[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5132":3,"related-5132":81},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":66,"slug":69,"view_count":21,"doi":8,"paper":70,"created_at":80},5132,"首席专家黄祖辉教授《浙江日报》发文:「双链耦合」建成现代农业大产业","http:\u002F\u002Fwww.crcd.zju.edu.cn\u002Findex.php?a=show&catid=141&id=7827","浙江大学中国农村发展研究院首席专家黄祖辉教授在《浙江日报》第六版发表题为《「双链耦合」建成现代农业大产业》的理论文章。文章指出「把农业建成现代化大产业」并不是单纯追求产业规模的扩大,而是要追求产业的现代性、包容性以及多功能性和高价值性。现代农业大产业是现代农业的产业体系、生产体系、经营体系和技术体系集成的大产业。农业科技链和农业全产业链的「双链耦合」是建成现代农业大产业的关键。提出三个体系:一是优化现代农业科技支撑体系,使改革创新、集成创新与科技创新融为一体;二是完善现代农业全链服务体系,建立与完善现代农业产业组织体系,充分发挥数字化和互联网平台服务小农户的功能;三是强化现代农业大产业制度适配体系。",null,"浙江日报 浙江大学区域协调发展研究中心 2026 年 10 月 12 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终审：刘玉峰","中国农村经济 2026-10-21","2026-10-21T00:00:00Z",82,{"impact":17,"substance":16,"depth":17,"authority":93,"freshness":94,"relevant":21,"comment":95},15,9,"核心期刊论文，多案例揭示农业产业园促进小农户融入数字化供应链的协同机制与制度逻辑，理论新意足、政策参考价值高，值得进入每日精选。",[97],{"name":89,"url":86},[26,54,99,100,101],"制度逻辑","数字化供应链","农业产业园",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":105},{"candidates":106,"agricultural_evidence":46},[107,111],{"code":32,"score":19,"supported":46,"title":108,"abstract":109,"tags":110},[54],[54],[54],{"code":57,"score":21,"supported":13,"title":112,"abstract":113,"tags":114},[],[],[26],[116,117],"农业产业园 小农户 数字化供应链","大荔县 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transformation","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1966579","The rapid digitalization of food retail through private quick commerce platforms has transformed urban food consumption patterns in India. Despite the investments in digital infrastructure, Uzhavar Sandhai (Farmers Market), a farmer-to-consumer direct marketing system in Tamil Nadu, is still limited to traditional physical markets. This study suggests a strategic rethinking of Farmers Market as a public quick commerce infrastructure layer, incorporating digital marketplaces, hyperlocal logistics and cooperative governance to boost farmer market competitiveness and promote public value, food system resilience and equitable digital market access. In this study, the Farmers Market Digital Platform model is developed by applying the conceptual policy design approach to convert the Farmers Market platform into a decentralized public quick commerce network. This study identifies the institutional, technological and governance reforms necessary to support the digital transformation of farmer markets using publicly governed digital infrastructure. This reorientation of Farmers Market to public digital infrastructure could form the policy roadmap for platformization of agriculture in Tamil Nadu, which would benefit small farmers, urban consumers, and municipalities and help alleviate inefficiencies in supply chains. As a conceptual policy framework, the proposed model has not been empirically tested and requires future pilot implementation and validation before adoption. The study contributes to the evolving literature on agricultural digitalization by offering a vision for the potential redesign of existing public agricultural markets as digitally enabled public service infrastructure, which can enhance farmer welfare and urban food system resilience.","印度食品零售通过私营即时商务平台的快速数字化，已改变了城市食品消费模式。