Pengyin Chen

Protein and sugar content are important seed quality traits in soybean because they improve the value and sustainability of soy food and feed products. Thus, identifying Quantitative Trait Loci (QTL) for soybean seed protein and sugar content can benefit plant breeders and the soybean market by accelerating the breeding process via marker-assisted selection. For this study, a population of recombinant inbred lines (RILs) was developed from a cross between R08-3221 (high protein and low sucrose) and R07-2000 (high sucrose and low protein). Phenotypic data for protein content were taken from the F2:4 and F2:5 generations. The DA7250 NIR analyzer and HPLC instruments were used to analyze total seed protein and sucrose content. Genotypic data were generated using analysis via the SoySNP6k chip. A total of four QTLs were identified in this study. Two QTLs for protein content were located on chromosomes 11 and 20, and two QTLs associated with sucrose content were located on chromosomes 14 and. 11, the latter of which co-localized with detected QTLs for protein, explaining 10% of the phenotypic variation for protein and sucrose content in soybean seed within the study population. Soybean breeding programs can use the results to improve soybean seed quality.

Cercospora leaf blight (CLB) of soybean, caused by Cercospora cf. flagellaris, C. kikuchii, and C. cf. sigesbeckiae, is an economically important disease in the southern United States. Cultivar resistance to CLB is inconsistent; therefore, fungicides in the quinone outside inhibitor (QoI) class have been relied on to manage the disease. Approximately 620 isolates from plants exhibiting CLB were collected between 2018 and 2021 from 19 locations in eight southern states. A novel polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay based on two genes, calmodulin and histone h3, was developed to differentiate between the dominant species of Cercospora, C. cf. flagellaris, and C. cf. sigesbeckiae. A multilocus phylogenetic analysis of actin, calmodulin, histone h3, ITS rDNA, and transcription elongation factor 1-α was used to confirm PCR-RFLP results and identify remaining isolates. Approximately 80% of the isolates collected were identified as C. cf. flagellaris, while 15% classified as C. cf. sigesbeckiae, 2% as C. kikuchii, and 3% as previously unreported Cercospora species associated with CLB in the United States. PCR-RFLP of cytochrome b (cytb) identified QoI-resistance conferred by the G143A substitution. Approximately 64 to 83% of isolates were determined to be QoI-resistant, and all contained the G143A substitution. Results of discriminatory dose assays using azoxystrobin (1 ppm) were 100% consistent with PCR-RFLP results. To our knowledge, this constitutes the first report of QoI resistance in CLB pathogen populations from Alabama, Arkansas, Kentucky, Mississippi, Missouri, Tennessee, and Texas. In areas where high frequencies of resistance have been identified, QoI fungicides should be avoided, and fungicide products with alternative modes-of-action should be utilized in the absence of CLB-resistant soybean cultivars.

Weeds can cause significant yield losses and will continue to be a problem for agricultural production due to climate change. Dicamba is widely used to control weeds in monocot crops, especially genetically engineered dicamba-tolerant (DT) dicot crops, such as soybean and cotton, which has resulted in severe off-target dicamba exposure and substantial yield losses to non-tolerant crops. There is a strong demand for non-genetically engineered DT soybeans through conventional breeding selection. Public breeding programs have identified genetic resources that confer greater tolerance to off-target dicamba damage in soybeans. Efficient and high throughput phenotyping tools can facilitate the collection of a large number of accurate crop traits to improve the breeding efficiency. This study aimed to evaluate unmanned aerial vehicle (UAV) imagery and deep-learning-based data analytic methods to quantify off-target dicamba damage in genetically diverse soybean genotypes. In this research, a total of 463 soybean genotypes were planted in five different fields (different soil types) with prolonged exposure to off-target dicamba in 2020 and 2021. Crop damage due to off-target dicamba was assessed by breeders using a 1–5 scale with a 0.5 increment, which was further classified into three classes, i.e., susceptible (≥3.5), moderate (2.0 to 3.0), and tolerant (≤1.5). A UAV platform equipped with a red-green-blue (RGB) camera was used to collect images on the same days. Collected images were stitched to generate orthomosaic images for each field, and soybean plots were manually segmented from the orthomosaic images. Deep learning models, including dense convolutional neural network-121 (DenseNet121), residual neural network-50 (ResNet50), visual geometry group-16 (VGG16), and Depthwise Separable Convolutions (Xception), were developed to quantify crop damage levels. Results show that the DenseNet121 had the best performance in classifying damage with an accuracy of 82%. The 95% binomial proportion confidence interval showed a range of accuracy from 79% to 84% (p-value ≤ 0.01). In addition, no extreme misclassifications (i.e., misclassification between tolerant and susceptible soybeans) were observed. The results are promising since soybean breeding programs typically aim to identify those genotypes with ‘extreme’ phenotypes (e.g., the top 10% of highly tolerant genotypes). This study demonstrates that UAV imagery and deep learning have great potential to high-throughput quantify soybean damage due to off-target dicamba and improve the efficiency of crop breeding programs in selecting soybean genotypes with desired traits.

