NC State
Van Duong, D., Schimleck, L., Lam Tran, D., and Dai Vo, H. (2022). "Radial and among-clonal variations of the stress-wave velocity, wood density, and mechanical properties in 5-year-old Acacia auriculiformis clones," BioResources 17(2), 2084-2096.


Radial and between-clone variations in stress-wave velocity, air-dry density (AD), and mechanical properties in six clones of 5-year-old Acacia auriculiformis trees planted in Vietnam were investigated. The potential to predict modulus of elasticity (MOE) and modulus of rupture (MOR) using stress-wave velocity of standing trees (SWVT) or small specimens (SWVS) was also examined. The examined SWVT, SWVS, and wood properties differed significantly among clones, particularly with two (clones 1 and 6) well suited for A. auriculiformis tree breeding programs focusing on lumber production, as they had the highest static bending values and no significant difference in AD between positions near pith and bark. At the specimen level, the best prediction of static bending properties could be achieved when both SWVS and AD were used in a model for calculation of dynamic modulus of elasticity (MOEd) in air-dry conditions. Significant correlations between SWVT and average MOE (r = 0.83) and MOR (r = 0.61) of test specimens indicated that the use of stress-wave technique for assessing MOE and MOR for selecting the best A. auriculiformis clones in terms of lumber performance was possible.

Download PDF

Full Article

Radial and Among-clonal Variations of the Stress-wave Velocity, Wood Density, and Mechanical Properties in 5-year-old Acacia auriculiformis Clones

Doan Van Duong,a,* Laurence Schimleck,b Dong Lam Tran,c and Hai Dai Vo c

Radial and between-clone variations in stress-wave velocity, air-dry density (AD), and mechanical properties in six clones of 5-year-old Acacia auriculiformis trees planted in Vietnam were investigated. The potential to predict modulus of elasticity (MOE) and modulus of rupture (MOR) using stress-wave velocity of standing trees (SWVT) or small specimens (SWVS) was also examined. The examined SWVT, SWVS, and wood properties differed significantly among clones, particularly with two (clones 1 and 6) well suited for A. auriculiformis tree breeding programs focusing on lumber production, as they had the highest static bending values and no significant difference in AD between positions near pith and bark. At the specimen level, the best prediction of static bending properties could be achieved when both SWVS and AD were used in a model for calculation of dynamic modulus of elasticity (MOEd) in air-dry conditions. Significant correlations between SWVT and average MOE (r = 0.83) and MOR (r = 0.61) of test specimens indicated that the use of stress-wave technique for assessing MOE and MOR for selecting the best A. auriculiformis clones in terms of lumber performance was possible.

DOI: 10.15376/biores.17.2.2084-2096

Keywords: Acacia auriculiformis; Wood density; MOE; MOR; Stress wave velocity

Contact information: a: Faculty of Forestry, Thai Nguyen University of Agriculture and Forestry, Thai Nguyen, Vietnam; b: Wood Science and Engineering, Oregon State University, 119 Richardson Hall, Corvallis, OR 97331, USA; c: Vietnamese Academy of Forest Sciences, Hanoi, Vietnam;

* Corresponding author:


Acacia auriculiformis A. Cunn. Ex Benth. occurs naturally in Australia, Papua New Guinea, and Indonesia, and it was introduced into Vietnam in the 1960s (Pinyopusarek et al. 1991; Hai 2009). In Vietnam, A. auriculiformis has become an important species especially in central and southern regions because it grows fast, fixes nitrogen, and displays adaptability to a wide range of environmental conditions. It produces acceptable pulp wood (pulp yield = 43-44%, fiber length approximately 1 mm (Jahan et al. 2008)) and small sawlogs in rotations as short as 7 to 10 years (Hai et al. 2008). The wood is recognized as being very attractive for furniture, wood turning and carving, as well as being suitable for construction work, e.g. framing and flooring (Hai 2009). Provenance trials of A. auriculiformis were established in the 1980s, and the best performing provenances (Coen River (Queensland, Australia), Mibini (PNG), and Morehead (PNG)) were selected to plant in several parts of Vietnam (Le 2001; Nguyen 2003). However, tree breeding programs for A. auriculiformis generally emphasize improvements in tree growth, stem form, and pest and disease resistance. There is little information available to A. auriculiformis breeding programs in Vietnam regarding wood properties, such as wood density and mechanical properties, which determine suitability for lumber production.

