[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3140":3,"related-3140":74},{"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":50,"slug":53,"view_count":21,"doi":54,"paper":55,"created_at":73},3140,"Validity of experience-based food insecurity scales among Indigenous Peoples","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1962514","Well-validated experience-based food insecurity (FI) scales have been extensively used to assess FI in general populations across the globe. However, the validity of these scales in Indigenous communities remains uncertain. The objective of this evidence-informed expert synthesis was to assess the validity of experience-based FI scales among Indigenous Peoples. We examined peer-reviewed articles that (1) used an experience-based FI scale; (2) were conducted in an Indigenous Community or involved a survey specifically targeting Indigenous or Aboriginal peoples, regardless of where they lived; and (3) included data that allowed assessment of scale psychometric and\u002For predictive and convergent validity. We identified 21 articles of studies conducted in Africa, Latin America, North America, and the Western Pacific regions. Findings suggest that, in Indigenous communities, experience-based FI scales have adequate face validity, psychometric validity, convergent validity (associations between FI and dietary intake), and predictive validity (associations between FI and socioeconomic or psycho-emotional stress indicators). However, these scales may not fully capture additional context-specific dimensions of FI related to traditional or ancestral food systems. Research is needed to identify core FI scale items that are broadly applicable across Indigenous population complemented with additional context-specific culturally relevant measures. Such efforts would improve large-scale assessments of FI in highly vulnerable communities that are often excluded from national surveillance and monitoring systems, provide a more accurate understanding of the distribution and magnitude of FI, and suggest ways to address it among Indigenous Peoples in different parts of the world.","经过充分验证的基于经验的粮食不安全（FI）量表已被广泛用于评估全球一般人群的粮食不安全状况。然而，这些量表在原住民社区中的有效性仍不确定。本项基于证据的专家综合研究旨在评估基于经验的粮食不安全量表在原住民群体中的有效性。我们审查了经同行评审的文章，这些文章（1）使用了基于经验的粮食不安全量表；（2）在原住民社区中开展，或涉及专门针对原住民或土著民族的调查，无论其居住何地；（3）包含可用于评估量表心理测量学和\u002F或预测效度及聚合效度的数据。我们识别出21篇在非洲、拉丁美洲、北美和西太平洋地区开展研究的文章。研究结果表明，在原住民社区中，基于经验的粮食不安全量表具有足够的表面效度、心理测量效度、聚合效度（粮食不安全与膳食摄入之间的关联）和预测效度（粮食不安全与社会经济或心理情绪压力指标之间的关联）。然而，这些量表可能无法完全捕捉与传统或祖传食物系统相关的额外情境特异性粮食不安全维度。需要开展研究以确定可广泛适用于原住民群体的核心粮食不安全量表条目，并辅以额外的情境特异性文化相关测量指标。此类努力将改善对高度脆弱社区粮食不安全的大规模评估——这些社区往往被排除在国家监测和监控系统之外——提供对粮食不安全分布和严重程度更准确的理解，并为应对世界不同地区原住民的粮食不安全问题提出对策。",null,"Frontiers in Sustainable Food Systems","2026-09-22T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},8,20,16,13,1,"系统评估经验性食物不安全量表在原住民社区效度的证据综述，对完善农村食物安全监测与信息化评估有参考价值，但属学术结论层面，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业信息化","粮食安全","原住民社区","食物不安全量表","农村监测",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"unclassified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":48,"reason":49},[38,43],{"code":39,"score":21,"supported":14,"title":40,"abstract":41,"tags":42},"A8",[],[],[27],{"code":44,"score":21,"supported":14,"title":45,"abstract":46,"tags":47},"I1",[],[],[26],true,"缺少足够的主题证据",[51,52],"Frontiers Sustainable Food Systems 食物不安全量表","原住民 食物不安全 