[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3051":3,"related-3051":82},{"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,"primary_topic":30,"secondary_topics":36,"classification":43,"search_phrases":66,"slug":69,"view_count":61,"doi":70,"paper":71,"created_at":81},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",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[37],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",{"status":44,"locked":13,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,55,59],{"code":31,"score":50,"supported":51,"title":52,"abstract":53,"tags":54},12,true,[26],[26],[26],{"code":38,"score":50,"supported":51,"title":56,"abstract":57,"tags":58},[25],[25],[25],{"code":60,"score":61,"supported":51,"title":62,"abstract":63,"tags":65},"G1",3,[],[64],"技术培训",[],[67,68],"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","10.3389\u002Ffsufs.2026.1748737\u002Fpdf",{"doi":70,"openalex_id":8,"authors":72,"venue":8,"cited_by_count":73,"oa_url":8,"card":74,"direction":78,"ingested_from":80},[],0,{"tldr":75,"method":76,"finding":77,"direction":78,"opportunity":79},"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":83,"page":20,"page_size":83,"items":84},6,[85,151,187,264,316,378],{"id":86,"title":87,"url":88,"summary":89,"summary_zh":8,"content":8,"source_name":90,"source_url":8,"published_at":91,"category":11,"cover_url":8,"hotness":12,"is_selected":51,"score":92,"score_detail":93,"sources":98,"tags":100,"primary_topic":104,"secondary_topics":105,"classification":113,"search_phrases":138,"slug":141,"view_count":142,"doi":8,"paper":143,"created_at":150},4194,"数字乡村建设的代价？数字乡村战略对粮食安全和农业碳循环的非对称影响——来自中国的证据","https:\u002F\u002Fideas.repec.org\u002Fa\u002Fspr\u002Fagfoec\u002Fv14y2026i1d10.1186_s40100-026-00503-9.html","Weilong Gao、Qian Zhang、Ying Peng（吉林大学国家发展与安全研究院、大连理工大学商学院、长春工业大学经济管理学院）。发表于Agricultural and Food Economics 2026, 14(1): 1-22。基于989个县和1835户农户的多层次数据，将县级数字乡村发展指数与家庭层面数字采纳指数链接，运用固定效应和两阶段最小二乘法模型。结果显示：农村数字化与粮食产量显著正相关，与农业碳排放显著负相关，但对作物生物量碳吸收或净碳足迹无显著改善。机制分析表明生产收益与土地利用集约化（特别是双季种植扩张）相关，减排与减少劳动力和农药使用一致。异质性分析显示数字基础设施驱动生产率增长，数字治理和数字生活与减排关联更密切。","Agricultural and Food Economics 2026, 14(1)","2026-10-01T00:00:00Z",88,{"impact":16,"substance":94,"depth":95,"authority":19,"freshness":96,"relevant":20,"comment":97},24,19,9,"基于989县1835户的全国性实证研究，揭示数字乡村对粮食安全与农业碳循环的非对称影响，数据规模大、结论有政策参考价值，值得进入每日精选。",[99],{"name":90,"url":88},[25,101,26,102,103],"数字基础设施","农业碳排放","农户数字化",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[106,112],{"code":107,"slug":108,"name":109,"path":110,"parent_code":111},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":13,"version":45,"method":46,"evidence":114},{"candidates":115,"agricultural_evidence":51},[116,123,128,132],{"code":107,"score":18,"supported":51,"title":117,"abstract":119,"tags":122},[118],"农业碳",[120,118,121],"碳排放","碳足迹",[102],{"code":38,"score":124,"supported":51,"title":125,"abstract":126,"tags":127},13,[25],[25],[25,101],{"code":31,"score":96,"supported":51,"title":129,"abstract":130,"tags":131},[26],[],[26],{"code":133,"score":61,"supported":51,"title":134,"abstract":135,"tags":137},"F4",[],[136],"农药",[],[139,140],"数字乡村 粮食安全 农业碳循环","吉林大学 数字乡村 