[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3295":3,"related-3295":81},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":76,"slug":79,"view_count":21,"doi":8,"paper":8,"created_at":80},3295,"FAO-China 高层次政策对话与考察访问 推动南南合作与三方合作伙伴关系——14国驻华使节聚焦数字农业与农食体系转型","https:\u002F\u002Fwww.fao.org\u002Fsstc-gateway\u002Fnews-and-events\u002Fnews\u002FFAO-China-high-level-study-tour-and-policy-dialogue","2026年9月14日至18日，FAO 与中国农业农村部（MARA）合作，在海南和北京举办高层政策对话与考察访问。来自佛得角、埃塞俄比亚、德国、印尼、意大利、肯尼亚、墨西哥、蒙古、巴基斯坦、南非、斯里兰卡、乌干达、乌兹别克斯坦、津巴布韦14国高级代表参会。访问内容覆盖热带农业与小农耕作、农产品加工与价值链、木薯与水果研究、种子育种与创新、农业研究与遗传资源、植物与动物科学、养蜂与数字农业。9月18日北京高层政策对话结束，FAO与中国正式启动多边务实合作。自2009年以来FAO-China南南合作计划已通过39个国家、区域、全球与三角合作项目支持粮食安全、可持续农业与农村发展。",null,"Senior representatives and Ambassadors from around 14 countries gathered in Hainan and Beijing, China, from 14 to 18 September 2026 for a high-level policy dialogue and study tour designed at advancing South-South and Triangular Cooperation as a driver of agrifood systems transformation.\n\nOrganized by the Food and Agriculture Organization of the United Nations (FAO), in collaboration with China’s Ministry of Agriculture and Rural Affairs (MARA), the event offered senior country representatives from Cabo Verde, Ethiopia, Germany, Indonesia, Italy, Kenya, Mexico, Mongolia, Pakistan, South Africa, Sri Lanka, Uganda, Uzbekistan and Zimbabwe, firsthand exposure to China’s agricultural innovations, while generating new opportunities to reinforce partnerships, exchange policy solutions and scale up cooperation under the FAO–China South-South Cooperation Programme.\n\nThe initiative created a space for countries to exchange priorities and identify new areas of cooperation.\n\nAcross Haikou, Danzhou and Sanya in Hainan Province, and later in Beijing, participants were exposed to a broad range of China’s agricultural development experiences. Visits covered tropical agriculture and smallholder farming, agricultural processing and value chains, cassava and fruit research, seed breeding and innovation, agricultural research and genetic resources, plant and animal sciences, apiculture and digital agriculture.\n\nThe delegation engaged with the Hainan Provincial Government, the Chinese Academy of Tropical Agricultural Sciences (CATAS), agricultural enterprises and cooperatives, Yazhou Bay Science and Technology City and the Nanfan seed R&D platforms, the Chinese Academy of Agricultural Sciences (CAAS) and China Agricultural University. These exchanges helped turn the study tour into a partnership-building platform, opening channels between participating countries and Chinese technical and research institutions for potential future cooperation.\n\nThe programme culminated on 18 September with a High-level Policy Dialogue in Beijing, where countries, MARA, FAO and partner institutions moved from learning and exchange to action, translating lessons from the week into future priorities and concrete partnerships.\n\nThe Dialogue underscored the role of the FAO-China SSC Programme as a practical platform for policy exchange and for matching country priorities with relevant knowledge, technologies and institutional expertise. Countries shared experiences as both recipients and providers of solutions, know-how and technologies, identified opportunities for future SSTC and triangular partnerships, and discussed practical ways to give SSTC a stronger and more systematic role across FAO’s Strategic Framework, programmes and priority corporate initiatives.