How social factors and behavioural strategies affect feeding and social interaction patterns in pigs

Flexible feeding: Dairy cow personality affects changes in feeding behavior and milk production under feed competition conditions

The objective of this study was to determine the effect of individual cow personality traits on feeding behavior and production under low levels of feeding competition, and to determine whether personality traits influence how feeding behavior changes in response to greater feeding competition. Forty-two Holstein cows were assigned to 1 automated feed bin per cow (low competition condition) from 15 to 28 d in milk (DIM; period 1, P1), and 2 feed bins per 3 cows (higher competition condition) from 63 to 76 DIM (period 2, P2). A total mixed ration (TMR) was fed into the automated feed bins which recorded each feed bin visit time, duration, and intake. Cow personality traits were assessed at 21 DIM during P1 and at 70 DIM during P2 using a combined arena test, measuring behavioral responses to a novel environment, novel object, and novel human. Principal components analysis of behaviors observed during the P1 combined arena test revealed 1 factor (interpreted as active-explorative) from the novel environment test explaining 51% of the variance, and 3 factors (interpreted as fearfulness, active-explorative, and sociability toward conspecifics) from each of the novel object (76% cumulative variance) and human (75% cumulative variance) tests. The principal components analysis of behaviors observed during the P2 combined arena test revealed 2 factors jointly from the environment, object, and human tests (interpreted as fearfulness and active-explorative) that together explained 68% of the variance. Fearfulness and active-explorative trait scores were correlated across P1 and P2, indicating stability of personality over a challenging period and advancing DIM. In P2 when competition for feed was increased at greater stage of lactation, the more active-explorative cows appeared to make few alterations to their feeding behavior, yet still maintained their milk yield, compared with lower competition in P1. In contrast, cows who were more fearful increased their feed bin visits from P1 to P2, and less fearful cows increased their eating rate, without increased milk production, despite advanced lactation. Overall, the results indicate that cows of different personalities adopt different feeding strategies in response to a change in their environment, and may benefit from tailored management during challenging periods.

Novel phenotypes of feeding and social behaviour and their relationship with individual rabbit growth and feed efficiency

Due to the lack of a recording system for individual consumption of group-housed rabbits, published studies about feeding behaviour are based on information recorded at the group- and not at the individual level and periods covering only a few days or, in some cases, only part of a day. Such information could be used to inform rabbit management systems but cannot be used for genetic selection. We aimed to generate and use information from a novel automated feeder for group-housed rabbits to identify new phenotypes for individual animals that could be incorporated into breeding programs to improve feed efficiency and social behaviour under different feeding regimens. At 39 d of age, rabbits from 15 batches were placed in cages and fed ad libitum to become used to the electronic feeder. From 42 to 58-59 d, one group of 1 086 rabbits was fed ad libitum (AL), while another group of 1 134 rabbits was fed on a restricted feeding schedule (R) by limiting the feeding time to the period between 1800 and 0600 h of the following day. We implemented a reliable multivariate method to remove anomalous feeding behaviour records. We then defined novel traits for feeding behaviour that apply to both types of feeding regimes, and for social behaviour that indicates an animal's rank within the cage hierarchy. We based these traits on feeder records and a biologically sound definition of a meal. Finally, we estimated the phenotypic correlations of those traits with growth and feed efficiency traits. Our findings demonstrate that variables about resource distribution among cage mates and an animal's priority for feed access were found to be good indicators of an animal's dominant or subordinate status within the cage. Based on results obtained in R animals (results were similar in AL animals), the most efficient animals were those that ate less frequently (phenotypic correlation with feed conversion ratio, rho = 0.6), and consumed smaller amounts per meal (rho = 0.7), spent less time at the feeder (rho = 0.4), and appeared to be subordinate, as they did not have priority access to the feeder (rho = -0.3), and had the smallest share of resources (range of rho = 0.2-0.6). We conclude that quantifying feeding and social behaviour traits can enhance the understanding of the mechanisms through which individuals exert their effects on the performance of their cage mates.

