Using Soil, Plant, Topographic and Remotely Sensed Data to Determine the Best Method for Defining Aflatoxin Contamination Risk Zones within Fields for Precision Management

Abstract

Contamination of crops by aflatoxins (AFs) is a real risk in the South-Eastern USA. Contamination risk at the county level based on soil type and weather in different years has been investigated. However, defining AFs contamination risk zones within fields has not yet been attempted. Drought conditions, particularly within the month of June have been linked to high levels of AFs contamination at the county level. Soil characteristics and topography are the factors influencing drought status that vary most within fields. Here, soil, plant, topography and remotely sensed information are used to define AFs contamination risk zones within two fields using different approaches. Normalized difference vegetation index (NDVI) data were used to indicate potential droughty areas and thermal IR data from LandSat imagery were used to identify hot areas. Topographic variables were also computed. Comparison tests showed that a combination of regression analysis of soil, plant and imagery data and bi-variate local Moran’s I analysis of NDVI and Thermal IR data from several years was the best way to define zones for mean and maximum AFs levels. An approach based on principal components analysis of soil, plant and imagery data from 2010, a high-risk year, was best for defining zones for minimum AFs levels. Analysis of imagery from several years suggested that the zones are likely to be relatively stable in time and could be defined using only freely available sensor, topographic and soil series data. Once defined, such zones can be managed to increase profitability and reduce waste.