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). Sclerotinia stem rot is infamously characterized by its challenging fungal promiscuity and longevity, and by the subsequently devastating crop losses; farmers in the north central region of the United States justifiably rank white mold management second behind soybean cyst nematode (Heterodera glycines) in significance and concern. Michigan farmers ranked Sclerotinia stem rot as the number one (tied with planting rates) soybean production issue to be researched over multiple meetings conducted over the 2017/2018 winter meeting season (Staton, 2018). 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
Spray regimes for white mold 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.

Research Goal
To develop a modern and highly integrated management plan for white mold of soybean.

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

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

Updated March 29, 2021:
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, which is ongoing. An updated analysis was performed over the winter of 2021 to deal with extreme variability across locations. 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 fungicide application had a significant effect on both DIX (P<0.01) with the interaction of row-spacing and population and fungicides significantly influencing yield (P < 0.01). DIX was lowest in 30” rows with added reductions where fungicide was used, especially in the 15” row-spacing where disease pressure was higher. However, yield was still higher in 15” planted soybeans in higher populations, despite the higher disease pressure. Our next step is too look at the economics of these programs to decipher where the balance of yield, reduction in DIX, and maximizing resources can be achieved. We expect the research publication to be submitted in the summer of 2021, with the extension summary posted on the Crop Protection Network website in late 2021.

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 and commercial companies are being received and integrated trials are being assembled for the 2021 season. We also have screened commercial germplasm for resistance to white mold in the winter of 2021, along with screening breeding material to be further evaluated in field trials in 2021.

We have also identified four soybean genotypes, referred to as ‘check lines’, which exhibit varying levels of resistance to white mold (Sclerotinia Stem Rot) (Webster et al. 2021). These four check lines include Dwight (public cultivar) which was identified as susceptible, 51-23 and SSR51-70 (breeding lines) identified as moderately resistant, and 52-82B (breeding line) identified as highly resistant. These four lines had also previously been tested for their field resistance in 2016 (McCaghey et al. 2017). More recently, these lines were identified as the check lines from greenhouse studies which examined their physiological resistance levels to white mold. From these greenhouse studies, the soybean genotypes were given their respective resistance rankings (Webster et al. 2021). We have published these results in the journal of Plant Disease (see citation below). We have also published a research summary on the Crop Protection Network website and promoting as a resource for breeders to use moving forward (see objective 4 below).

Citation for this objective:

Webster, R.W., Roth, M.G., Reed, H., Mueller, B., Groves, C.L., McCaghey, M., Chilvers, M.I., Mueller, D.S., Kabbage, M., and Smith, D.L. 2021. Identification of soybean (Glycine max) check lines for evaluating genetic resistance to Sclerotinia stem rot. Plant Disease. https://doi.org/10.1094/PDIS-10-20-2193-RE.

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

By incorporating the resistance levels from the check lines described in objective 2a into the already developed Sporecaster risk prediction model, error associated with differences between genetic resistance levels could be more accurately accounted for. We suggest that moving the action threshold (spray threshold) on the model based on resistance level may improve accuracy of the spray prediction. For example, if a producer had planted a susceptible variety, then the threshold would need to be lowered relative to the standard threshold, and if a highly resistant variety were planted then the threshold could be set at a higher level relative to standard.

In 2020, we established field trials in two separate locations, in the Hancock, WI and Rib Falls, WI. The Hancock location was at the Hancock Agricultural Research Station and the field has a high level of Sclerotinia sclerotiorum inoculum which helps to ensure disease development. This location is under irrigated conditions. The Rib Falls location is located on land belonging to a producer-collaborator with a history of high disease pressure. This field is under non-irrigated conditions. In both of the tested environments, disease development occurred, but disease developed at higher levels at our Rib Falls location.

When examining the white mold index (DIX) across both environments, the response to soybean genotype was highly significant (P < 0.01). As expected, Dwight had the greatest white mold which aligns well with our previous greenhouse studies. 52-82B and 51-23 resulted in similar disease response, and SSR51-70 had the lowest disease levels. In our previous greenhouse studies, SSR51-70 showed moderate resistance, but in field conditions in 2020 this genotype showed higher resistance. This may in part be due to the genotype having an escape mechanism which allows for it to avoid the development of disease. This escape mechanism could be due to SSR51-70 being the earliest maturity group of the four check lines, which allows for the flowering period to be completed prior to the largest release of ascospores into the soybean canopy. SSR51-70 may also exhibit certain mechanisms for inhibiting ascospore infection compared to the other three check lines.

