[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5017":3,"related-5017":117},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":76,"slug":79,"view_count":80,"doi":81,"paper":82,"created_at":116},5017,"Design and Construction of Web-Based and Mobile Cattle Feed Management System and Cattle Health Monitoring System Using the Prototyping Method","https:\u002F\u002Fdoi.org\u002F10.61436\u002Frietm\u002Fv5i4.pp394-406","Livestock management operations are often inefficient and error-prone because agricultural institutions frequently rely on manual recording of cattle feed and health data. This study aims to develop an integrated web and mobile information system for cattle feed management and health monitoring, implemented for the Herbal and Horticultural Science and Technology Park (TSTH2), Pollung, Humbang Hasundutan Regency, North Sumatra, Indonesia. The system was designed using a prototyping methodology through three iterative cycles, each involving stakeholder communication, rapid planning, design, modeling, prototype construction, and user evaluation. The resulting system includes 68 functional components: 24 for feed management and 44 for health monitoring. The web platform was implemented using React.js for the front end, with Express.js serving as the back end for the feed management subsystem and Django REST Framework for the health monitoring subsystem. In contrast, Flutter was used for the mobile platform of both subsystems. Functional testing used black-box testing with a 100% success rate on both devices, and user acceptance testing had a satisfaction score of 76.96% (good\u002Facceptable). This system successfully replaced previously manual steps in rural livestock management, especially in feed scheduling, consumption tracking, and health monitoring. To improve the system's intelligence, future research should add flexible scheduling algorithms and AI-based decision support. Keywords: Information system; Cattle feed management; Cattle health monitoring; Prototyping methodology; Web and mobile information system.","畜牧管理作业往往效率低下且容易出错，因为农业机构通常依赖人工记录牛只饲料与健康数据。本研究旨在开发一套面向牛只饲料管理与健康监测的集成化网页与移动信息系统，并在印度尼西亚北苏门答腊省洪邦哈孙杜坦县波隆的草药与园艺科技园（TSTH2）实施。该系统采用原型化方法设计，经过三个迭代周期，每个周期均包括利益相关者沟通、快速规划、设计、建模、原型构建和用户评估。最终系统包含68个功能组件：24个用于饲料管理，44个用于健康监测。网页平台前端采用React.js实现，饲料管理子系统后端采用Express.js，健康监测子系统后端采用Django REST Framework。而两个子系统的移动平台均采用Flutter。功能测试采用黑盒测试，在两个设备上均达到100%的成功率，用户验收测试的满意度得分为76.96%（良好\u002F可接受）。该系统成功替代了农村畜牧管理中此前的人工操作步骤，尤其是在饲料调度、消耗量追踪和健康监测方面。为提高系统的智能化水平，未来研究应增加灵活调度算法和基于人工智能的决策支持。关键词：信息系统；牛只饲料管理；牛只健康监测；原型化方法；网页与移动信息系统。",null,"Research in Education Technology and Multiculture","2026-10-14T00:00:00Z","论文",10,false,53,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,18,14,5,1,"印尼农村养牛饲料与健康监测系统的原型开发论文，方法完整、数据具体，但属区域性应用研究，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","畜牧养殖","健康监测","移动应用","农业信息系统",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,58,65,71],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},11,true,[48],"cattle",[48],[],{"code":36,"score":52,"supported":46,"title":53,"abstract":54,"tags":57},6,[],[55,56],"畜牧","livestock",[],{"code":59,"score":60,"supported":46,"title":61,"abstract":62,"tags":64},"C4",3,[],[63],"饲料",[],{"code":66,"score":60,"supported":46,"title":67,"abstract":68,"tags":70},"I2",[],[69],"decision support",[],{"code":72,"score":21,"supported":14,"title":73,"abstract":74,"tags":75},"I1",[],[],[26],[77,78],"TSTH2 