European Journal of Plant Pathology

, Volume 124, Issue 3, pp 413–425 | Cite as

A predictive model for early-warning of Septoria leaf blotch on winter wheat

  • D. E. te Beest
  • M. W. Shaw
  • S. Pietravalle
  • F. van den Bosch
Article

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.

Keywords

Septoria tritici Mycosphaerella graminicola Validation Window Pane Disease forecasting 

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Copyright information

© KNPV 2009

Authors and Affiliations

  • D. E. te Beest
    • 1
  • M. W. Shaw
    • 2
  • S. Pietravalle
    • 3
  • F. van den Bosch
    • 1
  1. 1.Biomathematics and Bioinformatics divisionRothamsted ResearchHarpendenUK
  2. 2.University of ReadingSchool of Biological SciencesReadingUK
  3. 3.Central Science LaboratorySand HuttonUK

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