Timber species grouping in Bangladesh: linking wood properties

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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: [email protected]

123

Wood Sci Technol

DOI 10.1007/s00226-013-0532-0

Author's personal copy

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

Wood Sci Technol

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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

Wood Sci Technol

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NR

ST

18

CO

18

L

Myr

isti

cali

nif

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a1M

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lin

My

rist

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0.3

68

.75

5.4

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18

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Syz

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msp

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yz

sp.

My

rtac

eae

0.6

10

80

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RIN

18

MT

18

W

Syz

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ran

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rtac

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0.6

81

1.2

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doc

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0.4

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0.7

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tho

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sis1

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tch

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po

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26

L

Wood Sci Technol

123


Ta

ble

1co

nti

nu

ed

Sp

ecie

sn

ame

Cod

eF

amil

yB

asic

den

sity

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ram

eles

nudifl

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nud

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ram

elac

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0.3

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41.5

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18

L

Sch

ima

wal

lich

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chw

alT

hea

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5.5

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IN18

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18

L

Gm

elin

aa

rbor

ea11

Gm

ear

bV

erb

enac

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0.4

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20

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T.

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ies,

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sa

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nv

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92b),

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72),

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li(1

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ban

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etal

.(1

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10

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atta

char

jee

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ub

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13

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ub

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18

Das

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dd

in(1

99

7),

19

Das

(19

84a)

,20

Das

(19

84b),

21

Das

(19

72),

22

Das

(19

70),

23

Mo

hiu

dd

in(1

99

3),

24

Das

(19

90),

25

Mo

hiu

dd

inan

dD

as(1

99

2),

26

Das

(19

99)

Wood Sci Technol

123


(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

Wood Sci Technol

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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


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


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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Timber species grouping in Bangladesh: linking wood properties

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