Browsing by Author "Sağlam, F."

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Dynamic site index models sensitive to ecoregional variability for Scots pine stands in Western Black Sea Region of Türkiye
(Springer Science and Business Media Deutschland GmbH, 2024) Sağlam, F.; Sakici, O.E.
Site productivity, defined as the production amount of the stand at a specific age, has a significant impact on the growth of the stand and site index is used as an indicator of site productivity. The objective of this study is to develop ecoregion-based dynamic site index models for Scots pine (Pinus sylvestris L.) stands in the Kastamonu and Sinop regions of Türkiye. The mixed-effects modeling approach allowing for the inclusion of ecoregions in the models was used to develop dynamic site index models, and the models derived from seven base models were tested. The best model was selected based on statistical criteria. As a result of statistical analyses and graphical examinations, the King-Prodan model was found to yield the best predictive results in terms of growth patterns. The site index model based on the King-Prodan method produced a coefficient of determination (R2) of 0.977. The statistical criteria for this model are as follows: Akaike information criterion (AIC) of 4931.052, Bayesian information criterion (BIC) of 4968.933, root mean square error (RMSE) of 1.218, and mean error (ME) of − 0.036. The F-test was used to test whether there was a statistically significant difference in dominant heights between ecoregions. The results demonstrated that the dominant heights exhibited statistically significant differences among the ecoregions. Consequently, it is of paramount importance to utilize ecoregion-based dynamic site index models in order to achieve reliable and accurate predictions.
Ecoregional height–diameter models for Scots pine in Turkiye
(Springer Nature, 2024) Sağlam, F.; Sakici, O.E.
Ecoregion-based height-diameter models were developed in the present study for Scots pine (Pinus sylvestris L.) stands in Turkiye and included several ecological factors derived from a pre-existing ecoregional classification system. The data were obtained from 2831 sample trees in 292 sample plots. Ten generalized height–diameter models were developed, and the best model (HD10) was selected according to statistical criteria. Then, nonlinear mixed-effects modeling was applied to the best model. The R2 for the generalized height‒diameter model (Richards function) modified by Sharma and Parton is 0.951, and the final model included number of trees, dominant height, and diameter at breast height, with a random parameter associated with each ecoregion attached to the inverse of the mean basal area. The full model predictions using the nonlinear mixed-effects model and the reduced model (HD10) predictions were compared using the nonlinear sum of extra squares test, which revealed significant differences between ecoregions; ecoregion-based height–diameter models were thus found to be suitable to use. In addition, using these models in appropriate ecoregions was very important for achieving reliable predictions with low prediction errors.