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Wood Science and TechnologyJournal of the International Academy ofWood Science ISSN 0043-7719 Wood Sci TechnolDOI 10.1007/s00226-013-0532-0
Timber species grouping in Bangladesh:linking wood properties
Md. Qumruzzaman Chowdhury, SwapanKumar Sarker, Jiban Chandra Deb &Sanjay Saha Sonet
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ORI GIN AL
Timber species grouping in Bangladesh: linking woodproperties
Md. Qumruzzaman Chowdhury •
Swapan Kumar Sarker • Jiban Chandra Deb •
Sanjay Saha Sonet
Received: 2 March 2012
� Springer-Verlag Berlin Heidelberg 2013
Abstract Timber species grouping (TSG) is essential for meaningful and cost-
optimal use of wood. Bangladesh forests are exceedingly diverse and comprise
many woody species which are potentially suitable for versatile uses including
structural materials. Traditionally, widely known tree species are used for structural
timber because technological properties of most of the species are poorly known. In
this study, a hierarchical agglomerative cluster analysis based on three selected
wood properties [i.e., wood density, modulus of elasticity (MOE) and modulus of
rupture (MOR)] of seventy-nine timber species was done. The clustering process led
to the formation of four distinct species groups [i.e., very low (TSG1), low (TSG2),
medium (TSG3) and high (TSG4)]. However, the species grouping patterns also
varied from trait to trait. This might be due to moderate relationship between density
and MOE (r2 = 0.46) or MOR (r2 = 0.52). Species of the TSG1 group are mainly
characterized by extremely low trait values, while the TSG4 group consists of
species having exceedingly high trait values. The TSG2 and TSG3 groups are
characterized by low and medium trait values. Hence, it is suggested to select
suitable species from these groups, particularly the lesser known high-quality spe-
cies in afforestation and reforestation programs to meet future timber demand in
Bangladesh.
Introduction
Timber species grouping based on wood properties is a common practice in many
countries and essential for meaningful use of wood (Davalos and Barcenas 1999;
Ali et al. 2008; EN-338 2009). Wood properties (e.g., physical and mechanical) that
Md. Q. Chowdhury (&) � S. K. Sarker � J. C. Deb � S. S. Sonet
Department of Forestry and Environmental Science, Shahjalal University of Science
and Technology, Sylhet 3114, Bangladesh
e-mail: qumrul-for@sust.edu
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DOI 10.1007/s00226-013-0532-0
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regulate the structural quality of wood are not in use in grouping or categorizing
timber species in Bangladesh. Indeed, timber species are grouped based on
stumpage appraisal method (for details see Pant 1990). At present, five different
timber species groups from superior to lower (e.g., special class, A, B, C and D
class) are in use, and based on these groups, royalties are scheduled for timber-
yielding species. Although the country has about 500 usable tree species (Sattar and
Akhtaruzzaman 1997), only a few widely known species are mostly exploited and
used (Sattar 1997). This overexploitation has resulted in decreased population size
of these species in the forests. As a consequence, the national timber markets have
been suffering from timber supply shortage for the last few decades (FAO 2009).
Under this circumstance, utilization of versatile or lesser known species is
considered essential to reduce pressure on the widely used timber species (Ali et al.
2008) and simultaneously to increase the resource base of the country. However,
lack of information on wood properties of these commercially least concerned
species has been a barrier for wider uses of these species. Depending on the tree
species, its wood properties vary, even within a single tree species (Panshin and de
Zeeuw 1980), and such kind of variation has resulted in a wood supply with great
variability, making difficulties in processing as well as utilization (Zobel and
van Buijtenen 1989). Hence, understanding the variation of wood properties
is imperative for processing and utilization of these species in Bangladesh
(Chowdhury et al. 2009a).
Teak (Tectona grandis) timber is considered as a standard with which other
locally grown or imported timbers are compared in Bangladesh. For example, the
strength properties of 40-year-old Chittagong-grown teak were used by Yakub et al.
(1978) to assess the relative suitability of other species for various purposes (Sattar
et al. 1997). However, use of wood properties rather than conventional stumpage
appraisal method in categorizing timber species is more logical because it provides
the scope to bring timber species into groups that are homogeneous in technological
properties. Furthermore, grouping technique based on wood properties may increase
the market value of unpopular species and may optimize uses of versatile species. In
this regard, the objectives of this work are to describe important wood properties of
timber species growing in Bangladesh based on a literature review, and then to
group the timber species based on the selected wood properties, such as basic
density (henceforth referred to as density), modulus of elasticity (MOE) and
modulus of rupture (MOR).
