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Quantitative Assessment of Spatial–Temporal Characteristics of Agricultural Development Level in China: A County-Level Analysis

Author

Listed:
  • Anna Jiang

    (School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China)

  • Wanshun Zhang

    (School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
    State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
    China Institute of Development Strategy and Planning, Wuhan University, Wuhan 430079, China)

  • Feng Zhou

    (School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China)

  • Hong Peng

    (Department of Hydrology and Water Resources Engineering, School of Water Resources and Hydropower, Wuhan University, Wuhan 430072, China)

  • Xin Liu

    (School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China)

  • Yue Wang

    (School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China)

  • Xiao Zhang

    (School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China)

Abstract

Main Functional Area Planning (MFAP) is a significant initiative in China, aimed at promoting coordinated socio-economic progress while ensuring resource capacity and environmental sustainability. However, there is a lack of quantitative assessments of China’s county-level agricultural development level (ADL) following the implementation of the MFAP. In this study, a coupled “agricultural product-agricultural space-agricultural population” evaluation index system which was based on plan requirements, remote sensing imagery, statistical data, and industry-specific information was proposed for assessing the development level of agricultural after implementing the MFAP, and we utilized the system to evaluate the ADL of 2850 counties across China from 2009 to 2015 at the county level. The results indicate that MFAP has played a positive role in driving agricultural development in China. From 2009 to 2015, the ADL of counties in China showed an upward trend, and the agricultural development within the “Seven regions & Twenty-three belts” reached a high level, with the proportion of the top three districts and counties in the agricultural development zone increasing from 86.78% to 88.72%. The spatial distribution of ADL ratings shows a central > east > northeast > west pattern, with the western regions exhibiting the fastest growth rate. Moreover, targeted policies were provided for pathway optimization and upgrading the level of agricultural development of regions with different levels of development within the seven main agricultural production areas and others.

Suggested Citation

  • Anna Jiang & Wanshun Zhang & Feng Zhou & Hong Peng & Xin Liu & Yue Wang & Xiao Zhang, 2023. "Quantitative Assessment of Spatial–Temporal Characteristics of Agricultural Development Level in China: A County-Level Analysis," Sustainability, MDPI, vol. 15(22), pages 1-20, November.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:15816-:d:1277567
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    References listed on IDEAS

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    1. Guo, Shan & Jiang, Li & Shen, Geoffrey Q.P., 2019. "Embodied pasture land use change in China 2000-2015: From the perspective of globalization," Land Use Policy, Elsevier, vol. 82(C), pages 476-485.
    2. You, Liangzhi & Wood, Stanley, 2006. "An entropy approach to spatial disaggregation of agricultural production," Agricultural Systems, Elsevier, vol. 90(1-3), pages 329-347, October.
    3. Xufeng Cui & Ting Cai & Wei Deng & Rui Zheng & Yuehua Jiang & Hongjie Bao, 2022. "Indicators for Evaluating High-Quality Agricultural Development: Empirical Study from Yangtze River Economic Belt, China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1101-1127, December.
    4. Shen, Zhiyang & Baležentis, Tomas & Chen, Xueli & Valdmanis, Vivian, 2018. "Green growth and structural change in Chinese agricultural sector during 1997–2014," China Economic Review, Elsevier, vol. 51(C), pages 83-96.
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