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支持畜群健康监测的传感器技术:利用反刍持续时间和活动指标作为非特异性变量来早期检测患有健康偏差的奶牛。

Sensor technology to support herd health monitoring: Using rumination duration and activity measures as unspecific variables for the early detection of dairy cows with health deviations.

作者信息

Gusterer Erika, Kanz Peter, Krieger Stefanie, Schweinzer Vanessa, Süss David, Lidauer Laura, Kickinger Florian, Öhlschuster Manfred, Auer Wolfgang, Drillich Marc, Iwersen Michael

机构信息

Clinical Unit for Herd Health Management, University Clinic for Ruminants, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine Vienna, 1210, Vienna, Austria.

Smartbow GmbH, Jutogasse 3, 4675, Weibern, Austria.

出版信息

Theriogenology. 2020 Nov;157:61-69. doi: 10.1016/j.theriogenology.2020.07.028. Epub 2020 Jul 29.

DOI:10.1016/j.theriogenology.2020.07.028
PMID:32805643
Abstract

A significant number of lactating dairy cows are affected by health disorders in the early postpartum period. Precision dairy farming technologies have tremendous potential to support farmers in detecting disordered cows before clinical manifestation of a disease. The objective of this study was to evaluate if activity and rumination measures obtained by a commercial 3D-accelerometer system, i.e. "lying", "high active", "inactive", and "rumination" times, can be used for early identification of cows with health deviations before the clinical manifestation of disease. A total of 312 Holstein cows equipped with an ear attached accelerometer (Smartbow GmbH, Weibern, Austria) were monitored and analyzed from 14 days prior to parturition to eight days in milk (DIM). Animals were checked daily for clinical disorders from zero to eight DIM using standard operating procedures and by blood β-hydroxybutyrate measurements at three, five, and eight DIM. Cows that presented no symptoms of health problems and with BHB concentrations <1.2 mmol/L in the first eight DIM were classified as healthy (n = 156) and used as the reference in this study. Cows with disorders were allocated in groups with one disorder (n = 65) and >1 disorders (n = 91). "Rumination" durations per day were already shorter five days before the clinical diagnosis (D0) in diseased cows (401.9 ± 147.4 min/day) compared with healthy controls (434.6 ± 140.3 min/day). "Rumination" time decreased before the diagnosis, with a nadir at Day -1 for healthy cows and cows with >1 disorder (392.0 ± 147.9 vs. 313.4 ± 162.6 min/day). Cows with one disorder reached a nadir on Day -3 (388.8 ± 158.6 min/day). Similarly, the "high active" time started to become shorter three days before the clinical diagnosis in diseased cows compared to healthy cows (164.1 ± 119.1 vs. 200.3 ± 111.5 min/day). The times cows spent "inactive" were significantly longer three days before clinical diagnosis in diseased cows compared to healthy cows (421.7 ± 168.3 vs. 362.8 ± 117.6 min/day). "Lying" time started to become significantly longer one day before the diagnosis of disorders in disordered cows compared to healthy cows (691.8 ± 183.3 vs. 627.3 ± 158.0 min/day). On average, these results indicated a strong disturbance of physiological parameters before the clinical onset of disease. In summary, it was possible to show differences between disordered and healthy cows based on activity and "rumination" data recorded by a 3D-accelerometer.

摘要

大量泌乳奶牛在产后早期受到健康问题的影响。精准奶牛养殖技术在帮助养殖户在疾病临床表现出来之前检测出患病奶牛方面具有巨大潜力。本研究的目的是评估通过商业3D加速度计系统获得的活动和反刍测量指标,即“躺卧”、“高活动”、“不活动”和“反刍”时间,是否可用于在疾病临床表现出来之前早期识别健康状况有偏差的奶牛。总共312头佩戴耳部加速度计(Smartbow GmbH,奥地利韦伯恩)的荷斯坦奶牛从分娩前14天到产奶8天(DIM)进行了监测和分析。在0至8 DIM期间,每天使用标准操作程序并通过在3、5和8 DIM时测量血液β-羟基丁酸来检查动物是否有临床疾病。在前8 DIM内没有健康问题症状且BHB浓度<1.2 mmol/L的奶牛被归类为健康奶牛(n = 156),并在本研究中用作参考。患有疾病的奶牛被分为患有一种疾病的组(n = 65)和患有多种疾病的组(n = 91)。与健康对照组(434.6±140.3分钟/天)相比,患病奶牛在临床诊断前5天(D0)的每日“反刍”持续时间已经缩短(401.9±147.4分钟/天)。在诊断前,“反刍”时间减少,健康奶牛和患有多种疾病的奶牛在第-1天达到最低点(392.0±147.9对313.4±162.6分钟/天)。患有一种疾病的奶牛在第-3天达到最低点(388.8±158.6分钟/天)。同样,与健康奶牛相比,患病奶牛在临床诊断前3天“高活动”时间开始变短(164.1±119.1对200.3±111.5分钟/天)。与健康奶牛相比,患病奶牛在临床诊断前3天“不活动”的时间明显更长(421.7±168.3对362.8±117.6分钟/天)。与健康奶牛相比,患病奶牛在疾病诊断前1天“躺卧”时间开始明显变长(691.8±183.3对627.3±158.0分钟/天)。平均而言,这些结果表明在疾病临床发作之前生理参数受到强烈干扰。总之,可以根据3D加速度计记录的活动和“反刍”数据显示患病奶牛和健康奶牛之间的差异。

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