Hostname: page-component-7479d7b7d-jwnkl Total loading time: 0 Render date: 2024-07-11T13:30:10.385Z Has data issue: false hasContentIssue false
  • English
  • Français

Dumb animals and smart machines: the implications of modern milking systems for integrated management of dairy cows

Published online by Cambridge University Press:  27 February 2018

T. T. Mottram  and
L. Masson
T. T. Mottram
Affiliation:
Silsoe Research Institute, Wrest Park, Silsoe, BEDFORD, MK45 4HS, UK
L. Masson
Affiliation:
Silsoe Research Institute, Wrest Park, Silsoe, BEDFORD, MK45 4HS, UK
Get access

Abstract

The dairy industry has continued to innovate to meet the needs of the consumers' specification of milk at a low price, of good hygienic quality and with rising expectations of animal welfare. The introduction of robotic milking offers the opportunity for the cost effective deployment of novel sensors for a variety of milk analytes. Traditional methods of monitoring health changes in animals are based entirely on the human senses. However, in modern milking systems humans rarely have enough time to see the cows to observe for signs of ill health, the extreme case is that of robotic milking. Novel sensors will allow closed loop control systems where the early detection of deviations from optimal performance will enable the farm manager to make management decisions before damage to potential milk yields is irreversible. Where a biological model already exists, for example, in the prediction of ovulation with milk progesterone analysis, rapid progress is being made towards an automated prediction system. Integrated management systems for dairy cows will not only have the traditional goals of efficient milk production but can also be tuned to reduce polluting outputs of ammonia, phosphorus and methane. The main metabolic markers in milk to be monitored are urea, fat, ketones and protein. The detection of mastitis can be achieved by the development of sensor systems to detect enzyme markers of inflammatory response such as Nagase. Multi-disciplinary research is needed to develop integrated management systems drawing all the different elements of dairy cow management into a single system. The major cause of death in dairy cows is dystocia and monitoring systems are needed to ensure that parturition is better managed.

Type
Offered Papers
Information
BSAP Occasional Publication , Volume 28: Integrated management systems for livestock , 2001 , pp. 77 - 84
Copyright
Copyright © British Society of Animal Science 2001

