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Systematic comparison of the empirical and factorial methods used to estimate the nutrient requirements of growing pigs

Published online by Cambridge University Press:  18 December 2009

L. Hauschild ,
C. Pomar  and
P. A. Lovatto
L. Hauschild
Affiliation:
Dairy and Swine Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, Québec, Canada, J1M 1Z3 Departamento de Zootecnia, Universidade Federal de Santa Maria, Santa Maria, RS 97105-900, Brazil
C. Pomar*
Affiliation:
Dairy and Swine Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, Québec, Canada, J1M 1Z3
P. A. Lovatto
Affiliation:
Departamento de Zootecnia, Universidade Federal de Santa Maria, Santa Maria, RS 97105-900, Brazil
*
†E-mail: candido.pomar@agr.gc.ca
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Abstract

Empirical and factorial methods are currently used to estimate nutrient requirements for domestic animals. The purpose of this study was to estimate the nutrient requirements of a given pig population using the empirical and factorial methods; to establish the relationship between the requirements estimated with these two methods; and to study the limitations of the methods when used to determine the level of a nutrient needed to optimize individual and population responses of growing pigs. A systematic analysis was carried out on optimal lysine-to-net-energy (Lys : NE) ratios estimated by the empirical and factorial methods using a modified InraPorc® growth model. Sixty-eight pigs were individually simulated based on detailed experimental data. In the empirical method, population responses were estimated by feeding pigs with 11 diets of different Lys : NE ratios. Average daily gain and feed conversion ratio were the chosen performance criteria. These variables were combined with economic information to estimate the economic responses. In the factorial method, the Lys : NE ratio for each animal was estimated by model inversion. Optimal Lys : NE ratios estimated for growing pigs (25 to 105 kg) differed between the empirical and the factorial method. When the average pig is taken to represent a population, the factorial method does not permit estimation of the Lys : NE ratio that maximizes the response of heterogeneous populations in a given time or weight interval. Although optimal population responses are obtained by the empirical method, the estimated requirements are fixed and cannot be used for other growth periods or populations. This study demonstrates that the two methods commonly used to estimate nutrient requirements provide different nutrient recommendations and have important limitations that should be considered when the goal is to optimize the response of individuals or pig populations.

Keywords

animal variability, lysine, nutrient requirements, pigs, simulation models
Type
Full Paper
Information
animal , Volume 4 , Issue 5 , May 2010 , pp. 714 - 723
Copyright
Copyright © The Animal Consortium 2009

