2017
DOI: 10.3168/jds.2016-11884
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Rumination time and reticuloruminal temperature as possible predictors of dystocia in dairy cows

Abstract: The objectives of this study were to explore changes of rumination time and reticuloruminal pH and temperature of dairy cows and heifers (means ± standard deviation; age = 5.8 ± 1.9; parity = 2.7 ± 1.4; body condition score = 3.2 ± 0.2) with eutocic (EUT, n = 10) and dystocic calving (DYS, n = 8). The recording period lasted from 3 d before calving until 7 d in milk. For the comparison of rumination time and reticuloruminal characteristics between groups, time to return to baseline (the time interval required … Show more

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Cited by 47 publications
(30 citation statements)
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“…Stangaferro et al (2016c) showed that rumination time combined with cow activity was effective for identifying cows with severe cases of metritis but less effective for identifying cows with mild cases of metritis. Kovács et al (2017) reported less rumination time 8 h before calving up until 4 d postpartum for cows with dystocia compared with cows that had a normal calving. Stangaferro et al (2016b) showed that monitoring rumination time in combination with cow activity was effective for identifying cows with clinical cases of mastitis caused by Escherichia coli but not when it was caused by other pathogens.…”
Section: Detection Of Parturition and Illness In Dairy Cowsmentioning
confidence: 90%
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“…Stangaferro et al (2016c) showed that rumination time combined with cow activity was effective for identifying cows with severe cases of metritis but less effective for identifying cows with mild cases of metritis. Kovács et al (2017) reported less rumination time 8 h before calving up until 4 d postpartum for cows with dystocia compared with cows that had a normal calving. Stangaferro et al (2016b) showed that monitoring rumination time in combination with cow activity was effective for identifying cows with clinical cases of mastitis caused by Escherichia coli but not when it was caused by other pathogens.…”
Section: Detection Of Parturition and Illness In Dairy Cowsmentioning
confidence: 90%
“…As normal daily variation in ruminating time is about 10% (>10% with finely chopped or high-grain diets; Dulphy et al, 1979), the assumption is that large reductions in rumination time by an individual cow on a particular day can be an indication of a change in cow health. For example, rumination time has been shown to be consistently reduced about 8 h before calving and increase about 6 h later, likely a result of limited feed intake (Schirmann et al, 2013;Pahl et al, 2014;Paudyal et al, 2016;Borchers et al, 2017;Kovács et al, 2017). Thus, monitoring rumination time could be useful in predicting time of calving.…”
Section: Detection Of Parturition and Illness In Dairy Cowsmentioning
confidence: 99%
“…The ability to detect drinking events using a thresholding method and by incorporating this information rather that removing it, as in previous studies (Cooper-Prado et al, 2011;Costa et al, 2016), has the potential to expand temperature monitoring in cows (Cooper-Prado et al, 2011;Timsit et al, 2011;Costa et al, 2016;Kovács et al, 2017). Drinking behavior in dairy cattle is important for the physical and emotional state of the animals (Baxter, 1983) and for farm economics because it is highly related to milk production (Steiger Burgos et al, 2001;Kramer et al, 2009;Daros et al, 2019).…”
Section: Discussionmentioning
confidence: 99%
“…In recent years, the development of precision livestock farming technology has facilitated not only the collection of real-time data but also the integration of this information within the overall monitoring of individual animals (Berckmans, 2014;Walton et al, 2018). Among these technologies is the collection of reticuloruminal temperature data through the use of reticular boluses that transmit data to a central computer using an active radiofrequency transmitter (Costa et al, 2016;Kovács et al, 2017;Lees et al, 2018). This allows farmers to continuously monitor cows' temperature, providing an alert when temperature goes out of a preset range; for example, during heat stress (Koltes et al, 2018).…”
Section: Introductionmentioning
confidence: 99%
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