Spatial variability of soil organic matter in a gravel-sand mulched jujube orchard at field scale

Abstract

In agricultural ecosystems, soil organic matter (SOM) is a major determinant and indicator of soil fertility and quality. The objectives of this study were to understand the spatial distribution of SOM and the accuracy of two interpolation methods evaluated: Kriging and inverse distance weighting (IDW) in a gravel-sand mulch of northwest China. We are measuring SOM in 256 soil samples collected from 0 to 10, 10–20, 20–30, and 30–50 cm layers at five sampling scales, 32 × 32, 28 × 28, 24 × 24, 20 × 20, and 16 × 16 m, and three sampling spacings, 4, 8, and 12 m. SOM content decreased with depth in each scale, varying from 2.41 to 5.75 g/kg. The SOM was weakly to moderately variable and has a strong spatial autocorrelation. The standard deviation was small for each soil layer, and the variability was low or weakly moderate, indicating that two interpolation methods were applicable to the entire data set. Kriging interpolation was more accurate than IDW. The distribution of SOM differed in the surface layers at the five sampling scales, which was more uniform as the sampling scale decreased. Eight meter is a reasonable sampling spacing. Through the scales effect and spacing change on SOM for fixed-point monitoring, combing the estimate of SOM to reduce the sampling workload will aid the supply of SOM in gravel-sand mulched fields in arid regions.

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Funding

The research was supported by National Natural Science Foundation of China (51869010), Ministry of Agriculture Open Fund Project (2017001) and Hong Liu supporting discipline of Lanzhou University of Technology.

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Correspondence to Wenju Zhao.

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Responsible Editor: Abdullah Al-Amri

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Zhao, W., Cao, T., Li, Z. et al. Spatial variability of soil organic matter in a gravel-sand mulched jujube orchard at field scale. Arab J Geosci 13, 446 (2020). https://doi.org/10.1007/s12517-020-05465-w

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Keywords

  • Gravel-sand mulch
  • Soil organic matter
  • Spatial variation
  • Kriging interpolation
  • Inverse distance weighting interpolation