| 摘要: |
| 目的 通过分析耕地利用综合效率的时空格局及影响因素，为促进边境地区农业可持续发展及保障粮食安全提供理论基础。方法 文章通过构建耕地利用效率“投入—产出”指标体系，运用DEA模型、Malmquist指数模型及Tobit模型从动静两方面研究了中国陆地边境地区的耕地利用综合效率的时空格局变化及分区域影响因素。结果 （1）2008—2018年中国陆地边境地区耕地利用综合效率经历“小幅下降—快速上升—稳步上升”3个阶段，耕地利用综合效率高值区总体上呈“多点式”向“带片状”转变的态势，连片集聚度不断上升。（2）边境地区耕地利用全要素生产率指数（TFP）波动幅度较大，由2008—2014年的“倒V”型反转向2014—2018年的“V”型增长过渡。从全要素生产率指数结果来看，西南边境地区>西北边境地区>东北边境地区，这与各片区发展的资源禀赋、区域定位等差异有关。（3）东北边境地区耕地利用综合效率的显著性影响因素有农业机械总动力与农业劳动力，西北边境地区耕地利用综合效率的显著性影响因素有化肥施用量、种植结构调整、农业机械总动力，西南边境地区耕地利用综合效率影响因素为农村居民人均纯收入、乡村户数、农林水利事务支出、种植结构调整、农业劳动力。结论 边境地区耕地利用综合效率的主导影响因素存在明显的空间差异性，应因地制宜实施差异化的耕地利用管控政策，有效促进边境地区耕地利用综合效率的持续增长，维护边境地区的社会稳定与国家粮食安全。 |
| 关键词: 耕地利用综合效率 DEA-Malmquist模型 Tobit模型 时空格局 影响因素 边境地区 |
| DOI：10.7621/cjarrp.1005-9121.20220902 |
| 分类号:F301.2 |
| 基金项目:2020年国家自然科学基金项目“‘四维度’的边境耕地多功能利用与国家战略农田划定研究——以广西边境地区为例”（42061043） |
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| THE SPATIAL AND TEMPORAL PATTERNS AND INFLUENCING FACTORS OF THE COMPREHENSIVE EFFICIENCY OF ARABLE LAND USE IN CHINA'S LAND BORDER AREAS |
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Qin Qiuyan1, Lu Rucheng1,2, Duan Lian1, Wei Shaoyin1, Lin Xiaonan1, Pang Xiaofei3
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1.School of Natural Resources and Surveying and Mapping, Nanning Normal University, Nanning 530001, Guangxi, China;2.Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning 530001, Guangxi, China;3.School of Geography Science and Planning, Nanning Normal University, Nanning 530001, Guangxi, China
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| Abstract: |
| To provide a theoretical basis for promoting sustainable agricultural development and ensuring food security in border areas by analyzing the spatial and temporal patterns of arable land use efficiency and its influencing mechanisms. Through constructing an "input-output" index system of arable land use efficiency, we measured the spatial and temporal patterns of arable land use efficiency and the sub-regional influencing factors in the border areas of China by using the DEA model, Malmquist index model and Tobit model from both dynamic and static aspects. The results were listed as follows. (1) From 2008 to 2018, the comprehensive efficiency of arable land use in China's land border areas experienced three stages of "small decline-rapid rise-steady rise". The areas with high values of overall efficiency of arable land use generally showed a "multi-point" to "band-like" transformation, the degree of contiguous agglomeration was rising. (2) The comprehensive efficiency of arable land use in border areas had gone through three stages of "small decline-rapid rise-steady rise". The total factor productivity index (TFP) of arable land use in border areas fluctuated greatly, and changed from "inverted V" type from 2008 to 2014 to "V" type from 2014 to 2018. From the results of TFP index, the order from high to low was: the southwest border region > the northwest border region > the northeast border region, which was related to the differences in resource endowment and regional positioning of the development of each area. (3) The significant influencing factors of the comprehensive efficiency of arable land utilization in the northeast border area were total power of agricultural machinery and agricultural labor, the main factors were fertilizer application, planting structure adjustment and total power of agricultural machinery in the northwest border area, and the main factors were rural per capita net income, number of rural households, expenditure on agriculture, forestry and water conservancy affairs, adjustment of planting structure, and agricultural labor in the southwest border area. In summary, there are obvious spatial differences in the dominant factors affecting the overall efficiency of cultivated land use in border areas. Differentiated control policies on arable land use should be implemented according to local conditions, so as to effectively promote the sustainable growth of the comprehensive efficiency of arable land use and maintain the social stability and national food security in border areas. |
| Key words: comprehensive efficiency of arable land use DEA-Malmquist model Tobit model spatial and temporal patterns influencing factors border areas |
