Processing quality is an important economic wheat trait. The marker-assisted selection (MAS) method plays a vital role in accelerating genetic improvement of processing quality. In the present study, processing quality in a panel of 165 cultivars grown in four environments was evaluated by mixograph. An association mapping analysis using 90 K and 660 K single nucleotide polymorphism (SNP) arrays identified 24 loci in chromosomes 1A, 1B (4), 1D, 2A, 2B (2), 3A, 3B, 3D (2), 4A (3), 4B, 5D (2), 6A, 7B (2) and 7D (2), explaining 10.2–42.5% of the phenotypic variances. Totally, 15 loci were stably detected in two or more environments. Nine loci coincided with known genes or QTL, whereas the other fifteen were novel loci. Seven candidate genes encoded 3-ketoacyl-CoA synthase, lipoxygenase, pyridoxal phosphate-dependent decarboxylase, sucrose synthase 3 and a plant lipid transfer protein/Par allergen. SNPs significantly associated with processing quality and accessions with more favorable alleles can be used for marker-assisted selection.

Soybean yield is governed by key agronomic traits such as main stem node number, lowest pod height, and branch number. These polygenic traits exhibit substantial environmental variation, and the instability of associated genetic loci identified in single-environment studies constrains their application in molecular breeding. To uncover their stable genetic basis, we conducted multi-environment phenotyping over three years and six locations using a panel of 320 soybean accessions. The Best Linear Unbiased Prediction (BLUP) model was employed to integrate phenotypic data and control for genotype-by-environment interactions. Subsequently, genome-wide association studies (GWAS) were performed using BLUP-integrated phenotypic values to capture stable genetic effects, with optimal model using the Mixed Linear Model (MLM). As a result, a total of 22 significant single-nucleotide polymorphism (SNP) loci were identified. Among these, 10, 7, and 5 significant loci were associated with main stem node number, lowest pod height, and branch number, respectively, representing stable genetic signals across multiple environments. These loci primarily clustered on chromosomes 19, 8, and 18. Within these associated regions, we predicted eight high-confidence candidate genes. Functional annotation revealed that these genes are significantly enriched in pathways related to cell wall biosynthesis, energy metabolism, and stress response. This study provides effective genomic resources derived from a multi-environment GWAS framework. These stable loci and candidate genes directly facilitate the molecular breeding of soybean varieties with improved yield-related traits and environmental adaptability.

Although green manure crops are widely recognized for ameliorating alkaline soils, some previous studies have focused mainly on laboratory germination screenings or single yield indicators, and field validation of the synergy between high yield and soil improvement remains comparatively limited. This study evaluated 16 species/accessions with varying salt tolerance in a typical alkaline soil (initial pH 9.01, EC 306 μS·cm⁻¹) in Heilongjiang Province during 2024–2025, based on prior laboratory screening. The experiment was conducted at a single location with three field replicates, and the same plots were monitored across both years. Plant height, fresh/dry grass yield, and soil properties (pH, EC, organic matter, nutrients) were monitored. Glycine max (L.) Merr. and Sesbania Scop. achieved dual synergy, with two-year average fresh yields of 40,733 and 35,750 kg·ha⁻¹, respectively, while reducing soil pH by 0.28–0.29 and electrical conductivity (EC) by 42.2–46.3%. Melilotus officinalis and Vicia villosa Roth exhibited the strongest alkali reduction (pH ↓0.29, EC ↓39.0–39.7%) with moderate yields. Sorghum sudanense showed the highest salt reduction (EC ↓49.0%). Principal component analysis classified the 16 species into four functional types, revealing preliminary ecological patterns for functional differentiation. This study proposes a “laboratory screening–field validation–functional classification” evaluation framework, which requires further validation across different regions and longer time scales for the biological amelioration of alkaline soils.