Vol. 63 No. 3 (2024)
Articles

“AFLA-peanut”, a mechanistic prototype model to predict aflatoxin B1 contamination

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  • Matteo CROSTA,
  • Marco CAMARDO LEGGIERI,
  • Paola BATTILANI
Matteo CROSTA
Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, Piacenza, Italy
Marco CAMARDO LEGGIERI
Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, Piacenza, Italy
Paola BATTILANI
Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, Piacenza, Italy
DOI: https://doi.org/10.36253/phyto-15771
Categories

Published 2024-12-30

Keywords

  • Aspergillus flavus,
  • model transfer,
  • weather,
  • phenology,
  • mycotoxin,
  • climate change
    • ...More
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How to Cite

[1]
M. CROSTA, M. CAMARDO LEGGIERI, and P. BATTILANI, “‘AFLA-peanut’, a mechanistic prototype model to predict aflatoxin B1 contamination”, Phytopathol. Mediterr., vol. 63, no. 3, pp. 481–488, Dec. 2024.

Copyright (c) 2024 Matteo CROSTA, Marco CAMARDO LEGGIERI, Paola BATTILANI

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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Abstract

Italian production of peanuts has recently increased. Aflatoxin B1 (AFB1) contamination of peanuts is currently not in Italy, but changing climatic conditions of the Mediterranean region may increase risks posed by this mycotoxin. A mechanistic weather-driven prototype model to predict AFB1 contamination in peanuts was developed by adapting the mechanistic AFLA-maize model for the Aspergillus flavus-peanut pathosystem. The peanut growth stages were examined to develop a phenology model based on growing degree days (GDD), which was linked to an A. flavus infection cycle model, and exploited to develop the “AFLA-peanut” prototype model. Starting from sowing, 686 GDD were required to reach flowering (as the critical growth stage for A. flavus infection), and 1925 GDD were required to reach harvesting, in a short season peanut variety. Variability of the AFB1 index, across years and locations, highlighted the capacity of AFLA-peanuts to account for weather data inputs in predicting AFB1 contamination risks. Although model validation will be mandatory to assess AFLA-peanut performance, this study has provided the first evidence that the prototype model could become an important tool for aflatoxin risk management.

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