[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2739":3,"related-2739":77},{"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":61,"slug":64,"view_count":65,"doi":8,"paper":66,"created_at":76},2739,"Bridging the Extension Gap: Mobile Phones and Agricultural Information Seeking among Smallholder Farmers in Kenya","https:\u002F\u002Fijsrm.net\u002Findex.php\u002Fijsrm\u002Farticle\u002Fview\u002F8204","肯尼亚Kericho郡Belgut分区小农户移动电话与农业信息寻求研究。调查显示,农业培训信息访问频率最高(39.6%),其次是投入品信息(19.8%),种植信息(14.4%)、病虫害管理(13.5%)、一般市场信息(13.5%)、商品价格信息(10.8%)和天气预报(9.9%)。整体利用率偏低表明,虽然移动电话具有作为能力建设工具的潜力,但系统性障碍(如数字素养、成本、本地化内容缺乏和对传统推广结构的依赖)阻碍其作为综合咨询机制的采用。",null,"IJSRM 2026年9月13日","2026-09-13T00:00:00Z","论文",10,false,59,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},8,18,15,12,6,1,"基于肯尼亚小农户调查的一手数据，揭示移动电话在农技信息获取中的结构性障碍，对数字乡村与农技推广信息化有参考价值，但属境外区域研究、影响层级有限。",[24],{"name":9,"url":6},[26,27,28,29,30],"数字乡村","数字素养","小农户","农技推广","移动农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,50,56],{"code":32,"score":19,"supported":45,"title":46,"abstract":48,"tags":49},true,[47],"smallholder",[28],[28],{"code":51,"score":52,"supported":13,"title":53,"abstract":54,"tags":55},"I6",2,[],[],[26,27],{"code":57,"score":21,"supported":13,"title":58,"abstract":59,"tags":60},"G1",[],[],[29],[62,63],"农技推广 数字乡村 数字素养 移动农业","农技推广 数字乡村","农技推广数字乡村数字素养移动农业-2739",3,{"doi":8,"openalex_id":8,"authors":67,"venue":8,"cited_by_count":68,"oa_url":8,"card":69,"direction":73,"ingested_from":75},[],0,{"tldr":70,"method":71,"finding":72,"direction":73,"opportunity":74},"研究肯尼亚小农户用手机获取农业信息的现状及障碍。","对肯尼亚Belgut分区小农户进行问卷调查与信息类型统计。","手机农业信息利用率整体偏低，受数字素养、成本、本地化内容缺乏等系统性障碍制约。","数字乡村与农业信息化","可探索低成本本地化内容与数字素养培训如何提升手机农业咨询的实际采纳率。","agent","2026-09-17T00:04:41.088694Z",{"total":20,"page":21,"page_size":20,"items":78},[79,127,171,249,331,373],{"id":80,"title":81,"url":82,"summary":83,"summary_zh":8,"content":8,"source_name":84,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":85,"score_detail":86,"sources":91,"tags":93,"primary_topic":96,"secondary_topics":97,"classification":98,"search_phrases":115,"slug":118,"view_count":21,"doi":8,"paper":119,"created_at":126},4972,"数字赋能:助推小农户融入现代农业发展轨道——基于全国农户微观数据的考察","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fjnxb\u002Fmulu\u002F633014.html","钟文晶、李丹、罗必良(华南农业大学经济管理学院)基于有限理性假设和信息不对称理论,探讨互联网信息技术助推小农户数字赋能的内在机理,并利用中国劳动力动态调查(CLDS)2018年的微观数据进行实证检验。结果表明,互联网使用有效促进了小农户农业生产的机械化、土地经营的规模化以及对农业的生产性投资;异质性分析表明,数字赋能在大规模农户、中老年农户、中部和东北地区的农户组中的作用尤为显著。文章强调应加快推进农村地区的数字基础设施建设,提升农户的数字素养。","《农业经济问题》",79,{"impact":87,"substance":88,"depth":17,"authority":89,"freshness":52,"relevant":21,"comment":90},22,23,14,"基于全国微观数据的实证研究，结论扎实且具政策参考价值，但时效性偏弱，适合主题聚合而非当日精选。",[92],{"name":84,"url":82},[26,27,28,94,95],"互联网使用","农村数字基建",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":99},{"candidates":100,"agricultural_evidence":45},[101,105,111],{"code":32,"score":19,"supported":45,"title":102,"abstract":103,"tags":104},[28],[28],[28],{"code":106,"score":65,"supported":45,"title":107,"abstract":108,"tags":110},"F5",[],[109],"机械化",[],{"code":51,"score":52,"supported":13,"title":112,"abstract":113,"tags":114},[],[],[26,27],[116,117],"华南农业大学 