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Multi-Scale Characterization of Spatial Variability of Soil Organic Carbon in a Semiarid Zone in Northern China

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  • Long Li

    (College of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
    Key Laboratory of Desert Ecosystem Conservation and Restoration, State Forestry and Grassland Administration of China, Hohhot 010018, China
    Key Laboratory of Aeolian Physics and Desertification Control Engineering from Inner Mongolia Autonomous Region, Hohhot 010018, China)

  • Yongjie Yue

    (Forestry College, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Fucang Qin

    (College of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
    Key Laboratory of Desert Ecosystem Conservation and Restoration, State Forestry and Grassland Administration of China, Hohhot 010018, China
    Key Laboratory of Aeolian Physics and Desertification Control Engineering from Inner Mongolia Autonomous Region, Hohhot 010018, China
    Forestry College, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Xiaoyu Dong

    (College of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Cheng Sun

    (College of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Yanqi Liu

    (College of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Peng Zhang

    (College of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

Abstract

The variation of soil organic carbon (SOC) spatial distribution is dependent on the relative contributions of different environmental factors, and the dominant factors change according to study scales. Here, geostatistical and remote sensing techniques were used to gain deep knowledge about SOC spatial distribution patterns and their dominant determinants at different study scales; specifically, the structure of the spatial variability of SOC content at the county, regional, and watershed scales in Aohan, China were analyzed. The results show that altitude and normalized difference vegetation index (NDVI) are the key predictors explaining 49.6% of the SOC variability at the county scale; NDVI and slope are the key predictors explaining 36.2% of the SOC variability at the regional scale; and terrain factors are the most significant factors at the watershed scale. These three scales have a moderate spatial correlation in terms of SOC content. As the study scale widens, the spatial variability attributable to the random factors increases gradually, whereas the variability attributable to the structural factors gradually weakens. Soil type and land use type are the key factors influencing the SOC content at these three scales. At all scales, the SOC contents of the different land use types differ significantly in the order forestland > shrubland > grassland. Conservation of regional soil and water and prevention of soil desertification are effective measures for improving SOC content.

Suggested Citation

  • Long Li & Yongjie Yue & Fucang Qin & Xiaoyu Dong & Cheng Sun & Yanqi Liu & Peng Zhang, 2022. "Multi-Scale Characterization of Spatial Variability of Soil Organic Carbon in a Semiarid Zone in Northern China," Sustainability, MDPI, vol. 14(15), pages 1-14, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9390-:d:877410
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    References listed on IDEAS

    as
    1. Valentina Brombin & Enrico Mistri & Mauro De Feudis & Camilla Forti & Gian Marco Salani & Claudio Natali & Gloria Falsone & Livia Vittori Antisari & Gianluca Bianchini, 2020. "Soil Carbon Investigation in Three Pedoclimatic and Agronomic Settings of Northern Italy," Sustainability, MDPI, vol. 12(24), pages 1-19, December.
    2. Daniela De Benedetto & Emanuele Barca & Mirko Castellini & Stefano Popolizio & Giovanni Lacolla & Anna Maria Stellacci, 2022. "Prediction of Soil Organic Carbon at Field Scale by Regression Kriging and Multivariate Adaptive Regression Splines Using Geophysical Covariates," Land, MDPI, vol. 11(3), pages 1-18, March.
    Full references (including those not matched with items on IDEAS)

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