Prediction of tail biting in pigs using partial least squares regression and artificial neural networks

Abstract

Tail biting in pig farming causes economic losses as well as pain and suffering to the affected pigs. The aim of the study was to predict tail lesions to provide the farmer with the opportunity to intervene early. To do this, tail lesions were recorded during rearing (REA) and fattening (FAT) on two farms (Farm 1: 10 batches REA, 5 batches FAT; Farm 2: 7 batches REA, 4 batches FAT). In addition, tail posture, skin lesions, musculoskeletal system issues, treatment index for suckling, weight at weaning, water consumption, activity time, humidity, NH3 and CO2 concentrations were observed. Processed to daily values forming a time series, these data was used to predict the prevalence of tail lesions per pen using partial least squares (PLS) regression models, focused time-delay neural networks (FTDNN) and nonlinear autoregressive neural networks with exogenous inputs (NARX). After training, validating and testing the methods for Farm 1, the respective methods were applied to the data of Farm 2. In addition, it was investigated whether the prevalence of tail lesions per pen could be predicted for an offset of one to five days before with PLS. The networks were used to test whether a delay of one to five days before should be included for prediction. NARX achieved the best prediction performance on both farms in REA and FAT: accuracy between true and estimated values (R 0.85–0.97), recall (REC 0.63–0.97), false positive rate (FPR 0.01–0.27), precision (PRC 0.43–0.92), F1-score (0.54–0.94) and area under the precision-recall curve (AUPRC 0.66–0.97). PLS (R 0.49–0.81, REC 0.32–0.86, FPR 0.02–0.36, PRC 0.42–0.70, F1-score 0.40–0.77, AUPRC 0.43–0.85) and FTDNN (R 0.45–0.90, REC 0.19–1.00, FPR 0.00–0.97, PRC 0.30–0.84, F1-score 0.28–0.75, AUPRC 0.30–0.86) performed less accurately than NARX but still with sufficient prediction performance, whereby the results for Farm 2 were worse than for Farm 1. Therefore, a generalisation of the methods to other farms is possible. No noticeable difference was observed between offsets 1 to 5 and delays 1 to 5. PLS, FTDNN and NARX with all offsets and delays are suitable for predicting the prevalence of tail lesions per pen in several farms as well as in REA and FAT.