Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-12-01T01:17:29.777Z Has data issue: false hasContentIssue false
  • English
  • Français

Accounting for variability among individual pigs in deterministic growth models

Published online by Cambridge University Press:  04 April 2013

B. Vautier ,
N. Quiniou ,
J. van Milgen  and
L. Brossard
B. Vautier
Affiliation:
INRA, UMR1348 Pegase, F-35590 Saint-Gilles, France Agrocampus Ouest, UMR1348 Pegase, F-35000 Rennes, France IFIP-Institut du Porc, BP 35104, F-35651 Le Rheu cedex, France
N. Quiniou
Affiliation:
IFIP-Institut du Porc, BP 35104, F-35651 Le Rheu cedex, France
J. van Milgen
Affiliation:
INRA, UMR1348 Pegase, F-35590 Saint-Gilles, France Agrocampus Ouest, UMR1348 Pegase, F-35000 Rennes, France
L. Brossard*
Affiliation:
INRA, UMR1348 Pegase, F-35590 Saint-Gilles, France Agrocampus Ouest, UMR1348 Pegase, F-35000 Rennes, France
*
†E-mail: [email protected]
Get access

Abstract

Inclusion of variation in deterministic nutritional models for growth by repeating simulations using different sets of parameters has been performed in literature without or with only hypothetic consideration of the covariance structure among parameters. However, a description of the structure of links among parameters describing individuals is required to generate realistic sets of parameters. In this study, the mean and covariance structure of model parameters describing feed intake and growth were analyzed from 10 batches of crossbred gilts and barrows. Data were obtained from different crossbreeds, originating from Large White × Landrace sows and nine sire lines. Pigs were group-housed (12 pigs/pen) and performance testing was carried out from 70 days of age to ∼110 kg BW. Daily feed intake (DFI) was recorded using automatic feeding stations and BW was measured at least every 3 weeks. A growth model was used to characterize individual pigs based on the observed DFI and BW. In this model, a Gompertz function was used to describe protein deposition and the resulting BW gain. A gamma function (expressing DFI as multiples of maintenance) was used to express the relationship between DFI and BW. Each pig was characterized through a set of five parameters: BW70 (BW at 70 days of age), BGompertz (a precocity parameter) PDm (mean protein deposition rate) and DFI50 and DFI100 (DFI at 50 and 100 kg BW, respectively). The data set included profiles for 1288 pigs for which no eating or growth disorders were observed (e.g. because of disease). All parameters were affected by sex (except for BW70) and batch, but not by the crossbreed (except for PDm). An interaction between sex and crossbreed was observed for PDm (P < 0.01) and DFI100 (P = 0.05). Different covariance matrices were computed according to the batch, sex, crossbreed, or their combinations, and the similarity of matrices was evaluated using the Flury hierarchy. As covariance matrices were all different, the unit of covariance (subpopulation) corresponded to the combination of batch, sex and crossbreed. Two generic covariance matrices were compared afterwards, with (median matrix) or without (raw matrix) taking into account the size of subpopulations. The most accurate estimation of observed covariance was obtained with the median covariance matrix. The median covariance matrix can be used, in combination with average parameters obtained on-farm, to generate virtual populations of pigs that account for a realistic description of mean performances and their variability.

Keywords

piggrowthmodelingvariabilityFlury hierarchy
Type
Nutrition
Information
animal , Volume 7 , Issue 8 , August 2013 , pp. 1265 - 1273
Copyright
Copyright © The Animal Consortium 2013 

