[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3343":3,"related-3343":94},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":65,"slug":68,"view_count":69,"doi":70,"paper":71,"created_at":93},3343,"Supply chain digitalization and proactive green innovation in agri-food enterprises: evidence from source-reduction patent bias","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1957657","Supply chain digitalization has emerged as a critical mechanism for advancing sustainable agriculture amidst intensifying climate and resource constraints. Utilizing panel data from A-share listed agri-food enterprises in China spanning 2013 to 2024, this study examines the impact of supply chain digitalization on corporate green innovation and its underlying transmission pathways. Our findings indicate that digital empowerment significantly enhances overall green innovation, with a pronounced structural preference for proactive source-reduction patents over reactive, end-of-pipe compliance technologies. This positive effect remains robust after addressing endogeneity concerns through instrumental variable (IV) analysis, Difference-in-Differences (DID) models, and double machine learning (DML) approaches. Mechanism tests reveal that digitalization fosters green innovation through three primary channels: enhancing supply chain synergies, strengthening R&D absorptive capacity, and elevating corporate risk-taking thresholds. Notably, this effect is amplified in environments characterized by high customer concentration and concentrated market structures. Furthermore, causal forest analysis underscores heterogeneous treatment effects, demonstrating that digital empowerment yields greater benefits for non-state-owned enterprises, firms with dominant market positions, and those deeply embedded in supply networks. These insights clarify the distributional implications of digital transformation and provide targeted policy guidance for accelerating the adoption of source-reduction technologies across the agri-food supply chain.","供应链数字化已成为在气候与资源约束日益加剧背景下推进可持续农业的关键机制。本研究利用2013年至2024年中国A股上市农业食品企业的面板数据，考察供应链数字化对企业绿色创新的影响及其内在传导路径。研究发现，数字赋能显著提升了整体绿色创新水平，且在结构上明显偏好主动型源头减量专利，而非末端治理型被动合规技术。在通过工具变量（IV）分析、双重差分（DID）模型和双重机器学习（DML）方法处理内生性问题后，这一正向效应依然稳健。机制检验表明，数字化通过三条主要渠道促进绿色创新：增强供应链协同效应、强化研发吸收能力以及提升企业风险承担阈值。值得注意的是，该效应在客户集中度高和市场结构集中的环境中更为显著。此外，因果森林分析揭示了异质性处理效应，表明数字赋能对非国有企业、具有市场主导地位的企业以及深度嵌入供应网络的企业带来更大收益。这些发现阐明了数字化转型的分配效应，并为加快农业食品供应链中源头减量技术的采用提供了有针对性的政策指引。",null,"Frontiers in Sustainable Food Systems","2026-09-23T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"基于A股农食企业面板数据的实证研究，方法扎实、结论新颖，对农业供应链数字化转型与源头减量技术政策有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字农业","农业供应链","农业企业","绿色创新","专利结构",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":41,"reason":64},[38,47,54,59],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":46},"H8",6,true,[],[44,45],"可持续农业","sustainable agriculture",[],{"code":48,"score":40,"supported":41,"title":49,"abstract":50,"tags":53},"I2",[],[51,52],"机器学习","machine learning",[],{"code":55,"score":21,"supported":14,"title":56,"abstract":57,"tags":58},"G2",[],[],[28],{"code":60,"score":21,"supported":14,"title":61,"abstract":62,"tags":63},"I1",[],[],[26],"多个产业证据相当，需要确认主归属",[66,67],"农业供应链 数字化 绿色创新","A股 农食企业 专利","农业供应链数字化绿色创新-3343",3,"10.3389\u002Ffsufs.2026.1957657",{"doi":70,"openalex_id":72,"authors":73,"venue":10,"cited_by_count":85,"oa_url":6,"card":86,"direction":90,"ingested_from":92},"W7214118553",[74,77,79,82],{"name":75,"orcid":76},"Siliang Guo","https:\u002F\u002Forcid.org\u002F0000-0001-9093-9588",{"name":78,"orcid":9},"Yan