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ARS Home » Southeast Area » Fort Pierce, Florida » U.S. Horticultural Research Laboratory » Subtropical Plant Pathology Research » Research » Publications at this Location » Publication #231542
Title: Site-specific risk factors for ray blight in Tasmanian pyrethrum fields

Author
item PETHYBRIDGE, S. J. - TASMANIAN INST. OF AG.
item Gent, David - Dave
item ESKER, P. D. - UNIV. OF WISCONSIN
item Turechek, William
item HAY, F. S. - TASMANIAN INST. OF AG.
item NUTTER, F. W. - IOWA STATE UNIVERSITY

Submitted to: Plant Disease
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/1/2009
Publication Date: 4/1/2009
Citation: Pethybridge, S., Gent, D.H., Esker, P., Turechek, W., Hay, F., Nutter, F. 2009. Site-specific risk factors for ray blight in Tasmanian pyrethrum fields. Plant Disease. 93:299-308.

Interpretive Summary: Ray blight is a fungal disease of pyrethrum that can cause significant reductions in crop growth and pyrethrin yield. Weather and site-specific disease risk factors for ray blight have not been identified or quantified in terms of relative risk, which has limited the efficiency of managing disease. Several statistical models were developed to describe the relationships among edaphic factors and weather variables with ray blight disease severity in Tasmanian pyrethrum fields. The models were based on the the number of days with rain of at least 0.1 mm, the moving average of maximum temperatures in the last 14 days, and for late-season models the stem height in the previos month. The accuracy of the prediction of disease severity for the models developed ranged between 65% and 79%. The models developed in this research are the first steps towards identifying and weighting site and weather disease risk variables to develop a decision-support aid for the prediction of ray blight of pyrethrum.

Technical Abstract: Ray blight of pyrethrum, caused by Phoma ligulicola var. inoxydablis can cause significant reductions in crop growth and pyrethrin yield. Weather and site-specific disease risk factors for ray blight have not been identified or quantified in terms of relative risk, which has limited the efficiency of managing this disease. Logistic regression and nonparametric discriminate analyses were used to model relationships among edaphic factors and weather variables with ray blight disease severity in Tasmanian pyrethrum fields. The response variable used for both statistical methods was the classification of crops with average defoliation severity above or below a disease intensity threshold of 40% in September and October. This intensity threshold was shown in previous studies to be the threshold above which additional defoliation leads to a linear increase in the incidence of flowers with ray blight. Temperature and rainfall data from all fields was interpolated using the Queensland Department of Natural Resources and Mines Data Drill algorithm. Both modelling approaches led to the selection of similar disease risk prediction variables and had similar classification accuracies. The logistic regression model for September defoliation severity included a variable for the product of number of days with rain of at least 0.1 mm and the moving average of maximum temperatures in the last 14 days, which correctly classified (accuracy) the disease severity threshold for 64.8% of data sets. The percentage of data sets where disease severity was correctly classified above the threshold (sensitivity) or below the threshold (specificity) using logistic regression was 55.8% and 71%, respectively. The discriminate function for September defoliation severity with the highest percentage of correct classifications (69.5%) included disease risk prediction variables that were identical to those in the logistic regression model, with sensitivity and specificity values of 60.5% and 75.8%, respectively. Both logistic regression and discriminate function models for October defoliation severity included the number of days with at least 1 mm of rain in the past 14 days, stem height in September, and the product of the number of days with at least 10 mm of rain in the last 30 days and September defoliation severity. Accuracy, sensitivity, and specificity were 72.6, 73.6, and 71.4% for the logistic regression model, and 78.9, 84.9, and 71.4% for the discriminate function model, respectively. Youden’s index, which considers both the sensitivity and specificity to be of equal weight, identified thresholds of 0.25 and 0.33 for the September logistic regression and discriminate function models, respectively. For the October models, Youden’s index identified thresholds of 0.57 and 0.66 for the logistic regression and discriminate function models, respectively. When economic considerations of the costs of false positive and false negative decisions were integrated into receiver operating characteristic curves for the October models, optimal thresholds to minimize average costs were 0.32 and 0.33 for the logistic regression and discriminate function models, respectively. The models developed in this research are the first steps towards identifying and weighting site and weather disease risk variables to develop a decision-support aid for the prediction of ray blight of pyrethrum.