Adjusting for heterogeneity of experimental data in genetic evaluation of dry matter intake in dairy cattle

The objectives of the present study were (i) to find the best fitted model for repeatedly measured daily dry matter intake (DMI) data obtained from different herds and experiments across lactations and (ii) to get better estimates of the genetic parameters and better genetic evaluations. After editing, there were 572,512 daily DMI records of 3,495 animals (Holstein cows) from 11 different herds across 13 lactations and the animals were under 110 different nutritional experiments. The fitted model for this data set was a univariate repeated‐measure animal model (called model 1) in which additive genetic and permanent environmental (within and across lactations) effects were fitted as random. Model 1 was fitted as two distinct models (called models 2 and 3) based on alternative fixed effect corrections. For unscaled data, each model (models 2 and 3) was fitted as a homoscedastic (HOM) model first and then as a heteroscedastic (HET) model. Then, data were scaled by multiplying with particular herd‐scaling factors, which were calculated by accounting for heterogeneity of phenotypic within‐herd variances. Models were selected based on cross‐validation and prediction accuracy results. Scaling factors were re‐estimated to determine the effectiveness of accounting for herd heterogeneity. Variance components and respective heritability and repeatability were estimated based on a pedigree‐based relationship matrix. Results indicated that the model fitted for scaled data sho...
Source: Journal of Animal Breeding and Genetics - Category: Genetics & Stem Cells Authors: Tags: ORIGINAL ARTICLE Source Type: research