[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2621":3,"related-2621":92},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":66,"slug":69,"view_count":70,"doi":71,"paper":72,"created_at":91},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",null,"Agricultural Systems","2026-09-16T00:00:00Z","论文",10,false,84,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,21,18,14,9,1,"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。",[24],{"name":9,"url":6},[26,27,28,29,30],"农业遥感","农业信息化","粮食安全","空间分析","粮食生产",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":36},"unclassified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":64,"reason":65},[38,44,49,54,59],{"code":39,"score":40,"supported":13,"title":41,"abstract":42,"tags":43},"A8",2,[],[],[28,30],{"code":45,"score":21,"supported":13,"title":46,"abstract":47,"tags":48},"A",[],[],[30],{"code":50,"score":21,"supported":13,"title":51,"abstract":52,"tags":53},"I1",[],[],[27],{"code":55,"score":21,"supported":13,"title":56,"abstract":57,"tags":58},"I3",[],[],[26],{"code":60,"score":21,"supported":13,"title":61,"abstract":62,"tags":63},"I4",[],[],[29],true,"缺少足够的主题证据",[67,68],"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":71,"openalex_id":73,"authors":74,"venue":9,"cited_by_count":89,"oa_url":8,"card":8,"direction":8,"ingested_from":90},"W7213276104",[75,78,81,84,87],{"name":76,"orcid":77},"Jingchen Wang","https:\u002F\u002Forcid.org\u002F0000-0003-2367-852X",{"name":79,"orcid":80},"Sufen Wang","https:\u002F\u002Forcid.org\u002F0000-0002-8767-2197",{"name":82,"orcid":83},"Yunfei Fan","https:\u002F\u002Forcid.org\u002F0000-0002-1363-6433",{"name":85,"orcid":86},"Yu Hou","https:\u002F\u002Forcid.org\u002F0000-0002-3746-2415",{"name":88,"orcid":8},"Ke Xu",0,"openalex","2026-09-16T23:30:05.185625Z",{"total":93,"page":21,"page_size":93,"items":94},6,[95,153,228,293,345,422],{"id":96,"title":97,"url":98,"summary":99,"summary_zh":8,"content":8,"source_name":100,"source_url":8,"published_at":101,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":102,"score_detail":103,"sources":105,"tags":107,"primary_topic":111,"secondary_topics":116,"classification":117,"search_phrases":139,"slug":142,"view_count":89,"doi":8,"paper":143,"created_at":152},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":18,"substance":16,"depth":18,"authority":19,"freshness":93,"relevant":21,"comment":104},"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。",[106],{"name":100,"url":98},[108,27,28,109,110],"数字农业","耦合协调","绿色生产",{"code":50,"slug":112,"name":113,"path":114,"parent_code":115},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":118,"locked":13,"version":34,"method":35,"evidence":119},"classified",{"candidates":120,"agricultural_evidence":64},[121,126,130,135],{"code":50,"score":122,"supported":64,"title":123,"abstract":124,"tags":125},13,[108],[108],[27,108],{"code":39,"score":21,"supported":13,"title":127,"abstract":128,"tags":129},[],[],[28],{"code":131,"score":21,"supported":13,"title":132,"abstract":133,"tags":134},"H8",[],[],[110],{"code":60,"score":21,"supported":13,"title":136,"abstract":137,"tags":138},[],[],[109],[140,141],"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":8,"openalex_id":8,"authors":144,"venue":8,"cited_by_count":89,"oa_url":8,"card":145,"direction":149,"ingested_from":151},[],{"tldr":146,"method":147,"finding":148,"direction":149,"opportunity":150},"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