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Ordinal regression models for predicting deoxynivalenol in winter wheat

机译：预测冬小麦脱氧雪茄烯醇的序数回归模型

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摘要

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.
机译：脱氧雪腐烯酚（DON）是镰刀菌感染的小麦样品中最普遍的毒素之一。准确的预测系统可预测DON的存在，有助于支撑杀真菌剂应用的决策，确定有风险的田地，并有助于将DON污染食品和饲料的风险降至最低。为此，现有的预测系统通常采用统计回归模型，其中尝试将DON值预测为连续变量。相反，本文提倡使用序数回归模型预测DON值，方法是定义用于将连续DON值转换为固定数量的精心选择的风险类别的阈值。提出了以有意义的方式选择这些阈值的客观标准。在比利时的一项规模较大的野外实验中对所产生的方法进行了评估，该实验在2002-2011年期间在18个地点收集了DON值的测量值和各种类型的预测变量。结果表明，按顺序对DON值进行建模和评估会导致更准确，更容易解释的预测性能。与传统回归模型相比，支持向量序数回归模型和比例赔率模型可以观察到改进。

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