Phytopathology. 2014 Jan;104(1):50-6. doi: 10.1094/PHYTO-02-13-0058-R.
Downy mildew caused by Peronospora sparsa has resulted in serious production losses in boysenberry (Rubus hybrid), blackberry (Rubus fruticosus), and rose (Rosa sp.) in New Zealand, Mexico, and the United States and the United Kingdom, respectively. Development of a model to predict downy mildew risk would facilitate development and implementation of a disease warning system for efficient fungicide spray application in the crops affected by this disease. Because detailed disease observation data were not available, a two-step approach was applied to develop an empirical risk prediction model for P. sparsa. To identify the weather patterns associated with a high incidence of downy mildew berry infections (dryberry disease) and derive parameters for the empirical model, classification and regression tree (CART) analysis was performed. Then, fuzzy sets were applied to develop a simple model to predict the disease risk based on the parameters derived from the CART analysis. High-risk seasons with a boysenberry downy mildew incidence >10% coincided with months when the number of hours per day with temperature of 15 to 20°C averaged >9.8 over the month and the number of days with rainfall in the month was >38.7%. The Fuzzy Peronospora Sparsa (FPS) model, developed using fuzzy sets, defined relationships among high-risk events, temperature, and rainfall conditions. In a validation study, the FPS model provided correct identification of both seasons with high downy mildew risk for boysenberry, blackberry, and rose and low risk in seasons when no disease was observed. As a result, the FPS model had a significant degree of agreement between predicted and observed risks of downy mildew for those crops (P = 0.002).
由鳞球茎白锈菌引起的霜霉病分别在新西兰、墨西哥、美国和英国导致了杂交茶藨子（Rubus hybrid）、黑莓（Rubus fruticosus）和玫瑰（Rosa sp.）的严重产量损失。开发一种预测霜霉病风险的模型将有助于为受该病影响的作物开发和实施疾病预警系统，以实现高效杀菌剂喷雾应用。由于缺乏详细的疾病观测数据，因此采用两步法开发鳞球茎白锈菌的经验风险预测模型。为了确定与高发生率霜霉病浆果感染（枯果病）相关的天气模式并得出经验模型的参数，进行了分类和回归树（CART）分析。然后，应用模糊集来开发一种简单的模型，基于从 CART 分析中得出的参数来预测疾病风险。高风险季节的杂交茶藨子霜霉病发病率>10%，与每天温度为 15 至 20°C 的小时数平均>9.8 且该月降雨天数>38.7%的月份一致。使用模糊集开发的模糊鳞球茎白锈菌（FPS）模型定义了高风险事件、温度和降雨条件之间的关系。在验证研究中，FPS 模型正确识别了杂交茶藨子、黑莓和玫瑰的高霜霉病风险季节和无病季节的低风险季节。结果，FPS 模型在这些作物的霜霉病风险预测和观察之间具有显著的一致性（P=0.002）。
