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A Statistical Comparison of the Blossom Blight Forecasts of MARYBLYT and Cougarblight with Receiver Operating Characteristic Curve Analysis

September 2007 , Volume 97 , Number  9
Pages  1,164 - 1,176

M. M. Dewdney, A. R. Biggs, and W. W. Turechek

First and third authors: Department of Plant Pathology, Cornell University, Geneva, NY 14456; and second author: West Virginia University, Tree Fruit Research and Education Center, P.O. Box 609, Kearneysville, WV 25430.

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Accepted for publication 4 April 2007.

Blossom blight forecasting is an important aspect of fire blight, caused by Erwinia amylovora, management for both apple and pear. A comparison of the forecast accuracy of two common fire blight forecasters, MARYBLYT and Cougarblight, was performed with receiver operating characteristic (ROC) curve analysis and 243 data sets. The rain threshold of Cougarblight was analyzed as a separate model termed Cougarblight and rain. Data were used as a whole and then grouped into geographic regions and cultivar susceptibilities. Frequency distributions of cases and controls, orchards or regions (depending on the data set), with and without observed disease, respectively, in all data sets overlapped. MARYBLYT, Cougarblight, and Cougarblight and rain all predicted blossom blight infection better than chance (P = 0.05). It was found that the blossom blight forecasters performed equivalently in the geographic regions of the east and west coasts of North America and moderately susceptible cultivars based on the 95% confidence intervals and pairwise contrasts of the area under the ROC curve. Significant differences (P < 0.05) between the forecasts of Cougarblight and MARYBLYT were found with pairwise contrasts in the England and very susceptible cultivar data sets. Youden's index was used to determine the optimal cutpoint of both forecasters. The greatest sensitivity and specificity for MARYBLYT coincided with the use of the highest risk threshold for predictions of infection; with Cougarblight, there was no clear single risk threshold across all data sets.

The American Phytopathological Society, 2007