\n\nSince its establishment in 2009, the Programme has played a catalytic role in supporting food security, sustainable agriculture and rural development through 39 national, regional, global and triangular cooperation projects, alongside more than 1 500 training, capacity-development, study tour and learning exchange activities reaching participants from over 100 countries.\n\nDuring her closing remarks, FAO Deputy Director-General, Beth Bechdol, underscored how SSTC must assume a stronger and more strategic role in how FAO supports Members and emphasized the need to mainstream SSTC more systematically across FAO’s work: moving beyond successful individual projects to stronger partnerships, investment and innovation.\n\nMs Hua Yang, Director of the South-South and Triangular Cooperation Division of FAO also underscored the need to translate the policy dialogue and study tour into concreate actions. “We need to broaden the discussion and look forward – drawing on what we have seen during the study tour and on the development priorities and capabilities of all participating countries – to identify opportunities for future cooperation”\n\nBy the end of the week, participants identified concrete follow-up opportunities in technical and institutional cooperation, technology adaptation, capacity development, triangular cooperation, investment and partnership building. FAO will work with participating countries, China and partner institutions to translate these priorities and new connections into cooperation that can deliver practical, scalable solutions for agrifood system transformation.","FAO 2026年09月","2026-09-18T00:00:00Z","报道",10,false,79,{"impact":17,"substance":18,"depth":19,"authority":19,"freshness":20,"relevant":21,"comment":22},26,18,15,5,1,"FAO与中国农业农村部联合举办的高层政策对话与考察，涉及14国使节与数字农业、南南合作，权威性与影响层级高，但属活动综述、信息增量中等且已过一周时效。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字农业","国际合作","南南合作","农食体系转型","农业科技交流",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":41,"reason":75},[38,45,52,58,64,70],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":44},"I1",12,true,[26],[26],[26],{"code":46,"score":47,"supported":41,"title":48,"abstract":49,"tags":51},"A8",3,[],[50],"粮食安全",[],{"code":53,"score":47,"supported":41,"title":54,"abstract":55,"tags":57},"E1",[],[56],"农产品加工",[],{"code":59,"score":47,"supported":41,"title":60,"abstract":61,"tags":63},"G",[],[62],"农村发展",[],{"code":65,"score":47,"supported":41,"title":66,"abstract":67,"tags":69},"H8",[],[68],"可持续农业",[],{"code":71,"score":21,"supported":14,"title":72,"abstract":73,"tags":74},"J3",[],[],[27],"多个产业证据相当，需要确认主归属",[77,78],"FAO 中国 南南合作 数字农业","14国驻华使节 海南 