尽管在数字基础设施方面已有投入，泰米尔纳德邦的“乌扎瓦尔桑代”（Uzhavar Sandhai，即农民市场）——一种农民对消费者的直接营销体系——仍局限于传统的实体市场。本研究建议对农民市场进行战略性的重新思考，将其打造为公共即时商务基础设施层，融合数字市场、超本地物流与合作治理，以提升农民市场的竞争力，并促进公共价值、食物系统韧性以及公平的数字市场准入。本研究运用概念性政策设计方法，构建了农民市场数字平台模型，将农民市场平台转化为去中心化的公共即时商务网络。本研究识别了支持农民市场数字化转型所必需的制度、技术与治理改革，以依托公共治理的数字基础设施加以推进。将农民市场重新定位为公共数字基础设施，可为泰米尔纳德邦农业的平台化提供政策路线图，惠及小农户、城市消费者和市政当局，并有助于缓解供应链中的低效问题。作为一个概念性政策框架，所提出的模型尚未经过实证检验，在采纳之前需要未来的试点实施与验证。本研究为农业数字化领域不断发展的文献作出了贡献，提出了一种愿景，即把现有公共农业市场重新设计为数字赋能的公共服务基础设施，从而提升农民福利与城市食物系统韧性。","Frontiers in Sustainable Food Systems","2026-10-13T00:00:00Z",76,{"impact":18,"substance":137,"depth":138,"authority":139,"freshness":94,"relevant":21,"comment":239},"提出将农民市场重构为公共即时零售数字基础设施的政策框架，视角新颖但属概念性研究、未经实证检验，对国内农业数字化政策设计有借鉴价值。",[241],{"name":235,"url":232},[26,243,244,245,246],"农产品流通","农民合作社","农业平台化","公共数字基础设施",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":250},{"candidates":251,"agricultural_evidence":46},[252,257,262],{"code":32,"score":253,"supported":46,"title":254,"abstract":255,"tags":256},4,[],[54],[244],{"code":258,"score":21,"supported":13,"title":259,"abstract":260,"tags":261},"E5",[],[],[243],{"code":57,"score":21,"supported":13,"title":263,"abstract":264,"tags":265},[],[],[26],[267,268],"Uzhavar Sandhai 农民市场","Tamil Nadu 农业数字化","UzhavarSandhai农民市场-4868","10.3389\u002Ffsufs.2026.1966579",{"doi":270,"openalex_id":272,"authors":273,"venue":235,"cited_by_count":72,"oa_url":279,"card":280,"direction":77,"ingested_from":285},"W7221594564",[274,276],{"name":275,"orcid":8},"Arunachalam Panchatcharam SriVidhya",{"name":277,"orcid":278},"Paul J. Mansingh","https:\u002F\u002Forcid.org\u002F0000-0003-3423-8618","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1966579\u002Fpdf",{"tldr":281,"method":282,"finding":283,"direction":77,"opportunity":284},"提出将印度农民市场重构为公共快速商业基础设施的政策框架。","概念性政策设计方法，构建农民市场数字平台模型。","需制度、技术与治理改革，将农民市场转为去中心化公共快速商业网络。","该框架未经实证，可开展试点验证，并探索公共数字平台对小农福利与供应链效率的影响。","openalex","2026-10-14T23:30:06.797196Z",{"id":288,"title":289,"url":290,"summary":291,"summary_zh":8,"content":8,"source_name":292,"source_url":8,"published_at":293,"category":11,"cover_url":8,"hotness":12,"is_selected":46,"score":294,"score_detail":295,"sources":298,"tags":300,"primary_topic":305,"secondary_topics":306,"classification":307,"search_phrases":327,"slug":330,"view_count":45,"doi":8,"paper":331,"created_at":338},4284,"When e-commerce meets smallholders: Boosting incomes and widening gaps in rural China's anti-poverty policy","https:\u002F\u002Fbesjournals.onlinelibrary.wiley.com\u002Ffeed\u002F14678276\u002Fmost-recent","Wenqi Duan等评估中国电子商务进农村(ECC)项目对小型农户农业经营收入水平与分配的影响。研究采用准实验设计发现：ECC项目显著增加家庭人均农业经营收入；通过扩大市场接入推动收入增长，三大机制为经营规模扩大、产出价格提高、向高附加值作物转移；然而这些收益分配不均，项目扩大小农户间收入差距，主要归因于人力、生产、数字资本初始差异。