Hostname: page-component-5b777bbd6c-xnzsz Total loading time: 0 Render date: 2025-06-22T23:39:40.242Z Has data issue: false hasContentIssue false
  • English
  • Français

Phenotypic and genetic relationships between growth and feed intake curves and feed efficiency and amino acid requirements in the growing pig

Published online by Cambridge University Press:  05 September 2014

R. Saintilan ,
L. Brossard ,
B. Vautier ,
P. Sellier ,
J. Bidanel ,
J. van Milgen  and
H. Gilbert
R. Saintilan
Affiliation:
INRA, Génétique Animale et Biologie Intégrative (GABI), F-78352 Jouy-en-Josas Cedex, France AgroParisTech, Génétique Animale et Biologie Intégrative (GABI), F-75005 Paris, France
L. Brossard
Affiliation:
INRA, Physiologie, Génétique Animale et Systèmes d’Elevage (PEGASE), F-35590 Saint-Gilles, France Agrocampus, Physiologie, Génétique Animale et Systèmes d’Elevage (PEGASE), F-35590 Rennes, France
B. Vautier
Affiliation:
INRA, Physiologie, Génétique Animale et Systèmes d’Elevage (PEGASE), F-35590 Saint-Gilles, France Agrocampus, Physiologie, Génétique Animale et Systèmes d’Elevage (PEGASE), F-35590 Rennes, France IFIP – Institut du porc, BP 35104, F-35651 Le Rheu Cedex, France
P. Sellier
Affiliation:
INRA, Génétique Animale et Biologie Intégrative (GABI), F-78352 Jouy-en-Josas Cedex, France AgroParisTech, Génétique Animale et Biologie Intégrative (GABI), F-75005 Paris, France
J. Bidanel
Affiliation:
IFIP – Institut du porc, BP 35104, F-35651 Le Rheu Cedex, France
J. van Milgen
Affiliation:
INRA, Physiologie, Génétique Animale et Systèmes d’Elevage (PEGASE), F-35590 Saint-Gilles, France Agrocampus, Physiologie, Génétique Animale et Systèmes d’Elevage (PEGASE), F-35590 Rennes, France
H. Gilbert*
Affiliation:
INRA, Génétique Animale et Biologie Intégrative (GABI), F-78352 Jouy-en-Josas Cedex, France AgroParisTech, Génétique Animale et Biologie Intégrative (GABI), F-75005 Paris, France INRA, Génétique, Physiologie et Systèmes d’Elevage (GenPhySE), F-31326 Castanet-Tolosan, France Université de Toulouse, INP, ENSAT, Génétique, Physiologie et Systèmes d’Elevage (GenPhySE), F-31326 Castanet-Tolosan, France INP, ENVT, GenPhySE (Génétique, Physiologie et Systèmes d’Elevage), Université de Toulouse, F-31076 Toulouse, France
*
†E-mail: helene.gilbert@toulouse.inra.fr
Get access

Abstract

Improvement of feed efficiency in pigs has been achieved essentially by increasing lean growth rate, which resulted in lower feed intake (FI). The objective was to evaluate the impact of strategies for improving feed efficiency on the dynamics of FI and growth in growing pigs to revisit nutrient recommendations and strategies for feed efficiency improvement. In 2010, three BWs, at 35±2, 63±9 and 107±7 kg, and daily FI during this period were recorded in three French test stations on 379 Large White and 327 French Landrace from maternal pig populations and 215 Large White from a sire population. Individual growth and FI model parameters were obtained with the InraPorc® software and individual nutrient requirements were computed. The model parameters were explored according to feed efficiency as measured by residual feed intake (RFI) or feed conversion ratio (FCR). Animals were separated in groups of better feed efficiency (RFI− or FCR−), medium feed efficiency and poor feed efficiency. Second, genetic relationships between feed efficiency and model parameters were estimated. Despite similar average daily gains (ADG) during the test for all RFI groups, RFI− pigs had a lower initial growth rate and a higher final growth rate compared with other pigs. The same initial growth rate was found for all FCR groups, but FCR− pigs had significantly higher final growth rates than other pigs, resulting in significantly different ADG. Dynamic of FI also differed between RFI or FCR groups. The calculated digestible lysine requirements, expressed in g/MJ net energy (NE), showed the same trends for RFI or FCR groups: the average requirements for the 25% most efficient animals were 13% higher than that of the 25% least efficient animals during the whole test, reaching 0.90 to 0.95 g/MJ NE at the beginning of the test, which is slightly greater than usual feed recommendations for growing pigs. Model parameters were moderately heritable (0.30±0.13 to 0.56±0.13), except for the precocity of growth (0.06±0.08). The parameter representing the quantity of feed at 50 kg BW showed a relatively high genetic correlation with RFI (0.49±0.14), and average protein deposition between 35 and 110 kg had the highest correlation with FCR (−0.76±0.08). Thus, growth and FI dynamics may be envisaged as breeding tools to improve feed efficiency. Furthermore, improvement of feed efficiency should be envisaged jointly with new feeding strategies.

