Abstract
Aim of the study: The aim of this study was to develop a model for above-ground biomass estimation for Pinus radiata D. Don in Asturias.Area of study: Asturias (NE of Spain).Material and methods: Different models were fitted for the different above-ground components and weighted regression was used to correct heteroscedasticity. Finally, all the models were refitted simultaneously by use of Nonlinear Seemingly Unrelated Regressions (NSUR) to ensure the additivity of biomass equations.Research highlights: A system of four biomass equations (wood, bark, crown and total biomass) was develop, such that the sum of the estimations of the three biomass components is equal to the estimate of total biomass. Total and stem biomass equations explained more than 92% of observed variability, while crown and bark biomass equations explained 77% and 89% respectively.Keywords: radiata pine; plantations; biomass.
Highlights
Tree biomass estimation is needed for the sustainable planning of forest resources, so the development of biomass equations has been, and remains, one of the main lines of work of many researchers (Cunia, 1986, 1988; Waring and Running, 1998)
The data used to develop above-ground biomass equations was based on a sample of 27 radiata pine trees in five stands managed by Forest Services in Asturias
In this study a system of equations of above ground biomass for radiata pine in Asturias was developed through simultaneous adjustment with the generalized least squares method known as NSUR (Nonlinear Seemingly Unrelated Regressions), which ensured the additivity of the equations
Summary
Tree biomass estimation is needed for the sustainable planning of forest resources, so the development of biomass equations has been, and remains, one of the main lines of work of many researchers (Cunia, 1986, 1988; Waring and Running, 1998). The importance of forests as carbon sinks is closely linked to the amount of biomass available in them, which makes it essential to have equations to quantify the contribution of forest areas to the global carbon cycle (IPCC, 2003). Research began to focus on the determination of the dry weight of tree components (e.g. wood, bark, branches), especially those components of greater importance for forest companies. Many forest studies develop equations using regression techniques for specific geographic areas and tree species. Tree variables commonly used are diameter at breast height (d) and total height (h), while in stand equations, stand variables like stocking (trees/ha), basal area (G) and dominant height (H0) are used
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