Literature DB >> 15788114

Partitioning of limiting protein and energy in the growing pig: description of the problem, possible rules and their qualitative evaluation.

Fredrik B Sandberg1, Gerry C Emmans, Ilias Kyriazakis.   

Abstract

A core part of any animal growth model is how it predicts the partitioning of dietary protein and energy to protein and lipid retention for different genotypes at different degrees of maturity. Rules of partitioning need to be combined with protein and energy systems to make predictions. The animal needs describing in relation to its genotype, live weight and, possibly, body composition. Some existing partitioning rules will apply over rather narrow ranges of food composition, animal and environment. Ideally, a rule would apply over the whole of the possible experimental space (scope). The live weight range over which it will apply should at least extend beyond the 'slaughter weight range', and ideally would include the period from the start of feeding through to maturity. Solutions proposed in the literature to the partitioning problem are described in detail and criticised in relation to their scope, generality and economy of parameters. They all raise the issue, at least implicitly, of the factors that affect the net marginal efficiency of using absorbed dietary protein for protein retention. This is identified as the crucial problem to solve. A problem identified as important is whether the effects of animal and food composition variables are independent of each other or not. Of the rules in the literature, several could be rejected on qualitative grounds. Those rules that survived were taken forward for further critical and quantitative analysis in the companion paper.

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Year:  2005        PMID: 15788114     DOI: 10.1079/bjn20041321

Source DB:  PubMed          Journal:  Br J Nutr        ISSN: 0007-1145            Impact factor:   3.718


  3 in total

1.  Predicting the growth performance of growing-finishing pigs based on net energy and digestible lysine intake using multiple regression and artificial neural networks models.

Authors:  Li Wang; Qile Hu; Lu Wang; Huangwei Shi; Changhua Lai; Shuai Zhang
Journal:  J Anim Sci Biotechnol       Date:  2022-05-13

Review 2.  Early detection of health and welfare compromises through automated detection of behavioural changes in pigs.

Authors:  Stephen G Matthews; Amy L Miller; James Clapp; Thomas Plötz; Ilias Kyriazakis
Journal:  Vet J       Date:  2016-09-28       Impact factor: 2.688

3.  Estimating the continuous-time dynamics of energy and fat metabolism in mice.

Authors:  Juen Guo; Kevin D Hall
Journal:  PLoS Comput Biol       Date:  2009-09-18       Impact factor: 4.475

  3 in total

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