Soybean Sudden Death Syndrome Research Articles

Assessing Seed Treatments to Control Sudden Death Syndrome Caused by Fusarium virguliforme and Seedling and Root Disease Caused by Rhizoctonia sp. on Mungbean (Vigna radiata)

There is growing interest in producing mungbean ( Vigna radiata L. Wilczek) in the United States, which is driven by increased consumer demand. Strategies are needed for United States farmers to take advantage of this high-value crop and its environmental sustainability. Recommendations for the management of mungbean seedling and root diseases are lacking. Preliminary research in central Iowa mungbean fields from 2019 to 2023 has shown that mungbean is particularly vulnerable to Fusarium virguliforme, the cause of soybean sudden death syndrome (SDS), and Rhizoctonia sp., the cause of damping-off, root decay, premature defoliation, and pod abortion. The purpose of this study was to screen a variety of fungicides as a seed treatment or a soil band application to control root rot in mungbean. We performed three field studies over 2 years using mungbean cultivars Berken and OK2000. Seed treatments Evergol Energy + fluopyram and Evergol Energy + pydiflumetofen showed the highest performance ( P < 0.05), with approximately 30% more healthy roots than nontreated controls. Other treatments exhibited about 20% more healthy roots than the nontreated control. Biotam in-furrow application showed the highest plant population for ‘OK2000’, but we observed no treatment differences in plant stand for ‘Berken’.

Presymptomatic Leaf Reflectance of Fusarium virguliforme-Infected Soybean Plants in Greenhouse Conditions

Sudden death syndrome of soybean is caused by a soilborne pathogen, Fusarium virguliforme. Prior to visible foliar symptoms, a destructive technique can be carried out to diagnose root infection. Hyperspectral sensors can be a presymptomatic and nondestructive alternative for plant disease diagnosis. This study was designed to relate leaf spectral reflectance to F. virguliforme root infection in the absence of foliar symptoms. Soybean plants were grown under controlled greenhouse conditions. The spectral reflectance of the plants was measured weekly beginning at 21 days after transplanting up until 42 days after transplanting using a swing hyperspectral imaging system fixed on a gantry. Destructive root sampling confirmed F. virguliforme root infection using real-time PCR. The most relevant wavelengths for discrimination were selected using the ReliefF algorithm. Three machine learning models (partial least squares discriminant analysis, support vector machine, and random forest) were evaluated for classification accuracy using the selected wavelengths. Relevant wavelengths for differentiating between the healthy and F. virguliforme-infected plants were found in the visible and red-edge region from 500 to 750 nm and the shortwave infrared region from 1,400 to 2,350 nm. In the absence of visible foliar symptoms, classification results showed over 79% mean F1 scores for all models. Partial least squares discriminant analysis was able to differentiate healthy and F. virguliforme-infected plants with a mean F1 score of 83.1 to 85.3% and a kappa statistic of 0.43 to 0.54. This work supports the use of hyperspectral remote sensing for early presymptomatic disease diagnosis under a controlled environment. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Multistate Sensitivity Monitoring of Fusarium virguliforme to the SDHI Fungicides Fluopyram and Pydiflumetofen in the United States.

Sudden death syndrome (SDS), caused by Fusarium virguliforme, is an important yield-limiting disease of soybean (Glycine max). From 1996 to 2022, cumulative yield losses attributed to SDS in North America totaled over 25 million metric tons, which was valued at over US $7.8 billion. Seed treatments are widely used to manage SDS by reducing early season soybean root infection by F. virguliforme. Fluopyram (succinate dehydrogenase inhibitor [SDHI] - FRAC 7), a fungicide seed treatment for SDS management, has been registered for use on soybean in the United States since 2014. A baseline sensitivity study conducted in 2014 evaluated 130 F. virguliforme isolates collected from five states to fluopyram in a mycelial growth inhibition assay and reported a mean EC50 of 3.35 mg/liter. This baseline study provided the foundation for the objectives of this research: to detect any statistically significant change in fluopyram sensitivity over time and geographical regions within the United States and to investigate sensitivity to the fungicide pydiflumetofen. We repeated fluopyram sensitivity testing on a panel of 80 historical F. virguliforme isolates collected from 2006 to 2013 (76 of which were used in the baseline study) and conducted testing on 123 contemporary isolates collected from 2016 to 2022 from 11 states. This study estimated a mean absolute EC50 of 3.95 mg/liter in isolates collected from 2006 to 2013 and a mean absolute EC50 of 4.19 mg/liter in those collected in 2016 to 2022. There was no significant change in fluopyram sensitivity (P = 0.1) identified between the historical and contemporary isolates. A subset of 23 isolates, tested against pydiflumetofen under the same conditions, estimated an absolute mean EC50 of 0.11 mg/liter. Moderate correlation was detected between fluopyram and pydiflumetofen sensitivity estimates (R = 0.53; P < 0.001). These findings enable future fluopyram and pydiflumetofen resistance monitoring and inform current soybean SDS management strategies in a regional and national context.

