Search Results (116)

Shanghai-native vegetable soybean (Glycine max [L.] Merril) landraces are valuable germplasms, but their systematic evaluation for agronomic and nutritional traits remains insufficient. This study aimed to assess their phenotypic and nutritional diversity to explore their potential for breeding and meeting dietary needs. Twenty-nine local landraces and one control cultivar (‘Qingsu 7’) were evaluated for key agronomic traits, yield components, nutritional traits, and isoflavone profiles, using hierarchical clustering, principal component analysis (PCA), and correlation analysis. Substantial phenotypic diversity was found, with the germplasm classified into four groups. First pod height and effective pods per plant were highly variable. Nutritional traits showed low variability for crude protein but high diversity for crude fat, soluble sugars (dominated by sucrose), vitamin C, and free amino acids. Total isoflavone content in dry seeds varied widely, with genistin, daidzin, and daidzein as the main forms. ‘Xiangshui Maodou’ had high free amino acids and vitamin C, ‘Heiyan Susudou’ showed superior soluble sugar content, and two landraces exceeded 1500 μg/g DW total isoflavones. The landraces possess rich phenotypic diversity and nutritional diversity. This germplasm represents a valuable resource for breeding programs to enhance crop quality and address global nutritional demands. Full article

Drought impairs osmotic adjustment and photosynthetic performance in legumes; however, the role of micronutrients in modulating these responses across genotypes remains unclear. This study investigated the effects of selenium on the osmolytes and photosynthetic efficiency in two vegetable-soybean (Glycine max L. Merrill) cultivars differing in drought responses: UVE14 (drought-tolerant) and UVE17 (drought-susceptible). Plants were grown under well-watered (100% soil water-holding capacity, WHC) and water-limited (30% soil WHC) conditions, with or without soil-applied selenium. Free amino acids, soluble sugars, chlorophyll pigments, vegetation indices, and chlorophyll fluorescence parameters were assessed at the flowering and pod-filling stages. Under drought conditions, selenium enhanced tolerance primarily by modulating free amino acid metabolism at flowering, increasing aspartate, asparagine, glutamine, and glutamate levels, alongside improvements in chlorophyll content, canopy greenness, and PSII photochemical efficiency. These responses indicate a coordinated adjustment between nitrogen metabolism and photosynthetic function. Both cultivars benefited from selenium application, although the responses were more pronounced in the susceptible cultivar (UVE17). Selenium-induced changes in soluble sugar content were greater under well-watered conditions in both cultivars. The limited accumulation of stress-associated osmolytes, such as proline, following selenium soil drench suggests reduced cellular disruption and mitigation of drought-induced stress. These findings highlight selenium as a context-dependent modulator of drought resilience and emphasize cultivar- and developmental stage-specific effects. Full article

The increasing frequency of high-temperature episodes associated with climate change poses challenges to crop productivity. Stress priming could help to mitigate these effects, with the capacity to enhance plant resilience through metabolic adjustments and memory mechanisms. We evaluated the efficacy of the Stress Memory Encoder biofertilizer (SME, TIMAC Agro) as a seed treatment to induce heat stress (HS) memory in soybean plants [Glycine max (L.) Merrill]. In Experiment 1, plants with SME (0, 2, and 4 mL kg⁻¹) were exposed to HS (35 °C for 48 h) at V3 and V6 vegetative stages. The 4 mL kg⁻¹ dose at V6 under HS consistently improved photosynthetic traits and reductions in reactive oxygen species and lipid peroxidation. Non-enzymatic antioxidants were detected to this dose at V3. Multivariate analysis revealed patterns consistent with dose-dependent physiological adjustments and potential memory acquisition. In Experiment 2, plants treated with SME were exposed to HS (34 °C for 48 h) consecutively (V3 + V6). The SME-primed plants had a higher expression of transcript factors and genes related to HS. Overall, the findings indicate that SME may act as a priming agent capable of inducing somatic memory and enhancing adaptive responses to HS in soybean. Full article

