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Variations in acoustic velocity and density with age, and their interrelationships in radiata pine
Forest Ecology and Management 229 (2006) 388–394 www.elsevier.com/locate/foreco
Variations in acoustic velocity and density with age, and their interrelationships in radiata pine S.S. Chauhan *, J.C.F. Walker School of Forestry, University of Canterbury, Post Box 4800, Christchurch, New Zealand Received 11 November 2005; received in revised form 11 April 2006; accepted 13 April 2006
Abstract Acoustic velocity by the Fakopp time of flight (ToF) tool was used to estimate outerwood stiffness of trees within stands and between stands of different age classes (ages 8, 16 and 25). The TOF acoustic velocity measured in the standing trees was generally higher than the acoustic velocity measured by the Hitman (resonance) tool on the associated logs. The difference between the two velocities tended to be greater in the older and large diameter trees. The large variability in acoustic velocity, or preferably V2, makes it an efficient wood quality variable for screening trees. Wood density variables did not exhibit any relationship with acoustic velocity or modulus of elasticity (MoE) within each age-class. The classic pseudo-relationship observed with pooled data from all stand ages is mainly due to a stand age-effect and so one should be cautious in relying on any significant association between density and MoE. # 2006 Elsevier B.V. All rights reserved. Keywords: Acoustic; Density; Modulus of elasticity; Outerwood; Radiata pine
1. Introduction In New Zealand the trend to harvest radiata pine at ever younger ages, certainly less than 30 years, is today subject to introspection. A cause for concern regarding these fast grown trees is the poorer wood quality (more corewood) compared to older slower grown trees. Furthermore, the quality and property variations within and between these trees are huge: several researchers have shown that within a stand the initial microfibril angle (MFA) may vary from 308 to 508 between trees, e.g. Donaldson (1992); and the density of outerwood may vary from 375 to 500 kg/m3, e.g. Cown and McConchie (1983). In these circumstances, where good and poor quality trees can be found together in fast growth plantations, an appropriate means of sorting is essential. The forest sector will not progress without moving decisively beyond simplistic secondary-source interpretations – of density, internal checking, resin pockets, stiffness or whatever is the issue of the day. There is a sense in which
* Corresponding author at: Institute of Wood Science and Technology, P.O. Malleswaram, Bangalore 560 003, India. Tel.: +91 80 23346811; fax: +91 80 23340529. E-mail address: [email protected] (S.S. Chauhan). 0378-1127/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2006.04.019
understanding of wood is roughly where metallurgical research was in the 1940s. The complexity of the wood cell wall is in sharp contrast to the simplicity of metals, which explains why metallurgical research has advanced more rapidly (Entwistle and Walker, 2005). The difficulty in securing molecular structural information for wood hinders understanding as to why wood is such an amazing natural triumph of nanotechnology, which will only be teased out by the interdisciplinary skills in biomechanics, cell biology and physical chemistry. Practical benefits will not forthcoming until this belief is part of the industry’s work culture. Meanwhile currently favoured mediums of exchange between practitioners and scientists, acoustics and near-infrared microscopy (NIR), can bring limited gains. This paper discusses the use of acoustics, noting only in passing the considerable potential offered by NIR (So et al., 2004). The traditional view has been that density is an important index of wood quality to which all end users could relate (Bamber and Burley, 1983; Walker, 1993). It appears to influence machinability, conversion, strength, paper yield and many other properties. However, in the last few years it has been shown that wood performance in young, fast grown pine is controlled by intrinsic characteristics of the cell wall, such as the cellulose microfibril angle (MFA), to a greater degree than by a complex trait like density (Butterfield, 1998; Walker and
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Butterfield, 1996; Walker, 1998). Further, for structural lumber it is more logical to select logs and standing timber according to stiffness rather than indirectly by emphasizing selection for density or even MFA. Acoustics is an effective surrogate measure of stiffness, while also capturing and reflecting in part wood characteristics like tracheid length, the microfibril angle in the S2 layer, and even chemical composition of cell walls (Albert et al., 2002; Evans, 2000). Consequently, there has been extensive research seeking to understand acoustic wave propagation in clear wood, lumber and logs and to establish the efficient strategies that use acoustic velocity to sort logs according to their stiffness (Ross and Pellerin, 1991; Sobue, 1986; Tsehaye et al., 2000; Wang et al., 2000, 2001). To measure stemwood stiffness researchers rely on a fundamental relationship that appears to hold for all materials, whereby MoE ¼ rV 2
(1)
where MoE is axial stiffness of the green log, r the green density (ca. 1000 kg/m3) and V is the acoustic velocity along the grain. This equation offers a non-destructive means of measuring stiffness. Reasonable correlations have been found when comparing the acoustic velocity of the tree or log and the mean stiffness of all dried lumber cut from each tree: for example, Weyerhaeuser filed a patent that included an R2 of 0.64 (US Patent 6,0026,689, 2000). More generally, it is sufficient to note that a wide range in correlation coefficients (0.30–0.90) have been observed between MoE derived from ToF tools and static MoE (Halabe et al., 1997; Wang and Ko, 1998; Booker and Sorensson, 1999; Tsehaye et al., 2000; Wang et al., 2000; Lindstrom et al., 2002). At the very least these tools have empirical utility. The two principal ways of using acoustics in forest operations are the resonance and time of flight (ToF) methods. Resonance based tools have an edge over ToF tools in terms of ease of use but their application is limited to cut logs or lumber. With resonance-based systems the acoustic wave travels within the log back-and-forth repeatedly from end-to-end resonating strongly at various frequencies, i.e. the fundamental frequency and its overtones. The velocity is calculated based on the first harmonic or more usually using one of its overtones (these are not true harmonics) and the log length. Since the acoustic wave travels back-and-forth, two cut ends are essential. This limitation restricts the application of the resonance method to felled logs or lumber and not to standing trees. Dynamic MoEs derived from the resonance technique have been reported to be in very close agreement with the static MoEs for standard 20 mm 20 mm 300 mm specimens, with a correlation coefficient as high as 0.98 (Lindstrom et al., 2002). On the other hand only ToF tools are able to measure acoustic velocity in standing trees, by timing the acoustic wave as it travels along the stem between points a known distance apart. The acoustic velocities based on the ToF measurements are roughly 10% higher than those derived by resonance. There have been questions regarding the flight path taken by the
