A new approach for fitting growth models in random environment

Abstract

Nonlinear growth models are widely employed in Animal sciences for describing growth of various species ofanimals. Nonlinear estimation procedures are generally employed for estimation of parameters. However, onelimitation of these models is that they are applicable only when the data are available at equidistant epochs. Another limitation is that the fluctuations in the system cannot be satisfactorily explained simply by adding an error term to the deterministic formulation. The purpose of this article is to bring to the notice of Animal scientists the new approach of Stochastic differential equation modelling, which is capable of incorporating both the above aspects. The methodology is discussed by considering Gompertz growth model. Relevant SAS codes for fitting the model are developed. Finally, the methodology is illustrated on secondary monthly pig weight data, collected at thepiggery farm of Indian Veterinary Research Institute, Izatnagar, Bareilly, India.