[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3051":3,"related-3051":46},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":45},3051,"数字乡村建设赋能粮食安全：机制、区域异质性与政策启示","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1748737\u002Fpdf","华中师范大学Muzi Li、中国农科院农业经济与发展研究所Shuai Hao、北京工业大学Haoqi Chang、江苏科技大学Xiaoyuan Zhao合作发表。使用2011-2023年中国31省面板数据采用双向固定效应模型评估数字乡村建设对粮食安全水平（FSL）影响及其空间效应。结果显示：数字乡村建设显著提升本地粮食安全水平，在粮食主产区效应尤其突出，但跨区域溢出效应有限。机制分析表明该效应通过农业技术培训覆盖率与土地生产率两个主要渠道发挥作用，政府规模发挥正向调节作用。",null,"Frontiers Sustain Food Syst 10:1748737","2026-09-20T00:00:00Z","论文",10,false,87,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":12,"relevant":20,"comment":21},22,23,18,14,1,"基于31省11年面板数据的实证研究，机制与异质性结论清晰，对数字乡村政策有参考价值，值得进入每日精选。",[23],{"name":9,"url":6},[25,26,27,28,29],"数字乡村","粮食安全","区域异质性","农业技术培训","土地生产率",[31,32],"华中师范大学 数字乡村 粮食安全","中国农科院 数字乡村 粮食安全","华中师范大学数字乡村粮食安全-3051",0,"10.3389\u002Ffsufs.2026.1748737\u002Fpdf",{"doi":35,"openalex_id":8,"authors":37,"venue":8,"cited_by_count":34,"oa_url":8,"card":38,"direction":42,"ingested_from":44},[],{"tldr":39,"method":40,"finding":41,"direction":42,"opportunity":43},"基于2011-2023年31省面板数据，评估数字乡村建设对粮食安全的影响及空间效应。","双向固定效应模型，中国31省面板数据，机制与空间溢出分析。","数字乡村建设显著提升本地粮食安全，主产区效应突出，但跨区域溢出有限。","数字乡村与农业信息化","可探究数字乡村跨区域溢出受限的成因，及如何通过协同政策增强主产区外溢效应。","agent","2026-09-21T00:04:39.562305Z",{"total":47,"page":20,"page_size":47,"items":48},6,[49,72,101,125,155,183],{"id":50,"title":51,"url":52,"summary":53,"summary_zh":8,"content":54,"source_name":55,"source_url":8,"published_at":56,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":57,"score_detail":58,"sources":61,"tags":63,"search_phrases":65,"slug":68,"view_count":69,"doi":70,"paper":8,"created_at":71},115,"Empowering food security through digital villages: mechanisms, regional heterogeneity and policy insights from China","https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffsufs.2026.1748737\u002Ffull","Muzi Li, Shuai Hao, Haoqi Chang, Xiaoyuan Zhao基于2011-2023年中国31个省份面板数据,采用双向固定效应模型评估数字乡村建设(DVC)对粮食安全水平(FSL)的影响及其空间效应。研究发现DVC显著提升本地粮食安全水平,在粮食主产区效应尤为突出。","## Abstract\n\n**Introduction:**\n\nFood security constitutes a fundamental pillar of national development. Digital-village construction (DVC) plays a critical role in enhancing food security levels (FSL), yet the full scope of its impacts has not been comprehensively examined.\n\n**Methods:**\n\nThis study uses panel data from 31 Chinese provinces from 2011 to 2023 and employs a two-way fixed effects model to evaluate the effects of DVC on FSL and its spatial impacts.\n\n**Results:**\n\nThe results indicate that DVC significantly improves local FSL, with particularly strong effects in major grain-producing regions. However, the cross-regional spillover of these effects remains limited. The mechanism analysis shows that this effect operates through two primary channels: agricultural technology training coverage and land productivity, with government scale playing a positive moderating role.\n\n**Discussion:**\n\nBased on these findings, regionally differentiated promotion mechanisms should be established. Prioritizing digital literacy cultivation, enhancing agricultural digital service capacity, improving platform-based data coordination systems, and strengthening fiscal support with performance-oriented governance are essential steps.\n\n## 1 Introduction\n\nThe global food security issue has shifted from phase-based shortages to systemic vulnerability under multiple shocks. The Global Food Crisis Report points out that conflict, economic shocks, extreme weather, and forced displacement continue to exacerbate global food insecurity and malnutrition (). In this uncertain context, traditional factor input and expansion-based production models face limitations, and building resilience in the global food system is urgent. To address this challenge, digital transformation is often seen as an opportunity to enable sustainable futures in agriculture and rural areas (), providing support for improving the resilience of food security systems and optimizing