北苏门答腊 养牛系统","农业信息系统 健康监测 智慧农业 畜牧养殖","TSTH2北苏门答腊养牛系统-5017",0,"10.61436\u002Frietm\u002Fv5i4.pp394-406",{"doi":81,"openalex_id":83,"authors":84,"venue":10,"cited_by_count":80,"oa_url":6,"card":108,"direction":114,"ingested_from":115},"W7221767999",[85,88,90,93,95,97,99,101,103,105],{"name":86,"orcid":87},"Hernawati Susanti Samosir","https:\u002F\u002Forcid.org\u002F0000-0002-8036-292X",{"name":89,"orcid":9},"Cynthia Deborah Nababan",{"name":91,"orcid":92},"Bella Wahmilyana Asril","https:\u002F\u002Forcid.org\u002F0009-0006-0953-6989",{"name":94,"orcid":9},"Tantri Harianti Silaen",{"name":96,"orcid":9},"Oktavia Letisya Simatupang",{"name":98,"orcid":9},"Keren Simanjuntak",{"name":100,"orcid":9},"Christian Jhon Pranata Panjaitan",{"name":102,"orcid":9},"Hasan Sinaga",{"name":104,"orcid":9},"Nita Herlinda Kurnyawati Simangunsong",{"name":106,"orcid":107},"Sahat Pandapotan Nainggolan","https:\u002F\u002Forcid.org\u002F0000-0001-6316-596X",{"tldr":109,"method":110,"finding":111,"direction":112,"opportunity":113},"开发基于Web和移动端的牛饲料管理与健康监测系统，替代人工记录。","原型法迭代开发，React\u002FExpress\u002FDjango\u002FFlutter，黑盒测","系统含68个功能组件，功能测试100%通过，用户满意度76.96%。","数字乡村与农业信息化","可引入灵活调度算法与AI决策支持，提升系统智能化与预测能力。","农业人工智能与决策模型","openalex","2026-10-15T23:30:55.216274Z",{"total":52,"page":21,"page_size":52,"items":118},[119,192,246,304,394,476],{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":9,"source_name":125,"source_url":122,"published_at":126,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":127,"score_detail":128,"sources":132,"tags":134,"primary_topic":138,"secondary_topics":143,"classification":145,"search_phrases":165,"slug":168,"view_count":21,"doi":169,"paper":170,"created_at":191},4975,"Detection of mounting behavior and mounted ewes in group-housed environments using HSM-YOLO algorithm","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102639","The detection of mounting behavior and the mounted ewe is critical for estrus monitoring in sheep. However, existing methods suffer from low detection efficiency and high false detection rates due to the similarity between non-sexual and sexual mounting behaviors and the complexity of group-housed environments. To address these challenges, this study introduced a fine-grained classification of mounting behaviors into sexual and non-sexual categories, and further proposed an improved detection algorithm named Hu Sheep Mounting-YOLO (HSM-YOLO) based on the YOLOv11n architecture for detecting sheep mounting behavior and the corresponding mounted ewes. The HSM-YOLO algorithm employed depthwise separable convolution (DWSConv) to reduce the model complexity. To improve behavioral feature extraction and pose discrimination capabilities, it further integrated the C3k2_RepViTBlock-RepBottleneck (C3k2_BiRep) module, an efficient multi-scale attention (EMA) module, and the Inner-SIoU loss function. To evaluate the performance of the proposed algorithm, comparative experiments were conducted on a multiple scenarios dataset. The experimental results showed that the HSM-YOLO model achieved an F1 score of 88.1%, a mean Average Precision (mAP) of 90.2%, with 2.2M parameters and 5.4G floating-point operations (FLOPs). Compared to the baseline YOLOv11n model, HSM-YOLO improved the F1 score by 5.8% and the mAP by 5.7%, while reducing both model parameters and computational complexity. When