Aggression In Pigs Research Articles

Risk factors for antimicrobial use in Dutch pig farms: A cross-sectional study

BackgroundAntimicrobial use (AMU) has decreased significantly in Dutch pig farms since 2009. However, this decrease has stagnated recently, with relatively high AMU levels persisting mainly among weaners. The aim of this study was to identify farm-level characteristics associated with: i) total AMU and ii) use of specific antimicrobial classes. MethodsIn 2020, cross-sectional data from 154 Dutch pig farms were collected, including information on AMU and farm characteristics. A mixed-effects conditional Random Forest analysis was applied to select the subset of features that was best associated with AMU. ResultsThe main risk factors for total AMU in weaners were vaccination for PRRS in sucklings, being a conventional farm (vs. not), high within-farm density, and early weaning. The main protective factors for total AMU in sows/sucklings were E. coli vaccination in sows and having boars for estrus detection from own production. Regarding antimicrobial class-specific outcomes, several risk factors overlapped for weaners and sows/sucklings, such as farmer's non-tertiary education, not having free-sow systems during lactation, and conventional farming. An additional risk factor for weaners was having fully slatted floors. For fatteners, the main risk factor for total AMU was PRRS vaccination in sucklings. ConclusionsSeveral factors found here to be associated with AMU. Some were known but others were novel, such as farmer's tertiary education, low pig aggression and free-sow systems which were all associated with lower AMU. These factors provide targets for developing tailor-made interventions, as well as an evidence-based selection of features for further causal assessment and mediation analysis.

Detection of Pig Movement and Aggression Using Deep Learning Approaches.

Motion and aggressive behaviors in pigs provide important information for the study of social hierarchies in pigs and can be used as a selection indicator for pig health and aggression parameters. However, relying only on visual observation or surveillance video to record the number of aggressive acts is time-consuming, labor-intensive, and lasts for only a short period of time. Manual observation is too short compared to the growth cycle of pigs, and complete recording is impractical in large farms. In addition, due to the complex process of assessing the intensity of pig aggression, manual recording is highly influenced by human subjective vision. In order to efficiently record pig motion and aggressive behaviors as parameters for breeding selection and behavioral studies, the videos and pictures were collected from typical commercial farms, with each unit including 8~20 pigs in 7~25 m2 space; they were bred in stable social groups and a video was set up to record the whole day's activities. We proposed a deep learning-based recognition method for detecting and recognizing the movement and aggressive behaviors of pigs by recording and annotating head-to-head tapping, head-to-body tapping, neck biting, body biting, and ear biting during fighting. The method uses an improved EMA-YOLOv8 model and a target tracking algorithm to assign a unique digital identity code to each pig, while efficiently recognizing and recording pig motion and aggressive behaviors and tracking them, thus providing statistics on the speed and duration of pig motion. On the test dataset, the average precision of the model was 96.4%, indicating that the model has high accuracy in detecting a pig's identity and its fighting behaviors. The model detection results were highly correlated with the manual recording results (R2 of 0.9804 and 0.9856, respectively), indicating that the method has high accuracy and effectiveness. In summary, the method realized the detection and identification of motion duration and aggressive behavior of pigs under natural conditions, and provided reliable data and technical support for the study of the social hierarchy of pigs and the selection of pig health and aggression phenotypes.

Efficient Aggressive Behavior Recognition of Pigs Based on Temporal Shift Module

Aggressive behavior among pigs is a significant social issue that has severe repercussions on both the profitability and welfare of pig farms. Due to the complexity of aggression, recognizing it requires the consideration of both spatial and temporal features. To address this problem, we proposed an efficient method that utilizes the temporal shift module (TSM) for automatic recognition of pig aggression. In general, TSM is inserted into four 2D convolutional neural network models, including ResNet50, ResNeXt50, DenseNet201, and ConvNext-t, enabling the models to process both spatial and temporal features without increasing the model parameters and computational complexity. The proposed method was evaluated on the dataset established in this study, and the results indicate that the ResNeXt50-T (TSM inserted into ResNeXt50) model achieved the best balance between recognition accuracy and model parameters. On the test set, the ResNeXt50-T model achieved accuracy, recall, precision, F1 score, speed, and model parameters of 95.69%, 95.25%, 96.07%, 95.65%, 29 ms, and 22.98 M, respectively. These results show that the proposed method can effectively improve the accuracy of recognizing pig aggressive behavior and provide a reference for behavior recognition in actual scenarios of smart livestock farming.

