Pesticide Impact on White Mold (Sclerotinia Stem Rot) and Soybean Yield

Updated in 2026, this version replaces the 2020 Pesticide Impact on White Mold (Sclerotinia Stem Rot) and Soybean Yield (Archived) publication.

Hope Renfroe-Becton, North Dakota State University; Maria Oros, University of Wisconsin-Madison; Jason Lo, University of Wisconsin-Madison; Adam M. Byrne, Michigan State University; Martin I. Chilvers, Michigan State University; Nathan Kleczewski, University of Illinois; Horacio D. Lopez-Nicora, The Ohio State University; Brian D. Mueller, University of Wisconsin-Madison; Daren S. Mueller, Iowa State University; Damon L. Smith, University of Wisconsin-Madison; Darcy E. P. Telenko, Purdue University; and Richard Wade Webster, North Dakota State University.

Summary

• Meta-analysis was conducted using 32 pesticide application programs corresponding to data across 8 states from 2017 to 2024 field seasons.

• The majority of spray programs were effective in reducing white mold development and preserving yield compared to not using a pesticide.

• Yield losses attributed to white mold were observed beginning at 5% disease index (DIX) and an estimated 10% yield loss was observed at a DIX of 15%.

• Economic models were generated under varying levels of white mold pressure to give farmers return on investment (ROI) predictions based on efficacy of treatments relative to different levels of disease.

• An interactive tool was developed incorporating economic models and ROI predictions to help farmers make informed pesticide application decisions for white mold management.

Overview

Sclerotinia sclerotiorum is a fungal pathogen causing white mold (or Sclerotinia stem rot) in soybean (Figure 1). Though only prevalent on soybeans in the North Central region, white mold was consistently among the top ten most devastating diseases of soybean between 2010 and 2019 (Allen et al. 2017; Bradley et al. 2021). In 2024, white mold contributed to yield losses exceeding 23.64 million bu (710 million kg) (Crop Protection Network 2025a).

White mold can be particularly challenging to manage due to the presence of survival structures, known as sclerotia, which remain viable in the soil for extended periods. S. sclerotiorum is also able to infect a broad range of host crops that are commonly grown in the Midwestern U.S. and Canada, such as canola, dry beans, pulses, and sunflower. Additionally, soybean varieties with high levels of resistance to white mold are not widely available commercially. Cultural practices such as increasing row spacing and reducing seeding rates have been documented to be effective at reducing white mold pressure. However, these practices have been associated with increased weed pressure and reduced yields. These limitations have led to a reliance on pesticide programs for protecting soybeans from infection during early reproductive stages (when flowers are present).

Research Goals

To provide evidence-based management recommendations, we compiled independent pesticide efficacy studies from across the North Central soybean growing region.

• Further investigate the impact of white mold disease severity on soybean yield based on findings from Willbur et al. (2019)

• Compare various pesticide programs for efficacy of white mold reduction and yield protection (Table 1)

• Develop an economic model to estimate expected production value and break-even probabilities for fungicide programs

• Develop a return on investment (ROI) interactive tool to guide farmers’ decisions for selecting the ‘best’ pesticide program for their field(s)

Pesticide trials were conducted in Illinois, Indiana, Iowa, Michigan, Minnesota, North Dakota, Ohio, and Wisconsin where 1,370 plot-level data points were collected between 2017 and 2024. Commonly-used commercially-available pesticides were evaluated either at standard application timings or guided by predictive models (formerly Sporecaster). These models are no longer available as a standalone mobile application but are currently available under the Crop Disease Forecasting Tool (Crop Protection Network 2025b; Willbur et al. 2018a, b; Webster et al. 2022, 2023). Effects of pesticide programs on white mold severity, yield, estimated sclerotial load, and application costs were considered for economic analysis.

Table 1. Pesticide programs evaluated including pesticide active ingredient(s), group, and abbreviation.

^a Active ingredients within the same formula are separated by + while active ingredients of separate products are separated by a semi-colon.

^b Total number of yield effect sizes generated from the primary analysis of variance, and used to evaluate each treatment in network meta-analysis.

^c Total number of studies (environment) used to evaluate each treatment.