Genome-Wide Informative Microsatellite Markers and Population Structure of Fusarium virguliforme from Argentina and the USA.

Soybean sudden death syndrome (SDS) is a destructive disease that causes substantial yield losses in South and North America. Whereas four Fusarium species were identified as the causal agents, F. virguliforme is the primary SDS-causing pathogen in North America and it also contributes substantially to SDS in Argentina. In this study, we comparatively analyzed genome assemblies of four F. virguliforme strains and identified 29 informative microsatellite markers. Sixteen of the 29 markers were used to investigate the genetic diversity and population structure of this pathogen in a collection of 90 strains from Argentina and the USA. A total of 37 multilocus genotypes (MLGs) were identified, including 10 MLGs in Argentina and 26 in the USA. Only MLG2, the most dominant MLG, was found in both countries. Analyses with three different approaches showed that these MLGs could be grouped into three clusters. Cluster IA consisting of four MLGs exclusively from the USA has much higher genetic diversity than the other two clusters, suggesting that it may be the ancestral cluster although additional data are necessary to support this hypothesis. Clusters IB and II consisted of 13 and 21 MLGs, respectively. MLGs belonging to these two clusters were present in all four sampled states in Argentina and all five sampled states in the USA.

Evaluation of soybean germplasm for resistance to Fusarium virguliforme, the major pathogen causing sudden death syndrome of soybean in the United States

AbstractSudden death syndrome (SDS) is one of the most important yield limiting diseases of soybean [Glycine max (L.) Merr.]. In North America, the most common causal species is Fusarium virguliforme O'Donnell &amp; T. Aoki. Yield losses of up to 80% have been reported. Host resistance is among the most sustainable tools to reduce SDS losses, and screening soybean accessions for sources of resistance is a fundamental part of discovering host resistance. This study evaluated 10,144 soybean plant introductions (PIs) in soybean maturity groups (MGs) 000 to X for resistance to F. virguliforme. Using a tiered testing sequence and a seedling‐layered inoculum method in the greenhouse, new sources of resistance were identified. In tier VI, 81 of 125 PIs evaluated had mean foliar severity that was equal to or significantly lower than that of the resistant check, PI 567374. In tier VII, 20 PIs with low foliar severity scores representing a range of MGs were further evaluated for foliar response, as well as “relative dry weights”—dry shoot and root weights expressed as a percentage of respective noninoculated plants. At 28 days after planting, 16 PIs had mean foliar severity ratings not significantly different from PI 567374; three of these PIs, 279081, 506757, and 507568, had significantly greater relative dry shoot, root, and total weight than PI 567374. The highly resistant entries reported here will be useful for further studies to understand the resistance mechanisms to F. virguliforme and for breeding programs focused on increasing SDS resistance in elite breeding lines.

Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean.

Using a reliable and accurate method to phenotype disease incidence and severity is essential to unravel the complex genetic architecture of disease resistance in plants, and to develop disease resistant cultivars. Genome-wide association studies (GWAS) involve phenotyping large numbers of accessions, and have been used for a myriad of traits. In field studies, genetic accessions are phenotyped across multiple environments and replications, which takes a significant amount of labor and resources. Deep Learning (DL) techniques can be effective for analyzing image-based tasks; thus DL methods are becoming more routine for phenotyping traits to save time and effort. This research aims to conduct GWAS on sudden death syndrome (SDS) of soybean [Glycine max L. (Merr.)] using disease severity from both visual field ratings and DL-based (using images) severity ratings collected from 473 accessions. Images were processed through a DL framework that identified soybean leaflets with SDS symptoms, and then quantified the disease severity on those leaflets into a few classes with mean Average Precision of 0.34 on unseen test data. Both visual field ratings and image-based ratings identified significant single nucleotide polymorphism (SNP) markers associated with disease resistance. These significant SNP markers are either in the proximity of previously reported candidate genes for SDS or near potentially novel candidate genes. Four previously reported SDS QTL were identified that contained a significant SNPs, from this study, from both a visual field rating and an image-based rating. The results of this study provide an exciting avenue of using DL to capture complex phenotypic traits from images to get comparable or more insightful results compared to subjective visual field phenotyping of traits for disease symptoms.

