PRE-SEASON MAIZE (Zea mays L.) PRODUCTION PLANNING FOR MANAGING CLIMATE RISKS IN ETHIOPIA

Abstract

Crop production in Ethiopia is predominantly rainfed which makes it highly sensitive to seasonal and intra-seasonal climate vagaries. Hence, availability of functional seasonal climate and agricultural production forecasting system is expected to provide rigorous climate risk management options which lead to greater economic and social values. Thus, the general objective of this study was, to develop an integrated decision support system that predicts seasonal maize production in advance over the country using an integrated climate and crop modeling frame work. Statistically downscaled (using empirical quantile mapping) outputs from Climate Forecast System (CFSv2) was used to forecast the seasonal climate. The seasonal climate forecast was integrated with Decision Support System for Agro technology Transfer (DSSAT) crop simulation model to predict maize production. The crop model run on a national scale simulating maize at 10 km grid points. The climate and crop model outputs was evaluated against observations. The study assesses the ability of the statistically downscaled seasonal forecast output in reproducing the observed seasonal features as well as evaluates whether any significant improvement in skill and value of the seasonal forecast were achieved, after the statistical downscaling were employed. The assessment suggested that the downscaled CFSv2 seasonal forecast gives fairly skillful prediction in Ethiopia and can be linked to crop model application. Accordingly, a maize decision support system (MDSS) integrates the output from seasonal forecast from CFSv2 in to crop simulation model to predict maize production on national scale over Ethiopia is developed. The first version of the integrated system is evaluated and it can be used to predict the spatial and temporal maize yield variability, and thereby support strategic maize production planning. However, further work is required to evaluate and refine the system at different spatial and temporal scales using measured long-term climate and crop data