Mibulo, T.; Kiggundu, N. Evaluation of FAO AquaCrop Model for Simulating Rainfed Maize Growth and Yields in Uganda. Agronomy2018, 8, 238.
Mibulo, T.; Kiggundu, N. Evaluation of FAO AquaCrop Model for Simulating Rainfed Maize Growth and Yields in Uganda. Agronomy 2018, 8, 238.
Mibulo, T.; Kiggundu, N. Evaluation of FAO AquaCrop Model for Simulating Rainfed Maize Growth and Yields in Uganda. Agronomy2018, 8, 238.
Mibulo, T.; Kiggundu, N. Evaluation of FAO AquaCrop Model for Simulating Rainfed Maize Growth and Yields in Uganda. Agronomy 2018, 8, 238.
Abstract
Uganda’s agriculture depends mainly on rainwater. As farmers are trying to increase on the food output to match the demands of a fast growing population, they are susceptible to make losses due to fluctuating weather patterns which are being caused by the global climate change. Therefore, it is necessary to explore ways of improving water use efficiency in rainfed agricultural systems to save farmers labour and input costs in situations where the grain harvest would be zero due to crop failure. The water driven FAO AquaCrop model is used as a support tool for making informed decisions during planning and situation analysis. In this study, AquaCrop model was evaluated for prediction of maize growth and yields at MUARIK in Uganda, for rainfed agriculture in three growing seasons. The model efficiency (E) and root mean square value (RMSE) for the maize canopy simulation during the September–December 2015 season was 0.945 and 7.24 respectively. The deviation of the simulated final biomass from measured data ranged from −15.4 to 11.6%, while the deviation of the final yield ranged from −2.8 to 2.0. The results suggest that the model can be used in the prediction of rainfed agricultural outputs, hence helping in guiding on management practices to increase food production.
Keywords
AquaCrop model; maize; rain fed; Uganda
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