A computer vision image differential approach for automatic detection of aggressive behavior in pigs using deep learning.

Pig aggression is a major problem facing the industry as it negatively affects both the welfare and the productivity of group-housed pigs. This study aimed to use a supervised deep learning (DL) approach based on a convolutional neural network (CNN) and image differential to automatically detect aggressive behaviors in pairs of pigs. Different pairs of unfamiliar piglets (N = 32) were placed into one of the two observation pens for 3 d, where they were video recorded each day for 1 h following mixing, resulting in 16 h of video recordings of which 1.25 h were selected for modeling. Four different approaches based on the number of frames skipped (1, 5, or 10 for Diff1, Diff5, and Diff10, respectively) and the amalgamation of multiple image differences into one (blended) were used to create four different datasets. Two CNN models were tested, with architectures based on the Visual Geometry Group (VGG) VGG-16 model architecture, consisting of convolutional layers, max-pooling layers, dense layers, and dropout layers. While both models had similar architectures, the second CNN model included stacked convolutional layers. Nine different sigmoid activation function thresholds between 0.1 and 1.0 were evaluated and a 0.5 threshold was selected to be used for testing. The stacked CNN model correctly predicted aggressive behaviors with the highest testing accuracy (0.79), precision (0.81), recall (0.77), and area under the curve (0.86) values. When analyzing the model recall for behavior subtypes prediction, mounting and mobile non-aggressive behaviors were the hardest to classify (recall = 0.63 and 0.75), while head biting, immobile, and parallel pressing were easy to classify (recall = 0.95, 0.94, and 0.91). Runtimes were also analyzed with the blended dataset, taking four times less time to train and validate than the Diff1, Diff5, and Diff10 datasets. Preprocessing time was reduced by up to 2.3 times in the blended dataset compared to the other datasets and, when combined with testing runtimes, it satisfied the requirements for real-time systems capable of detecting aggressive behavior in pairs of pigs. Overall, these results show that using a CNN and image differential-based deep learning approach can be an effective and computationally efficient technique to automatically detect aggressive behaviors in pigs.

Inflammation interferes with chemoreception in pigs by altering the neuronal layout of the vomeronasal sensory epithelium.

Chemical communication is widely used by animals to exchange information in their environment, through the emission and detection of semiochemicals to maintain social organization and hierarchical rules in groups. The vomeronasal organ (VNO) is one of the main detectors of these messages, and its inflammation has been linked to behavioral changes because it potentially prevents molecule detection and, consequently, the translation of the signal into action. Our previous study highlighted the link between the intensity of vomeronasal sensory epithelium (VNSE) inflammation, probably induced by farm contaminant exposure, and intraspecific aggression in pigs. The aim of this study was to evaluate the cellular and molecular changes that occur during vomeronasalitis in 76 vomeronasal sensorial epithelia from 38 intensive-farmed pigs. Histology was used to evaluate the condition of each VNO and classify inflammation as healthy, weak, moderate, or strong. These data were compared to the thickness of the sensorial epithelium and the number of type 1 vomeronasal receptor cells using anti-Gαi2 protein immunohistochemistry (IHC) and analysis. The presence of odorant-binding proteins (OBPs) in the areas surrounding the VNO was also analyzed by IHC and compared to inflammation intensity since its role as a molecule transporter to sensory neurons has been well-established. Of the 76 samples, 13 (17%) were healthy, 31 (41%) presented with weak inflammation, and 32 (42%) presented with moderate inflammation. No severe inflammation was observed. Epithelial thickness and the number of Gαi2+ cells were inversely correlated with inflammation intensity (Kruskal–Wallis and ANOVA tests, p < 0.0001), while OBP expression in areas around the VNO was increased in inflamed VNO (Kruskal–Wallis test, p = 0.0094), regardless of intensity. This study showed that inflammation was associated with a reduction in the thickness of the sensory epithelium and Gαi2+ cell number, suggesting that this condition can induce different degrees of neuronal loss. This finding could explain how vomeronasalitis may prevent the correct functioning of chemical communication, leading to social conflict with a potential negative impact on welfare, which is one of the most important challenges in pig farming.

