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Analysis of field-scale spatial correlations and variations of soil nutrients using geostatistics

  • Ruimin Liu
  • Fei Xu
  • Wenwen Yu
  • Jianhan Shi
  • Peipei Zhang
  • Zhenyao Shen
Article

Abstract

Spatial correlations and soil nutrient variations are important for soil nutrient management. They help to reduce the negative impacts of agricultural nonpoint source pollution. Based on the sampled available nitrogen (AN), available phosphorus (AP), and available potassium (AK), soil nutrient data from 2010, the spatial correlation, was analyzed, and the probabilities of the nutrient’s abundance or deficiency were discussed. This paper presents a statistical approach to spatial analysis, the spatial correlation analysis (SCA), which was originally developed for describing heterogeneity in the presence of correlated variation and based on ordinary kriging (OK) results. Indicator kriging (IK) was used to assess the susceptibility of excess of soil nutrients based on crop needs. The kriged results showed there was a distinct spatial variability in the concentration of all three soil nutrients. High concentrations of these three soil nutrients were found near Anzhou. As the distance from the center of town increased, the concentration of the soil nutrients gradually decreased. Spatially, the relationship between AN and AP was negative, and the relationship between AP and AK was not clear. The IK results showed that there were few areas with a risk of AN and AP overabundance. However, almost the entire study region was at risk of AK overabundance. Based on the soil nutrient distribution results, it is clear that the spatial variability of the soil nutrients differed throughout the study region. This spatial soil nutrient variability might be caused by different fertilizer types and different fertilizing practices.

Keywords

Nonpoint source pollution Soil nutrient Geostatistics Spatial variability Spatial correlation coefficient 

Notes

Acknowledgements

The research was funded by the Ministry of Education and Social Science Fund (14YJAZH048), CRSRI Open Research Program (CKWV2014223/KY), and the Water Specific Project of China (2008ZX07209-007).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ruimin Liu
    • 1
  • Fei Xu
    • 1
  • Wenwen Yu
    • 1
  • Jianhan Shi
    • 1
  • Peipei Zhang
    • 1
  • Zhenyao Shen
    • 1
  1. 1.State Key Laboratory of Water Environment Simulation, School of EnvironmentBeijing Normal UniversityBeijingChina

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