Nonlinear local height-diameter equations for five conifers in southwestern Puebla, Mexico
DOI:
https://doi.org/10.19136/era.a12n3.4610Keywords:
grouping by species, random coefficients, mixed-effects model, taxon-specific parameters, dummy variablesAbstract
Tree height (h) is a relevant variable for predicting other tree or stand attributes such as volume, biomass or carbon content. However, unlike diameter at breast height (d), the measurement of total height is difficult and costly, so h – d equations are used to estimate it. The objective was to generate local height - diameter equations for five conifer species of timber importance in southwestern Puebla, Mexico. In a first phase, the predictive capacity of nine nonlinear biparametric models was evaluated, using the global database integrated in 2012 of the five species. Through a quantitative and graphic evaluation system, the modified Hossfeld I model was identified as the one with the best predictive capacity because it exhibited a biologically realistic behavior. In a second phase, the selected model was adjusted using three fitting strategies: (1) nonlinear least squares (NLS) per species, (2) NLS with the incorporation of dummy variables (NLS-DV) for the global database, and (3) mixed-effects models (MEM) for the full base and using the species covariate as a grouping factor. According to R2adj and RMSE statistics it was determined that NLS-DV and MEM were better than NLS, subsequently MEM was diagnosed to be superior than NLS-DV (AIC = 10 442.2, BIC = 10481.24 and logLik = -5 214.1). It is concluded that MEM random effects values are useful for estimating h with primary use in forest inventories.
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