Abstract
Accurate forest biomass estimates require the selection of appropriate models of individual trees. Thus, two properties are required in tree biomass modeling: (1) additivity of biomass components and (2) estimator efficiency. This study aimed to develop a system of equations to estimate young eucalyptus aboveground biomass and guarantee additivity and estimator efficiency. Aboveground eucalyptus biomass models were calibrated using four methods: generalized least squares (GLS), weighted least squares (WLS), seemingly unrelated regression (SUR), and weighted seemingly unrelated regression (WSUR). The approaches were compared with regard to performance, additivity, and estimator efficiency. The methods did not differ with regard to the mean biomass estimation; therefore, their performance was similar. The GLS and WLS approaches did not satisfy the additivity principle, as the sum of the biomass components was not equal to total biomass. However, this was not observed with the SUR and WSUR approaches. With regard to estimator efficiency, the WSUR approach resulted in narrow confidence intervals and an efficiency gain of over 20%. The WSUR approach should be used in forest biomass modeling as it resulted in effective estimators while ensuring equation additivity, thus providing an easy and accurate alternative to estimate the initial biomass of eucalyptus stands in ecophysiological models.
Highlights
Biomass is the most important variable when evaluating carbon dynamics, carbon sequestration, and forest productivity (Fu, Zeng, & Tang, 2017)
The weighted seemingly unrelated regression (WSUR) approach should be used in forest biomass modeling as it resulted in effective estimators while ensuring equation additivity, providing an easy and accurate alternative to estimate the initial biomass of eucalyptus stands in ecophysiological models
The worst statistical evaluation of the power functions was found for the crown biomass components, while the best was observed for stem and total biomass
Summary
Biomass is the most important variable when evaluating carbon dynamics, carbon sequestration, and forest productivity (Fu, Zeng, & Tang, 2017). Alternative variables to biomass, such as root collar diameter, individual tree diameter at 10 cm height, diameter at breast height, total height, commercial height, live crown length, and age are compatible with individual or stand biomass models (Picard, Santi-André, & Henry, 2012; Wang, Zhao, Liu, Yang, & Teskey, 2018). Forest science has increasingly incorporated statistical models into individual and stand biomass estimations (Picard et al, 2012; Zhao, Kane, Markewitz, Teskey, & Clutter, 2015). Ecophysiological studies are required that evaluate the carbon balance and resource use (Binkley, Stape, & Ryan, 2004), nutrient allocation (Viera, Schumacher, Bonacina, Oliveira Ramos, & Rodriguez-Soalleiro, 2017), tree responses to planting density, water deficits (Hakamada, Hubbard, Ferraz, Stape, & Lemos, 2017), and forest products, such as pulpwood and bioenergy
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