量表效度","FrontiersSustainableFoodSystems食物不安全量表-3140","10.3389\u002Ffsufs.2026.1962514",{"doi":54,"openalex_id":56,"authors":57,"venue":10,"cited_by_count":65,"oa_url":6,"card":66,"direction":70,"ingested_from":72},"W7213967028",[58,60,63],{"name":59,"orcid":9},"Rafael Pérez-Escamilla",{"name":61,"orcid":62},"Sofía Segura‐Pérez","https:\u002F\u002Forcid.org\u002F0000-0001-8717-3315",{"name":64,"orcid":9},"Haydee Amalia Segura-Millán",0,{"tldr":67,"method":68,"finding":69,"direction":70,"opportunity":71},"综述评估经验性食物不安全量表在原住民群体中的效度，发现其基本有效但未涵盖传统食物系统维度。","证据信息专家综合，分析21篇非洲、拉美、北美和西太平洋地区原住民研究。","量表具表面、心理测量、收敛和预测效度，但未充分捕捉传统或祖先食物系统的情境维度。","其他","可开发融合文化相关指标的原住民食物不安全测量工具，以纳入国家监测并指导干预。","openalex","2026-09-22T23:30:07.744222Z",{"total":75,"page":21,"page_size":75,"items":76},6,[77,138,214,281,333,398],{"id":78,"title":79,"url":80,"summary":81,"summary_zh":9,"content":9,"source_name":82,"source_url":9,"published_at":83,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":84,"score_detail":85,"sources":90,"tags":92,"primary_topic":96,"secondary_topics":101,"classification":102,"search_phrases":124,"slug":127,"view_count":65,"doi":9,"paper":128,"created_at":137},5299,"刘宝涛等《统计与决策》:数字农业与粮食绿色生产效率的耦合协调特征及障碍机制","https:\u002F\u002Fguoyanwang.clcn.net.cn\u002FDRCNet.Mirror.Documents.Web\u002FDocSummary.aspx?DocID=8315929&leafID=18927","吉林农业大学经济管理学院刘宝涛副教授等基于2011—2023年中国31个省份面板数据,运用熵权TOPSIS法、超效率SBM模型测算数字农业发展水平和粮食绿色生产效率;研究期内数字农业发展水平呈\"小幅波动、平稳上升\"时序演化趋势,空间格局呈东南高、西北低、中部均衡;耦合协调度表现为波动上升趋势、由失调衰退向协调发展;粮食产量、科学研究和技术服务业固定资产投资额是主要障碍因子。","统计与决策(2026,11)","2026-10-15T00:00:00Z",78,{"impact":86,"substance":87,"depth":86,"authority":88,"freshness":75,"relevant":21,"comment":89},18,22,14,"基于31省11年面板数据的实证研究，方法规范、结论有政策参考价值，但属学术论文，公共影响有限，可入精选但非头条。",[91],{"name":82,"url":80},[93,26,27,94,95],"数字农业","耦合协调","绿色生产",{"code":44,"slug":97,"name":98,"path":99,"parent_code":100},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":103,"locked":14,"version":34,"method":35,"evidence":104},"classified",{"candidates":105,"agricultural_evidence":48},[106,110,114,119],{"code":44,"score":20,"supported":48,"title":107,"abstract":108,"tags":109},[93],[93],[26,93],{"code":39,"score":21,"supported":14,"title":111,"abstract":112,"tags":113},[],[],[27],{"code":115,"score":21,"supported":14,"title":116,"abstract":117,"tags":118},"H8",[],[],[95],{"code":120,"score":21,"supported":14,"title":121,"abstract":122,"tags":123},"I4",[],[],[94],[125,126],"刘宝涛 数字农业 粮食绿色生产效率","吉林农业大学 数字农业 耦合协调","刘宝涛数字农业粮食绿色生产效率-5299",{"doi":9,"openalex_id":9,"authors":129,"venue":9,"cited_by_count":65,"oa_url":9,"card":130,"direction":134,"ingested_from":136},[],{"tldr":131,"method":132,"finding":133,"direction":134,"opportunity":135},"测度2011—2023年中国31省数字农业与粮食绿色生产效率的耦合协调及障碍因子。","熵权TOPSIS法、超效率SBM模型，31省面板数据。","耦合协调度波动上升、由失调转向协调；粮食产量与科研投资是主要障碍因子。","农业绿色发展与碳","可深入探究障碍因子的省际异质性与动态传导机制，并检验数字农业对绿色效率的门槛或空间溢出效应。","agent","2026-10-19T00:07:48.451681Z",{"id":139,"title":140,"url":141,"summary":142,"summary_zh":143,"content":9,"source_name":144,"source_url":141,"published_at":145,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":146,"score_detail":147,"sources":149,"tags":151,"primary_topic":155,"secondary_topics":160,"classification":161,"search_phrases":189,"slug":192,"view_count":193,"doi":194,"paper":195,"created_at":213},4355,"Agricultural Flood Damage Assessment: A Systematic Review of Methods, Coverage, and Integration Gaps","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fegusphere-2026-3701","Abstract. Agricultural flooding causes tens of billions of dollars