^d fb = followed by

Quantifying the effects of white mold

To compare 32 unique pesticide application programs for managing white mold, disease incidence, disease severity, and yield were recorded. Disease incidence was recorded as the total number of plants within the rated area with white mold and severity was rated on a 0-3 scale; where 0 = no disease, 1 = infection on lateral branch, 2 = infection on main stem but not girdling the stem, and 3 = disease on main stem and girdling or dead (Figure 2). Disease incidence and severity were then used to calculate the disease index (DIX). Sclerotial load estimations were also calculated using disease incidence using the formula reported by Webster et al. (2025).

Key Findings

Our analysis estimated that a 15% increase in DIX is associated with nearly a 10% yield loss, which equates to up to approximately 6 bu/ac (376.59 kg/ha) yield loss. Fields with moderate to high DIX (40%-60%) could experience severe yield losses ranging from 23% to 34% loss without incorporating a pesticide program. In comparison to previous research, this analysis reported a higher predicted yield reduction of -16.59 bu/ac (1,041 kg/ha) at 40% DIX compared to the -12 bu/ac (−697 kg/ha) previously reported (Table 2).

Table 2. Estimated yield loss based on the parametric logistic model prediction Renfroe-Becton and Oros et al. 2025.

From meta-analysis, the top programs for reducing DIX included Endura R1 fb R3, Endura R3,  and Omega Sporecaster. A significant yield increase was observed with Delaro Complete R3 while DIX was only moderately deceased; this is likely due to the active ingredient boosting plant metabolism and suppressing the pathogen (Table 3).

Table 3. Mean yield increase and DIX reduction from meta-analysis, limited to treatment applications with ≥5 observations reporting both outcomes.

^a These means represent the averaged bu/ac (or kg/ha) that was observed with each treatment when compared to the non-treated plots. Means that are negative indicate a decrease in mean yield compared to the non-treated.

^b Means reflect an estimated reduction in DIX and decreases are reflected by negative values. Positive means indicate the treatment did not reduce DIX.

Economic analysis

The pesticide programs with the highest probability of being profitable for white mold management were Cobra at R1, Cobra at V4/V5, Endura at R3, and Delaro Complete at R3 across varying levels of DIX (Figures 3 and 4). Although the average yield increase associated with Cobra in the meta-analysis was relatively modest, its lower cost and moderate white mold control resulted in a high likelihood of positive net return. Notably, Cobra at R1 showed the highest breakeven probability across the full range of white mold severities, meaning it most frequently covered or exceeded its application cost. However, as a point of caution, applications of Cobra may have phytotoxic effects leading to leaf burn and flower abortion. It is important to make applications right before or right at the onset of flowering for optimal white mold management.

In contrast, programs requiring two fungicide applications, such as Endura R1 fb R3 and Omega R1 fb Miravis Neo R3, offered strong white mold suppression but generally had the lowest expected net benefit once application and product costs were considered. Ultimately, when a field has a history of moderate to high disease pressure, a chemical application is recommended for higher net expected benefits. An additional benefit of effective chemical application programs is the reduction in the production of new sclerotia, the inoculum of white mold, which could lead to lower disease pressure in subsequent seasons. However, the economic effects of lower inoculum are still not well-characterized.

These findings highlight an important distinction for decision-making. While programs with multiple applications of premium products can deliver excellent disease control and yield protection under severe pressure, programs including single applications of more affordable products often provide more reliable economic returns in fields with histories of no to low white mold pressure.

To assist farmers in making informed decisions tailored to their field conditions, our profitability calculator is available for use at the White Mold ROI Calculator and for practitioners interested in using our calculator in their software systems, we also maintain an Application Programming Interface (API). Our software is open source.

