Modelling the effects of the infectious environment on pig growth and intake

Abstract

Sub-clinical disease can have large effects on animal production with significant economic consequences. Animal health and welfare are increasingly important criteria in animal production, and the removal of antibiotic growth promoters has added pressure on production systems. No general model has yet been proposed for predicting the growth and performance of animals exposed to pathogens. A robust framework for predicting growth during health and disease may assist in the design of nutritional, environmental and genetic management strategies. 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. Solutions proposed in the literature to the partitioning problem were described in detail and criticised in relation to their scope, generality and economy of parameters (Chapter 1). They all raised the issue, often implicitly, of the factors that affect the net marginal efficiency of using absorbed dietary protein for protein retention. Partitioning rules that withstood qualitative criticisms were then tested against literature data and a general quantitative partitioning rule was concluded for that had two key parameters: the maximum marginal efficiency of protein retention and the energy to protein ratio at which the maximum efficiency is achieved (Chapter 2). A general rule was identified which was able to predict protein retention for both protein and energy limiting diets in healthy animals. In Chapter 3 a general model was developed for predicting effects of sub-clinical pathogen challenges of different doses and virulence on the intake of animals. Pathogen induced anorexia is the major consequence on hosts during the course of infection. The model was for the period from recognition of a pathogen through to acquisition and subsequent expression of immunity. It is crucial to define the pathogen challenge (in terms of dose and virulence) and the degree of resistance of different hosts, when comparing their responses in RFI. There is no general agreement on the consequences of pathogen challenges, other than anorexia, that need to be included in a predictive framework of growth. In Chapter 4 literature data was reviewed for different kinds of pathogen challenges of a range of hosts to identify reductions in growth beyond that caused by anorexia: these were host, dose and time dependent. In only some instances did anorexia fully explain the reductions in growth. Solutions were needed for describing the protein costs of innate and acquired immune responses and repair of damaged tissues. Increased energy requirements depended on immune responses, repair of damage and fever. In Chapter 5 a framework was proposed that predicts the performance of different genotypes (in terms of growth potential and disease resistance) when challenged by different doses of pathogens and given access to different foods. The model predicts amino acid and energy requirements due to growth and immune responses, and a partitioning rule was developed for partitioning scarce resources between growth and immune responses. Predictions can be made on the performance of different animal genotypes when they are given access to different quality foods and exposed to pathogens. The development of the model and its predictions, together with future testing, may contribute significantly towards our understanding of nutrition and genotype interactions during exposure to pathogens.