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RESEARCH ARTICLE

Comparison of in vivo and in silico growth performance and variability in pigs when applying a feeding strategy designed by simulation to control the variability of slaughter weight

L. Brossard A B C E , B. Vautier A B C D , J. van Milgen A B C , Y. Salaun A D and N. Quiniou A D
+ Author Affiliations
- Author Affiliations

A Unité Mixte Technologique ‘Ingénierie des Systèmes de Production Porcine’.

B INRA, UMR1348 PEGASE, 35590 Saint-Gilles, France.

C Agrocampus Ouest, UMR1348 PEGASE, 35000 Rennes, France.

D IFIP – Institut du Porc, BP 35104, 35651 Le Rheu cedex, France.

E Corresponding author. Email: ludovic.brossard@rennes.inra.fr

Animal Production Science 54(12) 1939-1945 https://doi.org/10.1071/AN14521
Submitted: 26 April 2014  Accepted: 26 July 2014   Published: 29 August 2014

Abstract

Variability in bodyweight (BW) among pigs complicates the management of feeding strategies and slaughter. Including variability among individuals in modelling approaches can help to design feeding strategies to control performance level, but also its variability. The InraPorc model was used to perform simulations on 10 batches of 84 crossbred pigs each to characterise the effect of feeding strategies differing in amino acid supply or feed allowance on the mean and variation in growth rate. Results suggested that a feed restriction reduces the coefficient of variation of BW at first departure for slaughter (BW1) by 34%. Growth performance obtained from an in silico simulation using ad libitum and restricted feeding plans was compared with results obtained in an in vivo experiment on a batch of 168 pigs. Pigs were offered feed ad libitum or were restricted (increase in feed allowance by 27 g/day up to a maximum of 2.4 and 2.7 kg/day for gilts and barrows, respectively). A two-phase feeding strategy was applied, with 0.9 and 0.7 g of digestible lysine per MJ of net energy (NE) in diets provided before or after 65 kg BW, respectively. Actual growth was similar to that obtained by simulation. Coefficient of variation of BW1 was similar in vivo and in silico for the ad libitum feeding strategy but was underestimated by 1 percentage point in silico for the restriction strategy. This study confirms the relevance of using simulations performed to predict the level and variability in performance of group housed pigs.

Additional keywords: feed allowance, feeding strategy, modelling.


References

Boys KA, Li N, Preckel PV, Schinckel A, Foster K (2007) Economic replacement of a heterogeneous herd. American Journal of Agricultural Economics 89, 24–35.
Economic replacement of a heterogeneous herd.Crossref | GoogleScholarGoogle Scholar |

Brossard L, Dourmad J-Y, 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.
Modelling the variation in performance of a population of growing pig as affected by lysine supply and feeding strategy.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXpvFejtbk%3D&md5=eaaf362efbe300831e43c484f8c08672CAS | 22444841PubMed |

Brossard L, Quiniou N, Dourmad J-Y, van Milgen J (2012) Prise en compte de la variabilité individuelle dans l’estimation des besoins en acides aminés et dans la modélisation de la réponse des porcs en croissance aux apports alimentaires. Productions Animales 25, 17–28.

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 de la Recherche Porcine 43, 229–230.

Gonyou HW, Chapple RP, Frank GR (1992) Productivity, time budgets and social aspects of eating in pigs penned in groups of 5 or individually. Applied Animal Behaviour Science 34, 291–301.
Productivity, time budgets and social aspects of eating in pigs penned in groups of 5 or individually.Crossref | GoogleScholarGoogle Scholar |

Hauschild L, Pomar C, Lovatto PA (2010) Systematic comparison of the empirical and factorial methods used to estimate the nutrient requirements of growing pigs. Animal 4, 714–723.
Systematic comparison of the empirical and factorial methods used to estimate the nutrient requirements of growing pigs.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXksVehtLk%3D&md5=3f88f562a30db7aa8ae42c3beeeedcb8CAS | 22444124PubMed |

Henry Y (1993) Affinement du concept de la protéine idéale pour le porc en croissance. Productions Animales 6, 199–212.

Kristensen AR, Nielsen L, Nielsen MS (2012) Optimal slaughter pig marketing with emphasis on information from on-line live weight assessment. Livestock Science 145, 95–108.
Optimal slaughter pig marketing with emphasis on information from on-line live weight assessment.Crossref | GoogleScholarGoogle Scholar |

Li N, Precke PV, Foster KA, Schinckel AP (2003) Analysis of economically optimal nutrition and marketing strategies for Paylean usage in hog production. Journal of Agricultural and Resource Economics 28, 272–286.

Mohn S, Gillis AM, Moughan PJ, de Lange CFM (2000) Influence of dietary lysine and energy intakes on body protein deposition and lysine utilization in the growing pigs. Journal of Animal Science 78, 1510–1519.

Morel PCH, Sirisatien D, Wood GR (2012) Effect of pig type, costs and prices, and dietary restraints on dietary nutrient specification for maximum profitability in grower-finisher pig herds: a theoretical approach. Livestock Science 148, 255–267.
Effect of pig type, costs and prices, and dietary restraints on dietary nutrient specification for maximum profitability in grower-finisher pig herds: a theoretical approach.Crossref | GoogleScholarGoogle Scholar |

Niemi JK, Sevón-Aimonen M-L, Pietola K, Stalder KJ (2010) The value of precision feeding technologies for grow–finish swine. Livestock Science 129, 13–23.
The value of precision feeding technologies for grow–finish swine.Crossref | GoogleScholarGoogle Scholar |

Payne H, Mullan B, Trezona M, Frey B (1999) A review – variation in pig production and performance. In ‘Proceedings of the 7th biennial conference of the Australasian Pig Science Association (APSA). Manipulating pig production VII’. pp. 13–26. (APSA: Adelaide)

Sauvant D, Perez JM, Tran G (Eds) (2004) ‘INRA-AFZ. Tables of composition and nutritive value of feed materials. Pigs, poultry, cattle, sheep, goats, rabbits, horses, fish.’ (Wageningen Academic Publishers: Wageningen, The Netherlands)

Schinckel AP, Li N, Preckel PV, Einstein ME, Miller D (2003) Development of a stochastic pig compositional growth model. The Professional Animal Scientist 19, 225–260.

Statistical Analysis Systems Institute (2012) ‘SAS/STAT users guide, version 9.4.’ (SAS Institute: Cary, NC)

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.
A new mathematical model for combining growth and energy intake in animals: the case of the growing pig.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD1MnptVWktw%3D%3D&md5=198a58119969f899f695ffea73db9fbbCAS | 19665033PubMed |

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.
InraPorc: a model and decision support tool for the nutrition of growing pigs.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXms1WjsLk%3D&md5=453649f2e8020269351ad476c6b5b9f4CAS |

Vautier B, Quiniou N, van Milgen J, Brossard L (2013) Accounting for variability among individual pigs in deterministic growth models. Animal 7, 1265–1273.
Accounting for variability among individual pigs in deterministic growth models.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhtVOktrjM&md5=2b54683d3cbac9ff4efe44ea0e753408CAS | 23552345PubMed |

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.