Characterization of growth parameters needed as inputs for pig growth models.

Data from 27 feeding trials conducted on growing pigs from different research institutes across India were subjected to mixed model regression analysis to derive requirements of digestible energy (DE), crude protein (CP) and essential amino acids for maintenance and body weight gains. The ranges of maintenance requirements were determined to be: DE 516 to 702 kJ/kg M 0.75 , CP 6.98 to 11.62, lysine 0.431 to 0.664, methionine 0.265 to 0.458, methionine þ cystine 0.327 to 0.466, cystine 0.055 to 0.184, threonine 0.205 to 0.511, arginine 0.377 to 1.21, isoleucine 0.241 to 0.775, leucine 0.604 to 1.54, phenylalanine þ tyrosine 0.496 to 1.33, tryptophan 0.078 to 0.213, and valine 0.330 to 0.892 g/kg M 0.75 , respectively for different body weight ranges. The corresponding requirements for 1 g gain in body weight were: DE 28.6 to 38.6 kJ, CP 0.27 to 0.44 g, lysine 0.0071 to 0.0126 g, methionine 0.0047 to 0.0133 g, methionine þ cystine 0.0151 to 0.0261 g, cystine 0.0043 to 0.0094 g, threonine 0.0052 to 0.0165 g, arginine 0.0045 to 0.0301 g, isoleucine 0.0023 to 0.0198 g, leucine 0.0150 to 0.0447 g, phenylalanine þ tyrosine 0.0091 to 0.0382 g, tryptophan 0.0005 to 0.0044 g, and valine 0.0061 to 0.0222 g. Regression equations had high R 2 values (ranging from 0.50 to 0.99 for different estimates), low coefficients of variation, low variance of error estimates and the coefficients were highly significant (P , 0.001). Regressed values were used to develop feeding standards. As the new standards derived in the present study are based on a thorough analysis of a larger database than previous Indian standards, the new feeding standard seems to be more appropriate for India and other tropical countries.

Section: Discussionmentioning

confidence: 99%

Determination of nutrient requirements for growth and maintenance of growing pigs under tropical condition

Paul

Mandal

Chatterjee

et al. 2007

“…Different methods have been proposed to achieve this, including serial slaughter at different BW (Ferguson and Gous, 1993). Repeated measurements can also be realised during growth on individual pigs (Schinckel and de Lange, 1996), allowing estimation of the betweenanimal variation. Alternatively, techniques of 'inverted modelling' or 'reverse simulation' have been developed to obtain model parameters DoeschlWilson et al, 2006 and.…”

Section: Discussionmentioning

confidence: 99%

“…As the animal is described by a limited number of parameters in most models, inclusion of between-animal variation in modelling studies requires the determination of individual values of these parameters. Consequently, methods based on the analysis of serial live measurements have been developed to obtain these values (Schinckel and de Lange, 1996;Knap et al, 2003;Schinckel et al, 2003;Doeschl-Wilson et al, 2006). The objective of this study is to develop an analysis method to summarise individual growth and feed intake profiles by five descriptive parameters of a growth model, and to study the relationships between these parameters.…”

Section: Introductionmentioning

confidence: 99%

Modelling the variation in performance of a population of growing pig as affected by lysine supply and feeding strategy

Brossard

Dourmad

Rivest

et al. 2009

Considerable progress has been made in the nutritional modelling of growth. Most models typically predict (or analyse) the response of a single animal. However, the response to nutrients of a single, representative animal is likely to be different from the response of the herd. To address the variation in response between animals, a stochastic approach towards nutritional modelling is required. In the present study, an analysis method is presented to describe growth and feed intake curves of individual pigs within a population of 192 pigs. This method was developed to allow end-users of InraPorc (a nutritional model predicting and analysing growth in pigs) to easily characterise their animals based on observed data and then use the model to test different scenarios. First, growth and intake data were curve-fitted to characterise individual pigs in terms of BW (Gompertz function of age) and feed intake (power function of BW) by a set of five parameters, having a biological or technico-economical meaning. This information was then used to create a population of virtual pigs in InraPorc, having the same feed intake and growth characteristics as those observed in the population. After determination of the mean lysine (Lys) requirement curve of the population, simulations were carried out for each virtual pig using different feeding strategies (i.e. 1, 2, 3 or 10 diets) and Lys supply (ranging from 70% to 130% of the mean requirement of the population). Because of the phenotypic variation between pigs and the common feeding strategies that were applied to the population, the Lys requirement of each individual pig was not always met. The percentage of pigs for which the Lys requirement was met increased concomitantly with increasing Lys supply, but decreased with increasing number of diets used. Simulated daily gain increased and feed conversion ratio decreased with increasing Lys supply ( P , 0.001) according to a curvilinear-plateau relationship. Simulated performance was close to maximum when the Lys supply was 110% of the mean population requirement and did not depend on the number of diets used. At this level of Lys supply, the coefficient of variation of simulated daily gain was minimal and close to 10%, which appears to be a phenotypic characteristic of this population. At lower Lys supplies, simulated performance decreased and variability of daily gain increased with an increasing number of diets ( P , 0.001). Knowledge of nutrient requirements becomes more critical when a greater number of diets are used. This study shows the limitations of using a deterministic model to estimate the nutrient requirements of a population of pigs. A stochastic approach can be used provided that relationships between the most relevant model parameters are known.

“…BW is estimated in the model by a quadratic function of time (t) fitted to data from the four weightings of each pig. The first derivative of this function is used to estimate daily weight gain (qBW/qt), while PD is estimated according to Schinckel and de Lange (1996) as follows,…”

Section: Pig Growth Modelingmentioning

confidence: 99%

Systematic comparison of the empirical and factorial methods used to estimate the nutrient requirements of growing pigs

Hauschild

Pomar

Lovatto

2010

Empirical and factorial methods are currently used to estimate nutrient requirements for domestic animals. The purpose of this study was to estimate the nutrient requirements of a given pig population using the empirical and factorial methods; to establish the relationship between the requirements estimated with these two methods; and to study the limitations of the methods when used to determine the level of a nutrient needed to optimize individual and population responses of growing pigs. A systematic analysis was carried out on optimal lysine-to-net-energy (Lys : NE) ratios estimated by the empirical and factorial methods using a modified InraPorc R growth model. Sixty-eight pigs were individually simulated based on detailed experimental data. In the empirical method, population responses were estimated by feeding pigs with 11 diets of different Lys : NE ratios. Average daily gain and feed conversion ratio were the chosen performance criteria. These variables were combined with economic information to estimate the economic responses. In the factorial method, the Lys : NE ratio for each animal was estimated by model inversion. Optimal Lys : NE ratios estimated for growing pigs (25 to 105 kg) differed between the empirical and the factorial method. When the average pig is taken to represent a population, the factorial method does not permit estimation of the Lys : NE ratio that maximizes the response of heterogeneous populations in a given time or weight interval. Although optimal population responses are obtained by the empirical method, the estimated requirements are fixed and cannot be used for other growth periods or populations. This study demonstrates that the two methods commonly used to estimate nutrient requirements provide different nutrient recommendations and have important limitations that should be considered when the goal is to optimize the response of individuals or pig populations.