Prediction of Tail Biting Events in Finisher Pigs from Automatically Recorded Sensor Data.

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-lif

SUBMITTER: Larsen MLV 

PROVIDER: S-EPMC6681100 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature