Soybean Breeding Programs Research Articles

Natural variations and geographical distributions of seed carotenoids and chlorophylls in 1 167 Chinese soybean accessions

Understanding the composition and contents of carotenoids in various soybean seed accessions is important for their nutritional assessment. This study investigated the variability in the concentrations of carotenoids and chlorophylls and revealed their associations with other nutritional quality traits in a genetically diverse set of Chinese soybean accessions comprised of cultivars and landraces. Genotype, planting year, accession type, seed cotyledon color, and ecoregion of origin significantly influenced the accumulation of carotenoids and chlorophylls. The mean total carotenoid content was in the range of 8.15–14.72 µg g–1 across the ecoregions. The total carotenoid content was 1.2-fold higher in the landraces than in the cultivars. Soybeans with green cotyledons had higher contents of carotenoids and chlorophylls than those with yellow cotyledons. Remarkably, lutein was the most abundant carotenoid in all the germplasms, ranging from 1.35–37.44 µg g–1. Carotenoids and chlorophylls showed significant correlations with other quality traits, which will help to set breeding strategies for enhancing soybean carotenoids without affecting the other components. Collectively, our results demonstrate that carotenoids are adequately accumulated in soybean seeds, however, they are strongly influenced by genetic factors, accession type, and germplasm origin. We identified novel germplasms with the highest total carotenoid contents across the various ecoregions of China that could serve as the genetic materials for soybean carotenoid breeding programs, and thereby as the raw materials for food sectors, pharmaceuticals, and the cosmetic industry.

Introgression of Asian soybean rust resistant genes into elite soybean lines from Uruguay

Soybean is the main agricultural crop in Uruguay. One of the diseases with the greatest damage potential is Asian soybean rust, with estimated grain yield losses up 80% in the region. Throughout history, different authors have stated the importance of genetic resistance as a fundamental tool to decrease the adverse effects of phytopathogens, leading to agriculture sustainability. The objective of this work was to introduce rust resistance genes into elite lines of INIA’s soybean breeding program and to evaluate the enhanced resistance. Introgression of resistant genes was carried out in a greenhouse, through three backcrosses from F1. Two donors: Py7-1-47 and No6-12-B handed by JIRCAS with two resistant genes combinations (Rpp1-b + Rpp5 and Rpp4 + Rpp5, respectively) and three elite lines: SJ10-122-040, SJ10-158-039, and SJ10-173-072 from INIA’s breeding program were used. After each backcross, plants carrying both genes were selected with marker assisted selection. Each third backcross was self-pollinated (BC3F2) and single plant families were inoculated in the greenhouse. Percentage of rust resistant plants within the introgressed families ranged from 42% to 98%, compared to fully susceptible recurrent parents.

Coordinate Inheritance of Seed Isoflavone and Protein in Soybean

Soybean (Glycine max (L.) Merr.) is an important nutritional crop, not only as a rich source of protein and oil, but also for the production of isoflavones. There is a demand to breed soybean cultivars bearing consistently high protein, oil and isoflavone yields, yet this requires a clear heritable genetic relationship among isoflavone, protein and oil production. Here, two soybean genotypes contrasting in terms of protein, oil and isoflavone contents and their 185 F8:10 recombinant inbred lines (RILs) were employed to characterize seed protein, oil and isoflavone contents over two years of field trials. In this population, protein, but not oil, was significantly correlated with isoflavone content. A high-density genetic linkage map containing 3943 SNP markers identified through genotyping-by-sequencing (GBS) technology was constructed for further genetic analysis, whereby a total of 25 integrated isoflavone loci were identified, including qISO1, qISO 6.1, qISO 6.3 and qISO 6.4, which are newly identified QTLs. Two major QTLs identified in this study, qISO5 and qISO6.2, were fine-mapped for production of daidzein and genistein derivatives, as well as for glycitein derivatives, in the sequences between nucleotide positions 41042159 and 42098680 on chromosome 5 and between 18449510 and 19395795 on chromosome 6, which, respectively, explain 9.3–20.4% and 7.8–24.8% of the phenotypic variation in these traits. Further combination of qISO5 and qISO6.2 resulted in additive impacts on isoflavone production. Among the 13 QTLs linked with seed protein content in this study, three also colocated with QTLs for isoflavone content, indicating that seed isoflavone and protein content may be coordinately inherited. These results contribute to understanding the relationships between isoflavone and protein or oil content in soybean seeds. This knowledge could be valuable for soybean breeding programs aiming to combine consistently high isoflavone production with high protein or oil content.

