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Predicting plant disease epidemics from functionally represented weather series.

D A Shah1, P A Paul2, E D De Wolf1

  • 11 Department of Plant Pathology, Kansas State University , 4024 Throckmorton PSC, Manhattan, KS 66506 , USA.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|May 7, 2019
PubMed
Summary

A new functional approach improves prediction of Fusarium head blight (FHB) epidemics in wheat by analyzing weather patterns over extended periods. This method identifies earlier weather correlations, enhancing disease risk forecasting.

Keywords:
Fusarium head blightscalar-on-function regressionwheat scab

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Area of Science:

  • Plant Pathology
  • Epidemiology
  • Biostatistics

Background:

  • Plant disease epidemics are influenced by weather patterns affecting pathogen and host.
  • Traditional methods like window-pane analysis search for early-season weather correlations with disease outcomes.
  • Predicting epidemics requires understanding complex relationships between environmental conditions and disease development over time.

Purpose of the Study:

  • To introduce a functional approach for analyzing weather time series in relation to plant disease epidemics.
  • To compare the efficacy of functional models with traditional logistic regression models for predicting Fusarium head blight (FHB) in wheat.
  • To identify novel weather-related predictors for improved epidemic forecasting.

Main Methods:

  • Utilized scalar-on-function regression to model the probability of a Fusarium head blight (FHB) epidemic.
  • Analyzed weather time series data spanning 140 days relative to wheat flowering.
  • Compared functional model performance against standard logistic regression (lr) models.

Main Results:

  • Functional models demonstrated a superior fit to the data compared to standard logistic regression.
  • Identified weather patterns occurring significantly earlier than previously recognized as associated with FHB epidemics.
  • Developed new weather summary variables that, when integrated into lr models, matched the performance of existing models for real-time risk prediction.

Conclusions:

  • A functional data analysis approach offers a more principled method for understanding weather-disease relationships in plant pathology.
  • Early-season weather conditions play a crucial role in predicting FHB epidemics.
  • The developed methods enhance the accuracy and timeliness of plant disease forecasting systems.