production efficiency. However, food security systems remain constrained by traditional measurement paradigms and policy logic, and this lag is weakening their resilience. How to establish an effective connection between digital transformation and food security systems, and whether digitalization can truly enhance the security and sustainability of food systems, has become a key issue to resolve. China’s practice provides a unique real-world example for this issue. As the largest developing country in the world, China has continuously promoted multiple institutional and policy explorations in agriculture and rural areas. In 2019, China proposed the implementation of the “Digital Village Strategy,” becoming an important practice for the concentrated promotion of digital transformation in the agricultural and rural sectors (). Digital-village construction (DVC) represents a new trajectory of agricultural and rural modernization (), the digital technologies it deploys are widely viewed as a key driving force for improving agricultural productivity and fostering high-quality development (). In this context, China’s DVC is both a leading global experiment in digital transformation and provides an observable institutional setting to identify how digitalization enhances food security levels (FSL), offering valuable insights for other developing countries.\n\nExisting literature has developed a substantial body of research around the two broad themes of food security and digital villages. However, studies that explicitly integrate these two strands remain relatively limited. In the field of food security research, traditional studies have predominantly focused on quantitative security, with most analyses characterizing food production conditions using single indicators such as total grain output or yield per unit area (; ; ). Only a small number of studies have incorporated total volume, structural balance, ecological sustainability, and quality security within a unified analytical framework (; ). Although existing research has expanded measurement approaches to food security, quantity-oriented perspectives continue to dominate, and a comprehensive and systematic framework has yet to be established. In addition, increasing attention has been paid to the effects of external factors such as climate change, agricultural insurance, labor prices and demographic structure, and industrial integration on food security (; ; ; ). With respect to research on digital villages, most studies have centered on the digital economy and examined its role in promoting green agriculture, farmland protection, rural industries, and common prosperity (; ; ; ; ). The development of the digital economy is widely regarded as a key factor in enhancing the resilience of food systems (), while the application of digital technologies can effectively increase grain yields through improvements in service provision, field management, and agricultural mechanization (). Existing studies have also confirmed the positive effects of digital industrial development and digital service capacity on agricultural ecological efficiency (). Although digital approaches have demonstrated considerable potential in improving agricultural productivity and safeguarding food security, most studies remain focused on the effects of the digital economy, thereby overlooking the systemic relationship between DVC and FSL.\n\nIn summary, existing research still has three main limitations: first, most studies simplify FSL indicators to quantitative security, a single measurement approach that is insufficient for explaining the structural vulnerability of production systems in the context of digitalization. This may affect the scientific accuracy and precision of policies. Second, existing research primarily focuses on the impact of the digital economy and digital technologies on agriculture, neglecting the role of DVC from the perspective of macro-level institutions and service environments. Furthermore, some studies commonly use unified digital indicators for urban and rural areas, lacking detailed characterization of the digital features at the rural level. As a result, the dual-indicator system of DVC and FSL remains underdeveloped. Third, relevant research often stops at verifying the superficial correlation between digital technologies and food security, lacking in-depth analysis of the mechanisms, spatial effects, and regional differences between the two. The intrinsic logic of how DVC enhances FSL through infrastructure improvement and optimization of public services has yet to be fully explained.