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Abstract
ABSTRACT In this study, it is aimed that the dynamic site index models were developed for Crimean Pine stands in Sarikaya-Cankiri forests located in middle northern Turkey. The data for this study are 153 sample trees obtained from the Crimean Pine stands. In modeling relationships between height and age of dominant or co-dominant trees, some dynamic site index equations such as Chapman-Richards (M1, M2, M3), Lundqvist (M4 and M6), Hossfeld (M5), Weibull (M7) and Schumacher (M8) based on the Generalized Algebraic Difference Approach (GADA) were used. The estimations for these eight-dynamic site index model parameters with well as various statistical values were obtained using the nonlinear regression technique. Among these equations, the Chapman-Richards’s equation, M3, was determined to be the most successful model, with accounted for 89.03 % of the total variance in height-age relationships with MSE: 1.7633, RMSE: 1.3279, SSE: 1165.6, Bias: -0.0380. After determination of the best predictive model, ARMA (1, 1) autoregressive prediction technique was used to account autocorrelation problems for time-series height measurements. When ARMA autoregressive prediction technique was applied to the Chapman-Richards function for solving autocorrelation problem, these success statistics were improved as SSE: 868.7, MSE: 1.3183, RMSE: 1.1482, Bias: -0.06369, R2: 0.918. Also, Durbin-Watson statistics displayed that autocorrelation problem was solved by the use of ARMA autoregressive prediction technique; DW test value=1.99, DW<P=0.5622, DW>P=0.4378. The dynamic site index model that was developed has provided results compatible with the growth characteristics expected in the modeling of height-age relations, such as polymorphism, multiple asymptote, and base-age invariance.
Highlights
One of the main factors that affect the growth and yields of trees is the site quality, which is known as the site index (Carmean, 1972)
When ARMA autoregressive prediction technique was applied to the Chapman-Richards function for solving autocorrelation problem, these success statistics were improved as Sum of Squared Error (SSE): 868.7, Mean Squared Error (MSE): 1.3183, RMSE: 1.1482, Bias: -0.06369, R2: 0.918
Durbin-Watson statistics displayed that autocorrelation problem was solved by the use of ARMA autoregressive prediction technique; DW test value=1.99, DWP=0.4378
Summary
One of the main factors that affect the growth and yields of trees is the site quality, which is known as the site index (Carmean, 1972). The forest site quality is a factor that should be considered in forestry applications (Elfving and Kiviste, 1997). In this respect, one of the main tasks for forest management should be to determine the forest site quality and to classify forests according to site index (Clutter et al, 1983). As distinct from this feature of medium height, the upper height, which is calculated on the heights of the dominant trees in the upper layer, is calculated within the scope of silvicultural practices by using uncut trees that have not been handled or manipulated. The dominant height is accepted as an indicator of site index values and is nowadays used as an indication of site quality (Stage, 1963; Curtis et al, 1974; Monserud, 1984)
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