There was no significant response due to fungicide applications, however, some trends were apparent based on the two locations in 2020. When Dwight was not treated, the largest amounts of white mold developed. The responsiveness of Dwight to fungicide treatments is quite dynamic, while the other three genotypes responded to fungicides more statically. Taken together, this shows that a susceptible soybean genotype requires fungicide applications at lower risk thresholds. This is in contrast to the other three genotypes with at least moderate resistance which show that fungicide applications may be needed under more higher pressure conditions.

Soybean genotypes were significantly different for their yield response across both environments (P < 0.01). Dwight had the highest yields despite having the greatest disease levels. 52-82B had the second highest yields which supports previous field trials showing both high yields and low disease levels. The other two genotypes, 51-23 and SSR51-70 had similar yields that were the lowest of the check lines.

Overall, these results show that susceptible genotypes such as Dwight are very responsive to fungicide applications suggesting that applications should be used even in low-risk conditions. Conversely, the genotypes exhibiting levels of resistance can withstand higher risk levels before a fungicide application may be necessary. More work is needed in 2021 to better determine action thresholds based on resistance type. This work is helping to add to the already developed Sporecaster algorithm to further improve prediction accuracy for producers. While making planting decisions, the level of genetic resistance within the variety should be considered. While a white mold susceptible variety may yield higher, there may be an increased cost associated with the need of an application of fungicide where a more resistant variety may not require that fungicide application. In addition to economics, the susceptible genotype will also result in higher disease levels which leads to the production of new white mold sclerotia. This will in turn create a higher inoculum load in that particular field for future production seasons.


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

Selection efforts continue to identify transgenic lines for this project. We have performed another glufosinate (herbicide tag used with our construct) screening in our growth room on the campus of the University of Wisconsin-Madison. From this, 67 putative lines were identified as being tolerant of glufosinate, and we will be inoculating them shortly with S. sclerotiorum isolate 1980 to examine their resistance levels. We believe we have identified a few lines with a stable construct, as the differences in herbicide screening were quite stark for most of the lines we progressed forward. The impending disease screening will be telling on the level of resistance imparted from the transgenic events.


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

We continue to develop outreach materials based on the work conducted under this proposal. The latest material was recently published on the Crop Protection Network. It describes the development and use of the soybean check panel for evaluating resistance to white mold. The citation for this new extension output is below. The link to the resource is here: https://cropprotectionnetwork.org/resources/publications/improved-screening-method-for-genetic-resistance-to-white-mold-sclerotinia-stem-rot-in-soybean

Roth, M. G., Webster, R. W., Reed, H., Mueller, B., Groves, C. L., McCaghey, M., Chilvers, M. I., Mueller, D. S., Kabbage, M., and Smith, D. 2021. Improved Screening Method for Genetic Resistance to White Mold (Sclerotinia stem rot) in Soybean. Crop Protection Network. CPN 5006. Doi.org/10.31274/cpn-20210318-1.

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

This objective was removed to offset the requested budget reduction for this grant-year.

Updated November 10, 2021:
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 duration of this project have been consolidated into a large analysis and submitted for peer-reviewed publication. Soybean farmers in the Upper Midwest region of the United States frequently experience severe yield losses due to Sclerotinia stem rot (SSR or white mold). Previous studies have revealed benefits of individual management practices on 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 multi-site field trials across the Upper Midwest between 2017-2019. These trials revealed that wide row spacings and low seeding rates individually reduced SSR levels but reduced yields. Yields were similar across the three higher seeding rates examined. However, site-years where SSR developed showed the highest partial profits in the intermediate seeding rates. This indicates that partial profits in diseased fields were negatively impacted by high seeding rates, but this trend was not seen 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 the non-treated control. 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 the use of wide rows and low seeding rates could be useful in fields with a history of SSR, while reserving narrow rows and higher seeding rates for fields without a history of SSR. We expect the research publication to be fully accepted (currently accepted with revision) in the Fall of 2021, with the extension summary posted on the Crop Protection Network website sometime in 2022.

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.

We have also identified four soybean genotypes, referred to as ‘check lines’, which exhibit varying levels of resistance to white mold (Sclerotinia Stem Rot) (Webster et al. 2021). These four check lines include Dwight (public cultivar) which was identified as susceptible, 51-23 and SSR51-70 (breeding lines) identified as moderately resistant, and 52-82B (breeding line) identified as highly resistant. These four lines had also previously been tested for their field resistance in 2016 (McCaghey et al. 2017). More recently, these lines were identified as the check lines from greenhouse studies which examined their physiological resistance levels to white mold. From these greenhouse studies, the soybean genotypes were given their respective resistance rankings (Webster et al. 2021). We have published these results in the journal of Plant Disease (see citation below). We have also published a research summary on the Crop Protection Network website and promoting as a resource for breeders to use moving forward (see objective 4 below).

