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

Abstract

SummaryThe 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 showed better fit than the models (HOM or HET) fitted for unscaled data. The heritability estimates of the models 2 and 3 fitted for scaled data were 0.30 and 0.08, respectively. The repeatability estimates of the model fitted for scaled data ranged from 0.51 to 0.63. The re-estimated scaling factor after accounting for heterogeneity of residual variances was close to 1.0, indicating the stabilization of residual variances and herd accounted for most of the heterogeneity. The rank correlation of EBVs between scaled and unscaled data ranged from 0.96 to 0.97. K E Y W O R D Sdairy cattle, dry matter intake, genetic evaluation, heritability, heterogeneity, repeatability

Lactation curves and model evaluation for feed intake and energy balance in dairy cows

“…The traits FI and EB can be measured at different intervals (e.g., test day, test week, and so on), which can be evaluated using different models. These include repeatability models, multi-trait models, and random regression models with different functions (Buttchereit et al, 2010;Spurlock et al, 2012;Li et al, 2018;Uddin et al, 2018).…”