小农户 数字赋能","CLDS 互联网使用 农业生产","华南农业大学小农户数字赋能-4972",{"doi":8,"openalex_id":8,"authors":120,"venue":8,"cited_by_count":68,"oa_url":8,"card":121,"direction":73,"ingested_from":75},[],{"tldr":122,"method":123,"finding":124,"direction":73,"opportunity":125},"基于CLDS数据实证检验互联网使用如何助推小农户融入现代农业发展。","有限理性与信息不对称理论，CLDS 2018农户微观数据实证。","互联网使用促进小农户机械化、规模化和生产性投资，中老年及中部东北农户效应更显著。","可探究数字素养培训与数字基础设施如何协同提升小农户数字赋能效果。","2026-10-15T00:06:09.096458Z",{"id":128,"title":129,"url":130,"summary":131,"summary_zh":8,"content":132,"source_name":133,"source_url":8,"published_at":134,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":135,"score_detail":136,"sources":139,"tags":141,"primary_topic":145,"secondary_topics":146,"classification":147,"search_phrases":158,"slug":161,"view_count":162,"doi":8,"paper":163,"created_at":170},5537,"农业产业园促进小农户融入数字化供应链：协同机制与制度逻辑","https:\u002F\u002Fkyy.nwafu.edu.cn\u002Fkjjz\u002Fxdkjjz\u002F557ffd662c8e4af98e46fbf90f35d91f.htm","西北农林科技大学阮俊虎教授团队联合德国哥廷根大学于晓华教授，以大荔县国家现代农业产业园等为典型案例，从交易成本理论出发围绕技术赋能、组织整合、规则治理与价值分配四个机制开展案例内分析与跨案例比较，揭示农业产业园作为中国特色制度平台促进小农户融入数字化供应链的深层逻辑。","近日，经济管理学院“数智农业运营管理团队”阮俊虎教授联合德国哥廷根大学于晓华教授在《中国农村经济》发表题为“农业产业园促进小农户融入数字化供应链：协同机制与制度逻辑”的研究论文，通过多案例研究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，并阐释机制形成与运行的制度逻辑。博士研究生刘晨欢为第一作者，阮俊虎教授为通讯作者，王月博士和于晓华教授为合作学者。\n\n数字技术正深刻重构农产品供应链格局，大型电商平台等供应链核心主体加速构建覆盖生产源头至消费终端的数字化供应链体系，该体系不仅体现为数字技术在生产、销售、物流等环节的应用，更强调不同环节与经营主体之间的系统协同与数据贯通，小农户亟须有效融入数字化供应链以应对市场竞争格局的深刻变化。然而，“大国小农”国情农情下，小农户经营规模较小、土地细碎化程度较高，难以独立对接数字化、规模化的现代供应链体系。促进小农户深度融入数字化供应链，进而实现小农户和现代农业发展有机衔接，成为农业现代化进程中值得关注的重要议题。农业产业园作为区域农业发展的综合性制度平台，兼具资源集聚、组织协同与制度保障三重优势，能够为地方政府及园区经营管理主体协同整合制度资源并协调多元主体关系提供平台支撑，但将资源集聚、组织协同和制度供给优势转化为小农户融入数字化供应链的现实能力，其内在机理尚未得到系统性揭示。\n\n本文立足“农业产业园促进小农户融入数字化供应链的内在机理”这一科学问题，从交易成本理论出发，以大荔县国家现代农业产业园等为典型案例，围绕技术赋能、组织整合、规则治理和价值分配四个机制开展案例内分析与跨案例比较分析，揭示不同园区作用机制的共性、差异及情境适配特征，并从制度层面阐释农业产业园作为中国特色制度平台，促进小农户融入数字化供应链的深层逻辑。研究旨在为农业经营呈现小规模、分散化特征的国家和地区促进小农户和现代农业发展有机衔接提供理论参考与实践借鉴。\n\n该研究得到国家社会科学基金重大项目“我国农产品供应链数字化转型的发展战略与实施路径研究”（23&ZD123）的资助。\n\n论文链接：\n\n[https:\u002F\u002Fzgncjj.ajcass.com\u002F?jumpnotice=201606270007#\u002FissueDetail?contentId=123722&year=2026&issue=10&title=%E5%BD%93%E6%9C%9F%E7%9B%AE%E5%BD%95](https:\u002F\u002Fzgncjj.ajcass.com\u002F?jumpnotice=201606270007#\u002FissueDetail?contentId=123722&year=2026&issue=10&title=%E5%BD%93%E6%9C%9F%E7%9B%AE%E5%BD%95)\n\n编辑：张晴 终审：刘玉峰","中国农村经济 2026-10-21","2026-10-21T00:00:00Z",82,{"impact":17,"substance":87,"depth":17,"authority":18,"freshness":137,"relevant":21,"comment":138},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。",[140],{"name":133,"url":130},[26,28,142,143,144],"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":148},{"candidates":149,"agricultural_evidence":45},[150,154],{"code":32,"score":19,"supported":45,"title":151,"abstract":152,"tags":153},[28],[28],[28],{"code":51,"score":21,"supported":13,"title":155,"abstract":156,"tags":157},[],[],[26],[159,160],"农业产业园 