[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3992":3,"related-3992":90},{"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":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":53,"slug":56,"view_count":57,"doi":58,"paper":59,"created_at":89},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。",null,"Frontiers in Sustainable Food Systems","2026-10-01T00:00:00Z","论文",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,22,17,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"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":33,"slug":34,"name":35,"path":36,"parent_code":37},"C6","smart-livestock","养殖环境与智慧畜牧","\u002Ftopic\u002Flivestock\u002Fsmart-livestock","C",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":52},6,true,[],[50,51],"动物福利","animal welfare",[],[54,55],"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",0,"10.3389\u002Ffsufs.2026.1900587",{"doi":58,"openalex_id":60,"authors":61,"venue":10,"cited_by_count":57,"oa_url":6,"card":82,"direction":86,"ingested_from":88},"W7215035584",[62,64,67,70,73,76,78,80],{"name":63,"orcid":9},"Michelyn Kawane Rosa Araújo",{"name":65,"orcid":66},"Diego Pierotti Procópio","https:\u002F\u002Forcid.org\u002F0000-0002-1622-3335",{"name":68,"orcid":69},"Jéssica Romagnoli Freire Campos","https:\u002F\u002Forcid.org\u002F0000-0002-6972-1039",{"name":71,"orcid":72},"Matheus Wemerson Gomes Pereira","https:\u002F\u002Forcid.org\u002F0000-0002-6052-6683",{"name":74,"orcid":75},"Kamilla Silva Melo","https:\u002F\u002Forcid.org\u002F0009-0006-5581-0934",{"name":77,"orcid":9},"Alexia Santos Barreto",{"name":79,"orcid":9},"Débora Andrade Silva",{"name":81,"orcid":9},"Luis Antônio França Barbacena",{"tldr":83,"method":84,"finding":85,"direction":86,"opportunity":87},"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。","openalex","2026-10-02T23:30:09.616122Z",{"total":46,"page":22,"page_size":46,"items":91},[92,154,226,282,336,400],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":9,"source_name":98,"source_url":95,"published_at":99,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":101,"sources":104,"tags":106,"primary_topic":111,"secondary_topics":112,"classification":113,"search_phrases":135,"slug":138,"view_count":57,"doi":139,"paper":140,"created_at":153},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。","International Journal of Advanced Research in Science Communication and Technology","2026-10-13T00:00:00Z",49,{"impact":17,"substance":102,"depth":20,"authority":46,"freshness":13,"relevant":22,"comment":103},12,"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。",[105],{"name":98,"url":95},[107,108,109,110],"智慧农业","RFID","畜牧监测","防碰撞算法",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":114},{"candidates":115,"agricultural_evidence":47},[116,122,127],{"code":37,"score":117,"supported":47,"title":118,"abstract":120,"tags":121},11,[119],"livestock",[119],[],{"code":33,"score":17,"supported":47,"title":123,"abstract":125,"tags":126},[124],"livestock monitoring",[],[],{"code":128,"score":129,"supported":47,"title":130,"abstract":131,"tags":134},"I1",7,[],[132,133],"精准农业","precision