农业考察","FAO中国南南合作数字农业-3295","2026-09-24T00:03:57.394547Z",{"total":82,"page":21,"page_size":82,"items":83},6,[84,147,204,263,305,381],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":8,"content":8,"source_name":89,"source_url":87,"published_at":90,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":92,"score_detail":93,"sources":96,"tags":98,"primary_topic":102,"secondary_topics":107,"classification":108,"search_phrases":123,"slug":126,"view_count":127,"doi":128,"paper":129,"created_at":146},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","2026-10-21T00:00:00Z","论文",38,{"impact":94,"substance":82,"depth":94,"authority":82,"freshness":13,"relevant":21,"comment":95},8,"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。",[97],{"name":89,"url":87},[26,99,100,101],"农业信息化","遥感","地图评估",{"code":39,"slug":103,"name":104,"path":105,"parent_code":106},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":109,"locked":14,"version":34,"method":35,"evidence":110},"classified",{"candidates":111,"agricultural_evidence":41},[112,118],{"code":39,"score":113,"supported":41,"title":114,"abstract":116,"tags":117},13,[115],"digital agriculture",[115],[99,26],{"code":119,"score":21,"supported":14,"title":120,"abstract":121,"tags":122},"I3",[],[],[100],[124,125],"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",0,"10.21203\u002Frs.3.rs-11283264\u002Fv1",{"doi":128,"openalex_id":130,"authors":131,"venue":89,"cited_by_count":127,"oa_url":87,"card":8,"direction":144,"ingested_from":145},"W7223099987",[132,135,138,141],{"name":133,"orcid":134},"Phillip Lanza","https:\u002F\u002Forcid.org\u002F0000-0002-5545-1744",{"name":136,"orcid":137},"David G. Rossiter","https:\u002F\u002Forcid.org\u002F0000-0003-4558-1286",{"name":139,"orcid":140},"Diana Stuart Sinton","https:\u002F\u002Forcid.org\u002F0000-0001-5828-9001",{"name":142,"orcid":143},"Louis Longchamps","https:\u002F\u002Forcid.org\u002F0000-0002-4761-6094","数字乡村与农业信息化","openalex","2026-10-22T11:20:47.463814Z",{"id":148,"title":149,"url":150,"summary":151,"summary_zh":152,"content":8,"source_name":153,"source_url":150,"published_at":90,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":154,"score_detail":155,"sources":160,"tags":162,"primary_topic":167,"secondary_topics":168,"classification":169,"search_phrases":186,"slug":189,"view_count":127,"doi":190,"paper":191,"created_at":203},5578,"Unlocking digital agriculture through new quality productive forces: nonlinear thresholds and spatial spillovers in China","https:\u002F\u002Fdoi.org\u002F10.1057\u002Fs41599-026-09510-z","New quality productive forces (NQPF) critically enable agricultural digitisation. Using China as a case study, this paper employs panel data from 284 prefecture-level cities in China from 2011 to 2022. The results show that NQPF has a significant promoting effect on agricultural digital transformation and exhibits positive spatial spillover effects, driving not only local transformation but also the development of surrounding regions. Labour skills and innovation capacity play mediating roles in this process. Notably, the enabling effect of NQPF on agricultural digitisation displays significant regional heterogeneity. Threshold analysis indicates that its promoting effect follows a non-linear pattern, characterised by a double threshold effect. Therefore, the development of NQPF should be accelerated, with emphasis placed on regional coordination and talent cultivation, to promote the sustained and in-depth advancement of agricultural digital transformation in accordance with local conditions.","新质生产力（New Quality Productive Forces, NQPF）对农业数字化具有关键赋能作用。