农户","数字乡村粮食安全农业碳循环-4194",4,{"doi":8,"openalex_id":8,"authors":144,"venue":8,"cited_by_count":73,"oa_url":8,"card":145,"direction":78,"ingested_from":80},[],{"tldr":146,"method":147,"finding":148,"direction":78,"opportunity":149},"基于中国县级与农户数据，评估数字乡村战略对粮食安全和农业碳循环的非对称影响。","989县与1835户多层次数据，固定效应和两阶段最小二乘法。","数字化提升粮食产量并减少碳排放，但未改善作物碳吸收或净碳足迹。","可探究数字乡村对碳吸收无显著影响的原因，并优化数字治理以协同减排与固碳。","2026-10-05T00:04:55.880239Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":8,"content":156,"source_name":157,"source_url":8,"published_at":158,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":159,"score_detail":160,"sources":163,"tags":165,"primary_topic":167,"secondary_topics":168,"classification":169,"search_phrases":181,"slug":184,"view_count":12,"doi":185,"paper":8,"created_at":186},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":161,"substance":16,"depth":18,"authority":19,"freshness":20,"relevant":20,"comment":162},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。",[164],{"name":157,"url":154},[25,26,166,27],"中国",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":44,"locked":13,"version":45,"method":46,"evidence":170},{"candidates":171,"agricultural_evidence":51},[172,177],{"code":31,"score":50,"supported":51,"title":173,"abstract":175,"tags":176},[174],"food security",[26],[26],{"code":38,"score":142,"supported":51,"title":178,"abstract":179,"tags":180},[],[25],[25],[182,183],"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","10.3389\u002Ffsufs.2026.1748737\u002Ffull","2026-07-31T00:00:56.112781Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":8,"source_name":193,"source_url":190,"published_at":194,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":195,"score_detail":196,"sources":201,"tags":203,"primary_topic":207,"secondary_topics":208,"classification":209,"search_phrases":240,"slug":243,"view_count":73,"doi":244,"paper":245,"created_at":263},5491,"Analysis of the Impact of Digital Technology on the Resilience of Anhui’s Agricultural Industry Chain: Threshold Effect of Relative Grain Yield","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010656","Improving the resilience of the agricultural industry chain to frequent external shocks and the intertwined agricultural production risks is critical for ensuring national food security and sustainable agricultural development. Taking the 16 prefecture-level cities in Anhui Province—a major grain-producing region in China—as the research sample, two composite proxy indices were constructed in this study; namely, an agricultural industry chain resilience index built upon aggregate official statistics (rather than direct observed shock-response outcomes) and an index capturing broad rural digital development instead of purely agriculture-specific digital adoption. Based on balanced panel data from 2015 to 2024, two-way fixed-effect, mediation-effect, and panel-threshold models were adopted in this study to examine the association, mediating mechanism, regional heterogeneity, and threshold characteristics conditioned on the relative grain yield. The empirical results show that the rural-oriented digital development index is significantly and robustly positively associated with