Technical note: An evaluation of technology-recorded rumination and feeding behaviors in dairy heifers

Abstract

Precision dairy monitoring technologies have become increasingly popular for recording rumination and feeding behaviors in dairy cattle. The objective of this study was to validate the rumination and feeding time functions of the CowManager SensOor (Agis, Harmelen, the Netherlands) against visual observation in dairy heifers. The study took place over a 44-d period beginning June 1, 2016. Holstein heifers equipped with CowManager SensOor tags attached according to manufacturer specifications (n = 49) were split into 2 groups based on age, diet, and housing type. Group 1 heifers (n = 24) were calves (mean ± SD) 2.0 ± 2.7 mo in age, fed hay and calf starter, and housed on a straw-bedded pack. Group 2 heifers (n = 25) were 17.0 ± 1.3 mo in age, fed a TMR, confirmed pregnant, and housed in freestalls. Visual observation shifts occurred at 1500, 1700, 1900, and 2100 h. Each heifer was observed for 2 hour-long periods, with both observation periods occurring on the same day. Visual observations were collected using a synchronized watch, and "start" and "stop" times were recorded for each rumination and feeding event. For correlations, data from CowManager SensOor tags and observations were averaged, so a single 1-h observation was provided per animal, reducing the potential for confounding repeated measures being collected for each animal. Concordance correlations (CCC; epiR package; R Foundation for Statistical Computing, Vienna, Austria) and Pearson correlations (r; CORR procedure; SAS Institute Inc., Cary, NC) were used to calculate association between visual observations and technology-recorded behaviors. Visually observed rumination time was correlated with the CowManager SensOor (r = 0.63, CCC = 0.55).Visually observed feeding time was also correlated with the CowManager SensOor (r = 0.88, CCC = 0.72). The difference between technology-recorded data and visual observation was treated as the dependent variable in a mixed linear model (MIXED procedure of SAS). Time of day, age in months, and group were treated as fixed effects. Individual heifers were treated as random and repeated effects. The effects of time of day, age, and group on rumination and feeding times were not significant. The CowManager SensOor was more effective at recording feeding behavior than rumination behavior in dairy heifers. The CowManager SensOor can be used to provide relatively accurate measures of feeding time in heifers, but its rumination time function should be used with caution.

Symposium review: Precision technologies for dairy calves and management applications

“…Validation studies compare behavior data recorded by the technology to a known measure of behavior, such as visual observation, or to another validated technology known as the gold standard. Validation studies have highlighted the importance of validating multiple PDT in one location (Borchers et al, 2016), as well as validating the same technology (ear-based accelerometer in this instance) in different settings such as on pasture (Pereira et al, 2018), in tiestalls (Zambelis et al, 2019), or in freestalls (Borchers et al, 2016), and in cattle of different ages (Reynolds et al, 2019). This type of validation of PDT is essential to establish that it is precise and accurate in multiple settings.…”

“…However, validation of accelerometers to measure rumination and feeding behavior for herd management has had mixed results. For example, one ear-tag accelerometer for herd management was validated for rumination against live observation in one study (Hill et al, 2017), but was not validated for rumination or feeding time against video observations in another study (Reynolds et al, 2019). A different in-ear herdmanagement accelerometer also had moderate agreement for rumination but poor agreement for feeding behaviors such as milk intake from a teat, water, and solid feed intake (Roland et al, 2018a) and milk intake from a bucket (Roland et al, 2018b).…”

“…However, automated monitoring of rumination in calves has had limited application. This is likely related to the variable ages at which accelerometers are able to measure rumination; for example, rumination was detected using herd-management accelerometers from 6 wk (Hill et al, 2017) to 8 wk of age (Reynolds et al, 2019), but there is debate about at what age true rumination occurs in calves (reviewed by Khan et al, 2016). Recent work evaluated rumination, activity, and eating with a herd-management accelerometer to assess calf performance on 4 different milk feeding programs (Dennis et al, 2018); although no differences were found in these behaviors between treatment groups, there is great potential for this technology to identify individual calves that may be performing poorly in response to particular feeding programs.…”

Using rumination time to manage health and reproduction in dairy cattle: a review

“…This sensor records rumination, feeding behavior and activity. On average, the tag is 5.1 cm long, 6.4 cm wide and 1.9 cm thick, weighing about 28 g (Reynolds et al 2019 ). Data from the sensor are collected through a router stored at the local computer.…”

Wearable Wireless Biosensor Technology for Monitoring Cattle: A Review

“…This variable was evaluated in both indoor intensive farming systems (such as freestall barns or tie-stall barns) and pasture systems (Supplementary Tables S1 and S2). In intensive farming, eating behavior is defined as the chewing or licking movement occurring when the animal's muzzle is located in or above the feed bunk [17][18][19][20][21][22][23][24][25][26][27][28]. In pasture systems, eating behavior is defined as the process of biting or chewing grass when the cow's muzzle is located near or above the grass [29][30][31][32][33][34].…”