Are models of volumetric production at the diametric distribution and total stand level mutually compatible? View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12-19

AUTHORS

Marcos Felipe de Oliveira Valeriano, Eder Pereira Miguel, Pedro Guilherme de Andrade Vasconcelos, Mauro Eloi Nappo, Humberto Angelo, Alba Valéria Rezende, Renan Augusto Miranda Matias, Leonardo Job Biali, Ilvan Medeiros Lustosa Junior

ABSTRACT

The objective of this work was to compare estimates generated by a diametric distribution model and a total stand model against the pre-cut inventory. The model efficiency was also evaluated. Data were evaluated from 30 permanent sample plots in a Eucalyptus urophylla stand, comprising 24 sample plots used for model fitting, and six sample plots for validation. The volume of wood per hectare was estimated for different productive units (sites), using 7 years as the reference age. The model adjustment quality was verified by adjustment and precision statistics: the correlation between observed and predicted variables, root mean square error percentage, graphical analysis of residual distribution, and a frequency histogram for classes of relative errors and validation. Although the two-parameter Weibull probability density function adhered to the data for tree evolution in diameter classes for the reference age (7 years) in the different productivity classes, it generated imprecise estimates of the number of individuals. Consequently, it produced inaccurate volumetric production estimates. The total stand model provided reliable projections of production volumes in different productivity classes for both adjustment types, showing compatibility with the pre-cut inventory according to a Tukey test. In summary, the total stand model generated estimates that were compatible with the pre-cut inventory while the diametric distribution model did not. More... »

PAGES

1-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11676-018-0868-2

DOI

http://dx.doi.org/10.1007/s11676-018-0868-2

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https://app.dimensions.ai/details/publication/pub.1110760052


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