[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4969":3,"related-4969":97},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":8,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":13,"score_detail":14,"sources":22,"tags":27,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":77,"slug":80,"view_count":55,"doi":81,"paper":82,"created_at":96},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。",null,"Frontiers in Sustainable Food Systems 2026","论文",25,false,77,{"impact":15,"substance":16,"depth":17,"authority":18,"freshness":19,"relevant":20,"comment":21},22,21,18,14,2,1,"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。",[23,24],{"name":9,"url":6},{"name":25,"url":26},"Frontiers in Sustainable Food Systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.2002109",[28,29,30,31,32],"数字农业","粮食安全","农业韧性","空间溢出","数字金融",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[],{"status":41,"locked":12,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":48},[46,53,61,67,72],{"code":34,"score":47,"supported":48,"title":49,"abstract":51,"tags":52},9,true,[50],"food security",[],[29],{"code":54,"score":55,"supported":48,"title":56,"abstract":57,"tags":60},"G2",6,[],[58,59],"土地流转","社会化服务",[],{"code":62,"score":63,"supported":48,"title":64,"abstract":65,"tags":66},"I1",4,[],[28],[28],{"code":68,"score":20,"supported":12,"title":69,"abstract":70,"tags":71},"G3",[],[],[32],{"code":73,"score":20,"supported":12,"title":74,"abstract":75,"tags":76},"I4",[],[],[31],[78,79],"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":81,"openalex_id":83,"authors":84,"venue":25,"cited_by_count":88,"oa_url":26,"card":89,"direction":93,"ingested_from":95},"W7223134273",[85],{"name":86,"orcid":87},"Yang Shen","https:\u002F\u002Forcid.org\u002F0000-0002-6781-6915",0,{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"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。","数字乡村与农业信息化","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。","openalex","2026-10-15T00:06:08.890950Z",{"total":55,"page":20,"page_size":55,"items":98},[99,179,246,309,369,475],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":8,"source_name":105,"source_url":102,"published_at":106,"category":10,"cover_url":8,"hotness":107,"is_selected":12,"score":108,"score_detail":109,"sources":116,"tags":118,"primary_topic":122,"secondary_topics":123,"classification":124,"search_phrases":156,"slug":159,"view_count":88,"doi":160,"paper":161,"created_at":178},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",10,74,{"impact":110,"substance":111,"depth":112,"authority":113,"freshness":114,"relevant":20,"comment":115},16,20,17,13,8,"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。",[117],{"name":105,"url":102},[28,119,29,120,121],"数字乡村","农业产业链韧性","安徽农业",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":12,"version":42,"method":43,"evidence":125},{"candidates":126,"agricultural_evidence":48},[127,132,137,144,151],{"code":34,"score":128,"supported":48,"title":129,"abstract":130,"tags":131},7,[],[29,50],[29],{"code":62,"score":128,"supported":48,"title":133,"abstract":134,"tags":136},[],[28,135],"digital agriculture",[28],{"code":138,"score":55,"supported":48,"title":139,"abstract":140,"tags":143},"G",[],[141,142],"农村发展","rural development",[],{"code":145,"score":146,"supported":48,"title":147,"abstract":148,"tags":150},"J1",3,[],[149],"农业政策",[],{"code":152,"score":20,"supported":12,"title":153,"abstract":154,"tags":155},"I6",[],[],[119],[157,158],"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":160,"openalex_id":162,"authors":163,"venue":105,"cited_by_count":88,"oa_url":102,"card":173,"direction":93,"ingested_from":95},"W7223005655",[164,167,169,171],{"name":165,"orcid":166},"Lan Ma","https:\u002F\u002Forcid.org\u002F0000-0003-2468-9917",{"name":168,"orcid":8},"Yuyan Li",{"name":170,"orcid":8},"张国亮",{"name":172,"orcid":8},"Yaoyao