Modeling pre-harvest aflatoxin incidence in groundnut (arachis hypogaea l.) using selected soil properties and ambient temperature

Abstract

Aflatoxin contamination of groundnut (Arachis hypogaea L.) and its products is a worldwide food safety concern. Contamination of kernels during crop growth is associated with adverse soil factors and weather conditions. Therefore, good agricultural practices are an important strategy to minimize the pre-harvest aflatoxin contamination risk in groundnut. The main objective of this study was to formulate regression models to predict pre-harvest aflatoxin contamination risk in groundnut using selected soil properties and ambient temperature. Field experiments were conducted in Lusaka and Chongwe districts of Zambia to evaluate the effects of soil amendments, namely, gypsum (15.6 % soluble calcium) and compost on pre-harvest aflatoxin incidence in groundnut. Additionally, the effects of soil moisture content and soil temperature during groundnut pod development stage on aflatoxin concentration were evaluated. The data generated from these experiments were analysed using appropriate tests at 5 % level of significance. Treatment effects were evaluated using the one or two-way ANOVA test as appropriate. The Tukey test was used to separate significantly different treatment means. Pearson correlation analysis was performed on the data to evaluate relationships between continuous variables. Simple and multiple linear regression analysis was performed on significantly correlated variables. All these tests were performed using the R-statistical software. Results showed that higher levels of compost were associated with lower aflatoxin contamination. The gypsum amendment did not have a significant effect on aflatoxin contamination of groundnut kernels. Further, regardless of the treatments applied, ambient temperature and soil temperature were positively correlated with aflatoxin contamination whereas soil moisture content was negatively correlated. Simple linear regression models gave R2 values of 0.30 for maximum ambient temperature, 0.24 for soil temperature and 0.38 for soil moisture content. Combining soil moisture content and soil temperature in a multivariate regression model explained 54 % of the variation in aflatoxin contamination. Therefore, soil moisture and soil temperature can be used to predict aflatoxin contamination risk in groundnut. The two predictive variables, soil moisture and soil temperature, can be manipulated through agronomic practices to reduce the risk of pre-harvest aflatoxin contamination in groundnut.