Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027014%3A_____%2F22%3AN0000080" target="_blank" >RIV/00027014:_____/22:N0000080 - isvavai.cz</a>
Alternative codes found
RIV/60076658:12310/22:43905038 RIV/60460709:41210/22:91158
Result on the web
<a href="http://nature.com" target="_blank" >http://nature.com</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41598-022-07174-8" target="_blank" >10.1038/s41598-022-07174-8</a>
Alternative languages
Result language
angličtina
Original language name
Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
40201 - Animal and dairy science; (Animal biotechnology to be 4.4)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Scientific Reports
ISSN
2045-2322
e-ISSN
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Volume of the periodical
12
Issue of the periodical within the volume
1
Country of publishing house
DE - GERMANY
Number of pages
10
Pages from-to
Article number: 3409
UT code for WoS article
000765922600036
EID of the result in the Scopus database
2-s2.0-85126079708