\n\nIn light of this, based on panel data from 31 provinces in China from 2011 to 2023, this paper systematically analyzes the impact of DVC on FSL and further deepens the study of its impact mechanisms, spatial effects, heterogeneity, and moderating effects. The potential contributions of this study are as follows: First, it improves the measurement system by constructing a dual-dimensional evaluation system for DVC and FSL, tailored to rural characteristics, providing a tool reference for related empirical research. Second, it deepens the mechanism analysis by breaking through the superficial analysis in existing studies, addressing the shortcomings in the literature regarding the explanation of the mechanisms of agricultural technology training coverage and land productivity. Third, from a spatial perspective, this paper identifies the spatial correlation of food security and introduces spatial econometric methods to supplement the baseline results. It further combines regional location, functional positioning, and quantile regression analysis to systematically describe the spatial differences and heterogeneity in the impact of DVC on FSL, thereby enriching the study of the boundary conditions of DVC’s empowering effects. Fourth, it empirically tests the positive moderating effect of government scale, clarifying the collaborative empowerment logic between DVC and government agricultural fiscal investment, providing new empirical evidence for related fields.\n\nThe remainder of this paper is structured as follows: Section 2 constructs the theoretical analysis framework and presents the research hypotheses; Section 3 introduces the data sources and research design; Section 4 presents the empirical results and analysis; Section 5 provides an extended discussion; Section 6 discusses the research findings; Section 7 concludes with the main conclusions, policy recommendations, and a summary of the research limitations.\n\n## 2 Theoretical framework and research hypotheses\n\nIn the context of digital transformation, DVC is not simply a technological investment, but rather a systematic embedding of information technologies such as big data, the Internet of Things, and artificial intelligence into agricultural production, rural life, and grassroots governance processes. It represents an important form of agricultural and rural informatization development in the new era. DVC promotes the deep integration of informational elements with traditional agricultural factors, systematically improving the technical conditions and operational environment for rural digital development. This paper systematically analyzes the impact of DVC on FSL from three perspectives: direct effects, intermediary mechanisms, and moderating effects, and proposes the research hypotheses.\n\n### 2.1 The direct effect of DVC on FSL\n\nIn the context of digital transformation, DVC improves the overall security of the food system by reallocating informational elements and optimizing the institutional environment, which enhances information efficiency and the way resources are allocated, ultimately optimizing the operation logic of food production and supply systems. From the perspective of information economics, agricultural production generally suffers from information asymmetry and high information search costs, leading to distorted resource allocation and reduced production efficiency. Rural digitalization significantly improves production decision-making efficiency by reducing information acquisition and transmission costs and increasing information transparency ().