Automatic Recognition and Quantification Feeding Behaviors of Nursery Pigs Using Improved YOLOV5 and Feeding Functional Area Proposals

A novel method is proposed based on the improved YOLOV5 and feeding functional area proposals to identify the feeding behaviors of nursery piglets in a complex light and different posture environment. The method consists of three steps: first, the corner coordinates of the feeding functional area were set up by using the shape characteristics of the trough proposals and the ratio of the corner point to the image width and height to separate the irregular feeding area; second, a transformer module model was introduced based on YOLOV5 for highly accurate head detection; and third, the feeding behavior was recognized and counted by calculating the proportion of the head in the located feeding area. The pig head dataset was constructed, including 5040 training sets with 54,670 piglet head boxes, and 1200 test sets, and 25,330 piglet head boxes. The improved model achieves a 5.8% increase in the mAP and a 4.7% increase in the F1 score compared with the YOLOV5s model. The model is also applied to analyze the feeding pattern of group-housed nursery pigs in 24 h continuous monitoring and finds that nursing pigs have different feeding rhythms for the day and night, with peak feeding periods at 7:00-9:00 and 15:00-17:00 and decreased feeding periods at 12:00-14:00 and 0:00-6:00. The model provides a solution for identifying and quantifying pig feeding behaviors and offers a data basis for adjusting the farm feeding scheme.

Circadian rhythms and diurnal patterns in the feed intake behaviour of growing-finishing pigs

The feeding behaviour of growing-finishing pigs is an important indicator of performance, health and welfare, but this use is limited by its large, poorly-understood variation. We explored the variation in basal feed intake of individual pigs by detecting circadian rhythms, extracting features of diurnal patterns and assessing consistency over time, from day-to-day and across age. Hourly feed intake data of individual pigs (n = 110) was obtained during one growing-finishing phase, using electronic feeding stations. We applied wavelet analysis to assess rhythms and a hurdle generalised additive model to extract features of diurnal patterns. We found that circadian rhythms could be detected during 58 ± 3% (mean ± standard error) of days in the growing-finishing phase (range 0-100%), predominantly at older ages. Although the group diurnal intake pattern was alternans (small morning peak, larger afternoon peak), individual pigs showed a range of diurnal patterns that changed with age, differing mostly in the extent of night fasting and day-to-day consistency. Our results suggest that the type, day-to-day consistency and age development of diurnal patterns in feed intake show general group patterns but also differ between pigs. Using this knowledge, promising features may be selected to compare against production, health and welfare parameters.

Effect of mixing at weaning and nutrient density of the weaner diet on growth performance and welfare of pigs to slaughter

BACKGROUND

Mixing pigs at weaning can compromise pig welfare and growth. Therefore, grouping littermates together may allow a diet nutrient and energy density reduction during the nursery period to reduce feed cost without affecting slaughter weight. This study investigated the combined effect of mixing and reducing dietary energy and nutrient density on growth performance, body lesions (BL), and behaviour in pigs from weaning to slaughter.

RESULTS

Forty-eight litters [554 pigs, 11-12 pigs/litter; Danish Duroc × (Large White × Landrace)] were included in the trial. At 28 days of age, pigs were weaned and housed in nursery rooms in litter groups (INTACT, n = 24) or mixed with other litters and grouped by weight to reduce within-pen pig weight variation (MIXED, n = 24). A dietary regimen meeting pigs' nutritional requirements (CON) and a low-density dietary regimen (LOW; -10% energy and protein) completed a 2 × 2 factorial arrangement (Mixing x Diet, n = 12). On day 74 of age, pigs moved to the grower-finisher accommodation without further mixing and all pigs received the CON dietary regimen. Mixing increased FCR by 4.0% during the nursery period (p = 0.003). Nursery pigs fed LOW experienced a growth retardation which was maintained until slaughter (-2.6 kg slaughter weight; p = 0.025). Initial differences in the coefficient of variation (CV) between MIXED (10.4%) and INTACT (17.6%; p < 0.001) pigs were reduced in CON pens but not in LOW pens (interaction p = 0.025) at the end of the nursery period. MIXED pigs had more fights and BL (p < 0.001) at weaning and showed more aggression (p = 0.003) after being moved to the grower-finisher rooms. At the end of the nursery period, MIXED pigs fed LOW showed the highest number of aggressive behaviours around the feeder (interaction; p = 0.003) and pigs fed LOW showed more damaging behaviour (p < 0.001).

CONCLUSIONS

Mixing animals at weaning had limited impact on growth performance but impaired welfare which was aggravated by energy and nutrient reduction in the nursery diet. Decreasing dietary nutrient density in the nursery stage retarded growth, which could not be compensated for during the growing-finishing period.