本文以中国为案例，采用2011—2022年中国284个地级市的面板数据。结果表明，新质生产力对农业数字化转型具有显著促进作用，并呈现正向空间溢出效应，不仅推动本地转型，也带动周边地区发展。劳动技能与创新能力在这一过程中发挥中介作用。值得注意的是，新质生产力对农业数字化的赋能效应表现出显著的区域异质性。门槛分析表明，其促进作用呈非线性特征，表现为双重门槛效应。因此，应加快新质生产力发展，注重区域协调与人才培养，因地制宜推动农业数字化转型持续深入推进。","Humanities and Social Sciences Communications",81,{"impact":18,"substance":156,"depth":18,"authority":157,"freshness":158,"relevant":21,"comment":159},22,14,9,"基于284个地级市面板数据的实证研究，揭示新质生产力对农业数字化的非线性门槛与空间溢出效应，方法规范、结论有政策参考价值，值得进入每日精选。",[161],{"name":153,"url":150},[26,163,164,165,166],"新质生产力","空间溢出","区域协调","农业数字化转型",{"code":39,"slug":103,"name":104,"path":105,"parent_code":106},[],{"status":109,"locked":14,"version":34,"method":35,"evidence":170},{"candidates":171,"agricultural_evidence":41},[172,176,181],{"code":39,"score":13,"supported":41,"title":173,"abstract":174,"tags":175},[115],[],[166,26],{"code":177,"score":21,"supported":14,"title":178,"abstract":179,"tags":180},"I4",[],[],[164],{"code":182,"score":21,"supported":14,"title":183,"abstract":184,"tags":185},"J2",[],[],[163],[187,188],"新质生产力 农业数字化","地级市 面板数据","新质生产力农业数字化-5578","10.1057\u002Fs41599-026-09510-z",{"doi":190,"openalex_id":192,"authors":193,"venue":153,"cited_by_count":127,"oa_url":150,"card":198,"direction":144,"ingested_from":145},"W7221723385",[194,196],{"name":195,"orcid":8},"Qingqing Huang",{"name":197,"orcid":8},"Wenjing Guo",{"tldr":199,"method":200,"finding":201,"direction":144,"opportunity":202},"基于中国284个地级市面板数据，检验新质生产力对农业数字化的促进、空间溢出与门槛效应。","2011—2022年284个地级市面板数据，空间计量与门槛回归，中介效应检验。","新质生产力显著促进农业数字化并具正向空间溢出，存在双门槛非线性效应与区域异质性。","可深入探究新质生产力影响农业数字化的微观机制，以及不同区域门槛条件下的差异化政策路径。","2026-10-22T11:20:47.354744Z",{"id":205,"title":206,"url":207,"summary":208,"summary_zh":8,"content":8,"source_name":209,"source_url":8,"published_at":210,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":211,"score_detail":212,"sources":215,"tags":217,"primary_topic":221,"secondary_topics":226,"classification":227,"search_phrases":249,"slug":252,"view_count":127,"doi":253,"paper":254,"created_at":262},5529,"数字农业技术采纳的驱动因素与障碍：基于复杂网络与演化博弈的分析","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1894648\u002Ffull","文章从演化博弈视角构建数字农业技术采纳的驱动与阻碍机制框架，系统梳理影响农户采纳数字农业技术的关键因素与障碍路径。研究强调通用大模型难以直接满足专业知识、结果依据与溯源能力要求，为政策设计提供了技术采纳侧的理论依据。","Frontiers in Sustainable Food Systems 2026-10-19","2026-10-19T00:00:00Z",67,{"impact":40,"substance":18,"depth":213,"authority":113,"freshness":94,"relevant":21,"comment":214},16,"从演化博弈视角分析农户采纳数字农业技术的驱动与障碍，方法有新意、结论有政策参考价值，但属理论性论文，影响范围限于细分领域。",[216],{"name":209,"url":207},[26,218,219,220],"智慧农业","技术采纳","农户行为",{"code":222,"slug":223,"name":224,"path":225,"parent_code":106},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":109,"locked":14,"version":34,"method":35,"evidence":228},{"candidates":229,"agricultural_evidence":41},[230,234,239,244],{"code":39,"score":113,"supported":41,"title":231,"abstract":232,"tags":233},[26],[26],[26,218],{"code":222,"score":47,"supported":41,"title":235,"abstract":236,"tags":238},[],[237],"大模型",[],{"code":240,"score":21,"supported":14,"title":241,"abstract":242,"tags":243},"G1",[],[],[219],{"code":245,"score":21,"supported":14,"title":246,"abstract":247,"tags":248},"G2",[],[],[220],[250,251],"数字农业 