Geographical location and climate
Bangladesh is located in the northeastern part of South Asia and lies between 20�340
to 26�380 north latitudes and 88�010 to 92�410 east longitudes (FAO 2009). The
country is bounded by India on the west, the north and the northeast and Myanmar
on the southeast and the Bay of Bengal on the south. It consists of mainly flood
plains with some hilly areas in the north-eastern and south-eastern regions, and the
total area is 14.40 million ha, of which 9.25 million ha is agricultural land, 1.92
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million ha public forests, 0.34 million ha private forests including tea and rubber
gardens, 1.16 million ha urban area and 0.94 million ha water bodies (FAO 1998).
It has a monsoonal climate with one dry and one wet season (Shaman et al.
2005). The monsoon has its onset in the beginning of June and ends in October with
some interannual variability. The pre-monsoon continues from March to May and
the post-monsoon from late October to November. The dry winter period lasts from
December to February. During this dry winter period, the mean temperature varies
from 16 �C in the north-west and north-eastern parts of the country to 20–21 �C in
the coastal areas. The mean annual rainfall in Bangladesh varies from 1,600 mm in
the west-central part to over 3,000 mm in the northeast and southeast part of the
country (Banglapedia 2012).
Current status and distribution of forests
Bangladesh forests are characterized by high species diversity due to the great
variability of physiographic and environmental conditions. Although it is stated by
FAO that forest land is approximately 16.7 % (FAO 2000), the recent report showed
that the forest cover extends around 6.7 % of the country (FAO 2009). Of the total
forest area, 84 % has been classified as natural forest and 16 % as plantation forest
(BFD 2010). The forests are broadly classified into three types, that is, tropical
evergreen/semievergreen, moist/dry deciduous and swamp (mangrove and fresh-
water) forests. The tropical evergreen/semievergreen forests are distributed from
south-eastern region (Chittagong, Rangamati, Khagrachari, Bandarbans and Cox’s
Bazar district) to north-eastern districts (Sylhet, Moulavibazar and Habigonj
district) (Salam et al. 1999). The moist/dry deciduous forests are presently
distributed in central (Dhaka, Gazipur, Mymensingh and Tangail district) and
northern (Dinajpur and Thakurgaon district) part of the country (Alam et al. 2008).
The natural mangrove forests widely known as Sundarbans are distributed in the
south-western part (Khulna, Stakhira and Bagerhat district), and plantation
mangroves are located along the coastline of the country (Chowdhury et al.
2008). The freshwater swamp forest is distributed in the wetland areas of greater
Sylhet region (Choudhury et al. 2004). In addition, there are homestead forests
(FAO 2000), which are the major sources of forest products in Bangladesh
(Muhammed et al. 2008; Choudhury and Hossain 2011).
Wood properties and natural durability variation
Table 1 summarizes some selected physical and mechanical properties and natural
decay resistance of different timber species from various literature sources to
provide a brief overview. All species are hardwood, except Podocarpus nerifoliawhich is the only indigenous softwood species in the country. As with most tropical
species, all hardwood species presented here are diffuse-porous, except Cedrelatoona, Lagerstroemia speciosa, Melia azadarach and T. grandis which are ring- to
semiring-porous (Das 1972; Mohiuddin and Das 1992; Priya and Bhat 1999). Of all
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indices that characterize wood properties, wood density is used universally to define
wood quality (Zobel and van Buijtenen 1989). The studied species have densities
ranging from 0.22 to 0.84 g/cm3 (Table 1). The lowest wood density is in Erythrinaorientalis, while Vitex peduncularis and Mesua ferrea show the highest density.
MOE is an indication of the stiffness, and MOR is an indication of the bending
strength of a board or structural materials (Panshin and de Zeeuw 1980; Kollman
and Cote 1984). In most of the species, the MOE and MOR are ranging from 3.6 to
20.3 GPa and 20.6 to 171.1 MPa, respectively (Table 1). E. ovalifolia shows the
lowest MOE, whereas M. ferrea shows the highest MOR.
