HIEGHT DIAMETER RELATIONSHIP MODEL FOR ACACIA NILOTICA IN RIVERINE FORESTS BLUE NILE

Abstract

HIEGHT DIAMETER RELATIONSHIP MODEL FOR ACACIA NILOTICA IN REVERINE FORESTS - BLUE NILE The optimal forests management is concerning with maintaining and increasing the yield of the forest. There for the accurate precise forest resource information in the inventory is very essential. This information can be utilized over time in the most effective way for planning purposes. Forest inventory studies usually measure the diameter of all trees in a plot and the height of some. Individual tree’s height and diameter at breast height (dbh) are essential inventory measures for estimating tree volume, site index and other important variables in forest growth and yield models. Tree diameter can be measured easily and at little cost. Total tree height, however, is more difficult and costly to measure due to several reasons: time required to complete measurements, chance of observer error, and visual obstruction. Therefore the aim of this study is to develop a generalized height-diameter relationship model for predicting tree height of Acacia nilotica plantations in the reverian forests that could account for the variability of site and stand conditions. Four commonly models used non-linear growth functions (i.e., Stage, Curtis, Arabatzis and Burkhart) were selected as candidate base models and were fitted to individual tree’s height-diameter data of Acacia nilotica. A total of 1127 trees from 20 riverine forests reserves of Acacia nilotica along the Blue Nile South Sinnar Dam up to northwest of El Roseiris Dam were measured. This area falls between Latitude 13.28836 – 11.99478 N and Longitude 33.88800 - 34.37298 E. Data collected from systematic sample plots with various spacing, stand age, and site quality were available for this study. The 4 models described in the literature review are non-linear, therefore model fitting was carried out with the NLIN procedure of the SAS/ STAT_ statistical programme] using the Gauss-Newton algorithm. By previously linearizing the equation and fitting it to the data by ordinary least squares, using the REG procedure of the same statistical programme. The results showed that the four base models show variation in fitting, Wykoff model performed slightly better than the other models based on the goodness of fit as well as bias and standard errors of the predictions. This model can be fitted easily and provided more satisfactory fit when additional variables were included into the model, hence was selected as the base model.