Key message: This review provides a comprehensive atlas of QTLs, genes, and alleles conferring resistance to 28 important diseases in all major soybean production regions in the world. Breeding disease-resistant soybean [Glycine max (L.) Merr.] varieties is a common goal for soybean breeding programs to ensure the sustainability and growth of soybean production worldwide. However, due to global climate change, soybean breeders are facing strong challenges to defeat diseases. Marker-assisted selection and genomic selection have been demonstrated to be successful methods in quickly integrating vertical resistance or horizontal resistance into improved soybean varieties, where vertical resistance refers to R genes and major effect QTLs, and horizontal resistance is a combination of major and minor effect genes or QTLs. This review summarized more than 800 resistant loci/alleles and their tightly linked markers for 28 soybean diseases worldwide, caused by nematodes, oomycetes, fungi, bacteria, and viruses. The major breakthroughs in the discovery of disease resistance gene atlas of soybean were also emphasized which include: (1) identification and characterization of vertical resistance genes reside rhg1 and Rhg4 for soybean cyst nematode, and exploration of the underlying regulation mechanisms through copy number variation and (2) map-based cloning and characterization of Rps11 conferring resistance to 80% isolates of Phytophthora sojae across the USA. In this review, we also highlight the validated QTLs in overlapping genomic regions from at least two studies and applied a consistent naming nomenclature for these QTLs. Our review provides a comprehensive summary of important resistant genes/QTLs and can be used as a toolbox for soybean improvement. Finally, the summarized genetic knowledge sheds light on future directions of accelerated soybean breeding and translational genomics studies.

This review summarizes the history and current state of the known genetic basis for soybean resistance to Soybean mosaic virus (SMV), and examines how the integration of molecular markers has been utilized in breeding for crop improvement. SVM causes yield loss and seed quality reduction in soybean based on the SMV strain and the host genotype. Understanding the molecular underpinnings of SMV–soybean interactions and the genes conferring resistance to SMV has been a focus of intense research interest for decades. Soybean reactions are classified into three main responses: resistant, necrotic, or susceptible. Significant progress has been achieved that has greatly increased the understanding of soybean germplasm diversity, differential reactions to SMV strains, genotype–strain interactions, genes/alleles conferring specific reactions, and interactions among resistance genes and alleles. Many studies that aimed to uncover the physical position of resistance genes have been published in recent decades, collectively proposing different candidate genes. The studies on SMV resistance loci revealed that the resistance genes are mainly distributed on three chromosomes. Resistance has been pyramided in various combinations for durable resistance to SMV strains. The causative genes are still elusive despite early successes in identifying resistance alleles in soybean; however, a gene at the Rsv4 locus has been well validated.

Southern root-knot nematode [SRKN, Meloidogyne incognita (Kofold & White) Chitwood] is a plant-parasitic nematode challenging to control due to its short life cycle, a wide range of hosts, and limited management options, of which genetic resistance is the main option to efficiently control the damage caused by SRKN. To date, a major quantitative trait locus (QTL) mapped on chromosome (Chr.) 10 plays an essential role in resistance to SRKN in soybean varieties. The confidence of discovered trait-loci associations by traditional methods is often limited by the assumptions of individual single nucleotide polymorphisms (SNPs) always acting independently as well as the phenotype following a Gaussian distribution. Therefore, the objective of this study was to conduct machine learning (ML)-based genome-wide association studies (GWAS) utilizing Random Forest (RF) and Support Vector Machine (SVM) algorithms to unveil novel regions of the soybean genome associated with resistance to SRKN. A total of 717 breeding lines derived from 330 unique bi-parental populations were genotyped with the Illumina Infinium BARCSoySNP6K BeadChip and phenotyped for SRKN resistance in a greenhouse. A GWAS pipeline involving a supervised feature dimension reduction based on Variable Importance in Projection (VIP) and SNP detection based on classification accuracy was proposed. Minor effect SNPs were detected by the proposed ML-GWAS methodology but not identified using Bayesian-information and linkage-disequilibrium Iteratively Nested Keyway (BLINK), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Enriched Compressed Mixed Linear Model (ECMLM) models. Besides the genomic region on Chr. 10 that can explain most of SRKN resistance variance, additional minor effects SNPs were also identified on Chrs. 10 and 11. The findings in this study demonstrated that overfitting in GWAS may lead to lower prediction accuracy, and the detection of significant SNPs based on classification accuracy limited false-positive associations. The expansion of the basis of the genetic resistance to SRKN can potentially reduce the selection pressure over the major QTL on Chr. 10 and achieve higher levels of resistance.

