Search Results (675)

Soybean is a key global crop for food and oil production, playing a vital role in ensuring food security and supplying plant-based proteins and oils. Accurate information on soybean distribution is essential for yield forecasting, agricultural management, and policymaking. In this study, we developed an Enhanced Chlorophyll Index (NRLI) to improve the separability between soybean and maize—two spectrally similar crops that often confound traditional vegetation indices. The proposed NRLI integrates red-edge, near-infrared, and green spectral information, effectively capturing variations in chlorophyll and canopy water content during key phenological stages, particularly from flowering to pod setting and maturity. Building upon this foundation, we further introduce a pixel-wise compositing strategy based on the peak phase of NRLI to enhance the temporal adaptability and spectral discriminability in crop classification. Unlike conventional approaches that rely on imagery from fixed dates, this strategy dynamically analyzes annual time-series data, enabling phenology-adaptive alignment at the pixel level. Comparative analysis reveals that NRLI consistently outperforms existing vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Greenness and Water Content Composite Index (GWCCI), across representative soybean-producing regions in multiple countries. It improves overall accuracy (OA) by approximately 10–20 percentage points, achieving accuracy rates exceeding 90% in large, contiguous cultivation areas. To further validate the robustness of the proposed index, benchmark comparisons were conducted against the Random Forest (RF) machine learning algorithm. The results demonstrated that the single-index NRLI approach achieved competitive performance, comparable to the multi-feature RF model, with accuracy differences generally within 1–2%. In some regions, NRLI even outperformed RF. This finding highlights NRLI as a computationally efficient alternative to complex machine learning models without compromising mapping precision. This study provides a robust, scalable, and transferable single-index approach for large-scale soybean mapping and monitoring using remote sensing. Full article

Background/Objectives: Equol, a gut microbial metabolite of the soy isoflavone, daidzein, is associated with estrogenic activity and potential benefits for women’s health. While equol production depends on individual gut microbial composition, its dietary and clinical correlates in young women remain incompletely characterized. This study explored the relationship between urinary equol production, dietary habits, and premenstrual symptom severity in healthy university-aged women. Methods: We conducted a cross-sectional study of 41 Japanese women, aged 19–20 years. Urinary equol was measured using a validated liquid chromatography–tandem mass spectrometry (LC–MS/MS) method, following enzymatic hydrolysis. Participants were classified as either equol producers or non-producers, based on urinary concentration thresholds. Dietary intake was evaluated using a dietary questionnaire focused on soy products and dietary fiber sources. Premenstrual symptoms were assessed using a standardized Japanese questionnaire for premenstrual syndrome and premenstrual dysphoric disorder. Results: Twelve percent of participants were classified as equol producers. Compared with non-producers, equol producers reported higher consumption of pumpkin, soybean sprouts, and green tea. Among non-producers, higher consumption of certain vegetables and fiber-rich foods, including broccoli, pickled radish, konjac, and konjac jelly, was associated with greater premenstrual symptom severity, whereas such associations were not observed among equol producers. The analytical method demonstrated high sensitivity and reproducibility for urinary equol measurement. Conclusions: These findings suggest that equol production status may be associated with distinct dietary patterns and with differences in the relationship between food intake and premenstrual symptom severity in young women. Although the cross-sectional design and limited sample size preclude causal inference, these findings suggest that urinary equol is a promising candidate biomarker for future research on diet-related modulation of premenstrual symptoms. Full article

Agrivoltaics (APV) offers a promising dual land-use solution for food and energy production, yet empirical data regarding its impact on leguminous crops in tropical monsoon climates remain limited. This study evaluated the microclimate, growth, and yield of soybean (Glycine max) under an APV system compared to an open-field control during the wet and dry seasons in Bogor, Indonesia. The APV structure reduced incident solar radiation by approximately 35%, significantly lowering soil temperatures and maintaining higher soil moisture across both seasons. In the wet season, the APV treatment significantly increased grain yield (3528.8 vs. 1708.3 kg ha⁻¹, +106%) relative to the open field by mitigating excessive heat and radiative loads, which enhanced pod retention. In the dry season, APV maintained a yield advantage (2025.6 vs. 1724.4 kg ha⁻¹, +17%), driven by improved water conservation and a higher harvest index. Notably, shading did not delay phenological development or hinder vegetative growth in either season. These findings demonstrate that APV systems can contribute to sustainably higher yields and stability in tropical environments by buffering against season-specific environmental stresses, suggesting a viable pathway for sustainable agricultural intensification in equatorial regions. Full article

