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Adaptive models for online estimation of individual milk yield response to concentrate intake and milking interval length of dairy cows

Published online by Cambridge University Press:  08 April 2011

G. ANDRÉ ,
B. ENGEL ,
P. B. M. BERENTSEN ,
G. VAN DUINKERKEN  and
A. G. J. M. OUDE LANSINK
G. ANDRÉ*
Affiliation:
Livestock Research, Wageningen University and Research Centre, P.O. Box 65, 8200 AB Lelystad, The Netherlands
B. ENGEL
Affiliation:
Biometris, Wageningen University and Research Centre, P.O. Box 100, 6700 AC Wageningen, The Netherlands
P. B. M. BERENTSEN
Affiliation:
Business Economics Group, Wageningen University and Research Centre, P.O. Box 8130, 6700 EW Wageningen, The Netherlands
G. VAN DUINKERKEN
Affiliation:
Livestock Research, Wageningen University and Research Centre, P.O. Box 65, 8200 AB Lelystad, The Netherlands
A. G. J. M. OUDE LANSINK
Affiliation:
Business Economics Group, Wageningen University and Research Centre, P.O. Box 8130, 6700 EW Wageningen, The Netherlands
*
*To whom all correspondence should be addressed. Email: geert.andre@wur.nl
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Summary

Automated feeding and milking of dairy cows enables the application of individual cow settings for concentrate supply and milking frequency. Currently, general settings are used, based on knowledge about energy and nutrient requirements in relation to milk production at the group level. Individual settings, based on the actual individual response in milk yield, have the potential for a marked increase in economic profits. In the present study, adaptive dynamic models for online estimation of milk yield response to concentrate intake and length of milking interval are evaluated. The parameters in these models may change over time and are updated through a Bayesian approach for online analysis of time series. The main use of dynamic models lies in their ability to determine economically optimal settings for concentrate intake and milking interval length for individual cows at any day in lactation. Three adaptive dynamic models are evaluated, a model with linear terms for concentrate intake and length of milking interval, a model that also comprises quadratic terms, and an enhanced model (EM) in order to obtain more stable parameter estimates. The linear model is useful only for forecasting milk production and the estimated parameters of the quadratic model were found to be unstable. The parsimony of the EM leads to far more stable parameter estimates. It is shown that the EM is suitable for control and monitoring, and therefore promises to be a valuable tool for application within precision livestock farming.

Type
Modelling Animal Systems
Information
The Journal of Agricultural Science , Volume 149 , Issue 6 , December 2011 , pp. 769 - 781
Copyright
Copyright © Cambridge University Press 2011

