Modelling of nutrient partitioning in growing pigs to predict their anatomical body composition. 1. Model description

  • Halas V
  • Dijkstra J
  • Babinszky L
  • et al.
27Citations
Citations of this article
19Readers
Mendeley users who have this article in their library.

Abstract

A dynamic mechanistic model was developed for growing and fattening pigs. The aim of the model was to predict growth rate and the chemical and anatomical body compositions from the digestible nutrient intake of gilts (20–105 kg live weight). The model represents the partitioning of digestible nutrients from intake through intermediary metabolism to body protein and body fat. State variables of the model were lysine, acetyl-CoA equivalents, glucose, volatile fatty acids and fatty acids as metabolite pools, and protein in muscle, hide–backfat, bone and viscera and body fat as body constituent pools. It was assumed that fluxes of metabolites follow saturation kinetics depending on metabolite concentrations. In the model, protein deposition rate depended on the availability of lysine and of acetyl-CoA. The anatomical body composition in terms of muscle, organs, hide–backfat and bone was predicted from the chemical body composition and accretion using allometric relationships. Partitioning of protein, fat, water and ash in muscle, organs, hide–backfat and bone fractions were driven by the rates of muscle protein and body fat deposition. Model parameters were adjusted to obtain a good fit of the experimental data from literature. Differential equations were solved numerically for a given set of initial conditions and parameter values. In the present paper, the model is presented, including its parameterisation. The evaluation of the model is described in a companion paper.

Cite

CITATION STYLE

APA

Halas, V., Dijkstra, J., Babinszky, L., Verstegen, M. W. A., & Gerrits, W. J. J. (2004). Modelling of nutrient partitioning in growing pigs to predict their anatomical body composition. 1. Model description. British Journal of Nutrition, 92(4), 707–723. https://doi.org/10.1079/bjn20041237

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free