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Predicting Fusarium Head Blight Epidemics with Boosted Regression Trees

July 2014 , Volume 104 , Number  7
Pages  702 - 714

D. A. Shah, E. D. De Wolf, P. A. Paul, and L. V. Madden

First and second authors: Department of Plant Pathology, Kansas State University, Manhattan 66506; and third and fourth authors: Department of Plant Pathology, The Ohio State University, Wooster 44691.

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Accepted for publication 16 January 2014.

Predicting major Fusarium head blight (FHB) epidemics allows for the judicious use of fungicides in suppressing disease development. Our objectives were to investigate the utility of boosted regression trees (BRTs) for predictive modeling of FHB epidemics in the United States, and to compare the predictive performances of the BRT models with those of logistic regression models we had developed previously. The data included 527 FHB observations from 15 states over 26 years. BRTs were fit to a training data set of 369 FHB observations, in which FHB epidemics were classified as either major (severity ≥ 10%) or non-major (severity < 10%), linked to a predictor matrix consisting of 350 weather-based variables and categorical variables for wheat type (spring or winter), presence or absence of corn residue, and cultivar resistance. Predictive performance was estimated on a test (holdout) data set consisting of the remaining 158 observations. BRTs had a misclassification rate of 0.23 on the test data, which was 31% lower than the average misclassification rate over 15 logistic regression models we had presented earlier. The strongest predictors were generally one of mean daily relative humidity, mean daily temperature, and the number of hours in which the temperature was between 9 and 30°C and relative humidity ≥ 90% simultaneously. Moreover, the predicted risk of major epidemics increased substantially when mean daily relative humidity rose above 70%, which is a lower threshold than previously modeled for most plant pathosystems. BRTs led to novel insights into the weather–epidemic relationship.

Additional keywords: disease modeling, disease forecasting, machine learning, plant disease epidemiology, wheat scab.

© 2014 The American Phytopathological Society