Abstract
Correlation analysis among different variables do not always fully express the real direct effect of one variable on another, especially in forestry. The objective of the present study was to evaluate the correlations among direct/indirect effects of tree diameter, height, and biomass components on total aboveground tree biomass and consequences when using path analysis. We also aimed to define which of the variables should be included in biomass modeling. The tree data were collected from eight sites located in Paraná and Rio Grande do Sul, where the diameter 1.30 m aboveground ( dbh ), total height ( h ), biomass of stems, branches, leaves and total aboveground biomass were measured. Spearman's correlation showed that dbh (0.93), stems (0.94), and branches (0.90) had the highest association with total aboveground biomass when the direct and indirect effects of these variables were deconstructed by path analysis. The stem (0.44) and branch (0.35) components provided greater direct effects when compared with dbh (0.17). For the component stem, branches, and leaves the total aboveground biomass presented the greatest direct effects of 0.59, 0.74, and 0.36, respectively. Thus, these results indicate the convenience of including at least one biomass component in the biomass regression model, along with dbh and height.
Highlights
The study of correlations between variables measured in trees requires a deeper practical implementation in all areas of forestry science
The study of correlation is important when quantifying the degree of association and the influence of the independent variables on the dependent variable (TENA et al, 2016). The degree of such real influence of the independent variables on the dependent variable is still unknown. To address this gap in knowledge we proposed to deconstruct the direct and indirect effects of independent variables on the dependent variable using path analysis
The objective of this paper was to evaluate correlations and their deconstruction into direct and indirect effects by path analysis, using the variables diameter, height, biomass components and the tree total aboveground biomass, and identify the variables that should be included in future forest biomass modeling studies
Summary
The study of correlations between variables measured in trees requires a deeper practical implementation in all areas of forestry science. In the modeling of biomass, which uses linear and non-linear regression estimators, such knowledge becomes important to identify which explanatory variables are more correlated with biomass In this case, to find the variable that has high correlation with the variable biomass, the modeler can progress faster testing all the independent variables and their combinations to reach such aim. To find the variable that has high correlation with the variable biomass, the modeler can progress faster testing all the independent variables and their combinations to reach such aim These studies apply estimation techniques by regression that normally uses the simple correlation between variables (ZHAO; KANE, 2017; OLIVEIRA et al, 2017; ARAÚJO et al, 2018).
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