in annual losses globally, yet the methods used to assess these losses systematically capture only a fraction of their true economic scope. This systematic review synthesizes 65 peer-reviewed studies published between 1984 and 2025 to evaluate how agricultural flood damage is measured, what damage categories are covered, and where critical methodological gaps persist. We find a structural asymmetry across the reviewed literature – all 65 reviewed studies address direct immediate damages – primarily crop yield loss estimated through depth-damage functions or remote sensing – while direct induced effects appear in only 8 studies, indirect damages in 9, and intangible damages in fewer than 5. This asymmetry has persisted for four decades, through a major remote sensing revolution, because the dominant methods are structurally incapable of measuring damage categories beyond direct immediate losses. We introduce a Methods-by-Damage-Category Matrix that maps eight assessment methods against five damage categories, making the coverage gap visible and providing a diagnostic framework for method selection. Three post-2013 damage frontiers – long-term land productivity degradation, supply chain finance disruption, and farmer mental health – are identified as emerging categories warranting dedicated methodological development. Existing agricultural flood damage estimates used in benefit–cost analyses and disaster compensation are systematically downward-biased; closing this bias requires integrating remote sensing-based physical detection with economic valuation methods capable of capturing the full damage taxonomy.","摘要：农业洪水每年在全球造成数百亿美元损失，然而用于系统评估这些损失的方法仅能捕捉其真实经济影响的一小部分。本系统综述综合了1984年至2025年间发表的65项同行评审研究，评估农业洪水损失如何被测量、涵盖了哪些损失类别，以及关键方法论空白存在于何处。我们发现，所综述文献中存在结构性不对称——全部65项研究均涉及直接即时损失，主要是通过深度-损失函数或遥感估算的作物产量损失——而直接诱发效应仅出现在8项研究中，间接损失出现在9项研究中，无形损失则不足5项。这种不对称已持续四十年，历经遥感技术的重大革命，因为主流方法在结构上无法测量直接即时损失之外的损失类别。我们引入“方法-损失类别矩阵”，将八种评估方法与五类损失类别进行对应，使覆盖缺口清晰可见，并为方法选择提供诊断框架。2013年后的三个损失前沿——长期土地生产力退化、供应链金融中断和农民心理健康——被识别为值得专门方法论开发的新兴类别。现有用于成本效益分析和灾害补偿的农业洪水损失估算存在系统性低估；弥合这一偏差需要将基于遥感的物理检测与能够捕捉完整损失分类的经济估值方法相结合。","OpenAlex","2026-10-06T00:00:00Z",79,{"impact":86,"substance":87,"depth":86,"authority":20,"freshness":17,"relevant":21,"comment":148},"系统综述揭示农业洪灾损失评估长期偏低估的方法学缺口，提出方法-损失类别矩阵，对灾害补偿与成本收益分析有实质参考价值。",[150],{"name":144,"url":141},[26,27,152,153,154],"农业保险","遥感监测","灾害评估",{"code":156,"slug":157,"name":158,"path":159,"parent_code":100},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":103,"locked":14,"version":34,"method":35,"evidence":162},{"candidates":163,"agricultural_evidence":48},[164,171,175,180,185],{"code":156,"score":165,"supported":48,"title":166,"abstract":167,"tags":170},7,[],[168,169],"遥感","remote sensing",[153],{"code":39,"score":21,"supported":14,"title":172,"abstract":173,"tags":174},[],[],[27],{"code":176,"score":21,"supported":14,"title":177,"abstract":178,"tags":179},"G3",[],[],[152],{"code":181,"score":21,"supported":14,"title":182,"abstract":183,"tags":184},"H7",[],[],[154],{"code":44,"score":21,"supported":14,"title":186,"abstract":187,"tags":188},[],[],[26],[190,191],"农业洪涝灾害 损失评估","遥感 农作物 灾害损失","农业洪涝灾害损失评估-4355",2,"10.5194\u002Fegusphere-2026-3701",{"doi":194,"openalex_id":196,"authors":197,"venue":9,"cited_by_count":65,"oa_url":141,"card":207,"direction":211,"ingested_from":72},"W7220668753",[198,201,204],{"name":199,"orcid":200},"Shokhrukh–Mirzo Jalilov","https:\u002F\u002Forcid.org\u002F0000-0002-2428-9094",{"name":202,"orcid":203},"Haluk