[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3623":3,"related-3623":56},{"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":28,"search_phrases":33,"slug":36,"view_count":37,"doi":38,"paper":39,"created_at":55},3623,"Artificial Intelligence Literacy as a Predictor of Readiness for Digital Agriculture among Agricultural Education Students in Colleges of Education in South-East Nigeria","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22979479","Artificial Intelligence Literacy as a Predictor of Readiness for Digital Agriculture among Agricultural Education Students in Colleges of Education in South-East Nigeria。Zenodo (CERN European Organization for Nuclear Research)","人工智能素养作为尼日利亚东南部教育学院农业教育专业学生数字农业准备度的预测因素。Zenodo（CERN欧洲核子研究组织）。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-26T00:00:00Z","论文",25,false,62,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,15,12,9,1,"尼日利亚农业教育学生AI素养与数字农业就绪度的实证研究，对农业信息化人才培养有参考价值，但属区域性学术成果，影响范围有限。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22979480",[29,30,31,32],"数字农业","农业人工智能","农业教育","尼日利亚",[34,35],"尼日利亚 农业教育 数字农业","AI素养 数字农业 就绪度","尼日利亚农业教育数字农业-3623",0,"10.5281\u002Fzenodo.22979479",{"doi":38,"openalex_id":40,"authors":41,"venue":10,"cited_by_count":37,"oa_url":6,"card":48,"direction":52,"ingested_from":54},"W7214458041",[42,44,46],{"name":43,"orcid":9},"Dr. Owo Emmanuel Ozuma",{"name":45,"orcid":9},"Abel Charles Izuchukwu",{"name":47,"orcid":9},"Adu Chukwuebuka Jeremiah",{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"研究尼日利亚东南部教育学院农教学生的人工智能素养对数字农业准备度的预测作用。","对教育学院农业教育学生进行问卷调查与回归分析。","人工智能素养显著正向预测学生参与数字农业的准备度。","数字乡村与农业信息化","可拓展至不同地区与院校，并探究AI素养培养课程对数字农业采纳的干预效果。","openalex","2026-09-27T23:30:57.296547Z",{"total":57,"page":22,"page_size":57,"items":58},6,[59,97,128,165,214,263],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":9,"source_name":10,"source_url":62,"published_at":65,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":66,"score_detail":67,"sources":72,"tags":76,"search_phrases":80,"slug":83,"view_count":37,"doi":84,"paper":85,"created_at":96},3482,"Digital Transformation and the Reconfiguration of Farming Systems: Understanding How Tech-nological Adoption and Institutional Conditions Shape Sustainable and Inclusive Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22932900","Digital agriculture is progressively reshaping farming through the integration of sensors, artificial intelligence, Internet of Things technologies, digital platforms, data analytics, robotics and automated decision-support systems. This paper examines this transformation by connecting three complementary dimensions: the socio-technical development of digital agriculture, the determinants and processes of technological adoption, and the economic, organizational and inclusive consequences of digitalization for farming systems. The literature indicates that agricultural digitalization cannot be reduced to the availability of increasingly sophisticated technologies. Adoption and sustained use depend on farmers’ perceptions of usefulness and compatibility, farm resources, human capital, infrastructure, institutional support, advisory systems and the capacity to integrate technologies into established production routines. Digital technologies may improve resource efficiency, information