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Evaluation of the MARYBLYT Computer Model for Predicting Blossom Blight on Apple in West Virginia and Maryland. T. VANDER ZWET, Research Plant Pathologist, USDA-ARS, Appalachian Fruit Research Station, Kearneysville, WV 25430. A. R. BIGGS, Associate Professor, West Virginia University Experiment Farm, Kearneysville, WV 25430; R. HEFLEBOWER, Regional Extension Fruit Specialist, University of Maryland, Western Maryland Research and Education Center, Keedysville 21756; and G. W. LIGHTNER, Computer Specialist, USDA-ARS, Appalachian Fruit Research Station, Kearneysville, WV 25430. Plant Dis. 78:225-230. Accepted for publication 23 November 1993. This article is in the public domain and not copyrightable. It may be freely reprinted with customary crediting of the source The American Phytopathological Society, 1994. DOI: 10.1094/PD-78-0225.

The MARYBLYT computer model was evaluated for its accuracy in forecasting apple blossom infection by Erwinia amylovora and the subsequent appearance of fire blight symptoms. Temperature and rainfall data were collected and disease observations recorded in bearing orchards in West Virginia and Maryland during 1984-1993. Among the 13 primary infection events identified by the model at all sites in eight of the 10 yr, blossom blight symptoms appeared 10 times within 1 day, twice within 2 days, and only once within 3 days of the MARYBLYT prediction. Only three times in 10 yr did MARYBLYT predict blossom infection without symptom development. In no instance did spurious symptoms appear that would indicate the model failed to identify an infection period. A blossom sampling procedure conducted during 5 yr (1985, 1987, 1988, 1990, and 1993) in which blossom blight occurred confirmed the presence of E. amylovora coincident with the model's threshold calculation of epiphytic infection potential. When blossoms were inoculated artificially by introducing a bacterial suspension (108 cfu/ml) into flower nectaries, blossom blight symptoms developed 0, 1-3, and >5 days prior to that predicted by the model in one, seven, and three trials, respectively. In 11 trials, an average of 57 degree days >12.7C was accumulated between artificial inoculations and symptom appearance, which is consistent with the model's algorithm for symptom occurrence. The results of our field evaluations of MARYBLYT for predicting blossom infection and subsequent symptom development show that the model is accurate. Treatment decisions based on MARYBLYT can be expected to improve the level of control of this destructive disease.

Keyword(s): Malus, Pyrus