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Abstract

Southern pine lumber 2 by 8s and 2 by 10s from across the Southeast were used as the parent material source for this study. Fundamental data were collected for each piece of lumber: growth rings per inch (RPI), presence of pith, and specific gravity (SG), among other information. After mechanical property evaluation through static bending, small clear specimens were cut from the lumber pieces and tested for compression perpendicular to grain (C⊥). Those values were then compared with 2 by 8 and 2 by 10 average RPI and density. The results were significant for both tests. Therefore, a segmentation of the growth ring data into groups of <3, 3 to 5, and >5 RPI was conducted. Correlations were run specific to each group, per both lumber sizes, and still significance was found. Segmentation of SG was not significant only for 2 by 8 SG <0.4. The results suggest that SG is a better predictor of C⊥ than RPI alone due to statistical significance found during these analyses.

Wood is a natural renewable material used in various construction and architectural applications. Wood is cost preferred when compared with steel and concrete and has a greater strength-to-weight ratio. However, a host of environmental factors affect tree growth and resultant properties of the wood. Wood properties can vary widely between different species of wood; also between different trees of the same species and even within individual trees of the same species. It is not uncommon for properties to vary within a single board (Panshin and deZeeuw 1980).

The southeastern United States provides a lush environment to grow a plethora of softwood and hardwood species. Tree growth and quality are key considerations when it comes to wood usage. As such, construction material is scrutinized for strength and stiffness. As the forest landscape has evolved, some of the mechanical properties have evolved as well (Biblis 2006).

When wood is used for construction purposes, high stress levels are applied, and if the load applied is above the elastic range of wood, plastic deformation or failure occurs. Design values are set to ensure safe and satisfactory performance of the material, and the most common strength properties measured for design purposes are bending, compression parallel and perpendicular to grain, tension parallel to grain, and shear parallel to grain. In some cases, tensile strength perpendicular to grain and side hardness measurements are also required (Green 2001).

The southern forest landscape has been changing over the past several decades. One can see the increasing establishment of southern pine plantations across tracts that were once mixed hardwood and southern pine intermingled (Doyle and Markwardt 1966, Fox et al. 2004, Zhang et al. 2010). This trend has garnered much interest from a material quality standpoint. The grades assigned for southern pine lumber were based primarily on knots and slope of grain and wane; later, annual rings and percentage of latewood properties were added into dense-grain grades (Kretschmann 2010, ASTM International 2019). The first published design value was developed using mature old-growth lumber, and the properties were highly correlated to the small clear specimens (Madsen and Nielsen 1976). However, the management of forests has changed through the years, and the properties of southern pine lumber available in the market have also changed (Allen and Fox 2005, Biblis 2006).

Many questions of the changing growth paradigm have altered the traditional lumber standards, and the design for southern pine lumber applications is now dated and not current. The Southern Pine Inspection Bureau (SPIB) initiated a series of evaluations to assess the changes in southern pine lumber due to new forest management practices, and these assessments were based on mean modulus of elasticity (MOE) determined using nondestructive measurements. However, other properties, such as the presence of pith, modulus of rupture (MOR), compression parallel and perpendicular to grain, and tension, should be analyzed to evaluate the changes in the source (Kretschmann et al. 1999). In 2010, SPIB decided to reevaluate southern pine lumber, resulting in an increase in the design value (Table 1). Recent studies have examined the southern pine growth stock in various capacities to encapsulate that very premise (Stoker et al. 2007; Dahlen et al. 2012, 2014, 2018; James et al. 2013; Yang et al. 2015a, 2015b; Butler et al. 2016; França et al. 2018a).

Table 1 Summary of changes in the design value for southern pine No. 2 grade lumber (American Forest and Paper Association 2005, American Lumber Standards Committee 2013).a
Table 1

A large proportion of recent research has focused on nondestructive means to verify those values (Brashaw et al. 2009) and visual characteristics that might help predict grade; portable nondestructive devices are now prevalent and show promise in reliability (Frese 2008; Wang 2013; França et al. 2018b, 2018c, 2020). With much emphasis on these and other studies related to evaluating southern pine lumber, common experimentation has been keen on MOR and MOE values.