Modulus of rupture (MOR) and modulus of elasticity (MOE) are important properties in terms of understanding the performance of lumber when used in construction (Zobel and van Buijtenen 1989). Commonly, static bending properties are measured destructively by some methods that are expensive, time consuming, and damage experimental samples to varying degrees. Therefore, non-destructive evaluation techniques have emerged as alternative approaches for the estimation of mechanical properties of lumber. Tree breeders prefer non-destructive methods because it makes the rapid assessment of wood properties of standing trees possible (Schimleck et al. 2019). One non-destructive technique that measures the speed of sound waves within standing trees has received considerable attention (Wang et al. 2001). Prior to cutting, trees can be evaluated by measuring the acoustic velocity to sort high-quality from low-quality trees (Apiolaza et al. 2011).

In studies based on small defect-free specimens, the potential of acoustics for predicting mechanical properties has been demonstrated (Wang et al. 2001; Duong et al. 2019; Duong and Hasegawa 2021). However, wood is a biological material and has many natural defects, such as knots, slope of grain, spiral grain, reaction wood, and decay, which may reduce wood mechanical properties. There has been little work on using acoustic method to estimate static bending properties of wood containing knots (Qin et al. 2018), which are frequently encountered when testing young trees. If non-destructive techniques are to be used operationally on small A. auriculiformis trees, it is very likely that readings will be influenced by knots as this species retains many small branches when young. Thus, for A. auriculiformis breeding programs the potential of acoustic measurement as a rapid and non-destructive method for stiffness and strength prediction should be based on both small clear specimens and also specimens with small knots.

In this study, the authors assessed acoustic and mechanical properties of six A. auriculiformis clones from a trial in north central Vietnam. The specific objectives were to: a) Clarify radial and among-clonal variation in stress-wave velocity, wood density, MOR, and MOE of small wood specimens including samples containing natural knots, and b) Examine the prediction of A. auriculiformis mechanical properties using stress wave velocity measured both on standing trees and small wood specimens. This information will be used to develop appropriate selection strategies for A. auriculiformis breeding programs for lumber production in Vietnam.



Sample trees were harvested from an A. auriculiformis clonal trial established by the Vietnamese Academy of Forest Sciences to assess the growth rate and stem quality of different clones. The site is located in Cam Hieu commune, Cam Lo district, Quang Tri province, north central Vietnam (16°45’60″N and 107°01’12″E). The plantlets were propagated by tissue culture technology and planted at the site in December 2015 using a randomized complete block design with four replicates. Each plot comprised 36 ramets from a clone (6 lines × 6 ramets/line). The initial spacing between ramet was 3.0 x 3.0 m2 (1100 tree ha-1). Fertilizer application at planting was 100 g nitrogen (N), phosphate (P2O5), and potassium oxide (K2O) (VADFCO, Hanoi, Vietnam) (elemental ratio 16 : 16 : 8) per ramet and 100 g NPK one year later.


A total of 30 ramets (5 per clone) were chosen based on straightness, branching, and absence of disease or pest symptoms in December 2020. Stress-wave velocity of standing tree (SWVT) was measured using a Fakopp Microsecond Timer for each tree (Serial No.: FN-12/2020, Fakopp Enterprise Bt., Fenyo u.26, Hungary) with start and stop sensors at heights of 1.5 m and 0.5 m, respectively. The stress-wave propagation time was measured six times at the same position of the stem by hitting the start sensor with a small hammer. The SWVT was calculated by dividing the distance between two sensors (1.0 m) by the averaged stress-wave propagation time. Before felling, stem diameter at a height of 1.3 m was measured and the north and south sides were marked for all sampled ramets, and once felled, total height was measured. Mean values for stem diameter and height for each clone are presented in Table 1.

