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Predicting Stripe Rust Severity on Winter Wheat Using an Improved Method for Analyzing Meteorological and Rust Data. Stella Melugin Coakley, Associate research professor, Department of Biological Sciences, University of Denver (mail address: National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307); Roland F. Line(2), and Larry R. McDaniel(3). (2)Research plant pathologist, Agricultural Research Service, U.S. Department of Agriculture, Washington State University, Pullman 99164; (3)Research associate, Department of Biological Sciences, University of Denver (mail address: National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307). Phytopathology 78:543-550. Accepted for publication 28 October 1987. 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, 1988. DOI: 10.1094/Phyto-78-543.

An improved method was used to determine more precisely than previous methods the relationship of meteorological factors and stripe rust (caused by Puccinia striiformis) on winter wheat cultivars Gaines, Nugaines, and Omar at Pullman, WA. A computer program WINDOW was written and used to analyze meteorological data for 1967–1984 in segments of 21–65 days beginning on 29 July of each year and ending on 24 July of the following year. Meteorological factors were used as the independent x-variables in multiple regression with disease index (DI) used as the dependent y-variable. For each cultivar, four statistical models (two two-variable and two three-variable) provided more accurate predictions than either the local or regional models previously used in the Pacific Northwest. The three-variable models had adjusted R2 = 0.73–0.88, and were 89–100% accurate for predicting rust severity. Contingency quadrants were used to evaluate accuracy of predicted DI versus actual DI. Winter temperature and spring precipitation factors were included in the proposed three-variable models and were positively correlated with DI. Two models for each cultivar were “predictive” in that they could have been used early enough in the season to allow application of fungicides if severe disease had been predicted. The number of days with maximum temperature greater than 25 C was important in each full-season model. For Gaines and Nugaines (cultivars with high-temperature, adult-plant resistance), high temperatures were necessary for their resistance. The frequency of this factor from 21 April to 26 June was highly correlated (r = –0.88 and –0.90) with DI. However, for Omar, a cultivar without resistance, that factor was not important until June. Model validation included making DI predictions for 1985 and 1986, years not used in model development. The models should be used with caution whenever input data exceeds the range of the modeled data.

Additional keywords: empirical models, linear regression, quantitative epidemiology.