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Modelling the variation in performance of a population of growing pig as affected by lysine supply and feeding strategy

Published online by Cambridge University Press:  01 August 2009

L. Brossard ,
J.-Y. Dourmad ,
J. Rivest  and
J. van Milgen
L. Brossard*
Affiliation:
INRA, UMR1079, SENAH, F-35590 Saint-Gilles, France Agrocampus Rennes, UMR1079, SENAH, F-35000 Rennes, France
J.-Y. Dourmad
Affiliation:
INRA, UMR1079, SENAH, F-35590 Saint-Gilles, France Agrocampus Rennes, UMR1079, SENAH, F-35000 Rennes, France
J. Rivest
Affiliation:
Centre de Développement du Porc du Québec inc., 2795 boulevard Laurier, bureau 340, Sainte-Foy, Québec, G1V 4M7, Canada
J. van Milgen
Affiliation:
INRA, UMR1079, SENAH, F-35590 Saint-Gilles, France Agrocampus Rennes, UMR1079, SENAH, F-35000 Rennes, France
*
†E-mail: Ludovic.Brossard@rennes.inra.fr
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Abstract

Considerable progress has been made in the nutritional modelling of growth. Most models typically predict (or analyse) the response of a single animal. However, the response to nutrients of a single, representative animal is likely to be different from the response of the herd. To address the variation in response between animals, a stochastic approach towards nutritional modelling is required. In the present study, an analysis method is presented to describe growth and feed intake curves of individual pigs within a population of 192 pigs. This method was developed to allow end-users of InraPorc (a nutritional model predicting and analysing growth in pigs) to easily characterise their animals based on observed data and then use the model to test different scenarios. First, growth and intake data were curve-fitted to characterise individual pigs in terms of BW (Gompertz function of age) and feed intake (power function of BW) by a set of five parameters, having a biological or technico-economical meaning. This information was then used to create a population of virtual pigs in InraPorc, having the same feed intake and growth characteristics as those observed in the population. After determination of the mean lysine (Lys) requirement curve of the population, simulations were carried out for each virtual pig using different feeding strategies (i.e. 1, 2, 3 or 10 diets) and Lys supply (ranging from 70% to 130% of the mean requirement of the population). Because of the phenotypic variation between pigs and the common feeding strategies that were applied to the population, the Lys requirement of each individual pig was not always met. The percentage of pigs for which the Lys requirement was met increased concomitantly with increasing Lys supply, but decreased with increasing number of diets used. Simulated daily gain increased and feed conversion ratio decreased with increasing Lys supply (P < 0.001) according to a curvilinear–plateau relationship. Simulated performance was close to maximum when the Lys supply was 110% of the mean population requirement and did not depend on the number of diets used. At this level of Lys supply, the coefficient of variation of simulated daily gain was minimal and close to 10%, which appears to be a phenotypic characteristic of this population. At lower Lys supplies, simulated performance decreased and variability of daily gain increased with an increasing number of diets (P < 0.001). Knowledge of nutrient requirements becomes more critical when a greater number of diets are used. This study shows the limitations of using a deterministic model to estimate the nutrient requirements of a population of pigs. A stochastic approach can be used provided that relationships between the most relevant model parameters are known.

Keywords

pigmodellingvariabilitylysine requirementgrowth
Type
Full Paper
Information
animal , Volume 3 , Issue 8 , August 2009 , pp. 1114 - 1123
Copyright
Copyright © The Animal Consortium 2009