References

Allen, T. W., Bradley, C. A., Sisson, A. J., Byamukama, E., Chilvers, M. I., Coker, C. M., Collins, A. A., Damicone, J. P., Dorrance, A. E., Dufault, N. S., Esker, P. D., Faske, T. R., Giesler, L. J., Grybauskas, A. P., Hershman, D. E., Hollier, C. A., Isakeit, T., Jardine, D. J., Kelly, H. M., Kemerait, R. C., Kleczewski, N. M., Koenning, S. R., Kurle, J. E., Malvick, D. K., Markell, S. G., Mehl, H. L., Mueller, D. S., Mueller, J. D., Mulrooney, R. P., Nelson, B. D., Newman, M. A., Osborne, L., Overstreet, C., Padgett, G. B., Phipps, P. M., Price, P. P., Sikora, E. J., Smith, D. L., Spurlock,. T. N., Tande, C. A., Tenuta, A.U., Wise, K. A., and Wrather, J. A. 2017. Soybean yield loss estimates due to diseases in the United States and Ontario, Canada, from 2010 to 2014. Plant Health Progress. 18:19-27.

Bradley, C. A., Allen, T. W., Sisson, A. J., Bergstrom, G. C., Bissonnette, K. M., Bond, J., Byamukama, E., Chilvers, M. I., Collins, A. A., Damicone, J. P., Dorrance, A. E., Dufault, N. S., Esker, P. D., Faske, T. R., Fioello, N. M., Giesler, L. J., Hartman, G. L., Hollier, C. A., Isakeit, T., Jardine, D. J., Kelly, H. M., Kemerait, R. C., Kleczewski, N. M., Koehler, A. M., Kratochvil, R. K., Kurle, J. E., Malvick, D. K., Markell, S. G., Mathew, F. M., Mehl, H. L., Mehl, K. M., Mueller, D. S., Mueller, J. D.,  Nelson, B. D., Overstreet, C., Padgett, G. B., Price, P. P., Sikora, E. J., Small, I., Smith, D. L., Spurlock., T. N., Tande, C. A., Telenko, D. E. P., Tenuta, A. U., Theissen, L. D., Warner, F., Wiebold, W. J., and Wise, K. A. Soybean yield loss estimates due to diseases in the United States and Ontario, Canada, from 2015 to 2019. Plant Health Progress. 22:483-495.

Crop Protection Network. 2025a. Soybean Disease Loss Estimates from the United States and Ontario, Canada — 2024. Sikora, Ed; Faske, Travis; Spurlock, Terry; Betts, Alyssa; Dufault, Nick; Grabau, Zane; Kemerait, Bob; Camiletti, Boris; Telenko, Darcy; Mueller, Daren; Sisson, Adam; Onofre, Rodrigo; Bradley, Carl; Padgett, Boyd; Price, Trey; Watson, Tristan; Chilvers, Marty; Malvick, Dean; Allen, Tom; Bish, Mandy; Mangel, Dylan; Bergstrom, Gary; Lux, LeAnn; Mathew, Febina; Webster, Wade; Lopez-Nicora, Horacio; Duffeck, Maira; Tenuta, Albert; Collins, Alyssa; Esker, Paul; Mueller, John; Plumblee, Michael; Shires, Madalyn; Kelly, Heather; Isakeit, Tom; Langston, David; Zeng, Yuan; Smith, Damon. CPN-1018-24. doi.org/10.31274/cpn-20250317-1.

Crop Protection Network. 2025b. Crop Risk Tool. Date cited: 17 July 2025. Website URL: https://connect.doit.wisc.edu/cpn-risk-tool/

Webster, R. W., Groves, C. L., Mueller, B. D., Renfroe-Becton, H., and Smith, D. L. 2025. Investigating the role of soybean genetic resistance on the production of Sclerotinia sclerotiorum sclerotia. Plant Dis. 109:1158-1164.

Webster, R. W., Mueller, B. D., Conley, S. P., and Smith, D. L. 2023. Integration of soybean (Glycine max) resistance levels to Sclerotinia stem rot into predictive Sclerotinia sclerotiorum apothecial models. Plant Dis. 107:2763-2768.

Webster, R. W., Roth, M. G., Mueller, B. D., Mueller, D. S., Chilvers, M. I., Willbur, J. F., Mourtzinis, S., Conley, S. P., and Smith, D. L. 2022. Integration of row spacing, seeding rate, and fungicide applications for control of Sclerotinia stem rot in Glycine max. Plant Dis. 106:1183-1191.