Table 1. Mean Values and Standard Deviations of Stem Diameter and Tree Height for Each Clone

A 1.0-m log was collected between 0.5 to 1.5 m from each sampled stem. These logs were dried in a room at ambient conditions for approximately 2 months without humidity control. After drying, eight 20 (radial) × 20 (tangential) × 300 (longitudinal) mm3 small wood specimens were cut from each log for additional stress wave measurements and destructive evaluation of wood strength (MOR) and stiffness (MOE). Because the average radius at breast height of the 30 ramets was small (approximately 60 mm), these specimens were carefully cut from locations near pith and bark with the aim of obtaining a representative sample for the examination of radial variation in wood properties. A total of 240 small wood specimens (120 specimens near pith and bark, respectively, and representing the north and south directions) were obtained from the 30 harvested stems. Small specimens in this study included both clear specimens and specimens with small knots (the knots were located near the ends; Fig. 1). Specimens were conditioned at 20 °C and 60% relative humidity for 4 weeks to constant weight. The average moisture content (MC) of the tested specimens at time of measurement was approximately 12%. Once equilibrium was reached, air-dry density (AD) of the specimens was determined as the ratio of weight and volume. Then, stress-wave propagation time (in the longitudinal direction) was measured for each specimen using a Fakopp Microsecond Timer and used to calculate stress wave velocity (SWVS) by dividing specimen length by propagation time (end to end). Dynamic modulus of elasticity of each specimen (MOEd) was estimated using Eq. 1,

MOEd = AD × SWVs2 × 10-9 (1)

where MOEd is the dynamic modulus of elasticity of specimen (GPa), AD is the air-dry density (kg/m3), and SWVS is the stress-wave velocity measured in small wood specimen (m/s).

Static bending tests were conducted using an Instron Tester (Autograph AG-G, Shimazu, Kyoto, Japan) in accordance with Japanese Industrial Standard, JIS Z2101:1994 (2000). The span length and cross head speed were 260 mm and 5 mm/min, respectively. The MOE and MOR were calculated with a data analyser attached with Instron Tester.

Fig. 1. Specimens with small knots

Data Analysis

Analysis of variance (ANOVA) was performed at a 5% significance level to determine the differences of SWVT, SWVS, AD, MOEd, MOE, and MOR among the A. auriculiformis clones. Tukey tests were used to further analyze the differences among means. The difference in SWVS and wood properties between the two radial positions was examined using a T-test. All analyses were conducted using R software version 4.0.0. (Version 4.0.0; RStudio, Boston, MA, USA).


Among-clonal Variation of Stress Wave Velocity, Wood Density, and Mechanical Properties

Table 2 shows average SWVS and wood properties for six A. auriculiformis clones planted in Vietnam. There were significant (P < 0.001) differences in SWVS among clones. The overall mean of SWVS among clones was 4242 m/s (at approximately 12% MC). The lowest and highest SWVS values were observed in clone 2 (3972 m/s) and clone 1 (4393 m/s), respectively. In radial direction, the SWVS values near the bark were significantly greater than those near the pith, except for clone 2. Hasegawa et al. (2015) reported that the longitudinal ultrasonic wave velocity of small clear wood specimens from 10-year-old A. auriculiformis is 4500 m/s. In 5-year-old Acacia mangium, Duong and Hasegawa (2021) found the average (200 kHz ultrasonic) velocity of small defect-free specimens as 4170 m/s.

There was a significant difference found in AD between positions near the pith and bark in clones 2 and 5. In contrast, no significant difference was found between inner and outer wood from pith for the other clones. The mean AD across the six A. auriculiformis clones was 0.54 g/cm3, varying from 0.53 g/cm3 (near pith) to 0.56 g/cm3 (near bark). These results were comparable with those reported for 5½-year-old A. auriculiformis clones planted in southern Vietnam (0.52 g/cm3 for heartwood and 0.56 g/cm3 for sapwood) (Hai et al. 2010). However, the authors’ results were lower than densities reported in older trees, for example in 8-year-old (0.66 g/cm3) and 11-year-old (0.69 g/cm3) A. auriculiformis (Shukla et al. 2007; Chowdhury et al. 2012).

The ANOVA showed significant differences in AD among clones (Table 2). The highest AD values were detected in clones 6 (0.59 g/cm3) and 1 (0.57 g/cm3), whereas clones 5 and 3 had the lowest (0.50 and 0.51 g/cm3, respectively). Wood density is considered as one of the most important wood properties because it is related to sawn timber quality and pulp yield (Zobel and van Buijtenen 1989), and is also an integrator of strength properties, making it an important selection criterion for Acacia breeding programs that incorporate wood properties (Chowdhury et al. 2013). Clones 1 and 6, which had little radial variation in wood density, may help improve juvenile (core wood) wood properties. When coupled with their higher wood density the authors’ data suggests that both have promise for breeding programs that focus on improving wood quality of A. auriculiformis grown in Vietnam.