deployed and tested on an RTX 4090, the system delivered an average detection speed of 47.9 frames per second (FPS). Moreover, despite considerable variation in detection performance across the four different imaging scenarios, HSM-YOLO consistently demonstrated superior detection accuracy. These results confirm that the HSM-YOLO provides a robust and efficient solution for detecting sheep mounting behaviors and mounted ewes in complex and variable scenarios, paving the way for the development of automated estrus monitoring systems.","检测羊只的爬跨行为及被爬跨母羊对于绵羊发情监测至关重要。然而，由于非性爬跨行为与性爬跨行为之间的相似性以及群养环境的复杂性，现有方法存在检测效率低和误检率高的问题。为解决这些挑战，本研究将爬跨行为细粒度分类为性爬跨和非性爬跨两类，并进一步提出了一种基于YOLOv11n架构的改进检测算法——湖羊爬跨-YOLO（HSM-YOLO），用于检测绵羊爬跨行为及相应的被爬跨母羊。HSM-YOLO算法采用深度可分离卷积（DWSConv）以降低模型复杂度。为提高行为特征提取和姿态判别能力，该算法进一步集成了C3k2_RepViTBlock-RepBottleneck（C3k2_BiRep）模块、高效多尺度注意力（EMA）模块以及Inner-SIoU损失函数。为评估所提算法的性能，在多场景数据集上进行了对比实验。实验结果表明，HSM-YOLO模型的F1分数达到88.1%，平均精度均值（mAP）达到90.2%，参数量为2.2M，浮点运算量为5.4G。与基线YOLOv11n模型相比，HSM-YOLO的F1分数提高了5.8%，mAP提高了5.7%，同时降低了模型参数量和计算复杂度。在RTX 4090上部署测试时，系统平均检测速度达到47.9帧每秒（FPS）。此外，尽管在四种不同成像场景下检测性能存在较大差异，HSM-YOLO始终展现出优越的检测精度。这些结果证实，HSM-YOLO为复杂多变场景下检测绵羊爬跨行为和被爬跨母羊提供了一种鲁棒且高效的解决方案，为自动化发情监测系统的开发奠定了基础。","Smart Agricultural Technology","2026-10-12T00:00:00Z",81,{"impact":18,"substance":129,"depth":18,"authority":19,"freshness":130,"relevant":21,"comment":131},22,9,"提出HSM-YOLO算法实现羊只爬跨行为与受体母羊检测，为自动化发情监测提供高效方案，方法新颖、指标扎实，具行业参考价值。",[133],{"name":125,"url":122},[26,135,136,27,137],"农业人工智能","目标检测","羊只发情监测",{"code":66,"slug":139,"name":140,"path":141,"parent_code":142},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[144],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":146},{"candidates":147,"agricultural_evidence":46},[148,154,161],{"code":66,"score":149,"supported":46,"title":150,"abstract":152,"tags":153},13,[151],"YOLO",[151],[135,136],{"code":32,"score":130,"supported":46,"title":155,"abstract":156,"tags":160},[],[157,158,159],"湖羊","绵羊","sheep",[],{"code":72,"score":21,"supported":14,"title":162,"abstract":163,"tags":164},[],[],[26],[166,167],"HSM-YOLO 羊只爬跨行为 检测","绵羊 发情监测 目标检测","HSM-YOLO羊只爬跨行为检测-4975","10.1016\u002Fj.atech.2026.102639",{"doi":169,"openalex_id":171,"authors":172,"venue":125,"cited_by_count":80,"oa_url":122,"card":186,"direction":114,"ingested_from":115},"W7221573347",[173,175,177,179,182,184],{"name":174,"orcid":9},"Yanchao Wang",{"name":176,"orcid":9},"Ming Gao",{"name":178,"orcid":9},"Zhixin Hua",{"name":180,"orcid":181},"Asim Shakeel","https:\u002F\u002Forcid.org\u002F0000-0003-2942-9081",{"name":183,"orcid":9},"Chengyu Ren",{"name":185,"orcid":9},"Kaiying Wang",{"tldr":187,"method":188,"finding":189,"direction":114,"opportunity":190},"提出HSM-YOLO算法，在群养环境下检测羊爬跨行为及被爬跨母羊，用于发情监测。","基于YOLOv11n改进，引入DWSConv、C3k2_BiRep、EMA和In","F1达88.1%、mAP达90.2%，参数量2.2M，较基线提升且更轻量，检测速度47.9 FPS。","可延伸至多物种发情行为细粒度识别与轻量化边缘部署，结合时序信息提升监测实时性。","2026-10-15T23:30:03.393878Z",{"id":193,"title":194,"url":195,"summary":196,"summary_zh":197,"content":9,"source_name":198,"source_url":195,"published_at":199,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":200,"score_detail":201,"sources":205,"tags":207,"primary_topic":209,"secondary_topics":210,"classification":211,"search_phrases":227,"slug":230,"view_count":52,"doi":231,"paper":232,"created_at":245},3969,"Fusion of multi-branch attention mechanism and hybrid graph convolutional network for accurate cow behavior recognition","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112485","Fusion of multi-branch attention mechanism and hybrid graph convolutional network for accurate cow behavior recognition。