Electronic monitoring of rumination activity as an indicator of health status and production traits in high-yielding dairy cows

Gáspárdy, András; Efrat, Gingis; Bajcsy, Árpád Csaba; Fekete, S.

doi:10.1556/avet.2014.026

Cited by 25 publications

(18 citation statements)

References 11 publications

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“…Researchers have previously examined associations of changes in rumination, activity, and milk yield data with health disorders in dairy cows, but fewer studies have created and validated prediction models. Daily milk production data followed obvious patterns before and up to 7 d after clinical ketosis diagnosis (Grӧhn et al, 1999) but were less responsive to cases of subclinical ketosis (SCK) and metritis (Gáspárdy et al, 2014). Daily rumination time and BW data changed relative to the day of SCK and metritis diagnoses (Gáspárdy et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Behavior and productivity of cows milked in automated systems before diagnosis of health disorders in early lactation

King

LeBlanc

Pajor

et al. 2018

Journal of Dairy Science

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Associations of electronically recorded data were examined before diagnosis of health disorders in early-lactation cows in herds with automated milking systems (AMS). Rumination time, activity, and milk yield data were collected for 8 mo for 605 early-lactation cows in 9 commercial AMS herds. Using multivariable generalized linear regression models controlling for parity and days in milk, data were examined relative to the day of diagnosis for health disorders occurring in absence of, or at least 14 d before, another disorder: mastitis (n = 13), new cases of lameness (n = 45), subclinical ketosis (SCK; n = 113), and purulent vaginal discharge (n = 49). All cases of displaced abomasum (DA; n = 8) occurred in conjunction with other disorders. Deviations from baseline among affected cows were examined, as well as differences compared with a group of healthy cows and an average group of all cows, who were given mock diagnosis days using the mean days in milk at diagnosis for each disorder. On 6 to 14 d of the 2 wk before diagnosis, cows with DA or mastitis had lower milk yield, rumination time, milking frequency, activity, and milk temperature compared with healthy cows as well as deviations from their own baseline rumination time and milk data starting 4 to 12 d before diagnosis. Cows with DA had lower AMS supplement intake than healthy cows and deviations from their baseline activity and milk temperature starting 6 and 4 d before diagnosis, respectively. Cows with mastitis had greater milk conductivity than healthy cows and deviated from their baseline milking frequency and conductivity 8 and 12 d before diagnosis, respectively. Compared with healthy cows, those with SCK or new cases of lameness generally had lower milk yield, rumination time, milk temperature, supplement intake, and milking and refusal frequencies. Only the milk temperature of lame cows deviated from baseline. Thus, acute health disorders (i.e., DA and mastitis) were associated with deviations from those cows' baseline AMS data, whereas more chronic disorders (i.e., SCK and lameness) were associated with significant but subtle longer term changes in milk production and behavior. Because cows with health disorders deviated from a group of healthy cows before they deviated from their own baseline and from the average of all other cows, including a healthy reference group in health alerts could refine the ability of detection models to identify subtle deviations in early lactation.

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Section: Introductionmentioning

confidence: 99%

Behavior and productivity of cows milked in automated systems before diagnosis of health disorders in early lactation

King

LeBlanc

Pajor

et al. 2018

Journal of Dairy Science

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“…According to de Ondarza et al (2017) changes in rumination time may indicate health problem earlier than clinical changes occur and before decrease of milk production. Optimal rumination time in cattle should be in range of 8 to 10 hours, eventually 12 hour a day in 4 to 20 time intervals that last from 10 to 60 minutes (Gáspárdy et al, 2014;Liboreiro et al, 2015). Decrease of rumination time from the average by 30-40 minutes already represents some sort of problem.…”

Section: Introductionmentioning

confidence: 99%

Rumination Time Monitoring as a Possible Tool to Improve Diagnostics of Heat and Parturition

Toušová

Ducháček

Codl

et al. 2020

Acta Univ. Agric. Silvic. Mendelianae Brun.

