Vocal expression of emotions has been observed across species and could provide a non-invasive and reliable means to assess animal emotions. We investigated if pig vocal indicators of emotions revealed in previous studies are valid across call types and contexts, and could potentially be used to develop an automated emotion monitoring tool. We performed an analysis of an extensive and unique dataset of low (LF) and high frequency (HF) calls emitted by pigs across numerous commercial contexts from birth to slaughter (7414 calls from 411 pigs). Our results revealed that the valence attributed to the contexts of production (positive versus negative) affected all investigated parameters in both LF and HF. Similarly, the context category affected all parameters. We then tested two different automated methods for call classification; a neural network revealed much higher classification accuracy compared to a permuted discriminant function analysis (pDFA), both for the valence (neural network: 91.5%; pDFA analysis weighted average across LF and HF (cross-classified): 61.7% with a chance level at 50.5%) and context (neural network: 81.5%; pDFA analysis weighted average across LF and HF (cross-classified): 19.4% with a chance level at 14.3%). These results suggest that an automated recognition system can be developed to monitor pig welfare on-farm.

Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production / E.F. Briefer, C.C. Sypherd, P. Linhart, L.M.C. Leliveld, M. Padilla de la Torre, E.R. Read, C. Guérin, V. Deiss, C. Monestier, J.H. Rasmussen, M. Špinka, S. Düpjan, A. Boissy, A.M. Janczak, E. Hillmann, C. Tallet. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - 12:1(2022 Mar 07), pp. 3409.1-3409.10. [10.1038/s41598-022-07174-8]

Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production

L.M.C. Leliveld;
2022

Abstract

Vocal expression of emotions has been observed across species and could provide a non-invasive and reliable means to assess animal emotions. We investigated if pig vocal indicators of emotions revealed in previous studies are valid across call types and contexts, and could potentially be used to develop an automated emotion monitoring tool. We performed an analysis of an extensive and unique dataset of low (LF) and high frequency (HF) calls emitted by pigs across numerous commercial contexts from birth to slaughter (7414 calls from 411 pigs). Our results revealed that the valence attributed to the contexts of production (positive versus negative) affected all investigated parameters in both LF and HF. Similarly, the context category affected all parameters. We then tested two different automated methods for call classification; a neural network revealed much higher classification accuracy compared to a permuted discriminant function analysis (pDFA), both for the valence (neural network: 91.5%; pDFA analysis weighted average across LF and HF (cross-classified): 61.7% with a chance level at 50.5%) and context (neural network: 81.5%; pDFA analysis weighted average across LF and HF (cross-classified): 19.4% with a chance level at 14.3%). These results suggest that an automated recognition system can be developed to monitor pig welfare on-farm.
Settore AGR/18 - Nutrizione e Alimentazione Animale
7-mar-2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/915270
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