Spatiotemporal Evolution and Driving Mechanisms of County-Level Agricultural Industry Agglomeration under High-Quality Development: A Case Study of Vegetable Production in Guangdong Province

doi:10.16258/j.cnki.1674-5906.2025.08.014

Ecology and Environmental Sciences ›› 2025, Vol. 34 ›› Issue (8): 1293-1304.DOI: 10.16258/j.cnki.1674-5906.2025.08.014

• Research Article [Environmental Science] • Previous Articles Next Articles

Spatiotemporal Evolution and Driving Mechanisms of County-Level Agricultural Industry Agglomeration under High-Quality Development: A Case Study of Vegetable Production in Guangdong Province

FENG Shanshan¹(), TAO Liang², LIANG Junfen¹^,^*()

1. Institute of Agricultural Economics and Information, Guangdong Academy of Agricultural Sciences/Key Laboratory of Urban Agriculture in South China, Ministry of Agriculture and Rural Affairs, Guangzhou 510640, P. R. China
2. National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China/Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management/Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510640, P. R. China

Received:2025-02-18 Online:2025-08-18 Published:2025-08-01

高质量发展视角下县域农业产业集聚的时空演化与驱动机制——以广东省蔬菜生产为例

冯珊珊¹(), 陶亮², 梁俊芬¹^,^*()

1.广东省农业科学院农业经济与信息研究所/农业农村部华南都市农业重点实验室，广东广州 510640
2.广东省科学院生态环境与土壤研究所/华南土壤污染控制与修复国家地方联合工程研究中心/广东省农业环境综合治理重点实验室，广东广州 510640

通讯作者: *E-mail: 251092539@qq.com
作者简介:冯珊珊（1994年生），女，助理研究员，博士，主要从事农业地理与遥感应用研究。E-mail: fengshanshan@gdaas.cn
基金资助:
广东省哲学社会科学规划2024年度一般项目(GD24CYJ33);国家自然科学基金青年项目(42401221);广东省农业科学院创新基金产业专项项目(202309);广东省农业科学院科技人才引进专项资金项目(R2022YJ-YB1002);2024年省级乡村振兴战略专项“耕地种植用途管控全域试点、动态监测与管理体系构建”(粤财农[2024]28号);广东省科技计划项目(2023B1212060044)

Abstract

Abstract:

The vegetable industry, characterized by its relatively high comparative benefits, has become the second-largest sector in China’s crop farming and plays an indispensable role in both agriculture and the broader national economy of China. Vegetable production is influenced by multiple factors, including natural endowments, technological advancements, industrial policies, transportation infrastructure, and market demand. Consequently, there are significant regional differences in the spatial and temporal characteristics of vegetable production in different areas. In Guangdong Province, vegetables represent the largest economic crop and a distinctive specialty industry, holding a critical position in the domestic market and exerting profound impacts on the global agricultural supply chain. With the acceleration of market-oriented economic development and agricultural modernization, Guangdong’s vegetable production has gradually evolved into a large-scale, billion-yuan industrial cluster. However, due to variations in resource endowments, economic levels, geographical positioning, and transportation conditions, there are substantial disparities in the layout and spatial agglomeration levels of vegetable production among counties within the province. Investigating the spatiotemporal characteristics of vegetable production and exploring the spatial-temporal patterns of agglomeration, along with their influencing factors, holds great significance for optimizing the industrial layout of Guangdong’s vegetable sector, leveraging regional comparative advantages, and promoting the development of industrial clusters in the province. This study, based on statistical data from 1992 to 2023 at the county level in Guangdong Province, employs geographic information system (GIS) technology to analyze Gini coefficients and global and local spatial autocorrelation indices. These analyses aim to reveal the spatiotemporal features of vegetable production agglomeration in Guangdong and explore the influencing factors of such agglomeration from aspects such as resource endowment, industrial policies, geographical indications and market demand. The findings of this study are expected to provide valuable insights and references for formulating relevant policies to promote the development of vegetable industrial clusters in Guangdong. The main findings are as follows. In terms of the overall change characteristics of vegetable production, from 1992 to 2023, the overall trend of vegetable production in Guangdong Province exhibited a fluctuating upward trend. Specifically, the sown area of vegetables expanded significantly, increasing from 631300 hectares in 1992 to 1453700 hectares in 2023. During this period, the number of counties with vegetable-sown areas exceeding 20000 hectares showed a continuous upward trend, rising from an initial count of two counties to 24 counties, accounting for 19.35% of the total number of counties in the province. Conversely, the number of counties with vegetable-sown areas below 3000 hectares decreased over time, from 56 to 29, representing a drop from 45.16% to 23.39% of the total number of counties. Regarding the temporal characteristics of agglomeration, the overall degree of vegetable production agglomeration in Guangdong Province has increased and has phased its characteristics. After 2017, the degree of agglomeration significantly improved. From the perspective of the spatial characteristics of agglomeration, vegetable production in Guangdong Province has undergone a development process from small-scale and scattered production to large-scale, regionalized, and specialized production. The overall degree of vegetable production agglomeration at the county level in Guangdong Province shows an upward trend and has phased its characteristics. After 2005, several geographical agglomeration areas of vegetable production gradually formed, namely: the “high-high” agglomeration area of vegetable production in the Pearl River Delta region mainly including Zengcheng District, Boluo County, and Huicheng District; the “high-high” contiguous agglomeration area of vegetable production in the western region of Guangdong, centered around Gaozhou City; and the “high-high” agglomeration area of vegetable production in the northern region of Guangdong mainly including Lianzhou City and Yangshan County. The geographical agglomeration of vegetable production in Guangdong is the result of interaction between natural conditions and external factors. Resource endowment serves as a foundational prerequisite for vegetable production agglomeration, whereas industrial policies act as a critical driving force. Geographical indications play an enabling role, and market demand is the primary driving force behind agglomeration. These factors collectively shape the spatial distribution and clustering patterns of vegetable production in Guangdong Province. Based on these conclusions, this study proposes a “Three-Zone Synergy” development strategy: prioritizing the integration of high-value-added facility-based vegetable production with smart agriculture in the Pearl River Delta; establishing large-scale winter vegetable production bases for northern markets in western Guangdong; and cultivating alpine summer-autumn vegetable supply hubs in northern Guangdong. A four-dimensional linkage mechanism—encompassing resource integration, policy empowerment, brand value enhancement, and market expansion—is designed to drive the upgrading of vegetable industry clusters in terms of scale and capacity. This strategy leverages regional comparative advantages through spatial-functional differentiation, technological innovation diffusion, and institutional synergy, ultimately facilitating the transformation of Guangdong’s vegetable industry toward intensive, intelligent, and sustainable development. This study provides valuable insights into the spatiotemporal dynamics of vegetable production agglomeration in Guangdong and offers practical policy suggestions for sustainable development in the vegetable industry. By combining empirical evidence with theoretical analysis, this study contributes to a deeper understanding of the complex interplay between resource allocation, industrial policies, and market forces in shaping vegetable production patterns in Guangdong Province. These findings can inform policymakers, practitioners, and researchers working to enhance the efficiency and sustainability of agricultural systems in China and beyond.

Key words: county-level scale, vegetable production, geographic agglomeration, spatiotemporal evolution, driving mechanisms, Guangdong Province

