Deciphering Cattle Temperament Measures Derived From a Four-Platform Standing Scale Using Genetic Factor Analytic Modeling
The animal's reaction to human handling (i.e., temperament) is critical for work safety, productivity, and welfare. Subjective phenotyping methods have been traditionally used in beef cattle production. Even so, subjective scales rely on the evaluator's knowledge and interpretation of temp...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-06-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2020.00599/full |
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| author | Haipeng Yu Gota Morota Elfren F. Celestino Carl R. Dahlen Sarah A. Wagner David G. Riley Lauren L. Hulsman Hanna |
| author_facet | Haipeng Yu Gota Morota Elfren F. Celestino Carl R. Dahlen Sarah A. Wagner David G. Riley Lauren L. Hulsman Hanna |
| author_sort | Haipeng Yu |
| collection | DOAJ |
| description | The animal's reaction to human handling (i.e., temperament) is critical for work safety, productivity, and welfare. Subjective phenotyping methods have been traditionally used in beef cattle production. Even so, subjective scales rely on the evaluator's knowledge and interpretation of temperament, which may require substantial experience. Selection based on such subjective scores may not precisely change temperament preferences in cattle. The objectives of this study were to investigate the underlying genetic interrelationships among temperament measurements using genetic factor analytic modeling and validate a movement-based objective method (four-platform standing scale, FPSS) as a measure of temperament. Relationships among subjective methods of docility score (DS), temperament score (TS), 12 qualitative behavior assessment (QBA) attributes and objective FPSS including the standard deviation of total weight on FPSS over time (SSD) and coefficient of variation of SSD (CVSSD) were investigated using 1,528 calves at weaning age. An exploratory factor analysis (EFA) identified two latent variables account for TS and 12 QBA attributes, termed difficult and easy from their characteristics. Inclusion of DS in EFA was not a good fit because it was evaluated under restraint and other measures were not. A Bayesian confirmatory factor analysis inferred the difficult and easy scores discovered in EFA. This was followed by fitting a pedigree-based Bayesian multi-trait model to characterize the genetic interrelationships among difficult, easy, DS, SSD, and CVSSD. Estimates of heritability ranged from 0.18 to 0.4 with the posterior standard deviation averaging 0.06. The factors of difficult and easy exhibited a large negative genetic correlation of −0.92. Moderate genetic correlation was found between DS and difficult (0.36), easy (−0.31), SSD (0.42), and CVSSD (0.34) as well as FPSS with difficult (CVSSD: 0.35; SSD: 0.42) and easy (CVSSD: −0.35; SSD: −0.4). Correlation coefficients indicate selection could be performed with either and have similar outcomes. We contend that genetic factor analytic modeling provided a new approach to unravel the complexity of animal behaviors and FPSS-like measures could increase the efficiency of genetic selection by providing automatic, objective, and consistent phenotyping measures that could be an alternative of DS, which has been widely used in beef production. |
| first_indexed | 2024-12-10T20:52:52Z |
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| id | doaj.art-37b5b0f4a7dd4918be7d9b5c6197780f |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-10T20:52:52Z |
| publishDate | 2020-06-01 |
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| series | Frontiers in Genetics |
| spelling | doaj.art-37b5b0f4a7dd4918be7d9b5c6197780f2022-12-22T01:34:02ZengFrontiers Media S.A.Frontiers in Genetics1664-80212020-06-011110.3389/fgene.2020.00599532890Deciphering Cattle Temperament Measures Derived From a Four-Platform Standing Scale Using Genetic Factor Analytic ModelingHaipeng Yu0Gota Morota1Elfren F. Celestino2Carl R. Dahlen3Sarah A. Wagner4David G. Riley5Lauren L. Hulsman Hanna6Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United StatesDepartment of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United StatesDepartment of Animal Sciences, North Dakota State University, Fargo, ND, United StatesDepartment of Animal Sciences, North