Lemmens Lena, Schodl Katharina, Fuerst-Waltl Birgit, Schwarzenbacher Hermann, Egger-Danner Christa, Linke Kristina, Suntinger Marlene, Phelan Mary, Mayerhofer Martin, Steininger Franz, Papst Franz, Maurer Lorenz, Kofler Johann
Department of Farm Animals and Veterinary Public Health, University Clinic for Ruminants, University of Veterinary Medicine Vienna, 1210 Vienna, Austria.
Department of Sustainable Agricultural Systems, Institute of Livestock Sciences, University of Natural Resources and Life Sciences Vienna, 1180 Vienna, Austria.
Animals (Basel). 2023 Mar 28;13(7):1180. doi: 10.3390/ani13071180.
This study aimed to develop a tool to detect mildly lame cows by combining already existing data from sensors, AMSs, and routinely recorded animal and farm data. For this purpose, ten dairy farms were visited every 30-42 days from January 2020 to May 2021. Locomotion scores (LCS, from one for nonlame to five for severely lame) and body condition scores (BCS) were assessed at each visit, resulting in a total of 594 recorded animals. A questionnaire about farm management and husbandry was completed for the inclusion of potential risk factors. A lameness incidence risk (LCS ≥ 2) was calculated and varied widely between farms with a range from 27.07 to 65.52%. Moreover, the impact of lameness on the derived sensor parameters was inspected and showed no significant impact of lameness on total rumination time. Behavioral patterns for eating, low activity, and medium activity differed significantly in lame cows compared to nonlame cows. Finally, random forest models for lameness detection were fit by including different combinations of influencing variables. The results of these models were compared according to accuracy, sensitivity, and specificity. The best performing model achieved an accuracy of 0.75 with a sensitivity of 0.72 and specificity of 0.78. These approaches with routinely available data and sensor data can deliver promising results for early lameness detection in dairy cattle. While experimental automated lameness detection systems have achieved improved predictive results, the benefit of this presented approach is that it uses results from existing, routinely recorded, and therefore widely available data.
本研究旨在通过整合来自传感器、自动计量系统（AMS）以及常规记录的动物和农场数据，开发一种用于检测轻度跛行奶牛的工具。为此，从2020年1月至2021年5月，每隔30 - 42天对10个奶牛场进行一次走访。每次走访时评估奶牛的运动评分（LCS，从非跛行的1分到严重跛行的5分）和体况评分（BCS），共记录了594头奶牛。完成了一份关于农场管理和饲养的问卷，以纳入潜在风险因素。计算了跛行发生率风险（LCS≥2），各农场之间差异很大，范围在27.07%至65.52%之间。此外，还检查了跛行对导出的传感器参数的影响，结果表明跛行对总反刍时间没有显著影响。与非跛行奶牛相比，跛行奶牛在进食、低活动和中等活动方面的行为模式存在显著差异。最后，通过纳入不同组合的影响变量，拟合了用于跛行检测的随机森林模型。根据准确性、敏感性和特异性对这些模型的结果进行了比较。表现最佳的模型准确率为0.75，敏感性为0.72，特异性为0.78。这些利用常规可用数据和传感器数据的方法，可为奶牛跛行的早期检测带来有前景的结果。虽然实验性的自动跛行检测系统已取得了更好的预测结果，但本方法的优势在于它利用了现有常规记录且广泛可用的数据所产生的结果。
