Phytopathology. 2004 Dec;94(12):1350-7. doi: 10.1094/PHYTO.2004.94.12.1350.
ABSTRACT Risk assessment models for gray leaf spot of maize, caused by Cercospora zeae-maydis, were developed using preplanting site and maize genotype data as predictors. Disease severity at the dough/dent plant growth stage was categorized into classes and used as the response variable. Logistic regression and classification and regression tree (CART) modeling approaches were used to predict severity classes as a function of planting date (PD), amount of maize soil surface residue (SR), cropping sequence, genotype maturity and gray leaf spot resistance (GLSR) ratings, and longitude (LON). Models were development using 332 cases collected between 1998 and 2001. Thirty cases collected in 2002 were used to validate the models. Preplanting data showed a strong relationship with late-season gray leaf spot severity classes. The most important predictors were SR, PD, GLSR, and LON. Logistic regression models correctly classified 60 to 70% of the validation cases, whereas the CART models correctly classified 57 to 77% of these cases. Cases misclassified by the CART models were mostly due to overestimation, whereas the logistic regression models tended to misclassify cases by underestimation. Both the CART and logistic regression models have potential as management decision-making tools. Early quantitative assessment of gray leaf spot risk would allow for more sound management decisions being made when warranted.
摘要 利用玉米叶斑病发病前的种植地点和玉米基因型数据作为预测因子，建立了玉米叶斑病发病风险评估模型。将玉米面团/萌芽期的病害严重程度分为不同等级，作为响应变量。采用逻辑回归和分类回归树（CART）建模方法，预测种植日期（PD）、玉米土壤表面残茬量（SR）、种植顺序、基因型成熟度和叶斑病抗性（GLSR）等级以及经度（LON）等因素与病害严重程度等级之间的关系。模型的开发使用了 1998 年至 2001 年间收集的 332 个案例。2002 年收集的 30 个案例用于验证模型。发病前的数据与后期叶斑病严重程度等级有很强的关系。最重要的预测因子是 SR、PD、GLSR 和 LON。逻辑回归模型正确分类了 60%至 70%的验证案例，而 CART 模型正确分类了 57%至 77%的案例。CART 模型误分类的案例主要是由于高估，而逻辑回归模型则倾向于低估而导致误分类。CART 和逻辑回归模型都有作为管理决策工具的潜力。早期对叶斑病风险的定量评估将允许在有必要时做出更合理的管理决策。