\n\nFirst, agricultural informatization constitutes a core driving force in the evolution of modern agricultural production systems. By embedding digital technologies into production processes, it reshapes information transmission and decision making structures within agricultural production (; ). As a fundamental pillar of DVC, digital infrastructure functions not only as the physical carrier of information flows but also as a prerequisite for the integration of digital technologies into production systems. Improvements in rural network coverage, data collection terminals, and information sensing facilities enable digital technologies to be embedded throughout grain production, management, and circulation processes, facilitating a shift from experience based to data-oriented production. On the one hand, enhanced digital infrastructure substantially reduces information acquisition and transaction costs and mitigates the information asymmetry that is pervasive in agricultural production (). This allows farmers to more promptly access information on climatic conditions, production environments, and market signals, thereby reducing uncertainty in production decisions, optimizing input allocation, and enhancing the stability and sustainability of grain production. On the other hand, the integration of digital technologies into production processes under improved infrastructure conditions increases precision and controllability, reduces resource waste and environmental pressure, and improves the stability of grain output while simultaneously accounting for ecological constraints and structural coordination.\n\nSecond, building upon the strengthened technical foundation provided by digital infrastructure, DVC further promotes FSL through digital services. Grain production inherently relies on the input of key factors, including labor, capital, and technology. By leveraging the inclusive effects of digital technologies, DVC expands access to credit resources and information channels for small scale producers (), enabling farmers to dynamically adjust input structures during the production process, enhance their capacity to respond to external shocks, and improve the stability of the grain production system. Capital constitutes a critical support for grain production activities, and digital inclusive finance alleviates financing constraints faced by farmers by virtue of its digital and inclusive characteristics, thereby effectively safeguarding food security (). Technology represents another important driving force, as agricultural technological innovation accelerates the transition toward green agricultural development, promotes resource saving and environmentally friendly production, and enhances the ecological security of grain production (). Through information feedback and service coordination mechanisms, digital services guide the rational allocation of production factors, mitigate risks of structural imbalance, and strengthen the overall resilience of the food system.\n\nTaken together, DVC can reinforce FSL through a mechanism chain encompassing information efficiency enhancement, factor optimization, and improvements in system resilience. Accordingly, Hypothesis 1 is proposed.\n\n> _H1_: DVC has a significant positive effect on FSL.\n\n### 2.2 Indirect effects of DVC on FSL\n\nDVC not only exerts a direct effect on FSL but also generates indirect effects by alleviating key constraints in grain production. In the production process, the achievement of FSL is often jointly constrained by technological limitations arising from insufficient farmers’ capacity to absorb technology and by factor constraints resulting from low efficiency in land factor allocation. By improving the information environment and the conditions for digital technology development, DVC provides critical support for relaxing these constraints. Based on this logic, this paper selects agricultural technology training coverage and land productivity as mediating variables to characterize the transmission pathways through which DVC influences FSL by easing technological and factor constraints.