Prediction of board MOE from increment cores - data

Steven Psaltis; Chandan Kumar; Ian W Turner; Elliot J Carr; Troy Farrell; Loic Brancheriau; Henri Bailleres; David Lee

doi:10.25907/00007

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Prediction of board MOE from increment cores - data

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Prediction of board MOE from increment cores - data

Steven Psaltis, Chandan Kumar, Ian W Turner, Elliot J Carr, Troy Farrell, Loic Brancheriau, Henri Bailleres and David Lee

University of the Sunshine Coast

2020

DOI:

https://doi.org/10.25907/00007

Appears in UniSC Research Data Collection

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Data for A new approach for predicting board MOE from increment cores - board data20.18 kBDownload View

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Data for A new approach for predicting board MOE from increment cores - core data134.12 kBDownload View

DataCC BY V4.0, Open Access

Abstract

Forestry sciences

Numerical and computational mathematics

wood quality

southern pine

ultrasound

MOE

Modulus of Elasticity

Data from 68 southern pine hybrid trees (Pinus elliottii var. elliottii x P. caribaea var. hondurensis) sampled in southern Queensland (note no data exists for tree 61, hence tree numbers range from 1 to 69). The trees were collected from three sites in the southeast Queensland plantation estate owned by HQPlantations. Tree sizes were stratified by diameter at breast height, to ensure we collected the full range of sizes in the plantations. Ages of the trees ranged from 19 to 28 years. Two csv data files: File: Data for A new approach for predicting board MOE from increment cores - board data.csv 6 variables in columns and 635 lines of hybrid pine data: 1. Tree#: Identifier for the tree 2. Stud centre distance from pith (mm): Distance from the pith to the centre of the board 3. Log end: The end of the log from which the static bending sample was taken. Butt end = 0, Top end = 1. 4. Static bending MOE: The standard reference MOE measured using the four-point bending test. 5. Predicted MOE - 3D approach: The board MOE predicted from our three-dimensional approach using four diametrical cores extracted from the tree. 6. The board MOE predicted from our two-dimensional approach using a single diametrical core extracted near breast height. File: Data for A new approach for predicting board MOE from increment cores - core data.csv 9 variables in columns and 3184 lines of hybrid pine data: 1. Tree#: Identifier for the tree 2. Stocking: Number of stems per hectare 3. Height Core#: The core number extracted from the log, from 1 (butt end) to 4 (top end). 4. Side#: The side of the pith the segment was taken from. 5. Segment Position#: Segment count from each side of the pith, starting from 1 (near bark). 6. Segment#: Overall segment number. Note some segments were rejected hence maximum segment number is 3204. 7. Distance Seg from pith (Matlab): Distance from the pith to the centre of the segment, calculated using image processing in Matlab. 8. Height (m): Height of the core in the tree. 9. Segment US MOE: Segment MOE (in MPa) measured using the ultrasound method.

Details

Title: Prediction of board MOE from increment cores - data
Authors: Steven Psaltis (Data Collector) - Queensland University of Technology
Chandan Kumar (Data Collector) - Queensland Department of Agriculture and Fisheries
Ian W Turner (Data Collector) - Queensland University of Technology
Elliot J Carr (Data Collector) - Queensland University of Technology
Troy Farrell (Data Collector) - Queensland University of Technology
Loic Brancheriau (Data Collector) - Université de Montpellier
Henri Bailleres (Data Collector) - Queensland Department of Agriculture and Fisheries
David Lee (Data Collector) - University of the Sunshine Coast, Queensland, Forest Industries Research Centre
Additional notes: This work was supported by Forest and Wood Products Australia (FWPA) through project PNC361-1415, and partners University of the Sunshine Coast, Queensland Department of Agriculture and Fisheries, HQPlantations, Forestry Corporation of NSW, Hyne and Son Pty Ltd, Queensland University of Technology and Hancock Victoria Plantations. The authors also wish to thank the following for their valuable contributions to this work: Bruce Hogg, Tony Burridge and John Oostenbrink (Department of Agriculture and Fisheries, Gympie) for plantation sampling, log selection and harvesting, and Dan Field, Eric Littee, Rhianna Robinson, and Rica Minett (Department of Agriculture and Fisheries, Salisbury Research Facility) for sawing and testing.
Format: 2 x MS Excel files
Location: Location: Tuan State Forest, Toolara State Forest and Beerburrum State Forest
Publisher: University of the Sunshine Coast
Date collected: 2015–2018
Date published: 2020
DOI: 10.25907/00007
Organisation Unit: Tropical Forests and People Research Centre; University of the Sunshine Coast, Queensland; Forest Industries Research Centre; Forest Research Institute
Language: English
Record Identifier: 99484008902621
Output Type: Dataset

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