Preferential associations in an unstable social network: applying social network analysis to a dynamic sow herd

Preferential associations are fitness-enhancing ties between individuals, documented in a range of taxa. Despite this, research into preferential associations remains underrepresented in commercial species, particularly pigs. This study investigates the development of preferential associations in a dynamic sow herd. Preferential associations were defined as approaching a resting sow and then sitting or lying with physical contact with the selected sow, separated by < 1 m from the head or directly next to her, with interaction tolerated for > 60 s. For individual identification, each sow was marked with colored dots, stripes, or both, corresponding to their ear-tag number. Preferential associations were measured over one production cycle of 21 days. Behavioral observations took place on 7 days of the study, with 3 h of behavior per day recorded during peak activity times (08:00-09:00, 15:00-16:00, 20:00-21:00 h). Behaviors were recorded using five cameras, each positioned within the barn to provide coverage of the functional areas. The network metrics applied included in-degree centrality (received ties), out-degree centrality (initiated ties), centralization (the extent to which an individual is central within the network), clustering coefficient (a measure of tie strength), and the E-I Index (a measure of assortment by trait: parity, familiarity, and sociality). Individuals were added and removed during the study, so the centrality metrics of missing sows were weighted. To describe the structure of the network, brokerage typologies were applied. Brokerage typologies include five positions, including coordinators, gatekeepers, representatives, consultants, and liaisons. The results revealed social discrimination in assortment by connectedness even when ties were not reciprocal, and the most connected sows were significantly more likely to be approached than less connected individuals. The most connected sows had significantly higher in-degree and out-degree centrality. With the application of brokerage typologies, the results showed a relationship between connectedness and brokering type, with the most connected sows predominantly engaging in coordinating behavior. The results suggest that the motivation for discrimination in the unstable preferential association network was not founded upon bidirectional interactions. These findings highlight the complexities involved when forming social preferences and present a platform for further exploring the motivations for preferential associations among intensively farmed pigs.

Are infrared thermography, feeding behavior, and heart rate variability measures capable of characterizing group-housed sow social hierarchies?

Group gestation housing is quickly becoming standard practice in commercial swine production. However, poor performance and welfare in group housed sows may result from the formation and maintenance of the social hierarchy within the pen. In the future, the ability to quickly characterize the social hierarchy via precision technologies could be beneficial to producers for identifying animals at risk of poor welfare outcomes. Therefore, the objective of this study was to investigate the use of infrared thermography (IRT), automated electronic sow feeding systems, and heart rate monitors as potential technologies for detecting the social hierarchy within five groups of sows. Behavioral data collection occurred for 12 h after introducing five sow groups (1-5; n = 14, 12, 15, 15, and 17, respectively) to group gestation housing to determine the social hierarchy and allocate individual sows to 1 of 4 rank quartiles (RQ 1-4). Sows within RQ1 were ranked highest while RQ4 sows were ranked lowest within the hierarchy. Infrared thermal images were taken behind the neck at the base of the ear of each sow on days 3, 15, 30, 45, 60, 75, 90, and 105 of the experiment. Two electronic sow feeders tracked feeding behavior throughout the gestation period. Heart rate monitors were worn by 10 randomly selected sows per repetition for 1 h prior to and 4 h after reintroduction to group gestation housing to collect heart rate variability (HRV). No differences were found between RQ for any IRT characteristic. Sows within RQ3 and RQ4 had the greatest number of visits to the electronic sow feeders overall (P < 0.04) but spent shorter time per visit in feeders (P < 0.05) than RQ1 and RQ2 sows. There was an interaction of RQ with hour for feed offered (P = 0.0003), with differences between RQ occurring in hour 0, 1, 2, and 8. Higher-ranked sows (RQ1 and RQ2) occupied the feeder for longer during the first hour than lower ranking sows (RQ3 and RQ4; P < 0.04), while RQ3 sows occupied the feeder longer than RQ1 sows during hour 6, 7, and 8 (P < 0.02). Heart beat interval (RR) collected prior to group housing introduction differed between RQ (P < 0.02 for all), with RQ3 sows exhibiting the lowest RR, followed by RQ4, RQ1, and RQ2. Rank quartile also affected standard deviation of RR (P = 0.0043), with RQ4 sows having the lowest, followed by RQ1, RQ3, and RQ2 sows. Overall, these results indicate that feeding behavior and HRV measures may be capable of characterizing social hierarchy in a group housing system.

