Deciphering genetic factors contributing to enhanced resistance against Cercospora leaf blight in soybean (Glycine max L.) using GWAS analysis.

Cercospora leaf blight (CLB), caused by Cercospora cf. flagellaris, C. kikuchii, and C. cf. sigesbeckiae, is a significant soybean [Glycine max (L.) Merr.] disease in regions with hot and humid conditions causing yield loss in the United States and Canada. There is limited information regarding resistant soybean cultivars, and there have been marginal efforts to identify the genomic regions underlying resistance to CLB. A Genome-Wide Association Study was conducted using a diverse panel of 460 soybean accessions from maturity groups III to VII to identify the genomic regions associated to the CLB disease. These accessions were evaluated for CLB in different regions of the southeastern United States over 3 years. In total, the study identified 99 Single Nucleotide Polymorphism (SNPs) associated with the disease severity and 85 SNPs associated with disease incidence. Across multiple environments, 47 disease severity SNPs and 23 incidence SNPs were common. Candidate genes within 10 kb of these SNPs were involved in biotic and abiotic stress pathways. This information will contribute to the development of resistant soybean germplasm. Further research is warranted to study the effect of pyramiding desirable genomic regions and investigate the role of identified genes in soybean CLB resistance.

Identification of Quantitative Trait Loci (QTL) for Sucrose and Protein Content in Soybean Seed.

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.

Relationships between protein and other chemical composition and texture of tofu made from soybeans grown in different locations.

Understanding quantitative relationships between protein and other chemical components in diverse soybean genotypes (lines) grown in different locations and the firmness of tofu can provide scientific insight for selecting soybean suitable for tofu making. Locations showed significant effects on seed components, including total protein, major storage proteins, subunits and polypeptides of the major storage proteins, and calcium, but not magnesium or phytic acid. Results showed that 11S content, but not 11S/7S ratio, was only correlated with filled tofu firmness when analyzed over all locations. A strong and positive correlation between firmness and A3 polypeptide of the 11S protein content was found for both pressed tofu (r = 0.80, p < 0.001) and filled tofu (r = 0.76, p < 0.001) over three locations (overall pooled data) and within most individual locations. The correlation of filled tofu firmness and A3 polypeptide was significant for each of the three individual locations. However, the correlation of pressed tofu firmness and A3 polypeptide content was significant at two of three locations. Mean calcium content was positively correlated with mean pressed and filled tofu firmness over all locations, but calcium was not correlated with pressed tofu firmness at any individual location, and only one location showed a significant correlation of calcium and filled tofu firmness. In addition, pressed tofu firmness was found to be negatively correlated with tofu yield. The findings that A3 polypeptide's strong relationship with tofu firmness within certain locations may be used by the food industry to select proper soybean for manufacturing tofu and to facilitate tofu soybean breeding for tofu making.

Characterization of QoI-Fungicide Resistance in Cercospora Isolates Associated with Cercospora Leaf Blight of Soybean from the Southern United States.

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.

Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning.

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.

Global, national, and regional burden of acute myeloid leukemia among 60-89 years-old individuals: insights from a study covering the period 1990 to 2019.

Background: Our study examined the global, national, and regional trends in the incidence, mortality, and disability-adjusted life years (DALYs) associated with older people's acute myeloid leukemia (AML) over a 30 years period. AML, which predominantly affects individuals aged 60-89, is known for its severity and unfavorable prognosis. By providing insights into the growing burden of AML, our research highlights the urgent need for effective interventions and support at various levels. Methods: In this study, we analyzed older people with AML aged 60-89 using the Global Burden of Disease (GBD) database for 2019. Our goal was to assess trends and characteristics by examining the incidence rate, mortality rate, DALYs, and estimated annual percentage change (EAPC). We aimed to provide a comprehensive understanding of the disease's trajectory and development. Results: In 2019, the older age group of 60 to 89 years reported 61,559 new cases of AML, with the corresponding number of deaths being 53,620, and the estimated DALYs standing at 990,656. Over the last 30 years, the incidence rate of AML in this age bracket increased by 1.67 per 100,000 people, the mortality rate rose by 1.57 per 100,000 people, and the rate of DALYs, indicative of disease burden, climbed by 1.42 per 100,000 people. High Socio-demographic Index (SDI) regions, particularly high-income North America and Australia, had the highest incidence rates. Germany had the highest incidence rate among the 204 countries analyzed, while Monaco reported the highest mortality and DALY rates. Smoking, high body mass index, occupational exposure to benzene, and formaldehyde were identified as significant risk factors associated with mortality from older people with AML in 2019. Conclusion: Our study showed that the incidence, mortality, and DALY rates of AML in the older population were strongly correlated with the SDI, and these rates have been steadily increasing. This had become an increasingly serious global health issue, particularly in areas with a high SDI. We highlighted the urgency to focus more on this disease and called for the prompt implementation of appropriate preventive and control measures.

