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Spatial variability of soil organic matter and nutrients in paddy fields at various scales in southeast China


The present study examines the spatial dependency of soil organic matter and nutrients in paddy fields at three different scales using geostatistics and geographic information system techniques (GIS). The spatial variability of soil organic matter (SOM), total nitrogen (TN) and available phosphorus (AP) has been characterized using a total of 460, 131 and 64 samples that were, respectively, collected from the Hangzhou–Jiaxing–Huzhou (HJH) Plain (10 km), Pinghu county (1,000 m) and a test plot area (100 m) within the Pinghu county, Zhejiang province of the southeast China. Semivariograms showed that the SOM and TN had moderate spatial dependency on the large scale of HJH plain and moderate scale of Pinghu county with long spatial correlation distances. At the moderate scale of Pinghu county and the small scale of a test plot area, the AP data did not show any spatial correlation, but had moderate spatial dependency in HJH plain. Spherical and exponential variogram models were best fitted to all these soil properties. Maps of SOM and TN were generated through interpolation of measured values by ordinary kriging, and AP by lognormal kriging. This study suggests that precision management of SOM and TN is feasible at all scales, and precision management of AP is feasible at large scales.

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This research was sponsored in part by the National Natural Science Foundation project of China (40601051) and the China Postdoctoral Science Foundation (20060391061). The authors would like to thank the laboratory assistants for their analysis of soil samples. Authors appreciate the reviewers (M. Shamsudduha) and the editors for their contribution to this paper.

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Correspondence to Jianming Xu.

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Liu, X., Zhao, K., Xu, J. et al. Spatial variability of soil organic matter and nutrients in paddy fields at various scales in southeast China. Environ Geol 53, 1139–1147 (2008).

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  • Geostatistics
  • Semivariogram
  • SOM and nutrients
  • Spatial variability