Legume Genomics and Genetics 2026, Vol.17, No.1, 68-79 http://cropscipublisher.com/index.php/lgg 69 uniformity alongside density effects to better understand population-level productivity variations. The objectives also encompass identifying optimal density ranges tailored for specific environmental contexts to guide practical recommendations for soybean cultivation. By addressing these questions through controlled field experiments and comprehensive data analysis, this work seeks to contribute actionable insights for improving soybean production efficiency in diverse agroecosystems (Xu et al., 2021; Yang et al., 2025). 2 Materials and Methods 2.1 Experimental site description The field experiments were conducted in a representative soybean production area characterized by specific geographical, climatic, and soil conditions conducive to soybean growth. The site is located in a temperate region with a semi-humid climate, featuring distinct growing seasons with adequate rainfall and temperature ranges suitable for soybean development. The soil type at the experimental site is typically a well-drained silt loam with moderate fertility, providing a balanced environment for root growth and nutrient uptake. These conditions reflect common agricultural settings where soybean cultivation is prominent, allowing the results to be applicable to similar agroecosystems (Liao et al., 2022). Climatic data during the experimental periods showed average temperatures ranging from 20°C to 28°C during the growing season, with total precipitation sufficient to support rainfed cultivation but supplemented by irrigation in some treatments. The soil’s physical and chemical properties, including pH, organic matter content, and nutrient availability, were monitored to ensure consistency across plots. This comprehensive characterization of the experimental site ensures that observed effects on soybean growth and yield can be attributed primarily to planting density variations rather than environmental heterogeneity (Ran et al., 2023). 2.2 Experimental design The experiment employed a randomized complete block design with multiple planting density gradients to evaluate their effects on soybean growth and yield. Planting densities ranged from low to high levels commonly used in commercial production, such as 135,000; 180,000; 225,000; 270,000; 315,000; and 360,000 plants per hectare. This gradient allowed for detailed analysis of density-dependent responses across a broad spectrum of plant populations. Each density treatment was replicated three times to ensure statistical reliability (Yang et al., 2025). Plots were arranged with uniform row spacing and plant distribution patterns to minimize confounding factors related to spatial variability. In some cases, uniform versus non-uniform plant spacing was also tested to assess the interaction between density and spatial arrangement on canopy light interception and yield components. Standard agronomic practices including fertilization and pest management were uniformly applied across all treatments. The layout facilitated precise measurement of growth traits and yield parameters under controlled yet field-relevant conditions (Figure 1) (Xu et al., 2021). 2.3 Measurement indicators and methods Key growth traits measured included plant height, leaf area index (LAI), branch number, pod number per plant, and biomass accumulation at critical growth stages such as flowering (R1-R2) and pod filling (R5-R6). LAI was assessed using direct leaf area measurements or indirect optical methods to quantify canopy development related to photosynthetic capacity. Plant height and branch number were recorded manually at designated sampling points within each plot (Yang et al., 2025). Yield components measured comprised seed number per pod, pods per unit area, 100-seed weight, total seed yield per hectare, and harvest index. Seed yield was determined by harvesting plants from a fixed central area within each plot to avoid edge effects. Dry matter accumulation was measured by oven-drying sampled plants at various stages to assess biomass partitioning between vegetative and reproductive organs. Data collection followed standardized protocols ensuring accuracy and repeatability across treatments (Matsuo et al., 2018). Additionally,
RkJQdWJsaXNoZXIy MjQ4ODYzNA==