Project Details:

Title:
Multi-pronged strategies to provide efficient, sustainable, and durable control to Sclerotinia stem rot

Parent Project: Multi-Pronged Strategies to Provide Efficient Sustainable and Durable Control to Sclerotinia Stem Rot
Checkoff Organization:North Central Soybean Research Program
Categories:Soybean diseases
Organization Project Code:MSN219805
Project Year:2020
Lead Principal Investigator:Damon Smith (University of Wisconsin)
Co-Principal Investigators:
Daren Mueller (Iowa State University)
Martin Chilvers (Michigan State University )
Mehdi Kabbage (University of Wisconsin)
Keywords: Sclerotinia stem rot, Soybean Diseases, White Mold

Contributing Organizations

Funding Institutions

Information and Results

Click a section heading to display its contents.

Project Summary

Impact of Sclerotinia sclerotiorum on soybean:
Sclerotinia stem rot (SSR; white mold) is caused by Sclerotinia sclerotiorum and consistently ranks in the top ten diseases plaguing global soybean crops. Between 2010 and 2014, SSR resulted in total soybean yield losses valued at an estimated $1.2 billion in the U.S. and Canada (Allen et al., 2017). Furthermore, according to a United Soybean Board report from 2011, SSR epidemics in the Great Lakes region alone were responsible for 94% of nationwide losses to the disease and cost regional farmers ~$138 million (USDA-NASS, 2015). Successful control requires farmers to use multiple tools in an integrated disease management plan. The most accessible tools are often simply manipulating standard soybean management practices to reduce pathogen inoculum and subsequent disease.

Management of Sclerotinia stem rot in soybean:
The integrated management of SSR utilizes a combination of cultural, chemical, and biological control practices (Peltier et al., 2012). Some practices may include, crop rotation using non-host crops (Garcia-Garza et al., 2002; Mueller et al., 2002; Rousseau et al., 2007), practicing reduced tillage (Garcia-Garza et al., 2002; Kurle et al., 2001; Mueller et al., 2002), using resistant cultivars (Grau et al., 1982; Hoffman et al., 2002; Kurle et al., 2001), modifying the soybean canopy through seeding rate and row spacing (Jaccoud-Filho et al., 2016; Kurle et al., 2001; Lee et al., 2005), and applying in-season chemical control (Mueller et al., 2004; Peltier et al., 2012; Sumida et al., 2015; Saharan and Mehta, 2008). Many of these practices manipulate the host environment to be unfavorable for diseases development, such as increasing air flow through the canopy or reducing inoculum development in the field.

In Wisconsin, agronomic studies have determined that soybeans planted on either a 7.5- or 15- inch row spacing will consistently yield 7-10% more than soybeans planted at a wider 30-inch row spacing (Bertram and Pedersen, 2004). Additionally, this study reports that, at a narrow 15- inch row spacing, optimal yields may be achieved at population densities of 173,000- 272,000 seeds/acre. Optimal population densities for 30-inch rows, however, range from 124,000-222,000 seeds/acre. The development of the SSR fungus, and subsequent soybean infection, is known to be dependent on canopy closure and favored by cool, moist conditions (Boland and Hall, 1988a). High-yielding soybean row spacing and seeding rates, therefore, inherently increase the risk of SSR development by reducing the time to full canopy closure and by reducing canopy ventilation.

Studies in Brazil have shown that narrow row spacing and high population density increases white mold disease severity and incidence (Jaccoud-Filho et al., 2016). The seeding rates used in this research, however, are not representative of the optimal populations recommended for soybeans grown in the North Central region. In Michigan, population was also found to be positively correlated with disease severity and negatively correlated with yield (Lee et al., 2005); this research, however, only considered a narrow range of high density seeding rates in 7.5- or 30- inch row spacings. As a result, it is difficult to give regionally appropriate SSR management recommendations in environments prone to SSR. Effective integrated management systems require integrated evaluation of regional standards in irrigation, row spacing, seeding rate, and fungicide treatment and their effects on white mold incidence and severity. Moreover, it is important to investigate how manipulation of these practices directly affects the biology surrounding fungal development, and as discussed below, the element of plant resistance

Resistance to S. sclerotiorum in soybean:
In the absence of elicitors of strong host resistance to S. sclerotiorum, polygenic alleles with minor effects are widely believed to contribute to resistance to S. sclerotiorum. Partially resistant soybean genotypes have been selected and identified (Bastein et al, 2014; Boland and Hall, 1987; Grau et al., 1982; Han et al., 2008; Huynh et al., 2010; Iquira et al., 2015; Kim and Diers, 2000; Li et al., 2010; McCaghey and Willbur et al., 2017; Sebastian et al., 2010; Zhao X et al., 2015). Overall, 103 quantitative trait loci (QTL) that contributed to resistance have been recorded in Soybase on 18 out of 20 chromosomes (Soybase, 2010). Identification of these loci provide an opportunity to use marker assisted selection (MAS) as a potential tool for the screening of lines resistant to SSR. However, such an approach presents practical challenges that must be overcome to deploy SSR resistance.

