PDF
Full
Outline
Strong agricultural towns have promoted the circulation of resources among towns in modern rural China. Local resources can be integrated to develop comparatively advantaged leading industries in sustainable agriculture. This study aims to clarify the spatial distribution and influencing factors of strong agricultural towns with various categories at the national level from 2018 to 2024. Nine categories were classified according to the leading industries. A combination of spatial analysis, including the average nearest neighbor index, kernel density estimation, and geographical detector, was adopted to explore the spatial pattern and driving factors. The results show that: (1) A total of 1 709 strong agricultural towns were approved in China, which were distributed in the third topographic step along water sources. The overall uneven spatial distribution exhibited a “northeast–southwest” pattern. Kernel density analysis revealed that the towns specializing in different product categories exhibited the distribution patterns of "category-region matching and core-led radiation". Among them, the largest number of grain and oil industry-strong towns reached 389, while edible fungus industry-strong towns were the smallest, with only 60. Overall, three major high-density clusters were formed in the border areas of Hebei-Shandong-Henan, Jiangsu-Zhejiang, and Sichuan-Chongqing. (2) At the provincial level, the conventional major agricultural provinces—Shandong, Sichuan, and Henan—shared a large number of such towns, indicating the sound quantity and spatial layout. According to the average nearest neighbor index and distribution density, 17 provinces shared the high dense distribution. Specifically, the dense agglomeration was found in the six regions (Shandong, Henan, Guangdong, Jiangsu, Hubei, and Chongqing); Beijing, Tianjin, and Shanghai were the dense uniformity; Three autonomous regions (Xinjiang, Inner Mongolia, and Tibet) presented the scattered agglomeration. (3) The geographical detector showed that the agricultural production scale and regional economic development level served as the significant single factors to explain the distribution of strong industry towns. Among them, the total output value of agriculture, forestry, animal husbandry, and fishery also presented the strongest explanatory effect, particularly for the agricultural development level and optimal industrial structure. In contrast, regional and policy factors shared the weak independent explanatory effects, with the stronger explanatory power after interactions with the other factors. In conclusion, the strong agricultural towns can be expected to position product categories, according to local resources, differentiated development, and comparative advantages. Planning and layout can promote the clustered development of factor efficiency in strong agricultural towns, leading to their differentiated, intensive, and high-quality development. The findings can also provide data support to construct strong industry towns.
PDF
244
107
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
