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.