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

Jaime F. Willbur, Michigan State University; Damon L. Smith, University of Wisconsin-Madison; Keith A. Ames, University of Illinois; Carl A. Bradley, University of Kentucky; Adam M. Byrne, Michigan State University; Scott A. Chapman, University of Wisconsin-Madison; Martin I. Chilvers, Michigan State University; Shawn P. Conley, University of Wisconsin-Madison; Mamadou L. Fall, Agriculture and Agri-Food Canada; Crystal M. Floyd, University of Minnesota; Mehdi Kabbage, University of Wisconsin-Madison; Nathan M. Kleczewski, University of Illinois; Dean K. Malvick, University of Minnesota; Paul D. Mitchell, University of Wisconsin-Madison; Brian D. Mueller, University of Wisconsin-Madison; Daren S. Mueller, Iowa State University; and Adam J. Sisson Iowa State University.

White mold (Sclerotinia stem rot) is caused by the fungal pathogen Sclerotinia sclerotiorum, and the disease frequently ranks among the top yield-reducing soybean diseases in the northern United States. Researchers estimate that white mold caused more than 101 million bushels of soybean yield loss (an estimated value $1.2 billion) in the U.S. and Ontario, Canada (Allen et al., 2017; USDA-NASS, 2017).

The pathogen can survive in the soil as sclerotia for a long time. Furthermore, S. slerotiorum has a broad host range. Both factors present major management challenges. Most commercial soybean cultivars exhibit little host resistance, so in-season management relies heavily on applying fungicides that protect the flowers from infection.

Researchers commonly test chemical products in white mold management trials across the soybean-growing region. For this publication, we used meta-analysis to collate information from these trials into a single database.

Research goals

• Evaluate the efficacy of pesticide treatments and timings

• Provide regional white mold management recommendations

This study compiled independent pesticide efficacy studies from across the North Central soybean growing region to:

• Investigate the impact of white mold on soybean yield

• Determine efficacy of disease reduction and yield protection for multiple chemical application programs

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

• Develop a smartphone application to guide pesticide decisions based on the probability of return on pesticide investment.

Researchers conducted chemical evaluations in Illinois, Iowa, Michigan, Minnesota, New Jersey, and Wisconsin from 2009 to 2016 and obtained more than 2,000 plot-level data points.They tested common active ingredients and application timings, measured white mold severity and grain yield, and combined chemical list prices and application costs for economic analysis (Tables 1 and 2).

Rating disease

Pesticide efficacy trials generally evaluate treatments with measures of disease severity or incidence and yield. For white mold, researchers typically record disease incidence and severity data using a rating scale and combine these into a disease severity index score (DIX). By combining DIX scores with yield loss analyses, we can help identify control thresholds for cost-effective management. These studies will help farmers select cost-effective chemical programs to manage white mold in soybean.

DIX and yield loss

There was a significant correlation between white mold DIX and soybean yield despite considerable variability across the dataset. According to the statistical model we used, little yield loss (0.4-0.9 bushels per acre) occurred at 25-30 percent DIX when the crop growth stage was between R6 and R7.When DIX was greater than 40 percent, yield decreased more dramatically – yield loss was considerable starting at approximately 65 percent DIX.

For every 10 percent increase in DIX after 65 percent, there was a corresponding soybean yield loss of approximately 10 bushels per acre.

Active ingredient

All of the products evaluated reduced overall mean of disease, which offered some level of control and potential yield benefits. While disease pressure did not significantly influence the effect of a treatment, all products provided greater yield benefits when used in high disease-severity situations, except Domark® (e.g., DIX>40 percent; Table 3).

In particular, we observed Cobra® only had positive yield benefits under high white mold severity, a previously observed phenomenon in soybean (Dann et al., 1999). These results are also evident in the yield loss model, which suggests greater yield impacts are observed at greater disease pressure. In the absence of considerable white mold pressure (less than 40 percent DIX), only three of the products we evaluated resulted in a consistent yield benefit over yield in non-treated plots (Table 3): 

• Endura®+Priaxor®

• Endura®

• Proline®+Stratego YLD®

In the North Central region, a single Endura® application or two Aproach® applications are standard recommendations for white mold management. In this study, these active ingredients, along with Endura®+Priaxor®, consistently reduced white mold and provided yield benefits under high and low disease pressure (Table 4).

These products reduced white mold 14-19 percent in areas with 60 percent DIX, and they provided yield benefits of 16-23 percent under high disease pressure. With a mean yield across studies of approximately 55 bushels per acre, these products improve yield potential by as much as 13 bushels per acre over non-treated plots when white mold was severe.

The researchers consistently found that the active ingredients Fortix®, Domark®, Cobra®, and Topsin® had among the lowest efficacies and/or yield benefits. Other researchers also observed limited white mold control or yield benefit by Topsin® (Huzar-Novakowiski et al., 2017). Moreover, researchers have identified Topsin® insensitivity as a concern (Lehner et al., 2015; Mueller et al., 2002). However, we have not found any reports that S. sclerotiorum is insensitive to Domark® or to Fortix®.

Application timing

In these studies, pesticide application timing significantly affected disease reduction and yield benefits.Two applications during flowering provided the most control and greatest yield benefits (Table 5). Single applications at beginning flower (R1) and full flower (R2) resulted in higher disease control than applications at beginning pod. Applications outside these flowering periods provided the lowest white mold control and yield benefits.