agriculture",[107],[136,137],"RFID 畜牧 防碰撞","多天线 RFID 牲畜识别","RFID畜牧防碰撞-4891","10.48175\u002Fijarsct-38829",{"doi":139,"openalex_id":141,"authors":142,"venue":98,"cited_by_count":57,"oa_url":95,"card":147,"direction":151,"ingested_from":88},"W7221675915",[143,145],{"name":144,"orcid":9},"Prof. P. T. Dhotre Prathamesh Dilip Jadhav",{"name":146,"orcid":9},"Dr. H. B. Jadhav Prof. G. R. Sarode",{"tldr":148,"method":149,"finding":150,"direction":151,"opportunity":152},"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。","2026-10-14T23:30:16.832710Z",{"id":155,"title":156,"url":157,"summary":158,"summary_zh":159,"content":9,"source_name":160,"source_url":157,"published_at":161,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":162,"score_detail":163,"sources":168,"tags":170,"primary_topic":174,"secondary_topics":175,"classification":182,"search_phrases":205,"slug":208,"view_count":22,"doi":209,"paper":210,"created_at":225},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":102,"substance":164,"depth":165,"authority":166,"freshness":17,"relevant":22,"comment":167},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。",[169],{"name":160,"url":157},[107,171,172,50,173],"农业人工智能","计算机视觉","精准畜牧",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[176],{"code":177,"slug":178,"name":179,"path":180,"parent_code":181},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":183},{"candidates":184,"agricultural_evidence":47},[185,190,196,201],{"code":33,"score":186,"supported":47,"title":187,"abstract":188,"tags":189},19,[51],[173,50,51],[50,173],{"code":177,"score":186,"supported":47,"title":191,"abstract":193,"tags":195},[192],"computer vision",[172,194,192],"artificial intelligence",[171,172],{"code":37,"score":46,"supported":47,"title":197,"abstract":198,"tags":200},[],[199,119],"畜牧",[],{"code":128,"score":22,"supported":14,"title":202,"abstract":203,"tags":204},[],[],[107],[206,207],"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":209,"openalex_id":211,"authors":212,"venue":160,"cited_by_count":57,"oa_url":219,"card":220,"direction":151,"ingested_from":88},"W7220695950",[213,216],{"name":214,"orcid":215},"Mariska Thalitha Bosschaert","https:\u002F\u002Forcid.org\u002F0000-0002-9160-7638",{"name":217,"orcid":218},"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":221,"method":222,"finding":223,"direction":151,"opportunity":224},"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":227,"title":228,"url":229,"summary":230,"summary_zh":9,"content":231,"source_name":232,"source_url":9,"published_at":233,"category":234,"cover_url":9,"hotness":13,"is_selected":14,"score":235,"score_detail":236,"sources":240,"tags":242,"primary_topic":247,"secondary_topics":248,"classification":249,"search_phrases":277,"slug":280,"view_count":22,"doi":9,"paper":9,"created_at":281},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":237,"substance":17,"depth":46,"authority":166,"freshness":238,"relevant":22,"comment":239},26,4,"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。",[241],{"name":232,"url":229},[171,243,244,245