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| 基于动态QCA的广州市耕地非农化时空演变格局及驱动机制分析 |
| Spatiotemporal evolution pattern and driving mechanism of non-agricultural conversion of cultivated land in Guangzhou based on dynamic QCA |
| Received:March 31, 2026 |
| DOI：10.13254/j.jare.2026.0364 |
| 中文关键词: 耕地利用非农化，时空变化，动态QCA，驱动机制，广州市 |
| 英文关键词: cultivated land conversion to non-agricultural uses, spatiotemporal change, dynamic QCA, driving mechanism, Guangzhou City |
| 基金项目:四川省重点研发计划项目（24ZDYF1583） |
| Author Name | Affiliation | E-mail | | Zhang Ying | College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Tangshan Vocational and Technical College, Tangshan 063000, China
Yanshan Late-maturing Peach Technology Innovation Center, Hebei Province, Tangshan 063000, China | | | Wang Wei | Shaoguan University, Shaoguan 512005, China | | | Hu Yueming | College of Tropical Crops, Hainan University, Haikou 570208, China | | | Mao Xiaoyun | College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China | xymao@scau.edu.cn |
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| 中文摘要: |
| 为揭示耕地非农化的时空演变特征及其驱动机制，实现耕地资源合理配置和有效管理，提高耕地利用效率，本研究针对广州市2005—2021年间耕地非农化情况，综合利用核密度分析、标准差椭圆分析等方法，深入剖析了耕地非农化时空分布特征，基于PSR框架构建驱动因子体系，借助R语言工具，利用动态QCA方法探索广州市耕地非农化驱动机制。核密度分析表明，广州市耕地非农化具有明显的空间聚集性，主要集中于中心城区及城市化发展较快的见中心地区。标准差椭圆分析显示，2005—2021年椭圆长轴与短轴之比从1.44增至1.62，离散特征持续增强。组态分析发现，两类组态可导致高非农化水平：一是城镇化-农业现代化协同驱动型（农业城镇协同型和人口城镇协同型，一致性分别为0.999和0.995，覆盖度0.380和0.583），二是资源驱动型（一致性0.938，覆盖度0.289）。其中，资源驱动型组态存在明显的时间效应，2018年组间一致性降至0.807，主要受当年土地政策收紧及极端降雨气候影响。高、低非农化组态中案例效果均不显著，组内一致性调整距离均小于0.1。研究表明，广州市耕地非农化具有一定的空间聚集性，非农化问题受资源、人口、经济发展、农业现代化水平等多重影响，建议在制定土地管理、耕地保护政策过程中，应着重考虑导致高非农化组态中的核心压力因子，以及其相互间的协同效应。 |
| 英文摘要: |
| This study aimed to reveal the spatiotemporal evolution characteristics and driving mechanisms of the non-agricultural conversion of cultivated land, facilitate the rational allocation and effective management of cultivated land resources, and improve the use efficiency of cultivated land in Guangzhou. Taking the non-agricultural conversion of cultivated land in Guangzhou from 2005 to 2021 as a case study, this research comprehensively applied kernel density analysis and standard deviational ellipse（SDE）analysis to characterize the spatiotemporal patterns of the non-agricultural conversion of cultivated land. A driving factor system was developed based on the Pressure-State-Response（PSR）framework. Using R language, the dynamic qualitative comparative analysis（QCA）method was employed to explore the driving mechanisms of the non-agricultural conversion of cultivated land. Kernel density estimation indicated that the conversion of cultivated land to non-agricultural uses in Guangzhou exhibited pronounced spatial clustering, with high-density areas mainly concentrated in the central urban districts and rapidly urbanizing sub-centers. The SDE analysis further showed that the ratio of the major axis to the minor axis increased from 1.44 to 1.62 between 2005 and 2021, indicating enhanced directional dispersion and spatial anisotropy in the conversion of cultivated land. The configuration analysis identified two types of configurations leading to a high level of non-agricultural conversion of cultivated land：（1） the urbanization-agricultural modernization synergistic type（Configuration1 and Configuration2）, with consistencies of 0.999 and 0.995, and coverages of 0.380 and 0.583, respectively; and（2）the resource-driven type （Configuration3）, with a consistency of 0.938 and coverage of 0.289. A significant temporal effect was detected in the resource-driven configuration, as the between-group consistency dropped to 0.807 in 2018, which was mainly influenced by the tightening of land policies and the occurrence of extreme rainfall events in that year. No significant case effects were found in either high or low non-agricultural conversion configurations of cultivated land, and the adjustment distances for within-group consistency were all less than 0.1. The study indicates that cultivated land conversion in Guangzhou exhibits a certain degree of spatial agglomeration, and the issue of cultivated land conversion is influenced by multiple factors such as resources, population, economic development, and the level of agricultural modernization. In the formulation of land management and cultivated land protection policies, emphasis should be placed on considering the core pressure factors that lead to high cultivated land conversion configurations, as well as their synergistic effects. |
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