[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4550":3,"related-4550":83},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":59,"slug":62,"view_count":21,"doi":63,"paper":64,"created_at":82},4550,"Blue water footprint and scarcity indicators in dairy cattle systems: a comparative study on water use","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105006","Blue water footprint and scarcity indicators in dairy cattle systems: a comparative study on water use。Agricultural Systems",null,"Agricultural Systems","2026-10-09T00:00:00Z","论文",10,false,71,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,20,17,14,8,1,"核心期刊发表的奶牛养殖水足迹比较研究，方法数据扎实，但属细分领域学术成果，产业影响有限。",[24],{"name":9,"url":6},[26,27,28,29],"水资源管理","农业可持续","奶牛养殖","水足迹",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":44},[43,49,54],{"code":31,"score":16,"supported":44,"title":45,"abstract":47,"tags":48},true,[46],"cattle",[46],[28],{"code":50,"score":21,"supported":13,"title":51,"abstract":52,"tags":53},"H3",[],[],[26],{"code":55,"score":21,"supported":13,"title":56,"abstract":57,"tags":58},"H8",[],[],[27],[60,61],"奶牛养殖 水足迹 水资源","农业可持续 水资源管理 奶牛养殖 水足迹","奶牛养殖水足迹水资源-4550","10.1016\u002Fj.agsy.2026.105006",{"doi":63,"openalex_id":65,"authors":66,"venue":9,"cited_by_count":80,"oa_url":6,"card":8,"direction":8,"ingested_from":81},"W7221119168",[67,69,72,74,76,78],{"name":68,"orcid":8},"Luciana Jennerich",{"name":70,"orcid":71},"María Celeste Schierano","https:\u002F\u002Forcid.org\u002F0000-0003-3380-4374",{"name":73,"orcid":8},"Marisol Farias",{"name":75,"orcid":8},"Gonzalo Gutiérrez",{"name":77,"orcid":8},"Carina Griffa",{"name":79,"orcid":8},"Rosana Boglione",0,"openalex","2026-10-10T23:30:03.711091Z",{"total":84,"page":21,"page_size":84,"items":85},6,[86,137,215,300,367,418],{"id":87,"title":88,"url":89,"summary":90,"summary_zh":8,"content":91,"source_name":92,"source_url":8,"published_at":93,"category":94,"cover_url":8,"hotness":12,"is_selected":13,"score":95,"score_detail":96,"sources":101,"tags":103,"primary_topic":108,"secondary_topics":109,"classification":110,"search_phrases":132,"slug":135,"view_count":80,"doi":8,"paper":8,"created_at":136},5276,"寒区奶牛液体粪肥还田技术入选2026黑龙江农业主推技术 累计推广83.31万亩","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697083643218477622\u002F","黑龙江省黑土保护利用研究院研究员高洪生团队依托国家重点研发计划、黑龙江省农科院创新工程等项目历经近10年攻关,成功研发出适配寒区农业生产节律的奶牛液体粪肥还田成套技术体系;2022—2024年累计推广应用面积83.31万亩、消纳还田奶牛液体粪肥467.41万立方米;2026年该技术正式入选黑龙江省农业主推技术名单。","[](https:\u002F\u002Fwww.toutiao.com\u002F)\n\n*   关注 \n*   推荐 \n*   北京   \n*   视频 \n*   财经 \n*   科技 \n*   热点 \n*   国际 \n*   更多 \n\n    *   军事 \n    *   体育 \n    *   娱乐 \n    *   历史 \n    *   美食 \n    *   [直播](https:\u002F\u002Flive.ixigua.com\u002F) \n    *   旅游 \n    *   [懂车帝](https:\u002F\u002Fwww.dongchedi.com\u002F?zt=tt_pc_home_channel) \n\n搜索\n\n[消息](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697083643218477622\u002F)\n\n[发布](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697083643218477622\u002F)\n\n[登录](https:\u002F\u002Fsso.toutiao.com\u002Flogin\u002F?service=https%3A%2F%2Fwww.toutiao.com%2Farticle%2F7697083643218477622%2F%3Fwid%3D1792368503998)\n\n2\n\n评论\n\n收藏\n\n分享\n\n*   转发到头条 \n*   复制链接 \n*   微信  微信扫码分享  \n*   新浪微博 \n*   QQ空间 \n\n# 寒区奶牛液体粪肥还田技术变废为宝\n\n2026-10-16 10:15·[中工网](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAMnblevmY0Qt-QgRg_pKWHVMrbv_lg0VMmEMpEEOk6Vw\u002F?source=tuwen_detail)\n\n科技日报记者 朱虹\n\n前不久，寒区奶牛液体粪肥还田技术推介观摩现场会暨畜禽液体粪污资源化利用专题培训会在哈尔滨举行。本次会议由黑龙江省黑土保护利用研究院牵头，联合黑龙江省奶业协会、省奶牛产业技术协同创新推广体系主办，聚焦寒区畜禽粪污资源化利用、绿色种养循环农业发展，开展技术研讨与现场观摩交流。\n\n黑龙江作为全国核心优质奶源基地，规模化奶牛养殖产业体量庞大。但受高寒气候制约，区域土壤封冻期长、粪污还田窗口期短，叠加粪污贮存风险高、转运难度大、施用技术粗放、专用装备短缺等痛点，长期存在奶牛液体粪污“存不住、运不出、还田难”困境。这造成养殖端有机养分资源与种植端耕地肥力需求时空错配，既污染养殖区域生态环境，又浪费珍贵的有机农业资源，制约种养循环农业高质量发展。