GENETIC EVALUATION OF FERTILITY TRAITS OF FRIESIAN X BUNAJI DAIRY COWS USING MULTIVARIATE MILK COMPOSITION BASED MODELS

Authors

  • Alphonsus C. Animal Science Department, Ahmadu Bello University, Zaria, Nigeria.
  • Akpa G. N. Animal Science Department, Ahmadu Bello University, Zaria, Nigeria.
  • Barje, P. P. National Animal Production Research Institute, Ahmadu Bello University, Shika, Zaria, Nigeria
  • Nwagu, B. I. National Animal Production Research Institute- Shika,
  • Orunmuyi, M. Animal Science Department, Ahmadu Bello University, Zaria, Nigeria.
  • Adeyinka I. A. National Animal Production Research Institute- Shika,
  • Alphonsus, M. Animal Science Department, Ahmadu Bello University, Zaria, Nigeria.
  • Tanko R. J. National Animal Production Research Institute- Shika,
  • Hassan A. M. Department of Animal Science, Faculty of Agriculture and Agricultural Technology, Kano University of Science and Technology, Wudil.
  • Achi N. P. National Animal Production Research Institute- Shika,

Keywords:

fertility, milk composition, milk yield, single-trait models, multiple-traitmodels

Abstract

The study investigated the improvement in genetic evaluation of fertility traits of dairy cows
using single trait models and multiple-trait models that combined information on milk
composition and milk yield traits. Data on fertility and milk production records of 61 Friesian x
Bunaji cows were used for this study. Four fertility traits; days to first insemination (DFI), days
open (DO), number of insemination per conception (NIC) and non-return rate after 56 days of
insemination (NRR56). The models prediction ability and stability were assessed by the
coefficients of determination adjusted (R2-adj), root mean square error of prediction (RMSEP)
and P-value of the resulted models. Based on the evaluation criteria, the models that combined
milk composition traits and one or more milk yield traits showed better model stability and
predictive ability than the single-trait models for all the fertility traits evaluated. In addition, the
single-trait models underestimated the prediction ability of the models. The estimates of h2 were
very low for the fertility traits (0.014 to 0.035) and moderate for milk yield and milk composition
traits (0.315-0.415). Moreover, there was a moderate correlation between most of the milk yield
and fertility traits. These estimates of parameters indicated that the accuracy of the prediction of
fertility traits would increase using multiple-trait model that include milk yield and milk
composition traits. These results suggested that genetic evaluation of fertility traits would be
improved using multiple-trait models that combine milk yield and composition traits.

Published

2016-12-30