Soybean Genotypes Research Articles

DELINEATING GENETIC DIVERSITY IN SOYBEAN (GLYCINE MAX L.) GENOTYPES: INSIGHTS FROM A-PAGE ANALYSIS OF GLOBULIN RESERVE PROTEINS

This study explores the genetic diversity of soybean (Glycine max) genotypes using polyacrylamide gel electrophoresis (A-PAGE) to analyze globulin reserve proteins. The research utilized 51 soybean genotypes from various origins, including Canada, Turkey, Uzbekistan and others. The A-PAGE method was employed to extract and analyze the globulin storage proteins in the grains of soybean samples. The proteins were categorized into ω, γ, β and α zones, revealing distinct electrophoretic patterns among the genotypes. A total of 22 spectra and 107 patterns were identified, demonstrating polymorphism based on the frequency of occurrence. The genetic diversity index, calculated using Nei’s formula, indicated varying levels of genetic diversity across the different zones.

Evaluation of Physiological and biochemical traits of selected soybean line in Makurdi

Twelve genotypes of soybean were selected for physiological and seed biochemical evaluations at Joseph Sarwuan Tarka University, Makurdi. These seeds were collected from NCRI Badegi; Niger State- Nigeria. Ten of these genotypes S1075-6-93, S1179-6-58, S1239-6-135, S1240-6-288, S1252-6-277, S1252-6-156, S1268-6-15, S1273-6-65, S1365-6L-20 and SC SENTINEL is newly introduced to Makurdi while SC SIGNA and SLO1 used as checks have been released. The seed crop field was laid out in a Randomized Complete Block Design (RCBD) with three replications in 4-rows of 5m long. Seed samples were collected and subjected to seed quality tests at the Department of Plant Breeding and Seed Science Laboratory of the University. Data were collected on germination count which was expressed as germination percentage and its (germination rate and germination rate index) were consequentially obtained. These seeds were also subjected to proximate analysis and percentage protein, fat, ash, moisture content and crude fibre were determined using soxhlet method. The result showed that significant differences existed for germination rate index (GRI) and germination index (GI) while other seedling traits were not different in magnitude from each other. Protein was highest in three varieties (S1075-6-93, S1239-6-135 and S1252-6-156) with 40.25%, respectively and significantly different from others and the checks. SC SENTINEL had the least protein content (37.19) and highest oil content (20.82). Meanwhile, genotype S1239-6-135 had the least oil content (18.83). Compositionally, an inverse relationship existed between protein oil in genotypes studied. Biochemical composition has more effect on seed quality of these soybean genotypes than physiological traits. Genotypes (S1075-6-93, S1239-6-135 and S1252-6-156) had higher protein content than the checks. Also, Sc SENTINEL, S1365-6L-20, S1273-6-65, S1268-6-15 and S1240-6-288 had higher oil content than the checks. Soybean oil producers could use SC SENTINEL for their productions and (S1075-6-93, S1239-6-135 and S1252-6-156) could be used for protein-based products.

Field screening of soybean genotypes against green semilooper, Chrysodeixis acuta (Fabricus)

Field screening of soybean genotypes against green semilooper, Chrysodeixis acuta (Fabricus)

Assessment of the genetic parameters of soybean genotypes for precocity and productivity in the various cultivation conditions