‘ShowMeSoy 4301’ (Reg. no. CV‐544, PI 699899) is a conventional (non‐genetically modified) early maturity group IV soybean [Glycine max (L.) Merr.] cultivar released by the University of Missouri–Fisher Delta Research Center soybean breeding program in 2019. It originated from the cross ‘S08‐17361’ × ‘S05‐11482’′ designed to develop new high‐yielding germplasm with multiple disease resistance and desirable seed composition. ShowMeSoy 4301 is resistant to charcoal rot, frogeye leaf spot, stem canker, sudden death syndrome, southern root‐knot nematode, and reniform nematode, moderately resistant to soybean cyst nematode races 1 (HG Type 2.5.7) and 2 (HG Type 1.2.5.7), and tolerant to salt stress. Seeds of ShowMeSoy 4301 have 234.9 g kg^–1 of oil, 402.4 g kg^–1 of protein, and 478.4 g kg^–1 of meal protein content on a dry weight basis, making it a high oil cultivar based on the industry's standards. ShowMeSoy 4301 was tested from 2014 to 2019 in a total of 150 geographically diverse environments across 13 states including Alabama, Arkansas, Illinois, Indiana, Kansas, Kentucky, Louisiana, Missouri, Mississippi, Ohio, Tennessee, Texas, and Virginia. It demonstrated high‐yielding potential and broad adaptability across multiple states. The combination of these traits with a comprehensive disease package and desirable seed composition makes it a good cultivar choice for growers in the mid‐southern United States.

The wide adoption of dicamba-tolerant (DT) soybean has led to numerous cases of off-target dicamba damage to non-DT soybean and dicot crops. This study aimed to develop a method to differentiate soybean response to dicamba using unmanned-aerial-vehicle-based imagery and machine learning models. Soybean lines were visually classified into three classes of injury, i.e., tolerant, moderate, and susceptible to off-target dicamba. A quadcopter with a built-in RGB camera was used to collect images of field plots at a height of 20 m above ground level. Seven image features were extracted for each plot, including canopy coverage, contrast, entropy, green leaf index, hue, saturation, and triangular greenness index. Classification models based on artificial neural network (ANN) and random forest (RF) algorithms were developed to differentiate the three classes of response to dicamba. Significant differences for each feature were observed among classes and no significant differences across fields were observed. The ANN and RF models were able to precisely distinguish tolerant and susceptible lines with an overall accuracy of 0.74 and 0.75, respectively. The imagery-based classification model can be implemented in a breeding program to effectively differentiate phenotypic dicamba response and identify soybean lines with tolerance to off-target dicamba damage.

‘S13‐3851C’ (Reg. no. CV‐541, PI 698653) is an early maturity group IV (relative maturity 4.4) conventional soybean [Glycine max (L.) Merr.] cultivar released by the University of Missouri–Fisher Delta Research Center Soybean Breeding Program. It was developed from a conventional breeding scheme focusing on parental traits including high oil content, multiple disease resistance, and high yielding potential. S13‐3851C has purple flowers, light tawny pubescence, tan pod wall, and indeterminate growth habit. Seeds of S13‐3851C have a black hilum and intermediate seed luster, with 230.8 g kg⁻¹ of oil and 404.1 g kg⁻¹ of protein content on a dry‐weight basis. S13‐3851C is resistant to stem canker, sudden death syndrome, charcoal rot, and Phytophthora root rot. S13‐3851C showed high yield potential and broad adaptability in 113 environments across 12 states, yielding 102% of the check average and 103% of the test average in Missouri and other southern states during the testing period (2014–2018).