginal. Similarly, results also revealed that paying farmers a price in the range of 1300-1700 Kenya shillings was most profitable and likely to motivate them to engage in maize production. It is recommended that CropSyst and Monte Carlo models be included among a bundle of tools for decision making. Further research is also required to test the two models under different locations, soil types, management styles and scales of production.