Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-27T19:36:49.810Z Has data issue: false hasContentIssue false

A time series model of daily milk yields and its possible use for detection of a disease (ketosis)

Published online by Cambridge University Press:  18 August 2016

R. M. Lark
Affiliation:
Mathematics and Decision Systems Group, Silsoe Research Institute, Wrest Park, Silsoe, Bedford MK45 4HS
B. L. Nielsen
Affiliation:
Scottish Agricultural College, Edinburgh Genetics and Behavioural Sciences Department, Bush Estate, Penicuik, Midlothian EH26 OQE
T. T. Mottram
Affiliation:
Livestock Engineering Group, Silsoe Research Institute, Wrest Park, Silsoe, Bedford MK45 4HS
Get access

Abstract

A time series model was used to describe the daily milk yield of healthy cows in the first 48 days of lactation. A moving average (MA) model of order 1 on the first-order differences of the data was selected — the same chosen in a previous study by Deluyker et al. (1990). When the model was used to generate predicted daily yields in a second data set, for cows in which clinical ketosis had been diagnosed, it was found that significant deviations of actual yield below the daily forecast occurred from 3 days before the day of diagnosis. The model appeared to be transportable to healthy cows from another herd. Threshold values were defined to identify ailing animals by their deviation from predicted yield. However, the thresholds were not very sensitive, and required that a fairly high level of false positives be accepted (25% of a healthy herd over a 5-day period).

Type
Research Article
Copyright
Copyright © British Society of Animal Science 1999

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

Baird, G. D. 1982. Primary ketosis in high-producing dairy cows: clinical and subclinical disorders, treatment, prevention and outlook. Journal of Dairy Science 65: 110.Google Scholar
Beever, D. E., Rook, J. A., France, J., Dhanoa, M. S. and Gill, M. 1991. A review of empirical and mechanistic models of lactational performance by the dairy cow. Livestock Production Science 29: 115130.CrossRefGoogle Scholar
Benjamíni, Y. and Hochberg, Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, series B. 57: 289300.Google Scholar
Box, G. E. P. and Jenkins, G. M. 1976. Time series analysis: forecasting and control. Holden-Day, San Francisco.Google Scholar
Burema, H. J. and Kerkhof, J. A. 1983. Health monitoring of lactating cows. Proceedings of the symposium on automation in dairying, Wageningen, 20-22 April 1983, IMAG, Wageningen, The Netherlands, pp. 187192.Google Scholar
Chatfield, C. 1984. The analysis of time series. Chapman and Hall, London.Google Scholar
Deluyker, H. A., Shumway, R. H., Wecker, W. E., Azari, A. S. and Weaver, L. D. 1990. Modeling daily milk yield in Holstein cows using time series analysis. Journal of Dairy Science 73: 539548.Google Scholar
Diggle, P. J. 1990. Time series, a biostatistical introduction. Oxford statistical science series, 5. Oxford University Press, Oxford.Google Scholar
Dobbelaar, P., Mottram, T., Nyabadza, C., Hobbs, P., Elliott-Martin, R. J. and Schukken, Y. H. 1996. Detection of ketosis in dairy cows by analysis of exhaled breath. Veterinary Quarterly 18: 151152.Google Scholar
Eddy, R. G. 1992. Major metabolic disorders, in Bovine medicine, disease and husbandry of cattle (ed. Andrews, A. H., Blowey, R. H., Boyd, H. and Eddy, R.), pp. 577600. Blackwell Science, Oxford.Google Scholar
Filby, D. E., Turner, M. J. B. and Street, M. J. 1979. A walkthrough weigher for dairy cows. Journal of Agricultural Engineering Research 24: 6778.Google Scholar
Hochberg, Y. and Tamhane, A. 1987. Multiple comparison procedures. Wiley, New York.Google Scholar
Jones, T. 1997. Empirical Bayes prediction of 305-day milk production. Journal of Dairy Science 80: 10601075.CrossRefGoogle ScholarPubMed
Lescourret, F. and Coulon, J. B. 1994. Modeling the impact of mastitis on milk production by dairy cows. Journal of Dairy Science 77: 22892301.Google Scholar
Lucey, S., Rowlands, G. J. and Russell, A. M. 1986. Short-term associations between disease and milk yield of dairy cows. Journal of Dairy Research 53: 715.CrossRefGoogle ScholarPubMed
Morant, S. V. and Gnanasakthy, A. 1989. A new approach to the mathematical formulation of lactation curves. Animal Production 49: 151162.Google Scholar
Mottram, T. T. 1997. Automatic monitoring of the health and metabolic status of dairy cows. Livestock Production Science 48: 209217.Google Scholar
Numerical Algorithms Group. 1988. The NAG Fortran library manual — mark 13. NAG Ltd, Oxford.Google Scholar
Payne, R. W. and Arnold, G. M. 1995. Genstat® 5 procedure library manual release 3[3]. Rothamsted Experimental Station, Harpenden.Google Scholar
Pérochon, L., Coulon, J. B. and Lescourret, F. 1996. Modelling lactation curves of dairy cows with emphasis on individual variability. Animal Science 63: 189200.Google Scholar
Rasmussen, L. K., Nielsen, B. L., Pryce, J. E., Mottram, T. T. and Veerkamp, R. F. 1999. Risk factors associated with the incidence of ketosis in dairy cows. Animal Science 68: 379386.Google Scholar
Snedecor, G. W. and Cochran, W. G. 1980. Statistical methods, 7th edition. Iowa State University Press, Ames, Iowa.Google Scholar
Svennersten-Sjaunja, K., Sjaunja, L.-O., Bertilsson, J. and Wiktorsson, H. 1997. Use of regular milking records versus daily records for nutrition and other kinds of management. Livestock Production Science 48: 167174.Google Scholar
Whitaker, D. A. 1997. Interpretation of metabolic profiles in dairy cows. Irish Veterinary Journal 50: 498501.Google Scholar
Wood, P. D. P. 1967. Algebraic model of the lactation curve in cattle. Nature 216: 164165.Google Scholar