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[Display omitted] • 57–86 % of diseases, oestrus and calving of cows are detected with random forest applied on activity data. • Some cow conditions can be detected from activity-one or two days before caretakers noticing them. • Highest activity level during the day and the cyclic components are the most discriminant features. • The proportion of false alarms remains very high (from 46 to 89 %). • The model was tested on 5 independent datasets (total, 120,000 cow*days) to check its robustness. Precision livestock farming (PLF) technologies help to detect animals experiencing specific conditions such as illness, stress, or oestrus. PLF systems either detect one or more conditions at a time, or several conditions together but without distinguishing them. The aim of this work was to detect and classify a wide range of conditions of health, stress and reproduction based on cow daily activity using machine learning on time series data derived from sensors. We used five datasets (120,000 cow*days) from experimental or commercial farms, with 28 to 300 Holstein cows per dataset and observations lasting from 1 to 12 months. Caretakers checked the animals every day and recorded any specific cow condition related to disease (lameness, mastitis, acidosis, accident, and so on), oestrus, calving, as well as potential stress due to mixing or handling. We labelled a number of days before and after the specific condition during which the behaviour might be altered. Sensors (location systems or accelerometers) captured the per-hour duration of key cow activities - eating, resting, in alleys - that served to compute the activity level. The distribution of the activity level in each 24 h time series was described by 21 time-domain or frequency-domain features. We then ran the random forest algorithm to relate the features of the time series to animal condition (1000 trees; 10 random splits of 70 % train / 30 % test subsets; oversampling applied on train subsets to reduce the imbalance between the occurrences of cow conditions). The most important features in the random forest algorithm were, in descending order: maximum, 90% quantile, Fourier harmonics 2 and 1, 50 % and 75 % quantiles, Fourier harmonics 3 and 4. This approach correctly classified over 90 % of the control series (i.e. with no specific condition) and classified 24 % of the specific condition-labelled series as non-control series. The condition-labelled series can be confused with control series and to a lesser extent with series labelled disturbances or oestrus. There was 56 %–86 % probability of successfully detecting at least one 24 h series around a disease, oestrus, or calving. The detection can occur 1–2 days before the day caretakers noticed the event. This study shows that machine learning on time series can detect and discriminate several conditions of cow impaired welfare and could thus be implemented in PLF systems to facilitate herd management. The sensitivity of the method must be improved before it can be spread out on farms. [ABSTRACT FROM AUTHOR]

Abstract: The objective was to investigate the effects of Burdizzo castration on the thermal nociception (stress-induced hypoalgesia) of Holstein–Friesian bull calves of different ages. Calves castrated at 5.5mo of age were compared with either intact calves of the same age, or calves castrated at 1.5, 2.5, 3.5, and 4.5mo of age (n =10 calves/treatment). Treatments were conducted on the same day for all calves. The baseline surface skin temperatures on the caudal part of metatarsi, and the latency of the calves to perform hind leg withdrawal (i.e., thermal nociception threshold) in response to a CO2 laser beam applied on the same area were measured 72h before, and 12, 24 and 48h after treatment. The thermal nociception threshold varied inversely with the baseline skin temperature (pooled correlations, r =−0.45, −0.31 and −0.48 at 12, 24 and 48h, respectively; P <0.01). There were no differences (P >0.05) in the skin temperatures between castrated and intact calves at 5.5mo of age. Calves castrated at 1.5mo-old had consistently lower skin temperatures than all other castrated calves throughout the study. These calves had markedly increased skin temperatures following castration, while the opposite trend was observed in older castrated calves, and no change was observed in intact calves. At −72h, the 1.5mo-old calves had higher thermal nociception thresholds than older calves. In all calves, the thermal nociception threshold increased after treatment. Calves castrated at 5.5mo of age tended to display higher thermal nociception threshold than intact calves. However, variations in the initial skin temperature accounted for these differences between treatments or interactions between time and treatment. In conclusion, the laser-based thermal nociception assay can be influenced by the surface skin temperature of the hind legs and the age of animals, particularly in calves less than 2mo of age which have lower skin temperatures and longer latency to respond to the laser. Burdizzo castration increased the skin temperature of 1.5mo-old calves, but had the opposite effects on older calves. Within the temporal limits of this study, no conclusive evidence was found to support the presence of acute stress-induced hypoalgesia following castration. [Copyright &y& Elsevier]

