Modelling mycotoxin contamination in maize to optimizae agricultural practices and improve the value chain

BATTILANI, P.; BANDYOPADHYAY, R.; AMRA, H.; CAMARDO LEGGIERI, M.; DZANTIEV, B.; MAGAN, N.; MAKUKU, G.; MESTERHÀZY, A.; MORETTI, A.; OZER, H; RAMOS, A.J.; TORRES, A.M.; LOGRIECO, A.

Rotterdam

Simposio; 7th Conference of the World Mycotoxin Forum and XIII Th IUPAC International Symposium on Mycotoxins and Phycotoxins; 2012

IUPAC

Maize is an essential crop for food, feed and fuel throughout the world. By 2050, the demand for maize in the developing world will doublé and by 2025 maize will have the greatest global production among all crops. A range of approaches are being used to improve maize productivity and cropping system for increasing quality and yield under various environmental and socio-economics scenarios.  However, kernel contamination by mycotoxin producing fungi remains an unresolved problem. Acute and chronic toxicity of mycotoxins pose grave risks to human health and curtail animal productivity. Therefore, regulatory agencies have set legal limits above which trade in agricultural products cannot take place. Good agricultural and management practices and hybrids with higher resistance are recommended during pre-and post-harvest stages of production and processing of maize, including analytical checks, such that the legal limits are not exceeded. However, these additional interventions involve additional costs, thereby reducing the profitability of the maize value chain improvement. Fusarium fujikuroi species complex and Aspergillus section Flavi frequently infect maize and produce highly potent toxins fumonisins asn aflatoxins, respectively. Two mechanistic models were developed to predict the infection cycle of A. flavus and F. verticillioides on maize. Host crop phenology from silk emergence to grains ripening was included in modeling. The mandatory input to run models were meteorological data (temperature, relative humidity and rain) and the output consisted of risk indexes for aflatoxins and fumonisins that were related to a probability to overcome a fixed threshold.  The index was calculated on a daily base and allowed  tracking of the relative risk dynamics during the growing season from silk emergence to harvest. The models were validated with data collected in six countries around the world. Field samples and related meteorological data were collected; observed data were compared to predicted data to evaluate the reliability of predictions. The results were promising, with a percentage of correct predictions commonly higher than 70%. Therefore, these models could be used in supporting decisions to add safety and value to the maize chain.