Weather-based logistic models to estimate total fumonisin levels in maize kernels at export terminals in Argentina

Abstract

Maize fumonisin (FB) contamination is strongly driven by the weather conditions and crop resistance. Logistic regression techniques were used to quantify the effect of weather-based variables on total FB content in kernel samples coming from many locations of the Argentinean Pampas region (over three growing seasons), and collected immediately after arriving at export terminals. The samples were analyzed by the HPLC method and grouped according to their proximity to the available weather stations (n = 52). The highest correlations between binary and ordinal FB levels and weather variables were found in an early critical period (17 December to 15 January) where maize silking phase (Si) frequently occurs and in a late period (15 February to 2 April) around physiological maturity (PM). The best-fitted models included variables calculated around Si that would meet the requirements of infection of F. verticillioides (precipitation-induced wetness events, high humidity and warm temperatures). Around PM, the effect of the number of days with precipitation combined with lower temperatures (13.3° to 25 °C) that would slow the kernel drying process was included, increasing the FB accumulation. An integrated system for FB management in the maize value chain should use validated weather-based models as tools for estimating seasonal kernel FB contamination levels in the Pampas region, being able to improve kernel sampling efficiency at export terminals and mills.