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.

A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and Its Potential to Assess Animal Welfare.

Several precision livestock farming (PLF) technologies, conceived for optimizing farming processes, are developed to detect the physical and behavioral changes of animals continuously and in real-time. The aim of this review was to explore the capacity of existing PLF technologies to contribute to the assessment of pig welfare. In a web search for commercially available PLF for pigs, 83 technologies were identified. A literature search was conducted, following systematic review guidelines (PRISMA), to identify studies on the validation of sensor technologies for assessing animal-based welfare indicators. Two validation levels were defined: internal (evaluation during system building within the same population that were used for system building) and external (evaluation on a different population than during system building). From 2,463 articles found, 111 were selected, which validated some PLF that could be applied to the assessment of animal-based welfare indicators of pigs (7% classified as external, and 93% as internal validation). From our list of commercially available PLF technologies, only 5% had been externally validated. The more often validated technologies were vision-based solutions (n = 45), followed by load-cells (n = 28; feeders and drinkers, force plates and scales), accelerometers (n = 14) and microphones (n = 14), thermal cameras (n = 10), photoelectric sensors (n = 5), radio-frequency identification (RFID) for tracking (n = 2), infrared thermometers (n = 1), and pyrometer (n = 1). Externally validated technologies were photoelectric sensors (n = 2), thermal cameras (n = 2), microphone (n = 1), load-cells (n = 1), RFID (n = 1), and pyrometer (n = 1). Measured traits included activity and posture-related behavior, feeding and drinking, other behavior, physical condition, and health. In conclusion, existing PLF technologies are potential tools for on-farm animal welfare assessment in pig production. However, validation studies are lacking for an important percentage of market available tools, and in particular research and development need to focus on identifying the feature candidates of the measures (e.g., deviations from diurnal pattern, threshold levels) that are valid signals of either negative or positive animal welfare. An important gap identified are the lack of technologies to assess affective states (both positive and negative states).

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

Animals living in groups compete for food resources and face food conflicts. These conflicts are affected by social factors (e.g. competition level) and behavioural strategies (e.g. avoidance). This study aimed to deepen our understanding of the complex interactions between social factors and behavioural strategies affecting feeding and social interaction patterns in animals. We focused on group-housed growing pigs, Sus scrofa, which typically face conflicts around the feeder, and of which patterns in various competitive environments (i.e. pig:feeder ratio) have been documented soundly. An agent-based model was developed to explore how interactions among social factors and behavioural strategies can affect various feeding and social interaction patterns differently under competitive situations. Model results show that pig and diet characteristics interact with group size and affect daily feeding patterns (e.g. feed intake and feeding time) and conflicts around the feeder. The level of competition can cause a turning point in feeding and social interaction patterns. Beyond a certain point of competition, meal-based (e.g. meal frequency) and social interaction patterns (e.g. displacements) are determined mainly by behavioural strategies. The average daily feeding time can be used to predict the group size at which this turning point occurs. Under the model's assumptions, social facilitation was relatively unimportant in the causation of behavioural patterns in pigs. To validate our model, simulated patterns were compared with empirical patterns in conventionally housed pigs. Similarities between empirical and model patterns support the model results. Our model can be used as a tool in further research for studying the effects of social factors and group dynamics on individual variation in feeding and social interaction patterns in pigs, as well as in other animal species.

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.

The importance of hormonal circadian rhythms in daily feeding patterns: An illustration with simulated pigs.

The interaction between hormonal circadian rhythms and feeding behaviour is not well understood. This study aimed to deepen our understanding of mechanisms underlying circadian feeding behaviour in animals, using pigs, Sus scrofa, as a case study. Pigs show an alternans feeding pattern, that is, a small peak of feed intake at the beginning of the day and a larger peak at the end of the day. We simulated the feeding behaviour of pigs over a 24h period. The simulation model contained mechanisms that regulate feeding behaviour of animals, including: processing of feed in the gastrointestinal tract, fluctuation in energy balance, circadian rhythms of melatonin and cortisol and motivational decision-making. From the interactions between these various processes, feeding patterns (e.g. feed intake, meal frequency, feeding rate) emerge. These feeding patterns, as well as patterns for the underlying mechanisms (e.g. energy expenditure), fitted empirical data well, indicating that our model contains relevant mechanisms. The circadian rhythms of cortisol and melatonin explained the alternans pattern of feeding in pigs. Additionally, the timing and amplitude of cortisol peaks affected the diurnal and nocturnal peaks in feed intake. Furthermore, our results suggest that circadian rhythms of other hormones, such as leptin and ghrelin, are less important in circadian regulation of feeding behaviour than previously thought. These results are relevant to animal species with a metabolic and endocrine system similar to that of pigs, such as humans. Moreover, the modelling approach to understand feeding behaviour can be applied to other animal species.