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

Black, JL, Mullan, BP, Lorschy, ML, Giles, LR 1993. Lactation in the sow during heat-stress. Livestock Production Science 35, 153–170.CrossRefGoogle Scholar
Boente, G, Rodriguez, D, Sued, M 2010. Inference under functional proportional and common principal component models. Journal of Multivariate Analysis 101, 464–475.Google Scholar
Brossard, L, Dourmad, JY, Rivest, J, van Milgen, J 2009. Modelling the variation in performance of a population of growing pig as affected by lysine supply and feeding strategy. Animal 3, 1114–1123.CrossRefGoogle ScholarPubMed
Casey, DS, Stern, HS, Dekkers, JCM 2005. Identification of errors and factors associated with errors in data from electronic swine feeders. Journal of Animal Science 83, 969–982.Google Scholar
Daumas, G, Causeur, D, Predin, J 2010. Validation de l’équation française de prédiction du taux de muscle des pièces (TMP) des carcasses de porc par la méthode CGM. Journées Recherche Porcine 42, 229–230.Google Scholar
Emmans, GC, Fisher, C 1986. Problems in nutritionnal theory. In Nutrient requirements of poultry and nutritionnal research (ed. C Fisher and KN Boorman), pp. 9–39. Butterworths, London.Google Scholar
Ferguson, NS, Kyriazis, ST 2003. Evaluation of the growth parameters of six commercial crossbred pig genotypes – 1. Under commercial housing conditions in individual pens. South African Journal of Animal Science 33, 11–20.Google 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.Google Scholar
Flury, B 1988. Common principal components and related multivariate models. Wiley, New York.Google Scholar
Green, DM, Brotherstone, S, Schofield, CP, Whittemore, CT 2003. Food intake and live growth performance of pigs measured automatically and continuously from 25 to 115 kg live weight. Journal of the Science of Food and Agriculture 83, 1150–1155.CrossRefGoogle Scholar
IFIP 2000. Mémento de l’éleveur de porc. p 156. IFIP, Paris, France.Google Scholar
InraPorc® 2006. A Model and Decision Support Tool for the Nutrition of Growing Pigs, Version 1.6.5.3. INRA-UMR PEGASE, Saint-Gilles, France. Retrieved November 15, 2011, from http://www.rennes.inra.fr/inraporc/Google 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 Pers, Wageningen, The Netherlands.Google Scholar
Knap, PW 2000. Time trends of Gompertz growth parameters in ‘meat-type’ pigs. Animal Science 70, 39–49.Google Scholar
Knap, PW, Schrama, JW 1996. Simulation of growth in pigs: approximation of protein turn-over parameters. Animal Science 63, 533–547.Google Scholar
Labroue, F, Guéblez, R, Sellier, P, Meunier-Salaün, MC 1994. Feeding-behavior of group-housed Large White and Landrace pigs in french central test stations. Livestock Production Science 40, 303–312.CrossRefGoogle Scholar
Lê, S, Husson, F, Pagès, J 2007. DMFA: dual multiple factor analysis. Conference at the 12th International Conference on Applied Stochastic Models and Data Analysis, Chania, Crete, Greece, 8pp.Google Scholar
Morel, PCH, Wood, GR, Sirisatien, D 2008. Effect of genotype, population size and genotype variation on optimal diet determination for growing pigs. In Acta Horticulturae (ed. P Barreiro, MLATM Hertog, FJ Arranz, B Diezma and EC Correa), pp. 287–292. International Society for Horticultural Science (ISHS), Leuven, Belgium.Google Scholar
Phillips, PC, Arnold, SJ 1999. Hierarchical comparison of genetic variance-covariance matrices. I. Using the Flury hierarchy. Evolution 53, 1506–1515.Google Scholar
Pomar, C, Dubeau, F, van Milgen, J 2009. La détermination des besoins nutritionnels, la formulation multicritère et l'ajustement progressif des apports de nutriments au besoin des porcs: des outils pour maîtriser les rejets d'azote et de phosphore. INRA Productions Animales 22, 49–54.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
Ripley, BD 1987. Stochastic simulation. Wiley, New York.Google Scholar
Statistical Analysis System Institute Inc. (SAS) 2000. SAS user's guide, version 8.01. SAS Institute Inc., Cary, NC, USA.Google Scholar
Strathe, AB, Sorensen, H, Danfaer, A 2009. A new mathematical model for combining growth and energy intake in animals: the case of the growing pig. Journal of Theoretical Biology 261, 165–175.CrossRefGoogle ScholarPubMed
van Milgen, J, Noblet, J 1999. Energy partitioning in growing pigs: the use of a multivariate model as an alternative for the factorial analysis. Journal of Animal Science 77, 2154–2162.Google 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.Google Scholar
Vautier, B, Quiniou, N, van Milgen, J, Brossard, L 2011. Modelling the dynamics of feed intake in growing pigs; interest for modelling populations of pigs. In Book of Abstracts of the 62nd Annual Meeting of the European Federation of Animal Science, p. 105. Wageningen Academic Publisher, Wageningen, The Netherlands.Google Scholar
Wellock, IJ, Emmans, GC, Kyriazakis, I 2004a. Describing and predicting potential growth in the pig. Animal Science 78, 379–388.CrossRefGoogle Scholar
Wellock, IJ, Emmans, GC, Kyriazakis, I 2004b. Modeling the effects of stressors on the performance of populations of pigs. Journal of Animal Science 82, 2442–2450.Google Scholar
Whittemore, CT, Kerr, JC, Cameron, ND 1995. An approach to prediction of feed-intake in growing pigs using simple body measurements. Agricultural Systems 47, 235–244.Google Scholar