management, productivity and sustainability, while also modifying labor organization, farmer autonomy, data governance and relationships among actors within agricultural value chains. These effects remain uneven across farms and territories, particularly where smallholders face financial, infrastructural and capability constraints. The paper therefore interprets digital agriculture as a multidimensional transformation whose outcomes depend on the interaction between technological capabilities, farmer behavior and institutional conditions. Inclusive and sustainable digitalization requires attention not only to innovation diffusion but also to governance, skills, accessibility and the distribution of technological benefits.","数字农业正通过传感器、人工智能、物联网技术、数字平台、数据分析、机器人技术和自动化决策支持系统的整合，逐步重塑农业生产方式。本文通过连接三个互补维度来考察这一转型：数字农业的社会技术发展、技术采纳的决定因素与过程，以及数字化对农业系统产生的经济、组织和包容性后果。文献表明，农业数字化不能简化为日益复杂的技术供给。采纳和持续使用取决于农民对有用性和兼容性的认知、农场资源、人力资本、基础设施、制度支持、咨询系统以及将技术整合到既定生产惯例中的能力。数字技术可能提高资源效率、信息管理、生产力和可持续性，同时也会改变劳动组织、农民自主性、数据治理以及农业价值链中参与者之间的关系。这些影响在农场和区域之间仍不均衡，尤其是小农户面临资金、基础设施和能力约束的地方。因此，本文将数字农业解释为一种多维转型，其结果取决于技术能力、农民行为和制度条件之间的相互作用。包容和可持续的数字化不仅需要关注创新扩散，还需要关注治理、技能、可及性和技术收益的分配。","2026-09-24T00:00:00Z",77,{"impact":18,"substance":68,"depth":69,"authority":70,"freshness":21,"relevant":22,"comment":71},20,17,13,"系统梳理数字农业技术采纳与制度条件的研究综述，对智慧农业政策与推广有参考价值，但属文献综述类，非重大突破。",[73,74],{"name":10,"url":62},{"name":10,"url":75},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22932899",[29,77,30,78,79],"智慧农业","小农户","技术采纳",[81,82],"农业人工智能 技术采纳 数字农业 智慧农业","农业人工智能 技术采纳","农业人工智能技术采纳数字农业智慧农业-3482","10.5281\u002Fzenodo.22932900",{"doi":84,"openalex_id":86,"authors":87,"venue":10,"cited_by_count":37,"oa_url":62,"card":90,"direction":95,"ingested_from":54},"W7214187008",[88],{"name":89,"orcid":9},"Chilufya Banda",{"tldr":91,"method":92,"finding":93,"direction":52,"opportunity":94},"综述数字农业转型，分析技术采纳与制度条件如何共同塑造可持续包容性农业。","文献综述，整合社会技术、技术采纳与数字化经济组织后果三维度。","数字化成效取决于技术能力、农户行为与制度条件的交互，小农面临多重约束。","可实证检验制度支持与农户能力如何调节数字技术对包容性和可持续性的影响。","智慧农业 \u002F 农业物联网","2026-09-25T23:30:18.463733Z",{"id":98,"title":99,"url":100,"summary":101,"summary_zh":9,"content":9,"source_name":102,"source_url":9,"published_at":103,"category":12,"cover_url":9,"hotness":104,"is_selected":14,"score":105,"score_detail":106,"sources":109,"tags":111,"search_phrases":114,"slug":117,"view_count":37,"doi":9,"paper":118,"created_at":127},3439,"《Agentic Artificial Intelligence in Agriculture: A Systematic Mapping Review of Reported Architectures, Applications, Challenges, and Future Directions》","https:\u002F\u002Fwww.mdpi.com\u002F2227-7080\u002F14\u002F9\u002F591","作者按PRISMA 2020方案从4111项记录筛选至181项研究，对2020—2026年农业代理式人工智能文献做系统映射综述：领域跨度30多年但LLM子集非常年轻（2024年才出现、46\u002F47项发表于2025—2026年）；82%研究报告合作能力，计划和推理分别仅31%、记忆6%、反思4%；只有33项研究报告现场或实际部署，68项仍停留在概念性阶段，仅11项报告了一个季度以上的评估。","《Technologies》2026, 14(9), 591 \u002F MDPI","2026-09-22T00:00:00Z",10,80,{"impact":18,"substance":107,"depth":18,"authority":70,"freshness":17,"relevant":22,"comment":108},23,"基于PRISMA的农业代理式AI系统映射综述，量化揭示LLM应用年轻化与落地不足，信息增量与专业深度突出，值得进入每日精选。",[110],{"name":102,"url":100},[29,77,30,112,113],"农业大模型","智能体",[115,116],"农业代理式人工智能 系统映射综述","农业人工智能 农业大模型 数字农业 智慧农业","农业代理式人工智能系统映射综述-3439",{"doi":9,"openalex_id":9,"authors":119,"venue":9,"cited_by_count":37,"oa_url":9,"card":120,"direction":124,"ingested_from":126},[],{"tldr":121,"method":122,"finding":123,"direction":124,"opportunity":125},"系统映射181项研究，梳理农业代理式AI的架构、应用、挑战与未来方向。","按PRISMA 