Soybean (Glycine max [L.] Merr) plays a crucial role in the advancement of agriculture in Kazakhstan, serving as a promising food crop and feed source. The primary challenge in boosting soybean production in Northern Kazakhstan lies in the absence of soybean cultivars suited to the region's conditions. As such, the foremost focus of breeding initiatives should be on creating soybean varieties that possess both early maturity and satisfactory yield potential. The objective of this research was to assess the impact of maturity time (MT) on both the yield formation and the adaptive characteristics of soybean varieties from different origins. This evaluation was conducted by analyzing the outcomes of their testing under diverse cultivation conditions in the northern region of Kazakhstan. The soybean cultivars that were examined, originating from various sources, were classified into three primary groups. These groups varied in terms of their growing season duration as well as their yield levels. The way the alleles of the E1–E4 flowering genes were spread out in the identified clusters showed that for soybean varieties where recessive alleles of the E1–E4 genes build up, the growing season usually shorter. Cultivars of Chinese, Russian, and domestic selections isolated as a result of the research were good initial material for use in local breeding programs. Within the framework of the clusters, an environmental assessment of soybean accessions was carried out, which made it possible to determine their degree of plasticity and, in general, their adaptive potential in the conditions of Northern Kazakhstan. The best cultivars were the Chinese selection ‘Dongnong 63’ and the Russian selection ‘SIBNIIK 315’. Hence, the present study successfully discovered soybean cultivars that possess exceptional adaptability and flexibility. These cultivars hold significant potential for cultivation and practical use in the specific environmental circumstances of northern Kazakhstan.

Assessment of Germination and Seedling Development Factors of Soybean Cultivars in Different Salinity Levels

Salinity poses a significant abiotic stress factor that exerts detrimental effects on plant growth during germination and early seedling stages. The global prevalence of high salt concentration has transformed salinity into a serious problem, impacting vast expanses of land worldwide. This experiment aims to examine the effects of various concentrations of Sodium chloride (NaCl), including 200 mM, 150 mM, 50 mM, and 50 mM, on the seed development at early stage and germination of different cultivars of soybean to determine the variety with the highest value of tolerance, while exploring the underlying mechanisms responsible for salt tolerance in these plants. The parameters considered for measurement included relative injury rate, mean germination time, germination percentage, water uptake percentage, seedling height reduction, seedling biomass, and salt tolerance. Among these parameters, seedling height was highly affected with up to 72.58% reduction in 200 mM, followed by fresh weight and water uptake percentage. The parameters with minimum changes from 0 mM to 200 mM were mean germination time and relative injury rate. By assessing these parameters, a comprehensive understanding of the effects of salinity on soybean genotypes can be obtained. In conclusion, the study suggests that seedling traits are a reliable way to identify genotypes with increased tolerance to salinity stress by farmers according to the salinity situation in their soils.

Soybean seed yield, protein, and oil concentration for a modern and old genotype under varying row spacings

Progress in soybean (Glycine max L.) breeding has led to a reduction in optimal seeding rates due to enhanced branching capacity over time. However, less is known about the changes in canopy architecture between old and modern soybean genotypes at varying row spacing and their impact on yield and seed quality through the main stem and branches. Therefore, this study aimed to i) evaluate yield and seed quality responses of an old and modern soybean genotype at different row spacings and ii) examine the yield and seed quality of branches and the main stem. Trials were conducted in Kansas (United States, US) during 2020 and 2021, comparing two genotypes (old, released in 1980, and modern, released in 2013) at four row spacings (0.19, 0.38, 0.76, and 1.52 m) under rainfed conditions. Seed yield and quality (protein and oil concentrations, %) were assessed at the end of each growing season. In 2021, both genotypes had low and similar yields at all row spacings (averaging 2481 kg ha−1) with 2.5 % less protein on branches compared to the main stem. However, 2022 resulted in a high-yielding environment, with the modern yielding 50 % more (3584 kg ha−1) than the old (2315 kg ha−1) genotype in narrow row spacings (<0.38 m). Additionally, the modern genotype showed a three-fold greater contribution to yield from branches (1113 kg ha−1) relative to the old genotype (379 kg ha−1). Despite the high yields observed in narrow rows, the modern genotype maintained protein levels. These results highlight the importance of row spacing as a key management practice for improving yield while maintaining protein levels in high yield conditions.

