中国智慧农场的时空演变及驱动因子研究

STUDY ON SPATIAL-TEMPORAL TRANSITION AND DRIVING FACTORS OF SMART FARMS IN CHINA

关键词:  数字经济时代  新质生产力  智慧农场  时空演变  关键因子

Key words:  digital economy era  new quality productivity  smart farm  spatial-temporal transition  key factors

摘要:

目的 智慧农场既是数字经济时代智慧农业发展的现实立足点，又是衡量新质生产力发展水平的重要指标。在“农业4.0”时代，从时空演变的源头追溯智慧农场发展规律，为优化智慧农场区域布局、推动农业高质量发展提供重要理论基础与决策参考。方法 文章采用空间自相关分析、核密度分析、标准差椭圆及地理探测器等方法，分析了2014—2023年中国智慧农场的时空演变特征及关键因子。结果 （1）从时间演变来看，智慧农场历经平缓发展期（2014—2018年）和高速增长期（2018—2023年）两个阶段。（2）从空间演变来看，2014—2023年智慧农场整体布局发生了“东北—西南”的空间演变，并呈现覆盖范围持续扩张、布局愈发趋于均衡、西部地区数量相对稀少的分异特征。（3）从关键因子来看，智慧农场分异格局受资源禀赋、财政支出、农业产能、农机技术等内外多重要素耦合影响。智慧农场分布遵循胡焕庸线规律，受到地形、气候等传统资源禀赋影响；内驱因子各维度解释力结果为“产业依赖> 经济支持> 科学技术> 数字基础”，且因子发生交互作用时会进一步强化影响效应。结论 智慧农场发展稳健，目前正处于黄金时期，部分地区发展相对落后，且新型智慧农业技术的融合运用与数字基础设施的建设方面还存在提升空间。

Abstract:

Smart farms are not only a practical foundation for the development of smart agriculture in the digital economy era, but also an important indicator of measuring the level of new quality productivity. In the era of "Agriculture 4.0", tracing the development patterns of smart farms from the perspective of spatial-temporal transition provides an important theoretical basis and a decision-making reference for optimizing the regional layout of smart farms and promoting high-quality agricultural development. This study employed methods such as spatial auto-correlation analysis, kernel density analysis, standard deviation ellipses, and geographical detectors to investigate the spatial-temporal transition characteristics of smart farms in China from 2014 to 2023 and identify key driving factors. The study revealed the following findings: (1) In terms of temporal transition, smart farms in China had undergone two stages of gradual development (2014—2018) and rapid growth (2018—2023). (2) Regarding spatial transition, from 2014 to 2023, the overall layout of smart farms had experienced a spatial transition from “Northeast to Southwest,” showing continuous expansion in coverage, a trend towards more balanced distribution, and a small quantity in the western regions. (3) Analysis of key factors indicated that the differentiation pattern of smart farms was influenced by the coupling effects of multiple factors such as resource endowment, financial expenditure, agricultural production capacity, and agricultural machinery. And the distribution of smart farms followed the Heihe-Tengchong Line, influenced by traditional resource endowments such as terrain and climate. Internal driving factors had varying explanatory power, with industrial dependence > financial support > science and technology > digital infrastructure, and the impact was strengthened when factors interacted. In summary, at present, smart farms are developing steadily and are in a golden age. The development of some regions is relatively backward, and there is still room for improvement in the integration and application of new smart agricultural technologies and the construction of digital infrastructure.