Determination of physical and mechanical properties is very time-consuming
(Kokutse et al. 2010). The strong relationship of wood density to mechanical
properties, fiber yield and other properties relevant to the end use of forest products,
and the relative ease of its determination, make it (density) a simple and good
predictor of the aforementioned properties (Panshin and de Zeeuw 1980; Kollman
and Cote 1984). However, in this study, linear regression analysis showed that MOE
and MOR are moderately related with density (r2 = 0.46 and r2 = 0.52, respec-
tively). Wood density is determined primarily by the cell wall thickness, and
therefore, it can be assumed that high-density woods have thick-walled fibers and
low-density woods have thin-walled fibers (Chowdhury et al. 2012). However,
moderate relationships reinforce the argument that density is not a sole predictor of
MOE or MOR and should not be used alone to predict timber strength. The lower r2
value indicates that there are other factors apart from density, for instance
microfibril angle which influence wood strength (Cave and Walker 1994; Nakada
et al. 2003; Zhu et al. 2005).
Wood is an extremely versatile material, and more than one property of wood
is important for the end product. Depending on the end use, the value of
appearance-type properties, such as texture and grain pattern, should be evaluated
with wood density and/or strength properties, because of their (appearance-type
properties) variations within them (density and/or strength properties) in the
tropical hardwoods (FPL 1999; Hernandez and Almeida 2003; Gupta and Sinha
2012). The studied species exhibit 26 % fine (cell arrangement is even), 57 %
coarse (cell arrangement is uneven) and 17 % moderate (mixed cell arrangement)
texture (Table 1). In this study, only grain types are presented which are often
used in reference to figures on wood and direction of the fibers (Desch and
Dinwoodie 1996). The grain types vary among the species, and 60 % trees exhibit
straight, 38 % interlocked and 2 % twisted grain (Table 1). However, the grain
types or textures do not show any distinct relationship with density or mechanical
strength (MOE and MOR), and thus, the occurrence of these properties might be a
result of either their inherent or induced structures by external influences (Desch
and Dinwoodie 1996).
Understanding natural durability of wood is important in utilization as well as
ecosystem management (Scheffer and Morrell 1998; Viitanen et al. 2010; Freschet
et al. 2012). The studied species show variations in wood decay resistance
(Table 1), and superior decay resistance is associated with higher wood density
(Fig. 1). Tree species with high natural decay resistance might evolve to produce
high-extractive compounds that can protect the wood against decaying organisms
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Wood Sci Technol
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(Scheffer and Morrell 1998). In addition to the chemical composition, the structure
of wood, such as higher fiber portion, and less conduits and parenchyma portions,
might influence less fungal movement within wood and reduce the spread of
infections (Fengel and Wegner 1984; Choat et al. 2008).
In fact, most of the information regarding the wood properties presented in this
work is rather old. In addition to inter- and/or intra-specific variation, wood
properties vary vertically along the main axis of the stem and/or radially from pith
to bark within individual trees (Zobel and van Buijtenen 1989; Savidge 2003;
Chowdhury et al. 2009a, b). Except for a few (Chowdhury et al. 2005, 2009a), most
of the data presented in Table 1 are based on single mean without providing the
information about the exact location of wood specimens or of the variation patterns
within trees. Presenting single-mean data might be erroneous due to within-tree
variation (e.g., Nogueira et al. 2008; Nock et al. 2009). Thus, future research should
be directed to determine their properties considering whole tree variations which are
likely to be beneficial for timber utilization in the country.
Methods
Data from various literature sources on selected physical and mechanical properties
of eighty tree species growing in Bangladesh (Table 1) were collected. In this
analysis, P. nerifolia was the only softwood species deducted. Hierarchical
agglomerative polythetic cluster analysis was performed using software PC-ORD
5.0 (McCune and Mefford 1999) to categorize seventy-nine tree species based on
three wood properties, namely density, MOE and MOR. Tree species only having
data on these three common properties were subjectively used in the analysis. In
Fig. 1 Variation of basic(wood) density in each decayresistance class. Decayresistance was categorized (VR,very resistant; RS, resistant;MR, moderately resistant; NR,nonresistant) according toScheffer and Morrell (1998).The plus signs indicate mean,horizontal bars are median,upper and lower limits of theboxes are interquartile range (75and 25 percentile), and verticalbars are the total variation inwood densities
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cluster analysis, complete linkage method and Euclidean distance were used as
group linkage method and distance measure, respectively. Wishart’s objective
function was used to scale the resulting dendrogram that measures loss of
information at each step of cluster formation. All dendrograms were pruned at
distance indicating 75 % information remaining.
Results
Timber species groups based on wood density
Based on wood density variation, cluster analysis identified four timber species
groups (TSGs) (Fig. 2). Species of TSG1 group (e.g., E. orientalis, E. ovalifolia,
Ficus spp, etc.) had extremely low wood density (0.28 ± 0.040 g/cm3), while TSG4
had timber species (e.g., V. peduncularis, M. ferrea, etc.) having very high wood
density (0.78 ± 0.049 g/cm3) (Table 2). TSG2 and TSG3 groups represented
timber species having low and medium wood density values, respectively.
