Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-24T04:30:52.936Z Has data issue: false hasContentIssue false

Application of Bayesian inference in the comparison of lactation curves of Merino ewes

Published online by Cambridge University Press:  02 September 2010

P. C. N. Groenewald
Affiliation:
Department of Mathematical Statistics, University of the Orange Free State, Bloemfontein, South Africa
A. V. Ferreira
Affiliation:
Department of Animal Science, Faculty of Agriculture, University of the Orange Free State, PO Box 339, Bloemfontein 9300, South Africa
H. J. van der Merwe
Affiliation:
Department of Animal Science, Faculty of Agriculture, University of the Orange Free State, PO Box 339, Bloemfontein 9300, South Africa
S. C. Slippers
Affiliation:
Department of Agriculture, University of Zululand, Kwadlangezwa, South Africa
Get access

Abstract

Bayesian theory is applied to compare the characteristics of the estimated lactation curves of two groups of 5-year-old Merino ewes. The diets of the two groups were supplemented respectively by DL-methionine and maleyl-DLmethionine. The purpose is to illustrate the Bayesian approach when analysing for the effect of supplement on the lactation pattern of the sheep. Using Wood's model, the posterior distributions of the model parameters are determined for the two groups. This is achieved by assuming a hierarchical Bayes model and applying the Gibbs sampler, a sampling based computer intensive algorithm that is very efficient in obtaining marginal distributions of functions of parameters. The Gibbs sampler enables us to obtain marginal posterior distribution of characteristics of the lactation curve such as peak yield, time of peak yield, persistency and total milk yield. The results are notable differences in the marginal posterior distributions of mean peak milk yield and mean total yield. The posterior probability that the mean peak milk yield of the group supplemented by maleyl-DL-methionine is higher than that of the group with DL-methionine supplement is 0·98, while the same probability for mean total yield is 0·83.

Type
Research Article
Copyright
Copyright © British Society of Animal Science 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aguillar, J. A., Aparico, J. J., Fernandez-Rivera, S. and Tovar, I. 1989. Lactation curves and milk yield of Corriedale, Rambouilett and Suffolk ewes. Journal of Dairy Science 72: 426427.Google Scholar
Blattberg, R. and George, E. I. 1991. Shrinkage estimation of price and promotional elasticities: seemingly unrelated equations. Journal of the American Statistical Association 86: 304315.CrossRefGoogle Scholar
Cappio-Borlino, A., Pulina, G., Cannas, A. and Rossi, G. 1989. The theoretical lactation curve of Sardinian ewes. Zootecnica e Nutrizione Animate 15: 5963.Google Scholar
Casella, G. and George, I. E. 1992. Explaining the Gibbs sampler. American Statistician 46: 167174.Google Scholar
Doney, J. M., Peart, J. N., Smith, W. F. and Louda, F. 1979. A consideration of the techniques for estimation of milk yield by suckled sheep and a comparison of estimates obtained by two methods in relation to the effect of breed, level of production and stage of lactation. Journal of Agricultural Science, Cambridge 92:123132.CrossRefGoogle Scholar
Gelfand, A. E., Hills, S. E., Racine-Poon, A. and Smith, A. E. M. 1990. Illustration of Bayesian inference in normal data models using Gibbs sampling. Journal of the American Statistical Association 85: 972985.CrossRefGoogle Scholar
Gelfand, A. E. and Smith, A. F. M. 1990. Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 85: 398409.CrossRefGoogle Scholar
Geman, S. and Geman, D. 1984. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6:721741.CrossRefGoogle ScholarPubMed
Gianola, D. and Fernando, R. L. 1986. Bayesian methods in animal breeding theory. Journal of Animal Science 63: 217244.CrossRefGoogle Scholar
Gianola, D. and Kachman, S. D. 1983. Prediction of breeding value in situations with nonlinear structure: categorical responses, growth functions and lactation curves. Proceedings of the thirty-fourth annual meeting of the European Association for Animal Production, Madrid, summaries, p. 172 (abstr.).Google Scholar
Goodall, E. A. and Sprevak, D. 1984. A note on a stochastic model to describe the milk yield of a dairy cow. Animal Production 38: 133136.Google Scholar
Goodall, E. A. and Sprevak, D. 1985. A Bayesian estimation of the lactation curve of a dairy cow. Animal Production 40: 189193.Google Scholar
Groenewald, P. C. N., Ferreira, A. V., Merwe, H. J. van der and Slippers, S. C. 1995. A mathematical model for describing and predicting the lactation curve of Merino ewes. Animal Science 61: 95101.CrossRefGoogle Scholar
Grossman, M. and Koops, W. J. 1988. Multiphasic analysis of lactation curves in dairy cattle. Journal of Dairy Science 71: 15981608.CrossRefGoogle Scholar
Jeffreys, H. 1961. Theory of probability. Claredon Press, Oxford.Google Scholar
Lindley, D. V. and Smith, A. F. M. 1972. Bayes estimates for the linear model (with discussion). Journal of the Royal Statistical Society, Series B 34:141.Google Scholar
McCance, I. 1959. The determination of milk yield in the Merino ewe. Australian Journal of Agricultural Research 10: 839853.CrossRefGoogle Scholar
Morant, S. V. and Gnanasakthy, A. 1989. A new approach to the mathematical formulation of lactation curves. Animal Production 49:151162.Google Scholar
National Research Council. 1985. Nutrient requirements of sheep. National Academy of Sciences, Washington, DC.Google Scholar
Naylor, J. C. and Smith, A. F. M. 1982. Applications of a method for the efficient computation of posterior distributions. Applied Statistics 31: 214225.CrossRefGoogle Scholar
Rook, A. J., France, J. and Dhanoa, M. S. 1993. On the mathematical description of lactation curves. Journal of Agricultural Science 121: 97102.CrossRefGoogle Scholar
Sakul, H. and Boylan, W. J. 1992. Lactation curves for several US sheep breeds. Animal Production 54: 229233.Google Scholar
Tierney, L. and Kadane, J. B. 1986. Accurate approximations for posterior moments and marginal densities. Journal of the American Statistical Association 81: 8286.CrossRefGoogle Scholar
Torres-Hernandez, G. and Hohenboken, W. D. 1980. Biometric properties of lactation in ewes raising single or twin lambs. Animal Production 30: 431436.Google Scholar
Wakefield, J. C., Smith, A. F. M., Racine-Poon, A. and Gelfand, A. E. 1994. Bayesian analysis of linear and nonlinear population models by using the Gibbs sampler. Applied Statistics 43: 201221.CrossRefGoogle Scholar
Wang, C. S., Rutledge, J. J. and Gianola, D. 1993. Marginal inferences about variance components in a mixed linear model using Gibbs sampling. Genetic Selection Evolution 25: 164165.CrossRefGoogle Scholar
Wood, P. D. P. 1967. Algebraic model of the lactation curve in cattle. Nature, London 216: 164165.CrossRefGoogle Scholar
Wood, P. D. P. 1968. Factors affecting persistency of lactation in cattle. Nature, London 218: 894.CrossRefGoogle Scholar