[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4891":3,"related-4891":84},{"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":30,"secondary_topics":36,"classification":37,"search_phrases":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":83},4891,"Adaptive Confidence-Aware Multi-Antenna RFID Anti-Collision Method for Precision Livestock Monitoring","https:\u002F\u002Fdoi.org\u002F10.48175\u002Fijarsct-38829","Radio Frequency Identification (RFID) enables automatic livestock identification in precision agriculture, but simultaneous tag replies can cause collisions and increase reading delay. Earlier work combined multiple receiving antennas with Dynamic Frame Slotted ALOHA (DFSA) to improve bulk tag reading. This paper proposes a confidence-aware extension, called CA-MA-DFSA, that combines slot-outcome feedback, antenna-level response confidence, and a bounded frame-size update. The reader estimates the remaining tag population from idle, successful, and collided slots, then adjusts the next frame conservatively. Antenna observations are used to prioritize repeated interrogation of uncertain tags rather than assuming that every antenna independently identifies a new tag. A simulation plan is specified for comparison with conventional DFSA and multi-antenna DFSA. No experimental results are claimed in this draft; performance must be established through implementation and reproducible testing.","射频识别（RFID）可实现精准农业中的牲畜自动识别，但多个标签同时应答会造成碰撞并增加读取延迟。前期工作将多接收天线与动态帧时隙ALOHA（DFSA）相结合，以提升批量标签读取性能。本文提出一种置信度感知的扩展方法，称为CA-MA-DFSA，融合了时隙结果反馈、天线级响应置信度以及有界帧长更新。阅读器根据空闲、成功和碰撞时隙估计剩余标签数量，然后保守地调整下一帧。天线观测用于优先对不确定标签进行重复询问，而非假设每根天线都能独立识别一个新标签。文中给出了与传统DFSA及多天线DFSA进行对比的仿真方案。本稿未声称任何实验结果；性能须通过实现和可重复测试来确立。",null,"International Journal of Advanced Research in Science Communication and Technology","2026-10-13T00:00:00Z","论文",10,false,49,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},8,12,13,6,1,"面向精准畜牧监测的RFID防碰撞方法学论文，仅提出仿真方案、无实验结果，方法有一定新意但证据不足，暂不宜进入每日精选。",[24],{"name":10,"url":6},[26,27,28,29],"智慧农业","RFID","畜牧监测","防碰撞算法",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"C6","smart-livestock","养殖环境与智慧畜牧","\u002Ftopic\u002Flivestock\u002Fsmart-livestock","C",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,55],{"code":35,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},11,true,[47],"livestock",[47],[],{"code":31,"score":17,"supported":45,"title":51,"abstract":53,"tags":54},[52],"livestock monitoring",[],[],{"code":56,"score":57,"supported":45,"title":58,"abstract":59,"tags":62},"I1",7,[],[60,61],"精准农业","precision agriculture",[26],[64,65],"RFID 畜牧 防碰撞","多天线 RFID 牲畜识别","RFID畜牧防碰撞-4891",0,"10.48175\u002Fijarsct-38829",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":67,"oa_url":6,"card":76,"direction":80,"ingested_from":82},"W7221675915",[72,74],{"name":73,"orcid":9},"Prof. P. T. Dhotre Prathamesh Dilip Jadhav",{"name":75,"orcid":9},"Dr. H. B. Jadhav Prof. G. R. Sarode",{"tldr":77,"method":78,"finding":79,"direction":80,"opportunity":81},"提出置信度感知的多天线RFID防碰撞方法CA-MA-DFSA，用于精准畜牧监测。","结合槽结果反馈、天线响应置信度与有界帧长更新，基于DFSA仿真对比。","方法尚处仿真设计阶段，未报告实验结果，性能需通过实现验证。","智慧农业 \u002F 农业物联网","可开展真实畜牧场景下的RFID多天线防碰撞实验，验证置信度机制对读取延迟的改善。","openalex","2026-10-14T23:30:16.832710Z",{"total":20,"page":21,"page_size":20,"items":85},[86,160,266,334,389,440],{"id":87,"title":88,"url":89,"summary":90,"summary_zh":91,"content":9,"source_name":92,"source_url":89,"published_at":93,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":94,"score_detail":95,"sources":100,"tags":102,"primary_topic":107,"secondary_topics":108,"classification":115,"search_phrases":139,"slug":142,"view_count":21,"doi":143,"paper":144,"created_at":159},4426,"Animal welfare in the world of digitalization: Computer Vision in the context of foundational structures","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43681-026-01417-4","Abstract Computer Vision is an emerging technology within Precision Livestock Farming (PLF), combining camera systems with artificial intelligence to monitor animal welfare. While animal ethicists recognize its potential benefits, they also raise concerns about possible negative implications for animal welfare. This paper argues that addressing ethical concerns cannot rely on solutions targeting isolated ethical risks; rather, potential solutions must account for the technology’s embeddedness within society-wide structures. The focus in this paper will be on foundational structures, these structures concern how the world is experienced and understood, what is taken as self-evident. Over centuries, humans have increasingly delegated tasks to machines and standardized the world in doing so. This process has fostered growing trust in the outcomes of (digital) technologies. These developments have changed human experience and understanding of the world in various ways. Farmers, as members of society, are unlikely to be exempt from this trend. Therefore, reflection on Computer Vision must go beyond the farming practice and take into account these foundational structures that are currently reshaping society at large. Computer Vision examined as embedded within foundational structures demonstrates that ethical issues are also embedded within these structures, consequently, potential solutions must take account of them to increase their effectiveness.","摘要 计算机视觉是精准畜牧业（PLF）中的一项新兴技术，它将摄像系统与人工智能相结合，以监测动物福利。尽管动物伦理学家认可其潜在益处，但也对其可能给动物福利带来的负面影响表示担忧。本文认为，解决伦理关切不能依赖于针对孤立伦理风险的方案；相反，潜在的解决方案必须考虑到该技术嵌入于全社会结构之中的事实。本文的关注重点将是基础性结构，这些结构关乎世界如何被体验和理解，以及什么被视为不言自明。几个世纪以来，人类越来越多地将任务委托给机器，并在此过程中使世界标准化。这一进程培育了人们对（数字）技术产物的日益信任。这些发展以多种方式改变了人类对世界的体验和理解。农民作为社会成员，不太可能置身于这一趋势之外。因此，对计算机视觉的反思必须超越养殖实践，并考虑到这些当前正在重塑整个社会的基础性结构。将计算机视觉置于基础性结构中加以审视表明，伦理问题同样嵌入于这些结构之中，因此，潜在的解决方案必须将其纳入考量，以提高其有效性。","AI and Ethics","2026-10-06T00:00:00Z",72,{"impact":18,"substance":96,"depth":97,"authority":98,"freshness":17,"relevant":21,"comment":99},20,18,14,"从社会基础结构层面反思计算机视觉在精准畜牧中的伦理嵌入，视角新颖、论证扎实，对智慧农业伦理治理有参考价值。",[101],{"name":92,"url":89},[26,103,104,105,106],"农业人工智能","计算机视觉","动物福利","精准畜牧",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[109],{"code":110,"slug":111,"name":112,"path":113,"parent_code":114},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":38,"locked":14,"version":39,"method":40,"evidence":116},{"candidates":117,"agricultural_evidence":45},[118,124,130,135],{"code":31,"score":119,"supported":45,"title":120,"abstract":122,"tags":123},19,[121],"animal welfare",[106,105,121],[105,106],{"code":110,"score":119,"supported":45,"title":125,"abstract":127,"tags":129},[126],"computer vision",[104,128,126],"artificial intelligence",[103,104],{"code":35,"score":20,"supported":45,"title":131,"abstract":132,"tags":134},[],[133,47],"畜牧",[],{"code":56,"score":21,"supported":14,"title":136,"abstract":137,"tags":138},[],[],[26],[140,141],"计算机视觉 精准畜牧 动物福利","农业人工智能 计算机视觉 动物福利 智慧农业","计算机视觉精准畜牧动物福利-4426","10.1007\u002Fs43681-026-01417-4",{"doi":143,"openalex_id":145,"authors":146,"venue":92,"cited_by_count":67,"oa_url":153,"card":154,"direction":80,"ingested_from":82},"W7220695950",[147,150],{"name":148,"orcid":149},"Mariska Thalitha Bosschaert","https:\u002F\u002Forcid.org\u002F0000-0002-9160-7638",{"name":151,"orcid":152},"Bart