Developing simultaneously modeling systems for improving the reliability of tree aboveground biomass- carbon and its components estimates for Machilus odoratissimus nees in the central highlands, Viet Nam
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Machilus odoratissimus Nees is a multi-purpose species with, high economic value and environmental protection, so this tree species is commonly used in agroforestry models. In plantation management, it demands modeling systems that predict accurately aboveground biomass- carbon and its components. At the same time, the developed models support computing carbon accumulation of forest trees in agroforestry models for the program of reducing emissions from deforestation and forest degradation (REDD). Twenty-two 300 m2 plots were measured within the full range of 1 to 7 ages in the Central Highlands of VietNam. A total of 22 quadratic mean diameter trees were destructively sampled to obtain a dataset of the dry iomass/carbon of the stem (Bst/Cst), bark (Bba/Cba), branches (Bbr/Cbr), leaves (Ble/Cle), and total tree aboveground biomass/carbon (AGB/AGC). We examined the performance of weighted nonlinear models fit by maximum likelihood and weighted nonlinear seemingly unrelated regression (SUR) fit by generalized least squares for predicting tree aboveground biomass- carbon and its components. The simultaneous estimation of AGB/AGC and its components produced a higher reliability than that of the models of tree components and the total developed separately. The selected forms of modeling systems were AGB = Bst + Bba + Bbr + Ble = a1×(D2H)b1 + a2×(D2H)b2 + a3×Db3 + a4×(D2H)b4 and AGC = Cst + Cba+ Cbr + Cle = a1×(D2H)b1 ++2×(D2H)b2 + a3×Db3 + a4×(D2H)b4 (where D is the diameter at breast height and H is the height of the tree).
Keywords: Agroforestry, Machilus odoratissimus, seemingly unrelated regression (SUR), tree biomass- carbon
ID: 3472953