Gedikoğlu","https:\u002F\u002Forcid.org\u002F0000-0002-0158-2597",{"name":205,"orcid":206},"Robert Maltsbarger","https:\u002F\u002Forcid.org\u002F0009-0003-7801-8390",{"tldr":208,"method":209,"finding":210,"direction":211,"opportunity":212},"系统综述65项农业洪水损失评估研究，揭示方法仅覆盖直接即时损失的结构性偏差。","系统综述1984-2025年65篇文献，构建方法-损失类别矩阵。","所有研究仅测直接即时损失，间接、诱导及无形损失严重缺失，致损失估计系统性偏低。","农业遥感与作物表型","可开发融合遥感与经济学估值的方法，量化长期土地退化、供应链金融与农户心理健康等新兴损失类别。","2026-10-07T23:30:25.887527Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":9,"content":9,"source_name":219,"source_url":217,"published_at":220,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":221,"score_detail":222,"sources":226,"tags":228,"primary_topic":9,"secondary_topics":232,"classification":233,"search_phrases":257,"slug":260,"view_count":261,"doi":262,"paper":263,"created_at":280},2621,"Spatiotemporal mismatches in China's grain production under dual perspectives of regional comparability and global benchmarking","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104986","Spatiotemporal mismatches in China's grain production under dual perspectives of regional comparability and global benchmarking。Agricultural Systems","Agricultural Systems","2026-09-16T00:00:00Z",84,{"impact":87,"substance":223,"depth":86,"authority":88,"freshness":224,"relevant":21,"comment":225},21,9,"核心期刊论文，从区域可比性与全球对标双视角揭示中国粮食生产时空错配，方法新颖、数据规模大，对粮食安全布局有参考价值，值得进入每日精选。",[227],{"name":219,"url":217},[229,26,27,230,231],"农业遥感","空间分析","粮食生产",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":234},{"candidates":235,"agricultural_evidence":48,"reason":49},[236,240,245,249,253],{"code":39,"score":193,"supported":14,"title":237,"abstract":238,"tags":239},[],[],[27,231],{"code":241,"score":21,"supported":14,"title":242,"abstract":243,"tags":244},"A",[],[],[231],{"code":44,"score":21,"supported":14,"title":246,"abstract":247,"tags":248},[],[],[26],{"code":156,"score":21,"supported":14,"title":250,"abstract":251,"tags":252},[],[],[229],{"code":120,"score":21,"supported":14,"title":254,"abstract":255,"tags":256},[],[],[230],[258,259],"农业信息化 农业遥感 空间分析 粮食安全","农业信息化 农业遥感","农业信息化农业遥感空间分析粮食安全-2621",4,"10.1016\u002Fj.agsy.2026.104986",{"doi":262,"openalex_id":264,"authors":265,"venue":219,"cited_by_count":65,"oa_url":9,"card":9,"direction":9,"ingested_from":72},"W7213276104",[266,269,272,275,278],{"name":267,"orcid":268},"Jingchen Wang","https:\u002F\u002Forcid.org\u002F0000-0003-2367-852X",{"name":270,"orcid":271},"Sufen Wang","https:\u002F\u002Forcid.org\u002F0000-0002-8767-2197",{"name":273,"orcid":274},"Yunfei Fan","https:\u002F\u002Forcid.org\u002F0000-0002-1363-6433",{"name":276,"orcid":277},"Yu Hou","https:\u002F\u002Forcid.org\u002F0000-0002-3746-2415",{"name":279,"orcid":9},"Ke Xu","2026-09-16T23:30:05.185625Z",{"id":282,"title":283,"url":284,"summary":285,"summary_zh":9,"content":9,"source_name":10,"source_url":284,"published_at":286,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":146,"score_detail":287,"sources":292,"tags":294,"primary_topic":9,"secondary_topics":296,"classification":297,"search_phrases":308,"slug":311,"view_count":312,"doi":313,"paper":314,"created_at":332},1251,"Digital technology and grain industrial chain resilience: mechanisms, heterogeneity, and threshold effects","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1847872","Introduction Against the backdrop of rising global food system vulnerabilities driven by climate shocks, geopolitical