Weather-Based Models for Assessing the Risk of Sclerotinia sclerotiorum Apothecial Presence in Soybean (Glycine max) Fields

Abstract

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).

Development and validation of a weather-based warning system to advise fungicide applications to control dollar spot on turfgrass

“…disease event) is going to occur based on binary data (1 = event occurrence, 0 = no event occurrence). To date logistic regression has not been widely used in turfgrass pathology, but has been tested extensively and proven effective in other plant pathogen systems [ 19 – 21 ]. The advantages of this technique in developing a plant disease prediction model include the ability to use simple binary data rather than more complicated approaches such as spore sampling or counting lesions, etc.…”

Identification of factors associated with white mould in snap bean using tree‐based methods

“…Forecasting when white mould is likely to occur is worthwhile and should be made before bloom, which is the optimal time for applying protectant fungicides (Lehner et al , ). Many studies over decades have focused on identifying agronomic and weather factors associated with white mould in bean and continue to do so (Willbur et al , ). Researchers have long recognized that white mould is the product of complex inter‐relationships among several contributory factors and have been frustrated (from a modelling standpoint) because these relationships are difficult to capture with classical linear models (Bom & Boland, ).…”

“…Predictive performance was maintained by remaining with a tree‐based methodology (random forests), which again alludes to an earlier point that the presence of white mould in snap bean is characterized by several interactions not easily captured by linear regression models (Bom & Boland, ). In this case the random forest model had what would be considered a very good estimated test performance (cv AUC of 0.819) for a Sclerotinia ‐based prediction model (Harikrishnan & del Río, ; Willbur et al , , b).…”

An overview of the Sclerotinia sclerotiorum pathosystem in soybean: impact, fungal biology, and current management strategies

“…Apothecial presence, therefore, is a promising candidate to use for SSR risk assessment in soybean fields. In the Great Lakes region, Willbur et al (2018a) developed SSR risk models using environmental parameters including maximum temperature, mean relative humidity, and maximum wind speed to predict apothecial presence. Models were used in a set of subsequent field validation experiments to test accuracy of prediction of end-ofseason disease levels.…”