小农户 数字化供应链","大荔县 国家现代农业产业园","农业产业园小农户数字化供应链-5537",7,{"doi":8,"openalex_id":8,"authors":164,"venue":8,"cited_by_count":68,"oa_url":8,"card":165,"direction":73,"ingested_from":75},[],{"tldr":166,"method":167,"finding":168,"direction":73,"opportunity":169},"以农业产业园为案例，揭示其促进小农户融入数字化供应链的机制与制度逻辑。","交易成本理论，大荔县等典型案例的案例内分析与跨案例比较。","技术赋能、组织整合、规则治理与价值分配四机制共同推动小农户融入数字化供应链。","可量化检验四机制对农户融入绩效的影响，并比较不同产业园模式的适用边界。","2026-10-22T00:07:10.192700Z",{"id":172,"title":173,"url":174,"summary":175,"summary_zh":176,"content":8,"source_name":177,"source_url":174,"published_at":178,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":179,"score_detail":180,"sources":183,"tags":185,"primary_topic":189,"secondary_topics":190,"classification":197,"search_phrases":228,"slug":231,"view_count":20,"doi":232,"paper":233,"created_at":248},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":87,"depth":17,"authority":181,"freshness":137,"relevant":21,"comment":182},13,"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。",[184],{"name":177,"url":174},[26,186,187,188,28],"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},[191],{"code":192,"slug":193,"name":194,"path":195,"parent_code":196},"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":198},{"candidates":199,"agricultural_evidence":45},[200,204,213,220,224],{"code":32,"score":18,"supported":45,"title":201,"abstract":202,"tags":203},[47],[28,47],[28],{"code":192,"score":12,"supported":45,"title":205,"abstract":206,"tags":212},[],[207,208,209,210,211],"机器学习","计算机视觉","machine learning","artificial intelligence","computer vision",[187],{"code":214,"score":162,"supported":45,"title":215,"abstract":216,"tags":219},"I1",[],[217,218],"数字农业","digital agriculture",[186],{"code":57,"score":21,"supported":13,"title":221,"abstract":222,"tags":223},[],[],[188],{"code":51,"score":21,"supported":13,"title":225,"abstract":226,"tags":227},[],[],[26],[229,230],"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":232,"openalex_id":234,"authors":235,"venue":177,"cited_by_count":68,"oa_url":174,"card":241,"direction":73,"ingested_from":247},"W7214205238",[236,238],{"name":237,"orcid":8},"Pravangkar Boruah",{"name":239,"orcid":240},"Rubul Kumar Bania","https:\u002F\u002Forcid.org\u002F0000-0001-6294-0231",{"tldr":242,"method":243,"finding":244,"direction":245,"opportunity":246},"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。","openalex","2026-09-25T23:30:39.745514Z",{"id":250,"title":251,"url":252,"summary":253,"summary_zh":254,"content":8,"source_name":255,"source_url":252,"published_at":178,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":179,"score_detail":256,"sources":258,"tags":260,"primary_topic":262,"secondary_topics":263,"classification":265,"search_phrases":299,"slug":302,"view_count":303,"doi":304,"paper":305,"created_at":330},3469,"ARTIFICIAL INTELLIGENCE FOR AGRICULTURE: A SYSTEMATIC REVIEW OF