Quantifying uncertainty in forest growth and yield systems is critical to evaluating the expected variability associated with future prediction. Three areas in forest growth and yield modelling were improved by introducing new mathematical and computational approaches for whole stand survival modelling, simultaneous parameter estimation for a system of yield equations, and systems of stochastic differential equations. The two-step regression method for whole stand survival modeling was improved by presenting a method to estimate the probability of survival model and whole stand survival method simultaneously in conjunction with modeling the uncertainty of the combined equation directly. A method was presented to simultaneously estimate the parameters of a system of non-linear forest yield equations, accounting for the cross-equation correlation of the error components. Finally, a method to model forest growth as a system of stochastic differential was introduced which uses numerical approximations. All methods presented in this research were shown improve over existing practices.