Time of Soybean Sudden Death Syndrome Foliar Symptom Onset Influences Final Disease Intensity, Yield, and Yield Components.

Sudden death syndrome (SDS), caused by Fusarium virguliforme, causes substantial yield losses in soybean. However, relationships between soybean yield and components of disease progress, including time of disease onset, are poorly understood. Individual soybean plants (2018) and quadrats (2016 to 2018) were monitored in commercial fields and experimental plots in Iowa to quantify the impact of SDS foliar symptom onset on final SDS intensity, soybean yield components, and yield. The date when SDS foliar symptoms were first detected (onset time) and progress of SDS incidence and severity were recorded weekly. Individual soybean plants and quadrats were harvested at the end of each season. Beta-regression showed that date of SDS onset had a consistent and stable effect on final disease intensity both at individual plant and quadrat levels. The slope of the relationship between date of SDS onset and final SDS severity was common across all field sites and years. Weighted linear regression revealed that SDS onset explained 60 to 83% of the variation in number of pods, number of seeds, and total seed weight in individual plants, and 94 to 97% of the variation in seed yield in quadrats. Soybean yield damage functions (slopes) indicated that for each day SDS onset was delayed, soybean yield increased by 30.5 to 31.3 kg/ha. This new quantitative information improves understanding of the impact of SDS on final disease intensity and soybean yield. Further experiments are needed to determine how this relationship is affected by site-specific factors.

Genetic mapping of host resistance to soybean sudden death syndrome

AbstractSoybean sudden death syndrome (SDS) is a disease caused by a soil‐borne fungus, Fusarium virguliforme. Many genetic studies have attempted to identify genes responsible for the quantitative host resistance to SDS. Three recombinant inbred line (RIL) populations were evaluated for foliar SDS resistance at a naturally infested field site in Decatur, MI, during the 2014 and 2015 growing seasons. Lines were evaluated for disease severity (DS) on a 1–9 scale, disease incidence (DI) as an estimate of the percentage of plants with symptoms per plot, and disease index (DX) as a metric which integrates DS and DI. Phenotypic data was spatially adjusted to account for uneven pathogen distribution in the naturally infested field. A subset of RILs from each population were genotyped with the SoySNP6K Illumina Infinium BeadChip. Linkage maps unique to each population were constructed using JoinMap ver. 2. Composite interval mapping was performed using WinQTLCartographer ver. 2.5. Three quantitative trait loci (QTL) were identified across 2 yr and/or multiple populations. One QTL on Chromosome 10 appeared to be colocalized with the E2 maturity locus. Another QTL identified was on Chromosome 18, in a region which has been demonstrated to provide soybean cyst nematode (SCN) and SDS resistance in many studies (rhg1/Rfs2).

AAC McRae soybean

AAC McRae is a high yielding food grade soybean [Glycine max (L.) Merr.] cultivar with yellow hilum, high protein concentration, and acceptable processing quality for foreign and domestic tofu, soymilk, and miso markets. It has excellent soybean cyst nematode (SCN) (Heterodera glycines Ichinohe) and soybean sudden death syndrome (SDS) (Fusarium virguliforme O'Donnell &amp; T. Aoki) resistance. AAC McRae was developed at the Agriculture and Agri-Food Canada (AAFC) Harrow Research and Development Centre (Harrow-RDC), Harrow, Ontario, and is adapted to areas of southwest Ontario with 3100 or more crop heat units and has a relative maturity group of 2.2 (MG 2.2).

Genome-wide transcriptional response of the causal soybean sudden death syndrome pathogen Fusarium virguliforme to a succinate dehydrogenase inhibitor fluopyram.

