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Visualizing clustering characteristics of multidimensional arable land quality indexes at the county level in mainland China

Author

Listed:
  • Sijing Ye
  • Changqing Song
  • Peichao Gao

    (State Key Laboratory of Earth Surface Processes and Resource Ecology, 47836Beijing Normal University, China
    Faculty of Geographical Science, 47836Beijing Normal University, China)

  • Chenyu Liu

    (Faculty of Geographical Science, 47836Beijing Normal University, China)

  • Changxiu Cheng

    (State Key Laboratory of Earth Surface Processes and Resource Ecology, 47836Beijing Normal University, China;
    Faculty of Geographical Science, 47836Beijing Normal University, China;
    National Tibetan Plateau Data Center, China)

Abstract

The evaluation of the arable land ecosystem services capacity and arable land use intensity is important for recognizing regional key factors that impact the change of arable land attributes. A chronic lack of cooperation persists between these two fields of study, which makes providing sufficient information to support developing arable land use management and control policies difficult. In this study, the clustering characteristics of four arable land quality indexes have been assessed using the K-means algorithm to indicate the regional coordination between arable land resource protection and arable land use. The clustering results have been visualized using circular cartogram. This study can contribute to the identification of key regional challenges in China's arable land use and help to build the framework of other countries’ arable land protection policies.

Suggested Citation

  • Sijing Ye & Changqing Song & Peichao Gao & Chenyu Liu & Changxiu Cheng, 2022. "Visualizing clustering characteristics of multidimensional arable land quality indexes at the county level in mainland China," Environment and Planning A, , vol. 54(2), pages 222-225, March.
    • Handle: RePEc:sae:envira:v:54:y:2022:i:2:p:222-225
    DOI: 10.1177/0308518X211062232
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    References listed on IDEAS

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    1. Ye, Sijing & Song, Changqing & Shen, Shi & Gao, Peichao & Cheng, Changxiu & Cheng, Feng & Wan, Changjun & Zhu, Dehai, 2020. "Spatial pattern of arable land-use intensity in China," Land Use Policy, Elsevier, vol. 99(C).
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    Cited by:

    1. Ye, Sijing & Ren, Shuyi & Song, Changqing & Du, Zhenbo & Wang, Kuangxu & Du, Bin & Cheng, Feng & Zhu, Dehai, 2024. "Spatial pattern of cultivated land fragmentation in mainland China: Characteristics, dominant factors, and countermeasures," Land Use Policy, Elsevier, vol. 139(C).
    2. Sijing Ye & Changqing Song & Yakov Kuzyakov & Feng Cheng & Xiangbin Kong & Zhe Feng & Peichao Gao, 2024. "Arable Land Quality in Developing China: An Integrated Exploration from Global Challenges to Localized Solutions," Land, MDPI, vol. 13(3), pages 1-9, March.
    3. Liu, Chenyu & Song, Changqing & Ye, Sijing & Cheng, Feng & Zhang, Leina & Li, Chao, 2023. "Estimate provincial-level effectiveness of the arable land requisition-compensation balance policy in mainland China in the last 20 years," Land Use Policy, Elsevier, vol. 131(C).
    4. Ting Du & Chao Li & Zhaolin Wang, 2023. "Spatial Differentiation and Influencing Mechanisms of Farmland Transfer Rents in Mountainous Areas: Evidence from Chongqing and Its Surrounding Areas," Land, MDPI, vol. 12(3), pages 1-19, March.
    5. Xin Yan & Yuejian Wang & Yuejiao Chen & Guang Yang & Baofei Xia & Hailiang Xu, 2022. "Study on the Spatial Allocation of Receding Land and Water Reduction under Water Resource Constraints in Arid Zones," Agriculture, MDPI, vol. 12(7), pages 1-18, June.
    6. Xiaoyang Han & Sijing Ye & Shuyi Ren & Changqing Song, 2023. "Using the DTFM Method to Analyse the Degradation Process of Bilateral Trade Relations between China and Australia," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    7. Han Liu & Yu Wang & Lingling Sang & Caisheng Zhao & Tengyun Hu & Hongtao Liu & Zheng Zhang & Shuyu Wang & Shuangxi Miao & Zhengshan Ju, 2023. "Evaluation of Spatiotemporal Changes in Cropland Quantity and Quality with Multi-Source Remote Sensing," Land, MDPI, vol. 12(9), pages 1-22, September.
    8. Sijing Ye & Changqing Song & Yakov Kuzyakov & Feng Cheng & Xiangbin Kong & Zhe Feng & Peichao Gao, 2022. "Preface: Arable Land Quality: Observation, Estimation, Optimization, and Application," Land, MDPI, vol. 11(6), pages 1-5, June.

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