Fig. 2 Dendrogram representing four timber species groups (TSGs) based on wood density and prunedat distance 8.7E-02 (75 % information remaining scale). Groups 1, 2, 3 and 4 indicate timber speciesgroup with very low, low, medium and high basic density of wood values, respectively
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Timber species groups based on MOE
Cluster analysis also produced four distinct timber species groups based on the
MOE variation among the species (Fig. 3). TSG1 group comprised 11 species
having very low MOE (5.20 ± 0.97 GPa). Ficus spp. exhibited the lowest MOE
value (3.6 GPa). TSG2 group contained higher number of species (49) with low
MOE in comparison with other species groups, while TSG3 group included only 16
timber species having medium MOE values (Table 2). Species of TSG4 group, such
as M. ferrea, Tamarindus indica and Bruguiera conjugate, showed exceedingly
higher MOE values than the other studied species.
Timber species groups based on MOR
Considerable variation in MOR also produced four distinct species groups (Fig. 4).
TSG1 group contained only four timber species (E. ovalifolia, E. orientalis, Pajanpelialongifolia and Bombax insigne) with low MOR values (31.5 ± 7.53 MPa). Tree species
of TSG2 group comprised the highest (41) number of species (Table 2). However, trees
of that group had lower MOR values in comparison with other groups. TSG3 group
represented 26 timber species with medium MOR values (91.1 ± 8.54 MPa). Timber
species of TSG4 group, such as M. ferrea, Sterospermum personatum and B. conjugate,had exceedingly higher MOR values than the other species.
Discussion
Based on the selected wood properties, the clustering process led to the formation of
four distinct species groups (Figs. 2, 3, 4). Trees of the TSG1 group are mainly
characterized by the lowest values of wood traits, while trees of the TSG4 group are
with the highest values. The other two groups (TSG2 and TSG3) are characterized
by substantially low and medium values, respectively. Concerning both wood
Table 2 Summary of the timber species groups identified by cluster analysis
Wood properties Description Timber Species Groups
TSG1 TSG2 TSG3 TSG4
Wood density
(g/cm3)
Range 0.22–0.33 0.36–0.53 0.54–0.69 0.71–0.84
Average ± SD 0.28 ± 0.040 0.45 ± 0.057 0.61 ± 0.048 0.78 ± 0.049
Number of species 6 32 33 8
MOE (GPa) Range 3.6–6.1 6.4–12.0 12.7–16.5 17.6–20.3
Average ± SD 5.20 ± 1.0 9.2 ± 1.7 14.2 ± 1.3 18.7 ± 1.4
Number of species 11 49 16 3
MOR (MPa) Range 20.6–37.8 41.5–78.8 80.5–106.7 119.2–171.1
Average ± SD 31.5 ± 7.5 59.5 ± 10.2 91.1 ± 8.5 135.7 ± 17.3
Number of species 4 41 26 8
SD standard deviation
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density and strength properties, a considerable number of species show lower
affinity to a certain group and species are segregated among the different groups.
For example, a number of species of TSG1 with lowest wood density fall into other
groups while MOE or MOR values were used in the analysis (Figs. 2, 3, 4). This
might be due to the moderate relationship between density and MOE or MOR.
Sattar et al. (1997) also reported four to five groups while classifying 45 tree species
on the basis of relative distance from T. grandis using wood density, MOE and
MOR. In that categorization, the species grouping also varies from trait to trait. For
example, wood density criterion shows four groups (e.g., A—light to D—high),
while MOE and MOR show five groups (e.g., A—light to E—very high).
Comparing the values of that category, the range of each group also varies in the
present study. For example, wood density in their study ranges from 0.29–0.43,
0.44–0.56, 0.60–0.67 and 0.73–0.84 gm/cm3 for A (light), B (medium), C (upper)
and D (high) groups, respectively. However, in this study, the values range from
0.22–0.33, 0.36–0.53, 0.54–0.69 and 0.71–0.84 gm/cm3 for TSG1, TSG2, TSG3
and TSG4 groups, respectively (Table 2).