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

Andersson, L., Gustafsson, A.H. and Emanuelson, U. 1991. Effect of hyperketonaemia and feeding on fertility in dairy cows. Theriogenology 36: 521–536 Google Scholar
Andersson, L. and Lundstrom, K. 1984. Milk and blood ketone bodies, blood isopropanol and plasma glucose in dairy cows; methodological studies and diurnal variations. Journal of Veterinary Medicine Series A 31: 340–349 Google ScholarPubMed
Anon, 1992, Council Directive 92/46/EEC, Health roles for the production and placing on the market of raw milk, heat treated milk and milk based products, Official Journal of the European Communities No. L 268, 1–32 Google Scholar
Bennett, R.M. and Done, J.T. 1986. Control of the Bovine pestivirus syndrome in cattle: a case for social cost benefit analysis? Proceedings of the Society of Veterinary Epidemiology and Preventative Medicine, M.V. Thrushfield, Edinburgh.Google Scholar
Bramley, A.J. 1992. Mastitis and Machine Milking, Chapter 10 of Machine Milking and Lactation. Ed. Bramley, A.J., Dodd, F.H., Mein, G.A., and Bramley, J.A., pp 343–372. Insight Books, Newbury, UK Google Scholar
Claycomb, R.W., Delwiche, M.J., Munro, CJ and Bondurant, R.H., 1995. Enzyme immunoassay biosensor for measurement of bovine progesterone during milking, ASAE Conference, 95, 3564, 25.Google Scholar
Cooper, K. and Parsons, D. J. 1999. An economic analysis of automatic milking using a simulation model. Journal of Agricultural Engineering Research 73: 311–321 Google Scholar
De Mol, R. M., Kroeze, G.H., Achten, J.M.F.H., Maatje, K. and Rossing, W. 1997. Results of a multivariate approach to automated oestrus and mastitis detection. Livestock Production Science 48: 219–227 Google Scholar
De Mol, R.M. and Ouweltjes, W. 2000. Detection model for mastitis in cows milked in an automatic milking system, in: Robotic Milking proceedings of the international symposium held in Lelystad, the Netherlands, 17-19 August 2000, pub. Wageningen Press, the Netherlands, pp 97–107 Google Scholar
Eckersall, P.D. Young, F.J. Mccomb, C., Hogarth, C.J., Safi, S., Weber, A., Mcdonald, T., Nolan, A.M., Fitzpatrick, J.L. 2001. Acute phase protein in serum and milk from dairy cows with clinical mastitis. Veterinary Record, in pressGoogle Scholar
Esslemont, R.J., Bailie, J.H. and Cooper, M.J. 1985. Fertility management in dairy cattle, Collins, London, UK Google Scholar
Gustafsson, A.H. 1993. Acetone and urea concentration in milk as indicators of the nutritional status and the composition of the diet of dairy cows. Dissertation. Report 222, Department of Animal Nutrition and Management, Swedish University of Agricultural Sciences, UppsalaGoogle Scholar
Gustafsson, A.H. and Emanuelson, U. 1996. Milk acetone as an indicator of hyperketonaemia in dairy cows: the critical value revised. Animal Science 63: 183–188 Google Scholar
Harding, F. 1995. Compositional Quality. In Milk Quality. Ed. Harding, F., p87, Blackie Academic and Professional, Glasgow, UK CrossRefGoogle Scholar
Hart, J.P. and Wring, S.A., 1997. Recent developments in the design and application of screen-printed electrochemical sensors for biomedical, environmental and industrial analyses, Trends in Analytical Chemistry, 16, 89–103.CrossRefGoogle Scholar
Heuer, C., Schukken, Y.H. and Dobbelaar, P. 1999. Postpartum body condition score and results for the first test day milk as predictors of disease, fertility, yield and culling in commercial dairy herds. Journal of Dairy Science 82: 295–304 Google Scholar
Heuer, C., Van Straalen, W.M., Schukken, Y.H., Dirkzwager, A. and Noordhuizen, J.P.T.M. 2000. Prediction of energy balance in a high yielding dairy herd in early lactation: model development and precision. Livestock Production Science 65: 91–105 CrossRefGoogle Scholar
Hobbs, and Mottram, 2000. Significant contributions of dimethyl sulphide from livestock to the atmosphere, Atmospheric Environment, 34, 3649–3650.Google Scholar
Hogeveen, H. and Meijering, A. 2000. Robotic Milking, proceedings of the international symposium held in Lelystad, the Netherlands, 17-19 August 2000, pub. Wageningen Press, the Netherlands Google Scholar
Jenkins, D.M., Delwiche, M.J., Depeters, E.J. and Bondurant, R.H. 1999. Chemical assay of urea for automated sensing in milk. Journal of Dairy Science 82: 1999–2004 Google Scholar
Jonker, J.S., Kohn, R.A. and Erdman, R.A. 1999. Milk urea nitrogen target concentrations for lactating dairy cows fed according to national research council recommendations. Journal of Dairy Science 82: 1261–1273 Google Scholar
Kitchen, B.J. 1981. Review of the progress of Dairy Science: Bovine mastitis: milk compositional changes and related diagnostic tests. Journal of Dairy Research 48: 167–188 Google Scholar