Flavonoids and their relationship with the physiological quality of seeds from different soybean genotypes

Flavonoids are compounds that result from the secondary metabolism of plants and play a crucial role in plant development and mitigating biotic and abiotic stresses. The highest levels of flavonoids are found in legumes such as soybean. Breeding programs aim to increase desirable traits, such as higher flavonoid contents and vigorous seeds. Soybeans are one of the richest sources of protein in the plant kingdom and the main source of flavonoid derivatives for human health. In view of this, the hypothesis of this study is based on the possibility that the concentration of isoflavones in soybean seeds contributes to the physiological quality of the seeds. The aim of this study was to analyze the content of flavonoids in soybean genotypes and their influence on the physiological quality of the seeds. Seeds from thirty-two soybean genotypes were obtained by carrying out a field experiment during the 2021/22 crop season. The experimental design was randomized blocks with four replications and thirty-two F3 soybean populations. The seeds obtained were subjected to germination, first germination counting, electrical conductivity and tetrazolium vigor and viability tests. After drying and milling the material from each genotype, liquid chromatography analysis was carried out to obtain flavonoids, performed at UPLC level. Data were submitted to analysis of variance and, when significant, the means were compared using the Scott-Knott test at 5% probability. The results found here show the occurrence of genotypes with higher amounts of flavonoids when compared to their peers. The flavonoid FLVD_G2 had the highest concentration and differed from the others. Thus, we can assume that the type and concentration of flavonoids does not influence the physiological quality of seeds from different soybean genotypes, but it does indirectly contribute to viability and vigor, since the genotypes with the highest FLVD_G2 levels had better FGC values. The findings indicate that there is a difference between the content of flavonoids in soybean genotypes, with a higher content of genistein. The content of flavonoids does not influence the physiological quality of seeds, but contributes to increasing viability and vigor.

COMPARATIVE ANALYSIS OF SOME SOYBEAN GENOTYPES BASED ON BIOMETRIC PARAMETERS

The study made a comparative analysis regarding the behavior of some soybean genotypes in non-irrigated crop system. Experiments were carried out within ARDS Lovrin, on a chernozem type soil with medium fertility. The following genotypes were cultivated: 'Calma', 'Caro', 'Isa', 'Karavukovo', 'Perla', and 'Ziana'. Sowing was done at the optimal time, at a distance of 70 cm between rows, and the crop technology ensured favorable conditions for plant growth. At physiological maturity, plant samples were taken and determinations were made regarding: plant density (PD, plants/m2), plant weight (PW, kg/m2), soybean pod weight (SpW, kg/m2), soybean grain weight (SgW, kg/m2), soybean pods shells weight (SpsW, kg/m2). Based on the recorded values, the yield of soybeans in relation to pods (Y, %) was calculated. PW/SpW, PW/SgW, and PsW/SgW ratios were also calculated. Very strong, positive correlation was recorded between SgW and SpW (r = 0.998), between Y (%) and SpW (r = 0.923), and between Y and SgW (r = 0.944). According to PCA, PC1 explained 74.369% of variance, and PC2 explained 14.769% of variance. The Cluster Analysis generated the dendrogram of soybean genotypes based on similarity (Coph.corr = 0.972), and a high level of similarity was recorded between 'Calma' and 'Isa' genotypes (SDI = 1.6926). In relation to the average value of the experiment, the 'Perla' genotype stood out for the PW parameter, the 'Ziana' genotype for the SpW parameter, the 'Ziana' genotype for the SgW parameter, and the 'Perla' genotype for yield (Y, %).

Determination of Correlation and Path Coefficient for Seed Yield and it’s Contributing Characters in Soybean Germplasm

Background: Soybean is one of the world’s most important legumes contributing significantly as an oilseed in terms of total production and international trade. It is a multipurpose crop. Selection of genotypes with higher yield is always given priority by breeders for developing high yielding varieties. The present experimentation was conducted to know character association and path coefficient analysis among yield and its components for enhancing the usefulness of selection criterion to be followed during development of cultivars. Methods: The experimental material consisted of 32 soybean genotypes sown at Sipani Krishi Anusandhan Farm, Changli, Mandsaur Madhya Pradesh in Kharif season, 2022 in RBD with three replication. The character association and path coefficient was analysed between seed yield and nine yield attributing quantitative characters. Result: Grain yield per plot showed highly significant and positive correlation with number of primary branches per plant (0.501** and 0.444**), number of pods per plant (0.714** and 0.684**), number of pods per cluster (0.709** and 0.673**), number of nods per plant (0.746** and 0.687**) and plant height (0.253* and 0.214*) respectively at both the genotypic and phenotypic levels. Out of ten, five characters had positive direct effect on seed yield per plot. Highest positive direct effect on seed yield per plot was exhibited by number of pods /plant followed by number of nods /plant, number of primary branches per plant, number of pods /cluster, days to 50% pod initiation.

