| Full text | |
| Author(s): |
Savegnago, R. P.
[1]
;
Rosa, G. J. M.
[2]
;
Valente, B. D.
[2]
;
Herrera, L. G. G.
[3]
;
Carneiro, R. L. R.
[4]
;
Sesana, R. C.
[4]
;
El Faro, L.
[5]
;
Munari, D. P.
[1]
Total Authors: 8
|
| Affiliation: | [1] Univ Estadual Paulista UNESP, FCAV, Dept Ciencias Exatas, BR-14884900 Sao Paulo - Brazil
[2] Univ Wisconsin, Dept Anim Sci, Madison, WI 53706 - USA
[3] Univ Tecnol Pereira, Fac Ciencias Salud, Pereira, Risaralda - Colombia
[4] CRV Lagoa, BR-14174000 Sao Paulo - Brazil
[5] Agencia Paulista Tecnol Agronegocios APTA Ctr Les, SAA, BR-14001970 Sao Paulo - Brazil
Total Affiliations: 5
|
| Document type: | Journal article |
| Source: | JOURNAL OF DAIRY SCIENCE; v. 96, n. 11, p. 7284-7293, NOV 2013. |
| Web of Science Citations: | 5 |
| Abstract | |
The objectives of the present study were to estimate genetic parameters of monthly test-day milk yield (TDMY) of the first lactation of Brazilian Holstein cows using random regression (RR), and to compare the genetic gains for milk production and persistency, derived from RR models, using eigenvector indices and selection indices that did not consider eigenvectors. The data set contained monthly TDMY of 3,543 first lactations of Brazilian Holstein cows calving between 1994 and 2011. The RR model included the fixed effect of the contemporary group (herd-month-year of test days), the covariate calving age (linear and quadratic effects), and a fourth-order regression on Legendre orthogonal polynomials of days in milk (DIM) to model the population-based mean curve. Additive genetic and nongenetic animal effects were fit as RR with 4 classes of residual variance random effect. Eigenvector indices based on the additive genetic RR covariance matrix were used to evaluate the genetic gains of milk yield and persistency compared with the traditional selection index (selection index based on breeding values of milk yield until 305 DIM). The heritability estimates for monthly TDMY ranged from 0.12 +/- 0.04 to 0.31 +/- 0.04. The estimates of additive genetic and nongenetic animal effects correlation were close to 1 at adjacent monthly TDMY, with a tendency to diminish as the time between DIM classes increased. The first eigenvector was related to the increase of the genetic response of the milk yield and the second eigenvector was related to the increase of the genetic gains of the persistency but it contributed to decrease the genetic gains for total milk yield. Therefore, using this eigenvector to improve persistency will not contribute to change the shape of genetic curve pattern. If the breeding goal is to improve milk production and persistency, complete sequential eigenvector indices (selection indices composite with all eigenvectors) could be used with higher economic values for persistency. However, if the breeding goal is to improve only milk yield, the traditional selection index is indicated. (AU) | |
| FAPESP's process: | 12/16087-5 - Application of neural networks and random regression models for predict the breeding value of milk production in Holstein cows |
| Grantee: | Rodrigo Pelicioni Savegnago |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| FAPESP's process: | 10/05148-8 - Application of neural networks and random regression models for predict the breeding value of milk production in Holstein cows |
| Grantee: | Rodrigo Pelicioni Savegnago |
| Support Opportunities: | Scholarships in Brazil - Doctorate |