Identification of genomic regions associated with soybean responses to off-target dicamba exposure.

The widespread adoption of genetically modified (GM) dicamba-tolerant (DT) soybean was followed by numerous reports of off-target dicamba damage and yield losses across most soybean-producing states. In this study, a subset of the USDA Soybean Germplasm Collection consisting of 382 genetically diverse soybean accessions originating from 15 countries was used to identify genomic regions associated with soybean response to off-target dicamba exposure. Accessions were genotyped with the SoySNP50K BeadChip and visually screened for damage in environments with prolonged exposure to off-target dicamba. Two models were implemented to detect significant marker-trait associations: the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) and a model that allows the inclusion of population structure in interaction with the environment (G×E) to account for variable patterns of genotype responses in different environments. Most accessions (84%) showed a moderate response, either moderately tolerant or moderately susceptible, with approximately 8% showing tolerance and susceptibility. No differences in off-target dicamba damage were observed across maturity groups and centers of origin. Both models identified significant associations in regions of chromosomes 10 and 19. The BLINK model identified additional significant marker-trait associations on chromosomes 11, 14, and 18, while the G×E model identified another significant marker-trait association on chromosome 15. The significant SNPs identified by both models are located within candidate genes possessing annotated functions involving different phases of herbicide detoxification in plants. These results entertain the possibility of developing non-GM soybean cultivars with improved tolerance to off-target dicamba exposure and potentially other synthetic auxin herbicides. Identification of genetic sources of tolerance and genomic regions conferring higher tolerance to off-target dicamba may sustain and improve the production of other non-DT herbicide soybean production systems, including the growing niche markets of organic and conventional soybean.

Incorporation of Soil-Derived Covariates in Progeny Testing and Line Selection to Enhance Genomic Prediction Accuracy in Soybean Breeding.

The availability of high-dimensional molecular markers has allowed plant breeding programs to maximize their efficiency through the genomic prediction of a phenotype of interest. Yield is a complex quantitative trait whose expression is sensitive to environmental stimuli. In this research, we investigated the potential of incorporating soil texture information and its interaction with molecular markers via covariance structures for enhancing predictive ability across breeding scenarios. A total of 797 soybean lines derived from 367 unique bi-parental populations were genotyped using the Illumina BARCSoySNP6K and tested for yield during 5 years in Tiptonville silt loam, Sharkey clay, and Malden fine sand environments. Four statistical models were considered, including the GBLUP model (M1), the reaction norm model (M2) including the interaction between molecular markers and the environment (G×E), an extended version of M2 that also includes soil type (S), and the interaction between soil type and molecular markers (G×S) (M3), and a parsimonious version of M3 which discards the G×E term (M4). Four cross-validation scenarios simulating progeny testing and line selection of tested-untested genotypes (TG, UG) in observed-unobserved environments [OE, UE] were implemented (CV2 [TG, OE], CV1 [UG, OE], CV0 [TG, UE], and CV00 [UG, UE]). Across environments, the addition of G×S interaction in M3 decreased the amount of variability captured by the environment (-30.4%) and residual (-39.2%) terms as compared to M1. Within environments, the G×S term in M3 reduced the variability captured by the residual term by 60 and 30% when compared to M1 and M2, respectively. M3 outperformed all the other models in CV2 (0.577), CV1 (0.480), and CV0 (0.488). In addition to the Pearson correlation, other measures were considered to assess predictive ability and these showed that the addition of soil texture seems to structure/dissect the environmental term revealing its components that could enhance or hinder the predictability of a model, especially in the most complex prediction scenario (CV00). Hence, the availability of soil texture information before the growing season could be used to optimize the efficiency of a breeding program by allowing the reconsideration of field experimental design, allocation of resources, reduction of preliminary trials, and shortening of the breeding cycle.