Society has a number of expectations around livestock farming that go beyond mere production and affordable food prices to now encompass high standards of animal welfare and environmental performance. Here we investigate whether and how it is possible to concurrently achieve good economic, environmental, and animal welfare performances on suckler cattle farms. We extracted economic indicators, proxies for animal welfare and environmental performances, and data describing farming practices and conditions from a technical-economic database featuring data collected from >250 French suckler farms over the period 2016–2022. We analysed the relationships between animal welfare performance, environmental performance and economic performance using a structural equation modelling (SEM) approach. We then used logit models to identify farming practices and conditions that promote 'multiperformance'. Farms that combine practices where nutritional needs of suckler cattle are synchronised with the grass availability cycle are more likely to multiperform. The synchronisation is managed by exploiting certain key animal characteristics (depletion and restoral of body reserves), choosing the right calving season, and selling animals well adapted to grass-feeding. Combining two analytical models—one establishing the relationships between several performance dimensions and one establishing the relationships between multiperformance and farming practices—allows to bridge the gap between theoretical concepts and concrete farming measures on the topical issue of achieving multiperformance in more than two dimensions, where the literature is still scarce. [Display omitted] • Society wants livestock farms to concurrently deliver animal welfare, environmental and economic performances. • We investigate whether and how multiperformance is achievable based on technical-economic data of 250 suckler cattle farms. • We use structural equation modelling to assess the concepts of animal welfare, environmental and economic performances. • There is a positive moderately-strong link between the three performance dimensions. • Combining practices that synchronise cattle nutritional needs with the grass growth cycle is a multiperformance strategy. [ABSTRACT FROM AUTHOR]

Maladjusted cubicles for dairy cattle may cause increased skin alterations, lameness, and dirtiness. The International Commission of Agricultural and Biosystems Engineering has produced several recommendations for cubicle design, but a previous study showed that not all of them seem efficient. Here, we aim to refine and complete these recommendations. We collected data on 76 dairy farms (2,404 cows). We modeled the association between combinations of cubicle properties (e.g., type of bedding litter) and dimensions (e.g., cubicle width) relative to cow size, and prevalence of cow skin alterations, lameness, and dirtiness. We used weighted multivariable logistic regression models to predict the presence of skin alteration on the carpus; the neck, shoulder, and back; the flank, side, and udder; and the tarsus or hindquarters. We also evaluated the presence of lameness as well as the dirtiness of the lower hind legs including hocks; the hindquarters, upper hind legs, and flank; the cow rear including tail; and the udder. The risk factors highlighted led us to recommend (1) position cubicles in a way that leaves more than 1 m of clearance from any obstacle in front of the cubicle; (2) if there is an obstacle on the lateral plane (i.e., where the cubicle partition is) in front ahead of the cow, put the obstacle in front of the fore knees; (3) if there is an obstacle in front of the cow on the median plane (e.g., neck or front rail), the position the obstacle between 1.25 and 1.5 of the cow length from the curb and between 1.0 and 1.25 of its height; (4) use curb height between 0.11 and 0.15 of cow height with no sharp edges on the curb; (5) use round or at least has no sharp edges brisket board; (6) use a stone-free soil instead of concrete or use a mattress thicker than 1 cm, with microrelief, and a soft fixing area at the curb, (7) litter with straw (rather than nothing or sawdust) and keep it dry. This risk factor analysis should be followed by experiments in controlled environments to further validate these conclusions and used to update the International Commission of Agricultural and Biosystems Engineering recommendations. [ABSTRACT FROM AUTHOR]

Within a 36-h time series, we use Fast Fourier transform (FFT) to model the circadian rhythm of activity during the first and last 24-h segments. After aligning the two models in time, we calculate the Euclidian distance between them and compare it to a given threshold, above which we conclude that the rhythm has changed. • Our Fourier transform-based method detects changes in circadian rhythm. • Circadian changes in cows link to disease, stress, or calving/oestrus events. • The method detects 95% of the rhythm anomalies due to reproductive or disease events. • It can help detect animals needing care. • It can also assess overall animal welfare status or health/stress-sensitive phenotypes. 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 ~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. [ABSTRACT FROM AUTHOR]

