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

Abstract

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.

The Effect of Stress on Reproduction and Reproductive Technologies in Beef Cattle—A Review

“…Additional behavioral indicators of beef cattle stress have been proposed. These include movement-measuring devices [ 63 ], a docility test [ 64 ], strain gauges [ 65 ], a race score [ 66 ], qualitative behavioral assessment [ 67 ] and a Four-Platform Standing Scale [ 68 ]. Most of these behavioral scales have been shown to be objective, repeatable and correlated with different biomarkers, including cortisol [ 19 , 61 ], haptoglobin [ 19 ], substance P and prolactin [ 20 ].…”

Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning

“…Exploratory factor analysis can reveal the latent structure among phenotypes when no hypotheses about the nature of the underlying factor can be assumed a priori. This section closely follows the work of Yu et al (2020). The aforementioned t = 45 phenotypes were analyzed using EFA by fitting…”

“…In a Bayesian setting, all unknowns in equations (1) and (2) were assigned priors. The assignment of priors was performed according to Yu et al (2019Yu et al ( , 2020 using the default priors in the blavaan R package (Merkle and Rosseel, 2018). A Gaussian distribution with a mean of zero and variance of 100 was assigned to the factor loading term.…”

“…Factor analysis is an effective method for handling many response variables in a quantitative genetic framework (Runcie and Mukherjee, 2013;Peñagaricano et al, 2015;Rocha et al, 2018;Yu et al, 2019Yu et al, , 2020. The central idea behind factor analysis is to model the observed phenotypes through unobserved latent factors by maximizing the common variance between correlated phenotypes.…”

Relationship between Temperament and Stage of Lactation, Productivity and Milk Composition of Dairy Cows

“…Cattle temperament is important not just for animal welfare, but also for their productivity, health, longevity and farm profitability [5][6][7][8][9][10][11] therefore, it may make sense to use this indicator in dairy cattle breeding programmes. Yu et al [12] argued that heritability coefficient values of cow temperament ranging from 0.17 to 0.40 may be sufficient for cattle selection. This statement has been confirmed by the research results of other authors.…”