While polygenic resistance (quantitative resistance) is thought be more durable than qualitative resistance; breeding using quantitative resistance is complicated. This includes the “drag” of deleterious and undesirable traits within and near QTL regions, existence of numerous QTL with minimal sole contribution to SSR resistance, and epistatic interactions that pose a challenge to heritability (Moellers et al., 2017). Furthermore, the genetics of physiological resistance to S. sclerotiorum are not well understood. Current ‘field tolerant’ soybean cultivars may be tolerant due to avoidance phenotypes such as flowering time and plant height or entangled environmental and genetic interactions. For example, Kim and Diers (2000) used Novartis S19-90 as a source of resistance in breeding lines and mapped three QTL that accounted for 8-10% of disease severity (DSI) variability. However, two were associated with disease escape mechanisms of greater height, increased lodging, and later flowering date. These escape mechanisms make screening for physiological disease resistance in a field setting difficult. Furthermore, flowering time or canopy closure may differentially align with apothecial development in varied environments, thus impacting disease resistance across environments. Additionally, screening for resistance is complicated by aggregated distributions of inoculum in field trials, if canopy closure and favorable microenvironments for infection differ in a field, resulting in differential disease pressure. To circumvent resistance conferred by escape mechanisms, breeders have mapped QTL and screened lines using inoculation methods that avoid this issue (Arahana et al., 2001; Guo et al., 2008; Vuong et al., 2008). However, other technologies such as genetic modification or gene editing could help advance the industry toward improved resistance to Sclerotinia stem rot.

Chemical Control:
As no complete resistance is available in commercial cultivars, in-season management relies heavily on chemical control targeted at protecting the flowers from S. sclerotiorum ascospore infection (Peltier et al., 2012). Spray regimes are most effective when targeting the flowering window, particularly at the R1 (beginning bloom) growth stage (Mueller et al., 2004). In greenhouse studies, certain fungicides have all demonstrated suppression of S. sclerotiorum signs and symptoms on leaves (Mueller et al., 2002). Chemical sprays may be ineffective and inconsistent when the incidence of SSR is high. The effectiveness of fungicides differs based on the chemical used and application timing in north-central regional studies (Byrne and Chilvers, 2016; Huzar and Novakowiski et al., 2017; Mueller et al., 2016; Smith et al., 2015). Furthermore, field trials demonstrate effective control against S. sclerotiorum by several pesticides and herbicides, but they do not provide complete control, and incidence after chemical sprays can range from 0-60% in plot trials (Mueller et al., 2002 and 2004). Application coverage is also important, with flat-fan spray nozzles with high-fine to mid-medium droplets (200-400 µm) being the most effective. Poor coverage, fungicide rate, mixing, sprayer calibration, and environmental conditions can all affect fungicide efficacy. Coverage is influenced by the density of the canopy, droplet size, and spray volume (Derksen et al., 2008). Additionally, the lactofen formulation used in Dann et al. (1999) had phytotoxic effects that resulted in a 10% yield decrease in the absence of SSR. Lactofen can also cause phenotypic effects such as stunting and discolored, malformed leaves (Huzar-Novakowiski et al., 2017).

Epidemiological modeling to improve management strategies:
Historically, S. sclerotiorum apothecia and SSR incidence were both spatially aggregated and correlated within sectors of soybean fields (Boland and Hall, 1988b). More recently, the distribution of SSR has been correlated with apothecia in both canola (Qandah and del Rio Mendoza 2012) and soybean (Wegulo et al., 2000). In both studies, disease incidence decreased as distance from apothecial inoculum sources increased. Furthermore, ascospores were deposited near the apothecia within soybean fields (Wegulo et al., 2000), which supports the relationship between apothecia and disease. Sclerotial load, determined by intensive soil sampling, was not found to describe white mold incidence in bean fields (McDonald and Boland, 2004). Apothecial presence, therefore, is a promising candidate to use for SSR risk assessment in soybean fields. In the Great Lakes region, Willbur et al. (2018a) combined much of this prior knowledge of SSR in other crops, with new data to develop SSR risk models using environmental parameters including maximum temperature, mean relative humidity, and maximum wind speed to predict apothecial presence. Models were used in a set of subsequent field validation experiments to test accuracy of prediction of end-of-season disease levels. In those validation efforts in Wisconsin, Iowa, and Michigan models predicted SSR over 80% of the time (Willbur et al., 2018b). Furthermore, sources of weather data were tested, including data from an open-source weather provider, darksky.net. Weather from this source were nearly as accurate as weather from on-site weather stations (Willbur et al., 2018b). Plant phenology information and canopy and row-spacing parameters have subsequently been combined with these prediction models into a smartphone application that can be used anywhere to predict the risk of apothecial presence during the soybean bloom period. Thus, timely fungicide applications can be made if weather is conducive or fungicide sprays can be saved if favorable conditions do not exist before and during bloom. The smartphone application is available on the Android and iPhone platforms and is called Sporecaster.