Keywords

growth curvepigresidual feed intakeamino acid requirementsfeed efficiency
Type
Research Article
Information
animal , Volume 9 , Supplement 1 , January 2015 , pp. 18 - 27
Copyright
© The Animal Consortium 2014 

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.)

Article purchase

Temporarily unavailable

References

Abegaz, S, van Wyk, JB and Olivier, JJ 2010. Estimation of genetic and phenotypic parameters of growth curve and their relationships with early growth and productivity in Horro Sheep. Archiv Tierzucht 53, 85–94.Google Scholar
Barea, R, Dubois, S, Gilbert, H, Sellier, P, van Milgen, J and Noblet, J 2010. Energy utilization in pigs selected for high and low residual feed intake. Journal of Animal Science 88, 2062–2072.Google Scholar
Black, J 2009. Models to predict feed intake. In Voluntary feed intake in pigs (ed. D Torrallardona and E Roura), pp 323–351. Wageningen Academic Publishers, Wageningen, The Netherlands.CrossRefGoogle Scholar
Brossard, L, Gilbert, H, Billon, Y and van Milgen, J 2012. Effet d’une sélection divergente pour la consommation journalière résiduelle chez le porc en croissance sur la réponse à une carence en acides aminés. Journées de la Recherche Porcine 44, 165–170.Google Scholar
Brossard, L, van Milgen, J, Lannuzel, PY, Bertinnoti, R and Rivest, J 2006. Analyse des relations entre croissance et ingestion à partir de cinétiques individuelles: implications dans la définition de profils animaux pour la modélisation. Journées de la Recherche Porcine 38, 217–224.Google Scholar
Cai, W, Casey, DS and Dekkers, JCM 2008. Selection response and genetic parameters for residual feed intake in Yorkshire swine. Journal of Animal Science 86, 287–298.Google Scholar
Cai, W, Kaiser, MS and Dekkers, JCM 2012. Bayesian analysis of the effect of selection for residual feed intake on growth and feed intake curves in Yorkshire swine. Journal of Animal Science 90, 127–141.Google Scholar
Clutter, AC 2011. Genetics of performance traits. In The genetics of the pig, 2nd edition (ed. MF Rothschild and A Ruvinsky), pp. 325–354. CABI Publishing, Wallingford, UK.Google Scholar
Daumas, G 2008. Taux de muscle des pièces et appréciation de la composition corporelle des carcasses. Journées de la Recherche Porcine 40, 61–68.Google Scholar
Doeschl-Wilson, AB, Knap, PW, Kinghorn, BP and van der Steen, HAM 2007. Using mechanistic animal growth models to estimate genetic parameters of biological traits. Animal 1, 489–499.CrossRefGoogle ScholarPubMed
Dunshea, FR, Allison, JRD, Bertram, M, Boler, DD, Brossard, L, Campbell, R, Crane, JP, Hennessy, DP, Huber, L, de Lange, C, Ferguson, N, Matzat, P, McKeith, F, PJU, Moraes, Mullan, BP, Noblet, J, Quiniou, N and Tokach, M 2013. The effect of immunization against GnRF on nutrient requirements of male pigs: a review. Animal 7, 1769–1778.CrossRefGoogle ScholarPubMed
Faure, J, Lefaucheur, L, Bonhomme, N, Ecolan, P, Meteau, K, Metayer Coustard, S, Kouba, M, Gilbert, H and Lebret, B 2013. Consequences of divergent selection for residual feed intake in pigs on muscle energy metabolism and meat quality. Meat Science 93, 37–45.CrossRefGoogle ScholarPubMed
Ferguson, NS and Gous, RM 1993. Evaluation of pig genotypes: 1. Theoretical aspects of measuring genetic parameters. Animal Production 56, 233–243.Google Scholar
Fowler, VR, Bichard, M and Pease, A 1976. Objectives in pig breeding. Animal Production 23, 365–387.Google Scholar
Gilbert, H, Bidanel, JP, Billon, Y, Lagant, H, Guillouet, P, Sellier, P, Noblet, J and Hermesch, S 2012. Correlated responses in sow appetite, residual feed intake, body composition, and reproduction after divergent selection for residual feed intake in the growing pig. Journal of Animal Science 90, 1097–1108.CrossRefGoogle ScholarPubMed
Gilbert, H, Bidanel, JP, Gruand, J, Caritez, J, Billon, Y, Guillouet, P, Lagant, H, Noblet, J and Sellier, P 2007. Genetic parameters for residual feed intake in growing pigs, with emphasis on genetic relationships with carcass and meat quality traits. Journal of Animal Science 85, 3182–3188.Google Scholar
Gilbert, H, Al Aïn, S, Sellier, P, Lagant, H, Billon, Y, Bidanel, J-P, Guillouet, P, Noblet, J, van Milgen, J and Brossard, L 2009. Relations génétiques entre efficacité alimentaire et cinétiques de croissance et d'ingestion chez le porc Large White. Journées de la Recherche Porcine 41, 1–6.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
