Deciphering cattle temperament measures derived from a four-platform standing scale using genetic factor analytic modeling Institutional Repository Document uri icon


  • AbstractThe animals 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 evaluators 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.

altmetric score

  • 3.1

author list (cited authors)

  • Yu, H., Morota, G., Celestino, E. F., Dahlen, C. R., Wagner, S. A., Riley, D. G., & Hanna, L.

citation count

  • 2

complete list of authors

  • Yu, Haipeng||Morota, Gota||Celestino, Elfren F||Dahlen, Carl R||Wagner, Sarah A||Riley, David G||Hanna, Lauren L Hulsman

Book Title

  • bioRxiv

publication date

  • January 2020