\n\nFirst, by integrating information platforms with educational systems, DVC enhances both the breadth and effectiveness of agricultural technology training, thereby alleviating technological constraints in grain production. According to information economics theory, the diffusion of the internet facilitates information exchange among governments, research institutions, and social organizations, improving the efficiency of knowledge dissemination (). However, in developing countries, farmers commonly face knowledge gaps, particularly in pesticide application and pest and disease identification, and grain production decisions often rely on experience based judgment (; ; ), which increases production risk. Although agricultural information infrastructure has continued to improve, disparities persist in farmers’ capacity to access and utilize information, especially among emerging agricultural operators (). Farmer behavior theory suggests that technology adoption is primarily driven by expected benefit maximization, while the positive externalities associated with the digital attributes of DVC improve the decision environment for technology adoption by reducing information search costs and replication costs (). Digital platforms not only lower training costs but also enable more precise matching of technical guidance. Existing studies show that improvements in farmers’ digital literacy significantly strengthen their sustainable cultivation practices (). Taken together, DVC mitigates technological constraints in grain production by expanding agricultural technology training coverage, thereby enhancing FSL.\n\nSecond, DVC alleviates factor constraints in grain production by promoting the flow and intensive utilization of land factors, thereby improving land productivity. According to induced technical change theory, agricultural technological progress is not exogenously determined, but is jointly induced by factor endowment structures and market demands (). When land factors are relatively scarce or large-scale operations are continuously advanced, technological progress will be directed towards improving land productivity. DVC accelerates the adoption of land-saving and efficiency-enhancing agricultural technologies by improving digital infrastructure and the technological application environment, allowing technological progress to better align with land resource constraints. In this process, the application of digital technologies in production enhances the precision and efficiency of land use. For example, automated machinery and autonomous operating systems improve field operation efficiency, reducing labor and resource consumption (). At the same time, digital technologies accelerate the dissemination of agricultural technologies, enabling high-yield technologies and high-quality varieties to be applied more quickly in frontline production. Furthermore, the flow of information through the internet promotes rural land transfers, improving land use efficiency and the level of large-scale operations (; ). Under the combined influence of digital technologies and the institutional environment, land factors are allocated more efficiently, thus strengthening food security.\n\nTaken together, DVC indirectly promotes FSL through knowledge diffusion and factor reallocation effects. Accordingly, Hypothesis 2 is proposed.\n\n> _H2_: DVC enhances FSL by improving agricultural technology training coverage and increasing land productivity.