Epsilon Archive for Student Projects

Abstract

Stand growth models are an important aid in contemporary sustainable forest management as a
major planning and decision-making tool. However, climate change influencing growth conditions
worldwide is a challenge to commonly used empirical models as they do not provide enough
flexibility, which can result in inaccurate predictions and thus wrong decisions and sub-optimal
results. Climate sensitive models based on plant physiology are a solution that provides greater
adaptability, but they are computationally demanding and require often unavailable input data, and
therefore they are rarely used in practice. For this reason, simple growth models for Scots pine
(Pinus sylvestris L.) monocultures based on the concept of Light-Use Efficiency (LUE) were
developed in this thesis. They use the yearly sums of absorbed photosynthetically active radiation
(APAR), readily available climate data (annual precipitation and mean air temperature), and basic
stand characteristics (age, number of trees, and site index) to predict the annual stem volume
increment. For model fitting and validation, stand data from a long-term thinning and fertilization
experiment established in Sweden and climate data from the Swedish Meteorological and
Hydrological Institute were used. Two approaches differing in the method of data preparation were
analysed. Model 1 (M1) was based on averaged variables and Model 2 (M2) used variables from the
beginning of the measurement periods. Both models fit reasonably well with the data, but the RMSE
exceeded 20% and they proved to provide reliable estimates for only the part of the productivity
range included in this study. The unsatisfactory performance can be attributed to the change of the
dependent variable in the LUE model to stem volume increment, the lack of good quality APAR
data, and the low degree of model hybridization.