The MOE and MOR are important wood properties for species mainly used for construction lumber. Overall, average MOE and MOR of the six A. auriculiformis clones was 8.07 and 92.12 MPa, respectively. The MOE values in this study were higher than MOE found by Sahri et al. (1998) in A. auriculiformis, but in the range of other studies of the same species (Chowdhury et al. 2012; Jusoh et al. 2014). Mean MOR was close to MOR of 8-year-old A. auriculiformis planted in India (99.7 MPa) (Shukla et al. 2007), but lower than values reported in 5½-year-old A. auriculiformis by Hai et al. (2010) (141.8 MPa) and 11-year-old A. auriculiformis (103.5 MPa) (Chowdhury et al. 2012).

The T-test results showed that the mean values of MOE and MOR in the outer wood were higher than those near the pith in all tested clones, except for MOR in clones 3 and 5. This radial variation pattern of static bending properties was like that in A. auriculiformis clones reported by Hai et al. (2010). The results of ANOVA revealed that there were significant differences among clones for MOE and MOR. Similar to AD, the highest average MOE and MOR were also detected in clones 6 (8.90 GPa and 99.51 MPa, respectively) and 1 (9.14 GPa and 101.43 MPa, respectively), indicating the importance of both for tree improvement for lumber production. Wood properties are generally more heritable than growth properties (Cornelius 1994). However, wood properties are controlled not only by genetic factors but also by environmental factors (Zobel and van Buijitenen 1989). Further research must be done to examine the effects of environmental variation on clonal variation in A. auriculiformis wood properties.

A number of studies have reported that values of dynamic modulus of elasticity based on stress wave velocity are higher than those obtained by destructive tests (Wang et al. 2001; Duong and Matsumura 2018; Duong and Ridley-Ellis 2021). In this study, the overall mean value of MOEd was 17.82% higher than that of MOE (Table 2). The results of ANOVA showed that MOEd values had similar patterns in radial and among-clone variation comparing with MOE.

Table 2. Average Stress Wave Velocity and Wood Properties for Six Acacia auriculiformis Clones Planted in Vietnam

Relationships among Measured Properties

Relationships among wood properties and SWVS are shown in Table 3. The correlations between SWVS and MOE were positive, ranging from 0.29 (clone 4) to 0.73 (clone 6). There was a significant, but weak correlation between SWVS and MOR in clones 1 (r = 0.48) and 6 (r = 0.32), although the properties have no relationship in the remaining clones (Table 3). There was a moderate (r = 0.56; P < 0.001) correlation between SWVS and MOE and a weak correlation (r = 0.14, P < 0.05) between the SWVS and MOR for combined clones (Table 3).

Correlation analyses were also performed between the mechanical properties and density for each clone and all clones combined (Table 3). For individual clones AD had significant positive correlations with both MOE and MOR, except for clone 6. At specimens level, all clones combined correlation coefficients of AD with MOE and MOR were 0.60 (P < 0.001) and 0.62 (P < 0.001), respectively. For A. auriculiformis grown in Bangladesh, Chowdhury et al. (2012) found a statistically significant coefficient of determination between AD and MOR (r2 = 0.63) and no correlation between AD and MOE.

Table 3. Pearson Correlation Coefficients (r) for Relationships between Variables (SWVS, AD, MOEd, MOE, and MOR)

There was no correlation between AD and SWVS except for clone 6 (r = -0.45; P < 0.01, Table 3). A possible explanation for the negative relationship in clone 6 was that the increase in density was not accompanied by a corresponding increase in wood stiffness, and therefore the propagation speed decreased with increasing density. Baar et al. (2012, 2013) reported that the velocity of wave propagation in tropical hardwoods (Afzelia bipindesis Harms, Astronium graveolens Jacq, Intsia bijuga Kuntze, and Millettia laurentii De Wild) is probably much more affected by the microstructure of particular species such as grain angle, the proportion of fibers, and vessel elements; and it is not recommendable to try to predict it based solely on density. In contrast to relationships between AD and mechanical properties, little data are available relating the relationship between AD and SWVS for A. auriculiformis. In other species, no significant correlation between acoustic velocity and wood density has been reported (Mishiro 1996; Ilic 2003; Yanez et al. 2021).