\n\n### 2.3 Moderating effect of government scale in the process of DVC influencing FSL\n\nIn the process of DVC promoting FSL, government actions play a critical institutional moderating role. According to public economics and agricultural policy theory, government size is not only associated with public resource allocation capacity, but also affects the coordination efficiency between digital infrastructure and the grain production system. When the proportion of government fiscal expenditure to GDP is high, it indicates that the public sector has greater input and regulatory capacity in the agricultural and rural sectors. The government’s role in resource provision, institutional protection, and policy incentives will significantly amplify the marginal effects of DVC ().\n\nUnder the same level of DVC, differences in government scale will influence the actual effectiveness of DVC implementation through fiscal input capacity. From a mechanism perspective, fiscal expansion enhances the construction of rural digital infrastructure, providing the necessary resource conditions for DVC to impact FSL. Studies have shown that government agricultural expenditure is positively correlated with agricultural output and food security (; ). In China, agricultural fiscal expenditure mainly focuses on farmland water conservancy, agricultural technology, and infrastructure construction (), which helps improve digital infrastructure such as rural internet, meteorological monitoring, and data services. The promotion of agriculture and food security through digitalization is institutionally dependent, and its effectiveness partially depends on government fiscal capacity and governance levels (). Strengthened fiscal support lowers the barriers to the application of digital technologies, accelerating the extension of agricultural informatization to the grassroots level, thereby improving the implementation efficiency of DVC in the field of grain production. Through subsidies and other fiscal support for agriculture, government departments promote the substitution of labor by agricultural machinery, increasing mechanization levels and food production efficiency (). A larger government scale can facilitate multi-party coop","Frontiers in Sustainable Food Systems 2026-03-17","2026-03-17T01:00:00Z",75,{"impact":59,"substance":16,"depth":18,"authority":19,"freshness":20,"relevant":20,"comment":60},20,"论文基于中国省级面板数据，实证分析数字乡村建设对粮食安全的影响机制与区域异质性，方法规范、结论有政策参考价值，但发表于3月，时效性差。",[62],{"name":55,"url":52},[25,26,64,27],"中国",[66,67],"区域异质性 数字乡村 粮食安全 中国","区域异质性 数字乡村","区域异质性数字乡村粮食安全中国-115",8,"10.3389\u002Ffsufs.2026.1748737\u002Ffull","2026-07-31T00:00:56.112781Z",{"id":73,"title":74,"url":75,"summary":76,"summary_zh":8,"content":77,"source_name":78,"source_url":8,"published_at":79,"category":80,"cover_url":8,"hotness":12,"is_selected":81,"score":82,"score_detail":83,"sources":89,"tags":91,"search_phrases":96,"slug":99,"view_count":34,"doi":8,"paper":8,"created_at":100},2816,"中央网信办专家解读:乘\"数\"而上 向\"智\"而行——大力发展智慧农业加快建设数字乡村","https:\u002F\u002Fwww.cac.gov.cn\u002F2026-09\u002F17\u002Fc_1789925195600209.htm","专家解读指出,\"十五五\"时期数字乡村将加快迈向数智乡村,智慧农业建设将进入创新发展、落地见效的关键阶段。《加快农业农村现代化\"十五五\"规划》明确\"推进人工智能运用和智慧农业发展\"。人工智能等数智技术正加速演进,数据产业加快培育、应用场景全链拓展、新兴产业加快培育,为智慧农业发展带来前所未有的新机遇:数据从辅助性工具发展为新型生产要素,农业大模型与智能装备在生物育种、农情监测、生产管理、动植物疫病识别与防控、产量预测等场景加速落地。","习近平总书记高度重视数字乡村建设和智慧农业发展，作出重要指示强调，“瞄准农业现代化主攻方向，提高农业生产智能化、经营网络化水平，帮助广大农民增加收入”“要用好现代信息技术，创新乡村治理方式，提高乡村善治水平”。2019年，中共中央办公厅、国务院办公厅印发了《数字乡村发展战略纲要》。此后，中央一号文件连续八年对推进数字乡村和智慧农业作出重要部署。各地区各有关部门持续推进数字技术与农业生产、乡村生活日益融合，数字乡村建设和智慧农业发展取得重要阶段性成效。近日，国家互联网信息办公室、农业农村部联合发布《中国数字乡村发展报告（2019—2025年）》（以下简称《报告》），系统总结了七年来我国数字乡村发展的成就和经验。《报告》立足新形势新要求，展示了以信息基础设施为底座、数据资源体系为核心、智慧农业与乡村数字经济为重点、数字文化与数字治理为支撑、信息服务与智慧美丽乡村为拓展、政策机制与人才队伍为保障的体系化发展路径。该《报告》不仅为全面了解发展成效、科学谋划“十五五”数字乡村发展蓝图提供了重要参考，也积极回应各方关切，向国际社会展示了我国借助数字技术推动农业与乡村治理数智化转型的经验。回顾七年历程，智慧农业作为数字乡村建设的重要内容，已由试点探索转向快速起步、由点状突破迈向系统推进，正乘“数”而上，向“智”而行，为推进农业农村现代化提供有力支撑。\n\n**一、智慧农业正从“盆景”走向“风景”**\n\n数字乡村涵盖乡村经济、治理、文化、服务等多个维度，内涵丰富。《报告》提出，智慧农业是“农业新质生产力的重要内容，是乡村产业数字化的关键着力点”。智慧农业为数字乡村高质量发展提供了坚实的产业支撑，成为推动数字乡村发展的关键动能。《报告》显示，七年的探索推进和快速发展，推动智慧农业实现了“四个跨越”。\n\n**第一，智慧农业基础设施实现从“基础覆盖”到“深化赋能”的跨越。**完善的网络基础设施为智慧农业在田间地头、池塘圈舍的落地拓展提供了基础支撑。截至2025年底，农村地区互联网普及率达69.5%，较2018年底提升31.1个百分点。传统基础设施数字化为智慧农业提供了更加坚实的硬件底座和场景支撑，农村水利、农田、电网、公路及寄递物流等持续升级完善。农业数据资源日益丰富，为智慧农业落地应用提供了基础资源和创新引擎，“天空地一体化”监测网络等新型基础设施加快建设，全国农产品批发市场价格信息等涉农数据开发利用不断深入。\n\n**第二，关键技术装备实现从“基础”到“核心”的跨越。