地理学报

地理学报 ›› 2023, Vol. 78 ›› Issue (9): 2105-2127.doi: 10.11821/dlxb202309001

• 土地利用与“双碳”研究 •    下一篇

中国县域耕地动态演变及其驱动机制

张婕1,2(), 刘玉洁1,2(), 张二梅1,2, 陈洁1,2, 谭清华1,2   

  1. 1.中国科学院地理科学与资源研究所 中国科学院陆地表层格局与模拟重点实验室，北京 100101
    2.中国科学院大学，北京 100049
  • 收稿日期:2022-11-18 修回日期:2023-06-30 出版日期:2023-09-25 发布日期:2023-09-28
  • 通讯作者: 刘玉洁(1982-), 女, 甘肃天水人, 博士, 研究员, 主要从事全球变化与粮食安全研究。E-mail: liuyujie@igsnrr.ac.cn
  • 作者简介:张婕(1996-), 女, 新疆伊犁人, 博士生, 主要从事全球变化与可持续发展研究。E-mail: zhangj.18s@igsnrr.ac.cn
  • 基金资助:
    中国科学院战略性先导科技专项(XDA28060200);国家自然科学基金项目(42122003);国家自然科学基金项目(72221002);中国科学院青年创新促进会会员人才专项(Y202016)

Dynamics and driving mechanisms of cultivated land at county level in China

ZHANG Jie1,2(), LIU Yujie1,2(), ZHANG Ermei1,2, CHEN Jie1,2, TAN Qinghua1,2   

  1. 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2022-11-18 Revised:2023-06-30 Published:2023-09-25 Online:2023-09-28
  • Supported by:
    The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA28060200);National Natural Science Foundation of China(42122003);National Natural Science Foundation of China(72221002);Youth Innovation Promotion Association of the Chinese Academy of Sciences(Y202016)

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摘要：

非农化和非粮化造成大量耕地不再用于农业用途和粮食生产，严重影响粮食综合生产能力，威胁国家粮食安全。识别非农化和非粮化时空演化特征及不同阶段的驱动因素是科学管控和决策的基础。本文以县域为基本研究单元，评估了非农化和非粮化动态演变特征，选择耕地资源本底、社会经济及农户主观因素构建综合指标体系，采用地理探测器模型量化了不同阶段的驱动因子。分析发现，中国耕地非农化和非粮化具有明显的空间集聚效应，“胡焕庸线”以东非农化程度较高，非粮化现象呈现由东北向西南加剧的空间格局。1980—2020年非农化程度呈现减弱且范围缩小的趋势，而非粮化经历了“增长—平稳”的变化过程。1980—2020年主产区非粮化进程减缓，而2010—2020年主销区非粮化增长速度分别为主产区和产销平衡区的1.49倍和1.33倍。与1980—2000年相比，2010—2020年主产区的非农化速度下降了77%，而产销平衡区非农化的增加速度分别是主销区和主产区的1.63倍和4.65倍。耕地资源禀赋是导致非粮化的基础原因，且土壤质量、地形因子与社会经济存在显著的交互作用，农民的逐利行为是决定耕地非粮化根本原因。农业劳动力为非农化的显著影响因子，城镇化对非农化的解释力2010—2020年有所提升。本文提出分类设定管制规则和补贴机制、分区推进管控政策、加强动态监测与风险预警、加强责任监督与考核的建议。

关键词: 非农化, 非粮化, 动态演变, 地理探测器, 驱动机制, 县域尺度

Abstract:

The land conversion processes concerning non-agricultural and non-grain production areas have prominently decreased arable land availability, which substantially impacted grain production capacity and threatened national food security. Thus, it is critical to establish a novel scientific approach to identify spatio-temporal evolution patterns of land conversion and its influencing factors in different stages. This study evaluates the evolutionary characteristics of non-agricultural and non-grain fields by constructing a comprehensive index system that considers factors like cultivated land resources, social and economic conditions, and farmers' subjective perspectives, using a county as the basic research unit. For a comprehensive analysis, a geographical detector model was utilized to quantify driving factors in different stages. The results indicated spatial clustering effects for non-agricultural and non-grain fields throughout China, particularly in the eastern region beyond the "Hu Huanyong Line". Further analysis revealed a spatial pattern for non-grain conversion phenomenon was more intense in the southwestern than the northeastern fields. Over the past four decades, non-agricultural fields recorded an area expansion, but the year-wise area increase was gradually reduced, while non-grain areas exhibited a "growth-stable" change pattern. Although progress in non-grain was less in primary producing areas over the last 40 years, an increase of 1.49 times and 1.33 times was recorded from 2010 to 2020 in PSB (production and sales balance area) and Mrt (marketing) areas, respectively. Compared to the period 1980-2000, the rate of non-agricultural conversion in primary producing areas decreased by 77% during 2010-2020, while the rate of non-agricultural conversion increased by 1.63 and 4.65 times for PSB and Mrt regions, respectively. Based on these findings, this paper puts forward suggestions, such as setting control rules and subsidy mechanisms according to area classification, promoting control policies based on regional considerations, strengthening dynamic monitoring and risk warning, as well as enhancing supervision and assessment.

Key words: non-agricultural, non-grain, dynamic evolution, geographic detector model, driving mechanism, county scale