Dimension Lumber Grade and Yield Estimates for Yellow-Poplar

Denig, Joseph; Wengert, Eugene M.; Brisbin, Robert L.; Schroeder, James G.

doi:10.1093/sjaf/8.3.123

Cited by 3 publications

(3 citation statements)

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“…Defects, such as knots, stains, knobs, and holes, as well as the length and diameter of logs, are the main variables that impact lumber yields (Denig et al 1984;Kretschmann 2010). Lumber yields can be predicted using only the diameter and length of the log, but the log quality is perhaps the most important and major factor for predicting the log yield.…”

Section: Hardwood Log Gradesmentioning

confidence: 99%

Analysis of hardwood lumber grade yields using Monte Carlo simulation

Quesada-Pineda

Adhikari

Bond

et al. 2019

BioRes

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The goal of this study was to develop a lumber grade yield prediction model with a probability-based technique known as the Monte Carlo simulation. The data to develop the prediction model was taken from an existing lumber grade yield database developed from red oak logs sawn at the Appalachian region of the United States. Statistical input analysis techniques were used to fit the lumber grade yields to hypothesized probability distributions. Inverse cumulative probability function distributions were developed from the fitted probability distributions to simulate and predict lumber grade yields. The predicted gross revenue was compared with the actual gross revenue and against the gross revenue predicted by a multiple linear regression (MLR) model. The predicted gross revenue using the Monte Carlo simulation had a 0.88% absolute error compared with the actual gross revenue, while the predicted gross revenue from the MLR model had an absolute error of 3.31%. The higher prediction power of the Monte Carlo method was more effective when predicting lumber grade yields from individual log groups. The Monte Carlo model developed in this research can be easily implemented to quickly predict lumber grade yields or gross revenue to support procurement, log inventory management, production, planning, and marketing operations.

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Section: Hardwood Log Gradesmentioning

confidence: 99%

Analysis of hardwood lumber grade yields using Monte Carlo simulation

Quesada-Pineda

Adhikari

Bond

et al. 2019

BioRes

View full text Add to dashboard Cite

show abstract

“…This process, now tried on numerous low-and medium-density hardwoods, has proved to be eminently successful. Extensive study of yellow-poplar studs at several locations has shown 90 to 100 percent of pieces manufactured by SDR to be of STUD grade (Denig and Wengert 1985;NHPMA 1978;. Results for other species such as paper birch , red alder (Layton 1982), and cottonwood (Trachsel 1982) have been very good, as have limited trials on basswood, red maple, black willow , sweetgum, blackgum, and sycamore.…”

Section: Structural Lumber From Aspen: Using the Saw-dry-rip (Sdr) Prmentioning

confidence: 99%

Aspen symposium '89 proceedings; 1989 July 25-27; Duluth, MN.

Fs¹

1990

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“…Assessing the yield and recovery of SGHL is crucial for determining SGHL's economic feasibility as a CLT raw material. Previous yield studies by Denig et al (1984), Koch andStenglein (1986), andAllison et al (1987) focused on specific methods and species and aimed to produce SGHL for different markets than CLT, so a comprehensive evaluation is necessary to align with current market dynamics and sawing technology. However, Azambuja et al (2022) studied to produce SGHL for the structural market by remanufacturing the NHLA grade lumber focusing on CLTs, which likely cost more and become less competitive in the current market.…”

Section: Introductionmentioning

confidence: 99%

Producing structural grade hardwood lumber as a raw material for cross-laminated timber: Yield and economic analysis

Adhikari,

Bond,

Quesada

2023

BioRes

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The economic feasibility of producing structural-grade hardwood lumber (SGHL) that qualifies as a raw material for structurally rated cross-laminated timber (CLT) was examined. 126 yellow poplar logs from diameters 12 to 15 inches were selected and divided into test and control samples. A log yield study was then conducted of the yield and revenue generated when producing lumber graded with National Hardwood Lumber Association (NHLA) rules, SGHL rules, and a mix of both rules (NHLA and SGHL-graded lumber). Producing mix-grade lumber added approximately 27% more revenue than producing NHLA-grade lumber on average if sawmills adopt a cant sawing method. Mix-grade lumber production resulted in 32% of the total volume produced as SGHL and the remaining 68% as NHLA lumber. As a result, 2 Common and lower-grade lumber board footage was reduced to only 29% in test samples and remained converted into SGHL compared to more than 85% of 2 Common and lower-grade lumber boards for control samples. 95% of the SGHL produced as mixed-graded lumber with NHLA-grade lumber met the specifications required to produce structural CLT, and the remaining 5% can be utilized to produce non-structural grade CLTs if they meet the minimum requirement of the materials for CLT production.

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Dimension Lumber Grade and Yield Estimates for Yellow-Poplar

Cited by 3 publications

References 0 publications

Analysis of hardwood lumber grade yields using Monte Carlo simulation

Analysis of hardwood lumber grade yields using Monte Carlo simulation

Aspen symposium '89 proceedings; 1989 July 25-27; Duluth, MN.

Producing structural grade hardwood lumber as a raw material for cross-laminated timber: Yield and economic analysis

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