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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","论文",false,81,{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":584,"relevant":455,"comment":585},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。",[587],{"name":578,"url":575},[589,590,334,591,592],"智慧农业","机器学习","动物福利","风险分级监管",{"code":380,"slug":381,"name":382,"path":383,"parent_code":332},[595],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":597,"locked":581,"version":4,"method":598,"evidence":599},"classified","rules",{"candidates":600,"agricultural_evidence":603},[601,608,613,618,622,628,633],{"code":380,"score":602,"supported":603,"title":604,"abstract":606,"tags":607},15,true,[605],"animal welfare",[591,605],[591],{"code":24,"score":602,"supported":603,"title":609,"abstract":611,"tags":612},[610],"machine learning",[590,610],[590],{"code":356,"score":614,"supported":603,"title":615,"abstract":616,"tags":617},6,[],[358,357],[],{"code":332,"score":400,"supported":603,"title":619,"abstract":620,"tags":621},[],[333],[334],{"code":348,"score":623,"supported":603,"title":624,"abstract":625,"tags":627},3,[],[626],"cattle",[],{"code":372,"score":623,"supported":603,"title":629,"abstract":630,"tags":632},[],[631],"veterinary",[],{"code":16,"score":455,"supported":581,"title":634,"abstract":635,"tags":636},[],[],[589],[638,639],"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","10.3389\u002Ffvets.2026.1938469",{"doi":641,"openalex_id":643,"authors":644,"venue":578,"cited_by_count":424,"oa_url":575,"card":671,"direction":675,"ingested_from":677},"W7214951739",[645,647,649,651,653,656,658,660,662,665,668],{"name":646,"orcid":11},"Eva Obenaus",{"name":648,"orcid":11},"Sabrina Kuchling",{"name":650,"orcid":11},"Tadej Zerak",{"name":652,"orcid":11},"Corina Schleicher",{"name":654,"orcid":655},"Ian Kopacka","https:\u002F\u002Forcid.org\u002F0000-0003-4106-1742",{"name":657,"orcid":11},"Sven Budik",{"name":659,"orcid":11},"Sabine Grimme",{"name":661,"orcid":11},"Jörg Hiesel",{"name":663,"orcid":664},"Peter Eckhardt","https:\u002F\u002Forcid.org\u002F0009-0005-5015-4846",{"name":666,"orcid":667},"Johannes Baumgartner","https:\u002F\u002Forcid.org\u002F0000-0001-8555-1378",{"name":669,"orcid":670},"Clair L. Firth","https:\u002F\u002Forcid.org\u002F0000-0002-4919-7786",{"tldr":672,"method":673,"finding":674,"direction":675,"opportunity":676},"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。","openalex","2026-10-01T23:30:54.115236Z",{"technologies":680,"keywords":688},[681,683,685,686],{"name":682,"count":400},"农业人工智能",{"name":684,"count":455},"农业物联网",{"name":590,"count":455},{"name":687,"count":455},"计算机视觉",[689,690,691,693,695,697,699,701,703,705,707,709],{"name":589,"count":400},{"name":591,"count":623},{"name":692,"count":623},"智慧畜牧",{"name":694,"count":69},"十五五规划",{"name":696,"count":69},"畜禽粪污",{"name":698,"count":69},"精准畜牧",{"name":700,"count":69},"绿色养殖",{"name":702,"count":455},"农业碳减排",{"name":704,"count":455},"农业补贴",{"name":706,"count":455},"动物疫病防控",{"name":708,"count":455},"动物防疫",{"name":710,"count":455},"数字乡村"]