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Rumination time is one of possible parameters for observing not only feed management and health status, but also some reproductive characteristics such as heat or periparturient period. The evaluation of rumination time in this study was done on Holstein dairy cows in period around heat (n = 297) and around parturition (n = 322) during one year. SAS 9.3 was used for statistic evaluation. Factors such as heat or periparturient period but also a parity or month within the year have significant impact on rumination time. The decrease of rumination time in the average of 19% (more than 100 minutes) was observed in heat time against days before heat. Similarly, decrease of rumination time more than 110 minutes was observed on the day of parturition in comparison with days before that day. However, the return to normal rumination time values was gradual within 3 day after calving. The results indicated that in healthy cows the rumination time can be a good indicator not only of approaching periparturient but also of incoming heat.

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“…The number of chews per bolus is associated with the fiber content of the feed, but in general rumination activity can be influenced by several environmental factors, such as the nature and amount of feed (Metz, 1975;Suzuki et al, 2014) or milking schedules and patterns of lighting (Beauchemin, 1991). Adult cows ruminate approximately 8 h per day in 4 to 24 periods, each lasting 10 to 60 min (Gáspárdy et al, 2014). Nevertheless, rumination time seems to have a physiological limit of approximately 10 h per day, but rumination times of 12 h per day have also been reported (Welch, 1982;Beauchemin, 1991;Liboreiro et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an ear-tag-based accelerometer for monitoring rumination in dairy cows

Reiter

Sattlecker²,

Lidauer³

et al. 2018

Journal of Dairy Science

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The objective of this study was to evaluate the ear-tag-based accelerometer system Smartbow (Smartbow GmbH, Weibern, Austria) for detecting rumination time, chewing cycles, and rumination bouts in indoor-housed dairy cows. For this, the parameters were determined by analyses of video recordings as reference and compared with the results of the accelerometer system. Additionally, we tested the intra- and inter-observer reliability as well as the agreement of direct cow observations and video recordings. Ten Simmental dairy cows in early lactation were equipped with 10-Hz accelerometer ear tags and kept in a pen separated from herd mates. A total mixed ration was fed twice a day via a roughage intake control system. During the study, cows' rumination and other activities were directly observed for 20 h by 2 trained observers. Additionally, cows were video recorded for 19 d, 24 h a day. After exclusion of unsuitable videos, 2,490 h of cow individual 1-h video sequences were eligible for further analyses. Out of this, one hundred 1-h video sequences were randomly selected and visually and manually classified by a trained observer using professional video analyses software. Based on these analyses, half of the data was used for development (based on data of 50-h video analyses) and testing (based on data of additional 50-h video analyses) of the Smartbow algorithms, respectively. Inter- and intra-observer reliability as well as the comparison of direct against video observations revealed in high agreements for rumination time and chewing cycles with Pearson correlation coefficients >0.99. The rumination time, chewing cycles, as well as rumination bouts detected by Smartbow were highly associated (r > 0.99) with the analyses of video recordings. Algorithm testing revealed in an underestimation of the average ± standard deviation rumination time per 1-h period by the Smartbow system of 17.0 ± 35.3 s (i.e., -1.2%), compared with visual observations. The average number ± standard deviation of chewing cycles and rumination bouts was overestimated by Smartbow by 59.8 ± 79.6 (i.e., 3.7%) and by 0.5 ± 0.9 (i.e., 1.6%), respectively, compared with the video analyses. In summary, the agreement between the Smartbow system with video analyses was excellent. From a practical and clinical point of view, the detected differences were negligible. However, further research is necessary to test the system under various field conditions and to evaluate the benefit of incorporating rumination data into herd management decisions.

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Electronic monitoring of rumination activity as an indicator of health status and production traits in high-yielding dairy cows

Cited by 25 publications

References 11 publications

Behavior and productivity of cows milked in automated systems before diagnosis of health disorders in early lactation

Behavior and productivity of cows milked in automated systems before diagnosis of health disorders in early lactation

Rumination Time Monitoring as a Possible Tool to Improve Diagnostics of Heat and Parturition

Evaluation of an ear-tag-based accelerometer for monitoring rumination in dairy cows

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