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acoustic wave and the precise moment when the acoustic signal ‘arrives’ at the piezoelectric detectors (Harris and Andrews, 1999; Andrews, 2000; Booker et al., 2000; Wang et al., 2000). Some of these concerns are discussed later. In view of such differences in acoustic velocity measurements by these two methods, the robustness of ToF measurements in assessing the stiffness of wood in standing trees needs to be established. This study compared ToF values for outerwood in standing trees in three stands of different ages using a Fakopp-2D tool with acoustic velocities of butt logs from these trees measured by Hitman, a resonance tool The paper also analyses the association of wood density (green and basic) with acoustic velocity and MoE in logs. There are limited opportunities to estimate cheaply, efficiently and quickly the intrinsic properties of the wood in standing trees. One can measure outerwood density (with an increment core or indirectly with a pilodyn) and axial acoustic velocity (with a ToF tool). The paper examines the relationship between the acoustic velocity measured on standing trees using a ToF tool (Fakopp-2D) and outerwood density (increment core) along with the association of these two variables with (1) the acoustic velocity of the equivalent log measured by a resonance tool (Hitman) after felling the tree, and (2) air-dry outerwood stiffness as measured by SilviScan derived from MFA, density and wood chemistry. 2. Materials and methods The study was a part of a comprehensive study initiated by a Government-Industry research consortium, ‘‘The Wood Quality Initiative Ltd.’’, to evaluate wood quality in three stands of Pinus radiata aged 8, 16 and 25 growing in close proximity to one another on the West Coast, New Zealand. The stocking in 8, 16 and 25 year-old stands was 798, 222 and 222 sph, respectively, at the time of the study. The 16-year-old stand had an initial stocking of 798 sph and was thinned at age 9 while 25-year-old stand had an initial stocking of 1725 sph and was thinned at age 5 and again at age 7. In each stand, 50 trees were selected, measured and assessed for a number of other attributes that are not discussed here. The Fakopp-2D ToF tool was used to measure acoustic velocity on standing trees. The tool was used in a three probe configuration with a passive start probe and two piezoelectric probes that detect the acoustic wave generated by lightly tapping the start probe. The lapse time between the first and second stop probes and the distance between them (1.3–1.4 m) was used to calculate the acoustic velocity. The acoustic velocity was measured on opposite sides on each tree at breast height and a simple average of the two velocities was used for most analyses. Two 5 mm diameter increment cores extracted at breast height from opposite sides of the stem were measured for basic density using the maximum moisture content method described by Smith (1954). Once standing tree measurements had been completed, the 8-year-old trees were felled and the acoustic velocity on a single 4 m butt log was determined by resonance (Hitman): a few logs were somewhat shorter (>3.1 m) where small end
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diameters were below 100 mm. For the 16-year-old trees, logs were trimmed to 4.9 m lengths. For the 25-year-old trees logs were trimmed to 4.6 m lengths. Discs were taken from the large and small ends of all the butt logs from each of the three stands. These discs were used to measure diameter over and under bark, as well as moisture content, green density and basic density of wood on a whole-log basis. A 10 mm increment core was extracted at the breast height on each of the selected trees and used for MFA and density measurements using SilviScan. 3. Results Summary statistics of the basic tree and wood characteristics are given in Table 1. Fisher’s LSD test indicated significant differences in the mean value of each of these variables between stands. All measured variables increased with stand age, except log green density, which was less in wood from older stands. This is attributed to the gradual formation of heartwood. Log densities values are included in Table 1 to show that the outerwood and whole log values are consistent, with the green outerwood values being greater than the whole log values in the two older stands due to the presence of heartwood. The whole log green density was determined from the initial green weight and volume of discs (by immersion). Further discussion of whole log values is inappropriate as they can only be obtained by destructive sampling. For all age classes the mean whole-section acoustic velocity of logs with Hitman was lower than the mean outerwood acoustic velocity of stems using Fakopp (Table 1). The average difference in the two velocities was 8.74% (S.D.: 4.98), 8.95% (S.D.: 4.05) and 17.55% (S.D.: 5.54) for 8, 16 and 25-year-old trees, respectively. There was a strong association between the two velocities for each age class (Fig. 1). If there were true correspondence between Fakopp and Hitman velocities, the velocity measured in any one log should have the same value (y = x) when using either tool. Within a stand there is a moderate negative trend in Fakopp velocity with increasing diameter at breast height (DBH) (Fig. 2). The relationship between Fakopp velocity and outerwood densities (basic and green) is shown in scatter diagrams (Fig. 3a and b). Overall, a very poor relationship was evident between
Fig. 1. Comparison between acoustic velocities in standing trees (Fakopp) and butt log of the same trees (Hitman). If the regressions were forced through the origin then the respective slopes would be 1.09, 1.09 and 1.17 implying that Fakopp velocities were on average 9%, 9% and 17% faster than Hitman velocities in these three age classes.
the two variables for individual age classes. However where data from all age classes were pooled together and analysed, acoustic velocity exhibited a significant statistical relationship with outerwood density (R2 = 0.51; P < 0.0001). This significantly higher correlation in pooled data is primarily due to the stand age effect as both Fakopp velocity and outerwood basic density increases with stand age. Thus any interpretation based on pooled data across different age groups could be misleading and one must be very cautious regarding any generalised relationship between density and acoustic velocity (and stiffness). 4. Discussion The with-stand variability in acoustic velocity was much greater than was the variability for the density measurements (Table 1). The coefficient of variation for acoustic velocity was in the range of 9–13% while the variation in log green density, basic density and outerwood basic density was in the range of 2–4%, 5–6% and 6.5–7% respectively. The larger variability in the acoustic velocity compared to that for density implies that
Table 1 Summary of some stand properties Variable
DBH (mm) OW basic density (kg/m3) OW green density (kg/m3) Log green density (kg/m3) Log basic density (kg/m3) Hitman velocity (km/s) green wood Fakopp Velocity (km/s) green wood OW green MoE (GPa) – Fakopp OW air-dry MoE (GPa) – SilviScan a
Stand age.
8-year olda
16-year olda
25-year olda
Mean
CV (%)
Mean
CV (%)
Mean
CV (%)
163.8 331 1110 1043 342 1.73 1.88 4.00 4.86
19.8 6.7 0.7 2.2 5.6 10.4 12.6 25.0 25.3
363.2 369 1123 1026 360 2.19 2.38 6.45 7.68
17.7 6.7 0.7 2.7 5.5 9.6 11.6 23.0 22.0
531.2 394 1131 1001 378 2.45 2.88 9.43 10.73
16.4 6.8 0.8 3.7 5.9 9.0 8.6 17.6 18.2
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Fig. 2. Relationship of log diameter (a) with Fakopp velocity (b) with the difference between Fakopp and Hitman velocities.
acoustic velocity is more efficient in screening the worst or the best trees within a population. The higher velocity measured by Fakopp (Fig. 1) is attributed, in part, to the fact that single pass transit-time velocities are sensitive to the high localised stiffness of the outerwood lying in the ‘‘flight path’’ between the two probes. At the same time, the transit time will not be retarded as much as one might expect by the presence of large knots (branches) and any other defects on the flight path. This is because part of the leading edge of the propagating wave-front will bypass reasonably rapidly between knots in branch whorls even though where confronted by a knot immediately ahead the local wavefront will be delayed by having to sweep round the knots. It is the leading edge of the propagating wave-front moving through the outerwood that is picked-up by the stop probes resulting in a higher velocity by the Fakopp tool. The Fakopp velocity was lower than the Hitman velocity in only three 8-year-old trees. These trees had huge branch-whorls near breast height as observed during the measurements. The Fakopp ToF tool measured the acoustic velocity over a maximum of 1.5 m at around breast height, so the presence of excessive branches and distorted grain within this short 1.5 m path length would reduce significantly the measured ToF velocities and so underestimate the outerwood stiffness. In contrast, with a 4 m butt log, the two vibrational nodes for the second harmonic occur at 1 and 3 m, respectively, i.e. fortuitously, Hitman would not sense large branch whorls at
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Fig. 3. Relationship of Fakopp velocity with (a) outerwood basic and (b) green density. Thick lines are corresponding to straight line fit for individual age groups and thin line is for pooled data.
these locations and so would overestimate log stiffness. The effect of nodes is illustrated by separate study where logs were evaluated using two resonance tools, Hitman and WoodSpec. WoodSpec captured data for both the first and second harmonics. These values are plotted against the Hitman data (Fig. 4). The perfect correspondence between the two tools when WoodSpec was analysing the second
Fig. 4. Comparing two resonant tools, Hitman (operating at the second harmonic) with WoodSpec (operating at both fundamental and second harmonics).
