RESUMEN
Vegetation types restrict soil structure and heterogeneous processes of elements, which result in difference in spatial distribution of soil nutrients. In this study, the differences in contents of soil nutrients, TN, TP, TK, and soil organic matter (SOM) among different vegetation types were analyzed, and the accuracy of ordinary kriging, regression model and regression model based on vegetation type in predicting soil nutrients was compared. The results showed that, TN, TK and SOM were significantly (P<0.05) correlated to vegetation type, and TP had no significant correlation with vegetation type (P=0.390). TN and SOM had significant difference between shrubbery and arable land. TK had significant difference between arbor and scrub-grassland, shrubbery and arable land, and scrub-grassland and arable land, respectively. In a non-continuous typical small karst catchment, because of high spatial heterogeneity of terrain, the accuracy of multivariate linear regression model based on the real terrain factors of various points was considerably higher than that of ordinary kriging prediction method considering the locations of the known points and prediction points. Moreover, the regression model based on vegetation type improved the prediction accuracy of the TN.
Asunto(s)
Suelo/química , Análisis Espacial , China , Nitrógeno/análisis , Fósforo/análisis , Plantas , Potasio/análisisRESUMEN
A total of 163 soil samples (0-20 cm layer) were collected from the grid sampling plots (80 m x 80 m) in Huanjiang Observation and Research Station of Karst Ecosystem in a small catchment in Karst cluster-peak depression area, South China. By using classical statistics and geostatistics, the spatial heterogeneity of mineral components (SiO2, Fe2O3, CaO, MgO, Al2O3, MnO, and TiO2) in the soils were studied. The contents of the seven soil mineral components in the study area differed greatly, being in the order of SiO2 > Al2O3 > CaO > MgO > Fe2O3 > TiO2 > MnO, and the variance coefficients also varied obviously, in the order of CaO > MgO > Fe2O3 > TiO2 > SiO2 > Al2O3 > MnO. The seven mineral components accounted for 69.4% of the total soil mass. The spatial patterns and the fittest models of the seven soil mineral components differed from each other. All the seven soil mineral components had a strong spatial autocorrelation, with shorter variation ranges and stronger spatial dependence. The Kriging contour maps indicated that the distribution patterns of soil SiO2, Fe2O3, Al2O3, MnO, and TiO2 were similar, being higher in south and east, lower in north and west, higher in depression, and lower in slope, while the distribution patterns of soil CaO and MgO were in adverse. Natural conditions (vegetation, bare rock rate, slope degree, and slope aspect, etc. ) and human disturbance were the most important factors affecting the spatial patterns of the soil mineral components.