Vijendra K. Boken

Monitoring peanut contamination in Mali (Africa) using AVHRR satellite data and a crop simulation model

Peanut (Arachis hypogaea L.) is the main legume crop of Mali, West Africa. It can be contaminated by aflatoxin, a natural toxin that can develop because of drought conditions at pre‐harvest stage or because of temperature‐ and humidity‐related factors that occur during post‐harvest storage. Consumption of aflatoxin‐contaminated peanut can cause liver diseases, such as jaundice, hepatitis or cancer. We present a case study for Mali, identifying weather‐ and satellite‐based variables that could be used to indicate aflatoxin in peanut. Based on this monitoring and predicting, a warning may be issued against eating contaminated peanut to keep the public health from deteriorating. Normalized difference vegetation index (NDVI) composites, derived from advanced very high resolution radiometer (AVHRR) satellite data, were averaged for the reproductive phase of peanut and examined for their relationship with the annual peanut yield, an indicator of drought and aflatoxin. The relationship was found to be moderate (R 2 = 0.56). The commencement and termination dates for the reproductive phase of peanut were determined by using a crop simulation model. Amounts of aflatoxin were measured for peanut samples collected from various locations across Mali and were found to be linked to the NDVI, total precipitation, and maximum temperature averaged over the reproductive phase of peanut.

Evaluation of on-farm irrigation applications using the simulation model EPIC

An understanding of water needs in agriculture is a critical input in resolving the water resource issues that confront many southeastern and other US states. The objective of this study was to evaluate on-farm irrigation applications for three major crops grown in Georgia, USA using the Environmental Policy Integrated Climate (EPIC) model. For cotton, 16, 58, and 75 farmers’ fields in 2000, 2001, and 2002, respectively, were selected from among the Agricultural Water Pumping (AWP) program sites across the state of Georgia. For maize, 9, 20, and 28 fields were selected in 2000, 2001, and 2002, respectively, and for peanut, 18, 51, and 54 fields were selected in 2000, 2001, and 2002, respectively. The majority of these fields were located in the southwest region of Georgia, where traditional row-crop agriculture is most dominant. We compared the simulated irrigation requirements with the amount of water that the farmers actually applied during the 2000, 2001, and 2002 growing seasons. For cotton and peanut, the means of farmer-applied irrigation amounts and simulated irrigation requirements agreed very well, with similar values for root mean squared deviation (RMSD) of the two crops. For maize, good agreement between simulated and farmer-applied irrigation amounts were found only in 2001. Farmers applied more water to their maize crop when compared to simulated irrigation requirements, especially when rainfall was very low and potential evapotranspiration was high during the 2000 and 2002 growing seasons. The component of the mean squared deviation (MSD = RMSD2) related to the pattern of variability in seasonal irrigation applications contributed most to MSD. Accurate estimates of the mean and the magnitude of variability in seasonal irrigation applications could be very useful for the estimation of overall water use by agriculture in Georgia and other southeastern states. This study showed that the EPIC model would be an adequate tool for this purpose; potential users could include policy makers, planners and regulators, including the Georgia Department of Natural Resources (DNR).

Estimating Water Demand For Irrigation Using A Crop Simulation Model

Crop yield and water demand for irrigation under rainfed and irrigated conditions for four major crops in Georgia were estimated using the Environmental Policy Integrated Climate (EPIC) model. Seasonal yield and irrigation data during 1990-2001 for Tifton, Plains and Midville in the Coastal Plain region, Griffin and Athens in the Piedmont region, and Calhoun in North Georgia were used for evaluating simulated yield and irrigation. Under rainfed conditions, the model performs fairly well for different crops, weather and soil conditions across Georgia. In general, the model tends to overpredict for low yielding conditions and underpredict for high yielding conditions. Under irrigated conditions, the model overpredicted to a greater extent for low yielding conditions and underpredicted to a greater extent for high yielding conditions. Only for cotton, the model simulated the year-to -year variability in measured irrigation fairly well.