Comparative study of linear mixed-effects and artificial neural network models for longitudinal unbalanced growth data of Madras Red sheep

Abstract

Sheep are efficient converters of unutilized poor quality grass and crop residues into meat and skin. Growth is a trait of economic importance in sheep as sheep rearing is an important livelihood for a large number of small and marginal farmers in India. Information about growth model parameters is very useful for selection studies. Growth in farm animals has been investigated for many years [1, 2]. Madras Red sheep, a native breed of Tamilnadu state of India and distributed in the northern parts of the state is known for its valuable meat and skin quality [3]. The growth performance of Madras Red sheep under farmer's flock was studied previously [4, 5]. In animal growth studies, body weight measurements, which are good indicators of growth rate, is often measured on the same animal at various ages (time points) such as weekly/monthly resulting in longitudinal growth data. Such data, collected on a group of animals, has many advantages as it can provide vital information about individual changes. That is, by collecting data over many time points, it can separate changes over time within individual animals from differences between animals yielding valuable information on the animals. The main goal here is to characterize the way the outcome changes over time, and to identify the predictors of that change. Recent developments in longitudinal data analysis have been discussed in detail in previous studies [6, 7]. Longitudinal growth data pose challenges for statistical analysis as the responses are correlated (not independent) and also the responses that are closer in time are more correlated than responses that are farther apart. In addition, variance of repeated measures often change and increase steadily with time (heteroscedasticity). So, these correlation and variation patterns combine to produce complicated covariance structure of repeated measurements which needs to be modeled suitably for drawing correct inferences. Under these circumstances, the standard ANOVA, MANOVA and Veterinary World, EISSN: 2231-0916 Available at www.veterinaryworld.org/Vol.7/Feb-2014/2.pdf