农业大数据研究发展（2025）
收稿日期: 2026-01-07
录用日期: 2026-02-05
网络出版日期: 2026-04-01
基金资助
国家农业科学数据中心课题“农业大数据研究发展报告（2025）研制”(NASDC2025XM00-03);中央高校基本科研业务费项目所级基本科研业务费基金项目“前沿交叉领域融合创新路径识别方法研究”(JBYW-AII-2025-11)
Progress of Agricultural Big Data Research (2025)
Received date: 2026-01-07
Accepted date: 2026-02-05
Online published: 2026-04-01
【背景】为系统描绘2020至2024年全球农业大数据研究图景，揭示其核心趋势、热点前沿及主要参与国的差异化发展路径。【方法】本研究基于Web of Science与Scopus数据库的50,502篇文献，采用科学计量学方法，运用PhraseLDA模型聚类主题，构建复合指标遴选热点前沿，并引入四阶段成熟度框架对技术热点前沿进行系统性定位。【内容】研究发现，全球农业大数据研究正从L3（系统智能）向L4（生态协同）阶段加速演进，其核心驱动力源于技术融合的内生变革与可持续发展的外在目标升级。研究识别出三种国家级发展路径，即中国的应用驱动与全链整合模式，欧盟的政策驱动与规范引领模式，以及美国的市场驱动与前沿探索模式。【应用与价值】研究提出，全球农业大数据研究已进入以智能化、绿色化、协同化为特征的快速发展期。尽管各国路径不同，但技术融合与可持续发展已成全球共识。未来，AI大模型、气候智能型农业、开放创新生态及数字育种技术将是重塑农业大数据研究领域的关键。
吴蕾 , 马晓敏 , 孙巍 , 张学福 . 农业大数据研究发展（2025）[J]. 农业大数据学报, 2026 , 8(1) : 1 -18 . DOI: 10.19788/j.issn.2096-6369.200008
To depict the global landscape of agricultural big data research from 2020 to 2024, this study reveals its core trends, emerging frontiers, and the differentiated development paths of key participating countries. Based on 50,502 papers from the Web of Science and Scopus databases, this research employs scientometric methods, utilizes the PhraseLDA model for topic clustering, constructs composite indicators to identify emerging frontiers, and introduces a four-stage maturity framework to systematically position technological frontiers. The study finds that global agricultural big data research is accelerating its evolution from Level 3 (System Intelligence) to Level 4 (Ecological Synergy), driven by both endogenous technological convergence and the external goal of sustainable development. Three national development models are identified: China's application-driven and whole-chain integration model, the EU's policy-driven and standard-led model, and the U.S.'s market-driven and frontier exploration model. The study indicates that global agricultural big data research has entered a rapid development phase characterized by intelligence, green innovation, and synergy. Despite differing national approaches, technological convergence and sustainable development have become global consensus. Future priorities include AI large models, climate-smart agriculture, open innovation ecosystems, and digital breeding, which will reshape the field of agricultural big data research.
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