Li",{"tldr":174,"method":175,"finding":176,"direction":93,"opportunity":177},"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",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":184,"content":8,"source_name":185,"source_url":182,"published_at":186,"category":10,"cover_url":8,"hotness":107,"is_selected":12,"score":187,"score_detail":188,"sources":190,"tags":192,"primary_topic":196,"secondary_topics":197,"classification":198,"search_phrases":229,"slug":232,"view_count":20,"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":110,"substance":111,"depth":112,"authority":113,"freshness":47,"relevant":20,"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},[28,193,29,194,195],"农业人工智能","农业经济结构","LSTM预测",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":12,"version":42,"method":43,"evidence":199},{"candidates":200,"agricultural_evidence":48},[201,205,209,215,220,225],{"code":34,"score":128,"supported":48,"title":202,"abstract":203,"tags":204},[],[29,50],[29],{"code":62,"score":128,"supported":48,"title":206,"abstract":207,"tags":208},[],[28,135],[28],{"code":210,"score":63,"supported":48,"title":211,"abstract":212,"tags":214},"I2",[],[213],"artificial intelligence",[193],{"code":138,"score":146,"supported":48,"title":216,"abstract":217,"tags":219},[],[218],"乡村振兴",[],{"code":152,"score":146,"supported":48,"title":221,"abstract":222,"tags":224},[],[223],"农业大数据",[],{"code":145,"score":146,"supported":48,"title":226,"abstract":227,"tags":228},[],[149],[],[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":88,"oa_url":182,"card":239,"direction":93,"ingested_from":95},"W7221088951",[237],{"name":238,"orcid":8},"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":251,"content":8,"source_name":25,"source_url":249,"published_at":252,"category":10,"cover_url":8,"hotness":107,"is_selected":12,"score":13,"score_detail":253,"sources":255,"tags":257,"primary_topic":260,"secondary_topics":261,"classification":262,"search_phrases":288,"slug":291,"view_count":146,"doi":292,"paper":293,"created_at":308},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":17,"substance":16,"depth":112,"authority":113,"freshness":114,"relevant":20,"comment":254},"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。",[256],{"name":25,"url":249},[119,29,30,258,259],"绿色金融","农村振兴",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":12,"version":42,"method":43,"evidence":263},{"candidates":264,"agricultural_evidence":48},[265,270,274,280,284],{"code":34,"score":266,"supported":48,"title":267,"abstract":268,"tags":269},15,[50],[29,50],[29],{"code":138,"score":146,"supported":48,"title":271,"abstract":272,"tags":273},[],[218],[],{"code":275,"score":146,"supported":48,"title":276,"abstract":277,"tags":279},"G6",[],[278],"rural income",[],{"code":68,"score":20,"supported":12,"title":281,"abstract":282,"tags":283},[],[],[258],{"code":152,"score":20,"supported":12,"title":285,"abstract":286,"tags":287},[],[],[119],[289,290],"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":292,"openalex_id":294,"authors":295,"venue":25,"cited_by_count":88,"oa_url":249,"card":302,"direction":306,"ingested_from":95},"W7214667116",[296,298,300],{"name":297,"orcid":8},"Zixiang Zhao",{"name":299,"orcid":8},"Chengcheng Lin",{"name":301,"orcid":8},"Po-Wen Tsai",{"tldr":303,"method":304,"finding":305,"direction":306,"opportunity":307},"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":310,"title":311,"url":312,"summary":313,"summary_zh":314,"content":8,"source_name":315,"source_url":312,"published_at":316,"category":10,"cover_url":8,"hotness":107,"is_selected":12,"score":317,"score_detail":318,"sources":321,"tags":323,"primary_topic":326,"secondary_topics":327,"classification":328,"search_phrases":346,"slug":349,"view_count":146,"doi":350,"paper":351,"created_at":368},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":17,"substance":111,"depth":17,"authority":319,"freshness":19,"relevant":20,"comment":320},12,"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。",[322],{"name":315,"url":312},[28,324,29,325],"系统综述","非洲农业",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":12,"version":42,"method":43,"evidence":329},{"candidates":330,"agricultural_evidence":48},[331,335,341],{"code":34,"score":266,"supported":48,"title":332,"abstract":333,"tags":334},[50],[29,50],[29],{"code":62,"score":107,"supported":48,"title":336,"abstract":337,"tags":340},[],[338,28,339,135],"精准农业","precision agriculture",[28],{"code":342,"score":20,"supported":12,"title":343,"abstract":344,"tags":345},"J3",[],[],[325],[347,348],"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":350,"openalex_id":352,"authors":353,"venue":315,"cited_by_count":88,"oa_url":312,"card":363,"direction":93,"ingested_from":95},"W7208759473",[354,357,359,361],{"name":355,"orcid":356},"Funso O. Alabuja","https:\u002F\u002Forcid.org\u002F0000-0002-7579-1041",{"name":358,"orcid":8},"Ejiofor D. Chukwubuike",{"name":360,"orcid":8},"Lateefah