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【目的】筛选出对土壤有机碳变化敏感且能反映有机碳稳定性的指标,为土壤有机碳稳定性动态监测与评价提供参考。【方法】以桂林毛村岩溶区石灰土和碎屑岩区红壤两种不同土壤为例,通过选取11种指标对表征土壤有机碳活性和团聚体稳定性的指标进行主成分分析和聚类分析。【结果】选取的11种指标可分为两大类,一类与土壤养分及其有机碳组分密切相关,包括有机碳、总氮、总磷、颗粒有机碳、易氧化有机碳和难降解有机碳;另一类与团聚体稳定性密切相关且潜在影响土壤有机碳,包括平均重量直径、几何平均直径、分形维数、偏倚系数和>0.25 mm粒径土壤团聚体比例。此外,岩溶区与碎屑岩区的旱地土壤有机碳含量均较低,次表层(20~50 cm土层)土壤有机碳含量也较低,林地和灌丛自然土壤的土壤团聚体稳定性均较好,而水田的团聚体稳定性最差。聚类分析结果表明,14个不同类型土壤样本可聚为5类,其中k D1、k D2、k F2、c F2、c S1可归为一类,k P2、c P2、c D2、c D1归为一类,c P1、c F1和k F1归为一类,三者土壤有机碳含量分别处于中等、最低、较高水平;k P1和k S1各自聚为一类,其土壤团聚体稳定性最差或有机碳及其组分含量及团聚体稳定性最高。【结论】应用多元统计方法不仅可筛选出对土壤有机碳稳定性响应较敏感的主成分,还可以建立主成分中各指标之间的关系;在喀斯特地区,两种不同地质背景土壤中的有机碳活性和土壤团聚体稳定很大程度上受土地利用方式的影响。
【Objective】 The objective of this study was to screen out the indexes that are sensitive to the change of soil organic carbon and reflect the stability of organic carbon, and provide references for the dynamic monitoring and evaluation of soil organic carbon stability. 【Method】 Taking two different soils of limestone and clastic red soil in Maokou karst area of Guilin for example, principal component analysis and clustering analysis were conducted on 11 indexes to select the indexes of soil organic carbon activity and aggregate stability. . 【Result】 The results showed that the eleven indicators could be divided into two categories. One was closely related to soil nutrients and their organic carbon components, including organic carbon, total nitrogen, total phosphorus, particulate organic carbon, organic carbon and refractory Organic carbon; the other is closely related to the stability of aggregates and potentially affects soil organic carbon, including average weight diameter, geometric mean diameter, fractal dimension, biased coefficient and soil aggregate fraction> 0.25 mm in diameter. In addition, soil organic carbon content in dryland soil in karst area and clastic rock area was low, soil organic carbon content in subsurface layer (20 ~ 50 cm soil layer) was also lower, and soil aggregate stability in natural forestland and shrub soil Better, while paddy field aggregates have the poorest stability. The results of cluster analysis showed that 14 different soil samples could be clustered into 5 categories, among which k D1, k D2, k F2, c F2 and c S1 could be classified into one category, k P2, c P2, c D2, c D1 C P1, c F1 and k F1 are grouped into three categories, and the soil organic carbon contents of the three are at the lowest, middle and highest levels, respectively; and k P1 and k S1 are clustered together and their soil aggregates are stable The worst or organic carbon and its components and aggregates the highest stability. 【Conclusion】 Multivariate statistical methods can not only screen out the principal components that are sensitive to soil organic carbon stability, but also establish the relationships among the indexes in the principal components. In the karst area, organic matter in two different geological backgrounds Carbon activity and soil aggregate stability are largely influenced by land use patterns.