Cows suffering from mammary inflammation display 'individual-centred' pain and/or sickness behaviours; however, how they interact with their surroundings (physical environment, social context) is less documented. This study examined the effects of mild mammary pain and/or sickness on cows' daily activity patterns, spatial organisation, and social behaviours (dyadic interactions and social network metrics). Twenty-seven dairy cows were studied using a repeated-measures design over six observation days, comprising three 'non-challenge days' and three 'challenge days'. On each challenge day, a subset of cows received an intramammary infusion of 25 μg E. coli lipopolysaccharide (LPS) into a healthy udder quarter after morning milking, while the remaining cows served as CONTROL. Cows receiving LPS were either left untreated (LPS status, n = 14) or immediately treated with a non-steroidal anti-inflammatory drug (LPS+NSAID status, n = 13). Cows' behaviour was continuously video-recorded between morning and evening milkings across all 6 days, which allowed to collect data on location, activity, proximity to neighbours, and social interactions. Data were analysed using mixed effects models and social network analysis. The results showed significant effects of pain and/or sickness status on behaviour (location; activity) and social dynamics. First, compared to the CONTROL status, cows in the LPS status spent less time at the self-locking barrier and more time in cubicles. They spent longer durations of time standing still and perching and less time eating, engaged in fewer social interactions and maintained greater distances from their nearest neighbours. Social network analysis found that cows in LPS status reduced their level of social engagement (i.e. lower Strength) and of social connectivity (i.e. lower betweenness). Second, comparisons between the LPS+NSAID and CONTROL conditions revealed no significant differences, whereas comparisons between the LPS+NSAID and LPS conditions showed significant contrasts. Compared to cows in LPS status, those in LPS+NSAID status spent more time in self-locking barriers, less time in cubicles, more time eating, less time perching, and were involved in a higher number of social interactions and were closer to their first neighbour. Together, these results indicate that NSAID treatment alleviated most behavioural effects associated with pain and/or sickness. These findings highlight the potential value of behavioural indicators including activities, space use and social behaviour for improving mild pain and/or sickness detection in dairy cows. This study can be seen as a first step to identify relevant behaviours, which would be followed by the development or refinement of practical tools in on-field applications.

After a first article dedicated to pain in livestock animals, this article addresses scientific knowledge on pain management in livestock species intended for human consumption, including fish. Pain relief is based for a part on its pharmacology. Four families of molecules can or could be useful for multimodal analgesia: local anaesthetics, alpha 2-agonists, anti-inflammatories, and opioids. In addition, pain experience may be modulated by a so-called ethological approach, which is based on emotional and attentional factors, or on animal training.

EURCAW Ruminants & Equines has published its response to a question received in relation to the need of veterinary care. This service, referred to as Questions to EURCAW (Q2E) is open to Competent Authorities and government policy workers of EU Member States.

This article addresses scientific knowledge on pain in livestockspecies intended for human consumption. Pain is an unpleasant sensory and emotional experience associated with or resembling that associated with actual or potential tissue damage. It has three components: sensory, emotional and cognitive. The mechanisms of pain are similar in most species: elaboration, transmission, integration. Throughout their lives, farm animals are confronted with various contexts that can generate pain, with a significant impact on their production and economic performances, as well as on their health and welfare. Pain caused by some farming practices is predictable and its management can be anticipated, unlike accidents or diseases whose occurrence is not or hardly predictable. When in pain, farm animals change their behaviour and physiological response. To detect it, we can mobilize observations focusing on the animals (e.g. posture, facial expression, reaction to palpation, etc.) but also on the way they interact with their physical environment (activities, locomotion, space use, etc.) and social environment (proximity, social interactions, synchronization).

Cet article propose de dresser un état des lieux des connaissances scientifiques sur la douleur chez les animaux de production destinés à la consommation humaine. La douleur est « une expérience sensitive et émotionnelle désagréable associée ou ressemblant à celle associée à une atteinte tissulaire réelle ou potentielle ». Elle comporte trois composantes : sensitive, émotionnelle et cognitive. Les mécanismes de la douleur sont similaires chez la plupart des espèces : transduction, transmission, intégration. Tout au long de leur vie, les animaux de production sont confrontés à divers contextes pouvant générer de la douleur, avec un impact important sur leurs performances zootechniques, économiques, sur leur santé et sur leur bien-être. La douleur provoquée par certaines interventions en élevage est prévisible et sa prise en charge peut être anticipée, à l’inverse des accidents ou des maladies dont la survenue n’est pas ou peu prévisible. Les animaux manifestent la douleur en modifiant leurs comportements et leurs réponses physiologiques. Pour la détecter, on peut mobiliser l’observation centrée sur les animaux (posture, expression faciale, réaction à la palpation…) mais aussi sur la manière dont ils interagissent avec leur environnement physique (activités, locomotion, utilisation de l’espace…) et social (proximité, interactions, synchronisation).