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harmonic demonstrates that Hitman looks only at the second harmonic. However there are noticeable differences when comparing the first and second harmonics. Thus the same log can give two acoustic velocities that differ by as much as 11%, which corresponds to a difference in the estimated stiffness of about 23%. These different calculated values arise because the overtones are not exact harmonics. The second harmonic or overtone has been used for acoustic velocity determination of logs. This preference is related to the resonance frequencies of a tapered homogeneous bar in which the difference between tapered and uniform bar frequencies decreases with the higher overtones (Timoshenko, 1964). On that basis the resonance frequencies ( f) of higher overtones (n) are better for calculating the MoE, based on rod theory, i.e. MoE = rV2 or r (2Lf n/n)2 where the sample length is L. In practice it is difficult to capture more than one or two overtones due to various energy loss mechanism (moisture, anisotropy, presence of defects, knots, etc.) in wood. There is the separate issue as to whether a particular harmonic observes a large branch whorl. This depends on its location relative to the nodes (where a whorl would be undetected and unaccounted for) or anti-nodes (detected) of that harmonic. For the fundamental frequency the nodal point will be at the mid point of the log along the log length while for the second harmonics two nodal points will be at 0.25L distance from the log ends. Clearly analysis based on a single harmonic will not be ‘‘correct’’ in all instances. If one is interested in intrinsic wood quality then the harmonic that gives the highest acoustic velocity would be the more appropriate. If one were interested in knot distributions and sizes then a comparison between overtones may provide some information. The relatively moderate coefficient of determination (R2 = 0.75) for the 25-year-old trees between Fakopp and Hitman (second harmonic) velocity points to the greater uncertainty in ranking trees for their whole-tree stiffness in the oldest stand using Fakopp: Fakopp measures outwood properties; Hitman senses whole-section properties (Fig. 1). Zhang (1995) observed that within a stand the bigger trees resulting from higher growth rate would generally produce wood of lower stiffness. Fig. 2a shows a moderate negative trend in Fakopp velocity with increasing diameter at breast height (DBH) in individual age groups with a modest coefficient of determination in the youngest and the oldest stand. The Pearson correlation coefficient values for 8-, 16- and 25-year-old stands were 0.43 (P = 0.002), 0.24 (P = 0.1) and 0.37 (P = 0.007), respectively. For trees of the same age, slow growing trees tend to have high outerwood stiffness to sustain trees under various stresses like wind, crown weight, etc. In fast growing trees the increased diameter with its larger second moment of inertia means there is less need for high material stiffness. Further Lasserre et al. (2004) have observed that outerwood stiffness increases dramatically with stocking (and so with smaller tree DBH), which is what is observed here at the individual tree level. For 11-year-old trees they observed that the whole-stem stiffness is reduced from 5.9 GPa for high stockings (2500 sph) to 4.1 GPa for low stockings (833 sph).
With our 16-year-old stand, a thinning operation had been carried out in year 9 reducing the stocking to 222 sph from an initial 632 sph. This would have accelerated growth in slow growing trees due to reduced within stand competition and this may explain the poor relationship between the acoustic velocity and DBH in this stand. To analyse the effect of log diameter on the difference in Fakopp and Hitman velocities, the difference in the two velocities was plotted against the diameter at breast height of logs (Fig. 2b). It is evident that in large diameter logs, especially from the 25-year-old stand, the difference in the two velocities was comparatively large. With increasing age, both wood density and MoE increase from pith to cambium in radiata pine. Therefore in the oldest stand there will be a large difference in stiffness between outerwood and corewood. Hitman velocity (resonance) is a cross-section weighted average velocity while the Fakopp velocity is influenced by the stiffness of the wood lying in the shortest time-of-flight path (stiff outerwood) between the detector probes. There are two obvious factors at work, one age related and the other diameter related. A further factor contributing to the difference in the Fakopp and Hitman velocity is the proportion of bark on logs. The gross bark volume in radiata pine butt logs (up to 10 m) is reported to be about 15–17% of the under bark round volume (Harris and Nash, 1973). Since bark has few fibres and a very poor tensile stiffness, the high percentage of bark would pull down the resonance velocity in log due to volume averaging. This bark volume or weight does not affect the transit-time velocity. 4.1. Wood density and acoustic velocity Wood basic density has long been the most popular measure of wood quality, as there are quantitative functions that relate it to mechanical and physical properties of straight-grained wood. The industrial standard for density assessment in standing trees involves extraction of outerwood cores and measurement of basic density with minimal delay. On the other hand wood stiffness, MoE, is the product of the acoustic velocity squared and the actual density at the time of measurement: and these two are treated as independent variables, at least as a first approximation (Eq. (1)). In a sense, acoustic velocity is assessing the quality of matter, being related largely to the inclination of the microfibrils in the S2 layer (MFA); whereas density is merely the quantity of matter in a given volume of material. The poor association of Fakopp velocity with outerwood density (Fig. 3) indicates that the two variables reflect different inherent wood quality parameters. However with increasing stand age the association between the two variables gets a little stronger. For the 25-year-old stand, acoustic velocity was significantly related with outerwood density (basic and green) with a correlation coefficient of 0.42 (P = 0.002). One can justify the modest improvement in correlation between basic density and Fakopp velocity with increasing stand age, since both density and acoustic velocity increase with distance from the pith. Strictly, density and acoustic velocity are not totally independent of each other. This is because the proportion of the
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cell wall that is occupied by the S2 layer varies with density – from between 60% in thin-walled cells to over 80% in thickwalled cells. The cellulose microfibrils in the other parts of the cell wall (P, S1, S3) have very high angles and make a negligible contribution to axial stiffness except in the youngest wood in the poorest of trees. As density increases with the accumulation of thick walled cells, the proportion of material in the highstiffness S2 layer increases disproportionately, i.e. as the quantity of matter increase so there is a concurrent modest increase in the quality of matter. The coefficient of determination for individual age groups and for pooled data are same in case of both basic and the green density (Fig. 3). Here, it is important to recognise that the outerwood green density was not measured; it was calculated from the basic density assuming a density of 1500 kg/m3 for oven-dry cell wall tissue. Therefore, in effect any association between basic density or green density with another property will give the same coefficient of determination as they are not independent variables. The derived outerwood (OW) green density is used to estimate the outerwood green MoE from the equation: OW MoEgreen = OW densitygreen Fakopp velocity2. The very low variability in outerwood green density (CV 0.7%) means that the acoustic velocity is the dominant variable in describing the variability in outerwood green MoE. This is shown by analysing coefficient of variations. The coefficient of variation of acoustic velocity, velocity square and outerwood green MoE were estimated for each stand (Table 2). It can be seen that the coefficient of variation of outerwood MoE is nearly the same as of square of acoustic velocity for all the age classes. This suggests that the total variation in the green outerwood MoE or the independently determined air-dry OW SilviScan MoE (using a totally different methodology) can be explained by the variation in square of the velocity variable. It is important to compare the efficiency of outerwood basic density and Fakopp velocity for sorting or screening trees and logs according to their stiffness class. Here the air-dry SilviScan MoE values were derived from the same increment cores as were used to determine outerwood density. SilviScan uses image analysis together with attenuation and diffraction of Xrays (to directly measure features such as cellulose crystallinity, density and MFA) to determine the intrinsic stiffness of outerwood. The method has been validated by comparison with small clearwood specimens (Evans and Ilic, 2001).