Computers and Electronics in Agriculture","融合多分支注意力机制与混合图卷积网络的奶牛行为精准识别","Computers and Electronics in Agriculture","2026-10-01T00:00:00Z",78,{"impact":202,"substance":203,"depth":18,"authority":19,"freshness":130,"relevant":21,"comment":204},16,21,"该论文提出多分支注意力与混合图卷积网络融合方法用于奶牛行为识别，方法新颖、发表于农业信息领域核心期刊，对智慧畜牧有参考价值，值得进入每日精选。",[206],{"name":198,"url":195},[26,135,27,208],"行为识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":212},{"candidates":213,"agricultural_evidence":46},[214,219,223],{"code":32,"score":60,"supported":46,"title":215,"abstract":216,"tags":218},[],[217],"奶牛",[],{"code":72,"score":21,"supported":14,"title":220,"abstract":221,"tags":222},[],[],[26],{"code":66,"score":21,"supported":14,"title":224,"abstract":225,"tags":226},[],[],[135],[228,229],"奶牛行为识别 图卷积网络","Computers and Electronics in Agriculture 奶牛","奶牛行为识别图卷积网络-3969","10.1016\u002Fj.compag.2026.112485",{"doi":231,"openalex_id":233,"authors":234,"venue":198,"cited_by_count":80,"oa_url":9,"card":240,"direction":114,"ingested_from":115},"W7215066466",[235,237],{"name":236,"orcid":9},"Yang Liu",{"name":238,"orcid":239},"Lei Liu","https:\u002F\u002Forcid.org\u002F0009-0000-0312-0815",{"tldr":241,"method":242,"finding":243,"direction":114,"opportunity":244},"提出融合多分支注意力与混合图卷积网络，实现奶牛行为精准识别。","多分支注意力机制与混合图卷积网络，用于奶牛行为识别。","该融合模型能有效提升奶牛行为识别准确率。","可探索轻量化图卷积模型在边缘设备上的实时奶牛行为监测与健康预警。","2026-10-02T23:30:02.089949Z",{"id":247,"title":248,"url":249,"summary":250,"summary_zh":9,"content":9,"source_name":125,"source_url":249,"published_at":251,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":252,"score_detail":253,"sources":256,"tags":258,"primary_topic":261,"secondary_topics":262,"classification":263,"search_phrases":283,"slug":286,"view_count":287,"doi":288,"paper":289,"created_at":303},3604,"HEART-Net: An ultra-lightweight multimodal neural network for on-device cattle live-weight estimation and precision phytogenic dosing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102587","HEART-Net: An ultra-lightweight multimodal neural network for on-device cattle live-weight estimation and precision phytogenic dosing。Smart Agricultural Technology","2026-09-25T00:00:00Z",77,{"impact":18,"substance":254,"depth":18,"authority":149,"freshness":17,"relevant":21,"comment":255},20,"提出超轻量多模态网络实现端侧肉牛活重估计与精准植物源饲料投喂，方法新颖且面向实际养殖场景，具备智慧畜牧领域参考价值。",[257],{"name":125,"url":249},[26,135,259,27,260],"智能装备","精准饲喂",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":264},{"candidates":265,"agricultural_evidence":46},[266,270,275,279],{"code":32,"score":45,"supported":46,"title":267,"abstract":268,"tags":269},[48],[48],[],{"code":271,"score":21,"supported":14,"title":272,"abstract":273,"tags":274},"F5",[],[],[259],{"code":72,"score":21,"supported":14,"title":276,"abstract":277,"tags":278},[],[],[26],{"code":66,"score":21,"supported":14,"title":280,"abstract":281,"tags":282},[],[],[135],[284,285],"HEART-Net 肉牛 体重估计","肉牛 活重估计 植物源饲料","HEART-Net肉牛体重估计-3604",2,"10.1016\u002Fj.atech.2026.102587",{"doi":288,"openalex_id":290,"authors":291,"venue":125,"cited_by_count":80,"oa_url":249,"card":9,"direction":9,"ingested_from":115},"W7214389961",[292,295,297,299,301],{"name":293,"orcid":294},"Ashif