Characterization of Heterodera sojae Virulence Phenotypes in Korea.

The white soybean cyst nematode Heterodera sojae, isolated from the roots of soybean in Korea, is widespread in most provinces of the country and has the potential to be as harmful to soybean as H. glycines. Determining the virulence phenotypes of H. sojae is essential to devising management strategies that use resistant cultivars. Consequently, virulence phenotypes of 15 H. sojae populations from Korea were determined on seven soybean lines and one susceptible check variety. Two different HS types were found to be present in Korea; the more common HS type 2.5.7, comprising 73.3% of the H. sojae populations and the less common HS type 0, constituting only 26.7% of the tested populations. Considering the high frequency of H. sojae adaptation to soybean indicator lines, the PI 88788 group may not be a possible source of resistance while PI 548402, PI 90763, PI 437654, and PI 89772 can be used as resistance sources for soybean breeding programs aimed at developing H. sojae-resistant soybean cultivars in Korea.

Linkage analysis and residual heterozygotes derived near isogenic lines reveals a novel protein quantitative trait loci from a Glycine soja accession.

Modern soybean [Glycine max (L.) Merr] cultivars have low overall genetic variation due to repeated bottleneck events that arose during domestication and from selection strategies typical of many soybean breeding programs. In both public and private soybean breeding programs, the introgression of wild soybean (Glycine soja Siebold and Zucc.) alleles is a viable option to increase genetic diversity and identify new sources for traits of value. The objectives of our study were to examine the genetic architecture responsible for seed protein and oil using a recombinant inbred line (RIL) population derived from hybridizing a G. max line (‘Osage’) with a G. soja accession (PI 593983). Linkage mapping identified a total of seven significant quantitative trait loci on chromosomes 14 and 20 for seed protein and on chromosome 8 for seed oil with LOD scores ranging from 5.3 to 31.7 for seed protein content and from 9.8 to 25.9 for seed oil content. We analyzed 3,015 single F4:9 soybean plants to develop two residual heterozygotes derived near isogenic lines (RHD-NIL) populations by targeting nine SNP markers from genotype-by-sequencing, which corresponded to two novel quantitative trait loci (QTL) derived from G. soja: one for a novel seed oil QTL on chromosome 8 and another for a novel protein QTL on chromosome 14. Single marker analysis and linkage analysis using 50 RHD-NILs validated the chromosome 14 protein QTL, and whole genome sequencing of RHD-NILs allowed us to reduce the QTL interval from ∼16.5 to ∼4.6 Mbp. We identified two genomic regions based on recombination events which had significant increases of 0.65 and 0.72% in seed protein content without a significant decrease in seed oil content. A new Kompetitive allele-specific polymerase chain reaction (KASP) assay, which will be useful for introgression of this trait into modern elite G. max cultivars, was developed in one region. Within the significantly associated genomic regions, a total of eight genes are considered as candidate genes, based on the presence of gene annotations associated with the protein or amino acid metabolism/movement. Our results provide better insights into utilizing wild soybean as a source of genetic diversity for soybean cultivar improvement utilizing native traits.