强调电商政策需配套措施缓解异质性初始禀赋带来的差距。","American Journal of Agricultural Economics 10.1002\u002Fajae.70086 2026","2026-10-04T00:00:00Z",89,{"impact":296,"substance":188,"depth":17,"authority":93,"freshness":94,"relevant":21,"comment":297},24,"顶刊准实验研究揭示电商进农村项目增收与扩大差距的双重效应，政策与学术价值兼具，值得入选每日精选。",[299],{"name":292,"url":290},[301,26,302,303,304],"农村电商","农户收入","收入差距","电商扶贫",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":308},{"candidates":309,"agricultural_evidence":46},[310,314,318,323],{"code":32,"score":45,"supported":46,"title":311,"abstract":312,"tags":313},[],[54],[],{"code":258,"score":21,"supported":13,"title":315,"abstract":316,"tags":317},[],[],[301],{"code":319,"score":21,"supported":13,"title":320,"abstract":321,"tags":322},"G6",[],[],[302],{"code":57,"score":21,"supported":13,"title":324,"abstract":325,"tags":326},[],[],[26],[328,329],"电子商务进农村 ECC 农户收入","农村电商 小农户 收入差距","电子商务进农村ECC农户收入-4284",{"doi":8,"openalex_id":8,"authors":332,"venue":8,"cited_by_count":72,"oa_url":8,"card":333,"direction":77,"ingested_from":79},[],{"tldr":334,"method":335,"finding":336,"direction":77,"opportunity":337},"评估中国电商进农村项目对农户收入及差距的影响","准实验设计，分析ECC项目对农户农业经营收入的作用","项目显著提高人均农业收入，但扩大农户间收入差距","可研究数字资本差异下电商政策配套措施，缩小农户收入差距","2026-10-06T00:05:04.824292Z",{"id":340,"title":341,"url":342,"summary":343,"summary_zh":344,"content":8,"source_name":345,"source_url":342,"published_at":346,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":347,"score_detail":348,"sources":350,"tags":352,"primary_topic":356,"secondary_topics":357,"classification":364,"search_phrases":397,"slug":400,"view_count":20,"doi":401,"paper":402,"created_at":416},3512,"AI-Driven Agricultural Advisory and Diagnostic Systems for Smallholder Farming: Technical Architectures, Evidence and Deployment Priorities for North-East India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Farja\u002F2026\u002Fv19i4919","Artificial intelligence (AI) is being introduced into agricultural advisory services through machine learning, computer vision, conversational large language models, retrieval-augmented generation and multimodal interfaces. For smallholder farming, the central question is not whether these technologies can produce technically plausible outputs, but whether they can provide locally correct, actionable and safe recommendations under heterogeneous agronomic, linguistic and connectivity conditions. This critical narrative review integrates evidence on digital extension, AI-enabled agricultural advice, image-based diagnosis and responsible digital agriculture, with particular reference to North-East India. Literature published from 1 January 2010 to 17 July 2026 was considered, with emphasis on peer-reviewed field evaluations, technical validation studies, reviews and regionally relevant research. Evidence from digital extension provides the strongest causal baseline: mobile and personalised advisory services frequently improve information recall, agronomic knowledge and adoption of recommended practices, yet effects on yield, profit and welfare are inconsistent. Recent generative-AI studies show that large language models can produce useful agricultural responses, but site-specific rates, timing and local practice remain recurrent failure points. Retrieval grounding and expert feedback improve local relevance, although multi-season farm-level effectiveness evidence remains scarce. Image-based plant-disease systems achieve high accuracy in curated datasets, but performance can deteriorate sharply under field domain shift, class novelty and variable image quality. North-East Indian studies of mobile advisory systems in Meghalaya, Nagaland and Tripura demonstrate a valuable institutional foundation based on interactive