Shuvo","https:\u002F\u002Forcid.org\u002F0009-0003-5734-1519",{"name":296,"orcid":9},"Sakibul Hasan",{"name":298,"orcid":9},"Abir Anjum",{"name":300,"orcid":9},"Asaduzzaman Shahed",{"name":302,"orcid":9},"Mohammad Al-Mamun","2026-09-27T23:30:05.905976Z",{"id":305,"title":306,"url":307,"summary":308,"summary_zh":309,"content":9,"source_name":310,"source_url":307,"published_at":311,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":312,"score_detail":313,"sources":315,"tags":317,"primary_topic":320,"secondary_topics":321,"classification":327,"search_phrases":357,"slug":360,"view_count":20,"doi":361,"paper":362,"created_at":393},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","2026-09-24T00:00:00Z",62,{"impact":17,"substance":18,"depth":202,"authority":13,"freshness":13,"relevant":21,"comment":314},"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。",[316],{"name":310,"url":307},[318,26,135,319,27],"数字农业","农业物联网",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[322],{"code":323,"slug":324,"name":325,"path":326,"parent_code":142},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":328},{"candidates":329,"agricultural_evidence":46},[330,337,343,349,353],{"code":323,"score":18,"supported":46,"title":331,"abstract":333,"tags":336},[332],"IoT",[334,335,332],"物联网","internet of things",[319],{"code":32,"score":19,"supported":46,"title":338,"abstract":340,"tags":342},[339],"goat",[341,339],"山羊",[],{"code":66,"score":344,"supported":46,"title":345,"abstract":346,"tags":348},7,[],[347,69],"artificial intelligence",[135],{"code":36,"score":52,"supported":46,"title":350,"abstract":351,"tags":352},[],[55,56],[],{"code":72,"score":287,"supported":14,"title":354,"abstract":355,"tags":356},[],[],[318,26],[358,359],"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":361,"openalex_id":363,"authors":364,"venue":310,"cited_by_count":80,"oa_url":307,"card":387,"direction":391,"ingested_from":115},"W7214075234",[365,367,369,371,373,375,378,381,383,385],{"name":366,"orcid":9},"Nicholas Renaldo",{"name":368,"orcid":9},"Sulaiman Musa",{"name":370,"orcid":9},"Jaswar Koto",{"name":372,"orcid":9},"Kristy Veronica",{"name":374,"orcid":9},"Umar Faruq",{"name":376,"orcid":377},"Yulvia Nora Marlim","https:\u002F\u002Forcid.org\u002F0009-0007-8624-5023",{"name":379,"orcid":380},"Rangga Rahmadian Yuliendi","https:\u002F\u002Forcid.org\u002F0000-0003-2288-3580",{"name":382,"orcid":9},"Wilda Susanti",{"name":384,"orcid":9},"Achmad Tavip Junaedi",{"name":386,"orcid":9},"Nabila Wahid",{"tldr":388,"method":389,"finding":390,"direction":391,"opportunity":392},"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。","智慧农业 \u002F 农业物联网","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":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":9,"source_name":400,"source_url":397,"published_at":401,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":200,"score_detail":402,"sources":404,"tags":406,"primary_topic":410,"secondary_topics":416,"classification":418,"search_phrases":444,"slug":447,"view_count":448,"doi":449,"paper":450,"created_at":475},1412,"Review: Opportunities for Data Based On-Farm Estimation of Greenhouse Gas Emissions from Ruminants Farming Systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.animal.2026.101946","Livestock farming has become increasingly digitalized in large parts of the world. Sensor technologies can be used to