Heart rate variability and behavioral alterations during prepartum period in dairy cows as predictors of calving: a preliminary study
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Objective: Parturition is crucial for dams, their calves, and cow managers. The prediction of calving time, which assists cow managers to decide on the relocation of cows to maternity pens and necessity of human supervision, is a pivotal aspect of livestock farming. However, existing methods of predicting calving time in dairy cows based on hormonal changes and clinical symptoms are time-consuming and yield unreliable predictions. Accordingly, we investigated whether heart rate variability (HRV) which is a non-invasive assessment of autonomic nervous system (ANS) activity and behavior during the prepartum period would be useful for predicting calving time in dairy cows.Methods: Eight pregnant cows were surveilled under electrocardiogram and video recordings for HRV and behavioral analyses, respectively. HRV parameters in time and frequency domains were evaluated. A 24-h time budget was calculated for each of six types of behavior (standing and lying with or without rumination, sleeping, and eating).Results: Heart rate on calving day is considerably higher than those recorded on the days preceding calving. Low frequency power declined, whereas high frequency power escalated on the calving day compared to the period between 24 and 48 h before calving. The time budget for ruminating while lying decreased and that while standing increased markedly on the calving day compared to those allocated on the preceding days; nonetheless, the total time budget for ruminating did not differ during the prepartum period.Conclusion: We elucidated the ANS activity and behavioral profiles during prepartum period. Our results confirm that HRV parameters and behavior are useful for predicting calving time, and interestingly indicate that the time budget for ruminating while standing (or lying) may serve as a valuable predictor of calving. Collectively, our findings lay the foundation for future investigations to determine other potential predictors and formulate an algorithm for predicting calving time.
Abstract
Objective: Parturition is crucial for dams, their calves, and cow managers. The prediction of calving time, which assists cow managers to decide on the relocation of cows to maternity pens and necessity of human supervision, is a pivotal aspect of livestock farming. However, existing methods of predicting calving time in dairy cows based on hormonal changes and clinical symptoms are time-consuming and yield unreliable predictions. Accordingly, we investigated whether heart rate variability (HRV) which is a non-invasive assessment of autonomic nervous system (ANS) activity and behavior during the prepartum period would be useful for predicting calving time in dairy cows. Methods: Eight pregnant cows were surveilled under electrocardiogram and video recordings for HRV and behavioral analyses, respectively. HRV parameters in time and frequency domains were evaluated. A 24-h time budget was calculated for each of six types of behavior (standing and lying with or without rumination, sleeping, and eating). Results: Heart rate on calving day is considerably higher than those recorded on the days preceding calving. Low frequency power declined, whereas high frequency power escalated on the calving day compared to the period between 24 and 48 h before calving. The time budget for ruminating while lying decreased and that while standing increased markedly on the calving day compared to those allocated on the preceding days; nonetheless, the total time budget for ruminating did not differ during the prepartum period. Conclusion: We elucidated the ANS activity and behavioral profiles during prepartum period. Our results confirm that HRV parameters and behavior are useful for predicting calving time, and interestingly indicate that the time budget for ruminating while standing (or lying) may serve as a valuable predictor of calving. Collectively, our findings lay the foundation for future investigations to determine other potential predictors and formulate an algorithm for predicting calving time.
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