河北省表层土壤有机碳和全氮空间变异特征性及影响因子分析

发布时间：2019-02-28 18:42

【摘要】：【目的】在陆地生态系统中,土壤全氮和有机碳是重要的生态因子。本研究基于土壤调查获得大量土壤剖面的空间和属性信息,研究河北的土壤有机碳和全氮的空间分布特征,为河北的土壤养分监测和管理提供科学依据,同时也为其他类似地区土壤采样提供参考,减少采样成本。【方法】运用传统统计学和地统计学分析方法,以变异函数为工具,初步分析了河北土壤全氮和有机碳的空间变异特征,并应用普通克立格法和回归克里格法进行插值,得出全氮和有机碳含量的分布格局。【结果】研究区土壤有机碳和全氮的平均值分别为15.25 g/kg和1.23 g/kg,变异系数分别为0.73和0.63,属于中等强度变异。经对数转换后,土壤有机碳和全氮均符合正态分布。选择球状模型作为土壤有机碳和全氮的半方差函数理论模型,土壤有机碳和全氮的块金值/基台值的比值分别为1.8%和1.2%,有机碳和全氮的块金系数均小于25%,表明有机碳和全氮具有强烈的空间相关性。有机碳和全氮空间变异的尺度范围不同,分别为50.400 km和59.200 km。研究区的有机碳总体空间分布规律是有机碳在北部较高、南部较低,呈自北向南递减趋势,土壤全氮与有机碳的空间分布趋势相似,但有机碳的空间变异特征较全氮明显,这种空间分布格局主要受环境因子、土壤质地、土壤类型以及土地利用类型等的影响,其中环境因子中的气温和海拔对有机碳和全氮的影响较大。通过比较普通克里格和回归克里格的预测结果,回归克里格能较好地反映东南部有机碳和全氮较低地区的局部变异外,对于西北部的山区也能更好地反映碳、氮与地形及气候等因素的关系。【结论】河北土壤有机碳和全氮的空间变异和分布特征较为类似,受地形地貌、气候等因素的影响。通过比较普通克里格法和回归克里格法的空间预测结果,回归克里格法可以消除环境因子的影响,从而得到更准确的空间预测结果,因此建议使用回归克里格法进行预测,以期获得一个更为准确的土壤有机碳和全氮的空间预测结果。
[Abstract]:Objective: soil total nitrogen and organic carbon are important ecological factors in terrestrial ecosystem. In this study, the spatial and attribute information of a large number of soil profiles were obtained based on soil investigation, and the spatial distribution characteristics of soil organic carbon and total nitrogen in Hebei Province were studied in order to provide scientific basis for monitoring and management of soil nutrients in Hebei Province. At the same time, it also provides reference for soil sampling in other similar areas and reduces sampling cost. [methods] using the traditional statistical and geostatistical analysis methods, the variation function is used as a tool. The spatial variation characteristics of soil total nitrogen and organic carbon in Hebei Province were analyzed, and the general Kriging method and the regression Kriging method were used to interpolate the total nitrogen and organic carbon. The distribution patterns of total nitrogen and organic carbon content were obtained. [results] the average values of soil organic carbon and total nitrogen in the study area were 15.25 g/kg and 1.23 g / kg respectively, and the coefficient of variation were 0.73 and 0.63, respectively, belonging to medium intensity variation. After logarithmic conversion, soil organic carbon and total nitrogen all accord with normal distribution. The spherical model was selected as the theoretical model of semi-variance function for soil organic carbon and total nitrogen. The ratio of block gold value to platform value of soil organic carbon and total nitrogen was 1.8% and 1.2%, respectively, and the block gold coefficient of organic carbon and total nitrogen was less than 25%. The results show that organic carbon and total nitrogen have strong spatial correlation. The spatial variation scales of organic carbon and total nitrogen are 50.400 km and 59.200 km., respectively. The overall spatial distribution of organic carbon in the study area is higher in the north and lower in the south, decreasing from north to south. The spatial distribution trend of soil total nitrogen is similar to that of organic carbon, but the spatial variation of organic carbon is more obvious than that of total nitrogen. The spatial distribution pattern is mainly affected by environmental factors, soil texture, soil types and land use types, among which the air temperature and altitude of environmental factors have a greater impact on organic carbon and total nitrogen. By comparing the prediction results of ordinary Kriging and regression Kriging, the regression Kriging can better reflect the local variation of the lower organic carbon and total nitrogen in the southeast, and it can also better reflect the carbon in the mountainous areas of the northwest. [conclusion] the spatial variation and distribution characteristics of soil organic carbon and total nitrogen in Hebei Province are similar and affected by topography, climate and other factors. [conclusion] the spatial variation and distribution characteristics of soil organic carbon and total nitrogen in Hebei Province are similar and affected by topography, climate and other factors. By comparing the spatial prediction results of the ordinary Kriging method and the regression Kriging method, the regression Kriging method can eliminate the influence of the environmental factors and obtain more accurate spatial prediction results. Therefore, it is suggested that the regression Kriging method be used for the prediction. In order to obtain a more accurate spatial prediction results of soil organic carbon and total nitrogen.
【作者单位】：中国农业科学院农业资源与农业区划研究所农业部面源污染控制重点实验室;中国科学院亚热带农业生态研究所;The
【基金】：宁夏回族自治区土系调查与土系志编制项目(2014FY110200A07)资助
【分类号】：S153.6

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