Spatial portability of numerical models of leaf wetness duration based on empirical approaches

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2010-07-15
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Kim, Kwang Soo
Taylor, S. Elwynn
Gleason, Mark
Coop, Leonard
Pfender, William
Seem, Robert
Sentelhas, Paulo
Gillespie, Terry
Marta, Anna
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Plant Pathology and Microbiology
The Department of Plant Pathology and Microbiology and the Department of Entomology officially merged as of September 1, 2022. The new department is known as the Department of Plant Pathology, Entomology, and Microbiology (PPEM). The overall mission of the Department is to benefit society through research, teaching, and extension activities that improve pest management and prevent disease. Collectively, the Department consists of about 100 faculty, staff, and students who are engaged in research, teaching, and extension activities that are central to the mission of the College of Agriculture and Life Sciences. The Department possesses state-of-the-art research and teaching facilities in the Advanced Research and Teaching Building and in Science II. In addition, research and extension activities are performed off-campus at the Field Extension Education Laboratory, the Horticulture Station, the Agriculture Engineering/Agronomy Farm, and several Research and Demonstration Farms located around the state. Furthermore, the Department houses the Plant and Insect Diagnostic Clinic, the Iowa Soybean Research Center, the Insect Zoo, and BugGuide. Several USDA-ARS scientists are also affiliated with the Department.
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Leaf wetness duration (LWD) models based on empirical approaches offer practical advantages over physically based models in agricultural applications, but their spatial portability is questionable because they may be biased to the climatic conditions under which they were developed. In our study, spatial portability of three LWD models with empirical characteristics – a RH threshold model, a decision tree model with wind speed correction, and a fuzzy logic model – was evaluated using weather data collected in Brazil, Canada, Costa Rica, Italy and the USA. The fuzzy logic model was more accurate than the other models in estimating LWD measured by painted leaf wetness sensors. The fraction of correct estimates for the fuzzy logic model was greater (0.87) than for the other models (0.85–0.86) across 28 sites where painted sensors were installed, and the degree of agreement k statistic between the model and painted sensors was greater for the fuzzy logic model (0.71) than that for the other models (0.64–0.66). Values of the kstatistic for the fuzzy logic model were also less variable across sites than those of the other models. When model estimates were compared with measurements from unpainted leaf wetness sensors, the fuzzy logic model had less mean absolute error (2.5 h day−1) than other models (2.6–2.7 h day−1) after the model was calibrated for the unpainted sensors. The results suggest that the fuzzy logic model has greater spatial portability than the other models evaluated and merits further validation in comparison with physical models under a wider range of climate conditions.

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This article is from Agricultural and Forest Meteorology 150 (2010): 871–880, doi:10.1016/j.agrformet.2010.02.006.

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