As stated by Wang et al. (2001), Posta et al. (2016), and Duong and Matsumura (2018), non-destructive methods based on the propagation of stress waves are suitable for predicting dynamic MOE and have a high correlation with the results of the destructive tests. For example, Duong and Matsumura (2018) obtained a good relationship (r = 0.92) between MOE and dynamic moduli determined from stress wave velocity in small clear samples of Melia azedarach L. However, the measurements of small clear samples may not reflect other wood characteristics such as the presence of knots, deviations from straight grain, and splits. In this study, MOEd was well related to MOE. The overall correlation for all combined samples was 0.87 and ranged from 0.72 to 0.87 among clones (Table 3). The relationships between MOE and MOEd for A. auriculiformis wood obtained in this study were weak compared with other species. This could be explained by effects of knots on stress wave propagation. Lin and Wu (2013) showed that knots have significant impact on longitudinal stress wave propagation in Korean pine (Pinus koraiensis Siebold & Zucc.). Stress wave propagation time in wood samples with knots is shorter than that in clear wood samples with similar moisture content and density. In future experiments, the effects of knots on stress wave propagation in A. auriculiformis wood requires clarification.

Table 3 shows the relationships between the MOR and MOEd for each clone and specimens combined for all clones. The correlation coefficient between MOR and MOEd in combined clones was 0.53 (P < 0.001) and ranged from 0.33 for clone 3 to 0.58 for clone 5 (Table 3). These correlations were weaker than the correlations between MOE and MOEd. Relationships between MOEd and MOR in this study were similar to those from other hardwood species using acoustic velocity. For example, De Olivera et al. (2002) reported the coefficients of determination between MOEd (predicted by the ultrasound technique) and MOR for Goupia glabra Aubl. and Hymenaea sp. as 0.36 and 0.55, respectively.

Prediction of Static Bending Properties by Stress Wave Velocity of Standing Trees

Acoustic technologies have been well established as material evaluation tools for assessing wood properties on standing trees before harvesting and wood processing to maximize value extracted from the resource (Schimleck et al. 2019). Many studies have demonstrated moderate to good relationships between tree acoustic velocity and MOE of structural products or MOE of small wood specimens cut from trees (Ishiguri et al. 2008; Vazquez et al. 2015; de Melo et al. 2020). However, most of the studies were on softwood species. The published information on using tree acoustic technique for predicting mechanical properties in hardwood species is currently limited to a few studies (Dickson et al. 2003; Ngadianto et al. 2020; Yanez et al. 2021). Results obtained from measuring SWVT for each clone and combined clones of A. auriculiformis planted in Vietnam are summarized in Table 4. The average SWVT was 3417 m/s and ranged from 3291 m/s (clone 3) to 3609 m/s (clone 1), with a notably small 4.48% coefficient of variation. The results of ANOVA analysis showed significant differences in SWVT among clones (Table 4). Clones 1 and 6 had significantly higher SWVT values than other clones examined in this study. In previous studies, Makino et al. (2012) and Ngadianto et al. (2020) reported different mean SWVT values for A. mangium which are 3590 and 3570 m/s. Prasetyo et al. (2017) reported the SWVT values in three Eucalyptus species in Indonesia ranged from 2640 to 3890 m/s. The current authors observed that the average value of stress wave velocity in small specimens was approximately 20% greater than that in standing trees. Wang and Chuang (2000) showed that the acoustic velocity increases rapidly with decreasing moisture content below the fiber saturation point.

Table 4. Descriptive Statistics for Stress Wave Velocity of Trees (SWVT) for Acacia auriculiformis Clones

Figure 2 shows the relationships between the SWVT and the average MOE or MOR in static bending of small specimens for all A. auriculiformis clones combined (where the average values of MOE and MOR were calculated by averaging all values for each property for specimens obtained from a ramet within a clone). There was a relatively high correlation (r = 0.83, P < 0.001) between SWVT and MOE of specimens. A statistically significant (r = 0.61, P < 0.001) correlation was also found between SWVT and MOR of specimens. This suggested that selection for increased SWVT may result in significant increases in MOE and MOR and that tree breeders could identify the best clones in terms of lumber quality based on stress wave velocity for crossing and/or propagation. Correlation coefficients between SWVT and static properties (MOE and MOR) of specimens were higher than those between SWVS and static properties of specimens when clone data were combined (Table 3). One probable explanation is the effects of knots on stress wave propagation. SWVT was measured in the outerwood of a tree to a depth of 20 to 30 mm over a distance between two sensors (1.0 m), while SWVS was measured for specimens both in outerwood and corewood of the stem. The corewood of A. auriculiformis presented more knots than the outerwood because this species retains many small branches when young.