SELECTION BETWEEN AND WITHIN SEGREGATING SOYBEAN POPULATIONS VIA SIMULATED INDIVIDUAL BLUP (BLUPIS)

The use of methods with high statistical accuracy in the selection of genotypes can provide substantial gains to breeding programs. This study evaluated the suitability of the best linear unbiased prediction at individual level (BLUPI) with simulated individual BLUP (BLUPIS) procedure to select individuals in segregating soybean populations. Fifteen segregating populations were evaluated in the field during the 2014/15 crop season. Data for grain yield (g/plant) and growth cycle in days (divided in early and late) were collected and analyzed via restricted maximum likelihood/best linear unbiased prediction (REML/BLUP). In general, 67–92% of the individuals were selected in the three best populations. The efficiency of BLUPIS relative to BLUPI was 4.66 and 1.46 for grain yield and growth cycle, respectively. There was strong correlation between BLUPIS and BLUPI for grain yield. The selection by BLUPIS was more efficient than BLUPI for all the traits studied and contributes to the selection of individuals in representative numbers of the best populations, thereby providing substantial gain for soybean breeding programs.

Registration of ‘S16‐14801C’: A high‐yielding determinate maturity group V soybean cultivar with multiple disease resistance, salt tolerance, and broad adaptation

AbstractThe demand for non–genetically modified soybean [Glycine max (L.) Merr.] cultivars has steadily increased in the last decade due to changes in consumers’ preferences and the growth of specialty niche markets. To meet this growing demand, the University of Missouri–Fisher Delta Research, Extension, and Education Center soybean breeding program has developed and released ‘S16‐14801C’ (Reg. no. CV‐553, PI 699901). It is a high‐yielding determinate maturity group 5 early (relative maturity 5.0) conventional soybean cultivar with resistance to multiple diseases, including soybean cyst nematode races 2 (HG type 1.2.5.7) and 5 (HG Type 2.5.7), southern root‐knot nematode, reniform nematode, stem canker, and salt tolerance. S16‐14801C was tested in a total of 111 environments across 12 states from 2017 to 2021, outyielding the commercial checks (on average) of similar maturity in 75 environments. Given its high‐yielding performance, multiple disease resistance package, and wide adaptability, S16‐14801C represents a good cultivar choice for soybean growers across the U.S. southern states, especially for crop systems targeting premium contracts associated with specialty markets.

Identification of Candidate Genes for a Major Quantitative Disease Resistance Locus From Soybean PI 427105B for Resistance to Phytophthora sojae

Phytophthora root and stem rot is a yield-limiting soybean disease caused by the soil-borne oomycete Phytophthora sojae. Although multiple quantitative disease resistance loci (QDRL) have been identified, most explain <10% of the phenotypic variation (PV). The major QDRL explaining up to 45% of the PV were previously identified on chromosome 18 and represent a valuable source of resistance for soybean breeding programs. Resistance alleles from plant introductions 427105B and 427106 significantly increase yield in disease-prone fields and result in no significant yield difference in fields with less to no disease pressure. In this study, high-resolution mapping reduced the QDRL interval to 3.1 cm, and RNA-seq analysis of near-isogenic lines (NILs) varying at QDRL-18 pinpointed a single gene of interest which was downregulated in inoculated NILs carrying the resistant allele compared to inoculated NILs with the susceptible allele. This gene of interest putatively encodes a serine–threonine kinase (STK) related to the AtCR4 family and may be acting as a susceptibility factor, based on the specific increase of jasmonic acid concentration in inoculated NILs. This work facilitates further functional analyses and marker-assisted breeding efforts by prioritizing candidate genes and narrowing the targeted region for introgression.