our composite agricultural industry chain resilience proxy. Although agricultural TFP exhibits a statistically detectable partial mediating pattern in this correlational relationship, the economic magnitude of this indirect channel is extremely modest. As such, this finding indicates a potential theoretical transmission pathway rather than providing definitive empirical evidence for a causal mechanism. The promotion effect exhibits clear regional heterogeneity, with the strongest effect in Northern Anhui, followed by Central Anhui, while the coefficient is positive but insignificant in Southern Anhui. Furthermore, a single-threshold effect of the relative grain yield is identified; that is, the positive association between rural-oriented digital development and resilience proxy cannot be statistically detected when the grain yield remains below the threshold. Only after the agricultural grain yield increases can digital-related rural development release its resilience-related empowering dividends. We note that both composite indices are subject to measurement constraints linked to the local agricultural economic scale and broad rural digital conditions; these limitations are further discussed in the paper. This study provides empirical correlational evidence for the implementation of differentiated digital agriculture policies in major grain-producing provinces.","提升农业产业链对频繁外部冲击与交织叠加的农业生产风险的韧性，对保障国家粮食安全和农业可持续发展至关重要。本研究以中国粮食主产省安徽省的16个地级市为研究样本，构建了两个复合代理指标：一是基于官方统计数据总量（而非直接观测的冲击—响应结果）构建的农业产业链韧性指数，二是刻画农村数字化广泛发展（而非单纯农业领域数字化应用）的指数。基于2015—2024年平衡面板数据，本研究采用双向固定效应模型、中介效应模型和面板门槛模型，考察二者之间的关联、中介机制、区域异质性以及以相对粮食产量为条件变量的门槛特征。实证结果表明，农村导向的数字化发展指数与本研究的农业产业链韧性复合代理指标之间存在显著且稳健的正向关联。尽管农业全要素生产率在这一相关关系中呈现出统计上可检测的部分中介模式，但该间接渠道的经济量级极为有限。因此，这一发现指示的是一种潜在的理论传导路径，而非为因果机制提供确凿的实证证据。其促进效应表现出明显的区域异质性，皖北效应最强，皖中次之，皖南系数为正但不显著。此外，还识别出相对粮食产量的单一门槛效应，即当粮食产量低于门槛值时，农村导向的数字化发展与韧性代理指标之间的正向关联在统计上无法被检测到；只有在农业粮食产量提高之后，数字相关农村发展才能释放其韧性赋能红利。我们注意到，两个复合指数均受到与地方农业经济规模和农村数字化总体状况相关的测度约束；这些局限性在文中进一步讨论。本研究为粮食主产省实施差异化数字农业政策提供了实证相关性证据。","Sustainability","2026-10-20T00:00:00Z",74,{"impact":197,"substance":161,"depth":198,"authority":124,"freshness":199,"relevant":20,"comment":200},16,17,8,"基于安徽16市面板数据的实证研究，方法规范、结论有区域异质性价值，但属省级学术论文，公共影响有限，适合作为主题页聚合素材而非头条精选。",[202],{"name":193,"url":190},[204,25,26,205,206],"数字农业","农业产业链韧性","安徽农业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":44,"locked":13,"version":45,"method":46,"evidence":210},{"candidates":211,"agricultural_evidence":51},[212,217,223,230,236],{"code":31,"score":213,"supported":51,"title":214,"abstract":215,"tags":216},7,[],[26,174],[26],{"code":218,"score":213,"supported":51,"title":219,"abstract":220,"tags":222},"I1",[],[204,221],"digital agriculture",[204],{"code":224,"score":83,"supported":51,"title":225,"abstract":226,"tags":229},"G",[],[227,228],"农村发展","rural development",[],{"code":231,"score":61,"supported":51,"title":232,"abstract":233,"tags":235},"J1",[],[234],"农业政策",[],{"code":38,"score":20,"supported":13,"title":237,"abstract":238,"tags":239},[],[],[25],[241,242],"安徽 农业产业链韧性 数字技术","安徽 粮食产量 数字乡村","安徽农业产业链韧性数字技术-5491","10.3390\u002Fsu182010656",{"doi":244,"openalex_id":246,"authors":247,"venue":193,"cited_by_count":73,"oa_url":190,"card":257,"direction":78,"ingested_from":262},"W7223005655",[248,251,253,255],{"name":249,"orcid":250},"Lan