Uncovering the environmental conditions required for Phyllachora maydis infection and tar spot development on corn in the United States for use as predictive models for future epidemics.

Phyllachora maydis is a fungal pathogen causing tar spot of corn (Zea mays L.), a new and emerging, yield-limiting disease in the United States. Since being first reported in Illinois and Indiana in 2015, P. maydis can now be found across much of the corn growing regions of the United States. Knowledge of the epidemiology of P. maydis is limited but could be useful in developing tar spot prediction tools. The research presented here aims to elucidate the environmental conditions necessary for the development of tar spot in the field and the creation of predictive models to anticipate future tar spot epidemics. Extended periods (30-day windowpanes) of moderate mean ambient temperature (18-23 °C) were most significant for explaining the development of tar spot. Shorter periods (14- to 21-day windowpanes) of moisture (relative humidity, dew point, number of hours with predicted leaf wetness) were negatively correlated with tar spot development. These weather variables were used to develop multiple logistic regression models, an ensembled model, and two machine learning models for the prediction of tar spot development. This work has improved the understanding of P. maydis epidemiology and provided the foundation for the development of a predictive tool for anticipating future tar spot epidemics.

Integration of Soybean (Glycine max) Resistance Levels to Sclerotinia Stem Rot into Predictive Sclerotinia sclerotiorum Apothecial Models.

Sclerotinia stem rot (SSR) is a major disease of soybean across the Upper Midwest region of the United States. Management of this disease has relied on fungicide applications, but due to the environmental conditions necessary for SSR to develop, many of these applications are unnecessary. To mitigate this, predictive models have been developed using localized weather data for predicting the formation of Sclerotinia sclerotiorum apothecia, the inoculum source of SSR, and these models were integrated into a decision support system called Sporecaster. However, these models do not account for the soybean resistance levels to SSR. In this study, fungicide trials were performed across seven site-years in Wisconsin between 2020 and 2022 examining fungicide applications applied at one of three action thresholds (low, moderate, and high) following Sporecaster recommendations in combination with four soybean varieties representing three SSR resistance levels (susceptible, moderately resistant, and resistant). From these trials, the low and moderate action thresholds resulted in similarly low disease severity index (DIX) levels comparable to the standard across all varieties. However, the low action threshold was most accurate for predicting SSR development in the susceptible variety, and the high action threshold was most accurate for predicting SSR development for the three resistant varieties. Both the susceptible soybean and a moderately resistant line yielded similarly high results. Additionally, the use of all fungicide applications led to similar partial profits at grain sale prices of either $0.44 or $0.55 kg-1. Overall, this study uncovered relationships between soybean resistance levels to SSR and Sporecaster, allowing for improved recommendations for fungicide applications.

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.

Identification of Soybean (Glycine max) Check Lines for Evaluating Genetic Resistance to Sclerotinia Stem Rot.

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.