Beyond the contributions to advances in the research and development of current southern pine lumber provided by MOR and MOE, there is still a need for information on other strength properties yet to be evaluated extensively (Mascia and Cramer 2009). Each mechanical property is important, and different structural applications will require a specific mechanical property. For example, when wood is used for short posts or columns, the knowledge of compression perpendicular to grain is needed to determine how much load can be applied in this circumstance (Shmulsky and Jones 2019). Only a few studies have focused on minor properties (Kenesh 1968, Kretschmann and Bendtsen 1992, Kretschmann 1997), and no data are available on the relationship between growth rings per inch (RPI) and specific gravity (SG) with compression perpendicular to grain (C⊥).

This study intends to explore if changes in forest management have an impact on C⊥ and to investigate the relationship between SG and RPI with C⊥.

Materials and Methods

Materials

Southern pine 2 by 8 and 2 by 10 lumber was collected from various retail stores in the southeastern United States in an effort to acquire current production stock for mechanical property evaluation. For this study, No. 2 grade lumber was chosen because it represents the grade most produced in this region (Southern Pine Forest Production Association [SFPA] 2005). The lumber pieces were reevaluated for grade verification by a certified grader from two grading agencies: SPIB and Timber Products Inspection. This sampling method tried to mimic the in-grade lumber used by SPIB to derive published design values for southern pine (SPIB 2014). After regrading the specimens, several data were collected including, counting RPI and SG. Other data, such as the presence of pith and the percentage of latewood, were reported and described in França et al. (2018c). The overall moisture content (MC) for the lumber pieces averaged around 12 percent. Table 2 describes the sample size and dimensions for the specimens used in this study.

Table 2 Summary of sample size and dimensions of No. 2 2 by 8 and 2 by 10 southern pine lumber pieces.
Table 2

Methods

The measurement of RPI was done by counting the rings in each sample on both ends of the specimens following the procedures from SPIB grading rules (SPIB 2014); then the number of rings was divided by the thickness or the width, depending on the grain orientation of the piece (radial or tangential). Further analysis used a data segmentation approach. RPI data were split into three distinct groups: <3 RPI, 3 to 5 RPI, and >5 RPI. The ASTM D2395 standard (ASTM International 2017) was used to determine the density SG measured at 12 percent MC. The SG was also divided into three groups for further analysis (<0.4, 0.4 to 0.5, and >0.5).

The full-size lumber specimens were broken in a four-point bending apparatus with a span-to-depth ratio of 17-to-1 in accordance with ASTM D198 (ASTM International 2015) on an Instron universal testing machine. For the C⊥ test, small clear specimens were obtained from full-size lumber pieces after a bending test was completed. This test followed the ASTM D143 standard (ASTM International 2014). The rate for the load applied was 0.003 in./in. (0.00762 cm/cm) of nominal specimen length/min. Because 2 by 8 lumber (5.5 MPa) exhibited a higher MOR value than 2 by 10 lumber (5.2 MPa), small clear specimens were sorted according to the previous nominal size. The results for compression parallel to the grain were reported in Irby et al. (2020).

Statistical Analyses

Statistical analyses were conducted using SPSS version 25 (IBM 2018) using the Pearson correlation to test C⊥ and RPI for significance (α = 0.05) with subsequential testing of SG. A linear regression fit was added to the scatter plots for evaluation of any relationship. Data were segmented in various groups: RPI (<3, 3 to 5, and >5) and SG (<0.4, 0.4 to 0.5, and >0.5) to test if any significance arose among more defined groupings.

Single-variable linear regression analyses (α = 0.01) were built for each cross-section lumber group to correlate the segmentation group's outputs to compression parallel to grain values. The linear regressions were conducted given the independent variables (x, which can be represented by RPI groups and SG classes) and the dependent variable (C⊥). The coefficient of determination (r2), which measures the strength of the relationship variables, was the main focus.

The mathematical regression models between the independent variables and C⊥ were assumed to be linear and of the following form:

Results and Discussion

Table 3 illustrates the average growth rate RPI, SG, and C⊥ values in psi for each lumber group. Averages for each variable were very similar for 2 by 8 and 2 by 10 lumber sizes. Table 4 shows the attempt to discern any mean differences between the two lumber sizes. The t test revealed a significant difference for all three variables. When comparing the number of RPI and SG with previous studies, it is possible to see a decrease of RPI along the years; where Markwardt and Wilson (1935) and Newlin and Wilson (1917) found an average value of 9 RPI and an SG average around 0.54 for southern yellow pine lumber, in this study RPI averaged 4 and SG 0.48.

