Modelling the influence of radiata pine log variables on structural lumber production
We run logit models to explain the variability of Pinus radiata structural lumber in 71 second and third unpruned logs. The response variable was the proportion of lumber with a static modulus of elasticity greater or equal than 8 GPa, pMSG8+, and the explanatory variables were log volume, branch i...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Universidad del Bío-Bío
2022-09-01
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| Series: | Maderas: Ciencia y Tecnología |
| Subjects: | |
| Online Access: | https://revistas.ubiobio.cl/index.php/MCT/article/view/5654 |
| _version_ | 1797806019629285376 |
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| author | Elvis Gavilán Rosa M. Alzamora Luis A. Apiolaza Katia Sáez Juan Pedro Elissetche Antonio Pinto |
| author_facet | Elvis Gavilán Rosa M. Alzamora Luis A. Apiolaza Katia Sáez Juan Pedro Elissetche Antonio Pinto |
| author_sort | Elvis Gavilán |
| collection | DOAJ |
| description |
We run logit models to explain the variability of Pinus radiata structural lumber in 71 second and third unpruned logs. The response variable was the proportion of lumber with a static modulus of elasticity greater or equal than 8 GPa, pMSG8+, and the explanatory variables were log volume, branch index, largest branch, log internode index, wood basic density, and acoustic velocity. The average pMSG8+ volume was 44,30 % and 36,18 % in the second and third log respectively. Ten models were selected based on meeting statistical assumptions, their goodness of fit, and the statistical significance of their parameters. The best models (R2 - adj. > 0,75) included acoustic velocity (AV) as explanatory variable, which explained 56,25 % of the variability of pMSG8+. Models without AV presented goodness of fit ranging from 0,60 to 0,75 (R2 - adj.), and variables with the highest weight to explain the variability of pMSG8+ were volume, followed by wood basic density, branch index, and largest branch. It is possible to model pMSG8+ from log variables even when acoustic velocity is not available; however, this requires wood basic density models calibrated for the Pinus radiata growing zone.
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| first_indexed | 2024-03-13T05:59:57Z |
| format | Article |
| id | doaj.art-3b7e1eb8c76d4aa0bef6b415a12b5f9f |
| institution | Directory Open Access Journal |
| issn | 0717-3644 0718-221X |
| language | English |
| last_indexed | 2024-03-13T05:59:57Z |
| publishDate | 2022-09-01 |
| publisher | Universidad del Bío-Bío |
| record_format | Article |
| series | Maderas: Ciencia y Tecnología |
| spelling | doaj.art-3b7e1eb8c76d4aa0bef6b415a12b5f9f2023-06-12T16:58:05ZengUniversidad del Bío-BíoMaderas: Ciencia y Tecnología0717-36440718-221X2022-09-012510.4067/s0718-221x2023000100402Modelling the influence of radiata pine log variables on structural lumber productionElvis GavilánRosa M. AlzamoraLuis A. ApiolazaKatia SáezJuan Pedro ElissetcheAntonio Pinto We run logit models to explain the variability of Pinus radiata structural lumber in 71 second and third unpruned logs. The response variable was the proportion of lumber with a static modulus of elasticity greater or equal than 8 GPa, pMSG8+, and the explanatory variables were log volume, branch index, largest branch, log internode index, wood basic density, and acoustic velocity. The average pMSG8+ volume was 44,30 % and 36,18 % in the second and third log respectively. Ten models were selected based on meeting statistical assumptions, their goodness of fit, and the statistical significance of their parameters. The best models (R2 - adj. > 0,75) included acoustic velocity (AV) as explanatory variable, which explained 56,25 % of the variability of pMSG8+. Models without AV presented goodness of fit ranging from 0,60 to 0,75 (R2 - adj.), and variables with the highest weight to explain the variability of pMSG8+ were volume, followed by wood basic density, branch index, and largest branch. It is possible to model pMSG8+ from log variables even when acoustic velocity is not available; however, this requires wood basic density models calibrated for the Pinus radiata growing zone. https://revistas.ubiobio.cl/index.php/MCT/article/view/5654Acoustic technologylog variablesPinus radiataregression modelsstructural lumber |
| spellingShingle | Elvis Gavilán Rosa M. Alzamora Luis A. Apiolaza Katia Sáez Juan Pedro Elissetche Antonio Pinto Modelling the influence of radiata pine log variables on structural lumber production Maderas: Ciencia y Tecnología Acoustic technology log variables Pinus radiata regression models structural lumber |
| title | Modelling the influence of radiata pine log variables on structural lumber production |
| title_full | Modelling the influence of radiata pine log variables on structural lumber production |
| title_fullStr | Modelling the influence of radiata pine log variables on structural lumber production |
| title_full_unstemmed | Modelling the influence of radiata pine log variables on structural lumber production |
| title_short | Modelling the influence of radiata pine log variables on structural lumber production |
| title_sort | modelling the influence of radiata pine log variables on structural lumber production |
| topic | Acoustic technology log variables Pinus radiata regression models structural lumber |
| url | https://revistas.ubiobio.cl/index.php/MCT/article/view/5654 |
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