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References

REFERENCES

André, G., Berentsen, P. B. M., van Duinkerken, G., Engel, B. & Oude Lansink, A. G. J. M. (2010 a). Economic potential of individual variation in milk yield response to concentrate intake of dairy cows. Journal of Agricultural Science, Cambridge 148, 263–276.CrossRefGoogle Scholar
André, G., Berentsen, P. B. M., Engel, B., de Koning, C. J. A. M. & Oude Lansink, A. G. J. M. (2010 b). Increasing the revenues from automatic milking by using individual variation in milking characteristics. Journal of Dairy Science 93, 942–953.CrossRefGoogle ScholarPubMed
André, G., Ouweltjes, W., Zom, R. L. G. & Bleumer, E. J. B. (2007). Increasing economic profit of dairy production utilizing individual real time process data. In 3rd European Conference on Precision Livestock Farming (Ed. Cox, S.), pp. 179–186. Wageningen, The Netherlands: Wageningen Academic Publishers.CrossRefGoogle Scholar
van Bebber, J., Reinsch, N., Junge, W. & Kalm, E. (1999). Monitoring daily milk yields with a recursive test day repeatability model (Kalman filter). Journal of Dairy Science 82, 2421–2429.CrossRefGoogle ScholarPubMed
Bieleman, J. (2005). Technological innovation in Dutch cattle breeding and dairy farming, 1850–2000. Agricultural History Review 53, 229–250.Google Scholar
Bieleman, J. (2008). Boeren in Nederland. Geschiedenis van de Landbouw 1500–2000 (Farmers in The Netherlands. History of agriculture 1500–2000). In Dutch. Amsterdam, The Netherlands: Uitgeverij Boom.Google Scholar
Broster, W. H. & Thomas, C. (1981). The influence of level and pattern of concentrate input on milk output. In Recent Advances in Animal Nutrition (Ed. Haresign, W.), pp. 49–69. London: Butterworths.CrossRefGoogle Scholar
Cox, S. (2002). Information technology: the global key to precision agriculture and sustainability. Computers and Electronics in Agriculture 36, 93–111.CrossRefGoogle Scholar
DeLuyker, H. A., Shumway, R. H., Wecker, W. E., Azari, A. S. & Weaver, L. D. (1990). Modeling daily milk yield in Holstein cows using time series analysis. Journal of Dairy Science 73, 539–548.CrossRefGoogle Scholar
France, J. & Thornley, J. H. M. (1984). Mathematical Models in Agriculture. A Quantitative Approach to Problems in Agriculture and Related Sciences. London: Butterworths.Google Scholar
Frost, A. R., Schofield, C. P., Beaulah, S. A., Mottram, T. T., Lines, J. A. & Wathes, C. M. (1997). A review of livestock monitoring and the need for integrated systems. Computers and Electronics in Agriculture 17, 139–159.CrossRefGoogle Scholar
Garnsworthy, P. C., Lock, A., Mann, G. E., Sinclair, K. D. & Webb, R. (2008 a). Nutrition, metabolism, and fertility in dairy cows: 1. Dietary energy source and ovarian function. Journal of Dairy Science 91, 3814–3823.CrossRefGoogle ScholarPubMed
Garnsworthy, P. C., Lock, A., Mann, G. E., Sinclair, K. D. & Webb, R. (2008 b). Nutrition, metabolism, and fertility in dairy cows: 2. Dietary fatty acids and ovarian function. Journal of Dairy Science 91, 3824–3833.CrossRefGoogle ScholarPubMed
Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. (1995). Bayesian Data Analysis. London: Chapman and Hall.CrossRefGoogle Scholar
Goodall, E. A. & Sprevak, D. (1985). A Bayesian estimation of the lactation curve of a dairy cow. Animal Production 40, 189–193.Google Scholar
Ingvartsen, K. L. & Andersen, J. B. (2000). Integration of metabolism and intake regulation: a review focusing on periparturient animals. Journal of Dairy Science 83, 1573–1597.CrossRefGoogle ScholarPubMed
Lark, R. M., Nielsen, B. L. & Mottram, T. T. (1999). A time series model of daily milk yields and its possible use for detection of a disease (ketosis). Animal Science 69, 573–582.CrossRefGoogle Scholar
de Mol, R. M., Keen, A., Kroeze, G. H. & Achten, J. M. F. H. (1999). Description of a detection model for oestrus and diseases in dairy cattle based on time series analysis combined with a Kalman filter. Computers and Electronics in Agriculture 22, 171–185.CrossRefGoogle Scholar
Montgomery, D. C. & Peck, E. A. (1982). Introduction to Linear Regression Analysis. New York: Wiley.Google Scholar
Thomas, C. (2004). Feed into Milk: A New Applied Feeding System for Dairy Cows. Nottingham, UK: Nottingham University Press.Google Scholar
Van Knegsel, A. T. M., Van den Brand, H., Dijkstra, J., Tamminga, S. & Kemp, B. (2005). Effect of dietary energy source on energy balance, production, metabolic disorders and reproduction in lactating dairy cattle. Reproduction Nutrition Development 45, 665–688.CrossRefGoogle ScholarPubMed
Van Knegsel, A. T. M., De Vries Reilingh, G., Meulenberg, S., Van den Brand, H., Dijkstra, J., Kemp, B. & Parmentier, H. K. (2007 a). Natural antibodies related to energy balance in early lactation dairy cows. Journal of Dairy Science 90, 5490–5498.CrossRefGoogle ScholarPubMed
Van Knegsel, A. T. M., Van den Brand, H., Dijkstra, J., Van Straalen, W. M., Jorritsma, R., Tamminga, S. & Kemp, B. (2007 b). Effect of glucogenic vs. lipogenic diets on energy balance, blood metabolites, and reproduction in primiparous and multiparous dairy cows in early lactation. Journal of Dairy Science 90, 3397–3409.CrossRefGoogle ScholarPubMed
Vetharaniam, I., Davis, S. R., Upsdell, M., Kolver, E. S. & Pleasants, A. B. (2003). Modeling the effect of energy status on mammary gland growth and lactation. Journal of Dairy Science 86, 3148–3156.CrossRefGoogle ScholarPubMed
Wathes, C. M., Kristensen, H. H., Aerts, J.-M. & Berckmans, D. (2008). Is precision livestock farming an engineer's daydream or nightmare, an animal's friend or foe, and a farmer's panacea or pitfall? Computers and Electronics in Agriculture 64, 2–10.CrossRefGoogle Scholar
West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models, 2nd edn. New York: Springer-Verlag.Google Scholar
Wood, P. D. P. (1967). Algebraic model of the lactation curve in cattle. Nature 216, 164–165.CrossRefGoogle Scholar
Woods, V. B., Kilpatrick, D. J. & Gordon, F. J. (2003). Development of empirical models to describe the response in lactating dairy cattle to changes in nutrient intake as defined in terms of metabolisable energy intake. Livestock Production Science 80, 229–239.CrossRefGoogle Scholar
Zom, R. L. G., Van Riel, J. W., André, G. & Van Duinkerken, G. (2002). Voorspelling voeropname met Koemodel 2002 (Prediction of Feed Intake using the 2002 Dairy Cow Model). In Dutch. Lelystad, The Netherlands: Praktijkrapport 11, Praktijkonderzoek Veehouderij.Google Scholar