Ma","https:\u002F\u002Forcid.org\u002F0000-0003-2468-9917",{"name":252,"orcid":8},"Yuyan Li",{"name":254,"orcid":8},"张国亮",{"name":256,"orcid":8},"Yaoyao Li",{"tldr":258,"method":259,"finding":260,"direction":78,"opportunity":261},"以安徽16市面板数据检验农村数字发展对农业产业链韧性的影响及粮食产量门槛效应。","2015—2024年安徽16市平衡面板，双向固定效应、中介效应与面板门槛模型。","数字发展显著提升产业链韧性，存在粮食相对产量单门槛，且皖北效应最强、皖南不显著。","可细化数字技术指标、引入真实冲击响应韧性测度，并探究门槛形成机制与皖南失效原因。","openalex","2026-10-21T23:30:29.219265Z",{"id":265,"title":266,"url":267,"summary":268,"summary_zh":8,"content":269,"source_name":270,"source_url":8,"published_at":271,"category":272,"cover_url":8,"hotness":12,"is_selected":51,"score":273,"score_detail":274,"sources":277,"tags":279,"primary_topic":283,"secondary_topics":284,"classification":285,"search_phrases":311,"slug":314,"view_count":73,"doi":8,"paper":8,"created_at":315},4928,"专家解读:乘'数'而上 向'智'而行——《中国数字乡村发展报告(2019—2025年)》发布","https:\u002F\u002Fwww.ztnews.net\u002Farticle\u002Fshow-496095.html","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)》。报告指出,七年来,智慧农业实现了'四个跨越',从'基础覆盖'到'深化赋能',从'试点探索'到'快速起步',从'点状突破'到'系统推进',从'盆景'走向'风景'。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。报告全面展示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。","![Image 1](https:\u002F\u002Fwww.ztnews.net\u002FnewsImage\u002F202510\u002F507474e9510ac7c2.jpg)\n\n![Image 2](https:\u002F\u002Fwww.ztnews.net\u002FnewsImage\u002F202609\u002F28c83f013022f584.jpg)\n\n![Image 3](https:\u002F\u002Fwww.ztnews.net\u002FnewsImage\u002F202610\u002Ff0a75ae49ce17c47.gif)\n\n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）\n\n* * *\n\n来源：中国网信网\n\n![Image 4](https:\u002F\u002Fwww.ztnews.net\u002FnewsImage\u002F202609\u002F0946bb54de14ff91.jpg)\n\n![Image 5](https:\u002F\u002Fwww.ztnews.net\u002FnewsImage\u002F202610\u002Fe74b87da1aae8515.gif)\n\n![Image 6](https:\u002F\u002Fwww.ztnews.net\u002FnewsImage\u002F202411\u002F5630c109c52e879c.jpg)","昭通新闻网 2026 年 10 月 14 日","2026-10-14T00:00:00Z","报道",91,{"impact":275,"substance":17,"depth":18,"authority":124,"freshness":96,"relevant":20,"comment":276},28,"国家网信办与农业农村部联合发布的七年期权威报告解读，含农村互联网普及率69.5%、农机北斗终端350万台套等系统数据，政策与产业价值突出，值得进入每日精选。",[278],{"name":270,"url":267},[25,280,281,26,282],"智慧农业","农业人工智能","农业农村数据",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[],{"status":44,"locked":13,"version":45,"method":46,"evidence":286},{"candidates":287,"agricultural_evidence":51},[288,292,296,302,306],{"code":38,"score":50,"supported":51,"title":289,"abstract":290,"tags":291},[25],[25],[25],{"code":218,"score":142,"supported":51,"title":293,"abstract":294,"tags":295},[],[280],[280],{"code":297,"score":61,"supported":51,"title":298,"abstract":299,"tags":301},"G5",[],[300],"乡村治理",[],{"code":31,"score":20,"supported":13,"title":303,"abstract":304,"tags":305},[],[],[26],{"code":307,"score":20,"supported":13,"title":308,"abstract":309,"tags":310},"I2",[],[],[281],[312,313],"中国数字乡村发展报告 2019—2025","国家互联网信息办公室 农业农村部 