Succinate dehydrogenase inhibitors (SDHIs) have been widely used to manage plant diseases caused by phytopathogenic fungi. Although attention to and use of SDHI fungicides has recently increased, molecular responses of fungal pathogens to SDHIs have often not been investigated. A SDHI fungicide, fluopyram, has been used as a soybean seed treatment and has displayed effective control of Fusarium virguliforme, one of the causal agents of soybean sudden death syndrome. To examine genome-wide gene expression of F. virguliforme to fluopyram, RNA-seq analysis was conducted on two field strains of F. virguliforme with differing SDHI fungicide sensitivity in the absence and presence of fluopyram. The analysis indicated that several xenobiotic detoxification-related genes, such as those of deoxygenase, transferases and transporters, were highly induced by fluopyram. Among the genes, four ATP-binding cassette (ABC) transporters were characterized by the yeast expression system. The results revealed that expression of three ABCG transporters was associated with reduced sensitivity to multiple fungicides including fluopyram. In addition, heterologous expression of a major facilitator superfamily (MFS) transporter that was highly expressed in the fluopyram-insensitive F. virguliforme strain in the yeast system conferred decreased sensitivity to fluopyram. This study demonstrated that xenobiotic detoxification-related genes were highly upregulated in response to fluopyram, and expression of ABC or MFS transporter genes was associated with reduced sensitivity to the SDHI fungicide. This is the first transcriptomic analysis of the fungal species response to fluopyram and the finding will help elucidate the molecular mechanisms of SDHI resistance. © 2021 Society of Chemical Industry.

Influence of Fusarium virguliforme Temporal Colonization of Corn, Tillage, and Residue Management on Soybean Sudden Death Syndrome and Soybean Yield.

The asymptomatic host range of Fusarium virguliforme includes corn, a common crop rotated with soybean that we hypothesize may alter F. virguliforme population dynamics and disease management. A field-based approach explored the temporal dynamics of F. virguliforme colonization of corn and soybean roots under different tillage and residue managements. Experiments were conducted in Iowa, Indiana, Michigan, and Wisconsin, United States and Ontario, Canada from 2016 to 2018. Corn and soybean roots were sampled at consecutive timepoints between 1 and 16 weeks after planting. DNA was extracted from all roots and analyzed by real-time quantitative PCR for F. virguliforme quantification. Trials were rotated between corn and soybean, containing a two-by-two factorial of tillage (no-tilled or tilled) and corn residue (with or without) in several experimental designs. In 2016, low amounts (approximately 100 fg per 10 mg of root tissue) of F. virguliforme were detected in the inoculated Iowa, Indiana, and Michigan locations and noninoculated Wisconsin corn fields. However, in 2017, greater levels of F. virguliforme DNA were detected in Iowa, Indiana, and Michigan across sampling timepoints. Tillage practices showed inconsistent effects on F. virguliforme root colonization and sudden death syndrome (SDS) foliar symptoms among trials and locations. However, residue management did not alter root colonization of corn or soybean by F. virguliforme. Plots with corn residue had greater SDS foliar disease index in Iowa in 2016. However, this trend was not observed across the site-years, indicating that corn residue may occasionally increase SDS foliar symptoms depending on the disease level and soil and weather factors.

Current recommendations and novel strategies for sustainable management of soybean sudden death syndrome.

The increase in food production requires reduction of the damage caused by plant pathogens, minimizing the environmental impact of management practices. Soil-borne pathogens are among the most relevant pathogens that affect soybean crop yield. Soybean sudden death syndrome (SDS), caused by several distinct species of Fusarium, produces significant yield losses in the leading soybean-producing countries in North and South America. Current management strategies for SDS are scarce since there are no highly resistant cultivars and only a few fungicide seed treatments are available. Because of this, innovative approaches for SDS management need to be developed. Here, we summarize recently explored strategies based on plant nutrition, biological control, priming of plant defenses, host-induced gene silencing, and the development of new SDS-resistance cultivars using precision breeding techniques. Finally, sustainable management of SDS should also consider cultural control practices with minimal environmental impact. © 2021 Society of Chemical Industry.

