Use of Multivariate Analysis to Extract Latent Variables Related to Level of Production and Lactation Persistency in Dairy Cattle

Abstract

Multivariate factor analysis and principal component analysis were used to decompose the correlation matrix of test-day milk yields of 48,374 lactations of 21,721 Italian Simmental cows. Two common latent factors related to level of production in early lactation and lactation persistency, and 2 principal components associated with the whole lactation yield and persistency were obtained. Factor and principal component scores were treated as new quantitative phenotypes related to prominent features of lactation curve shape. Genetic parameters were estimated by univariate and bivariate animal models. Estimates of heritability were moderately low for both latent factors (0.13 for persistency and yield early in lactation). Heritabilities of the principal component related to total lactation yield and 305-d yield were similar (0.19 and 0.20, respectively). Finally, heritability was quite low for the principal component related to lactation persistency (0.07). Repeatabilities between lactations were about 0.27 for both latent factors, around 0.4 for the first principal component and 305-d yield, and 0.11 for the second principal component. Moderate genetic correlation among common factors (0.26) and their high genetic correlation with total lactation yield (>0.60) suggest that selection can be used to change the shape of lactation curve as well as improve yield. Scores of the second principal component can be used to genetically improve persistency while maintaining constant total lactation yield.

The mathematical description of lactation curves in dairy cattle

“…Cows with curves characterized by lower peaks and higher persistency are less exposed to health and fertility problems and can exploit efficiently cheaper feeds (Jakobsen et al, 2002). Several models for measuring these traits have been proposed in literature but none of them has been widely accepted (Macciotta et al, 2006;Rekaya et al, 2001). Lactation curve traits are characterised by low to moderate values of heritability, even though a a relevant variation could be observed in estimates reported in literature.…”

“…The first component, explaining 75% of the original variance, is equally related to all milk tests whereas the second (about 15% of the original variance) has negative relationship with the first and positive with the second part of the lactation respectively. These new variables can be used as lactation curve traits, expressing average lactation yield and lactation curve shape, respectively (Macciotta et al, 2006;Wilmink, 1987b) The same result has been also obtained at genetic level, by performing eigenvalue decomposition on genetic covariance matrix of TD ( Van der Werf et al, 1998;Druet et al, 2003) or the genetic (co)variance matrix of random regression test day models (Jamrozik et al, 2002). A part from supplying new variables that may be of help for the genetic improvement of lactation curve traits, results of the multivariate analysis that underline the existence of a latent structure made of two latent variable seems to give an indirect confirmation of the hypothesis of Grossman and Koops (1988) of a diphasic structure of the lactation pattern.…”

Use of principal component analysis to classify forages and predict their calculated energy content

“…in vitro rumen fluid or enzymatic digestibility) parameters was used to predict the energy value of feeds and forages (Givens et al, 1989;De Boever et al, 1996). More recently, some authors used multivariate analysis techniques to extract latent variables linked to the milk yield and lactation persistency of dairy cows (Macciotta et al, 2006), to study the influence of quality parameters on the methanogenic potential of different forages (Jayanegara et al, 2011) or the between-goat variability in feeding behaviour (Desnoyers et al, 2011).…”

Non-nutritional factors affecting lactation persistency in dairy ewes: a review

“…Some measures of persistency are highly correlated with total lactation yield, even though some authors state that a robust measure of persistency should be independent from total yield (real persistency) (Gengler, 1996) or that the total lactation yield should be included as a (co)variate in the genetic model used to estimate genetic parameters and breeding values for lactation persistency (Swalve, 1995). In any case, most scientists agree that persistency possesses a certain degree of genetic variation, with moderate heritability (0.15-0.20) (Macciotta et al, 2006), and that selection for this trait is feasible.…”