With global warming and regional decreases in precipitation, drought has become a problem worldwide. As the number of arid regions in the world is increasing, drought has become a major factor leading to significant crop yield reductions and food crises. Soybean is a crop that is relatively sensitive to drought. It is also a crop that requires more water during growth and development. The aim of this study was to identify the quantitative trait locus (QTL) that affects drought tolerance in soybean by using a recombinant inbred line (RIL) population from a cross between the drought-tolerant cultivar ‘Jindou21’ and the drought-sensitive cultivar ‘Zhongdou33’. Nine agronomic and physiological traits were identified under drought and well-watered conditions. Genetic maps were constructed with 923,420 polymorphic single nucleotide polymorphism (SNP) markers distributed on 20 chromosomes at an average genetic distance of 0.57 centimorgan (cM) between markers. A total of five QTLs with a logarithm of odds (LOD) value of 4.035–8.681 were identified on five chromosomes. Under well-watered conditions and drought-stress conditions, one QTL related to the main stem node number was located on chromosome 16, accounting for 17.177% of the phenotypic variation. Nine candidate genes for drought resistance were screened from this QTL, namely Glyma.16G036700, Glyma.16G036400, Glyma.16G036600, Glyma.16G036800, Glyma.13G312700, Glyma.13G312800, Glyma.16G042900, Glyma.16G043200, and Glyma.15G100700. These genes were annotated as NAC transport factor, GATA transport factor, and BTB/POZ-MATH proteins. This result can be used for molecular marker-assisted selection and provide a reference for breeding for drought tolerance in soybean.

Soybean, an important cash crop, is often affected by soil salinity, which is one of the important types of abiotic stress that affects its growth. Poly (acrylic) acid coated Mn₃O₄ (PMO) has been reported to play a vital role in defending against a variety of abiotic stresses in plants. To date, the effects of PMOs on soybean have not been reported; this study explored the mechanism of PMO-enhanced soybean germination under salt stress. In this experiment, 100 mg/L PMO was used as an immersion agent with a salt treatment of 150 mM NaCl. The results showed that when compared with the PMO treatment, salt stress significantly decreased the germination rate, fresh weight, carbohydrate content, and antioxidant enzyme activity of soybean and significantly increased the contents of reactive oxygen species, malondialdehyde, and osmoregulatory substances. However, PMO treatment enhanced the antioxidant defense system and significantly reduced the malondialdehyde content of soybean. Moreover, the activities of H⁺-ATPase and Ca²⁺-ATPase were significantly higher in treated soybean than in the control, and the content of ATP was also higher in treated soybean than in the control. Generally, PMO regulates the homeostasis of reactive oxygen species and reduces ATP consumption, thereby improving the ability of soybeans to germinate under salt stress. This study provides new insights into how nanomaterials improve plant salt tolerance.

Southern Xinjiang is an important soybean production region in China. However, the short growing season and the cultivation of winter crops (such as wheat) in the region limit the expansion of soybean planting areas. An increased planting density can compensate for the loss in yield due to delayed sowing. To identify the quantitative relationship between increased density and delayed days, a two-year field experiment was conducted at the Tarim University Agronomy Experiment Station. Two sowing dates (April 7 (S1) and May 7 (S2)) and three planting densities of 206,800 plants·ha⁻¹ (D1), 308,600 plants·ha⁻¹ (D2), and 510,200 plants·ha⁻¹ (D3) were used to compare various plant growth parameters and canopy characteristics. Late sowing and a high planting density significantly increased the plant height (S2 was 37.3% higher than S1, and D3 was 17.6% and 8.8% higher than D1 and D2), main stem internode, petiole length, and the mean tilt angle of the leaves (S2 was 22.5% higher than S1, and D3 was 11.7% higher than D2) but reduced the stem diameter (D3 was 28.6% and 12.5% lower than D1 and D2), branch number (S2 was 26.7% lower than S1, and D2 was 75% lower than D1), canopy light transmittance (S2 was 49.2% lower than S1, and D3 was 36.7% and 20.8% lower than D1 and D2), photosynthetic rate, and dry matter. The highest yield was achieved at S1D1, but the lowest yield was found for S2D1. Overall, the results suggest that earlier sowing and a lower planting density contribute to achieving an optimum canopy structure and higher yield. Our conclusions provide a reference for soybean production in southern Xinjiang.

