我国发展智慧农业的现实基础、制约因素、实现路径

Smart Agriculture Development in China: Current Foundations, Key Constraints and Implementation Pathways

  • 摘要: 智慧农业作为人工智能技术驱动下的农业转型方向,对提升农业国际竞争力具有战略意义,同时也是我国实现农业现代化目标的必然选择。本研究基于“政策—技术—产业”分析框架,探讨我国智慧农业发展的现实基础、制约因素和实现路径。研究发现:当前我国智慧农业发展面临农业基础设施薄弱、土地细碎化限制规模经营、农业数据分散阻碍共享应用、复合型人才匮乏和资金投入不足等问题。通过比较研究美国、日本、英国及以色列的发展经验,结合我国农业发展现状,提出以下实现路径:实施以发展智慧农业需求为导向的高标准农田建设,推进以“三权”分置为重点的土地制度改革,打破农业信息壁垒构建全民共享“智慧图”,创新协同育人机制实施新型职业农民培育工程,创新投融资机制探索智慧农业降本增效路径及加强构建农业大模型提高生产决策科学化。

     

    Abstract: Smart agriculture, as a direction of agricultural transformation driven by artificial intelligence technology, has strategic significance in enhancing the international competitiveness of agriculture and is also an inevitable choice for China to achieve the goal of agricultural modernization. Based on the “policy technology industry” analysis framework, this study explored the practical foundation, constraints and implementation path of the development of smart agriculture in China. Research has found that, the current development of smart agriculture in China has been facing weak agricultural infrastructure, limited scale operation due to land fragmentation, scattered agricultural data hindering shared applications, a shortage of versatile talents, and insufficient funding. By comparing and studying the experiences of the United States, Japan, the United Kingdom, and Israel in policy support, technology transformation, and industrial synergy, and combining with the current situation of agricultural development in China, the following Implementation paths are proposed: implementing high standard farmland construction guided by the demand for developing smart agriculture, promoting land system reform with a focus on “separation of three rights” , breaking down agricultural information barriers to build a “smart map” shared by the whole nation, innovating collaborative education mechanisms to implement new vocational farmer cultivation projects, innovating investment and financing mechanisms to explore cost reduction and efficiency improvement paths for smart agriculture, and strengthening the construction of intelligent agriculture large model to improve scientific production decision-making.

     

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