Center for Integrated Fungal Research, Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, 27695, USA.
Department of Plant and Environmental Sciences, Clemson University, Charleston, SC, 29414, USA.
Int J Biometeorol. 2018 Apr;62(4):655-668. doi: 10.1007/s00484-017-1474-2. Epub 2017 Nov 25.
Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good performance in predicting the risk of cucurbit downy mildew outbreak in the eastern United States.
由专性卵菌 Pseudoperonospora cubensis 引起的瓜类霜霉病被认为是世界范围内瓜类最重要的经济疾病之一。在美国大陆,病原体在佛罗里达州南部和墨西哥湾沿岸越冬。北方各州的疾病爆发每年都会通过感染源地的远距离空中运输孢子来发生。已经开发了一个综合的空气传播病原体建模系统,以预测疾病发生的风险,并及时利用杀菌剂进行疾病管理。该预测系统结合了已知接种源、远距离大气孢子传播和孢子沉积模块的信息,用于测试其预测疾病爆发风险的准确性。在 2013 年、2014 年和 2015 年生长季节,在阿拉巴马州、佐治亚州、路易斯安那州、纽约州、北卡罗来纳州、俄亥俄州、宾夕法尼亚州和南卡罗来纳州的疾病监测点每周采集雨水样本,从种植到田间出现症状的第一时间。使用针对 P. cubensis 的引物的常规 PCR 检测法检测雨水样本中孢子囊的存在。在每次降雨后,监测并记录每个地点的疾病预测情况,直到出现初始疾病症状。在研究期间采集的 187 个雨水样本中,有 38 个样本检测到了病原体。该预测系统根据孢子囊的存在或初始疾病症状的出现,正确地预测了疾病爆发的风险,总体准确率分别为 66%和 75%。此外,预测系统正确分类疾病存在或不存在的概率≥73%。基于瓜类田间植物出现疾病症状计算的真技能统计值在 0.42 到 0.58 之间,表明该疾病预测系统在美国东部预测瓜类霜霉病爆发的风险具有可接受的良好性能。