Modeling individual tree mortality for crimean pine plantations.

Individual tree mortality model was developed for crimean pine (Pinus nigra subsp. pallasiana) plantations in Turkey. Data came from 5 year remeasurements of the permanent sample plots. The data comprises of 115 sample plots with 5029 individual trees. Parameters of the logistic equation were estimated using weighted nonlinear regression analysis. Approximately 80% of the observations were used for model development and 20% for validation. The explicatory variables in the model were ratio of diameter of the subject tree and basal area mean diameter of the sample plot as measure of competition index for individual trees, basal area and site index. All parameter estimates were found highly significant (p < 0.001) in predicting mortality model. Chi-square statistics indicate that the most important variable is d / d(q), the second most important is site index, and the third most important predictor is stand basal area. Examination of graphs of observed vs. predicted mortality rates reveals that the mortality model is well behaved and match the observed mortality rates quite well. Although the phenomenon of mortality is a stochastic, rare and irregular event, the model fit was fairly good. The logistic mortality model passed a validation test on independent data not used in parameter estimation. The key ingredient for obtaining a good mortality model is a data set that is both large and representative of the population under study and the data satisfy both requirements. The mortality model presented in this paper is considered to have an appropriate level of reliability.

Characterization of soil erosion and its implication to forest management.

Forests have traditionally been managed to maximize timber production or economic profit, completely neglecting other forest values. Nowadays, however, forests are being managed for multiple uses. The basic requirement of multiple use forestry is to identify and quantify forest values and to determine management objectives. The priorities of management objectives, however, must be decided. In this study, a model predicting the soil loss for multi objective forest management was developed. The model was based on data from remeasurement of permanent sample plots. The data were gathered from 132 sample plots. Approximately 80% of the observations were used for model development and 20% for validation. The model was designed for even aged and uneven aged forests, as well as for forests with mixed and pure species composition. The explicatory variables in the model were mean diameter and number of trees. All parameter estimates were found highly significant (p < 0.001) in predicting soil loss. The model fit and validation tests were fairly good. The soil loss model presented in this paper was considered to have an appropriate level of reliability. It can be used in the overall multi-objective forest management planning, but, it should be limited to the conditions for which the data were gathered.