Early-season predictions of aerial spores to enhance infection model efficacy for Cercospora leaf spot management in sugarbeet

Authors: HERNANDEZ, ALEXANDRA - Michigan State University; BLOOMINGDALE, CHRIS - Michigan State University; RUTH, SARAH - Michigan State University; CUSHNIE, ERICA - University Of Guelph; TRUEMAN, CHERYL - University Of Guelph; Hanson, Linda; WILLBUR, JAMIE - Michigan State University

Submitted to: Plant Disease
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/19/2025
Publication Date: 9/22/2025
Citation: Hernandez, A., Bloomingdale, C., Ruth, S., Cushnie, E., Trueman, C., Hanson, L.E., Willbur, J.F. 2025. Early-season predictions of aerial spores to enhance infection model efficacy for Cercospora leaf spot management in sugarbeet. Plant Disease. 109(9):1805-2001. https://doi.org/10.1094/PDIS-10-24-2153-RE.
DOI: https://doi.org/10.1094/PDIS-10-24-2153-RE

Interpretive Summary: Cercospora beticola is a fungus that causes one of the most economically important foliar diseases of sugarbeet in many growing areas. Management of the disease, Cercospora leaf spot (CLS), relies heavily on timely fungicide applications. There are several disease prediction models used to improve treatment timing. All consider conditions good for infection, but none include factors for presence and abundance of pathogen spores, which are important for starting disease. Both mechanical and biological samplers (highly susceptible beets) were used to test early season spore presence in sugarbeet fields in Michigan and in Ontario, Canada from 2019-2022. These spore levels were compared to weather data. How long leaves were wet, air temperature, and wind speed were found to predict the risk of higher Cercospora spore concentrations with 67.9% accuracy. In 2022 and 2023, a model was tested with a set of potential spray thresholds, in addition to the current disease prediction model (known as the BEETcast model). Model-based programs integrating 50% and 40% action thresholds with canopy closure information resulted in CLS, plant yield, and sugar levels comparable to the grower standard with one less fungicide application. In additional modeling, a model that included relative humidity showed a higher testing accuracy of 73.2%. Based on these studies it is evident that including spore presence and level for C. beticola has potential to improve application timing for targeted CLS management.

Technical Abstract: Cercospora beticola causes one of the most destructive foliar diseases of sugarbeet in many growing regions. Management of Cercospora leaf spot (CLS) relies heavily on timely and repeated fungicide applications. Current treatment initiation is often supported by models predicting conditions favorable for infection; however, these models lack information of C. beticola presence and abundance. Burkard volumetric mechanical samplers and highly CLS-susceptible sentinel beets (biological samplers) were used to assess early-season aerial C. beticola conidia from sugarbeet fields in Michigan and in Ontario, Canada from 2019-2022. In initial correlation and logistic regression analyses (n=449), duration of leaf wetness, air temperature, and wind speed were found to predict the risk of elevated Cercospora spore concentrations with 67.9% accuracy. In 2022 and 2023, a select model and a limited set of action thresholds, in addition to the BEETcast model, were tested for fungicide application timing. When CLS pressure was high, extending the interval between applications showed reduced management of CLS (P < 0.001), sugar percentage, and RWS (P < 0.05) compared to the grower standard. Model-based programs integrating 50% and 40% action thresholds with canopy closure information resulted in CLS, yield, and sugar metrics comparable to the grower standard despite one less fungicide application. In additional training analysis (n=402), an ensemble model included leaf wetness, air temperature, relative humidity, and wind speed variables with a testing accuracy of 73.2% (n=101). Based on model development, refinement, and field validation studies, assessment of elevated early-season C. beticola presence and abundance has potential to improve application timing for targeted CLS management.