Fig. 2. Relationship between the SWVT and the average MOE or MOR in static bending of small specimens


In this study, the SWVT, SWVS, AD, MOEd, MOE, and MOR properties were evaluated for six clones of A. auriculiformis planted in a trial site at Quang Tri, Vietnam.

  1. The stress-wave velocity and other wood properties examined in this study significantly differed from those obtained among the six clones. Coupled with no significant difference in AD between inner and outer woods from pith, clones 1 and 6 had greater AD, MOE, and MOR than the other clones examined. Therefore, clones 1 and 6 might be appropriate for A. auriculiformis tree breeding programs focused on improving wood quality specifically for lumber production in the north central region of Vietnam.
  2. A good coefficient of correlation was found between MOEd measured by stress wave method and MOE measured by destructive test. This could allow for the prediction of static bending properties of A. auriculiformis wood with knots using the stress wave technique.
  3. Regarding the relationships of SWVT and static bending properties, the increase of SWVT may result in significant increase in MOE and MOR. Therefore, tree breeders could identify the best trees in term of the performance of lumber based on the stress wave velocity for crossing and/or propagation.


This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED), Grant No. 106.06-2019.319.


Apiolaza, L. A., Butterfield, B., Chauhan, S., and Walker, J. C. F. (2011). “Characterization of mechanically perturbed young stems: Can it be used for wood quality screening?,” Ann. For. Sci. 68(2), 407-414. DOI: 10.1007/s13595-011-0028-8

Baar, J., Tippner, J., and Gryc, V. (2012). “The influence of wood density on longitudinal wave velocity determined by the ultrasound method in comparison to the resonance longitudinal method,” Eur. J. Wood Wood Prod. 70(5), 767-769. DOI: 10.1007/s00107-011-0550-2

Baar, J., Tippner, J., and Gryc, V. (2013). “The relation of fibre length and ray dimensions to sound propagation velocity on wood of selected tropical hardwood,” IAWA J. 34(1), 49-60. DOI: 10.1163/22941932-00000005

Chowdhury, M. Q., Ishiguri, F., Hiraiwa, T., Takashima, Y., Iizuka, K., Yokota, S., and Yoshizawa, N. (2012). “Radial variation of bending property in plantation grown Acacia auriculiformis in Bangladesh,” For. Sci. Technol. 8(3), 1-4. DOI: 10.1080/21580103.2012.704961

Chowdhury, M. Q., Ishiguri, F., Hiraiwa, T., Takashima, Y., Iizuka, K., Yokota, S., and Yoshizawa, N. (2013). “Anatomical property variation in Acacia auriculiformis growing in Bangladesh.” Inter. Wood Pro. J. 4(2), 75-80. DOI: 10.1179/2042645313Y.0000000032

Cornelius, J. (1994). “Heritabilities and additive genetic coefficients of variation in forest trees,” Canadian J. For. Res. 24(2), 372-379. DOI: 10.1139/x94-050

De Melo, R. R., Barbosa, K. T., Beltrame, R., Acosta, A. P., Pimenta, A. S., and Mascarenhas, A. R. P. (2020). “Ultrasound to determine physical-mechanical properties of Eucalyptus camaldulensis wood,” Wood Mater. Sci. Eng. 1(1), 1-7. DOI: 10.1080/17480272.2020.1830435

De Oliveira, F. G. R., de Campos, J. A. O., and Sales, A. (2002). “Ultrasonic measurements in Brazilian hardwood,” Mater. Res. 5(1), 51-55. DOI: 10.1590/S1516-14392002000100009

Dickson, R. L., Raymond, C. A., and Joe Wilkinson, C. A. (2003). “Segregation of Eucalyptus dunnii logs using acoustics,” For. Ecol. Manag. 179(1-3), 243-251. DOI: 10.1016/S0378-1127(02)00519-4

