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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

Decision tree generated by the C4.5 algorithm.
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animals-03-00923-f007: Decision tree generated by the C4.5 algorithm.

Mentions: The decision tree was generated using the C4.5 algorithm, considering that the minimum number of objects per leaf is equal to nine (Figure 7). Through the generated rules, it was possible to classify the four types of distress with an accuracy rate of 81.69%. The precision rate (a type of accuracy for a specific class of the data) can be thought of as a measure of exactness. The class with the highest precision was the pain distress (0.99), followed by the normal welfare status (baseline) (0.90), the cold distress (0.89), and the hunger distress (0.69) (Table 3).


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)

Decision tree generated by the C4.5 algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

animals-03-00923-f007: Decision tree generated by the C4.5 algorithm.
Mentions: The decision tree was generated using the C4.5 algorithm, considering that the minimum number of objects per leaf is equal to nine (Figure 7). Through the generated rules, it was possible to classify the four types of distress with an accuracy rate of 81.69%. The precision rate (a type of accuracy for a specific class of the data) can be thought of as a measure of exactness. The class with the highest precision was the pain distress (0.99), followed by the normal welfare status (baseline) (0.90), the cold distress (0.89), and the hunger distress (0.69) (Table 3).

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