mitigate emissions from farming in several ways including improved efficiency of operations. The data that farmers collect to monitor and optimize their production offer a possibility to increase the accuracy of (GHG) emission estimation if utilized fully. However, technologies developed for farm management may not meet requirements for monitoring of GHG greenhouse gas emissions where high accuracy is required. The cost benefit and reliability of farm level data collection need to be considered before investing effort in their widespread adoption. The lack of access to data and poor interoperability of the data across different software systems also currently limits the use of data, however there are several data sharing initiatives, such as data spaces, that aim to develop the farmers right to data sovereignty and provide contractual and technical solutions for increasing data availability. This review assesses the state-of-the-art of the digital data collection and sharing usable for GHG accounting from ruminant farming systems perspective with focus on commercially available on-farm technology. A review of the accuracy of different methods and two case studies on the impact of the uncertainty of digital data sources on CH 4 emission prediction uncertainty quantified using Monte Carlo simulation are presented. In conclusion the greatest potential for automating the LCA process and coverage of production for national inventories exists for dairy cattle, sheep and goat farms with daily milk yield recording and high technology feeding systems and lowest for pasture based meat production systems. The use of on-farm data for GHG accounting provides an opportunity for more targeted mitigation and even differential product pricing favouring the lowest emitting farms within a region. The reliability as well as contribution to overall emissions of each additional data source collected from farms should be assessed before it is taken into use and calibration protocols for farmers to ensure accurate data should be developed and their application monitored.","畜牧业在全球大部分地区已日益数字化。传感器技术可通过多种方式减少农业排放，包括提高作业效率。农民为监测和优化生产所收集的数据，若得到充分利用，有望提高温室气体（GHG）排放估算的准确性。然而，为农场管理开发的技术可能无法满足对高精度要求的温室气体排放监测需求。在投入精力广泛采用这些技术之前，需权衡农场级数据收集的成本效益与可靠性。目前，数据获取渠道有限以及不同软件系统间数据互操作性差也制约了数据的使用，不过已有多个数据共享倡议（如数据空间）致力于发展农民的数据主权，并提供合同和技术解决方案以增强数据可用性。本综述从反刍动物养殖系统的视角，评估了可用于温室气体核算的数字数据收集与共享的现状，重点关注市售的农场技术。文中综述了不同方法的准确性，并展示了两项案例研究，利用蒙特卡洛模拟量化了数字数据源不确定性对甲烷（CH₄）排放预测不确定性的影响。结论表明，对于奶牛、绵羊和山羊养殖场，若具备每日产奶量记录和高度技术化的饲喂系统，其生命周期评估（LCA）流程自动化及生产覆盖国家清单的潜力最大；而对于基于放牧的肉类生产系统，这一潜力最低。将农场数据用于温室气体核算，为实现更有针对性的减排乃至差异化产品定价提供了机会，可惠及区域内排放最低的养殖场。在采用从农场收集的每一项额外数据源之前，应评估其可靠性及对整体排放的贡献，并应制定供农民使用的校准协议以确保数据准确性，同时对其应用进行监测。","animal","2026-09-01T00:00:00Z",{"impact":18,"substance":129,"depth":18,"authority":149,"freshness":344,"relevant":21,"comment":403},"综述评估了利用农场数字数据估算反刍动物温室气体排放的潜力与挑战，对农业碳核算和减排有重要参考价值。",[405],{"name":400,"url":397},[26,407,27,408,409],"温室气体","数据共享","碳排放核算",{"code":411,"slug":412,"name":413,"path":414,"parent_code":415},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[417],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":419},{"candidates":420,"agricultural_evidence":46},[421,427,431,435,440],{"code":411,"score":19,"supported":46,"title":422,"abstract":424,"tags":426},[423],"greenhouse",[425,423],"温室",[],{"code":32,"score":130,"supported":46,"title":428,"abstract":429,"tags":430},[],[217,158,341,48,159,339],[],{"code":36,"score":52,"supported":46,"title":432,"abstract":433,"tags":434},[],[55,56],[],{"code":436,"score":21,"supported":14,"title":437,"abstract":438,"tags":439},"H4",[],[],[407],{"code":72,"score":21,"supported":14,"title":441,"abstract":442,"tags":443},[],[],[26],[445,446],"碳排放核算 