A predictive model for early-warning of Septoria leaf blotch on winter wheat |
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Authors: | D E te Beest M W Shaw S Pietravalle F van den Bosch |
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Institution: | (1) Biomathematics and Bioinformatics division, Rothamsted Research, Harpenden, Hertfordshire, AL5 2 JQ, UK;(2) University of Reading, School of Biological Sciences, Reading, RG6 6AS, UK;(3) Central Science Laboratory, Sand Hutton, York, YO41 1LZ, UK |
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Abstract: | Disease–weather relationships influencing Septoria leaf blotch (SLB) preceding growth stage (GS) 31 were identified using
data from 12 sites in the UK covering 8 years. Based on these relationships, an early-warning predictive model for SLB on
winter wheat was formulated to predict the occurrence of a damaging epidemic (defined as disease severity of 5% or > 5% on
the top three leaf layers). The final model was based on accumulated rain > 3 mm in the 80-day period preceding GS 31 (roughly
from early-February to the end of April) and accumulated minimum temperature with a 0°C base in the 50-day period starting
from 120 days preceding GS 31 (approximately January and February). The model was validated on an independent data set on
which the prediction accuracy was influenced by cultivar resistance. Over all observations, the model had a true positive
proportion of 0.61, a true negative proportion of 0.73, a sensitivity of 0.83, and a specificity of 0.18. True negative proportion
increased to 0.85 for resistant cultivars and decreased to 0.50 for susceptible cultivars. Potential fungicide savings are
most likely to be made with resistant cultivars, but such benefits would need to be identified with an in-depth evaluation. |
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Keywords: | Septoria tritici Mycosphaerella graminicola Validation Window Pane Disease forecasting |
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