FARMERS' PERCEPTIONS, ACCEPTANCE, ADOPTION AND BARRIERS","https:\u002F\u002Fdoi.org\u002F10.64013\u002Fbbasrjlifess.v2026i1.70","Artificial intelligence (AI) is rapidly reshaping agriculture, enabling the application of data for efficient decision-making, precision farming, crop monitoring, pest management, disease detection, smart irrigation, yield prediction, and various aspects of farm automation. Yet, to successfully gain a foothold in farms, factors like farmers' perceptions, their acceptance, desire, and readiness to adopt, as well as the capability to surmount socioeconomic, technological, and institutional challenges, must also make a difference. This article systematically collates the available information on farmers' perspectives on, acceptance of, readiness to adopt, and the obstacles related to AI in the agricultural context. The PRISMA 2020 guideline has been followed to select studies that are related, evaluated for inclusion, and finally, per the criteria, combined into a systematic synthesis. The review mainly discusses the aspects that influence the use of AI, like perceived usefulness, ease of use, trust, digital literacy, affordability, farm size, socioeconomic characteristics, availability of digital infrastructure, and access to agricultural technical advisory services. Main challenges identified involve the high cost of implementing AI, poor connectivity, weak rural infrastructure, low levels of technological knowledge, unavailability of support services, concerns about data privacy, distrust of algorithmic bias, language barriers, and inequalities impacting smallholder farmers. Besides barriers, the review indicates the potential of leveraging AI through extension services, agricultural mobile apps, precision farming, climate-smart agriculture, early-warning systems, and tailored farm advisories. By drawing out technical, behavioral, socioeconomic, and institutional perspectives, the review pinpoints important research questions and presents a farmholder-oriented setup to explain the process of taking up AI in farming. The outcomes can be used by scientific experts, policymakers, extension personnel, and software developers to create low-cost, reliable, and accessible AI systems aimed at promoting sustainable agricultural development.","人工智能（AI）正迅速重塑农业，使数据得以应用于高效决策、精准农业、作物监测、病虫害管理、病害检测、智能灌溉、产量预测以及农场自动化的各个方面。然而，要在农场中成功立足，农民的认知、接受度、采用意愿和准备程度，以及克服社会经济、技术和制度挑战的能力，同样至关重要。本文系统梳理了现有关于农民对农业领域人工智能的看法、接受度、采用准备程度及相关障碍的信息。研究遵循PRISMA 2020指南，筛选相关研究，评估其纳入资格，并最终依据标准进行系统性综合。综述主要探讨了影响人工智能使用的因素，包括感知有用性、易用性、信任、数字素养、可负担性、农场规模、社会经济特征、数字基础设施的可用性以及农业技术咨询服务的获取。识别出的主要挑战包括人工智能实施成本高昂、网络连接不佳、农村基础设施薄弱、技术水平低下、支持服务缺乏、数据隐私担忧、对算法偏见的疑虑、语言障碍以及影响小农户的不平等问题。除障碍外，综述还指出了通过推广服务、农业移动应用、精准农业、气候智慧型农业、预警系统和定制化农场咨询来利用人工智能的潜力。通过梳理技术、行为、社会经济和制度层面的视角，本综述指出了重要的研究问题，并提出了一个以农场主为导向的框架，以解释在农业中采用人工智能的过程。