Gremmen","https:\u002F\u002Forcid.org\u002F0000-0002-9652-6407","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs43681-026-01417-4.pdf",{"tldr":155,"method":156,"finding":157,"direction":80,"opportunity":158},"论文论证计算机视觉用于动物福利时，伦理问题须置于社会基础结构中理解。","概念分析与伦理反思，结合精准畜牧中计算机视觉技术。","伦理问题嵌入社会基础结构，孤立风险方案无效，需整体考量。","可实证研究农民对计算机视觉的信任如何受社会基础结构影响，并设计嵌入伦理的监测系统。","2026-10-08T23:30:12.350082Z",{"id":161,"title":162,"url":163,"summary":164,"summary_zh":165,"content":9,"source_name":166,"source_url":163,"published_at":167,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":168,"score_detail":169,"sources":173,"tags":175,"primary_topic":179,"secondary_topics":180,"classification":182,"search_phrases":224,"slug":227,"view_count":228,"doi":229,"paper":230,"created_at":265},3917,"Risk-based prioritization of official animal welfare inspections using machine learning","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffvets.2026.1938469","Introduction For official animal welfare inspections in Austria, at least 2% of agricultural livestock farms must be selected and inspected on the basis of a risk-based random sample. In the federal state of Styria, more than 19,000 farms keep livestock, while the inspection capacity of official veterinarians is limited, requiring efficient prioritization of farm checks. This study aimed to develop a machine learning model leveraging routinely collected administrative and animal health data to identify farms in Styria at higher risk of animal welfare violations. Methods The statistical analyses incorporated the outcomes of animal welfare inspections from 2019 to 2023, aggregated by farm and year. A total of 2,647 aggregated inspection results were used to train an Extreme Gradient Boosting (XGBoost) model, with a further 475 results used for testing purposes. Information about farms (e.g., production type, farmer and regional data, livestock data), as well as slaughter and meat inspection data, was acquired from the Consumer Health Information System database ( Verbrauchergesundheitsinformationssystem , VIS). Additionally, data from local fallen stock rendering plants and information on antibiotics dispensed to farmers were available and incorporated into the model. Results Results showed a moderate model performance with AUC (Area under the curve) of 0.75 (95%-CI [0.66, 0.82]), MCC (Matthews Correlation Coefficient) of 0.25 (95%-CI [0.19, 0.37]) and AP (Average Precision) of 0.19 (95%-CI [0.11–0.31]) for the test set. Post-hoc model interpretation revealed as most important influencing features antibiotic dispensing in cattle production, particularly overall quantities and European Medicines Agency Category D antibiotics, followed by farmer-related characteristics such as farmer age and the distance between the farm and the farmer’s residence. Membership in the Animal Health Service ( Tiergesundheitsdienst , TGD) was associated with a decrease in the model-predicted probability of animal welfare violations. Other relevant factors included the number of livestock units (cattle and poultry) on farm, cattle mortality recorded by rendering plants, and certain findings at meat inspection, such as pericarditis in pigs. Discussion These results indicate that routinely recorded administrative and veterinary data can provide meaningful indicators for identifying farms with a higher risk of animal welfare violations. Although some predictors appear indirectly linked to welfare, they likely reflect broader farm management practices and animal health status. Risk-based sampling could support veterinary authorities in prioritizing farms for inspection and allocating inspection resources more efficiently.","引言 在奥地利，官方动物福利检查要求至少对2%的农业牲畜养殖场进行基于风险的随机抽样检查。在施泰尔马克州，超过19,000个养殖场饲养牲畜，而官方兽医的检查能力有限，因此需要高效地确定养殖场检查的优先顺序。本研究旨在开发一种机器学习模型，利用常规收集的行政和动物健康数据，识别施泰尔马克州动物福利违规风险较高的养殖场。方法 统计分析纳入了2019年至2023年动物福利检查的结果，按养殖场和年份汇总。共有2,647条汇总检查结果用于训练极端梯度提升（XGBoost）模型，另有475条结果用于测试。