NMR Fingerprinting of Conventional and Genetically Modified Soybean Plants with AtAREB1 Transcription Factors.

Drought stress impacts soybean yields and physiological processes. However, the insertion of the activated form of the AtAREB1 gene in the soybean cultivar BR16, which is sensitive to water deficit, improved the drought response of the genetically modified plants. Thus, in this study, we used 1H NMR in solution and solid-state NMR to investigate the response of genetically modified soybean overexpressing AtAREB1 under water deficiency conditions. We achieved that drought-tolerant soybean yields high content of amino acids isoleucine, leucine, threonine, valine, proline, glutamate, aspartate, asparagine, tyrosine, and phenylalanine after 12 days of drought stress conditions, as compared to drought-sensitive soybean under the same conditions. Specific target compounds, including sugars, organic acids, and phenolic compounds, were identified as involved in controlling sensitive soybean during the vegetative stage. Solid-state NMR was used to study the impact of drought stress on starch and cellulose contents in different soybean genotypes. The findings provide insights into the metabolic adjustments of soybean overexpressing AREB transcription factors in adapting to dry climates. This study presents NMR techniques for investigating the metabolome of transgenic soybean plants in response to the water deficit. The approach allowed for the identification of physiological and morphological changes in drought-resistant and drought-tolerant soybean tissues. The findings indicate that drought stress significantly alters micro- and macromolecular metabolism in soybean plants. Differential responses were observed among roots and leaves as well as drought-tolerant and drought-sensitive cultivars, highlighting the complex interplay between overexpressed transcription factors and drought stress in soybean plants.

Across-environment seed protein stability and genetic architecture of seed components in soybean

The recent surge in the plant-based protein market has resulted in high demands for soybean genotypes with improved grain yield, seed protein and oil content, and essential amino acids (EAAs). Given the quantitative nature of these traits, complex interactions among seed components, as well as between seed components and environmental factors and management practices, add complexity to the development of desired genotypes. In this study, the across-environment seed protein stability of 449 genetically diverse plant introductions was assessed, revealing that genotypes may display varying sensitivities to such environmental stimuli. The EAAs valine, phenylalanine, and threonine showed the highest variable importance toward the variation in stability, while both seed protein and oil contents were among the explanatory variables with the lowest importance. In addition, 56 single nucleotide polymorphism (SNP) markers were significantly associated with various seed components. Despite the strong phenotypic Pearson’s correlation observed among most seed components, many independent genomic regions associated with one or few seed components were identified. These findings provide insights for improving the seed concentration of specific EAAs and reducing the negative correlation between seed protein and oil contents.

Assessment of Novel Protein Ingredient Arthrospira platensis and Soybean Genotype Amino Acid and Oil Selection Improvements on Broiler Performance for a 28–42 d Feeding Period