Table 3 Average characteristics of 2 by 8 and 2 by 10 No. 2 southern pine lumber pieces.a
Table 3
Table 4 Two-sample t test for rings per inch (RPI), specific gravity (SG), and compression perpendicular to grain (C⊥) of small clear specimens from 2 by 8 and 2 by 10 No. 2 southern pine lumber.
Table 4

Simple scatter plots were used to show the relationship between RPI and C⊥. The data widely varied for both lumber sizes; see Figure 1. Table 5 shows the attempt to identify any relationship between RPI and SG with C⊥. Compression perpendicular to grain data for 2 by 8 lumber showed no significance in relation to the categorized RPI data (r2 = 0.007). For 2 by 10 lumber, r2 = 0.021 exhibited a low relationship, but the result was significant.

Figure 1.—. Relationship between compression perpendicular to grain (psi) and growth rings per inch for 2 by 8 and 2 by 10 southern pine lumber.Figure 1.—. Relationship between compression perpendicular to grain (psi) and growth rings per inch for 2 by 8 and 2 by 10 southern pine lumber.Figure 1.—. Relationship between compression perpendicular to grain (psi) and growth rings per inch for 2 by 8 and 2 by 10 southern pine lumber.
Figure 1 Relationship between compression perpendicular to grain (psi) and growth rings per inch for 2 by 8 and 2 by 10 southern pine lumber.

Citation: Forest Products Journal 70, 2; 10.13073/FPJ-D-19-00043

Table 5 Rings per inch (RPI) and specific gravity (SG) averages, coefficients of determination (r2) values, and P values for the compression perpendicular to grain (C⊥) prediction of 2 by 8 and 2 by 10 southern pine lumber.
Table 5

SG was used as an independent variable to see if stronger relationships were present. The test consisting of C⊥ as the dependent variable and using SG as the independent analysis was done across all data for each lumber size: 2 by 8 and 2 by 10 (see Fig. 2). The results were significant for both lumber sizes, and the r2 for 2 by 8 was 0.242 and for 2 by 10 was 0.348.

Figure 2.—. Relationship between compression perpendicular to grain (psi) and specific gravity for 2 by 8 and 2 by 10 southern pine lumber.Figure 2.—. Relationship between compression perpendicular to grain (psi) and specific gravity for 2 by 8 and 2 by 10 southern pine lumber.Figure 2.—. Relationship between compression perpendicular to grain (psi) and specific gravity for 2 by 8 and 2 by 10 southern pine lumber.
Figure 2 Relationship between compression perpendicular to grain (psi) and specific gravity for 2 by 8 and 2 by 10 southern pine lumber.

Citation: Forest Products Journal 70, 2; 10.13073/FPJ-D-19-00043

Further refinement of the data was conducted to better understand the relationships. This approach attempted to look at SG as an independent variable for RPI groups: <3 RPI, 3 to 5 RPI, and >5 RPI in both lumber sizes. Table 6 shows the data breakout per RPI groups and the test variables for the 2 by 8 and 2 by 10 specimens plus statistical output. Figures 3 through 5 exhibit the relationship for the groups: <3, 3 to 5, and >5 RPI, respectively.

Table 6 Growth rings per inch (RPI) categories, compression perpendicular to grain (C⊥) averages per group, coefficients of determination (r2), and P values for 2 by 8 and 2 by 10 lumber.
Table 6
Figure 3.—. Relationship between compression perpendicular to grain (psi) and specific gravity derived from growth rings per inch (RPI) segmentation <3 RPI for 2 by 8 and 2 by 10 southern pine lumber.Figure 3.—. Relationship between compression perpendicular to grain (psi) and specific gravity derived from growth rings per inch (RPI) segmentation <3 RPI for 2 by 8 and 2 by 10 southern pine lumber.Figure 3.—. Relationship between compression perpendicular to grain (psi) and specific gravity derived from growth rings per inch (RPI) segmentation <3 RPI for 2 by 8 and 2 by 10 southern pine lumber.
Figure 3 Relationship between compression perpendicular to grain (psi) and specific gravity derived from growth rings per inch (RPI) segmentation <3 RPI for 2 by 8 and 2 by 10 southern pine lumber.

