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Highlights • We assess potential of management techniques to reduce the need for fungicides. • Experimental field trials data (1996–2014) were used to analyse impact on yield. • Fungicide use increased yields by, on average, 0.74 t/ha in trials run 1996–2014. • Resistance, rainfall, and disease impacted the yield effect of fungicide use. • Resistant varieties and forecasting disease could reduce fungicide needed on barley. Abstract This paper assesses potential for Integrated Pest Management (IPM) techniques to reduce the need for fungicide use without negatively impacting yields. The impacts of three disease management practices of relevance to broad acre crops –disease resistance, forecasting disease pressure, and fungicide use – were analysed to determine impact on yield using a long-term field trials database of Scottish spring barley, with information from experiments across the country regarding yield, disease levels, and fungicide treatment. Due to changes in data collection practices, data from 1996 to 2010 were only available at trial level, while data from 2011 to 2014 were available at plot level. For this reason, data from 1996 to 2014 were analysed using regression models, while a subset of farmer relevant varieties was taken from the 2011–2014 data, and analysed using ANOVA, to provide additional information of particular relevance to current farm practice. While fungicide use reduced disease severity in 51.4%of a farmer-relevant subset of trials run 2011–2014, and yields were decreased by 0.62 t/ha on average, this was not statistically significant in 65% of trials. Fungicide use had only a minor impact on profit in these trials, with an average increase of 4.4% for malting and 4.7% for feed varieties, based on fungicide cost and yield difference; potential savings such as reduced machinery costs were not considered, as these may vary widely. Likewise, the1996–2014 database showed an average yield increase of 0.74 t/ha due to fungicide use, across a wide range of years, sites, varieties, and climatic conditions. A regression model was developed to assess key IPM and site factors which influenced the difference between treated and untreated yields across this 18-year period. Disease resistance, season rainfall, and combined disease severity of the three fungal diseases were found to be significant factors in the model. Sowing only highly resistant varieties and, as technology improves, forecasting disease pressure based on anticipated weather would help to reduce and optimise fungicide use. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.