Spatial variability of available nutrients contents in cropland soils in Gaoyang County of Hebei Province, China

This study used GIS and geostatistics to analyze the spatial variability and content distribution of available N, P and K as part of a comprehensive management of soil nutrients in Gaoyang County of Hebei Province. Results showed that available N and P distribution was lognormal while that of available K was normal. The averages of soil available N, P and K were respectively 76.32 mg·kg^-1, 22.28 mg·kg^-1 and 128.34 mg·kg^-1. The coefficients of variation ranged from 36.11% to 79.71%, which suggested that the variations were at medium levels. The result showed that C0/(C+C0) of available N, P and K were respectively 38.79%, 74.27% and 32.33%, which suggested moderate spatial self-correlations. The spatial variability was caused by structural and random factors. Available K had the longest correlation range (51.94 km), available P the shortest (1.05 km) and that of available N was 43.96 km. Integrated comparisons in interpolation errors were conducted, and the best theoretical model of semivariogram of soil available N, P and K were established, which turned out to be spherical, exponential, spherical models, respectivley, with preferable 0-order trend effect. Spatial distribution maps of available N, P and K contents in cropland soils constructed by using universal Kriging interpolation objectively reflected nutrient abundance/deficiency in the study area. The maps suggested that the characteristics of the spatial distribution of available N was insignificant, available P was mainly with a banding distribution and available K was with both banding and island distribution. The content of available N was low, the area of land with 60~90 mg·kg^-1 available N accounted for 93.13% of the investigated region. This suggested that there was the need to increase soil nitrogen in the study area. The contents of available P and K were in the medium-to-high range in most of the study area. Also the spatial distribution of available P showed that areas of low, medium, high and very high grades were respectively 0.34%, 31.97%, 46.98% and 20.71% of the study area. Available K map showed that the areas of low, medium, high and very high grades were respectively 0.04%, 40.36%, 54.12% and 5.48% of the study area. The figures of the GIS-based nutrient variability reflected the spatial distribution of soil nutrients and provided the theoretical basis for decision-making and soil nutrient management in the study area.