\n\n针对寒区养殖粪污资源化利用的技术短板，黑龙江省黑土保护利用研究院研究员高洪生团队，依托国家重点研发计划、黑龙江省农业科学院创新工程等项目，历经近10年持续攻关、多点田间试验与示范验证，成功研发出适配寒区农业生产节律的奶牛液体粪肥还田成套技术体系，破解高寒地区粪污资源化利用的核心难题。\n\n这套技术构建起一条“冬天存得住、春天运得出、田里施得准”的流水线：先把粪水集中起来，固液分离后把液体粪肥密封贮存发酵，解决封冻期不能还田、容易污染的问题；等土地解冻，用像“脐带”一样的泵和软管把粪肥直接从贮存池送到田间，替代罐车拉运，降低成本；还田前检测粪肥养分、测算地块承受力，再按季节用喷洒、深埋注入或滴灌等方式精准施入，抓住春、夏、秋的还田窗口。这样就把粪污从“污染负担”变成了“肥料资源”。同时，项目配套的托管式（脐带）输送装备国产化率超95%，大幅降低大型牧场粪肥田间输送成本、提升作业效率，为技术规模化落地筑牢装备根基。\n\n多年示范应用数据显示，该技术提质增效成果显著。2022—2024年，黑龙江省累计推广应用面积达83.31万亩，消纳还田奶牛液体粪肥467.41万立方米。示范片区实现化肥减量10%—20%，玉米粮食作物增产7.58%以上，同步达成粪污无害化处置、化肥减量增效、黑土地力培肥三重效益，构建起生态、农业、产业共赢的发展模式。2026年，寒区奶牛液体粪肥还田技术正式入选黑龙江省农业主推技术名单，标志着该项科研成果全面完成试验示范迭代，迈入标准化、规范化、规模化推广新阶段。\n\n来源：科技日报\n\n举报\n\n评论 0\n\n请先 登录 后发表评论～\n\n今日头条推荐引擎有 9 条更新\n\n[稻花香里说丰年｜向科技要丰收，看大国粮仓如何“藏粮于技”？](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7696519606655124009\u002F)\n*   [![Image 1: 稻花香里说丰年｜向科技要丰收，看大国粮仓如何“藏粮于技”？-图1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fbdf7992fbac1d9184325c16ee97c9901~tplv-tt-cs0:1280:853.jpg?_iz=31826&bid=10&from=article.headline&gid=7696519606655124009&lk3s=06827d14&x-expires=1794960507&x-signature=UVjsVt3yiBPoAmtiKXAKGfjAM48%3D)](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7696519606655124009\u002F) \n*   [![Image 2: 稻花香里说丰年｜向科技要丰收，看大国粮仓如何“藏粮于技”？-图2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F41252e896104b44737e8937b61d290cd~tplv-tt-cs0:600:461.jpg?_iz=31826&bid=10&from=article.headline&gid=7696519606655124009&lk3s=06827d14&x-expires=1794960507&x-signature=8F6QAbPohV55KRdkKGB%2BigcxbcY%3D)](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7696519606655124009\u002F) \n*   [![Image 3: 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16](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-6w9my0ksvp\u002F8a6d886dd69946e9a343cf9355dafc05~tplv-tt-cs0:554:368.jpg?_iz=31826&bid=10&from=article.headline&gid=7697081607077528098&lk3s=06827d14&x-expires=1794960507&x-signature=SOViRuqlWP3%2Fy5hYkmK81aqn1ks%3D)](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697081607077528098\u002F \"他达拉非怎么选？先看这几点，再决定选哪个\")\n\n[他达拉非怎么选？先看这几点，再决定选哪个](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697081607077528098\u002F)\n\n[39健康网](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAuTdxeswYIRb0iF7f1sp1Ayyja48nFyh9W6IYWgBORBY\u002F?source=tuwen_detail)\n\n11小时前\n\n[![Image 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[#AI礼堂职场](https:\u002F\u002Fm.ixigua.com\u002Fdx\u002F7697463378411466355 \"#AI礼堂职场精英写真感 #抖音ai创作 #正能量 #抖音推广 #上热搜\")\n*   [《冰雪奇缘》艾](https:\u002F\u002Fm.ixigua.com\u002Fdx\u002F7697463344193621263 \"《冰雪奇缘》艾莎摔门不是自由 《冰雪奇缘》系列10个隐藏细节：从安娜脸上的印记，到《随它吧》最后摔上的那扇门，再到仍守着空城堡的棉花糖。 #冰雪奇缘   #动画解说   #艾莎\")\n*   [海上明月🌙月](https:\u002F\u002Fm.ixigua.com\u002Fdx\u002F7697463317413551730 \"海上明月🌙月光洒在海面，氛围感拉满 深蓝夜色笼罩大海，一轮满月悬于上空。 月光碎成粼粼波光，轻轻浮在海面。 安安静静画画，感受独属于月夜的温柔🌙 #抖音画画人的日常 #艺术在抖音 #丙烯画 #青年创作者成长计划\")\n*   [莫迪刚喊完追赶](https:\u002F\u002Fm.ixigua.com\u002Fdx\u002F7697463295875353894 \"莫迪刚喊完追赶美国卢比就暴跌，申办2036奥运又被全球群嘲 莫迪刚喊完追赶美国卢比就暴跌，申办2036奥运又被全球群嘲，印度凭什么敢这么自信？印度把关税从20%一口气提到70%，制造业占比反而掉到14%，核心零件还得靠中国进口，印度一口气删掉一亿三千万选民，底层穷人连投票资格都没了，莫迪这套选举到底想干嘛？前1%印度人拿走四成财富，班加罗尔还在限水限电，梵我合一真能让人安于贫困吗？印度暂停印度河用水条约又擅改拉达克地名，内部搞不定就拿民族主义往外甩锅，几亿印度年轻人找不到工作，政府还封城断网抓抗议，所谓人口红利正在变成火药桶\")\n*   [#爱国正能量](https:\u002F\u002Fm.ixigua.com\u002Fdx\u002F7697463307624114858 \"#爱国正能量\")\n*   [中式台球历史上](https:\u002F\u002Fm.ixigua.com\u002Fdx\u002F7697463233511173307 \"中式台球历史上最精彩的一场双赛点决胜局，没有之一。#中式八球#台球#赵汝亮#张泰艺\")\n*   [风力发电项目，](https:\u002F\u002Fm.ixigua.com\u002Fdx\u002F7697463227273644122 \"风力发电项目，重型车辆压坏，至今没有修复 \")\n*   [Token 到](https:\u002F\u002Fm.ixigua.com\u002Fdx\u002F7697463199754423606 \"Token 到底是什么？为什么同一句话的字数和收费单位不一样 二百字的商品介绍，用量为什么不是二百？只补一句“再活泼一点”，怎么还要算上前面的内容？从一次翻译任务出发，讲清 Token 怎么数、费用怎么算，以及怎样省钱才不白忙。