Conceptual paper for modelling protein and lipid accretion in different body parts of growing and fattening pigs

Halas, Veronika; Babinszky, L.; Verstegen, M.W.A.

doi:10.1080/0003942031000107307

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…2. In empirical models, only protein and energy inputs are used to simulate growth of pigs (Halas et al 2003). The present model uses nutrients derived from the digestion of protein, fat, starch and sugar and from fermentation of cell wall components.…”

Section: Representation Of the Metabolic Part Of The Modelmentioning

confidence: 99%

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

et al. 2004

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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. Modelling: Anatomical body composition: Chemical body composition: PigSince the introduction of pig growth models, applicable in a scientific and a practical environment in the 1970s and 1980s (for example, see Whittemore & Fawcett, 1976;Moughan et al. 1987;Black et al. 1988), interest in prediction of pig growth has increased over the years. New models have been introduced, each serving their own objective: some models have focused on nutrient digestion processes (Bastianelli et al. 1996), on protein digestion in the small intestine (Rivest et al. 2000) or on estimating amino acid requirements (Moughan, 1989). Others have aimed to model growth rate and its composition in terms of protein and lipid (Burlacu et al. 1989;Pomar et al. 1991;Danfaer, 2000;Birkett & de Lange, 2001b), or especially fatty acid composition of the body fat (Lizardo et al. 2002), or improving understanding of different processes, such as protein turnover and ion pumping (Gill et al. 1989b), or the process of growth (Lovatto & Sauvant, 2003). In addition, pig growth modelling efforts have been reviewed and various approaches have been discussed extensively (Black, 1995;Gerrits & Dijkstra, 2000;Halas & Babinszky, 2000;Birkett & de Lange, 2001a). Most pig growth simulation models until the 1990s considered protein and energy as separate entities (de Lange, 1995).As acknowledged in more recently developed models, this approach ignored the effects of differences in the composition of the dietary energy (Danfaer, 2000;Birkett & de Lange, 2001a). In addition to models predicting che...

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Section: Representation Of the Metabolic Part Of The Modelmentioning

confidence: 99%

“…The turnover of the muscle protein can be manipulated by nutrition. Both synthesis and degradation increase with increasing nutrient supply as reviewed by Halas et al (2003). A high protein turnover due to the excess nutrient supply results in a high protein deposition rate.…”

Section: Protein Metabolismmentioning

confidence: 99%

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

et al. 2004

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The Effect of Decreased Protein Levels in Sow Diets on Nitrogen Content of Faeces and Physiological Parameters of Blood

Korniewicz

Grela

Matras

et al. 2012

Annals of Animal Science

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The Effect of Decreased Protein Levels in Sow Diets on Nitrogen Content of Faeces and Physiological Parameters of BloodThe research material consisted of 42 sows divided into 3 feeding groups, 14 individuals in each group, which received feed mixtures with different protein levels. Protein and amino acid levels in the control group were in accordance with recommendations contained in Polish standards. The level of protein in experimental groups was decreased by 10 and 20%, respectively, and the levels of lysine, methionine, threonine and tryptophan were adjusted to those of the control group. During the gestation and lactation periods all sows were fed individually. The content of dry matter, nitrogen, fibre, fat and ash was determined in samples of faeces collected at 60 and 105 days of pregnancy, and the total nitrogen content was assessed in urine. Blood from sows was collected on day 14 of lactation, and indices of protein, fat transformation and mineral components content were determined in blood serum. The results of the analysis of faeces and urine prove that pregnant sows from the experimental groups excreted 7% and 10% less nitrogen in faeces, and 18% and 23% less urine in faeces. The reduced protein level in feed mixtures did not significantly influence the content of dry matter, fibre and ash in faeces. A significant decrease in the content of total protein, globulins, urea and an increase in cholesterol level was observed in the blood serum of sows from both experimental groups. No influence of the reduced protein level in feed on the content of mineral compounds in blood serum or haematological parameters of the sows' blood was noted.

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Deciphering Seasonal Patterns in Animal Feeding: A Mechanistic Approach to Analyzing the Restricted Growth of Iberian Pigs

Sánchez-Esquiliche,

Ferreira,

Nieto

et al. 2024

Animals

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The growth of animals is influenced by various factors including genetics, nutrition, and environmental conditions. This study focuses on understanding the traditional growth phases of Iberian pigs raised in semi-free-range environments prior to the finishing period within the Mediterranean forest. Using a comprehensive dataset of 5329 pigs, we evaluated the abilities of several mathematical models to predict growth patterns during the early growing and pre-finishing phases. The Gompertz model demonstrated the best fit for the age–weight relationship, indicating its effectiveness in characterizing growth dynamics in these animals. A novel compartmental mathematical model was developed, which integrates age-based estimations for certain body components (protein, water, and ash) with linear body weight predictions for fat. Our findings indicate that this mechanistic model serves as an effective tool for understanding and optimizing management strategies and enhancing growth predictions in traditional pig breeds.

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Conceptual paper for modelling protein and lipid accretion in different body parts of growing and fattening pigs

Cited by 3 publications

References 26 publications

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

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

The Effect of Decreased Protein Levels in Sow Diets on Nitrogen Content of Faeces and Physiological Parameters of Blood

Deciphering Seasonal Patterns in Animal Feeding: A Mechanistic Approach to Analyzing the Restricted Growth of Iberian Pigs

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