Qu",{"name":80,"orcid":81},"Yang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-0789-4938",{"name":83,"orcid":84},"Bingqian Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8212-452X",0,{"tldr":87,"method":88,"finding":89,"direction":90,"opportunity":91},"基于2013-2024年中国农食上市企业面板数据，研究供应链数字化如何促进企业主动型绿色创新。","A股农食企业面板数据，IV、DID、双重机器学习及因果森林分析。","数字化显著提升绿色创新，且更偏向源头减量专利而非末端治理技术。","农业绿色发展与碳","可探究数字化驱动源头减量专利在中小农食企业及不同供应链结构中的扩散机制与政策激励。","openalex","2026-09-24T23:30:07.915956Z",{"total":40,"page":21,"page_size":40,"items":95},[96,153,211,266,326,384],{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":9,"source_name":10,"source_url":99,"published_at":102,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":103,"sources":105,"tags":107,"primary_topic":9,"secondary_topics":111,"classification":112,"search_phrases":132,"slug":135,"view_count":136,"doi":137,"paper":138,"created_at":152},2921,"Can digital technology adoption enhance supply chain resilience? Evidence from listed food firms in China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1930717","As a sector vital to national welfare and people’s livelihoods, the food industry has supply chains characterized by perishable raw materials, pronounced seasonality, stringent safety standards, extended chain structures, and highly fragmented participants. These features make food supply chains particularly vulnerable to external shocks, making the enhancement of supply chain resilience essential for safeguarding food safety and industrial and supply chain security. Using a sample of food firms listed on the Shanghai and Shenzhen A-share markets from 2009 to 2024, we systematically examine the effect of digital technology adoption on supply chain resilience, its potential channels, and heterogeneous effects. First, digital technology adoption significantly enhances the supply chain resilience of food firms. Second, digital technology adoption is significantly associated with greater information transparency, weaker financing constraints, and lower supply chain concentration. Third, this resilience-enhancing effect is more pronounced among firms facing lower environmental uncertainty, firms with stronger corporate innovation capability, and firms located in regions with a higher level of regional marketization. Focusing on the unique context of the food industry, we examine the potential channels and heterogeneous effects of digital technology adoption and provide micro-level empirical evidence for advancing the digital transformation of food firms, safeguarding food safety, and maintaining stable supply.","作为关乎国计民生的重要产业，食品产业的供应链具有原料易腐、季节性显著、安全标准严格、链条长、参与主体高度分散等特征。这些特征使食品供应链更易受到外部冲击，因此提升供应链韧性对于保障食品安全和产业链供应链安全至关重要。本文以2009—2024年沪深A股上市食品企业为样本，系统考察数字技术应用对供应链韧性的影响、潜在渠道及异质性效应。第一，数字技术应用显著提升了食品企业的供应链韧性。第二，数字技术应用与更高的信息透明度、更弱的融资约束和更低的供应链集中度显著相关。第三，这种韧性提升效应在环境不确定性较低的企业、创新能力较强的企业以及所在地区市场化水平较高的企业中更为明显。本文聚焦食品产业的独特情境，考察数字技术应用的潜在渠道和异质性效应，为推进食品企业数字化转型、保障食品安全和维持供应稳定提供微观层面的经验证据。","2026-09-18T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":104},"基于2009-2024年A股食品企业样本的实证研究，为农业食品行业数字化转型与供应链安全提供微观证据，方法规范、结论有政策参考价值。",[106],{"name":10,"url":99},[26,108,109,28,110],"供应链韧性","数字化转型","食品供应链",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":113},{"candidates":114,"agricultural_evidence":14,"reason":131},[115,122,127],{"code":116,"score":40,"supported":41,"title":117,"abstract":118,"tags":121},"E4",[],[119,120],"食品安全","food safety",[],{"code":60,"score":123,"supported":14,"title":124,"abstract":125,"tags":126},2,[],[],[26,109],{"code":55,"score":21,"supported":14,"title":128,"abstract":129,"tags":130},[],[],[28],"农业相关性需要复核",[133,134],"食品企业 数字化转型 供应链韧性","A股食品企业 