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Identifying drivers of land degradation in Xilingol, China, between 1975 and 2015

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  • Batunacun,
  • Wieland, Ralf
  • Lakes, Tobia
  • Yunfeng, Hu
  • Nendel, Claas

Abstract

Land degradation occurs in all kinds of landscapes over the world, but the drivers of land degradation vary from region to region. Identifying these drivers at the appropriate spatial scale is an essential prerequisite for developing and implementing appropriate area-specific policies. In this study, we investigate nine different driving factors in three categories: human disturbance, water condition, and urbanisation. Using partial order theory and the Hasse diagram technique, we analyse the temporal and spatial dynamics of these drivers and identify the major drivers of land degradation at the county level in the Xilingol League, China. Our findings indicate that: (i) in eight out of the region’s 12 counties, human disturbance was the dominant driver responsible for land degradation up to 2000, followed by water conditions, while urbanisation was the dominant driver in only four counties; (ii) the effects resulting from human disturbance and water availability decreased after 2000, while urbanisation became the dominant driver for land degradation in seven counties. The influence of human disturbance in this region has decreased, which suggests that ecological protection policies that were designed to control population and livestock numbers have worked as intended for this region. However, land degradation has continued and new policy measures are required to ease the effect of urbanisation.

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  • Batunacun, & Wieland, Ralf & Lakes, Tobia & Yunfeng, Hu & Nendel, Claas, 2019. "Identifying drivers of land degradation in Xilingol, China, between 1975 and 2015," Land Use Policy, Elsevier, vol. 83(C), pages 543-559.
  • Handle: RePEc:eee:lauspo:v:83:y:2019:i:c:p:543-559
    DOI: 10.1016/j.landusepol.2019.02.013
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    References listed on IDEAS

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    Cited by:

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    2. Haojie Liu & Jinyue Liu & Weixin Yang & Jianing Chen & Mingyang Zhu, 2020. "Analysis and Prediction of Land Use in Beijing-Tianjin-Hebei Region: A Study Based on the Improved Convolutional Neural Network Model," Sustainability, MDPI, vol. 12(7), pages 1-25, April.
    3. Xu, Xin & Zhang, Daojun & Zhang, Yu & Yao, Shunbo & Zhang, Jinting, 2020. "Evaluating the vegetation restoration potential achievement of ecological projects: A case study of Yan’an, China," Land Use Policy, Elsevier, vol. 90(C).
    4. Zhenhua Dong & Ah Rong & Jiquan Zhang & Zhijun Tong & Aru Han & Feng Zhi, 2022. "Study on the Evolutionary Features and Driving Factors of Land-Use System in Xilingol, China," Land, MDPI, vol. 11(4), pages 1-19, April.
    5. Bayarmaa Byambaa & Walter T. de Vries, 2021. "The Production of Pastoral Space: Modeling Spatial Occupation of Grazing Land for Environmental Impact Assessment Using Structural Equation Modeling," Land, MDPI, vol. 10(2), pages 1-18, February.
    6. Xinhai Lu & Yanwei Zhang & Chaoran Lin & Feng Wu, 2021. "Evolutionary Overview and Prediction of Themes in the Field of Land Degradation," Land, MDPI, vol. 10(3), pages 1-23, March.
    7. Hualin Xie & Yanwei Zhang & Zhilong Wu & Tiangui Lv, 2020. "A Bibliometric Analysis on Land Degradation: Current Status, Development, and Future Directions," Land, MDPI, vol. 9(1), pages 1-37, January.
    8. Diriye, Abdishakur W. & Jama, Osman M. & Diriye, Jama Warsame & Abdi, Abdulhakim M., 2022. "Public preference for sustainable land use policies – Empirical results from multinomial logit model analysis," Land Use Policy, Elsevier, vol. 114(C).

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