发现每头奶牛8-10平米时玩耍行为最多,且可用自动视觉系统持续监测。
From Manual Observation to Automated Monitoring: Space Allowance Effects on Play Behaviour in Group-Housed Dairy Calves
- 用计算机视觉自动识别奶牛玩耍行为,准确率97.6%。
- 每头牛8-10平米时玩耍占比达1.6%,高于其他空间范围。
- 适合关注动物福利与自动化监测的养殖管理者参考。
玩耍行为是奶牛福利的良好指标,但在商业条件下,中高空间范围(6-20平方米/头)内空间分配的影响仍不明确。本研究在荷兰14个牧场对60头群养奶牛进行调查,空间范围为2.66-17.98平方米/头,分析了空间分配与玩耍行为的关系,并开发了一套可扩展的自动化计算机视觉监测流程。视频数据基于详细行为图谱分析,玩耍行为以观察时段占比(%OP)表示。采用线性混合模型分析,农场作为随机效应。计算机视觉模型基于6个牧场共108小时的手动标注数据训练,并在预留测试集上验证,主动玩耍检测准确率达97.6%,召回率99.4%。奶牛平均每天玩耍约10分钟(占17小时观察期的1.0%)。空间与玩耍关系呈非线性,8-10平方米/头时玩耍占比最高(1.6%OP),6-8平方米和12-14平方米时均低于0.6%OP。控制年龄、健康状况和群体规模后,空间仍具显著影响。结果表明,8-10平方米/头是兼顾福利与经济可行性的实用目标,同时证明自动化监测可将小规模标注项目扩展为持续福利评估系统。
原文摘要 · Abstract (English)
Play behaviour serves as a positive welfare indicator in dairy calves, yet the influence of space allowance under commercial conditions remains poorly characterized, particularly at intermediate-to-high allowances (6-20 m2 per calf). This study investigated the relationship between space allowance and play behaviour in 60 group-housed dairy calves across 14 commercial farms in the Netherlands (space range: 2.66-17.98 m2 per calf), and developed an automated computer vision pipeline for scalable monitoring. Video observations were analyzed using a detailed ethogram, with play expressed as percentage of observation period (%OP). Statistical analysis employed linear mixed models with farm as a random effect. A computer vision pipeline was trained on manual annotations from 108 hours on 6 farms and validated on held-out test data. The computer vision classifier achieved 97.6% accuracy with 99.4% recall for active play detection. Calves spent on average 1.0% of OP playing reflecting around 10 minutes per 17-hour period. The space-play relationship was non-linear, with highest play levels at 8-10 m2 per calf (1.6% OP) and lowest at 6-8 m2 and 12-14 m2 (<0.6% OP). Space remained significant after controlling for age, health, and group size. In summary, these findings suggest that 8-10 m2 per calf represents a practical target balancing welfare benefits with economic feasibility, and demonstrate that automated monitoring can scale small annotation projects to continuous welfare assessment systems.
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