Citation for this objective:

Webster, R.W., Roth, M.G., Reed, H., Mueller, B., Groves, C.L., McCaghey, M., Chilvers, M.I., Mueller, D.S., Kabbage, M., and Smith, D.L. 2021. Identification of soybean (Glycine max) check lines for evaluating genetic resistance to Sclerotinia stem rot. Plant Disease. https://doi.org/10.1094/PDIS-10-20-2193-RE.

We are also seeking plant variety patents (PVP) on four new soybean lines that have resulted from this work. We have disclosed these four new lines to the patenting arm at UW-Madison, the Wisconsin Alumni Research Foundation (WARF). Three of these lines are non-GMO lines appropriate for organic production or other production where non-GMO soybeans are warranted. One of these does have a clear hilum, which would be suitable for a food-grade production system. The other two lines have dark hilum. All lines are highly resistant to SSR or white mold as determined by both greenhouse inoculations and field trialing. The fourth line of interest is also highly resistant to white mold and is non-GMO. This line has a black seed coat and may be appropriate for specialty markets in Asia. We plan to increase these four liens and release them through the Wisconsin Crop Improvement Association for sub-licensing to seed producers.

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

By incorporating the resistance levels from the check lines described in objective 2a into the already developed Sporecaster risk prediction model, error associated with differences between genetic resistance levels could be more accurately accounted for. We suggest that moving the action threshold (spray threshold) on the model based on resistance level may improve accuracy of the spray prediction. For example, if a producer had planted a susceptible variety, then the threshold would need to be lowered relative to the standard threshold, and if a highly resistant variety were planted then the threshold could be set at a higher level relative to standard.

In 2020, we established field trials in two separate locations, in the Hancock, WI and Rib Falls, WI. The Hancock location was at the Hancock Agricultural Research Station and the field has a high level of Sclerotinia sclerotiorum inoculum which helps to ensure disease development. This location is under irrigated conditions. The Rib Falls location is located on land belonging to a producer-collaborator with a history of high disease pressure. This field is under non-irrigated conditions. In both tested environments, disease development occurred, but disease developed at higher levels at our Rib Falls location.

When examining the white mold index (DIX) across both environments, the response to soybean genotype was highly significant (P < 0.01). As expected, Dwight had the greatest white mold which aligns well with our previous greenhouse studies. 52-82B and 51-23 resulted in similar disease response, and SSR51-70 had the lowest disease levels. In our previous greenhouse studies, SSR51-70 showed moderate resistance, but in field conditions in 2020 this genotype showed higher resistance. This may in part be due to the genotype having an escape mechanism which allows for it to avoid the development of disease. This escape mechanism could be due to SSR51-70 being the earliest maturity group of the four check lines, which allows for the flowering period to be completed prior to the largest release of ascospores into the soybean canopy. SSR51-70 may also exhibit certain mechanisms for inhibiting ascospore infection compared to the other three check lines.

There was no significant response due to fungicide applications, however, some trends were apparent based on the two locations in 2020. When Dwight was not treated, the largest amounts of white mold developed. The responsiveness of Dwight to fungicide treatments is quite dynamic, while the other three genotypes responded to fungicides more statically. Taken together, this shows that a susceptible soybean genotype requires fungicide applications at lower risk thresholds. This contrasts with the other three genotypes with at least moderate resistance which show that fungicide applications may be needed under more higher-pressure conditions.

Soybean genotypes were significantly different for their yield response across both environments (P < 0.01). Dwight had the highest yields despite having the greatest disease levels. 52-82B had the second highest yields which supports previous field trials showing both high yields and low disease levels. The other two genotypes, 51-23 and SSR51-70 had similar yields that were the lowest of the check lines.

