[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5383":3,"related-5383":89},{"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":29,"primary_topic":35,"secondary_topics":41,"classification":42,"search_phrases":65,"slug":68,"view_count":69,"doi":70,"paper":71,"created_at":88},5383,"The transition towards CSA as a relational model: shaping relational processes between farmers and governments, agri-food corporations and the financial sector","https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.34701594","Climate-smart agriculture (CSA) can enhance the mitigation and adaptation capacity of the sector. Yet progress towards CSA remains slow. Durable and scalable transformation requires more than technological adoption or changes in farmers’ capabilities; it depends on the relationships through which actors coordinate resources, interests, and institutional conditions. However, these interdependencies remain fragmented across the literature. This article aims to address this gap by presenting a relational framework for this transformation by focusing on the most impactful relational processes through an integrative review. We identified three relational mechanisms shaping CSA transitions: resource-mobilising, interest-aligning, and institutional-shaping. Across these mechanisms, transformative capacity depends less on the actors involved or the resources exchanged than on how the underlying relationship is configured. Drawing on Emerson’s power-dependence framework and building on Pahl-Wostl’s conceptualisation of governance modes, the proposed framework shows how relational mechanisms and their specific configuration can reproduce hierarchical arrangements or contribute to more networked forms of governance. This study contributes to the body of research on agri-food sustainability transitions and governance by shifting attention from individual adoption and actor roles to the relational configurations through which CSA transitions are enabled or constrained. Moreover, it provides practitioners with insights into mechanisms enhancing CSA adoption.","气候智慧型农业（climate-smart agriculture, CSA）能够增强该领域的减缓与适应能力。然而，迈向气候智慧型农业的进展仍然缓慢。持久且可扩展的转型不仅需要技术采纳或农民能力的变化，还依赖于行动者用以协调资源、利益与制度条件的关系。然而，这些相互依存关系在现有文献中仍呈碎片化状态。本文旨在通过整合性综述聚焦最具影响力的关系过程，提出一个用于该转型的关系框架，以弥补这一空白。我们识别出塑造气候智慧型农业转型的三种关系机制：资源动员、利益协调与制度塑造。在这些机制中，转型能力与其说取决于所涉及的行动者或所交换的资源，不如说取决于底层关系的配置方式。借鉴Emerson的权力—依赖框架，并以Pahl-Wostl关于治理模式的概念化为基础，所提出的框架展示了关系机制及其特定配置如何能够再生产等级化安排，或促进更具网络化形式的治理。本研究通过将关注点从个体采纳和行动者角色转向使气候智慧型农业转型得以实现或受到制约的关系配置，为农业食品可持续转型与治理研究作出了贡献。此外，本研究为实践者提供了关于促进气候智慧型农业采纳机制的洞见。",null,"Figshare","2026-10-19T00:00:00Z","论文",25,false,60,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,18,16,6,8,1,"该文以整合性综述提出气候智慧农业转型的关系性框架，学术增量明确，但属理论探讨、来源为预印本平台，公共影响有限，不宜进入每日精选。",[25,26],{"name":10,"url":6},{"name":27,"url":28},"International Journal of Agricultural Sustainability","https:\u002F\u002Fdoi.org\u002F10.1080\u002F14735903.2026.2742575",[30,31,32,33,34],"气候智慧农业","农业可持续","农业转型","农业治理","农户关系",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":43,"locked":14,"version":44,"method":45,"evidence":46},"classified","2026-10-22.2","rules",{"candidates":47,"agricultural_evidence":50},[48,55,60],{"code":36,"score":49,"supported":50,"title":51,"abstract":52,"tags":54},3,true,[],[53],"smart agriculture",[],{"code":56,"score":22,"supported":14,"title":57,"abstract":58,"tags":59},"H7",[],[],[30],{"code":61,"score":22,"supported":14,"title":62,"abstract":63,"tags":64},"H8",[],[],[31],[66,67],"气候智慧农业 农业可持续 农业治理 农业转型","气候智慧农业 