":154,"title":155,"url":156,"summary":157,"summary_zh":158,"content":8,"source_name":159,"source_url":156,"published_at":160,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":161,"score_detail":162,"sources":165,"tags":167,"primary_topic":171,"secondary_topics":175,"classification":176,"search_phrases":204,"slug":207,"view_count":40,"doi":208,"paper":209,"created_at":227},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":18,"substance":16,"depth":18,"authority":122,"freshness":163,"relevant":21,"comment":164},8,"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。",[166],{"name":159,"url":156},[27,28,168,169,170],"农业保险","遥感监测","灾害评估",{"code":55,"slug":172,"name":173,"path":174,"parent_code":115},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":118,"locked":13,"version":34,"method":35,"evidence":177},{"candidates":178,"agricultural_evidence":64},[179,186,190,195,200],{"code":55,"score":180,"supported":64,"title":181,"abstract":182,"tags":185},7,[],[183,184],"遥感","remote sensing",[169],{"code":39,"score":21,"supported":13,"title":187,"abstract":188,"tags":189},[],[],[28],{"code":191,"score":21,"supported":13,"title":192,"abstract":193,"tags":194},"G3",[],[],[168],{"code":196,"score":21,"supported":13,"title":197,"abstract":198,"tags":199},"H7",[],[],[170],{"code":50,"score":21,"supported":13,"title":201,"abstract":202,"tags":203},[],[],[27],[205,206],"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","10.5194\u002Fegusphere-2026-3701",{"doi":208,"openalex_id":210,"authors":211,"venue":8,"cited_by_count":89,"oa_url":156,"card":221,"direction":225,"ingested_from":90},"W7220668753",[212,215,218],{"name":213,"orcid":214},"Shokhrukh–Mirzo Jalilov","https:\u002F\u002Forcid.org\u002F0000-0002-2428-9094",{"name":216,"orcid":217},"Haluk Gedikoğlu","https:\u002F\u002Forcid.org\u002F0000-0002-0158-2597",{"name":219,"orcid":220},"Robert Maltsbarger","https:\u002F\u002Forcid.org\u002F0009-0003-7801-8390",{"tldr":222,"method":223,"finding":224,"direction":225,"opportunity":226},"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":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":8,"source_name":234,"source_url":231,"published_at":235,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":236,"score_detail":237,"sources":239,"tags":241,"primary_topic":245,"secondary_topics":250,"classification":251,"search_phrases":267,"slug":270,"view_count":271,"doi":272,"paper":273,"created_at":292},3529,"Cultivated land system resilience in China’s major rice-producing areas: spatiotemporal patterns and zonal regulation strategies","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1942721","Introduction Cultivated land systems face convergent pressures from land-use change, farmland marginalization, and dietary upgrading, and thus understanding their resilience is essential for sustaining agricultural functions and food security. Methods Focusing on China’s major rice-producing areas (MRPAs), we developed a spatially explicit resilience framework that integrates internal adaptive capacities and external socio-economic conditions, and examined the spatial and temporal patterns of cultivated land system resilience using three representative years (2000, 2010, and 2023). We quantified resilience, diagnosed dominant constraints via an obstacle model, and developed differentiated regulation pathways based on the identified constraints. Results Results show that: (1) overall resilience increased with fluctuations, with a growing role of socio-economic conditions alongside persistent environmental constraints. This improvement coincided with infrastructure development and policy interventions, although urbanization pressures may have constrained resilience in peri-urban areas. (2) resilience exhibited marked spatial differentiation. Large-scale agricultural regions maintained higher resilience, coinciding with favorable natural endowments and relatively well-developed infrastructure, whereas fragmented and highly urbanized areas remained more vulnerable amid intensified human–land conflicts. (3) dominant obstacles shifted from natural background constraints to socio-economic factors over time. Four regulation zones were identified: background-restricted low-resilience, external-stress resilience risk, resource-constrained lagging recovery, and market-sensitive hidden risk. Discussion These findings provide a diagnostic basis for differentiated management by tailoring strategies to region-specific resilience constraints.","引言 耕地系统面临土地利用变化、农田边际化和饮食升级等多重趋同压力，因此理解其韧性对于维持农业功能和粮食安全至关重要。方法 本文以中国水稻主产区为研究区，构建了一个整合内部适应能力与外部社会经济条件的空间显式韧性框架，并选取2000年、2010年和2023年三个代表性年份，考察耕地系统韧性的时空格局。我们量化了韧性水平，通过障碍度模型诊断了主导约束因子，并基于识别出的约束条件提出了差异化调控路径。结果 结果表明：(1) 总体韧性呈波动上升趋势，社会经济条件的作用不断增强，而环境约束持续存在。这一改善与基础设施建设和政策干预相吻合，尽管城市化压力可能制约了城郊地区的韧性提升。(2) 韧性表现出显著的空间分异。大规模农业区域维持了较高的韧性，这与有利的自然禀赋和相对完善的基础设施相吻合，而破碎化程度高和高度城市化的区域在人地矛盾加剧的背景下仍较为脆弱。(3) 主导障碍因子随时间推移从自然本底约束转向社会经济因素。研究识别出四类调控分区：本底限制型低韧性区、外部胁迫型韧性风险区、资源约束型滞后恢复区和市场敏感型隐性风险区。讨论 这些发现为因地制宜制定差异化策略、针对区域特定韧性约束实施差异化管理提供了诊断依据。","Frontiers in Sustainable Food Systems","2026-09-25T00:00:00Z",80,{"impact":18,"substance":16,"depth":18,"authority":122,"freshness":20,"relevant":21,"comment":238},"基于2000—2023年三时相数据构建耕地系统韧性评估框架并划分四类调控区，方法新颖、结论具体，对主产区差异化治理有参考价值。",[240],{"name":234,"url":231},[26,28,242,243,244],"耕地韧性","水稻主产区","分区调控",{"code":246,"slug":247,"name":248,"path":249,"parent_code":45},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice",[],{"status":118,"locked":13,"version":34,"method":35,"evidence":252},{"candidates":253,"agricultural_evidence":64},[254,258,263],{"code":246,"score":19,"supported":64,"title":255,"abstract":256,"tags":257},[247],[248,247],[],{"code":39,"score":180,"supported":64,"title":259,"abstract":260,"tags":262},[],[28,261],"food security",[28],{"code":55,"score":21,"supported":13,"title":264,"abstract":265,"tags":266},[],[],[26],[268,269],"中国水稻主产区 耕地韧性","耕地系统韧性 时空格局","中国水稻主产区耕地韧性-3529",5,"10.3389\u002Ffsufs.2026.1942721",{"doi":272,"openalex_id":274,"authors":275,"venue":234,"cited_by_count":89,"oa_url":231,"card":287,"direction":149,"ingested_from":90},"W7214283232",[276,279,282,285],{"name":277,"orcid":278},"Renjing Wang","https:\u002F\u002Forcid.org\u002F0009-0003-3257-5140",{"name":280,"orcid":281},"Xinrui Li","https:\u002F\u002Forcid.org\u002F0000-0002-8653-5828",{"name":283,"orcid":284},"Qi Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5672-7095",{"name":286,"orcid":8},"Jiayan