Breeding for disease resistance in soybean: a global perspective.

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.

Decades of Genetic Research on Soybean mosaic virus Resistance in Soybean.

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.

Exploring Machine Learning Algorithms to Unveil Genomic Regions Associated With Resistance to Southern Root-Knot Nematode in Soybeans.

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.

Genome-wide association analysis of sucrose and alanine contents in edamame beans.

The sucrose and Alanine (Ala) content in edamame beans significantly impacts the sweetness flavor of edamame-derived products as an important attribute to consumers' acceptance. Unlike grain-type soybeans, edamame beans are harvested as fresh beans at the R6 to R7 growth stages when beans are filled 80-90% of the pod capacity. The genetic basis of sucrose and Ala contents in fresh edamame beans may differ from those in dry seeds. To date, there is no report on the genetic basis of sucrose and Ala contents in the edamame beans. In this study, a genome-wide association study was conducted to identify single nucleotide polymorphisms (SNPs) related to sucrose and Ala levels in edamame beans using an association mapping panel of 189 edamame accessions genotyped with a SoySNP50K BeadChip. A total of 43 and 25 SNPs was associated with sucrose content and Ala content in the edamame beans, respectively. Four genes (Glyma.10g270800, Glyma.08g137500, Glyma.10g268500, and Glyma.18g193600) with known effects on the process of sucrose biosynthesis and 37 novel sucrose-related genes were characterized. Three genes (Gm17g070500, Glyma.14g201100 and Glyma.18g269600) with likely relevant effects in regulating Ala content and 22 novel Ala-related genes were identified. In addition, by summarizing the phenotypic data of edamame beans from three locations in two years, three PI accessions (PI 532469, PI 243551, and PI 407748) were selected as the high sucrose and high Ala parental lines for the perspective breeding of sweet edamame varieties. Thus, the beneficial alleles, candidate genes, and selected PI accessions identified in this study will be fundamental to develop edamame varieties with improved consumers' acceptance, and eventually promote edamame production as a specialty crop in the United States.

Genome-wide association study and genomic selection for yield and related traits in soybean.

Soybean [Glycine max (L.) Merr.] is a crop of great interest worldwide. Exploring molecular approaches to increase yield genetic gain has been one of the main challenges for soybean breeders and geneticists. Agronomic traits such as maturity, plant height, and seed weight have been found to contribute to yield. In this study, a total of 250 soybean accessions were genotyped with 10,259 high-quality SNPs postulated from genotyping by sequencing (GBS) and evaluated for grain yield, maturity, plant height, and seed weight over three years. A genome-wide association study (GWAS) was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model. Genomic selection (GS) was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that 20, 31, 37, and 23 SNPs were significantly associated with maturity, plant height, seed weight, and yield, respectively; Many SNPs were mapped to previously described maturity and plant height loci (E2, E4, and Dt1) and a new plant height locus was mapped to chromosome 20. Candidate genes were found in the vicinity of the two SNPs with the highest significant levels associated with yield, maturity, plant height, seed weight, respectively. A 11.5-Mb region of chromosome 10 was associated with both yield and seed weight. Overall, the accuracy of GS was dependent on the trait, year, and population structure, and high accuracy indicates that these agronomic traits can be selected in molecular breeding through GS. The SNP markers identified in this study can be used to improve yield and agronomic traits through the marker-assisted selection and GS in breeding programs.

Qualification of Soybean Responses to Flooding Stress Using UAV-Based Imagery and Deep Learning.

