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.
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Characteristics of the physical childcare environment are associated with children’s sedentary behavior (SB) and physical activity (PA) levels. This study examines whether these associations are moderated by child characteristics. A total of 152 1- to 3-year-old children from 22 Dutch childcare centers participated in the study. Trained research assistants observed the physical childcare environment, using the Environment and Policy Assessment Observation (EPAO) protocol. Child characteristics (age, gender, temperament and weight status) were assessed using parental questionnaires. Child SB and PA was assessed using Actigraph GT3X+ accelerometers. Linear regression analyses including interaction terms were used to examine moderation of associations between the childcare environment and child SB and PA. Natural elements and portable outdoor equipment were associated with less SB and more PA. In addition, older children, boys and heavier children were less sedentary and more active, while more use of childcare and an anxious temperament were associated with more SB. There were various interactions between environmental factors and child characteristics. Specific physical elements (e.g., natural elements) were especially beneficial for vulnerable children (i.e., anxious, overactive, depressive/withdrawn, overweight). The current study shows the importance of the physical childcare environment in lowering SB and promoting PA in very young children in general, and vulnerable children specifically. Moderation by child characteristics shows the urgency of shaping childcare centers that promote PA in all children, increasing equity in PA promotion in childcare.
Several studies have suggested that precision livestock farming (PLF) is a useful tool foranimal 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 systemcan 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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Gebruik van sensoren en data voor het monitoren van welzijn en gezondheid van mens en dier, raakt steeds meer ingeburgerd. Ook voor de paardenhouderij is het interessant om met behulp van sensoren de gezondheid en het welzijn van de paarden te volgen en in geval van ziekte of stress preventief te kunnen handelen. In tegenstelling tot het ruime aanbod voor de veehouderij, zijn er voor paarden nog weinig of geen sensoren beschikbaar voor gezondheidsmonitoring. In dit project zullen halsbanden voor paarden worden ontwikkeld met activiteitssensoren (accelerometers), die gedragsdata verzamelen. Deze data worden vertaald in informatie over het normale en afwijkende gedrag van de paarden. Activiteit en gedrag worden gekoppeld aan gezondheid en het welzijn van het paard. Doel is om een systeem te ontwikkelen waarbij gezondheid en welzijn van de paarden gemonitord wordt met behulp van deze sensor, en waarbij de eigenaar gewaarschuwd wordt wanneer veranderingen in gedrag optreden die voorspellend zijn voor ziekte, stress of afwijkingen.