Citation: Forest Products Journal 70, 2; 10.13073/FPJ-D-19-00043

Figure 4.—. Relationship between compression perpendicular to grain (psi) and specific gravity derived from growth rings per inch (RPI) segmentation from 3 to 5 RPI for 2 by 8 and 2 by 10 southern pine lumber.Figure 4.—. Relationship between compression perpendicular to grain (psi) and specific gravity derived from growth rings per inch (RPI) segmentation from 3 to 5 RPI for 2 by 8 and 2 by 10 southern pine lumber.Figure 4.—. Relationship between compression perpendicular to grain (psi) and specific gravity derived from growth rings per inch (RPI) segmentation from 3 to 5 RPI for 2 by 8 and 2 by 10 southern pine lumber.
Figure 4 Relationship between compression perpendicular to grain (psi) and specific gravity derived from growth rings per inch (RPI) segmentation from 3 to 5 RPI for 2 by 8 and 2 by 10 southern pine lumber.

Citation: Forest Products Journal 70, 2; 10.13073/FPJ-D-19-00043

Figure 5.—. Relationship between compression perpendicular to grain (psi) and specific gravity derived from growth rings per inch (RPI) segmentation >5 RPI for 2 by 8 and 2 by 10 southern pine lumber.Figure 5.—. Relationship between compression perpendicular to grain (psi) and specific gravity derived from growth rings per inch (RPI) segmentation >5 RPI for 2 by 8 and 2 by 10 southern pine lumber.Figure 5.—. Relationship between compression perpendicular to grain (psi) and specific gravity derived from growth rings per inch (RPI) segmentation >5 RPI for 2 by 8 and 2 by 10 southern pine lumber.
Figure 5 Relationship between compression perpendicular to grain (psi) and specific gravity derived from growth rings per inch (RPI) segmentation >5 RPI for 2 by 8 and 2 by 10 southern pine lumber.

Citation: Forest Products Journal 70, 2; 10.13073/FPJ-D-19-00043

The linear regression results for 2 by 8 lumber were r2 = 0.279, 0.290, and 0.086 for each RPI group, respectively. For 2 by 10 lumber r2 = 0.420, 0.226, and 0.369. All the 2 by 8 and 2 by 10 groups showed a significant relationship for RPI and C⊥. Lower significance was found only in 2 by 8 with >5 RPI. The results suggest stronger statistical significance across all SG/RPI segments in 2 by 10 pieces compared with similar 2 by 8 segments.

Similar analyses looked at SG as an independent variable for predicting C⊥. The segmentation of the data was based on SG classes: <0.4, 0.4 to 0.5, and >0.5 for C⊥ for 2 by 8 and 2 by 10 lumber. Figures 6 through 8 show the relationship in each SG class for 2 by 8 and 2 by 10 lumber sizes.

Figure 6.—. Relationship between compression perpendicular to grain (psi) and specific gravity segmentation <0.4 for 2 by 8 and 2 by 10 southern pine lumber.Figure 6.—. Relationship between compression perpendicular to grain (psi) and specific gravity segmentation <0.4 for 2 by 8 and 2 by 10 southern pine lumber.Figure 6.—. Relationship between compression perpendicular to grain (psi) and specific gravity segmentation <0.4 for 2 by 8 and 2 by 10 southern pine lumber.
Figure 6 Relationship between compression perpendicular to grain (psi) and specific gravity segmentation <0.4 for 2 by 8 and 2 by 10 southern pine lumber.

Citation: Forest Products Journal 70, 2; 10.13073/FPJ-D-19-00043

Figure 7.—. Relationship between compression perpendicular to grain (psi) and specific gravity segmentation from 0.4 to 0.5 for 2 by 8 and 2 by 10 southern pine lumber.Figure 7.—. Relationship between compression perpendicular to grain (psi) and specific gravity segmentation from 0.4 to 0.5 for 2 by 8 and 2 by 10 southern pine lumber.Figure 7.—. Relationship between compression perpendicular to grain (psi) and specific gravity segmentation from 0.4 to 0.5 for 2 by 8 and 2 by 10 southern pine lumber.
Figure 7 Relationship between compression perpendicular to grain (psi) and specific gravity segmentation from 0.4 to 0.5 for 2 by 8 and 2 by 10 southern pine lumber.