Kanis, E, de Greef, KH, Hiemstra, A and van Arendonk, JA 2005. Breeding for societally important traits in pigs. Journal of Animal Science 83, 948–957.Google Scholar
Kloareg, M, Noblet, J and van Milgen, J 2006. Estimation of whole body lipid mass in finishing pigs. Animal Science 82, 241–251.CrossRefGoogle Scholar
Koivula, M, Sevon-Aimonen, M-L, Strandén, I, Matilainen, K, Serenius, T, Stalder, KJ and Mäntysaari, EA 2008. Genetic (co)variances and breeding value estimation of Gompertz growth curve parameters in Finnish Yorkshire boars, gilts and barrows. Journal of Animal Breeding and Genetics 125, 168–175.Google Scholar
Le Naou, T, Le Floc’h, N, Louveau, I, Gilbert, H and Gondret, F 2012. Metabolic changes and tissue responses to selection on residual feed intake in growing pigs. Journal of Animal Science 90, 4771–4780.Google Scholar
Métayer, A and Daumas, G 1998. Estimation par découpe de la teneur en viande maigre des carcasses de porc. Journées de la Recherche Porcine 30, 7–11.Google Scholar
Meunier-Salaün, MC, Guérin, C, Billon, Y, Priet, A, Sellier, P, Noblet, J and Gilbert, H 2014. Divergent selection for residual feed intake in group-housed growing pigs: characteristics of physical and behavioural activity according to line and sex. Animal, first published online 24 July 2014, doi:10.1017/S1751731114001839.Google Scholar
Meyer, K 2006. ‘WOMBAT’ – digging deep for quantitative genetic analyses by restricted maximum likelihood. In Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, 13–18 August 2006, Belo Horizonte, Brazil, pp. 27–14.Google Scholar
Mignon-Grasteau, S, Beaumont, C, Le Bihan-Duval, E, Poivey, JP, de Rochembeau, H and Ricard, FH 1999. Genetic parameters of growth curve parameters in male and female chicken. British Poultry Science 40, 44–51.Google Scholar
N’Dri, AL, Mignon-Grasteau, S, Sellier, N, Tixier-Boichard, M and Beaumont, C 2006. Genetic relationships between feed conversion ratio, growth curve and body composition in slow-growing chickens. British Poultry Science 47, 273–280.Google Scholar
Pomar, C, Hauschild, L, Zhang, G-H, Pomar, J and Lovatto, PA 2009. Applying precision feeding techniques in growing-finishing pigs operations. Revista Brasileira de Zootecnia 38, 226–237.CrossRefGoogle Scholar
Pomar, C, Hauschild, L, Zhang, GH, Pomar, J and Lovatto, PA 2010. Precision feeding can significantly reduce feeding cost and nutrient excretion in growing animals. In Modelling nutrition digestion and utilization in farm animals (ed. D Sauvant, J van Milgen, P Faverdin and N Friggens), pp 327–334. Wageningen Academic Publishers, Wageningen, The Netherlands.Google Scholar
Quiniou, N, Courboulay, V, Salaün, Y and Chevillon, P 2010. Conséquences de la non castration des porcs mâles sur les performances de croissance et le comportement: comparaison avec les mâles castrés et les femelles. Journées de la Recherche Porcine 42, 113–118.Google Scholar
Saintilan, R, Mérour, I, Brossard, L, Tribout, T, Dourmad, JY, Sellier, P, Bidanel, J, van Milgen, J and Gilbert, H 2013. Genetics of residual feed intake in growing pigs: relationships with production traits, and nitrogen and phosphorus excretion. Journal of Animal Science 91, 2542–2554.Google Scholar
SAS Institute 2010. Statistical analysis system release 8.01. SAS Institute Inc., Cary, NC, USA.Google Scholar
Shirali, M, Doeschl-Wilson, A, Knap, PW, Kanis, E, Duthie, C, van Arendonk, JAM and Roehe, R 2014. Growth modeling for energy and nitrogen efficiency. In Improvement of energy and nitrogen utilisation in pork production: genetics and growth models. PhD Thesis, Wageningen University, Wageningen, The Netherlands.Google Scholar
van Milgen, J, Valancogne, A, Dubois, S, Dourmad, JY, Seve, B and 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, Brossard, L, van Milgen, J and Quiniou, N 2013. Accounting for variability among individual pigs in deterministic growth models. Animal 7, 1265–1273.Google Scholar
Wellock, IJ, Emmans, GC and Kyriazakis, I 2004. Describing and predicting potential growth in the pig. Animal Science 78, 379–388.Google Scholar
Supplementary material: File

Saintilan Supplementary Material

Figure S1

Download Saintilan Supplementary Material(File)
File 73.7 KB
Supplementary material: File

Saintilan Supplementary Material

Figure S2

Download Saintilan Supplementary Material(File)
File 74.3 KB