Journal of Integrative Agriculture ›› 2026, Vol. 25 ›› Issue (2): 424-435.DOI: 10.1016/j.jia.2025.04.010

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西南典型喀斯特地区土壤养分的变异及其驱动因素

  

  • 收稿日期:2024-12-09 修回日期:2025-04-04 接受日期:2025-03-09 出版日期:2026-02-20 发布日期:2026-01-06

Variations and major driving factors for soil nutrients in a typical karst region in Southwest China

Miaomiao Wang1, 2, Hongsong Chen2, 3#, Wei Zhang2, 3, Kelin Wang2, 3   

  1. 1 College of Life and Environmental Science, Central South University of Forestry and Technology, Changsha 410004, China

    2 Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China

    3 Guangxi Key Laboratory of Karst Ecological Processes and Services, Huanjiang Observation and Research Station for Karst Ecosystems, Chinese Academy of Sciences, Huanjiang 547100, China

  • Received:2024-12-09 Revised:2025-04-04 Accepted:2025-03-09 Online:2026-02-20 Published:2026-01-06
  • About author:Miaomiao Wang, E-mail: miaomiaowang@csuft.edu.cn; #Correspondence Hongsong Chen, E-mail: hbchs@isa.ac.cn
  • Supported by:

    This research was supported by the National Natural Science Foundation of China (U2344201 and 42101316), the Natural Science Foundation of Hunan Province, China (2022JJ40866), and the Outstanding Youth Project of Education Bureau of Hunan Province, China (20B613).

摘要:

厘清关键土壤养分的空间分布及其变异机制有助于脆弱喀斯特生态系统的可持续发展。生态修复举措涉及土地利用变化等,因其实施导致的土壤养分空间分布特征变化仍不清楚。本研究基于区域尺度的样品采集利用描述性统计、地统计和空间分析方法探究0-15 cm表层土壤养分的空间变异及其驱动因素。结果表明:土壤有机碳(SOC)、全氮(TN)、全磷(TP)和全钾(TK)含量皆属中等程度变异,其变异系数分别为0.600.600.710.72;其全局莫兰指数分别为0.680.770.640.68,表明存在明显正向空间自相关;其局部莫兰指数皆较低,表明存在较大的空间变异;其最优半变异模型分别是球状、高斯、指数和指数模型。据中国第二次土壤普查养分分级标准,SOCTN是相对丰裕的,其极丰富和丰富等级面积累计占比分别为90.996.0%TP属于中等-贫乏水平,其中等和贫乏等级面积占比分别为33.730.1%TK是贫乏的,其贫乏、非常贫乏和极其贫乏等级面积累计占比87.6%。因此,相较于土壤氮,研究区内的陆地生态系统更易受土壤磷和钾的限制。此外,影响因素的方差分解表明,相较空间和环境变量,其它土壤特性对养分变异的贡献更大。以上结果可为研究区内陆地生态系统的管理提供理论依据。

Abstract:

Understanding the spatial distributions and corresponding variation mechanisms of key soil nutrients in fragile karst ecosystems can assist in promoting sustainable development.  However, due to the implementation of ecological restoration initiatives such as land-use conversions, novel changes in the spatial characteristics of soil nutrients remain unknown.  To address this gap, we explored nutrient variations and the drivers of the variation in the 0–15 cm topsoil layer using a regional-scale sampling method in a typical karst area in northwest Guangxi Zhuang Autonomous Region, Southwest China.  Descriptive statistics, geostatistics, and spatial analysis were used to assess the soil nutrient variability.  The results indicated that soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), and total potassium (TK) concentrations showed moderate variations, with coefficients of variance being 0.60, 0.60, 0.71, and 0.72, respectively.  Moreover, they demonstrated positive spatial autocorrelations, with global Moran’s indices being 0.68, 0.77, 0.64, and 0.68, respectively.  However, local Moran’s index values were low, indicating large spatial variations in soil nutrients.  The best-fitting semi-variogram models for SOC, TN, TP, and TK concentrations were spherical, Gaussian, exponential, and exponential, respectively.  According to the classification criteria of the Second National Soil Census in China, SOC and TN concentrations were relatively sufficient, with the proportions of rich and very rich levels being up to 90.9 and 96.0%, respectively.  TP concentration was in the medium-deficient level, with the areas of medium and deficient levels accounting for 33.7 and 30.1% of the total, respectively.  TK concentration was deficient, with the cumulative area of extremely deficient, very deficient, and deficient levels accounting for 87.6% of the total area.  Consequently, the terrestrial ecosystems in the study area were more vulnerable to soil P and K than soil N deficiencies.  Furthermore, variance partitioning analysis of the influencing factors showed that, except for the interactions, the single effect of other soil properties accounted more for soil nutrient variations than spatial and environmental variables.  These results will aid in the future management of terrestrial ecosystems.

Key words: dominant factor , geostatistics ,  karst ecosystem ,  soil nutrient classification ,  spatial variation