Citation: Forest Products Journal 70, 2; 10.13073/FPJ-D-19-00043

Figure 8.—. Relationship between compression perpendicular to grain (psi) and specific gravity segmentation >0.5 for 2 by 8 and 2 by 10 southern pine lumber.Figure 8.—. Relationship between compression perpendicular to grain (psi) and specific gravity segmentation >0.5 for 2 by 8 and 2 by 10 southern pine lumber.Figure 8.—. Relationship between compression perpendicular to grain (psi) and specific gravity segmentation >0.5 for 2 by 8 and 2 by 10 southern pine lumber.
Figure 8 Relationship between compression perpendicular to grain (psi) and specific gravity segmentation >0.5 for 2 by 8 and 2 by 10 southern pine lumber.

Citation: Forest Products Journal 70, 2; 10.13073/FPJ-D-19-00043

Table 7 exhibits the linear relationship between C⊥ and SG segmentations for 2 by 8 and 2 by 10 lumber sizes. The linear regression results for each SG class in 2 by 8 lumber were r2 = 0.001 for SG <0.4, 0.064 for 0.4 SG ≤0.5, and 0.075 for SG >0.4. The results did not reveal statistical significance for the <0.4 segmentation for 2 by 8 lumber data. For 2 by 10 lumber, r2 = 0.173, 0.180, and 0.088. All three SG segmentations showed statistical significance for 2 by 10 lumber.

Table 7 Specific gravity (SG) categories, compression perpendicular to grain (C⊥) averages per group, coefficients of determination (r2), and P values for 2 by 8 and 2 by 10 lumber.
Table 7

The results also suggest stronger statistical significance across all SG class segments in 2 by 10 compared with similar 2 by 8 segments. The approach of SG as the independent variable for RPI groups exhibited stronger prediction of C⊥ compared to SG segmentation alone.

To summarize the findings, Table 8 illustrates r2 values for each test. The full list of independent variables is listed as the first column headings. Lumber sizes are represented by rows. Slope, intercept, r2, standard error of estimate, and P value are for the compression parallel test and the various independent variables. SG as an independent variable predicts C⊥ better than RPI.

Table 8 Summary of r2 values for tests of compression perpendicular to grain and the various independent variables.a
Table 8

Conclusions

The No. 2 southern pine 2 by 8 and 2 by 10 lumber specimens utilized in this study were characteristic offerings for that grade material (SPIB 2014). As seen in Figures 1 and 4, the correlation of RPI in relation to C⊥ was not significant for 2 by 8 lumber but was significant for 2 by 10 lumber. When RPI data were segmented, no significance was seen for either lumber size for any RPI group (Table 2). Wide data disbursement in addition to small sample size for at least one of the segments could have been key factors. Analysis with SG incorporated as another independent variable was conducted. Figures 3 and 4 show average SG values with r2 values 0.242 and 0.348 for 2 by 8 and 2 by 10 lumber, respectively, across all data. Data for SG were then segmented two ways: per the RPI groups and per the SG groups. The SG segmentations per RPI data showed slightly stronger relationships than SG alone for both lumber sizes. Although only one group showed statistical significance for 2 by 8 (SG <4 RPI, P = 0.96), the 2 by 10 lumber test results were statistically significant across all SG/RPI groups. The segmented SG groups were not statistically significant for either lumber size or SG classes. The approach using SG as an independent variable for RPI groups exhibited stronger prediction of C⊥ compared to SG segmentation alone.

Acknowledgments

The authors wish to acknowledge the US Department of Agriculture (USDA); Research, Education and Economics (REE); Agriculture Research Service (ARS); Administrative and Financial Management (AFM); Financial Management and Accounting Division (FMAD); and Grants and Agreements Management Branch (GAMB) under agreement no. 58-0202-4-001. In addition, special thanks to the USDA Forest Service Forest Products Laboratory, Southern Pine Inspection Bureau, and Timber Products Inspection for the assistance in the larger study from which this project stemmed. This article was approved as journal article SB 958 of the Forest and Wildlife Research Center, Mississippi State University.

Literature Cited

  • Allen, H. L.
    and
    T. Fox.
    2005. What is ahead for intensive pine plantation silviculture in the South? South. J. Appl. Forestry29(
    2
    ):62–69.

  • American Forest
    and Paper Association (AFPA)
    . 2005. National design specification (NDS) for wood construction with commentary and supplement: Design values for wood construction 2005 edition.
    AFPA
    ,
    Washington, D.C.