Purpose: Cross-sectional analysis using open data has revealed an association between the traditional Japanese diet score (TJDS) and healthy life expectancy (HALE). This study aimed to clarify the association of the TJDS with the HALE and average life expectancy (LE) via a longitudinal analysis. Methods: Data regarding the food supply and total energy were extracted from the database of the Food and Agriculture Organization of the United Nations, and data regarding HALE and LE were obtained from the Global Burden of Disease Study 2019. The supply of items consumed frequently (rice, fish, soybeans, vegetables, and eggs) and less frequently (wheat, milk, and red meat) in the Japanese diet were scored (total: −8 to 8 points) and stratified into tertiles by country. The gross domestic product, aging rates, years of education, smoking rate, physical activity, and obesity rate were used as covariates. Longitudinal analyses were conducted for 143 countries, using the HALE and LE for each country from 2010 to 2019 as dependent variables and the 2010 TJDS as an independent variable. Results: The fixed effects (standard errors) were HALE 0.424 (0.102) and LE 0.521 (0.119), indicating significance (p < 0.001). Conclusion: The nine-year longitudinal analysis using international data suggests that the traditional Japanese diet based on rice may prolong the HALE and LE. Full article

This paper proposed a two-level hybrid stacking model for the classification of crops—wheat, soybean, and barley—based on multispectral orthomosaics obtained from uncrewed aerial vehicles. The proposed method unites gradient boosting algorithms (LightGBM, XGBoost, CatBoost) and tree ensembles (RandomForest, ExtraTrees, Attention-MLP deep neural network), whose predictions fuse at the meta-level using ExtraTreesClassifier. Spectral channels, along with a wide range of vegetation indices and their statistical characteristics, are used to construct the feature space. Experiments on an open dataset showed that the proposed model achieves high classification accuracy (Accuracy ≈ 95%, macro-F1 ≈ 0.95) and significantly outperforms individual algorithms across all key metrics. An analysis of the seasonal dynamics of vegetation indices confirmed the feasibility of monitoring phenological phases and early detection of stress factors. Furthermore, spatial segmentation of orthomosaics achieved approximately 99% accuracy in constructing crop maps, making the developed approach a promising tool for precision farming. The study’s results showed the high potential of hybrid ensembles for scaling to other crops and regions, as well as for integrating them into digital agricultural information systems. Full article

Understanding how supplemental nitrogen (N) interacts with biological N₂ fixation (BNF) in modern soybean cultivars is essential for designing fertilization strategies that avoid unnecessary N inputs. We investigated N partitioning among soil, fertilizer and symbiotic sources in soybean grown in a greenhouse pot experiment on a tropical Oxisol. Plants were inoculated with Bradyrhizobium and subjected to four N managements: no external N, soil-applied ¹⁵N-urea (20 kg N ha⁻¹), foliar ¹⁵N-urea (2 kg N ha⁻¹, 0.7% w/v), and the combination of soil + foliar N. Using ¹⁵N isotope dilution, we quantified N derived from the atmosphere (NDFA), fertilizer (NDFF) and soil (NDFS) at organ and whole-plant scales, and related these fractions to nodulation, nitrogenase activity and yield. In the absence of external N, NDFA exceeded 97% in all organs, indicating a strong reliance on BNF and efficient internal N remobilization during grain filling, accompanied by higher leaf nitrate reductase activity. Soil and soil + foliar N markedly increased NDFF and NDFS while suppressing nodulation (particularly at V4) and reducing nitrogenase activity, yet they did not improve grain yield or vegetative biomass. Foliar N alone had only modest effects on N partitioning and did not enhance yield. Under these tropical soil conditions, symbiotic fixation and internal N remobilization were sufficient to meet grain N demand, highlighting the limited agronomic benefit and potential ecological cost of supplemental N during reproductive growth. Full article

The non-destructive estimation of grain yield could increase the efficiency of soybean breeding through early genotype testing, allowing for more precise selection of superior varieties. High-throughput phenotyping (HTPP) data can be combined with machine learning (ML) to develop accurate prediction models. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was utilized to collect data on plant density (PD), plant height (PH), canopy cover (CC), biomass (BM), and various vegetation indices (VIs) from different stages of soybean development. These traits were used within random forest (RF) and partial least squares regression (PLSR) algorithms to develop models for soybean yield estimation. The initial RF model produced more accurate results, as it had a smaller error between actual and predicted yield compared with the PLSR model. To increase the efficiency of the RF model and optimize the data collection process, the number of predictors was gradually decreased by eliminating highly correlated VIs and selecting the most important variables. The final prediction was based only on several VIs calculated from a few mid-soybean stages. Although the reduction in the number of predictors increased the yield estimation error to some extent, the R² in the final model remained high (R² = 0.79). Therefore, the proposed ML model based on specific HTPP variables represents an optimal balance between efficiency and prediction accuracy for in-season soybean yield estimation. Full article

