ACS (1-aminocyclopropane-1-carboxylic acid synthase) is a member of the aminotransferase superfamily and a pyridoxal phosphate-dependent enzyme. ACS is also a rate-limiting enzyme for the biosynthesis of ethylene and has been linked with plant development, growth, and stress responses. However, information on ACS genes in the soybean genome is limited. In this study, we identified ACS genes in soybean through phylogenetic trees and conserved motifs and analyzed their cis-acting elements, subcellular localization, and expression patterns. Twenty-two members of the ACS family were identified in soybean, and they were divided into four subfamilies based on phylogenetic relationships. Moreover, the results of Arabidopsis mesophyll protoplasts showed that GmACS1, GmACS8, and GmACS15 were all localized in the nucleus and cell membrane. Cis-regulatory elements and qRT-PCR analyses indicated markedly increased levels of GmACS transcripts under hormone treatments and abiotic stress conditions (drought, alkalinity, and salt). In addition, under different abiotic stresses, the potential functional variations across the GmACS isoforms were mirrored in their differential expression. The analysis of transcriptional response to salinity indicated that salt stress might primarily be mediated by the GmACS15 gene. GmACS15 was also found to reduce salt-induced oxidative damage by modulating the ROS-scavenging system, cellular redox homeostasis, and maintaining intracellular Na⁺/K⁺ balance. The results of this investigation revealed the involvement of the ACS gene family in soybean stress-response pathways, including the identification of a potential target for enhancing salt tolerance in soybean.

Accurate and efficient soybean yield prediction is essential for ensuring food security and optimizing agricultural management. Remote-sensing approaches based on a single phenological stage or sensor may not fully capture the physiological and structural dynamics associated with soybean yield formation, thereby constraining prediction accuracy under field-level spatial heterogeneity. This study therefore developed and evaluated an integrated multi-source and multi-temporal UAV-based framework for plot-scale soybean yield prediction under production-field conditions. A UAV platform equipped with multispectral, RGB, and LiDAR sensors was used to extract multidimensional remote-sensing features at four key growth stages: beginning pod, full seed, beginning maturity, and full maturity. By integrating spectral indices, texture metrics, and three-dimensional canopy structural parameters, seven machine-learning algorithms—Ridge, LASSO, Random Forest, MLP, LightGBM, XGBoost, and CatBoost—were evaluated under single-temporal and multi-temporal scenarios. Nonlinear tree-based ensemble models generally achieved higher predictive accuracy than the linear models. On the independent test set, CatBoost performed best under the full multi-temporal, three-sensor fusion scenario, with an R² of 0.816 and an RMSE of 245 kg ha⁻¹. Among individual growth stages, the full seed stage (R6) produced the highest single-stage prediction accuracy. Three-sensor fusion improved prediction relative to the single-source multispectral scheme, and integrating features across phenological stages further improved performance by representing cumulative crop-growth dynamics. The proposed framework provides methodological support for UAV-based precision management and data-driven decision-making in soybean production.

The Kunitz trypsin inhibitor (KTI) gene family encompasses a category of trypsin inhibitors, and the KTI proteins are important components of the 2S storage protein fraction in soybeans. In this study, fifty members of the GmKTI family were identified in the soybean genome, and their physicochemical properties, domain compositions, phylogenetic relationships, gene structures, and expression patterns were comprehensively analyzed to explore their impact on soybean seed protein content. The results revealed significant gene expansion within the GmKTI family in soybean. The gene structures and conserved motifs of GmKTI members exhibited both regularity and diversity, with distinct expression patterns across different soybean tissues. Haplotype analysis identified 7 GmKTI genes significantly associated with seed storage protein content, and the combination of superior haplotypes was found to enhance seed storage protein content. This is crucial for the improvement of soybean varieties and the enhancement of storage protein content. Additionally, the GmKTI family demonstrated evolutionary conservation, with its functions likely linked to light induction, biotic stress, and growth development. This study characterizes the structure, expression, genomic haplotypes, and molecular features of the soybean KTI domain for the first time, providing a foundation for functional analyses of the GmKTI domain in soybean and other plants.