voice response, local expert networks and user-centred service design; they do not, however, establish the effectiveness of autonomous AI. The most defensible deployment model is therefore an offline-tolerant, multilingual, multimodal and human-supervised architecture that grounds recommendations in curated regional knowledge, represents uncertainty, preserves provenance and escalates high-risk or out-of-distribution cases. Future research should prioritise prospective district- and season-spanning evaluations that connect model quality to farmer decisions, agronomic outcomes, equity, safety and cost-effectiveness.","人工智能（AI）正通过机器学习、计算机视觉、对话式大语言模型、检索增强生成和多模态界面被引入农业咨询服务。对于小农户而言，核心问题不在于这些技术能否产生技术上看似合理的输出，而在于它们能否在异质的农艺、语言和网络连接条件下提供本地正确、可操作且安全的建议。本批判性叙事综述整合了数字推广、AI赋能的农业建议、基于图像的诊断和负责任数字农业方面的证据，并特别关注印度东北部。本文考察了2010年1月1日至2026年7月17日期间发表的文献，重点关注同行评议的田间评估、技术验证研究、综述及区域相关研究。来自数字推广的证据提供了最强的因果基线：移动化和个性化咨询服务经常改善信息记忆、农艺知识和对推荐措施的采纳，但对产量、利润和福利的影响并不一致。近期生成式AI研究表明，大语言模型能够产生有用的农业回答，但针对具体地点的用量、时机和本地实践仍是反复出现的失败点。检索 grounding 和专家反馈可提高本地相关性，但多季农场层面的有效性证据仍然稀缺。基于图像的植物病害系统在精选数据集上达到高准确率，但在田间域偏移、类别新颖性和图像质量多变的情况下，性能可能急剧下降。印度东北部在梅加拉亚邦、那加兰邦和特里普拉邦开展的移动咨询系统研究展示了基于交互式语音应答、本地专家网络和以用户为中心的服务设计的宝贵制度基础；然而，这些研究并未确立自主AI的有效性。因此，最可辩护的部署模式是一种容忍离线、多语言、多模态且有人工监督的架构，该架构将建议建立在精选的区域知识之上，表征不确定性，保留来源信息，并对高风险或分布外案例进行升级处理。未来研究应优先开展前瞻性的跨区县和跨季节评估，将模型质量与农户决策、农艺结果、公平性、安全性和成本效益联系起来。","Asian Research Journal of Agriculture","2026-09-24T00:00:00Z",80,{"impact":17,"substance":16,"depth":17,"authority":139,"freshness":94,"relevant":21,"comment":349},"系统综述AI农业咨询与诊断系统在印度东北小农场景的技术架构与落地证据，指出人机协同、离线多语言与检索增强是可行路径，对智慧农业落地有参考价值。",[351],{"name":345,"url":342},[26,353,354,355,54],"智慧农业","农业人工智能","农业技术推广",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[358],{"code":359,"slug":360,"name":361,"path":362,"parent_code":363},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":13,"version":40,"method":41,"evidence":365},{"candidates":366,"agricultural_evidence":46},[367,372,381,388,393],{"code":32,"score":93,"supported":46,"title":368,"abstract":370,"tags":371},[369],"smallholder",[54,369],[54],{"code":359,"score":12,"supported":46,"title":373,"abstract":374,"tags":380},[],[375,376,377,378,379],"机器学习","计算机视觉","machine learning","artificial intelligence","computer vision",[354],{"code":382,"score":119,"supported":46,"title":383,"abstract":384,"tags":387},"I1",[],[385,386],"数字农业","digital agriculture",[353],{"code":389,"score":21,"supported":13,"title":390,"abstract":391,"tags":392},"G1",[],[],[355],{"code":57,"score":21,"supported":13,"title":394,"abstract":395,"tags":396},[],[],[26],[398,399],"印度东北部 农业AI 小农户","农业智能诊断 多语言 离线","印度东北部农业AI小农户-3512","10.9734\u002Farja\u002F2026\u002Fv19i4919",{"doi":401,"openalex_id":403,"authors":404,"venue":345,"cited_by_count":72,"oa_url":342,"card":410,"direction":77,"ingested_from":285},"W7214205238",[405,407],{"name":406,"orcid":8},"Pravangkar Boruah",{"name":408,"orcid":409},"Rubul Kumar Bania","https:\u002F\u002Forcid.org\u002F0000-0001-6294-0231",{"tldr":411,"method":412,"finding":413,"direction":414,"opportunity":415},"综述AI农业咨询与诊断系统，聚焦印度东北小农，提出人监督多模态部署架构。","批判性叙述综述，整合2010-2026年数字推广、生成式AI与图像诊断证据。","AI输出技术可行但本地化、安全与田间效果证据不足，需人监督与检索增强。","农业人工智能与决策模型","可开展跨区跨季前瞻评估，连接模型质量与农户决策、产量、公平及成本效益。","2026-09-25T23:30:39.745514Z"]