技术采纳 演化博弈","农户行为 技术采纳 数字农业 智慧农业","数字农业技术采纳演化博弈-5529","10.3389\u002Ffsufs.2026.1894648\u002Ffull",{"doi":253,"openalex_id":8,"authors":255,"venue":8,"cited_by_count":127,"oa_url":8,"card":256,"direction":144,"ingested_from":261},[],{"tldr":257,"method":258,"finding":259,"direction":144,"opportunity":260},"构建演化博弈框架分析农户采纳数字农业技术的驱动因素与障碍路径。","演化博弈建模与复杂网络分析，梳理采纳影响因素。","通用大模型难以满足专业知识、结果依据与溯源要求，需政策引导。","可结合实证数据检验博弈模型，并探索面向溯源与专业知识的专用农业模型设计。","agent","2026-10-22T00:07:09.498742Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":8,"content":268,"source_name":269,"source_url":8,"published_at":210,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":270,"score_detail":271,"sources":274,"tags":276,"primary_topic":281,"secondary_topics":287,"classification":288,"search_phrases":300,"slug":303,"view_count":127,"doi":8,"paper":8,"created_at":304},5517,"上海农科院代表团赴马来西亚与新西兰 深化农产品质量安全与溯源合作","https:\u002F\u002Fwww.saas.sh.cn\u002Fhzjl\u002Fgjhz\u002Fcontent_57130","10月7日至12日院党委委员姚训率代表团一行4人出访，与马来西亚国立大学签署合作谅解备忘录并出席食品质量安全联合实验室揭牌，交流农业新兴污染物监测与农产品多源指纹真实性鉴别成果。赴新期间访问威世物流新西兰公司、新西兰生物经济科学研究所与梅西大学，调研有机食品认证与追溯管理经验。","2026年10月7-12日，院党委委员、纪委书记姚训率代表团一行4人赴马来西亚和新西兰参加国际学术会议并开展访问交流。代表团成员包括院农业生物安全研究中心副主任董茂锋、刘星副研究员和王献礼助理研究员。此次出访重点围绕农产品质量安全、真实性与产地溯源、农业新兴污染物监测及农食产品跨境供应链管理等领域，深化与两国高校、科研机构及企业的交流合作。\n\n7-9日，姚训代表我院参加与马来西亚国立大学合作谅解备忘录（MOU）交换仪式，出席双方食品质量安全联合实验室揭牌仪式并致辞。联合实验室的揭牌标志着双方合作进一步深化，为推动食品质量安全领域的联合科研、人才培养和成果转化搭建了合作平台。双方围绕科研项目合作、人员互访、研究生联合培养及科研资源共享等事项进行了深入交流。\n\n代表团参加了第三届马来西亚国立大学-宋卡王子大学亚洲科学技术国际研讨会（ISSTUP-Asia 2026）。会议期间，我院宋卫国研究员以线上方式作学术报告，介绍农业新兴污染物环境行为与食品安全风险防控方面的研究进展；刘星现场分享了农产品多源指纹真实性鉴别与产地溯源研究成果；王献礼通过学术墙报展示了绿色超分子溶剂在全氟和多氟烷基物质（PFAS）提取分析中的研究成果，并与参会专家开展交流。此外，代表团还参访了马来西亚国立大学食品科学相关实验室、甜食技术创新中心及马来西亚可可局可可创新与技术中心，并调研当地有机食品市场，了解食品质量安全检测、产品研发、成果转化及质量追溯管理等方面的实践经验。\n\n10-12日，代表团赴新西兰开展科研与产业交流。代表团访问了威世物流新西兰公司，围绕农产品跨境运输、冷链仓储、质量控制及供应链追溯等开展交流，了解新西兰农食产品出口及进入中国市场的物流与质量管理经验。代表团还与我院柔性引进专家Karyne Rogers教授就农产品真实性鉴别与产地溯源等领域的合作进展及后续研究计划进行交流。随后，代表团访问新西兰生物经济科学研究所及梅西大学马纳瓦图校区相关科研平台，围绕农产品质量安全、环境污染物监测研究及科研成果转化等开展学术交流，并探讨联合研究与人才培养等合作方向。访问期间，代表团还调研了新西兰有机食品市场的认证、质量保障及追溯管理情况。\n\n此次出访进一步深化了我院与马来西亚、新西兰相关高校、科研机构和企业的交流合作，展示了我院在农产品质量安全、真实性与产地溯源及农业新兴污染物研究方面的科技创新成果。下一步，我院将以食品质量安全联合实验室等合作平台为依托，推动联合科研、人才交流及成果转化，促进国际合作取得更多实质性成效，为提升农业科技创新能力提供支持。","上海市农业科学院 2026-10-19",65,{"impact":213,"substance":272,"depth":113,"authority":113,"freshness":82,"relevant":21,"comment":273},17,"省级农科院出访交流，有联合实验室揭牌与学术报告等实质内容，但属机构外事动态，公共影响有限。",[275],{"name":269,"url":266},[27,277,278,279,280],"农产品质量安全","产地溯源","农业污染物","跨境供应链",{"code":282,"slug":283,"name":284,"path":285,"parent_code":286},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",[],{"status":109,"locked":14,"version":34,"method":35,"evidence":289},{"candidates":290,"agricultural_evidence":41},[291,296],{"code":282,"score":158,"supported":41,"title":292,"abstract":294,"tags":295},[293],"农产品质量",[],[277],{"code":71,"score":21,"supported":14,"title":297,"abstract":298,"tags":299},[],[],[27],[301,302],"上海农科院 马来西亚 联合实验室","农产品 真实性鉴别 