**智能农机装备研发应用取得重要进展，新一代信息技术与农业装备深度融合，正推动农业生产方式从“靠天吃饭”向“知天而作”加速转变。产学研用相衔接的智慧农业创新体系加快形成，支撑取得一批关键智慧农业技术装备创新成果。《报告》显示，截至2025年底，累计建设智慧农业创新中心、分中心34个，智慧农业创新应用项目116个，104项关键智慧农业技术和62项整机智能装备研发取得突破。智慧农业技术装备质量管控更加严格、应用推广不断拓展，布局建设国家农机装备产业计量测试中心，强化农机装备产业计算测试技术研究与应用。\n\n**第三，主要产业数字化实现从“单点试验”到“面上推广”的跨越。**大田种植领域，天空地一体化农情感知与数据驱动模式初步构建，实现水稻、小麦、玉米苗情长势动态监测。截至2025年底，累计推广应用各类农机北斗终端超350万台套，农用无人机保有量超过30万架、年作业面积突破4.6亿亩。智能农机共享租赁加速普及。畜禽养殖领域，精准饲喂、环境控制、行为分析等智能技术广泛应用于生猪养殖和家禽立体高效养殖中。全国659个动物防疫通道纳入信息化管理，动物检疫监督更加智能化、便捷化和高效化。渔业领域，数字技术持续赋能多元化养殖模式，智能化网箱设备、投料机器人等智能装备加速迭代，海洋养殖智能化水平不断提升。\n\n**第四，粮食安全保障实现从“人工管控”到“数智赋能”的跨越。**粮食安全是“国之大者”，数智技术正在为其构筑起坚实保障。在耕地保护方面，“三区三线”等“一张图”相关基础数据库进一步完善，让“藏粮于地”有了更坚实的数据底座，助力守牢18亿亩耕地红线。在种业振兴方面，中国种业大数据平台建成运行，全国农作物种质资源信息平台已上线58.8万份国家级库圃种质资源信息，为育种创新提供了坚实的资源基础。在防灾减损领域，气象预警信息全面接入全国123万个应急广播终端并在16个省份386个市县试行开展“闪信”技术应用，以气象预警为先导的应急响应联动机制更加健全。在仓储方面，借助数字化仓储技术，粮库储粮周期内综合损耗率控制在1%以内，支撑节粮减损效果明显。从种到收、从田间到粮仓，数智技术正在全链条赋能国家粮食安全保障体系。\n\n**二、智慧农业发展需要坚定走好符合国情农情的路子**\n\n七年来，在信息革命加速农业深刻变革的进程中，智慧农业加快发展、数字乡村建设深入推进，推动农业成为更有奔头的产业、农村成为更加宜居宜业的家园，为网络强国、农业强国建设贡献了重要力量。回顾七年实践，我们进一步深化了对智慧农业发展的规律性认识。\n\n**一是政府引导与市场机制协同发力。**党中央、国务院印发的《加快建设农业强国规划（2024—2035年）》、农业农村部印发的《关于大力发展智慧农业的指导意见》《全国智慧农业行动计划（2024—2028年）》等文件构建了智慧农业“四梁八柱”。七年来，从智慧农业创新中心布局到创新应用项目建设实施，从主推技术遴选、典型案例推介到智慧农业创新大赛，政府的“有形之手”在搭建平台、降低门槛、推动产业化等方面发挥了重要作用。同时，平台经济等推动拓展创业空间，返乡青年、家庭农场、农民合作社和农村个体商户以平台化方式进入市场、链接消费和重构经营模式，农村电商、数字服务等新业态加速发展，市场的“无形之手”进一步增强了智慧农业发展的动力活力。\n\n**二是技术创新与农情农艺深度结合。**农业不同于工业、农村不同于城市，发展智慧农业、建设数字乡村必须坚持问题导向、应用导向，走适宜化、低成本、易操作的技术路线。近年来，农机北斗终端实现快速规模化推广，在于其有效契合了播种、收获等关键环节的实际生产需求，让农民“用得上、用得起、用得好”。\n\n**三是智慧农业与小农户有机衔接。**“大国小农”的基本国情农情决定了智慧农业要实现大规模落地应用，必须坚持让小农户共享数字红利的现实路径。各类农业社会化服务组织加速布点，通过集采智能装备、统一调度作业、提供“菜单式”服务，将智能农机、无人机植保、精准施肥等先进技术和装备转化为小农户“点单即享”的标准化服务，有效破解小农户“买不起、用不好”的难题。以社会化服务为纽带，智慧农业正成为促进小农户与现代农业发展有机衔接的重要手段。\n\n**三、奋力推进“十五五”时期智慧农业建设**\n\n“十五五”时期是基本实现农业农村现代化的关键时期。展望未来五年，数字乡村将加快迈向数智乡村，智慧农业建设将进入创新发展、落地见效的关键阶段。《加快农业农村现代化“十五五”规划》明确，要“推进人工智能运用和智慧农业发展”。这要求我们既要总结运用好实践中积累形成的宝贵经验，又要准确把握未来数智技术和农业发展新趋势。\n\n当前，人工智能等数智技术加速演进，深刻重塑农业发展的底层逻辑，为智慧农业发展带来了前所未有的新机遇。**一是数据产业加快培育，释放要素价值潜能。**数据从支撑农业农村发展的辅助性工具，逐步发展为具有独立价值、可市场化运营的新型生产要素，其基础资源和创新引擎作用日渐显现，数智技术加速内化成为农业农村领域的发展动能，要抓实数据这个根本，进一步加快“统筹部署农业农村数据基础设施”“发展农业农村领域数据产业”。**二是应用场景全链拓展，场景驱动成为重要引擎。**农业大模型、智能装备加速在生物育种、农情监测、生产管理、动植物疫病识别与防控、产量预测等场景落地，场景驱动技术迭代的效能日益凸显，要打造丰富多样的应用场景，“加快农业人工智能应用场景拓展”**。三是新兴产业加快培育，拓展农业发展新空间。**智能设计育种、新能源农机、农业低空经济等先导性产业规模化发展，开辟智慧农业高质量发展全新赛道，要从智能育种等产业急需领域做起，加快“培育发展乡村新产业新业态”。\n\n《报告》的发布既是阶段性总结，更是新征程的动员。面向“十五五”，我们要坚决贯彻党中央、国务院关于大力推进“人工智能+”农业的部署要求，在基础设施上强基固本，在关键技术装备上聚力攻坚，在产业数智化上扩面提效，在粮食安全保障上筑牢数字防线，加快推动智慧农业从“点上突破”迈向“面上成势”，从“量的积累”转向“质的跃升”。以智慧农业的创新发展，推动数字乡村高质量发展，为加快农业农村现代化、扎实推进乡村全面振兴注入更加澎湃的数智动能。（作者：李韶民 农业农村部信息中心副主任）","中央网信办 \u002F 网信中国","2026-09-17T00:00:00Z","政策",true,94,{"impact":84,"substance":85,"depth":18,"authority":86,"freshness":87,"relevant":20,"comment":88},28,24,15,9,"中央网信办与农业农村部联合发布七年数字乡村发展报告，含大量权威数据与十五五部署方向，政策层级高、信息增量足，值得进入每日精选。",[90],{"name":78,"url":75},[92,25,93,94,26,95],"十五五","智慧农业","农业人工智能","农业数据要素",[97,98],"农业人工智能 农业数据要素 数字乡村 智慧农业","农业人工智能 农业数据要素","农业人工智能农业数据要素数字乡村智慧农业-2816","2026-09-18T00:03:24.723951Z",{"id":102,"title":103,"url":104,"summary":105,"summary_zh":8,"content":106,"source_name":107,"source_url":8,"published_at":108,"category":80,"cover_url":8,"hotness":12,"is_selected":81,"score":109,"score_detail":110,"sources":113,"tags":115,"search_phrases":120,"slug":123,"view_count":34,"doi":8,"paper":8,"created_at":124},2694,"加快农业保险高质量发展实施方案印发——2030年保险深度1.9%、密度1200元\u002F人","https:\u002F\u002Fjcs.moa.gov.cn\u002Fgzdt\u002F202609\u002Ft20260915_6487691.htm","财政部、农业农村部、金融监管总局、国家林草局联合印发《关于加快农业保险高质量发展的实施方案》,到2030年农业保险深度达到1.9%、密度达到1200元\u002F人、科技投入强度达到1%,稻谷、小麦、玉米三大粮食作物覆盖率稳定在90%左右,综合费用率不高于20%。","各省、自治区、直辖市人民政府，新疆生产建设兵团，国务院有关部委、有关直属机构：\n\n经国务院同意，现将《关于加快农业保险高质量发展的实施方案》印发给你们，请抓好贯彻落实。\n\n财政部 农业农村部\n\n金融监管总局 国家林草局\n\n2026 年 9 月 8 日\n\n**关于加快农业保险高质量发展的实施方案**\n\n为进一步加快农业保险高质量发展，稳定农户收益，服务保障国家粮食安全和农业生产能力，经国务院同意，制定本实施方案。\n\n一、总体要求\n\n坚持以习近平新时代中国特色社会主义思想为指导，深入贯彻党的二十大和二十届历次全会精神，认真落实四中全会部署，全面贯彻习近平总书记关于“三农”工作的重要论述，完整准确全面贯彻新发展理念，加快构建新发展格局，着力推动高质量发展，遵循政府引导、市场运作、自主自愿、协同推进、改革创新的原则，充分发挥农业保险在价格、补贴、保险“三位一体”农业支持政策体系中的重要一环作用。