Two experiments were conducted to assess the efficiency of including the novel protein ingredient Arthrospira platensis or improved soybean meal in a broiler diet. The first experiment aimed to determine the feeding value of soybean meal produced from varieties of soybeans bred for increased amino acid content (SBAA) and improved oil content (SBO) compared to a conventional soybean variety in an ANOVA design fed to Cobb 500 female broilers for 28–42 d. The SBAA and SBO soybeans contained overall higher amino acid content and lower oligosaccharide content compared to the conventional soybean variety in addition to improved oil quality. The second experiment assessed the novel protein ingredient microalgae, Arthrospira platensis (algae), and was conducted to evaluate algae and corn distillers’ grain (DDGS) inclusion on broiler performance for a 28–42 d feeding period in Cobb CF05 male broilers with a 2 × 2 factorial treatment array. Prior to the experimental period, all birds were reared on common feeds. In Experiment 1, birds were fed a diet containing 20% inclusion of an experimental soybean source in the form of full-fat soybean meal. In Experiment 2, the four dietary treatments consisted of diets containing algae at inclusion levels of either 0 or 2% and DDGS at inclusion levels of 0% and 8%. Diets were fed to 288 female broilers (Experiment 1) and 384 male broilers (Experiment 2), placed in eight replicate pens of twelve birds, and live performance was assessed from d 28 to 42. At d 42, six birds from each pen were randomly selected and processed for evaluation of carcass traits and incidence of woody breast. For Experiment 1, all performance data were analyzed using a one-way ANOVA using JMP Pro 16 software with diet as the fixed effect and block as a random effect. Statistical significance was considered at p ≤ 0.05. No significant responses were observed for any recorded measurement for live performance, carcass traits, or woody breast. All data in Experiment 2 were analyzed as a full factorial with a mixed model using JMP software with algae, DDGS, and algae × DDGS as fixed effects and block as a random effect. The F-protected Fisher’s LSD test was used to separate means when p ≤ 0.05. No significant responses were observed for the algae, DDGS, and algae × DDGS influences on BWG, FI, and FCR or processing characteristics; the ingredient source did not affect bird performance. Experimental soybean lines developed at the University of Arkansas were able to be incorporated into broiler diets without decreasing performance. Algae has the potential to be a protein-contributing ingredient for broilers.

Combining AMMI and BLUP analysis to select high‐yielding soybean genotypes in Benin

AbstractThirty soybean [Glycine max (L.) Merr.] genotypes, along with three checks, were evaluated over three seasons across five communes in Benin. The experiments were laid out in an alpha lattice design with three replicates. Additive multiplicative mean interaction (AMMI) and best linear unbiased predictor (BLUP) analysis were combined to assess differential agronomic performance and yield stability among genotypes. There was significant variation (p &lt; 0.001) between genotypes for all traits, with highly significant environmental and genotype × environment interaction (GEI) effects on soybean grain yield (p &lt; 0.001). The likelihood ratio test indicated that both genotype and interaction effects were highly significant (p &lt; 0.001). The low R2 (0.21) for GEI reflected the presence of high residual variation in the GEI component, in contrast to the AMMI analysis of variance, which explained a high proportion of the GEI through the first two interaction principal component axes (52%). The very high value of the predictive accuracy (0.89) confirmed the model's reliability in selecting superior genotypes. The low (0.33) genotypic correlation between environments indicated that it was difficult to select superior genotypes for each environment. Based on the superiority index (weighted average absolute scores from BLUP for yield) of BLUP, simultaneous selection led to the identification of Jenguma 2.67 ± 0.06 t ha−1 as the most stable and productive genotype across environments, followed by Favour 2.34 ± 0.08 t ha−1, and Afayak 2.46 ± 0.08 t ha−1. The agronomic performance of soybean in this study suggested great potential for diversifying cotton‐based cropping systems in Benin, thereby improving their sustainability. The effect of these soybean genotypes on the productivity of intercrop combinations and sequences of cash crops, such as cotton, is yet to be investigated.

