Prediction of parturition in Holstein dairy cattle using electronic data loggers

Abstract

The objective of the present study was to assess the effect of parturition on behavioral activity [steps, standing time, lying time, lying bouts (LB), and duration of LB] 4 d before calving using electronic data loggers. Animals (n=132) from 3 herds were housed in similar freestall barns using a prepartum pen 21 d before the expected calving date and were moved into a contiguous individual maternity pen for parturition. Electronic data loggers were placed on a hind leg of prepartum heifers (heifers, n=33) and cows (cows, n=99) at 7±3 d before the expected calving date and removed at 14±3 d in milk. Calving ease (scale 1-4), parity, calving date and time, and stillbirth (born dead or died within 24h) were recorded. The number of steps (no./d), standing time (min/d), lying time (min/d), number of LB (no./d), and duration of LB (min/b) were recorded. Data were analyzed using MIXED procedures of SAS, adjusting for the herd effect. Only cows experiencing unassisted births (calving ease=1) were included in the study. An activity index was developed to predict calving time. Heifers and cows with unassisted births had significantly higher number of steps and longer standing time, decreased lying time, and more LB of shorter duration 24h before calving compared with d -4, -3, and -2. Additionally, the number of LB increased as both heifers and cows approached labor starting on d -2 and peaked at the day of calving. The time since the activity index increased over 50% to parturition did not differ between heifers and cows, and the activity index revealed the shift in activity on average 6h 14min (range from 2h to 14h 15min) before calf birth. This study provided evidence that heifers and cows approaching parturition showed a similar, but distinct, behavioral pattern that can be observed on average 6h before calf birth. The potential benefits of electronic data loggers as predictors of parturition along with proactive management practices should improve the overall survival and welfare of both the dam and calf.

The utility of sensor technology to support reproductive management on dairy farms

“…Previous studies have observed changes in feeding and resting behaviour determined by sensors in the last 24 hours before calving (Bucher and Sundrum, 2014, Clark, et al, 2015, Schirmann, et al, 2013, Titler, et al, 2015. No such effects were observed in the data of the sensor parameters "feeding" and "not active" in the current study.…”

“…It should be noted that these studies generated alerts every two hours instead of every hour and that reduces the total number of alerts including the false positive alerts. Other studies have also reported activity to increase during the 24 hours before calving (Clark, et al, 2015, Miedema, et al, 2011b, Saint-Dizier and Chastant-Maillard, 2015, Titler, et al, 2015. For "temperature", a decrease of about 3°C was observed during the hour in which calving started, whereas other studies have reported decreases in the range of 0.2-0.5 °C (Burfeind, et al, 2011, Ouellet, et al, 2016.…”

“…Time spent on feeding also decreases, dry matter intake tends to decrease slightly (Schirmann, et al, 2013); (Bucher and Sundrum, 2014), and activity changes in the 24 hours before calving (Clark, et al, 2015, Miedema, et al, 2011b, Saint-Dizier and Chastant-Maillard, 2015. Titler et al (Titler, et al, 2015) demonstrated that an activity index could be used to predict whether a cow would calve in the six hours following an increase in the activity index. Previous studies have shown that temperature (measured at the vulva, rectum, and rumen) decreases during the 24 hours prior to calving (Saint-Dizier and Chastant-Maillard, 2015).…”

“…Such sensors could potentially be used as an alternative to visual observation for onset and progression of calving , Ouellet, et al, 2016, Rutten, et al, 2017, Titler, et al, 2015. For instance, the walking behaviour and postures of cows prior to calving have been analyzed with video camera's (Cangar, et al, 2008).…”

“…Another example is temperature, which has been shown to decrease before the start of calving and this can be monitored with sensors (Burfeind, et al, 2011, Saint-Dizier andChastantMaillard, 2015). Lastly, changes in rumination, lying time and lying bouts determined by sensors have been associated with the start of calving (Bar and Solomon, 2010, Bucher and Sundrum, 2014, Ouellet, et al, 2016, Rutten, et al, 2017, Titler, et al, 2015.…”