• Location Big data of 158 cows during four months to study their individual and group social dynamics. • We found that sociality is an individual trait, unrelated to the group and relatively persistent once established. • Findings have the potential to help understanding the social dynamic when mixing dairy cows. In many species, animals live in highly structured groups. In these groups, individual differences in the number and identity of social contacts (alters) of each ego define the social network structure of the animal group. The composition of groups can be disturbed by grouping animals according to age or production stage, which can in turn induce stress. We investigated whether the preference of two animals to stay together depends on the sociality of each individual (defined by the time it spends close to other animals) or on the composition of the group. We observed 158 dairy cows distributed in 6 pens during 17 weeks. The precise positions of the cows were monitored with positional loggers 24/7 in two groups with fixed independent populations and during the formation of new groups with varying population. In fixed groups, the sociality of a cow was maintained over the entire observation period, but this trait depended on the area contacts occurred (inside vs. outside the resting area). When introducing foreign individuals into social groups, the sociality of individual cows was maintained independently of the group; this sociality was therefore not necessarily influenced by the time spent in the group but by the social characteristics of individual cows. During the formation of new groups, newly introduced cows dynamically interacted with resident ones, forming a few strong short-lasting contacts between newcomers and resident cows. However, a few long-lasting interactions occurred between resident and newcomers and the whole network is weakened. Our study suggests that each cow has its own sociality independent of the group and tend to establish relations with specific partners when the population is fixed. In addition, when introducing cows into a group, the social network of the group is weakened, with absence of strong links between newcomers and resident cows and a decrease in the links between resident cows for at least 2 weeks. [ABSTRACT FROM AUTHOR]

Design of cubicles and self-locking barriers can affect cow skin alterations, lameness, and dirtiness. We investigated whether the International Commission of Agricultural and Biosystems Engineering (CIGR, Gainesville, FL)-recommended cattle housing design and dimensions actually improve cow welfare. We recorded individual cow body dimensions and assessed skin alterations, dirtiness, and lameness on 3,841 cows from 131 loose housing dairy farms (76 farms with cubicles and 55 straw-yard systems). We recorded the dimensions of cubicles (e.g., width, length, and so on) and of the self-locking barrier (e.g., top rail height and so on) for each farm. We then compared whether these dimensions would match with the individual cow body dimensions and whether compliance was associated with the occurrence of skin alterations, lameness, and dirtiness. Most cows (69.2%) had at least one skin alteration, on the tarsus (41.2%); neck, shoulder, or back (28.2%); hindquarters (22.0%); carpus (21.2%); and flank, side, or udder (11.6%). Lameness prevalence reached 17.9%. Most cows (83.1%) were scored dirty in at least one body region, mainly on the lower hind legs including hocks (81.4%), hindquarters (41.8%), and udder (21.0%). Cubicle recommendations were mostly met for cubicle resting length (75.9% of cubicles) and neck rail distance (NRD, 60.7%) but less so for overall cubicle length (CL, 38.8%), cubicle width (CW, 30.9%), neck rail height (NRH, 22.5%), head space (HS, 15.8%), partition zone for controlling lying position (ii zone, 15.7%), head and lunging space (HLS, 10.6%), partition head zone (i zone, 9.4%), and partition zone for pelvis freedom (iii zone, 6.0%). Compliance with design recommendations was associated with fewer skin alterations on neck (recommendation met for CW, CL, HS, and NRD and i zone), hindquarters (CW, CL, NRD), tarsus (CW, i and ii zones), and carpus (CW, HS, i and ii zones); less lameness (CW, NRH, i zone); and less dirtiness on the hindquarters (CW, HLS, NRD), lower hind legs (iii zone), and udder (CW, CL, HLS, NRD). Compliance with recommended i zone and ii zone design was associated with less injury and lameness but more dirtiness, whereas the opposite was true for the iii zone. Self-locking barrier recommendations were mostly met for bottom rail height (68.2%), separation wall width (SWW, 68.3%), and top rail height (TRH, 56.9%) and less often met for separation wall height (36.3%) and height difference between feeding floor and walking alley (26.5%). Risk for skin alterations on carpus and neck only decreased when SWW and TRH met recommendations. In conclusion, compliance with CIGR recommendations for some cubicle dimensions and neck rail position was associated with lower risk for cow welfare. However, the CIGR recommendations on cubicle partitions and self-locking barriers still leave welfare at risk and so need to be refined through further research with close observation of cow lying and feeding behavior. [ABSTRACT FROM AUTHOR]

Long-term monitoring of animal activity can yield key information for both researchers in ethology and engineers in charge of developing precision livestock farming tools. First, a barn is segmented into delimited areas (e.g. cubicles) with which an activity can be associated (e.g. resting), then a real-time location system (RTLS) can be used to automatically convert cow position into behaviour. Working within the EU-PLF project, we tested a system already able to determine basic activities (resting, moving, eating...) and logged a "big data" set of billions of data points (123 days × 190 cows × 1 location-per-second readings). We then focused on integrating image analysis techniques to help visualise and analyse the dataset, first to validate the data and then to enrich the information extracted. The algorithm developed using freely available tools quickly confirmed the ability of the system to determine cows' main activities (except drinking behaviour), even with 11% of positions missing. The good localisation precision (16 cm) made it possible to enrich the time-budget with new activities such as using brushes and licking mineral blocks. For both activities, using visual observations as gold standard, activity profiles with excellent sensitivity (nearly 80%) were extracted. This validation procedure is both necessary and generalisable to other situations. The improvement of biological information contained in such data holds promise for people designing alarm devices and health and welfare indicators for farmers and/or vets. Graphical abstract Image 1 Highlights • Real-time location can be used to automatically convert cow position into behaviour. • Image analysis helps visualise and analyse big dataset from positioning system. • Tracking to monitor activity: a necessary and generalisable data assessment procedure. [ABSTRACT FROM AUTHOR]