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References

Bertolo, R, Moehn, S, Pencharz, P, Ball, R 2005. Estimate of the variability of the lysine requirement of growing pigs using the indicator amino acid oxidation technique. Journal of Animal Science 83, 2535–2542.CrossRefGoogle ScholarPubMed
Boys, K, Li, N, Preckel, PV, Schinckel, A, Foster, K 2007. Economic replacement of a heterogeneous herd. American Journal of Agricultural Economics 89, 24–35.CrossRefGoogle Scholar
Corzo, A, McDaniel, C, Kidd, M, Miller, E, Boren, B, Fancher, BI 2004. Impact of dietary amino acid concentration on growth, carcass yield, and uniformity of broilers. Australian Journal of Agricultural Research 55, 1133–1138.CrossRefGoogle Scholar
Doeschl-Wilson, AB, Knap, PW, Kinghorn, BP 2006. Evaluating animal genotypes through model inversion. In Mechanistic modelling in pig and poultry production (ed. R Gous, T Morris and C Fisher), pp. 163–187. CABI Publishing, Wallingford, UK.CrossRefGoogle Scholar
Doeschl-Wilson, AB, Knap, PW, Kinghorn, BP, Van der Steen, HAM 2007. Using mechanistic animal growth models to estimate genetic parameters of biological traits. Animal 1, 489–499.CrossRefGoogle ScholarPubMed
Ferguson, NS, Gous, RM 1993. Evaluation of pig genotypes. 1. Theoretical aspects of measuring genetic parameters. Animal Science 56, 233–243.CrossRefGoogle Scholar
Ferguson, NS, Gous, RM, Emmans, GC 1994. Preferred components for the construction of a new simulation-model of growth, feed-intake and nutrient-requirements of growing pigs. South African Journal of Animal Science 24, 10–17.Google Scholar
Ferguson, NS, Gous, RM, Emmans, GC 1997. Predicting the effects of animal variation on growth and food intake in growing pigs using simulation modelling. Animal Science 64, 513–522.CrossRefGoogle Scholar
Gous, RM, Berhe, ET 2006. Modelling populations for purposes of optimization. In Mechanistic modelling in pig and poultry production (ed. R Gous, T Morris and C Fisher), pp. 76–96. CABI Publishing, Wallingford, UK.CrossRefGoogle Scholar
InraPorc ® 2006. Un outil pour évaluer des stratégies alimentaires chez le porc. Version 1.0.4.0. INRA-UMR SENAH, www.rennes.inra.fr/inraporcGoogle Scholar
Knap, PW 1995. Aspects of stochasticity: variation between animals. In Modelling growth in the pig (ed. PJ Moughan, MWA Verstegen and MI Visser-Reyneveld), pp. 165–172. Wageningen Press, Wageningen, The Netherlands.Google Scholar
Knap, PW 1999. Simulation of growth in pigs: evaluation of a model to relate thermoregulation to body protein and lipid content and deposition. Animal Science 68, 655–679.CrossRefGoogle Scholar
Knap, PW 2000. Variation in maintenance requirements of growing pigs in relation to body composition. A simulation study. PhD, Wageningen University.Google Scholar
Knap, PW, Roehe, R, Kolstad, K, Pomar, C, Luiting, P 2003. Characterisation of pig genotypes for growth modeling. Journal of Animal Science 81, E187–E195.Google Scholar
Kyriazakis, I (ed.) 1999. Future directions for models in pig biology. In A quantitative biology of the pig, pp. 381–388. CABI Publishing, Wallingford, UK.Google Scholar
Leclercq, B, Beaumont, C 2000. Etude par simulation de la réponse des troupeaux de volailles aux apports d’acides aminés et de protéines. INRA Productions Animales 13, 47–59.CrossRefGoogle Scholar
Magowan, E, Mccann, MEE, Beattie, VE, McCracken, KJ, Henry, W, Smyth, S, Bradford, R, Gordon, FJ, Mayne, CS 2007. Investigation of growth rate variation between commercial pig herds. Animal 1, 1219–1226.CrossRefGoogle ScholarPubMed
van Milgen, J, Quiniou, N, Noblet, J 2000. Modelling the relation between energy intake and protein and lipid deposition in growing pigs. Animal Science 71, 119–130.CrossRefGoogle Scholar
van Milgen, J, Valancogne, A, Dubois, S, Dourmad, JY, Sève, B, Noblet, J 2008. InraPorc: a model and decision support tool for the nutrition of growing pigs. Animal Feed Science and technology 143, 387–405.CrossRefGoogle Scholar