摘要：

基于1992－2023年广东省县域面板数据，综合运用基尼系数、空间自相关分析及GIS技术，系统揭示蔬菜生产集聚的时空演化规律及其驱动机制。结果发现：1）时间维度上，蔬菜生产经历了从小规模、分散生产逐渐转向规模化、区域化和专业化生产的发展历程，蔬菜生产集聚程度总体呈上升趋势并具有“分散-稳定-集中”的阶段性特征；2）空间格局上，2005年后蔬菜生产形成三大核心集聚区：珠三角“增城-博罗-惠城”设施蔬菜高值集聚区、粤西以高州为中心的冬种北运连片集聚区、粤北“连州-阳山”高山夏秋蔬菜集聚区；3）驱动机制分析显示，资源禀赋构成空间集聚的自然基底，产业政策发挥重要驱动作用，地理标志促成品牌溢价，市场需求提供核心驱动力。研究构建了“时空演化-驱动机制-政策响应”的理论分析框架，提出“三区协同”发展战略：在珠三角侧重高附加值设施蔬菜与智慧农业融合、粤西重点打造“冬种北运”规模化生产基地、粤北培育高山夏秋蔬菜供应基地，通过要素整合、政策赋能、品牌增值和市场拓展四维联动推动蔬菜产业集群规模能级跃升。研究成果为粤港澳大湾区“菜篮子”工程提供空间优化方案，对同类地区农业产业集群发展具有重要借鉴价值。

关键词: 县域尺度, 蔬菜生产, 地理集聚, 时空演化, 驱动机制, 广东省

CLC Number:

FENG Shanshan, TAO Liang, LIANG Junfen. Spatiotemporal Evolution and Driving Mechanisms of County-Level Agricultural Industry Agglomeration under High-Quality Development: A Case Study of Vegetable Production in Guangdong Province[J]. Ecology and Environmental Sciences, 2025, 34(8): 1293-1304.

冯珊珊, 陶亮, 梁俊芬. 高质量发展视角下县域农业产业集聚的时空演化与驱动机制——以广东省蔬菜生产为例[J]. 生态环境学报, 2025, 34(8): 1293-1304.

Figures/Tables 9

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播种面积/ (10⁴ hm²)	1992年		1998年		2006年		2016年		2023年
播种面积/ (10⁴ hm²)	个数	比例/%	个数	比例/%	个数	比例/%	个数	比例/%	个数	比例/%
≥2.0	2	1.61	10	8.06	19	15.32	21	16.94	24	19.35
1.5－2.0	4	3.23	7	5.65	16	12.90	15	12.10	8	6.45
1.0－1.5	9	7.26	25	20.16	15	12.10	14	11.29	21	16.94
0.5－1.0	39	31.45	28	22.58	32	25.81	33	26.61	30	24.19
0.3－0.5	14	11.29	16	12.90	13	10.48	13	10.48	12	9.68
<0.3	56	45.16	38	30.65	29	23.39	28	22.58	29	23.39

播种面积/ (10⁴ hm²)	1992年		1998年		2006年		2016年		2023年
播种面积/ (10⁴ hm²)	个数	比例/%	个数	比例/%	个数	比例/%	个数	比例/%	个数	比例/%
≥2.0	2	1.61	10	8.06	19	15.32	21	16.94	24	19.35
1.5－2.0	4	3.23	7	5.65	16	12.90	15	12.10	8	6.45
1.0－1.5	9	7.26	25	20.16	15	12.10	14	11.29	21	16.94
0.5－1.0	39	31.45	28	22.58	32	25.81	33	26.61	30	24.19
0.3－0.5	14	11.29	16	12.90	13	10.48	13	10.48	12	9.68
<0.3	56	45.16	38	30.65	29	23.39	28	22.58	29	23.39