2020筛选4111项记录至181项，做系统映射综述。","LLM代理2024年才出现，多具合作能力但规划、记忆、反思薄弱，实际部署少。","农业人工智能与决策模型","农业LLM代理的长期田间部署、记忆与反思机制及跨季度评估仍是明显空白。","agent","2026-09-25T00:09:33.995675Z",{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":9,"source_name":134,"source_url":131,"published_at":103,"category":12,"cover_url":9,"hotness":104,"is_selected":14,"score":135,"score_detail":136,"sources":139,"tags":141,"search_phrases":144,"slug":147,"view_count":37,"doi":148,"paper":149,"created_at":164},3367,"Digital Technology Adoption Conditioning Analysis Model in Agriculture","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.1880.v1","Technological advancements have been responsible for a significant part of the growth in agricultural productivity in recent years. Digital technologies have a high potential to enable the development of the agricultural sector, reshape value chains, and significantly contribute to more productive, resilient, and transparent food systems; however, their adoption in Brazil remains uneven due to regional disparities and structural bottlenecks. The research investigated this problem to build and validate the Digital Technology Adoption Conditioning Analysis Model (MAC-AgriTech), through a case study with Brazilian agricultural data, encompassing the identification of conditioning factors, their territorial evaluation, and the proposition of actions, while providing structured data collection and analysis instruments. The spatial analysis revealed deep territorial asymmetries, concentrating resources and infrastructure in the South and Southeast regions. Econometric modeling demonstrated that digital adoption is primarily driven by the producer’s digital familiarity, connectivity quality, and property scale, with 77% of producers identifying acquisition and maintenance costs as the primary barrier. The transition to digital agriculture in Brazil requires targeted, multidimensional public policies—such as expanded rural connectivity, technical training, and subsidized credit—to overcome regional gaps, and to increase agricultural competitiveness, efficiency, and sustainability.","近年来，技术进步对农业生产力增长贡献显著。数字技术具有巨大潜力，能够推动农业部门发展、重塑价值链，并为构建更高产、更具韧性且更透明的粮食体系作出重要贡献；然而，由于区域差异和结构性瓶颈，其在巴西的采用仍不均衡。本研究针对这一问题，通过一项基于巴西农业数据的案例研究，构建并验证了数字技术采用条件分析模型（MAC-AgriTech），涵盖条件因素的识别、其区域性评估以及行动建议的提出，同时提供了结构化的数据收集与分析工具。空间分析揭示了深刻的区域不对称性，资源和基础设施集中在南部和东南部地区。计量经济建模表明，数字采用主要受生产者数字熟悉度、连接质量和财产规模的驱动，其中77%的生产者将购置和维护成本视为主要障碍。巴西向数字农业的转型需要有针对性的、多维度的公共政策——如扩大农村连接、技术培训和补贴信贷——以克服区域差距，并提高农业竞争力、效率和可持续性。","Preprints.org",67,{"impact":137,"substance":68,"depth":69,"authority":57,"freshness":17,"relevant":22,"comment":138},16,"基于巴西农业数据的数字技术采纳条件分析模型研究，方法系统、结论有实证支撑，但属预印本且聚焦巴西，对国内参考价值有限。",[140],{"name":134,"url":131},[29,77,30,142,143],"巴西农业","农村数字化",[145,146],"巴西 数字农业 技术采纳","MAC-AgriTech 模型","巴西数字农业技术采纳-3367","10.20944\u002Fpreprints202609.1880.v1",{"doi":148,"openalex_id":150,"authors":151,"venue":134,"cited_by_count":37,"oa_url":131,"card":159,"direction":52,"ingested_from":54},"W7214109425",[152,155,157],{"name":153,"orcid":154},"Isabela Santos","https:\u002F\u002Forcid.org\u002F0009-0002-3659-2020",{"name":156,"orcid":9},"Eduardo Dias",{"name":158,"orcid":9},"Lidia