产地溯源","上海农科院马来西亚联合实验室-5517","2026-10-22T00:07:04.117432Z",{"id":306,"title":307,"url":308,"summary":309,"summary_zh":310,"content":8,"source_name":311,"source_url":308,"published_at":312,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":313,"score_detail":314,"sources":317,"tags":319,"primary_topic":323,"secondary_topics":328,"classification":329,"search_phrases":358,"slug":361,"view_count":127,"doi":362,"paper":363,"created_at":380},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",74,{"impact":213,"substance":315,"depth":272,"authority":113,"freshness":94,"relevant":21,"comment":316},20,"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。",[318],{"name":311,"url":308},[26,320,50,321,322],"数字乡村","农业产业链韧性","安徽农业",{"code":46,"slug":324,"name":325,"path":326,"parent_code":327},"yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[],{"status":109,"locked":14,"version":34,"method":35,"evidence":330},{"candidates":331,"agricultural_evidence":41},[332,338,342,347,353],{"code":46,"score":333,"supported":41,"title":334,"abstract":335,"tags":337},7,[],[50,336],"food security",[50],{"code":39,"score":333,"supported":41,"title":339,"abstract":340,"tags":341},[],[26,115],[26],{"code":59,"score":82,"supported":41,"title":343,"abstract":344,"tags":346},[],[62,345],"rural development",[],{"code":348,"score":47,"supported":41,"title":349,"abstract":350,"tags":352},"J1",[],[351],"农业政策",[],{"code":354,"score":21,"supported":14,"title":355,"abstract":356,"tags":357},"I6",[],[],[320],[359,360],"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":362,"openalex_id":364,"authors":365,"venue":311,"cited_by_count":127,"oa_url":308,"card":375,"direction":144,"ingested_from":145},"W7223005655",[366,369,371,373],{"name":367,"orcid":368},"Lan Ma","https:\u002F\u002Forcid.org\u002F0000-0003-2468-9917",{"name":370,"orcid":8},"Yuyan Li",{"name":372,"orcid":8},"张国亮",{"name":374,"orcid":8},"Yaoyao Li",{"tldr":376,"method":377,"finding":378,"direction":144,"opportunity":379},"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":382,"title":383,"url":384,"summary":385,"summary_zh":386,"content":8,"source_name":387,"source_url":384,"published_at":312,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":388,"score_detail":389,"sources":391,"tags":393,"primary_topic":398,"secondary_topics":404,"classification":405,"search_phrases":424,"slug":427,"view_count":127,"doi":428,"paper":429,"created_at":444},5479,"Comparative Scientific Evaluation of Five Coffee Cooperative Organizations in Oromia, Ethiopia: Digital Traceability, Value Conversion, Institutional Dependencies and Five-Year Transformation Strategies","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23504407","Comparative Scientific Evaluation of Five Coffee Cooperative Organizations in Oromia, Ethiopia: Digital Traceability, Value Conversion, Institutional Dependencies and Five-Year Transformation Strategies Authors: Kedir Nefo Biftu, Kebede Hail, and Tadila Tadessa QajelaPublication year: 2026Publication type: Comparative scientific research article \u002F Preprint Description This scientific article presents a comparative evaluation of five coffee cooperative organizations in Oromia, Ethiopia: Chercher Oda Bultum Farmers Cooperative Union, Homa Multipurpose Farmers Cooperative, Kata Muduga Multipurpose Farmers Cooperative Union, Torban Anfillo Multipurpose Farmers Cooperative Union, and Sadetan Chora Farmers Cooperative Union. The research investigates variability