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

TY - JOUR

T1 - Prediction of tail biting events in finisher pigs from automatically recorded sensor data

AU - Larsen, Mona Lilian Vestbjerg

AU - Pedersen, Lene Juul

AU - Jensen, Dan Børge

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Artificial neural network

KW - Bayesian ensemble

KW - Bayes’ Theorem

KW - Computational ethology

KW - Drinking behaviour

KW - Dynamic linear models

KW - Pen temperature

KW - Precision livestock farming

KW - Sus scrofa domesticus

KW - Water flow

U2 - 10.3390/ani9070458

DO - 10.3390/ani9070458

M3 - Journal article

C2 - 31330973

AN - SCOPUS:85071361208

SN - 2076-2615

VL - 9

JO - Animals

JF - Animals

IS - 7

M1 - 458

ER -