Yang",{"tldr":288,"method":289,"finding":290,"direction":149,"opportunity":291},"构建耕地系统韧性评估框架，分析中国水稻主产区2000-2023年韧性时空格局并提出分区调控策略。","构建空间显式韧性框架，结合内部适应能力与外部社会经济条件，用障碍度模型诊断主导约","韧性总体波动上升，主导障碍由自然背景转向社会经济因素，识别出四类调控分区。","可探究城市化边缘区韧性下降机制，并将韧性框架与碳汇\u002F生态服务耦合，支撑分区精准调控。","2026-09-26T23:30:07.427444Z",{"id":294,"title":295,"url":296,"summary":297,"summary_zh":298,"content":8,"source_name":234,"source_url":296,"published_at":299,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":300,"score_detail":301,"sources":305,"tags":307,"primary_topic":8,"secondary_topics":311,"classification":312,"search_phrases":323,"slug":326,"view_count":21,"doi":327,"paper":328,"created_at":344},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。","2026-09-22T00:00:00Z",67,{"impact":163,"substance":302,"depth":303,"authority":122,"freshness":12,"relevant":21,"comment":304},20,16,"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。",[306],{"name":234,"url":296},[27,28,308,309,310],"原住民社区","食物不安全量表","农村监测",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":313},{"candidates":314,"agricultural_evidence":64,"reason":65},[315,319],{"code":39,"score":21,"supported":13,"title":316,"abstract":317,"tags":318},[],[],[28],{"code":50,"score":21,"supported":13,"title":320,"abstract":321,"tags":322},[],[],[27],[324,325],"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":327,"openalex_id":329,"authors":330,"venue":234,"cited_by_count":89,"oa_url":296,"card":338,"direction":342,"ingested_from":90},"W7213967028",[331,333,336],{"name":332,"orcid":8},"Rafael Pérez-Escamilla",{"name":334,"orcid":335},"Sofía Segura‐Pérez","https:\u002F\u002Forcid.org\u002F0000-0001-8717-3315",{"name":337,"orcid":8},"Haydee Amalia Segura-Millán",{"tldr":339,"method":340,"finding":341,"direction":342,"opportunity":343},"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。","2026-09-22T23:30:07.744222Z",{"id":346,"title":347,"url":348,"summary":349,"summary_zh":350,"content":8,"source_name":351,"source_url":348,"published_at":352,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":353,"score_detail":354,"sources":358,"tags":360,"primary_topic":363,"secondary_topics":369,"classification":370,"search_phrases":398,"slug":401,"view_count":40,"doi":402,"paper":403,"created_at":421},3060,"Geospatial Intelligence for Peri-Urban Land-Use Conflicts: Evaluating Agricultural Suitability against Rapid Urbanisation using the Analytical Hierarchy Process and Cloud Computing","https:\u002F\u002Fdoi.org\u002F10.59543\u002F6mpcwr41","This paper presents a geospatial multi-criteria evaluation of agricultural potential in the suburban region of Bapatla using eight physical and land-use characteristics: elevation, slope, road accessibility, proximity to water bodies, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), Land Use\u002FLand Cover (LULC), and soil texture, processed using Google Earth Engine. The Analytic Hierarchy Process (AHP) was used to determine the relative weights of each criterion. NDVI received the highest weight (26.33%), followed by LST, slope, and proximity to water bodies (14.96% each), while elevation received the lowest weight (4.6%) due to the region's flat terrain. Weighted overlay analysis classified the 142.25 km² study area into Suitable (108.90 km²; 76.55%), Not Suitable (32.60 km²; 22.92%), and Highly Suitable (0.75 km²; 0.53%) categories. Suitable areas are mainly distributed across the southern and peripheral agricultural zones, whereas unsuitable areas are concentrated within Bapatla Urban and its surroundings. The limited extent of highly suitable land highlights the scarcity of optimal agricultural sites. The results reveal land-use conflicts driven primarily by urbanisation rather than environmental constraints. The AHP-weighted suitability map provides an evidence-based tool for agricultural land conservation, water-resource management, and sustainable urban expansion.","本文基于八项自然与土地利用特征，对巴帕特拉（Bapatla）郊区农业潜力进行了地理空间多准则评价，这些特征包括：海拔、坡度、道路可达性、距水体远近、地表温度（LST）、归一化植被指数（NDVI）、土地利用\u002F土地覆盖（LULC）以及土壤质地，并利用Google Earth Engine进行处理。