Guava decline: updating its etiology from ‘Fusarium solani’ to Neocosmospora falciformis

Since the early 2000s, over 5000 hectares of guava orchards have been decimated in Brazil by guava decline. The disease has caused tens of millions of dollars in yield losses, the loss of thousands of jobs, and the closing of many fruit pulp-processing plants. Guava decline has been described as a synergistic interaction between the nematode Meloidogyne enterolobii and the fungus Fusarium solani. Nonetheless, the etiology of the disease has had to be reappraised because recent molecular studies have reassigned the whole ‘F. solani species complex’ to the genus Neocosmospora, with several plant-parasitic species being reclassified or synonymized. Hence, from a collection of ‘F. solani’ isolates associated with guava decline, we chose four of the most aggressive isolates, from which we sequenced RNA polymerase II subunit 1 (RPB1), RNA polymerase II subunit 2 (RPB2), and translation elongation factor 1-alpha (TEF1) genes. A GenBank® BLASTn search showed that the isolates belonged to Neocosmospora sp. Phylogenetic analyses using the maximum likelihood (ML) and Bayesian inference (BI) methods, with individual and combined datasets for TEF1, RPB1 and RPB2 genes, clustered all isolates within the N. falciformis clade, with maximum support in the ML and BI multilocus analyses. This and previous studies show that N. falciformis is the causal agent of guava decline and soybean sudden death syndrome, as well as of crown and root rots in other annual and perennial crops.

Economic Impact of Fluopyram-Amended Seed Treatments to Reduce Soybean Yield Loss Associated with Sudden Death Syndrome.

Soybean (Glycine max) sudden death syndrome (SDS), caused by Fusarium virguliforme, is a key limitation in reaching soybean yield potential, stemming from incomplete disease management through cultural practices and partial host resistance. A fungicidal seed treatment was released in 2014 with the active ingredient fluopyram and was the first chemical management strategy to reduce soybean yield loss stemming from SDS. Although farm level studies have found fluopyram profitable, we were curious to discover whether fluopyram would be beneficial nationally if targeted to soybean fields at risk for SDS yield loss. To estimate economic benefits of fluopyram adoption in SDS at-risk acres, in the light of U.S. public research and outreach from a privately developed product, we applied an economic surplus approach, calculating ex ante net benefits from 2018 to 2032. Through this framework of logistic adoption of fluopyram for alleviation of SDS-associated yield losses, we projected a net benefit of $5.8 billion over 15 years, considering the costs of public seed treatment research and future extension communication. Although the sensitivity analysis indicates that overall net benefits from fluopyram adoption on SDS at-risk acres are highly dependent upon the market price of soybean, the incidence of SDS, the adoption path, and ceiling of this seed treatment, the net benefits still exceeded $407 million in the worst-case scenario.

A Gated Recurrent Units (GRU)-Based Model for Early Detection of Soybean Sudden Death Syndrome through Time-Series Satellite Imagery

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.

Advanced material modulation of nutritional and phytohormone status alleviates damage from soybean sudden death syndrome.

Customized Cu3(PO4)2 and CuO nanosheets and commercial CuO nanoparticles were investigated for micronutrient delivery and suppression of soybean sudden death syndrome. An ab initio thermodynamics approach modelled how material morphology and matrix effects control the nutrient release. Infection reduced the biomass and photosynthesis by 70.3 and 60%, respectively; the foliar application of nanoscale Cu reversed this damage. Disease-induced changes in the antioxidant enzyme activity and fatty acid profile were also alleviated by Cu amendment. The transcription of two dozen defence- and health-related genes correlates a nanoscale Cu-enhanced innate disease response to reduced pathogenicity and increased growth. Cu-based nanosheets exhibited a greater disease suppression than that of CuO nanoparticles due to a greater leaf surface affinity and Cu dissolution, as determined computationally and experimentally. The findings highlight the importance and tunability of nanomaterial properties, such as morphology, composition and dissolution. The early seedling foliar application of nanoscale Cu to modulate nutrition and enhance immunity offers a great potential for sustainable agriculture.