An estimation of the financial consequences of reducing pig aggression.

Animal welfare scientists have accumulated knowledge and developed interventions to improve livestock welfare, but these are poorly adopted in commercial practice. Animal welfare interventions are rarely tested for economic viability and this limits their uptake. This study employs Stochastic Partial Budgeting (SPB) to determine the viability of animal welfare improvements. Aggression between pigs is used as an example because there is a large literature base from which to draw interventions, and the problem has persisted for decades without resolution. Costs and benefits of three interventions to control aggression (pre-weaning socialisation, synthetic maternal pheromones and large social groups) were estimated by reviewing the academic and industry literature and by conducting a survey of sixteen pig farmers. The net effects were compared to farmers’ willingness to pay (WTP) for interventions to reduce aggression as identified by recent research. Results are consistent with prior research which indicates that improving animal welfare generally comes at a cost to producers. Nevertheless, pre-weaning socialisation resulted in a neutral or positive net effect 38% of the time and should be central to campaigns promoting the control of aggression in the industry. Exposing pigs to synthetic maternal pheromones did not improve profitability but the net costs were small and within the realms of WTP for a sub-group of farmers with animal welfare goals. The net costs of converting existing buildings in order to house pigs in large social groups were beyond the realms of farmers’ WTP. The approach adopted in this study, of combining SPB with WTP from the sector, should be extended to other animal welfare issues.

Identifying blood-based biomarkers associated with aggression in weaned pigs after mixing

Animal behavior, including agonistic behavior, is regulated by various neurotransmitters and hormones in the blood. To Identify blood-based biomarkers associated with aggression in weaned pigs after mixing, a total of 500 weaned pigs from 65 litters were selected and mixed into 51 pens with littermates separately placed into different pens, and their behavior was recorded for 36 h. Blood samples were taken at 24 h before mixing and 24 h after mixing. An aggressive behavior index composite aggressive score was used to assess the aggressive behavior of weaned pigs after mixing. The most aggressive (n = 51) and the least aggressive pigs (n = 51) were selected in each pen. The concentrations of plasma triiodothyronine (T3) in the most aggressive pigs were lower (P < 0.01) than those of the least aggressive pigs at 24 h before (3.42 vs. 5.56 ng/mL) and 24 h after regrouping (3.88 vs. 5.51 ng/mL). In addition, skin lesion scores on the front body in the most aggressive pigs was greater (Pearson Chi-Square = 14.053; P = 0.001) than those of the least aggressive pigs 2 h after mixing. The concentrations of salivary cortisol from pen at 2, 24, and 48 h after mixing were higher than that at 24 h before mixing (2.2, 1.8, 2.1 vs. 0.9 ng/mL; P < 0.05). Plasma triiodothyronine levels at −24 (3.42 vs. 5.56 ng/mL; P < 0.05) and 24 h (3.88 vs. 5.51 ng/mL; P < 0.05) of mixing were higher in the most aggressive pigs than in the least aggressive pigs. The most aggressive pigs had less whole blood 5-hydroxytryptamine (293 vs. 330 ug/mL; P < 0.05) and more platelet number (1926 vs. 1571 ×109; P < 0.05) than the least aggressive pigs at 24 h after mixing. Number of defeat was positively related (r = 0.241, P < 0.05) to plasma tryptophan at 24 h after mixing. A significant canonical correlation (r = 0.7734, P = 0.006) was found between blood-based biomarkers and aggressive behavior indicators at 24 h before mixing. In conclusion, in this research blood-based biomarkers were partly associated with social stress-induced aggression in pigs, but the relationships are complicated.