MORPHO-PHYSIOLOGICAL DISSECTION OF HEAT TOLERANCE IN CERTAIN POOL OF SOYBEAN GENOTYPES

Under the scenario of climate change, crop diversification through the incorporation of non-conventional oilseed crops like soybean in the cropping pattern is a need of the time. However, being native to regions having mild temperatures, soybean faces difficulty in adaptation in areas of lower latitude with high temperatures and short day lengths during growing periods. This situation directs the researchers to determine the tolerance of soybean genotypes against high temperatures and to establish a selection criterion for the identification of adaptable traits. For this purpose, a study was conducted at Oilseeds Research Institute, Faisalabad. Forty soybean genotypes including approved varieties were screened for tolerance against high temperatures in the growth chamber and glass house. At the seedling stage, genotypes were categorized into tolerant, moderately tolerant, and susceptible categories based on their STI values in the growth chamber and values of certain morpho-physiological parameters in a glass house. Considering the seed yield an ultimate goal in the adaptation process, its study is crucial in field conditions. Tolerant and moderately tolerant genotypes from previous experiments were sown in the field. Data of yield and contributing traits were recorded at maturity and subjected to analysis. Results showed great variability among genotypes for all the traits. AARI Soybean gave the best yield followed by Faisal Soybean, ORI-SOY-91, and ORI-SOY-102. All of these genotypes/varieties also performed well in terms of electrolyte leakage and leaf-relative water content. To conclude, these varieties would show better adaptation in high-temperature areas. Moreover, both the varieties along with 2 best-performing genotypes may prove to be better parents in future hybridization programs.

Identification of Resistant Genotypes against Stem Fly, Melanagromyza sojae (Zehntner) in Soybean

Background: Soybean, Glycine max has gained paramount importance in the agriculture of this country. Contribution of soybean in the oilseed basket of the world has been maximum. In India, of late, the cultivation and utilization of soybean has picked up rapidly. Insect infestation is considered to be the single largest impediment among several abiotic and biotic factors. Among them, stem fly, Melanagromyza sojae (Zehnter) is the number one pest of soybean in southern India causing infestation up to a level of 75 to 90 per cent. Hence, the study was carried out to identify the potential donar of resistance in soybean to stem fly. Methods: The trial was carried out at Main Agricultural Research Station, University of Agricultural Sciences, Dharwad, Karnataka in randomized block design (RBD) with two replications during kharif 2022 and 2023. Twenty-one soybean genotypes were screened against stem fly, M. sojae with one check variety JS 335 under in-vivo condition. Observation on seedling mortality was recorded at weekly intervals up to 30 days after germination. Stem fly incidence and stem tunneling due to stem fly at flowering and harvesting stage were recorded and percentage was worked out. Result: The pooled data of two years concluded that, the soybean genotypes DSb 34, DLSb 1, KDS 1096 and NRC 197 could be found under resistant genotype, while DLSb 3, JS 23-09, MAUS 795, NRC 195, NRC 196 and MAUS 791 as moderately resistant. JS 23-03, JS 335, VLS 102, DSb 21, RSC 11-42 and RVS 13-20 were categorized as low resistant genotypes as against SL 1282, RSC 11-35 and PS 1682 as susceptible and CAUMS-2 and AS 24 genotypes as highly susceptible irrespective of the crop growth stages.

Evaluation of soybean genotypes for resistance to collar rot (Sclerotium rolfsii) under field and glasshouse condition

Evaluation of soybean genotypes for resistance to collar rot (Sclerotium rolfsii) under field and glasshouse condition

Dry matter distribution, yield and seed quality of soybean (Glycine max) genotypes as affected by water stress

Water stress is a major constraint for crop productivity and culturing right cultivars may produce a considerable yield under such stressful condition. An experiment was conducted inside a vinyl house to evaluate the effect of water stress on dry matter distribution, yield, and seed quality of eight soybean genotypes, viz. G00006, BD2336, AGS383, PK472, BCS-1, NCS-1, BU Soybean-1 and BARI Soybean-6. They were grown in pots and subjected to water stress (20% of field capacity, FC) and control (80% of FC). The water stress reduced plant height, leaf number, leaf, stem, and root dry matter by 23, 45, 46, 45 and 19%, respectively, across the genotypes. Under water stress, the soybean genotypes G00006, BCS-1, NCS-1 and BARI Soybean-6 beard only 6 to 30% pod and 5 to 34% seed compared to the control condition. The results further indicated that yield of BD2336 and AGS383 were less affected by the stress than those of other genotypes. Interestingly, water stress exerted positive effect on seed germination, viability, speed of germination and vigor index in BD2336 and AGS383, respectively. Nitrogen and seed protein content were found the highest in BCS-1 under control (9.82 and 58.42% respectively) followed by AGS383. Phosphorus content in seed also reduced by the stress in the tested genotypes, except BD2336 (0.29% in control and 0.96% in water stress) and BARI Soybean-6. Potassium content in seed was reduced by the stress in the tested genotypes, except G00006 and BARI Soybean-6. Based on the findings related to water stress effects on yield and seed quality, particularly seed protein of the tested eight soybean genotypes, it was concluded that genotypes AGS383 and BD2336 might be considered for field trial under water deficit condition.

