Influence of Fusarium virguliforme Temporal Colonization of Corn, Tillage, and Residue Management on Soybean Sudden Death Syndrome and Soybean Yield.

The asymptomatic host range of Fusarium virguliforme includes corn, a common crop rotated with soybean that we hypothesize may alter F. virguliforme population dynamics and disease management. A field-based approach explored the temporal dynamics of F. virguliforme colonization of corn and soybean roots under different tillage and residue managements. Experiments were conducted in Iowa, Indiana, Michigan, and Wisconsin, United States and Ontario, Canada from 2016 to 2018. Corn and soybean roots were sampled at consecutive timepoints between 1 and 16 weeks after planting. DNA was extracted from all roots and analyzed by real-time quantitative PCR for F. virguliforme quantification. Trials were rotated between corn and soybean, containing a two-by-two factorial of tillage (no-tilled or tilled) and corn residue (with or without) in several experimental designs. In 2016, low amounts (approximately 100 fg per 10 mg of root tissue) of F. virguliforme were detected in the inoculated Iowa, Indiana, and Michigan locations and noninoculated Wisconsin corn fields. However, in 2017, greater levels of F. virguliforme DNA were detected in Iowa, Indiana, and Michigan across sampling timepoints. Tillage practices showed inconsistent effects on F. virguliforme root colonization and sudden death syndrome (SDS) foliar symptoms among trials and locations. However, residue management did not alter root colonization of corn or soybean by F. virguliforme. Plots with corn residue had greater SDS foliar disease index in Iowa in 2016. However, this trend was not observed across the site-years, indicating that corn residue may occasionally increase SDS foliar symptoms depending on the disease level and soil and weather factors.

Neonicotinoid seed treatments of soybean provide negligible benefits to US farmers.

Neonicotinoids are the most widely used insecticides worldwide and are typically deployed as seed treatments (hereafter NST) in many grain and oilseed crops, including soybeans. However, there is a surprising dearth of information regarding NST effectiveness in increasing soybean seed yield, and most published data suggest weak, or inconsistent yield benefit. The US is the key soybean-producing nation worldwide and this work includes soybean yield data from 194 randomized and replicated field studies conducted specifically to evaluate the effect of NSTs on soybean seed yield at sites within 14 states from 2006 through 2017. Here we show that across the principal soybean-growing region of the country, there are negligible and management-specific yield benefits attributed to NSTs. Across the entire region, the maximum observed yield benefits due to fungicide (FST = fungicide seed treatment) + neonicotinoid use (FST + NST) reached 0.13 Mg/ha. Across the entire region, combinations of management practices affected the effectiveness of FST + NST to increase yield but benefits were minimal ranging between 0.01 to 0.22 Mg/ha. Despite widespread use, this practice appears to have little benefit for most of soybean producers; across the entire region, a partial economic analysis further showed inconsistent evidence of a break-even cost of FST or FST + NST. These results demonstrate that the current widespread prophylactic use of NST in the key soybean-producing areas of the US should be re-evaluated by producers and regulators alike.

Predicting Soybean Yield and Sudden Death Syndrome Development Using At-Planting Risk Factors.

In the United States, sudden death syndrome (SDS) of soybean is caused by the fungal pathogen Fusarium virguliforme and is responsible for important yield losses each year. Understanding the risk of SDS development and subsequent yield loss could provide growers with valuable information for management of this challenging disease. Current management strategies for F. virguliforme use partially resistant cultivars, fungicide seed treatments, and extended crop rotations with diverse crops. The aim of this study was to develop models to predict SDS severity and soybean yield loss using at-planting risk factors to integrate with current SDS management strategies. In 2014 and 2015, field studies were conducted in adjacent fields in Decatur, MI, which were intensively monitored for F. virguliforme and nematode quantities at-planting, plant health throughout the growing season, end-of-season SDS severity, and yield using an unbiased grid sampling scheme. In both years, F. virguliforme and soybean cyst nematode (SCN) quantities were unevenly distributed throughout the field. The distribution of F. virguliforme at-planting had a significant correlation with end-of-season SDS severity in 2015, and a significant correlation to yield in 2014 (P < 0.05). SCN distributions at-planting were significantly correlated with end-of-season SDS severity and yield in 2015 (P < 0.05). Prediction models developed through multiple linear regression showed that F. virguliforme abundance (P < 0.001), SCN egg quantity (P < 0.001), and year (P < 0.01) explained the most variation in end-of-season SDS (R2 = 0.32), whereas end-of-season SDS (P < 0.001) and end-of-season root dry weight (P < 0.001) explained the most variation in soybean yield (R2 = 0.53). Further, multivariate analyses support a synergistic relationship between F. virguliforme and SCN, enhancing the severity of foliar SDS. These models indicate that it is possible to predict patches of SDS severity using at-planting risk factors. Verifying these models and incorporating additional data types may help improve SDS management and forecast soybean markets in response to SDS threats.