  • American Lumber Standards Committee (ALSC). 2013. American lumber standards committee board of review: Board of review minutes. 1 February 2013.

    ALSC
    ,
    Germantown, Maryland
    .

  • ASTM International. 2014. Standard test methods for small clear specimens of timber. ASTM D143.

    ASTM International
    ,
    West Conshohocken, Pennsylvania
    .

  • ASTM International. 2015. Standard test methods of statics of lumber in structural sizes. ASTM D198.

    ASTM International
    ,
    West Conshohocken, Pennsylvania
    .

  • ASTM International. 2017. Standard test methods for density and specific gravity (relative density) of wood and wood-based materials. ASTM D2395.

    ASTM International
    ,
    West Conshohocken, Pennsylvania
    .

  • ASTM International. 2019. Standard practice for establishing allowable properties for visually-graded dimension lumber from in-grade tests of full-size specimens. ASTM D1990.

    ASTM International
    ,
    West Conshohocken, Pennsylvania
    .

  • Biblis, E. J.
    2006. Flexural properties and compliance to visual grade requirements of 2 by 4 and 2 by 6 loblolly pine lumber obtained from 19 year old plantations. Forest Prod. J. 56(
    9
    ):71–73.

  • Brashaw, B. K.,
    V. Bucur,
    F. Divos,
    R. Goncalves,
    J. X. Lu,
    R. Meder,
    R. F. Pellerin,
    S. Potter,
    R. J. Ross,
    X. Wang,
    and
    Y. F. Yin.
    2009. Nondestructive testing and evaluation of wood: A worldwide research update. Forest Prod. J. 59(
    3
    ):7–14.

  • Butler, M. S.,
    J. Dahlen,
    F. Antony,
    M. Kane,
    T. L. Eberhardt,
    H. Jin,
    K. Love-Myers,
    and
    J. P. McTague.
    2016. Relationships between loblolly pine small clear specimens and dimension lumber tested in static bending. Wood Fiber Sci. 48(
    2
    ):81–95.

  • Dahlen, J.,
    D. Auty,
    and
    T. L. Eberhardt.
    2018. Models for predicting density and ring width for loblolly pine from intensively managed plantations, and implications for wood utilization. Forests9(
    292
    ):1–20.

  • Dahlen, J.,
    P. D. Jones,
    R. D. Seale,
    and
    R. Shmulsky.
    2012. Bending strength and stiffness of in-grade Douglas-fir and southern pine No. 2 2 × 4 lumber. Can. J. Forest Res. 42(
    5
    ):858–867.

  • Dahlen, J.,
    P. D. Jones,
    R. D. Seale,
    and
    R. Shmulsky.
    2014. Bending strength and stiffness of wide dimension southern pine No. 2 lumber. Eur. J. Wood Wood Prod. 72(
    6
    ):759–768.

  • Doyle, D. V.
    and
    L. J. Markwardt.
    1966. Properties of southern pine in relation to strength grading of dimension lumber. Research Paper FPL-RP-64.
    USDA Forest Service
    ,
    Forest Products Laboratory, Madison, Wisconsin
    .
    62
    pp.

  • Fox, T. R.,
    E. J. Jokela,
    and
    H. L. Allen.
    2004. The evolution of pine plantation silviculture in the southern United States. In:Southern Forest Science: Past, Present, and Future.
    H. M. Rauscher
    and
    K. Johnsen
    (Eds.). Forest Service General Technical Report SRS-75.
    US Department of Agriculture
    ,
    Washington, D.C.
    pp. 63–82.

  • França, F. J. N.,
    T. S. F. A. França,
    R. D. Seale,
    and
    R. Shmulsky.
    2020. Nondestructive evaluation of 2 by 8 and 2 by 10 southern pine dimensional lumber. Forest Prod. J. 70(
    1
    ):79–87.

  • França, F. J. N.,
    R. D. Seale,
    R. Shmulsky,
    and
    T. S. F. A. França.
    2018 b. Assessing southern pine 2 × 4 and 2 × 6 lumber quality: Longitudinal and transverse vibration. Wood Fiber Sci. 51(
    1
    ):2–15.

  • França, F. J. N.,
    R. D. Seale,
    R. Shmulsky,
    and
    T. S. F. A. França.
    2018 c. Modeling mechanical properties of 2 by 4 and 2 by 6 southern pine lumber using longitudinal vibration and visual characteristics. Forest Prod. J. 68(
    3
    ):286–294.