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Table 2 Coefficients of variation in outerwood Stand age
Fakopp velocity
Fakopp velocity2
OW green MoE
OW SilviScan MoE
8-year old 16-year old 25-year old
12.6 11.6 8.6
25.0 22.8 17.3
25.0 23.0 17.6
25.3 22.0 18.2
A correlation analysis was performed to find the strength of association between outerwood basic density, and Fakopp velocity with: theoretical outerwood green MoE (outerwood green density Fakopp velocity2), air-dry outerwood MoE (SilviScan), and log MoE (log green density Hitman velocity2). The Pearson correlation matrix for individual age class reflecting the correlation coefficients are given in Table 3. It is evident from Table 3 that for the young stand (8-year old), outerwood basic density is not related to any of the MoEs (outerwood green, SilviScan MoE and log MoE). However with increasing stand age, the correlations of density with these MoEs gets stronger. For the 25-year-old stand, MoE variables were significantly related with outerwood basic density (P < 0.001). A strong association between Fakopp velocity and air-dry outerwood SilviScan MoE and green log MoE demonstrates the effectiveness of acoustic velocity measurements in standing tree using ToF tools for assessing the wood stiffness as compared to outerwood basic density (green or basic). 5. Conclusions This study, shows that within a stand acoustic velocity was much more variable than the outerwood basic density indicating that acoustic velocity, or acoustic velocity2, is a better parameter for the purpose of screening for outerwood modulus of elasticity (MoE). A strong positive association was observed between acoustic velocity measured on standing trees using the Fakopp instrument and the Hitman velocity measured on butt logs of the corresponding trees after felling. The Fakopp velocity was on an average higher by 9% in 8- and 16-year-old trees while 17% higher in 25-year-old trees. Though a
Table 3 Pearson correlation matrix Variables
Stand age
OW green MoE
SS 50 mm MoE
Hitman log MoE
OW basic density
8-year old 16-year old 25-year old
0.16 ns 0.29* 0.46***
0.17 ns 0.41** 0.50***
0.22 ns 0.26* 0.49***
Fakopp velocity
8-year old 16-year old 25-year old
0.996*** 0.997*** 0.997***
0.82*** 0.84*** 0.81***
0.93*** 0.96*** 0.85***
ns: non-significant at P = 0.05. * Significant at P = 0.05. ** Significance at P = 0.01. *** Significance at P = 0.001.
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reasonably good correlation was observed between the two velocities, the strength of association was poorer for the 25year-old stand. A few trees exhibited very large difference (25– 30%) in the two velocities. This difference might be attributed to the stand age, tree diameter and the proportion of bark on the trees. This variation may also be due to presence of a branch whorl or any other defects in the wave propagation path. The association between log green or basic density with acoustic velocity was very poor for young stand but tends to improve in older stands. An excellent association of Fakopp velocity with air-dry outerwood MoE (SilviScan) and log MoE (Hitman) makes acoustic velocity measurement on standing trees a better indicator than the outerwood basic density for the purpose of selecting or screening radiata pine trees according to their stiffness. Acknowledgement Authors thank The Wood Quality Initiative Ltd., New Zealand for permission to report on aspects of their broader study. References Albert, D.J., Clark, T.A., Dickson, R.L., Walker, J.C.F., 2002. Using acoustic to sort radiata pine pulp logs according to fibre characteristics and paper properties. Int. Forestry Rev. 4 (1), 12–19. Andrews, M., 2000. Where are we with Sonics? WTRC Workshop – 2000: capturing the benefits of forestry research. University of Canterbury, Christchurch, New Zealand. Bamber, R.K., Burley, J., 1983. The Wood Properties of Radiata Pine. Commonwealth Agriculture Bureau, Slough. Booker, R.E., Sorensson, C.T., 1999. New tools and techniques to determine mechanical wood properties. In: Proceedings of the 3rd Wood Quality Symposium on Emerging Technologies for Evaluating Wood Quality for Wood Processing, Rotorua, New Zealand. Booker, R.E., Ridoutt, B.G., Wealleans, K.R., McConchie, D.L., Ball, R.D., 2000. Evaluation of tools to measure sound velocity and stiffness in green radiata pine logs. In: Proceedings of the 12th International Symposium on NDT Wood, Sopron, Hungary, pp. 307–317. Butterfield, B.G., 1998. Microfibril angle in wood. In: IAWA/IUFRO International Workshop at Westport, New Zealand November 1997. University of Canterbury, p. 410. Cown, D.J., McConchie, D.L., 1983. Studies of the intrinsic properties of new crop radiata pine: wood characteristics of 10 trees from a 24-year-old stand grown in the central North Island. NZ Forest Service, Forest Research Institute Bulletin No. 37. Donaldson, L.A., 1992. Within – and between tree variation in microfibril angle in Pinus radiata. N. Z. J. Forestry Sci. 22 (1), 77–86. Entwistle, K., Walker, J.C.F., 2005. Workshop assessment. In: In The Hemicelluloses Workshop 10–13th January 2005. Wood Technology Research Centre, University of Canterbury, New Zealand, pp. 179–186.