Background: INDETERMINATE DOMAIN proteins (IDDs) are a plant-specific transcription factor family, and members of this family play crucial roles in regulating growth and development as well as environmental adaptation. However, a comprehensive analysis of the IDD family in soybean [Glycine max (L.) Merrill] is limited. Methods and Results: A total of 27 GmIDD genes were identified in the soybean genome, unevenly distributed across 14 chromosomes, and their encoded proteins all harbor a conserved INDETERMINATE (ID) domain with two Cys2His2 (C2H2) and two Cys2HisCys (C2HC) zinc finger motifs. Phylogenetic analysis classified these GmIDD genes into three subgroups. Soybean GmIDD genes exhibit high homology with their Arabidopsis thaliana IDD counterparts. Cis-acting element analysis indicated that the promoters of GmIDD genes are enriched in light-responsive elements (such as Box4), hormone-responsive elements (such as ABRE and AuxRR-core), and abiotic stress-responsive elements (such as MBS and LTR). The qRT-PCR results showed that GmIDD3/5/14/22/26 were upregulated under salt stress, while GmIDD8/9/10/12/16/17/19/20/23/24/25/27 were obviously downregulated during treatment. Under drought stress, the expression levels of GmIDD4/6/7/10/14/16/19/22/24/25/26/27 were upregulated during the treatment. The expression levels of GmIDD1/2/3/4/12/14/15/16/17/18/22/23/25/26 were induced by short-day conditions, whereas GmIDD9/13/19/21 were induced by long-day conditions in soybean leaves. Conclusions: This study provides a theoretical basis for further understanding the functions of the soybean IDD gene family in abiotic stress tolerance and photoperiod adaptability. Full article

This study develops and validates an integrated framework that combines UAV multispectral imagery and canopy structural metrics with gridded climatic variables to predict soybean (Glycine max (L.) Merrill) foliar potassium (K) status and grain yield. Field experiments were conducted over three consecutive growing seasons (2022–2023, 2023–2024, and 2024–2025) under different potassium fertilisation strategies and environmental conditions. Machine learning models, particularly the random forest algorithm, were applied to multisource datasets, including UAV-derived canopy structural traits (height and canopy area), spectral indices (NDVI), meteorological variables, and fertilisation information. The foliar K prediction models achieved high accuracy (R² up to 0.85), while the yield prediction models achieved R² values between 0.71 and 0.81. The inclusion of the potassium rate and fertilisation strategy further improved model performance, highlighting the strong influence of potassium supply and fertilisation management on plant physiological responses. Interestingly, compared with those required to stabilise grain yield, foliar potassium saturation occurred at substantially higher K₂O rates, indicating the occurrence of luxury potassium uptake. The association of UAV-derived canopy metrics with this pattern suggests that remote sensing may help detect subtle nutritional dynamics that are not directly reflected in yield responses. Model interpretability using SHAP analysis identified relationships within the analysed dataset that were consistent with physiological expectations, with positive contributions associated with canopy vigour and negative contributions associated with thermal stress. In addition, probabilistic SHAP analysis provided a decision-oriented perspective by quantifying yield probabilities under contrasting potassium management regimes and climate scenarios. Overall, within the experimental conditions studied, the proposed framework enabled a rapid assessment of crop nutritional status, yield prediction, and the evaluation of fertilisation strategies. The integration of UAV data, climatic variables, and machine learning provides an interpretable basis for potassium management and soybean yield forecasting within the experimental conditions studied, while broader transferability requires external validation. Full article

The soybean aphid, Aphis glycines Matsumura, is an important pest in soybean, Glycine max (L.) Merrill. To investigate the adaptability of various A. glycines generations to high temperatures, this study assessed various life parameters of A. glycines exposed to a diurnal temperature of 33 °C and a nocturnal temperature of 20 °C (33 °C/20 °C) over three generations, compared to a diurnal temperature of 25 °C and a nocturnal temperature of 20 °C (25 °C/20 °C), by life table approach. The adult survival rates of A. glycines in the first (G₁), second (G₂), and third generations (G₃) at 33 °C/20 °C were found to be lower than those at 25 °C/20 °C. Additionally, exposure to 33 °C/20 °C reduced aphid total longevity, oviposition day, and fecundity for G₁, G₂, and G₃ compared to 25 °C/20 °C. These findings indicate that A. glycines can develop and reproduce at a diurnal temperature of 33 °C across the three tested generations, albeit with variations in certain life parameters compared to 25 °C. The results are important for understanding the adaptability of A. glycines to temperature fluctuations and for predicting the population dynamics of this pest in soybeans in Heilongjiang, China, which is currently experiencing rising environmental temperatures. Full article