#人工智能 #Token #分词器 #API计费 #省钱技巧\")\n*   [得有眼力见啊](https:\u002F\u002Fm.ixigua.com\u002Fdx\u002F7697465649861851251 \"得有眼力见啊 死心眼子 大势得明白啊\")\n\n[![Image 18: 中工网 头像](https:\u002F\u002Fsf1-cdn-tos.toutiaostatic.com\u002Fimg\u002Fuser-avatar\u002F18143f27479eac65f70b88df5d2035da~300x300.image)](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAMnblevmY0Qt-QgRg_pKWHVMrbv_lg0VMmEMpEEOk6Vw\u002F?source=tuwen_detail&entrance_gid=7697083643218477622)[中工网](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAMnblevmY0Qt-QgRg_pKWHVMrbv_lg0VMmEMpEEOk6Vw\u002F?source=tuwen_detail&entrance_gid=7697083643218477622)\n中工网官方账号\n\n关注\n\nTA的热门作品\n\n*   [](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697982998863528486\u002F)[从卖产品到卖方案！广交会上“产品+服务”新模式受青睐](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697982998863528486\u002F)\n158阅读 11小时前 \n*   [](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697988220289598002\u002F)[中国制造从“我会做”走到“我懂你日子怎么过” 外贸向技术、方案、服务出口升级](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697988220289598002\u002F)\n157阅读 11小时前 \n*   [](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697985591312466459\u002F)[我在现场｜动人的泪光，也是洒向国际舞台的星光](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697985591312466459\u002F)\n35阅读 11小时前 \n*   [](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697983130728301092\u002F)[视频｜一年4次向靖国神社献祭 高市要干什么？](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697983130728301092\u002F)\n12阅读 11小时前 \n*   [](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697983380868186675\u002F)[视频｜连逛70年！98岁老人说：每一届广交会都有新东西学](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7697983380868186675\u002F)\n4阅读 11小时前 \n\n[查看更多](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAMnblevmY0Qt-QgRg_pKWHVMrbv_lg0VMmEMpEEOk6Vw\u002F?source=tuwen_detail&entrance_gid=7697083643218477622)\n\n## 头条热榜\n\n换一换\n\n1.   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Markerless computer vision may enable objective and repeatable locomotion monitoring under routine farm conditions. This study aimed to develop a 38-keypoint whole-body pose-estimation framework for automated dairy cattle lameness assessment and evaluate its agreement with blinded Sprecher locomotion scoring. Materials and Methods: A You Only Look Once version 8 (YOLOv8)-pose framework was developed using 12,000 annotated images obtained from 449 Holstein cows. Thirty-eight anatomical keypoints represented the head, spine, pelvis, forelimbs, hindlimbs, and hooves. Four model and input resolution configurations were evaluated. Joint-angle time series, coefficients of variation, and whole-body asymmetry features were derived from detected keypoints. For independent clinical validation, 120 cattle videos, obtained from animals with no overlap at the animal level with the 449-cow model-development cohort, were evaluated blindly by three assessors using the five-point Sprecher scale. Inter-observer reliability, consensus scores, rank associations, group differences, and exploratory receiver operating characteristic analyses were determined. Results: YOLOv8l-pose at 2560 × 2560 pixels showed the best performance, achieving pose mean average precision at an intersection over union threshold of 0.5 of 83.11%, mean average precision across thresholds of 0.5–0.95 of 37.39%, and recall of 91.74%. All three assessors assigned identical Sprecher scores to 87.5% of videos. Pairwise quadratic-weighted Cohen κ ranged from 0.843 to 0.926, Fleiss κ was 0.779, and intraclass correlation coefficients were 0.883 and 0.958 for single and average measurements, respectively. The continuous artificial intelligence (AI) score correlated significantly with consensus Sprecher scores (Spearman ρ = 0.234, p = 0.010), and score distributions differed among consensus groups (Kruskal–Wallis H = 13.09, p = 0.0014). Exploratory discrimination