Project Objectives

Objective 1) To evaluate current, standard soybean management practices, including irrigation, row spacing, population density, and fungicide treatment applied using an advisory tool, for use in integrated Sclerotinia stem rot management.

Objective 2.a) To identify new germplasm lines resistant to Sclerotinia sclerotiorum that can be incorporated into integrated management programs or into soybean breeding programs.

Objective 2.b) To refine the existing soybean SSR advisory tool to incorporate model output for different forms of resistance.

Objective 3). Exploitation of transgenic soybean silenced in NADPH oxidases to achieve abiotic and biotic stress tolerance.

Objective 4.a) Develop outreach publications and tools based on results generated here and disseminate through the national Crop Protection Network portal.

Objective 4.b) Develop an electronic book compiling information about Sclerotinia stem rot and management of the disease for a diverse audience.

Project Deliverables

The results of this research will be used to not only increase our understanding of the biology and epidemiology of SSR on soybean, but will be used to formulate improved, modern integrated management decisions for SSR control in soybean. Several important outcomes and deliverables will result from this research. These include:
-Peer-reviewed publications detailing the findings pertaining to integrated management of SSR
-A second peer-reviewed publication detailing adjustment to fungicide regime based on soybean SSR resistance level
-Further validation of Sporecaster on soybean
-Demonstration plots will be available for field day and other educational opportunities in the participating states (Iowa, Michigan, and Wisconsin) where integrated strategies for managing SSR will be showcased
-Fact sheets and publications will be generated using the most current information as a result of this coordinated effort (three personnel on this proposal have extension appointments in addition to their research appointments).
-Results of research will be presented at stakeholder meetings
-Blog articles will be written on extension personnel websites
-An electronic book will be developed for Sclerotinia stem rot management

Progress of Work

Updated March 31, 2020:
The major updates for each objective are listed below. The objective is listed first and then the update. These updates are the most significant during the funding period that started October 1, 2019.

Objective 1) To evaluate current, standard soybean management practices, including irrigation, row spacing, population density, and fungicide treatment applied using an advisory tool, for use in integrated Sclerotinia stem rot management.

Data from the last several years have been consolidated into a large analysis over the winter of 2020. This study examined the effects of integrating row spacing, planting population, and foliar fungicide applications using a smartphone app on SSR disease severity index (DIX) and soybean yield potential using multi-state, multi-year field trials from 2017-2019. The interaction of row spacing and planting population had a significant effect on both DIX (P = 0.04) and yield (P < 0.01). DIX was lowest with a planting population of less than 140,000 seeds/a in a 30-in row spacing. Conversely, DIX trended higher in the 15-in row spacing and was highest when a planting population of 200,000 seeds/a was used with a 15-in row spacing. However, yields were highest in 15-in rows and decreased as planting populations were reduced at both row spacings. Fungicide application had a significant effect on DIX (P < 0.01) and yield (P < 0.01). The greatest reduction of DIX and the highest yields were observed when fungicide was applied at both R1 and R3 growth stages. While our 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. A research publication is currently being drafted describing these results. An extension fact sheet will also be developed after the research publication has been peer-reviewed.

Objective 2.a) To identify new germplasm lines resistant to Sclerotinia sclerotiorum that can be incorporated into integrated management programs or into soybean breeding programs.

Germplasm entry lists from breeders, companies and for integrated trials are being assembled for the 2020 season. We are also in the process of analyzing germplasm and variety field trials from the 2018 and 2019 field seasons. Results from these analyses should inform us on background genetics to be developed into varieties, or to be used in future soybean crosses to improve resistance to white mold. A research publication is currently being drafted to describe these results.

Objective 2.b) To refine the existing soybean SSR advisory tool to incorporate model output for different forms of resistance.

In 2018, Sporecaster was made available to the public as a free download on the Google Play Store and iPhone app store. 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. Sporebuster is used to determine if a crop is at risk for white mold and advises if a fungicide application should be made. This app is meant to be run in-season and uses site-specific weather information to provide the risk prediction.

