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Optimal Weather Variables for Estimation of Leaf Wetness Duration Using an Empirical Method  

K. S. Kim (Department of Agronomy, Iowa State University)
S. E. Taylor (Department of Agronomy, Iowa State University)
M. L. Gleason (Department of Plant Pathology, Iowa State University)
K. J. Koehler (Department of Statistics, Iowa State University)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.4, no.1, 2002 , pp. 23-28 More about this Journal
Abstract
Sets of weather variables for estimation of LWD were evaluated using CART(Classification And Regression Tree) models. Input variables were sets of hourly observations of air temperature at 0.3-m and 1.5-m height, relative humidity(RH), and wind speed that were obtained from May to September in 1997, 1998, and 1999 at 15 weather stations in iowa, Illinois, and Nebraska, USA. A model that included air temperature at 0.3-m height, RH, and wind speed showed the lowest misidentification rate for wetness. The model estimated presence or absence of wetness more accurately (85.5%) than the CART/SLD model (84.7%) proposed by Gleason et al. (1994). This slight improvement, however, was insufficient to justify the use of our model, which requires additional measurements, in preference to the CART/SLD model. This study demonstrated that the use of measurements of temperature, humidity, and wind from automated stations was sufficient to make LWD estimations of reasonable accuracy when the CART/SLD model was used. Therefore, implementation of crop disease-warning systems may be facilitated by application of the CART/SLD model that inputs readily obtainable weather observations.
Keywords
Disease warning system; integrated pest management; weather models;
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