Soybean is sensitive to flooding stress that may result in poor seed quality and significant yield reduction. Soybean production under flooding could be sustained by developing flood-tolerant cultivars through breeding programs. Conventionally, soybean tolerance to flooding in field conditions is evaluated by visually rating the shoot injury/damage due to flooding stress, which is labor-intensive and subjective to human error. Recent developments of field high-throughput phenotyping technology have shown great potential in measuring crop traits and detecting crop responses to abiotic and biotic stresses. The goal of this study was to investigate the potential in estimating flood-induced soybean injuries using UAV-based image features collected at different flight heights. The flooding injury score (FIS) of 724 soybean breeding plots was taken visually by breeders when soybean showed obvious injury symptoms. Aerial images were taken on the same day using a five-band multispectral and an infrared (IR) thermal camera at 20, 50, and 80 m above ground. Five image features, i.e., canopy temperature, normalized difference vegetation index, canopy area, width, and length, were extracted from the images at three flight heights. A deep learning model was used to classify the soybean breeding plots to five FIS ratings based on the extracted image features. Results show that the image features were significantly different at three flight heights. The best classification performance was obtained by the model developed using image features at 20 m with 0.9 for the five-level FIS. The results indicate that the proposed method is very promising in estimating FIS for soybean breeding.

TILLING-by-Sequencing+ Reveals the Role of Novel Fatty Acid Desaturases (GmFAD2-2s) in Increasing Soybean Seed Oleic Acid Content.

Soybean is the second largest source of oil worldwide. Developing soybean varieties with high levels of oleic acid is a primary goal of the soybean breeders and industry. Edible oils containing high level of oleic acid and low level of linoleic acid are considered with higher oxidative stability and can be used as a natural antioxidant in food stability. All developed high oleic acid soybeans carry two alleles; GmFAD2-1A and GmFAD2-1B. However, when planted in cold soil, a possible reduction in seed germination was reported when high seed oleic acid derived from GmFAD2-1 alleles were used. Besides the soybean fatty acid desaturase (GmFAD2-1) subfamily, the GmFAD2-2 subfamily is composed of five members, including GmFAD2-2A, GmFAD2-2B, GmFAD2-2C, GmFAD2-2D, and GmFAD2-2E. Segmental duplication of GmFAD2-1A/GmFAD2-1B, GmFAD2-2A/GmFAD2-2C, GmFAD2-2A/GmFAD2-2D, and GmFAD2-2D/GmFAD2-2C have occurred about 10.65, 27.04, 100.81, and 106.55 Mya, respectively. Using TILLING-by-Sequencing+ technology, we successfully identified 12, 8, 10, 9, and 19 EMS mutants at the GmFAD2-2A, GmFAD2-2B, GmFAD2-2C, GmFAD2-2D, and GmFAD2-2E genes, respectively. Functional analyses of newly identified mutants revealed unprecedented role of the five GmFAD2-2A, GmFAD2-2B, GmFAD2-2C, GmFAD2-2D, and GmFAD2-2E members in controlling the seed oleic acid content. Most importantly, unlike GmFAD2-1 members, subcellular localization revealed that members of the GmFAD2-2 subfamily showed a cytoplasmic localization, which may suggest the presence of an alternative fatty acid desaturase pathway in soybean for converting oleic acid content without substantially altering the traditional plastidial/ER fatty acid production.

Improve Soybean Variety Selection Accuracy Using UAV-Based High-Throughput Phenotyping Technology.

The efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield, achieved in 1 year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials (PTs) due to their large population, complex genetic behavior, and high genotype-environment interaction. The goal of this study was to investigate the performance of selecting superior soybean breeding lines using image-based secondary traits by comparing them with the selection of breeders. A total of 11,473 progeny rows (PT) were planted in 2018, of which 1,773 genotypes were selected for the preliminary yield trial (PYT) in 2019, and 238 genotypes advanced for the advanced yield trial (AYT) in 2020. Six agronomic traits were manually measured in both PYT and AYT trials. A UAV-based multispectral imaging system was used to collect aerial images at 30 m above ground every 2 weeks over the growing seasons. A group of image features was extracted to develop the secondary crop traits for selection. Results show that the soybean seed yield of the selected genotypes by breeders was significantly higher than that of the non-selected ones in both yield trials, indicating the superiority of the breeder's selection for advancing soybean yield. A least absolute shrinkage and selection operator model was used to select soybean lines with image features and identified 71 and 76% of the selection of breeders for the PT and PYT. The model-based selections had a significantly higher average yield than the selection of a breeder. The soybean yield selected by the model in PT and PYT was 4 and 5% higher than those selected by breeders, which indicates that the UAV-based high-throughput phenotyping system is promising in selecting high-yield soybean genotypes.

