[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-115":3,"related-115":62},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":56,"slug":59,"view_count":13,"doi":60,"paper":8,"created_at":61},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。",null,"## 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","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":21,"comment":22},20,22,18,14,1,"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。",[24],{"name":10,"url":6},[26,27,28,29],"数字乡村","粮食安全","中国","区域异质性",{"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",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50],{"code":31,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},12,true,[47],"food security",[27],[27],{"code":51,"score":52,"supported":45,"title":53,"abstract":54,"tags":55},"I6",4,[],[26],[26],[57,58],"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",{"total":63,"page":21,"page_size":63,"items":64},6,[65,122,200,264,333,425],{"id":66,"title":67,"url":68,"summary":69,"summary_zh":8,"content":8,"source_name":70,"source_url":8,"published_at":71,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":72,"score_detail":73,"sources":76,"tags":78,"primary_topic":81,"secondary_topics":82,"classification":88,"search_phrases":106,"slug":109,"view_count":101,"doi":110,"paper":111,"created_at":121},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。","Frontiers Sustain Food Syst 10:1748737","2026-09-20T00:00:00Z",87,{"impact":18,"substance":74,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":75},23,"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。",[77],{"name":70,"url":68},[26,27,29,79,80],"农业技术培训","土地生产率",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[83],{"code":51,"slug":84,"name":85,"path":86,"parent_code":87},"digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",{"status":38,"locked":14,"version":39,"method":40,"evidence":89},{"candidates":90,"agricultural_evidence":45},[91,95,99],{"code":31,"score":44,"supported":45,"title":92,"abstract":93,"tags":94},[27],[27],[27],{"code":51,"score":44,"supported":45,"title":96,"abstract":97,"tags":98},[26],[26],[26],{"code":100,"score":101,"supported":45,"title":102,"abstract":103,"tags":105},"G1",3,[],[104],"技术培训",[],[107,108],"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":110,"openalex_id":8,"authors":112,"venue":8,"cited_by_count":113,"oa_url":8,"card":114,"direction":118,"ingested_from":120},[],0,{"tldr":115,"method":116,"finding":117,"direction":118,"opportunity":119},"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",{"id":123,"title":124,"url":125,"summary":126,"summary_zh":127,"content":8,"source_name":128,"source_url":125,"published_at":129,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":130,"score_detail":131,"sources":137,"tags":139,"primary_topic":143,"secondary_topics":144,"classification":145,"search_phrases":176,"slug":179,"view_count":113,"doi":180,"paper":181,"created_at":199},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":132,"substance":17,"depth":133,"authority":134,"freshness":135,"relevant":21,"comment":136},16,17,13,8,"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。",[138],{"name":128,"url":125},[140,26,27,141,142],"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":38,"locked":14,"version":39,"method":40,"evidence":146},{"candidates":147,"agricultural_evidence":45},[148,153,159,166,172],{"code":31,"score":149,"supported":45,"title":150,"abstract":151,"tags":152},7,[],[27,47],[27],{"code":154,"score":149,"supported":45,"title":155,"abstract":156,"tags":158},"I1",[],[140,157],"digital agriculture",[140],{"code":160,"score":63,"supported":45,"title":161,"abstract":162,"tags":165},"G",[],[163,164],"农村发展","rural development",[],{"code":167,"score":101,"supported":45,"title":168,"abstract":169,"tags":171},"J1",[],[170],"农业政策",[],{"code":51,"score":21,"supported":14,"title":173,"abstract":174,"tags":175},[],[],[26],[177,178],"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":180,"openalex_id":182,"authors":183,"venue":128,"cited_by_count":113,"oa_url":125,"card":193,"direction":118,"ingested_from":198},"W7223005655",[184,187,189,191],{"name":185,"orcid":186},"Lan