Abstract:GM(1,N) model of the Gray System Theory has been extensively applied to the analyses of systems of agriculture, forestry, ecology and many other life-related systems. The model is mathematically a linear system model with constants coefficients. However the development of both individuals and the population in biological systems is always, to different extent, affected or limited by finite resources and the competition among individuals and populations, which is known to be a primary contributor to the system nonlinearity. Hence the assumption of linearity behind GM (1,N) Model in general is not justified except some special cases. Even though the residual model of Gray Theory can be used to improve the accuracy of the system prediction, it contributes very little to the primary purpose of system modelling, i.e., to gain insight into the system and to capture the essence of system mechanism behind the observed data, because the residual model is usually difficult to interpret. This research is a study on the applicabil ityof GM(1,N) model on biological systems. Two different measurements of system behavior regarding the linearity, the system linearity (SL) and the significance of system nonlinearity (SSN) are defined to provide criteria and justification for the application of GM (1,N) model based on the system observations before final model solution is attempted. When the system does not satisfy the linearity criterion, it is better to seek alternative nonlinear models instead of relying on the residual model, Hence this paper is the also a early step to handle system nonlinearity regarding the Gray Model applications, The developed system behavior measurements were applied to computer-simulated systems of different nonlinearity, with results showing consistent measurements. In addition, a different formulation compared to the original GM(1, N) Model is used to obtain the model constants with more precise predictions and, in some cases, less amount of computation.