Genetic Associations of Novel Behaviour Traits Derived from Social Network Analysis with Growth, Feed Efficiency, and Carcass Characteristics in Pigs

Reducing harmful aggressive behaviour remains a major challenge in pig production. Social network analysis (SNA) showed the potential in providing novel behavioural traits that describe the direct and indirect role of individual pigs in pen-level aggression. Our objectives were to (1) estimate the genetic parameters of these SNA traits, and (2) quantify the genetic associations between the SNA traits and commonly used performance measures: growth, feed intake, feed efficiency, and carcass traits. The animals were video recorded for 24 h post-mixing. The observed fighting behaviour of each animal was used as input for the SNA. A Bayesian approach was performed to estimate the genetic parameters of SNA traits and their association with the performance traits. The heritability estimates for all SNA traits ranged from 0.01 to 0.35. The genetic correlations between SNA and performance traits were non-significant, except for weighted degree with hot carcass weight, and for both betweenness and closeness centrality with test daily gain, final body weight, and hot carcass weight. Our results suggest that SNA traits are amenable for selective breeding. Integrating these traits with other behaviour and performance traits may potentially help in building up future strategies for simultaneously improving welfare and performance in commercial pig farms.

Genetic Analysis of Novel Behaviour Traits in Pigs Derived from Social Network Analysis

Social network analysis (SNA) has provided novel traits that describe the role of individual pigs in aggression. The objectives were to (1) estimate the genetic parameters for these SNA traits, (2) quantify the genetic association between SNA and skin lesion traits, and (3) investigate the possible response to selection for SNA traits on skin lesion traits. Pigs were video recorded for 24 h post-mixing. The observed fight and bullying behaviour of each animal was used as input for the SNA. Skin lesions were counted on different body parts at 24 h (SL24h) and 3 weeks (SL3wk) post-mixing. A Bayesian approach estimated the genetic parameters of SNA traits and their association with skin lesions. SNA traits were heritable (h2 = 0.09 to 0.26) and strongly genetically correlated (rg > 0.88). Positive genetic correlations were observed between all SNA traits and anterior SL24h, except for clustering coefficient. Our results suggest that selection for an index that combines the eigenvector centrality and clustering coefficient could potentially decrease SL24h and SL3wk compared to selection for each trait separately. This study provides a first step towards potential integration of SNA traits into a multi-trait selection index for improving pigs’ welfare.

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.

Welfare indicators associated with feed conversion ratio and daily feed intake of growing-finishing pigs

Context Understanding the welfare indicators that affect animal performance can facilitate modifications that improve both animal welfare and profitability. Aims A cross-sectional study was conducted to investigate the prevalence of animal welfare indicators and quantify their possible associations with feed conversion ratio (FCR) and daily feed intake (DFI) of growing-finishing pigs (Sus scrofa). Methods Data from 46 farms were collected. The herds ranged from 360 to 2500 pigs, which were aged between 75 and 173 days, and were managed on an all-in all-out basis. The welfare indicators were evaluated once on each farm using the methodology of the Welfare Quality® assessment protocol for pigs. Multiple linear mixed models were used to assess the associations of welfare with FCR and DFI according to the production stage at which the pigs were evaluated on the farm. Key results The welfare indicators with the highest average prevalence were frequency of coughing (35.7%), moderate bursitis (31.1%), and moderate and severe soiling with manure (18.8 and 27.7% respectively). Most of the remaining indicators related to poor welfare had prevalence values of less than 1%. The mean prevalence of positive social behaviour (such as sniffing/nosing/licking) was 14.4% and that of negative social behaviour (NSB; such as aggressive interactions) was 3.1%. The average space allowance (measured in 460 pens) was 1.04 ± 0.13 m2/pig (ranging from 0.78 to 1.36 m2/pig). Better FCRs were associated with a low prevalence of NSB (P &lt; 0.05), a low prevalence of coughing (P &lt; 0.01), absence of lameness problems (P &lt; 0.001), and small space allowances (P &lt; 0.05). Lower DFI values were associated with a low prevalence of NSB (P &lt; 0.05), a high prevalence of moderate hernias (P &lt; 0.01), a low prevalence of other active behaviours (such as eating and drinking) (P &lt; 0.001), and a high prevalence of animals with wounds on the body (P &lt; 0.05). Conclusions Few indicators related to the impairment of welfare were detected with a high prevalence, and the results suggest that the conditions related to poor welfare were associated with an impairment in animal performance. Implications The results of this study can provide the industry with comparative information to promote improvements in pig welfare. Some welfare indicators could be used on farm as predictors of performance variables; however, these indicators need validation.

