SUMMARYThe dynamic of the natural pine trees renewal process in Taiga zone based on mathematical modelling is examined. Dynamic modelling is used for prediction of natural renewal success depending on seeds' yield and bumper crop years' recurrence. In current study mean bumper crop recurrence is assumed to be once per 5 years, this means that a bumper crop year may occur in 4 years as well as in 6 years. The analysis of the state of natural renewal depending on maternal stand among main forest types is made. The bearing age of 30 years is assumed, with the maximum seed productivity age of 110-130 years, depending on the forest type. The data on trunk quantity distribution depending on age was obtained as a result of a simple sum. The algorithm of the sum has been implemented in Interpol procedure contained in the initial text file gav4.cpp. The initial distribution of trunk quantity depending on age was taken from the growth progress tables. The change of trunk quantity by decades was determined using extrapolation of known values according to power function N= 424316,4хА-1,3533 . The value of approximation certainty factor equals R 2 = 0.9857. The age of stands changes from 20 to 190 years. The amount of bearing trees aged 31-40 years old was assumed as 5-10%, whereas at the maximum bearing age (110-130 years old) the share of bearing trees totals 70-90% depending on the forest type. The presented model is discrete, dynamic, stochastic and descriptive. Stochasticity of the process is caused by the uncertainty in the quantity of bearing trees depending on age, uncertainty in the quantity of seeds from a single tree, uncertainty in the quantity of sprouts and in the occurrence of normal and bumper crops. To account for randomness a function ravnom (a,b) has been introduced, which generates uniformly distributed random numbers from a to b. Implementation of this function is possible using C++ language. Due to the stochasticity of the natural renewal process more implementations of the program are needed for a more reliable prediction.