Skip to main content
ARS Home » Midwest Area » East Lansing, Michigan » Sugarbeet and Bean Research » Research » Publications at this Location » Publication #378072

Research Project: Genetic Characterization for Sugar Beet Improvement

Location: Sugarbeet and Bean Research

Title: Weather conditions conducive for the 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, J - Retired ARS Employee
item Hanson, Linda

Submitted to: Plant Disease
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/3/2021
Publication Date: 10/26/2021
Citation: Bublitz, D.M., McGrath, J.M., Hanson, L.E. 2021. Weather conditions conducive for the early-season production and dispersal of Cercospora beticola spores in the Great Lakes Region of North America. Plant Disease. 105(10):3063-3071. https://doi.org/10.1094/PDIS-09-20-2004-RE.
DOI: https://doi.org/10.1094/PDIS-09-20-2004-RE

Interpretive Summary: In many parts of the world, including the Great Lakes region of North America, Cercospora leaf spot, caused by the fungal pathogen Cercospora beticola, is a major disease of sugar beet. Management of Cercospora leaf spot involves combining several methods. One of these methods is the use of fungicides. To identify the best timing for management, disease prediction models have been widely used in North America. These models have not included information about the presence of the pathogen. Adding such information could make the models more effective. This study used live beets as a way to test for presence of C. beticola spores in the environment early in the season, when initial infection is likely to occur. Plants were placed in commercial fields in 2017 and 2018. The presence of Cercospora lesions on the plants was correlated with weather data to assist in determining factors important for spore release and dispersal before the disease was visible in commercial fields. Spores were detected during mid-April both years, which is much earlier than previously reported. A possible relationship was found between spore levels and several weather characters, but not temperature. Temperature is important for infection. The factors that were significantly correlated with spore presence in both years of the study include rainfall and relative humidity. Based on this work, work is continuing to determine how these factors affect spore production and spread, and to improve disease models.

Technical Abstract: 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 the 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 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.