Duong, D. V., and Hasegawa, M. (2021). “Predicting mechanical properties of clear wood from Acacia mangium provenances using ultrasound,” BioResources 16(4), 8309-8319. DOI: 10.15376/biores.16.4.8309-8319

Duong, D. V., Hasegawa, M., and Matsumura, J. (2019). “The relations of fiber length, wood density, and compressive strength to ultrasonic wave velocity within stem of Melia azedarach,” J. Ind. Acad. Wood Sci. 16(1), 1-8. DOI: 10.1007/s13196-018-0227-0

Duong, D. V., and Matsumura, J. (2018). “Within-stem variations in mechanical properties of Melia azedarach planted in northern Vietnam,” J. Wood Sci. 64(1), 329-337. DOI: 10.1007/s10086-018-1725-9

Duong, D. V., and Ridley-Ellis, D. (2021). “Estimating mechanical properties of clear wood from ten-year-old Melia azedarach trees using the stress wave method,” Eur. J. Wood Wood Prod. 79(4), 941-949. DOI: 10.1007/s00107-021-01664-8

Hai, P. H. (2009). Genetic Improvement of Plantation-Grown Acacia auriculiformis for Sawn Timber Production, Ph.D. Dissertation, Swedish University of Agricultural Sciences, Uppsala, Sweden.

Hai, P. H., Hannrup, B., Harwood, C., Jansson, G., and Ban, D. V. (2010). “Wood stiffness and strength as selection traits for sawn timber in Acacia auriculiformis A. Cunn. ex Benth,” Canadian J. For. Res. 40(2), 322-329. DOI: 10.1139/X09-191

Hai, P. H., Jansson, G., Harwood, C., Hannrup, B., and Ha, T. H. (2008). “Genetic variation in growth, stem straightness and branch thickness in clonal trials of Acacia auriculiformis at three contrasting sites in Vietnam,” For. Ecol. Manag. 255(1), 156-167. DOI: 10.1016/j.foreco.2007.09.017

Hasegawa, M., Mori, M., and Matsumura, J. (2015). “Relations of fiber length to within-tree variation of ultrasonic wave velocity in fast-growing trees,” Wood Fiber Sci. 47(3), 313-318.

Ilic, J. (2003). “Dynamic MOE of 55 species using small wood beams,” Holz. Roh. Werkst. 61(3), 167-172. DOI: 10.1007/s00107-003-0367-8

Ishiguri, F., Matsui, R., Iizuka, K., Yokota, S., and Yoshizawa, N. (2008). “Prediction of the mechanical properties of lumber by stress-wave velocity and Pilodyn penetration of 36-year-old Japanese larch trees,” Holz. Roh. Werkst. 66(4), 275-280. DOI: 10.1007/s00107-008-0251-7

Jahan, M. S., Sabina, R., and Rubaiyat, A. (2008). “Alkaline pulping and bleaching of Acacia auriculiformis grown in Bangladesh,” Turk. J. Agric. For. 32(4), 339-347.

JIS Z2101:1994 (2000). “Methods of test for woods,” Japanese Standard Association, Tokyo, Japan.

Jusoh, I., Zaharin, F. A., and Adam, N. S. (2014). “Wood quality of Acacia hybrid and second-generation Acacia mangium,” BioResources 9(1), 150-160. DOI: 10.15376/biores.9.1.150-160

Le, D. K. (2001). Studies on the Use of Natural Hybrids Between Acacia mangium and Acacia auficuliformis in Vietnam, Agriculture Publisher, Hanoi, Vietnam.

Lin, W., and Wu, J. (2013). “Non-destructive testing of wood defects for Korean pine in northeast China based on ultrasonic technology,” in: International Conference on Signal Processing, Communication and Computing (ICSPCC), Yunnan, China. DOI: 10.1109/ICSPCC.2013.6664057

Makino, K., Ishiguri, F., Wahyudi, I., Takashima, Y., Iizuka, K., Yokota, S., and Yoshizawa, N. (2012). “Wood properties of young Acacia mangium trees planted in Indonesia,” Forest Prod. J. 62(2), 102-106. DOI: 10.13073/0015-7473-62.2.102

