Sclerotinia sclerotiorum is a fungus that forms durable resting structures, called sclerotia, that allows it to survive dormant in the soil for up to 8 years (Adams and Ayers, 1979). Under certain environmental conditions, the sclerotia will germinate and form small mushroom-like structures, called apothecia, which can launch spores into the air and onto soybean plants. Often, the environmental conditions for sclerotia germination occur during soybean flowering, so fungal spores commonly land on flowers, colonize them, and infect into the stem. Relying on this natural infection process in soybean breeding programs can complicate research because soybean genotypes vary widely in their flowering times. Even susceptible soybean genotypes can appear resistant if their flowering time is not in sync with sclerotia germination and spore release in the field. A more robust method to test for resistance is to use a direct inoculation method by removing a portion of a soybean petiole and placing the fungus on the cut-petiole (Figure 1, left). This allows S. sclerotiorum to colonize the soybean stem, mimicking a natural infection and allowing breeders to better evaluate physiological or active resistance to white mold in soybeans.
Many university, industry, and government laboratories use a direct inoculation method to screen soybean genotypes for enhanced resistance to white mold. However, each institution tends to use different soybean genotypes as checks or controls, and different rating scales to report levels of susceptibility or resistance. This has led to confusion on how to interpret disease resistance ratings of public and commercial soybean genotypes.
Develop a set of soybean genotypes with consistent responses to diverse S. sclerotiorum isolates that can be used as checks across breeding programs
Screen additional soybean genotypes for resistance to white mold with a greenhouse inoculation assay
Encourage soybean breeding programs to adopt this screening protocol to improve the reliability and rate of resistance identification
Resistance to white mold in soybean is challenging to identify using only field experiments for a number of reasons. First, there is a strong influence of environment on S. sclerotiorum‘s life cycle, which can influence white mold severity year to year in the same locations (Peltier et al., 2012). Second, different agronomic traits such as maturity group, lodging, and plant height are highly correlated with disease resistance in field settings (Kim and Diers, 2000), which complicate our understanding of resistance to white mold. Furthermore, genetic resistance to white mold appears to be controlled by many genes, with each gene contributing small amounts of resistance to the disease (Arahana et al., 2001; Vuong et al., 2008). Therefore, improved genetic resistance is still desired by farmers, and actively sought by soybean breeders.
Soybean breeding efforts often generate thousands of new genotypes with different genetic combinations that significantly affect agronomic and disease resistance traits. Screening thousands of genotypes and the check genotypes with nine isolates of S. sclerotiorum is not possible. However, this method is highly valuable in early breeding stages to screen soybean genotypes as potential parents for future crosses to develop resistant varieties. In subsequent stages of the breeding process, the method presented here can be used with one aggressive S. sclerotiorum isolate like isolate 20 (as shown in Figures 2B and 2D), and the four check genotypes to screen the offspring of crosses. This approach can help speed up identification of resistant offspring and allow for comparisons across different soybean breeding programs at all institutions.
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This research was based on the following manuscript
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Mitchell G. Roth, University of Wisconsin-Madison; Richard W. Webster, University of Wisconsin-Madison; Hannah Reed, University of Wisconsin-Madison; Brian Mueller, University of Wisconsin-Madison; Carol L. Groves, University of Wisconsin-Madison; Megan McCaghey, University of California-Davis; Martin I. Chilvers, Michigan State University; Daren S. Mueller, Iowa State University; Mehdi Kabbage, University of Wisconsin-Madison; and Damon Smith, University of Wisconsin-Madison.
Carl Bradley, University of Kentucky; Travis Faske, University of Arkansas; and Kiersten Wise, University of Kentucky.