Previous View
 
APSnet Home
 
Phytopathology Home


VIEW ARTICLE

Ecology and Epidemiology

Quantitative Relationships Between Climatic Variables and Stripe Rust Epidemics on Winter Wheat. Stella Melugin Coakley, Department of Biological Sciences, University of Denver, research location: National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307; Roland F. Line, Department of Biological Sciences, University of Denver, research location, AR-SEA, U.S. Department of Agriculture, Pullman, WA 99164. Phytopathology 71:461-467. Accepted for publication 18 September 1980. Copyright 1981 The American Phytopathological Society. DOI: 10.1094/Phyto-71-461.

Climatic variation at Pullman, WA, since 1958 has contributed to an increase in the frequency of epidemics and severity of stripe rust (caused by Puccinia striiformis) on winter wheat. For 19631979, rust intensities on cultivar Omar (CI 13072) (very susceptible) and on cultivar Gaines (CI 13448) (susceptible in the seedling stage at all temperatures, but resistant at later growth stages at high temperatures) were positively correlated with January temperatures and negatively correlated with April and June temperatures. Spring temperatures were more highly correlated with disease development in Gaines than in Omar. Frequency of precipitation in June was correlated with stripe rust intensity in both Gaines and Omar. To mathematically relate stripe rust intensity to cumulative temperatures, we calculated negative degree days (NDD) and positive degree days (PDD) by using a 7 C base. Disease intensity was negatively correlated with NDD and PDD; the best correlations were with NDD that accumulated between 1 December and 31 January and PDD that accumulated between 1 April and 30 June. Slopes of linear regression equations for disease intensity on Gaines vs NDD in December and January and vs PDD in April through June differed significantly from zero at P <0.001. Regression equations for disease intensity on Omar as a function of NDD or PDD had significantly different Y-intercepts but slopes that were similar to the comparable equations for Gaines. These relationships help explain why stripe rust was not severe between 1941 and 1958, and may be useful for predicting stripe rust intensity in the Pacific Northwest.

Additional keywords: model for disease prediction, quantitative epidemiology, statistical model for stripe rust prediction.