Developing simultaneously modeling systems for improving reliability of tree aboveground biomass - carbon and its components estimates for Machilus odoratissimus Nees in the Central Highlands of Viet Nam
Keywords:
Machilus odoratissimus, biomass,, carbon, seemingly unrelated regression (SUR)Abstract
Machilus odoratissimus Nees is a species of multi - purposes, hight economic value and environmental protection. In plantation business, it demands modeling system that predicts accurately aboveground biomass and its components; At the same time, the developed models support to compute carbon accumulation of forest trees for program of reducing emissions fro deforestation and forest degradation. Twenty - two 300 m2 plots within the full range of 1 - 7 ages in the Central Highlands were measured. A total of 22 averaged - diameter trees were destructively sampled to obtain a dataset of the dry biomass/carbon of the stem (Bst/Cst), bark (Bba/Cba), branches (Bbr/Cbr), leaves (Ble/Cle), and total aboveground biomass/carbon (AGB/AGC). The study compared two methods: developing independent equations was weighted nonlinear regression fit by maximum likelihood and building simultaneous modeling system was weighted nonlinear fit by seemingly unrelated regression (SUR). As a result, the modeling system devloped simultaneously using SUR produced higher reliability than the models established independently. The selected forms of modeling systems for estimating tree aboveground biomass/carbon and its components were AGB = Bst + Bba+ Bbr + Ble = a1×(D2H) b1 + a2×(D2H) b2 + a3×D b3 + a4×(D2H) b4 and AGC = Cst + Cba+ Cbr + Cle = a1×(D2H) b1 + a2×(D2H) b2 + a3×D b3 + a4×(D2H) b4
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