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| 大都市区耕地非粮化驱动机制及变化趋势预测——以北京市为例 |
| Driving mechanism and change trend prediction of non-grain conversion of cultivated land in metropolitan areas:taking Beijing as an example |
| 投稿时间：2024-07-20 |
| DOI：10.13254/j.jare.2024.0552 |
| 中文关键词: 耕地非粮化，土地利用转移矩阵，全局空间自相关，GM-ARIMA-BP组合模型，驱动因素 |
| 英文关键词: non-grain conversion of cultivated land, land use transfer matrix, global spatial autocorrelation, GM-ARIMA-BP combined model, driving factor |
| 基金项目:河南农业大学创新基金项目（30201181） |
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| 中文摘要: |
| 为探究大都市区耕地非粮化现状和空间分异特征，并识别其驱动因素，本研究以北京市为研究对象，采用土地利用转移矩阵分析2010—2020年土地利用变化，利用全局空间自相关对其耕地非粮化水平时空演变特征进行分析，利用Stata模型探究其驱动因素，并通过GM-ARIMA-BP组合模型预测2023—2035年非粮化变化趋势。结果表明：北京市的耕地面积先减少后增加，耕地利用转型明显，主要流向建设用地。在时间上，耕地非粮化程度由2010年的29.57%上涨到2020年的52.13%，呈波动上升趋势；在空间上，呈现出“中心-四周”扩散的态势，并且存在正相关关系。常住人口密度、到市中心的距离、第一产业劳动力占比、单位面积产量是主要影响因素，其作用依次减弱。组合模型预测显示，北京市整体以及各区县的耕地非粮化程度均呈波动上升趋势。研究表明，北京市耕地非粮化受自然、经济和社会条件共同影响，加大监管的同时需分类治理与管控，对保护大都市区粮食安全具有重要意义。 |
| 英文摘要: |
| To explore the current status and spatial differentiation characteristics of non-grain conversion of cultivated land（NGCOCL）in metropolitan areas and identify its driving factors, this study took Beijing as the research object. We employed a land use transfer matrix to analyze land use changes in Beijing from 2010 to 2020. Additionally, global spatial autocorrelation was utilized to examine the spatial and temporal evolution of the NGCOCL, and the Stata model was applied to investigate the driving factors. Furthermore, we implemented a GMARIMA-BP combined model to predict the trends in non-grain cultivation from 2023 to 2035. Our findings indicated that the area of arable land in Beijing initially decreased before subsequently increasing, with a notable transformation in arable land utilization, primarily shifting towards construction land. Over time, the proportion of non-grain production of farmland rose from 29.57% in 2010 to 52.13% in 2020, exhibited a fluctuating upward trend. Spatially, a“center-surround”diffusion pattern was observed, characterized by positive correlation. Key influencing factors included the density of the resident population, distance to the city center, the share of the labor force in the primary sector, and production per unit area, all of which demonstrated diminishing effects. Predictions from the combined model suggested that the NGCOCL in Beijing, as well as across all districts and counties, would continue to exhibit a fluctuating upward trend. This study concludes that the NGCOCL in Beijing is influenced by a combination of natural, economic, and social conditions. Therefore, enhanced regulation should be accompanied by classified management and control measures, which are crucial for safeguarding food security in the metropolitan area. |
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