Maize is a staple food crop in Kenya that is grown in all agro-ecological zones and on two out of every three farms. It accounts for about 40 percent of daily calories and has a percapita consumption of 98 kilograms (www.fao.org). Over 85 percent of the rural population derives its livelihood from agriculture with a majority engaging in maize production.

Deaths from acute aflatoxis from highly contaminated maize have been documented in Kenya. In 2004 alone 125 deaths were reported (Lewis et al., 2005). Aflatoxins are a group of mycotoxins of fungal origin that are produced mainly by Aspergillus flavus and Aspergillus parasiticus. These mycotoxins contaminate agricultural commodities before and/or under post-harvest conditions threatening food security.

Field dying of maize is widely practiced by farmers in the region. Climate change and the coincidence of wet weather persisting with the harvest is a growing concern.  Delayed harvesting under these high humidity and temperature conditions is thought to be responsible for increased field losses from insect damage, moulds and aflatoxin contamination (Kaaya, Warren, Kyamanywa, & Kyamuhangire, 2005; Wagacha & Muthomi, 2008).

A Computational Methods Approach to Aflatoxin Contamination Prediction and Prevention in Field Drying of Maize (Zea Mays L.)

This study aims to characterize field drying of maize with application of Computational Fluid Dynamics (CFD) and to, through simulation modelling, determine the complex correlation between the multi-variable weather, environment and crop interactions, necessary for predicting and preventing aflatoxin contamination of maize in the post physiological maturation period before the harvest.

Climate change is inevitable and both mitigation and adaptation are seen as key to enhancing our chances of survival (Schmidt, 2009). The findings of this study will go a long way in guiding the formulation of postharvest adaptation strategies and enhance food security by minimising field losses.

Projektverantwortlich : Isaiah Muchilwa