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Comparing two concentrate allowances in an automatic milking system

Published online by Cambridge University Press:  09 March 2007

I. Halachmi ,
S. Ofir  and
J. Miron
I. Halachmi*
Affiliation:
Institute of Agricultural Engineering, Agricultural Research Organization (ARO), The Volcani Centre, PO Box 6, Bet Dagan 50250, Israel
S. Ofir
Affiliation:
Ambar Feed Mills, Granot, Israel
J. Miron
Affiliation:
Institute of Animal Science, ARO, The Volcani Centre, PO Box 6, Bet Dagan 50250, Israel
*
†E-mail: [email protected]
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Abstract

This study investigated the potential for applying an automatic milking system (AMS) to the management of high-yielding cows offered a total mixed ration (TMR). The null hypothesis was that it is desirable to maintain even in AMS, the TMR feeding management practice recommended for high-yielding cows and therefore it can be attained by ‘reducing the concentrate allocation in the robot without reducing the number of milkings’. Two feeding regimes were used: the ‘candy concept’, with only 1·2 kg of food concentrate – the minimum to attract the cow – provided at each visit to the milking robot; and the provision of a maximum of 7 kg of food concentrate per day. Approximately 100 cows were subjected to one or other of these two treatments. Although the cows in the first treatment consumed approximately 3·5 kg of concentrate per day and those in the second treatment approximately 5 kg per day, no significant differences were observed in the numbers of voluntary milkings.

Keywords

concentratesfood intakeindividual feedingmilking intervalmilking machines (automatic)
Type
Research Article
Information
Animal Science , Volume 80 , Issue 3 , June 2005 , pp. 339 - 343
Copyright
Copyright © British Society of Animal Science 2005

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References

Halachmi, I. 2000. Designing the optimal robotic barn. 2. Behaviourbased simulation. Journal of Agricultural Engineering Research 77: 67–79.CrossRefGoogle Scholar
Halachmi, I. 2004. Designing the automatic milking farm in a hot climate. Journal of Dairy Science 87: 764–775.CrossRefGoogle Scholar
Halachmi, I., Adan, I. J. B. F., Wal van der, J., Heesterbeek, J. A. P. and Beek, P. van. 2000a. The design of robotic dairy barns using closed queuing networks. European Journal of Operational Research 124: 437–446.CrossRefGoogle Scholar
Halachmi, I., Dzidic, A., Metz, J. H. M., Speelman, L., Dijkhuizen, A. A. and Kleijnen, J. P. C. 2001. Validation of simulation model for robotic milking barn design. European Journal of Operational Research 134: 165–176.CrossRefGoogle Scholar
Halachmi, I., Edan, Y., Maltz, E., Peiper, U. M., Brukental, I. and Moalem, U. 1998a A real-time control system for individual dairy cow food intake. Computers and Electronics in Agriculture 20: 131–144.CrossRefGoogle Scholar
Halachmi, I., Edan, Y., Moallem, U. and Maltz, E. 2004. Predicting feed intake of the individual dairy cow. Journal of Dairy Science 87: 2254–2267.CrossRefGoogle ScholarPubMed
Halachmi, I., Heesterbeek, J. A. P. and Metz, J. H. M. 1998b. Designing the optimal robotic milking barn, using simulation. Proceedings of Dutch-Japanese workshop on precision dairy farming, 8–11 09 1998, pp. 13–16. Wageningen Press, The Netherlands.Google Scholar
Halachmi, I., Metz, J. H. M., Land van't, A., Halachmi, S. and Kleijnen, J. P. C. 2002. Optimal facility allocation in a robotic milking barn. Transactions of the American Society of Agricultural Engineers 45: 1539–1546.CrossRefGoogle Scholar
Halachmi, I., Metz, J. H. M., Maltz, E., Dijkhuizen, A. A. and Speelman, L. 2000b. Designing the optimal robotic barn. 1. Quantifying facility usage. Journal of Agricultural Engineering Research 76: 37–49.Google Scholar
Hermans, G. G. N., Ipema, A. H., Stefanowska, J. and Metz, J. H. M. 2003. The effect of two traf. c situations on the behavior and performance of cows in an automatic milking system. Journal of Dairy Science 86: 1997–2004.CrossRefGoogle Scholar
Ketelaar-De Lauwere, C. C., Ipema, A. H., Metz, J. H. M., Noordhuizen, J. P. T. M. and Schouten, W. G. P. 1999. The in. uence of the accessibility of concentrate on the behaviour of cows milked in an automatic milking system. Netherlands Journal of Agricultural Science 47: 1–16.Google Scholar
Livshin, N., Maltz, E. and Edan, Y. 1995. Regularity of dairy cow feeding behavior with computer-controlled feeders. Journal of Dairy Science 78: 296–304.CrossRefGoogle ScholarPubMed
Miron, J., Yosef, E., Nikbachat, M., Zenou, A., Maltz, E., Halachmi, I. and Ben-Ghedalia, D. 2004. Feeding behavior and performance of dairy cows fed pelleted non-roughage fiber byproducts. Journal of Dairy Science 87: 1372–1379.CrossRefGoogle Scholar
National Research Council. 2001. Nutrient requirements of dairy cattle, seventh revised edition. National Academy Press, Washington, DC.Google Scholar
Wierenga, H. K. and Hopster, H. 1991. Behaviour of dairy cows when fed concentrates with an automatic concentrates feeding system. Applied Animal Behaviour Science 30: 223–246.CrossRefGoogle Scholar