[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5491":3,"related-5491":104},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":78,"slug":81,"view_count":82,"doi":83,"paper":84,"created_at":103},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。",null,"Sustainability","2026-10-20T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,20,17,13,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字农业","数字乡村","粮食安全","农业产业链韧性","安徽农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,58,66,73],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},7,true,[],[29,50],"food security",[29],{"code":53,"score":46,"supported":47,"title":54,"abstract":55,"tags":57},"I1",[],[27,56],"digital agriculture",[27],{"code":59,"score":60,"supported":47,"title":61,"abstract":62,"tags":65},"G",6,[],[63,64],"农村发展","rural development",[],{"code":67,"score":68,"supported":47,"title":69,"abstract":70,"tags":72},"J1",3,[],[71],"农业政策",[],{"code":74,"score":22,"supported":14,"title":75,"abstract":76,"tags":77},"I6",[],[],[28],[79,80],"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",0,"10.3390\u002Fsu182010656",{"doi":83,"openalex_id":85,"authors":86,"venue":10,"cited_by_count":82,"oa_url":6,"card":96,"direction":100,"ingested_from":102},"W7223005655",[87,90,92,94],{"name":88,"orcid":89},"Lan Ma","https:\u002F\u002Forcid.org\u002F0000-0003-2468-9917",{"name":91,"orcid":9},"Yuyan Li",{"name":93,"orcid":9},"张国亮",{"name":95,"orcid":9},"Yaoyao Li",{"tldr":97,"method":98,"finding":99,"direction":100,"opportunity":101},"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。","数字乡村与农业信息化","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",{"total":60,"page":22,"page_size":60,"items":105},[106,179,246,314,377,428],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":9,"content":9,"source_name":111,"source_url":9,"published_at":9,"category":12,"cover_url":9,"hotness":112,"is_selected":14,"score":113,"score_detail":114,"sources":121,"tags":126,"primary_topic":130,"secondary_topics":131,"classification":132,"search_phrases":162,"slug":165,"view_count":60,"doi":166,"paper":167,"created_at":178},4969,"Digital technology, agricultural resilience, and food security: A mini review","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.2002109\u002Ffull","Shen在《Frontiers in Sustainable Food Systems》(2026)发表的综述综合了国际数字农业文献与中国'数字经济—农业经济韧性'研究两条主线。提出三点发现:一是数字技术通过不同机制强化食物安全的四个维度——可获得性(生产力与投入效率)、可及性(市场信息与数字金融)、稳定性(溯源、预警与保险)、利用性(溯源、营养信息与移动支付);二是准自然实验证据显示数字基础设施、数字金融和人工智能主要通过社会化服务、土地流转、产业融合、人力资本而非仅'采纳'提升粮食生产与价值链韧性;三是收益分布不均,空间溢出方向取决于吸收能力、市场整合与要素流动性。","Frontiers in Sustainable Food Systems 2026",25,77,{"impact":115,"substance":116,"depth":117,"authority":118,"freshness":119,"relevant":22,"comment":120},22,21,18,14,2,"核心期刊综述，系统梳理数字技术强化粮食安全四维度的机制与准自然实验证据，兼具国际文献与中国经验，但发布日期不明、时效性偏弱，适合作为主题页深度聚合素材。",[122,123],{"name":111,"url":109},{"name":124,"url":125},"Frontiers in Sustainable Food Systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.2002109",[27,29,127,128,129],"农业韧性","空间溢出","数字金融",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":133},{"candidates":134,"agricultural_evidence":47},[135,140,147,152,157],{"code":33,"score":136,"supported":47,"title":137,"abstract":138,"tags":139},9,[50],[],[29],{"code":141,"score":60,"supported":47,"title":142,"abstract":143,"tags":146},"G2",[],[144,145],"土地流转","社会化服务",[],{"code":53,"score":148,"supported":47,"title":149,"abstract":150,"tags":151},4,[],[27],[27],{"code":153,"score":22,"supported":14,"title":154,"abstract":155,"tags":156},"G3",[],[],[129],{"code":158,"score":22,"supported":14,"title":159,"abstract":160,"tags":161},"I4",[],[],[128],[163,164],"Frontiers