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SPATIAL VARIABILITY OF SOIL PHYSICAL PROPERTIES IN A REGION OF THE LOESS PLATEAU OF PR CHINA SUBJECT TO WIND AND WATER EROSION

Authors

  • Y. Q. Wang,

    1. College of Resources and Environment, Northwest A&F University, Shaanxi, PR, China
    2. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Science and the Ministry of Water Resources, Shaanxi, PR, China
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  • M. A. Shao

    Corresponding author
    1. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, PR, China
    • State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Science and the Ministry of Water Resources, Shaanxi, PR, China
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Correspondence to: M. A. Shao, No. 26, Xinong Road, Institute of Soil and Water Conservation, Chinese Academy of Science, Yangling, Shaanxi Province 712100, PR China.

E-mail: mashao@ms.iswc.ac.cn

ABSTRACT

The analysis of the spatial variability of soil properties is important for land management and construction of an ecological environment. The objectives of this study were to investigate the spatial variability of saturated hydraulic conductivity (KS), total porosity (TP), capillary porosity (CP) and bulk density (BD) in relation to land use in a 0·54 km2 watershed on the Loess Plateau. Topsoil samples (0–5 cm) from 154 sites within the watershed were collected and analyzed by classical and geostatistical statistics in the summer of 2009. The results from the classical statistical analyses indicated that TP, CP and BD had low variability whereas KS had high variability with the watershed. Farmland had significantly lower BD and higher TP and CP than grassland, shrubland and woodland (p < 0·05). Geostatistical analyses revealed that the KS semivariogram was best fit by a spherical model, the CP semivariogram was best fit by an exponential model and the TP and BD semivariograms were best fit by Gaussian models. The nugget to sill ratios and fractal dimension values indicated that all four soil properties had strong spatial dependence. Moran's I analysis showed that a 100-m sampling interval would be adequate for detecting the spatial structure of the four soil physical properties within the watershed. Spatial interpolation maps could provide useful information for precision agriculture practices and ecological management. Copyright © 2011 John Wiley & Sons, Ltd.

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