数字技术 供应链","食品企业数字化转型供应链韧性-2921",7,"10.3389\u002Ffsufs.2026.1930717",{"doi":137,"openalex_id":139,"authors":140,"venue":10,"cited_by_count":85,"oa_url":99,"card":146,"direction":150,"ingested_from":92},"W7213555796",[141,144],{"name":142,"orcid":143},"Meihui Li","https:\u002F\u002Forcid.org\u002F0009-0004-2665-2629",{"name":145,"orcid":9},"Yuechao Zhu",{"tldr":147,"method":148,"finding":149,"direction":150,"opportunity":151},"基于2009-2024年中国A股食品上市公司，实证检验数字技术采用对供应链韧性的影响及机制。","使用沪深A股食品企业面板数据，实证分析数字技术采用与供应链韧性的关系。","数字技术采用显著提升食品企业供应链韧性，通过提高信息透明度、缓解融资约束和降低供应链集中度实现。","数字乡村与农业信息化","可探究数字技术不同维度对食品供应链韧性的差异化影响，以及环境不确定性的调节机制。","2026-09-19T23:30:08.651264Z",{"id":154,"title":155,"url":156,"summary":157,"summary_zh":9,"content":9,"source_name":158,"source_url":156,"published_at":159,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":160,"score_detail":161,"sources":164,"tags":166,"primary_topic":170,"secondary_topics":175,"classification":176,"search_phrases":190,"slug":193,"view_count":85,"doi":194,"paper":195,"created_at":210},5579,"A Composite Field-Evaluation Framework for Digital Agriculture Maps","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11283264\u002Fv1","A Composite Field-Evaluation Framework for Digital Agriculture Maps。Research Square","Research Square","2026-10-21T00:00:00Z",38,{"impact":162,"substance":40,"depth":162,"authority":40,"freshness":13,"relevant":21,"comment":163},8,"预印本提出数字农业地图的田间复合评估框架，方法有一定新意，但摘要信息极少、未经同行评审，公共影响有限，暂不宜进入每日精选。",[165],{"name":158,"url":156},[26,167,168,169],"农业信息化","遥感","地图评估",{"code":60,"slug":171,"name":172,"path":173,"parent_code":174},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":177,"locked":14,"version":34,"method":35,"evidence":178},"classified",{"candidates":179,"agricultural_evidence":41},[180,185],{"code":60,"score":19,"supported":41,"title":181,"abstract":183,"tags":184},[182],"digital agriculture",[182],[167,26],{"code":186,"score":21,"supported":14,"title":187,"abstract":188,"tags":189},"I3",[],[],[168],[191,192],"Research Square 数字农业","农业信息化 地图评估 数字农业 遥感","ResearchSquare数字农业-5579","10.21203\u002Frs.3.rs-11283264\u002Fv1",{"doi":194,"openalex_id":196,"authors":197,"venue":158,"cited_by_count":85,"oa_url":156,"card":9,"direction":150,"ingested_from":92},"W7223099987",[198,201,204,207],{"name":199,"orcid":200},"Phillip Lanza","https:\u002F\u002Forcid.org\u002F0000-0002-5545-1744",{"name":202,"orcid":203},"David G. Rossiter","https:\u002F\u002Forcid.org\u002F0000-0003-4558-1286",{"name":205,"orcid":206},"Diana Stuart Sinton","https:\u002F\u002Forcid.org\u002F0000-0001-5828-9001",{"name":208,"orcid":209},"Louis Longchamps","https:\u002F\u002Forcid.org\u002F0000-0002-4761-6094","2026-10-22T11:20:47.463814Z",{"id":212,"title":213,"url":214,"summary":215,"summary_zh":216,"content":9,"source_name":217,"source_url":214,"published_at":159,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":218,"score_detail":219,"sources":222,"tags":224,"primary_topic":229,"secondary_topics":230,"classification":231,"search_phrases":248,"slug":251,"view_count":85,"doi":252,"paper":253,"created_at":265},5578,"Unlocking digital agriculture through new quality productive forces: nonlinear thresholds and spatial spillovers in China","https:\u002F\u002Fdoi.org\u002F10.1057\u002Fs41599-026-09510-z","New quality productive forces (NQPF) critically enable agricultural digitisation. Using China as a case study, this paper employs panel data from 284 prefecture-level cities in China from 2011 to 2022. The results show that NQPF has a significant promoting effect on agricultural digital transformation and exhibits positive spatial spillover