Ordinal regression models for predicting deoxynivalenol in winter wheat

Authors

  • S. Landschoot,

    Corresponding author
    1. Faculty of Applied Bioscience Engineering, University College Ghent, Gent, Belgium
    2. KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Gent, Belgium
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  • W. Waegeman,

    1. KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Gent, Belgium
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  • K. Audenaert,

    1. Faculty of Applied Bioscience Engineering, University College Ghent, Gent, Belgium
    2. Department of Crop Protection, Laboratory of Phytopathology, Ghent University, Gent, Belgium
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  • G. Haesaert,

    1. Faculty of Applied Bioscience Engineering, University College Ghent, Gent, Belgium
    2. Department of Crop Protection, Laboratory of Phytopathology, Ghent University, Gent, Belgium
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  • B. De Baets

    1. KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Gent, Belgium
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Abstract

Deoxynivalenol (DON) is one of the most prevalent toxins in Fusarium-infected wheat samples. Accurate forecasting systems that predict the presence of DON are useful to underpin decision making on the application of fungicides, to identify fields under risk, and to help minimize the risk of food and feed contamination with DON. To this end, existing forecasting systems often adopt statistical regression models, in which attempts are made to predict DON values as a continuous variable. In contrast, this paper advocates the use of ordinal regression models for the prediction of DON values, by defining thresholds for converting continuous DON values into a fixed number of well-chosen risk classes. Objective criteria for selecting these thresholds in a meaningful way are proposed. The resulting approach was evaluated on a sizeable field experiment in Belgium, for which measurements of DON values and various types of predictor variables were collected at 18 locations during 2002–2011. The results demonstrate that modelling and evaluating DON values on an ordinal scale leads to a more accurate and more easily interpreted predictive performance. Compared to traditional regression models, an improvement could be observed for support vector ordinal regression models and proportional odds models.

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