of consensus Sprecher scores ≥3, using a threshold internally derived from and evaluated within the same stratified validation cohort (not externally validated), yielded an area under the curve of 0.875; at an AI threshold of 2.9, sensitivity and specificity were 62.5% and 98.2%, respectively. Conclusion: The 38-keypoint whole-body model demonstrates technical feasibility as an anatomically comprehensive, sensor-free approach for quantifying dairy cattle locomotion. Because the clinical validation cohort was deliberately stratified and diagnostic thresholds were internally derived, its agreement with independent visual assessment should be interpreted as evidence of potential decision-support utility rather than established clinical performance. Prospective external validation and independent calibration of diagnostic thresholds in an unselected cohort are warranted before clinical or commercial deployment. Keywords: artificial intelligence, bovine lameness, computer vision, dairy cattle, locomotion assessment, pose-estimation, Sprecher score, whole-body keypoints.","背景与目的：跛行是奶牛重要的健康、福利和生产问题，而传统的视觉运动评分具有间歇性且易受观察者差异影响。无标记计算机视觉可能有助于在常规农场条件下实现客观且可重复的运动监测。本研究旨在开发一个38关键点全身姿态估计框架，用于自动化奶牛跛行评估，并评估其与盲法Sprecher运动评分的一致性。材料与方法：使用来自449头荷斯坦奶牛的12,000张标注图像，开发了You Only Look Once第8版（YOLOv8）-pose框架。38个解剖关键点代表头部、脊柱、骨盆、前肢、后肢和蹄部。评估了四种模型和输入分辨率配置。从检测到的关键点中提取关节角度时间序列、变异系数和全身不对称特征。为进行独立临床验证，由三名评估者使用五分制Sprecher量表对120头牛的视频进行盲法评估，这些动物在个体层面与449头模型开发队列无重叠。确定了观察者间可靠性、共识评分、秩相关、组间差异和探索性受试者工作特征分析。结果：2560×2560像素下的YOLOv8l-pose表现最佳，在交并比阈值为0.5时姿态平均精度为83.11%，在0.5–0.95阈值范围内的平均精度为37.39%，召回率为91.74%。三名评估者对87.5%的视频给出了相同的Sprecher评分。两两二次加权Cohen κ范围为0.843至0.926，Fleiss κ为0.779，单次测量和平均测量的组内相关系数分别为0.883和0.958。连续人工智能（AI）评分与共识Sprecher评分显著相关（Spearman ρ = 0.234，p = 0.010），且各共识组之间的评分分布不同（Kruskal–Wallis H = 13.09，p = 0.0014）。使用在同一分层验证队列内部推导并评估的阈值（未经外部验证），对共识Sprecher评分≥3进行探索性判别时，曲线下面积为0.875；在AI阈值为2.9时，敏感性和特异性分别为62.5%和98.2%。结论：38关键点全身模型表现","Veterinary World","2026-10-03T00:00:00Z",80,{"impact":147,"substance":97,"depth":147,"authority":148,"freshness":149,"relevant":21,"comment":150},18,13,9,"基于12000张标注图像与120头独立验证视频的38关键点YOLOv8姿态估计框架，为奶牛跛行无接触客观评估提供技术可行性证据，方法新颖、数据扎实，但阈值内推、缺乏外部验证，属细分领域技术进展。",[152],{"name":143,"url":140},[154,155,156,157,28],"智慧农业","农业人工智能","计算机视觉","动物健康",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[160],{"code":161,"slug":162,"name":163,"path":164,"parent_code":165},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":38,"locked":13,"version":39,"method":40,"evidence":167},{"candidates":168,"agricultural_evidence":44},[169,174,181],{"code":31,"score":170,"supported":44,"title":171,"abstract":172,"tags":173},15,[46],[115,46],[28],{"code":161,"score":175,"supported":44,"title":176,"abstract":177,"tags":180},11,[],[156,178,179],"artificial intelligence","computer vision",[155,156],{"code":182,"score":21,"supported":13,"title":183,"abstract":184,"tags":185},"I1",[],[],[154],[187,188],"YOLOv8 奶牛 跛行 姿态估计","奶牛跛行 自动评估 计算机视觉","YOLOv8奶牛跛行姿态估计-4244","10.14202\u002Fvetworld.2026.4286-4306",{"doi":190,"openalex_id":192,"authors":193,"venue":143,"cited_by_count":80,"oa_url":206,"card":207,"direction":213,"ingested_from":81},"W7218963807",[194,197,200,203],{"name":195,"orcid":196},"А В Греченева","https:\u002F\u002Forcid.org\u002F0000-0002-7341-5237",{"name":198,"orcid":199},"Maxim Baknin","https:\u002F\u002Forcid.org\u002F0000-0002-3124-6252",{"name":201,"orcid":202},"Sergey Vladimirovich Akchurin","https:\u002F\u002Forcid.org\u002F0000-0002-6822-0013",{"name":204,"orcid":205},"Ирина Владимировна