A better understanding of leaf rust progress curves is highly desired by wheat breeders and pathologists to achieve more precise and ambitious predictors of disease development. High variations in leaf rust progress across different wheat cropping systems can cause difficulties and uncertainties for study of disease increase rate. A number of standard curves were fitted to a four-season data set on leaf rust severity ratings to find further descriptive disease curve elements for wheat cultivars under different sowing dates. A Gaussian model provided the best fitted parameters to be considered as wheat leaf rust progress curve elements. According to H-test results, sowing date and cultivar resistance factors significantly affected the area under disease progress curve (AUDPC), maximum disease severity, and Gaussian parameters m and s. Multivariate analysis indicated considerable association among AUDPC, leaf rust onset, maximum disease severity, and three Gaussian parameters. With this new proposed approach, a better understanding of the variables to be measured for estimating wheat leaf rust development will be obtained.

小麦育种者和病理学家强烈希望更好地了解叶锈病进展曲线，以实现更精确，更雄心勃勃的疾病发展预测指标。在不同的小麦种植体系中，叶锈病进展的高度差异可能会给疾病增加率的研究带来困难和不确定性。将许多标准曲线拟合到有关叶锈病严重程度等级的四个季节数据集，以查找不同播种日期下小麦品种的进一步描述性疾病曲线元素。高斯模型提供了最佳拟合参数，可以将其视为小麦叶锈病进展曲线元素。根据H试验结果，播期和品种抗性因素显着影响疾病进展曲线（AUDPC）下的面积，最大病害严重程度以及高斯参数m和s。多变量分析表明，AUDPC，叶锈病发作，最大病害严重程度和三个高斯参数之间存在显着关联。通过这种新提出的方法，可以更好地理解为估计小麦叶锈病发展而需要测量的变量。