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A good understanding of the distribution characteristics of soil nutrients is important to achieve the best management of soil nutrients. Kenli County, a typical delta area in Shandong Province in North China, was chosen as the study area of this paper. There were 1 278 soil samples (0- 20 cm) collected, processed and analyzed in laboratory. Geostatistical analysis was conducted to elucidate the spatial variations of soil nutrients and interpolate key soil nutrients in space. The semivariogram models of alkali- hydrolyzable nitrogen (AN), available phosphorus (AP) and available potassium (AK) were estimated. The best fitting models were selected based on the residual sum of squares (RSS) and coefficient of determination (R2). The R2 of AP’s spherical model was 0.951, followed by R2 of AN’s exponential model (0.892) and R2 of AK’s spherical model (0.787). The interpolations were employed with Ordinary Kriging (OK), Inverse Distance Weighted (IDW)，Universal Kriging (UK)，Radial Basis Function (RBF) and Local Polynomial (LP) in GIS software. The prediction accuracy was validated with test data. In order to explore the self-adaptive ability of OK, IDW, RBF and LP, eight datasets with different spatial distribution patterns were designed with AN data. The default optimized model of each method was used on the eight datasets and the results were compared. It comes to the following conclusions: 1) The spatial variations and autocorrelation of AN, AP and AK in the study area are moderate. The best fitted semivariogram models are spherical model, exponential model and spherical model, whose coefficients of determination were 0.951, 0.892 and 0.787, respectively. 2) The spatial distribution patterns of AN, AP and AK are closely related to the terrain and land use types in the study area, i.e., the contents of key soil nutrients are high in the southwest part of region where there are irrigated land and dry land and northeast part of the region where the cropland is along the Yellow River and affected by the freshwater of the river, and the nutrient contents are lower in the central region where there is paddy field. 3) Compared with Nugget/sill, Moran’s I, the most widely used index to describe spatial autocorrelation, is more robust and effective for measuring spatial autocorrelation of soil nutrients. 4) The influence factors of spatial interpolation are spatial distribution pattern, sample number, spatial autocorrelation and spatial clustered degree (measured with Nearest Neighbor Rate); in dispersed pattern, all methods perform bad; in random pattern, IDW and RBF perform better than OK and LP; in clustered pattern, the adaptabilities of the methods are related with sample number and spatial autocorrelation, and all methods perform almost the same when there are enough samples. This paper ascertained the best interpolation prediction method for the main soil nutrients in the study area, analyzed the variation and the spatial distribution of soil nutrients, and provided a theoretical basis for soil nutrients management and agricultural sustainable development in typical area of Yellow River Delta. [ABSTRACT FROM AUTHOR]

Studying the spatial variability and its affecting factors of surface soil organic matter and total nutrients can provide reference for soil management in the ecological fragile zone of the Loess Plateau. Based on the 93 soil sampling points in Wangdonggou watershed, this paper used a variety of geostatistical methods to analyze the spatial variability and its influencing factors of soil nutrients in the gully region of the Loess Plateau. The results showed that soil nutrient contents in the study area were at a moderate level. The spatial variability was in the following order: Total phosphorus > total nitrogen > organic matter > total potassium, and they were dominated by structural factors. Compared with total phosphorus and total potassium, organic matter and total nitrogen had smaller variation scale, weaker spatial autocorrelation and higher spatial complexity. Soil organic matter and total nitrogen presented a distribution pattern of high in the south and low in the north, and their main influencing factors included elevation, slope, curvature and land use. Total phosphorus was opposite to the former two, namely high in the north and low in the south. Elevation, slope, land use, soil mechanical composition and human activity had great influence on it. Total potassium was high in the center and low in the periphery, and its distribution was relatively scattered, and aspect and soil mechanical composition had a stronger effect on it. In view of the above results, regression prediction models between nine environmental factors and soil nutrients were established to provide data support for the sustainable use of soil resources in the study area. [ABSTRACT FROM AUTHOR]