Prediction of tail biting events in finisher pigs from automatically recorded sensor data

Mona Lilian Vestbjerg Larsen*, Lene Juul Pedersen, Dan Børge Jensen

*Corresponding author for this work
    7 Citations (Scopus)
    12 Downloads (Pure)

    Abstract

    Tail biting in pigs is an animal welfare problem, and tail biting should be prevented from developing into tail damage. One strategy could be to predict events of tail biting so that the farmer can make timely interventions in specific pens. In the current investigation, sensor data on water usage (water flow and activation frequency) and pen temperature (above solid and slatted floor) were included in the development of a prediction algorithm for tail biting. Steps in the development included modelling of data sources with dynamic linear models, optimisation and training of artificial neural networks and combining predictions of the single data sources with a Bayesian ensemble strategy. Lastly, the Bayesian ensemble combination was tested on a separate batch of finisher pigs in a real-life setting. The final prediction algorithm had an AUC > 0.80, and thus it does seem possible to predict events of tail biting from already available sensor data. However, around 30% of the no-event days were false alarms, and more event-specific predictors are needed. Thus, it was suggested that farmers could use the alarms to point out pens that need greater attention.

    Original languageEnglish
    Article number458
    JournalAnimals
    Volume9
    Issue number7
    ISSN2076-2615
    DOIs
    Publication statusPublished - 2019

    Keywords

    • Artificial neural network
    • Bayesian ensemble
    • Bayes’ Theorem
    • Computational ethology
    • Drinking behaviour
    • Dynamic linear models
    • Pen temperature
    • Precision livestock farming
    • Sus scrofa domesticus
    • Water flow

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