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应用生态学报 ›› 2011, Vol. 22 ›› Issue (12): 3163-3168.

• 研究论文 • 上一篇    下一篇

黄水河小流域土壤养分分布及其与地形的关系

宋轩1,李立东1,寇长林2,陈杰1**   

  1. 1郑州大学水利与环境学院, 郑州 450001;2河南省农业科学院植物营养与资源环境研究所, 郑州 450002
  • 出版日期:2011-12-18 发布日期:2011-12-18

Soil nutrient distribution and its relations with topography in Huangshui River drainage basin. 

SONG Xuan1, LI Li-dong1, KOU Chang-lin2, CHEN Jie1   

  1. 1School of Water Conservancy and Environment, Zhengzhou University, Zhengzhou 450001, China;2Institute of Plant Nutrition & Resource Environment, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
  • Online:2011-12-18 Published:2011-12-18

摘要: 基于GIS和地统计学原理,对丹江口水库水源区黄水河小流域土壤养分空间分布及其与地形因子间的关系进行分析.结果表明: 研究区土壤全氮、全磷、有机质的变异系数分别为51%、66%和85%,为中等变异,土壤速效磷的变异系数为161%,属强变异;土壤全氮和有机质表现为显著正向空间自相关,速效磷和全磷的空间自相关性较弱;海拔是影响该区土壤养分分布的主要因素,其对全氮、全磷和有机质都具有极显著影响,坡度和剖面曲率显著影响全氮和有机质分布.建立了地形因子与土壤养分含量空间分布的回归预测方程,并进行了数字化制图输出,为研究区土壤资源的精确管理提供了数据支持.

关键词: 土壤养分, 地统计学, 空间自相关, 地形因子

Abstract: By using GIS and geostatistic techniques, this paper studied the spatial distribution patterns of soil nutrients and their relationships with topographic factors in Huangshui River drainage basin, a water source of Danjiangkou Reservoir. In the study area, the soil total nitrogen, total phosphorus, and organic matter varied spatially at medium level, with the variation coefficients being 51%, 66%, and 85%, respectively, whereas the soil available phosphorus displayed a strong spatial variation, with the variation coefficient reached 161%. The soil total nitrogen and organic matter exhibited a spatially positive autocorrelation, while the soil total and available phosphorus presented a spatially weak autocorrelation.Altitude was one of the main topographic factors affecting the spatial distribution patterns of the soil nutrients, having significant effects on the spatial distribution of total nitrogen, total phosphorus, and organic matter. Slope and profile curvature also had significant effects on the spatial distribution of the soil total nitrogen and organic matter. Based on these, the regression prediction models of topographic factors and soil nutrient spatial distribution were established, and the digital mappings of the soil nutrients were made, which provided data support for the precise management of soil resources in the study area.

Key words: soil nutrient, geostatistcs, spatial autocorrelation, topographic factor