刘洪彬, 王红红, 金子位, 潘春玲. 下辽河平原区耕地非农化时空演变特征及驱动机制[J]. 中国生态农业学报 (中英文), 2024, 32(0): 1−12. DOI: 10.12357/cjea.20240171
引用本文: 刘洪彬, 王红红, 金子位, 潘春玲. 下辽河平原区耕地非农化时空演变特征及驱动机制[J]. 中国生态农业学报 (中英文), 2024, 32(0): 1−12. DOI: 10.12357/cjea.20240171
LIU H B, WANG H H, JIN Z W, PAN C L. Spatial and temporal evolution characteristics and driving mechanism of cultivated land conversion in Lower Liaohe River Plain[J]. Chinese Journal of Eco-Agriculture, 2024, 32(0): 1−12. DOI: 10.12357/cjea.20240171
Citation: LIU H B, WANG H H, JIN Z W, PAN C L. Spatial and temporal evolution characteristics and driving mechanism of cultivated land conversion in Lower Liaohe River Plain[J]. Chinese Journal of Eco-Agriculture, 2024, 32(0): 1−12. DOI: 10.12357/cjea.20240171

下辽河平原区耕地非农化时空演变特征及驱动机制

Spatial and temporal evolution characteristics and driving mechanism of cultivated land conversion in Lower Liaohe River Plain

  • 摘要: 探索耕地非农化的时空演变特征及其驱动机制是耕地保护的前提, 分析下辽河平原区2000—2020年耕地非农化的时空演变特征及驱动机制, 可以为该地区耕地保护政策的制定与防止耕地非农化提供理论依据。本文基于2000年、2005年、2010年、2015年和2020年5个时期的土地利用数据, 以下辽河平原区为研究区域, 结合社会经济数据, 运用ArcGIS 10.2空间叠加分析, 计算耕地非农化面积, 采用重心迁移模型、核密度分析、地理探测器等统计学和地理信息系统的空间分析方法, 揭示下辽河平原区耕地非农化特征及其演变趋势, 并探讨其耕地非农化的驱动因素。研究结果表明: 1)在时间上, 下辽河平原区耕地非农化面积和非农化率呈现周期性波动, 耕地非农化总面积为2201.52 km2, 耕地非农化率为7.11%; 2)在空间分布上, 耕地非农化的重心主要从辽中区向东北方向迁移至于洪区, 下辽河平原区耕地非农化东部地区高于西部地区, 核密度最大值1667.3, 主要在于洪区、浑南区、沈北新区、苏家屯区附近; 耕地非农化的次集中区域是新民市和昌图县, 其核密度最大值分别是1033.18和1018.49; 3)在驱动因素上, 耕地非农化驱动因素影响依次是农业机械总动力>户籍人口>城镇人口>固定资产投资>第三产业增加值>GDP>粮食产量>第二产业增加值, 交互作用探测器表现为双因子增强或非线性增强关系。综合来看, 耕地非农化是社会、经济因素综合作用的复杂结果。鉴于此, 本文建议通过严格控制城市建设用地占用耕地的规模、挖掘农村居民点利用潜力、转变经济发展方式、推动农业产业结构调整等措施治理耕地非农化。

     

    Abstract: Exploring the spatial and temporal evolution characteristics of cultivated land conversion and its driving mechanism is the premise of cultivated land protection. The spatial and temporal evolution characteristics and driving mechanism of cultivated land conversion in Lower Liaohe Plain from 2000 to 2020 are analyzed, which provides a theoretical basis for the formulation of cultivated land protection policies and the prevention of cultivated land conversion in this area. Using land-use and socio-economic data from 5 years (2000, 2005, 2010, 2015, and 2020) of the study area, we employed ArcGIS 10.2 spatial overlay analysis to calculate the area and percentage of cultivated land conversion in Lower Liaohe River Plain. Statistical and geographic information system methods, such as the gravity center migration model, kernel density analysis, and geographical detectors, were employed for spatial analysis. The results revealed that from 2000 to 2020, there were cyclical fluctuations in the area and rates of agricultural land conversion in Lower Liaohe River Plain. Moreover, we found that cultivated land with a total area of 2201.52 km2 had undergone conversion, indicating a conversion rate of 7.11%, with most of the conversion occurring in the vicinity of Shenyang City. The converted land mainly transitioned to urban and rural residential area, and other construction land, with conversion to rural residential land accounting for the highest proportion. Spatially, during the study period, the gravity center of converted cultivated land shifted from Liaozhong District to Yuhong District, representing a distance of 83.17 km. The conversion of cultivated land was found to show spatial agglomeration characteristics, with agglomeration mainly observed in Hunnan District, Sujiatun District, and Yuhong District, and the surrounding areas, being characterized by a concentrated pattern toward the east and dispersion toward the west. With respect to the factors driving this conversion of cultivated land, single factor analysis revealed that the impact levels of the non-agricultural factors influencing such conversion were as follows: total power of agricultural machinery > registrated population > urban population > investments in fixed asset > added value of the tertiary industry > GDP > grain output > added value of the secondary industry. This ranking indicates that advances in agricultural technology and demographic changes are the key factors influencing the cultivated land conversion, whereas economic and industrial factors act as direct drivers. Two-factor analysis further revealed an two-factor enhancement or non-linear enhancement, highlighting that multiple factors collectively influence the cultivated land conversion rather than single factors acting independently. In summary, the cultivated land conversion is a complex process driven by the interplay of societal and economic factors, resulting in a dynamically evolving spatial phenomenon. On the basis of our findings, we recommend that such land conversion should be managed by applying strict regulations on the scale of land converted for urban purposes, maximizing the utility of rural residential areas, transforming economic development models, and promoting changes in the agricultural industrial structure.

     

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