Evans, R., 2000. Measuring wood and fibre properties. In: In WTRC Workshop 2000. Wood Technology Research Centre, University of Canterbury, pp. 15–20. Evans, R., Ilic, J., 2001. Rapid prediction of wood stiffness from microfibril angle and density. Forest Prod. J. 51 (3), 53–57. Halabe, U.B., Bidigalu, G.M., GangaRao, H.V.S., Ross, R., 1997. Nondestructive evaluation of green wood using stress wave and transverse vibration techniques. Mater. Eval. 1013–1018. Harris, P., Andrews, M., 1999. Tools and acoustic techniques for measuring wood stiffness. In: Proceedings of the 3rd Wood Quality Symposium: Emerging Technologies for Evaluating Wood Quality for Wood Processing, Rotorua, New Zealand. Harris, J.M., Nash, H.T., 1973. Physical propertie of bark from radiata pine, Corsican pine and Douglas fir. In: Proceedings of Bark Utilisation Symposium, University of Canterbury, Christcurch, New Zealand, pp. 29–35. Lasserre, J.-P., Mason, E., Watt, M., 2004. The influence of initial stocking on corewood stiffness in a clonal experiment of 11 year-old Pinus radiata D. Don. N. Z. J. Forestry 49 (2), 18–23. Lindstrom, H., Harris, P., Nakada, R., 2002. Methods for measuring stiffness of young trees. Holz als Roh und Werkstoff 60 (3), 165–174. Ross, R., Pellerin, R., 1991. NDE of green material with stress waves: preliminary results using dimension lumber. Forest Prod. J. 41 (6), 57–59. Smith, D.M., 1954. Maximum moisture content method for determining specific gravity of small wood samples. USDA Forest Service, Forest Product Laboratory Report No. 2014. So, C.-L., Via, B.K., Groom, L.H., Schimleck, L.R., Shupe, T.F., Kelly, S.S., Rials, T.G., 2004. Near infrared spectroscopy in the forest products industry. Forest Prod. J. 54 (3), 6–16. Sobue, N., 1986. Instantaneous measurement of elastic constants by analysis of the tap tone of wood. Mokuzai Gakkaishi 32 (4), 274–279. Timoshenko, S., 1964. Vibration Problems in Engineering. D. Van Nostrand C Inc., New York. Tsehaye, A., Buchanan, A.H., Walker, J.C.F., 2000. Sorting of logs using acoustics. Wood Sci. Technol. 34 (4), 337–344. US Patent 6,0026,689, 2000. Log cutting optimization system. US Patent 22 February 2000. Wang, S.Y., Ko, C.Y., 1998. Dynamic modulus of elasticity and bending properties of large beams of Taiwan-grown Japanese cedar from different plantation spacing sites. J. Wood Sci. 44 (1), 62–68. Wang, X., Ross, R.J., McClellan, M., Barbour, R.J., Erickson, J.R., Forsman, J.W., McGinnis, G.D., 2000. Strength and stiffness assessment of standing trees using a nondestructive stress wave technique. Research Paper Forest Products Laboratory, USDA Forest Service (FPL-RP-585), 9 pp. Wang, X., Ross, R.J., McClellan, M., Barbour, R.J., Erickson, J.R., Forsman, J.W., McGinnis, G.D., 2001. Nondestructive evaluation of standing trees with a stress-wave method. Wood Fiber Sci. 33 (4), 522–533. Walker, J.C.F., Butterfield, B.G., 1996. The importance of microfibril angle for the processing industries. N. Z. J. Forestry 40 (4), 34–40. Walker, J.C.F., 1993. Primary Wood Processing – Principles and Practices. Chapman and Hall Publication, London. Walker, J.C.F., 1998. Corewood: docking the dog’s tail. Part 1. An alternative road map. N. Z. Forestry 42 (4), 5–6. Zhang, S.Y., 1995. Effect of growth rate on wood specific gravity and selected mechanical properties in individual species from distinct wood categories. Wood Sci. Technol. 29 (6), 451–466.
Variations in acoustic velocity and density with age, and their interrelationships in radiata pine S.S. Chauhan *, J.C.F. Walker School of Forestry, University of Canterbury, Post Box 4800, Christchurch, New Zealand Received 11 November 2005; received in revised form 11 April 2006; accepted 13 April 2006
Abstract Acoustic velocity by the Fakopp time of flight (ToF) tool was used to estimate outerwood stiffness of trees within stands and between stands of different age classes (ages 8, 16 and 25). The TOF acoustic velocity measured in the standing trees was generally higher than the acoustic velocity measured by the Hitman (resonance) tool on the associated logs. The difference between the two velocities tended to be greater in the older and large diameter trees. The large variability in acoustic velocity, or preferably V2, makes it an efficient wood quality variable for screening trees. Wood density variables did not exhibit any relationship with acoustic velocity or modulus of elasticity (MoE) within each age-class. The classic pseudo-relationship observed with pooled data from all stand ages is mainly due to a stand age-effect and so one should be cautious in relying on any significant association between density and MoE. # 2006 Elsevier B.V. All rights reserved. Keywords: Acoustic; Density; Modulus of elasticity; Outerwood; Radiata pine
1. Introduction In New Zealand the trend to harvest radiata pine at ever younger ages, certainly less than 30 years, is today subject to introspection. A cause for concern regarding these fast grown trees is the poorer wood quality (more corewood) compared to older slower grown trees. Furthermore, the quality and property variations within and between these trees are huge: several researchers have shown that within a stand the initial microfibril angle (MFA) may vary from 308 to 508 between trees, e.g. Donaldson (1992); and the density of outerwood may vary from 375 to 500 kg/m3, e.g. Cown and McConchie (1983). In these circumstances, where good and poor quality trees can be found together in fast growth plantations, an appropriate means of sorting is essential. The forest sector will not progress without moving decisively beyond simplistic secondary-source interpretations – of density, internal checking, resin pockets, stiffness or whatever is the issue of the day. There is a sense in which
* Corresponding author at: Institute of Wood Science and Technology, P.O. Malleswaram, Bangalore 560 003, India. Tel.: +91 80 23346811; fax: +91 80 23340529. E-mail address: [email protected] (S.S. Chauhan). 0378-1127/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2006.04.019
understanding of wood is roughly where metallurgical research was in the 1940s. The complexity of the wood cell wall is in sharp contrast to the simplicity of metals, which explains why metallurgical research has advanced more rapidly (Entwistle and Walker, 2005). The difficulty in securing molecular structural information for wood hinders understanding as to why wood is such an amazing natural triumph of nanotechnology, which will only be teased out by the interdisciplinary skills in biomechanics, cell biology and physical chemistry. Practical benefits will not forthcoming until this belief is part of the industry’s work culture. Meanwhile currently favoured mediums of exchange between practitioners and scientists, acoustics and near-infrared microscopy (NIR), can bring limited gains. This paper discusses the use of acoustics, noting only in passing the considerable potential offered by NIR (So et al., 2004). The traditional view has been that density is an important index of wood quality to which all end users could relate (Bamber and Burley, 1983; Walker, 1993). It appears to influence machinability, conversion, strength, paper yield and many other properties. However, in the last few years it has been shown that wood performance in young, fast grown pine is controlled by intrinsic characteristics of the cell wall, such as the cellulose microfibril angle (MFA), to a greater degree than by a complex trait like density (Butterfield, 1998; Walker and
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Butterfield, 1996; Walker, 1998). Further, for structural lumber it is more logical to select logs and standing timber according to stiffness rather than indirectly by emphasizing selection for density or even MFA. Acoustics is an effective surrogate measure of stiffness, while also capturing and reflecting in part wood characteristics like tracheid length, the microfibril angle in the S2 layer, and even chemical composition of cell walls (Albert et al., 2002; Evans, 2000). Consequently, there has been extensive research seeking to understand acoustic wave propagation in clear wood, lumber and logs and to establish the efficient strategies that use acoustic velocity to sort logs according to their stiffness (Ross and Pellerin, 1991; Sobue, 1986; Tsehaye et al., 2000; Wang et al., 2000, 2001). To measure stemwood stiffness researchers rely on a fundamental relationship that appears to hold for all materials, whereby MoE ¼ rV 2
(1)