Акчурина","https:\u002F\u002Forcid.org\u002F0000-0002-1975-2929","https:\u002F\u002Fveterinaryworld.org\u002FVol.19\u002FOctober-2026\u002F3.pdf",{"tldr":208,"method":209,"finding":210,"direction":211,"opportunity":212},"构建38关键点全身YOLOv8姿态框架，实现奶牛跛行自动评估并与Sprecher评分比对。","YOLOv8l-pose，12000张449头荷斯坦牛图像，120视频盲评验证。","模型mAP@0.5达83.11%，AI评分与共识Sprecher评分显著相关，AUC达0.875。","农业人工智能与决策模型","可扩展至多牧场外部验证，并结合时序行为与多模态数据提升跛行早期预警泛化性。","数字乡村与农业信息化","2026-10-05T23:30:33.217120Z",{"id":216,"title":217,"url":218,"summary":219,"summary_zh":220,"content":8,"source_name":9,"source_url":218,"published_at":221,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":222,"score_detail":223,"sources":225,"tags":227,"primary_topic":232,"secondary_topics":233,"classification":234,"search_phrases":272,"slug":275,"view_count":21,"doi":276,"paper":277,"created_at":299},2747,"Simulation of the entire process and multi-dimensional sustainability assessment of regional water footprint supply chain using the SCOR-SD-BN coupled framework","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104984","Context Global water resources face critical challenges such as pollution, over-exploitation, and inequitable distribution. This necessitates the development of scientific frameworks to effectively evaluate the sustainability of water resource management. Objective This study establishes a theoretical framework for the agricultural water footprint supply chain, directly elucidates the transformation mechanisms and sustainable evaluation methods from farmland water use to final products, and constructs a system dynamics model for the agricultural water footprint in Heilongjiang Province. This model simulates dynamic processes, enables real-time evaluation of multidimensional sustainability, and analyzes dominant paths. Methods Drawing on the Supply Chain Operations Reference (SCOR) model, this study treated water footprint (WF) and water value as equivalent to the supply chain's total cost and total revenue, respectively. It established a WF supply chain transformation mechanism from water consumption to the product side and used a system dynamics (SD) model to simulate the entire WF supply chain process in Heilongjiang Province, quantitatively analyzing supply chain structure diversity and disturbance amplification. Based on this, a multidimensional evaluation index system covering economic, environmental, and social dimensions was constructed and, in combination with Bayesian Network (BN), fuzzy logic, and semantic mapping, facilitated multidimensional dynamic evaluation and dominant path identification of regional WF supply chain sustainability. Results and conclusions The research findings demonstrate that (1) From 2000 to 2050, both the total cost and total revenue of Heilongjiang Province's WF supply chain consistently exhibit an initial increase followed by a decrease, with the disturbance amplification effect present across all sectoral WF supply chains. (2) In agriculture, apple and beef cattle supply chains possess the strongest sustainability, while soybean and sheep supply chains display the weakest. In the industry, the food manufacturing sector has the strongest sustainability, while the electric heating and metallurgical sectors are the weakest. (3) The comprehensive sustainability index of the WF Supply Chain pursues a distinctly fluctuating upward trajectory from 2000 to 2025. Under the established scenario, the index is projected to continue rising overall from 2026 to 2050, reaching approximately 0.83 around 2045, entering the high sustainability level, and becoming largely stable by 2050. Environmental improvement stands out as the primary strategy when sustainability is low, while economic factors become the principal constraint when sustainability is high. Significance This research establishes a novel analytical framework and quantitative tool for assessing the sustainability of water resource management. The findings decisively guide the promotion of sustainable utilization and management of regional water resources.","背景 全球水资源面临污染、过度开采和分配不均等严峻挑战，亟需构建科学框架以有效评估水资源管理的可持续性。