Ma","https:\u002F\u002Forcid.org\u002F0000-0003-2468-9917",{"name":188,"orcid":8},"Yuyan Li",{"name":190,"orcid":8},"张国亮",{"name":192,"orcid":8},"Yaoyao Li",{"tldr":194,"method":195,"finding":196,"direction":118,"opportunity":197},"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":201,"title":202,"url":203,"summary":204,"summary_zh":8,"content":8,"source_name":205,"source_url":8,"published_at":206,"category":12,"cover_url":8,"hotness":13,"is_selected":45,"score":207,"score_detail":208,"sources":213,"tags":215,"primary_topic":219,"secondary_topics":220,"classification":228,"search_phrases":252,"slug":255,"view_count":52,"doi":8,"paper":256,"created_at":263},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":18,"substance":209,"depth":210,"authority":20,"freshness":211,"relevant":21,"comment":212},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。",[214],{"name":205,"url":203},[26,216,27,217,218],"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},[221,227],{"code":222,"slug":223,"name":224,"path":225,"parent_code":226},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",{"code":51,"slug":84,"name":85,"path":86,"parent_code":87},{"status":38,"locked":14,"version":39,"method":40,"evidence":229},{"candidates":230,"agricultural_evidence":45},[231,238,242,246],{"code":222,"score":19,"supported":45,"title":232,"abstract":234,"tags":237},[233],"农业碳",[235,233,236],"碳排放","碳足迹",[217],{"code":51,"score":134,"supported":45,"title":239,"abstract":240,"tags":241},[26],[26],[26,216],{"code":31,"score":211,"supported":45,"title":243,"abstract":244,"tags":245},[27],[],[27],{"code":247,"score":101,"supported":45,"title":248,"abstract":249,"tags":251},"F4",[],[250],"农药",[],[253,254],"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",{"doi":8,"openalex_id":8,"authors":257,"venue":8,"cited_by_count":113,"oa_url":8,"card":258,"direction":118,"ingested_from":120},[],{"tldr":259,"method":260,"finding":261,"direction":118,"opportunity":262},"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":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":8,"source_name":270,"source_url":267,"published_at":271,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":272,"score_detail":273,"sources":276,"tags":278,"primary_topic":282,"secondary_topics":283,"classification":284,"search_phrases":312,"slug":315,"view_count":101,"doi":316,"paper":317,"created_at":332},3766,"The food security dividend: how green finance-induced rural revitalisation strengthens agricultural resilience","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1708484","Introduction Green finance (GF) has become crucial for improving agricultural infrastructure in rural areas. This study aims to identify the role of GF-induced rural revitalisation in enhancing agricultural resilience (AR). At the same time, the association between food security and AR is determined, followed by the impact of education and environmental regulation on AR. Methods The quantitative data were collected from the “Food and Agriculture Organisation of the United Nations” for different Asian countries (Cambodia, China, Pakistan, Myanmar, India, Indonesia, Laos, Mongolia, Nepal, Sri Lanka, the Philippines, Thailand, Uzbekistan and Vietnam). The selected timeframe ranged from 2012 to 2022. The descriptive statistics, correlation analysis and multiple old regression models were applied to the data. Results The results obtained show that GF, environmental regulation, and education boost agricultural productivity ( p \u003C 0.05), whereas higher climate risk reduces it. GF and rural income significantly improve productivity ( p \u003C 0.05), leading to an enhancement of AR. This helps in decreasing food insecurity. Environmental regulation and education are both key drivers of better productivity. AR has generally increased over the years with some ups and downs. Moreover, GF levels differ widely between countries. However, agricultural leaders such as China, Thailand, Indonesia and India tend to have stronger AR and GF. Higher education scores are associated with better agricultural productivity and stronger environmental regulations to attain AR. Finally, productivity levels vary greatly between the selected countries. Discussion This study emphasises the integration of GF in agricultural infrastructure to enhance AR and food security.","引言 绿色金融（GF）已成为改善农村地区农业基础设施的关键。