So far, digital technology development in agriculture has mainly dealt with precision agriculture, often associated with conventional large-scale systems. The emergence of digital agriculture - based on the triptych of "new data sources / new processing methods / new inter-connection capacities (internet)" - opens up prospects for mobilizing digital technologies to accelerate the deployment of other forms of agriculture, such as agroecology. A specific research agenda must therefore be built to redirect researchers specialized in digital technologies towards these new issues. This construction is significant because digital technology and agroecology are disruptive innovations that shake up the actors' practices, agricultural innovation ecosystems, and value chains. An interdisciplinary group of INRAE researchers (covering 10 scientific departments) was mandated to carry out this reflection, with the objective of developing a research agenda to better couple digitalization and agroecology, in order to pave the way for responsible digital farming. The group used the framework of responsible research and innovation. Over 18 months, the group met monthly by video-conference, to overcome the interdisciplinarity barrier, and at three face-to-face seminars, where creative design exercises were carried out (based on a world café format, and "remember the future" method). This work gave rise to three prospective lines of research aimed at putting digitalization at the service of agroecology and local food systems. These topics prioritize research that fosters innovations in digital technology, as well as organisations and policies that (1) accelerate the agroecological transition on the farm and in the territories, (2) manage the territories as commons, (3) empower farmers and consumers. Then, the group examined these three prospective lines of research from an RRI perspective as well as three current research topics on digital agriculture (digital soil mapping, precision agriculture, technologies for food wastage reduction). This work allowed us to highlight the gaps between current research on digital agriculture and the RRI expectations, and the tensions (between rationalization and diversity of farming systems, between complexity of agroecological systems and the need for simplification of models, and finally between data speculation and frugality). We were also able to refine the specific scientific questions of each prospective line of research and finally to draw attention to the key levers that will have to be integrated if these research efforts are to be approached from an RRI perspective. This contribution shows RRI can be used not only to reflect on research practices but also as a framework to build a research agenda paving the way for responsible digital agriculture. [Display omitted] • So far, research on digital technologies has focused on precision agriculture and has rarely addressed smallholders' issues. • Our interdisciplinary group aims at building a research agenda to foster an agroecology-based digitalization of agriculture. • Three lines of research were built in creative sessions and studied via Responsible Research and Innovation (RRI) principles. • RRI helped us both to express original research questions and to raise key issues for correctly addressing them. • RRI has been useful for building this research agenda on smart agriculture, by combatting researchers' own path dependency. [ABSTRACT FROM AUTHOR]

• Cows activity is modified under an episode of subacute ruminal acidosis. • Thanks to Machine Learning (ML) one can predict cow activity from one day to another. • Discrepancies between ML-predicted and observed activity can reveal abnormal behaviour. • KNNR seems the most efficient algorithm for detecting abnormal activity and disease. Sickness behaviour is characterised by a lethargic state during which the animal reduces its activity, sleeps more and at times when normally awake, reduces its feed and water intake, and interacts less with its environment. Subtle modifications in behaviour can materialise just before clinical signs of a disease. Recent sensor developments enable continuous monitoring of animal behaviour, but the shift to abnormal animal activity remains difficult to detect. We explored the use of Machine Learning (ML) to detect abnormal behaviour from continuous monitoring. We submitted 14 cows (Bos taurus) to Sub-Acute Ruminal Acidosis (SARA), a disease known to induce changes in behaviour. Another 14 control cows were not submitted to SARA. We used a ruminal bolus to monitor pH and detect when a cow experienced SARA. We used a positioning system to infer an animal's activity based on its position in relation to specific elements in the barn (feeder, resting area, and alleys). We tested several ML algorithms: K Nearest Neighbours for Regression (KNNR); Decision Tree for Regression (DTR); MultiLayer Perceptron (MLP); Long Short-Term Memory (LSTM); and an algorithm where activity is assumed to be similar from one day to the next. First, we developed ML models to predict activity on a given day from the previous 24 h, considering all cows together. Then, we calculated the error between observed and predicted values for a given cow. Finally, we compared the error to a threshold chosen to optimise the distinction between normal and abnormal values. KNNR performed best, detecting 83% of SARA cases (true-positives), but it also produced 66% of false-positives, which limits its use in practice. In conclusion, ML can help detect anomalies in behaviour. Further improvements could probably be obtained by applying ML on very large datasets at animal rather than group level. [ABSTRACT FROM AUTHOR]

