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ARS Home » Midwest Area » East Lansing, Michigan » Sugarbeet and Bean Research » Research » Publications at this Location » Publication #348524

Research Project: Genetic Dissection of Traits for Sugar Beet Improvement

Location: Sugarbeet and Bean Research

Title: Assessment of spore presence for Cercospora beticola as demonstrated by sentinel beets

item Hanson, Linda
item BUBLITZ, DANIEL - Michigan State University
item Campbell, Larry
item McGrath, Jon

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 3/27/2018
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Cercospora beticola, the causal agent of Cercospora leaf spot (CLS) in Beta vulgaris (sugar, table, and leaf beet), is an important pathogen globally. Disease forecasting models are widely used to aid in CLS management for sugar beet. Most models rely on weather data to predict infection periods but do not include pathogen presence or conditions for spore release. Spore traps have been used to test for pathogen presence but correlate poorly with disease occurrence. This may be due to imprecision in differentiating spores of varying Cercospora species. Further, genetic evidence indicates that a sexual stage may occur, but spore trapping only identified asexual spores. To reduce issues with spore identification, a highly susceptible sugar beet germplasm was used as a live spore trap. Germplasm F1042 was grown in the greenhouse to at least the 10 leaf stage. These sentinel plants were placed in six different field locations for 7 d and replaced weekly for 6 weeks. Plants were incubated in a humidity chamber for 5 d to allow spores to infect, and monitored for CLS for 21 d. Weather data was taken at the same locations and compared to level of lesion development. Results indicated that infectious spores were present as early as April. A high incidence of infection on sentinel beets was detected 14 d before disease was observed in local fields. This method can be used to aid in identification of risk periods for spore production to add to forecasting models.