  • França, T. S. F. A.,
    F. J. N. França,
    R. D. Seale,
    and
    R. Shmulsky.
    2018 a. Bending strength and stiffness of No. 2 grade southern pine lumber. Wood Fiber Sci. 50(
    2
    ):1–15.

  • Frese, M.
    2008. Visual strength grading supported by mechanical grading. In:ConfCOST E53, October 29–30, 2008, Delft, the Netherlands;
    Delft University of Technology
    ,
    the Netherlands
    . pp. 19–30.

  • Green, D. W.
    2001. Wood: Strength and stiffness. In:Encyclopedia of Materials: Science and Technology.
    K. H. J. Buschow
    (Ed.).
    Elsevier
    ,
    Amsterdam and New York
    . pp. 9732–9736

  • IBM. 2018. IBM SPSS statistics for windows, version 25.0.

    IBM Corporation
    ,
    Armonk, New York
    .

  • Irby, N. E.,
    F. J. N. França,
    H. M. Barnes,
    R. Seale,
    and
    R. Shmulsky.
    2020. Effect of growth rings per inch and density on compression parallel to grain in southern pine lumber. BioResources15(
    1
    ).

  • James, E.,
    D. E. Kretschmann,
    C. C. Hatfield,
    and
    D. W. Green.
    2013. Analysis of the Part I Southern Pine In-Grade Program data. Report GTR-221.
    USDA Forest Products Laboratory
    ,
    Madison, Wisconsin
    .
    15
    pp.

  • Kenesh, R. H.
    1968. Strength and elastic properties of wood in perpendicular compression. Forest Prod. J. 18(
    1
    ):65–72.

  • Kretschmann, D. E.
    1997. Effect of juvenile wood on shear parallel and compression perpendicular-to-grain strength of loblolly pine. Research Paper FPL-RP-647.
    USDA Forest Service
    ,
    Forest Products Laboratory, Madison, Wisconsin
    .
    25
    pp.

  • Kretschmann, D. E.
    2010. Stress grades and design properties for lumber, round timber, and ties. In:Wood Handbook.
    W. RamsaySmith
    (Ed.). General Technical Report FPL-GTR-190.
    USDA Forest Service, Forest Products Laboratory
    ,
    Madison, Wisconsin
    . pp. 7-1–7-16.

  • Kretschmann, D. E.
    and
    B. A. Bendtsen.
    1992. Ultimate tensile stress and modulus of elasticity of fast-grown plantation loblolly pine lumber. Wood Fiber Sci. 24(
    2
    ):189–203.

  • Kretschmann, D. E.,
    J. W. Evans,
    and
    L. Brown.
    1999. Monitoring of visually graded structural lumber. Research Paper FPL-RP-576.
    USDA Forest Service
    ,
    Forest Products Laboratory, Madison, Wisconsin
    .
    18
    pp.

  • Madsen, B.
    and
    L. F. Nielsen.
    1976. In-grade testing: Size investigations on lumber subjected to bending. Structural Research Series Report 15.
    Department of Civil Engineering, University of British Columbia
    ,
    Vancouver
    .

  • Markwardt, L. J.
    and
    T. R. C. Wilson.
    1935. Strength and related properties of woods grown in the United States. Technical Bulletin 479.
    USDA Forest Service
    ,
    Forest Products Laboratory, Washington, D.C
    .
    99
    pp.

  • Mascia, N. T.
    and
    S. M. Cramer.
    2009. On the effect of the number of annual growth rings, specific gravity and temperature on redwood elastic modulus. Maderas Cienc. Tecnol. 11(
    1
    ):47–60.

  • Newlin, J. A.
    and
    T. R. C. Wilson.
    1917. Mechanical properties of woods grown in the United States. Technical Bulletin 556.
    USDA Forest Service
    ,
    Forest Products Laboratory, Washington, D.C
    . pp. 551–575.

  • Panshin, A. J.
    and
    D. deZeeuw.
    1980. Textbook of Wood Technology. Vol. 1, 4th ed.
    McGraw-Hill
    ,
    New York
    .

  • Shmulsky, R.
    and
    P. D. Jones.
    2019. Forest Products and Wood Science: An Introduction. 7th ed.
    John Wiley & Sons
    ,
    Hoboken, New Jersey
    .
    482
    pp.

