A mechanistic weather-driven model was developed based on the infection cycle of Aspergillus flavus on maize to predict the risk of aflatoxin contamination in field on a daily basis from silk emergence to harvest; hourly data of temperature, relative humidity and rain were used as model input. The work was done in four steps: (i) development of the model prototype; (ii) collection of Italian field data on aflatoxin contamination in maize with related crop and weather data; (iii) development of a probability index to exceed the legal limit of 5μg of aflatoxin B1 per kg of unprocessed maize by combining model predictions and field data in a logistic regression; and (iv) validation with Italian data of the probability index and release of the predictive model, named AFLA-maize. Predictions of maize contamination above the threshold of 5μg/kg in the data set used for parameterization of the regression equation were correct for 73% of field samples; 59% and 14%, respectively, were not contaminated and contaminated. In a second independent data set, 68% of samples were correctly predicted. The model AFLA-maize provides prediction of A. flavus infection and aflatoxin contamination along the growing season and at harvest. This information is useful to support decision-making for (i) crop management, (ii) harvest timing, (iii) maize lots cleaning and logistic, and (iv) maize sampling for aflatoxin analysis at consignment.

Battilani, P., Camardo Leggieri, M., Rossi, V., Giorni, P., AFLA-maize, a mechanistic model for Aspergillus flavus infection and aflatoxin B1 contamination in maize, <<COMPUTERS AND ELECTRONICS IN AGRICULTURE>>, 2013; 2013/94 (N/A): 38-46 [http://hdl.handle.net/10807/45391]

AFLA-maize, a mechanistic model for Aspergillus flavus infection and aflatoxin B1 contamination in maize

Battilani, Paola;Camardo Leggieri, Marco;Rossi, Vittorio;Giorni, Paola
2013

Abstract

A mechanistic weather-driven model was developed based on the infection cycle of Aspergillus flavus on maize to predict the risk of aflatoxin contamination in field on a daily basis from silk emergence to harvest; hourly data of temperature, relative humidity and rain were used as model input. The work was done in four steps: (i) development of the model prototype; (ii) collection of Italian field data on aflatoxin contamination in maize with related crop and weather data; (iii) development of a probability index to exceed the legal limit of 5μg of aflatoxin B1 per kg of unprocessed maize by combining model predictions and field data in a logistic regression; and (iv) validation with Italian data of the probability index and release of the predictive model, named AFLA-maize. Predictions of maize contamination above the threshold of 5μg/kg in the data set used for parameterization of the regression equation were correct for 73% of field samples; 59% and 14%, respectively, were not contaminated and contaminated. In a second independent data set, 68% of samples were correctly predicted. The model AFLA-maize provides prediction of A. flavus infection and aflatoxin contamination along the growing season and at harvest. This information is useful to support decision-making for (i) crop management, (ii) harvest timing, (iii) maize lots cleaning and logistic, and (iv) maize sampling for aflatoxin analysis at consignment.
Inglese
Battilani, P., Camardo Leggieri, M., Rossi, V., Giorni, P., AFLA-maize, a mechanistic model for Aspergillus flavus infection and aflatoxin B1 contamination in maize, <<COMPUTERS AND ELECTRONICS IN AGRICULTURE>>, 2013; 2013/94 (N/A): 38-46 [http://hdl.handle.net/10807/45391]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10807/45391
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