农业可持续","气候智慧农业农业可持续农业治理农业转型-5383",0,"10.6084\u002Fm9.figshare.34701594",{"doi":70,"openalex_id":72,"authors":73,"venue":10,"cited_by_count":69,"oa_url":6,"card":80,"direction":86,"ingested_from":87},"W7222983506",[74,76,78],{"name":75,"orcid":9},"Eduardo Garcia-Igualada",{"name":77,"orcid":9},"Amparo Merino de Diego",{"name":79,"orcid":9},"Victor Luis de Nicolás",{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"通过整合综述提出关系框架，解释农民与政府、企业、金融部门的关系配置如何影响气候智慧型农业转型。","整合综述，基于Emerson权力依赖框架与Pahl-Wostl治理模式概念化三类","转型能力更取决于关系配置方式而非参与主体或交换资源，关系机制可复制等级安排或推动网络化治理。","农业绿色发展与碳","可将关系配置框架操作化为可测指标，结合中国小农户与政府、企业、金融互动数据检验其对CSA采纳的因果影响。","智慧农业 \u002F 农业物联网","openalex","2026-10-20T23:30:08.809392Z",{"total":20,"page":22,"page_size":20,"items":90},[91,141,197,278,325,369],{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":94,"published_at":98,"category":12,"cover_url":9,"hotness":99,"is_selected":14,"score":100,"score_detail":101,"sources":104,"tags":106,"primary_topic":111,"secondary_topics":112,"classification":113,"search_phrases":124,"slug":127,"view_count":69,"doi":128,"paper":129,"created_at":140},4654,"The Role of Indigenous Knowledge in Climate-Smart Agriculture: A Case Study of Weather Forecasting in Uganda","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23283885","The Role of Indigenous Knowledge in Climate-Smart Agriculture: A Case Study of Weather Forecasting in Uganda。Zenodo (CERN European Organization for Nuclear Research)","本土知识在气候智慧型农业中的作用：乌干达天气预报案例研究。Zenodo（CERN欧洲核子研究组织）","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z",10,67,{"impact":17,"substance":18,"depth":19,"authority":102,"freshness":21,"relevant":22,"comment":103},13,"非洲本土知识与气候智慧农业结合的案例研究，对国内农业气象信息化有借鉴价值，但地域相关性有限。",[105],{"name":97,"url":94},[107,30,108,109,110],"智慧农业","乌干达","传统知识","气象预报",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":114},{"candidates":115,"agricultural_evidence":50},[116,120],{"code":36,"score":17,"supported":50,"title":117,"abstract":118,"tags":119},[53],[53],[107],{"code":56,"score":22,"supported":14,"title":121,"abstract":122,"tags":123},[],[],[30],[125,126],"乌干达 气象预报 传统知识","气候智慧农业 土著知识","乌干达气象预报传统知识-4654","10.5281\u002Fzenodo.23283885",{"doi":128,"openalex_id":130,"authors":131,"venue":97,"cited_by_count":69,"oa_url":94,"card":134,"direction":86,"ingested_from":87},"W7221309644",[132],{"name":133,"orcid":9},"Academians Publishers",{"tldr":135,"method":136,"finding":137,"direction":138,"opportunity":139},"研究乌干达土著知识在气候智慧型农业天气预测中的作用。","案例研究，结合土著知识与气候智慧型农业。","土著天气预测知识对气候智慧型农业有重要价值。","数字乡村与农业信息化","可将土著天气预测知识与现代气象数据融合，开发本地化农业决策支持系统。","2026-10-11T23:30:07.345273Z",{"id":142,"title":143,"url":144,"summary":145,"summary_zh":146,"content":9,"source_name":147,"source_url":144,"published_at":148,"category":12,"cover_url":9,"hotness":99,"is_selected":14,"score":149,"score_detail":150,"sources":153,"tags":155,"primary_topic":160,"secondary_topics":161,"classification":162,"search_phrases":180,"slug":183,"view_count":69,"doi":184,"paper":185,"created_at":196},4421,"Impact of Climate-Smart Agricultural Education on Youth Livelihood in Bayelsa Riverine Communities","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijaes.vol.12.no9.2026.pg56.66","This study evaluated the impact of climate-smart agricultural education on youth livelihoods across riverine communities in Bayelsa State. Guided by three research questions and three null hypotheses, the author adopted a mixed-method design integrating a cross-sectional descriptive survey with a quasi-experimental field trial. The target population consisted of active farming youths within Ekeremor and Southern Ijaw Local Government Areas. From this frame, the Taro Yamane formula determined a descriptive survey sample of 195 youths, while a nested cohort of 