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Fungicide use is integral to reduce yield loss from Sclerotinia sclerotiorum on dry bean and soybean. Increasing fungicide use against this fungus may lead to resistance to the most common fungicides. Resistance has been reported in Brazil ( Glycine max ) and China ( Brassica napus subsp. napus ), however, few studies have investigated fungicide sensitivity of S. sclerotiorum in the United States. This work was conducted to determine if there was a difference in fungicide sensitivity of S. sclerotiorum isolates in the United States from: (i) dry bean versus soybean and (ii) fields with different frequencies of fungicide application. We further hypothesized that isolates with fungicide applications of a single active ingredient from tropical Brazil and subtropical Mexico were less sensitive than temperate U.S. isolates due to different management practices and climates. The EC 50(D) fungicide sensitivity of 512 S. sclerotiorum isolates from the United States (443), Brazil (36), and Mexico (33) was determined using a discriminatory concentration (DC) previously identified for tetraconazole (2.0 ppm; EC 50(D) range of 0.197 to 2.27 ppm), boscalid (0.2; 0.042 to 0.222), picoxystrobin (0.01; 0.006 to 0.027), and thiophanate-methyl, which had a qualitative DC of 10 ppm. Among the 10 least sensitive isolates to boscalid and picoxystrobin, 2 presented mutations known to confer resistance in the SdhB (qualitative) and SdhC (quantitative) genes; however, no strong resistance was found. This study established novel DCs that can be used for further resistance monitoring and baseline sensitivity of S . sclerotiorum to tetraconazole worldwide plus baseline sensitivity to boscalid in the United States., Competing Interests: The author(s) declare no conflict of interest.

Soybean production in the upper midwestern United States is affected by Sclerotinia stem rot (SSR) caused by the fungal pathogen Sclerotinia sclerotiorum . Genetic resistance is an important management strategy for this disease; however, assessing genetic resistance to S. sclerotiorum is challenging because a standardized method of examining resistance across genotypes is lacking. Using a panel of nine diverse S. sclerotiorum isolates, four soybean lines were assessed for reproducible responses to S. sclerotiorum infection. Significant differences in SSR severity were found across isolates ( P < 0.01) and soybean lines ( P < 0.01), including one susceptible, two moderately resistant, and one highly resistant line. These four validated lines were used to screen 11 other soybean genotypes to evaluate their resistance levels, and significant differences were found across genotypes ( P < 0.01). Among these 11 genotypes, five commercial and public cultivars displayed high resistance and were assessed during field studies across the upper midwestern United States growing region to determine their response to SSR and yield. These five cultivars resulted in low disease levels ( P < 0.01) in the field that were consistent with greenhouse experiment results. The yields were significantly different in fields with disease present ( P < 0.01) and disease absent ( P < 0.01), and the order of cultivar performance was consistent between environments where disease was present or absent, suggesting that resistance prevented yield loss to disease. This study suggests that the use of a soybean check panel can accurately assess SSR resistance in soybean germplasm and aid in breeding and commercial soybean development.

In soybean, Sclerotinia sclerotiorum apothecia are the sources of primary inoculum (ascospores) critical for Sclerotinia stem rot (SSR) development. We recently developed logistic regression models to predict the presence of apothecia in irrigated and nonirrigated soybean fields. In 2017, small-plot trials were established to validate two weather-based models (one for irrigated fields and one for nonirrigated fields) to predict SSR development. Additionally, apothecial scouting and disease monitoring were conducted in 60 commercial fields in three states between 2016 and 2017 to evaluate model accuracy across the growing region. Site-specific air temperature, relative humidity, and wind speed data were obtained through the Integrated Pest Information Platform for Extension and Education (iPiPE) and Dark Sky weather networks. Across all locations, iPiPE-driven model predictions during the soybean flowering period (R1 to R4 growth stages) explained end-of-season disease observations with an accuracy of 81.8% using a probability action threshold of 35%. Dark Sky data, incorporating bias corrections for weather variables, explained end-of-season disease observations with 87.9% accuracy (in 2017 commercial locations in Wisconsin) using a 40% probability threshold. Overall, these validations indicate that these two weather-based apothecial models, using either weather data source, provide disease risk predictions that both reduce unnecessary chemical application and accurately advise applications at critical times.

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).

Take-all, caused by Gaeumannomyces graminis var. tritici, is one of the most important root diseases of wheat worldwide. Because of the lack of highly effective chemical control, cultural practices, such as crop rotation, play a major role in managing disease severity. In Kansas, many producers do not use these measures and continue to suffer losses from take-all. Greenhouse and field experiments were established to assess the effect of horizontal versus vertical distribution of G. graminis var. tritici inoculum on disease severity. Oat kernel inoculum was placed at 0 (seed level), 5, 10, or 15 cm below the wheat seed or 5, 10, or 15 cm to the side of the wheat seed at a depth of 5 cm. Inoculum spatial location and distance greatly influenced take-all. Experiments showed more severe losses due to take-all when inoculum was placed below the seed than to the side of the seed. Regression analyses were used to develop take-all risk models relating inoculum distance from the seed to yield loss. Quadratic models were a better fit for data from experiments where inoculum was placed to the side of the seed, whereas linear models significantly fit data from experiments where inoculum was positioned below the seed. Within the same direction, take-all decreased as the inoculum was placed at greater distances from the seed, often to insignificant levels at 10 to 15 cm. According to the regression models, significant reduction (≥50%) in take-all might be achieved by plowing under the infested residues (crowns) to depths greater than 15 cm, or placing seed >6.0 cm to the side of inoculum. Therefore, under no-till conditions, sowing parallel to and exactly between the previous years' stubble rows (inoculum) might help manage take-all. These possibilities need to be investigated under field conditions.