Predicting Soybean Yield and Sudden Death Syndrome Development Using At-Planting Risk Factors

Abstract

In the United States, sudden death syndrome (SDS) of soybean is caused by the fungal pathogen Fusarium virguliforme and is responsible for important yield losses each year. Understanding the risk of SDS development and subsequent yield loss could provide growers with valuable information for management of this challenging disease. Current management strategies for F. virguliforme use partially resistant cultivars, fungicide seed treatments, and extended crop rotations with diverse crops. The aim of this study was to develop models to predict SDS severity and soybean yield loss using at-planting risk factors to integrate with current SDS management strategies. In 2014 and 2015, field studies were conducted in adjacent fields in Decatur, MI, which were intensively monitored for F. virguliforme and nematode quantities at-planting, plant health throughout the growing season, end-of-season SDS severity, and yield using an unbiased grid sampling scheme. In both years, F. virguliforme and soybean cyst nematode (SCN) quantities were unevenly distributed throughout the field. The distribution of F. virguliforme at-planting had a significant correlation with end-of-season SDS severity in 2015, and a significant correlation to yield in 2014 (P < 0.05). SCN distributions at-planting were significantly correlated with end-of-season SDS severity and yield in 2015 (P < 0.05). Prediction models developed through multiple linear regression showed that F. virguliforme abundance (P < 0.001), SCN egg quantity (P < 0.001), and year (P < 0.01) explained the most variation in end-of-season SDS (R2 = 0.32), whereas end-of-season SDS (P < 0.001) and end-of-season root dry weight (P < 0.001) explained the most variation in soybean yield (R2 = 0.53). Further, multivariate analyses support a synergistic relationship between F. virguliforme and SCN, enhancing the severity of foliar SDS. These models indicate that it is possible to predict patches of SDS severity using at-planting risk factors. Verifying these models and incorporating additional data types may help improve SDS management and forecast soybean markets in response to SDS threats.