Soil salinization is considered a major global environmental problem due to its adverse effects on agricultural sustainability and production. Compost is an environmentally friendly and sustainable measure used for reclaiming saline–sodic soil. However, the responses of the physiological characteristics of alfalfa and the structure and function of rhizosphere fungal communities after compost application in saline–sodic soil remain elusive. Here, a pot experiment was conducted to explore the effect of different compost application rates on soil properties, plant physiological traits, and rhizosphere fungal community characteristics. The results showed that compost significantly increased soil nutrients and corresponding soil enzyme activities, enhanced leaf photosynthesis traits, and ion homeostasis compared with the control treatment. We further found that the rhizosphere fungal communities were dominated by Sodiomyces at the genus level, and the relative abundance of pathogenic fungi, such as Botryotrichum, Plectosphaerella, Pseudogymnoascus, and Fusarium, declined after compost application. Moreover, the α-diversity indexes of the fungal community under compost application rates of 15% and 25% significantly decreased in comparison to the control treatment. The soil SOC, pH, TP, and TN were the main environmental factors affecting fungal community composition. The leaf photosynthetic traits and metal ion contents showed significantly positive correlations with Sodiomyces and Aspergillus. The fungal trophic mode was dominated by Pathotroph–Saprotroph–Symbiotroph and Saprotroph. Overall, our findings provide an important basis for the future application of microbial-based strategies to improve plant tolerance to saline-alkali stress.

In recent years, genomic selection (GS) has been widely adopted in plant breeding; however, its practical application is constrained by the high cost of genotyping large segregating populations. To address this issue, this study employed a Quantitative Trait Nucleotide (QTN)-assisted GS strategy to evaluate its efficiency in reducing genotyping costs for soybean seed oil content (OC) and protein content (PC). Based on six multi-parent F₄ populations (n = 4404) derived from seven elite soybean cultivars, which were genotyped using a 20K SNP chip, we identified 83 and 110 QTNs that were significantly associated with OC and PC, respectively. Among these loci, 37 and 62 QTNs were specific to OC and PC, respectively. Genomic prediction accuracies were evaluated across different training population (TP) sizes using three marker panels: genome-wide SNPs, all detected QTNs, and trait-specific QTNs. The panel consisting of all detected QTNs exhibited significantly higher prediction accuracy than the other two panels, except for PC when using 90% of the population as the training set. Phenotypic verification of the selected individuals showed that the PC-specific QTN panel yielded higher PC values and increased OC + PC values compared with the other marker panels. These results demonstrate that a small set of QTNs provides a cost-effective approach for genomic selection in practical soybean breeding programs.

Oil content (OC) and protein content (PC) are two important traits determining the quality and yield in soybean. However, these traits are quantitative in nature, governed by polygenes possessing minor effects. In this study, we performed multi-locus genome-wide association studies (GWAS) to identify the quantitative trait nucleotides (QTNs) associated with OC and PC by using the 4404 multiparent F4 individuals genotyping with 20K SNP chip. A total of 83 and 110 QTNs significantly associated with OC and PC, respectively were detected by using six multi-locus GWAS methods. The identified QTNs as well as genome-wide SNPs (9,942 SNPs) were tested with training populations (TP) of different sizes for genomic selection (GS) analysis. Our results revealed that using the QTNs only has allowed to provide the higher prediction accuracy of 0.70 at reduced TP size of 10%; besides, using the QTNs-specific to each trait viz., OC and PC for GS selection minimizes the negative correlations among these traits. The present study, provided the detailed genetic architecture of PC and OC in soybean, besides provided a new method for developing soybean cultivars with both high PC and OC, which otherwise was the long-term unachievable goal of the soybean breeders.

Soybean (Glycine max L. Merr.) is rich in proteins, fats, and other nutrients, and the genetic improvement of soybean protein content has long been a key research focus in breeding programs. Based on the chromosome segment substitution line (CSSL) population, this study screened target lines within this population using genotypic and phenotypic information to establish an initial mapping population for soybean seed protein content. Through single-marker analysis, a quantitative trait locus (QTL) interval was mapped to the region between 26,705,080 bp and 33,180,908 bp on chromosome 16, designated as qSPJ_1. A secondary segregating population was constructed based on the initial mapping results for fine mapping, which narrowed the interval to 0.076 Mb. A total of 9 candidate genes were identified within this interval. By comparing amino acid and promoter sequences between the two parents, performing quantitative real-time PCR (qRT-PCR) analysis, and conducting haplotype analysis, Glyma.16G165100 was preliminarily predicted as a candidate gene affecting soybean seed protein content. The single nucleotide polymorphism (SNP) variation sites in its promoter region were significantly associated with the variation in protein content in the resource population. This study provides important theoretical guidance for dissecting the genetic mechanism of soybean seed protein content and advancing its breeding improvement.