Corrigendum to "Do International Commission of Agricultural and Biosystems Engineering (CIGR) dimension recommendations for loose housing of cows improve animal welfare?" Table 2 International Commission of Agricultural and Biosystems Engineering (CIGR, 2014) recommendations on dimensions for cubicles and self-locking barriers and requirement for each recommendation to be met 1 A. de Boyer des Roches, R. Lardy, J. Capdeville, L. Mounier, I. Veissier, Do International Commission of Agricultural and Biosystems Engineering (CIGR) dimension recommendations for loose housing of cows improve animal welfare?. [Extracted from the article]

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., (© 2023. The Author(s).)

The Open Science movement aims at ensuring accessibility, reproducibility, and transparency of research. The adoption of Open Science practices in animal science, however, is still at an early stage. To move ahead as a field, we here provide seven practical steps to embrace Open Science in animal science. We hope that this paper contributes to the shift in research practices of animal scientists towards open, reproducible, and transparent science, enabling the field to gain additional public trust and deal with future challenges to guarantee reliable research. Although the paper targets primarily animal science researchers, the steps discussed here are also applicable to other research domains., (© The Author(s) 2022. Published by Oxford University Press on behalf of the National Academy of Sciences.)

Many biological studies seek to confirm the effect of a treatment on the levels of chemical markers in biological tissues. This requires choosing the analytical method best able to detect the difference between basal levels and those found after treatment. We propose a new approach to calculate a criterion we call the 'discrimination threshold' (DT), and we applied it to an analytical method that we developed to determine cortisol in cattle plasma to detect stress. DT is derived from the measurement uncertainty (MU), and combines the variabilities of both the analytical method and the marker. The uncertainty of the analytical method comes from the method validation study. The marker variability (here cortisol) is modeled from a literature review. The graphical representation of DT allows estimating the applicability of the method. Our analytical method is shown to perform well when the difference in blood cortisol exceeds 18 ng. mL -1 ., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Under temperate climates, cattle are often at pasture in summer and are not necessarily provided with shade. We aimed at evaluating in a temperate region (Belgium) to what extent cattle may suffer from heat stress (measured through body temperature, respiration rate and panting score, cortisol or its metabolites in milk, and feces on hot days) and at assessing the potential benefits of shade. During the summer of 2012, 20 cows were kept on pasture without access to shade. During the summer of 2011, ten cows had access to shade (young trees with shade cloth hung between them), whereas ten cows had no access. Climatic conditions were quantified by the Heat Load Index (HLI). In animals without access to shade respiration rates, panting scores, rectal temperatures, and milk cortisol concentrations increased as HLI increased in both 2011 and 2012. Fecal cortisol metabolites varied with HLI in 2011 only. When cattle had access to shade, their use of shade increased as the HLI increased. This effect was more pronounced during the last part of the summer, possibly due to better acquaintance with the shade construction. In this case, shade use increased to 65% at the highest HLI (79). Shade tempered the effects on respiration, rectal temperature, and fecal cortisol metabolites. Milk cortisol was not influenced by HLI for cows using shade for > 10% of the day. Therefore, even in temperate areas, cattle may suffer from heat when they are at pasture in summer and providing shade can reduce such stress.

One of the major topics of applied ethology is the welfare of animals reared by humans. Welfare can be defined as a state of harmony between an individual and its environment. Any marked deviation from this state, if perceived by the individual, results in a welfare deficit due to negative emotional experiences. In humans, verbal language helps to assess emotional experiences. In animals, only behavioural and physiological measurements help to detect emotions. However, how to interpret these responses in terms of emotional experiences remains an open question. The information on the cognitive abilities of farm animals, which are available but scattered, could help the understanding of their emotions. We propose a behavioural approach based on cognitive psychology: emotions can be investigated in farm animals in terms of the individual's appraisal of the situation. This evaluative process depends on: (a) the intrinsic characteristics of the eliciting event (suddenness, novelty, pleasantness); (b) the degree of conflict of that event with the individual's needs or expectations; and (c) the individual's coping possibilities offered by the environment. The result of such an evaluation determines the negative versus positive emotions. We propose an analysis of the emotional repertoire of farm animals in terms of the relationship between the evaluative process of the event on the one hand and the behavioural and physiological responses on the other hand.