Après un premier article consacré à la douleur chez les animaux de production, cet article propose de dresser un état des lieux des connaissances scientifiques sur la gestion de la douleur chez les animaux de production destinés à la consommation humaine, à la fois chez les mammifères et les poissons. Le soulagement de la douleur repose notamment sur sa prise en charge pharmacologique. Quatre familles de molécules sont ou pourraient être mobilisables pour une analgésie multimodale : les anesthésiques locaux, les α2-agonistes, les anti-inflammatoires et les opioïdes. En complément, la douleur peut être gérée par une approche dite éthologique, qui repose sur les facteurs émotionnels et attentionnels, ou sur l’entraînement des animaux.

Cattle suffering from inflammatory infection display sickness and pain-related behaviours. As these behaviours may be transient and last only a few hours, one may miss them. The aim of this study was to assess the benefit of combining continuous monitoring of cow behaviour via collar-attached accelerometers with direct visual observations to detect sickness and pain-related behavioural responses after a systemic inflammatory challenge (intravenous lipopolysaccharide injection) in cows of two different ages, proven by clinical, physiological and blood parameters. Twelve cloned Holstein cows (six ‘old’ cows aged 10–15 years old and six ‘young’ cows aged 6 years old) were challenged and either directly observed at five time-points from just before the lipopolysaccharide injection up to 24 h post-injection (hpi) or continuously monitored using collar-attached accelerometers in either control or challenge situations. Direct observations identified specific sickness and pain behaviours (apathy, changes in facial expression and body posture, reduced motivation to feed) expressed partially at 3 hpi and fully at 6 hpi. These signs of sickness and pain behaviours then faded, and quicker for the young cows. Accelerometers detected changes in basic activities (low ingesting, low ruminating, high inactivity) and position (high time standing up) earlier and over a longer period of time than direct observations. The combination of sensors and direct observations improved the detection of behavioural signs of sickness and pain earlier on and over the whole study period, even when direct signs were weak especially in young cows. This system could provide great benefit for better earlier animal care.

Disease and stress can disrupt the circadian rhythm of activity in animals. Sensor technologies can automatically detect variations in daily activity, but it remains difficult to detect exactly when the circadian rhythm disruption starts. Here we report a mathematical Fourier-Based Approximation with Thresholding (FBAT) method designed to detect changes in the circadian activity rhythm of cows whatever the cause of change (typically disease, stress, oestrus). We used data from an indoor positioning system that provides the time per hour spent by each cow resting, in alleys, or eating. We calculated the hourly activity level of each cow by attributing a weight to each activity. We considered 36-h time series and used Fourier transform to model the variations in activity during the first and last 24 h of these 36-h series. We then compared the Euclidian distance between the two models against a given threshold above which we considered that rhythm had changed. We tested the method on four datasets (giving a cumulative total of similar to 120000 cow*days) that included disease episodes (acidosis, lameness, mastitis or other infectious diseases), reproductive events (oestrus or calving) and external stimuli that can stress animals (e. g. relocation). The method obtained over 80% recall of normal days and detected 95% of abnormal rhythms due to health or reproductive events. FBAT could be implemented in precision livestock farming system monitoring tools to alert caretakers to individual animals needing specific care. The FBAT method also has the potential to detect anomalies in humans to guide healthcare intervention or in wild animals to detect disturbances. We anticipate that chronobiological studies could apply FBAT to help relate circadian rhythm anomalies to specific events.

Enting concluded lameness, from an economic perspective, as the third most costly health disease, following mastitis and reproductive failure issues, in cattle units. Archer estimated the incidence rate of lameness in the United Kingdom cattle herds roughly 50 cases/100 cows in a year; nevertheless, due to poor correlation between incidence rates and records of treatments in farms, the actual number seems to be higher. Surprisingly, the significance of lameness associated with cattle welfare, health and profitability of the unit has been greatly underestimated. Recent works have shown a clear link between BCS and hook condition of cows with the development of lameness in these animals. Lameness is a multifactorial and progressive issue where different detriments contribute to its development via complex interactions. Detection of lame cattle can be facilitated through description of the animals' gait characteristics in a numerical scaling system known as locomotion scoring. The total number of visual (manual) locomotion scoring systems can reach up to 25, where differences lie mostly in the used scales, characterization of cows' gait, and posture. Automated locomotion scoring tools would be a big advantage for regular monitoring of lameness in the herd. Three methods that are commonly engaged with automated systems are: kinetic, kinematic and indirect. The kinetic and kinematic approaches measure the forces, involved in locomotion, and time and distance of variables, associated to limb movement, respectively. The indirect method simply exploits behavioral or production data as indicators for impaired locomotion. The automated tools/instruments that will be developed, based upon either of the aforementioned approaches, need to be validated with a ‘reference’ method. This usually is done by comparing with manual scoring; however, it is noteworthy that manual scoring systems have their own set of limitations.

