Ordinal regression models for predicting deoxynivalenol in winter wheat

Abstract

Deoxynivalenol (DON) is one of the most prevalent toxins inFusarium‐infected wheat samples. Accurate forecasting systems that predict the presence ofDONare 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 withDON. To this end, existing forecasting systems often adopt statistical regression models, in which attempts are made to predictDONvalues as a continuous variable. In contrast, this paper advocates the use of ordinal regression models for the prediction ofDONvalues, by defining thresholds for converting continuousDONvalues 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 ofDONvalues and various types of predictor variables were collected at 18 locations during 2002–2011. The results demonstrate that modelling and evaluatingDONvalues 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.