A field-specific web tool for the prediction of Fusarium head blight and deoxynivalenol content in Belgium

  • Authors:

  • S. Landschoot;W. Waegeman;K. Audenaert;P. Van Damme;J. Vandepitte;B. De Baets;G. Haesaert

  • Affiliations:

  • Faculty of Applied Bioscience Engineering, University College Ghent, Valentin Vaerwyckweg 1, BE-9000 Ghent, Belgium and KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, ...;KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, BE-9000 Ghent, Belgium;Faculty of Applied Bioscience Engineering, University College Ghent, Valentin Vaerwyckweg 1, BE-9000 Ghent, Belgium and Department of Crop Protection, Laboratory of Phytopathology, Ghent Universit ...;Soil Service of Belgium, Willem de Croylaan 48, BE-3001 Leuven, Belgium;KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, BE-9000 Ghent, Belgium;KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, BE-9000 Ghent, Belgium;Faculty of Applied Bioscience Engineering, University College Ghent, Valentin Vaerwyckweg 1, BE-9000 Ghent, Belgium and Department of Crop Protection, Laboratory of Phytopathology, Ghent Universit ...

  • Venue:
  • Computers and Electronics in Agriculture
  • Year:
  • 2013

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Abstract

Fusarium head blight is a worldwide problem in wheat growing areas. In addition to yield loss, Fusarium species can also synthesise mycotoxins and thus threaten animal and human health. Models for predicting Fusarium head blight and deoxynivalenol content in wheat provide farmers with a tool for preventing yield loss and mycotoxin contamination. Growers may use the predictions to underpin decision making on cultivation techniques and the application of fungicides. At the end of the growing season, the food and feed industry may use the predictions to make marketing decisions. Furthermore, the predictions are helpful to identify regions with a higher disease pressure and thus improve sampling efficiency. Based on the data of 3100 wheat samples from 18 locations throughout Belgium between 2002 and 2011, various predictive models were evaluated. The most accurate models were implemented in a web tool to provide growers with field-specific predictions of Fusarium head blight incidence and deoxynivalenol content. The predictions are based on the agronomic variables of a specific wheat field and weather data from the nearest weather station. During the growing season several predictions can be asked. The web tool provides a graphical representation of the predicted results together with an advice on management strategies and recommendations for fungicide application.