基于参数最优地理探测器的粤东北耕地非农化特征与影响因素研究

Characteristics and Influencing Factors of Farmland Conversion to Non-agricultural Uses in Northeast Guangdong Province Based on Optimal Parameter Geographic Detector

[目的]探究粤东北丘陵山区耕地非农化格局及影响机制,为该地区防治耕地非农化提供理论指导。[方法]以粤东北为例,基于该地区1990,2000,2010和2020年4期土地利用影像提取非农化信息,运用空间自相关分析和参数最优地理探测器等方法探究耕地非农化时空分布特征与影响因素。[结果]①1990-2020年,粤东北耕地非农化呈“平稳发展到骤降”的变化趋势,累计非农化面积2.29×105 hm^(2),非农化率25.31%,林地和建设用地是主要的非农化类型。②垂直方向上,海拔1000 m以上和坡度25°~35°的耕地更容易非农化;水平方向上,与建设用地距离50 m以内和与河流距离1000~1500 m的耕地更容易非农化。③粤东北耕地非农化存在显著的正向集聚,全局莫兰指数由0.371逐渐下降至0.255,“高-高”聚类和“低-低”聚类的变化主导着耕地非农化率空间自相关格局的演变。④农业人口和耕地破碎度始终对耕地非农化空间分布具有较强的解释力,近10 a来社会经济因素对非农化的解释力有所减弱。多因素特别是农业因素和社会经济因素的交互能够进一步增加对耕地非农化的解释力。[结论]近30 a来,粤东北耕地非农化面积和空间集聚呈下降趋势,但农业人口和耕地破碎化对非农化的影响进一步增强。应合理控制城镇扩张速度,整合破碎耕地、引入小型农机改善生产条件,同时通过普及农村电子商务等方式鼓励农民种植,以应对耕地非农化问题。

[Objective]The patterns and mechanisms of farmland conversion to non-agricultural were explored in the hilly regions of Northeast Guangdong Province(NGP)to provide theoretical guidance for preventing Farmland Conversion to Non-agricultural.[Methods]Using NGP as an example,information on farmland conversion was extracted land-use images for the years 1990,2000,2010,and 2020.Spatial autocorrelation analysis and optimal parameter geographic detector methods were employed to investigate the spatiotemporal distribution characteristics and factors influencing farmland conversion.[Results]①From 1990 to 2020,farmland conversion to non-agricultural in NGP exhibited a trend of“steady development followed by a sharp decline”,with a cumulative non-agriculturalized area spanning 2.29×105 hm^(2) at a rate of 25.31%.Forestland and constructed land were identified as the primary types of non-agricultural land.②Vertically,croplands above 1000 m in elevation and with a slope of 25°-35°were more prone to non-agriculturalization;horizontally,croplands within 50 m of construction land and 1000-1500 m from rivers were more likely to undergo non-agriculturalization.③Cropland non-agriculturalization in NGP exhibited significant positive clustering,with Global Molan’s I gradually decreasing from 0.371 to 0.255.The changes in“high-high”and“low-low”clustering dominated the evolution of the spatial autocorrelation pattern of cropland non-agriculturalization rates.④Agricultural population and cropland fragmentation consistently had strong explanatory power for the spatial distribution of cropland non-agriculturalization.In contrast,the explanatory power of socioeconomic factors for non-agriculturalization has weakened over the past decade.The interaction of multiple factors,especially agricultural and socioeconomic factors,can further enhance the explanatory power of cropland non-agriculturalization.[Conclusion]From 1990 to 2020,the area and spatial clustering of farmland conversion to non-agricultural in NGP decreased.However,the impact of the agricultural population and farmland fragmentation on farmland conversion to non-agricultural has intensified.It is suggested to reasonably control the speed of urban expansion,consolidate fragmented farmland,introduce small-scale agricultural machinery to improve farming conditions,and encourage farmers to cultivate by promoting rural e-commerce to address the issue of farmland conversion to non-agricultural.