Browsing by Author "Sakici, Oytun Emre"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • No Thumbnail Available
    Pubmed
    Compatible above-ground biomass equations and carbon stock estimation for small diameter Turkish pine (Pinus brutia Ten.).
    (2018-04-15T00:00:00Z) Sakici, Oytun Emre; Kucuk, Omer; Ashraf, Muhammad Irfan
    Small trees and saplings are important for forest management, carbon stock estimation, ecological modeling, and fire management planning. Turkish pine (Pinus brutia Ten.) is a common coniferous species and comprises 25.1% of total forest area of Turkey. Turkish pine is also important due to its flammable fuel characteristics. In this study, compatible above-ground biomass equations were developed to predict needle, branch, stem wood, and above-ground total biomass, and carbon stock assessment was also described for Turkish pine which is smaller than 8 cm diameter at breast height or shorter than breast height. Compatible biomass equations are useful for biomass prediction of small diameter individuals of Turkish pine. These equations will also be helpful in determining fire behavior characteristics and calculating their carbon stock. Overall, present study will be useful for developing ecological models, forest management plans, silvicultural plans, and fire management plans.
  • No Thumbnail Available
    Pubmed
    Dynamic site index models sensitive to ecoregional variability for Scots pine stands in Western Black Sea Region of Türkiye.
    (2024-10-08) Sağlam, Fadime; Sakici, Oytun Emre
    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 (R) 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.