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Predicting Fusarium Head Blight Epidemics With Weather-Driven Pre- and Post-Anthesis Logistic Regression Models

September 2013 , Volume 103 , Number  9
Pages  906 - 919

D. A. Shah, J. E. Molineros, P. A. Paul, K. T. Willyerd, L. V. Madden, and E. D. De Wolf

First and sixth authors: Department of Plant Pathology, Kansas State University, Manhattan 66506; second author: Department of Entomology and Plant Pathology, Oklahoma State University, Stillwater 74078; and third, fourth, and fifth authors: Department of Plant Pathology, The Ohio State University, Wooster 44691.

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Accepted for publication 14 March 2013.

Our objective was to identify weather-based variables in pre- and post-anthesis time windows for predicting major Fusarium head blight (FHB) epidemics (defined as FHB severity ≥ 10%) in the United States. A binary indicator of major epidemics for 527 unique observations (31% of which were major epidemics) was linked to 380 predictor variables summarizing temperature, relative humidity, and rainfall in 5-, 7-, 10-, 14-, or 15-day-long windows either pre- or post-anthesis. Logistic regression models were built with a training data set (70% of the 527 observations) using the leaps-and-bounds algorithm, coupled with bootstrap variable and model selection methods. Misclassification rates were estimated on the training and remaining (test) data. The predictive performance of models with indicator variables for cultivar resistance, wheat type (spring or winter), and corn residue presence was improved by adding up to four weather-based predictors. Because weather variables were intercorrelated, no single model or subset of predictor variables was best based on accuracy, model fit, and complexity. Weather-based predictors in the 15 final empirical models selected were all derivatives of relative humidity or temperature, except for one rainfall-based predictor, suggesting that relative humidity was better at characterizing moisture effects on FHB than other variables. The average test misclassification rate of the final models was 19% lower than that of models currently used in a national FHB prediction system.

Additional keywords: additive logistic regression, data mining, multiple imputation.

© 2013 The American Phytopathological Society