Soybean (Glycine max (L.) Merr.) isoflavone is important for human health and plant defense system. To identify novel quantitative trait loci (QTL) and epistatic QTL underlying isoflavone content in soybean, F_5:6, F_5:7 and F_5:8 populations of 130 recombinant inbred (RI) lines, derived from the cross of soybean cultivar ‘Zhong Dou 27′ (high isoflavone) and ‘Jiu Nong 20′ (low isoflavone), were analyzed with 95 new SSR markers. A new linkage map including 194 SSR markers and covering 2,312 cM with mean distance of about 12 cM between markers was constructed. Thirty four QTL for both individual and total seed isoflavone contents of soybean were identified. Six, seven, ten and eleven QTL were associated with daidzein (DZ), glycitein (GC), genistein (GT) and total isoflavone (TI), respectively. Of them 23 QTL were newly identified. The qTIF_1 between Satt423 and Satt569 shared the same marker Satt569 with qDZF_2, qGTF_1 and qTIF_2. The qGTD2_1 between Satt186 and Satt226 was detected in four environments and explained 3.41%-10.98% of the phenotypic variation. The qGTA2_1, overlapped with qGCA2_1 and detected in four environments, was close to the previously identified major QTL for GT, which were responsible for large a effects. QTL (qDZF_2, qGTF_1 and qTIF_2) between Satt144-Satt569 were either clustered or pleiotropic. The qGCM_1, qGTM_1 and qTIM_1 between Satt540-Sat_244 explained 2.02%–9.12% of the phenotypic variation over six environments. Moreover, the qGCE_1 overlapped with qGTE_1 and qTIE_1, the qTIH_2 overlapped with qGTH_1, qGCI_1 overlapped with qDZI_1, qTIL_1 overlapped with qGTL_1, and qTIO_1 overlapped with qGTO_1. In this study, some of unstable QTL were detected in different environments, which were due to weak expression of QTL, QTL by environment interaction in the opposite direction to a effects, and/or epistasis. The markers identified in multi-environments in this study could be applied in the selection of soybean cultivars for higher isoflavone content and in the map-based gene cloning.

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.

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.

12-oxo-phytodienoic acid reductase (OPR) is a core component of the jasmonic acid (JA) biosynthetic pathway and participates in JA synthesis by catalyzing the reduction in the precursor 12-oxo-phytodienoic acid (OPDA), as well as broadly regulating plant development, stress response, and hormone signaling networks. This study analyzed the OPR gene family using 28 soybean genomes. A total of 15 OPR gene family members in soybean were identified, including 14 core genes and one variable gene. Analysis of gene duplication types showed that whole-genome duplication (WGD)/segmental duplication was the main mode of duplication in GmOPRs. The phylogenetic tree constructed from multiple species showed that the OPRs in subgroup VII were functionally important OPR genes and that the OPRs underwent Leguminosae and Cruciferae divergence, and large-scale duplication occurred in Leguminosae. Analysis of natural selection pressures on 28 soybean accessions indicated that the overall evolutionary pressures on GmOPRs were dominated by purifying selection, but there were also potential positive selection signals. Analysis of cis-acting elements revealed a large number of light- and hormone-responsive cis-acting elements in the GmOPRs. Some specific cis-acting elements were only present in a few genes or accessions. The protein interaction network consisted of 12 GmOPR proteins, 4 allene oxide synthase (AOS) proteins, and 6 allene oxide cyclase (AOC) proteins, where AOCs interact with GmOPRs and AOSs. Tissue transcriptome expression profiling showed that GmOPR3, GmOPR7, and GmOPR15 were specifically expressed in roots, whereas GmOPR2, GmOPR10, and GmOPR14 were specifically expressed in leaves, suggesting that these genes play an important role in the growth and development of the tissues. Moreover, GmOPRs usually responded to salt stress, and GmOPR3, GmOPR8, GmOPR9, GmOPR10, and GmOPR11 were significantly up-regulated in roots and leaves under salt stress. This suggests that these genes may be involved in biological processes such as osmoregulation, ion homeostasis, and scavenging of reactive oxygen species, thus helping soybeans to resist salt stress. This study comprehensively analyzed the OPR gene family in soybean based on the 28 soybean accessions and clarified the salt stress response pattern, which provides a new and more effective and reliable way to analyze the soybean gene family.

In salt-affected soil, different agronomic practices can alter crop productivity and rhizosphere microbial dynamics, yet whether their short-term responses are consistent remains unclear. A single-site, one-season field experiment was conducted in 2025 in Zepu County, southern Xinjiang, China. Three tillage treatments, conventional tillage (CT), no-tillage (NT), and no-tillage with straw mulching (NT&SM), were compared for soybean yield, rhizosphere soil properties, and bacterial communities. The results showed that the soybean yield under CT was significantly higher than that under NT&SM. Contrary to the yield trend, the NT&SM treatment exhibited the highest mean values for the Chao1 and Shannon indices (the overall treatment effect was significant). PERMANOVA based on Bray–Curtis distance indicated that short-term tillage significantly altered the rhizosphere bacterial community structure, explaining 34.6% of the variation in composition. Tillage practices produced different rhizosphere bacterial community profiles. However, these short-term shifts in bacterial diversity and composition did not parallel the yield response. This suggests that both crop productivity and rhizosphere ecological responses should be considered simultaneously when evaluating conservation tillage practices. These findings represent short-term responses under the conditions of a single site and growing season and require validation through multi-year, multi-site experiments.