The quantitative principles of animal growth

Filipe, JAN; Leinonen, I; Kyriazakis, I

doi:10.3920/978-90-8686-854-4

The quantitative principles of animal growth

Lookup NU author(s): Dr Joao Filipe, Dr Ilkka Leinonen, Professor Ilias Kyriazakis

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This is the authors' accepted manuscript of a book chapter that has been published in its final definitive form by Wageningen Academic Publishers, 2018.

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Abstract

The principles and a quantitative theory of growth for monogastric livestock animals aredescribed here, focusing on the period from birth to slaughter. The theory helps to understandkey relationships between the outcomes that may be desired in a production system and theirinfluencing factors, e.g. nutritional, genetic, and environmental, and forms the basis of currentmechanistic models for predicting animal performance. We describe general physical,chemical and biological principles that should underlie any description of animal growth; andaddress growth as a three-faceted problem: 1) growth under ‘normal’ conditions; 2) growthunder ‘limiting’ conditions; and 3) recovery growth when ‘normal’ conditions are restored. Wepropose a theoretical framework that incorporates these situations as facets of a whole thatencompasses growth conditions across modern livestock production systems, and in whichterms such as ‘normal’ and ‘limiting’ acquire a meaning. To make this framework quantitative,we apply a body of theoretical and empirical principles in the derivation of mathematicalmodels of growth. The usefulness of existing theories and mathematical models depends onthe availability of data for testing hypotheses and parameterising models under a range ofconditions. A challenge ahead, therefore, is in obtaining extensive data for further testing andconsolidating modelling prediction of growth under a wide range of conditions, or as animalsrespond to change in limitations. Another challenge lies in addressing practical problems posedby heterogeneity in performance within groups of animals: i.e. how to manage groups towardsoptimal resource use and performance, and how to identify best phenotypical characteristicswithin breeds, strains etc. and predict their performance. Further progress will require muchgreater availability of rich individual data cross-sectioning herds and breeds.