强力推进智慧农业建设 加快形成农业新质生产力

doi:10.19788/j.issn.2096-6369.000054

Abstract

Abstract:

Agriculture is the most fundamental and representative traditional industry, and accelerating the construction of an agricultural powerhouse requires the cultivation and development of new quality productive forces in agriculture. Digital technology is the leading force of the new round of scientific and technological revolution and industrial transformation, and comprehensively promoting the integration of digital agriculture will become an important focus for accelerating the formation of new quality productive forces in agriculture. This article studies the scientific connotation of new quality productive forces, the basic characteristics of agricultural new quality productive forces, the realistic needs, development trends, and key directions of smart agriculture construction, combining domestic and foreign literature, statistical data, and survey results. It proposes measures to promote the construction of smart agriculture and accelerate the formation of new quality productive forces in agriculture. China's smart agriculture construction is moving from "scenic spots" to "scenery," and has entered a new stage of synergistic and efficient integration of big data, IoT, blockchain, artificial intelligence, satellite remote sensing, and BeiDou Navigation Satellite System (BDS) and other modern information technologies.It is necessary to start from key industries and accelerate the formation of a favorable ecological environment for the development of smart agriculture. We should regard smart agriculture as an important lever for advancing the construction of an agricultural powerhouse, addressing the biggest constraint factor of data, promoting the deep integration of modern information technology with agricultural industries, cultivating a high-quality innovative workforce, fully leveraging the role of enterprises as the main innovation actors, strengthening key core technology R&D, aligning with the development trends of digitalization and greenization, and accelerating the cultivation and development of new agricultural productive forces.

Key words: new quality productive forces, new quality productive forces of agriculture, smart agriculture, data, high-quality development

WANG XiaoBing, LIU Yang, LIANG Dong, KANG Ting, KANG ChunPeng, YIN RuiFeng, CHEN Sha, REN YuJue, XU Yang. Strongly Promote the Construction of Smart Agriculture, Accelerate the Formation of New Quality Productive Forces in Agriculture[J].Journal of Agricultural Big Data, 2024, 6(4): 469-475.

References 11

[1]	张燕, 朱春筱. 发展新质生产力是推动高质量发展的内在要求[N]. 安徽日报,2024-03-05.
[2]	任丽梅. 新质生产力理论:引领未来新型工业化的思想革命[J]. 中国矿业大学学报(社会科学版), 2024, 26(3):1-12.
[3]	吴德进. 培育新质生产力赋能高质量发展[N]. 福建日报, 2024-02-20.
[4]	习近平经济思想研究中心. 新质生产力的内涵特征和发展重点[N]. 人民日报,2024-03-01.
[5]	石枕. 怎样理解和计算“全要素生产率”的增长—评一个具体技术经济问题的计量分析[J]. 数量经济技术经济研究, 1988(12):68-71.
[6]	刘俊鹏. 加快形成“新质生产力”推动全要素生产率的全面提升[N]. 中国航空报,2024-02-09.
[7]	万劲波, 王雪. 科技创新是发展新质生产力的核心要素[N]. 学习时报,2024-04-03.
[8]	罗必良. 论农业新质生产力[J]. 改革, 2024(4):19-30.
[9]	叶兴庆. 把准农业领域发展新质生产力的着力点[N]. 农民日报,2024-03-29.
[10]	国家数据局. 数字中国发展报告(2023年),2024.
[11]	全国数据资源调查工作组. 全国数据资源调查报告(2023年),2024.

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Strongly Promote the Construction of Smart Agriculture, Accelerate the Formation of New Quality Productive Forces in Agriculture

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