Willbur, J. F., Fall, M. L., Bloomingdale, C., Byrne, A., M., Chapman, S. A., Isard, S. A., Magarey, R. D., McCaghey, M. M., Mueller, B. D., Russo, J. M., Schlegel, J., Chilvers, M. I., Mueller, D. S., Kabbage, M., and Smith, D. L. 2018a. Weather-based models for assessing the risk of Sclerotinia sclerotiorum apothecial presence in (Glycine max) fields. Plant Dis. 102:73-84.

Willbur, J. F., Fall, M. L., Byrne, A., M., Chapman, McCaghey, M. M., Mueller, B. D., Schmidt, R., Chilvers, M. I., Mueller, D. S., Kabbage, M., Giesler, L. J., Conley, S. P., and Smith, D. L. 2018b. Validating Sclerotinia sclerotiorum apothecial models to predict Sclerotinia stem rot in soybean (Glycine max) fields. Plant Dis. 102:2592-2601.

Willbur, J. F., Mitchell, P. D., Fall, M. L., Byrne, A. M., Chapman, S. A., Floyd, C. M., Bradley, C. A., Ames, K. A., Chilvers, M. I., Kleczewski, N. M., Malvick, D. K., Mueller, B. D., Mueller, D. S., Kabbage, M., Conley, S. P., and Smith, D. L. 2019. Meta-Analytic and Economic Approaches for Evaluation of Pesticide Impact on Sclerotinina Stem Rot Control and Soybean Yield in the North Central United States. Phytopathology 109:1157-1170.

This research was based on the following manuscript

Renfroe-Becton, H., Oros, M., Byrne, A.M., Chilvers, M.I., Kleczewski, N., Lopez-Nicora, H.D., Mueller, B.D., Mueller, D.S., Smith, D.L., Telenko, D.E.P., and Webster, R.W. 2025. Meta-analytic and economic evaluation of fungicide programs applied for managing Sclerotinia stem rot in soybean across the North-Central United States. PhytoFrontiers. Doi: 10.1094/PHYTOFR-07-25-0068-R.

Acknowledgements

Authors

Hope Renfroe-Becton, North Dakota State University; Maria Oros, University of Wisconsin-Madison; Jason Lo, University of Wisconsin-Madison; Adam M. Byrne, Michigan State University; Martin I. Chilvers, Michigan State University; Nathan Kleczewski, University of Illinois; Horacio D. Lopez-Nicora, The Ohio State University; Brian D. Mueller, University of Wisconsin-Madison; Daren S. Mueller, Iowa State University; Damon L. Smith, University of Wisconsin-Madison; Darcy E. P. Telenko, Purdue University; and Richard Wade Webster, North Dakota State University.

Reviewers

Travis Faske, University of Arkansas and Albert Tenuta, Ontario Ministry of Agriculture, Food and Agribusiness.

This research was supported by the North Central Soybean Research Program (NCSRP) under the projects titled “Multi-dimensional approaches for improved productivity, sustainability, and management of major soybean diseases in the North Central U.S.” and “Advancing soybean health: field trials to decision support tools to maximize disease management”; and in part by Ohio Soybean Council (24-R-37).

The authors would like to thank Heidi and Spencer Eslinger for their assistance in managing field trials at the Oakes Irrigation Research Site in Dickey Co., ND; Stith Wiggs for managing field trials in Iowa; John F. Boyse, William Widdicombe, and Micalah Herendeen for managing field trials in Michigan; Su Shim, Steven Brand, and Jeffrey Ravellette for managing field trials in Indiana; Zak Ralston and his team for managing field trials in Ohio.

The United Soybean Board and the United States Department of Agriculture - National Institute of Food and Agriculture (USDA-NIFA) support Crop Protection Network infrastructure and resources.

How to cite: Renfroe-Becton, H., Oros, M., Lo, J., Byrne, A. M., Chilver, M. I., Kleczewski, N., Lopez-Nicora, H. D., Mueller, B. D., Mueller, D. S., Smith, D. L., Telenko, D. E. P., Webster, R. W. 2026. Pesticide Impact on White Mold (Sclerotinia Stem Rot) and Soybean Yield. Crop Protection Network. CPN-5001. doi.org/10.31274/cpn-20191022-000.