Workflow for the Quantification of Soluble and Insoluble Carbohydrates in Soybean Seed.

Soybean seed composition has a profound impact on its market value and commercial use as an important commodity. Increases in oil and protein content have been historically pursued by breeders and genetic engineers; consequently, rapid methods for their quantification are well established. The interest in complete carbohydrate profiles in mature seeds, on the other hand, has recently increased due to numerous attempts to redirect carbohydrates into oil and protein or to offer specialty seed with a specific sugar profile to meet animal nutritional requirements. In this work, a sequential protocol for quantifying reserve and structural carbohydrates in soybean seed was developed and validated. Through this procedure, the concentrations of soluble sugars, sugar alcohols, starch, hemicellulose, and crystalline cellulose can be determined in successive steps from the same starting material using colorimetric assays, LC-MS/MS, and GC-MS. The entire workflow was evaluated using internal standards to estimate the recovery efficiency. Finally, it was successfully applied to eight soybean genotypes harvested from two locations, and the resulting correlations of carbohydrate and oil or protein are presented. This methodology has the potential not only to guide soybean cultivar optimization processes but also to be expanded to other crops with only slight modifications.

Effect of Planting Date and Cultivar Maturity in Edamame Quality and Harvest Window.

Edamame is a food-grade soybean [Glycine max (L.) Merr.] that is harvested immature between the R6 and R7 reproductive stages. To be labeled as a premium product, the edamame market demands large pod size and intense green color. A staggered harvest season is critical for the commercial industry to post-harvest process the crop in a timely manner. Currently, there is little information to assist in predicting the optimum time to harvest edamame when the pods are at their collective largest size and greenest color. The objectives of this study were to assess the impact of cultivar, planting date, and harvest date on edamame color, pod weight, and a newly minted Edamame Harvest Quality Index combining both aforementioned factors. And to predict edamame harvest quality based on phenological stages, thermal units, and planting dates. We observed that pod color and weight depended on the cultivar, planting date, and harvest date combination. Our results also indicated that edamame quality is increased with delayed planting dates and that quality was dependent on harvest date with a quadratic negative response to delaying harvest. Maximum quality depended on cultivar and planting and harvest dates, but it remained stable for an interval of 18-27 days around the peak. Finally, we observed that the number of days between R1 and harvest was consistently identified as a key factor driving edamame quality by both stepwise regression and neural network analysis. These research results will help define a planting and harvest strategy for edamame production in Arkansas and the United States Mid-South.

The importance of slow canopy wilting in drought tolerance in soybean.

Slow canopy wilting (SW) is a water conservation trait controlled by quantitative trait loci (QTLs) in late maturity group soybeans [Glycine max (L.) Merr.]. Recently, two exotic (landraces) plant introductions (PI 567690 and PI 567731) were identified as new SW lines in early maturity groups. Here, we show that the two PIs share the same water conservation strategy of limited maximum transpiration rates as PI 416937. However, in contrast to PI 416937, the transpiration rates of these PIs were sensitive to an aquaporin inhibitor, indicating an independence between limited maximum transpiration and the lack of silver-sensitive aquaporins. Yield tests of selected recombinant inbred lines from two elite/exotic crosses provide direct evidence to support the benefit of SW in drought tolerance. Four SW QTLs mapped in a Pana×PI 567690 cross at multiple environments were found to be co-located with previous reports. Moreover, two new SW QTLs were mapped on chromosomes 6 and 10 from a Magellan×PI 567731 cross. These two QTLs explain the observed relatively large contributions of 20-30% and were confirmed in a near-isogenic background. These findings demonstrate the importance of SW in yield protection under drought and provide genetic resources for improving drought tolerance in early maturity group soybeans.