Koelsch, R.K., Aneshansley, D.J. and Butler, W.R. 1994: Milk Progesterone Sensor for Application with Dairy Cattle. Journal of Agricultural Engineering Research 58, 115–120.CrossRefGoogle Scholar
Legin, A., Smirnova, A., Rudnitskaya, A., Lvova, L., Suglobova, E. and Vlasov, Y. 1999. Chemical sensor array for multicomponent analysis of biological liquids. Analytica Chimica Acta 385: 131–135 Google Scholar
Lind, O., Ipema, A.H., de Koning, C., Mottram, T.T. and Hermann, H.J. 2000. Automatic Milking, Bulletin of the IDF, 348, 3–14 Google Scholar
Lui, D., Ge, K., Chen, K., Nie, L. and Yao, S. 1996. Sensitive specialization analysis of urea in human blood by surface acoustic wave urea sensor system. Microchemical Journal 53: 6–17 Google Scholar
Marchant, J.A., Schofield, C.P. and White, R.P. 1999. Pig growth and conformation monitoring using image analysis. Animal Science 68: 141–150.Google Scholar
Mottram, T.T. 1997. Automatic monitoring of the health and metabolic status of dairy cows. Livestock Production Science 48: 209–217 CrossRefGoogle Scholar
Mottram, T.T. and Frost, A.R. 1997. Report on the state of the art of current and emerging methods of oestrus detection on UK dairy farms. Contract Report CR/775/97/1681. (Silsoe Research Institute for Milk Development Council)Google Scholar
Mottram, T.T., Hart, J. and Pemberton, R.M. 2000. Biosensing techniques for detecting abnormal and contaminated milk, In: Robotic Milking proceedings of the international symposium in Lelystad, The Netherlands, 17-19 August 2000, Eds: Hogeveen, H., Meijering, A., Wageningen Press, Wageningen, 108–113 Google Scholar
Mottram, T.T., Velasco-Garcia, M., Hart, J.P. and Pemberton, R.M. 2001. The optimum time of sampling during milking with a biosensor based system for Automatic Ovulation Prediction of dairy cows, proceedings of the ASAE International Meeting, Sacramento, 30th July-1st August 2001, in pressGoogle Scholar
Mottram, T.T., Whay, H.R., Hobbs, P.J., Richards, P., Short, L. and Hartung, J. 1999. Automatic Cow Breath Sampling: an experiment to measure emissions of polluting compounds in the breath of unrestrained dairy cows, In: Proceedings of 4th International Conference on Livestock Production, Engineering and the Environment, 9-10 March 1999, Freising, Germany Google Scholar
Nix, J. 2000. Farm Management Pocketbook: Dairy Cows, pp 65–74, Wye College, University of London, Ashford, UKGoogle Scholar
Oltner, R., Emanuelson, M. and Wiktorsson, H. 1985. Urea concentrations in milk in relation to milk yield, liveweight, lactation number and amount and composition of feed given to dairy cows. Livestock Production Science 12: 47–57 Google Scholar
Oltner, R. and Wiktorsson, H. 1983. Urea concentrations in milk and blood as influenced by feeding varying amounts of protein and energy to dairy cows. Livestock Production Science 10: 457–467 Google Scholar
Pehrson, B. 1996. Milk analysis as an indicator of the nutritional and disease status of dairy cows. In Recent Advanced in Animal Nutrition. Eds. Garnsworthy, P.C., Wiseman, J. and Haresign, W., pp7–33, Nottingham University Press, Nottingham, UK Google Scholar
Pemberton, R.M., Hart, J.P. and Foulkes, J.A. 1998. Development of a sensitive, selective electrochemical immunoassay for progesterone in cow's milk based on a disposable screen-printed amperometric biosensor. Electrochimica Acta 43: 3567–3574 Google Scholar
Pope, G.S. and Swinburne, J.K. 1980. Reviews of the progress of Dairy Science: Hormones in milk: their physiological significance and value as diagnostic aids. Journal of Dairy Research 47, 427–449 Google Scholar
Steen, A., Osteras, O. and Gronstol, H. 1996. Evaluation of additional acetone and urea analysis, and the fat-lactose-quotient in cow milk samples in the herd recording system in Norway. Journal of Veterinary Medicine Series A 43: 181–191 Google Scholar
Tang, X. and Delwiche, M.J. 1998: On-line measurement of progesterone during milking for estrus detection. In: AgEng98. (Ed: Anon, .) CIGR, Oslo, 59–60.Google Scholar
Tsenkova, R.N., Iordanov, K.I. and Shinde, J. 1992. Near-infrared spectroscopy for evaluating milk quality. Proceedings “Prospects for Automatic Milking” Wageningen, the Netherlands, EAAP Publication 65. pp 185–192 Google Scholar
Urech, E., Puhan, Z. and Schallibaum, M. 1999. Changes in milk protein fraction as affected by sub-clinical mastitis. Journal of Dairy Science 82: 2402–2411 Google Scholar
Webster, J. 1993. Understanding the Dairy Cow 2nd ed., pp189–198. Blackwell Science Ltd., Oxford, UK Google Scholar
Whitaker, D.A. 2000. Use and interpretation of metabolic profiles. In The Health of Dairy Cattle. Ed. Andrews, A.H., pp89– 107, Blackwell Science Ltd. Oxford, UK Google Scholar