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

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 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 validation, assessment of elevated early-season C. beticola presence and abundance has potential to improve application timing and efficacy for preventative CLS management.