in biological production, institutional organization, processing efficiency, digital traceability, product quality, market participation and financial value realization. The study uses institutional questionnaires, cooperative administrative records and previously developed case assessments, supported by recent international, African and Ethiopian scientific literature. Its integrated analytical framework comprises 18 end-to-end value-chain components, nine value-creation and conversion gates, three institutional levels and the eight-stage Big Win methodology. The comparative results demonstrate significant differences in organizational scale, coffee-growing ecology, farmer registration, geospatial mapping, production measurement, processing infrastructure, quality assurance, certification, export-market participation, financial settlement and digital readiness. The findings also identify common weaknesses in upstream traceability, data consistency, coffee-lot genealogy, processing mass balance, producer-level quality differentiation and verifiable links between commercial transactions and farmer payments. The scientific discussion examines how biological productivity, environmental sustainability, quality conservation, processing efficiency, value addition, buyer acceptance, and realized commercial value depend on reliable institutional coordination and information exchange. The analysis draws on recent research concerning digitalization and sustainability (Hidalgo et al., 2023), Ethiopian coffee traceability and buyer trust (Mbakop et al., 2023), digital traceability capabilities (Tadesse et al., 2024), geographical-origin compliance (Urugo et al., 2025), Ethiopian coffee-quality differentiation (Worku, Astatkie, and Boeckx, 2023), and sustainability performance in southwestern Oromia (Botola, Werke, and Birbirsa, 2026). International institutional frameworks from the International Coffee Organization, UNIDO, FAO, GS1, and ISO inform the proposed approaches to value-chain resilience, circular resource management, digital interoperability, and product traceability. The article proposes an integrated Smart Digital Coffee Value Chain architecture linking market intelligence, genetic resources, nursery development, producer and plot identity, extension services, production, harvesting, aggregation, processing, quality assurance, storage, certification, market contracts, buyer acceptance, financial settlement and feedback. A five-year scientific transformation proposal identifies differentiated improvement priorities for each organization, together with harmonized Business Requirements (BR), Functional Requirements (FR), institutional data dependencies, Key Performance Indicators (KPIs), Key Risk Indicators (KRIs) and corrective and preventive actions. The principal scientific contribution is a comparative framework demonstrating how cooperative organizations with different ecological, institutional, and commercial characteristics can implement a common, interoperable digital value-chain system while following locally appropriate transformation pathways. The study contributes to scientific understanding of cooperative value-chain performance, digital agriculture, institutional