\n\n到 2030 年，基本建成与农户风险保障需求相契合、与现代农业发展相适应、中央与地方分工负责的多层次农业保险体系，总体达到国际先进水平，实现补贴有效率、产业有保障、农民得实惠、机构可持续的多赢格局，政策更加普惠，发展更加均衡。农业保险深度（保费\u002F第一产业增加值）达到 1.9%，农业保险密度（保费\u002F第一产业就业人数）达到 1200 元\u002F人，农业保险科技投入强度（农业保险机构科技创新投入\u002F保费）达到 1%。稻谷、小麦、玉米三大粮食作物农业保险覆盖率稳定在 90%左右，提升特色农业保险对特色农业产业的保障水平，更好发挥富农强农作用。农业保险综合费用率不高于 20%。\n\n二、聚焦农业保险发展重点\n\n**（一）健全多层次农业保险体系。**坚持中央与地方共同推动、各部门分工负责，构建复合、多元、多层次的农业保险体系。加强对关系国计民生和粮食安全的大宗农产品以及地方特色农业的保障，稳步推进物化成本保险、完全成本保险、收入保险等险种发展，加强对小农户和农民合作社、家庭农场等新型农业经营主体的保障，强化对防止返贫致贫对象的支持。建立健全投保人、直保端、再保端等多方参与的风险分散链条，发挥好大灾风险准备金作用。\n\n**（二）增强种植业农产品风险保障。**加强主粮、棉花、油料、糖料、橡胶等大宗农产品风险保障，提高区域间农业保险发展均衡性，完善省以下保费分担机制。逐步降低产粮大县农业保险县级保费补贴承担比例，推动扩大稻谷、小麦、玉米、大豆完全成本保险和种植收入保险投保面积。完善对马铃薯、油菜、花生、甜菜、青稞等农产品以及稻谷、小麦、玉米制种的保险支持保障。\n\n**（三）促进养殖业保险规范发展。**完善养殖企业和养殖场（户）养殖保险模式，采用电子耳标、生物识别等技术加强养殖数量监控。动态调整能繁母猪、育肥猪、奶牛等养殖业保险保障水平，鼓励根据不同养殖方式提供差异化风险保障方案。做好牦牛、藏系羊等涉藏特定品种保险支持保障。\n\n**（四）推动林草保险提质增效。**坚持预防与保障并重，与现代林草业发展相适应，推动构建林草综合保险体系。完善公益林保险和商品林保险政策，探索适合相关林业保险险种特点的承保理赔模式。保险机构要合理确定森林保险防灾减损费用计提比例，据实列支。鼓励保险机构开展草原、湿地、国家公园、碳汇等保险试点，丰富林草保险产品。\n\n**（五）支持地方特色发展。**鼓励各地结合实际制定特色农业保险发展政策，加强相关资金统筹，加大支持力度，扩大保险范围，积极发展蔬菜、水果、肉牛、肉羊、禽类、渔业、林下经济等特色农业保险。\n\n**（六）完善产品供给体系。**健全“政策险+商业险”、“基本险+补充险”的农业保险产品供给体系。鼓励各地结合实际在物化成本保险等提供基本保险保障的险种基础上，设计开发相关补充保险，形成更高保障。鼓励各地因地制宜发展农业气象指数保险。支持保险机构发展商业性农业保险，开发便利农户投保的综合型保险保障方案。\n\n**（七）推广“农业保险+”模式。**深化农业保险与信贷、担保、期货等金融工具协同，探索保单质押、联合分担风险等模式。发挥农业保险增信功能，提高投保农户信用等级，促进涉农数据、科技、资产等要素资源融合，引导加大金融资源投入。\n\n**（八）逐步拓宽服务领域。**鼓励保险机构根据农业发展需要以市场化方式提供覆盖农林牧渔、涉农产业上下游、农民人身安全等方面风险的保险产品，探索开展农产品加工、仓储、运输、质量保险及高标准农田工程质量保险，为农业对外合作提供保险服务。鼓励地方和保险机构提供大棚、农房、农机、仓库等农业生产设施设备保险，以及农业绿色发展、智慧农业、民族村寨等领域保险。\n\n三、优化农业保险运行机制\n\n**（九）优化完善承保管理。**财政、发展改革、农业农村、保险监管、林业草原等部门及保险机构加强信息互通，共享农（林）地确权、流转、目标价格补贴面积以及农产品成本调查、畜禽存出栏量、病死猪无害化处理等农业生产经营数据。逐步推行“一户一单”承保模式。保险监管部门指导保险机构制定农业保险精准承保规范，提升可操作性。保险机构应加强农业保险承保精准性管理，开展验标和承保信息审核，集体投保类业务逐步提高抽查验标比例，规模经营主体投保类业务实现全部验标。\n\n**（十）加强财政补贴管理。**强化保费补贴资金管理，加强补贴资金所涉投保信息与农业生产经营数据、历史投保数据的比对核验，提高财政资金使用效益。及时向保险机构拨付保费补贴资金，优化资金拨付方式。鼓励各地实行省级财政部门与保险机构省级分公司“省级对省级”结算等模式。\n\n**（十一）完善查勘定损制度。**农业农村、保险监管、林业草原等部门结合当地种植、养殖、林业生产实际，指导保险机构制定分类或分险种查勘定损操作规范，明确查勘、抽样、定损等工作流程和标准。农业农村、林业草原等部门配合做好灾后查勘和灾损核定工作。\n\n**（十二）提升理赔服务质效。**保险监管部门指导保险机构制定完善农业保险理赔实施规范，提升理赔效率和精准度。保险机构应完善基层服务网络，简化手续、优化流程、赔款到户，建立农业大灾快速理赔响应机制，针对重大自然灾害和事故合理预付部分赔偿金，支持农户恢复生产。保险机构应通过银行转账等非现金方式将保险赔款直接支付给被保险人。\n\n**（十三）鼓励开展风险减量管理。**健全风险减量管理机制，增强事前防灾减灾、事中救灾减损、事后及时赔付相结合的一体化服务能力，推动农业保险从单一事后理赔向全流程风险管理转型。保险机构可与农业农村、林业草原、气象等领域社会化服务机构合作，开展防灾减损工作。\n\n**（十四）健全监督管理机制。**健全农业保险领域常态化监督机制，财政、农业农村、保险监管、林业草原等部门加强联动，加大监督力度。对虚构虚增保险标的、虚假理赔、骗取套取保费补贴等违法违规行为，加大打击和惩戒力度，依法依规予以处理。\n\n四、夯实农业保险发展基础\n\n**（十五）强化信息化支撑。**持续推进全国农业保险数据信息系统、全国农业保险信息管理平台建设。统一农业保险数据标准，加强部门间、中央与地方间农业保险数据信息共享。加快推动农业保险行业数字化转型，促进业务流程智能化、标准化。\n\n**（十六）推进风险区划工作。**推进三大粮食作物、主要畜产品等重要农产品保险风险区划工作，适时发布风险区划结果和保险费率参考。探索开展地方特色农业保险风险区划工作。\n\n**（十七）加强科技应用。**保险机构应加大科技投入强度，合理应用无人机、遥感、物联网、人工智能、区块链、云计算等技术工具，提高保险服务精准度。鼓励保险机构探索开发农业灾害风险模型。制定农业保险科技应用规范或标准。保险机构应开展承保理赔信息实名校验和身份认证。\n\n**（十八）加强遴选管理。**按照适度竞争原则，结合服务能力、风控能力合理确定农业保险承保机构。各省份根据保费规模等因素统筹确定承保机构数量，承保机构一经确定应保持相对稳定，服务有效期一般不少于 3 年。开展承保机构绩效评价，强化绩效评价结果应用，做好承保机构动态调整工作。审慎评估农业保险市场环境和保险机构服务能力，加强经营资格管理，优化准入退出机制。\n\n**（十九）健全政策法规。**适应农业保险发展态势和高质量发展需要，推动适时修订《农业保险条例》，进一步加强农业保险法治建设。\n\n五、健全农业保险大灾风险分散机制\n\n**（二十）完善大灾风险准备金管理。**科学设置农业保险各险种大灾风险准备金计提比例，完善大灾风险准备金触发使用标准。保险机构按照独立运作、因地制宜、分级管理、统筹使用的原则，加强大灾风险准备金使用规范性管理，进一步发挥大灾风险准备金作用。\n\n**（二十一）大力发展农业再保险。**按照约定分保和市场化分保相结合的发展模式，健全农业再保险制度。完善约定分保机制和流程，优化大灾超赔保障。鼓励发展商业性再保险，逐步提高市场化业务比重。推动完善农业再保险机构治理机制，提高运行规范性和发展可持续性。\n\n**（二十二）健全风险分散机制。**研究建立农业保险大灾风险基金，完善农业保险大灾风险分散链条。加强农业保险赔付资金与政府救灾资金协同运用。\n\n六、做好组织实施工作\n\n各地区、各有关部门要高度重视农业保险工作，夯实工作基础，形成工作合力，更好满足“三农”主体风险保障需求。各省级人民政府加强组织领导，统筹推进本行政区域内农业保险规划发展、资源统筹、政策宣传等各项工作，发挥农业保险工作小组作用，组织有关部门和保险机构加强协同配合，确保各项任务落实见效。各有关部门按职责分工做好政策设计、资金安排、监督管理等工作。保险机构切实提高服务质效，可根据业务需要设立协保员，为承保理赔等服务提供便利。","农业农村部计划财务司 2026年9月8日","2026-09-08T00:00:00Z",92,{"impact":84,"substance":85,"depth":111,"authority":86,"freshness":47,"relevant":20,"comment":112},19,"四部门联合印发的全国性农业保险高质量发展实施方案，明确2030年保险深度1.9%、密度1200元\u002F人等量化目标，条款与数据详实，权威性和政策影响力突出，值得进入每日精选。",[114],{"name":107,"url":104},[25,116,26,117,118,119],"农业遥感","农业保险","风险区划","财政补贴",[121,122],"农业保险 农业遥感 数字乡村 粮食安全","农业保险 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