数字乡村","中国数字乡村发展报告2019—2025-4928","2026-10-15T00:05:53.301569Z",{"id":317,"title":318,"url":319,"summary":320,"summary_zh":8,"content":8,"source_name":321,"source_url":8,"published_at":322,"category":11,"cover_url":8,"hotness":323,"is_selected":13,"score":324,"score_detail":325,"sources":327,"tags":332,"primary_topic":336,"secondary_topics":337,"classification":338,"search_phrases":353,"slug":356,"view_count":357,"doi":358,"paper":359,"created_at":377},4406,"Extreme Temperature, Digital Rural Development, and Grain Production Resilience in the Yellow River Basin","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1912640\u002Ffull","王、于、杨、张基于 2013-2022 年黄河流域 9 省面板数据,构建包含抵抗力、恢复力、转型力的粮食生产韧性指数,实施双向固定效应模型、稳健性检验、内生性检验、异质性分析与调节效应模型;研究发现极端温度事件对粮食生产韧性具有显著负向影响,在非粮食主产区和农业低消费地区负相关性更明显;数字乡村发展在极端温度事件与粮食生产韧性之间发挥正向调节作用。","Frontiers in Sustainable Food Systems 2026-10-05 接受","2026-10-05T00:00:00Z",25,80,{"impact":18,"substance":16,"depth":18,"authority":124,"freshness":96,"relevant":20,"comment":326},"基于黄河流域9省十年面板数据的实证研究，方法规范、结论明确，对数字乡村应对气候风险的决策有参考价值。",[328,329],{"name":321,"url":319},{"name":330,"url":331},"Frontiers in Sustainable Food Systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1912640",[25,26,333,334,335],"农业韧性","黄河流域","极端气候",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[],{"status":44,"locked":13,"version":45,"method":46,"evidence":339},{"candidates":340,"agricultural_evidence":51},[341,345,349],{"code":224,"score":199,"supported":51,"title":342,"abstract":343,"tags":344},[228],[],[],{"code":38,"score":142,"supported":51,"title":346,"abstract":347,"tags":348},[],[25],[25],{"code":31,"score":20,"supported":13,"title":350,"abstract":351,"tags":352},[],[],[26],[354,355],"黄河流域 粮食生产韧性 数字乡村","极端温度 粮食韧性 调节效应","黄河流域粮食生产韧性数字乡村-4406",2,"10.3389\u002Ffsufs.2026.1912640\u002Ffull",{"doi":358,"openalex_id":360,"authors":361,"venue":330,"cited_by_count":73,"oa_url":331,"card":371,"direction":375,"ingested_from":262},"W7222684710",[362,365,367,369],{"name":363,"orcid":364},"Wenwen Wang","https:\u002F\u002Forcid.org\u002F0000-0003-1547-5714",{"name":366,"orcid":8},"Yanru Yu",{"name":368,"orcid":8},"Yuanyi Yang",{"name":370,"orcid":8},"Yanwei Zhang",{"tldr":372,"method":373,"finding":374,"direction":375,"opportunity":376},"基于黄河流域9省面板数据，研究极端温度对粮食生产韧性的影响及数字乡村的调节作用。","2013-2022年黄河流域9省面板数据，双向固定效应模型与调节效应模型。","极端温度显著降低粮食生产韧性，非主产区更明显；数字乡村发展起正向调节作用。","农业绿色发展与碳","可探究数字乡村通过哪些具体路径（如信息获取、保险、技术采纳）缓解极端温度对粮食韧性的冲击。","2026-10-08T00:07:47.198797Z",{"id":379,"title":380,"url":381,"summary":382,"summary_zh":383,"content":8,"source_name":330,"source_url":381,"published_at":91,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":384,"score_detail":385,"sources":387,"tags":389,"primary_topic":393,"secondary_topics":394,"classification":395,"search_phrases":419,"slug":422,"view_count":357,"doi":423,"paper":424,"created_at":442},4003,"Does rural digitalization improve food production security? Evidence from China’s digital village construction policy","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1914989","Introduction Food production security (FPS) remains a core challenge for developing economies. Although rural digitalization serves as a fundamental strategy to reshape agricultural systems in developing countries, its impact on food supply resilience remains underexplored. Methods This study investigates the