Dietary influences on nutrient partitioning and anatomical body composition of growing pigs; modelling and experimental approaches

Prediction of pig performance from data on nutrient intake and animal properties makes it easier to obtain a better productivity. It provides tools to arrive at desired outputs, or to calculate required inputs. Thus it enables production to be flexible, safe and less erratic. It is to be expected that the results will give a more profitable pig production. In practice, different types of models are used, mostly by feed producers, but also in farm management programmes. Each of these existing models was designed to meet a certain objective. The classification of different types of models, and the benefits of using them, are presented in the literature overview of the thesis. After a general overview of modelling, a critical evaluation was provided on existing models. It was concluded from the literature, that a comprehensive model, which predicts the chemical composition in different parts of the body, like in lean or in the meat, does not exist. It was also concluded that mechanistic approach should be used to modelling growth. The conceptual basis of a mechanistic model was developed in accordance with basic properties of protein and lipid metabolism. Since nutrients are almost exclusively absorbed in the hydrolyzed form, simulation of use of nutrients for growth should, at least to some extent, make use of biochemical pathways. Therefore, a biological approach to simulation of anatomical body composition is pretended as it follows nutrients from ingestion through intermediary metabolism to deposition as body fat and protein, preferably in distinct tissues or tissue groups. Prediction of anatomical body composition therefore has to be based on deposition of the chemical entities.Therefore the scope of the present thesis was 1) to develop a mechanistic-dynamic model for growing and fattening pigs which predicts anatomical and chemical body composition at slaughter; 2) to determine which model parameters are sensitive to changes in the model; 3) to determine the model accuracy by quantitative and qualitative prediction of the model tested with independent data; 4) to complete an experiment to define fat production potential of different energy sources at low and high feeding levels, and 5) to study the effect of different energy sources at two feeding levels on the distribution of fat deposition during the fattening period.The thesis presents both the description and the evaluation of the growth model. It was concluded that the developed model predicts growth rate as well as chemical and anatomical body compositions of gilts in the 20-105 kg live weight range, from nutrient intake. The model represents partitioning of nutrients from feed intake through intermediary metabolism to synthesis of body protein and body fat. State variables of the model are lysine, acetyl-CoA equivalents, glucose, VFA, and fatty acids as metabolite pools, as well as protein in muscle, hide, bone and viscera and body fat as body constituent pools. It is assumed that fluxes of metabolites follow saturation kinetics depending on metabolite concentrations. Anatomical body composition is predicted from chemical body composition and accretion. Partitioning of protein, fat, water and ash into muscle, organs, hide and bone fractions are described by allometric equations, driven by rates of muscle protein and body fat deposition. Two experiments were used in the model calibration process, one with 95 growing pigs (20-45 kg) fed different ileal digestible lysine intakes at two feeding levels, and another with 100 growing and fattening pigs (20-105 kg), which received different energy intakes. Differential equations were solved numerically for a given set of initial conditions and parameter values. The integration interval used was 0.01 day, with the fourth-order fixed-step-length Runge-Kutta algorithm. The muscle protein and body fat deposition rates were considered in different weight ranges and for the whole fattening period. Results presented were not sensitive to small changes in initial conditions, or to smaller integration step sizes.In the model evaluation the predicted response of the pigs to changes in model parameters, and to changes in nutrient intakes, are shown. As a result of the sensitivity analysis, the model was responsive to changes in a number of the model parameters examined. Changes in maintenance energy requirements, and the fractional degradation rate of muscle protein, have the largest impact on tissue deposition rates. The model is highly sensitive to changes in the maximal velocity and steepness parameter of lysine utilisation for muscle protein synthesis. Those parameters which directly affect the size of the lysine pool generally have a considerable influence on model predictions. Furthermore, it should be noted that results of this sensitivity analysis depend on nutrient intakes of the reference simulation. The model was relatively insensitive to changes of parameters regarding energy metabolism. It was concluded that