Mishiro, A. (1996). “Effect of density on ultrasonic velocity in wood,” Mokuzai Gakkaishi 42, 887-894. (In Japanese)

Ngadianto, A., Ishiguri, F., Nezu, I., Takahashi, Y., Tanabe, J., Hidayati, F., Irawati, D., Ohshima, J., and Yokota, S. (2020). “Wood properties and simulated modulus of elasticity of glulam in three fast-growing trees species grown in community forests in Yogyakarta, Java Island, Indonesia,” Tropics 29(3), 89-104. DOI: 10.3759/tropics.MS20-02

Nguyen, H. N. (2003). Development of Acacia Species in Vietnam, Agriculture Publisher, Hanoi, Vietnam. (In Vietnamese)

Pinyopusarek, K., Williams, E., and Boland, D. (1991). “Geographic-variation in seedling morphology of Acacia auriculiformis A-cunn ex-Benth,” Aust. J. Bot. 39(3), 247-260. DOI: 10.1071/BT9910247

Posta, J., Ptacek, P., Jara, R., Terebesyova, M., Kuklik, P., and Dolejs, J. (2016). “Correlations and differences between methods for non-destructive evaluation of timber elements,” Wood Res. 61(1), 129-140.

Prasetyo, A., Aiso, H., Ishiguri, F., Wahyudi, I., Wijaya, I. P. G., Ohshima, J., and Yokota, S. (2017). “Variations on growth characteristics and wood properties of three Eucalyptus species planted for pulpwood in Indonesia,” Tropics 26(2), 59-69. DOI: 10.3759/tropics.MS16-15

Qin, J., Liu, X., van Den Abeele, K., and Cui, G. (2018). “The study of wood knots using acoustic non-destructive testing methods,” Ultrasonics 88, 43-50. DOI: 10.1016/j.ultras.2018.01.004

Sahri, M. H., Ashaari, Z., Kader, R. A., and Mohmod, A. L. (1998). “Physical and mechanical properties of Acacia mangium and Acacia auriculiformis from different provenances,” Pertanika J. Trop. Agri. Sci. 21(2), 73-81.

Schimleck, L., Dahlen, J., Apiolaza, L. A., Downes, G., Emms, G., Evans, R., Moore, J., Paques, L., Van den Bulcke, J., and Wang, X. (2019). “Non-destructive evaluation techniques and what they tell us about wood property variation,” Forests 10(9), Article Number 728. DOI: 10.3390/f10090728

Shukla, S. R., Rao, R. V., Sharma, S. K., Kumar, P., Sudheendra, R., and Shashikala, S. (2007). “Physical and mechanical properties of plantation-grown Acacia auriculiformis of three different ages,” Aust. For. 70(2), 86-92. DOI: 10.1080/00049158.2007.10675007

Vazquez, C., Goncalves, R., Bertoldo, C., Bano, V., Vega, A., Crespo, J., and Guaita, M. (2015). “Determination of the mechanical properties of Castanea sativa Mill. using ultrasonic wave propagation and comparison with static compression and bending methods,” Wood Sci. Technol. 49(3), 607-622. DOI: 10.1007/s00226-015-0719-7

Wang, S. Y., and Chuang, S. T. (2000). “Experimental data correction of the dynamic elastic moduli velocity and density of solid wood as a function of moisture content above the fiber saturation point,” Holzforschung 54(3), 309-314. DOI: 10.1515/HF.2000.052

Wang, X., Ross, R. J., McClellan, M., Barbour, R. J., Erickson, J. R., Forsman, J. W., and McGinnis, G. D. (2001). “Nondestructive evaluation of standing trees with a stress wave method,” Wood Fiber Sci. 33(4), 522-533.

Yanez, M. A., Zamudio, F., Espinoza, S., Ponce, M., Gajardo, J., and Espinosa, C. (2021). “Assessing wood properties on hybrid poplars using rapid phenotyping tools,” New Forests 52(3), 397-410. DOI: 10.1007/s11056-020-09799-x

Zobel, B. J., and van Buijtenen, J. P. (1989). Wood Variation, Its Causes and Control, Springer, Heidelberg.

Article submitted: December 4, 2021; Peer review completed: February 5, 2022; Revised version received and accepted: February 8, 2022; Published: February 10, 2022.

DOI: 10.15376/biores.17.2.2084-2096