Soybean, Glycine max (L.) Merrill, is usually grown on a large scale, with pest control based on chemical insecticides. However, the overuse of chemicals has led to several adverse effects requiring more sustainable approaches to pest control. Results from Integrated Pest Management (IPM) employed on Brazilian soybean farms indicate that adopters of the technology have reduced insecticide use by approximately 50% relative to non-adopters, with yields comparable to or slightly higher than those of non-adopters. This reduction can be explained not only by the widespread use of Bt soybean cultivars across the country but also by the adoption of economic thresholds (ETs) in a whole Soybean-IPM package, which has reduced insecticide use. However, low refuge compliance has led to the first cases of pest resistance to Cry1Ac, thereby leading to the return of overreliance on chemical control and posing additional challenges for IPM practitioners. The recent global agenda for decarbonized agriculture might help to support the adoption of IPM since less chemical insecticides sprayed over the crops reduces CO₂-equivalent emissions from its application. In addition, consumers’ demand for less pesticide use in food production has favored the increased use of bio-inputs in agriculture, helping mitigate overdependence of agriculture on chemical inputs to preserve yields. Despite the challenges of adopting IPM discussed in this review, the best way to protect soybean yield and preserve the environment remains as IPM, integrating plant resistance (including Bt cultivars), ETs, scouting procedures, selective insecticides, biological control, and other sustainable tools, which help sustain environmental quality in an ecological and economical manner. Soon, those tools will include RNAi, CRISPR-based control strategies, among other sustainable alternatives intensively researched around the world. Full article

Silicon (Si) plays a critical role in regulating plant physiological processes, particularly through its influence on non-enzymatic antioxidant systems and amino acid metabolism. This study aims to assess soybean performance in response to both soil and foliar Si applications under well-watered and drought conditions, with the goal of enhancing Si accumulation in plant tissues and potentially strengthening the crop’s physiological responses to water deficit stress. This is especially pertinent given that the mechanisms underlying Si fertilization and its contribution to drought tolerance in soybean remain poorly understood. Greenhouse experiments were conducted using a 3 × 2 factorial design. The factors were: (i) three foliar Si treatments: control (no Si), potassium silicate (SiK; 128 g L⁻¹ Si, 126.5 g L⁻¹ K₂O, pH 12.0), and sorbitol-stabilized potassium silicate (SiKe; 107 g L⁻¹ Si, 28.4 g L⁻¹ K₂O, 100 mL L⁻¹ sorbitol, pH 11.8); and (ii) two soil water levels: well-watered (80% field capacity) and water-restricted (40% field capacity), the latter simulating tropical dry spells. Silicon was applied to the soil via irrigation and to the leaves via foliar spraying prior to the onset water restriction. All Si solutions were adjusted to pH 7.0 with 1 M HCl immediately before application. Potassium (K) levels were standardized across treatments through supplementary applications of KCl to both soil and foliage. Biometric and physiological parameters were subsequently measured. Sorbitol-stabilized Si enhanced Si accumulation in soybean tissues and improved plant resilience under both well-watered and drought conditions by promoting key physiological traits, including increased levels of daidzein and ascorbic acid levels, along with reduced amino acid concentrations. It also improved biometric parameters such as leaf area, root development, and number of pods per plant. These findings further support the role of Si as a beneficial element in enhancing stress tolerance and contributing to sustainable agricultural practices. Full article

The aim of the study was to determine the effect of cultivar and nitrogen fertilization on the morphological and physiological traits and yield of soybean (Glycine max (L.) Merrill) grown in central-eastern Poland. In a strict, two-factor field experiment, four soybean cultivars were used: ‘Abelina’, ‘Malaga’, ‘Coraline’, and ‘Petrina’, and three nitrogen rates: 0, 30, and 60 kg N ha⁻¹. The moderate rate (N30) was applied before sowing, while the higher rate (N60) was divided into two parts, with 50% applied before sowing and 50% top-dressed at BBCH 61. The studies were conducted during two growing seasons. It was shown that both the cultivar and nitrogen fertilization significantly affected plant height, leaf area index (LAI), leaf greenness index (SPAD), and chlorophyll fluorescence indices (Fv/Fm, PI). The interaction among cultivar, fertilization, and years was significant for SPAD and Fv/Fm index, indicating a strong influence of environmental factors on plant response. Nitrogen fertilization increased plant height and chlorophyll content but reduced fluorescence indices. Among the cultivars studied, the late-season cultivar ‘Malaga’ was characterized by the highest SPAD index (502), Fv/Fm (0.800), and PI values (4.3), and achieved the highest seed yield (5.06 t ha⁻¹) and thousand-seed weight (230 g). In contrast, the medium-season cultivar ‘Abelina’ showed the lowest SPAD (454), and significantly lower Fv/Fm and PI values (0.790 and 3.51, respectively), resulting in the lowest yield (4.25 t ha⁻¹) and TSW (169.7 g). The application of a moderate rate of nitrogen (N30) improved the physiological indicators of plants and elements of yield structure without reducing the potential photochemical efficiency of PSII, while a higher rate (N60) did not result in a significant increase in yield, despite a greater number of pods and seeds per plant, which may have been due to a reduction in thousand-seed weight. The results highlight the importance of cultivar selection and moderate N fertilization of soybean grown in temperate climates and indicate the need for further research on the physiological mechanisms that determine cultivar-specific nitrogen use efficiency and yield stability under environmental stress. Full article