养殖场相关信息（如生产类型、养殖者及区域数据、牲畜数据）以及屠宰和肉类检验数据均获取自消费者健康信息系统数据库（Verbrauchergesundheitsinformationssystem，VIS）。此外，当地病死动物无害化处理厂的数据以及养殖者抗生素发放信息也可获取，并纳入模型。结果 结果显示模型性能中等，测试集的AUC（曲线下面积）为0.75（95%-CI [0.66, 0.82]），MCC（马修斯相关系数）为0.25（95%-CI [0.19, 0.37]），AP（平均精度）为0.19（95%-CI [0.11–0.31]）。事后模型解释显示，最重要的影响因素是养牛生产中的抗生素发放，特别是总体用量和欧洲药品管理局D类抗生素，其次是养殖者相关特征，如养殖者年龄和养殖场与养殖者住所之间的距离。加入动物健康服务（Tiergesundheitsdienst，TGD）与模型预测的动物福利违规概率降低相关。其他相关因素包括养殖场牲畜单位数（牛和家禽）、无害化处理厂记录的牛死亡率，以及肉类检验中的某些发现，如猪心包炎。讨论 这些结果表明，常规记录的行政和兽医数据可为识别动物福利违规风险较高的养殖场提供有意义的指标。尽管一些预测因素似乎间接相关","Frontiers in Veterinary Science","2026-09-30T00:00:00Z",81,{"impact":97,"substance":170,"depth":97,"authority":98,"freshness":171,"relevant":21,"comment":172},22,9,"奥地利用XGBoost整合屠宰、抗生素与病死畜数据预测动物福利违规风险，为有限兽医检验资源提供风险分级抽样依据，方法可迁移至我国畜牧监管信息化。",[174],{"name":166,"url":163},[26,176,177,105,178],"机器学习","畜牧兽医","风险分级监管",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[181],{"code":110,"slug":111,"name":112,"path":113,"parent_code":114},{"status":38,"locked":14,"version":39,"method":40,"evidence":183},{"candidates":184,"agricultural_evidence":45},[185,190,195,202,207,214,220],{"code":31,"score":186,"supported":45,"title":187,"abstract":188,"tags":189},15,[121],[105,121],[105],{"code":110,"score":186,"supported":45,"title":191,"abstract":193,"tags":194},[192],"machine learning",[176,192],[176],{"code":196,"score":20,"supported":45,"title":197,"abstract":198,"tags":201},"C3",[],[199,200],"家禽","poultry",[],{"code":35,"score":203,"supported":45,"title":204,"abstract":205,"tags":206},4,[],[47],[177],{"code":208,"score":209,"supported":45,"title":210,"abstract":211,"tags":213},"C2",3,[],[212],"cattle",[],{"code":215,"score":209,"supported":45,"title":216,"abstract":217,"tags":219},"C5",[],[218],"veterinary",[],{"code":56,"score":21,"supported":14,"title":221,"abstract":222,"tags":223},[],[],[26],[225,226],"奥地利 施蒂里亚 动物福利 机器学习","XGBoost 动物福利 风险评估","奥地利施蒂里亚动物福利机器学习-3917",2,"10.3389\u002Ffvets.2026.1938469",{"doi":229,"openalex_id":231,"authors":232,"venue":166,"cited_by_count":67,"oa_url":163,"card":259,"direction":263,"ingested_from":82},"W7214951739",[233,235,237,239,241,244,246,248,250,253,256],{"name":234,"orcid":9},"Eva Obenaus",{"name":236,"orcid":9},"Sabrina Kuchling",{"name":238,"orcid":9},"Tadej Zerak",{"name":240,"orcid":9},"Corina Schleicher",{"name":242,"orcid":243},"Ian Kopacka","https:\u002F\u002Forcid.org\u002F0000-0003-4106-1742",{"name":245,"orcid":9},"Sven Budik",{"name":247,"orcid":9},"Sabine Grimme",{"name":249,"orcid":9},"Jörg Hiesel",{"name":251,"orcid":252},"Peter Eckhardt","https:\u002F\u002Forcid.org\u002F0009-0005-5015-4846",{"name":254,"orcid":255},"Johannes Baumgartner","https:\u002F\u002Forcid.org\u002F0000-0001-8555-1378",{"name":257,"orcid":258},"Clair L. Firth","https:\u002F\u002Forcid.org\u002F0000-0002-4919-7786",{"tldr":260,"method":261,"finding":262,"direction":263,"opportunity":264},"用机器学习基于常规行政与兽医数据，对奥地利农场动物福利违规风险进行优先级排序。","XGBoost模型，使用2019-2023年2647条检查结果及VIS等行政兽医","模型AUC 0.75，抗生素发放量、农户年龄和距离等是动物福利违规的重要预测因子。","农业人工智能与决策模型","可探索将常规兽医行政数据与机器学习结合，用于其他农业合规监管的精准抽查与风险预警。","2026-10-01T23:30:54.115236Z",{"id":267,"title":268,"url":269,"summary":270,"summary_zh":271,"content":9,"source_name":272,"source_url":269,"published_at":273,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":168,"score_detail":274,"sources":276,"tags":278,"primary_topic":281,"secondary_topics":282,"classification":284,"search_phrases":313,"slug":316,"view_count":317,"doi":318,"paper":319,"created_at":333},2746,"Fostering artificial intelligence in the livestock sector: Adoption factors and policy insights from the case of Spain","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104956","CONTEXT AND PURPOSE The integration of Artificial Intelligence (AI) and Precision Livestock