Fusarium oxysporum (Fo) is an important pathogen that reduces soybean yield by causing seedling disease and root rot. This study assessed the effects of pH and temperature on Fo fungal growth and seedling disease. In an in vitro assay, 14 Fo isolates collected from symptomatic soybean roots across Iowa in 2007 were grown on artificial culture media at five pH levels (4, 5, 6, 7, and 8) and incubated at four temperatures (15, 20, 25, or 30°C). In a rolled-towel assay, soybean seeds from Fo-susceptible cultivar Jack were inoculated with a suspension of a pathogenic or a nonpathogenic Fo isolate; both isolates were previously designated for their relative aggressiveness in causing root rot at 25°C. The seeds were placed in rolled germination paper, and the rolls were incubated in all combinations of buffer solutions at four pH levels (4, 5, 6, and 7), and four temperatures (15, 20, 25, or 30°C). There was a significant interaction between temperature and pH (P < 0.05) for in vitro radial growth and root rot severity. Isolates showed the most in vitro radial growth after incubation at pH 6 and 25°C. For the rolled-towel assay, the pathogenic isolate caused the most severe root rot at pH 6 and 30°C. Gaussian regression analysis estimates for optimal conditions were pH 6.3 at 27.1°C for maximal fungal growth and pH 5.9 at 30°C for maximal root rot severity. These results indicate that optimal pH and temperature conditions are similar for Fo growth and disease in soybean seedlings and suggest that Fo may be a more important seedling pathogen when soybeans are planted under warm conditions in moderately acidic soils.

Current management of sudden death syndrome (SDS) of soybean, caused by Fusarium virguliforme, focuses on planting resistant varieties and improving soil drainage; however, these measures are not completely effective. A 6-year study evaluated the effects of cropping system diversification on SDS and soybean yield. SDS, root health, yield, and F. virguliforme density in soil were assessed in a naturally infested field trial comparing a 2-year cropping system consisting of a corn-soybean rotation and synthetic fertilizer applications with 3- and 4-year cropping systems consisting of corn-soybean-oat + red clover and corn-soybean-oat +alfalfa-alfalfa rotations, respectively, with both manure and low synthetic fertilizer rates. In 5 of 6 years, SDS incidence and severity were lower and yield higher in the 3- and 4-year systems than in the 2-year system. SDS severity and incidence were up to 17-fold lower in the diversified systems than in the 2-year system. Incidence and severity of SDS explained 45 to 87% of the variation in yield. Plants in the 2-year system generally showed more severe root rot and lower plant weights than plants in the diversified systems. F. virguliforme density in soil was up to fivefold greater in the 2-year system compared with the 4-year system. The processes responsible for the suppression of SDS and yield protection in the diversified cropping systems still need to be determined.

Sudden death syndrome (SDS) is one of the major yield-limiting soybean diseases in the Midwestern United States. Effective management for SDS requires accurate detection in soybean fields. Since traditional scouting methods are time-consuming, labor-intensive, and often destructive, alternative methods to monitor SDS in large soybean fields are needed. This study explores the potential of using high-resolution (3 m) PlanetScope satellite imagery for detection of SDS using the random forest classification algorithm. Image data from blue, green, red, and near-infrared (NIR) spectral bands, the calculated normalized difference vegetation index (NDVI), and crop rotation information were used to detect healthy and SDS-infected quadrats in a soybean field experiment with different rotation treatments, located in Boone County, Iowa. Datasets collected during the 2016, 2017, and 2018 soybean growing seasons were analyzed. The results indicate that spectral features, when combined with ground-based information, can detect areas in soybean plots that are at risk for disease, even before foliar symptoms develop. The classification of healthy and diseased soybean quadrats was >75% accurate and the area under the receiver operating characteristic curve (AUROC) was >70%. Our results indicate that high-resolution satellite imagery and random forest analyses have the potential to detect SDS in soybean fields, and that this approach may facilitate large-scale monitoring of SDS (and possibly other economically important soybean diseases). It may also be useful for guiding recommendations for site-specific management in current and future seasons.

In general, early detection and timely management of plant diseases are essential for reducing yield loss. Traditional manual inspection of fields is often time-consuming and laborious. Automated imaging techniques have recently been successfully applied to detect plant diseases. However, these methods mostly focus on the current state of the crop. This paper proposes a gated recurrent unit (GRU)-based model to predict soybean sudden death syndrome (SDS) disease development. To detect SDS at a quadrat level, the proposed method uses satellite images collected from PlanetScope as the training set. The pixel image data include the spectral bands of red, green, blue and near-infrared (NIR). Data collected during the 2016 and 2017 soybean-growing seasons were analyzed. Instead of using individual static imagery, the GRU-based model converts the original imagery into time-series data. SDS predictions were made on different data scenarios and the results were compared with fully connected deep neural network (FCDNN) and XGBoost methods. The overall test accuracy of classifying healthy and diseased quadrates in all methods was above 76%. The test accuracy of the FCDNN and XGBoost were 76.3–85.5% and 80.6–89.2%, respectively, while the test accuracy of the GRU-based model was 82.5–90.4%. The calculation results show that the proposed method can improve the detection accuracy by up to 7% with time-series imagery. Thus, the proposed method has the potential to predict SDS at a future time.

Sudden death syndrome (SDS) of soybean is favored by planting in cool soil but epidemics can be severe even when planting occurs later in the season into warmer soil. Our objective was to determine how soil temperature affects susceptibility of plants exposed to Fusarium virguliforme at different ages. Soybean plants were grown in rhizotrons in water baths at 17, 23, and 29°C. Subsets of plants were inoculated 0, 3, 7, and 13 days after planting (DAP) by drenching soil with a conidial suspension. Root rot developed in all inoculated plants but severity decreased with increasing temperature and plant age at inoculation. Severity of foliar symptoms also decreased with increasing plant age. Whereas plants inoculated 0 DAP developed severe foliar symptoms at all temperatures, plants inoculated 3 and 7 DAP developed symptoms only at 17 and 23°C, and those inoculated 13 DAP never developed foliar symptoms at any temperature. Root length at inoculation was negatively correlated with disease severity. Our findings suggest that roots are most susceptible to infection during the first days after seed germination and that accelerated root growth in warmer temperatures reduces susceptibility to root infection conducive to foliar symptoms. However, soil temperature may not affect infections that occur as soon as seeds germinate.