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Population parameter estimation of daily milk yield of the Chios sheep using test-day random regression models and Gibbs sampling

Published online by Cambridge University Press:  09 March 2007

G. Banos ,
G. Arsenos ,
Z. Abas  and
Z. Basdagianni
G. Banos*
Affiliation:
Aristotle University of Thessaloniki, Department of Animal Production, School of Veterinary Medicine, GR-54124 Thessaloniki, Greece
G. Arsenos
Affiliation:
Aristotle University of Thessaloniki, Department of Animal Production, School of Veterinary Medicine, GR-54124 Thessaloniki, Greece
Z. Abas
Affiliation:
Democritus University of Thrace, Department of Agricultural Development, GR-68200 Orestiada, Greece
Z. Basdagianni
Affiliation:
Aristotle University of Thessaloniki, Department of Animal Production, School of Veterinary Medicine, GR-54124 Thessaloniki, Greece Chios Sheep Breeders' Cooperative ‘Macedonia’, Thessaloniki, Greece
*
E-mail: [email protected]
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Abstract

Parameters of daily milk yield during the first three lactations of Chios ewes were estimated with random regression models. Data consisted of 42 675 test-day records of 7121 ewes from 75 flocks that had lambed between 1998 and 2000. Models fitted fourth order fixed regressions on Legendre polynomials of the number of days post partum and fourth order random regressions on the individual animal. (Co)variance components were estimated with Gibbs sampling. Lactations were analysed separately. The four eigen values accounted for 0·80 to 0·84, 0·11 to 0·15, 0·04 to 0·05 and about 0·01 of the animal variance, respectively, depending on lactation number. Animal variance estimates, including genetic and, partly, permanent environment effects, were high at the beginning of each lactation and decreased as lactation progressed, suggesting that the animal effect is most important to early daily records. Residual variance was highest in the middle of lactation, suggesting that non-systematic environmental factors play a bigger at that time. Animal correlation estimates between daily yield records ranged from 0·26 to 0·99, were highest for adjacent days and decreased for days further apart. The decline had a different shape in the three lactations and was more evident in the first, suggesting that the three lactations may be biologically distinct traits. Animal correlation estimates between daily and total lactation milk yield ranged from 0·61 to 0·98 and were highest in the middle and lowest towards the end of lactation. Early lactation daily yield had an animal correlation of 0·70 to 0·80 with total lactation milk yield, in all three lactations. Results of this study suggest that daily milk yield records in the early stages of lactation may be useful for selection of ewes with high producing ability and accurate prediction of total lactation milk yield.

Keywords

dairy sheepGibbs samplingmilk yieldpopulation parametersregression
Type
Research Article
Information
Animal Science , Volume 81 , Issue 2 , October 2005 , pp. 233 - 238
Copyright
Copyright © British Society of Animal Science 2005