disruptions and pandemic aftershocks, enhancing grain industrial chain resilience has become a strategic priority for countries worldwide. Existing literature has widely documented the productivity gains of digital technology in agriculture, yet the causal pathways and boundary conditions through which digitalization shapes grain industrial chain resilience remain under-explored. Methods Drawing on panel data for 285 prefecture-level cities in China spanning 2014 to 2024, this study develops a composite digital technology index and assesses the resilience of the grain industrial chain across three dimensions grounded in resilience theory: resistance, recovery, and transformation capacity. Employing a two-way fixed effects model, instrumental variable estimation, panel threshold model and mediated effects framework, we examine the causal impact, transmission mechanisms and heterogeneous patterns of digital technology on grain industrial chain resilience. Results The results indicate that digital technology exerts a significant positive effect on grain industrial chain resilience, and this finding remains robust after endogeneity adjustment and multiple robustness checks. Mechanism analysis demonstrates that digital technology enhances resilience through two complementary channels: improving resource allocation efficiency and fostering industrial synergy and integration. Heterogeneity analysis shows that the resilience-enhancing effect of digital technology is stronger in eastern regions and plain areas, while dimensional differences appear only as a point-estimate pattern and lack statistical verification. Threshold tests confirm two single-threshold effects, with digital infrastructure and farmers' digital literacy serving as boundary conditions. Further dimensional analysis shows a differentiated constraint pattern. Discussion This study provides empirical evidence and targeted policy references for digital transformation in the grain sector.","2026-08-25T00:00:00Z",{"impact":87,"substance":288,"depth":289,"authority":290,"freshness":193,"relevant":21,"comment":291},24,19,12,"基于中国地级市面板数据的实证研究，揭示数字技术提升粮食产业链韧性的机制与边界条件，对政策制定有参考价值。",[293],{"name":10,"url":284},[93,26,27,295],"产业链韧性",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":298},{"candidates":299,"agricultural_evidence":48,"reason":49},[300,304],{"code":44,"score":193,"supported":14,"title":301,"abstract":302,"tags":303},[],[],[26,93],{"code":39,"score":21,"supported":14,"title":305,"abstract":306,"tags":307},[],[],[27],[309,310],"产业链韧性 农业信息化 数字农业 粮食安全","产业链韧性 农业信息化","产业链韧性农业信息化数字农业粮食安全-1251",3,"10.3389\u002Ffsufs.2026.1847872",{"doi":313,"openalex_id":315,"authors":316,"venue":10,"cited_by_count":65,"oa_url":325,"card":326,"direction":330,"ingested_from":72},"W7204197128",[317,320,322],{"name":318,"orcid":319},"Hao Li","https:\u002F\u002Forcid.org\u002F0009-0007-0547-5071",{"name":321,"orcid":9},"Yuqiao Ma",{"name":323,"orcid":324},"Zihan