effects, driving not only local transformation but also the development of surrounding regions. Labour skills and innovation capacity play mediating roles in this process. Notably, the enabling effect of NQPF on agricultural digitisation displays significant regional heterogeneity. Threshold analysis indicates that its promoting effect follows a non-linear pattern, characterised by a double threshold effect. Therefore, the development of NQPF should be accelerated, with emphasis placed on regional coordination and talent cultivation, to promote the sustained and in-depth advancement of agricultural digital transformation in accordance with local conditions.","新质生产力（New Quality Productive Forces, NQPF）对农业数字化具有关键赋能作用。本文以中国为案例，采用2011—2022年中国284个地级市的面板数据。结果表明，新质生产力对农业数字化转型具有显著促进作用，并呈现正向空间溢出效应，不仅推动本地转型，也带动周边地区发展。劳动技能与创新能力在这一过程中发挥中介作用。值得注意的是，新质生产力对农业数字化的赋能效应表现出显著的区域异质性。门槛分析表明，其促进作用呈非线性特征，表现为双重门槛效应。因此，应加快新质生产力发展，注重区域协调与人才培养，因地制宜推动农业数字化转型持续深入推进。","Humanities and Social Sciences Communications",81,{"impact":17,"substance":18,"depth":17,"authority":220,"freshness":20,"relevant":21,"comment":221},14,"基于284个地级市面板数据的实证研究，揭示新质生产力对农业数字化的非线性门槛与空间溢出效应，方法规范、结论有政策参考价值，值得进入每日精选。",[223],{"name":217,"url":214},[26,225,226,227,228],"新质生产力","空间溢出","区域协调","农业数字化转型",{"code":60,"slug":171,"name":172,"path":173,"parent_code":174},[],{"status":177,"locked":14,"version":34,"method":35,"evidence":232},{"candidates":233,"agricultural_evidence":41},[234,238,243],{"code":60,"score":13,"supported":41,"title":235,"abstract":236,"tags":237},[182],[],[228,26],{"code":239,"score":21,"supported":14,"title":240,"abstract":241,"tags":242},"I4",[],[],[226],{"code":244,"score":21,"supported":14,"title":245,"abstract":246,"tags":247},"J2",[],[],[225],[249,250],"新质生产力 农业数字化","地级市 面板数据","新质生产力农业数字化-5578","10.1057\u002Fs41599-026-09510-z",{"doi":252,"openalex_id":254,"authors":255,"venue":217,"cited_by_count":85,"oa_url":214,"card":260,"direction":150,"ingested_from":92},"W7221723385",[256,258],{"name":257,"orcid":9},"Qingqing Huang",{"name":259,"orcid":9},"Wenjing Guo",{"tldr":261,"method":262,"finding":263,"direction":150,"opportunity":264},"基于中国284个地级市面板数据，检验新质生产力对农业数字化的促进、空间溢出与门槛效应。","2011—2022年284个地级市面板数据，空间计量与门槛回归，中介效应检验。","新质生产力显著促进农业数字化并具正向空间溢出，存在双门槛非线性效应与区域异质性。","可深入探究新质生产力影响农业数字化的微观机制，以及不同区域门槛条件下的差异化政策路径。","2026-10-22T11:20:47.354744Z",{"id":267,"title":268,"url":269,"summary":270,"summary_zh":271,"content":9,"source_name":10,"source_url":269,"published_at":159,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":272,"score_detail":273,"sources":277,"tags":279,"primary_topic":9,"secondary_topics":282,"classification":283,"search_phrases":310,"slug":313,"view_count":85,"doi":314,"paper":315,"created_at":325},5545,"Regional digital finance and food supply-chain management resilience: evidence on the regional-to-firm conversion gap in China’s emerging listed food enterprises","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1948192","Food supply chains face climate shocks, logistics disruption, food-safety incidents and volatile demand, yet the relevance of regional digital finance for firm-level resilience remains uncertain. This study examines the association between a province-level digital-finance environment and an accounting-based food supply-chain resilience proxy among emerging listed food enterprises in China. Using public data for 122 eligible firms, we construct annual resistance, recovery and stability measures from revenue, operating cash flow and net margin; the common two-way fixed-effects sample contains 732 firm-year observations from 119 firms during 2017–2023. Regional digital