Soybean sudden death syndrome: Fungal pathogenesis and plant response

AbstractSoybean sudden death syndrome (SDS) is a fungal disease caused by members of clade 2 of the Fusarium solani species complex (FSSC). These fungi are soilborne pathogens that infect soybean plants through the roots and produce toxins that translocate to aerial parts of the plant, inducing foliar chlorosis and necrosis followed by premature defoliation. Here, we first give the current state of knowledge of early pathogen detection and infection establishment for the SDS pathosystem. Subsequently, we discuss the nature and activity of secreted toxins, followed by an overview of changes in plant metabolism and factors that influence fungus–soybean interaction. Finally, we summarize the advances in plant disease resistance, symptom evaluation, and treatment.

Remote thermal infrared imaging for rapid screening of sudden death syndrome in soybean

Sudden death syndrome (SDS), a fungal infection in soybean caused by Fusarium virguliforme, greatly affects the plant health and in some cases, can cause yield losses of more than 70%. Infected plants are scored by visual assessment based on severity and extent of infection. This manual process is time intensive and not practical for large acreages. Diseased and stress in plants show elevated canopy temperatures that can potentially lead to identification of unhealthy plants without manual scoring. The infection decreases nutrient distribution causing stress that results in internal plant temperature to increase. Thermal infrared (TIR) sensors have the ability to measure the emitted radiation of an object in the infrared region of the electromagnetic spectrum to estimate canopy temperatures. However, TIR sensors have not yet been utilized to capture changes in canopy temperatures to detect SDS in soybean. Therefore, the goal of this study was to (1) use a TIR sensor to assess plant health and vitality, and (2) evaluate canopy temperatures over the growing season to quantify disease development. A thermal infrared camera was mounted on a small unmanned aerial system to capture aerial imagery over the growing season. The first flight was achieved once SDS foliar symptoms began initial development. The remaining three flights occurred before, during, and after full pod fill when symptoms had reached their apex. Results show increasing correlations over the four days. Elevated canopy temperature changes were observed on canopies at early SDS symptom development. Symptoms at the end of the growing season displayed strong correlations to the canopy temperature with ρ = 0.7404. Disease severity showed the strongest correlation throughout the four flights with the last at ρ = 0.7245. The four flights exhibit a decreasing trend with Spearman's rho (R2 = 0.86 for disease severity). Therefore, thermal imaging can be utilized to detect diseased plots. Future studies will be conducted to understand how to mitigate for SDS using thermal detection.

Effects of ILeVO Seed Treatment on Heterodera glycines Reproduction and Soybean Yield in Small-Plot and Strip-Trial Experiments in Iowa.

ILeVO (fluopyram) is a fungicide seed treatment for soybean sudden death syndrome (SDS) that also has nematicidal activity. ILeVO is sold with a base of insecticide Poncho (clothianidin), nematode-protectant VOTiVO (Bacillus firmus), and Acceleron fungicides (metalaxyl, fluxapyroxad, and pyraclostrobin). Yield and reproduction of the soybean cyst nematode (SCN) (Heterodera glycines) on soybean plants grown from seed treated with ILeVO plus the base were compared with those treated with only the base in 27 small-plot experiments and 12 strip-trial experiments across Iowa from 2015 to 2017. To increase the likelihood that yield results were related to effects on SCN, data were used only from 26 small-plot experiments and 12 strip trials in which symptoms of SDS were low or nonexistent. An SCN reproductive factor (RF) was calculated for each experimental unit by dividing the SCN population density at harvest by the population density at planting. ILeVO significantly reduced SCN RF by 50% in one strip-trial experiment and by 36 to 60% in four small-plot experiments but yields were not increased by ILeVO in any of those five experiments. Soybean yields were 2.8 to 3.7 bushels/acre (bu/ac) (188.3 to 248.8 kg/ha) greater with ILeVO in three small-plot experiments but SCN RF was not reduced in those experiments. Also, yield was 1.9 bu/ac (127.8 kg/ha) greater with ILeVO in one strip-trial experiment in 2016 but SCN samples were not collected at harvest from the study to assess the possible effects of ILeVO on SCN reproduction. When strip-trial data from 2015 and 2016 were combined, there was a small but significant 0.8 bu/ac (52.2 kg/ha) yield increase with ILeVO. Overall, the effects of ILeVO on SCN reproduction and soybean yield were variable in these field studies.

Exploring the Potential of High-Resolution Satellite Imagery for the Detection of Soybean Sudden Death Syndrome

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 &gt;75% accurate and the area under the receiver operating characteristic curve (AUROC) was &gt;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.