Application of Principal Component Analysis of Sows' Behavioral Indicators of the Welfare Quality Protocol to Determine Main Components of Behavior

Research output: Contribution to journalJournal articleResearchpeer-review

Standard

Application of Principal Component Analysis of Sows' Behavioral Indicators of the Welfare Quality Protocol to Determine Main Components of Behavior. / Friedrich, Lena; Krieter, Joachim; Kemper, Nicole; Czycholl, Irena.

In: Frontiers in Animal Science, Vol. 2, 40, 2021.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Friedrich, L, Krieter, J, Kemper, N & Czycholl, I 2021, 'Application of Principal Component Analysis of Sows' Behavioral Indicators of the Welfare Quality Protocol to Determine Main Components of Behavior', Frontiers in Animal Science, vol. 2, 40. https://doi.org/10.3389/fanim.2021.728608

APA

Friedrich, L., Krieter, J., Kemper, N., & Czycholl, I. (2021). Application of Principal Component Analysis of Sows' Behavioral Indicators of the Welfare Quality Protocol to Determine Main Components of Behavior. Frontiers in Animal Science, 2, [40]. https://doi.org/10.3389/fanim.2021.728608

Vancouver

Friedrich L, Krieter J, Kemper N, Czycholl I. Application of Principal Component Analysis of Sows' Behavioral Indicators of the Welfare Quality Protocol to Determine Main Components of Behavior. Frontiers in Animal Science. 2021;2. 40. https://doi.org/10.3389/fanim.2021.728608

Author

Friedrich, Lena ; Krieter, Joachim ; Kemper, Nicole ; Czycholl, Irena. / Application of Principal Component Analysis of Sows' Behavioral Indicators of the Welfare Quality Protocol to Determine Main Components of Behavior. In: Frontiers in Animal Science. 2021 ; Vol. 2.

Bibtex

@article{aed4ca1a823c47f79b00a186030ae4ac,
title = "Application of Principal Component Analysis of Sows' Behavioral Indicators of the Welfare Quality Protocol to Determine Main Components of Behavior",
abstract = "Understanding behavior is important in terms of welfare assessments to be able to evaluate possible changes in behavior among different husbandry systems. The present study applied principal component analysis (PCA) to reveal relationships between behavioral indicators to identify the main components of sows' behavior promoting feasibility of welfare assessments by providing possibilities for variable reduction and aggregation. The indicators of the Welfare Quality{\textregistered} protocol's principle to assess behavior were repeatedly applied by two observers on 13 farms in Northern Germany. This included Qualitative Behavior Assessments (QBA) to evaluate animals' body language using 20 pre-defined adjectives, assessments of social and exploratory behavior, stereotypies, and human–animal relationship tests. Two separate PCA were performed with respect to the QBA: (1) adjectives were included as independent variables and (2) adjectives were pre-aggregated using the calculation rules of the Welfare Quality{\textregistered} protocol for fattening pigs since a calculation for sows does not yet exist. In both analyses, two components described sows' behavior. Most variance was explained by the solution with adjectives as independent variables (51.0%). Other behavioral elements not captured as indicators by the protocol may still be important for all-inclusive welfare assessments as the required variance of 70% was not achieved in the analyses. Component loadings were used to determine components' labels as (1) “satisfaction of exploratory behavior” and (2) “social resting”. Both components reflected characteristics of sows' natural behavior and can subsequently be used for variable reduction but also for development of component scores for aggregation. As defined for PCA, component 1 explained more variance than component 2. PCA is useful to determine the main components of sows' behavior, which can be used to enhance feasibility of welfare assessments.",
author = "Lena Friedrich and Joachim Krieter and Nicole Kemper and Irena Czycholl",
year = "2021",
doi = "10.3389/fanim.2021.728608",
language = "English",
volume = "2",
journal = "Frontiers in Animal Science",
issn = "2673-6225",
publisher = "Frontiers Media",

}

RIS

TY - JOUR

T1 - Application of Principal Component Analysis of Sows' Behavioral Indicators of the Welfare Quality Protocol to Determine Main Components of Behavior

AU - Friedrich, Lena

AU - Krieter, Joachim

AU - Kemper, Nicole

AU - Czycholl, Irena

PY - 2021

Y1 - 2021

N2 - Understanding behavior is important in terms of welfare assessments to be able to evaluate possible changes in behavior among different husbandry systems. The present study applied principal component analysis (PCA) to reveal relationships between behavioral indicators to identify the main components of sows' behavior promoting feasibility of welfare assessments by providing possibilities for variable reduction and aggregation. The indicators of the Welfare Quality® protocol's principle to assess behavior were repeatedly applied by two observers on 13 farms in Northern Germany. This included Qualitative Behavior Assessments (QBA) to evaluate animals' body language using 20 pre-defined adjectives, assessments of social and exploratory behavior, stereotypies, and human–animal relationship tests. Two separate PCA were performed with respect to the QBA: (1) adjectives were included as independent variables and (2) adjectives were pre-aggregated using the calculation rules of the Welfare Quality® protocol for fattening pigs since a calculation for sows does not yet exist. In both analyses, two components described sows' behavior. Most variance was explained by the solution with adjectives as independent variables (51.0%). Other behavioral elements not captured as indicators by the protocol may still be important for all-inclusive welfare assessments as the required variance of 70% was not achieved in the analyses. Component loadings were used to determine components' labels as (1) “satisfaction of exploratory behavior” and (2) “social resting”. Both components reflected characteristics of sows' natural behavior and can subsequently be used for variable reduction but also for development of component scores for aggregation. As defined for PCA, component 1 explained more variance than component 2. PCA is useful to determine the main components of sows' behavior, which can be used to enhance feasibility of welfare assessments.

AB - Understanding behavior is important in terms of welfare assessments to be able to evaluate possible changes in behavior among different husbandry systems. The present study applied principal component analysis (PCA) to reveal relationships between behavioral indicators to identify the main components of sows' behavior promoting feasibility of welfare assessments by providing possibilities for variable reduction and aggregation. The indicators of the Welfare Quality® protocol's principle to assess behavior were repeatedly applied by two observers on 13 farms in Northern Germany. This included Qualitative Behavior Assessments (QBA) to evaluate animals' body language using 20 pre-defined adjectives, assessments of social and exploratory behavior, stereotypies, and human–animal relationship tests. Two separate PCA were performed with respect to the QBA: (1) adjectives were included as independent variables and (2) adjectives were pre-aggregated using the calculation rules of the Welfare Quality® protocol for fattening pigs since a calculation for sows does not yet exist. In both analyses, two components described sows' behavior. Most variance was explained by the solution with adjectives as independent variables (51.0%). Other behavioral elements not captured as indicators by the protocol may still be important for all-inclusive welfare assessments as the required variance of 70% was not achieved in the analyses. Component loadings were used to determine components' labels as (1) “satisfaction of exploratory behavior” and (2) “social resting”. Both components reflected characteristics of sows' natural behavior and can subsequently be used for variable reduction but also for development of component scores for aggregation. As defined for PCA, component 1 explained more variance than component 2. PCA is useful to determine the main components of sows' behavior, which can be used to enhance feasibility of welfare assessments.

U2 - 10.3389/fanim.2021.728608

DO - 10.3389/fanim.2021.728608

M3 - Journal article

VL - 2

JO - Frontiers in Animal Science

JF - Frontiers in Animal Science

SN - 2673-6225

M1 - 40

ER -

ID: 328549300