60 youths was purposively selected and split evenly into experimental and control groups (n=30 each) for experimental tracking. Data collection relied on the verified Youth Climate-Smart Agriculture and Livelihood Questionnaire and the objective Climate-Smart Agriculture Competency Assessment Test. Descriptive questions were analyzed using mean and standard deviation, whereas the null hypotheses were tested via Analysis of Covariance (ANCOVA) at a 0.05 level of significance. The empirical findings demonstrated that targeted education in flood resilient crop production, advanced fish farming, and aquaponics significantly boosted practical vocational competencies, prompting the rejection of all three null hypotheses. The study concluded that institutionalized climate-smart agricultural training enhances economic resilience and promotes livelihood diversification in ecological flood zones, recommending that relevant state agencies provide financial tools, specialized equipment, and demonstration hubs for trained cohorts","本研究评估了气候智能型农业教育对巴耶尔萨州沿河社区青年生计的影响。在三个研究问题和三个零假设的指导下，作者采用混合方法设计，将横断面描述性调查与准实验性田间试验相结合。目标人群为埃克雷莫尔和南伊乔地方政府区域内活跃从事农业的青年。在此基础上，采用Taro Yamane公式确定描述性调查样本为195名青年，同时目的性选取60名青年作为嵌套队列，将其平均分为实验组和对照组（每组n=30）进行实验追踪。数据收集依托经过验证的《青年气候智能型农业与生计问卷》和客观的《气候智能型农业能力评估测试》。描述性问题采用均值和标准差进行分析，零假设则通过协方差分析（ANCOVA）在0.05显著性水平下进行检验。实证结果表明，针对抗洪作物生产、先进养鱼技术和鱼菜共生系统的定向教育显著提升了实际职业能力，因此三个零假设均被拒绝。研究得出结论：制度化的气候智能型农业培训能够增强经济韧性并促进生态洪泛区的生计多样化，建议相关州级机构为受训群体提供金融工具、专业设备和示范中心。","INTERNATIONAL JOURNAL OF AGRICULTURE AND EARTH SCIENCE","2026-10-07T00:00:00Z",70,{"impact":17,"substance":151,"depth":19,"authority":102,"freshness":21,"relevant":22,"comment":152},21,"尼日利亚河流州气候智慧农业教育对青年生计影响的实证研究，方法规范、结论明确，但属区域性案例，对国内三农信息化参考价值有限。",[154],{"name":147,"url":144},[156,30,157,158,159],"水产养殖","农业教育","青年生计","洪涝适应",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":163},{"candidates":164,"agricultural_evidence":50},[165,172,176],{"code":166,"score":167,"supported":50,"title":168,"abstract":169,"tags":171},"D",4,[],[170],"fish farming",[156],{"code":36,"score":49,"supported":50,"title":173,"abstract":174,"tags":175},[],[53],[],{"code":56,"score":22,"supported":14,"title":177,"abstract":178,"tags":179},[],[],[30],[181,182],"Bayelsa 气候智慧农业 青年","aquaponics 洪水适应 农业培训","Bayelsa气候智慧农业青年-4421","10.56201\u002Fijaes.vol.12.no9.2026.pg56.66",{"doi":184,"openalex_id":186,"authors":187,"venue":147,"cited_by_count":69,"oa_url":190,"card":191,"direction":86,"ingested_from":87},"W7220862726",[188],{"name":189,"orcid":9},"Ugo Panebi","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJAES\u002FVOL. 12 NO. 9 2026\u002FIMPACT OF CLIMATE-SMART AGRICULTURAL 56-66.pdf",{"tldr":192,"method":193,"finding":194,"direction":138,"opportunity":195},"评估气候智慧型农业教育对巴耶尔萨河岸社区青年生计的影响。","混合方法：195人问卷调查加60人准实验，用ANCOVA检验。","抗洪种植、养鱼和鱼菜共生培训显著提升青年职业技能与生计韧性。","可探索将气候智慧型农业培训与数字平台、远程教育结合，扩大河岸社区覆盖。","2026-10-08T23:30:10.938615Z",{"id":198,"title":199,"url":200,"summary":201,"summary_zh":202,"content":9,"source_name":203,"source_url":200,"published_at":204,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":205,"score_detail":206,"sources":213,"tags":218,"primary_topic":222,"secondary_topics":223,"classification":224,"search_phrases":250,"slug":253,"view_count":254,"doi":255,"paper":256,"created_at":277},3470,"AI-generated advice as a reinforcement layer in climate-smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.landusepol.2026.108336","Climate-smart agricultural (CSA) practices are central to food-system decarbonisation, yet adoption often falls short of farmers’ stated intentions, weakening the impact of incentives and extension under capacity constraints. We