Six commercial bread wheat (Triticum aestivum L.) cultivars were evaluated against leaf rust (Puccinia triticina) at two different locations in Egypt during three successive growing seasons, i.e., 2016/2017, 2017/2018 and 2018/2019. Correlation between eight weekly environmental factors: solar radiation, total precipitation, average wind speed, maximum wind speed, minimum air temperature, maximum air temperature, minimum relative humidity and maximum relative humidity to leaf rust severity (%) and yield losses (%), was determined using stepwise regression analysis. Predictive models for each tested cultivar for final leaf rust severity (%) and yield losses using environmental data were determined. Generally, maximum relative humidity was positively correlated with final leaf rust severity (%). Significant correlation between yield losses (%) and the two factors, i.e., final leaf rust severity (%) and maximum relative humidity, was also found in this study. Meanwhile, negative correlation was found between final leaf rust severity (%) and yield losses (%) and the two environmental factors, i.e., solar radiation and minimum relative humidity. The effectiveness of tested models was estimated by using R² values for all models, and these models were validated to predict leaf rust severity (%) and yield loss.

中文翻译：

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基于环境因素的小麦叶锈病严重程度和产量损失预测

对6个商品面包小麦（Triticum aestivum L.）品种的叶锈病（Puccinia triticina）进行了评价。）在三个连续的生长季节，即2016 / 2017、2017 / 2018和2018/2019，分别位于埃及的两个不同地点。每周八个环境因素之间的相关性：太阳辐射，总降水量，平均风速，最大风速，最低气温，最高气温，最低相对湿度和最高相对湿度对叶锈病的严重程度（％）和产量损失（％），使用逐步回归分析确定。使用环境数据确定每种测试品种的最终叶锈病严重程度（％）和产量损失的预测模型。通常，最大相对湿度与最终叶锈病严重程度（％）正相关。在这项研究中还发现了产量损失（％）与两个因素之间的显着相关性，即最终叶锈病严重程度（％）和最大相对湿度。同时，最终叶锈病严重度（％）和产量损失（％）与两个环境因素（即太阳辐射和最小相对湿度）之间存在负相关关系。通过使用来评估测试模型的有效性所有模型的R ²值，并且这些模型均经过验证，可以预测叶锈病严重程度（％）和产量损失。