University of Veterinary & Animal Sciences, Department of Livestock Management, Lahore, Pakistan.
University of Veterinary & Animal Sciences, Department of Animal Nutrition, Lahore, Pakistan.
Braz J Biol. 2022 May 4;84:e257884. doi: 10.1590/1519-6984.257884. eCollection 2022.
Buffalo is one of the leading milk-producing dairy animals. Its production and reproduction are affected due to some factors including inadequate monitoring around parturition, which cause economic losses like delayed birth process, increased risk of stillbirth, etc. The appropriate calving monitoring is essential for dairy herd management. Therefore, we designed a study its aim was, to predict the calving based on automated machine measured prepartum behaviors in buffaloes. The data were collected from n=40 pregnant buffaloes of 2nd to 5th parity, which was synchronized. The NEDAP neck and leg logger tag was attached to each buffalo at 30 days before calving and automatically collected feeding, rumination, lying, standing, no. of steps, no. of switches from standing to lying (lying bouts) and total motion activity. All behavioral data were reduced to -10 days before the calving date for statistical analysis to use mixed model procedure and ANOVA. Results showed that feeding and rumination time significantly (P<0.05) decreased from -10 to -1 days before calving indicating calving prediction. Moreover, Rumination time was at lowest (P<0.001) value at 2h before the calving such behavioral changes may be useful to predict calving in buffaloes. Similarly, lying bouts and standing time abruptly decreased (P<0.05) from -3 to -1 days before calving, while lying time abruptly increased (P<0.01) from -3 to -1 days before calving (531.57±23.65 to 665.62±18.14, respectively). No. of steps taken and total motion significantly (P<0.05) increased from -10 to -1 days before calving. Feeding time was significantly (P<0.02) lowered in 3rd parity buffaloes compared with 2nd, 4th and 5th parity buffaloes, while standing time of 5th parity buffaloes were lowered (P<0.05) as compared to 2nd to 4th parity buffalos at -1 day of prepartum. However, rumination, lying, no. of steps taken and total motion activity at -1 day of prepartum was independent (P>0.05) of parity in buffaloes. Neural network analysis for combined variables from NEDAP technology at the daily level yielded 100.0% sensitivity and 98% specificity. In conclusion NEDAP technology can be used to measured behavioral changes -10 day before calving as it can serve as a useful guide in the prediction calving date in the buffaloes.
水牛是主要的产奶家畜之一。其生产和繁殖受到一些因素的影响，包括围产期监测不足，这会导致分娩过程延迟、死产风险增加等经济损失。适当的产犊监测对奶牛群管理至关重要。因此，我们设计了一项研究，旨在根据水牛产前的自动机器测量行为来预测产犊。数据来自 n=40 头处于 2 至 5 胎次的怀孕水牛，这些水牛被同步。在产犊前 30 天，每个水牛都被贴上 NEDAP 颈和腿记录仪标签，并自动收集进食、反刍、躺着、站立、步数、从站立到躺着的次数（躺着时间）和总运动活动。所有行为数据都减少到产犊日期前 10 天进行统计分析，以使用混合模型程序和 ANOVA。结果表明，从产犊前 10 天到 1 天，进食和反刍时间显著（P<0.05）减少，表明可以进行产犊预测。此外，反刍时间在产犊前 2 小时达到最低值（P<0.001），这种行为变化可能有助于预测水牛产犊。同样，躺着时间和站立时间从产犊前 3 天到 1 天突然减少（P<0.05），而躺着时间从产犊前 3 天到 1 天突然增加（P<0.01）（分别为 531.57±23.65 到 665.62±18.14）。从产犊前 10 天到 1 天，步数和总运动显著（P<0.05）增加。与 2 胎、4 胎和 5 胎相比，3 胎水牛的进食时间显著（P<0.02）降低，而 5 胎水牛的站立时间在产犊前 1 天降低（P<0.05）与 2 胎至 4 胎水牛相比。然而，水牛在产犊前 1 天的反刍、躺着、步数和总运动活动不受胎次的影响（P>0.05）。基于 NEDAP 技术的组合变量的神经网络分析在每日水平上产生了 100.0%的灵敏度和 98%的特异性。总之，NEDAP 技术可以在产犊前 10 天测量行为变化，这可以作为预测水牛产犊日期的有用指南。