where MoE is axial stiffness of the green log, r the green density (ca. 1000 kg/m3) and V is the acoustic velocity along the grain. This equation offers a non-destructive means of measuring stiffness. Reasonable correlations have been found when comparing the acoustic velocity of the tree or log and the mean stiffness of all dried lumber cut from each tree: for example, Weyerhaeuser filed a patent that included an R2 of 0.64 (US Patent 6,0026,689, 2000). More generally, it is sufficient to note that a wide range in correlation coefficients (0.30–0.90) have been observed between MoE derived from ToF tools and static MoE (Halabe et al., 1997; Wang and Ko, 1998; Booker and Sorensson, 1999; Tsehaye et al., 2000; Wang et al., 2000; Lindstrom et al., 2002). At the very least these tools have empirical utility. The two principal ways of using acoustics in forest operations are the resonance and time of flight (ToF) methods. Resonance based tools have an edge over ToF tools in terms of ease of use but their application is limited to cut logs or lumber. With resonance-based systems the acoustic wave travels within the log back-and-forth repeatedly from end-to-end resonating strongly at various frequencies, i.e. the fundamental frequency and its overtones. The velocity is calculated based on the first harmonic or more usually using one of its overtones (these are not true harmonics) and the log length. Since the acoustic wave travels back-and-forth, two cut ends are essential. This limitation restricts the application of the resonance method to felled logs or lumber and not to standing trees. Dynamic MoEs derived from the resonance technique have been reported to be in very close agreement with the static MoEs for standard 20 mm 20 mm 300 mm specimens, with a correlation coefficient as high as 0.98 (Lindstrom et al., 2002). On the other hand only ToF tools are able to measure acoustic velocity in standing trees, by timing the acoustic wave as it travels along the stem between points a known distance apart. The acoustic velocities based on the ToF measurements are roughly 10% higher than those derived by resonance. There have been questions regarding the flight path taken by the
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acoustic wave and the precise moment when the acoustic signal ‘arrives’ at the piezoelectric detectors (Harris and Andrews, 1999; Andrews, 2000; Booker et al., 2000; Wang et al., 2000). Some of these concerns are discussed later. In view of such differences in acoustic velocity measurements by these two methods, the robustness of ToF measurements in assessing the stiffness of wood in standing trees needs to be established. This study compared ToF values for outerwood in standing trees in three stands of different ages using a Fakopp-2D tool with acoustic velocities of butt logs from these trees measured by Hitman, a resonance tool The paper also analyses the association of wood density (green and basic) with acoustic velocity and MoE in logs. There are limited opportunities to estimate cheaply, efficiently and quickly the intrinsic properties of the wood in standing trees. One can measure outerwood density (with an increment core or indirectly with a pilodyn) and axial acoustic velocity (with a ToF tool). The paper examines the relationship between the acoustic velocity measured on standing trees using a ToF tool (Fakopp-2D) and outerwood density (increment core) along with the association of these two variables with (1) the acoustic velocity of the equivalent log measured by a resonance tool (Hitman) after felling the tree, and (2) air-dry outerwood stiffness as measured by SilviScan derived from MFA, density and wood chemistry. 2. Materials and methods The study was a part of a comprehensive study initiated by a Government-Industry research consortium, ‘‘The Wood Quality Initiative Ltd.’’, to evaluate wood quality in three stands of Pinus radiata aged 8, 16 and 25 growing in close proximity to one another on the West Coast, New Zealand. The stocking in 8, 16 and 25 year-old stands was 798, 222 and 222 sph, respectively, at the time of the study. The 16-year-old stand had an initial stocking of 798 sph and was thinned at age 9 while 25-year-old stand had an initial stocking of 1725 sph and was thinned at age 5 and again at age 7. In each stand, 50 trees were selected, measured and assessed for a number of other attributes that are not discussed here. The Fakopp-2D ToF tool was used to measure acoustic velocity on standing trees. The tool was used in a three probe configuration with a passive start probe and two piezoelectric probes that detect the acoustic wave generated by lightly tapping the start probe. The lapse time between the first and second stop probes and the distance between them (1.3–1.4 m) was used to calculate the acoustic velocity. The acoustic velocity was measured on opposite sides on each tree at breast height and a simple average of the two velocities was used for most analyses. Two 5 mm diameter increment cores extracted at breast height from opposite sides of the stem were measured for basic density using the maximum moisture content method described by Smith (1954). Once standing tree measurements had been completed, the 8-year-old trees were felled and the acoustic velocity on a single 4 m butt log was determined by resonance (Hitman): a few logs were somewhat shorter (>3.1 m) where small end
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diameters were below 100 mm. For the 16-year-old trees, logs were trimmed to 4.9 m lengths. For the 25-year-old trees logs were trimmed to 4.6 m lengths. Discs were taken from the large and small ends of all the butt logs from each of the three stands. These discs were used to measure diameter over and under bark, as well as moisture content, green density and basic density of wood on a whole-log basis. A 10 mm increment core was extracted at the breast height on each of the selected trees and used for MFA and density measurements using SilviScan. 3. Results Summary statistics of the basic tree and wood characteristics are given in Table 1. Fisher’s LSD test indicated significant differences in the mean value of each of these variables between stands. All measured variables increased with stand age, except log green density, which was less in wood from older stands. This is attributed to the gradual formation of heartwood. Log densities values are included in Table 1 to show that the outerwood and whole log values are consistent, with the green outerwood values being greater than the whole log values in the two older stands due to the presence of heartwood. The whole log green density was determined from the initial green weight and volume of discs (by immersion). Further discussion of whole log values is inappropriate as they can only be obtained by destructive sampling. For all age classes the mean whole-section acoustic velocity of logs with Hitman was lower than the mean outerwood acoustic velocity of stems using Fakopp (Table 1). The average difference in the two velocities was 8.74% (S.D.: 4.98), 8.95% (S.D.: 4.05) and 17.55% (S.D.: 5.54) for 8, 16 and 25-year-old trees, respectively. There was a strong association between the two velocities for each age class (Fig. 1). If there were true correspondence between Fakopp and Hitman velocities, the velocity measured in any one log should have the same value (y = x) when using either tool. Within a stand there is a moderate negative trend in Fakopp velocity with increasing diameter at breast height (DBH) (Fig. 2). The relationship between Fakopp velocity and outerwood densities (basic and green) is shown in scatter diagrams (Fig. 3a and b). Overall, a very poor relationship was evident between
Fig. 1. Comparison between acoustic velocities in standing trees (Fakopp) and butt log of the same trees (Hitman). If the regressions were forced through the origin then the respective slopes would be 1.09, 1.09 and 1.17 implying that Fakopp velocities were on average 9%, 9% and 17% faster than Hitman velocities in these three age classes.
the two variables for individual age classes. However where data from all age classes were pooled together and analysed, acoustic velocity exhibited a significant statistical relationship with outerwood density (R2 = 0.51; P < 0.0001). This significantly higher correlation in pooled data is primarily due to the stand age effect as both Fakopp velocity and outerwood basic density increases with stand age. Thus any interpretation based on pooled data across different age groups could be misleading and one must be very cautious regarding any generalised relationship between density and acoustic velocity (and stiffness). 4. Discussion The with-stand variability in acoustic velocity was much greater than was the variability for the density measurements (Table 1). The coefficient of variation for acoustic velocity was in the range of 9–13% while the variation in log green density, basic density and outerwood basic density was in the range of 2–4%, 5–6% and 6.5–7% respectively. The larger variability in the acoustic velocity compared to that for density implies that
Table 1 Summary of some stand properties Variable
DBH (mm) OW basic density (kg/m3) OW green density (kg/m3) Log green density (kg/m3) Log basic density (kg/m3) Hitman velocity (km/s) green wood Fakopp Velocity (km/s) green wood OW green MoE (GPa) – Fakopp OW air-dry MoE (GPa) – SilviScan a
Stand age.
