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Demethylation inhibitor (DMI) and succinate dehydrogenase inhibitor (SDHI) fungicides are currently relied upon for the control of septoria tritici blotch (STB) in European wheat fields. However, multiple mutations have occurred over time in the genes encoding the targeted proteins that have led to a practical loss of fungicide efficacies. Among the different amino acid substitutions in Zymoseptoria tritici associated with resistance to these fungicides, S524T in CYP51 (DMI target) and H152R in SdhC (SDHI target) are regarded as conferring the highest resistance factors to DMI and SDHI, respectively. To facilitate further studies on the monitoring and selection of these substitutions in Z. tritici populations, a multiplex allele‐specific quantitative PCR (qPCR) assay allowing for estimation of both allele frequencies in bulk DNA matrices was developed. The assay was then used on complex DNA samples originating from a spore trap network set up in Belgium, Denmark, Sweden, and Ireland in 2017 and 2018, as well as on leaf samples with symptoms. The S524T allele was present in all field samples and its proportion was significantly higher in Ireland than in Belgium, whereas the proportion of H152R was only sporadically present in both countries. The frequency of S524T varied greatly in the airborne inoculum of all four countries; however, the H152R allele was never detected in the airborne inoculum. The method developed in this study can be readily adopted by other laboratories and used for multiple applications including resistance monitoring in field populations of Z. tritici. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]