| 刘 序1，2，冯珊珊 1，胡韵菲1，梁俊芬 1，罗旖文 1，刘淑娴 1，黄继川 3，周灿芳 1，2.粤港澳大湾区蔬菜生产时空格局演变及其影响因素分析[J].广东农业科学,2023,50(1):40-49 |
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粤港澳大湾区蔬菜生产时空格局演变及其影响因素分析 |
| Analysis of Spatial-temporal Pattern Evolution and Influencing Factors of Vegetable Production in Guangdong - Hong Kong - Macao Greater Bay Area |
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| DOI：10.16768/j.issn.1004-874X.2023.01.004 |
| 中文关键词: 空间自相关 蔬菜生产 时空格局 影响因素 粤港澳大湾区 |
| 英文关键词: spatial autocorrelation vegetable production spatial-temporal pattern influence factors Guangdong-Hong Kong-Macao Greater Bay Area |
| 基金项目:广 东 省 农 业 科 学 院 协 同 创 新 中 心 项 目（XTXM202201）； 广 州 市 农 村 科 技 特 派 员 项 目（20212100049）；广东省现代农业产业技术体系建设创新团队（2022KJ110）；广东省科技计划项目重点领域研发
计划（2020B0202090002）；农业农村部都市农业重点试验室开放基金（UA201706） |
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| 中文摘要: |
| 【目的】粤港澳大湾区人口众多，蔬菜需求旺盛。研究蔬菜生产格局演变及其影响因素，为大湾区蔬菜产业空间布局优化和高质量发展提供参考。【方法】利用 2010—2020 年粤港澳大湾区县域蔬菜统计数据和当年行政区划图，计算每年蔬菜产量的全局自相关指数，判断蔬菜生产的区域相互关联关系；借助局部自相关指数探索分析县域蔬菜生产的聚集区域及其空间格局演变过程；运用空间回归模型分析蔬菜生产在空间相关作用下的社会经济影响因素。【结果】2012—2020 年全局自相关指数通过检验且数值大于 0，表明县域蔬菜生产形成空间正向相关格局，蔬菜生产在空间上具有一定程度的聚集现象，并随年际变化不断增强。通过局部空间自相关检验和分析，发现龙门、博罗、惠城等蔬菜生产区域呈现“高 - 高”聚集格局，并逐渐在大湾区东北部形成聚集格局。而蔬菜生产“高 - 低”“低 - 高”空间格局逐渐受相邻区域的正向相关作用影响，最终与周边区域形成相同格局。通过空间回归模型分析影响蔬菜生产的主要因素，发现 2012 年主要因素为土地、经济水平、劳动力和农业技术投入；2016 年后主要因素为土地，而其他因素的影响作用不显著，说明蔬菜种植规模的扩大是产量增加的第一因素。【结论】粤港澳大湾区蔬菜生产存在正向空间关联，并逐渐形成产业核心区。今后需在博罗、龙门、惠城、从化等蔬菜生产核心区域，加强农业科技投入，进一步提升生产的现代化水平，不断带动周边区域提升产业综合效益，推进蔬菜产业高质量发展。 |
| 英文摘要: |
| 【Objective】There is a large population and strong demand for vegetables in Guangdong-Hong KongMacao Greater Bay Area (hereinafter called “the Greater Bay Area”). The evolution of vegetable production pattern and its influencing factors are studied in order to provide references for the spatial layout optimization and high-quality development of vegetable industry in the Greater Bay Area.【Method】The global autocorrelation indexes of vegetable gross outputs of each year were calculated to judge the regional correlation of vegetable production by using the statistical data at counties of the Greater Bay Area from 2010 to 2020 and the administrative map of the same year. The aggregation area of vegetable production and its spatial pattern evolution in counties were analyzed by local autocorrelation index. Spatial regression model was used to analyze the socio-economic factors affecting vegetable production under spatial correlation.【Result】From 2012 to 2020, the global autocorrelation index passes the test and the value is greater than 0, indicating that the vegetable production at county level forms a positive spatial correlation pattern, and the vegetable production has a certain degree of aggregation in space and increases with inter-annual changes. Through local spatial autocorrelation test and analysis, it is found that vegetable production areas such as Longmen county, Boluo county and Huicheng district shows a high-high aggregation pattern, and gradually forms an aggregation pattern in the northeast of the Greater Bay Area over time. The high-low and low-high spatial patterns of vegetable production are gradually affected by the positive correlation of adjacent areas, and finally forms the same pattern with the surrounding areas. Through the analysis of spatial regression model, it is found that the main factors affecting vegetable production are land, economic level, labor force and agricultural technology input in 2012. After 2016, the main influencing factor is land, while the influence of other factors is not obvious or significant, indicating that the expansion of vegetable planting scale is the first factor of yield increase.【Conclusion】There is a positive spatial correlation in vegetable production in Guangdong-Hong Kong-Macao Greater Bay Area, and gradually forms an industrial core area. In the future, it is necessary to increase investment in agricultural science and technology in the core areas of vegetable production such as Boluo, Longmen, Huicheng and Conghua, to further improve the modernization level of production, continuously drive the surrounding areas to improve the comprehensive industrial benefits, and promote the high-quality development of the vegetable industry. |
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