Please use this identifier to cite or link to this item: https://repositorio.inpa.gov.br/handle/1/15384
Title: Yield modeling in sawing logs of Manilkara spp. (Sapotaceae) in sawmill in the state of Roraima, Brazil
Other Titles: Modelagem do rendimento no desdobro de toras de Manilkara spp. (Sapotaceae) em serraria do estado de Roraima, Brasil
Authors: Danielli, Filipe Eduardo
Gimenez, Bruno Oliva
Oliveira, Criscian Kellen Amaro de
Santos, Joaquim dos
Higuchi, Niro
Keywords: Lumber
Saw Mills
Wood Products
Coefficient Of Determination
Diameter Class
Homogeneous Distribution
Roraima , Brazil
Sawnwoods
Standard Errors
Statistical Differences
Yield Modeling
Sawing
Byproducts
Logs
Saw Mills
Issue Date: 2016
metadata.dc.publisher.journal: Scientia Forestalis/Forest Sciences
metadata.dc.relation.ispartof: Volume 44, Número 111, Pags. 641-651
Abstract: The aim of this study was to estimate the yield of sawing Manilkara spp. logs, to quantify the wood by products generated, to evaluate differences in the yield between the diameter classes and to adjust models to estimate the yield in lumber and to estimate the volume of the hollow part of the logs. Seventy-one logs were sampled and grouped into diameter classes. Log volumes were determined by the Smalian method and the volume of lumber was calculated to determine the yield. Twelve models were tested to estimate the sawn lumber and twelve models to estimate the volume of the hollow logs. The choice of the best models was made based on the highest adjusted coefficient of determination (Rajust2), lowest standard error of estimate (Syx%) and homogeneous distribution of the residues. The average yield was 30.1% and showed no statistical differences in yield between the diameter classes and between hollow logs and non-hollow logs. Class 5 (70<79,9 cm) was the one that presented the best yield. To estimate the yield, the best equation was the number seven. To estimate the volume of the hollow part of the logs, the best equations were number two, three and ten.
metadata.dc.identifier.doi: 10.18671/scifor.v44n111.10
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