全球智慧农业的模式比较与演化趋势——杨明、胡冰川《智慧农业(中英文)》2026年第3期

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Smart Agriculture ›› 2026, Vol. 8 ›› Issue (3): 1-12.doi: 10.12133/j.smartag.SA202605001

• 专刊--数字技术赋能与农业经济范式转型 •下一篇

全球智慧农业的模式比较与演化趋势

杨明, 胡冰川()

1. 中国社会科学院农村发展研究所，北京 100029，中国

• 收稿日期:2026-04-30 出版日期:2026-05-30
• 基金项目:国家社会科学基金项目(23ZDA045)
• 作者简介:杨 明，博士，助理研究员，研究方向为社会创业与智慧农业。E-mail：[email protected]
• 通信作者:胡冰川，博士，研究员，教授，研究方向为农产品贸易与智慧农业。E-mail：[email protected]

Comparison and Evolutionary Trends of Global Smart Agriculture Models

YANG Ming, HU Bingchuan()

1. Rural Development Institute, Chinese Academy of Social Sciences, Beijing 100029, China

• Received:2026-04-30 Online:2026-05-30
• Foundation items:The National Social Science Fund of China(23ZDA045)
• About author:YANG Ming, E-mail: [email protected]
• Corresponding author:HU Bingchuan, E-mail: [email protected]

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摘要/Abstract

摘要：

【目的/意义】 智慧农业是全球应对粮食安全、气候变化与劳动力老龄化挑战、培育农业新质生产力的战略选择。本文旨在系统比较全球智慧农业模式、揭示其分化机制与演化趋势，以期为中国构建自主可控且开放协同的智慧农业体系提供理论参考与实践依据。 【进展】 基于诱致性创新理论与制度比较分析视角，构建“驱动主体-制度环境-技术路径-价值取向”四维分析框架，识别出商业化大农场智慧农业的美国模式、绿色化集约智慧农业的欧盟模式、精细化协同智慧农业的日本模式、节水化创新智慧农业的以色列模式，以及集成化协同智慧农业的中国模式。分析发现，全球智慧农业正从单点技术引入、国别分散发展，向数据驱动、智能决策、开放协同的系统集成与趋同演化转变；然而仍面临核心技术封闭、数据治理碎片化、小农户接入鸿沟扩大与国际规则博弈加剧等挑战。全球智慧农业呈现出技术上从数字化精准作业到智能化自主决策、组织上从单主体推动走向开放生态共治、数据上从孤岛走向跨境互操作与要素市场化、价值上从单一增产走向绿色低碳与韧性导向、治理上从规则碎片化走向多边协同共建的演化趋势。 【结论/展望】 中国作为大国小农国情的代表性经济体，可在五维协同推进中承担技术供给者、制度试验者与规则共建者的复合角色，与各国共同推动全球智慧农业走向更具包容性的共生格局。

关键词:智慧农业,数据驱动,智能决策,新质生产力,农业基础模型,开放协同,大国小农

Abstract:

[Significance] Smart agriculture represents a strategic choice for addressing the global challenges of food security, climate change, and an aging agricultural labor force, and for cultivating new-quality productive forces in agriculture. Shaped by factors such as factor endowments, institutional traditions, and value preferences, economies have not converged upon a single optimal path, but have instead formed highly heterogeneous practical configurations across driving actors, institutional environments, technological pathways, and value orientations. Existing research remains largely confined to the parallel enumeration of country-specific experiences and lack a unified comparative framework; their mechanistic explanations seldom move beyond the single dimension of factor endowments, leaving the multidimensional, co-evolutionary dynamics inadequately understood. In view of this, the archetypal models of global smart agriculture were systematically compared, the mechanisms underlying their divergence were uncovered, and their evolutionary trends were assessed, so as to provide theoretical references and practical guidance for China to build an autonomous, controllable, and openly collaborative smart agriculture system and in advancing from technological follower to co-builder of rules. [Progress] Drawing on the theory of induced innovation and a comparative institutional analysis perspective, a four-dimensional analytical framework of "driving actors-institutional environment-technological pathway-value orientation" was constructed, and five representative economies, the United States, the European Union, Japan, Israel, and China, were selected to distill five archetypal models. The commercialized large-scale farm model exemplified by the United States is one in which the private sector leads and releases economies of scale through large-scale field operations. The green and intensive model exemplified by the European Union is one in which intensive production is steered by binding green constraints. The refined collaborative model exemplified by Japan is one in which platform-based co-governance alleviates super-aging and smallholder fragmentation. The water-saving innovative model exemplified by Israel is one in which a full-chain water-saving technology system has developed under acute water scarcity. The integrated collaborative model exemplified by China is one in which national strategy integrates multiple actors, coupling the 'large-country-with-smallholders' condition with leading digital infrastructure such as BeiDou, 5G, and AI. On this basis, four mechanisms, induced innovation, path dependence, the antitrust tradition, and rule-based contestation, were introduced to provide a process-tracing account of model divergence in terms of sequential triggering and tension-laden coupling, the underlying drivers of this divergence were traced across the three dimensions of endowment constraints, organizational systems, and value hierarchies, and thereby the distinct value sequences of the five models were identified. Five dimensions of global evolutionary trends were assessed: technology, organization, data, value, and governance. Technologically, from digital precision operations toward intelligent autonomous decision-making. Organizationally, from single-actor-driven development toward open-ecosystem co-governance. In data terms, from isolated silos toward cross-border interoperability and factor marketization. In value terms, from a singular pursuit of yield growth toward green, low-carbon, and resilience-oriented goals, and in governance, from national fragmentation toward global regulatory coopetition. [Conclusions and Prospects] Model divergence constitutes a stable equilibrium under the multiple coupling of factor endowments, institutional structures, and historical paths, and no universally optimal model exists; beneath this divergence lies a convergent core in which data serve as a factor of production, models as the decision-making engine, ecosystems as the organizational form, and sustainability as the value baseline. Looking ahead, global smart agriculture should evolve along the trajectory of technological inclusiveness, ecological openness, value pluralism, and governance synergy. China is well positioned to assume the composite role of technology provider, institutional experimenter, and rule co-builder, working with other nations to advance global smart agriculture toward a more inclusive and symbiotic configuration.