,246],"十五五规划","畜禽粪污","绿色养殖","智慧畜牧",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":250},{"candidates":251,"agricultural_evidence":47},[252,257,261,268,272],{"code":37,"score":18,"supported":47,"title":253,"abstract":255,"tags":256},[199,254],"畜禽",[199,254],[],{"code":33,"score":19,"supported":47,"title":258,"abstract":259,"tags":260},[244],[246,244],[246,244,245],{"code":262,"score":263,"supported":47,"title":264,"abstract":265,"tags":267},"G1",3,[],[266],"成果转化",[],{"code":177,"score":22,"supported":14,"title":269,"abstract":270,"tags":271},[],[],[171],{"code":273,"score":22,"supported":14,"title":274,"abstract":275,"tags":276},"J1",[],[],[243],[278,279],"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":283,"title":284,"url":285,"summary":286,"summary_zh":9,"content":287,"source_name":288,"source_url":9,"published_at":161,"category":289,"cover_url":9,"hotness":13,"is_selected":14,"score":290,"score_detail":291,"sources":294,"tags":296,"primary_topic":301,"secondary_topics":302,"classification":303,"search_phrases":331,"slug":334,"view_count":57,"doi":9,"paper":9,"created_at":335},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":165,"substance":186,"depth":292,"authority":102,"freshness":129,"relevant":22,"comment":293},15,"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。",[295],{"name":288,"url":285},[297,298,246,299,300],"数字乡村","农业物联网","奶业数字化","奶牛大数据",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":304},{"candidates":305,"agricultural_evidence":47},[306,310,314,321,326],{"code":33,"score":21,"supported":47,"title":307,"abstract":308,"tags":309},[246],[],[246],{"code":37,"score":17,"supported":47,"title":311,"abstract":312,"tags":313},[199],[],[],{"code":315,"score":46,"supported":47,"title":316,"abstract":317,"tags":320},"C2",[],[318,319],"奶牛","奶业",[],{"code":322,"score":22,"supported":14,"title":323,"abstract":324,"tags":325},"I5",[],[],[298],{"code":327,"score":22,"supported":14,"title":328,"abstract":329,"tags":330},"I6",[],[],[297],[332,333],"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":337,"title":338,"url":339,"summary":340,"summary_zh":9,"content":341,"source_name":342,"source_url":9,"published_at":233,"category":234,"cover_url":9,"hotness":343,"is_selected":47,"score":344,"score_detail":345,"sources":349,"tags":357,"primary_topic":361,"secondary_topics":362,"classification":363,"search_phrases":395,"slug":398,"view_count":263,"doi":9,"paper":9,"created_at":399},3927,"农业农村部印发《全国畜牧兽医行业发展\"十五五\"规划》","https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fart\u002F2026\u002F9\u002F30\u002Fart_9391_2706676.html","规划部署了5方面重点任务：强化产业发展综合调控、推动产业提质增效、提升动物疫病风险防控效能、促进产业绿色安全发展、强化科技装备支撑；其中明确提出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。