Leaf blotch diseases (LBD), such as Septoria nodorum bloch (Parastagnospora nodorum), Septoria tritici blotch (Zymoseptoria tritici) and Tan spot (Pyrenophora tritici-repentis) can cause severe yield losses (up to 50%) in Norwegian spring wheat (Triticum aestivum) and are mainly controlled by fungicide applications. A forecasting model to predict disease risk can be an important tool to optimize disease control. The association between specific weather variables and the development of LBD differs between wheat growth stages. In this study, a mathematical model to estimate phenological development of spring wheat was derived based on sowing date, air temperature and photoperiod. Weather factors associated with LBD severity were then identified for selected phenological growth stages by a correlation study of LBD severity data (17 years). Although information regarding host resistance and previous crop were added to the identified weather factors, two purely weather-based risk prediction models (CART, classification and regression tree algorithm) and one black box model (KNN, based on K nearest neighbor algorithm) were most accurate to predict moderate to high LBD severity (>5% infection). The predictive accuracy of these models (76–83%) was compared to that of two existing models used in Norway and Denmark (60 and 61% accuracy, respectively). The newly developed models performed better than the existing models, but still had the tendency to overestimate disease risk. Specificity of the new models varied between 49 and 74% compared to 40 and 37% for the existing models. These new models are promising decision tools to improve integrated LBD management of spring wheat in Norway.

挪威黑春小麦（Triticum）的叶斑病（LBD），例如诺氏斑霉（Parastagnospora nodorum），小麦色斑（Zymoseptoria tritici）和棕褐色斑病（Pyrenophora tritici-repentis）可能导致挪威春小麦（Triticum）严重减产（高达50％）小麦），并且主要由杀菌剂的使用来控制。预测疾病风险的预测模型可能是优化疾病控制的重要工具。在小麦生长期之间，特定天气变量与LBD发育之间的关联是不同的。在这项研究中，基于播种日期，气温和光周期，推导了估算春小麦物候发育的数学模型。然后通过对LBD严重性数据（17年）的相关研究，为选定的物候生长阶段确定了与LBD严重性相关的天气因素。尽管已将有关寄主抗性和先前作物的信息添加到了确定的天气因素中，但有两个纯粹基于天气的风险预测模型（CART，分类和回归树算法）和一个黑盒模型（KNN，基于K最近邻算法）最准确地预测中度至高的LBD严重程度（> 5％感染）。将这些模型的预测准确性（76-83％）与挪威和丹麦使用的两个现有模型的预测准确性（分别为60％和61％）进行了比较。新开发的模型比现有模型表现更好，但仍然倾向于高估疾病风险。新模型的特异性介于49％和74％之间，而现有模型的特异性为40％和37％。这些新模型是改善挪威春小麦LBD综合管理的有前途的决策工具。新开发的模型比现有模型表现更好，但仍然倾向于高估疾病风险。新模型的特异性介于49％和74％之间，而现有模型的特异性为40％和37％。这些新模型是改善挪威春小麦LBD综合管理的有前途的决策工具。新开发的模型比现有模型表现更好，但仍然倾向于高估疾病风险。新模型的特异性介于49％和74％之间，而现有模型的特异性为40％和37％。这些新模型是改善挪威春小麦LBD综合管理的有前途的决策工具。