test whether spatially targeted, AI-generated advice can narrow this intention-action gap in a randomised field experiment with 1529 row crop farmers in Iowa, Illinois and Indiana during the cover crop decision window. Farmers assigned to receive four AI-generated emails were 4.45 %age points more likely to plant cover crops than controls (z = 2.81, p = 0.005), despite high baseline intentions in both groups. Effects operated on the extensive margin: there was no detectable change in the share of land planted among adopters. Survey responses indicate high engagement and a shift from untested optimism to more calibrated trust after exposure. Supervised AI advice can provide a low-cost, scalable complement to existing extension, improving follow-through and modestly expanding uptake without displacing human expertise.","气候智慧型农业（CSA）实践是食品系统脱碳的核心，然而在能力受限的情况下，农户的实际采用往往低于其声称的意愿，削弱了激励措施和推广服务的效果。我们在爱荷华州、伊利诺伊州和印第安纳州开展了一项随机田间试验，覆盖1529名大田作物种植户，在覆盖作物决策窗口期测试了空间靶向的AI生成建议能否缩小这一意愿—行动差距。被分配接收四封AI生成电子邮件的农户种植覆盖作物的概率比对照组高4.45个百分点（z = 2.81，p = 0.005），尽管两组基线意愿均较高。效应体现在广延边际上：采用者中种植土地比例未检测到显著变化。调查回复表明参与度较高，且在接触建议后，农户从未经检验的乐观转向更为校准的信任。有监督的AI建议可作为现有推广服务的低成本、可扩展补充，改善后续落实并适度扩大采用，而不会取代人类专业知识。","Land Use Policy","2026-09-24T00:00:00Z",87,{"impact":207,"substance":208,"depth":209,"authority":210,"freshness":211,"relevant":22,"comment":212},22,23,19,14,9,"随机对照试验证实AI生成建议可低成本缩小农户意愿与行动差距，对智慧农业推广具参考价值。",[214,215],{"name":203,"url":200},{"name":216,"url":217},"Apollo","https:\u002F\u002Fdoi.org\u002F10.17863\u002Fcam.134741",[107,219,220,221,30],"农业人工智能","农业技术推广","覆盖作物",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":225},{"candidates":226,"agricultural_evidence":50},[227,231,236,241,245],{"code":36,"score":211,"supported":50,"title":228,"abstract":229,"tags":230},[53],[],[107],{"code":232,"score":22,"supported":14,"title":233,"abstract":234,"tags":235},"A7",[],[],[221],{"code":237,"score":22,"supported":14,"title":238,"abstract":239,"tags":240},"G1",[],[],[220],{"code":56,"score":22,"supported":14,"title":242,"abstract":243,"tags":244},[],[],[30],{"code":246,"score":22,"supported":14,"title":247,"abstract":248,"tags":249},"I2",[],[],[219],[251,252],"AI生成建议 覆盖作物","爱荷华 伊利诺伊 印第安纳 覆盖作物","AI生成建议覆盖作物-3470",7,"10.1016\u002Fj.landusepol.2026.108336",{"doi":255,"openalex_id":257,"authors":258,"venue":203,"cited_by_count":69,"oa_url":200,"card":271,"direction":86,"ingested_from":87},"W7214223143",[259,262,264,266,269],{"name":260,"orcid":261},"Callum Alexander","https:\u002F\u002Forcid.org\u002F0009-0007-5275-4583",{"name":263,"orcid":9},"Aiora Zabala",{"name":265,"orcid":9},"Andreas Kontoleon",{"name":267,"orcid":268},"Shalamar Armstrong","https:\u002F\u002Forcid.org\u002F0000-0002-1326-9936",{"name":270,"orcid":9},"Anuoluwa Sangotayo",{"tldr":272,"method":273,"finding":274,"direction":275,"opportunity":276},"随机试验检验AI生成建议能否缩小农户覆盖作物种植的意图-行动差距。","1529户美国中西部农户随机对照试验，四次AI生成邮件干预。","AI建议使覆盖作物种植率提高4.45个百分点，效果体现在是否采纳而非种植面积。","农业人工智能与决策模型","可探索AI建议与人工推广协同、长期持续效果及不同作物区域的异质性影响。","2026-09-25T23:30:09.887867Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":9,"content":9,"source_name":283,"source_url":281,"published_at":284,"category":12,"cover_url":9,"hotness":99,"is_selected":14,"score":18,"score_detail":285,"sources":288,"tags":290,"primary_topic":293,"secondary_topics":294,"classification":295,"search_phrases":310,"slug":313,"view_count":167,"doi":314,"paper":315,"created_at":324},2936,"Green Climate Smart Agriculture For Sustainable Farming","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.25577.45920","Green Climate Smart Agriculture For Sustainable 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