Farmer Perceptions of Pig Aggression Compared to Animal-Based Measures of Fight Outcome.

Simple SummaryAggression between pigs is a major animal welfare issue in commercial farming, however only a minority of farmers believe that aggression is a problem that needs to be addressed. We investigated whether the farmers’ reluctance to reduce aggression is linked to desensitization as a result of their frequent exposure to the behavior. We showed farmers video clips of pigs during and immediately after a fight and they judged through a questionnaire the severity of what they saw. These judgments were compared to (a) animal-based measures of injury (skin lesions) and exhaustion (blood lactate), and (b) human observers with and without experience of working with pigs. Farmers perceived fights as severe and were motivated to prevent them continuing. They were not desensitized to aggression as their judgments were similar to those of participants who had never worked with pigs. When farmers (and comparison groups) did not see the fight occurring, they judged exhaustion and injuries to be lower than indicated by the animal-based measures. Farmers could benefit from information on how to better assess the impact of aggression by scoring lesions and from evidence of the economic and welfare impact of these lesions.Several animal welfare issues persist in practice despite extensive research which has been linked to the unwillingness of stakeholders to make changes. For example, most farmers do not perceive pig aggression to be a problem that requires action despite the fact that stress and injuries are common, and that several solutions exist. Frequent exposure to animal suffering could affect farmer responses to distressed animals. This study investigated for the first time whether this occurs, using pig aggression as a focus. Using video clips, 90 pig farmers judged the severity of aggression, level of pig exhaustion and the strength of their own emotional response. Their judgments were compared to objective measures of severity (pigs’ skin lesions and blood lactate), and against control groups with similar pig experience (10 pig veterinarians) and without experience (26 agricultural students; 24 animal science students). Famers did not show desensitization to aggression. However, all groups underestimated the outcome of aggression when they did not see the fight occurring as compared to witnessing a fight in progress. We suggest that farmers be provided with evidence of the economic and welfare impact of aggression as indicated by lesions and that they be advised to score lesions on affected animals.

Suitability of traits related to aggression and handleability for integration into pig breeding programmes: Genetic parameters and comparison between Gaussian and binary trait specifications.

Changes in husbandry systems as well as consumers’ increasing demands for animal welfare lead to increasing importance of traits such as handleability and aggressiveness in pigs. However, before using such novel traits for selection decisions, information on genetic parameters for these traits for the specific population is required. Therefore, weight gain and behaviour-related traits were recorded in 1004 pigs (814 Pietrain x German Landrace crossbred, 190 German Landrace purebred) at different ages. Behaviour indicators and tests were assessed and conducted, respectively under commercial farm conditions and included scoring of skin lesions (twice) and behaviour during backtests (twice), injections (once), handling (twice) and weighing (three times). Since behaviour scores often exhibit suboptimal statistical properties for parametric analyses, variance components were estimated using an animal model assuming a normal (Gaussian, GA; all traits) and additionally a binary distribution of variables (BI; using a logit-link function for all behaviour traits). Heritabilities for behavioural traits ranged from 0.02 ± 0.04 (finishing pig handling test; BI) to 0.36 ± 0.08 (backtest 2; GA) suggesting that some of the traits are potentially useful for genetic selection (e.g. finishing pig weighing test: h2 (GA) = 0.20 ± 0.07). Only minor differences were observed for results from binary and Gaussian analyses of the same traits suggesting that either approach might yield valid results. However, four-fold cross-validation using correlations between breeding values of a sub-set of animals for the sample trait finishing pig weighing score indicated slight superiority of the logit model (r = 0.85 ± 0.04 vs. r = 0.77 ± 0.03). Generally, only weak to moderate associations were found between behavioural reactions to the same test at different ages (rp ≤ 0.11 for weighing at different ages; rp = 0.30 but rg (GA) = 0.84 ± 0.11 for the backtests) as well as between reactions to different tests. Therefore, for inclusion of behaviour traits into breeding programmes, and considering high labour input required for some tests such as the backtest, it is recommended to assess behaviour during situations that are relevant and identical to practical conditions, while the use of indicator traits generally does not appear to be a very promising alternative.