Sporecaster was previously validated (2016 and 2017) in commercial fields and research trials. In those validations, Sporecaster was over 80% accurate in predicting yield-limiting epidemics of white mold. Additional field validations were performed in 2018. While white mold severity was much less compared to 2016 and 2017, epidemics were present in some fields. In the 2018 validations of 16 commercial fields, Sporecaster was accurate ~80% of the time in predicting yield-limiting epidemics. In 2019 a smaller number of commercial validations were performed. Generally the app worked well, except in Northwest Iowa. In this region, the app made widespread misses. We have spent the winter of 2020 back-validating and making adjustments to the app. The major adjustments have been made to weather inputs to improve accuracy. We have also added 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 update or download for the 2020 field season.

We are also continuing to work on understanding how cultivar resistance can be included in the Sporecaster prediction to improve accuracy. 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. We spent the winters of 2019 and 2020 conducting controlled inoculations to develop a panel of check varieties that can then be used to compare commercial germplasm for resistance level. We can also use this check panel for field testing to develop new spray thresholds for Sporecaster based on resistance. Two trials will be deployed in 2020 to validate this approach.

Objective 3). Exploitation of transgenic soybean silenced in NADPH oxidases to achieve abiotic and biotic stress tolerance.

We performed qPCR using primers of the silencing construct in transformed plants in an attempt to identify transgenic lines that most highly expressed the construct. the assumption is that this would translate to identifying lower RBOH expression. From there, we took the top 6 lines with the highest construct expression, the empty-vector control, and wild-type Williams 82 plants and challenged them with two isolates of Sclerotinia sclerotiorum (1980 and #20) using our published petiole inoculation assay. No significant differences were found in disease levels. We are currently conducting a follow-up experiment of the same 8 lines, to be inoculated soon, so that we can measure the RBOH expression levels at 96 hpi compared to 0 hpi. We hope the results of this experiment will help us further refine which line(s) are stable transformants to pursue in future disease assays.

Objective 4.a) Develop outreach publications and tools based on results generated here and disseminate through the national Crop Protection Network portal.

We recently updated the white mold fungicide efficacy publication (https://crop-protection-network.s3.amazonaws.com/publications/pesticide-impact-on-white-mold-sclerotinia-stem-rot-and-soybean-yield-filename-2020-02-18-181018.pdf) on the Crop Protection Network (CPN) Website. Information from this publication was used to further update efficacy ratings for white mold fungicides on the fungicide efficacy table (https://crop-protection-network.s3.amazonaws.com/publications/fungicide-efficacy-for-control-of-soybean-foliar-diseases-filename-2020-03-18-150123.pdf) also housed on the CPN website. We are actively updating the general white mold information fact sheet (https://crop-protection network.s3.amazonaws.com/publications/cpn-1005-white-mold.pdf). Many of the updates to this fact sheet are based on new research results from this work. This update should be available during the late spring of 2020.

Objective 4.b) Develop an electronic book compiling information about Sclerotinia stem rot and management of the disease for a diverse audience.

The framework for the electronic book is now in place and content will start to be added to the draft during the Spring/Summer of 2020. This will be a major focus for the next few months.

Final Project Results

Benefit to Soybean Farmers

Soybean farmers and agriculture scientists will benefit from this research by:

-Gaining an improved understanding of key, modern management strategies for SSR on soybean
-Improved management of SSR in soybean resulting in improved yield and profitability
-Improved timing of necessary fungicide applications through use of the advisory tool will improve fungicide efficacy and disease control
-Reduced unnecessary fungicide inputs i.e. where weather conditions are non-conducive to apothecia production during flowering a fungicide application can be avoided
-New and improved outreach materials will be developed, including updated web pages and handouts
-An electronic book will be developed to bring a quick, modern, usable reference into the hands of the next generation of farmers and scientists

Performance Metrics

1. Peer-reviewed publication detailing the findings pertaining to integrated management of SSR will be written in winter of 2020. Data for this publication were generated based on this work.
2. A second peer-reviewed publication detailing adjustment to fungicide regime based on soybean SSR resistance level is planned based on data from this project.
3. Further validation of Sporecaster on soybean. These data will be used to improve accuracy.
4. Demonstration plots were available for field days and other educational opportunities in the participating states (Iowa, Michigan, and Wisconsin) where integrated strategies for managing SSR will be showcased.
5. Fact sheets and publications will be generated using the most current information as a result of this coordinated effort.
6. Results of research will be presented at stakeholder meetings in all states involved.
7. Blog articles will be written on extension personnel websites.
8. An electronic book will be developed for white mold management based on data from this work.

Project Years