Fast pyrolysis of vegetable oils and residues generates bio-oil (BO), a renewable hydrocarbon source with high acidity that limits its direct use in refineries. In this study, BOs were produced from refined soybean oil (RSO) and waste cooking oil (WCO) at 525 °C in a continuous bench-scale pyrolysis at 525 °C, with a 390 ± 8 g h⁻¹ feed rate, under steady-state conditions. The resulting bio-oils exhibited high acidity (acid index of 145 and 127 mg KOH g⁻¹, respectively) and elevated olefinic and oxygen contents, making them corrosive and unsuitable for co-refining with petroleum. To reduce acidity, ethyl esterification was performed using lipase B from Candida antarctica (CALB), using a Box–Behnken 3³ factorial design. Variables included temperature (40–60 °C), bio-oil:ethanol mass ratio (1:1–1:5), and catalyst concentration (3–10% w/w). The acid index was reduced by up to 76%, with optimal conditions (62 °C, 1:1 mass ratio, 11% CALB) yielding a final value of 28 mg KOH g⁻¹. Similar reductions were obtained for waste cooking oil bio-oil, confirming robustness across feedstocks. CALB retained over 70% activity after three cycles, demonstrating stability. This enzymatic esterification process shows strong potential for lowering bio-oil acidity, enabling integration into petroleum refineries, diversifying feedstocks, and advancing renewable fuel production. Full article

Accurate soybean mapping is critical for food–oil security and cropping assessment, yet spatiotemporal heterogeneity arising from fragmented parcels and phenological variability reduces class separability and robustness. This study aims to deliver a high-resolution, reusable pipeline and quantify the marginal benefits of feature selection and architecture design. We built a full-season multi-temporal Sentinel-1/2 stack and derived candidate optical/SAR features (raw bands, vegetation indices, textures, and polarimetric terms). Jeffries–Matusita (JM) distance was used for feature–phase joint selection, producing four comparable feature sets. We propose a lightweight APM-UNet: an Attention Sandglass Layer (ASL) in the shallow path to enhance texture/boundary details, and a Parallel Vision Mamba layer (PVML with Mamba-SSM) in the middle/bottleneck to model long-range/global context with near-linear complexity. Under a unified preprocessing and training/evaluation protocol, the four feature sets were paired with U-Net, SegFormer, Vision-Mamba, and APM-UNet, yielding 16 controlled configurations. Results showed consistent gains from JM-guided selection across architectures; given the same features, APM-UNet systematically outperformed all baselines. The best setup (JM-selected composite features + APM-UNet) achieved PA 92.81%, OA 97.95, Kappa 0.9649, Recall 91.42%, IoU 0.7986, and F1 0.9324, improving PA and OA by ~7.5 and 6.2 percentage points over the corresponding full-feature counterpart. These findings demonstrate that JM-guided, phenology-aware features coupled with a lightweight local–global hybrid network effectively mitigate heterogeneity-induced uncertainty, improving boundary fidelity and overall consistency while maintaining efficiency, offering a potentially transferable framework for soybean mapping in complex agricultural landscapes. Full article

The objective of this study is to evaluate the effects of botanical oils on the mortality of the phytophagous mite Tetranychus urticae, the predatory mite Amblyseius swirskii, and on gas exchange in papaya seedlings. Two vegetable oils (soybean and corn), two essential oils (lavender and oregano), a synthetic pesticide (abamectin), and a control (water) were evaluated on papaya seedlings infested with T. urticae. In laboratory assays, within the first day after application, abamectin caused 100% mortality of T. urticae adults, followed closely by soybean (96%), corn (94.7%), and lavender (94.7%) oils. In A. swirskii, abamectin caused 100% mortality within 24 h; at 72 h, corn and lavender oils reached 96%, while oregano oil caused the least mortality (67.3%). In field trials, both abamectin and botanical oils statistically reduced eggs per leaf 24 h after application relative to the control, and a similar pattern was observed for nymphs 48 h after treatment. Botanical oils equaled abamectin in T. urticae adult suppression by 72 h, and soybean caused complete adult mortality by day 14. Regarding gas exchange, abamectin significantly affected the photosynthesis and transpiration processes. Thus, botanical oils represent viable biorational options for managing T. urticae in papaya, with lower ecological and physiological costs than abamectin. Full article

