ABSTRACT
Pollarding of oak trees for livestock and animal feeding is a traditional application, and it has been used for centuries from generation to generation in southern and southeastern Turkey. Estimation of the fresh sprout biomass (FSB) potential of pollarded oak forests in high accuracy is important for sustainable forest management. In the present study, 40 trees were sampled from Turkey oak (Quercus cerris L.) stands that have been irregularly pollarded for animal husbandry in Adiyaman, southeastern Turkey. In order to estimate FSB, a multiple logarithmic linear model was developed with explanatory variables such as tree diameter at breast height (DBH), total tree height (H), mean sprout length (SL), and mean sprout age (SA), which are in a significant relationship with FSB. Stepwise multiple regression analysis was used to fit this multiple logarithmic linear model and to determine the best independent variable set. As a result of stepwise regression analysis, three models were obtained in which SL, DBH, and SA are independent variables. Model 1 estimates the FSB by taking only SL, Model 2 uses SL and DBH, and Model 3 uses SL, DBH, and SA as independent variables. All models were significant at p = 0.001 level. Model 1 explained the variation in FSB by 65%, Model 2 by 81%, and Model 3 by 86%. Inclusion of DBH in the model (Model 2) decreased the mean absolute error (MAE) of FSB by 26% and the inclusion of SA (Model 3) decreased MAE by 43%.
Subject(s)
Quercus , Trees , Biomass , Environmental Monitoring , Forests , TurkeyABSTRACT
Biomass equations were developed for different components of oak trees (Quercus cerris L.), which have been managed in coppices at different development stages-small-diameter forest (SDF) and medium-diameter forest (MDF). In this context, four biomass regression models-two based on diameter at breast height (DBH) alone and two based on DBH and total tree height (H)-were developed for each of the crown, stem, and total aboveground biomass components. Akaike's information criterion (AIC), root mean square error percentage (RMSE (%)), mean absolute error percentage (MAE (%)), adjusted coefficient of determination (Adj.R2), and bias values were used to evaluate and compare the suitability of a total of 12 regression models developed for biomass components. As a result, in the estimation of crown biomass, only DBH-based models provided higher estimation accuracy than DBH-H-based models. For the most suitable model, estimated values were Adj.R2 = 0.60, bias = - 0.009, RMSE = 66%, and MAE = 41%. In models developed to estimate stem biomass, the estimation accuracy of DBH-H-based models was higher. In the goodness-of-fit statistics calculated for the most suitable model, Adj.R2, bias, RMSE, and MAE were 0.89, 0.010, 38%, and 23%, respectively. The models developed to estimate the total aboveground biomass were all close in terms of estimation accuracy. The biomass components (crown and stem) in the total aboveground biomass were proportionally as follows: crown at 38% and stem at 62% in the SDF stage, and crown at 35% and stem at 65% in the MDF stage, indicating lower crown and higher stem partitioning as the development stage increased.
