4.7 Article

Weather Conditions Conducive for the Early-Season Production and Dispersal of Cercospora beticola Spores in the Great Lakes Region of North America

Journal

PLANT DISEASE
Volume 105, Issue 10, Pages 3063-3071

Publisher

AMER PHYTOPATHOLOGICAL SOC
DOI: 10.1094/PDIS-09-20-2004-RE

Keywords

Beta vulgaris; Cercospora leaf spot; live spore trap; Mycosphaerellaceae; sugarbeet

Categories

Funding

  1. Beet Sugar Development Foundation
  2. Michigan Sugar Company

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This study assessed the presence of C. beticola spores in the environment using sentinel beets and found correlations between spore levels and weather variables such as rainfall and relative humidity. The findings suggest that rainfall and relative humidity are significantly associated with the presence of spores, providing valuable information for improving disease prediction models and management strategies for CLS.
In many parts of the world including the Great Lakes region of North America, Cercospora leaf spot (CLS), caused by the fungal pathogen Cercospora beticola, is a major foliar disease of sugar beet (Beta vulgaris). Management of CLS involves an integrated approach which includes the application of fungicides. To guide fungicide application timings, disease prediction models are widely used by sugar beet growers in North America. While these models have generally worked well, they have not included information about pathogen presence. Thus, incorporating spore production and dispersal could make them more effective. The current study used sentinel beets to assess the presence of C. beticola spores in the environment early in the 2017 and 2018 growing seasons. Weather variables including air temperature, relative humidity, rainfall, leaf wetness, wind speed, and solar radiation were collected. These data were used to identify environmental variables that correlated with spore levels during a time when CLS is not generally observed in commercial fields. C. beticola spores were detected during mid-April both years, which is much earlier than previously reported. A correlation was found between spore data and all the weather variables examined during at least one of the two years, except for air temperature. In both years, spore presence was significantly correlated with rainfall (P < 0.0001) as well as relative humidity (P < 0.0090). Rainfall was particularly intriguing, with an adjusted R-2 of 0.3135 in 2017 and 0.1652 in 2018. Efforts are ongoing to investigate information on spore presence to improve prediction models and CLS management.

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