Research Of Spatial Variability On Soil Nutrients In Camellia Oleifera Based On GIS And Geostatistics

In recent years, with the rapid development of Precision Agriculture and aggravate of the water erosion, On our soil nutrient management research more and more. The problems such as Soil fertility degradation and water erosion in camellia forest are increasingly serious. In order to make the ecological and economic win-win, by using the method of combined with ASI, Geostatistics and GIS on camellia forest in Yongfeng county, discuss the characteristics and rules of soil nutrients, to providing a scientific basis for precise fertilization and controlling Nutrient losses in camellia forest. the results were as follows:(1) The 14 nutritive elements all have different levels of variation by traditional statistical analysis. CV in between 4.04-150.15%. P and Mg content showed strong variability, pH and OM content showed weak variability, others were moderate. Only NH4-N, AA, S have not skewed. The result of classification:the concentration of S, Fe are in a rich station; the concentration of B, Cu are balance in four level and others have a low concentration.(2) It is normal distribution for pH, AA, K, S, B, Mn and others present logarithmic normal distribution. Through the Semi variance characteristic parameter of faye anomaly in soil Nutrient. Most of them fit the exponential model and different nutrient has different self-correlative distance. The biggest change is AA (181.69 m), the smallest is K (3.81 m). The sequence of their spatial dependency:A A> N> Cu> Fe> OM> pH> P=Ca> S> B= Zn> Mn>Mg> K. Among 14 types of nutrients, OM, Fe, Cu, N, pH are in the range between 25% and 75%, which show their medium spatial correlations; while K, Ca, Mg, P, Zn, Mn, B, S, AA are lower than 25%, which show their strong spatial correlations. The result of fractal analysis:the distribution of B, Ca and K are more uniform than others in test bed.(3) Choosing the best fitting model, we get the spatial distribution maps by using Kriging interpolation, Show that the soil nutrient contents are highly spatial heterogeneity, By analyzing the factors which affect the spatial variance of these nutrients, we get the reasons for spatial variability derived from Soil parent material, topography, land use, fertilization, farming, etc.