年份	莫兰指数	期望值	方差	Z得分	p值	显著性水平
1992	0.0658	−0.0081	0.0035	1.2400	0.2151	－
1993	0.0371	−0.0081	0.0036	0.7501	0.4532	－
1994	0.0421	−0.0081	0.0036	0.8376	0.4022	－
1995	0.0358	−0.0081	0.0036	0.7293	0.4658	－
1996	0.0405	−0.0081	0.0036	0.8052	0.4207	－
1997	0.0368	−0.0081	0.0036	0.7444	0.4566	－
1998	0.0367	−0.0081	0.0037	0.7401	0.4593	－
1999	0.0235	−0.0081	0.0037	0.5216	0.6020	－
2000	0.0320	−0.0081	0.0037	0.6633	0.5071	－
2001	0.0123	−0.0081	0.0036	0.3374	0.7358	－
2002	0.0121	−0.0081	0.0036	0.3333	0.7389	－
2003	0.0673	−0.0081	0.0036	1.2499	0.2114	－
2004	0.0799	−0.0081	0.0036	1.4592	0.1445	－
2005	0.1057	−0.0081	0.0036	1.8833	0.0597	10%
2006	0.1129	−0.0081	0.0036	2.0044	0.0450	5%
2007	0.1101	−0.0081	0.0036	1.9562	0.0504	10%
2008	0.1110	−0.0081	0.0037	1.9707	0.0488	5%
2009	0.1156	−0.0081	0.0037	2.0468	0.0407	5%
2010	0.1293	−0.0081	0.0037	2.2713	0.0231	5%
2011	0.1328	−0.0081	0.0037	2.3283	0.0199	5%
2012	0.1408	−0.0081	0.0037	2.4605	0.0139	5%
2013	0.1672	−0.0081	0.0037	2.8968	0.0038	1%
2014	0.1843	−0.0081	0.0037	3.1819	0.0015	1%
2015	0.1977	−0.0081	0.0037	3.4044	0.0007	1%
2016	0.2005	−0.0081	0.0037	3.4504	0.0006	1%
2017	0.2418	−0.0081	0.0037	4.1363	0.0000	1%
2018	0.2454	−0.0081	0.0037	4.1935	0.0000	1%
2019	0.2507	−0.0081	0.0037	4.2804	0.0000	1%
2020	0.2533	−0.0081	0.0037	4.3213	0.0000	1%
2021	0.2589	−0.0081	0.0037	4.4144	0.0000	1%
2022	0.2667	−0.0081	0.0037	4.5399	0.0000	1%
2023	0.2679	−0.0081	0.0037	4.5401	0.0000	1%

年份	莫兰指数	期望值	方差	Z得分	p值	显著性水平
1992	0.0658	−0.0081	0.0035	1.2400	0.2151	－
1993	0.0371	−0.0081	0.0036	0.7501	0.4532	－
1994	0.0421	−0.0081	0.0036	0.8376	0.4022	－
1995	0.0358	−0.0081	0.0036	0.7293	0.4658	－
1996	0.0405	−0.0081	0.0036	0.8052	0.4207	－
1997	0.0368	−0.0081	0.0036	0.7444	0.4566	－
1998	0.0367	−0.0081	0.0037	0.7401	0.4593	－
1999	0.0235	−0.0081	0.0037	0.5216	0.6020	－
2000	0.0320	−0.0081	0.0037	0.6633	0.5071	－
2001	0.0123	−0.0081	0.0036	0.3374	0.7358	－
2002	0.0121	−0.0081	0.0036	0.3333	0.7389	－
2003	0.0673	−0.0081	0.0036	1.2499	0.2114	－
2004	0.0799	−0.0081	0.0036	1.4592	0.1445	－
2005	0.1057	−0.0081	0.0036	1.8833	0.0597	10%
2006	0.1129	−0.0081	0.0036	2.0044	0.0450	5%
2007	0.1101	−0.0081	0.0036	1.9562	0.0504	10%
2008	0.1110	−0.0081	0.0037	1.9707	0.0488	5%
2009	0.1156	−0.0081	0.0037	2.0468	0.0407	5%
2010	0.1293	−0.0081	0.0037	2.2713	0.0231	5%
2011	0.1328	−0.0081	0.0037	2.3283	0.0199	5%
2012	0.1408	−0.0081	0.0037	2.4605	0.0139	5%
2013	0.1672	−0.0081	0.0037	2.8968	0.0038	1%
2014	0.1843	−0.0081	0.0037	3.1819	0.0015	1%
2015	0.1977	−0.0081	0.0037	3.4044	0.0007	1%
2016	0.2005	−0.0081	0.0037	3.4504	0.0006	1%
2017	0.2418	−0.0081	0.0037	4.1363	0.0000	1%
2018	0.2454	−0.0081	0.0037	4.1935	0.0000	1%
2019	0.2507	−0.0081	0.0037	4.2804	0.0000	1%
2020	0.2533	−0.0081	0.0037	4.3213	0.0000	1%
2021	0.2589	−0.0081	0.0037	4.4144	0.0000	1%
2022	0.2667	−0.0081	0.0037	4.5399	0.0000	1%
2023	0.2679	−0.0081	0.0037	4.5401	0.0000	1%

