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Understanding Vocalization Might Help to Assess Stressful Conditions in Piglets.

da Silva Cordeiro AF, de Alencar Nääs I, Oliveira SR, Violaro F, de Almeida AC, Neves DP - Animals (Basel) (2013)

Bottom Line: A unidirectional microphone positioned about 15 cm from the animals' mouth was used for recording the acoustic signals.The microphone was connected to a digital recorder, where the signals were digitized at the 44,100 Hz frequency.The J48 decision tree algorithm available at the Weka(®) data mining software was used for stress classification.

View Article: PubMed Central - PubMed

Affiliation: Agricultural Engineering College, State University of Campinas, Ave. Candido Rondon, 501, Campinas, SP, 13083-875, Brazil. alexandracordeiro6@gmail.com.

ABSTRACT
Assessing pigs' welfare is one of the most challenging subjects in intensive pig farming. Animal vocalization analysis is a noninvasive procedure and may be used as a tool for assessing animal welfare status. The objective of this research was to identify stress conditions in piglets reared in farrowing pens through their vocalization. Vocal signals were collected from 40 animals under the following situations: normal (baseline), feeling cold, in pain, and feeling hunger. A unidirectional microphone positioned about 15 cm from the animals' mouth was used for recording the acoustic signals. The microphone was connected to a digital recorder, where the signals were digitized at the 44,100 Hz frequency. The collected sounds were edited and analyzed. The J48 decision tree algorithm available at the Weka(®) data mining software was used for stress classification. It was possible to categorize diverse conditions from the piglets' vocalization during the farrowing phase (pain, cold and hunger), with an accuracy rate of 81.12%. Results indicated that vocalization might be an effective welfare indicator, and it could be applied for assessing distress from pain, cold and hunger in farrowing piglets.

No MeSH data available.


Related in: MedlinePlus

Number of rules (%) vs. minimum number of objects per leaf.
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animals-03-00923-f006: Number of rules (%) vs. minimum number of objects per leaf.

Mentions: An increase of the accuracy rate (%) occurred with the increase of the minimum number of objects per leaf up to the maximum value (82%). Then, the accuracy rate (%) tended to decrease (Figure 4). The same behavior was observed for the Kappa statistic values (Figure 5), and for the number of rules (Figure 6). The results indicated that, for this dataset, the best model (decision tree) was the one whose minimum number of objects per leaf is equal to nine.


Understanding Vocalization Might Help to Assess Stressful Conditions in Piglets.

da Silva Cordeiro AF, de Alencar Nääs I, Oliveira SR, Violaro F, de Almeida AC, Neves DP - Animals (Basel) (2013)

Number of rules (%) vs. minimum number of objects per leaf.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4494434&req=5

animals-03-00923-f006: Number of rules (%) vs. minimum number of objects per leaf.
Mentions: An increase of the accuracy rate (%) occurred with the increase of the minimum number of objects per leaf up to the maximum value (82%). Then, the accuracy rate (%) tended to decrease (Figure 4). The same behavior was observed for the Kappa statistic values (Figure 5), and for the number of rules (Figure 6). The results indicated that, for this dataset, the best model (decision tree) was the one whose minimum number of objects per leaf is equal to nine.

Bottom Line: A unidirectional microphone positioned about 15 cm from the animals' mouth was used for recording the acoustic signals.The microphone was connected to a digital recorder, where the signals were digitized at the 44,100 Hz frequency.The J48 decision tree algorithm available at the Weka(®) data mining software was used for stress classification.

View Article: PubMed Central - PubMed

Affiliation: Agricultural Engineering College, State University of Campinas, Ave. Candido Rondon, 501, Campinas, SP, 13083-875, Brazil. alexandracordeiro6@gmail.com.

ABSTRACT
Assessing pigs' welfare is one of the most challenging subjects in intensive pig farming. Animal vocalization analysis is a noninvasive procedure and may be used as a tool for assessing animal welfare status. The objective of this research was to identify stress conditions in piglets reared in farrowing pens through their vocalization. Vocal signals were collected from 40 animals under the following situations: normal (baseline), feeling cold, in pain, and feeling hunger. A unidirectional microphone positioned about 15 cm from the animals' mouth was used for recording the acoustic signals. The microphone was connected to a digital recorder, where the signals were digitized at the 44,100 Hz frequency. The collected sounds were edited and analyzed. The J48 decision tree algorithm available at the Weka(®) data mining software was used for stress classification. It was possible to categorize diverse conditions from the piglets' vocalization during the farrowing phase (pain, cold and hunger), with an accuracy rate of 81.12%. Results indicated that vocalization might be an effective welfare indicator, and it could be applied for assessing distress from pain, cold and hunger in farrowing piglets.

No MeSH data available.


Related in: MedlinePlus