The current study aimed to investigate the monitoring behaviors of the NEDAP system in buffaloes, to evaluate the validation, accuracy, and precision over visual observation and video recording. The NEDAP neck and leg tags were attached on the left side of the neck and left front leg of multiparous dairy buffaloes (n = 30). The feeding, rumination, lying, and standing behaviors were monitored by the NEDAP system, visual observation, and video recording. The feeding time monitored by NEDAP was 25.2 ± 2.7 higher (p < 0.05) than visual observation and video recording. However, the rumination, lying, and standing time was lower (p < 0.05) in buffaloes when monitored by the NEDAP technology than by visual observation and video recording. The Pearson correlation between NEDAP technology with visual observation and video recording for feeding, rumination, lying, and standing was 0.91, 0.85, 0.93, and 0.87, respectively. The concordance correlation coefficient between the NEDAP with visual observation and video recording was high for rumination and standing (0.91 for both), while moderate for feeding and lying (0.85 and 0.88, respectively). The Bland–Altman plots were created to determine the association between NEDAP and visual observation and video recording, showing no bias. Therefore, a high level of agreement was found. In conclusion, the current finding showed that the NEDAP system can be used for monitoring feeding, rumination, lying, and standing behaviors in buffaloes. Moreover, these results revealed that the buffalo behavior was monitored precisely using NEDAP technology than visual observation and video recording. This technology will be useful for the diagnosis of diseases.

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.