Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-30T20:20:55.476Z Has data issue: false hasContentIssue false

Non-parametric lactation curves

Published online by Cambridge University Press:  02 September 2010

D. A. Elston
Affiliation:
Scottish Agricultural Statistics Service, University of Edinburgh, King's Buildings, Edinburgh EH9 3JZ
C. A. Glasbey
Affiliation:
Scottish Agricultural Statistics Service, University of Edinburgh, King's Buildings, Edinburgh EH9 3JZ
D. R. Neilson
Affiliation:
Edinburgh School of Agriculture, West Mains Road, Edinburgh EH9 3JG
Get access

Abstract

Lactation curves are fitted to data as a preliminary to estimating summary statistics. Two widely quoted curves are atbe-ct (Wood, 1967) and a(1 - e-bt) - ct (Cobby and Le Du, 1978), each of which has three parameters. Restriction to either of these curves imposes limitations on the fit to the data and can result in biased estimation of summary statistics. Alternatively, lactation curves can be generated by the use of a non-parametric method which requires only weak assumptions about the signs of derivatives of the curves. Because the non-parametric curves are more flexible, estimates of summary statistics are less likely to be biased than those based on parametric models. Use of the non-parametric curves is particularly advantageous around the time of peak yield, where the curves of Wood and Cobby and Le Du are known to fit data poorly.

Type
Papers
Copyright
Copyright © British Society of Animal Science 1989

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

REFERENCES

Cobby, J. M. and Le Du, Y. L. P. 1978. On fitting curves to lactation data. Animal Production 26: 127133.Google Scholar
Glasbey, C. A. 1987. Tolerance-distribution-free analyses of quantal dose-response data. Applied Statistics 36: 251259.Google Scholar
Glasbey, C. A. 1988. Examples of regression with serially correlated errors. The Statistician 37: 277291.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
Johnson, E. G. and Routledge, R. D. 1985. The line transect method: a nonparametric estimator based on shape restrictions. Biometrics 41: 669679.CrossRefGoogle Scholar
Madsen, O. 1975. A comparison of some suggested measures of persistency of milk yield in dairy cows. Animal Production 20: 191197.Google Scholar
Neilson, D. R., Whittemore, C. T., Lewis, M., Alliston, J. C., Roberts, D. J., Hodgson-jones, L. S., Mills, J., Parkinson, H. and Prescott, J. H. D. 1983. Production characteristics of high-yielding dairy cows. Animal Production 36: 321334.Google Scholar
Numerical Algorithms Group. 1987. Fortran Library Manual, Mark 12. Numerical Algorithms Group, Oxford.Google Scholar
Rowlands, G. J., Lucey, S. and Russell, A. M. 1982. A comparison of different models of the lactation curve in dairy cattle. Animal Production 35: 135144.Google Scholar
Wood, P. D. P. 1967. Algebraic model of the lactation curve in cattle. Nature, London 216: 164165.CrossRefGoogle Scholar