Vegetable soybean is a major crop in China with significant economic value. However, it is prone to yellowing and browning during postharvest storage, which reduces quality, marketability, and competitiveness. ‘Tongdou No. 6’ was used to evaluate postharvest quality preservation through combined cold shock treatment and nanocomposite polymer packaging. The results demonstrate that the combined treatment effectively slows the green-to-yellow color change by significantly reducing chroma a* value, weight loss, and chlorophyll degradation. Additionally, it markedly reduces the accumulation of malondialdehyde (MDA) and reactive oxygen species (H₂O₂ and O₂·⁻), while decreasing the activities of polyphenol oxidase (PPO) and peroxidase (POD). The treatment also significantly enhanced the levels of antioxidant compounds, including ascorbic acid (AsA), total phenolics, and total flavonoids, and boosted the activities of key antioxidant enzymes—ascorbate peroxidase (APX), superoxide dismutase (SOD), phenylalanine ammonia-lyase (PAL), and catalase (CAT). Moreover, the combined application of cold shock and nanocomposite polymer packaging significantly enhanced the scavenging capacity against DPPH and hydroxyl (OH⁻) radicals. Overall, combining these two techniques effectively delayed senescence-related discoloration by activating the antioxidant system, regulating ROS metabolism, and reducing oxidative damage. This approach is highly effective in maintaining postharvest quality and offers a promising solution to the storage-induced deterioration of vegetable soybeans. 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

Smart self-healing polymer materials are breaking open new pathways in industry, minimizing waste, and enhancing the long-term reliability of applications. Moreover, when they possess anti-corrosive properties, they effectively protect surfaces from wear and corrosion, leading to improved and more robust products. In the present work, we develop a series of new self-healing polyurethane coatings activated by temperature, through the encapsulation of vegetable oils (VO), namely olive, soybean, and castor oil, in the core of polyurea microcapsules (VO-MCs). Using a green method, water-dispersible microcapsules were embedded in water-based polyurethane matrices. Both the self-healing ability and the anti-corrosive properties of the respective films were evaluated after mechanical damage. Encapsulation allowed for the direct release of VOs into the damaged area; subsequently, the temperature increase reduced the viscosity of the oils, facilitating their flow and diffusion into the damaged area and accelerating the healing process. Soybean oil and olive oil showed remarkable performance in terms of self-healing and high anti-corrosion ability for the polyurethane coatings, while castor oil showed a limited anti-corrosion effect but quite satisfactory effectiveness in terms of self-healing. Overall, the study highlights the potential of using encapsulated oils in environmentally friendly, active coatings with dual action: corrosion protection and self-repair of damage. Full article

Efficient biological nitrogen fixation (BNF) is crucial for sustainable soybean productivity. Current strategies involve the use of Bradyrhizobium diazoefficiens and co-inoculation with plant growth-promoting bacteria like Azospirillum brasilense. To further optimize BNF and plant performance, we investigated the effect of co-inoculation with A. brasilense and B. diazoefficiens combined with the strategic application of the micronutrients Molybdenum (Mo), Cobalt (Co), and Nickel (Ni) on soybean grown under greenhouse conditions. We evaluated plant growth, photosynthetic parameters, accumulation of N, nitrate reductase activity, and nifH gene expression at the R1 reproductive stage. Our main finding was that the co-inoculation combined with the simultaneous application of Mo, Co, and Ni significantly maximized vegetative growth, photochemical efficiency, and BNF. Specifically, this triple supplementation increased nifH gene expression (0.22) compared to the inoculated control (0.003), leading to a substantial enhancement of photosynthetic parameters, including photosystem II (PSII) efficiency and net carbon assimilation (A). For example, the total dry mass was 14.36 g in the Mo + Co + Ni + AZO + BRADY combination and 6.50 g in the non-inoculated and non-micronutrient-treated plants. The total N content was also higher in the plants treated with Mo + Co + Ni + AZO + BRADY (73.20 g kg⁻¹). Crucially, the data also demonstrated that excessive levels of Co impaired the symbiosis, underscoring the necessity of precise dose management. These results confirm the strong synergistic potential of combining microbial co-inoculation with targeted mineral nutrition as a high-impact, sustainable strategy for boosting soybean productivity. Full article

Flexible polyurethane foam (PUF) is a vital material across diverse applications, and its global market is projected to continue growing. Driven by regulatory and consumer demand for sustainable materials, the PUF industry is exploring alternatives to petroleum-derived raw materials, such as vegetable oil-derived bio-polyols. Although bio-based alternatives to fossil-derived foams have been developed, their environmental benefits remain to be fully assessed. Therefore, this study evaluates the environmental performance of flexible PUF production by comparing a conventional fossil-based formulation with a bio-based alternative using a cradle-to-gate Life Cycle Assessment (LCA). The bio-based PUF reduced global warming (6%), fossil resource scarcity (9%), and mineral resource scarcity (6%), but caused significant increases in freshwater eutrophication (91%) and marine eutrophication (19%), mainly due to agricultural processes associated with soybean cultivation. Regardless of the formulation, polyol and toluene diisocyanate production were identified as major environmental hotspots. These results highlight both the decarbonization potential and the trade-offs of bio-based raw materials, underlining the complexity of achieving sustainable PUF production. Overall, the findings provide quantitative insights to guide more sustainable design and sourcing strategies for flexible PUF in the transition from fossil to renewable feedstocks. Full article