JOURNAL OF NATURAL RESOURCES ›› 2018, Vol. 33 ›› Issue (3): 489-503.doi: 10.11849/zrzyxb.20170067

• Resource Evaluation • Previous Articles     Next Articles

Spatial Prediction of Cultivated Land Soil Nutrients in Typical Region of Yellow River Delta

LI Yan-linga, b, ZHAO Geng-xingb   

  1. a. College of Information Science and Engineering, b. College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China
  • Received:2017-01-20 Revised:2017-04-11 Online:2018-03-20 Published:2018-03-20
  • Supported by:
    The Sci-tech Pillar Project of the “Twelfth Five Year” Plan of China, No. 2013BAD05B06 and 2015BAD23B0202; National Natural Science Foundation of China, No. 41271235.

Abstract: 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 semi-variogram 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.

Key words: Moran's I, Nearest Neighbor Rate, soil nutrients, spatial distribution pattern, spatial prediction, Yellow River Delta

CLC Number: 

  • S153.6