Progression of powdery mildew in susceptible-resistant wheat (Triticum aestivum) cultivars sown at different dates

A better understanding of non-climatic predictors of temporal dynamics of wheat powdery mildew is needed to improve accuracy of disease measurement for breeding and control programmes. The present study examined fourteen types of standard curves to model powdery mildew progress data collected from 282 plots of commercial wheat cultivars sown at different dates over four growing seasons (2013-2017). An exponential model was fitted to disease progress data, and then, relationships between commonly used disease descriptors and exponential parameters were examined. From exponential function, parameters b and r defined disease increase factor and rate of disease increase, respectively. This model described a slow increase of wheat powdery mildew at first, followed by a more rapid increase lacking bound. According to Kruskal-Wallis one-way ANOVA results, wheat resistance and sowing date were significantly effective on area under disease progress curve (AUDPC), maximum disease severity and exponential parameter b. Greatest mean values for AUDPC, maximum severity and exponential parameter b were detected in later sowings of susceptible cultivars. There were significant correlations between powdery mildew curve elements. Based on principal component analysis, AUDPC, disease onset time, maximum severity and exponential parameters b and r accounted for 77% of total data variance. This information on specific predictors of wheat powdery mildew progress may improve accuracy of future disease estimates in breeding and control programmes.

Automated summary

This study explored non-climatic predictors of wheat powdery mildew dynamics and developed an exponential model to describe disease progress. Results showed significant effects of sowing date and wheat resistance on disease progress curve area (AUDPC), maximum severity, and exponential parameter b.