序号	地区	产业园蔬菜类型	序号	地区	产业园蔬菜类型
1	广州市增城区	迟菜心	16	汕尾市海丰县	蔬菜
2	广州市增城区	菜稻轮作	17	韶关市乳源县	蔬菜
3	广州市白云区	设施蔬菜	18	韶关市武江区	蔬菜
4	惠州市博罗县	蔬菜	19	韶关市新丰县	蔬菜
5	惠州市惠东县	蔬菜	20	韶关市乐昌市	香芋
6	惠州市惠阳区	蔬菜	21	韶关市始兴县	蔬菜
7	肇庆市怀集县	蔬菜	22	韶关市曲江区	食用菌
8	湛江市坡头区	莲藕	23	韶关市武江区	食用菌
9	湛江市徐闻县	玉米	24	梅州市梅江区	蔬菜
10	清远市连州市	菜心	25	梅州市丰顺县	特色蔬菜
11	清远市阳山县	蔬菜	26	梅州市五华县	蔬菜
12	清远市英德市	连樟村果菜茶	27	汕头市澄海区	蔬菜
13	清远市英德市	西牛麻竹笋	28	揭阳市揭东区	竹笋
14	河源市源城区	蔬菜	29	揭阳市揭西县	苦笋
15	汕尾市陆丰市	萝卜	30	揭阳市普宁市	稻蔬

Spatiotemporal Evolution and Driving Mechanisms of County-Level Agricultural Industry Agglomeration under High-Quality Development: A Case Study of Vegetable Production in Guangdong Province

高质量发展视角下县域农业产业集聚的时空演化与驱动机制——以广东省蔬菜生产为例

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序号	产品名称	地区	序号	产品名称	地区
1	增城迟菜心	广州市增城区	17	西牛麻竹笋	清远市英德市
2	派潭凉粉草	广州市增城区	18	阳山淮山	清远市阳山县
3	庙南粉葛	广州市南沙区	19	阳山西洋菜	清远市阳山县
4	新垦莲藕	广州市南沙区	20	连州菜心	清远市连州市
5	炭步槟榔香芋	广州市花都区	21	星子红葱	清远市连州市
6	合水粉葛	佛山市高明区	22	连山大肉姜	清远市连山县
7	三水黑皮冬瓜	佛山市三水区	23	竹山粉葛	清远市佛冈县
8	矮陂梅菜	惠州市惠城区	24	北乡马蹄	韶关市乐昌市
9	福田菜心	惠州市博罗县	25	张溪香芋	韶关市乐昌市
10	杜阮凉瓜	江门市蓬江区	26	火山粉葛	韶关市曲江区
11	鹤山粉葛	江门市鹤山市	27	新丰佛手瓜	韶关市新丰县
12	恩平簕菜	江门市恩平市	28	三圳淮山	梅州市蕉岭县
13	甜水萝卜	江门市新会区	29	蕉岭冬笋	梅州市蕉岭县
14	活道粉葛	肇庆市高要区	30	高堂菜脯	潮州市饶平县
15	水东芥菜	茂名市电白区	31	埔田竹笋	揭阳市揭东区
16	徐闻良姜	湛江市徐闻县	32	吴厝淮山	揭阳市揭东区
			33	虎噉金针菜	汕尾市海丰县

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