Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-28T18:06:10.111Z Has data issue: false hasContentIssue false
  • English
  • Français

Adaptation of a non-ruminant nutrient-based growth model to rainbow trout (Oncorhynchus mykiss Walbaum)

Published online by Cambridge University Press:  01 June 2009

K. HUA ,
S. BIRKETT ,
C. F. M. DE LANGE  and
D. P. BUREAU
K. HUA*
Affiliation:
Department of Animal and Poultry Science, University of Guelph, Guelph, OntarioN1G 2W1, Canada
S. BIRKETT
Affiliation:
Department of Systems Design Engineering, University of Waterloo, Waterloo, OntarioN2L 3G1, Canada
C. F. M. DE LANGE
Affiliation:
Department of Animal and Poultry Science, University of Guelph, Guelph, OntarioN1G 2W1, Canada
D. P. BUREAU
Affiliation:
Department of Animal and Poultry Science, University of Guelph, Guelph, OntarioN1G 2W1, Canada
*
*To whom all correspondence should be addressed. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Models that accurately describe and predict growth and nutrient utilization of fish can be useful in developing strategies to improve the economic and environmental sustainability of aquaculture operations. Current bioenergetics models are not sufficiently flexible to be applied to the wide range of conditions encountered in aquaculture. There is a need to move from bioenergetics approaches to more mechanistic approaches based on nutrient utilization by fish. A non-ruminant nutrient-based growth model has been successfully used in pig production. The model explicitly describes the utilization of energy-yielding nutrients and metabolites for body protein deposition (Pd) and body lipid deposition (Ld) at the whole animal level. Partitioning of intake of energy-yielding nutrients between Pd and Ld is governed by a minimum ratio (minLP) of the body lipid mass (L) to protein mass (P), a maximum daily rate of Pd (PdMax), or maximum efficiency of using intake of the first limiting dietary essential amino acid (AA) for body Pd. The growth model was adapted to rainbow trout (Oncorhynchus mykiss (Walbaum 1792)) through parameterization and various modifications consistent with its framework. The fish nutrient-based model was evaluated by comparing model simulations with data from various experiments carried out with rainbow trout. Significant discrepancies between model predictions and experimental observations were observed. The model predicted energy retention well but did not always accurately predict growth rate, nor Pd and Ld. Overall, the model underestimated growth rate (expressed as thermal-unit growth coefficient (TGC)) by 37% and Pd by 15% and overestimated Ld by 13%. These discrepancies are probably attributable to differences in nutrient utilization and partitioning mechanisms between fish and pigs. The development of more reliable models requires better understanding of the nutritional and endogenous determinants of fish growth.

Type
Modelling Animal Systems Paper
Information
The Journal of Agricultural Science , Volume 148 , Issue 1 , February 2010 , pp. 17 - 29
Copyright
Copyright © Cambridge University Press 2009