Scoton",{"tldr":160,"method":161,"finding":162,"direction":52,"opportunity":163},"构建并验证MAC-AgriTech模型，分析巴西农业数字技术采纳的条件因素与区域差异。","巴西农业数据案例研究，空间分析与计量经济建模。","采纳主要由数字熟悉度、连接质量和农场规模驱动，77%生产者视成本为首要障碍。","可延伸至中国等发展中国家，探究数字素养、基础设施与政策组合对技术采纳的因果效应。","2026-09-24T23:30:27.046035Z",{"id":166,"title":167,"url":168,"summary":169,"summary_zh":170,"content":9,"source_name":134,"source_url":168,"published_at":171,"category":12,"cover_url":9,"hotness":104,"is_selected":14,"score":172,"score_detail":173,"sources":176,"tags":178,"search_phrases":181,"slug":184,"view_count":37,"doi":185,"paper":186,"created_at":213},3358,"One Toolchain, Six Domains: A Multiple-Case, Document-Based Study of Rapid IoT Prototypes Built in a One-Week Immersive Course on a Master’s Program in Applied Artificial Intelligence","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.2011.v1","This paper reports a document-based, multiple-case study of six Internet-of-Things (IoT) prototypes designed and simulated during a one-week immersive course, “IoT for Data Intelligence,” delivered in July 2026 within the professional Master in Applied Artificial Intelligence (Maestría en Inteligencia Artificial Aplicada, MNA) at Tecnológico de Monterrey. Six teams followed the same five-day toolchain IoT theory; Oracle Application Express (APEX), SQL, and REST service design; MIT App Inventor; ESP32\u002FWokwi simulation; and generative-AI integration and produced Wokwi-simulated prototypes spanning industrial energy monitoring, agricultural hazard response, residential automation, cardiovascular telemonitoring, industrial waste reduction, and precision agriculture. A fixed coding framework was applied across architecture, AI-integration pattern, platform-level failure modes, security debt, and Sustainable Development Goal alignment, distinguishing findings that the course structure itself prescribes from findings the teams introduced independently. The six cases converged on a shared five-layer architecture and, in a pattern only partly prescribed by the course, on keeping generative AI in an advisory or fail-safe-wrapped role. Deposited results were also compared, for illustrative purposes only, against the course’s internal competency rubric. An observed proposal from a Pontifical Catholic University of Chile’s collaboration is discussed as an informal reference point rather than as evidence for generalization. This paper discusses the implications and limits of this small, single-institution, single-cohort, simulation-only case set.","本文报告了一项基于文档的多案例研究，研究对象为六项物联网（Internet of Things, IoT）原型，这些原型是在2026年7月于蒙特雷理工学院（Tecnológico de Monterrey）应用人工智能专业硕士（Maestría en Inteligencia Artificial Aplicada, MNA）项目内开设的一周沉浸式课程“面向数据智能的物联网”（IoT for Data Intelligence）中设计与仿真的。六个团队遵循了相同的五日工具链——物联网理论；Oracle Application Express（APEX）、SQL与REST服务设计；MIT App Inventor；ESP32\u002FWokwi仿真；以及生成式AI集成——并产出了基于Wokwi仿真的原型，涵盖工业能源监测、农业灾害响应、住宅自动化、心血管远程监护、工业减废和精准农业。研究采用固定编码框架，从架构、AI集成模式、平台级失效模式、安全债务和可持续发展目标对齐五个维度进行分析，并区分了课程结构本身所规定的发现与各团队独立引入的发现。六个案例收敛于一个共享的五层架构，并在一种仅部分由课程规定的模式中，将生成式AI保持在顾问性或故障安全包裹的角色中。所提交的成果还仅出于示例目的与课程内部能力量规进行了比较。智利天主教大学一项合作中提出的方案作为非正式参照点加以讨论，而非作为可推广的证据。