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Genetic parameters for test-day yield of milk, fat and protein in buffaloes estimated by random regression models

Published online by Cambridge University Press:  23 March 2012

Rúsbel R. Aspilcueta-Borquis
Affiliation:
Department of Animal Science, São Paulo State University (FCAV/UNESP), Jaboticabal 14884 900, SP, Brazil
Francisco R. Araujo Neto
Affiliation:
Department of Animal Science, São Paulo State University (FCAV/UNESP), Jaboticabal 14884 900, SP, Brazil
Fernando Baldi
Affiliation:
Department of Animal Science, São Paulo State University (FCAV/UNESP), Jaboticabal 14884 900, SP, Brazil
Daniel J. A. Santos
Affiliation:
Department of Animal Science, São Paulo State University (FCAV/UNESP), Jaboticabal 14884 900, SP, Brazil
Lucia G. Albuquerque
Affiliation:
Department of Animal Science, São Paulo State University (FCAV/UNESP), Jaboticabal 14884 900, SP, Brazil Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq) and Instituto Nacional de Ciência e Tecnologia – Ciência Animal (INCT – CA), Viçosa 36570 000, MG, Brazil
Humberto Tonhati*
Affiliation:
Department of Animal Science, São Paulo State University (FCAV/UNESP), Jaboticabal 14884 900, SP, Brazil Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq) and Instituto Nacional de Ciência e Tecnologia – Ciência Animal (INCT – CA), Viçosa 36570 000, MG, Brazil
*
*For correspondence; e-mail: tonhati@fcav.unesp.br

Abstract

The test-day yields of milk, fat and protein were analysed from 1433 first lactations of buffaloes of the Murrah breed, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, born between 1985 and 2007. For the test-day yields, 10 monthly classes of lactation days were considered. The contemporary groups were defined as the herd-year-month of the test day. Random additive genetic, permanent environmental and residual effects were included in the model. The fixed effects considered were the contemporary group, number of milkings (1 or 2 milkings), linear and quadratic effects of the covariable cow age at calving and the mean lactation curve of the population (modelled by third-order Legendre orthogonal polynomials). The random additive genetic and permanent environmental effects were estimated by means of regression on third- to sixth-order Legendre orthogonal polynomials. The residual variances were modelled with a homogenous structure and various heterogeneous classes. According to the likelihood-ratio test, the best model for milk and fat production was that with four residual variance classes, while a third-order Legendre polynomial was best for the additive genetic effect for milk and fat yield, a fourth-order polynomial was best for the permanent environmental effect for milk production and a fifth-order polynomial was best for fat production. For protein yield, the best model was that with three residual variance classes and third- and fourth-order Legendre polynomials were best for the additive genetic and permanent environmental effects, respectively. The heritability estimates for the characteristics analysed were moderate, varying from 0·16±0·05 to 0·29±0·05 for milk yield, 0·20±0·05 to 0·30±0·08 for fat yield and 0·18±0·06 to 0·27±0·08 for protein yield. The estimates of the genetic correlations between the tests varied from 0·18±0·120 to 0·99±0·002; from 0·44±0·080 to 0·99±0·004; and from 0·41±0·080 to 0·99±0·004, for milk, fat and protein production, respectively, indicating that whatever the selection criterion used, indirect genetic gains can be expected throughout the lactation curve.

Type
Research Article
Copyright
Copyright © Proprietors of Journal of Dairy Research 2012

