Pethybridge Sarah J, Gent David H, Esker Paul D, Turechek William W, Hay Frank S, Nutter Forrest W
Botanical Resources Australia - Agricultural Services Pty. Ltd., Ulverstone, Tasmania, 7315, Australia.
United States Department of Agriculture - Agricultural Research Services (USDA-ARS), Forage Seed and Cereal Research Unit and Oregon State University, Department of Botany and Plant Pathology, Corvallis, OR 97331.
Plant Dis. 2009 Mar;93(3):229-237. doi: 10.1094/PDIS-93-3-0229.
Ray blight of pyrethrum (Tanacetum cinerariifolium), caused by Phoma ligulicola var. inoxydablis, can cause defoliation and reductions of crop growth and pyrethrin yield. Logistic regression was used to model relationships among edaphic factors and interpolated weather variables associated with severe disease outbreaks (i.e., defoliation severity ≥40%). A model for September defoliation severity included a variable for the product of number of days with rain of at least 0.1 mm and a moving average of maximum temperatures in the last 14 days, which correctly classified (accuracy) the disease severity class for 64.8% of data sets. The percentage of data sets where disease severity was correctly classified as at least 40% defoliation severity (sensitivity) or below 40% defoliation severity (specificity) were 55.8 and 71%, respectively. A model 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%, respectively. Youden's index identified predictive thresholds of 0.25 and 0.57 for the September and October models, respectively. When economic considerations of the costs of false positive and false negative decisions and disease prevalence were integrated into receiver operating characteristic (ROC) curves for the October model, the optimal predictive threshold to minimize average management costs was 0 for values of disease prevalence greater than 0.2 due to the high cost of false negative predictions. ROC curve analysis indicated that management of the disease should be routine when disease prevalence is greater than 0.2. The models developed in this research are the first steps toward identifying and weighting site and weather disease risk variables to develop a decision-support aid for the management of ray blight of pyrethrum.
除虫菊（白花除虫菊）的叶疫病由不锈茎点霉变种引起，可导致落叶，并使作物生长和除虫菊酯产量降低。采用逻辑回归对土壤因子与与严重病害爆发（即落叶严重程度≥40%）相关的插值气象变量之间的关系进行建模。9月落叶严重程度模型包括至少0.1毫米降雨天数与过去14天最高温度移动平均值的乘积这一变量，该模型对64.8%的数据集正确分类了病害严重程度等级。病害严重程度被正确分类为至少40%落叶严重程度（灵敏度）或低于40%落叶严重程度（特异度）的数据集百分比分别为55.8%和71%。10月落叶严重程度模型包括过去14天至少1毫米降雨天数、9月茎高以及过去30天至少10毫米降雨天数与9月落叶严重程度的乘积。准确率、灵敏度和特异度分别为72.6%、73.6%和71.4%。约登指数分别确定了9月和10月模型的预测阈值为0.25和0.57。当将假阳性和假阴性决策成本以及疾病流行率的经济因素纳入10月模型的受试者工作特征（ROC）曲线时，由于假阴性预测成本高昂，对于疾病流行率大于0.2的值，使平均管理成本最小化的最佳预测阈值为0。ROC曲线分析表明，当疾病流行率大于0.2时，应常规管理该病害。本研究中开发的模型是朝着识别和权衡场地及气象病害风险变量迈出的第一步，以便为除虫菊叶疫病管理开发决策支持工具。
