Spatiotemporal Distribution Pattern of Sclerotinia sclerotiorum Apothecia is Modulated by Canopy Closure and Soil Temperature in an Irrigated Soybean Field.

Abstract

Identifying the optimal timing for fungicide application is crucial in order to maximize the control of Sclerotinia stem rot (SSR), which is caused by Sclerotinia sclerotiorum. In this study, the impact of canopy closure and soil temperature on apothecia production was investigated to optimize fungicide application timing. Replicated soybean plots with a row spacing of 0.36 and 0.38 or 0.76 m were established in 2015 and 2016 in an irrigated soybean field at Michigan State University's Montcalm Research Center. The number of apothecia and ascospores and the incidence of SSR were monitored two times per week for 10 to 12 weeks. In both row-spacing trials, apothecia were observed earlier in 2016 (before the R1 growth stage) than in 2015 (between R1 and R2). The maximum number of apothecia was 50 times higher with the 0.36-m row spacing than with the 0.76-m row spacing in 2015 but was 2.5 times higher with the 0.76-m row spacing than with the 0.38-m row spacing in 2016, though the overall numbers were much lower in 2016. The apothecia distribution pattern was synchronized with the canopy closure pattern and the soil temperature profile. The peak number of apothecia was observed when canopy closure reached at least 50% and when average soil temperature in the row was between 21.5 and 23.5°C. In 91% of the cases, the presence of apothecia was observed when the percentage of light blocked was 70%, and no apothecia germinated in the absence of light or under full light exposure. During the first 50 days after plant emergence, the rate of canopy closure was higher in 2016 than in 2015, and the first diseased plant was observed earlier in 2016 (R2) than in 2015 (R5). Canopy closure and the distance of the sampling point from the soybean row explained much of the variability in the number of apothecia. These results can partially explain the inconsistent efficacy of fungicide applications based on the soybean growth stage and will be helpful for informing disease models and fine-tuning fungicide application strategies.