Farming (PLF) offers significant opportunities to improve the productivity, animal welfare and sustainability of the livestock sector. However, the adoption of these technologies remains highly uneven. Moving beyond descriptive statistics, this study investigates the complex, interacting factors associated with AI adoption in the Spanish livestock sector. METHODOLOGY Utilising a survey-based sample of 666 Spanish livestock holdings, drawn from a national survey whose sampling was designed to be proportional to the 2020 Agricultural Census by subsector and region, a Partial Least Squares Structural Equation Modelling (PLS-SEM) approach was employed to evaluate a comprehensive adoption framework across different farming systems and operational scales. AI adoption was modelled as a formative composite of five self-reported AI system categories, and the framework was tested through mediation, moderation, subgroup and out-of-sample predictive analyses with a 10,000-resample bootstrap. MAIN FINDINGS The empirical results indicate that AI adoption is most strongly associated with a holding's baseline technological infrastructure (β = 0.6464, p \u003C 0.001); advanced predictive tools appear to presuppose a pre-existing ecosystem of digital farm books, automated feeding systems and interoperable sensors. A ‘perception-capability gap’ is formally supported: perceived benefits are associated with adoption only indirectly, through the infrastructure baseline (indirect-only mediation), and their association strengthens as infrastructure increases (positive interaction). Recognition of AI's theoretical benefits or awareness of public subsidies is thus not independently associated with adoption in the absence of foundational digital capability. Furthermore, the study identifies a stark structural divide: adoption reaches 56.3% in intensive and semi-intensive holdings but only 24.4% in extensive ones (χ2 = 57.61, p \u003C 0.001), as traditional extensive systems face severe physical and connectivity barriers that leave them systematically disadvantaged. Results are robust across reflective, count, reduced-indicator and logistic specifications, and the model shows substantial out-of-sample predictive power (Q 2 predict ≈ 0.53). CONCLUSIONS To facilitate a more inclusive digital transformation, the findings suggest that policymakers should adopt a ‘readiness-conditional’ model, ensuring farms meet baseline digital prerequisites and have access to robust extension services before subsidising advanced AI tools.","背景与目的 人工智能（Artificial Intelligence, AI）与精准畜牧业（Precision Livestock Farming, PLF）的融合为提升畜牧部门的生产力、动物福利和可持续性提供了重要机遇。然而，这些技术的采用仍然极不均衡。本研究超越描述性统计，探讨了西班牙畜牧部门中与AI采用相关的复杂交互因素。方法 基于一项全国性调查中抽取的666个西班牙畜牧养殖场的样本（该调查的抽样设计按2020年农业普查的子部门和地区比例进行），采用偏最小二乘结构方程模型（Partial Least Squares Structural Equation Modelling, PLS-SEM）方法，评估了一个跨不同养殖系统和经营规模的综合采用框架。AI采用被建模为五个自我报告的AI系统类别的形成性复合变量，并通过中介、调节、子组和样本外预测分析（10,000次重抽样自助法）对框架进行了检验。主要发现 实证结果表明，AI采用与养殖场的基线技术基础设施关联最为密切（β = 0.6464，p \u003C 0.001）；先进的预测工具似乎以已有的数字牧场记录、自动化饲喂系统和可互操作传感器生态系统为前提。“感知—能力差距”得到正式支持：感知收益仅通过基础设施基线间接与采用相关（仅间接中介），且其关联随基础设施的增加而增强（正向交互）。因此，在缺乏基础数字能力的情况下，对AI理论效益的认可或对公共补贴的知晓与采用并无独立关联。此外，研究识别出一条鲜明的结构性分界线：集约化和半集约化养殖场的采用率达到56.3%，而粗放型养殖场仅为24.4%（χ2 = 57.61，p \u003C 0.001），因为传统粗放型系统面临严重的物理和连接障碍，使其处于系统性劣势。结果在反映性、计数、简化指标和逻辑斯谛设定下均稳健，且模型显示出显著的样本外预测能力（Q 2 predict ≈ 0.53）。