内蒙古雅鲁河流域土壤有机碳空间分布特征及主控因素研究

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

    • 摘要:
      研究目的 土壤有机碳是生态系统碳储存、碳汇和碳循环的关键组成部分,显著影响区域生态系统功能。以内蒙古东部雅鲁河流域为研究区域,采集676个表层土壤样本(0~20 cm),阐释该区域土壤有机碳的空间分布特征及其影响因素。
      研究方法 采用地统计学方法分析有机碳含量的空间分布格局,运用相关分析、方差分析、回归分析等方法,探讨地形地貌、土地利用方式、pH值及N、P、K等元素对有机碳含量空间异质性的影响,并通过半方差方法分析确定最佳理论模型和空间变异特征。
      研究结果 研究结果表明,区内表层土壤有机碳含量介于1.10~61.20 g/kg之间,平均值25.54 g/kg,明显高于全国平均水平。变异系数为0.48,表现出中等程度的空间变异性。半方差分析结果表明,土壤有机碳的空间分布特征最适合用指数模型描述,块金效应为49.97%,变程为63.64 km,说明其空间分布特征既受结构性因素影响,也受随机性因素影响,且具有较大的自相关范围。回归分析结果显示,在环境因素中,海拔和年均气温是影响土壤有机碳空间分布的主导因素;在土壤因素中,含水率的影响最显著,其次是全氮和容重。通过通径分析进一步发现,环境因素中海拔影响最大,全氮在土壤因子中表现出最显著的直接作用,而容重主要通过间接途径影响。
      结论 通过综合统计分析,揭示了环境因素和土壤因素对土壤有机碳分布的作用机制,为深入理解土壤有机碳的空间变异规律提供了新的研究思路。

       

      Abstract:
      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.

       

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