Fig. 3 Dendrogram representing four timber species groups (TSGs) based on MOE while pruned atdistance 6.2E?1 (75 % information remaining scale). Groups 1, 2, 3 and 4 indicate timber species groupwith very low, low, medium and high MOE values, respectively
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The present grouping system also varies from the previously mentioned
categorization of Pant (1990). For example, Syzigium sp. is a lower (C)-class timber
species in that category, whereas same species belongs to higher (TSG3) group in
this study here. Wood properties of many lesser known species have higher values
than those of well-known species (Table 1) and grouped with them (Figs. 2, 3, 4).
For instance, V. peduncularis, M. ferrea, Homalium bhamoense, Anogeissusacuminate and Protium serratum have higher wood density and strength properties
than other species and belong to higher groups. At present, homestead forests and
social forestry plantations are the major sources of timber in Bangladesh (Choudhury
and Hossain 2011), and government has initiated collaborative forest management to
secure sustainable supply of timber from these forests (Sarker et al. 2011). Hence,
sustainable use of lesser known species that are abundant in the homesteads and
introduction of these species in the ongoing social forestry plantations may offer a
solution in relieving the pressure on the widely used species in the country.
The quality of a grouping system can be assessed on the basis of requirements
that are related to the utilization of timber. For example, if a company/user needs
only timber of higher strength, then the grouping system might be able to optimize
Fig. 4 Dendrogram representing four timber species groups (TSGs) based on MOR when pruned atdistance 5.2E?03 (75 % information remaining scale). Groups 1, 2, 3 and 4 indicate timber species groupwith very low, low, medium and high MOR values, respectively
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the yield for these groups. Several timber species grouping systems exist on an
international level. For example, Davalos and Barcenas (1999) made five categories
(very low to very high) using 500 tree species growing in Mexico on the basis of the
probability distribution of MOE and MOR values. Compared to that grouping, the
MOE of the present study resembles low to high categories and MOR resembles
very low to medium categories. In Europe, the strength classes are divided into C
classes for softwood and D classes for hardwood incorporating the standard
requirements of the end users (EN-338 2009). In Bangladesh, this type of grading
system is lacking yet. However, the mean density, MOE and MOR of the
investigated timber (hardwood) species of TSG2 to TSG4 are comparable to those
of European strength classes of EN-338, for example, D18 to D70. On the other
hand, density and MOR of the softwood species (P. nerrifolia) are comparable with
C40, and MOE is similar to C24 class (EN-338 2009). These findings support the
argument that in an attempt to ensure quality control, timbers should be grouped on
the basis of the uniformity in strength results and used for similar structural
applications (Zziwa et al. 2006).
It is necessary to add new species to this grouping, and therefore, additional data
collection is required as Bangladesh forests are highly diverse. In plantations and
homestead forests, most of the species are fast growing having short (6–18 years)
and long rotations (40 years to upward) (Sarker et al. 2011). Hence, it is
recommended to select suitable species from these groups in future afforestation and
reforestation programs to meet the future timber demand. From this study, it is also
evident that some of these species may not be popular traditionally (Table 1), but
possess desirable properties that the users would like to have, such as M. ferrea. In
Uganda, Zziwa et al. (2010) suggested that timber species with MOR equal to or
greater than 16 MPa could be used for high-load-bearing timbers, whereas those
with MOR equal to or less than 4 MPa could be used for relatively low-load-bearing
timbers. With the wide range of density and strength values, species of TSG3 and
TSG4 groups appear to have potential to meet the wood quality requirements for
medium to large construction and furniture, whereas those of TSG1 and TSG2
groups could be used for lighter uses where relatively low-load-bearing- capacity of
wood is necessary.
Conclusion
In this study, wood properties of seventy-nine tree species were analyzed by
comparing among the species. The variation in density and strength properties of the
studied timber species indicate that the timbers can be categorized into four
different groups: TSG1, TSG2, TSG3 and TSG4. Concerning both density and
strength properties, differences among the groups were also found and this might be
due to having moderate relationships between them. The purpose of this species
grouping protocol is not to make every user a certified lumber grader but rather to
provide a simple tool with sufficient supporting documentation to facilitate the
decision-making process concerning the structural capacity of woody species. The
results obtained in this study have quite significance from the perspective of the
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Bangladesh timber promotion, for instance selecting lesser known timber species in
the wood industry.
Acknowledgments This study was partly supported by a research grant from Shahjalal University of
Science and Technology (SUST), Sylhet, Bangladesh. The authors wish to thank Bangladesh Forest
Research Institute (BFRI), Chittagong, for using their library. Special thanks to Mr. Swaikat Haldar for
his help in literature collection. We are grateful to two anonymous reviewers for their critical comments
on an earlier version of the manuscript.
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