Abstract
ABSTRACT The objectives of this work were to propose additive equations for biomass components (stem and crown) and total biomass for black wattle (Acacia mearnsii De Wild.) and show the inconsistency of independently adjusted biomass equations. Two procedures were used to fit nonlinear equations of biomass: i) independent and ii) systems of equations. The second procedure, defined by the application of the seemingly unrelated regression model, has better biological and statistical properties to estimate allometric equations of biomass components and total biomass when compared with the independent estimation. An effective property of this procedure is the additivity, i.e., the estimates of component biomass are compatible with those of total biomass. Independent fitted adjusted equations do not consider the dependence between the biomass components, thus, besides the estimates being non-additive, which is an undesirable property, they will result in estimates with larger variance.
Highlights
Energy generation from forest biomass has the potential to reduce carbon emissions when compared with other energy sources, especially fossil fuels
The field research was carried out in commercial stands of black wattle (Acacia mearnsii de Wild.) in areas where the species is prevalent in the state of Rio Grande do Sul, in June and July 2014
We obtained the weights for the biomass of the components and total biomass using the structures of variance in Equations 5 to 19
Summary
Energy generation from forest biomass has the potential to reduce carbon emissions when compared with other energy sources, especially fossil fuels. Accurate biomass estimates have been fundamental for diverse applications. Information on biomass stocks is especially useful for forest managers to determine the role of forest biomass in the global carbon cycle and to manage energy production from biomass. The importance of establishing equations and enabling forest biomass inventories. The development and testing of allometric biomass models depend on the availability of data from destructive tree evaluations and is a time-consuming and expensive task (Chave et al, 2014). To avoid this work being carried out frequently, it is possible to use existing biomass equations and to estimate biomass stocks using forest inventory information
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