Powdery mildew (caused by Blumeria graminis DC. f. sp. avenae Em. Marchal) is the most important disease of common oat (Avena sativa L.) in cooler and humid regions of the world including India. In spite of this, no prediction model for assessing the high risk (>30% severity) of powdery mildew in common oat is available. In the present study, a logistic regression model which assesses the high risk of powdery mildew in common oat was developed using weather and disease data collected from 15 years (2004–05 to 2018–19) observations in a monitoring experiment conducted at Palampur, India. The model incorporated increasing weekly average temperature (between 11.5 and 21.9 °C) coupled with decreasing relative humidity (between 40 and 60%) and sunshine (between 5.4 and 8.7 h) as key predictors for high (>30% severity) powdery mildew severity. The model was validated for its accuracy using cross validation technique with area under receiver operating characteristic curve value (AUC) of 0.89 during development and 0.87 on cross validation. Since the model depends on weekly values of commonly available macro-climatic weather variables, the fungicide spraying can be done in advance which will help in reducing the losses caused by powdery mildew of common oat. To our knowledge, this is the first model for predicting powdery mildew disease of common oat in India and probably around the world.

白粉病（由小麦白粉病DC，F。属蚜他们。Marchal的）是常见的燕麦中最重要的疾病（燕麦L.）在包括印度在内的世界上较凉和潮湿的地区。尽管如此，目前尚无用于评估普通燕麦中白粉病高风险（严重程度> 30％）的预测模型。在本研究中，使用从印度Palampur进行的15年（2004-05至2018-19）观察到的天气和疾病数据，开发了一种评估普通燕麦白粉病高风险的逻辑回归模型。 。该模型结合了每周平均温度的升高（11.5至21.9°C之间）以及相对湿度（40％至60％之间）和日照强度（5.4至8.7 h之间）的降低，作为高（> 30％严重度）白粉病严重程度的关键预测指标。使用交叉验证技术验证了模型的准确性，开发过程中接收器工作特性曲线值（AUC）下的面积为0.89，交叉验证时为0.87。由于该模型取决于常用的宏观气候天气变量的每周值，因此可以提前喷洒杀菌剂，这将有助于减少普通燕麦白粉病造成的损失。据我们所知，这是预测印度乃至世界范围内普通燕麦白粉病的第一个模型。