数据共享 智慧农业 温室气体","碳排放核算 数据共享","碳排放核算数据共享智慧农业温室气体-1412",4,"10.1016\u002Fj.animal.2026.101946",{"doi":449,"openalex_id":451,"authors":452,"venue":400,"cited_by_count":80,"oa_url":468,"card":469,"direction":391,"ingested_from":115},"W7204954019",[453,456,459,461,463,465],{"name":454,"orcid":455},"Matti Pastell","https:\u002F\u002Forcid.org\u002F0000-0002-5810-4801",{"name":457,"orcid":458},"Vasileios Anestis","https:\u002F\u002Forcid.org\u002F0000-0003-4260-1763",{"name":460,"orcid":9},"A. Romera",{"name":462,"orcid":9},"A.M. Mazzetto",{"name":464,"orcid":9},"P. Ricci",{"name":466,"orcid":467},"Maguy Eugène","https:\u002F\u002Forcid.org\u002F0000-0002-2111-0597","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS175173112600193X\u002Fpdf",{"tldr":470,"method":471,"finding":472,"direction":473,"opportunity":474},"综述利用农场数字数据估算反刍动物温室气体排放的机遇与挑战。","综述现有技术，结合蒙特卡洛模拟案例评估数据不确定性影响。","奶牛、绵羊、山羊场数据潜力大，肉牛放牧系统潜力低。","农业绿色发展与碳","可研究农场数据用于碳核算的校准协议及数据共享机制，提升估算准确性。","2026-09-02T23:30:13.638840Z",{"id":477,"title":478,"url":479,"summary":480,"summary_zh":9,"content":9,"source_name":481,"source_url":479,"published_at":482,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":483,"score_detail":484,"sources":486,"tags":488,"primary_topic":492,"secondary_topics":493,"classification":494,"search_phrases":506,"slug":509,"view_count":80,"doi":510,"paper":511,"created_at":526},5542,"Beyond profitability: heterogeneous associations between ESG-related mechanisms and farm economic sustainability-evidence from Spanish sheep and goat farming","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105022","Beyond profitability: heterogeneous associations between ESG-related mechanisms and farm economic sustainability-evidence from Spanish sheep and goat farming。Agricultural Systems","Agricultural Systems","2026-10-21T00:00:00Z",66,{"impact":17,"substance":18,"depth":202,"authority":19,"freshness":13,"relevant":21,"comment":485},"西班牙羊山羊养殖ESG与经济可持续性实证研究，方法有一定新意但属区域性细分议题，公共影响有限，不宜进入每日精选。",[487],{"name":481,"url":479},[489,27,490,491],"可持续农业","ESG","农场经济",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":495},{"candidates":496,"agricultural_evidence":46},[497,501],{"code":32,"score":129,"supported":46,"title":498,"abstract":499,"tags":500},[159,339],[159,339],[],{"code":502,"score":21,"supported":14,"title":503,"abstract":504,"tags":505},"H8",[],[],[489],[507,508],"西班牙 养殖 ESG","可持续农业 农场经济 畜牧养殖 ESG","西班牙养殖ESG-5542","10.1016\u002Fj.agsy.2026.105022",{"doi":510,"openalex_id":512,"authors":513,"venue":481,"cited_by_count":80,"oa_url":479,"card":9,"direction":9,"ingested_from":115},"W7223120996",[514,517,520,523],{"name":515,"orcid":516},"Maria Llanos Soria-Oliver","https:\u002F\u002Forcid.org\u002F0009-0005-2631-1135",{"name":518,"orcid":519},"Carolina Pontones Rosa","https:\u002F\u002Forcid.org\u002F0000-0001-6989-7969",{"name":521,"orcid":522},"Inmaculada Alonso Carrillo","https:\u002F\u002Forcid.org\u002F0000-0002-0813-9934",{"name":524,"orcid":525},"Rosario Pérez Morote","https:\u002F\u002Forcid.org\u002F0000-0001-5502-3646","2026-10-22T11:20:12.622588Z"]