Quantifying how climatic factors affect variation in plant disease severity: a general method using a new way to analyze meteorological data [USA]
1987
Coakley, S.M. | McDaniel, L.R. | Line, R.F.
A general method is presented for analyzing how climatic conditions affect plant disease severity. An example of its application is given for the analysis of stripe rust (caused by Puccinia striiformis) data on winter wheat cultivar Gaines and climatic data collected at Bullman, Washington [USA] for 1968-1986. A computer program WINDOW was written to identify the climatic factors most highly correlated with disease. This program is designed to utilize meteorological data for an entire growing season of a crop as well as to include climatic conditions preceding planting. This program uses an iterative process to examine variable-length segments of meteorological data in a more exhaustive analysis than previously possible. Climatic factors considered include: mean maximum, minimum, and average temperature; total and frequency of precipitation; consecutive days with and without precipitation; accumulation of negative and positive degree days; and number of days with extreme temperature events. Variables that were highly correlated with disease were the basis for regression models that were developed to predict disease severity index for each of the three cultivars. Two- and three-variable models explained, respectively, 75 and 76% of the variation in disease from year to year. Predictions (which could be made early enough in the growing season to allow application of chemical control) were evaluated on the basis of whether years with severe disease were accurately predicted. Models were validated using Allen's PRESS statistics and by application to new data. The method is potentially applicable to studies of how climatic conditions affect the populations or productivity of other types of organisms.
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