A GENERALIZED ABOVEGROUND BIOMASS MODEL FOR JUVENILE INDIVIDUALS OF RHODODENDRON ARBOREUM (Sm.) IN NEPAL

• Autor(a) principal: BK, Rana Bahadur
• Data de Publicação: 2019
• Outros Autores: Sharma, Ram Prasad, Bhandari, Shes Kanta
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Cerne (Online)
• Texto Completo: https://cerne.ufla.br/site/index.php/CERNE/article/view/2002
• topic: allometric modelling; community forest; crown measures, crown spread ratio; height-to-diameter ratio

Carbon in the juvenile plants contributes significant share to the total carbon stock in forests. A precise estimate of aboveground biomass of the juvenile sages of trees is therefore very important. We developed a generalized allometric biomass model for the prediction of aboveground biomass of the juvenile individuals of Rhododendron arboreum (Sm.). We used biomass and other dendrometric data from 66 destructively sampled juveniles of this species in Gorkha district- a mountainous district in Nepal, for the purpose. Using eight nonlinear functions of different mathematical forms (power, exponential, fractional forms), we evaluated several individual-level characteristics, such as size (diameter, height), crown measures (crown ratio, crown width, crown spread ratio, crown index, crown fullness ratio), height-to-diameter ratio (slenderness coefficient), number of branches and wood density, and stand-level characteristics, such as altitude and slope for their potential contributions to the biomass variations of the juvenile individuals. A simple power function with cross product of the square-diameter and height as a main predictor and crown spread ratio as a covariate predictor showed the best fit to the data (R2adj = 0. 9013); RMSE = 59.349 g) with no substantial trends in the residuals and with fitted curves perfectly covering to the measured biomass data. Our model is site-specific, and its application should therefore be limited to those that formed the basis of this study.  Further research on recalibration, validation, and verification of our model, with a larger dataset collected from sites with a wider range of these characteristics, is recommended.