Social Network Analysis of Agonistic Behaviour and Its Association with Economically Important Traits in Pigs

Simple Summary

Aggression behaviour has several negative consequences on the performance and welfare of pigs. Here, a Social Network Analysis (SNA) approach was employed to (1) identify individual traits that describe the role of each animal in the aggression; (2) investigate the association of these traits with performance and feeding behaviour traits. The study was conducted on 326 Duroc pigs reared in 29 pens. Several individual centrality traits were identified and used to calculate the Social Rank Index. The Dominant, Subordinate, and Isolated animals represented 21.1%, 57.5% and 21.4%, respectively. No significant correlations were observed between out-degree (number of initiated agonistic behaviours) and growth traits, indicating the similarity of growth patterns for dominant and non-dominant animals. Furthermore, out-degree was correlated positively with average daily occupation time (time at the feeder/day) and average daily feeding frequency (number of visits to the feeder/day), but negatively with average daily feeding rate (gr/min). This may indicate the ability of non-dominant pigs to modify their behaviour to obtain their requirements. The Hamming distances between networks showed that there is no common behaviour pattern between pens. In conclusion, SNA showed potential for extracting behaviour traits that could be used to improve pig performance and welfare.

Abstract

Aggression behaviour has several negative consequences on the performance and welfare of pigs. Here, the Social Network Analysis (SNA) approach was employed to (1) identify individual traits that describe the role of each animal in the aggression; (2) investigate the association of these traits with performance and feeding behaviour traits. The study was conducted on 326 Duroc pigs reared in 29 pens. Several individual centrality traits were identified and used to calculate the Social Rank Index. The Dominant, Subordinate, and Isolated animals represented 21.1%, 57.5% and 21.4%, respectively. No significant correlations were observed between out-degree (number of initiated agonistic behaviours) and growth traits, indicating the similarity of growth patterns for dominant and non-dominant animals. Furthermore, out-degree was correlated positively with average daily occupation time (time at the feeder/day) and average daily feeding frequency (number of visits to the feeder/day) but negatively with average daily feeding rate (gr/min). This may indicate the ability of non-dominant pigs to modify their behaviour to obtain their requirements. The Hamming distances between networks showed that there is no common behaviour pattern between pens. In conclusion, SNA showed the potential for extracting behaviour traits that could be used to improve pig performance and welfare.

A Proposed Role for Pro-Inflammatory Cytokines in Damaging Behavior in Pigs

Sickness can change our mood for the worse, leaving us sad, lethargic, grumpy and less socially inclined. This mood change is part of a set of behavioral symptoms called sickness behavior and has features in common with core symptoms of depression. Therefore, the physiological changes induced by immune activation, for example following infection, are in the spotlight for explaining mechanisms behind mental health challenges such as depression. While humans may take a day off and isolate themselves until they feel better, farm animals housed in groups have only limited possibilities for social withdrawal. We suggest that immune activation could be a major factor influencing social interactions in pigs, with outbreaks of damaging behavior such as tail biting as a possible result. The hypothesis presented here is that the effects of several known risk factors for tail biting are mediated by pro-inflammatory cytokines, proteins produced by the immune system, and their effect on neurotransmitter systems. We describe the background for and implications of this hypothesis.

Unravelling variation in feeding, social interaction and growth patterns among pigs using an agent-based model

Domesticated pigs, Sus scrofa, vary considerably in feeding, social interaction and growth patterns. This variation originates partly from genetic variation that affects physiological factors and partly from behavioural strategies (avoid or approach) in competitive food resource situations. Currently, it is unknown how variation in physiological factors and in behavioural strategies among animals contributes to variation in feeding, social interaction and growth patterns in animals. The aim of this study was to unravel causation of variation in these patterns among pigs. We used an agent-based model to explore the effects of physiological factors and behavioural strategies in pigs on variation in feeding, social interaction and growth patterns. Model results show that variation in feeding, social interaction and growth patterns are caused partly by chance, such as time effects and coincidence of conflicts. Furthermore, results show that seemingly contradictory empirical findings in literature can be explained by variation in pig characteristics (i.e. growth potential, positive feedback, dominance, and coping style). Growth potential mainly affected feeding and growth patterns, whereas positive feedback, dominance and coping style affected feeding patterns, social interaction patterns, as well as growth patterns. Variation in behavioural strategies among pigs can reduce aggression at group level, but also make some pigs more susceptible to social constraints inhibiting them from feeding when they want to, especially low-ranking pigs and pigs with a passive coping style. Variation in feeding patterns, such as feeding rate or meal frequency, can indicate social constraints. Feeding patterns, however, can say something different about social constraints at group versus individual level. A combination of feeding patterns, such as a decreased feed intake, an increased feeding rate, and an increased meal frequency might, therefore, be needed to measure social constraints at individual level.