Developing statistical models of aflatoxin risk in peanuts using historical weather data

Abstract

Aflatoxin contamination in peanuts (Arachis hypogaea L.) is a significant public health risk. Aflatoxin is detected postharvest after inspection of loads associated with grading at peanut buying points, leaving growers and shellers in a precarious position. Stricter limits on aflatoxin contamination could restrict the United States access to international markets. Predicting aflatoxin risk remains challenging, but improved tools could help inform postharvest storage segregation decisions and alert industry stakeholders to seasonal threats. This study aimed to develop and evaluate multiple statistical models that estimate the regional status of peanut aflatoxin contamination based on preharvest weather conditions. Our approach expanded on an existing peanut aflatoxin model for which a new geographic area and time period were tested. Weather variables served as independent variables to predict the risk of aflatoxin as the proportion of samples with greater than 20 ppb and 4 ppb aflatoxin (PGT20 [the proportion of samples with greater than 20 ppb aflatoxin] and PGT4 [the proportion of samples with greater than 4 ppb aflatoxin], respectively) across 10 counties in Georgia for 2018–2022. Best-performing models were developed through multiple linear stepwise regression explaining more than 72% and 41% of the variability in PGT20 and PGT4, respectively. Model performance further varied whether it was a year of low or high aflatoxin incidence, with temperature observed as a key influencing factor across best-performing models. This study established an adaptive approach to monitoring and managing aflatoxin risk through statistical predictive modeling, with output targeting farmers, industry, regulators, and public health officials. Future model development will aim to improve interpretation and confidence with in-season aflatoxin prediction and efficacy testing of this approach across space and time.