Overall, these results show that susceptible genotypes such as Dwight are very responsive to fungicide applications suggesting that applications should be used even in low-risk conditions. Conversely, the genotypes exhibiting levels of resistance can withstand higher risk levels before a fungicide application may be necessary. More work was performed in 2021 to better determine action thresholds based on resistance type. This work is helping to add to the already developed Sporecaster algorithm to further improve prediction accuracy for producers. While making planting decisions, the level of genetic resistance within the variety should be considered. While a white mold susceptible variety may yield higher, there may be an increased cost associated with the need of an application of fungicide where a more resistant variety may not require that fungicide application. In addition to economics, the susceptible genotype will also result in higher disease levels which leads to the production of new white mold sclerotia. This will in turn create a higher inoculum load in that field for future production seasons.

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

Selection efforts continue to identify transgenic lines for this project. We have performed another glufosinate (herbicide tag used with our construct) screening in our growth room on the campus of the University of Wisconsin-Madison. From this, 67 putative lines were identified as being tolerant of glufosinate, and we will be inoculating them shortly with S. sclerotiorum isolate 1980 to examine their resistance levels. We believe we have identified a few lines with a stable construct, as the differences in herbicide screening were quite stark for most of the lines we progressed forward. The impending disease screening will be telling on the level of resistance imparted from the transgenic events. Currently these lines are progressing through the disease screening process, and we are hopeful that we will have 2-3 lines that are stable and resistant to white mold.

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

We continue to develop outreach materials based on the work conducted under this proposal. The latest material was recently published on the Crop Protection Network. It describes the development and use of the soybean check panel for evaluating resistance to white mold. The citation for this new extension output is below. The link to the resource is here: https://cropprotectionnetwork.org/resources/publications/improved-screening-method-for-genetic-resistance-to-white-mold-sclerotinia-stem-rot-in-soybean

Roth, M. G., Webster, R. W., Reed, H., Mueller, B., Groves, C. L., McCaghey, M., Chilvers, M. I., Mueller, D. S., Kabbage, M., and Smith, D. 2021. Improved Screening Method for Genetic Resistance to White Mold (Sclerotinia stem rot) in Soybean. Crop Protection Network. CPN 5006. Doi.org/10.31274/cpn-20210318-1.

We also recently updated the general knowledge page on white mold on the Crop Protection Network. This page was updated to include the results of the work that was funded here. This updated page can be found here: https://cropprotectionnetwork.org/resources/publications/white-mold

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.

Overall, this study suggests the use of wide rows and low seeding rates could be useful in fields with a history of SSR, while reserving narrow rows and higher seeding rates for fields without a history of SSR. Soybean farmers in the Upper Midwest region of the United States frequently experience severe yield losses due to Sclerotinia stem rot (SSR or white mold). Previous studies have revealed benefits of individual management practices on 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 multi-site field trials across the Upper Midwest between 2017-2019. These trials revealed that wide row spacings and low seeding rates individually reduced SSR levels but reduced yields. Our results indicate that partial profits in diseased fields were negatively impacted by high seeding rates, but this trend was not seen 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 the non-treated control. Additionally, the production of new inoculum was predicted from disease incidence, serving as an indicator of increased risk for SSR development in future years.

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.

We have identified four soybean genotypes, referred to as ‘check lines’, which exhibit varying levels of resistance to white mold (Sclerotinia Stem Rot) (Webster et al. 2021). These four check lines include Dwight (public cultivar) which was identified as susceptible, 51-23 and SSR51-70 (breeding lines) identified as moderately resistant, and 52-82B (breeding line) identified as highly resistant. We are promoting these lines as a resource for breeders to use moving forward (see objective 4 below).

Citation for this objective:
Webster, R.W., Roth, M.G., Reed, H., Mueller, B., Groves, C.L., McCaghey, M., Chilvers, M.I., Mueller, D.S., Kabbage, M., and Smith, D.L. 2021. Identification of soybean (Glycine max) check lines for evaluating genetic resistance to Sclerotinia stem rot. Plant Disease. https://doi.org/10.1094/PDIS-10-20-2193-RE.

We are also seeking plant variety patents (PVP) on four new soybean lines that have resulted from this work. We have disclosed these four new lines to the patenting arm at UW-Madison, the Wisconsin Alumni Research Foundation (WARF). Three of these lines are non-GMO lines appropriate for organic production or other production where non-GMO soybeans are warranted. One of these does have a clear hilum, which would be suitable for a food-grade production system. The other two lines have dark hilum. All lines are highly resistant to SSR or white mold as determined by both greenhouse inoculations and field trialing. The fourth line of interest is also highly resistant to white mold and is non-GMO. This line has a black seed coat and may be appropriate for specialty markets in Asia. We plan to increase these four liens and release them through the Wisconsin Crop Improvement Association for sub-licensing to seed producers.

