Assessment of the Variability of Yield of Maize in Lilongwe District (Malawi) in Response to Climate Change Using DSSAT Model.

The study generates information on seasonal rainfall characteristics that will be vital in exploiting the possibilities offered by climatic variability and offers opportunities for adapting to seasonal distribution to improve and stabilize maize crop yields in Malawi. The need to generate agronomically relevant seasonal rainfall and temperature characteristics to guide decision making, for instance, in terms of adaptation and mitigation strategies in agriculture.  DSSAT model was used to run the crop simulations for the cropping season of 1996/1997 to 2007/2008 for growth, development and yields of hybrid Maize at Chitedze Research Station, and to assess which agronomic management practices can help adapt to climate change. The DSSAT model was used to provide information concerning management options such as the timing of planting, specifically the impact on the yield with reference to different planting dates at Chitedze Research Station in Malawi.

The results showed that planting maize early December increases yield than late and early November, late December; late and early January for Chitedze, with Index of Agreement of 0.861 (d-stat) which signifies the closeness of the relationship between the observed and the simulated yield, also the efficiency of DSSAT model to simulate yield with little Root Mean Square of Error (220.689 kg/ha), R2=0.770, mean difference of –143.41 kg/ha. The mean observed Maize yield was 1350 kg/ha and the mean simulated being 1206.59 kg/ha through regression analysis showing positive correlation. Planting date is directly related to the yield of maize with reference to rainfall received with minimal variability. Maize yield depends upon the amount and frequency of rainfall as well as its distribution on temporal and spatial scales, especially the reproductive and the vegetative phases that are prone to rainfall variability.

Key words: Crop model, planting date, climate variability, maize yield, DSSAT model, climate change