These findings corroborate other studies that identified effective application programs during the early flowering periods (Huzar-Novakowiski et al., 2017; Mueller et al., 2004). We further considered the economics of one- and two-spray programs for the best performing products at typically recommended rates and timings.

Under high disease severity (that is, in areas with a history of severe white mold epidemics), the two-application Aproach® program had a comparable return-on-investment to the Cobra® and Endura® single-application programs. However, fungicide products that require two applications for effective control could be competitive options when the price per unit area is less expensive.

Economic analysis

Based on expected farmer returns or break-even probabilities, the active ingredients that maximized ROI were Topsin® and Cobra® (Figure 5). Topsin® was one of the least effective products, but it costs less, which provides favorable returns, especially when disease pressure is considered low. Cobra® was among the more effective active ingredients in high-disease pressure situations. Its relatively low cost compared to other products resulted in high estimated ROI.

Often, products that are more effective for white mold management are also more expensive. These more expensive products have a better ROI potential when disease pressure is higher.

Economical disease management balances efficacy and cost. For that reason, Endura® (a highly effective active ingredient) was less likely to offer a positive ROI in some situations because it cost more than products like Cobra®. However, growers who take the time to seek out the best prices for products like Endura® can improve their chances for a positive ROI with more effective products.

Something to consider is fungicide resistance. Fungicide resistance in S. sclerotiorum is a major concern that could have a long-term negative ROI despite a short-term positive ROI.

This analysis does not incorporate the benefits of resistance management and rotating modes of action across fields and seasons. All treatments generate positive benefits when disease severity and crop value are sufficiently high. For that reason, they can be part of an economical resistance management program.

Sporebuster

The information and models described in this publication were used to develop Sporebuster, a smartphone application for iPhone and Android platforms. Sporebuster helps farmers make economic decisions when they are selecting pesticides and deciding when to apply them to manage white mold. Sporebuster can run various scenarios – when disease pressure is low, medium, or high; when revenue potential is different – while adjusting pesticide application cost. This is a dynamic tool growers can use to tailor pesticide programs to specific farms and situations.

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References

Allen, T. W., et al. 2017. Soybean yield loss estimates due to diseases in the United States and Ontario, Canada from 2010 to 2014. Plant Health Progress 17:211-222. Article / Google Scholar

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This research was based on the following manuscripts

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 Sclerotinia stem rot control and soybean yield in the North Central U.S. Phytopathology. Article / Google Scholar

Acknowledgements

Authors

Jaime F. Willbur, Michigan State University; Damon L. Smith, University of Wisconsin-Madison; Keith A. Ames, University of Illinois; Carl A. Bradley, University of Kentucky; Adam M. Byrne, Michigan State University; Scott A. Chapman, University of Wisconsin-Madison; Martin I. Chilvers, Michigan State University; Shawn P. Conley, University of Wisconsin-Madison; Mamadou L. Fall, Agriculture and Agri-Food Canada; Crystal M. Floyd, University of Minnesota; Mehdi Kabbage, University of Wisconsin-Madison; Nathan M. Kleczewski, University of Illinois; Dean K. Malvick, University of Minnesota; Paul D. Mitchell, University of Wisconsin-Madison; Brian D. Mueller, University of Wisconsin-Madison; Daren S. Mueller, Iowa State University; and Adam J. Sisson Iowa State University.

Reviewers

Tom Allen, Mississippi State University; Gary Bergstrom, Cornell University; Travis Faske, University of Arkansas; Samuel Markell, North Dakota State University; Edward Sikora, Auburn University; Albert Tenuta, Ontario Ministry of Agriculture, Food and Rural Affairs; Darcy Telenko, Purdue University; and Lindsey Thiessen, North Carolina State University. 

All photos were provided by and are the property of the authors and reviewers.

The authors wish to acknowledge the Illinois Soybean Association, Iowa Soybean Association, Michigan Soybean Promotion Committee, Wisconsin Soybean Marketing Board, Minnesota Soybean Research and Promotion Council, and North Central Soybean Research Program for support of this research. For Michigan studies, additional funding was provided in part by DuPont Crop Protection and the USDA-ARS Specific Cooperative Agreement #58-5442-4-017 (National Sclerotinia Initiative). 

We express appreciation to E. Adee, M. Johnson, L. Paul, and G. Steckel for technical assistance in maintaining the research trials in Illinois. Technical editing by Kevin Leigh Smith, Purdue Agricultural Sciences Education and Communication.

This research update is a multi-state and international collaboration partially sponsored by the United Soybean Board and the North Central Soybean Research Program (NCSRP). We also thank the United States Department of Agriculture - National Institute of Food and Agriculture (USDA-NIFA) and the Grain Farmers of Ontario for their support. The Agricultural Adaption Council assists in the delivery of GF2 in Ontario.

This publication was developed by the Crop Protection Network, a multi-state and international collaboration of university/provincial extension specialists and public/ private professionals that provides unbiased, research-based information to farmers and agricultural personnel.

This information in this publication is only a guide, and the authors assume no liability for practices implemented based on this information. Reference to products in this publication is not intended to be an endorsement to the exclusion of others that may be similar. Individuals using such products assume responsibility for their use in accordance with current directions of the manufacturer.

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