研究结果可供科学专家、政策制定者、推广人员和软件开发者用于创建低成本、可靠且可及的人工智能系统，以促进可持续农业发展。","Journal of Life and Social Sciences",{"impact":17,"substance":87,"depth":17,"authority":181,"freshness":137,"relevant":21,"comment":257},"基于PRISMA的系统综述，系统梳理农户对AI的认知、接受度与采纳障碍，对智慧农业推广与政策设计有实质参考价值。",[259],{"name":255,"url":252},[186,187,27,261,28],"农户采纳",{"code":192,"slug":193,"name":194,"path":195,"parent_code":196},[264],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":266},{"candidates":267,"agricultural_evidence":45},[268,272,276,282,289,295],{"code":192,"score":19,"supported":45,"title":269,"abstract":270,"tags":271},[210],[210],[187],{"code":32,"score":16,"supported":45,"title":273,"abstract":274,"tags":275},[],[28,47],[261,28],{"code":214,"score":162,"supported":45,"title":277,"abstract":278,"tags":281},[],[279,280],"精准农业","smart agriculture",[186],{"code":283,"score":20,"supported":45,"title":284,"abstract":285,"tags":288},"H3",[],[286,287],"灌溉","irrigation",[],{"code":290,"score":65,"supported":45,"title":291,"abstract":292,"tags":294},"H8",[],[293],"可持续农业",[],{"code":51,"score":21,"supported":13,"title":296,"abstract":297,"tags":298},[],[],[27],[300,301],"农民 AI 采纳 障碍","农业人工智能 系统综述","农民AI采纳障碍-3469",5,"10.64013\u002Fbbasrjlifess.v2026i1.70",{"doi":304,"openalex_id":306,"authors":307,"venue":255,"cited_by_count":68,"oa_url":252,"card":324,"direction":329,"ingested_from":247},"W7214189410",[308,310,312,314,316,318,320,322],{"name":309,"orcid":8},"MM JAMEEL",{"name":311,"orcid":8},"M SAEED",{"name":313,"orcid":8},"SA SHER",{"name":315,"orcid":8},"Z ALI",{"name":317,"orcid":8},"Q HAYYAT",{"name":319,"orcid":8},"S KIRBAG",{"name":321,"orcid":8},"S KHAN",{"name":323,"orcid":8},"HN AHMAD",{"tldr":325,"method":326,"finding":327,"direction":73,"opportunity":328},"系统综述农民对农业AI的感知、接受度、采纳意愿及障碍。","遵循PRISMA 2020指南，系统筛选并综合相关文献。","成本、基础设施、数字素养与信任是主要障碍，小农户受影响最大。","可研究低成本、本地化AI采纳模型及小农户数字包容机制。","智慧农业 \u002F 农业物联网","2026-09-25T23:30:09.670568Z",{"id":332,"title":333,"url":334,"summary":335,"summary_zh":8,"content":8,"source_name":336,"source_url":8,"published_at":337,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":338,"score_detail":339,"sources":342,"tags":344,"primary_topic":348,"secondary_topics":349,"classification":350,"search_phrases":361,"slug":364,"view_count":303,"doi":8,"paper":365,"created_at":372},3434,"《农业数字供应链金融何以助力小农户采纳农业新质技术?》","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F165468.html","刘彦君、申云基于园区交易信任视角从理论与实证维度考查以现代农业产业园为载体的农业数字供应链金融何以助力小农户采纳农业新质技术。研究发现：农业数字供应链金融显著促进园区内信任机制的形成，有效激励农户采纳农业新质技术，不仅提升采纳率而且显著改善应用水平与推广效果；机制上通过农业供应链整合、信息渠道数字化及同群效应三条路径发挥作用；该促进效应在市级及以上等级园区、近郊型园区以及高学历农户群体中更为突出。","《农业经济与管理》2026年第2期","2026-09-20T00:00:00Z",75,{"impact":340,"substance":87,"depth":17,"authority":89,"freshness":303,"relevant":21,"comment":341},16,"核心期刊实证研究，揭示数字供应链金融促进小农户采纳新质技术的机制，对数字乡村与农业科技推广有参考价值。",[343],{"name":336,"url":334},[26,28,345,346,347],"现代农业产业园","数字供应链金融","农业新质技术",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":351},{"candidates":352,"agricultural_evidence":45},[353,357],{"code":32,"score":19,"supported":45,"title":354,"abstract":355,"tags":356},[28],[28],[28],{"code":51,"score":21,"supported":13,"title":358,"abstract":359,"tags":360},[],[],[26],[362,363],"农业数字供应链金融 