Online warning systems for individual fattening pigs based on their feeding pattern

Maselyne, Jarissa; Nuffel, Annelies Van; Briene, Petra; Vangeyte, Jürgen; Ketelaere, Bart De; Millet, Sam; Hof, Janne Van den; Maes, Dominiek; Saeys, Wouter

doi:10.1016/j.biosystemseng.2017.08.006

Cited by 28 publications

(18 citation statements)

References 21 publications

(50 reference statements)

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“…This is especially relevant since advanced technology is currently available to automatically monitor feeding behaviour in pigs and to collect large amounts of data on individual level [e.g. 43,44]. Understanding what data should be collected, and how it could be analysed and interpreted, can be very useful to find behavioural feeding patterns that can be used as indicator for animal health, welfare and productivity.…”

Section: Discussionmentioning

confidence: 99%

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

Boumans

Boer

Hofstede

et al. 2018

Physiology & Behavior

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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.

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Section: Discussionmentioning

confidence: 99%

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

Boumans

Boer

Hofstede

et al. 2018

Physiology & Behavior

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“…A first reason was that we wanted to demonstrate the value of existing information in prediction. Nowadays, the focus in prediction is merely on collecting data using new (sensor) techniques (e.g., Ferrari et al, 2008; Maselyne et al, 2018; Pezzuolo et al, 2018) and the step to integrate with existing information is often neglected (Rutten et al, 2013). According to Cornou and Kristensen (2013), decision making is based on a combination of observations of the animals and their environment, as well as production results.…”

Section: Discussionmentioning

confidence: 99%

Boosted trees to predict pneumonia, growth, and meat percentage of growing-finishing pigs1

Mollenhorst

Ducro

Greef

et al. 2019

Journal of Animal Science

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In pig production, efficiency is benefiting from uniform growth in pens resulting in single deliveries from a pen of possibly all animals in the targeted weight range. Abnormalities, like pneumonia or aberrant growth, reduce production efficiency as it reduces the uniformity and might cause multiple deliveries per batch and pigs delivered with a low meat yield or outside the targeted weight range. Early identification of pigs prone to develop these abnormalities, for example, at the onset of the growing-finishing phase, would help to prevent heterogeneous pens through management interventions. Data about previous production cycles at the farm combined with data from the piglet’s own history may help in identifying these abnormalities. The aim of this study, therefore, was to predict at the onset of the growing-finishing phase, that is, at 3 mo in advance, deviant pigs at slaughter with a machine-learning technique called boosted trees. The dataset used was extracted from the farm management system of a research center. It contained over 70,000 records of individual pigs born between 2004 and 2016, including information on, for example, offspring, litter size, transfer dates between production stages, their respective locations within the barns, and individual live-weights at several production stages. Results obtained on an independent test set showed that at a 90% specificity rate, the sensitivity was 16% for low meat percentage, 20% for pneumonia and 36% for low lifetime growth rate. For low lifetime growth rate, this meant an almost three times increase in positive predictive value compared to the current situation. From these results, it was concluded that routine performance information available at the onset of the growing-finishing phase combined with data about previous production cycles formed a moderate base to identify pigs prone to develop pneumonia (AUC > 0.60) and a good base to identify pigs prone to develop growth aberrations (AUC > 0.70) during the growing-finishing phase. The mentioned information, however, was not a sufficient base to identify pigs prone to develop low meat percentage (AUC < 0.60). The shown ability to identify growth aberrations and pneumonia can be considered a good first step towards the development of an early warning system for pigs in the growing-finishing phase.

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“…Microphones for cough detection to identify sick pigs was applied in two studies. Moreover, RFID data were used to detect deviations in individual pigs' feeding patterns to point diseases or other disturbances, correlating it with the Welfare Quality R protocol assessment (looking for skin, ear and tail lesions, soiling, abnormalities in body condition, respiration, locomotion, bursitis, lameness, or diarrhea) (137). Finally, water usage data from flow meters have been tested as an early indicator of potential presence of diseases at group level, demonstrating that changes in diurnal drinking patterns of pigs can predict, for example, a diarrhea outbreak before clinical signs show up (38).…”

Section: Health-related Traitsmentioning

confidence: 99%

“…When working with RFID, loadcells can monitor feeding and drinking patterns and growing performance at individual level, overcoming one of the challenges that cannot be achieved by current welfare protocols, which can only monitor these aspects at group level. Although a normal growth pattern may have little predictive value in terms of good animal welfare, growth deviations or retardations have been used to identify health issues and other welfare problems (137). Automatic feeders with RFID are a promising technique to understand animals' requirements and anticipate welfare problems based on feeding patterns deviations, allowing the implementation of corrective measures and thus improving animal health and welfare (46).…”

Section: Load Cells and Flow Metersmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2021

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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).

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Online warning systems for individual fattening pigs based on their feeding pattern

Cited by 28 publications

References 21 publications

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

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

Boosted trees to predict pneumonia, growth, and meat percentage of growing-finishing pigs1

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

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