O. Lama-Abdul",{"name":362,"orcid":8},"Olabiyi Fatimah Adebisi",{"tldr":364,"method":365,"finding":366,"direction":93,"opportunity":367},"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",{"id":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":8,"source_name":375,"source_url":372,"published_at":316,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":376,"score_detail":377,"sources":379,"tags":383,"primary_topic":386,"secondary_topics":387,"classification":394,"search_phrases":458,"slug":461,"view_count":20,"doi":462,"paper":463,"created_at":474},1721,"Climate-Smart Agriculture for Sustainable Food Security","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22301142","Climate change is creating serious challenges for agriculture and food security, particularly for rural and smallholder farming communities. Rising temperatures, irregular rainfall, droughts, floods, water scarcity, soil degradation and biodiversity loss can reduce agricultural productivity and affect rural livelihoods. Climate-Smart Agriculture (CSA) provides an integrated approach to address these challenges by improving agricultural productivity, strengthening resilience to climate change and promoting sustainable management of natural resources. This study examines the role of CSA in achieving sustainable food security from a global perspective. The study adopts a descriptive and analytical research design based on secondary data collected from the Food and Agriculture Organization (FAO), World Bank, Intergovernmental Panel on Climate Change (IPCC), United Nations and other published sources. The study covers the period 2019–2025 and reviews major CSA practices, including conservation agriculture, water-smart agriculture, climate-resilient crop varieties, agroforestry, integrated crop-livestock systems and digital agriculture. The findings indicate that CSA can improve agricultural productivity, farmer income, climate resilience, nutritional security and resource conservation. However, limited climate finance, high technology costs, smallholder constraints, digital inequality, weak extension services and policy gaps continue to restrict adoption. The study recommends stronger financial support, affordable technologies, improved extension services and farmer-centred policies to promote inclusive CSA and strengthen sustainable food security.","气候变化正给农业和粮食安全带来严峻挑战，尤其对农村和小农社区影响深远。气温升高、降雨不规律、干旱、洪涝、水资源短缺、土壤退化和生物多样性丧失，均可能降低农业生产力，并影响农村生计。气候智能型农业（Climate-Smart Agriculture, CSA）提供了一种综合性应对方案，通过提升农业生产力、增强对气候变化的适应能力以及促进自然资源的可持续管理，来应对上述挑战。本研究从全球视角探讨了CSA在实现可持续粮食安全中的作用。研究采用描述性与分析性相结合的研究设计，基于从联合国粮食及农业组织（FAO）、世界银行、政府间气候变化专门委员会（IPCC）、联合国及其他公开来源收集的二手数据展开分析。研究覆盖2019—2025年期间，并梳理了主要的CSA实践，包括保护性农业、节水型农业、气候适应性作物品种、农林业、种养结合系统以及数字农业。研究结果表明，CSA能够提升农业生产力、增加农民收入、增强气候韧性、改善营养安全并促进资源保护。然而，气候融资有限、技术成本高昂、小农面临诸多制约、数字鸿沟、推广服务薄弱以及政策缺口，仍持续限制着CSA的推广应用。研究建议加强资金支持、推动技术可负担化、完善推广服务并制定以农民为中心的政策，以促进包容性CSA发展，进而强化可持续粮食安全。","International Journal for Research Trends in Social Science & Humanities.",66,{"impact":17,"substance":111,"depth":110,"authority":107,"freshness":19,"relevant":20,"comment":378},"全球视角综述CSA实践与挑战，数据较新，但时效性低，适合作为背景参考。",[380,381],{"name":375,"url":372},{"name":375,"url":382},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22301141",[29,30,384,385],"气候智慧农业","小农",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[388],{"code":389,"slug":390,"name":391,"path":392,"parent_code":393},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":41,"locked":12,"version":42,"method":43,"evidence":395},{"candidates":396,"agricultural_evidence":48},[397,402,406,416,423,430,436,441,447,453],{"code":62,"score":112,"supported":48,"title":398,"abstract":400,"tags":401},[399],"smart agriculture",[28,399,135],[],{"code":34,"score":266,"supported":48,"title":403,"abstract":404,"tags":405},[50],[29,50],[29],{"code":389,"score":107,"supported":48,"title":407,"abstract":408,"tags":415},[],[409,410,411,412,413,414],"气候变化","气候适应","干旱","洪涝","climate change","climate resilience",[384],{"code":417,"score":55,"supported":48,"title":418,"abstract":419,"tags":422},"H2",[],[420,421],"土壤","soil",[],{"code":424,"score":55,"supported":48,"title":425,"abstract":426,"tags":429},"H6",[],[427,428],"生物多样性","biodiversity",[],{"code":431,"score":146,"supported":48,"title":432,"abstract":433,"tags":435},"C",[],[434],"livestock",[],{"code":54,"score":146,"supported":48,"title":437,"abstract":438,"tags":440},[],[439],"smallholder",[],{"code":442,"score":146,"supported":48,"title":443,"abstract":444,"tags":446},"H3",[],[445],"水资源",[],{"code":448,"score":146,"supported":48,"title":449,"abstract":450,"tags":452},"H8",[],[451],"conservation