8-year olda
16-year olda
25-year olda
Mean
CV (%)
Mean
CV (%)
Mean
CV (%)
163.8 331 1110 1043 342 1.73 1.88 4.00 4.86
19.8 6.7 0.7 2.2 5.6 10.4 12.6 25.0 25.3
363.2 369 1123 1026 360 2.19 2.38 6.45 7.68
17.7 6.7 0.7 2.7 5.5 9.6 11.6 23.0 22.0
531.2 394 1131 1001 378 2.45 2.88 9.43 10.73
16.4 6.8 0.8 3.7 5.9 9.0 8.6 17.6 18.2
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Fig. 2. Relationship of log diameter (a) with Fakopp velocity (b) with the difference between Fakopp and Hitman velocities.
acoustic velocity is more efficient in screening the worst or the best trees within a population. The higher velocity measured by Fakopp (Fig. 1) is attributed, in part, to the fact that single pass transit-time velocities are sensitive to the high localised stiffness of the outerwood lying in the ‘‘flight path’’ between the two probes. At the same time, the transit time will not be retarded as much as one might expect by the presence of large knots (branches) and any other defects on the flight path. This is because part of the leading edge of the propagating wave-front will bypass reasonably rapidly between knots in branch whorls even though where confronted by a knot immediately ahead the local wavefront will be delayed by having to sweep round the knots. It is the leading edge of the propagating wave-front moving through the outerwood that is picked-up by the stop probes resulting in a higher velocity by the Fakopp tool. The Fakopp velocity was lower than the Hitman velocity in only three 8-year-old trees. These trees had huge branch-whorls near breast height as observed during the measurements. The Fakopp ToF tool measured the acoustic velocity over a maximum of 1.5 m at around breast height, so the presence of excessive branches and distorted grain within this short 1.5 m path length would reduce significantly the measured ToF velocities and so underestimate the outerwood stiffness. In contrast, with a 4 m butt log, the two vibrational nodes for the second harmonic occur at 1 and 3 m, respectively, i.e. fortuitously, Hitman would not sense large branch whorls at
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Fig. 3. Relationship of Fakopp velocity with (a) outerwood basic and (b) green density. Thick lines are corresponding to straight line fit for individual age groups and thin line is for pooled data.
these locations and so would overestimate log stiffness. The effect of nodes is illustrated by separate study where logs were evaluated using two resonance tools, Hitman and WoodSpec. WoodSpec captured data for both the first and second harmonics. These values are plotted against the Hitman data (Fig. 4). The perfect correspondence between the two tools when WoodSpec was analysing the second
Fig. 4. Comparing two resonant tools, Hitman (operating at the second harmonic) with WoodSpec (operating at both fundamental and second harmonics).
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harmonic demonstrates that Hitman looks only at the second harmonic. However there are noticeable differences when comparing the first and second harmonics. Thus the same log can give two acoustic velocities that differ by as much as 11%, which corresponds to a difference in the estimated stiffness of about 23%. These different calculated values arise because the overtones are not exact harmonics. The second harmonic or overtone has been used for acoustic velocity determination of logs. This preference is related to the resonance frequencies of a tapered homogeneous bar in which the difference between tapered and uniform bar frequencies decreases with the higher overtones (Timoshenko, 1964). On that basis the resonance frequencies ( f) of higher overtones (n) are better for calculating the MoE, based on rod theory, i.e. MoE = rV2 or r (2Lf n/n)2 where the sample length is L. In practice it is difficult to capture more than one or two overtones due to various energy loss mechanism (moisture, anisotropy, presence of defects, knots, etc.) in wood. There is the separate issue as to whether a particular harmonic observes a large branch whorl. This depends on its location relative to the nodes (where a whorl would be undetected and unaccounted for) or anti-nodes (detected) of that harmonic. For the fundamental frequency the nodal point will be at the mid point of the log along the log length while for the second harmonics two nodal points will be at 0.25L distance from the log ends. Clearly analysis based on a single harmonic will not be ‘‘correct’’ in all instances. If one is interested in intrinsic wood quality then the harmonic that gives the highest acoustic velocity would be the more appropriate. If one were interested in knot distributions and sizes then a comparison between overtones may provide some information. The relatively moderate coefficient of determination (R2 = 0.75) for the 25-year-old trees between Fakopp and Hitman (second harmonic) velocity points to the greater uncertainty in ranking trees for their whole-tree stiffness in the oldest stand using Fakopp: Fakopp measures outwood properties; Hitman senses whole-section properties (Fig. 1). Zhang (1995) observed that within a stand the bigger trees resulting from higher growth rate would generally produce wood of lower stiffness. Fig. 2a shows a moderate negative trend in Fakopp velocity with increasing diameter at breast height (DBH) in individual age groups with a modest coefficient of determination in the youngest and the oldest stand. The Pearson correlation coefficient values for 8-, 16- and 25-year-old stands were 0.43 (P = 0.002), 0.24 (P = 0.1) and 0.37 (P = 0.007), respectively. For trees of the same age, slow growing trees tend to have high outerwood stiffness to sustain trees under various stresses like wind, crown weight, etc. In fast growing trees the increased diameter with its larger second moment of inertia means there is less need for high material stiffness. Further Lasserre et al. (2004) have observed that outerwood stiffness increases dramatically with stocking (and so with smaller tree DBH), which is what is observed here at the individual tree level. For 11-year-old trees they observed that the whole-stem stiffness is reduced from 5.9 GPa for high stockings (2500 sph) to 4.1 GPa for low stockings (833 sph).
With our 16-year-old stand, a thinning operation had been carried out in year 9 reducing the stocking to 222 sph from an initial 632 sph. This would have accelerated growth in slow growing trees due to reduced within stand competition and this may explain the poor relationship between the acoustic velocity and DBH in this stand. To analyse the effect of log diameter on the difference in Fakopp and Hitman velocities, the difference in the two velocities was plotted against the diameter at breast height of logs (Fig. 2b). It is evident that in large diameter logs, especially from the 25-year-old stand, the difference in the two velocities was comparatively large. With increasing age, both wood density and MoE increase from pith to cambium in radiata pine. Therefore in the oldest stand there will be a large difference in stiffness between outerwood and corewood. Hitman velocity (resonance) is a cross-section weighted average velocity while the Fakopp velocity is influenced by the stiffness of the wood lying in the shortest time-of-flight path (stiff outerwood) between the detector probes. There are two obvious factors at work, one age related and the other diameter related. A further factor contributing to the difference in the Fakopp and Hitman velocity is the proportion of bark on logs. The gross bark volume in radiata pine butt logs (up to 10 m) is reported to be about 15–17% of the under bark round volume (Harris and Nash, 1973). Since bark has few fibres and a very poor tensile stiffness, the high percentage of bark would pull down the resonance velocity in log due to volume averaging. This bark volume or weight does not affect the transit-time velocity. 4.1. Wood density and acoustic velocity Wood basic density has long been the most popular measure of wood quality, as there are quantitative functions that relate it to mechanical and physical properties of straight-grained wood. The industrial standard for density assessment in standing trees involves extraction of outerwood cores and measurement of basic density with minimal delay. On the other hand wood stiffness, MoE, is the product of the acoustic velocity squared and the actual density at the time of measurement: and these two are treated as independent variables, at least as a first approximation (Eq. (1)). In a sense, acoustic velocity is assessing the quality of matter, being related largely to the inclination of the microfibrils in the S2 layer (MFA); whereas density is merely the quantity of matter in a given volume of material. The poor association of Fakopp velocity with outerwood density (Fig. 3) indicates that the two variables reflect different inherent wood quality parameters. However with increasing stand age the association between the two variables gets a little stronger. For the 25-year-old stand, acoustic velocity was significantly related with outerwood density (basic and green) with a correlation coefficient of 0.42 (P = 0.002). One can justify the modest improvement in correlation between basic density and Fakopp velocity with increasing stand age, since both density and acoustic velocity increase with distance from the pith. Strictly, density and acoustic velocity are not totally independent of each other. This is because the proportion of the