采用层次分析法（AHP）确定各准则的相对权重。NDVI权重最高（26.33%），其次为LST、坡度和距水体远近（均为14.96%），而海拔因该地区地形平坦权重最低（4.6%）。加权叠加分析将142.25 km²的研究区划分为适宜（108.90 km²；76.55%）、不适宜（32.60 km²；22.92%）和高适宜（0.75 km²；0.53%）三类。适宜区主要分布于南部及外围农业区，而不适宜区集中于巴帕特拉城区及其周边。高适宜土地面积有限，凸显了优质农业用地的稀缺性。结果表明，土地利用冲突主要由城市化驱动，而非环境限制。基于AHP的适宜性地图为农业用地保护、水资源管理和可持续城市扩张提供了循证工具。","Journal of Urban Intelligence and Smart Systems","2026-09-19T00:00:00Z",70,{"impact":355,"substance":302,"depth":356,"authority":122,"freshness":163,"relevant":21,"comment":357},12,17,"该论文利用遥感与AHP方法评估城郊农业用地冲突，方法新颖、数据详实，对农业土地保护有参考价值，但属细分领域研究，影响范围有限。",[359],{"name":351,"url":348},[361,26,27,183,362],"智慧农业","土地利用",{"code":364,"slug":365,"name":366,"path":367,"parent_code":368},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":118,"locked":13,"version":34,"method":35,"evidence":371},{"candidates":372,"agricultural_evidence":64},[373,378,385,389,393],{"code":364,"score":93,"supported":64,"title":374,"abstract":375,"tags":377},[],[376,365],"土壤",[],{"code":379,"score":380,"supported":64,"title":381,"abstract":382,"tags":384},"H3",3,[],[383],"水资源",[],{"code":50,"score":40,"supported":13,"title":386,"abstract":387,"tags":388},[],[],[27,361],{"code":55,"score":40,"supported":13,"title":390,"abstract":391,"tags":392},[],[],[26,183],{"code":394,"score":21,"supported":13,"title":395,"abstract":396,"tags":397},"H6",[],[],[362],[399,400],"Bapatla 农业用地 城市化","Google Earth Engine 农业适宜性","Bapatla农业用地城市化-3060","10.59543\u002F6mpcwr41",{"doi":402,"openalex_id":404,"authors":405,"venue":351,"cited_by_count":89,"oa_url":414,"card":415,"direction":420,"ingested_from":90},"W7213644047",[406,408,410,412],{"name":407,"orcid":8},"Sreerama Naik Naik S R",{"name":409,"orcid":8},"T K Prasad",{"name":411,"orcid":8},"Feba Jose Jasmine",{"name":413,"orcid":8},"Jayapal G","https:\u002F\u002Fjuiss.org\u002Findex.php\u002Fjuiss\u002Farticle\u002Fdownload\u002F366\u002F231",{"tldr":416,"method":417,"finding":418,"direction":225,"opportunity":419},"用AHP与云平台评估印度Bapatla城郊农业适宜性，揭示城市化引发的土地利用冲突。","Google Earth Engine处理8个因子，AHP加权叠加分析142.2","76.55%区域适宜农业，但高度适宜仅0.53%，冲突主因是城市化而非环境限制。","可引入时序遥感与动态城市扩张模拟，构建城郊农业保护与城市增长协同优化模型。","智慧农业 \u002F 农业物联网","2026-09-21T23:30:09.448414Z",{"id":423,"title":424,"url":425,"summary":426,"summary_zh":427,"content":8,"source_name":428,"source_url":425,"published_at":429,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":430,"score_detail":431,"sources":433,"tags":435,"primary_topic":439,"secondary_topics":443,"classification":448,"search_phrases":489,"slug":492,"view_count":380,"doi":493,"paper":494,"created_at":506},3058,"Climate Change and Crop Production Vulnerability in Somalia: A VECM Analysis for Sustainable Agriculture","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.412749","Somali crop production is vulnerable to climate change, as agriculture is heavily reliant on rainfall. Increased temperatures, sporadic rainfall, and higher levels of CO2 emissions threaten food security as well as the livelihoods in rural areas. While global studies discuss these issues, there is a lack of empirical research on Somalia. This research attempts to study the gap created by the absence of research regarding the vulnerability of crop production to climate change and its sustainable agriculture aspects. The study