Research HighlightsMulti-pronged Strategies Provide Efficient, Sustainable and Durable Control to Sclerotinia Stem Rot
By Julie Meyer
Sclerotinia stem rot (SSR), also called white mold, is caused by the fungus Sclerotinia sclerotiorum. Farmers in the north central region of the United States justifiably rank white mold management second only to the soybean cyst nematode in significance and concern. Successful control of SSR requires knowledge of field history and skillful use of management strategies. Complicating the situation is the fact that SSR development is highly dependent on weather conditions during the mid-season period.
Management of Sclerotinia stem rot in soybean
Successful management of SSR requires an integrated management plan that combines cultural, chemical, and biological control practices. Practices include crop rotation using non-host crops, practicing reduced tillage, using resistant cultivars, modifying the soybean canopy through seeding rate and row spacing, and applying in-season chemical control. Many of these practices manipulate the host environment to be unfavorable for disease development, such as increasing air flow through the canopy.
With checkoff funding provided by the North Central Soybean Research Program, plant pathologists Damon Smith and Medhi Kabbage at the University of Wisconsin work closely with colleagues in Iowa and Michigan conducting multi-state, multi-year field trials over many years to evaluate combinations of SSR soybean management practices, and bring those results to farmers.
Demonstration plots in Iowa, Michigan, and Wisconsin are set up each season for field days and other educational opportunities so that farmers and ag advisors can see results of various integrated strategies for managing SSR first-hand.
This year, a large analysis was completed on data from research trials and on-farm data from the last several years. The specific focus of the analysis was the effects of integrating row spacing, planting population, and foliar fungicide applications on SSR disease severity and soybean yield potential.
Here are some of the results:
- In fields where white mold is a significant problem, growers can reduce planting populations down to around 120,000 seeds/acre and move to 30-inch row spacings without dramatic yield losses. Incidence of SSR was lowest with a planting population of less than 140,000 seeds/acre in a 30-inch row spacing, and highest in a 15-inch row spacing and a planting population of 200,000 seeds/acre.
- Analysis suggests wide row spacing and lower planting populations can reduce disease, it can also decrease yield potential. Therefore, additional factors such as field history and environmental factors need to be considered for field specific SSR management, but the combination of wide row spacing, and low populations is recommended for high-pressure SSR fields.
- Fungicide application had a significant effect on SSR and yield. The greatest reduction of SSR and the highest yields were observed when fungicide was applied at both R1 and R3 growth stages.
Using white mold resistant soybean varieties is critical to a complete white mold management plan. Although varieties with complete resistance are not yet available, there are commercial varieties with distinctly different levels of SSR resistance. Soybean breeders, including the team in Wisconsin, Iowa and Michigan, are working to improve varieties that have good resistance and yield well.
In 2020, 25 breeding lines with potential resistance to white mold were evaluated in multi-state field trials. The breeding lines were previously screened in the greenhouse for white mold resistance. The goal is to identify a handful of lines that can be used as new cultivars or as subsequent breeding material.
Updates on current outreach publications and tools
Because SSR management requires the integration of several management strategies, farmers need current, reliable sources of information in order to devise the best strategy for their farm.
A big focus of the white mold research team is to get information into farmers’ hands as quickly as possible. Three of the most popular and widely consulted publications on white mold have been updated with current information. An e-book on white mold management is also in the works.
In 2018, the Sporecaster app was made publicly available as a free download on the Google Play Store and iPhone app store. Sporecaster is used to determine if a crop is at risk for white mold and advises if a fungicide application should be made. The app is meant to be run in-season and uses site-specific weather information to provide the risk prediction.
By using this app, growers can improve the timing of necessary fungicide applications and reduce unnecessary fungicide inputs when weather conditions are not conducive to apothecia production.
As of this report, Sporecaster was downloaded over 3,500 times from the Apple and Android stores. Daily use rates during the major “white mold season” (July and August) ranged between 600 and 800 users per day.
The white mold team continues to test and improve the Sporecaster tool. Recent adjustments have been made to weather inputs to improve accuracy. A new feature is the ability for the user to adjust a spray action threshold to what they feel comfortable with.
The new version (version 1.35) is now available on both platforms for the 2021 field season.
The next frontier in the development of the app is to incorporate cultivar resistance into the Sporecaster prediction. This could be done by modifying the action thresholds based on resistance type. Work is underway to understand how this could be implemented, using greenhouse and field trials on varieties with known resistance levels. Spray thresholds would then be based on known resistance levels in soybean varieties.
Published: Feb 22, 2021
The materials on SRIN were funded with checkoff dollars from United Soybean Board and the North Central Soybean Research Program. To find checkoff funded research related to this research highlight or to see other checkoff research projects, please visit the National Soybean Checkoff Research Database.