impact of rural digitalization on regional FPS by exploiting China’s Digital Village Construction policy as a quasi-natural experiment. We employ the DID model on a panel dataset comprising 334 prefecture-level cities from 2015 to 2024. Results The empirical results demonstrate that rural digitalization significantly improves regional FPS. Specifically, the digital intervention increases the FPS of treated cities by an average of 8.2 percent. Regarding the mechanisms, we find that rural digitalization enhances FPS by facilitating smallholders’ food production viability and promoting food-oriented land reallocation. Furthermore, heterogeneity analyses reveal that the impact is more pronounced in regions equipped with superior agricultural socialized services, advanced agricultural industry structures, and major food producing area designations. Discussion These findings demonstrate that comprehensive rural digitalization strengthens the resilience of food production systems in developing economies and offer guidance for other Global South countries pursuing similar rural development paradigms.","引言 粮食生产安全（Food Production Security, FPS）仍是发展中经济体面临的核心挑战。尽管乡村数字化是发展中国家重塑农业体系的基础性战略，但其对粮食供给韧性的影响仍缺乏深入探讨。方法 本研究以中国数字乡村建设政策作为准自然实验，考察乡村数字化对区域粮食生产安全的影响。我们基于2015年至2024年334个地级市的面板数据集，采用双重差分（DID）模型进行实证分析。结果 实证结果表明，乡村数字化显著提升了区域粮食生产安全。具体而言，数字干预使处理组城市的粮食生产安全平均提高8.2%。在机制方面，我们发现乡村数字化通过促进小农户的粮食生产可行性和推动粮食导向的土地再配置来增强粮食生产安全。此外，异质性分析显示，在农业社会化服务较优、农业产业结构较先进以及被划定为粮食主产区的地区，该影响更为显著。讨论 这些发现表明，全面的乡村数字化增强了发展中经济体粮食生产体系的韧性，并为其他全球南方国家追求类似乡村发展范式提供了借鉴。",86,{"impact":94,"substance":16,"depth":18,"authority":124,"freshness":96,"relevant":20,"comment":386},"基于中国数字乡村政策准自然实验，用DID方法量化农村数字化对粮食生产安全的提升效应，数据规模大、结论可靠，对数字乡村与粮食安全主题有较高参考价值。",[388],{"name":330,"url":381},[25,26,390,391,392],"发展中国家","农业社会化服务","准自然实验",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[],{"status":44,"locked":13,"version":45,"method":46,"evidence":396},{"candidates":397,"agricultural_evidence":51},[398,404,411,415],{"code":38,"score":399,"supported":51,"title":400,"abstract":402,"tags":403},15,[401],"digital village",[25,401],[25],{"code":405,"score":213,"supported":51,"title":406,"abstract":407,"tags":410},"G2",[],[408,409],"社会化服务","小农户",[391],{"code":224,"score":61,"supported":51,"title":412,"abstract":413,"tags":414},[],[228],[],{"code":31,"score":20,"supported":13,"title":416,"abstract":417,"tags":418},[],[],[26],[420,421],"数字乡村建设 粮食生产安全","农村数字化 粮食安全","数字乡村建设粮食生产安全-4003","10.3389\u002Ffsufs.2026.1914989",{"doi":423,"openalex_id":425,"authors":426,"venue":330,"cited_by_count":73,"oa_url":381,"card":437,"direction":78,"ingested_from":262},"W7215123040",[427,430,432,435],{"name":428,"orcid":429},"Meng Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-2049-9147",{"name":431,"orcid":8},"Jianhua Wang",{"name":433,"orcid":434},"Jiaqing Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-4702-9495",{"name":436,"orcid":8},"Zixuan Ai",{"tldr":438,"method":439,"finding":440,"direction":78,"opportunity":441},"以中国数字乡村建设政策为准自然实验，评估农村数字化对区域粮食生产安全的影响。","基于334个地级市2015—2024年面板数据，采用双重差分（DID）模型。","农村数字化使处理组城市粮食生产安全平均提升8.2%，通过小农户生产可行性与耕地再配置实现。","可探究数字乡村政策对粮食安全的长期动态效应及社会化服务、产业结构的门槛机制。","2026-10-02T23:30:10.524948Z"]