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Cercospora Leaf Spot Management Decisions: Uses of a Correlation Between Rainfall and Disease Severity to Evaluate the Virginia Leaf Spot Advisory. C. S. Johnson, Former graduate research assistant, Department of Plant Pathology, North Carolina State University, Raleigh 27695-7616; P. M. Phipps(2), and M. K. Beute(3). (2)Associate professor of Plant Pathology, Tidewater Research and Continuing Education Center, Suffolk, VA 23437; (3)Professor, Department of Plant Pathology, North Carolina State University, Raleigh 27695-7616. Phytopathology 76:860-863. Accepted for publication 2 December 1985. Copyright 1986 The American Phytopathological Society. DOI: 10.1094/Phyto-76-860.

Classes of a variable (MINRAIN) consisting of the number of days with rainfall ≥0.254 cm from June through September over the period 1978-1983 were correlated with areas under disease progress curves (AUDPCs) for Cercospora leaf spot of peanut. A regression model was used (Y= - 7,813.81 + 5,191 (logc MINRAIN) (R2=0.83) to describe the relationship between AUDPC and MINRAIN. This model was used with precipitation data from the period 1933-1983 to estimate a cumulative probability distribution function for peanut leaf spot severity. Probabilities of disease conditions similar to those observed during 1980-1983 were estimated from the probability distribution function and used to weigh annual fungicide test results averaged over similar disease conditions. These weighted average results were then summed for each treatment to predict and compare expected long-term economic returns using the Virginia peanut leaf spot advisory system with those obtained using peanut leaf spot fungicides on the standard 14-day schedule. Predicted use of the advisory system or the 14-day schedule resulted in average increases in expected net return of 16.5 and 8.2%, respectively, compared with the unsprayed control. Use of the advisory system vs. the 14-day schedule resulted in an average increase in expected net return of $174.36/ha.

Additional keywords: Cercospora arachidicola, Cercosporidium personatum, decision theory, disease forecast models, disease management, epidemiology.