Research On The Spatial Characteristics Of Soil Nutrients In Karstic Depressions Of Long Ma Village In Tiandeng County

Soil exists in the form of a temporal and spatial continuum displaying the quality of heterogeneity and variability and possesses the spatial variability to a great degree. Research on the spatial variability of soil nutrients can provide guidance for the development of precision agriculture in Guangxi and at the same time help realize a sustainable development in agriculture.This research is based on a test zone of 6.58 hectare in Longma Town of Tiandeng County, Guangxi. By employing the principles and approaches in the traditional statistics and geographical statistics as well as GIS technology and the optimal interpolation method, an analysis and comparison of the spatial variability of Soil Organic Matters, Soil Available Phosphor and Soil Ammonium Nitrogen in the test zone has been conducted respectively and conclusions are drawn here:In terms of variability coefficients, there is no significant difference among the five soil nutrients in the test zone with a variability coefficient of 6.57% for pH Value, 50.56% for organic matters, 27.04% for Available Phosphor, 46.65% for Soil Ammonium Nitrogen and 26.11% for Available Potassium. All these fall into average variability.By the semi-variance function model analysis, the five nutrients have spatial correlations to a certain degree with a range of 93.6m for pH Value, 73.3m for organic matters, 54.3m for Available Phosphor, 94m for Ammonium Nitrogen and 64.8m for Available Potassium. The nugget of Ammonium Nitrogen and Available Phosphor and Available Potassium accounts for less than 25% of the sill, showing a fairly strong spatial correlation. Meanwhile, the nugget of pH Value and organic matters accounts for 25% to 75% of the sill, showing a medium spatial correlation. Statistics from the five nutrients with the help of semi-variance functions models analyzed by Kriging spatial interpolation respectively and in this way come the spatial variability figure of the nutrients according to the gradations of nutrient data. The result of Kriging interpolation shows that the nutrient content of soil surface has a high degree of spatial heterogeneity. With the analysis of the factors in the variability of the five nutrients, it concludes that factors such as soil types, soil quality, and fertilization, contribute to spatial variability.