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ARS Home » Midwest Area » East Lansing, Michigan » Sugarbeet and Bean Research » Research » Publications at this Location » Publication #379429
Research Project: Genetic Characterization for Sugar Beet Improvement

Location: Sugarbeet and Bean Research

Title: Early-season production and dispersal of Cercospora beticola spores in the Great Lakes Region of North America

Author
item BUBLITZ, DANIEL - Michigan State University
item MCGRATH, JON - Retired ARS Employee
item Hanson, Linda

Submitted to: Journal of Sugar Beet Research
Publication Type: Abstract Only
Publication Acceptance Date: 1/1/2021
Publication Date: 2/28/2021
Citation: Bublitz, D.M., Mcgrath, J.M., Hanson, L.E. 2021. Early-season production and dispersal of Cercospora beticola spores in the Great Lakes Region of North America [abstract]. Journal of Sugar Beet Research. 58:91.

Interpretive Summary:

Technical Abstract: Cercospora leaf spot (CLS), caused by the fungal pathogen Cercospora beticola, is a major foliar disease of sugar beet (Beta vulgaris). In the Great Lakes region of North America as well as several other parts of the world, this disease can have a major impact on yield, with losses of up to 40% reported. 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. It is hypothesized that incorporating information about spore production and dispersal into the models could make them more effective. To increase our understanding of the factors involved in spore release and dispersal, the current study used sentinel beets to assess the presence of the C. beticola spores in the environment prior to the main epidemic 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 R2 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.