本文讨论了这一小型、单一机构、单一批次、仅仿真案例集的启示与局限。","2026-09-23T00:00:00Z",50,{"impact":57,"substance":137,"depth":19,"authority":174,"freshness":21,"relevant":22,"comment":175},4,"单校单期小样本的预印本教学案例研究，含农业物联网原型与生成式AI集成经验，但样本与仿真局限明显，公共价值有限。",[177],{"name":134,"url":168},[77,30,179,180,31],"农业物联网","精准农业",[182,183],"Tecnológico de Monterrey 物联网 课程","ESP32 Wokwi 农业物联网 原型","TecnológicodeMonterrey物联网课程-3358","10.20944\u002Fpreprints202609.2011.v1",{"doi":185,"openalex_id":187,"authors":188,"venue":134,"cited_by_count":37,"oa_url":168,"card":207,"direction":95,"ingested_from":54},"W7214071608",[189,192,195,198,201,204],{"name":190,"orcid":191},"Antonio Carlos Bento","https:\u002F\u002Forcid.org\u002F0000-0001-8264-4771",{"name":193,"orcid":194},"Alexandro Ortiz","https:\u002F\u002Forcid.org\u002F0000-0002-3945-6908",{"name":196,"orcid":197},"Grettel Barceló-Alonso","https:\u002F\u002Forcid.org\u002F0009-0004-3373-6441",{"name":199,"orcid":200},"Jose Reinaldo Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2796-1613",{"name":202,"orcid":203},"Luis E. Falcón-Morales","https:\u002F\u002Forcid.org\u002F0000-0001-8760-5640",{"name":205,"orcid":206},"Sérgio Camacho-León","https:\u002F\u002Forcid.org\u002F0000-0002-5996-9997",{"tldr":208,"method":209,"finding":210,"direction":211,"opportunity":212},"基于六组一周IoT课程原型文档，分析其架构、AI集成与安全模式。","文档多案例研究，固定编码框架，Wokwi仿真与生成式AI集成。","六案例收敛于五层架构，生成式AI多限于建议或故障保护角色。","其他","可探究仿真原型向真实农田部署时，安全债务与AI角色如何演变。","2026-09-24T23:30:13.353443Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":219,"content":9,"source_name":220,"source_url":217,"published_at":65,"category":12,"cover_url":9,"hotness":104,"is_selected":14,"score":15,"score_detail":221,"sources":223,"tags":225,"search_phrases":227,"slug":230,"view_count":37,"doi":231,"paper":232,"created_at":262},3347,"Integrating Material Flow Cost Accounting and IoT-Based Monitoring for Eco-Efficient Goat Farm Management","https:\u002F\u002Fdoi.org\u002F10.35145\u002F6e5wnv18","Goat farming plays an important role in supporting rural livelihoods, food production, and agricultural sustainability. However, conventional goat farm management often separates environmental monitoring, financial accounting, and livestock management, limiting the ability to identify resource inefficiencies and associated environmental impacts. This study aims to develop and implement GEMBALA (Green Eco-smart Management-Based Automation for Livestock and Accounting), an integrated digital platform that combines Internet of Things (IoT)-based environmental monitoring, Material Flow Cost Accounting (MFCA), emission analysis, artificial intelligence-based livestock management, and analytical reporting. The research employed a research and development approach in collaboration with CV Cahaya Firdaus (Fathur Farm). An IoT sensor prototype was developed, installed, and tested in a real goat farming environment to monitor temperature, humidity, Heat Index (THI), ammonia gas, and dust density. The platform also incorporates MFCA, emission, AI Estrus, AI Health, and analytical reporting modules. The results demonstrate progress toward integrating environmental, economic, and livestock management information within a unified digital platform. However, further validation is required to improve sensor data transmission, synchronization, emission calculations, MFCA data consistency, and AI performance evaluation. The study provides a foundation for eco-economic decision support, sustainable livestock management, and future commercialization of digital livestock technologies.","山羊养殖在支撑农村生计、粮食生产和农业可持续性方面发挥着重要作用。