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References

Baker, DH 1986. Problems and pitfalls in animal experiments designed to establish dietary requirements for essential nutrients. Journal of Nutrition 116, 2339–2349.CrossRefGoogle ScholarPubMed
Baker, DH, Batal, AB, Parr, TM, Augspurger, NR, Parsons, CM 2002. Ideal ratio (relative to lysine) of tryptophan, threonine, isoleucine, and valine for chicks during the second and third weeks posthatch. Poultry Science 81, 485–494.CrossRefGoogle ScholarPubMed
Benchaar, C, Rivest, J, Pomar, C, Chiquette, J 1998. Prediction of methane production from dairy cows using existing mechanistic models and regression equations. Journal of Animal Science 76, 617–627.CrossRefGoogle ScholarPubMed
Bertolo, RF, Moehn, S, Pencharz, PB, Ball, RO 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
Bikker, P, Verstegen, MW, Campbell, RG, Kemp, B 1994. Digestible lysine requirement of gilts with high genetic potential for lean gain, in relation to the level of energy intake. Journal of Animal Science 72, 1744–1753.CrossRefGoogle 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, 1110–1114.CrossRefGoogle ScholarPubMed
Cline, TR, Cromwell, GL, Crenshaw, TD, Ewan, RC, Hamilton, CR, Lewis, AJ, Mahan, DC, Southern, LL 2000. Further assessment of the dietary lysine requirement of finishing gilts. Journal of Animal Science 78, 987–992.CrossRefGoogle ScholarPubMed
Curnow, RN 1973. A smooth population response curve based on an abrupt threshold and plateau model for individuals. Biometrics 29, 1–10.Google Scholar
de Lange, CFM, Schreurs, HWE 1995. Principles of model application. In Modelling the growth in the pigs (ed. PJ Moughan, MWA Verstegen and MI Visser-Reyneveld), pp. 187–208. European Association for Animal Production Publication, Wageningen, The Netherlands.Google Scholar
Fédération des Producteurs de Porcs du Québec 2008. Informations sur le prix du porc. Retrieved November 22, 2008, from http://www.fppq.upa.qc.ca/macros/prix.mac/anneeGoogle 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
Jean Dit Bailleul, P, Bernier, JF, van Milgen, J, Sauvant, D, Pomar, C 2000. The utilization of prediction models to optimize farm animal production systems: the case of a growing pig model. In Modelling nutrient utilization in farm animals (ed. JP McNamara, J France and D Beever), pp. 379–392. CABI International, Wallingford, UK.CrossRefGoogle Scholar
Knap, PW 2000. Stochastic simulation of growth in pigs: relations between body composition and maintenance requirements as mediated through protein turn-over and thermoregulation. Animal Science 71, 11–30.CrossRefGoogle Scholar
Lassiter, JW, Edwards, HM 1982. Animal nutrition. Reston Publishing Company, Reston, VA, USA.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
Letourneau Montminy, MP, Boucher, C, Pomar, C, Dubeau, F, Dussault, J-P 2005. Impact de la méthode de formulation et du nombre de phases d’alimentation sur le coût d’alimentation et les rejets d’azote et de phosphore chez le porc charcutier. Journées de la Recherche Porcine 37, 22–35.Google Scholar
Main, RG, Dritz, SS, Tokach, MD, Goodband, RD, Nelseen, JL 2008. Determining an optimum lysine:calorie ratio for barrows and gilts in a commercial finishing facility. Journal of Animal Science 86, 2190–2207.CrossRefGoogle Scholar
Nam, DS, Aherne, FX 1994. The effects of lysine:energy ratio on the performance of weanling pigs. Journal of Animal Science 72, 1247–1256.CrossRefGoogle Scholar
National Research Council 1998. Nutrient requirements of swine, 10th revised edition. National Academy Press, Washington, DC, USA.Google Scholar
Noblet, J, Fortune, H, Shi, XS, Dubois, S 1994. Prediction of net energy value of feeds for growing pigs. Journal of Animal Science 72, 344–354.CrossRefGoogle ScholarPubMed
O’Connell, MK, Lynch, PB, O’Doherty, JV 2005. Determination of the optimum dietary lysine concentration for growing pigs housed in pairs and in groups. Animal Science 81, 249–255.CrossRefGoogle Scholar
Owen, KQ, Knabe, DA, Burgoon, KG, Gregg, EJ 1994. Self-selection of diets and lysine requirements of growing-finishing swine. Journal of Animal Science 72, 554–564.CrossRefGoogle ScholarPubMed
Pack, M, Hoehler, D, Lemme, A 2003. Economic assessment of amino acid response in growing poultry. In Amino acids in animal nutrition (ed. FJP D’Mello), pp. 459–483. CAB International, Wallingford, UK.CrossRefGoogle Scholar
Patience, JF, Engele, K, Beaulieu, AD, Gonyou, HW, Zijlstra, RT 2004. Variation: costs and consequences. In Advances in pork production (ed. Ball RO), vol. 15, pp. 257–266. University of Alberta, Alberta, Canada.Google Scholar
Pomar, C, Dubeau, F, van Milgen, J 2009. Maîtrise des rejets d’azote et de phosphore à l’aide d’une formulation multicritère et d’un ajustement progressif des apports en nutriments aux besoins des animaux. INRA Productions Animales 22, 49–54.CrossRefGoogle 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
Pomar, C, Pomar, J, Babot, D, Dubeau, F 2007. Effet d’une alimentation multiphase quotidienne sur les performances zootechniques, la composition corporelle et les rejets d’azote et de phosphore du porc charcutier. Journées de la Recherche Porcine 39, 23–30.Google Scholar
Pomar, C, Rivest, J 1996. The effect of body position and data analysis on the estimation of body composition of pigs by dual energy X-ray absorptiometry (DEXA). Conference at the 46th Annual conference of the Canadian Society of Animal Science, Lethbridge, Alberta, 26pp.Google Scholar
Remmenga, MD, Milliken, GA, Kratzer, D, Schwenke, JR, Rolka, HR 1997. Estimating the maximum effective dose in a quantitative dose-response experiment. Journal of Animal Science 75, 2174–2183.CrossRefGoogle 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
Smith, JW, Tokach, MD, O’Quinn, PR, Nelssen, JL, Goodband, RD 1999. Effects of dietary energy density and lysine:calorie ratio on growth performance and carcass characteristics of growing-finishing pigs. Journal of Animal Science 77, 3007–3015.CrossRefGoogle Scholar
Statistical Analysis Systems Institute 1999. SAS/STAT Software, version 8. SAS Institute Inc., Cary, NC.Google Scholar
Theil, H 1966. Applied economic forecasting. North-Holland Publishing Company, Amsterdam, NL.Google Scholar
van Milgen, J, Valancogne, A, Dubois, S, Dourmad, J-Y, 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
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 2004. Modeling the effects of stressors on the performance of populations of pigs. Journal of Animal Science 82, 2442–2450.CrossRefGoogle ScholarPubMed
Whittemore, CT, Green, DM, Knap, PW 2001. Technical review of the energy and protein requirements of growing pigs: food intake. Animal Science 73, 3–17.CrossRefGoogle Scholar