Silicon-enhanced tolerance to cadmium toxicity in soybean by enhancing antioxidant defense capacity and changing cadmium distribution and transport

Cadmium (Cd) is a widely distributed heavy metal that is toxic to plants and humans. Although silicon (Si) has been reported to reduce Cd accumulation and toxicity in plants, evidence on the functions of Si and its mechanisms in the possible alleviation of soybean are limited. Therefore, a controlled experiment was conducted to investigate the impacts and mechanisms of Si on Cd retention in soybean. Here, we determined the growth index, Cd distribution, and antioxidant activity systems of Si, as well as expression levels of differentially expressed genes (DEGs) in Si under Cd stress, and conducted RNA-seq analysis. We not only found that Si can significantly promote soybean plant growth, increase plant antioxidant activities, and reduce the Cd translocation factor, but also revealed that a total of 636 DEGs were shared between CK and Cd, CK and Cd + Si, and Cd and Cd + Si. Moreover, several genes were significantly enriched in antioxidant systems and Cd distribution and transport systems. Therefore, the expression status of Si-mediated Cd stress response genes is likely involved in improving oxidative stress and changing Cd uptake and transport, as well as improving plant growth that contributes to Si alleviating Cd toxicity in plants. Moreover, numerous potential target genes were identified for the engineering of Cd-tolerant cultivars in soybean breeding programs.

Registration of ‘S16‐3747GT’: A high‐yielding determinate maturity group V soybean cultivar with broad biotic and abiotic stressors tolerance

AbstractGiven the magnitude of production losses caused by biotic and abiotic stressors in soybean [Glycine max (L.) Merr.], breeding programs have devoted great efforts to developing high‐yielding soybean cultivars with enhanced genetic resistance to multiple biotic and abiotic stressors. In this context, the University of Missouri–Fisher Delta Research, Extension, and Education Center developed and released the soybean cultivar ‘S16‐3747GT’ (Reg. no. CV‐552, PI 700001). It is a determinate maturity group 5 early (relative maturity 5.0) Roundup Ready 2 (glyphosate‐tolerant) soybean that combines high‐yielding potential with resistance to soybean cyst nematode, southern root‐knot nematode, stem canker, and Phytophthora root rot and with tolerance to salt stress. S16‐3747GT was evaluated in 155 environments across 13 states from 2017 to 2020 and yielded numerically and/or significantly higher than the commercial checks’ average in 9 of 13 states in the southern United States. The broad adaptability and competitiveness across multiple states combined with resistance to biotic and abiotic stressors make S16‐3747GT a compelling cultivar choice for growers in this region as well as an option for public and private soybean breeding programs to incorporate this genetic background into their breeding pipeline.

Profiling seed soluble sugar compositions in 1164 Chinese soybean accessions from major growing ecoregions

Soluble sugar is a key quality trait of soybean seeds. We developed rapid and economic extraction and quantification methods for seed soluble sugars using an ultra-high performance liquid chromatography system with a refractive index detector. We evaluated the soluble sugar compositions of 1164 soybean accessions collected from diverse ecoregions and grown in multiple locations and years. Total soluble sugar (TSS) content was influenced by accession type, year of cultivation, and ecoregion. The mean contents of fructose, glucose, sucrose, raffinose, stachyose and TSS were 3.31, 5.21, 55.60, 6.60, 35.47, and 106.19 mg g−1, respectively. The highest mean TSS content (108.71 mg g−1) was observed in accessions from Northern Region of China. Cultivars contained higher contents of sucrose, raffinose, and TSS, whereas landraces had a higher content of stachyose. Fourteen accessions with mean TSS contents >130 mg g−1 were identified as elite soybean resources. TSS was correlated with sucrose, raffinose, stachyose, protein, oil and total tocopherol. The main soluble sugar components were correlated with latitude and longitude, indicating that the geographical origin of the accessions affected their seed soluble sugar compositions. The developed methods and elite identified accessions can be used in the food and feed industry and in soybean breeding programs aimed at improving soybean seed nutrition.