Cumulative temperature (CT) serves as a critical factor influencing soybean growth and yield, particularly under changing climatic conditions. This study investigated the relationship between CT, growth traits, and yield components of two vegetable soybean (Glycine max (L.) Merrill) cultivars, ‘Pungsannamulkong’ and ‘Aram’, across four sowing dates (late May to late June) in a mid-mountainous region of Korea during 2023–2024. Yield exhibited strong positive correlations with the number of pods per plant (r = 0.88, p < 0.001) and 100-seed weight (r = 0.86, p < 0.001), both indirectly influenced by CT. Structural analysis indicated that CT was indirectly responsible for yield by pod number per plant, which being affected by stem elongation at the R2 stage. The optimal CT range for stable yield was identified as being between 3100 °C and 3500 °C, corresponding to early to mid-June sowing. These findings highlight that optimizing sowing time to secure adequate CT during vegetative growth is a practical adaptation strategy to sustain soybean productivity in mid-mountainous regions under climate warming scenarios. Full article

Cobalt, molybdenum, and nickel are elements directly involved in biological nitrogen fixation in legume plants. However, there is a lack of information about the effects of the interaction among these elements on seed vigor and plant development. This study aimed to evaluate the effects of different doses of these elements on soybean seeds with higher and lower vigor, focusing on nitrogen metabolism and plant development under controlled conditions. The two lots of soybean seeds (higher and lower vigor) were treated with doses of 0, 2, 4, 6, and 8 mL kg⁻¹ of seeds of a liquid commercial product composed of cobalt, molybdenum, and nickel. At the full flowering stage, urease and nitrogenase activities, dry biomass of shoots, roots, and nodules, nitrogen concentration in shoots, plant height, number of nodules, and the efficiency of biological nitrogen fixation (measured by nitrogen-15 isotopic ratio) were assessed. Urease activity increased by 191% in high-vigor seed plants and 65% in low-vigor seed plants. Nitrogenase activity was higher in higher-vigor plants. Nodule dry biomass increased by 42% in lower-vigor plants compared to the control treatment, while in higher-vigor plants, it decreased with increasing doses. Shoot biomass was 30% higher than the control at the 2 mL kg⁻¹ dose. In general, the best responses to the application of the elements in the evaluated variables were observed with the doses of 2 and 4 mL kg⁻¹. It is concluded that the appropriate application of cobalt, molybdenum, and nickel on seeds enhances growth and symbiotic efficiency. However, excessive doses may cause phytotoxic effects. Full article

Drought stress severely affects the physio-biochemical processes and yield of nutritious crops like vegetable soybean (Glycine max L. Merrill), threatening global food security and emphasising the need for effective strategies to improve drought tolerance. This study, conducted under controlled conditions in a greenhouse, investigates the effects of three selenium application methods (seed priming, foliar spray, and soil application) on photosynthesis efficiency, relative water content (RWC), hydrogen peroxide (H₂O₂), antioxidative responses, and yield traits of two vegetable soybean cultivars, UVE14 (drought-tolerant) and UVE17 (drought-susceptible), under drought stress. Among the three Se application methods, soil application was the most effective in improving drought tolerance and yield performance in both cultivars. In UVE17 (drought-susceptible), soil application significantly increased the number of seeds per plant (SPP) and the number of pods per plant (PPP), while in UVE14 (drought-tolerant) SPP increased. Selenium foliar spray and seed priming treatments did not increase yield in drought-stressed UVE14, suggesting that they are unlikely to further enhance tolerance in drought-tolerant cultivars. For UVE17 under drought conditions, selenium soil application improved key physio-biochemical indicators of drought tolerance, including photosynthesis efficiency (total performance of photosystems I and II, total chlorophyll content, and stomatal conductance), water retention (RWC), and carotenoid content. These improved physio-biochemical responses directly impacted yield outcomes. Notably, RWC and total chlorophyll content at the pod-filling stage in drought-stressed UVE17 were positively correlated with an increase in PPP under selenium soil application. Selenium soil application stands out as the most effective method for enhancing drought tolerance in vegetable soybean, presenting a promising and practical solution for enhanced crop production under climate change. Full article

