Using seasonal climate forecasts to improve maize production decision support in Zimbabwe

Maize production in marginal tropical regions is at great risk due to rainfall variability and climate change. Climate change is set to increase the variability and uncertainty of inter-annual rainfall. Farmers who depend on rainfed maize production for their livelihoods would therefore benefit from improved climate based forecasting of production likelihood. In this study we developed a simple maize production decision support tool for Masvingo by using seasonal climate forecasts and a crop model to forecast maize yields likelihood prior to the season. We follow up on earlier studies carried out in Zimbabwe which show that the El Nino Southern Oscillation (ENSO) can be used to forecast rainfall and maize yields in Zimbabwe. An ENSO based seasonal climate analysis tool (RAINMAN) was used to produce probabilistic monthly climate forecasts for Masvingo corresponding to the phases of the Southern Oscillation Index (SOI). The climate forecasts were used to run a crop model (AquaCrop) for a variety of scenarios relevant to maize production (monthly rainfall, cultivar selection, planting date, and fertility level). The results of the simulations were similar to those observed by Phillips et al. (1997) and formed the basis for the development of an operational decision support tool. Simulated maize yields varied from 1.2 t/ha to 5.8 t/ha. The simulated yields were higher than expected average yields in a marginal region like Masvingo especially under small holder farming. The work suggested that optimal use of forecasts may lead to improved maize production in Masvingo. The study set a platform for the development of operational climate based maize production decision support tools in Zimbabwe. (C) 2011 Elsevier B.V. All rights reserved.