Estimation of soybean grain yield from multispectral high-resolution UAV data with machine learning models in West Africa

Soybean (Glycine max (L.) Merr.) is a leguminous and oil crop with rapidly growing importance and demand in Africa following the increasing demand for oil and livestock and poultry feed in sub-Saharan Africa. However, soybean productivity is low in most countries of sub-Saharan Africa, especially in West Africa, where productivity is below one ton per ha. Hence, concerted soybean varietal development and testing efforts have been underway by the International Institute of Tropical Agriculture (IITA), collaborating with the various African and US-based soybean breeding programs. Integrating new varietal evaluation approaches based on advanced phenotyping techniques into IITA's soybean breeding program is crucial for designing efficient crop genetic improvement techniques. Hence, this work aims to investigate machine learning (ML) models and Unmanned Aerial vehicles (UAVs) to aid rapid high throughput phenotypic workflow for soybean yield estimation. We acquired multispectral images through a Sequoia® camera aboard a senseFly eBee X UAV from five variety trials during the 2020 growing season in Nigeria. UAV-based spectral bands, canopy height, vegetation indices (VI), and texture features were generated by gray level co-occurrence matrix (GLCM) and integrated to predict crop grain yield using five machine learning (ML) regression models, including Cubist, Extreme Gradient Boosting (XGBoost), Stochastic Gradient Boosting (GBM), Support vector machine (SVM), and Random Forest (RF). The main findings are the textural information generated using gray level co-occurrence matrix (GLCM) slightly outperformed predictors based mainly on vegetation indices (VI) and provided a promising alternative to the conventional use of VI in crop yield estimation. All the five ML models performed moderately well in predicting grain yield for all the soybean trials investigated, though the Cubist and RF model stood out, with R2 reaching 0.89. The study provides a framework to perform crop breeding trial assessments more effectively and consistently at high spatial scales that African crop breeding programs did not commonly apply. The workflow can also be successfully modified and applied for high throughput phenotyping of breeding platforms in other crops.

Parámetros genéticos de rasgos agronómicos y componentes de rendimiento de genotipos de soja en la Altillanura colombiana

Soybean cultivation expansion in Colombia has generated the need to seek varieties with high genetic merit. A soybean genetic breeding program seeks to generate varieties with competitive and sustainable yields, and high-quality grain adapted to the conditions of the low tropics. The aim was to determine the genetic parameters and expected gains in quantitative traits of interest in a soybean breeding process using biometric tools. Sixty lines and four commercial varieties were evaluated in an 8x8 alpha lattice design (genotypes x blocks) planted in oxisols from the Altillanura region of Colombia. The estimation of the parameters was performed by applying mixed models with a random-effects adjustment through the REML method. Wide genetic variability was observed for all the evaluated traits, except for plant height. High heritability values were registered for the characters at flowering and maturity. Heritability was high for yield components such as the number of pods and the weight of 100 grains. Overall, the environmental effect was low, with values close to or higher than 1, except for embryonic abortions, number of empty pods, and grain yield. The genetic gain as a mean percentage was high for plant height, moderate for grain yield, and low for the rest of the characters. Moderate heritability and grain yield gain indicate an additive action of the genes ideal for recurrent selection. The most discriminating characters in the evaluated conditions were the grain yield, weight of 100 grains, and number of pods.

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components.

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.

Environmental stratification in trials of unbalanced multiyear soybean (Glycine max (l.) Merril) via the integration of GGE Biplot graphs and networks of environmental similarity