Moughan, PJ 1998. Protein metabolism in the growing pig. In A quantitative biology of the pig (ed. I Kyriazakis), pp. 299–331. CABI Publishing, Oxon, UK.Google Scholar
Moughan, P, Kerr, R, Smith, W 1995. The role of simulation models in the development of economically-optimal feeding regimens for the growing pig. In Modelling growth in the pig (ed. PJ Moughan, MWA Verstegen and MI Visser-Reyneveld), pp. 209–222. Wageningen Press, Wageningen, The Netherlands.Google Scholar
Moughan, P, Smith, W, Pearson, G 1987. Description and validation of a model simulating growth in the pig (20–90 kg liveweight). New Zealand Journal of Agricultural Research 30, 481–489.CrossRefGoogle Scholar
National Research Council (NRC) 1998. Nutrient requirements of swine, 10th revised edition. National Academic Press, Washington, DC, USA.Google Scholar
Noblet, J, Quiniou, N 1999. Principaux facteurs de variation du besoin en acides aminés du porc en croissance. Techni-Porc 22, 9–16.Google Scholar
Noblet, J, Sève, B, Jondreville, C 2002. Valeurs nutritives pour les porcs. In Tables de composition et de valeur nutritive des matières premières destinées aux animaux d’élevage (ed. D Sauvant, J-M Perez and G Tran), pp. 25–35. INRA Editions, Paris, France.Google Scholar
Pomar, C, Kyriazakis, I, Emmans, GC, Knap, PW 2003. Modeling stochasticity: dealing with populations rather than individual pigs. Journal of Animal Science 81, E178–E186.Google Scholar
Quiniou, N, Brossard, L, Gaudre, D, van Milgen, J, Salaün, Y 2007. Optimum économique du niveau en acides aminés dans les aliments pour porcs charcutiers. Impact du contexte de prix des matières premières et de la conduite d’élevage. Techni-Porc 30, 25–36.Google Scholar
Quiniou, N, Hamelin, E, Noblet, J 2006. Le besoin en lysine digestible relativement à l’énergie nette des porcs rationnés est-il plus élevé que celui des porcs alimentés à volonté? Journées de la Recherche Porcine 38, 149–156.Google Scholar
Rivest, J 2004. Epreuve 16. Performances des animaux en station. Rapport final. Evaluation des verrats terminaux: Duroc et P76. Centre de développement du porc du Québec, inc., Sainte Foy, Canada, 44p.Google Scholar
Schinckel, AP, de Lange, CFM 1996. Characterization of growth parameters needed as inputs for pig growth models. Journal of Animal Science 74, 2021–2036.CrossRefGoogle ScholarPubMed
Schinckel, A, Li, N, Preckel, PV, Einstein, M, Miller, D 2003. Development of a stochastic pig compositional growth model. The Professional Animal Scientist 19, 255–260.CrossRefGoogle Scholar
Schnute, J 1981. A versatile growth model with statistically stable parameters. Canadian Journal of Fisheries and Aquatic Sciences 38, 1128–1140.CrossRefGoogle Scholar
Sève, B 1994. Alimentation du porc en croissance: intégration des concepts de protéine idéale, de digestibilité digestive des acides aminés et d’énergie nette. INRA Productions Animales 7, 275–291.CrossRefGoogle Scholar
Statistical Analysis Systems Institute 2000. SAS/STAT users guide, version 8.01. SAS Institute, Cary, NC, USA.Google Scholar
Warnants, N, Van Oeckel, MJ, De Paepe, M 2003. Response of growing pigs to different levels of ileal standardised digestible lysine using diets balanced in threonine, methionine and tryptophan. Livestock Production Science 82, 201–209.CrossRefGoogle Scholar
Wellock, IJ, Emmans, GC, Kyriazakis, I 2003. Modelling the effects of thermal environment and dietary composition on pig performance: model logic and concepts. Animal Science 77, 255–266.CrossRefGoogle Scholar
Wellock, IJ, Emmans, GC, Kyriazakis, I 2004. Modeling the effects of stressors on the performance of populations of pigs. Journal of Animal Science 82, 2442–2450.CrossRefGoogle ScholarPubMed
Whittemore, C, Fawcett, R 1976. Theoretical aspects of a flexible model to simulate protein and lipid growth in pigs. Animal Production 22, 87–96.Google Scholar