Poster Title

Evaluating the ability to detect calving time in dairy cattle using a precision technology that monitors tail movement

Presenter Information

Sarah MacFollow

Grade Level at Time of Presentation

Senior

Institution

University of Kentucky

KY House District #

6

KY Senate District #

6

Department

Animal and Food Science

Abstract

Predicting calving time allows the farmer to be present during the time of calving to assist in cases of dystocia, or difficulty calving. Dystocia has the potential to increase calf mortality, decrease milk yield, lower conception rate, and increase uterine disorders. The objective of this study was to evaluate the ability of a precision technology (Moocall, Dublin, Ireland) that measures tail movement to detect and alert the onset of calving. Accuracy of the calving device was evaluated by comparing the alert times to the actual time of calving. The calving detection device was attached to the tail 4 ± 3 days (mean ± SD) before expected calving date, and video was recorded for tail behavior analysis. Monitoring tail behavior was analyzed into three categories: an hour before the hour of the first alert (control period), the hour before the first alert (alert one data) and the hour before the second alert (alert two data). Using PROC TTEST (SAS Institute Inc., Cary, NC) a lower one-sided (H0=150) analysis for significance was performed. The average time interval between the first alert and calving was 119 ± 69 minutes (mean ± SD, P<0.01) and the average time interval of the second alert and calving was 87 ± 52 minutes (mean ± SD, P<0.01). Video was evaluated for the frequency and duration of tail lifts during the control period, hour of the first alert, and the hour of the second alert. Mean frequencies were 3.37, 7.95, and 8.47, respectively. Mean durations of tail lifts were 55, 124, and 134 seconds, respectively. The calving detection device has the potential to alert farmers around two hours before calving. The farmer being present during birth creates the potential to save a dam or calf’s life, decrease disease, prevent milk loss, and overall save the farmer money.

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Evaluating the ability to detect calving time in dairy cattle using a precision technology that monitors tail movement

Predicting calving time allows the farmer to be present during the time of calving to assist in cases of dystocia, or difficulty calving. Dystocia has the potential to increase calf mortality, decrease milk yield, lower conception rate, and increase uterine disorders. The objective of this study was to evaluate the ability of a precision technology (Moocall, Dublin, Ireland) that measures tail movement to detect and alert the onset of calving. Accuracy of the calving device was evaluated by comparing the alert times to the actual time of calving. The calving detection device was attached to the tail 4 ± 3 days (mean ± SD) before expected calving date, and video was recorded for tail behavior analysis. Monitoring tail behavior was analyzed into three categories: an hour before the hour of the first alert (control period), the hour before the first alert (alert one data) and the hour before the second alert (alert two data). Using PROC TTEST (SAS Institute Inc., Cary, NC) a lower one-sided (H0=150) analysis for significance was performed. The average time interval between the first alert and calving was 119 ± 69 minutes (mean ± SD, P<0.01) and the average time interval of the second alert and calving was 87 ± 52 minutes (mean ± SD, P<0.01). Video was evaluated for the frequency and duration of tail lifts during the control period, hour of the first alert, and the hour of the second alert. Mean frequencies were 3.37, 7.95, and 8.47, respectively. Mean durations of tail lifts were 55, 124, and 134 seconds, respectively. The calving detection device has the potential to alert farmers around two hours before calving. The farmer being present during birth creates the potential to save a dam or calf’s life, decrease disease, prevent milk loss, and overall save the farmer money.