Genotype × environment (GE) interaction can difficult soybean breeding programs to achieve the aim of more productive cultivars. Environment stratification is a way to circumvent this problem. However, multiyear data studies are difficult to work with, because they are, usually, unbalanced. GGE Biplot is an efficient method to find mega-environments, however, it allows for, at most, 30% of the unbalanced data. Thinking about how we could resolve this problem we came up with the idea of test a method that englobes GGE Biplot graphs, environment coincidence matrices and networks of environment. Wherefore, this work aimed to gather GGE Biplot graphs of a network of trials unbalance multiyear soybean via matrices of coincidence and networks of environment to optimize environmental stratification. Data from an experimental network of 43 trials was used, these experiments were implanted in 23 municipalities during the crop seasons of 2011/2012, 2012/2013, 2013/2014 and 2015/2016 in Brazil. The trials were implanted under an experimental block design with randomized treatments and approximately 30 genotypes were evaluated, of which most of these genotypes were not repeated between the trials evaluated. The GE interaction were statistically significant for all 43 trials. The step by step of our analyses was: GGE Biplots graphs were obtained; the environment coincidence matrices were calculated; the values of matrices were used to obtain the networks of environmental similarity. The study demonstrated that by the method was possible to identify, using unbalanced multiyear data, four mega-environments. The region under study can be represented by the municipalities of Palotina, Maracaju, Bela Vista do Paraíso and Rolândia. Therefore, integrating GGE Biplot graphs and networks of environmental similarity is an efficient method to optimize a soybean program by environment stratification.

Association between unmanned aerial vehicle high‐throughput canopy phenotyping and soybean yield

AbstractIdentifying agronomic traits correlated to grain yield can be very useful for soybean [Glycine max (L.) Merr.] breeding, especially if these traits can be measured through unmanned aerial vehicle high‐throughput phenotyping rather than through manual measurements. The objective of the present study was to assess the association between canopy coverage and soybean grain yield through different statistical methodologies. A panel with 97 soybean genotypes was evaluated in two field experiments conducted in Paraná State, Brazil. Canopy coverage was determined by using an RGB camera coupled to a drone. Images taken during flights at phenological stages V3‐V4, V5‐V6, V7‐V8, and V9‐R1 were used to calculate canopy coverage based on the green pixel ratio in each experimental unit. There were significant genotype × environment interactions in all evaluated traits. Selective accuracy values (0.73–0.96) revealed indirect yield selection efficiency based on canopy coverage. High genetic correlation estimates (0.76) were observed between grain yield and canopy coverage at flowering in one of the assessed environments. These results were confirmed through genetic correlation coefficient decomposition in direct and indirect effects and of gain estimates presenting indirect selection. Thus, canopy coverage data remotely collected using drones to soybean indirect selection for grain yield can be a promising strategy to accelerate genetic gains in soybean breeding programs.

Physiological insights on soybean response to drought

As most of the soybean in Europe is grown in rainfed conditions, climate aberrations are causing significant crop yield variability. To stabilise the yield and shift the sources of supply to European-grown soybean, stable and adaptable cultivars must be created by breeding. To better understand the mechanism of drought tolerance in specific germplasm, more knowledge needs to be acquired by research. Therefore, this research aimed to examine the physiological insights on soybean response to drought exposed plants (50% available water holding capacity) by monitoring the relative water content (RWC), lipid peroxidation (TBARS), pigments (Chl a and b), proteins and photosynthetic efficiency during five days in the pod stage. The decrease of RWC in both tested genotypes due to the drought effect, coupled with TBARS increase, indicates lipid peroxidation, which resulted in photosynthetic pigment and protein changes. Most of the chlorophyll a fluorescence parameters suggested plants initiated a defence mechanism to water deficiency, i.e. there was an initial adjustment on the first and second day of drought exposed plants, followed by drastic changes in electron flow in photosystem II (PSII). According to the correlation analysis, the effect of drought was prevalent for Chl b, Chl a/b, relative variable fluorescence at 30 ms (VI), the density of reaction centres (RC/CS0), absorption, trapping, electron transport and dissipation flux per reaction centre (ABS/RC, TR0/RC, ET0/RC and DI0/RC), plastoquinone at quinone A binding niche reducing RCs per PSII antenna chlorophylls (RC/ABS) and performance index (PIABS), meaning these parameters are good indicators of drought stress, that can facilitate the selection of genotypes and increase the efficiency of breeding programmes. Among tested materials, Korana had lower sensitivity to drought and smaller reduction of yield components than OS-211, indicating a better response of its defence mechanism to oxidative stress caused by lack of water. The methods tested and confirmed in this research could be used for screening large numbers of genotypes to determine in the soybean breeding programs aiming to select superior genotypes adaptable to weather changes.