目的 本研究建立农业水足迹供应链的理论框架，直接阐明从农田用水到最终产品的转化机制与可持续评价方法，并构建黑龙江省农业水足迹的系统动力学模型，模拟动态过程，实现多维可持续性的实时评估，并分析主导路径。方法 借鉴供应链运作参考（SCOR）模型，本研究将水足迹（WF）和水价值分别等同于供应链的总成本和总收益，建立了从水消耗到产品端的WF供应链转化机制，并运用系统动力学（SD）模型模拟黑龙江省整个WF供应链过程，定量分析供应链结构多样性和扰动放大效应。在此基础上，构建涵盖经济、环境和社会维度的多维评价指标体系，并结合贝叶斯网络（BN）、模糊逻辑和语义映射，实现区域WF供应链可持续性的多维动态评价和主导路径识别。结果与结论 研究结果表明：（1）2000年至2050年，黑龙江省WF供应链的总成本和总收益均呈现先增后减的趋势，扰动放大效应存在于所有部门WF供应链中。（2）在农业中，苹果和肉牛供应链的可持续性最强，而大豆和绵羊供应链的可持续性最弱。在工业中，食品制造业的可持续性最强，而电热和冶金部门的可持续性最弱。（3）WF供应链的综合可持续性指数在2000年至2025年间呈现明显的波动上升轨迹。在既定情景下，该指数预计在2026年至2050年间总体继续上升，约在2045年达到约0.83，进入高可持续","2026-09-16T00:00:00Z",81,{"impact":147,"substance":97,"depth":147,"authority":19,"freshness":149,"relevant":21,"comment":224},"以SCOR-SD-BN耦合框架模拟黑龙江农业水足迹供应链并做多维可持续评估，方法新颖、数据跨度大，对区域水资源管理有参考价值，但属学术论文，产业落地影响有限。",[226],{"name":9,"url":218},[228,229,230,29,231],"农业供应链","农业水资源","智慧水利","可持续评价",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":235},{"candidates":236,"agricultural_evidence":44},[237,244,251,258,265],{"code":31,"score":149,"supported":44,"title":238,"abstract":239,"tags":243},[],[240,241,46,242],"肉牛","绵羊","sheep",[],{"code":50,"score":245,"supported":44,"title":246,"abstract":247,"tags":250},7,[],[248,249],"水资源","water resource",[229],{"code":252,"score":84,"supported":44,"title":253,"abstract":254,"tags":257},"A4",[],[255,256],"大豆","soybean",[],{"code":259,"score":84,"supported":44,"title":260,"abstract":261,"tags":264},"B2",[],[262,263],"苹果","apple",[],{"code":266,"score":267,"supported":44,"title":268,"abstract":269,"tags":271},"E1",3,[],[270],"food manufacturing",[],[273,274],"农业供应链 农业水资源 可持续评价 智慧水利","农业供应链 农业水资源","农业供应链农业水资源可持续评价智慧水利-2747","10.1016\u002Fj.agsy.2026.104984",{"doi":276,"openalex_id":278,"authors":279,"venue":9,"cited_by_count":80,"oa_url":218,"card":293,"direction":297,"ingested_from":81},"W7213431804",[280,282,285,287,290],{"name":281,"orcid":8},"He Peng",{"name":283,"orcid":284},"Youzhu Zhao","https:\u002F\u002Forcid.org\u002F0000-0002-2706-8780",{"name":286,"orcid":8},"Meiyun Tao",{"name":288,"orcid":289},"Qiuxiang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-5790-2334",{"name":291,"orcid":292},"Zilong Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3143-0906",{"tldr":294,"method":295,"finding":296,"direction":297,"opportunity":298},"构建SCOR-SD-BN耦合框架，模拟黑龙江农业水足迹供应链并评估多维可持续性。","SCOR模型、系统动力学、贝叶斯网络、模糊逻辑与语义映射。","水足迹供应链成本收益先增后减，苹果和肉牛链可持续性最强，大豆和羊链最弱。","农业绿色发展与碳","可将该框架推广至其他区域或作物，结合实时数据实现动态水足迹可持续性预警。","2026-09-17T23:30:04.921468Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":8,"source_name":306,"source_url":303,"published_at":307,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":308,"score_detail":309,"sources":312,"tags":314,"primary_topic":318,"secondary_topics":319,"classification":320,"search_phrases":338,"slug":341,"view_count":21,"doi":342,"paper":343,"created_at":366},1714,"A simplified compartmental model for assessing nitrogen flows and losses in dairy effluent management systems: evidence from intensive farms in the Po Valley (Italy)","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1926333","Introduction Nitrogen management in dairy production systems is a major determinant of environmental performance and nutrient use efficiency, as substantial nitrogen losses can occur along the manure management chain. Although life cycle assessment and nutrient balance approaches are commonly used to estimate emissions, they often provide limited insight into internal nitrogen transformations and provide restricted support for farm-scale decision making. This study aimed to develop a simplified compartmental model to quantify nitrogen flows and losses across the main components of intensive