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cell wall that is occupied by the S2 layer varies with density – from between 60% in thin-walled cells to over 80% in thickwalled cells. The cellulose microfibrils in the other parts of the cell wall (P, S1, S3) have very high angles and make a negligible contribution to axial stiffness except in the youngest wood in the poorest of trees. As density increases with the accumulation of thick walled cells, the proportion of material in the highstiffness S2 layer increases disproportionately, i.e. as the quantity of matter increase so there is a concurrent modest increase in the quality of matter. The coefficient of determination for individual age groups and for pooled data are same in case of both basic and the green density (Fig. 3). Here, it is important to recognise that the outerwood green density was not measured; it was calculated from the basic density assuming a density of 1500 kg/m3 for oven-dry cell wall tissue. Therefore, in effect any association between basic density or green density with another property will give the same coefficient of determination as they are not independent variables. The derived outerwood (OW) green density is used to estimate the outerwood green MoE from the equation: OW MoEgreen = OW densitygreen Fakopp velocity2. The very low variability in outerwood green density (CV 0.7%) means that the acoustic velocity is the dominant variable in describing the variability in outerwood green MoE. This is shown by analysing coefficient of variations. The coefficient of variation of acoustic velocity, velocity square and outerwood green MoE were estimated for each stand (Table 2). It can be seen that the coefficient of variation of outerwood MoE is nearly the same as of square of acoustic velocity for all the age classes. This suggests that the total variation in the green outerwood MoE or the independently determined air-dry OW SilviScan MoE (using a totally different methodology) can be explained by the variation in square of the velocity variable. It is important to compare the efficiency of outerwood basic density and Fakopp velocity for sorting or screening trees and logs according to their stiffness class. Here the air-dry SilviScan MoE values were derived from the same increment cores as were used to determine outerwood density. SilviScan uses image analysis together with attenuation and diffraction of Xrays (to directly measure features such as cellulose crystallinity, density and MFA) to determine the intrinsic stiffness of outerwood. The method has been validated by comparison with small clearwood specimens (Evans and Ilic, 2001).
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Table 2 Coefficients of variation in outerwood Stand age
Fakopp velocity
Fakopp velocity2
OW green MoE
OW SilviScan MoE
8-year old 16-year old 25-year old
12.6 11.6 8.6
25.0 22.8 17.3
25.0 23.0 17.6
25.3 22.0 18.2
A correlation analysis was performed to find the strength of association between outerwood basic density, and Fakopp velocity with: theoretical outerwood green MoE (outerwood green density Fakopp velocity2), air-dry outerwood MoE (SilviScan), and log MoE (log green density Hitman velocity2). The Pearson correlation matrix for individual age class reflecting the correlation coefficients are given in Table 3. It is evident from Table 3 that for the young stand (8-year old), outerwood basic density is not related to any of the MoEs (outerwood green, SilviScan MoE and log MoE). However with increasing stand age, the correlations of density with these MoEs gets stronger. For the 25-year-old stand, MoE variables were significantly related with outerwood basic density (P < 0.001). A strong association between Fakopp velocity and air-dry outerwood SilviScan MoE and green log MoE demonstrates the effectiveness of acoustic velocity measurements in standing tree using ToF tools for assessing the wood stiffness as compared to outerwood basic density (green or basic). 5. Conclusions This study, shows that within a stand acoustic velocity was much more variable than the outerwood basic density indicating that acoustic velocity, or acoustic velocity2, is a better parameter for the purpose of screening for outerwood modulus of elasticity (MoE). A strong positive association was observed between acoustic velocity measured on standing trees using the Fakopp instrument and the Hitman velocity measured on butt logs of the corresponding trees after felling. The Fakopp velocity was on an average higher by 9% in 8- and 16-year-old trees while 17% higher in 25-year-old trees. Though a
Table 3 Pearson correlation matrix Variables
Stand age
OW green MoE
SS 50 mm MoE
Hitman log MoE
OW basic density
8-year old 16-year old 25-year old
0.16 ns 0.29* 0.46***
0.17 ns 0.41** 0.50***
0.22 ns 0.26* 0.49***
Fakopp velocity
8-year old 16-year old 25-year old
0.996*** 0.997*** 0.997***
0.82*** 0.84*** 0.81***
0.93*** 0.96*** 0.85***
ns: non-significant at P = 0.05. * Significant at P = 0.05. ** Significance at P = 0.01. *** Significance at P = 0.001.
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reasonably good correlation was observed between the two velocities, the strength of association was poorer for the 25year-old stand. A few trees exhibited very large difference (25– 30%) in the two velocities. This difference might be attributed to the stand age, tree diameter and the proportion of bark on the trees. This variation may also be due to presence of a branch whorl or any other defects in the wave propagation path. The association between log green or basic density with acoustic velocity was very poor for young stand but tends to improve in older stands. An excellent association of Fakopp velocity with air-dry outerwood MoE (SilviScan) and log MoE (Hitman) makes acoustic velocity measurement on standing trees a better indicator than the outerwood basic density for the purpose of selecting or screening radiata pine trees according to their stiffness. Acknowledgement Authors thank The Wood Quality Initiative Ltd., New Zealand for permission to report on aspects of their broader study. References Albert, D.J., Clark, T.A., Dickson, R.L., Walker, J.C.F., 2002. Using acoustic to sort radiata pine pulp logs according to fibre characteristics and paper properties. Int. Forestry Rev. 4 (1), 12–19. Andrews, M., 2000. Where are we with Sonics? WTRC Workshop – 2000: capturing the benefits of forestry research. University of Canterbury, Christchurch, New Zealand. Bamber, R.K., Burley, J., 1983. The Wood Properties of Radiata Pine. Commonwealth Agriculture Bureau, Slough. Booker, R.E., Sorensson, C.T., 1999. New tools and techniques to determine mechanical wood properties. In: Proceedings of the 3rd Wood Quality Symposium on Emerging Technologies for Evaluating Wood Quality for Wood Processing, Rotorua, New Zealand. Booker, R.E., Ridoutt, B.G., Wealleans, K.R., McConchie, D.L., Ball, R.D., 2000. Evaluation of tools to measure sound velocity and stiffness in green radiata pine logs. In: Proceedings of the 12th International Symposium on NDT Wood, Sopron, Hungary, pp. 307–317. Butterfield, B.G., 1998. Microfibril angle in wood. In: IAWA/IUFRO International Workshop at Westport, New Zealand November 1997. University of Canterbury, p. 410. Cown, D.J., McConchie, D.L., 1983. Studies of the intrinsic properties of new crop radiata pine: wood characteristics of 10 trees from a 24-year-old stand grown in the central North Island. NZ Forest Service, Forest Research Institute Bulletin No. 37. Donaldson, L.A., 1992. Within – and between tree variation in microfibril angle in Pinus radiata. N. Z. J. Forestry Sci. 22 (1), 77–86. Entwistle, K., Walker, J.C.F., 2005. Workshop assessment. In: In The Hemicelluloses Workshop 10–13th January 2005. Wood Technology Research Centre, University of Canterbury, New Zealand, pp. 179–186.
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