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

REFERENCES

Armstrong, D. G. (1969). Cell bioenergetics and metabolism. In Handbuch der Tierernährung (Band 1) (Handbook of Animal Nutrition) (Eds Lenkheit, W., Breirem, K. & Crasemann, E.), pp. 385–414. Hamburg, Germany: Paul Parey.Google Scholar
Azevedo, P. A., Cho, C. Y., Leeson, S. & Bureau, D. P. (1998). Effects of feeding level and water temperature on growth nutrient and energy utilization and waste outputs of rainbow trout (Oncorhynchus mykiss). Aquatic Living Resources 11, 227–238.CrossRefGoogle Scholar
Azevedo, P. A., Leeson, S., Cho, C. Y. & Bureau, D. P. (2004 a). Growth, nitrogen and energy utilization of juveniles from four salmonid species: diet, species and size effects. Aquaculture 234, 393–414.CrossRefGoogle Scholar
Azevedo, P. A., Leeson, S., Cho, C. Y. & Bureau, D. P. (2004 b). Growth and feed utilization of large size rainbow trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar) reared in freshwater: diet and species effects, and responses over time. Aquaculture Nutrition 10, 401–411.CrossRefGoogle Scholar
Azevedo, P. A., van Milgen, J., Leeson, S. & Bureau, D. P. (2005). Comparing efficiency of metabolizable energy utilization by rainbow trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar) using factorial and multivariate approaches. Journal of Animal Science 83, 842–851.CrossRefGoogle ScholarPubMed
Baldwin, R. L. (1995). Modeling Ruminant Digestion and Metabolism, 1st edn.London: Chapman & Hall.Google Scholar
Bar, N. S., Sigholt, T., Shearer, K. D. & Krogdahl, Å. (2007). A dynamic model of nutrient pathways, growth, and body composition in fish. Canadian Journal of Fisheries and Aquatic Sciences 64, 1669–1682.CrossRefGoogle Scholar
Birkett, S. & de Lange, K. (2001 a). Limitations of conventional models and a conceptual framework for a nutrient flow representation of energy utilization by animals. British Journal of Nutrition 86, 647–659.CrossRefGoogle Scholar
Birkett, S. & de Lange, K. (2001 b). A computational framework for a nutrient flow representation of energy utilization by growing monogastric animals. British Journal of Nutrition 86, 661–674.CrossRefGoogle ScholarPubMed
Birkett, S. & de Lange, K. (2001 c). Calibration of a nutrient flow model of energy utilization by growing pigs. British Journal of Nutrition 86, 675–689.CrossRefGoogle ScholarPubMed
Black, J. L. (1974). Manipulation of body composition through nutrition. Proceedings of the Australian Society of Animal Production 10, 211–218.Google Scholar
Bureau, D. P. & Hua, K. (2008). Models of nutrient utilization by fish and potential applications for fish culture operations. In Mathematical Modelling in Animal Nutrition (Eds France, J. & Kebreab, E.), pp. 442–461. Wallingford, UK: CAB International.CrossRefGoogle Scholar
Bureau, D. P., Kirkland, J. B. & Cho, C. Y. (1998). The partitioning of energy from digestible carbohydrate by rainbow trout (Oncorhynchus mykiss). In Energy Metabolism of Farm Animals (Eds McCracken, K. J., Unsworth, E. F. & Wylie, A. R. G.), pp. 163–166. CAB Wallingford, UK: International Press.Google Scholar
Bureau, D. P., Kaushik, S. J. & Cho, C. Y. (2002). Bioenergetics. In Fish Nutrition, 3rd edn. (Eds Halver, J. E. & Hardy, R. W.), pp. 2–62. San Diego, CA: Academic Press.Google Scholar
Bureau, D. P., Gunther, S. J. & Cho, C. Y. (2003). Chemical composition and preliminary theoretical estimates of waste outputs of rainbow trout reared on commercial cage culture operations in Ontario. North American Journal of Aquaculture 65, 33–38.2.0.CO;2>CrossRefGoogle Scholar
