ASSBT Biennial Meeting – Feb. 24 – Feb 27, 2025 in Long Beach, CA
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Detection of latent Cercospora leaf spot infection in commercial sugarbeet fields.


¹Sugarbeet and Potato Research Unit, Edward T. Schafer Agricultural Research Center, 1616 Albrecht Blvd, NCSL SBPR, Fargo, ND 58102, ²Plant Pathology Department, North Dakota State University, NDSU Dept 7660, PO Box 6050 Fargo, ND 58108-6050


Cercospora beticola, causal agent of Cercospora leaf spot (CLS), is the most economically damaging pathogen of sugar beet (Beta vulgaris subsp. vulgaris). Management of CLS is reliant on timely fungicide applications and CLS tolerant sugar beet varieties. In response to fungicide applications, C. beticola populations evolved resistance, with documented resistant isolates identified for all major classes of fungicides except for the ethylene bisdithiocarbamate class (EBDC) of fungicides. The economic importance of fungicide resistance cannot be overstated, with CLS epidemics occurring in the upper mid-west in three of four years from 2016 to 2020. To discourage fungicide resistance from developing, disease forecasting models are used to inform growers of the best time to apply fungicides. These models indicate that fungicides should be applied when symptoms of CLS first appear or when environmental conditions are favorable for CLS symptom development. However, the lifecycle of C. beticola includes a long latency period of infection where little to no symptoms of infection are observed. To better understand the temporal infection dynamics of CLS during this important early infection stage, we undertook a large cooperative project spanning the full RRV growing region with the goal of identifying latent infection in commercially grown fields. For five weeks in June and July, 280 field samples were collected each week. Each sample was screened for the presence of C. beticola using a qPCR assay. Additional, qPCR assays for fungicide resistance to systemic fungicides were simultaneously done to detect resistance to benzimidazoles, triazoles (DMIs), and quinone outside inhibitors (QoIs). Results from this study provide the first step in developing more precise timelines of CLS disease progression and information that should be incorporated into CLS disease forecasting models.


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