Hostname: page-component-745bb68f8f-d8cs5 Total loading time: 0 Render date: 2025-02-06T07:38:32.914Z Has data issue: false hasContentIssue false
  • English
  • Français

Applications of process control techniques in poultry production

Published online by Cambridge University Press:  27 February 2018

J.-M. Aerts ,
C. M. Wathes  and
D. Berckmans
J.-M. Aerts
Affiliation:
Laboratory for Agricultural Buildings Research, Department of Agro-engineering and –economics, Catholic University of Leuven, Kasteelpark Arenberg 30, B-3001 Leuven, Belgium
C. M. Wathes
Affiliation:
Silsoe Research Institute, Bio-Engineering Division, Wrest Park, Silsoe, Bedford MK45 4HS, UK
D. Berckmans
Affiliation:
Laboratory for Agricultural Buildings Research, Department of Agro-engineering and –economics, Catholic University of Leuven, Kasteelpark Arenberg 30, B-3001 Leuven, Belgium
Get access

Abstract

The application of modern process control techniques to poultry production is outlined. Compact dynamic data-based models are proposed to describe and control the metabolic responses of broiler chickens to variations in the micro-environment. The dynamic response of heat production to step changes in air temperature and light intensity could be modelled with a , on average, of 0.83 and 0.93 respectively. Using recursive parameter estimation techniques, the time-variant response of animal growth to food supply could be predicted on-line with a prediction error of a maximum of 5%, three to seven days ahead depending on the type of feeding schedule. We argue that the potential conflicts between the environmental, financial and biological pressures on sustainable poultry production can be resolved through the development of integrated management systems using process control techniques.

Type
Offered Papers
Information
BSAP Occasional Publication , Volume 28: Integrated management systems for livestock , 2001 , pp. 147 - 154
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