To establish an accurate and interpretable prediction framework for soybean lodging grade and clarify the core regulatory traits and differentiated driving mechanisms of soybean lodging under high-density drip irrigation cultivation, 356 spring soybean germplasm accessions were used as experimental materials in this study. Morphological and mechanical traits including plant height (PH), stem pulling force (SPF), internode number (IN) and petiole length (PL) were measured over two consecutive years of field phenotyping. Two composite evaluation indices, plant height/stem pulling force ratio (PH/SPF) and plant height/internode number ratio (PH/IN), were further constructed. Four machine learning algorithms were adopted to develop multi-classification models for soybean lodging grade prediction. SHAP analysis combined with three global sensitivity approaches (perturbation analysis, Sobol’ method and Morris screening) was applied to decipher the regulatory patterns of key traits. The results showed that lodging grade significantly affected soybean grain yield and explained 25–28% of the phenotypic yield variation; yield reduction tended to plateau under severe lodging. Compared with single indicators such as SPF and PL, the two derived composite indices could stably distinguish soybean accessions with different lodging grades and exhibited stronger discriminatory power. Model comparison revealed that the XGBoost model achieved optimal prediction accuracy and generalization stability for lodging grade, with a weighted F1-score of 95.34% on the test set, significantly outperforming the conventional linear model. Interpretability analysis demonstrated that the PH/IN, PH, and PH/SPF acted as the primary positive traits promoting lodging, while SPF was the sole protective trait. Driving factors of lodging presented obvious gradient heterogeneity: mild lodging was dominated by the imbalance of plant architecture ratio, whereas severe lodging was governed by the cumulative effects of PH and IN. Strong interactions existed among all measured traits. The interpretable machine learning framework established in this study can provide theoretical support and technical references for lodging-resistant germplasm screening and targeted plant architecture regulation for densely planted soybean under drip irrigation systems.

Cadmium (Cd) pollution seriously threatens food safety. Plant-derived nanomaterials, due to their environmental friendliness, have emerged as a promising approach for crop stress tolerance regulation; however, their mechanisms under Cd stress remain unclear. In this study, licorice–wolfberry-derived nanocomposites (LW-CNs) were synthesized via a one-step hydrothermal reaction following an established protocol, and their physicochemical properties have been characterized in our previous studies. These were foliarly applied at 200 mg/L to soybean seedlings under 300 μmol/L Cd stress, and the physiological mechanisms underlying their alleviation of Cd toxicity were systematically analyzed. The results showed that Cd stress significantly inhibited soybean seedling growth, reducing net photosynthetic rate from 6.42 to 0.29 μmol·m⁻²·s⁻¹, and induced substantial accumulation of H₂O₂, O₂·⁻, and MDA in leaves. After LW-CN application, plant height, shoot fresh weight, and root fresh weight increased by 5.8%, 29.0%, and 28.2%, respectively, while Pn recovered to 0.94 μmol·m⁻²·s⁻¹. Mechanistic analysis revealed that the ameliorative effects of LW-CNs resulted from four synergistic actions: (i) maintaining chloroplast ultrastructural integrity, with Pn recovery accompanied by stable intercellular CO₂ concentration, suggesting that the relief of photosynthetic limitation was unlikely to be attributable to stomatal factors alone; (ii) systemically upregulating SOD, POD, CAT, APX, DHAR, and GR activities, modulating the redox status of the AsA-GSH cycle, and significantly reducing H₂O₂ (2.77 to 2.14 μmol/g), O₂·⁻ (284.73 to 247.40 nmol/g), and MDA (79.29 to 58.40 nmol/g) contents, thereby maintaining ROS homeostasis; (iii) decreasing Cd concentrations in roots, stems, and leaves, and reducing chloroplast Cd co-localization signals (0.774 to 0.171), thus mitigating direct damage to photosynthetic machinery at the source; and (iv) organ-specifically reconfiguring nitrogen metabolism, enhancing NR, GOGAT, and GDH activities and glutamate accumulation in roots, and promoting nitrogen redistribution among roots, stems, and leaves. These findings provide a multidimensional physiological framework—covering Cd-accumulation reduction, photosynthesis protection, redox stabilization, and nitrogen redistribution—for understanding how a plant-derived nanomaterial enhances soybean Cd tolerance, and offer a reference for developing plant-based strategies to sustain soybean production in Cd-contaminated farmlands.