Bureau, D. P., Hua, K. & Cho, C. Y. (2006). Effect of feeding level on growth and nutrient deposition in rainbow trout (Oncorhynchus mykiss Walbaum) growing from 150 to 600 g. Aquaculture Research 37, 1090–1098.CrossRefGoogle Scholar
Campbell, R. G. (1988). Nutritional constraints to lean tissue accretion in farm animals. Nutrition Research Reviews 1, 233–253.CrossRefGoogle ScholarPubMed
Cho, C. Y. (1992). Feeding systems for rainbow trout and other salmonids with reference to current estimates of energy and protein requirements. Aquaculture 100, 107–123.CrossRefGoogle Scholar
Cho, C. Y. & Bureau, D. P. (1998). Development of bioenergetic models and the Fish-PrFEQ software to estimate production, feeding ration and waste output in aquaculture. Aquatic Living Resources 11, 199–210.CrossRefGoogle Scholar
Cho, C. Y. & Kaushik, S. J. (1990). Nutritional energetics in fish: energy and protein utilization in rainbow trout (Salmo gairdneri). World Review of Nutrition and Dietetics 61, 132–172.CrossRefGoogle ScholarPubMed
Conceição, L. E. C., Verreth, J. A. J., Verstegen, M. W. A. & Huisman, E. A. (1998). A preliminary model for dynamic simulation of growth in fish larvae: application to the African catfish (Clarias gariepinus) and turbot (Scophthalmus maximus). Aquaculture 163, 215–235.CrossRefGoogle Scholar
Cui, Y. & Xie, S. (1999). Modelling growth in fish. In Feeding Systems and Feed Evaluation Models (Eds Theodorou, M. K. & France, J.), pp. 413–434. Wallingford, UK: CAB International.Google Scholar
de Lange, C. F. M. & Birkett, S. H. (2005). Characterization of useful energy content in swine and poultry feed ingredients. Canadian Journal of Animal Science 85, 269–280.CrossRefGoogle Scholar
Dumas, A., de Lange, C. F. M., France, J. & Bureau, D. P. (2007). Quantitative description of body composition and rates of nutrient deposition in rainbow trout (Oncorhynchus mykiss). Aquaculture 273, 165–181.CrossRefGoogle Scholar
Emmans, G. C. (1994). Effective energy: a concept of energy utilization applied across species. British Journal of Nutrition 71, 801–821.CrossRefGoogle ScholarPubMed
Encarnação, P. M., de Lange, C. F. M., Rodehutscord, M., Hoehler, D., Bureau, W. & Bureau, D. P. (2004). Diet digestible energy content affects lysine utilization, but not dietary lysine requirement of rainbow trout (Oncorhynchus mykiss) for maximum growth. Aquaculture 235, 569–586.CrossRefGoogle Scholar
Encarnação, P. M., de Lange, C. F. M. & Bureau, D. P. (2006). Diet energy source affects lysine utilization for protein deposition in rainbow trout (Oncorhynchus mykiss). Aquaculture 261, 1371–1381.CrossRefGoogle Scholar
Fournier, V., Gouillou-Coustans, M. F., Métailler, R., Vachot, C., Guedes, M. J., Tulli, F., Oliva-Teles, A., Tibaldit, E. & Kaushik, S. J. (2002). Protein and arginine requirements for maintenance and nitrogen gain in four teleosts. British Journal of Nutrition 87, 459–469.CrossRefGoogle ScholarPubMed
Guillaume, J., Kaushik, S., Bergot, P. & Métailler, R. (2001). Nutrition and Feeding of Fish and Crustaceans. Chichester, UK: Praxis Publishing Ltd.Google Scholar
Kaushik, S. J. (1998). Nutritional bioenergetics and estimation of waste production in non-salmonids. Aquatic Living Resources 11, 211–217.CrossRefGoogle Scholar
Kyriazakis, I. (1999). Future directions for models in pig biology. In A Quantitative Biology of the Pig (Ed. Kyriazakis, I.), pp. 381–388. Wallingford, UK: CABI Publishing.Google Scholar
Lehninger, A. L. (2000). Principles of Biochemistry, 2nd edn.New York, USA: Worth Publishers.Google Scholar
Lopez, G. & Leeson, S. (2008). Energy partitioning in broiler chickens. Canadian Journal of Animal Science 88, 205–212.CrossRefGoogle Scholar