结论 为促进更具包容性的数字化转型，研究结果表明政策制定者应采取“就绪条件”模型，确保养殖场","Agricultural Systems","2026-09-16T00:00:00Z",{"impact":97,"substance":170,"depth":97,"authority":98,"freshness":171,"relevant":21,"comment":275},"基于666个西班牙牧场样本的实证研究，揭示AI采纳受数字基础设施制约的“感知—能力鸿沟”，并提出“就绪条件式”补贴政策思路，对智慧畜牧推广与数字乡村政策设计有参考价值。",[277],{"name":272,"url":269},[26,103,279,280,106],"数字素养","农业补贴",{"code":110,"slug":111,"name":112,"path":113,"parent_code":114},[283],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":285},{"candidates":286,"agricultural_evidence":45},[287,291,295,299,304,308],{"code":35,"score":98,"supported":45,"title":288,"abstract":289,"tags":290},[47],[133,47],[],{"code":110,"score":18,"supported":45,"title":292,"abstract":293,"tags":294},[128],[128],[103],{"code":31,"score":13,"supported":45,"title":296,"abstract":297,"tags":298},[],[106,105,121],[106],{"code":300,"score":21,"supported":14,"title":301,"abstract":302,"tags":303},"G3",[],[],[280],{"code":56,"score":21,"supported":14,"title":305,"abstract":306,"tags":307},[],[],[26],{"code":309,"score":21,"supported":14,"title":310,"abstract":311,"tags":312},"I6",[],[],[279],[314,315],"农业人工智能 农业补贴 数字素养 智慧农业","农业人工智能 农业补贴","农业人工智能农业补贴数字素养智慧农业-2746",5,"10.1016\u002Fj.agsy.2026.104956",{"doi":318,"openalex_id":320,"authors":321,"venue":272,"cited_by_count":67,"oa_url":269,"card":327,"direction":331,"ingested_from":82},"W7213425399",[322,324],{"name":323,"orcid":9},"Carlos Parra-Lopez",{"name":325,"orcid":326},"Carmen Carmona‐Torres","https:\u002F\u002Forcid.org\u002F0000-0002-6982-1363",{"tldr":328,"method":329,"finding":330,"direction":331,"opportunity":332},"基于西班牙666家养殖场调查，用PLS-SEM分析AI采纳的影响因素与政策启示。","666份养殖场问卷，PLS-SEM建模，含中介、调节与分组分析。","AI采纳最依赖既有数字基础设施；感知收益仅间接起作用，集约场采纳率远高于粗放场。","数字乡村与农业信息化","可研究粗放养殖场数字基础设施与连接性短板，设计‘就绪度条件’式推广与补贴机制。","2026-09-17T23:30:04.844104Z",{"id":335,"title":336,"url":337,"summary":338,"summary_zh":9,"content":339,"source_name":340,"source_url":9,"published_at":341,"category":342,"cover_url":9,"hotness":13,"is_selected":14,"score":343,"score_detail":344,"sources":347,"tags":349,"primary_topic":354,"secondary_topics":355,"classification":356,"search_phrases":384,"slug":387,"view_count":21,"doi":9,"paper":9,"created_at":388},4387,"全国畜牧兽医「十五五」规划部署畜禽粪污处理利用与人工智能运用","https:\u002F\u002Fgu.qq.com\u002Fresources\u002Fshy\u002Fnews\u002Fdetail-v2\u002Findex.html?_tentrees_trans=0&id=SN20260929115344b63ffa8d","《全国畜牧兽医行业发展「十五五」规划》共部署 5 方面重点任务,其中第四方面「促进产业绿色安全发展」提出全面提升养殖产品质量安全水平、规范畜禽屠宰行业管理、推进畜禽粪污处理利用;第五方面「强化科技装备支撑」强调强化科技创新和成果转化推广、全面推行养殖业节粮、加快推进人工智能运用和智慧畜牧业发展。","风吹散了这一页，去看看新故事吧\n\n 今日资讯热榜 \n\n 1  假期十大消息|财政部重磅发声；恒生科技指数将把成份股扩充至50只 \n\n 2  美股收评|债市风暴重燃，三大指数集体收跌，标普、纳指自历史高位小幅回落 \n\n 3  夜读|告别情绪交易，把直觉决策换成规则交易 \n\n风吹散了这一页，去看看新故事吧\n\n 今日资讯热榜 \n\n 1  假期十大消息|财政部重磅发声；恒生科技指数将把成份股扩充至50只 \n\n 2  美股收评|债市风暴重燃，三大指数集体收跌，标普、纳指自历史高位小幅回落 \n\n 3  夜读|告别情绪交易，把直觉决策换成规则交易","农业农村部 腾讯证券 2026-09-29","2026-09-29T00:00:00Z","政策",58,{"impact":345,"substance":17,"depth":20,"authority":98,"freshness":203,"relevant":21,"comment":346},26,"全国性畜牧兽医十五五规划部署粪污处理与AI应用，层级高但正文缺失、信息增量有限且已过时效。",[348],{"name":340,"url":337},[103,350,351,352,353],"十五五规划","畜禽粪污","绿色养殖","智慧畜牧",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":357},{"candidates":358,"agricultural_evidence":45},[359,364,369,375,379],{"code":35,"score":170,"supported":45,"title":360,"abstract":362,"tags":363},[133,361],"畜禽",[133,361],[],{"code":31,"score":365,"supported":45,"title":366,"abstract":367,"tags":368},17,[351],[353,351],[353,351,352],{"code":370,"score":209,"supported":45,"title":371,"abstract":372,"tags":374},"G1",[],[373],"成果转化",[],{"code":110,"score":21,"supported":14,"title":376,"abstract":377,"tags":378},[],[],[103],{"code":380,"score":21,"supported":14,"title":381,"abstract":382,"tags":383},"J1",[],[],[350],[385,386],"畜牧兽医 十五五规划","畜禽粪污 资源化利用","畜牧兽医十五五规划-4387","2026-10-08T00:06:07.462630Z",{"id":390,"title":391,"url":392,"summary":393,"summary_zh":9,"content":394,"source_name":395,"source_url":9,"published_at":93,"category":396,"cover_url":9,"hotness":13,"is_selected":14,"score":397,"score_detail":398,"sources":400,"tags":402,"primary_topic":407,"secondary_topics":408,"classification":409,"search_phrases":435,"slug":438,"view_count":67,"doi":9,"paper":9,"created_at":439},4381,"数字赋能传统牧业 黑龙江林甸打造智慧畜牧高质量发展新样板","http:\u002F\u002Fwww.hlj.xinhuanet.com\u002F20261006\u002Fb85b3ee101f4435d85eac39d69ce98d2\u002Fc.html","大庆市林甸县聚焦奶牛产业,搭建全域奶牛产业大数据平台,覆盖 17 处规模牧场、5.9 万头奶牛,集成 DWeb\u002FDFeed\u002FDCalf\u002FDSmart 四大智能系统,实现良种覆盖率 100%、犊牛成活率稳定 99%、奶牛空怀率下降,推动当地奶业由粗放规模型向智能精细、绿色高效转型,日均商品奶超千吨,稳居全国县域前列。","  大庆市林甸县是黑龙江省内重点畜牧县域、全国优质奶源核心产区。