Prediction of infection risk of hop by Pseudoperonspora humuli

Authors: Gent, David - Dave; OCAMB, CYNTHIA - Oregon State University

Submitted to: Phytopathology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/10/2009
Publication Date: 8/31/2009
Citation: Gent, D.H., Ocamb, C.M. 2009. Prediction of infection risk of hop by Pseudoperonspora humuli. Phytopathology. 99:1190-1198.

Interpretive Summary: Downy mildew, caused by Pseudoperonospora humuli, is one of the most destructive diseases of hop. Weather factors associated with infection risk by P. humuli in the maritime region of western Oregon were examined and models developed to predict the probability of infection for a 24- or 48-hour period. Use of the 24-hour model and 48-hour model was estimated to reduce average management costs during vegetative development when disease prevalence was less than 0.31 and 0.16, respectively. Using economic assumptions near harvest, management decisions informed by the models reduced average costs when disease prevalence was less than 0.21 and 0.1 for the 24-h and 48-h models, respectively. The value of the models in management decision making appears to be greatest when disease prevalence is relatively low during vegetative development, which generally corresponds to the normally drier period from late spring to mid-summer in the Pacific Northwestern U.S.

Technical Abstract: Downy mildew, caused by Pseudoperonospora humuli, is one of the most destructive diseases of hop. Weather factors associated with infection risk by P. humuli in the maritime region of western Oregon were examined for 24 and 48-h periods and quadratic discriminant function models were developed to classify periods as favorable for disease development. For the 24-h data sets, the model with superior predictive ability included variables for hours of relative humidity >80%, degree-hours of wetness, and mean night temperature. The same variables were selected for the 48-h data sets, with the addition of a product variable for mean night temperature and hours of relative humidity > 80%. Cut-points (pT ) on receiver operating characteristic curves that minimized the overall error rate were identified by selecting the cut-point with the highest value of Youden’s index. For the 24-h model and 48-h model these were pT = 0.49 and pT = 0.39, respectively. With these thresholds, the sensitivity and specificity of the models in cross validation by jackknife exclusion were 83.3% and 88.8% for the 24-h model, and 87.5% and 84.4% for the 48-h model, respectively. Cut-points that minimized the average costs associated with disease control and crop loss due to classification errors were determined using estimates of economic damage during vegetative development and on cones near harvest. Use of the 24-h model and 48-h model was estimated to reduce average management costs during vegetative development when disease prevalence was less than 0.31 and 0.16, respectively. Using economic assumptions near harvest, management decisions informed by the models reduced average costs when disease prevalence was less than 0.21 and 0.1 for the 24-h and 48-h models, respectively. The value of the models in management decision making appears to be greatest when disease prevalence is relatively low during vegetative development, which generally corresponds to the normally drier period from late spring to mid-summer in the Pacific Northwestern U.S.