Key words:smart agriculture,data-driven,intelligent decision-making,new quality productive forces,agricultural foundation models,open collaboration,large country with smallholder farming

中图分类号:

• S126

• F323

引用本文

杨明, 胡冰川. 全球智慧农业的模式比较与演化趋势[J]. 智慧农业(中英文), 2026, 8(3): 1-12.

YANG Ming, HU Bingchuan. Comparison and Evolutionary Trends of Global Smart Agriculture Models[J]. Smart Agriculture, 2026, 8(3): 1-12.

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链接本文:https://www.smartag.net.cn/CN/10.12133/j.smartag.SA202605001

https://www.smartag.net.cn/CN/Y2026/V8/I3/1

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表1

全球智慧农业典型模式比较

比较维度	美国	欧盟	日本	以色列	中国
模式类型	商业化大农场智慧农业	绿色化集约智慧农业	精细化协同智慧农业	节水化创新智慧农业	集成化协同智慧农业
驱动主体	风险资本+装备厂商+农化巨头	超国家规制机构+农户协会	国家研究机构+农协+私营企业	国家研发体系+军工+风投	国家战略+龙头企业+科研院所
制度供给重心	立法授权+连接性补贴+研发资助	CAP+绿色硬约束+数据空间立法	基本计划+开放API指南+协议会共治	国家研发资助+创新政策+水资源立法	国家行动计划+政策密度迭代+示范项目
技术路径	大农场+自动导航+变量喷雾+数据平台	智能温室+变量施肥+绿色合规	小尺度+共享平台+机器替人	滴灌+再生水+精准节水+AI水肥	北斗+5G+植保无人机+集成大模型
代表平台/项目	Operations Center、Climate FieldView	Common European Agricultural Data Space、SmartAgriHubs、DataConnect	WAGRI、SIP、Smart Food Chain	Netafim、Volcani Center、GrowingIL	伏羲农场、国家智慧农业大数据平台
价值取向	商业回报+规模经济	绿色低碳+生物多样性+多功能	替人增效+老龄化适应	资源效率+技术输出	粮食安全+新质生产力

表1

图1

全球智慧农业演化阶段 注：数据来源于USDA ERS《数字时代的精准农业》报告（2023年）、欧盟智慧农业枢纽（SmartAgriHubs）项目年度评估报告（2019—2024年）、日本农林水产省《食物·农业·农村基本计划》（2024—2025年）及日本农业数据协同平台协议会成员数据、欧盟农业数据空间蓝图（CEADS Blueprint, 2024年）。

图1

图2

日本WAGRI平台演化曲线 注：数据来源于日本农林水产省（Ministry of Agriculture, Forestry and Fisheries, MAFF）、NARO官方公告、亚太地区粮食与肥料技术中心（Food and Fertilizer Technology Center, FFTC）技术报告（2019）、WAGRI协议会成员页（截至2026年1月5日协议会总成员数530社）。

图2

图3

全球农业食品科技投资规模演化 注：数据来源于 AgFunder Global AgriFoodTech Investment Report 2019—2025。

图3

图4

全球智慧农业“五维共演”趋势框架

图4

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