Case Study of an Epidemiological Approach Dissecting Historical Soybean Sclerotinia Stem Rot Observations and Identifying Environmental Predictors of Epidemics and Yield Loss

An Innovative Binding-Protein-Based dsRNA Extraction Method: Comparison of Cost-Effectiveness of Virus Detection Methods Using High-Throughput Sequencing

Viral diseases represent a threat to global food production. Managing the impact of viruses on crop production requires the ability to monitor viruses, study their ecology and anticipate outbreaks. Double-stranded RNA (dsRNA) sequencing is a well-established and reliable method of detecting viruses and studying virome-host interactions and ecology. Compared to total RNA extraction, dsRNA extraction eliminates the majority of host RNAs, improving the recovery of viral RNAs. In this study, we developed and evaluated a novel dsRNA extraction method for high-throughput sequencing (HTS) applications based on the Flock House virus (FHV) B2 protein (B2-based method), and compared its performance with that of established cellulose-based and DRB4-based methods (commercial kit), as well as total RNA extraction techniques. The electrostatic properties of B2 have been instrumental in developing a bead-free and resin-free dsRNA extraction method. The B2-based method demonstrated high viral read recovery, achieving proportions exceeding 20% in most samples, and provided better dsRNA purity with less low weight molecule co-extracted RNA than the DRB4-based method and cellulose-based methods. Despite producing overall fewer total reads than the DRB4-based method, the B2-based enrichment for viral-derived dsRNA was better, with a higher percentage of viral reads, making it effective in virome profiling. Furthermore, it had an excellent detection specificity (0.97) and a good detection sensitivity (0.71), minimising false positives and false negatives. In addition, the B2-based method proved to be highly cost-effective, with a per-reaction cost of $4.47, compared to $35.34 for the DRB4-based method. This method offers a practical solution for laboratories with limited resources or for large-scale sampling for viral ecology studies. Future improvements to the B2-based method should focus on optimising sensitivity to Vitivirus species and developing scalable, automated workflows for high-throughput viral detection.

Occurrence of Ascospores and White Mold Caused by Sclerotinia sclerotiorum in Dry Bean Fields in Alberta, Canada

White mold caused by the fungal pathogen Sclerotinia sclerotiorum (Lib.) de Bary is one of the most important biological constraints to dry bean (Phaseolus vulgaris L.) production in Canada. Disease forecasting is one tool that could help growers manage the disease while reducing fungicide use. However, predicting white mold epidemics has remained difficult due to their sporadic occurrence. In this study, over the course of four growing seasons (2018 to 2021), we surveyed dry bean fields in Alberta and collected daily in-field weather data and daily in-field ascospore counts. White mold levels were variable and generally high in all years, confirming that the disease is ubiquitous and a constant threat to dry bean production. Ascospores were present throughout the growing season, and mean ascospore levels varied by field, month, and year. Models based on in-field weather and ascospore levels were not highly predictive of final disease incidence in a field, suggesting that environment and pathogen presence were not limiting factors to disease development. Rather, significant effects of market class on disease were found, with pinto beans, on average, having the highest disease incidence (33%) followed by great northern (15%), black (10%), red (6%), and yellow (5%). When incidence of these market classes was modeled separately, different environmental variables were important in each model; however, average wind speed was a significant variable in all models. Taken together, these findings suggest that white mold management in dry bean should focus on fungicide use, plant genetics, irrigation management, and other agronomic factors.

Integration of Row Spacing, Seeding Rates, and Fungicide Applications for Control of Sclerotinia Stem Rot in Glycine max

Soybean (Glycine max) farmers in the Upper Midwest region of the United States often experience severe yield losses due to Sclerotinia stem rot (SSR). Previous studies have revealed benefits of individual management practices for SSR. This study examined the integration of multiple control practices on the development of SSR, yield, and the economic implications of these practices. Combinations of row spacings, seeding rates, and fungicide applications were examined in multisite field trials across the Upper Midwest from 2017 to 2019. These trials revealed that wide row spacing and low seeding rates individually reduced SSR levels but also reduced yields. Yields were similar across the three highest seeding rates examined. However, site-years where SSR developed showed the highest partial profits at the intermediate seeding rates. This finding indicates that partial profits in diseased fields were reduced by high seeding rates, but this trend was not observed when SSR did not develop. Fungicides strongly reduced the development of SSR while also increasing yields. However, there was a reduction in partial profits due to their use at a low soybean sale price, but at higher sale prices fungicide use was similar to not treating. Additionally, the production of new inoculum was predicted from disease incidence, serving as an indicator of increased risk for SSR development in future years. Overall, this study suggests using wide rows and low seeding rates in fields with a history of SSR while reserving narrow rows and higher seeding rates for fields without a history of SSR.