当地立足寒地牧业优势，抢抓数字农业机遇，以大数据、物联网技术赋能传统养殖，通过搭建智慧平台、升级智能系统、完善配套服务，推动畜牧业由粗放规模型向智能精细、绿色高效转型，打造现代农业发展新样板。\n\n  畜牧业是林甸支柱富民产业，规模优势显著。全县现有规模化奶牛牧场32处，含3处万头牧场、8处中型牧场，奶牛存栏超10万头，日均产商品奶超千吨，生鲜乳产能稳居全国县域前列。针对传统养殖管理粗放、效率不高、风险难控等短板，林甸将奶业数字化转型列为重点工程，全面推进牧场智能升级，构建全流程数字化管控体系。\n\n  当地建成全域奶牛产业大数据平台，实现重点牧场数字化全覆盖。目前平台覆盖17处规模牧场、5.9万头奶牛，累计归集数千万条养殖、生产、防疫、环境数据，打通政企数据壁垒，实现监管、生产、决策数据协同。依托物联网智能设备，养殖全过程可自动采集、实时分析、智能预警，形成精准高效的数字化治理模式。\n\n  依托四大智能系统，林甸建成全链条精准养殖体系。DWeb系统为奶牛建立专属数字档案，实现良种覆盖率100%；DFeed系统依托大数据智能配比日粮，降低饲料损耗、节约养殖成本；DCalf系统精准培育犊牛，县域犊牛成活率稳定达99%；DSmart系统依托电子耳标实现牛只精准溯源，有效提升管理精度与效率。\n\n  数字化转型持续释放降本提质增效红利。智能设备替代传统人工巡查统计，大幅精简人力投入；通过监测牛只体征、活动数据，可提前预判疫病风险、及时处置，筑牢养殖安全底线。智能发情识别、标准化电子系谱，有效降低奶牛空怀率、提升繁育质量，助力牧场实现人力精简、效益提升。\n\n  为护航智慧畜牧发展，林甸县同步搭建全方位服务体系。县域组建专项服务专班，常态化驻场指导解决养殖、防疫、繁育难题；深化与省内农业高校校地合作，推动先进养殖技术落地转化，常态化开展技能培训，持续提升从业人员数字化、标准化养殖水平。\n\n  下一步，林甸县将持续迭代升级智慧畜牧体系，完善数据标准与产业数据库，优化AI分析、疫病预警、品质追溯等功能，搭建生鲜乳全链条溯源体系。持续打通政企数据链路，构建协同高效的数字畜牧发展格局，擦亮优质奶源品牌，以智慧牧业赋能乡村振兴和县域高质量发展。","新华网黑龙江频道 2026-10-06","报道",71,{"impact":97,"substance":119,"depth":186,"authority":18,"freshness":57,"relevant":21,"comment":399},"县域奶业数字化转型的实质案例，数据与系统细节充分，具行业示范价值。",[401],{"name":395,"url":392},[403,404,353,405,406],"数字乡村","农业物联网","奶业数字化","奶牛大数据",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":410},{"candidates":411,"agricultural_evidence":45},[412,416,420,426,431],{"code":31,"score":171,"supported":45,"title":413,"abstract":414,"tags":415},[353],[],[353],{"code":35,"score":17,"supported":45,"title":417,"abstract":418,"tags":419},[133],[],[],{"code":208,"score":20,"supported":45,"title":421,"abstract":422,"tags":425},[],[423,424],"奶牛","奶业",[],{"code":427,"score":21,"supported":14,"title":428,"abstract":429,"tags":430},"I5",[],[],[404],{"code":309,"score":21,"supported":14,"title":432,"abstract":433,"tags":434},[],[],[403],[436,437],"林甸 智慧畜牧 奶牛","黑龙江 奶业数字化 大数据平台","林甸智慧畜牧奶牛-4381","2026-10-08T00:06:06.982819Z",{"id":441,"title":442,"url":443,"summary":444,"summary_zh":445,"content":9,"source_name":446,"source_url":443,"published_at":447,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":448,"score_detail":449,"sources":451,"tags":453,"primary_topic":459,"secondary_topics":460,"classification":461,"search_phrases":468,"slug":471,"view_count":67,"doi":472,"paper":473,"created_at":502},3992,"Determinants of purchase intention for carbon neutral meat beef: an evaluation among Brazilian consumers","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1900587","Background Despite Brazil’s position as the world’s largest beef exporter, consumer purchase intention for Carbon Neutral Meat (CNM)-certified beef remains poorly understood. This study addresses that gap by examining the determinants of purchase intention for CNM-labeled beef among a sample of Brazilian consumers, extending the Theory of Planned Behavior (TPB) with sustainability-related constructs. Given the convenience and snowball sampling approach, findings should be interpreted as applying to the surveyed profile—predominantly young, highly educated, and female—rather than the general Brazilian population. Methods Data were collected through a national online survey ( n = 703) and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The model incorporated eight latent constructs: attitude, subjective norms, perceived