  • Southern Pine Forest Production Association (SFPA). 2005. Industry statistics: Annual production from 2000 to 2005. White Paper.

    SFPA
    ,
    Kenner, Louisiana
    .

  • Southern Pine Inspection Bureau (SPIB). 2014. Standard grading rules for southern pine lumber.

    SPIB
    ,
    Pensacola, Florida
    .
    152
    pp.

  • Stoker, D. L.,
    R. G. Pearson,
    D. E. Kretschmann,
    Y. Tang,
    and
    B. Simpson.
    2007. Effect of press-drying on static bending properties of plantation-grown No. 2 loblolly pine lumber. Forest Prod. J. 57(
    11
    ):70–73.

  • Wang, X.
    2013. Stress wave E-rating of structural timber—Size and moisture content effects. General Technical Report FPL-GTR-226. In:Proceedings of the 18th International Nondestructive Testing and Evaluation of Wood Symposium,
    R. J. Ross
    and
    X. Wang,
    (Eds.), September 24–27, 2013, Madison, Wisconsin;
    USDA Forest Service, Forest Products Laboratory
    ,
    Madison, Wisconsin
    . pp. 38–46.

  • Yang, B. Z.,
    R. D. Seale,
    R. Shmulsky,
    J. Dahlen,
    and
    X. Wang.
    2015 a. Comparison of nondestructive testing methods for evaluating No. 2 southern pine lumber. Part A, modulus of elasticity. Wood Fiber Sci. 47(
    4
    ):1–10.

  • Yang, B. Z.,
    R. D. Seale,
    R. Shmulsky,
    J. Dahlen,
    and
    X. Wang.
    2015 b. Comparison of nondestructive testing methods for evaluating No. 2 southern pine lumber. Part B, modulus of rupture. Wood Fiber Sci. 47(
    4
    ):1–10.

  • Zhang, Y.,
    I. Majumdar,
    and
    J. Schelhas.
    2010. Changes in woodland use from longleaf pine to loblolly pine. Sustainability2:2734–2745.

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<italic>Figure 1</italic>
Figure 1

Relationship between compression perpendicular to grain (psi) and growth rings per inch for 2 by 8 and 2 by 10 southern pine lumber.

<italic>Figure 2</italic>
Figure 2

Relationship between compression perpendicular to grain (psi) and specific gravity for 2 by 8 and 2 by 10 southern pine lumber.

<italic>Figure 3</italic>
Figure 3

Relationship between compression perpendicular to grain (psi) and specific gravity derived from growth rings per inch (RPI) segmentation <3 RPI for 2 by 8 and 2 by 10 southern pine lumber.

<italic>Figure 4</italic>
Figure 4

Relationship between compression perpendicular to grain (psi) and specific gravity derived from growth rings per inch (RPI) segmentation from 3 to 5 RPI for 2 by 8 and 2 by 10 southern pine lumber.

<italic>Figure 5</italic>
Figure 5

Relationship between compression perpendicular to grain (psi) and specific gravity derived from growth rings per inch (RPI) segmentation >5 RPI for 2 by 8 and 2 by 10 southern pine lumber.

<italic>Figure 6</italic>
Figure 6

Relationship between compression perpendicular to grain (psi) and specific gravity segmentation <0.4 for 2 by 8 and 2 by 10 southern pine lumber.

<italic>Figure 7</italic>
Figure 7

Relationship between compression perpendicular to grain (psi) and specific gravity segmentation from 0.4 to 0.5 for 2 by 8 and 2 by 10 southern pine lumber.

<italic>Figure 8</italic>
Figure 8

Relationship between compression perpendicular to grain (psi) and specific gravity segmentation >0.5 for 2 by 8 and 2 by 10 southern pine lumber.

Contributor Notes

The authors are, respectively, Graduate Research Assistant, Assistant Research Professor, Warren S. Thompson Professor, Warren S. Thompson Professor, and Head and Warren S. Thompson Professor, Dept. of Sustainable Bioproducts, Mississippi State Univ., Starkville (neirby@up.com, fn90@msstate.edu [corresponding author], hb1@msstate.edu, rds9@msstate.edu, rs26@msstate.edu). This paper was received for publication in September 2019. Article no. 19-00043.