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References

Basdagianni, Z., Banos, G., Abas, Z., Arsenos, G., Sinapis, E. and Zygoyiannis, D. 2004. Evaluation and definition of reference lactation length in Chios dairy sheep. Proceedings of the 34th International Committee on Animal Recording session of the 29 May–3 June, 2004, Sousse, Tunisia, pp. 121–125.Google Scholar
Brotherstone, S., White, I. M. S. and Meyer, K. 2000. Genetic modelling of daily milk yield using orthogonal polynomials and parametric curves. Animal Science 70: 407–415.CrossRefGoogle Scholar
Greek Ministry of Agricultural Development and Food. 2002. Results of the genetic improvement programme of the Chios sheep. Centre for the Genetic Improvement of Animals, Nea Mesimvria, Greece.Google Scholar
Horstick, A., Hamann, H. and Distl, O. 2002. Estimation of genetic parameters for daily milk performance of East Friesian milk sheep by random regression models. Proceedings of the seventh world congress on genetics applied to livestock production, Montpellier, France, communication no. 01–53.Google Scholar
Jamrozik, J. and Schaeffer, L. R. 1997. Estimates of genetic parameters for a test day model with random regressions for yield traits of first lactation Holsteins. Journal of Dairy Science 80: 762–770.CrossRefGoogle ScholarPubMed
Kettunen, A., Mäntysaari, E., Strandén, I., Pösö, J. and Lidauer, M. 1998. Estimation of genetic parameters for first lactation test day milk production using random regression models. Proceedings of the sixth world congress on genetics applied to livestock production, Armidale, Australia, vol. 23, pp. 307–310.Google Scholar
Kirkpatrick, M., Lofsvold, D. and Bulmer, M. 1990. Analysis of inheritance, selection and evolution of growth trajectories. Genetics 124: 979–993.CrossRefGoogle ScholarPubMed
Kominakis, A., Volanis, M. and Rogdakis, E. 2000. Genetic modelling of test day records in dairy sheep using orthogonal Legendre polynomials. Small Ruminant Research 39: 209–217.CrossRefGoogle Scholar
Ligda, Ch., Gabriilidis, G., Papadopoulos, Th. and Georgoudis, A. 2000. Estimation of genetic parameters for production traits of Chios sheep using a multitrait animal model. Livestock Production Science 66: 217–221.CrossRefGoogle Scholar
Mavrogenis, A. P. and Papachristoforou, C. 2000. Genetic and phenotypic relationships between milk production and body weight in Chios sheep and Damascus goats. Livestock Production Science 67: 81–87.CrossRefGoogle Scholar
Meyer, K. 1998. “DXMRR” – a program to estimate covariance functions for longitudinal data by restricted maximum likelihood. Proceedings of the sixth world congress on genetics applied to livestock production, Armidale, Australia, vol. 27, pp. 465–466.Google Scholar
Meyer, K. 2002. “RRGIBBS” – a program for simple random regression analyses via Gibbs sampling. Proceedings of the. seventh world congress on genetics applied to livestock production, Montpellier, communication no. 28–27.Google Scholar
Misztal, I., Strabel, T., Jamrozik, J., Mäntysaari, E. A. and Meuwissen, T. H. E. 2000. Strategies for estimating the parameters needed for different test-day models. Journal of Dairy Science 83: 1125–1134.CrossRefGoogle ScholarPubMed
Pool, M. H., Janss, L. L. G. and Meuwissen, T. H. E. 2000. Genetic parameters of Legendre polynomials for first parity lactation curves. Journal of Dairy Science 83: 2640–2649.CrossRefGoogle ScholarPubMed
Raftery, A. E. and Lewis, S. M. 1992. One long run with diagnostics: implementation strategies for Markov chain Monte Carlo. Statistical Science 7: 493–497.CrossRefGoogle Scholar
Rekaya, R., Carabaño, M. J. and Toro, M. A. 1999. Use of test day yields for the genetic evaluation of production traits in Holstein-Friesian cattle. Livestock Production Science 57: 203–217.CrossRefGoogle Scholar
Schaeffer, L. R. and Dekkers, J. C. M. 1994. Random regressions in animal models for test-day production in dairy cattle. Proceedings of the fifth world congress on genetics applied to livestock production, Guelph, vol. 88, pp. 443–446.Google Scholar
Smith, J. B. 2003. Bayesian output analysis manual, version 1·0. Department of Biostatistics, University of Iowa, College of Public Health.Google Scholar
Veerkamp, R. F., Koenen, E. P. C. and de Jong, G. 2001. Genetic correlations among body condition score, yield, and fertility in first-parity cows estimated by random regression models. Journal of Dairy Science 84: 2327–2335.CrossRefGoogle ScholarPubMed
Werf, J. H. J. van der, Goddard, M. E. and Meyer, K. 1998. The use of covariance functions and random regressions for genetic evaluation of milk production based on test-day records. Journal of Dairy Science 81: 3300–3308.CrossRefGoogle ScholarPubMed