Lupatsch, I. & Kissil, G. Wm. (2005). Feed formulations based on energy and protein demands in white grouper (Epinephelus aeneus). Aquaculture 248, 83–95.CrossRefGoogle Scholar
Machiels, M. A. M. & Henken, A. M. (1986). A dynamic simulation model for growth of the African catfish, Clarias gariepinus (Burchell 1822) 1. Effect of feeding level on growth and energy metabolism. Aquaculture 56, 29–52.CrossRefGoogle Scholar
Meyer-Burgorff, K. H., Osma, M. F. & Gunther, K. D. (1989). Energy metabolism in Oreochromis niloticus. Aquaculture 79, 283–291.CrossRefGoogle Scholar
McNamara, J. P. (2004). Research, improvement and application of mechanistic, biochemical, dynamic models of metabolism in lactating dairy cattle. Animal Feed Science and Technology 112, 155–176.CrossRefGoogle Scholar
Moughan, P. J. (1999). Protein metabolism in the growing pig. In A Quantitative Biology of the Pig (Ed. Kyriazakis, I.), pp. 299–331. Wallingford, UK: CAB International.Google Scholar
Moughan, P. J. (2003). Simulating the partitioning of dietary amino acids: New directions. Journal of Animal Science 81, E60–E67.Google Scholar
Olsen, R. E., Sundell, K., Mayhew, T. M., Myklebust, R. & Ringø, E. (2005). Acute stress alters intestinal function of rainbow trout, Oncorhynchus mykiss (Walbaum). Aquaculture 250, 480–495.CrossRefGoogle Scholar
Rodehutscord, M., Becker, A., Pack, M. & Pfeffer, E. (1997). Response of rainbow trout (Oncorhynchus mykiss) to supplements of individual essential amino acids in a semipurified diet, including an estimate of the maintenance requirement for essential amino acids. Journal of Nutrition 127, 1166–1175.CrossRefGoogle Scholar
Rodehutscord, M., Borchert, F., Gregus, Z., Pack, M. & Pfeffer, E. (2000). Availability and utilization of free lysine in rainbow trout (Oncorhynchus mykiss): 1. Effect of dietary crude protein level. Aquaculture 187, 163–176.CrossRefGoogle Scholar
Sandberg, F. B., Emmans, G. C. & Kyriazakis, I. (2005 a). Partitioning of limiting protein and energy in the growing pig: description of the problem, possible rules and their qualitative evaluation. British Journal of Nutrition 93, 205–212.CrossRefGoogle ScholarPubMed
Sandberg, F. B., Emmans, G. C. & Kyriazakis, I. (2005 b). Partitioning of limiting protein and energy in the growing pig: testing quantitative rules against experimental data. British Journal of Nutrition 93, 213–224.CrossRefGoogle ScholarPubMed
Shearer, K. D. (1994). Factors affecting the proximate composition of cultured fishes with emphasis on salmonids. Aquaculture 119, 63–88.CrossRefGoogle Scholar
Shirkey, T. W., Siggers, R. H., Goldade, B. G., Marshall, J. K., Drew, M. D., Laarveld, B. & Van Kessel, A. G. (2006). Effects of commensal bacteria on intestinal morphology and expression of proinflammatory cytokines in the gnotobiotic pig. Experimental Biology and Medicine 231, 1333–1345.CrossRefGoogle ScholarPubMed
Thornley, J. H. M. & France, J. (2007). Mathematical Models in Agriculture, 2nd edn.Wallingford, UK: CAB International.Google Scholar
van Dam, A. A. & Penning de Vries, F. W. T. (1995). Parameterization and calibration of a model to simulate effects of feeding level and feed composition on growth of Oreochromis niloticus (L.) and Oncorhynchus mykiss (Walbaum). Aquaculture Research 26, 415–425.Google Scholar
van Es, A. J. H. (1980). Energy cost of protein deposition. In Protein Deposition in Animals (Eds Buttery, P. & Lindsay, D.), pp. 215–224. London, UK: Butterworths.CrossRefGoogle Scholar
Zhou, A., Xie, S., Lei, W., Zhu, X. & Yang, Y. (2005). A bioenergetic model to estimate feed requirement of gibel carp, Carassius auratus gibelio. Aquaculture 248, 287–297.CrossRefGoogle Scholar