Investigation of reward quality-related behaviour as a tool to assess emotions

Trialling Locally Made, Low-Cost Bits to Improve Bit-Related Welfare Problems in Cart Horses: Findings from a Study in Senegal

Bits used for cart horses in Senegal are typically made of recovered construction iron and often have defects related to design, shape, fit and metal quality. Consequently, there is widespread presence of bit-related oral injury amongst these equids. It was hypothesised that improving bit design would ameliorate bit-related welfare issues for working cart horses. This study aimed to develop locally made alternative bit prototypes and test their efficacy as less harmful to working horses, and their acceptability to their drivers. Eight animal-based welfare indicators (four physical and four behavioural) were designed to measure positive or negative effects of the new bits. Following a testing phase to appraise and mitigate potential animal welfare risk associated with the alternative bit designs, a total of 540 driver/horse combinations were opportunistically selected across five municipalities in Senegal. Welfare indicators were observed when new bits were introduced and again after 21 weeks of daily use. The results indicated statistically significant improvements in all welfare indicators measured (i.e., lesions on lip commissures, tongue, buccal mucosa and bars; and open mouth, tongue loll, head toss/shake, and head tilt/turn behaviours). None of the drivers reported any difficulty with horse control, nor chose to revert back to their original bits. Whilst acknowledging the limitation of inability to control all potential confounding variables, these preliminary findings suggest the bit itself as an important contributor to oral injury, and the possibility to improve this through alternative bit design that is low-cost, locally produced and acceptable to drivers.

Humans and Goats: Improving Knowledge for a Better Relationship

Simple Summary

A good relationship between humans (e.g., farmers, owners) and farm animals is vital for the well-being of both parties: on the one hand, people are satisfied with their work, which becomes less stressful and more profitable, and may receive social benefits in terms of education or animal-assisted therapy; on the other hand, animals are rewarded by the presence of humans and are not afraid of them. Goats have high cognitive and communicative abilities towards humans: recognising these abilities helps humans to work properly on the quality of this relationship that is built from the first hours of the goat kids’ life, thanks to frequent and positive contacts (e.g., stroking, talking in a calm voice). Improving the quality of this relationship is an investment in the future of livestock farming and meets public demands for ethical and sustainable production. This review outlines the characteristics and predisposing factors for the establishment of a good human–goat relationship and for its evaluation.

Abstract

There is consensus that the quality of the human–animal relationship (HAR) is relevant to guarantee appropriate levels of animal welfare. Given the impact that HAR may have on both goats and human beings, the aim of the present review is to elucidate: (1) how humans and goats communicate; (2) which are the factors affecting human–goat interactions; (3) how we can measure the quality of this relationship. The systematic review led to the selection of 58 relevant articles. Effective human–goat communication takes place by means of visual, tactile and auditory stimuli and, to a less extent, via olfactory and gustative stimuli. Goats have well-developed socio-cognitive abilities and rely on humans to get relevant information. A deep knowledge of goats’ communication means and socio-cognitive abilities may greatly help improving the human–goat relationship. Management practices (e.g., rearing methods, amount and quality of interactions), as well as genetic selection for suitable individual traits, may contribute to improving HAR. Several measures to assess the quality of HAR have been validated, including avoidance in the pen and at the feeding rack and latency to first contact. Finally, farmers’ attitudes and empathy with goats, as well as their motivation to work with animals, should be improved through appropriate training.

Affective State Recognition in Livestock—Artificial Intelligence Approaches

Simple Summary

Emotions or affective states recognition in farm animals is an underexplored research domain. Despite significant advances in animal welfare research, animal affective state computing through the development and application of devices and platforms that can not only recognize but interpret and process the emotions, are in a nascent stage. The analysis and measurement of unique behavioural, physical, and biological characteristics offered by biometric sensor technologies and the affiliated complex and large data sets, opens the pathway for novel and realistic identification of individual animals amongst a herd or a flock. By capitalizing on the immense potential of biometric sensors, artificial intelligence enabled big data methods offer substantial advancement of animal welfare standards and meet the urgent needs of caretakers to respond effectively to maintain the wellbeing of their animals.

Abstract

Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are not scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time-consuming, interrupt farming processes and involve subjective judgments. Biometric sensor data enabled by artificial intelligence is an emerging smart solution to unobtrusively monitoring livestock, but its potential for quantifying affective states and ground-breaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and behaviour in real time are a near-term possibility.

Positive Welfare in Science and Society: Differences, Similarities and Synergies

Societal and scientific perspectives of animal welfare have an interconnected history. However, they have also, somewhat, evolved separately with scientific perspectives often focusing on specific aspects or indicators of animal welfare and societal perspectives typically taking a broader and more ethically oriented view of welfare. In this conceptual paper, we examine the similarities and differences between scientific and societal perspectives of positive welfare and examine what they may mean for future discussions of animal welfare considered as a whole. Reviewing published studies in the field we find that (UK and Republic of Ireland) farmers and (UK) members of the public (i.e., society) typically consider both negatives (i.e., minimising harms) and positives (i.e., promoting positive experiences) within the envelope of positive welfare and prioritise welfare needs according to the specific context or situation an animal is in. However, little consideration of a whole life perspective (e.g., the balance of positive and negative experiences across an animal's lifetime) is evident in these societal perspectives. We highlight how addressing these disparities, by simultaneously considering scientific and societal perspectives of positive welfare, provides an opportunity to more fully incorporate positive welfare within a comprehensive understanding of animal welfare. We suggest that a consideration of both scientific and societal perspectives points to an approach to welfare which accounts for both positive and negative experiences, prioritises them (e.g., by seeing positive experiences as dependent on basic animal needs being fulfilled), and considers the balance of positives and negatives over the lifetime of the animals. We expand on this view and conclude with its potential implications for future development of how to understand and assess animal welfare.

Piglets vocally express the anticipation of pseudo-social contexts in their grunts

Emotions not only arise in reaction to an event but also while anticipating it, making this context a means of accessing the emotional value of events. Before now, anticipatory studies have rarely considered whether vocalisations carry information about emotional states. We studied both the grunts of piglets and their spatial behaviour as they anticipated two (pseudo)social events known to elicit positive emotions of different intensity: arrival of familiar conspecifics and arrival of a familiar human. Piglets spatially anticipated both pseudo-social contexts, and the spectro temporal features of grunts differed according to the emotional context. Piglets produced low-frequency grunts at a higher rate when anticipating conspecifics compared to anticipating a human. Spectral noise increased when piglets expected conspecifics, whereas the duration and frequency range increased when expecting a human. When the arrival of conspecifics was delayed, the grunt duration increased, whereas when the arrival of the human was delayed, the spectral parameters were comparable to those during isolation. This shows that vocal expressions in piglets during anticipation are specific to the expected reward. Vocal expressions-both their temporal and spectral features- are thus a good way to explore the emotional state of piglets during the anticipation of challenging events.