2009
DOI: 10.1007/s10342-009-0286-2
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Abstract: Four variable-exponent taper equations and their modified forms were evaluated for lodgepole pine (Pinus contorta var. latifolia Engelm.) trees in Alberta, Canada. A nonlinear mixed-effects modeling approach was applied to account for within-and between-tree variations in stem form. Even though a direct modeling of within-tree autocorrelation by a variance-covariance structure failed to achieve convergence, most of the autocorrelation was accounted for when random-effects parameters were included in the models… Show more

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Cited by 69 publications
(71 citation statements)
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References 27 publications
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“…Garber & Maguire (2003) and Yang et al (2009) found significant functions using D/H ratio (a proxy for tree form) in their taper models for Abies grandis, P. ponderosa and P. contorta in Oregon (the former), and for lodgepole pine in Alberta (the latter). Our results for P. occidentalis shows that D/H ratio was not significant in our model.…”
Section: DImentioning
confidence: 99%
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“…Garber & Maguire (2003) and Yang et al (2009) found significant functions using D/H ratio (a proxy for tree form) in their taper models for Abies grandis, P. ponderosa and P. contorta in Oregon (the former), and for lodgepole pine in Alberta (the latter). Our results for P. occidentalis shows that D/H ratio was not significant in our model.…”
Section: DImentioning
confidence: 99%
“…The correlation coefficient of g1 and g2 was low (0.3) indicating that variation has been accounted by current covariates. Yang et al (2009) also incorporated one random parameter into the exponent (b0 parameter) and another for a1 to obtain the best results for several candidate variable-exponent taper equations for lodgepole pine in Alberta, Canada.…”
Section: DImentioning
confidence: 99%
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“…represents the estimated random parameters for the localized plot, is the 2x2 variance-covariance matrix for the among-plot variability (common for all plots), is the estimated kxk variance-covariance matrix for the within-plot variability, is the kxq matrix of partial derivatives of the function with respect to random parameters , ê ij is the residual value, which was calculated as the difference between the observed DBH value and the one predicted by using the DBH model including merely the fixed parameters in subsamples (Gregoire, 1987;Yang et. al., 2009).…”
Section: Calibration Response Evaluationmentioning
confidence: 99%
“…These sampling alternatives for calibration responses included the selection of previous trees based on certain stand size categories, i. e. largest, smallest and medium trees in the sample plots (Calama and Montero, 2004;Yang et. al., 2009).…”
Section: Calibration Response Evaluationmentioning
confidence: 99%