the probable reason was that protein and/or lysine was more limiting within the simulated conditions. The model was further tested by independent published data. In general, the model satisfactorily predicted qualitative pig responses to a wide range of variations in nutrient supply. The predicted chemical and anatomical body composition, and also the distribution of protein and fat, were satisfactory in model testing. In most cases, errors in the predicted parameters attributed to the deviation of the regression slope were minor. It was assumed that the major factors contributing to the relatively large bias, observed for most predicted growth characteristics, was variation in pig performance among genotypes, or differences in environmental conditions. Based on the comparison of model simulations with independent data sets, it was recommended to improve the model regarding prediction of protein and fat deposition rates from nutrient intake of different energy sources.It was found that literature data on the effect of different energy sources on fat deposition was limited. In non-protein energy fraction of the diet, dietary lipids, starch and rapidly fermentable non-starch polysaccharides (NSP) are major energy sources. Lipids are absorbed as long-chain fatty acids and starch as glucose. Dietary NSP is fermented and the short-chain fatty acids produced enter intermediary metabolism as an energy source. Equal intakes of energy from glucose, long-chain fatty acids and short-chains fatty acids might result in different fat deposition rates, and quite likely, result in different distributions of body fat over the tissues. There is, however, little quantitative data available on effects of energy source on partitioning of body lipids. Therefore a fattening trial was completed to: 1) study the effect of extra energy intake from fermentable NSP, digestible starch and digestible fat used for fat deposition under protein limiting conditions; 2) determine the location of the fat deposition resulting from extra intake fermentable NSP, digestible starch and digestible fat; 3) determine if the extra fat deposition from different energy sources depends on the level of feed intake, and 4) quantify potential interactions between feed intake level and energy source on the location of extra body fat deposition.A total of 58 hybrid individually housed pigs were used in the trial with an initial body weight of 48±4 kg. The experimental treatments were arranged in a 3x2 factorial design, with three energy sources (i.e. fermentable NSP, digestible starch and digestible fat, all added to a control diet) at each of two energy levels. Within each energy level, daily nutrient intakes were the same with regard to digestible protein, ileal digestible lysine and other amino acids, vitamins and minerals. Treatments had an isocaloric proportion of daily nutrient intake derived from each energy source (0.2 MJ DE/kg 0.75 ), in addition to the nutrients from control diet. It was equal with 11 g/kg 0.75 highly fermentable NSP, 11 g/kg 0.75 starch or 5 g/kg 0.75 digestible fat daily. The DE intakes were 2.0 and 3.0 maintenance requirement in control groups. The additional energy from different sources increased DE intake up to 2.4 and 3.4 times maintenance requirement at low and high feeding levels, respectively. To obtain initial values, ten pigs were slaughtered at 48±4 kg and the treatment pigs at 106±3 kg body weight. Each body was dissected into four fractions being: 1) lean, 2) organs, 3) hide and subcutaneous fat, and 4) offal. Chemical body composition was determined in each body fraction. The differences between fat deposition of body parts in the control group, and the other treatments, resulted in the additional energy derived from each energy source. As a conclusion from the study, under protein limiting conditions, extra energy intake from fermentable NSP, digestible starch and digestible fat resulted in similar fat deposition. Preferential deposition of extra energy intake in various fat depots did not depend on the energy source. The extra fat deposition from fermentable NSP, digestible starch and digestible fat deposited as body fat was similar at both the low and high levels of feed intake.In the General Discussion, some consequences of the mechanistic approach were discussed and then substantial attention was devoted to the practical aspects of the model. The later part of the General Discussion focuses on representation of different energy sources as an aspect of the model. The energetic efficiency of the different dietary energy sources is discussed, based on data from Chapter 6. The growth model is further evaluated by results of the fattening study. Moreover, data from the fattening study are analysed regarding the distribution of fat deposition. Consequences of the fattening study on the model is discussed regarding the effect of energy sources on energetic efficiency, and on location of fat deposition in the pig. Finally, a new application of the present model is introduced in addition to development of feeding strategies and identifying research priorities.