Weather-based models for assessing the risk of sclerotinia sclerotiorum apothecial presence in soybean (Glycine max) fields

Jaime F. Willbur, Mamadou L. Fall, Christopher Bloomingdale, Adam M. Byrne, Scott A. Chapman, Scott A. Isard, Roger D. Magarey, Megan M. McCaghey, Brian D. Mueller, Joseph M. Russo, Jay Schlegel, Martin I. Chilvers, Daren S. Mueller, Mehdi Kabbage, Damon L. Smith

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Sclerotinia stem rot (SSR) epidemics in soybean, caused by Sclerotinia sclerotiorum, are currently responsible for annual yield reductions in the United States of up to 1 million metric tons. In-season disease management is largely dependent on chemical control but its efficiency and cost-effectiveness depends on both the chemistry used and the risk of apothecia formation, germination, and further dispersal of ascospores during susceptible soybean growth stages. Hence, accurate prediction of the S. sclerotiorum apothecial risk during the soybean flowering period could enable farmers to improve in-season SSR management. From 2014 to 2016, apothecial presence or absence was monitored in three irrigated (n = 1,505 plot-level observations) and six nonirrigated (n = 2,361 plot-level observations) field trials located in Iowa (n = 156), Michigan (n = 1,400), and Wisconsin (n = 2,310), for a total of 3,866 plot-level observations. Hourly air temperature, relative humidity, dew point, wind speed, leaf wetness, and rainfall were also monitored continuously, throughout the season, at each location using high-resolution gridded weather data. Logistic regression models were developed for irrigated and nonirrigated conditions using apothecial presence as a binary response variable.Agronomic variables (row width) and weather-related variables (defined as 30-day moving averages, prior to apothecial presence) were tested for their predictive ability. In irrigated soybean fields, apothecial presence was best explained by row width (r = -0.41, P < 0.0001), 30-day moving averages of daily maximum air temperature (r = 0.27, P < 0.0001), and daily maximum relative humidity (r = 0.16, P < 0.05). In nonirrigated fields, apothecial presence was best explained by using moving averages of daily maximum air temperature (r = -0.30, P < 0.0001) and wind speed (r = -0.27, P < 0.0001). These models correctly predicted (overall accuracy of 67 to 70%) apothecial presence during the soybean flowering period for four independent datasets (n = 1,102 plot-level observations or 30 daily mean observations).

Original languageEnglish (US)
Pages (from-to)73-84
Number of pages12
JournalPlant disease
Issue number1
StatePublished - Jan 2018
Externally publishedYes

Bibliographical note

Funding Information:
We thank our undergraduate research assistants T. Blackwell, D. Holtz, H. Lucas, M. Weber, and M. Young (in Wisconsin); J. Bunner, Z. Sundin, C. Meredith, and K. Naasko (in Michigan); and R. Kempker (in Iowa) for their contributions to the intensive field studies conducted in this work; C. Groves for her laboratory support; and the iPiPE personnel for their continued technical support in using modeled weather data in our apothecial prediction model. The iPiPE CAP is supported by the United States Department of Agriculture (USDA) National Institute of Food and Agriculture Agriculture and Food Research Initiative Competitive Grants Program Food Security Challenge Area Grant 2015-68004-23179. We also thank the USDA Hatch program, the University of Wisconsin-Madison Science and Medicine Graduate Research Scholars fellowship program, the Wisconsin Soybean Marketing Board, the Michigan Soybean Promotion Council, the North Central Soybean Research Program, and the United Soybean Board for their generous support.

Publisher Copyright:
© 2018 The American Phytopathological Society.


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