agriculture",[],{"code":152,"score":146,"supported":48,"title":454,"abstract":455,"tags":457},[],[456],"数字鸿沟",[],[459,460],"气候智慧农业 农业韧性 粮食安全 小农","气候智慧农业 农业韧性","气候智慧农业农业韧性粮食安全小农-1721","10.5281\u002Fzenodo.22301142",{"doi":462,"openalex_id":464,"authors":465,"venue":375,"cited_by_count":88,"oa_url":372,"card":468,"direction":473,"ingested_from":95},"W7208735871",[466],{"name":467,"orcid":8},"N. Thilagavathi",{"tldr":469,"method":470,"finding":471,"direction":306,"opportunity":472},"综述气候智慧农业（CSA）对可持续粮食安全的作用，分析其效益与推广障碍。","基于FAO、世界银行等二手数据，采用描述性和分析性研究设计，覆盖2019-202","CSA能提升生产力、韧性、营养与资源保护，但受资金、技术、小农限制等制约。","可研究数字农业如何降低CSA技术成本，或设计针对小农的普惠金融与政策机制以促进采纳。","智慧农业 \u002F 农业物联网","2026-09-05T23:30:09.026027Z",{"id":476,"title":477,"url":478,"summary":479,"summary_zh":8,"content":8,"source_name":25,"source_url":478,"published_at":480,"category":10,"cover_url":8,"hotness":107,"is_selected":12,"score":13,"score_detail":481,"sources":483,"tags":485,"primary_topic":489,"secondary_topics":490,"classification":491,"search_phrases":502,"slug":505,"view_count":19,"doi":506,"paper":507,"created_at":526},1240,"Food security governance in complex environments: how digital technologies can enhance the stability of agricultural food systems?","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1914452","Introduction The Chinese agricultural food system is confronted with multiple complex environmental challenges, such as extreme climate events and market fluctuations. Digital technology (DT), as a key element in reshaping the resilience of the entire value chain, provides important support for enhancing the stability of agricultural food systems (SAFS) and ensuring stable food consumption. Methods Based on panel data from 31 provinces in China from 2011 to 2023, this study employs a two-way fixed-effects model and a moderation model to examine the effect of DT on the SAFS. It further investigates regional heterogeneity in this effect and the moderating role of the urbanization rate (UR). Results First, DT has a significant positive effect on the SAFS. Second, the effect of DT exhibits regional heterogeneity, with a particularly pronounced positive effect in western China. Third, UR positively moderates the relationship between DT and the SAFS. Discussion The findings suggest that government departments should strengthen policy coordination and institutional support for the digital transformation of the agricultural food system, improve digital infrastructure and data governance, promote coordinated urbanization, and enhance resilience and risk-warning capabilities throughout the agricultural food value chain, thereby improving the capacity of agricultural food systems to cope with external risks and promoting sustainable agricultural development.","2026-08-26T00:00:00Z",{"impact":15,"substance":15,"depth":17,"authority":113,"freshness":19,"relevant":20,"comment":482},"基于省级面板数据的实证研究，揭示数字技术对农业食物系统稳定性的促进作用及区域异质性，为政策制定提供依据。",[484],{"name":25,"url":478},[486,29,30,487,488],"智慧农业","数字技术","城镇化",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":12,"version":42,"method":43,"evidence":492},{"candidates":493,"agricultural_evidence":48},[494,498],{"code":34,"score":47,"supported":48,"title":495,"abstract":496,"tags":497},[50],[],[29],{"code":62,"score":19,"supported":12,"title":499,"abstract":500,"tags":501},[],[],[487,486],[503,504],"农业韧性 数字技术 智慧农业 粮食安全","农业韧性 数字技术","农业韧性数字技术智慧农业粮食安全-1240","10.3389\u002Ffsufs.2026.1914452",{"doi":506,"openalex_id":508,"authors":509,"venue":25,"cited_by_count":88,"oa_url":520,"card":521,"direction":243,"ingested_from":95},"W7204270185",[510,512,514,517],{"name":511,"orcid":8},"Yuang Guo",{"name":513,"orcid":8},"Yujie Yang",{"name":515,"orcid":516},"Qing Qin","https:\u002F\u002Forcid.org\u002F0000-0001-9176-5727",{"name":518,"orcid":519},"Ying Bai","https:\u002F\u002Forcid.org\u002F0000-0003-4887-2716","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1914452\u002Fpdf",{"tldr":522,"method":523,"finding":524,"direction":243,"opportunity":525},"研究数字技术对中国农业食物系统稳定性的影响及机制。","基于2011-2023年省级面板数据，采用双向固定效应和调节效应模型。","数字技术显著提升农业食物系统稳定性，西部效应更强，城镇化率正向调节。","可探索数字技术在不同农业子系统（如生产、供应链）中的差异化作用，以及与其他政策因素的交互效应。","2026-09-01T04:03:09.255875Z"]