Social network properties predict chronic aggression in commercial pig systems.

Post-mixing aggression in pigs is a harmful and costly behaviour which negatively impacts both animal welfare and farm efficiency. There is vast unexplained variation in the amount of acute and chronic aggression that dyadic behaviours do not fully explain. This study hypothesised that certain pen-level network properties may improve prediction of lesion outcomes due to the incorporation of indirect social interactions that are not captured by dyadic traits. Utilising current SNA theory, we investigate whether pen-level network properties affect the number of aggression-related injuries at 24 hours and 3 weeks post-mixing (24hr-PM and 3wk-PM). Furthermore we compare the predictive value of network properties to conventional dyadic traits. A total of 78 pens were video recorded for 24hr post-mixing. Each aggressive interaction that occurred during this time period was used to construct the pen-level networks. The relationships between network properties at 24hr and the pen level injuries at 24hr-PM and 3wk-PM were analysed using mixed models and verified using permutation tests. The results revealed that network properties at 24hr could predict long term aggression (3wk-PM) better than dyadic traits. Specifically, large clique formation in the first 24hr-PM predicted fewer injuries at 3wk-PM and high betweenness centralisation at 24hr-PM predicted increased rates of injury at 3wk-PM. This study demonstrates that network properties present during the first 24hr-PM have predictive value for chronic aggression, and have potential to allow identification and intervention for at risk groups.

Effectiveness of Pet Remedy in reducing aggression in pigs

Effectiveness of Pet Remedy in reducing aggression in pigs

Complex contests and the influence of aggressiveness in pigs

Animal contests vary greatly in behavioural tactics used and intensity reached, with some encounters resolved without physical contact while others escalate to damaging fighting. However, the reasons for such variation remains to be fully explained. Aggressiveness, in terms of a personality trait, offers a potentially important source of variation that has typically been overlooked. Therefore, we studied how aggressiveness as a personality trait influenced escalation between contestants matched for resource-holding potential (RHP), using detailed observations of the contest behaviour, contest dynamics and escalation levels. We predicted that winner and loser behaviour would differ depending on personality. This was tested by examining 52 dyadic contests between pigs, Sus scrofa. Aggressiveness was assayed in resident–intruder tests prior to the contest. Contests were then staged between pigs matched for RHP in terms of body weight but differing in their aggressiveness. In 27% of the contests a winner emerged without escalated physical fighting, demonstrating that a fight is not a prerequisite between RHP-matched contestants. However, the duration of contests with or without fighting was the same. In contests without a fight, opponents spent more time on mutual investigation and noncontact displays such as parallel walking, which suggests that ritualized display may facilitate assessment and decision making. Winners low in aggressiveness invested more time in opponent investigation and display and showed substantially less aggression towards the loser after its retreat compared to aggressive winners. Aggressiveness influenced contest dynamics but did not predict the level of escalation. Prominent behavioural differences were found for the interaction between personality and outcome and we therefore recommend including this interaction in models where personality is considered. Analyses based on contest duration only would miss many of the subtleties shown here and we therefore encourage more detailed analyses of animal contests, irrespective of the level of contest escalation.