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References

Aspilcueta, RB, Araujo Neto, FR, Baldi, F, Bignardi, AB, Albuquerque, LG & Tonhati, H 2010a Genetic parameters for buffalo milk yield and milk quality traits using Bayesian inference. Journal of Dairy Science 93 21952201 CrossRefGoogle Scholar
Aspilcueta, RB, Bignardi, AB, Seno, LO, Camargo, GM, Muñoz-Berrocal, M, Albuquerque, LG, Di Palo, R & Tonhati, H 2010b Genetic parameters for milk yield analyzed by test day models in Murrah buffaloes in Brazil. Italian Journal of Animal Science 9(2) 179182 Google Scholar
Aspilcueta, RB, Sesana, RC, Muñoz-Berrocal, M, Seno, LO, Bignardi, AB, El Faro, L, Albuquerque, LG, Camargo, GM & Tonhati, H 2010c Genetic parameters for milk, fat and protein yields in Murrah buffaloes (Bubalus bubalis Artiodactyla, Bovidae). Genetics and Molecular Biology 33 7177 CrossRefGoogle Scholar
Bignardi, AB, El Faro, L, Albuquerque, LG, Cardoso, VL & Machado, PF 2009 Random regression models to estimate testday milk yield genetic parameters Holstein cows in southeastern Brazil. Livestock Production Science 123 17 CrossRefGoogle Scholar
Breda, FC, Albuquerque, LG, Euclydes, RF, Bignardi, AB, Baldi, F, Torres, RA, Barbosa, L & Tonhati, H 2010 Estimation of genetic parameters for milk yield in Murrah buffaloes by Bayesian inference. Journal of Dairy Science 93 784791 CrossRefGoogle ScholarPubMed
De Groot, BJ, Keown, JF, Van Vleck, LD & Kachman, SD 2007 Estimates of genetic parameters for Holstein cows for testday yield traits with a random regression cubic spline model. Genetics and Molecular Research 6 434444 Google ScholarPubMed
De Roos, APW, Harbers, AGF & Jong, G 2004 Random herd curves in a test-day model for milk, fat and protein production of dairy cattle in the Netherlands. Journal of Dairy Science 87 26932701 CrossRefGoogle Scholar
El Faro, L & Albuquerque, LG 2003 Utilização de modelos de regressão aleatória para a produção de leite no dia do controle, com diferentes estruturas de variâncias residuais. Revista Brasileira de Zootecnia 32(5) 11041113 CrossRefGoogle Scholar
Gengler, N, Tijini, A, Wiggans, GR & Philpot, JC 1997 Estimation of (co)variance components of test day yields for U.S. Holsteins. Proceeding of the 1997 Interbull meeting, Vienna, Austria, August 28–29 1997 Interbull Bull 16 3942 Avaliable at http://www-interbull.slu.se/bulletins/bulletin16/Gengler.pdf Google Scholar
Jakobsen, JH, Madsen, P, Jensen, J, Pedersen, J, Christensen, LG & Sorensen, DA 2002a Genetic parameters for milk production and persistency for Danish Holsteins estimated in random regression models using REML. Journal of Dairy Science 85(6) 16071616 CrossRefGoogle ScholarPubMed
Jakobsen, JH, Madsen, P & Pedersen, J 2002b Multivariate covariance functions for test day production in Danish breeds. Proceeding of the 2002 Interbull Meeting, Interlaken, Switzerland, May 26–27 2002 Interbull Bull 29 95102 Available at http://www-interbull.slu.se/bulletins/bulletin29/Jakobsen.pdf Google Scholar
Legarra, A, Misztal, I & Bertrand, JK 2004 Constructing covariance functions for random regression models for growth in Gelbvieh beef cattle. Journal Animal Science 82 15641571 CrossRefGoogle ScholarPubMed
Liu, Z, Reinhardt, F & Reents, R 2000 Estimating parameters of a random regression test day model for first three lactation milk production traits using the covariance function approach. Proceeding of the 2000 Interbull meeting, Interlaken, Switzerland, May 14–15 2000 Interbull Bull 25 7480 Available at http://www-interbull.slu.se/bulletins/bulletin25/liu.pdf Google Scholar
López-Romero, P & Carabaño, MJ 2003 Comparing alternative random regression models to analyse first-lactation daily milk yield data in Holstein-Friesian cattle. Livestock Production Science 82 8186 CrossRefGoogle Scholar
Malhado, CH, Ramos, A, Carneiro, P, Souza, J & Piccinin, A 2007 Genetic and phenotypic parameters for milk production of Murrah buffaloes. Brazilian Journal Animal Science 37 376379 Google Scholar
Meyer, K 2006 “WOMBAT” – Digging deep for quantitative genetic analyses by restricted maximum likelihood. CD-ROM Eighth World Congress on Genetic Applied to Livestock Production, Proceedings…Communication No. 27–14.Google Scholar
Muir, BL, Kistemaker, G, Jamrozik, J & Canavesci, F 2007 Genetic parameters for a multiple-trait multiple-lactation random regression test-day model in Italian Holsteins. Journal of Dairy Science 90 15641574 CrossRefGoogle ScholarPubMed
Pool, MH, Janss, LLG & Meuwissen, THE 2000 Genetic parameters of Legendre polynomials for first parity lactation curves. Journal of Dairy Science 83 26402649 CrossRefGoogle ScholarPubMed
Rekaya, R, Carabaño, MJ & Toro, MA 2000 Assessment of heterogeneity of residual variance using changing points techniques. Genetics Selection Evolution 32 339346 CrossRefGoogle Scholar
Schaeffer, LR & Jamrozik, J 2008 Random regression models: a longitudinal perspective. Journal of Animal Breeding and Genetics 125 145146 CrossRefGoogle ScholarPubMed
Seno, LO, Cardoso, VL & Tonhati, H 2007 Valores econômicos para as características de produção de leite de búfalas no Estado de São Paulo. Revista Brasileira de Zootecnia 36 20162022 CrossRefGoogle Scholar
Sesana, RC, Bignardi, AB, Aspilcueta, RA, El Faro, L, Baldi, F, Albuquerque, LG & Tonhati, H 2010 Random regression models to estimate genetic parameters for test-day milk yield in Brazilian Murrah buffaloes. Journal of Animal Breeding and Genetics 127 369376 CrossRefGoogle ScholarPubMed
Silvestre, AM, Petim-Batista, F & Colaço, J 2005 Genetic parameters estimates of Portuguese dairy cows for milk, fat and protein using a Spline test-day model. Journal Dairy Science 88 12251230 CrossRefGoogle ScholarPubMed
Tonhati, H, Baruselli, PS, Oliveira, JFS, Vasconcellos, BF & Toledo, LM 1996 Calving season, peak of lactation and milk production of the buffalo in Ribeira Valley, São Paulo State Brazil. Rev. Bubalus Bubalis, Salerno, Italia 3 6367 Google Scholar
Wolfinger, R 1993 Covariance structure selection in general mixed models. Communications in Statistics 22(4) 10791106 CrossRefGoogle Scholar