Les techniques d'élevage de précision ont été développées essentiellement pour augmenter la rentabilité et réduire la charge de travail en appliquant des processus automatiques de surveillance des animaux et de leur environnement. Par exemple la détection de l'œstrus permet une insémination rapide, tandis que la détection des boiteries à un stade précoce ou d’un déséquilibre nutritionnel ou même des paramètres d'ambiance anormaux dans l'étable peuvent aider à prendre des mesures correctives rapidement. Les données générées par les capteurs pourraient également contribuer au bien-être des animaux. Un système détectant les problèmes de santé (par exemple, mammite ou cétose chez les vaches laitières) peut faire partie de la gestion du bien-être. En plus et surtout, certains dispositifs de l’élevage de précision sont basés sur la détection du comportement animal directement ou indirectement par la position des animaux : temps passé à se nourrir, ruminer, se reposer, marcher, etc. Des changements subtils de comportement peuvent indiquer l'état mental d'un animal : hyper-réactivité vs apathie, isolement social, modification du rythme quotidien d'activité, réduction du comportement de jeu ou du toilettage, hyper-agressivité. Ces changements peuvent être autant de signes de malaise dus à la maladie, au stress, à l'instabilité sociale, etc. Ainsi les techniques de l’élevage de précision offrent un large éventail de possibilités d'utiliser des signes de comportement animal pour aborder le bien-être dans des élevages modernes, qu’il s’agisse du bien-être lié à l'état de santé, aux relations sociales, aux relations homme-animal ou à un environnement quelconque stressant. À l'heure actuelle, ces possibilités sont peu explorées. Par ailleurs, l’élevage de précision modifie le travail des agriculteurs et potentiellement leurs interactions avec les animaux. Il est nécessaire que les animaux restent au centre de l'attention si l'on veut respecter leur bien-être et ce en harmonie avec celui de l’éleveur.

Precision Livestock Farming techniques have been developed essentially to increase profitability and reduce workload by applying automatic processes to monitor animals and their environment. For instance detecting oestrus allows timely insemination, while detecting lameness at an early stage or imbalance in the nutritional status or even abnormal ambiance parameters in the barn can help take remedial actions quickly. The data generated by Precision Livestock Farming sensors could also support animal welfare. A system detecting health problems (e.g. mastitis, ketosis in dairycows)can be part of welfare management. In addition and maybe more importantly, some Precision Livestock Farming devices are based on animal behaviour detection directly, or indirectly through the position of animals: time spent feeding, ruminating, resting, walking, etc. Subtle changes in behaviour can indicate the mental state of an animal hyper-reactivity vs. apathy, social isolation, changes in the daily rhythm of activity, reduction in play behaviour and grooming, hyper-agressivity. These changes can be all signs of malaise due to disease, stress, social instability, etc. We argue that Precision Livestock Farming techniques offer a wide range of possibilities to use animal behavioural signs to address animal welfare in modern livestock farming, be the welfare related to health status, social relations, human-animal relationship or more general effects of a stressful environment. At present, these possibilities have been little explored, and they deserve more research. In addition, the use of Precision Livestock Farming is changing farmers' work and potentially their interactions with animals. It is necessary that the animals remain at the centre of attention if one wants to address adequately their welfare in harmony with the farmer.

La prise en compte du bien-être animal devient une nécessité pour le vétérinaire praticien, de plus en plus sollicité sur ce champ d’action. Si certaines de ses compétences nécessitent d’être approfondies notamment en observations comportementales en élevage et plus généralement en bien-être animal, le vétérinaire devrait être en mesure d’appréhender l’ensemble des dimensions du bien-être d’un animal. Des indicateurs ont été scientifiquement validés dans la plupart des espèces de rente et peuvent être utilisés. Les capacités d’analyse et de synthèse doivent permettre au vétérinaire d’identifier les facteurs de risque face à un problème diagnostiqué en élevage. Interlocuteur privilégié de l’éleveur, il peut, en collaboration avec les autres intervenants, l’accompagner dans la mise en place d’actions correctives.