finance is measured as a province-level external financial environment rather than firm-level digital transformation or adoption. Operational capability and regional consumption capacity are accounting- and province-level proxies and do not directly observe supplier coordination, traceability, cold-chain performance, or product-level demand. The preferred estimate is close to zero [beta = −0.005, SE = 0.063, 95% CI (−0.128, 0.117)]. Alternative covariance estimators, outcome definitions and timing specifications do not establish a stable positive within-firm association. The proxy-based operational-capability pathway and the conditional consumption-capacity association are likewise not established. Equivalence tests at 0.05 and 0.10 SCR-standard-deviation bounds are not significant, and the approximate 80% minimum detectable effect is 1.575 SCR standard deviations. The evidence therefore cannot distinguish a small contextual association from limited within-firm statistical precision. The findings support a bounded regional-to-firm conversion interpretation: regional finance may widen resource availability, but whether it is associated with resilience depends on unobserved operational conversion routines and food-sector coordination conditions.","食品供应链面临气候冲击、物流中断、食品安全事件和需求波动，但区域数字金融与企业层面韧性之间的关联仍不确定。本研究考察省级数字金融环境与中国新兴上市食品企业基于会计的食品供应链韧性代理指标之间的关联。利用122家合格企业的公开数据，我们从营业收入、经营现金流和净利润率构建年度抵抗能力、恢复能力和稳定性指标；共同双向固定效应样本包含2017—2023年间119家企业的732个企业—年度观测值。区域数字金融被衡量为省级外部金融环境，而非企业层面的数字化转型或采用。运营能力和区域消费能力为会计层面和省级层面的代理变量，并不直接观测供应商协调、可追溯性、冷链绩效或产品层面需求。首选估计值接近于零[beta = −0.005，SE = 0.063，95% CI（−0.128，0.117）]。替代协方差估计量、结果定义和时序设定均未能确立稳定的企业内正向关联。基于代理变量的运营能力路径和条件性消费能力关联同样未能确立。在0.05和0.10个SCR标准差边界上的等价性检验不显著，约80%的最小可检测效应为1.575个SCR标准差。因此，证据无法区分微小的情境性关联与有限的企业内统计精度。研究结果支持有界的情境到企业转化解释：区域金融可能拓宽资源可得性，但其是否与韧性相关联取决于未观测到的运营转化惯例和食品行业协调条件。",69,{"impact":162,"substance":274,"depth":275,"authority":19,"freshness":13,"relevant":21,"comment":276},21,17,"基于122家上市食品企业的严谨实证研究，发现区域数字金融与企业韧性关联接近于零，对数字乡村金融政策有边界性提示价值，但属细分学术结论，公共影响有限。",[278],{"name":10,"url":269},[280,281,108,28,110],"数字乡村","数字金融",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":284},{"candidates":285,"agricultural_evidence":14,"reason":131},[286,292,296,300,305],{"code":287,"score":69,"supported":41,"title":288,"abstract":289,"tags":291},"E3",[],[290],"冷链",[],{"code":116,"score":69,"supported":41,"title":293,"abstract":294,"tags":295},[],[119],[],{"code":55,"score":21,"supported":14,"title":297,"abstract":298,"tags":299},[],[],[28],{"code":301,"score":21,"supported":14,"title":302,"abstract":303,"tags":304},"G3",[],[],[281],{"code":306,"score":21,"supported":14,"title":307,"abstract":308,"tags":309},"I6",[],[],[280],[311,312],"区域数字金融 食品供应链韧性","中国上市食品企业 供应链韧性","区域数字金融食品供应链韧性-5545","10.3389\u002Ffsufs.2026.1948192",{"doi":314,"openalex_id":316,"authors":317,"venue":10,"cited_by_count":85,"oa_url":269,"card":320,"direction":150,"ingested_from":92},"W7223086503",[318],{"name":319,"orcid":9},"Jieyu Zhao",{"tldr":321,"method":322,"finding":323,"direction":150,"opportunity":324},"检验省级数字金融环境与中国新兴上市食品企业供应链韧性之间的关系。","基于122家上市食品企业2017–2023年公开数据，构建双向固定效应模型与等价","区域数字金融与企业供应链韧性无稳定正相关，估计值接近零且不显著。","可研究区域金融资源如何经企业运营惯例转化为供应链韧性，并补充冷链、溯源等微观数据。","2026-10-22T11:20:15.297878Z",{"id":327,"title":328,"url":329,"summary":330,"summary_zh":9,"content":9,"source_name":331,"source_url":9,"published_at":332,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":333,"score_detail":334,"sources":338,"tags":340,"primary_topic":344,"secondary_topics":348,"classification":349,"search_phrases":370,"slug":373,"view_count":85,"doi":374,"paper":375,"created_at":383},5529,"数字农业技术采纳的驱动因素与障碍：基于复杂网络与演化博弈的分析","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1894648\u002Ffull","文章从演化博弈视角构建数字农业技术采纳的驱动与阻碍机制框架，系统梳理影响农户采纳数字农业技术的关键因素与障碍路径。