文章摘要
耕地集约利用的空间特征及影响因素研究——以甘肃省东部四市为例
The spatial characteristics and the influencing factors of intensive use of farmland: A case study of four cities in Eastern Gansu Province
投稿时间:2018-10-28  最后修改时间:2019-05-23
DOI:10.13872/j.1000-0275.2019.0062
中文关键词: 耕地集约利用  熵值法  时空分异  GWR模型  甘肃省东部
英文关键词: farmland intensive use  entropy method  spatial-temporal difference  GWR model  Eastern Gansu Province
基金项目:甘肃省自然科学基金项目(GSAN-ZL-2015-045)
作者单位E-mail
刘永康 甘肃农业大学 管理学院 1640071935@qq.com 
刘学录 甘肃农业大学 资源与环境学院 liuxl@gsau.edu.cn 
张一达 甘肃农业大学 管理学院 1468669764@qq.com 
任君 青海大学 研究生院 1105707558@qq.com 
王全喜 甘肃农业大学 管理学院 2480115068@qq.com 
李晓丹 甘肃农业大学管理学院 314889592@qq.com 
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中文摘要:
      为了准确把握耕地集约利用水平和空间差异特征以及不同区域间的影响因素,本文采用多因素综合评价、K-mean聚类法和空间计量模型对甘肃省东部四市的耕地集约利用度的变化情况及空间水平分布规律进行了研究,并对影响耕地集约利用水平的内部因素进行探索。结果表明:1)2016年研究区耕地集约利用度整体处于较粗放利用水平(均值0.377 2),区域差异比较明显,其中Ⅲ、Ⅳ级位于研究区东北部和西南部,Ⅰ、Ⅱ级位于研究区中部,整体呈“哑铃”状结构;2)全局莫兰指数为0.142 2,表明研究区各县(区)耕地集约利用度存在显著为正的全局自相关,呈现空间集聚特征,LISA聚集图空间特征与集约度等级图基本吻合;3)劳动力指数、地均机械总动力和部分区域农业科技发展率、有效灌溉率与耕地利用集约度成正相关关系,由于空间的辐射性和依赖性,各回归系数值分布具有明显的“片”或“带”状区域特征。
英文摘要:
      In order to accurately understand the intensive use level, the spatial difference characteristics of farmland and the influencing factors among different regions, this paper applied the multi-factor comprehensive evaluation and the K-mean clustering methods and the spatial measurement model to study the change of intensive use degree of farmland and to explore the internal factors affecting the level of intensive use of farmland in the four eastern cities of Gansu Province. Results show that: 1) in 2016, the intensive use level of farmland in the research area was generally at a relatively coarse utilization level (mean value were 0.377 2) with obvious regional differences. Grades III and IV were located in the northeast and southwest of the study area, and I and II were located in the middle of the study area. So a“dumbbell”shape was formed from regional perspective; 2) the global Moran index was 0.142 2, indicating that the intensive utilization degree of farmland in each county of the research area had significant positive global autocorrelation, representing spatial agglomeration characteristics. The spatial characteristics of LISA aggregate map were basically consistent with the intensive degree map; and 3) the labor index, the average mechanical power of the geography and the development rate of agricultural science and technology in some regions, and the effective irrigation rates were positively correlated with the intensive use of farmland. Due to the radiation and dependence of space, the distribution of regression coefficient values has obvious area features of “slices” or “bands”.
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