Deciphering Cattle Temperament Measures Derived From a Four-Platform Standing Scale Using Genetic Factor Analytic Modeling

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dc.contributor.authorYu, Haipengen
dc.contributor.authorMorota, Gotaen
dc.contributor.authorCelestino, Elfren F., Jr.en
dc.contributor.authorDahlen, Carl R.en
dc.contributor.authorWagner, Sarah A.en
dc.contributor.authorRiley, David G.en
dc.contributor.authorHulsman Hanna, Lauren L.en
dc.contributor.departmentAnimal and Poultry Sciencesen
dc.date.accessioned2020-08-04T17:38:26Zen
dc.date.available2020-08-04T17:38:26Zen
dc.date.issued2020-06-12en
dc.description.abstractThe 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, termeddifficultandeasyfrom 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 thedifficultandeasyscores discovered in EFA. This was followed by fitting a pedigree-based Bayesian multi-trait model to characterize the genetic interrelationships amongdifficult, 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 ofdifficultandeasyexhibited a large negative genetic correlation of -0.92. Moderate genetic correlation was found between DS anddifficult(0.36),easy(-0.31), SSD (0.42), and CVSSD (0.34) as well as FPSS withdifficult(CVSSD: 0.35; SSD: 0.42) andeasy(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.en
dc.description.notesThis work was supported by the North Dakota Agricultural Experiment Station funds to LH.en
dc.description.sponsorshipNorth Dakota Agricultural Experiment Station fundsen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fgene.2020.00599en
dc.identifier.eissn1664-8021en
dc.identifier.other599en
dc.identifier.pmid32595702en
dc.identifier.urihttp://hdl.handle.net/10919/99479en
dc.identifier.volume11en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectbeef cattleen
dc.subjectfactor analysisen
dc.subjectfour-platform standing scaleen
dc.subjectprecision agricultureen
dc.subjecttemperamenten
dc.titleDeciphering Cattle Temperament Measures Derived From a Four-Platform Standing Scale Using Genetic Factor Analytic Modelingen
dc.title.serialFrontiers In Geneticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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