小农户 新质技术","现代农业产业园 园区信任 技术采纳","农业数字供应链金融小农户新质技术-3434",{"doi":8,"openalex_id":8,"authors":366,"venue":8,"cited_by_count":68,"oa_url":8,"card":367,"direction":73,"ingested_from":75},[],{"tldr":368,"method":369,"finding":370,"direction":73,"opportunity":371},"研究农业数字供应链金融如何通过园区交易信任促进小农户采纳农业新质技术。","基于现代农业产业园调研数据，构建理论与实证模型检验信任机制与中介路径。","数字供应链金融显著提升农户新质技术采纳率与应用水平，通过供应链整合、信息数字化和同群效应实现。","可探究不同园区类型与农户禀赋下数字供应链金融的异质性作用及信任机制的长期演化。","2026-09-25T00:09:33.652154Z",{"id":374,"title":375,"url":376,"summary":377,"summary_zh":8,"content":8,"source_name":378,"source_url":8,"published_at":379,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":85,"score_detail":380,"sources":383,"tags":385,"primary_topic":388,"secondary_topics":389,"classification":390,"search_phrases":422,"slug":425,"view_count":137,"doi":426,"paper":427,"created_at":434},3327,"Digitalization of smallholder agriculture: Adoption and impacts——特刊导论（Sun et al. 2026）","https:\u002F\u002Fwww.sciopen.com\u002Flocal\u002Farticle_pdf\u002F10.1016\u002Fj.jia.2026.04.021.pdf","《Journal of Integrative Agriculture》2026年专刊导论，IAMO孙占力博士、华中农大熊航教授、北大易红梅教授等担任客座编辑。本专刊收录Liu et al. (2026)研究——使用面板数据和多种估计方法调查数字技术使用如何影响中国易地搬迁农户收入稳定性，发现数字技术显著改善收入水平和稳定性；Hu et al. (2026)使用来自5省1,833个农场的倾向得分匹配数据，发现网络信息使用实际上增加了小农户的化肥支出，反思了农业数字化的复杂性；Bai et al. (2026)研究数字素养通过改善农民对气候灾害风险的感知促进气候适应性生产行为；Amolegbe et al. (2026)研究尼日利亚数字知识与电商估值。","《Journal of Integrative Agriculture》2026专刊","2026-09-17T00:00:00Z",{"impact":17,"substance":87,"depth":381,"authority":89,"freshness":20,"relevant":21,"comment":382},19,"国际期刊专刊导论，汇集多国小农户数字化实证研究，方法扎实、结论具反思性，对农业信息化研究与实践有参考价值。",[384],{"name":378,"url":376},[217,386,27,28,387],"农村电商","数字鸿沟",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":391},{"candidates":392,"agricultural_evidence":45},[393,397,403,409,413,418],{"code":32,"score":19,"supported":45,"title":394,"abstract":395,"tags":396},[47],[28],[28],{"code":398,"score":65,"supported":45,"title":399,"abstract":400,"tags":402},"G6",[],[401],"农户收入",[],{"code":404,"score":65,"supported":45,"title":405,"abstract":406,"tags":408},"H7",[],[407],"气候适应",[],{"code":51,"score":52,"supported":13,"title":410,"abstract":411,"tags":412},[],[],[27,387],{"code":414,"score":21,"supported":13,"title":415,"abstract":416,"tags":417},"E5",[],[],[386],{"code":214,"score":21,"supported":13,"title":419,"abstract":420,"tags":421},[],[],[217],[423,424],"Journal of Integrative Agriculture 数字农业 专刊","易地搬迁农户 数字技术 收入稳定性","JournalofIntegrativeAgriculture数字农业专刊-3327","10.1016\u002Fj.jia.2026.04.021.pdf",{"doi":426,"openalex_id":8,"authors":428,"venue":8,"cited_by_count":68,"oa_url":8,"card":429,"direction":73,"ingested_from":75},[],{"tldr":430,"method":431,"finding":432,"direction":73,"opportunity":433},"该专刊导论综述小农户农业数字化的采纳与影响，涵盖中国及尼日利亚的多项实证研究。","基于面板数据、倾向得分匹配等方法的专刊导论，整合多国小农户实证研究。","数字技术可提升收入稳定性，但也可能增加化肥支出，数字化影响具有复杂性。","可深入探究数字技术对不同农户群体的异质性影响及化肥增投的机制与调控路径。","2026-09-24T00:04:03.016677Z"]