Bayesian Neural Network Learning for Prediction in the Australian Dairy Industry

  • Paula E. Macrossan
  • Hussein A. Abbass
  • Kerry Mengersen
  • Michael Towsey
  • Gerard Finn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1642)


One of the most common problems encountered in agriculture is that of predicting a response variable from covariates of interest. The aim of this paper is to use a Bayesian neural network approach to predict dairy daughter milk production from dairy dam, sire, herd and environmental factors. The results of the Bayesian neural network are compared with the results obtained when the regression relationship is described using the traditional neural network approach. In addition, the “baseline” results of a multiple linear regression employing both frequentist and Bayesian methods are presented. The potential advantages of the Bayesian neural network approach over the traditional neural network approach are discussed.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    H. A. Abbass, W. Bligh, M. Towsey, M. Tierney, and G. D. Finn. Knowledge discovery in a dairy cattle database: automated knowledge acquisition. 5th Inter. Conf. of the Inter. Society for DSS, Melbourne, Australia, 1999.Google Scholar
  2. 2.
    H. A. Abbass, M. Towsey, and G. D. Finn. An intelligent decision support system for dairy cattle mate-allocation. Proceedings of the third Australian workshop on Intelligent Decision Support and Knowledge Management, pages 45–58, 1998.Google Scholar
  3. 3.
    R. Andrews and J. Diederich. Rules and networks. Proceedings of the Rule Extraction from Trained ANN Workshop, Univ. of Sussex, Brighton, U.K., 1996.Google Scholar
  4. 4.
    J. Besag, P. Green, D. Higdon, and K. Mengersen. Bayesian computation and stochastic systems. Statistical Science, 10:3–66.Google Scholar
  5. 5.
    T. G. Dietterich. Machine learning. Annual Rev. of Comp. Sci., 4:225–306, 1990.Google Scholar
  6. 6.
    J. Dommerholt and J. B. M. Wilmink. Optimal selection response under varying milk prices and margins for milk production. Livestock Production Science, 14:109–121, 1986.CrossRefGoogle Scholar
  7. 7.
    G. D. Finn, R. Lister, R. Szabo, D. Simonetta, H. Mulder, and R. Young. Neural networks applied to a large biological database to analyse dairy breeding patterns. Neural Computing and Applications, 4:237–253, 1996.CrossRefGoogle Scholar
  8. 8.
    A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysis: Texts in Statistical Science. Chapman and Hall, London, 1995.Google Scholar
  9. 9.
    D. Gianola and R. L. Fernando. Bayesian methods in animal breeding theory. Animal Science, 63:217–244, 1986.Google Scholar
  10. 10.
    L. N. Hazel. The genetic basis for constructing selection indices. Genetics, 28:476–490, 1943.Google Scholar
  11. 11.
    C. R. Henderson. Use of all relatives in intraherd prediction of breeding values and producing abilities. Dairy Science, 58:1910–1916, 1975.CrossRefGoogle Scholar
  12. 12.
    R. Lacroix, F. Salehi, X. Z. Yang, and K. M. Wade. Effects of data preprocessing on the performance of Artificial neural networks for dairy yield prediction and cow culling clasification. Transactions of the ASAE, 40:839–846, 1997.Google Scholar
  13. 13.
    R. Lacroix, K. M. Wade, R. Kok, and J. F. Hayes. Predicting 305-day milk, fat and protein production with an Artificial neural network. Proceeding of the third International Dairy Housing Conference: Dairy Systems for the 21st Century, pages 201–208, 1994.Google Scholar
  14. 14.
    D. J. C. MacKay. A practical bayesian framework for backpropagation networks. Neural Computation, 4:448–472, 1992.CrossRefGoogle Scholar
  15. 15.
    MIT. Tlearn software. version 1.0.1. Exercises In Rethinking Innateness: A Handbook for Connectionist Simulations. MIT Press, 1997.Google Scholar
  16. 16.
    R. M. Neal. Bayesian Learning for Neural Networks, Lecture Notes in Statistics No. 11. Springer-Verlag, 1996.Google Scholar
  17. 17.
    R. M. Neal. Software that implements flexible bayesian models based on neural networks, gaussian processes, and mixtures and that demonstrates markov chain monte carlo methods., 1998.
  18. 18.
    D. E. Rumelhart, B. Widrow, and M. A. Lehr. The basic ideas in neural networks. Communications of the ACM, 37:87–92, 1994.CrossRefGoogle Scholar
  19. 19.
    G. H. Schmidt and L. D. Van Vleck. Principles of Dairy Science. W. H. Freeman and Company, 1974.Google Scholar
  20. 20.
    D. J. Spiegelhalter, A. Thomas, N. G. Best, and W. R. Gilks. BUGS: Bayesian Inference using Gibbs Sampling, Version 0.50. MRC Biostatistics Unit, Cambridge., 1995.Google Scholar
  21. 21.
    M. Towsey. The use of neural networks in the automated analysis of the electroencephalogram. PhD thesis, 1998.Google Scholar
  22. 22.
    P. M. Visscher and M. E. Goddard. Genetic analyses of profit for australian dairy cattle. Animal Science, 61:9–18, 1995.CrossRefGoogle Scholar
  23. 23.
    K. M. Wade and R. Lacroix. The role of Artificial neural networks in animal breeding. The Fifth World Congress on Genetics Applied to Livestock Production, pages 31–34, 1994.Google Scholar
  24. 24.
    B. Widrow, D. E. Rumelhart, and M. A. Lehr. Neural networks: Applications in industry, business and science. Communications of the ACM, 37:93–105, 1994.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Paula E. Macrossan
    • 1
  • Hussein A. Abbass
    • 2
  • Kerry Mengersen
    • 1
  • Michael Towsey
    • 2
  • Gerard Finn
    • 2
  1. 1.Queensland University of TechnologyAustralia
  2. 2.Queensland University of TechnologySchool of Computing Science Machine Learning Research CentreAustralia

Personalised recommendations