dairy farms. Methods The model combines process-based relationships with a limited set of farm-specific input data, including herd composition, feeding strategy, housing systems, and manure management practices. Nitrogen dynamics are represented through interconnected compartments describing the animal, barn, treatment, storage, and land application phases, enabling the identification of key stages contributing to gaseous nitrogen losses. The model was applied to ten commercial Holstein dairy farms located in the Po Valley (Northern Italy). Results Total gaseous nitrogen losses averaged 7.20 ± 6.43% of nitrogen intake in untreated systems, with ammonia volatilization representing the dominant loss pathway. Farms adopting anaerobic digestion showed increased nitrogen retention in digestate, despite higher overall losses associated with subsequent handling stages. A preliminary comparison between simulated and measured nitrogen concentrations in selected manure and digestate matrices indicated a generally consistent representation of nitrogen partitioning, although systematic overestimations were observed for liquid matrices during the early management stages. Discussion Given the limited number of monitored farms and the absence of a comprehensive quantitative validation, the results should be interpreted as indicative of nitrogen flow patterns rather than precise emission estimates. Nevertheless, the proposed framework provides a transparent and data-efficient tool for exploring farm-specific nitrogen balances, supporting scenario analyses and nutrient management strategies, and contributing to the transition toward more circular and environmentally efficient dairy production systems.","引言 奶牛生产系统中的氮管理是决定环境绩效和养分利用效率的关键因素，因为在粪污管理链条中可能发生大量氮损失。尽管生命周期评价和养分平衡方法常用于估算排放量，但它们往往对系统内部氮转化过程的揭示有限，对牧场尺度的决策支持也较为受限。本研究旨在开发一个简化的分室模型，以量化集约化奶牛场主要组成部分中的氮流动与损失。方法 该模型将基于过程的机理关系与有限的牧场特定输入数据相结合，包括畜群结构、饲喂策略、圈舍系统及粪污管理措施。氮动态通过描述动物、牛舍、处理、储存和农田施用等阶段的相互关联分室进行表征，从而识别导致气态氮损失的关键环节。该模型应用于位于波河平原（意大利北部）的十个商业荷斯坦奶牛场。结果 在未处理系统中，总气态氮损失平均占氮摄入量的7.20±6.43%，其中氨挥发是主要的损失途径。采用厌氧消化的牧场在消化液中表现出更高的氮保留率，尽管后续处理环节相关的总损失有所增加。对选定粪污和消化液基质中模拟氮浓度与实测氮浓度的初步比较表明，模型对氮分配的模拟总体一致，但在管理早期阶段对液态基质存在系统性高估。讨论 鉴于监测牧场数量有限且缺乏全面的定量验证，研究结果应被解读为氮流动模式的指示性参考，而非精确的排放估算值。尽管如此，所提出的框架为探索牧场特定氮平衡提供了一种透明且数据高效的工​​具，可支持情景分析和养分管理策略制定，并有助于推动奶牛生产系统向更加循环和环境高效的方向转型。","Frontiers in Sustainable Food Systems","2026-09-04T00:00:00Z",68,{"impact":16,"substance":17,"depth":147,"authority":148,"freshness":310,"relevant":21,"comment":311},5,"论文提出简化分区模型量化奶牛场氮流与损失，为农场氮管理提供决策工具，具行业参考价值。",[313],{"name":306,"url":303},[315,316,317,28],"氮管理","循环农业","农业环保",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":321},{"candidates":322,"agricultural_evidence":44},[323,328,334],{"code":31,"score":324,"supported":44,"title":325,"abstract":326,"tags":327},4,[],[115],[28],{"code":329,"score":267,"supported":44,"title":330,"abstract":331,"tags":333},"F3",[],[332],"养分管理",[],{"code":55,"score":21,"supported":13,"title":335,"abstract":336,"tags":337},[],[],[316],[339,340],"农业环保 奶牛养殖 循环农业 氮管理","农业环保 奶牛养殖","农业环保奶牛养殖循环农业氮管理-1714","10.3389\u002Ffsufs.2026.1926333",{"doi":342,"openalex_id":344,"authors":345,"venue":306,"cited_by_count":80,"oa_url":360,"card":361,"direction":297,"ingested_from":81},"W7208709391",[346,349,351,354,357],{"name":347,"orcid":348},"Giulia Ferronato","https:\u002F\u002Forcid.org\u002F0000-0001-9153-8540",{"name":350,"orcid":8},"Anna Simonetto",{"name":352,"orcid":353},"V. Caprarulo","https:\u002F\u002Forcid.org\u002F0000-0003-3691-4608",{"name":355,"orcid":356},"Elena Scaglia","https:\u002F\u002Forcid.org\u002F0000-0002-4225-5234",{"name":358,"orcid":359},"Gianni