Indirect Genetic Effects and Housing Conditions in Relation to Aggressive Behaviour in Pigs

Indirect Genetic Effects (IGEs), also known as associative effects, are the heritable effects that an individual has on the phenotype of its social partners. Selection for IGEs has been proposed as a method to reduce harmful behaviours, in particular aggression, in livestock and aquaculture. The mechanisms behind IGEs, however, have rarely been studied. The objective was therefore to assess aggression in pigs which were divergently selected for IGEs on growth (IGEg). In a one generation selection experiment, we studied 480 offspring of pigs (Sus scrofa) that were selected for relatively high or low IGEg and housed in homogeneous IGEg groups in either barren or enriched environments. Skin lesion scores, a proxy measure of aggression, and aggressive behaviours were recorded. The two distinct IGEg groups did not differ in number of skin lesions, or in amount of reciprocal fighting, both under stable social conditions and in confrontation with unfamiliar pigs in a 24 h regrouping test. Pigs selected for a positive effect on the growth of their group members, however, performed less non-reciprocal biting and showed considerably less aggression at reunion with familiar group members after they had been separated during a 24 h regrouping test. The enriched environment was associated with more skin lesions but less non-reciprocal biting under stable social conditions. Changes in aggression between pigs selected for IGEg were not influenced by G×E interactions with regard to the level of environmental enrichment. It is likely that selection on IGEg targets a behavioural strategy, rather than a single behavioural trait such as aggressiveness.

How do pigs behave before starting an aggressive interaction? Identification of typical body positions in the early stage of aggression using video labelling techniques.

The aim of this study was to identify, quantify, and describe pre-signs of aggression in pigs and the early stages of aggressive interactions. The experiment was carried out at a commercial farm on a group of 11 male pigs weighing on average 23 kg and kept in a pen of4 m x 2.5 m. In total 8 hours were videorecorded during the first 3 days after mixing. As a result, 177 aggressive interactions were identified and labelled to find pre-sign body positions before aggressive interactions, attack positions and aggressive acts performed from these positions. A total of 12 positions were classified as pre-signs (P1-P12) and 7 of them were identified immediately at the start of aggressive interactions (P6-P12). Most common pre-sign positions were P3-pigs approaching and facing each other (24%) and P2-initiator pigs approaching from the lateral side (18%). In 80% of the cases the duration of pre-signs was 1-2 sec 72% of all aggressive interactions were short (1 to 10 sec). The most frequent attack positions were P12-inverse parallel (39.5%), P7-nose to nose, 90 degrees (19.77%) and P9-nose to head (13.5%). The most frequent aggressive acts from attack positions were head knocking (34.4%), pressing (34.4%) and biting of different body parts (29.4%). Head knocking was mostly observed in relation to P7 and P2 positions and biting was common in the P7 position. In conclusion, pigs adopt specific pre-signs and body positions before the escalation of aggressive interactions. This could be used as potential sign to identify a beginning aggression.

Selection against aggressiveness in pigs at re-grouping: practical application and implications for long-term behavioural patterns

AbstractThe routine mixing of pigs causes aggression that cannot be greatly reduced by low-cost environmental changes. The variability and heritability of aggressiveness are discussed and both appear adequate to make selection against aggressiveness worthwhile in grower-stage pigs. Selection would require rapid phenotyping of many animals for which a validated indicator genetically correlated to aggressive behaviour is required. Three potential indicators are discussed (attack latency, number of skin lesions and relationship to non-social behavioural traits). Attack latency correlates with post-mixing aggressiveness under research conditions but attacks are delayed under commercial conditions reducing the practicability of the trait for selection. Correlations between aggressiveness and responses to non-social challenges, such as the back-test, are not always consistent. Lastly, the counting of skin lesions is rapid, and the number of lesions has a moderate heritability and is genetically correlated with involvement in aggressive behaviour. The wider effects of selection against post-mixing aggressiveness are discussed. Examining the behavioural strategies of unaggressive pigs, especially their response to defeat, would reveal how selection may alter aggressive tactics. Selection against lesions from mixing is also expected to reduce their number in more stable social conditions, but the implications for aggression between sows and that of sows towards their piglets and humans needs to be investigated. Aggressiveness is genetically correlated with response to handling involving components of social isolation, human presence and novelty. Identifying how unaggressive pigs respond to other challenging situations differing in these components may be worthwhile. Selection against aggression using skin lesions appears to be achievable although the full value of this would benefit from estimations of the genetic correlations with the traits outlined above.