研究强调通用大模型难以直接满足专业知识、结果依据与溯源能力要求，为政策设计提供了技术采纳侧的理论依据。","Frontiers in Sustainable Food Systems 2026-10-19","2026-10-19T00:00:00Z",67,{"impact":335,"substance":17,"depth":336,"authority":19,"freshness":162,"relevant":21,"comment":337},12,16,"从演化博弈视角分析农户采纳数字农业技术的驱动与障碍，方法有新意、结论有政策参考价值，但属理论性论文，影响范围限于细分领域。",[339],{"name":331,"url":329},[26,341,342,343],"智慧农业","技术采纳","农户行为",{"code":48,"slug":345,"name":346,"path":347,"parent_code":174},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":177,"locked":14,"version":34,"method":35,"evidence":350},{"candidates":351,"agricultural_evidence":41},[352,356,361,366],{"code":60,"score":19,"supported":41,"title":353,"abstract":354,"tags":355},[26],[26],[26,341],{"code":48,"score":69,"supported":41,"title":357,"abstract":358,"tags":360},[],[359],"大模型",[],{"code":362,"score":21,"supported":14,"title":363,"abstract":364,"tags":365},"G1",[],[],[342],{"code":55,"score":21,"supported":14,"title":367,"abstract":368,"tags":369},[],[],[343],[371,372],"数字农业 技术采纳 演化博弈","农户行为 技术采纳 数字农业 智慧农业","数字农业技术采纳演化博弈-5529","10.3389\u002Ffsufs.2026.1894648\u002Ffull",{"doi":374,"openalex_id":9,"authors":376,"venue":9,"cited_by_count":85,"oa_url":9,"card":377,"direction":150,"ingested_from":382},[],{"tldr":378,"method":379,"finding":380,"direction":150,"opportunity":381},"构建演化博弈框架分析农户采纳数字农业技术的驱动因素与障碍路径。","演化博弈建模与复杂网络分析，梳理采纳影响因素。","通用大模型难以满足专业知识、结果依据与溯源要求，需政策引导。","可结合实证数据检验博弈模型，并探索面向溯源与专业知识的专用农业模型设计。","agent","2026-10-22T00:07:09.498742Z",{"id":385,"title":386,"url":387,"summary":388,"summary_zh":389,"content":9,"source_name":390,"source_url":387,"published_at":391,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":392,"score_detail":393,"sources":396,"tags":398,"primary_topic":402,"secondary_topics":408,"classification":409,"search_phrases":438,"slug":441,"view_count":85,"doi":442,"paper":443,"created_at":460},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":336,"substance":394,"depth":275,"authority":19,"freshness":162,"relevant":21,"comment":395},20,"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。",[397],{"name":390,"url":387},[26,280,399,400,401],"粮食安全","农业产业链韧性","安徽农业",{"code":403,"slug":404,"name":405,"path":406,"parent_code":407},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[],{"status":177,"locked":14,"version":34,"method":35,"evidence":410},{"candidates":411,"agricultural_evidence":41},[412,417,421,428,434],{"code":403,"score":136,"supported":41,"title":413,"abstract":414,"tags":416},[],[399,415],"food security",[399],{"code":60,"score":136,"supported":41,"title":418,"abstract":419,"tags":420},[],[26,182],[26],{"code":422,"score":40,"supported":41,"title":423,"abstract":424,"tags":427},"G",[],[425,426],"农村发展","rural development",[],{"code":429,"score":69,"supported":41,"title":430,"abstract":431,"tags":433},"J1",[],[432],"农业政策",[],{"code":306,"score":21,"supported":14,"title":435,"abstract":436,"tags":437},[],[],[280],[439,440],"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":442,"openalex_id":444,"authors":445,"venue":390,"cited_by_count":85,"oa_url":387,"card":455,"direction":150,"ingested_from":92},"W7223005655",[446,449,451,453],{"name":447,"orcid":448},"Lan Ma","https:\u002F\u002Forcid.org\u002F0000-0003-2468-9917",{"name":450,"orcid":9},"Yuyan Li",{"name":452,"orcid":9},"张国亮",{"name":454,"orcid":9},"Yaoyao Li",{"tldr":456,"method":457,"finding":458,"direction":150,"opportunity":459},"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。","2026-10-21T23:30:29.219265Z"]