Determination of flavonoid and anthocyanin contents, and antioxidant activity of selected Philippine soybean (Glycine max) genotypes

Soybeans contain flavonoids and anthocyanins that are associated with health benefits related to antioxidant activities. In this study, the total flavonoid content (TFC), total anthocyanin content (TAC), and the relative antioxidant activities (AOA) of selected Philippine soybean genotypes were screened in order to select and recommend promising genotypes that may be used for further breeding and varietal development. Using the aluminum trichloride method, the genotypes POP 1-44 (TFC = 0.619 g/100 g), PHL 29272 (TFC = 0.616 g/100 g), and Tiwala 6 (TFC = 0.608 g/100 g) were determined to have the highest calculated TFC values. pH differential and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays distinguished all three black-seeded soybean genotypes PHL 29272 (TAC = 177.010 mg/100 g; AOA = 24.476%), PHL 29502 (TAC = 143.750 mg/100 g; AOA = 25.745%), and PHL 29552 (TAC = 123.572 mg/100 g; AOA = 29.297%) with the highest anthocyanin contents and relative antioxidant activities. Genotype PHL 29272 was consistently found among those with the highest results for the three tests. Significant correlations (α = 0.05) were found between flavonoid content and antioxidant activity (p &lt; 0.0001), and between anthocyanin content and antioxidant activity (p &lt; 0.0001). In conclusion, the study was able to identify genotypes that can be recommended as the most suitable candidates for soybean breeding activities.

Seed physiological traits and environmental factors influence seedling establishment of vegetable soybean (Glycine max L.).

Edamame (Glycine max (L.) Merr.), a specialty soybean prized for its nutritional value and taste, has witnessed a surge in demand within the U.S. However, subpar seedling stands have hindered its production potential, necessitating increased inputs for farmers. This study aims to uncover potential physiological factors contributing to low seedling emergence in edamame. We conducted comprehensive assessments on thirteen prominent edamame genotypes alongside two food-grade and two grain-type soybean genotypes, focusing on germination and emergence speed in both laboratory and field settings. Additionally, we employed single electrical conductivity tests and identified and quantified seed leachate components to distinguish among soybean types. Furthermore, using a LabField™ simulation table, we examined seed emergence across a wide soil temperature range (5°C to 45°C) for edamame and other soybean types. All seeds were produced under the same environmental conditions, harvested in Fall 2020, and stored under uniform conditions to minimize quality variations. Our findings revealed minimal divergence in emergence percentages among the seventeen genotypes, with over 95% germination and emergence in laboratory conditions and over 70% emergence in the field. Nonetheless, edamame genotypes typically exhibited slower germination speeds and higher leachate exudates containing higher soluble sugars and amino acids. Seed size did not significantly impact total emergence but was negatively correlated with germination and emergence speed, although this effect could be mitigated under complex field conditions. Furthermore, this study proposed differences that distinguish edamame from other soybean types regarding ideal and base temperatures, as well as thermal time. The finds offer valuable insights into edamame establishment, potentially paving the way for supporting local edamame production in the U.S.

Classification of Soybean Genotypes as to Calcium, Magnesium, and Sulfur Content Using Machine Learning Models and UAV–Multispectral Sensor

Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field.