规划还部署\"生猪产能常态化精准调控\"\"畜牧业提质增效\"\"动物疫病风险防控\"等专栏，明确区域布局，细化任务措施。","今天是 2026年10月2日 星期五[简体](javascript:zh_tran('n');)[繁体](javascript:zh_tran('t');)[无障碍阅读](javascript:;)\n\n![Image 1](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fpicture\u002F0\u002F2105191407404717973.png)\n\n[首页](https:\u002F\u002Fxmsyj.jining.gov.cn\u002F)[济宁畜牧](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fcol\u002Fcol9358\u002Findex.html)[政务公开](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fcol\u002Fcol9359\u002Findex.html)[互动交流](http:\u002F\u002Fwww.jining.gov.cn\u002Fjact\u002Ffront\u002Fmailwrite.do?sysid=1)[办事服务](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fcol\u002Fcol9361\u002Findex.html)[行业动态](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fcol\u002Fcol9362\u002Findex.html)[畜牧科技](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fcol\u002Fcol9363\u002Findex.html)\n\n![Image 2](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fimages\u002F510\u002Fsdxm_dishi_pic_tywzy.jpg)当前位置：[首页](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Findex.html)>[政务公开](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fcol\u002Fcol9359\u002Findex.html)>[工作动态](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fcol\u002Fcol9386\u002Findex.html)>[政务动态](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fcol\u002Fcol9391\u002Findex.html)\n\n推进畜牧业提质增效 加快构建高质量发展新格局——农业农村部畜牧兽医局负责人就《全国畜牧兽医行业发展“十五五”规划》答记者问 * * * 发布日期：2026-09-30 16:32:45 来源： 农业农村部新闻办公室 字号：[ [大](javascript:doZoom(16))[中](javascript:doZoom(14))[小](javascript:doZoom(12)) ][![Image 3](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fpicture\u002F0\u002F1711081543339464232.png)](javascript:void(0)) 近期，农业农村部印发《全国畜牧兽医行业发展“十五五”规划》（以下简称《规划》）。日前，农业农村部畜牧兽医局负责人就《规划》有关情况回答了记者提问。 **问：《规划》出台有什么背景考虑？** 答：现代畜牧业是践行大食物观、构建多元化食物供给体系的战略产业，是农业农村经济的支柱产业。“十四五”时期，现代畜牧业建设取得新成效。畜禽产品综合生产能力跃上新台阶。2025年，我国肉蛋奶总产量超过1.75亿吨，连续3年保持在1.7亿吨以上，其中肉类总产量首次突破1亿吨。以规模养殖为主体的现代生产方式基本形成。全国畜禽养殖规模化率达到76.9%，带动先进适用技术和装备广泛应用，畜牧业科技贡献率达到70%，畜禽粪污综合利用率超过80%。重大动物疫病防控能力逐步增强。非洲猪瘟等重大动物疫病得到有效防控，养殖环节整体生物安全防护水平大幅提升，动物疫情总体平稳可控。现代产业体系趋于完备。构建了饲草料、兽药、种畜禽生产以及设施装备、销售流通、屠宰加工等相配套的产业体系。工业饲料产量连续15年位居世界第一；国产种源市场占有率超过80%。产业调控管理体系进一步健全。畜禽产品质量安全保持较高水平，抽检合格率稳定在99%以上；产能综合调控机制和政策制度体系逐步完善。畜牧业高质量发展基础不断夯实，但也面临着畜禽产品供需形势发生变化、产业结构不够合理、疫情风险长期存在等挑战，还存在优质饲草料供应短缺、绿色发展不充分、优质特色产品供给不足、种业创新推广有差距等短板，亟需加快转型升级步伐，构建高质量发展新格局。 党中央、国务院高度重视畜牧兽医工作。《中华人民共和国国民经济和社会发展第十五个五年规划纲要》和《加快农业农村现代化“十五五”规划》均对畜牧兽医工作作出部署安排。2026年中央政治局会议两次对稳定生猪等农畜产品生产和价格提出明确要求。农业农村部贯彻落实党中央、国务院决策部署，立足新阶段、适应新形势，在深入调查研究、广泛征求意见的基础上，制定印发《规划》，旨在明确发展思路、总体目标、重点任务，引导行业准确把握发展方向，凝心聚力共同推进畜牧业高质量发展。 **问：《规划》的总体思路目标是什么？** 答：《规划》坚持以习近平新时代中国特色社会主义思想为指导，深入贯彻落实党的二十大和二十届历次全会精神，认真落实四中全会部署，全面贯彻习近平总书记关于“三农”工作的重要论述和重要指示精神，完整准确全面贯彻新发展理念，坚持稳中求进工作总基调，树立和践行大食物观，以稳产、提质、增效为目标，统筹发展和安全，坚持产量产能、生产生态、增产增收一起抓，促进全产业链提档升级，加快构建供需动态适配、规模结构合理、产业链协同提升、环境资源可持续的畜牧业高质量发展新格局。 《规划》明确提出，到2030年，畜牧业高质量发展迈上新台阶，主要畜禽产品稳定安全供给更有保障，产业素质提升更有质量。综合生产能力进一步巩固，产品结构持续优化，优质特色畜产品供应更加丰富，养殖规模化、设施化水平稳步提升，科技支撑作用进一步增强。规模结构和产业生态更加优化，大型企业联农带农作用进一步强化，利益分配机制逐步完善，上下游产业生态更加协同，中小养殖场户发展质量明显提升。重大动物疫病得到有效防控，种畜禽疫病净化取得积极成效，人畜共患病风险稳步降低，兽医公共卫生安全水平进一步提高。畜禽产品质量安全保持更高水平，单位畜禽产品使用兽用抗菌药数量总体下降。绿色转型取得新成效，畜禽粪污资源化利用水平持续提升，种养结合农牧循环格局基本形成。 **问：《规划》在具体发展目标设定上有哪些考虑？** 答：兼顾前瞻性和可行性，《规划》提出15项定量目标，包括肉类总产量、禽蛋产量、奶类产量、工业饲料总产量、饲草总产量等5项产量指标，畜禽养殖规模化率、国产种源市场占有率、科技进步贡献率、畜禽粪污综合利用率、畜禽产品抽检合格率等5项产业综合素质指标，新兽药产品注册数量、跨省调运畜禽落地报告率、执业兽医数量、规模以上生猪屠宰厂（场）数量占比以及动物疫病净化场、无疫小区、无疫区数量等5项专项领域发展指标，综合反映了畜牧兽医行业各个领域和环节的发展需要，在稳定生产基础上，突出了提质增效、优化结构等目标取向。其中，肉类总产量指标未设定增量目标，主要考虑是适应生猪综合生产能力快速提升、猪肉消费基本达峰的供需新形势，落实生猪产能综合调控要求，引导生猪产业由数量增长转向高质量发展。 **问：《规划》部署了哪些重点任务？** 答：《规划》着眼构建高质量发展新格局，坚持发展生产和优化结构相结合，着力防风险、固安全、增绿色，共部署了5方面重点任务。一是强化产业发展综合调控。建立健全产业综合调控机制，规范引导大型养殖企业有序发展，推动中小养殖场户融入现代生产体系。二是推动产业提质增效。稳定大宗畜禽产品生产，培育推广优良畜禽品种，发展优质特色畜禽产业，发展壮大饲草产业，提升畜禽产品加工流通水平，加强畜牧业品牌建设。三是提升动物疫病风险防控效能。强化重大动物疫病防控，加快推进重点动物疫病净化，增强动物检疫监管能力，加强动物诊疗管理。四是促进产业绿色安全发展。全面提升养殖产品质量安全水平，规范畜禽屠宰行业发展，推进畜禽粪污处理利用，防范安全生产风险。五是强化科技装备支撑。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。 **问：如何保障《规划》落实落地？** 答：结合重大工程项目和重点工作计划，《规划》设置了“生猪产能常态化精准调控”、“畜牧业提质增效”、“动物疫病风险防控”等专栏，明确区域布局，细化任务措施，量化任务目标，确保《规划》可落实、见实效。 同时，《规划》还提出了4项保障措施。一是强化责任落实。推动落实地方各级人民政府、行业管理部门、生产经营主体责任，引导加强行业自律。二是加强法治保障。健全畜牧兽医领域法律法规体系，完善相关配套规章制度及技术标准体系，完善基层综合执法工作机制。三是强化政策协同。强化产业政策、环保监管、产能调控等政策的协同联动，推动完善部门工作机制。加强项目资金全过程监管。四是加强人才队伍建设。加强基层动物防疫和畜牧技术推广队伍建设，强化官方兽医队伍管理，开展执业兽医和乡村兽医继续教育，发挥畜牧业产业技术体系作用，打造高素质专业队伍。 [【复制链接】](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fart\u002F2026\u002F9\u002F30\u002Fart_9391_2706676.html#)[【打印本页】](javascript:window.print())[【关闭窗口】](javascript:window.close()) 如果您认为本网转载的内容涉及侵权，请作品的作者尽快与我们联系。电话：0537-2966776，如转载本网内容请注明。\n\n[关于我们](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fcol\u002Fcol9428\u002Findex.html) |[联系我们](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fcol\u002Fcol9429\u002Findex.html) |[版权声明](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fcol\u002Fcol9431\u002Findex.html) |[网站地图](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fcol\u002Fcol9450\u002Findex.html) |\n\n[![Image 4](https:\u002F\u002Fdcs.conac.cn\u002Fimage\u002Fblue.png)](https:\u002F\u002Fbszs.conac.cn\u002Fsitename?method=show&id=64211F07810FDB09E053022E1AAC770C)版权所有：济宁市畜牧兽医事业发展中心 电话：0537-2966776 邮箱：xmjbgs@ji.shandong.cn 地址：山东省济宁市太白湖新区省运会指挥中心F区0303室 网站标识码：3708000070 [网站地图](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fcol\u002Fcol9450\u002Findex.html) [鲁ICP备05021223号](https:\u002F\u002Fbeian.miit.gov.cn\u002F)[![Image 5](https:\u002F\u002Fxmsyj.jining.gov.cn\u002Fpicture\u002F0\u002F1801231116515936459.png)鲁公网安备 37080202000131号](http:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=37080202000131)![Image 6](https:\u002F\u002Fzfwzgl.www.gov.cn\u002Fexposure\u002Fimages\u002Fjiucuo.png?v=3708000070)","济宁市畜牧兽医事业发展中心·2026年09月29日",40,91,{"impact":346,"substance":347,"depth":165,"authority":166,"freshness":129,"relevant":22,"comment":348},28,24,"农业农村部印发的全国性畜牧兽医五年规划，含15项定量目标与五方面重点任务，政策层级高、信息增量足，值得进入每日精选。",[350,351,354],{"name":342,"url":339},{"name":352,"url":353},"中宏网 农业农村部畜牧兽医局 2026-09-29","https:\u002F\u002Fwww.zhonghongwang.com\u002Fshow-258-474010-1.html",{"name":355,"url":356},"经济日报 2026-10-06","https:\u002F\u002Fwww.jingjiribao.cn\u002Fstatic\u002Fdetail.jsp?id=687271",[243,358,359,245,246,360],"畜牧业","动物疫病防控","畜禽种业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":364},{"candidates":365,"agricultural_evidence":47},[366,370,375,381,387,391],{"code":37,"score":102,"supported":47,"title":367,"abstract":368,"tags":369},[199],[199],[358],{"code":33,"score":371,"supported":47,"title":372,"abstract":373,"tags":374},5,[],[246],[246,245],{"code":376,"score":238,"supported":47,"title":377,"abstract":378,"tags":380},"C5",[],[379],"动物疫病",[359],{"code":382,"score":263,"supported":47,"title":383,"abstract":384,"tags":386},"C1",[],[385],"生猪",[],{"code":262,"score":263,"supported":47,"title":388,"abstract":389,"tags":390},[],[266],[],{"code":273,"score":22,"supported":14,"title":392,"abstract":393,"tags":394},[],[],[243],[396,397],"农业农村部 