The soybean Glycine max (L.) Merrill is a key crop in Brazil’s agricultural sector and is essential for both domestic food security and international trade. However, water stress severely impacts its productivity. In this study, we examined the physiological and biochemical responses of soybean plants to various water regimes via hyperspectral reflectance (350–2500 nm) and machine learning (ML) models. The plants were subjected to eleven distinct water regimes, ranging from 100% to 0% field capacity, over 14 days. Seventeen key physiological parameters, including chlorophyll, carotenoids, flavonoids, proline, stress markers and water content, and hyperspectral data were measured to capture changes induced by water deficit. Principal component analysis (PCA) revealed significant spectral differences between the water treatments, with the first two principal components explaining 88% of the variance. Hyperspectral indices and reflectance patterns in the visible (VIS), near-infrared (NIR), and shortwave-infrared (SWIR) regions are linked to specific stress markers, such as pigment degradation and osmotic adjustment. Machine learning classifiers, including random forest and gradient boosting, achieved over 95% accuracy in predicting drought-induced stress. Notably, a minimal set of 12 spectral bands (including red-edge and SWIR features) was used to predict both stress levels and biochemical changes with comparable accuracy to traditional laboratory assays. These findings demonstrate that spectroscopy by hyperspectral sensors, when combined with ML techniques, provides a nondestructive, field-deployable solution for early drought detection and precision irrigation in soybean cultivation. Full article

The precise identification, classification, sorting, and rapid and accurate quality assessment of soybean seeds are extremely important in terms of the continuity of agricultural production, varietal purity, seed processing, protein extraction, and food safety. Currently, commonly used methods for the identification and quality assessment of soybean seeds include morphological analysis, chemical analysis, protein electrophoresis, liquid chromatography, spectral analysis, and image analysis. The use of image analysis and artificial intelligence is the aim of the presented research, in which a method for the automatic classification of soybean varieties, the assessment of the degree of damage, and the identification of geometric features of soybean seeds based on numerical models obtained using a 3D scanner has been proposed. Unlike traditional two-dimensional images, which only represent height and width, 3D imaging adds a third dimension, allowing for a more realistic representation of the shape of the seeds. The research was conducted on soybean seeds with a moisture content of 13%, and the seeds were stored in a room with a temperature of 20–23 °C and air humidity of 60%. Individual soybean seeds were scanned to create 3D models, allowing for the measurement of their geometric parameters, assessment of texture, evaluation of damage, and identification of characteristic varietal features. The developed 3D-CNN network model comprised an architecture consisting of an input layer, three hidden layers, and one output layer with a single neuron. The aim of the conducted research is to design a new, three-dimensional 3D-CNN architecture, the main task of which is the classification of soybean seeds. For the purposes of network analysis and testing, 22 input criteria were defined, with a hierarchy of their importance. The training, testing, and validation database of the SB3D-NET network consisted of 3D models obtained as a result of scanning individual soybean seeds, 100 for each variety. The accuracy of the training process of the proposed SB3D-NET model for the qualitative classification of 3D models of soybean seeds, based on the adopted criteria, was 95.54%, and the accuracy of its validation was 90.74%. The relative loss value during the training process of the SB3D-NET model was 18.53%, and during its validation process, it was 37.76%. The proposed SB3D-NET neural network model for all twenty-two criteria achieves values of global error (GE) of prediction and classification of seeds at the level of 0.0992. Full article

Soybean (Glycine max (L.) Merrill) is highly sensitive to water deficit, particularly during the vegetative phase, when morphological and metabolic plasticity support continued growth and photosynthetic efficiency. We applied eleven water regimes, from full irrigation (W100) to total water withholding (W0), to plants grown under controlled conditions. After 14 days, we quantified morphophysiological, biochemical, leaf optical, gas exchange, and chlorophyll a fluorescence traits. Drought induces significant reductions in leaf area, biomass, pigment pools, and photosynthetic rates (A, g_s, ΦPSII) while increasing the levels of oxidative stress markers (electrolyte leakage, ROS) and proline accumulation. OJIP transients and JIP test metrics revealed reduced electron-transport efficiency and increased energy dissipation for many parameters under severe stress. Principal component analysis (PCA) clearly separated those treatments. PC1 captured growth and water status variation, whereas PC2 reflected photoprotective adjustments. These data show that progressive drought limits carbon assimilation via coordinated diffusive and biochemical constraints and that the accumulation of proline, phenolics, and lignin is associated with osmotic adjustment, antioxidant buffering, and cell wall reinforcement under stress. The combined use of hyperspectral sensors, gas exchange, chlorophyll fluorescence, and multivariate analyses for phenotyping offers a rapid, nondestructive diagnostic tool for assessing drought severity and the possibility of selecting drought-resistant genotypes and phenotypes in a changing stress environment. Full article