The impact of chronic environmental stressors on the social behaviour of growing pigs, Sus scrofa

Introduction The effects of common and current environmental stressors on the social behaviour of farm animals are poorly understood and have never before been tested in controlled conditions. Here, we report the results of a longitudinal, multi-factorial experiment designed specifically to examine the individual, additive or interactive effects of elevated ammonia, noise and dim light levels on the social behaviour of growing pigs under strictly controlled environmental conditions. Material and methods Two batches of 126 4-week-old hybrid gilts (50% Pietrain, 25% white Duroc, 12.5% Large White, 12.5% Landrace) were obtained at weaning. Eight rooms were allocated to eight experimental treatments in a 23 multifactorial design. Each treatment comprised elevated or low levels of light intensity (nominally 200 vs. 40 lux), atmospheric ammonia concentration (nominally 20 ppm) and dim light intensities (<40 lux) can affect aggression in pigs. Aggression in pigs is signalled with odour and visual cues. Group-mate recognition can be olfactory, and odour cues may be masked by the ammonia, affecting perception and modulating the formation of stability in the group. The findings reported here may have implications for the welfare of farmed pigs in the UK. Ventilation systems should be designed further to minimise the aerial ammonia concentrations to avoid potential exacerbation of aggressive acts early in the development of the pigs. In addition, it seems that dim lighting may increase aggressive acts, again, early in the development of the pigs, and the industry should take account of this when designing facilities in the future

Responses to multiple chain enrichment in post weaned pigs

The social stressors associated with the abrupt weaning process on commercial farms can result in significant welfare issues for pigs; including behavioural problems and aggression, decreased immunological responses, reduced live weight gain and efficiency of feed conversion (van de Weerd and Day, 2008). Slats and slurry systems in commercial housing systems for grower pigs provide a barren environment which may exacerbate these problems. Enriched environments have been shown to reduce aggression in pigs, particularly where devices that have manipulative and destructive properties are used (Scott et al, 2007). However, studies often include a single device that does not permit all animals to interact with the enrichment at any one time. The aim of the present study was to investigate the effects of multiple metal chain enrichment on levels of aggression in growing pigs.

Bayesian Analysis of Genetic Associations of Skin Lesions and Behavioural Traits to Identify Genetic Components of Individual Aggressiveness in Pigs

There is increasing interest in genetic selection against behavioural traits that impact negatively on welfare and productivity in commercial livestock production. Post-mixing aggressiveness in pigs shows wide phenotypic variation, affects health, welfare and growth performance and is a routine feature of production. A Bayesian approach was used to estimate the heritability of three traits associated with aggressiveness in pigs during the 24 h post-mixing; duration in reciprocal aggression, and in receipt of, or delivery of non-reciprocal aggression (NRA). For the purposes of genetic selection, recording aggressive behaviour is excessively labour intensive. The genetic correlations were quantified between the behavioural traits and an easily measurable indicator trait; the number of skin lesions following mixing (lesion score, LS). The heritabilities for the three behavioural traits ranged from 0.17 to 0.46 (receipt of NRA and reciprocal aggression respectively). The duration in reciprocal aggression and in delivery of NRA showed a strong genetic correlation (r g = 0.79 with 95% Bayesian credibility interval of 0.62-0.94). The genetic correlation between LS and these two behaviours indicated that selection on breeding values of LS could be used to reduce aggressiveness. The duration in receipt of NRA appeared to be regulated by different genes or genomic effects compared with the other behavioural traits and LS. Although duration in receipt of NRA was not genetically associated with LS, it was lowly but significantly environmentally associated with the residuals of central and caudal LS (r e = 0.28-0.32), indicating that pigs that received NRA also received bites on the central and caudal third of the body. The pen that the animals were mixed into was found to be a very important factor for the analysed traits, in particular those representing behavioural characteristics. Based on the estimated genetic parameters, it is concluded that selection on breeding values for reduced LS (especially central LS) is expected to reduce reciprocal aggression and the delivery of NRA but will not change the receipt of NRA directly.

