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Predicting aflatoxin risk with seasonal meteorological forecast

Bibliographic Data

ID15549405
AuthorsDarina Balkova (0000-0002-5485-7101, Università Cattolica del Sacro Cuore, corresponding author), Richa Raj (0009-0006-6622-181X, Central Institution for Meteorology and Geodynamics), Harald E Rieder (0000-0003-2705-0801, Central Institution for Meteorology and Geodynamics), Marco Camardo Leggieri (0000-0002-6547-7702, Università Cattolica del Sacro Cuore), Paola Battilani (0000-0003-1287-1711, Università Cattolica del Sacro Cuore)
Year2026
Volume21
Issue2
Pages024016-024016
Publication date2026-01-01
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ae3629
OpenAlexW7120132197
LanguageEN
References cited59

Aflatoxin contamination in maize ( Zea mays ), primarily caused by Aspergillus flavus , is strongly influenced by meteorological conditions and remains a major food safety concern. Predictive models have been developed to support contamination risk assessment and management strategies, typically relying on meteorological data from local weather stations. While these inputs offer high accuracy, their limited spatial coverage and lack of forecasting capability reduce their application as early warning systems and in-season decision support. This study evaluates the integration of ERA5-Land reanalysis and seasonal climate forecasts into AFLA-maize, a mechanistic model for predicting aflatoxin B1 contamination, to extend its spatial and temporal applicability. Using historical data from the Emilia-Romagna, Italy (2008–2025), we tested bias-adjusted ERA5-Land inputs and developed a hybrid forecasting approach combining reanalysis data for the early-season with adjusted forecasts for the later crop stages. Results show that simulations driven by ERA5-Land reproduced contamination probability in 91% of years compared to station-based results, supporting its use in regions with low quality observations or missing station networks. Hybrid integrations of bias-adjusted forecasts provided contamination risk assessments 3–8 weeks ahead of harvest, showing the trade-off between lead time and accuracy. Forecast initialized later in the season (August) achieved much higher accuracy (up to 91%) but offer less time for action, whereas earlier initializations (June, July) bear larger uncertainty (accuracy of 88%–93%) but extended the decision windows. Furthermore, the availability of ensemble forecasts allows to quantify the uncertainty, providing probability ranges across members that support risk communication and early warning outputs. In summary, the presented approach extends AFLA-maize into a scalable and transferable tool for anticipatory risk management, supporting climate-resilient agriculture and food safety through publicly available meteorological data

Aflatoxin · Climate Forecast System · Contamination · Decision support system · Early warning system · Forecast skill · Risk assessment · Warning system · Indoor Air Quality and Microbial Exposure · Mycotoxins in Agriculture and Food · Pesticide Residue Analysis and Safety

  • ERA5-Land

    Open Access•Joaquín Muñoz‐Sabater, Emanuel Dutra et al.•Earth System Science Data•2021

  • The ERA5 global reanalysis

    Open Access•Hans Hersbach, Bill Bell et al.•Quarterly Journal of the Royal…•2020

Highly citedNo

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