本研究旨在明确绿色金融驱动的乡村振兴在增强农业韧性（AR）中的作用。同时，确定粮食安全与农业韧性之间的关联，并进一步探讨教育和环境规制对农业韧性的影响。方法 定量数据收集自联合国粮食及农业组织，涵盖多个亚洲国家（柬埔寨、中国、巴基斯坦、缅甸、印度、印度尼西亚、老挝、蒙古、尼泊尔、斯里兰卡、菲律宾、泰国、乌兹别克斯坦和越南）。所选时间范围为2012年至2022年。对数据应用了描述性统计、相关性分析和多元旧回归模型。结果 所得结果表明，绿色金融、环境规制和教育提升了农业生产力（p \u003C 0.05），而较高的气候风险则降低了农业生产力。绿色金融和农村收入显著提高了生产力（p \u003C 0.05），从而增强了农业韧性。这有助于减少粮食不安全。环境规制和教育都是提高生产力的关键驱动因素。农业韧性总体上多年来有所提升，但伴有起伏。此外，各国绿色金融水平差异较大。然而，中国、泰国、印度尼西亚和印度等农业领先国家往往具有更强的农业韧性和绿色金融。较高的教育得分与更好的农业生产力和更强的环境规制相关联，从而实现农业韧性。最后，所选国家之间的生产力水平差异很大。讨论 本研究强调将绿色金融纳入农业基础设施，以增强农业韧性和粮食安全。","Frontiers in Sustainable Food Systems","2026-09-28T00:00:00Z",77,{"impact":19,"substance":274,"depth":133,"authority":134,"freshness":135,"relevant":21,"comment":275},21,"基于FAO多国面板数据的实证研究，揭示绿色金融通过乡村振兴提升农业韧性与粮食安全的机制，对农业信息化与农村金融政策有参考价值。",[277],{"name":270,"url":267},[26,27,279,280,281],"农业韧性","绿色金融","农村振兴",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":285},{"candidates":286,"agricultural_evidence":45},[287,292,297,303,308],{"code":31,"score":288,"supported":45,"title":289,"abstract":290,"tags":291},15,[47],[27,47],[27],{"code":160,"score":101,"supported":45,"title":293,"abstract":294,"tags":296},[],[295],"乡村振兴",[],{"code":298,"score":101,"supported":45,"title":299,"abstract":300,"tags":302},"G6",[],[301],"rural income",[],{"code":304,"score":21,"supported":14,"title":305,"abstract":306,"tags":307},"G3",[],[],[280],{"code":51,"score":21,"supported":14,"title":309,"abstract":310,"tags":311},[],[],[26],[313,314],"绿色金融 农业韧性 粮食安全","亚洲 农业韧性 绿色金融","绿色金融农业韧性粮食安全-3766","10.3389\u002Ffsufs.2026.1708484",{"doi":316,"openalex_id":318,"authors":319,"venue":270,"cited_by_count":113,"oa_url":267,"card":326,"direction":330,"ingested_from":198},"W7214667116",[320,322,324],{"name":321,"orcid":8},"Zixiang Zhao",{"name":323,"orcid":8},"Chengcheng Lin",{"name":325,"orcid":8},"Po-Wen Tsai",{"tldr":327,"method":328,"finding":329,"direction":330,"opportunity":331},"研究绿色金融驱动的乡村振兴如何通过提升农业生产率增强农业韧性并保障粮食安全。","基于FAO 2012-2022年14个亚洲国家面板数据，用描述统计、相关分析和多","绿色金融、环境规制与教育显著提升农业生产率和农业韧性，气候风险则降低生产率。","农业绿色发展与碳","可深入探究绿色金融影响农业韧性的微观传导机制，并比较不同国家制度环境的调节效应。","2026-09-29T23:30:07.494934Z",{"id":334,"title":335,"url":336,"summary":337,"summary_zh":8,"content":338,"source_name":339,"source_url":8,"published_at":340,"category":341,"cover_url":8,"hotness":13,"is_selected":14,"score":342,"score_detail":343,"sources":345,"tags":347,"primary_topic":352,"secondary_topics":353,"classification":359,"search_phrases":420,"slug":423,"view_count":113,"doi":8,"paper":8,"created_at":424},5499,"辽宁省印发「十五五」县域经济高质量发展规划 建设22个单产提升整建制县","https:\u002F\u002Fwww.ln.gov.cn\u002Fweb\u002Fzwgkx\u002Fzfwj\u002Fszfbgtwj\u002F2026n\u002F2026101915441826587\u002Findex.shtml","规划要求农产品主产区县域深入实施新一轮千亿斤粮食产能提升行动，建设18个玉米单产提升整建制推进县、2个大豆与2个水稻整建制推进县。同时提出大力发展智慧农业，推广先进适用农机装备，稳步推进沿海盐碱地与辽西风沙地综合利用，构建「省级品牌+区域公用品牌+企业品牌」体系并提升特色农产品电商化率。","（十三）推进基础设施补短板。积极融入全省“六张网”建设，找准节点位置，打通基础设施“最后一公里”，提升县域安全韧性、产业要素、市场流通保障能力。融入全省水网建设格局，重点推动盘山、开原等灌区现代化改造，全面提升县域供水安全、粮食安全等保障能力，到2030年，农村自来水普及率达到98%。融入全省新型电网建设格局，重点推进康平、瓦房店等新型储能项目建设实施，提高城乡供电可靠性和防灾抗灾水平。实施县城老旧管网更新改造，统筹城乡防洪排涝，高质量推进城市更新，推进宜居县城建设，开展县城体检。健全县乡村三级物流配送体系，加强地邮协作，打造集中共配型县级客货邮综合服务中心、兼具中转和末端服务的乡镇客货邮综合服务站、“一站式”村级客货邮综合服务点，建有村级寄递物流综合服务站的建制村比例达到90%。实施新一轮农村公路提升行动，实现乡镇100%通双车道，建制村100%通等级路，农村公路铺装率达到98%。加快“双千兆”网络向农村延伸，缩小城乡数字基础设施差距，加快县城、乡镇和有条件的行政村接入网络10G-PON提质升级改造。加快建设提升一批农产品产地冷藏保鲜设施、农产品冷链仓储、农副产品批发市场，持续补齐农产品仓储物流产销等县域消费基础设施短板。\n\n（十四）加快推进基本公共服务均等化。强化中心镇和重点镇公共服务节点功能，引导公共服务直达基层末梢。落实县域普通高中振兴行动计划，到2030年，支持50所以上县域普通高中改善办学条件、提升办学质量，新建续建12所县域普通高中。根据人口变化统筹教育资源，优化农村学校布局，提高农村教育质量，加快城乡义务教育学校标准化建设。持续提升县级医院综合能力，深化紧密型县域医共体建设，提高乡镇卫生院基本医疗卫生服务水平。推进生育支持政策和激励措施落实，有效降低家庭生育养育教育成本，加强妇女和儿童健康服务。加强乡镇区域养老服务中心能力建设，探索村级邻里互助点、农村幸福院等农村互助性养老模式。深化农村移风易俗，提高社会文明程度，鼓励争创县级全国文明城市。","辽宁省人民政府 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