Registration of ‘S16‐11651C’, a conventional soybean cultivar with high yield, resistance to multiple diseases, and broad adaptation

Abstract‘S16‐11651C’ (Reg. no. CV‐549, PI 699631) is an early maturity group (MG) V, semi‐determinate, conventional, high‐yielding soybean [Glycine max (L) Merr.] cultivar developed and released in 2020 by the University of Missouri–Fisher Delta Research Center Soybean Breeding Program. High global demand for soybean oil and animal feed and U.S. mid‐southern growers’ interest in early‐MG V conventional cultivars with high yield potential, desired protein and oil content, and broad disease resistance motivated the development and release of S16‐11651C soybean. S16‐11651C was tested in 94 environments in Missouri and 11 other mid‐southern states from 2017 to 2020 for yield and other agronomic traits. On average, S16‐11651C yielded 4,327 kg ha–1, which is 292 kg ha–1 higher than the all‐tests mean of 4,035 kg ha–1. S16‐11651C seed contains 413 (41.3%) and 220 (22%) g kg–1 of protein and oil, respectively, on a dry weight basis. S16‐11651C exhibits relative maturity of 5.3 and has white flowers, tawny pubescence, and tan pod walls. The seed is yellow with intermediate luster and black hila with a weight of 13.5 g 100–1 seed. S16‐11561C is 88 cm tall on average with resistance to lodging. It is resistant to cyst nematode (races 1, 2, and 5 with moderate resistance to race 3), peanut and southern root‐knot nematode, reniform nematode, brown stem rot, charcoal rot, and purple seed stain and moderately resistant to Phomopsis seed decay. S16‐11561C has the capacity to exclude chloride. In addition to its high yield potential, high seed protein and oil content, and early maturity, S16‐11561C has exhibited broad adaptation across mid‐southern states.

‘ShowMeSoy 4301’: High‐yielding soybean with multiple disease resistance and elevated seed oil content

Abstract‘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.

Registration of ‘S15‐10434C’ soybean cultivar with high yield, resistance to multiple diseases, and wide adaptation

Abstract‘S15‐10434C’ (Reg. no. CV‐548, PI 699630) is a maturity group V, high‐yielding conventional soybean [Glycine max (L) Merr.] cultivar developed and released in 2019 by the University of Missouri–Fisher Delta Research Center Soybean Breeding Program. S15‐10434C was developed following conventional breeding from a cross between ‘S11‐17025C’ and ‘S11‐14954’, which are Missouri soybean lines. ‘S11‐17025C’ is a high‐yielding cultivar with resistance to multiple diseases, and ‘S11‐14954’ is a line with high yield potential and soybean cyst nematode resistance developed by the University of Missouri soybean breeding program. Before release, S15‐10434C was tested in 107 environments in Missouri and other southern states from 2016 to 2019 for yield and other agronomic traits. On average, S15‐10434C yielded 4,102 kg ha–1, which is higher than the all‐tests mean of 3,907 kg ha–1. S15‐10434C seed contains 40.7% protein and 21.7% oil on a dry‐seed‐weight basis. It exhibits relative maturity of 5.5, determinate growth habit, purple flowers, tawny pubescence, and tan pod wall. Seeds are yellow with intermediate luster and black hila, with seed weight of 13.1 g 100–1 seed. S15‐10434C is 76 cm tall and has resistance to lodging. It is resistant to cyst nematode (races 1, 2, 3, 5, and 14), reniform nematode, peanut and southern root knot nematodes, sudden death syndrome, and frogeye leaf spot and has field tolerance to phytophthora root rot. It is also a chloride excluder. In addition to its high yield potential and desired seed protein content, S15‐10434C has broad adaptation across southern states.