Spatial distribution and influencing factors of soil organic carbon in the Yalu River Basin, Inner Mongolia

Objective Soil organic carbon (SOC) serves as a vital component in ecosystem carbon storage, sequestration, and cycling, substantially affecting regional ecosystem functions. This study investigated the spatial distribution patterns of SOC and its influencing factors in the Yalu River Basin, eastern Inner Mongolia, by analyzing 676 topsoil samples (0~20 cm).

Methods We employed geostatistical methods to examine the spatial distribution of SOC content and used correlation analysis, variance analysis, and regression analysis to explore how topography, land use patterns, pH, and soil nutrients (N, P, K) affect SOC spatial heterogeneity. The optimal theoretical model and spatial variation characteristics were determined through semi−variance analysis.

Results The results showed that topsoil SOC content ranged from 1.10 g/kg to 61.20 g/kg, with a mean value of 25.54 g/kg, which was notably higher than the national average. The coefficient of variation was 0.48, indicating moderate spatial variability. Semi−variance analysis revealed that the spatial distribution of SOC was best described by an exponential model, with a nugget effect of 49.97% and a range of 63.64 km, suggesting that both structural and random factors influenced its spatial distribution with a considerable autocorrelation range.Regression analysis identified elevation and mean annual temperature as the primary environmental factors controlling SOC spatial distribution, while soil water content emerged as the most influential soil factor, followed by total nitrogen and bulk density. Path analysis further revealed that elevation exerted the strongest direct effect, total nitrogen showed the most significant direct influence among soil factors, and bulk density primarily exhibited indirect effects.

Conclusions Through comprehensive statistical analysis, this study comprehensively elucidated the mechanisms by which environmental and soil factors influence SOC spatial distribution, offering new perspectives for understanding SOC spatial variation patterns.