Animal welfare is a multidimensional phenomenon and currently its on-farm assessment requires complex, multidimensional frameworks involving farm audits which are time-consuming, infrequent and expensive. The core principle of precision agriculture is to use sensor technologies to improve the efficiency of resource use by targeting resources to where they give a benefit. Precision livestock farming (PLF) enables farm animal management to move away from the group level to monitoring and managing individual animals. A range of precision livestock monitoring and control technologies have been developed, primarily to improve livestock production efficiency. Examples include using camera systems monitoring the movement of housed broiler chickens to detect problems with feeding systems or disease and leg-mounted accelerometers enabling the detection of the early stages of lameness in dairy cows. These systems are already improving farm animal welfare by, for example, improving the detection of health issues enabling more rapid treatment, or the detection of problems with feeding systems helping to reduce the risk of hunger. Environmental monitoring and control in buildings can improve animal comfort, and automatic milking systems facilitate animal choice and improve human-animal interactions. Although these precision livestock technologies monitor some parameters relevant to farm animal welfare (e.g. feeding, health), none of the systems yet provide the broad, multidimensional integration that is required to give a complete assessment of an animal’s welfare. However, data from PLF sensors could potentially be integrated into automated animal welfare assessment systems, although further research is needed to define and validate this approach.

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.

Cows housed indoors with cubicles are probably more restricted in their choice of lying posture and orientation compared with cows housed on pasture. We therefore compared lying postures on pasture in Uruguay and the Netherlands with lying postures in cubicles in the Netherlands, also recording orientation on pasture in Uruguay and divider and bedding type in Dutch cubicles. We visited one farm with four herds in Uruguay, doing live observations, and 25 Dutch farms, taking pictures of cows. Observations of 205 cows on pasture in Uruguay showed more long postures, lying on their belly with their neck stretched. Two herds preferred lying towards north and south, while one herd preferred west and east. Pictures of 217 cows on pasture in the Netherlands showed more wide postures (lying on the side with three or four legs stretched out). Pictures of 527 cows in cubicles in the Netherlands showed more narrow postures (lying on the side with hind legs folded). More long postures (lying on the belly with a stretched neck) and less short postures (lying with the head folded back) were seen in cubicles with soft floors and English dividers; more narrow postures were seen in cubicles with concrete floors. Wide postures were seen more in cubicles with mattresses and free-hanging dividers. We conclude that since cows in cubicles show more narrow postures than on pasture and cannot choose their orientation, their choice in showing preferred behavior is restricted. More research is needed to study the consequences of restricted choice in lying behavior on the health and welfare of dairy cows.

Precision Livestock Farming systems can help pig farmers prevent health and welfare issues around farrowing. Five sows were monitored in two field studies. A Sorama L642V sound camera, visualising sound sources as coloured spots using a 64-microphone array, and a Bascom XD10-4 security camera with a built-in microphone were used in a farrowing unit. Firstly, sound spots were compared with audible sounds, using the Observer XT (Noldus Information Technology), analysing video data at normal speed. This gave many false positives, including visible sound spots without audible sounds. In total, 23 of 50 piglet births were visible, but none were audible. The sow’s behaviour changed when farrowing started. One piglet was silently crushed. Secondly, data were analysed at a 10-fold slower speed when comparing sound spots with audible sounds and sow behaviour. This improved results, but accuracy and specificity were still low. When combining audible sound with visible sow behaviour and comparing sound spots with combined sound and behaviour, the accuracy was 91.2%, the error was 8.8%, the sensitivity was 99.6%, and the specificity was 69.7%. We conclude that sound cameras are promising tools, detecting sound more accurately than the human ear. There is potential to use sound cameras to detect the onset of farrowing, but more research is needed to detect piglet births or crushing.

Ketosis is a metabolic disorder associated with high production and low energy intake in dairy cows. Fat reserves are mobilized, and gluconeogenesis occurs. Traditionally, rapid tests for blood, milk or urine are used to detect increased ketone body levels in case of ketosis. Breath analysis is relatively new and relevant for the development of automatic sensor systems for early warning. This study aims to determine whether and when a postpartum rise in ketone bodies occurs in breath with elevated blood β-hydroxybutyrate (BHB) in cows at risk of ketosis. Postpartum breath, blood, urine and milk samples were taken daily until day 7, with one prepartum sample excluding milk, and ketone body concentrations were determined. Concentrations of blood BHB were 0.4–2.6 mmol/L (lab) and 0.3–3.1 mmol/L (rapid test), breath acetone was 2.3–20.0 ppm, urine acetoacetate 0–8 mmol/L and milk BHB 0–500 µmol/L. A rise in blood BHB was related to that in urine acetoacetate and milk BHB and followed by a rise in breath acetone. However, breath acetone levels of ketotic and non-ketotic cows were similar. We conclude that rising ketone bodies can be detected in blood, urine, milk and breath, but to use breath analysis as an alternative to rapid tests, longitudinal sampling is needed.