uses a vector error correction model (VECM) to evaluate how climate factors, including temperature, rainfall, CO2 emissions, land designated for agriculture, and labour, affect crop production in the short and long term. The study found that CO₂, temperature, and rainfall lead to boosted crop production in the short term because of the traditional adaptation strategies employed by farmers. In the long term, a 1% increase in CO2 emissions leads to a 57% reduction in crop production, and a similar rise in temperature causes production levels to fall over 100%. Rainfall, agricultural land, and labour continue to positively influence production levels in both periods. These results strongly indicate the necessity of climate-resilient strategies, better irrigation systems, and new forms of farming. The promotion of climate-smart agriculture will help mitigate the negative effects of climate change on the agricultural sector of Somalia while simultaneously catering to the issue of food security.","索马里作物生产易受气候变化影响，因为农业高度依赖降雨。气温升高、降雨不稳定以及二氧化碳排放量增加，威胁着粮食安全以及农村地区的生计。尽管全球研究讨论了这些问题，但缺乏关于索马里的实证研究。本研究试图填补因缺乏关于作物生产对气候变化的脆弱性及其可持续农业方面的研究而造成的空白。该研究采用向量误差修正模型（VECM），评估气候因素（包括气温、降雨、二氧化碳排放、农业用地和劳动力）在短期和长期内如何影响作物生产。研究发现，由于农民采用传统适应策略，二氧化碳、气温和降雨在短期内会促进作物生产。从长期来看，二氧化碳排放量增加1%会导致作物生产减少57%，气温类似上升会导致生产水平下降超过100%。降雨、农业用地和劳动力在两个时期都持续对生产水平产生积极影响。这些结果强烈表明，有必要采取气候韧性策略、改善灌溉系统并采用新型耕作方式。推广气候智能型农业将有助于减轻气候变化对索马里农业部门的负面影响，同时兼顾粮食安全问题。","AgEcon Search (University of Minnesota, USA)","2026-09-20T00:00:00Z",71,{"impact":355,"substance":17,"depth":356,"authority":122,"freshness":163,"relevant":21,"comment":432},"以VECM量化索马里气候因子对作物生产的短长期影响，结论具政策参考价值，但属区域国别研究，公共影响有限。",[434],{"name":428,"url":425},[26,28,436,437,438],"气候智慧农业","灌溉系统","索马里农业",{"code":196,"slug":440,"name":441,"path":442,"parent_code":368},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[444],{"code":131,"slug":445,"name":446,"path":447,"parent_code":368},"sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":118,"locked":13,"version":34,"method":35,"evidence":449},{"candidates":450,"agricultural_evidence":64},[451,458,464,468,474,480,485],{"code":196,"score":452,"supported":64,"title":453,"abstract":455,"tags":457},15,[454],"climate change",[456,454],"气候变化",[436],{"code":131,"score":19,"supported":64,"title":459,"abstract":461,"tags":463},[460],"sustainable agriculture",[462,460],"可持续农业",[],{"code":39,"score":180,"supported":64,"title":465,"abstract":466,"tags":467},[],[28,261],[28],{"code":379,"score":93,"supported":64,"title":469,"abstract":470,"tags":473},[],[471,472],"灌溉","irrigation",[],{"code":475,"score":380,"supported":64,"title":476,"abstract":477,"tags":479},"H4",[],[478],"碳排放",[],{"code":50,"score":380,"supported":64,"title":481,"abstract":482,"tags":484},[],[483],"smart agriculture",[],{"code":55,"score":21,"supported":13,"title":486,"abstract":487,"tags":488},[],[],[26],[490,491],"索马里 作物生产 气候变化","VECM 气候智慧农业","索马里作物生产气候变化-3058","10.22004\u002Fag.econ.412749",{"doi":493,"openalex_id":495,"authors":496,"venue":428,"cited_by_count":89,"oa_url":425,"card":501,"direction":420,"ingested_from":90},"W7213772032",[497,499],{"name":498,"orcid":8},"Mohamed Nur Abdulkadir",{"name":500,"orcid":8},"Yassin Sheikh Ali Ali",{"tldr":502,"method":503,"finding":504,"direction":149,"opportunity":505},"用VECM分析索马里气候变化对作物生产的短期与长期影响。","VECM模型，变量含温度、降雨、CO2、农地、劳动力。","短期CO2、温度、降雨促产，长期CO2增1%减产57%，温度增1%减产超100%。","可结合气候智慧型农业与灌溉技术，研究小农户适应策略的长期有效性。","2026-09-21T23:30:09.204353Z"]