in Sustainable Food Systems 数字农业","数字技术 农业韧性 粮食安全","FrontiersinSustainableFoodSystems数字农业-4969","10.3389\u002Ffsufs.2026.2002109\u002Ffull",{"doi":166,"openalex_id":168,"authors":169,"venue":124,"cited_by_count":82,"oa_url":125,"card":173,"direction":100,"ingested_from":102},"W7223134273",[170],{"name":171,"orcid":172},"Yang Shen","https:\u002F\u002Forcid.org\u002F0000-0002-6781-6915",{"tldr":174,"method":175,"finding":176,"direction":100,"opportunity":177},"综述数字技术通过多机制强化食物安全四维度并提升农业韧性，但收益分布不均。","综合国际数字农业文献与中国数字经济—农业经济韧性研究两条主线。","数字技术经社会化服务、土地流转等提升韧性，收益不均且空间溢出取决于吸收能力。","可探究数字技术收益不均的成因及吸收能力、市场整合对空间溢出方向的调节机制。","2026-10-15T00:06:08.890950Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":184,"content":9,"source_name":185,"source_url":182,"published_at":186,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":187,"score_detail":188,"sources":190,"tags":192,"primary_topic":196,"secondary_topics":197,"classification":198,"search_phrases":229,"slug":232,"view_count":22,"doi":233,"paper":234,"created_at":245},4600,"Optimization model of agricultural economic structure based on LSTM","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-026-09594-x","With the development of artificial intelligence (AI) and agricultural big data, the adjustment of agricultural economic structure has shifted from empirical judgment to data-driven decision-making. This study uses the provincial panel data of agricultural economy in Hubei Province from 2010 to 2023 to construct a comprehensive framework of prediction and optimization. The forecasting layer uses the Long Short-Term Memory (LSTM) model to capture the dynamic relationship among agricultural GDP growth rate, main crop output, rural employment structure, agricultural product price index, financial subsidies and agricultural infrastructure investment. The optimization layer uses the prediction results as parameters. It defines three objectives. overall agricultural benefit, structural deviation and food security risk. It also includes restrictions on cultivated land area, budget, labor supply and food security. Based on these backgrounds, the adjustment scheme of agricultural economic structure was formulated. The results show that the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and r of LSTM model are 0.32, 0.45 and 0.91 respectively. Its prediction performance is better than autoregressive moving average (ARIMA), back propagation neural network (BPNN), support vector regression (SVR) and random forest (RF). These findings show that the combination of multivariate collaborative forecasting and structural optimization constraints can improve the efficiency of agricultural resource allocation. This method also provides quantitative support for rural revitalization, food security and digital agriculture policy design.","随着人工智能（AI）与农业大数据的发展，农业经济结构调整已从经验判断转向数据驱动决策。本研究利用2010—2023年湖北省农业经济省级面板数据，构建了预测与优化的综合框架。预测层采用长短期记忆（LSTM）模型，捕捉农业GDP增长率、主要作物产量、农村就业结构、农产品价格指数、财政补贴与农业基础设施投资之间的动态关系。优化层以预测结果作为参数，设定三个目标：农业整体效益、结构性偏差与粮食安全风险，并纳入耕地面积、预算、劳动力供给与粮食安全等约束条件。在此基础上，制定农业经济结构调整方案。结果表明，LSTM模型的平均绝对误差（MAE）、均方根误差（RMSE）与r分别为0.32、0.45和0.91，其预测性能优于自回归移动平均（ARIMA）、反向传播神经网络（BPNN）、支持向量回归（SVR）与随机森林（RF）。这些发现表明，多变量协同预测与结构优化约束的结合能够提高农业资源配置效率。