An approach combining the information generated from different stochastic differential equations was developed to improve the predictive qualities of stem taper and volume. The stochastic differential equations and the stem taper and volume models were fitted to data from Scots pine and Norway spruce trees that were collected from across the entire Lithuanian territory. New models deduced from the Gompertz and Ornstein-Uhlenbeck shape stochastic differential equations were tested against the classical Kozak's stem taper model, q-exponential segmented stem taper model, classical Schumacher-Hall’s volume model, and q-exponential volume model based on allometric and geometric concepts. Comparison of the predicted stem taper and stem volume values with those obtained using regression fixed-effects models demonstrated the predictive power of the stochastic differential equations models.

Key words: stochastic differential equation, stem taper model, volume model