A multilevel nonlinear mixed-effects approach to model growth in pigs1

Abstract

Growth functions have been used to predict market weight of pigs and maximize return over feed costs. This study was undertaken to compare 4 growth functions and methods of analyzing data, particularly one that considers nonlinear repeated measures. Data were collected from an experiment with 40 pigs maintained from birth to maturity and their BW measured weekly or every 2 wk up to 1,007 d. Gompertz, logistic, Bridges, and Lopez functions were fitted to the data and compared using information criteria. For each function, a multilevel nonlinear mixed effects model was employed because it allowed for estimation of all growth profiles simultaneously, and different sources of variation (i.e., sex, pig, and litter effects) were incorporated directly into the parameters. Furthermore, variance in-homogeneity and within-pig correlation were introduced to the functions. Inclusion of a variance of power function and a continuous autoregressive process of first order rendered a substantially improved fit to data for all 4 growth functions. The Lopez function provided the best fit to the data set and was used for characterizing mean growth curves for the 3 sexes (barrows, boars, and gilts). It was estimated that the maximum growth rate occurs at 117, 134, and 96 kg of BW for barrows, boars, and gilts, respectively. Hence, the gilts reached their maximum growth rate at an earlier stage in life compared with boars. Mature size of pigs varied systematically with sex and was estimated to be 466, 537, and 382 kg of BW for the barrows, boars, and gilts, respectively. These estimates are significantly affected by the duration of the experimental period, and it is recommended that future studies looking at estimating the mature size in animals are conducted long enough so that the BW visually stabilizes. Furthermore, studies should consider adding continuous autoregressive process when analyzing nonlinear mixed models with repeated measures.

A review of mathematical functions for the analysis of growth in poultry

“…Craig and Schinckel (2001), Strathe et al (2010b) 1 Thornley and France (2007) In the von Bertalanffy equation, the assumptions are that nutrients are non-limiting, and the growth process is the difference between anabolism and catabolism. It has a flexible inflexion point that occurs at W = (1 − n) 1/n W f .…”

Morphometric Growth Characteristics and Body Composition of Fish and Amphibians

“…Growth curves which involve a series of measurements of some interest over time (body weight, body composition, diameter, and longitude) [38] are usually adjusted under controlled conditions, and are the first steps in predicting nutrient requirements for animals of different genotypes [26,39]. Moreover, they evaluate various parameters such as growth rate, maturity rate at different ages and weight at slaughter time and thus allow and help in establishing zoo technical breeding programs [15].…”

Bayesian analysis of energy balance data from growing cattle using parametric and non-parametric modelling

“…In particular, maintenance requirements and energetic partial efficiencies have been extensively examined (e.g. Lofgreen and Garrett 1968;Moe et al 1972;France et al 1989;Baldwin 1995;Strathe et al 2010) and several biological principles governing energy utilisation by farm animals have been established (Baldwin 1995). In this context, a statistically valid framework, which combines the prior knowledge on energy utilisation with new data is attractive.…”

“…Non-linear functions have been extensively utilised for modelling energy utilisation in food production animals (e.g. France et al 1989;Kebreab et al 2003;Strathe et al 2010). These models are usually selected through the underlying biological mechanisms and model parameters often have a biological interpretation.…”