LAUSR

Uganda’s agriculture is mainly rainfed. While farmers make efforts to increase food output to respond to the demands of a fast growing population, they are vulnerable to losses attributed to fluctuating weather patterns due to the global climate change. Therefore, it is necessary to explore ways of improving production in rainfed agricultural systems to save farmers labour and input costs in situations where the grain harvest would be zero due to crop failure. In this study, the Food and Agriculture Organization (FAO) AquaCrop model was evaluated for its predictability potential of maize growth and yields. The study was conducted at Makerere University Agricultural Research Institute Kabanyolo (MUARIK) in Uganda for three seasons. Maize growth and yield data was collected during the following seasons: Season 1, September to December 2014; Season 2, March to July 2015; and Season 3, September to December 2015. The model was calibrated using season 1 canopy cover data. The relative errors of simulated canopy cover ranged from −0.3% to −13.58% for different stages of the crop growth. The deviation of the simulated final biomass from measured data for the three seasons ranged from −15.4% to 11.6%, while the deviation of the final yield ranged from −2.8 to 2.0. These results suggest that FAO AquaCrop can be used in the prediction of rainfed agricultural outputs, and hence, has greater potential to guide management practices towards increasing food production.