Borchers M R, Chang Y M, Tsai I C, Wadsworth B A, Bewley J M
Department of Animal and Food Sciences, University of Kentucky, Lexington 40546.
Research Support Office, Royal Veterinary College, University of London, London, United Kingdom NW1 0TU.
J Dairy Sci. 2016 Sep;99(9):7458-7466. doi: 10.3168/jds.2015-10843. Epub 2016 Aug 8.
The objective of this study was to evaluate commercially available precision dairy technologies against direct visual observations of feeding, rumination, and lying behaviors. Primiparous (n=24) and multiparous (n=24) lactating Holstein dairy cattle (mean ± standard deviation; 223.4±117.8 d in milk, producing 29.2±8.2kg of milk/d) were fitted with 6 different triaxial accelerometer technologies evaluating cow behaviors at or before freshening. The AfiAct Pedometer Plus (Afimilk, Kibbutz Afikim, Israel) was used to monitor lying time. The CowManager SensOor (Agis, Harmelen, Netherlands) monitored rumination and feeding time. The HOBO Data Logger (HOBO Pendant G Acceleration Data Logger, Onset Computer Corp., Pocasset, MA) monitored lying time. The CowAlert IceQube (IceRobotics Ltd., Edinburgh, Scotland) monitored lying time. The Smartbow (Smartbow GmbH, Jutogasse, Austria) monitored rumination time. The Track A Cow (ENGS, Rosh Pina, Israel) monitored lying time and time spent around feeding areas for the calculation of feeding time. Over 8 d, 6 cows per day were visually observed for feeding, rumination, and lying behaviors for 2 h after morning and evening milking. The time of day was recorded when each behavior began and ended. These times were used to generate the length of time behaviors were visually observed. Pearson correlations (r; calculated using the CORR procedure of SAS Version 9.3, SAS Institute Inc., Cary, NC), and concordance correlations (CCC; calculated using the epiR package of R version 3.1.0, R Foundation for Statistical Computing, Vienna, Austria) evaluated association between visual observations and technology-recorded behaviors. Visually recorded feeding behaviors were moderately correlated with the CowManager SensOor (r=0.88, CCC=0.82) and Track A Cow (r=0.93, CCC=0.79) monitors. Visually recorded rumination behaviors were strongly correlated with the Smartbow (r=0.97, CCC=0.96), and weakly correlated with the CowManager SensOor (r=0.69, CCC=0.59). Visually recorded lying behaviors were strongly correlated with the AfiAct Pedometer Plus (r >0.99, CCC >0.99), CowAlert IceQube (r >0.99, CCC >0.99), and Track A Cow (r >0.99, CCC >0.99). The HOBO Data Loggers were moderately correlated (r >0.83, CCC >0.81) with visual observations. Based on these results, the evaluated precision dairy monitoring technologies accurately monitored dairy cattle behavior.
本研究的目的是针对直接肉眼观察到的采食、反刍和躺卧行为,评估市售的精准奶牛技术。对初产(n = 24)和经产(n = 24)的泌乳荷斯坦奶牛(均值±标准差;产奶223.4±117.8天,日产奶量29.2±8.2千克)安装6种不同的三轴加速度计技术,以评估奶牛在产犊时或产犊前的行为。使用AfiAct计步器升级版(以色列阿菲金基布兹阿菲米尔克公司)监测躺卧时间。使用奶牛管理器传感器(荷兰哈默伦阿吉斯公司)监测反刍和采食时间。使用HOBO数据记录器(美国马萨诸塞州波卡西特奥恩塞特计算机公司的HOBO吊坠G加速度数据记录器)监测躺卧时间。使用奶牛警报冰立方(苏格兰爱丁堡冰机器人有限公司)监测躺卧时间。使用智能碗(奥地利尤托加斯的智能碗有限公司)监测反刍时间。使用追踪奶牛(以色列罗什皮纳的ENGS公司)监测躺卧时间以及在采食区域周围停留的时间,以计算采食时间。在8天时间里,每天对6头奶牛在早晚挤奶后进行2小时的肉眼观察,记录采食、反刍和躺卧行为。记录每种行为开始和结束的时间。这些时间用于得出肉眼观察到的行为时长。采用Pearson相关性(r;使用SAS 9.3版(美国北卡罗来纳州卡里SAS研究所)的CORR程序计算)和一致性相关性(CCC;使用R 3.1.0版(奥地利维也纳R统计计算基金会)的epiR软件包计算)评估肉眼观察与技术记录行为之间的关联。肉眼记录的采食行为与奶牛管理器传感器(r = 0.88,CCC = 0.82)和追踪奶牛(r = 0.93,CCC = 0.79)监测结果中度相关。肉眼记录的反刍行为与智能碗(r = 0.97,CCC = 0.96)高度相关,与奶牛管理器传感器(r = 0.69,CCC = 0.59)弱相关。肉眼记录的躺卧行为与AfiAct计步器升级版(r > 0.99,CCC > 0.99)、奶牛警报冰立方(r > 0.99,CCC > 0.99)和追踪奶牛(r > 0.99,CCC > 0.99)高度相关。HOBO数据记录器与肉眼观察结果中度相关(r > 0.83,CCC > 0.81)。基于这些结果,所评估的精准奶牛监测技术能够准确监测奶牛行为。