Phytochrome-interacting factors (PIFs) play a crucial role in regulating plant growth and development. However, studies on soybean PIFs are limited. Here, we identified 22 GmPIF genes from the soybean genome and classified the GmPIF proteins into 13 subfamilies based on amino acid sequence homology, secondary and tertiary structures, protein structure, and conserved motifs. Genome-wide collinearity analysis revealed that fragment duplication events play a dominant role in expanding the GmPIF gene family. Cis-acting element analysis revealed that the GmPIF gene family is involved in light response, hormone response, biotic–abiotic stress response elements, and plant growth and development. Gene expression analysis in different temperature environments showed that the GmPIF family was found to be induced by phytohormone treatments, with a significant increase in the expression level of GmPIF3g. GmPIF3g plays a key role in the regulation of the entire network, and in addition, 30 proteins interacting with the GmPIF3g promoter were identified through the use of a novel biofilm interference technique. This technique showed that the transcription factor Dof (DNA binding with one finger) binds to the GmPIF3g promoter, and Y1H assays indicated that Dof regulates its expression by binding to the PIF promoter. These results provide a theoretical basis for further studies on the regulatory network of GmPIF genes to improve the structure of soybean plants under shade environments, as well as a new method for analyzing regulatory elements that interact with gene promoters.

Soybean (Glycine max) is an economically important cash crop and food source that serves as a key source of high-quality plant-derived protein and oil. Seed vigor is an important trait that influences the growth and development of soybean plants in an agricultural setting, underscoring a need for research focused on identifying seed vigor-related genetic loci and candidate genes. In this study, a population consisting of 207 chromosome segment substitution lines (CSSLs) derived from the crossing and continuous backcrossing of the Suinong14 (improved cultivar, recurrent parent) and ZYD00006 (wild soybean, donor parent) soybean varieties was leveraged to identify quantitative trait loci (QTLs) related to seed vigor. The candidate genes detected using this approach were then validated through RNA-seq, whole-genome resequencing, and qPCR approaches, while the relationship between specific haplotypes and seed vigor was evaluated through haplotype analyses of candidate genes. Phenotypic characterization revealed that the seed vigor of Suinong14 was superior to that of ZYD00006, and 20 total QTLs were identified using the selected CSSLs. Glyma.03G256700 was also established as a seed vigor-related gene that was upregulated in high-vigor seeds during germination, with haplotypes for this candidate gene also remaining consistent with observed soybean seed vigor. The QTLs identified herein can serve as a foundation for future marker-assisted and convergent breeding efforts aimed at improving seed vigor. In addition, future molecular and functional research focused on Glyma.03G256700 has the potential to elucidate the signaling network and key regulatory mechanisms that govern seed germination in soybean plants.

Soybean (Glycine max), as an important crop for both oil and grains, is a major source of high-quality plant proteins for humans. Among various natural disasters affecting soybean production, waterlogging is one of the key factors leading to yield reduction. It can cause root rot and seedling death, and in severe cases, even total crop failure. Given the significant differences in responses to waterlogging stress among different soybean varieties, traditional single-trait indicators are insufficient to comprehensively evaluate flood tolerance. In this study, relative seedling length (RSL) was used as a comprehensive evaluation index for flood tolerance. Using a chromosome segment substitution line (CSSL) population derived from SN14 and ZYD00006, we successfully identified seven quantitative trait loci (QTLs) associated with seed waterlogging tolerance. By integrating RNA-Seq transcriptome sequencing and phenotypic data, the functions of candidate genes were systematically verified. Phenotypic analysis indicated that Suinong14 had significantly better flood tolerance than ZYD00006. Further research revealed that the Glyma.05G160800 gene showed a significantly up-regulated expression pattern in Suinong14; qPCR analysis revealed that this gene exhibits higher expression levels in submergence-tolerant varieties. Haplotype analysis demonstrated a significant correlation between different haplotypes and phenotypic traits. The QTLs identified in this study can provide a theoretical basis for future molecular-assisted breeding of flood-tolerant varieties. Additionally, the functional study of Glyma.05G161800 in regulating seed flood tolerance can offer new insights into the molecular mechanism of seed flood tolerance. These findings could accelerate the development of submergence-tolerant rice varieties, enhancing crop productivity and stability in flood-prone regions.