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县域尺度表层土壤质地空间变异与因素分析
引用本文:张世文,黄元仿,苑小勇,王睿,叶回春,段增强,龚关.县域尺度表层土壤质地空间变异与因素分析[J].中国农业科学,2011,44(6):1154-1164.
作者姓名:张世文  黄元仿  苑小勇  王睿  叶回春  段增强  龚关
作者单位:(中国农业大学资源与环境学院/教育部植物-土壤相互作用重点实验室/农业部土壤和水重点实验室);
基金项目:国家"863"计划,国家科技支撑计划项目,教育部新世纪优秀人才支持计划
摘    要:【目的】以北京市平谷区为研究区域,采用传统统计和地统计学相结合的方法研究县域尺度下土壤质地空间变异的规律,探究土壤质地空间变异的机理。【方法】采用Levine’s方法进行方差奇次性检验,根据检验结果选取最小显著性差方法(least-significant difference,LSD)对土壤颗粒组成与高程、母质、土地利用和水域分布关系进行研究。空间预测采用普通克里格法,鉴于土壤质地属于成分数据,插值前对原始数据进行对称对数比转换。【结果】不同高程组、母质类型、土地利用类型及水域缓冲区组各颗粒平均含量存在一定的差异性,不同高程组和母质类型的土壤颗粒组成之间的差异性比不同土地利用类型和水域缓冲区明显。总体而言,研究区内随着海拔高度的降低,土壤颗粒呈现由粗变细的趋势;由石英含量较高的酸性岩母质发育的土壤土壤颗粒较粗;菜地土壤颗粒相对较细;随着离水域距离的增大,土壤砂粒含量呈增加趋势。地统计分析结果显示,土壤质地各颗粒表现出极强的空间自相关性,空间变异主要由结构性因素引起。各颗粒空间插值结果表明,土壤各颗粒组成空间分布总体趋势特征比较明显。【结论】传统统计分析和地统计学相结合的方法能够系统和全面地揭示土壤质地空间变异的确定性和随机性,研究区土壤质地空间格局主要受地形、母质等自然因素影响。经过对称对数比转换后,土壤质地各颗粒组成空间预测结果满足成分数据空间插值的要求。

关 键 词:土壤质地  空间变异  成分数据  因素分析
收稿时间:2010-06-25;

The Spatial Variability and Factor Analyses of Top Soil Texture on a County Scale
ZHANG Shi-wen,HUANG Yuan-fang,YUAN Xiao-yong,WANG Rui,YE Hui-chun,DUAN Zeng-qiang,GONG Guan.The Spatial Variability and Factor Analyses of Top Soil Texture on a County Scale[J].Scientia Agricultura Sinica,2011,44(6):1154-1164.
Authors:ZHANG Shi-wen  HUANG Yuan-fang  YUAN Xiao-yong  WANG Rui  YE Hui-chun  DUAN Zeng-qiang  GONG Guan
Affiliation:ZHANG Shi-wen1,HUANG Yuan-fang1,YUAN Xiao-yong1,WANG Rui1,YE Hui-chun1,DUAN Zeng-qiang1,GONG Guan1,2(1College of Resources and Environment,China Agriculture University/Key Laboratory of Plant-Soil Interactions,Ministry of Education/KeyLaboratory of Soil and Water,Ministry of Agriculture,Beijing 100193,2College of Economics and Management,Henan Polytechnic University,Zhengzhou 454000)
Abstract:【Objective】Taking Pinggu district as the research region, the paper studied spatial variability and explored mechanism of spatial variability of soil texture on a county scale by traditional statistics methods and geo-statistics.【Method】 The paper analyzed the relationship between different soil particle composition and some factors by the Least Significant Difference (LSD) according to the test results by Levine’s method. As soil textures are the composition data, they were transformed by SLR and then the spatial distribution of the particle composition was predicted by Ordinary Kriging.【Result】Mean values of soil particle composition of different elevation groups, parent material, and land use and water buffer groups had some differences, and the differences were more significant between elevation, parent material types and soil particle composition. Generally speaking, soil particles became finer gradually with the decrease of elevation. Soils developed from acid rock with high quartz content are coarser. Soil particles of vegetable plot were relatively finer. Sand content increased with distance increased from water. Results analyzed by geo-statistics showed that strong spatial autocorrelation of different soil particle compositions and spatial variability was caused by structural properties. Interpolation results showed that the overall trend characteristics of spatial distribution of different soil particle compositions are obvious. 【Conclusion】By using methods of combining statistical analysis and geostatistics, deterministic and stochastic of soil texture can be comprehensively and systematically reflected. Distributions of soil particles mainly were affected by natural factors, such as topography and parent material. After translated by SLR,the prediction results of soil texture met the requirements for composition data interpolation.
Keywords:soil texture  spatial variability  composition data  factor analyses  
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