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Background: Insensitivity of Zymoseptoria tritici to demethylation inhibitor (DMI) and quinone outside inhibitor (QoI) fungicides has been widely reported from laboratory studies, but the relationships between laboratory sensitivity phenotype or target site genotype and field efficacy remain uncertain. This article reports field experiments quantifying dose-response curves, and investigates the relationships between field performance and in vitro half maximal effective concentration (EC 50 ) values for DMIs, and the frequency of the G143A substitution conferring QoI resistance., Results: Data were analysed from 83 field experiments over 21 years. Response curves were fitted, expressed as percentage control, rising towards an asymptote with increasing dose. Decline in DMI efficacy over years was associated with a decrease in the asymptote, and reduced curvature. Field ED 50 values were positively related to in vitro EC 50 values for isolates of Z. tritici collected over a 14-year period. Loss of QoI efficacy was expressed through a change in asymptote. Increasing frequency of G143A was associated with changes in field dose-response asymptotes., Conclusion: New resistant strains are often detected by resistance monitoring and laboratory phenotyped/genotyped before changes in field performance are detected. The relationships demonstrated here between laboratory tests and field performance could aid translation between laboratory and field for other fungicide groups. © 2017 Society of Chemical Industry., (© 2017 Society of Chemical Industry.)

The statistical evaluation of probabilistic disease forecasts often involves calculation of metrics defined conditionally on disease status, such as sensitivity and specificity. However, for the purpose of disease management decision making, metrics defined conditionally on the result of the forecast-predictive values-are also important, although less frequently reported. In this context, the application of scoring rules in the evaluation of probabilistic disease forecasts is discussed. An index of separation with application in the evaluation of probabilistic disease forecasts, described in the clinical literature, is also considered and its relation to scoring rules illustrated. Scoring rules provide a principled basis for the evaluation of probabilistic forecasts used in plant disease management. In particular, the decomposition of scoring rules into interpretable components is an advantageous feature of their application in the evaluation of disease forecasts.

The article offers information on a research on need of experience, evidence, and expertise while making decisions about protection of plants from disease. Topics discussed include forecasting eyespot development and yield loss in winter wheat crops, field walking, looking and observation, inspection and identification of plant diseases, and selecting risk factors and collecting evidence on epidemiology of plant diseases.