该方法也为乡村振兴、粮食安全与数字农业政策设计提供了量化支持。","Discover Applied Sciences","2026-10-09T00:00:00Z",75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":136,"relevant":22,"comment":189},"基于湖北省级面板数据构建LSTM预测与多目标优化框架，方法对比充分、结论有量化支撑，对数字农业与乡村振兴政策设计具参考价值。",[191],{"name":185,"url":182},[27,193,29,194,195],"农业人工智能","农业经济结构","LSTM预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":199},{"candidates":200,"agricultural_evidence":47},[201,205,209,215,220,225],{"code":33,"score":46,"supported":47,"title":202,"abstract":203,"tags":204},[],[29,50],[29],{"code":53,"score":46,"supported":47,"title":206,"abstract":207,"tags":208},[],[27,56],[27],{"code":210,"score":148,"supported":47,"title":211,"abstract":212,"tags":214},"I2",[],[213],"artificial intelligence",[193],{"code":59,"score":68,"supported":47,"title":216,"abstract":217,"tags":219},[],[218],"乡村振兴",[],{"code":74,"score":68,"supported":47,"title":221,"abstract":222,"tags":224},[],[223],"农业大数据",[],{"code":67,"score":68,"supported":47,"title":226,"abstract":227,"tags":228},[],[71],[],[230,231],"湖北 农业经济结构 LSTM","农业经济结构 优化模型","湖北农业经济结构LSTM-4600","10.1007\u002Fs42452-026-09594-x",{"doi":233,"openalex_id":235,"authors":236,"venue":185,"cited_by_count":82,"oa_url":182,"card":239,"direction":100,"ingested_from":102},"W7221088951",[237],{"name":238,"orcid":9},"Xue Gong",{"tldr":240,"method":241,"finding":242,"direction":243,"opportunity":244},"用LSTM预测湖北农业经济指标，并构建多目标优化模型调整农业结构。","LSTM预测2010-2023年湖北面板数据，结合多目标优化与约束条件。","LSTM预测精度优于ARIMA、BPNN等，优化方案可提升农业资源配置效率。","农业人工智能与决策模型","可探索将LSTM与多目标优化耦合框架推广至其他省份或作物类型，并引入实时数据流。","2026-10-10T23:30:34.807708Z",{"id":247,"title":248,"url":249,"summary":250,"summary_zh":9,"content":9,"source_name":251,"source_url":9,"published_at":252,"category":12,"cover_url":9,"hotness":13,"is_selected":47,"score":253,"score_detail":254,"sources":258,"tags":260,"primary_topic":264,"secondary_topics":265,"classification":277,"search_phrases":301,"slug":304,"view_count":148,"doi":9,"paper":305,"created_at":313},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":115,"substance":255,"depth":256,"authority":118,"freshness":136,"relevant":22,"comment":257},24,19,"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。",[259],{"name":251,"url":249},[28,261,29,262,263],"数字基础设施","农业碳排放","农户数字化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[266,272],{"code":267,"slug":268,"name":269,"path":270,"parent_code":271},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",{"code":74,"slug":273,"name":274,"path":275,"parent_code":276},"digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":278},{"candidates":279,"agricultural_evidence":47},[280,287,291,295],{"code":267,"score":117,"supported":47,"title":281,"abstract":283,"tags":286},[282],"农业碳",[284,282,285],"碳排放","碳足迹",[262],{"code":74,"score":20,"supported":47,"title":288,"abstract":289,"tags":290},[28],[28],[28,261],{"code":33,"score":136,"supported":47,"title":292,"abstract":293,"tags":294},[29],[],[29],{"code":296,"score":68,"supported":47,"title":297,"abstract":298,"tags":300},"F4",[],[299],"农药",[],[302,303],"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":9,"openalex_id":9,"authors":306,"venue":9,"cited_by_count":82,"oa_url":9,"card":307,"direction":100,"ingested_from":312},[],{"tldr":308,"method":309,"finding":310,"direction":100,"opportunity":311},"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。","agent","2026-10-05T00:04:55.880239Z",{"id":315,"title":316,"url":317,"summary":318,"summary_zh":319,"content":9,"source_name":124,"source_url":317,"published_at":320,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":113,"score_detail":321,"sources":323,"tags":325,"primary_topic":328,"secondary_topics":329,"classification":330,"search_phrases":356,"slug":359,"view_count":68,"doi":360,"paper":361,"created_at":376},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。","2026-09-28T00:00:00Z",{"impact":117,"substance":116,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":322},"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。",[324],{"name":124,"url":317},[28,29,127,326,327],"绿色金融","农村振兴",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":331},{"candidates":332,"agricultural_evidence":47},[333,338,342,348,352],{"code":33,"score":334,"supported":47,"title":335,"abstract":336,"tags":337},15,[50],[29,50],[29],{"code":59,"score":68,"supported":47,"title":339,"abstract":340,"tags":341},[],[218],[],{"code":343,"score":68,"supported":47,"title":344,"abstract":345,"tags":347},"G6",[],[346],"rural income",[],{"code":153,"score":22,"supported":14,"title":349,"abstract":350,"tags":351},[],[],[326],{"code":74,"score":22,"supported":14,"title":353,"abstract":354,"tags":355},[],[],[28],[357,358],"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":360,"openalex_id":362,"authors":363,"venue":124,"cited_by_count":82,"oa_url":317,"card":370,"direction":374,"ingested_from":102},"W7214667116",[364,366,368],{"name":365,"orcid":9},"Zixiang Zhao",{"name":367,"orcid":9},"Chengcheng Lin",{"name":369,"orcid":9},"Po-Wen Tsai",{"tldr":371,"method":372,"finding":373,"direction":374,"opportunity":375},"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":378,"title":379,"url":380,"summary":381,"summary_zh":9,"content":9,"source_name":382,"source_url":9,"published_at":383,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":384,"score_detail":385,"sources":388,"tags":390,"primary_topic":394,"secondary_topics":395,"classification":397,"search_phrases":415,"slug":418,"view_count":68,"doi":419,"paper":420,"created_at":427},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":115,"substance":386,"depth":117,"authority":118,"freshness":13,"relevant":22,"comment":387},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。",[389],{"name":382,"url":380},[28,29,391,392,393],"区域异质性","农业技术培训","土地生产率",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[396],{"code":74,"slug":273,"name":274,"path":275,"parent_code":276},{"status":40,"locked":14,"version":41,"method":42,"evidence":398},{"candidates":399,"agricultural_evidence":47},[400,405,409],{"code":33,"score":401,"supported":47,"title":402,"abstract":403,"tags":404},12,[29],[29],[29],{"code":74,"score":401,"supported":47,"title":406,"abstract":407,"tags":408},[28],[28],[28],{"code":410,"score":68,"supported":47,"title":411,"abstract":412,"tags":414},"G1",[],[413],"技术培训",[],[416,417],"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":419,"openalex_id":9,"authors":421,"venue":9,"cited_by_count":82,"oa_url":9,"card":422,"direction":100,"ingested_from":312},[],{"tldr":423,"method":424,"finding":425,"direction":100,"opportunity":426},"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。","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。","2026-09-21T00:04:39.562305Z",{"id":429,"title":430,"url":431,"summary":432,"summary_zh":433,"content":9,"source_name":434,"source_url":431,"published_at":435,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":436,"score_detail":437,"sources":439,"tags":441,"primary_topic":444,"secondary_topics":445,"classification":446,"search_phrases":464,"slug":467,"view_count":68,"doi":468,"paper":469,"created_at":486},1746,"Digitization in Agriculture and Food Security in Sub-Saharan