Biosynthesis of silver nanoparticles employing Trichoderma harzianum with enzymatic stimulation for the control of Sclerotinia sclerotiorum

Biogenic synthesis of silver nanoparticles employing fungi offers advantages, including the formation of a capping from fungal biomolecules, which provides stability and can contribute to biological activity. In this work, silver nanoparticles were synthesized using Trichoderma harzianum cultivated with (AgNP-TS) and without enzymatic stimulation (AgNP-T) by the cell wall of Sclerotinia sclerotiorum. The nanoparticles were evaluated for the control of S. sclerotiorum. The specific activity of the T. harzianum hydrolytic enzymes were determined in the filtrates and nanoparticles. Cytotoxicity and genotoxicity were also evaluated. Both the nanoparticles exhibited inhibitory activity towards S. sclerotiorum, with no new sclerotia development, however AgNP-TS was more effective against mycelial growth. Both the filtrates and the nanoparticles showed specific enzymatic activity. Low levels of cytotoxicity and genotoxicity were observed. This study opens perspectives for further exploration of fungal biogenic nanoparticles, indicating their use for the control of S. sclerotiorum and other agricultural pests.

Performance and Profitability of Fungicides for Managing Soybean White Mold: A 10-Year Summary of Cooperative Trials

White mold, caused by Sclerotinia sclerotiorum, is a yield-limiting disease of soybean in Brazil. Uniform fungicide trials have been conducted annually since 2009. Data from 74 cooperative field trials conducted over a 10-year period were assembled. We selected five fungicides applied two times around flowering: dimoxystrobin plus boscalid (DIMO+BOSC), carbendazim plus procymidone (CARB+PROC), fluazinam (FLUZ), fluopyram (FLUO), and procymidone (PROC). For comparison, thiophanate-methyl (TMET) applied four times was also included as a low-cost treatment. Network models were fitted to the log of white mold incidence (percentages) and log of sclerotia mass data (grams/hectare) and to the nontransformed yield data (kilograms/hectare) for each treatment, including the untreated check. Back-transformation of the meta-analytic estimates indicated that the lowest and highest mean (95% confidence interval [CI]) percent reductions in incidence and sclerotia mass were 54.2 (49.3 to 58.7) and 51.6% (43.7 to 58.3) for TMET and 83.8 (79.1 to 87.5) and 87% (81.9 to 91.6) for CARB+PROC, respectively. The overall mean (95% CI) yield responses ranged from 323 kg/ha (247.4 to 400.3) for TMET to 626 kg/ha (521.7 to 731.7) for DIMO+BOSC, but the variance was significantly reduced by a binary variable (30% threshold) describing disease incidence in the untreated check. On average, an increment of 352 kg/ha was estimated for trials where the incidence was >30% compared with the low-disease scenario. Hence, the probability of breaking even on fungicide costs for the high-disease scenario was >65% for the more effective, but more expensive fungicide (FLUZ) than TMET. For the low-disease scenario, profitability was less likely and depended more on variations in fungicide cost and soybean price.

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.

Soilborne Inoculum Density and Environmental Parameters Influence the Development of Pythium Stunt Caused by Pythium tracheiphilum in Head Lettuce Crops

In Quebec muck soils, Pythium stunt (Pythium tracheiphilum Matta) is responsible for important yield losses in head lettuce crops each year, which can reach up to 50% in certain cases. Despite the significance of the disease, factors influencing its development remain poorly documented, and no disease risk indicators are available, which makes the development of management strategies difficult. Hence, growers systematically use chemical fungicides throughout the growing season to reduce crop losses. However, it is known that soilborne disease incidence or severity may be influenced by soil inoculum density and environmental parameters. Therefore, the objectives of this study were to investigate the influence of inoculum density on lettuce growth under controlled conditions and evaluate the influence of soil inoculum density, air temperature, relative humidity, and rainfall on disease incidence under field conditions. In particular, this study aims to develop accurate predictors for Pythium stunt incidence. Results showed that, under controlled environment, thresholds of inoculum density of 97 and 46 propagules per gram of dry soil were needed to reduce lettuce dry weight by one-half for cultivars Estival and Prestige, respectively. These results were confirmed under field conditions, where a soil inoculum density >132 propagules per gram of dry soil combined with air temperatures <18°C for the first 2 weeks and rain accumulation >64 mm for the first 3 weeks after transplanting accurately predicted disease incidence 79% of the time. These relationships improve understanding of seasonal Pythium stunt development and will provide useful tools to develop sustainable management strategies.

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.