Dakota State University, Fargo, ND, United StatesDepartment of Animal Sciences, North Dakota State University, Fargo, ND, United StatesDepartment of Animal Science, Texas A&M University, College Station, TX, United StatesDepartment of Animal Sciences, North Dakota State University, Fargo, ND, United StatesThe animal's reaction to human handling (i.e., temperament) is critical for work safety, productivity, and welfare. Subjective phenotyping methods have been traditionally used in beef cattle production. Even so, subjective scales rely on the evaluator's knowledge and interpretation of temperament, which may require substantial experience. Selection based on such subjective scores may not precisely change temperament preferences in cattle. The objectives of this study were to investigate the underlying genetic interrelationships among temperament measurements using genetic factor analytic modeling and validate a movement-based objective method (four-platform standing scale, FPSS) as a measure of temperament. Relationships among subjective methods of docility score (DS), temperament score (TS), 12 qualitative behavior assessment (QBA) attributes and objective FPSS including the standard deviation of total weight on FPSS over time (SSD) and coefficient of variation of SSD (CVSSD) were investigated using 1,528 calves at weaning age. An exploratory factor analysis (EFA) identified two latent variables account for TS and 12 QBA attributes, termed difficult and easy from their characteristics. Inclusion of DS in EFA was not a good fit because it was evaluated under restraint and other measures were not. A Bayesian confirmatory factor analysis inferred the difficult and easy scores discovered in EFA. This was followed by fitting a pedigree-based Bayesian multi-trait model to characterize the genetic interrelationships among difficult, easy, DS, SSD, and CVSSD. Estimates of heritability ranged from 0.18 to 0.4 with the posterior standard deviation averaging 0.06. The factors of difficult and easy exhibited a large negative genetic correlation of −0.92. Moderate genetic correlation was found between DS and difficult (0.36), easy (−0.31), SSD (0.42), and CVSSD (0.34) as well as FPSS with difficult (CVSSD: 0.35; SSD: 0.42) and easy (CVSSD: −0.35; SSD: −0.4). Correlation coefficients indicate selection could be performed with either and have similar outcomes. We contend that genetic factor analytic modeling provided a new approach to unravel the complexity of animal behaviors and FPSS-like measures could increase the efficiency of genetic selection by providing automatic, objective, and consistent phenotyping measures that could be an alternative of DS, which has been widely used in beef production.https://www.frontiersin.org/article/10.3389/fgene.2020.00599/fullbeef cattlefactor analysisfour-platform standing scaleprecision agriculturetemperament |
| spellingShingle | Haipeng Yu Gota Morota Elfren F. Celestino Carl R. Dahlen Sarah A. Wagner David G. Riley Lauren L. Hulsman Hanna Deciphering Cattle Temperament Measures Derived From a Four-Platform Standing Scale Using Genetic Factor Analytic Modeling Frontiers in Genetics beef cattle factor analysis four-platform standing scale precision agriculture temperament |
| title | Deciphering Cattle Temperament Measures Derived From a Four-Platform Standing Scale Using Genetic Factor Analytic Modeling |
| title_full | Deciphering Cattle Temperament Measures Derived From a Four-Platform Standing Scale Using Genetic Factor Analytic Modeling |
| title_fullStr | Deciphering Cattle Temperament Measures Derived From a Four-Platform Standing Scale Using Genetic Factor Analytic Modeling |
| title_full_unstemmed | Deciphering Cattle Temperament Measures Derived From a Four-Platform Standing Scale Using Genetic Factor Analytic Modeling |
| title_short | Deciphering Cattle Temperament Measures Derived From a Four-Platform Standing Scale Using Genetic Factor Analytic Modeling |
| title_sort | deciphering cattle temperament measures derived from a four platform standing scale using genetic factor analytic modeling |
| topic | beef cattle factor analysis four-platform standing scale precision agriculture temperament |
| url | https://www.frontiersin.org/article/10.3389/fgene.2020.00599/full |
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