畜牧兽医局 答记者问","动物疫病防控 十五五规划 智慧畜牧 畜禽种业","农业农村部畜牧兽医局答记者问-3927","2026-10-02T11:53:08.773753Z",{"id":401,"title":402,"url":403,"summary":404,"summary_zh":405,"content":9,"source_name":406,"source_url":403,"published_at":407,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":408,"score_detail":409,"sources":411,"tags":413,"primary_topic":417,"secondary_topics":418,"classification":420,"search_phrases":457,"slug":460,"view_count":461,"doi":462,"paper":463,"created_at":498},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":165,"substance":18,"depth":165,"authority":166,"freshness":21,"relevant":22,"comment":410},"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。",[412],{"name":406,"url":403},[107,414,415,50,416],"机器学习","畜牧兽医","风险分级监管",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[419],{"code":177,"slug":178,"name":179,"path":180,"parent_code":181},{"status":40,"locked":14,"version":41,"method":42,"evidence":421},{"candidates":422,"agricultural_evidence":47},[423,427,432,439,443,448,453],{"code":33,"score":292,"supported":47,"title":424,"abstract":425,"tags":426},[51],[50,51],[50],{"code":177,"score":292,"supported":47,"title":428,"abstract":430,"tags":431},[429],"machine learning",[414,429],[414],{"code":433,"score":46,"supported":47,"title":434,"abstract":435,"tags":438},"C3",[],[436,437],"家禽","poultry",[],{"code":37,"score":238,"supported":47,"title":440,"abstract":441,"tags":442},[],[119],[415],{"code":315,"score":263,"supported":47,"title":444,"abstract":445,"tags":447},[],[446],"cattle",[],{"code":376,"score":263,"supported":47,"title":449,"abstract":450,"tags":452},[],[451],"veterinary",[],{"code":128,"score":22,"supported":14,"title":454,"abstract":455,"tags":456},[],[],[107],[458,459],"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":462,"openalex_id":464,"authors":465,"venue":406,"cited_by_count":57,"oa_url":403,"card":492,"direction":496,"ingested_from":88},"W7214951739",[466,468,470,472,474,477,479,481,483,486,489],{"name":467,"orcid":9},"Eva Obenaus",{"name":469,"orcid":9},"Sabrina Kuchling",{"name":471,"orcid":9},"Tadej Zerak",{"name":473,"orcid":9},"Corina Schleicher",{"name":475,"orcid":476},"Ian Kopacka","https:\u002F\u002Forcid.org\u002F0000-0003-4106-1742",{"name":478,"orcid":9},"Sven Budik",{"name":480,"orcid":9},"Sabine Grimme",{"name":482,"orcid":9},"Jörg Hiesel",{"name":484,"orcid":485},"Peter Eckhardt","https:\u002F\u002Forcid.org\u002F0009-0005-5015-4846",{"name":487,"orcid":488},"Johannes Baumgartner","https:\u002F\u002Forcid.org\u002F0000-0001-8555-1378",{"name":490,"orcid":491},"Clair L. Firth","https:\u002F\u002Forcid.org\u002F0000-0002-4919-7786",{"tldr":493,"method":494,"finding":495,"direction":496,"opportunity":497},"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"]