Meta-Analytic and Economic Approaches for Evaluation of Pesticide Impact on Sclerotinia Stem Rot Control and Soybean Yield in the North Central United States.

As complete host resistance in soybean has not been achieved, Sclerotinia stem rot (SSR) caused by Sclerotinia sclerotiorum continues to be of major economic concern for farmers. Thus, chemical control remains a prevalent disease management strategy. Pesticide evaluations were conducted in Illinois, Iowa, Michigan, Minnesota, New Jersey, and Wisconsin from 2009 to 2016, for a total of 25 site-years (n = 2,057 plot-level data points). These studies were used in network meta-analyses to evaluate the impact of 10 popular pesticide active ingredients, and seven common application timings on SSR control and yield benefit, compared with not treating with a pesticide. Boscalid and picoxystrobin frequently offered the best reductions in disease severity and best yield benefit (P < 0.0001). Pesticide applications (one- or two-spray programs) made during the bloom period provided significant reductions in disease severity index (DIX) (P < 0.0001) and led to significant yield benefits (P = 0.0009). Data from these studies were also used in nonlinear regression analyses to determine the effect of DIX on soybean yield. A three-parameter logistic model was found to best describe soybean yield loss (pseudo-R2 = 0.309). In modern soybean cultivars, yield loss due to SSR does not occur until 20 to 25% DIX, and considerable yield loss (-697 kg ha-1 or -10 bu acre-1) is observed at 68% DIX. Further analyses identified several pesticides and programs that resulted in greater than 60% probability for return on investment under high disease levels.

Validating Sclerotinia sclerotiorum Apothecial Models to Predict Sclerotinia Stem Rot in Soybean (Glycine max) Fields.

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.

Spatiotemporal Distribution Pattern of Sclerotinia sclerotiorum Apothecia is Modulated by Canopy Closure and Soil Temperature in an Irrigated Soybean Field.

Identifying the optimal timing for fungicide application is crucial in order to maximize the control of Sclerotinia stem rot (SSR), which is caused by Sclerotinia sclerotiorum. In this study, the impact of canopy closure and soil temperature on apothecia production was investigated to optimize fungicide application timing. Replicated soybean plots with a row spacing of 0.36 and 0.38 or 0.76 m were established in 2015 and 2016 in an irrigated soybean field at Michigan State University's Montcalm Research Center. The number of apothecia and ascospores and the incidence of SSR were monitored two times per week for 10 to 12 weeks. In both row-spacing trials, apothecia were observed earlier in 2016 (before the R1 growth stage) than in 2015 (between R1 and R2). The maximum number of apothecia was 50 times higher with the 0.36-m row spacing than with the 0.76-m row spacing in 2015 but was 2.5 times higher with the 0.76-m row spacing than with the 0.38-m row spacing in 2016, though the overall numbers were much lower in 2016. The apothecia distribution pattern was synchronized with the canopy closure pattern and the soil temperature profile. The peak number of apothecia was observed when canopy closure reached at least 50% and when average soil temperature in the row was between 21.5 and 23.5°C. In 91% of the cases, the presence of apothecia was observed when the percentage of light blocked was 70%, and no apothecia germinated in the absence of light or under full light exposure. During the first 50 days after plant emergence, the rate of canopy closure was higher in 2016 than in 2015, and the first diseased plant was observed earlier in 2016 (R2) than in 2015 (R5). Canopy closure and the distance of the sampling point from the soybean row explained much of the variability in the number of apothecia. These results can partially explain the inconsistent efficacy of fungicide applications based on the soybean growth stage and will be helpful for informing disease models and fine-tuning fungicide application strategies.

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

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).

Effect of Soybean Cyst Nematode Resistance Source and Seed Treatment on Population Densities of Heterodera glycines, Sudden Death Syndrome, and Yield of Soybean.

A three-year study was conducted in Illinois, Indiana, Iowa, Michigan, and Ontario, Canada, from 2013 through 2015 to determine the effect of soybean (Glycine max) cultivars' source of soybean cyst nematode (SCN; Heterodera glycines) resistance on SCN population densities, sudden death syndrome (SDS; caused by Fusarium virguliforme), and yield of soybean. Five cultivars were evaluated with and without fluopyram seed treatment at each location. Cultivars with no SCN resistance had greater SDS severity, greater postharvest SCN egg counts (Pf), and lower yield than cultivars with plant introduction (PI) 548402 (Peking) and PI 88788-type of SCN resistance (P < 0.05). Cultivars with Peking-type resistance had lower Pf than those with PI 888788-type and no SCN resistance. In two locations with HG type 1.2-, cultivars with Peking-type resistance had greater foliar disease index (FDX) than cultivars with PI 88788-type. Fluopyram seed treatment reduced SDS and improved yield compared with a base seed treatment but did not affect SCN reproduction and Pf (P > 0.05). FDX and Pf were positively correlated in all three years (P < 0.01). Our results indicate that SDS severity may be influenced by SCN population density and HG type, which are important to consider when selecting cultivars for SCN management.