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Madera y bosques

On-line version ISSN 2448-7597Print version ISSN 1405-0471

Abstract

CUEVAS CRUZ, Juan Carlos; AQUINO RAMIREZ, Martín; CRUZ KU CHALE, Ramón de la  and  MORALES SOSA, Isidro Javier. Additive allometric equations to estimate aboveground biomass and carbon concentration of Piscidia piscipula (L.) Sarg. Madera bosques [online]. 2022, vol.28, n.3, e2832356.  Epub Apr 14, 2023. ISSN 2448-7597.  https://doi.org/10.21829/myb.2022.2832356.

Specific allometric equations are a key tool to quantify total and component aboveground biomass and carbon of tropical forests. The aim of this study was to fit allometric models to estimate aboveground biomass and structural components of Piscidia piscipula trees of semi-evergreen tropical forest in Escarcega, Campeche, Mexico. Through a destructive sampling of 17 individuals of different diameter categories (5 to 55 cm), samples of the stem, branches, and twigs with leaves were obtained to determine their dry weight. We fitted two systems of nonlinear equations using tree diameter at breast height (Dn) and total height (AT) as predictor variables with the Generalized Method of Moments (GMM). The additivity property was ensured by establishing the total aboveground biomass as the sum of the estimates of the biomass of each structural component of the tree. The carbon concentration expressed as a percentage of the biomass was determined in each component. The system of allometric equations based on the model y=e[-b0+b1ln(Dn)+b2ln(AT)] showed the best fit statistics and explained more than 89% of the variability of the biomass of the components and the total for the tree. In P. piscipula, the highest proportion of biomass was concentrated firstly in the stem (60.4%), secondly in the branches (36.8%), and thirdly, in twigs with leaves (2.7%). The average carbon concentration was 49.9 ± 0.48 %. The allometric equations generated provide reliable estimates of biomass ensuring additivity between structural components of P. piscipula.

Keywords : tropical trees; carbon; biomass distribution; jabin; allometric models; destructive sampling.

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