Gilioli","https:\u002F\u002Forcid.org\u002F0000-0001-7672-2577","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1926333\u002Fpdf",{"tldr":362,"method":363,"finding":364,"direction":297,"opportunity":365},"开发简化分区模型量化奶牛场氮流与损失，应用于意大利波河河谷集约化农场。","结合过程关系与农场数据，构建动物、牛舍等分区模型，应用于10个荷斯坦奶牛场。","未处理系统气态氮损失占氮摄入7.2%，氨挥发为主；厌氧消化增加消化物氮保留。","模型需全面验证，可扩展至其他地区或加入碳氮耦合，优化管理决策。","2026-09-05T23:30:07.545453Z",{"id":368,"title":369,"url":370,"summary":371,"summary_zh":8,"content":8,"source_name":9,"source_url":370,"published_at":372,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":373,"score_detail":374,"sources":376,"tags":378,"primary_topic":383,"secondary_topics":384,"classification":385,"search_phrases":397,"slug":400,"view_count":80,"doi":401,"paper":402,"created_at":417},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","2026-10-21T00:00:00Z",66,{"impact":20,"substance":147,"depth":99,"authority":19,"freshness":12,"relevant":21,"comment":375},"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。",[377],{"name":9,"url":370},[379,380,381,382],"可持续农业","畜牧养殖","ESG","农场经济",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":386},{"candidates":387,"agricultural_evidence":44},[388,393],{"code":31,"score":97,"supported":44,"title":389,"abstract":391,"tags":392},[242,390],"goat",[242,390],[],{"code":55,"score":21,"supported":13,"title":394,"abstract":395,"tags":396},[],[],[379],[398,399],"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":401,"openalex_id":403,"authors":404,"venue":9,"cited_by_count":80,"oa_url":370,"card":8,"direction":8,"ingested_from":81},"W7223120996",[405,408,411,414],{"name":406,"orcid":407},"Maria Llanos Soria-Oliver","https:\u002F\u002Forcid.org\u002F0009-0005-2631-1135",{"name":409,"orcid":410},"Carolina Pontones Rosa","https:\u002F\u002Forcid.org\u002F0000-0001-6989-7969",{"name":412,"orcid":413},"Inmaculada Alonso Carrillo","https:\u002F\u002Forcid.org\u002F0000-0002-0813-9934",{"name":415,"orcid":416},"Rosario Pérez Morote","https:\u002F\u002Forcid.org\u002F0000-0001-5502-3646","2026-10-22T11:20:12.622588Z",{"id":419,"title":420,"url":421,"summary":422,"summary_zh":8,"content":8,"source_name":423,"source_url":8,"published_at":424,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":425,"score_detail":426,"sources":428,"tags":430,"primary_topic":435,"secondary_topics":436,"classification":438,"search_phrases":450,"slug":453,"view_count":80,"doi":8,"paper":454,"created_at":463},5534,"DC-YOLO：应对遮挡与光照变化的湖羊行为检测模型","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-10-19\u002Fdoc-inivumaf7730137.shtml","中国农业大学团队针对集约化养殖中多目标严重遮挡、光照多变与类别不均问题，引入自适应细节—上下文注意力机制构建模块，并采用边界框相似度软抑制防止误删遮挡目标，预训练光照编码器迁移参数应对光照变化。数据集来自湖羊养殖场全天候监控，含505张标注图像、5类行为。","智慧农业（中英文）2026-10-19","2026-10-19T00:00:00Z",75,{"impact":147,"substance":98,"depth":18,"authority":148,"freshness":84,"relevant":21,"comment":427},"论文提出面向湖羊行为检测的DC-YOLO模型，方法有新意、数据来自真实养殖场，属智慧养殖细分领域实质进展，值得精选。",[429],{"name":423,"url":421},[155,431,432,433,434],"智慧养殖","目标检测","湖羊","动物行为检测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[437],{"code":161,"slug":162,"name":163,"path":164,"parent_code":165},{"status":38,"locked":13,"version":39,"method":40,"evidence":439},{"candidates":440,"agricultural_evidence":44},[441,445],{"code":31,"score":16,"supported":44,"title":442,"abstract":443,"tags":444},[433],[433],[433],{"code":161,"score":12,"supported":44,"title":446,"abstract":448,"tags":449},[447],"YOLO",[],[155,432],[451,452],"中国农业大学 湖羊 行为检测","DC-YOLO 遮挡 光照","中国农业大学湖羊行为检测-5534",{"doi":8,"openalex_id":8,"authors":455,"venue":8,"cited_by_count":80,"oa_url":8,"card":456,"direction":460,"ingested_from":462},[],{"tldr":457,"method":458,"finding":459,"direction":460,"opportunity":461},"提出DC-YOLO模型，用于湖羊行为检测，解决遮挡与光照变化问题。","自适应细节-上下文注意力、边界框相似度软抑制、光照编码器迁移，505张标注图像。","模型在遮挡和光照变化下有效检测湖羊行为，减少误删。","智慧农业 \u002F 农业物联网","可探索多模态融合与轻量化部署，提升复杂养殖场景下的实时行为监测能力。","agent","2026-10-22T00:07:09.998247Z"]