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Variability Among Forecast Models for the Apple Sooty Blotch/Flyspeck Disease Complex

September 2011 , Volume 95 , Number  9
Pages  1,179 - 1,186

Daniel R. Cooley, Department of Plant, Soil, & Insect Sciences, University of Massachusetts, Amherst; David A. Rosenberger, Hudson Valley Lab, Cornell University, Highland, NY; Mark L. Gleason, Department of Plant Pathology, Iowa State University, Ames; Glen Koehler, Pest Management Office, University of Maine, Orono; Kerik Cox, Hudson Valley Lab, Cornell University, Highland, NY; Jon M. Clements, Department of Plant, Soil, & Insect Sciences, University of Massachusetts, Amherst; Turner B. Sutton, Department of Plant Pathology, North Carolina State University, Raleigh; Angela Madeiras, Department of Plant, Soil, & Insect Sciences, University of Massachusetts, Amherst; and John R. Hartman, Department of Plant Pathology, University of Kentucky, Lexington

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Accepted for publication 9 May 2011.

Several disease forecast models have been developed to guide treatment of the sooty blotch and flyspeck (SBFS) disease complex of apple. Generally, these empirical models are based on the accumulation of hours of leaf wetness (leaf wetness duration [LWD]) from a biofix at or near the phenological growth stage petal fall, when apple flower petals senesce and drop. The models recommend timing of the initial fungicide application targeting SBFS. However, there are significant differences among SBFS forecast models in terms of biofix and the length of LWD thresholds. A comparison of models using a single input data set generated recommendations for the first SBFS fungicide application that differed by up to 5 weeks. In an attempt to improve consistency among models, potential sources for differences were examined. Leaf wetness (LW) is a particularly variable parameter among models, depending on whether on-site or remote weather data were used, the types of sensors and their placement for on-site monitors, and the models used to estimate LW remotely. When SBFS models are applied in the field, recommended treatment thresholds do not always match the method of data acquisition, leading to potential failures. Horticultural factors, such as tree size, canopy density, and cultivar, and orchard site factors such as the distance to potential inoculum sources can impact risk of SBFS and should also be considered in forecast models. The number of fungal species identified as contributors to the SBFS disease complex has expanded tremendously in recent years. A lack of understanding of key epidemiological factors for different fungi in the complex, and which fungi represent the most challenging management problems, are obvious issues in the development of improved SBFS models. If SBFS forecast models are to be adopted, researchers will need to address these issues.

© 2011 The American Phytopathological Society