Resident–Intruder Trait Aggression is Associated with Differences in Lysine Vasopressin and Serotonin Receptor 1A (5‐HT1A) mRNA Expression in the Brain of Pre‐Pubertal Female Domestic Pigs (Sus scrofa)

Aggressive behaviour exhibited by domestic pigs following encounters with unfamiliar individuals is a serious welfare and economical problem. Aggression resulting in skin lesions is similarly prevalent in prepubertal pigs of either sex. Little is known about the neural circuits and neuropeptides that control aggression in the pig. Because there is evidence for the involvement of the vasopressin and serotonergic systems in the regulation of aggressive behaviour in male mammals, we sought differences using quantitative in situ hybridisation of vasopressin and serotonin 1A receptor (5-HT1A) mRNA expression within specific brain regions of aggressive and nonaggressive prepubertal female pigs. The number of cells expressing vasopressin mRNA was significantly higher in aggressive pigs in the medial amygdala, lateral septum (LS) and showed a similar trend in the bed nucleus of the stria terminalis (BnST) but not the paraventricular nucleus (PVN) or supraoptic nucleus. The 5-HT1A receptor was widely expressed through the porcine brain and a significantly lower intensity (silver grain density) of 5-HT1A mRNA expression was observed in the BnST. In the medial amygdala and LS fewer cells expressed 5-HT1A mRNA in aggressive pigs but no differences were found in the PVN. In the absence of inbred strains or selection lines, these findings have shown that prior identification of phenotypic behavioural extremes in a population in advance of neural studies is a useful technique. Moreover, these findings support a central role for vasopressin and serotonin in the mediation of high trait aggression in prepubertal female pigs.

The accumulation of skin lesions and their use as a predictor of individual aggressiveness in pigs

Post-mixing aggression in commercially housed pigs can be intense and often results in the accumulation of skin lesions. The number of lesions (lesion score, LS) has often been used as a proxy indicator of post-mixing aggression. There has been no previous attempt to quantify the role of different behaviours in the accumulation of lesions. The objective of this work was to investigate the factors that contribute to LS in order to assess the validity of LS as a measure of individual aggressiveness. After mixing into new groups of 12, the durations of reciprocal fighting and bullying, the proportions of fights initiated, won and lost and physical attributes of the pigs were investigated for their impact on LS using multiple regression on a sample of 342 growing pigs. Pig liveweight was the single greatest determinant of LS. The duration spent in reciprocal fighting and being bullied were also significant determinants of the LS and contributed to the total LS in proportions of 0.17 and 0.14, respectively. The proportion of fights initiated, won and lost did not influence the LS for pigs of a given weight. On an individual pig basis, it was impossible to establish the relative importance of reciprocal fighting and the receipt of bullying to the accumulation of lesions by reference to the total LS alone. Engagement in reciprocal fighting was found to result in lesions to the anterior third of the body, whilst the receipt of bullying resulted in lesions accruing on the caudal third of the body. Reference to the location of lesions, in addition to their number, is a refinement of the methodology that potentially allows discrimination of pigs that accumulate lesions as a result of reciprocal fighting or receipt of bulling. A partial replication of the experiment at a second unit ( n = 84 pigs) with different genotypic, environmental and husbandry conditions confirmed that the duration spent in reciprocal fighting and being bullied were significant determinants of the LS and that the LS approach is applicable across units. Liveweight was not recorded at Unit 2. These results suggest that the LS methodology provides a rapid means of estimating aggressive behavioural phenotypes when reference is made to both the number and location of lesions and pig weight and pen identity effects are accounted for.