Aerts, J.-M., Berckmans, D., Saevels, P., Decuypere, E., Buyse, J. 2000. Modelling the static and dynamic response of total heat production of broiler chickens to step changes in air temperature and light intensity. British Poultry Science, 41: 651–659.CrossRefGoogle ScholarPubMed
Berckmans, D., and Goedseels, V. 1986. Development of new control techniques for heating and ventilation of livestock buildings. Journal of Agricultural Engineering Research 33: 1–12.Google Scholar
Black, J.L., Campbell, R.G., Williams, I.H., James, K.J., and Davies, G.T. 1986. Simulation of energy and amino acid utilisation in the pig. Research and Development in Agriculture 3(3): 121–145.Google Scholar
Bloemen, H., Aerts, J.-M., Berckmans, D., and Goedseels, V. 1997. Image analysis to measure activity index of animals. Equine Veterinary Journal, Suppl. 23: 16–19.Google Scholar
Bridges, T.C., Gates, R.S., Chao, K.L., Turner, L.W., and Minagawa, H. 1995. Techniques for development of swine performance response surfaces. Transactions of the ASAE 38(5): 1505–1511.CrossRefGoogle Scholar
Brody, S. 1945. Bioenergetics and growth. New York: Reinhold Publ. Co. Google Scholar
Bruce, J.M., and Clark, J.J. 1979. Models of heat production and critical temperature for growing pigs. Animal Production 28: 353–369.Google Scholar
Buyse, J., Michels, H., Vloeberghs, J., Saevels, P., Aerts, J.-M., Ducro, B., Berckmans, D., Decuypere, E. 1998. Energy and protein metabolism between 3 and 6 weeks of age of male broiler chickens selected for growth rate or for improved food efficiency. British Poultry Science, 39: 264–272.Google Scholar
Camacho, E.F., and Bordons, C. 1999. Model predictive control. Berlin: Springer-Verlag.Google Scholar
Cole, G.W. 1980. The application of control systems theory to the analysis of ventilated animal housing environments. Transactions of the ASAE 23(2): 431–436.Google Scholar
Cumby, T.R. and Phillips, V.R. 2001. Environmenatl impacts of livestock production. This proceedings.Google Scholar
Curtis, S.E. 1983. Environmental management in animal agriculture. Ames: Iowa State University Press.Google Scholar
Gates, R.S., Overhults, D.G., and Turner, L.W. 1992. A survey of electronic environmental controllers. Transactions of the ASAE 35(3): 993–998.Google Scholar
Fitzhugh, H.A. 1976. Analysis of growth curves and strategies for altering their shape. Journal of Animal Science 42(4): 1036–1051.Google Scholar
Geers, R., Berckmans, D., Goedseels, V., Wijnhoven, J., and Maes, F. 1984a. A case-study of fattening pigs in Belgian contract farming. Mortality, efficiency of food utilization and carcass value of growing pigs, in relation to environmental engineering and control. Animal Production 38: 105–111.Google Scholar
Geers, R., Goedseels, V., Berckmans, D., and Huybrechts, W. 1984b. Mortality, feed efficiency and carcass value of growing pigs in relation to environmental engineering and control. Livestock Production Science 11: 235–241.Google Scholar
Golten, J., and Verwer, A. 1991. Control system design and simulation. London: McGraw-Hill.Google Scholar
Goodwin, G.C., and Sin, K.S. 1984. Adaptive filtering, prediction and control. New York: Prentice-Hall.Google Scholar
Jones, B.W., and Ogawa, Y. 1992. Transient interaction between the human and the thermal environment. ASHRAE Transactions 98(2): 189–195.Google Scholar
Korthals, R.L., Hahn, G.L., and Nienaber, J.A. 1994. Evaluation of neural networks as a tool for management of swine environments. Transactions of the ASAE 37(4): 1295–1299.Google Scholar
Lacey, B., Hamrita, T.K., and Mitchel, B. 2000. Feasability of using neural networks for real-time prediction of poultry deep body temperature responses to stressful changes in ambient temperature. Applied Engineering in Agriculture 16(3): 303–308.Google Scholar
Leynen, M., Van den Berckt, T., Aerts, J.M., Castelein, B., Berckmans, D., and Ollevier, F. 1999. The use of Tubificidae in a biological early warning system. Environmental Pollution 105: 151–154.Google Scholar
Ljung, L. 1987. System identification. Theory for the user. New Jersey: Prentice Hall.Google Scholar
Mitchell, B.W. 1993. Process control system for poultry house environment. Transactions of the ASAE 36(6): 1881–1886.Google Scholar
Mount, L.E. 1979. Adaption to thermal environment. Man and his productive animals. London: Edward Arnold.Google Scholar
Newberry\R.C., , Hunt, J.R., and Gardiner, E.E. 1988. Influence of light intensity on behavior and performance of broiler chickens. Poultry Science 67: 1020–1025.Google Scholar
Nicholson, A.J. 1954. An outline of the dynamics of animal populations. Australian Journal of Zoology 2: 9–65.Google Scholar
Nienaber, J.A., LeRoy Hahn, G., and Yen, J.T. 1987. Thermal Environment Effects on Growing-Finishing Swine. Part I: Growth, Feed Intake and Heat Production. Transactions of the ASAE 30(6): 1772–1775.Google Scholar
Oltjen, J.W., Bywater, A.C., Baldwin, R.L., and Garett, W.N. 1986. Development of a dynamic model of beef cattle growth and composition. Journal of Animal Science 62: 86–97.Google Scholar
Oltjen, J.W., and Owens, F.N. 1987. Beef cattle feed intake and growth: empirical bayes derivation of the kalman filter applied to a nonlinear dynamic model . Journal of Animal Science 65: 1362–1370.Google Scholar
Oltjen, J.W. 1993. Integration of energy concepts by modeling techniques. Journal of Dairy Science 6: 1812–1816.Google Scholar
Parks, J. 1982. A theory of feeding and growth in animals. Berlin: Springer-Verlag.Google Scholar
Parmar, R.S., Diehl, K.C., Collins, E.R. Jr., and Hulet, M.R. 1992. Simulation of a turkey house environment. Agricultural Systems 4: 425–445.Google Scholar
Reece, F.N., Lott, B.D., and Deaton, J.W. 1980. Ammonia in the atmosphere during brooding affects performance of broiler chickens. Poultry Science 59(3): 486–488.CrossRefGoogle Scholar
Reece, F.N., and Lott, B.D. 1982. Education and production: Optimizing poultry house design for broiler chickens. Poultry Science 61: 25–32.CrossRefGoogle Scholar
Reece, F.N., Lott, B.D., and Bates, B.J. 1985. The performance of a computerized system for control of broiler-house environment. Poultry Science 64: 261–265.CrossRefGoogle Scholar
Soeterboek, A.R.M. 1992. Predictive control. A unified approach. New York: Prentice Hall.Google Scholar
Turnpenny, J.A., McArthur, A.J., Clark, J.A. and Wathes, C.M. 2000 Thermal balance of livestock. 1. A parsimonious model. Agricultural and Forest Meteorology 101: 15–27.Google Scholar
Van't Klooster, C.E., Bontsema, J., and Salomons, L. 1995. Dynamic model to tune a climatic control algorithm in pig houses with natural ventilation. Transactions of the ASAE 38(3): 911–918.Google Scholar
Vranken, E. 1999. Analysis and optimisation of ventilation control in livestock buildings. PhD thesis, Katholieke Universiteit Leuven.Google Scholar
Xin, H., DeShazer, J.A., Feddes, J.J.R., and Rajurkar, K.P. 1992. Data dependent systems analysis of stochastic swine energetic responses. Journal of Thermal Biology 17(4/5): 225–234.Google Scholar
Wathes, C.M., Abeyesinghe, S.M. and Frost, A.R. 2001 Environmental design and management for livestock in the 21st Century: resolving conflicts by integrated solutions. In Proceedings of the Sixth International Livestock Environment Symposium, Louisville, Kentucky, USA. May 21-23, 2001 pp5–14.Google Scholar
Young, P.C. 1984. Recursive estimation and time-series analysis. Berlin: Springer-Verlag.CrossRefGoogle Scholar