Africa: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.70382\u002Fajbar.v13i1.032","Digitization is increasingly recognized as a transformative force in Sub-Saharan Africa’s (SSA) agricultural sector, with significant implications for productivity enhancement and food security improvement. This systematic review examines the role of digital technologies in agriculture, focusing on their contributions to food availability, accessibility, utilization, and stability across SSA. The study adopts a systematic literature review (SLR) methodology, drawing evidence from peer-reviewed journals and reports published by institutions such as the World Bank, FAO, IFAD, and the African Development Bank between 2015 and 2025. Findings reveal that digital agriculture interventions, including mobile advisory systems, precision agriculture tools, digital financial services, and e-market platforms, significantly improve agricultural productivity, reduce transaction costs, strengthen market integration, and enhance farmers’ access to financial services. However, adoption remains uneven due to infrastructural deficits, digital illiteracy, gender inequality, and weak institutional frameworks. The review concludes that while digitization holds strong potential to transform food systems in SSA, its effectiveness depends on enabling environments such as infrastructure development, inclusive policies, and capacity building. The study recommends strengthening digital infrastructure, improving literacy programs, and expanding public–private partnerships to accelerate inclusive digital transformation in agriculture.","数字化日益被视为撒哈拉以南非洲（SSA）农业部门的变革性力量，对提高生产力和改善粮食安全具有重大意义。本系统综述考察了数字技术在农业中的作用，重点关注其对撒哈拉以南非洲粮食可得性、可及性、利用和稳定性的贡献。研究采用系统文献综述（SLR）方法，借鉴了2015年至2025年间同行评审期刊以及世界银行、联合国粮农组织（FAO）、国际农业发展基金（IFAD）和非洲开发银行等机构发布的报告中的证据。研究结果表明，包括移动咨询系统、精准农业工具、数字金融服务和电子市场平台在内的数字农业干预措施，显著提高了农业生产力，降低了交易成本，加强了市场整合，并增强了农民获取金融服务的机会。然而，由于基础设施不足、数字文盲、性别不平等和制度框架薄弱，数字技术的采用仍不均衡。本综述得出结论：尽管数字化在改变撒哈拉以南非洲粮食系统方面具有巨大潜力，但其有效性取决于有利环境，如基础设施发展、包容性政策和能力建设。研究建议加强数字基础设施建设，改善扫盲计划，并扩大公私伙伴关系，以加速农业领域的包容性数字化转型。","Journal of Biotechnology and Agricultural Research","2026-09-04T00:00:00Z",70,{"impact":117,"substance":18,"depth":117,"authority":401,"freshness":119,"relevant":22,"comment":438},"系统综述非洲数字农业对粮食安全的影响，证据扎实，但时效性低，适合精选。",[440],{"name":434,"url":431},[27,442,29,443],"系统综述","非洲农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":447},{"candidates":448,"agricultural_evidence":47},[449,453,459],{"code":33,"score":334,"supported":47,"title":450,"abstract":451,"tags":452},[50],[29,50],[29],{"code":53,"score":13,"supported":47,"title":454,"abstract":455,"tags":458},[],[456,27,457,56],"精准农业","precision agriculture",[27],{"code":460,"score":22,"supported":14,"title":461,"abstract":462,"tags":463},"J3",[],[],[443],[465,466],"数字农业 粮食安全 系统综述 非洲农业","数字农业 粮食安全","数字农业粮食安全系统综述非洲农业-1746","10.70382\u002Fajbar.v13i1.032",{"doi":468,"openalex_id":470,"authors":471,"venue":434,"cited_by_count":82,"oa_url":431,"card":481,"direction":100,"ingested_from":102},"W7208759473",[472,475,477,479],{"name":473,"orcid":474},"Funso O. Alabuja","https:\u002F\u002Forcid.org\u002F0000-0002-7579-1041",{"name":476,"orcid":9},"Ejiofor D. Chukwubuike",{"name":478,"orcid":9},"Lateefah O. Lama-Abdul",{"name":480,"orcid":9},"Olabiyi Fatimah Adebisi",{"tldr":482,"method":483,"finding":484,"direction":100,"opportunity":485},"系统综述撒哈拉以南非洲农业数字化对粮食安全的影响，发现其提升生产力但采用不均。","系统文献综述，分析2015-2025年同行评审文献及国际机构报告。","数字农业提高生产力、降低交易成本，但受基础设施、数字素养等制约。","可研究数字农业采用不均的驱动因素，如性别差异、制度障碍，探索包容性数字化路径。","2026-09-05T23:30:35.595189Z"]