Description of Behavioral Patterns Displayed by a Recently Weaned Cohort of Healthy Dairy Calves

Alawneh, J. I.; Barreto, Michelle; Bome, Kealeboga; Soust, Martin

doi:10.3390/ani10122452

Cited by 5 publications

(4 citation statements)

References 62 publications

(88 reference statements)

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“…Possibly young calves are less able to do so as they have a smaller stomach, or possibly a frequency of two milk feedings per day is simply too far from the voluntary milk feeding frequency of calves, which seems to be around 7 to 8 feedings per day (Webb et al, 2014), leading the calves to visit the AMFs more frequently. In terms of height of peaks, the height of peaks showed that calves become more and more active across the day, from morning to evening, which is consistent with previous research (Alawneh et al, 2020). The respective height of peaks increased as calves grew older, especially the first and the second peaks between week 8 and 20.…”

Section: Discussionsupporting

confidence: 89%

Decoding calf patterns : Advancing automated health monitoring with algorithmic insights

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World milk production reached 887 million tonnes in 2020 (FAO, 2022). With such high demand worldwide, dairy cows who produce the milk need to keep giving birth to renew the lactation in order to produce milk. Most female calves will be kept as heifers for the dairy industry, while the male (and some female) calves are not needed. The veal industry was therefore developed (mainly in Europe and North America) to make use of the dairy calves that are not wanted or needed for herd replacement, commonly referred to as 'surplus' calves.Although there are a number of possible routes for surplus dairy calves (Haskell, 2020;Webb et al., 2023), those calves destined for veal production are typically transported from different source dairy farms, sometimes across country borders, at the early age of around two to four weeks to a fattening veal farm (Marcato et al., 2018). At the veal farm, the calves are grouphoused, typically in small groups of five to ten, but sometimes in large groups of 40 to 70 calves, with typically 1.8 m 2 per calf, on wooden slatted floors. Veal calves are fed milk replacer in open troughs or buckets if housed in small groups, or via an automated milk feeders if house in large groups, until slaughter for the 'white' veal calves, and for eight weeks for the 'rosé' veal calves. Next to milk replacer, the calves receive solid feed, typically a mixture of concentrates and roughage (e.g. straw) until slaughter at approximately six (white) to eight (rosé) months of age (EFSA AHAW Panel, 2023). Veal calves do not typically receive enrichment, unless housed in the large groups, and do not typically have access to outdoor spaces. This production system allows veal calves to be raised efficiently, allowing each step of the chain to be considered and controlled (Renaud and Pardon, 2022), to end up with relatively high-quality meat products which fetch a high price with consumers, while using minimal resources.The Dutch veal sector is the biggest veal producer in the EU, accounting for 36% of the total production in 2020 (Berkhout et al., 2021). With around one million veal calves being kept in approximately 1,600 fattening farms 1 , the Dutch veal sector produces 1.6 to 1.7 million calves per year (Berkhout et al., 2021). These calves are transported from their source dairy farms all across Europe to the collection centres first, where they are sorted based on weight, breed, or conformation, before being sent to the fattening farms (Damiaans et al., 2019). This dairyveal chain presents potential concerns for calf welfare, including transportation of young animals, high risk of disease, and barren housing (Webb et al., 2023). One important concern for the farmers in this production system is the high morbidity (Sandelin et al., 2022) and high mortality rates (

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Section: Discussionsupporting

confidence: 89%

Decoding calf patterns : Advancing automated health monitoring with algorithmic insights

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“…Nevertheless, GPS can also be used inside barns, though there are some limitations regarding the accuracy and high energy consumption compared with alternatives such as video cameras [ 134 ]. Alawneh et al [ 135 ] fitted eleven weaned Holstein calves with GPS data loggers (I got-U GT600, Mobile Action Technology Inc., Taipei, Taiwan) for two weeks during spring in a paddock with 105 × 30 m to describe and analyse movement patterns. Among other observations, Alawneh et al [ 135 ] found that those calves were most active during the afternoon and at night.…”

Section: Technological Applications To Monitor Calves’ Health and Wel...mentioning

confidence: 99%

“…Alawneh et al [ 135 ] fitted eleven weaned Holstein calves with GPS data loggers (I got-U GT600, Mobile Action Technology Inc., Taipei, Taiwan) for two weeks during spring in a paddock with 105 × 30 m to describe and analyse movement patterns. Among other observations, Alawneh et al [ 135 ] found that those calves were most active during the afternoon and at night. Individual or group observations could greatly interest calf welfare evaluations, such as substrate preference and a specific position in the barn or paddock due to thermal conditions.…”

Section: Technological Applications To Monitor Calves’ Health and Wel...mentioning

confidence: 99%

Literature Review on Technological Applications to Monitor and Evaluate Calves’ Health and Welfare

Silva

Conceição

Pereira

et al. 2023

Animals

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Precision livestock farming (PLF) research is rapidly increasing and has improved farmers’ quality of life, animal welfare, and production efficiency. PLF research in dairy calves is still relatively recent but has grown in the last few years. Automatic milk feeding systems (AMFS) and 3D accelerometers have been the most extensively used technologies in dairy calves. However, other technologies have been emerging in dairy calves’ research, such as infrared thermography (IRT), 3D cameras, ruminal bolus, and sound analysis systems, which have not been properly validated and reviewed in the scientific literature. Thus, with this review, we aimed to analyse the state-of-the-art of technological applications in calves, focusing on dairy calves. Most of the research is focused on technology to detect and predict calves’ health problems and monitor pain indicators. Feeding and lying behaviours have sometimes been associated with health and welfare levels. However, a consensus opinion is still unclear since other factors, such as milk allowance, can affect these behaviours differently. Research that employed a multi-technology approach showed better results than research focusing on only a single technique. Integrating and automating different technologies with machine learning algorithms can offer more scientific knowledge and potentially help the farmers improve calves’ health, performance, and welfare, if commercial applications are available, which, from the authors’ knowledge, are not at the moment.

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“…GNSS data offer the possibility to monitor animal behaviour, such as foraging, walking and resting, with an accuracy ranging from 57 to 87.5% depending on the circumstances [55][56][57][58][59][60][61][62]. However, the potential of GNSS for behavioural monitoring might benefit from combination with other sensors such as accelerometers [63][64][65][66][67][68][69][70][71][72].…”

Section: Global Navigation Satellite Systems (Gnss)mentioning

confidence: 99%

Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review

Hofstra

Roelofs

Rutter

et al. 2022

Dairy

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Several studies have suggested that precision livestock farming (PLF) is a useful tool for animal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for automated monitoring of the location of individual animals, ranging from Global Navigation Satellite Systems (GNSS) to ultra-wideband (UWB), RFID, wireless sensor networks (WSN) and even computer vision. These techniques and developments all yield potential to manage and assess animal welfare, but also have their constraints, such as range and accuracy. Combining sensors such as accelerometers with any location determining technique into a sensor fusion system can give more detailed information on the individual cow, achieving an even more reliable and accurate indication of animal welfare. We conclude that location systems are a promising approach to determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use of technology in animal welfare monitoring is needed.

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Description of Behavioral Patterns Displayed by a Recently Weaned Cohort of Healthy Dairy Calves

Cited by 5 publications

References 62 publications

Decoding calf patterns : Advancing automated health monitoring with algorithmic insights

Decoding calf patterns : Advancing automated health monitoring with algorithmic insights

Literature Review on Technological Applications to Monitor and Evaluate Calves’ Health and Welfare

Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review

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