behavioral control, environmental awareness, animal welfare, traceability, consumer information level, and adoption of conservation technologies. Reliability and validity were confirmed through Composite Reliability (CR > 0.70) and Average Variance Extracted (AVE > 0.50). Results The model explained 62.5% of the variance in purchase intention ( R 2 = 0.625; adjusted R 2 = 0.621). Six of the eight proposed hypotheses were supported. Attitude emerged as the strongest predictor ( β = 0.306, p \u003C 0.001), followed by environmental awareness ( β = 0.255), perceived behavioral control ( β = 0.161), subjective norms ( β = 0.127, p = 0.001), traceability ( β = 0.119, p = 0.005), and adoption of conservation technologies ( β = 0.065, p = 0.058). Animal welfare and information level did not significantly influence purchase intention. Implications The findings advance an extended TPB framework for sustainable meat certification in a developing-country context and provide actionable insights for marketing strategies, certification bodies, and policymakers seeking to expand the CNM beef market, particularly among consumer segments with higher environmental awareness.","背景 尽管巴西是全球最大的牛肉出口国，但消费者对碳中和牛肉（Carbon Neutral Meat, CNM）认证牛肉的购买意愿仍缺乏充分了解。本研究通过考察巴西消费者样本中对CNM标签牛肉购买意愿的决定因素，以可持续性相关构念拓展计划行为理论（Theory of Planned Behavior, TPB），填补了这一空白。鉴于采用便利抽样和滚雪球抽样方法，研究结果应被解释为适用于所调查的样本特征——以年轻、高学历和女性为主——而非巴西总体人口。方法 数据通过全国性在线调查（n = 703）收集，并采用偏最小二乘结构方程模型（Partial Least Squares Structural Equation Modeling, PLS-SEM）进行分析。模型包含八个潜变量：态度、主观规范、感知行为控制、环境意识、动物福利、可追溯性、消费者信息水平和保护技术采用。信度和效度通过组合信度（Composite Reliability, CR > 0.70）和平均方差提取量（Average Variance Extracted, AVE > 0.50）得到确认。结果 该模型解释了购买意愿62.5%的方差（R² = 0.625；调整后R² = 0.621）。八个假设中有六个得到支持。态度是最强的预测因素（β = 0.306，p \u003C 0.001），其次是环境意识（β = 0.255）、感知行为控制（β = 0.161）、主观规范（β = 0.127，p = 0.001）、可追溯性（β = 0.119，p = 0.005）和保护技术采用（β = 0.065，p = 0.058）。动物福利和信息水平对购买意愿无显著影响。启示 研究结果推进了发展中国家背景下可持续肉类认证的拓展TPB框架，并为营销策略、认证机构和政策制定者提供了可操作的见解，以拓展CNM牛肉市场，尤其是在环境意识较高的消费者群体中。","Frontiers in Sustainable Food Systems","2026-10-01T00:00:00Z",69,{"impact":17,"substance":170,"depth":365,"authority":19,"freshness":171,"relevant":21,"comment":450},"基于703份样本的扩展TPB实证研究，揭示巴西消费者对碳中和认证牛肉的购买意愿驱动因素，对农业低碳认证与市场推广有参考价值，但属境外消费者研究，国内产业影响有限。",[452],{"name":446,"url":443},[454,455,456,457,458],"农业碳减排","食品溯源","碳中和牛肉","消费者行为","可持续认证",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":462},{"candidates":463,"agricultural_evidence":45},[464],{"code":31,"score":20,"supported":45,"title":465,"abstract":466,"tags":467},[],[105,121],[],[469,470],"巴西 碳中和牛肉 消费者","碳标签 牛肉 购买意愿","巴西碳中和牛肉消费者-3992","10.3389\u002Ffsufs.2026.1900587",{"doi":472,"openalex_id":474,"authors":475,"venue":446,"cited_by_count":67,"oa_url":443,"card":496,"direction":500,"ingested_from":82},"W7215035584",[476,478,481,484,487,490,492,494],{"name":477,"orcid":9},"Michelyn Kawane Rosa Araújo",{"name":479,"orcid":480},"Diego Pierotti Procópio","https:\u002F\u002Forcid.org\u002F0000-0002-1622-3335",{"name":482,"orcid":483},"Jéssica Romagnoli Freire Campos","https:\u002F\u002Forcid.org\u002F0000-0002-6972-1039",{"name":485,"orcid":486},"Matheus Wemerson Gomes Pereira","https:\u002F\u002Forcid.org\u002F0000-0002-6052-6683",{"name":488,"orcid":489},"Kamilla Silva Melo","https:\u002F\u002Forcid.org\u002F0009-0006-5581-0934",{"name":491,"orcid":9},"Alexia Santos Barreto",{"name":493,"orcid":9},"Débora Andrade Silva",{"name":495,"orcid":9},"Luis Antônio França Barbacena",{"tldr":497,"method":498,"finding":499,"direction":500,"opportunity":501},"基于扩展计划行为理论，调查巴西消费者购买碳中和牛肉意愿的影响因素。","对703名巴西消费者在线问卷，用PLS-SEM分析八个潜变量。","态度、环境意识、感知行为控制等六因素显著影响购买意愿，模型解释62.5%方差。","农业绿色发展与碳","可在中国等发展中国家验证扩展TPB模型，并探究动物福利与信息水平不显著的原因。","2026-10-02T23:30:09.616122Z"]