Yang","https:\u002F\u002Forcid.org\u002F0000-0003-4305-049X","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1847872\u002Fpdf",{"tldr":327,"method":328,"finding":329,"direction":330,"opportunity":331},"研究数字技术对中国粮食产业链韧性的影响、机制与边界条件。","基于2014-2024年285个城市面板数据，采用双向固定效应、工具变量、面板门","数字技术显著提升粮食产业链韧性，通过优化资源配置和产业协同，且存在基础设施和农民数字素养的门槛效应。","农业人工智能与决策模型","可探索数字技术对粮食产业链韧性的非线性影响，特别是不同维度（如恢复力）的差异化约束机制。","2026-09-01T04:03:09.775335Z",{"id":334,"title":335,"url":336,"summary":337,"summary_zh":338,"content":9,"source_name":339,"source_url":336,"published_at":340,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":341,"score_detail":342,"sources":344,"tags":346,"primary_topic":350,"secondary_topics":355,"classification":356,"search_phrases":382,"slug":385,"view_count":65,"doi":386,"paper":387,"created_at":397},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",74,{"impact":290,"substance":87,"depth":86,"authority":290,"freshness":13,"relevant":21,"comment":343},"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[345],{"name":339,"url":336},[347,348,27,168,349],"智慧农业","农业人工智能","气候分析",{"code":351,"slug":352,"name":353,"path":354,"parent_code":100},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":103,"locked":14,"version":34,"method":35,"evidence":357},{"candidates":358,"agricultural_evidence":48},[359,364,370,374,378],{"code":351,"score":224,"supported":48,"title":360,"abstract":362,"tags":363},[361],"artificial intelligence",[],[348],{"code":365,"score":312,"supported":48,"title":366,"abstract":367,"tags":369},"I5",[],[368],"农业传感",[],{"code":39,"score":21,"supported":14,"title":371,"abstract":372,"tags":373},[],[],[27],{"code":44,"score":21,"supported":14,"title":375,"abstract":376,"tags":377},[],[],[347],{"code":156,"score":21,"supported":14,"title":379,"abstract":380,"tags":381},[],[],[168],[383,384],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":386,"openalex_id":388,"authors":389,"venue":339,"cited_by_count":65,"oa_url":336,"card":392,"direction":330,"ingested_from":72},"W7223111766",[390],{"name":391,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":393,"method":394,"finding":395,"direction":211,"opportunity":396},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","2026-10-22T11:21:02.682497Z",{"id":399,"title":400,"url":401,"summary":402,"summary_zh":9,"content":9,"source_name":403,"source_url":401,"published_at":340,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":404,"score_detail":405,"sources":407,"tags":409,"primary_topic":411,"secondary_topics":412,"classification":413,"search_phrases":425,"slug":428,"view_count":65,"doi":429,"paper":430,"created_at":446},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",38,{"impact":17,"substance":75,"depth":17,"authority":75,"freshness":13,"relevant":21,"comment":406},"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。",[408],{"name":403,"url":401},[93,26,168,410],"地图评估",{"code":44,"slug":97,"name":98,"path":99,"parent_code":100},[],{"status":103,"locked":14,"version":34,"method":35,"evidence":414},{"candidates":415,"agricultural_evidence":48},[416,421],{"code":44,"score":20,"supported":48,"title":417,"abstract":419,"tags":420},[418],"digital agriculture",[418],[26,93],{"code":156,"score":21,"supported":14,"title":422,"abstract":423,"tags":424},[],[],[168],[426,427],"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":429,"openalex_id":431,"authors":432,"venue":403,"cited_by_count":65,"oa_url":401,"card":9,"direction":445,"ingested_from":72},"W7223099987",[433,436,439,442],{"name":434,"orcid":435},"Phillip Lanza","https:\u002F\u002Forcid.org\u002F0000-0002-5545-1744",{"name":437,"orcid":438},"David G. Rossiter","https:\u002F\u002Forcid.org\u002F0000-0003-4558-1286",{"name":440,"orcid":441},"Diana Stuart Sinton","https:\u002F\u002Forcid.org\u002F0000-0001-5828-9001",{"name":443,"orcid":444},"Louis Longchamps","https:\u002F\u002Forcid.org\u002F0000-0002-4761-6094","数字乡村与农业信息化","2026-10-22T11:20:47.463814Z"]