| Soil is an independent natural body, It is formed by the interaction of soil-forming factors such as the complexity natural and human activities, With a high degree of spatial heterogeneity; The spatial heterogeneity change with the changes in the soil environment, landform types and other factors, Select optimal spatial interpolation methods for different environments and landforms types help to improve the prediction accuracy of soil fertility factor. In this study, Select three different landscape types of the new capital area, Santai County, Huidong County As the study area, using four kinds of the most common and effective spatial interpolation method of Inverse distance interpolation method, ordinary kriging, co-kriging, regression kriging to spatial interpolation on Farmland survey of soil fertility factors (pH, organic matter, alkaline hydrolysis nitrogen, phosphorus, potassium) of Three different geomorphic regions, Research the optimal spatial interpolation method in different geomorphic regions conventional soil fertility elements of larger scale with plains, hills, Mountain, TO Provide a Actual reference with The ongoing nationwide Fertilization Evaluation of Farmland. The study results showed that:(1) The soil fertility factor of Plains District space predict overall recommended ordinary kriging. ordinary kriging is The optimal Method of Soil organic matter, nitrogen, potassium, interpolation mean absolute error (MRE) of Soil organic matter average reduce the nearly 1 percentage point, compared with the rest of several ways, Root mean square error (RMSE) is reduced by an average of 0.3, This is mainly due to the plain area is flat, terrain conditions, the other interpolation methods Consider the the topography conditions is Not as good as ordinary kriging. Since the soil pH and soil available phosphorus space correlation is weak, the piece of gold in proportion to the value and the base station respectively 79.60%,87.17%; Spatial variation is mainly influenced by random factors; At the same time corresponding auxiliary variable DEM correlation is stronger, among them, the correlation coefficient of available P in soil is -0.280**, are remarkably at P=0.01; So collaborative kriging interpolation method showed certain advantages, but due to large scale of county soil fertility factor space interpolation, due to the sample density is bigger, so the interpolation accuracy of interpolation method overall small differences.(2) Hilly region (santai) soil fertility factor space prediction recommended collaborative kriging interpolation method as a whole. Including soil pH, organic matter, alkali-hydro nitrogen related auxiliary variables respectively terrain slope, topography and humidity index (TI), humidity index (TI); Fertility through correlation analysis, found that the factors associated with related are strong, the correlation coefficients are remarkably at P= 0.01; So combined with auxiliary variable space has a better collaborative kriging interpolation method. Nugget value and base stations available p in soil,76.83% since the spatial correlation is weak, the spatial variation is strongly influenced by random factors; At the same time and the terrain factors have no obvious spatial correlation, make inverse distance weight interpolation method of soil effective phosphorus is relatively optimal spatial interpolation method to provide the possibility. Soil available k and terrain factors have no obvious spatial correlation, the elevation and the related auxiliary variables (DEM), the correlation coefficient for-0.061, only the nugget value and base stations at the same time,65.81% as medium spatial correlation, so combined with GS+half an ordinary kriging variance analysis method is slightly better than other space interpolation method.(3) Area (east county) soil fertility factor space forecast overall recommended regression kriging interpolation method and collaborative kriging interpolation method. Including soil pH, and soil alkaline hydrolysis n the relatively optimal interpolation method for regression kriging interpolation method; After analysis found that, soil pH, soil alkaline hydrolysis n, and the better environment factor regression equation fitting, the regression equation are remarkably at P= 0.01; At the same time the space since the correlation between the regression residuals are better, so the mountain soil pH in the study area and soil alkaline hydrolysis n to regression kriging interpolation method is better. Slope soil organic matter, available phosphorus and strong correlation, the correlation coefficient were 0.259**,0.131**, are extremely significant (P=0.01; So research in mountain area using gradient as auxiliary variables with collaborative kriging interpolation effect is better; Through the analysis found that, due to the mountainous region soil phosphorus and potassium in the study area terrain environment factors such as weak spatial correlation, the correlation coefficient is less than 0.1, so ordinary kriging interpolation method is superior to other spatial interpolation method based on the terrain factors. |
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