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

Overall, our results show that susceptible genotypes such as Dwight are very responsive to fungicide applications suggesting that applications should be used even in low-risk conditions. Conversely, the genotypes exhibiting levels of resistance can withstand higher risk levels before a fungicide application may be necessary. Soybean genotypes were significantly different for their yield response across both environments (P < 0.01).

By incorporating the resistance levels from the check lines described in objective 2a into the already developed Sporecaster risk prediction model, error associated with differences between genetic resistance levels could be more accurately accounted for. We suggest that moving the action threshold (spray threshold) on the model based on resistance level may improve accuracy of the spray prediction. For example, if a producer had planted a susceptible variety, then the threshold would need to be lowered relative to the standard threshold, and if a highly resistant variety were planted then the threshold could be set at a higher level relative to standard.

When examining the white mold index (DIX) across two environments, the response to soybean genotype was highly significant (P < 0.01). As expected, Dwight had the greatest white mold which aligns well with our previous greenhouse studies. 52-82B and 51-23 resulted in similar disease response, and SSR51-70 had the lowest disease levels. In our previous greenhouse studies, SSR51-70 showed moderate resistance, but in field conditions in 2020 this genotype showed higher resistance. This may in part be due to the genotype having an escape mechanism which allows for it to avoid the development of disease. This escape mechanism could be due to SSR51-70 being the earliest maturity group of the four check lines, which allows for the flowering period to be completed prior to the largest release of ascospores into the soybean canopy. SSR51-70 may also exhibit certain mechanisms for inhibiting ascospore infection compared to the other three check lines.

There was no significant response due to fungicide applications, however, some trends were apparent based on the two locations in 2020. When Dwight was not treated, the largest amounts of white mold developed. The responsiveness of Dwight to fungicide treatments is quite dynamic, while the other three genotypes responded to fungicides more statically. Taken together, this shows that a susceptible soybean genotype requires fungicide applications at lower risk thresholds. This contrasts with the other three genotypes with at least moderate resistance which show that fungicide applications may be needed under more higher-pressure conditions.

Dwight had the highest yields despite having the greatest disease levels. 52-82B had the second highest yields which supports previous field trials showing both high yields and low disease levels. The other two genotypes, 51-23 and SSR51-70 had similar yields that were the lowest of the check lines.

More work was performed in 2021 to better determine action thresholds based on resistance type. This work is helping to add to the already developed Sporecaster algorithm to further improve prediction accuracy for producers. While making planting decisions, the level of genetic resistance within the variety should be considered. While a white mold susceptible variety may yield higher, there may be an increased cost associated with the need of an application of fungicide where a more resistant variety may not require that fungicide application. In addition to economics, the susceptible genotype will also result in higher disease levels which leads to the production of new white mold sclerotia. This will in turn create a higher inoculum load in that field for future production seasons.

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

Selection efforts continue to identify transgenic lines for this project. We have performed another glufosinate (herbicide tag used with our construct) screening in our growth room on the campus of the University of Wisconsin-Madison. From this, 67 putative lines were identified as being tolerant of glufosinate, and we will be inoculating them shortly with S. sclerotiorum isolate 1980 to examine their resistance levels. We believe we have identified a few lines with a stable construct, as the differences in herbicide screening were quite stark for most of the lines we progressed forward. The impending disease screening will be telling on the level of resistance imparted from the transgenic events. Currently these lines are progressing through the disease screening process, and we are hopeful that we will have 2-3 lines that are stable and resistant to white mold.

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

We continue to develop outreach materials based on the work conducted under this proposal. The latest material was recently published on the Crop Protection Network. It describes the development and use of the soybean check panel for evaluating resistance to white mold. The citation for this new extension output is below. The link to the resource is here: https://cropprotectionnetwork.org/resources/publications/improved-screening-method-for-genetic-resistance-to-white-mold-sclerotinia-stem-rot-in-soybean

Roth, M. G., Webster, R. W., Reed, H., Mueller, B., Groves, C. L., McCaghey, M., Chilvers, M. I., Mueller, D. S., Kabbage, M., and Smith, D. 2021. Improved Screening Method for Genetic Resistance to White Mold (Sclerotinia stem rot) in Soybean. Crop Protection Network. CPN 5006. Doi.org/10.31274/cpn-20210318-1.

We also recently updated the general knowledge page on white mold on the Crop Protection Network. This page was updated to include the results of the work that was funded here. This updated page can be found here: https://cropprotectionnetwork.org/resources/publications/white-mold

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