然而，传统的山羊养殖场管理往往将环境监测、财务核算和畜牧管理相互分离，限制了识别资源低效利用及相关环境影响的能力。本研究旨在开发并实施GEMBALA（基于绿色生态智能管理的畜牧与会计自动化平台），这是一个集成了基于物联网（IoT）的环境监测、物料流成本会计（MFCA）、排放分析、基于人工智能的畜牧管理以及分析报告的综合数字平台。研究采用研发方法，与CV Cahaya Firdaus（Fathur Farm）合作开展。研究开发了物联网传感器原型，并在真实山羊养殖环境中进行安装和测试，用于监测温度、湿度、热指数（THI）、氨气和粉尘密度。该平台还整合了MFCA、排放、AI发情检测、AI健康和分析报告模块。结果表明，在将环境、经济和畜牧管理信息整合到统一数字平台方面取得了进展。然而，仍需进一步验证，以改进传感器数据传输、同步、排放计算、MFCA数据一致性以及AI性能评估。本研究为生态经济决策支持、可持续畜牧管理以及数字畜牧技术的未来商业化提供了基础。","Journal of Applied Business and Technology",{"impact":17,"substance":18,"depth":137,"authority":104,"freshness":104,"relevant":22,"comment":222},"论文提出融合物联网监测与物料流成本核算的山羊养殖数字平台，方法有创新但尚处原型验证阶段，产业影响有限。",[224],{"name":220,"url":217},[29,77,30,179,226],"畜牧养殖",[228,229],"GEMBALA 山羊养殖 物联网","MFCA 畜牧 环境监测","GEMBALA山羊养殖物联网-3347","10.35145\u002F6e5wnv18",{"doi":231,"openalex_id":233,"authors":234,"venue":220,"cited_by_count":37,"oa_url":217,"card":257,"direction":95,"ingested_from":54},"W7214075234",[235,237,239,241,243,245,248,251,253,255],{"name":236,"orcid":9},"Nicholas Renaldo",{"name":238,"orcid":9},"Sulaiman Musa",{"name":240,"orcid":9},"Jaswar Koto",{"name":242,"orcid":9},"Kristy Veronica",{"name":244,"orcid":9},"Umar Faruq",{"name":246,"orcid":247},"Yulvia Nora Marlim","https:\u002F\u002Forcid.org\u002F0009-0007-8624-5023",{"name":249,"orcid":250},"Rangga Rahmadian Yuliendi","https:\u002F\u002Forcid.org\u002F0000-0003-2288-3580",{"name":252,"orcid":9},"Wilda Susanti",{"name":254,"orcid":9},"Achmad Tavip Junaedi",{"name":256,"orcid":9},"Nabila Wahid",{"tldr":258,"method":259,"finding":260,"direction":95,"opportunity":261},"开发集成物联网监测与物料流成本核算的山羊养殖数字平台GEMBALA。","研发方法，在真实羊场部署物联网传感器并集成MFCA、排放分析与AI模块。","平台初步实现环境、经济与养殖信息整合，但传感器传输与数据一致性仍需验证。","可延伸研究物联网数据与MFCA实时耦合的算法优化及AI模块的长期性能验证。","2026-09-24T23:30:09.863218Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":268,"content":9,"source_name":10,"source_url":266,"published_at":103,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":269,"score_detail":270,"sources":272,"tags":276,"search_phrases":278,"slug":281,"view_count":37,"doi":282,"paper":283,"created_at":295},3272,"Smart Agriculture in Northern Nigeria: Prospects and Challenges for Graduate Farmers","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22891268","Smart Agriculture in Northern Nigeria: Prospects and Challenges for Graduate Farmers。Zenodo (CERN European Organization for Nuclear Research)","尼日利亚北部智慧农业：研究生农民的前景与挑战。Zenodo（CERN欧洲核子研究组织）",45,{"impact":17,"substance":57,"depth":104,"authority":20,"freshness":21,"relevant":22,"comment":271},"主题相关但为尼日利亚区域研究，缺乏新数据与实质结论，仅具参考价值，不宜进入每日精选。",[273,274],{"name":10,"url":266},{"name":10,"url":275},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22891267",[29,77,32,277],"农业人才",[279,280],"尼日利亚 智慧农业 毕业生","农业人才 尼日利亚 数字农业 智慧农业","尼日利亚智慧农业毕业生-3272","10.5281\u002Fzenodo.22891268",{"doi":282,"openalex_id":284,"authors":285,"venue":10,"cited_by_count":37,"oa_url":266,"card":290,"direction":95,"ingested_from":54},"W7214014972",[286,288],{"name":287,"orcid":9},"Baman Abubakar",{"name":289,"orcid":9},"A.A Akinsoru",{"tldr":291,"method":292,"finding":293,"direction":52,"opportunity":294},"调查尼日利亚北部毕业农户对智慧农业的前景认知与实际挑战。","针对毕业农户的问卷调查与定性分析。","毕业农户认可智慧农业潜力，但受基础设施、资金与技能制约。","可延伸至小农户数字素养培训与低成本智慧农业适配方案研究。","2026-09-23T23:30:09.435022Z"]