coordination, biological and economic efficiency, environmental sustainability and commercial value realization. The findings are based principally on documentary and organizational evidence. Reported performance indicators are distinguished from calculated measures and proposed interventions. Differences in reporting periods, measurement units and data completeness limit direct statistical comparability, while the proposed five-year improvements require implementation and independent evaluation. Keywords Smart Digital Coffee Value Chain; Comparative Scientific Evaluation; Oromia; Ethiopia; Coffee Cooperative Unions; Chercher Oda Bultum; Homa Cooperative; Kata Muduga; Torban Anfillo; Sadetan Chora; Digital Traceability; Value Creation; Value Conservation; Value Addition; Biological Efficiency; Economic Efficiency; Institutional Dependency; Big Win Methodology; Sustainable Coffee Production.","埃塞俄比亚奥罗米亚地区五家咖啡合作社组织的比较科学评估：数字可追溯性、价值转化、制度依赖与五年转型战略\n\n作者：Kedir Nefo Biftu、Kebede Hail、Tadila Tadessa Qajela\n\n出版年份：2026年\n\n出版类型：比较科学研究论文\u002F预印本\n\n描述\n\n本科学论文对埃塞俄比亚奥罗米亚地区五家咖啡合作社组织进行了比较评估：Chercher Oda Bultum农民合作社联盟、Homa多用途农民合作社、Kata Muduga多用途农民合作社联盟、Torban Anfillo多用途农民合作社联盟和Sadetan Chora农民合作社联盟。该研究考察了生物生产、制度组织、加工效率、数字可追溯性、产品质量、市场参与和财务价值实现方面的变异性。研究采用机构问卷、合作社行政记录和先前开发的案例评估，并以近期国际、非洲和埃塞俄比亚科学文献为支撑。其综合分析框架由18个端到端价值链组成部分、九个价值创造与转化关口、三个制度层面和八阶段“大赢”方法组成。比较结果揭示了在组织规模、咖啡种植生态、农民登记、地理空间测绘、生产计量、加工基础设施、质量保证、认证、出口市场参与、财务结算和数字准备度方面存在显著差异。研究结果还识别了上游可追溯性、数据一致性、咖啡批次谱系、加工物料平衡、生产者层面质量分级以及商业交易与农民付款之间可验证联系方面的共同弱点。科学讨论审视了生物生产力、环境可持续性、质量保持、加工效率、价值增值、买方接受度和已实现商业价值如何依赖于可靠的制度协调和信息交换。分析借鉴了近期关于数字化与可持续性（Hidalgo等，2023）、埃塞俄比亚咖啡可追溯性与买方信任（Mbakop等，2023）、数字可追溯能力（Tadesse等，2024）、地理原产地合规（Urugo等，2025）、埃塞俄比亚咖啡质量分级（Worku","Zenodo (CERN European Organization for Nuclear Research)",59,{"impact":94,"substance":18,"depth":19,"authority":158,"freshness":158,"relevant":21,"comment":390},"该研究以埃塞俄比亚咖啡合作社为对象，提出数字价值链框架，方法有一定新意，但属区域性案例研究，对国内三农信息化仅有间接参考价值。",[392],{"name":387,"url":384},[26,394,395,396,397],"价值链","合作社","数字溯源","咖啡产业",{"code":399,"slug":400,"name":401,"path":402,"parent_code":403},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[],{"status":109,"locked":14,"version":34,"method":35,"evidence":406},{"candidates":407,"agricultural_evidence":41},[408,414,420],{"code":399,"score":19,"supported":41,"title":409,"abstract":411,"tags":413},[410],"coffee",[412,410],"咖啡",[397],{"code":245,"score":415,"supported":41,"title":416,"abstract":417,"tags":419},4,[],[418],"农民合作社",[395],{"code":39,"score":415,"supported":41,"title":421,"abstract":422,"tags":423},[],[115],[26],[425,426],"埃塞俄比亚 咖啡合作社 数字溯源","咖啡产业 数字农业 数字溯源 价值链","埃塞俄比亚咖啡合作社数字溯源-5479","10.5281\u002Fzenodo.23504407",{"doi":428,"openalex_id":430,"authors":431,"venue":387,"cited_by_count":127,"oa_url":384,"card":438,"direction":443,"ingested_from":145},"W7223041235",[432,434,436],{"name":433,"orcid":8},"Kedir Biftu",{"name":435,"orcid":8},"Kebede Hailu",{"name":437,"orcid":8},"Tadila Tadessa Qajela",{"tldr":439,"method":440,"finding":441,"direction":144,"opportunity":442},"比较埃塞俄比亚奥罗米亚五个咖啡合作社的数字化追溯与价值转化差异。","机构问卷、合作社行政记录与案例评估，构建18环节价值链分析框架。","各社在组织规模、数字追溯与支付可验证性差异显著，上游追溯普遍薄弱。","可研究小农户合作社低成本数字追溯与批次谱系方案，打通交易与农户付款的可验证链路。","智慧农业 \u002F 农业物联网","2026-10-21T23:30:09.167175Z"]