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A reassessment of carbon content in wood: variation within and between 41 North American species


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At present, 50% (w/w) carbon is widely promulgated as a generic value for wood; however, the literature yields few data and indicates that very little research has actually been done. C contents in heartwood of forty-one softwood and hardwood species were determined. C in kiln-dried hardwood species ranged from 46.27% to 49.97% (w/w), in conifers from 47.21% to 55.2%. The higher C in conifers agrees with their higher lignin content (∼30%, versus ∼20% for hardwoods). Wood-meal samples drilled from discrete early wood and late wood zones of seven of the forty-one species were also investigated. C contents of early woods were invariably higher than those in corresponding late woods, again in agreement with early wood having higher lignin content. Further investigation was made into freshly harvested wood of some species to determine how much volatile C is present, comparing oven-dried wood meal with wood meal dried at ambient temperature over a desiccant. C contents of oven-dried woods were significantly lower, indicating that all past data on C content in oven- or kiln-dried woods may be inaccurate in relation to the true C content of forests. We conclude that C content varies substantially among species as well as within individual trees. Clearly, a 50% generic value is an oversimplification of limited application in relation to global warming and the concept of “carbon credits”.
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Biomass and Bioenergy 25 (2003) 381 388
A reassessment of carbon content in wood:
variation within and between 41 North American species
S.H. Lamlom, R.A. Savidge
University of New Brunswick, Faculty of Forestry and Environmental Management Fredericton, N.B., E3B 6C2 Canada
Received 4 September 2002; received in revised form 6 January 2003; accepted 23 January 2003
At present, 50% (w/w) carbon is widely promulgated as a generic value for wood; however, the literature yields few data
and indicates that very little research has actually been done. C contents in heartwood of forty-one softwood and hardwood
species were determined. C in kiln-dried hardwood species ranged from 46.27% to 49.97% (w/w), in conifers from 47.21% to
55.2%. The higher C in conifers agrees with their higher lignin content (30%, versus 20% for hardwoods). Wood-meal
samples drilled from discrete early wood and late wood zones of seven of the forty-one species were also investigated.
C contents of early woods were invariably higher than those in corresponding late woods, again in agreement with early wood
having higher lignin content. Further investigation was made into freshly harvested wood of some species to determine how
much volatile C is present, comparing oven-dried wood meal with wood meal dried at ambient temperature over a desiccant.
C contents of oven-dried woods were signicantly lower, indicating that all past data on C content in oven- or kiln-dried
woods may be inaccurate in relation to the true C content of forests. We conclude that C content varies substantially among
species as well as within individual trees. Clearly, a 50% generic value is an oversimplication of limited application in
relation to global warming and the concept of “carbon credits”.
?2003 Elsevier Ltd. All rights reserved.
Keywords: Annual rings; Carbon content; Carbon sequestration; Conifers; Earlywood; Latewood; Elemental analysis; Hardwoods; Wood
1. Introduction
Since the Industrial Revolution, atmospheric CO2
concentration has risen from 280 to 365 ppm, and pre-
dictions are that it could reach 700 ppm by the second
half of the 21st century [1]. Rising atmospheric CO2
contributes to global warming and is expected to alter
the earth’s climate [24].
Forests cover more than one third of the world’s
land area and constitute the major terrestrial carbon
Corresponding author. Tel.: +1-506-453-4919; fax: +1-506-
E-mail address: (R.A. Savidge).
pool [5,6]. Trees and other forest plants x CO2
through photosynthesis, and all forest organisms
release CO2through respiration [7,8]. Thus, forests
are both sinks and sources for atmospheric CO2. In-
creasing the biomass or carbon content of the world’s
existing forests through improved forest management
and/or decreased harvesting of less than fully devel-
oped trees is a potential option for enhancing seques-
tration of atmospheric CO2and ameliorating global
warming [8,9]. Once wood has formed, its durability
and inertness enable it, and therefore its carbon, to
remain in organic form over very long periods. For
example, coals contain carbon sequestered several
hundred million years ago, and we note here that
0961-9534/03/$ - see front matter ?2003 Elsevier Ltd. All rights reserved.
382 S.H. Lamlom, R.A. Savidge / Biomass and Bioenergy 25 (2003) 381 388
carbon can also still be found in imperfectly petri-
ed (“permineralized”) woods more than 200 million
years old. These considerations suggest that accumu-
lation and better management of the long-term dura-
bility of wood products could be another important
means of reducing atmospheric CO2content.
To determine the role of forests in mitigating atmo-
spheric CO2content globally, as a starting point it is
essential to have accurate inventory of carbon content
in forest organic matter. Carbon occurs in innumer-
able forms within forest ecosystems; however, wood
represents the dominant pool wherever trees at normal
stocking density are at sapling stage or larger [8]. At
present, there are actually few research data sets on
carbon content in woods [1012]. A generic carbon
concentration of 50% (w/w) has been assumed and
widely promulgated [1319], but other reports sup-
ported by little if any data claimed that carbon con-
tent of wood varies, depending on the species, at least
over a range from 47–59% [17,2024]. In defense of
the latter reasoning, each kind of wood tends to be
chemically as well as anatomically unique. Therefore,
it would seem reasonable to expect that each could
have a characteristic carbon content [25].
1.1. Variation in carbon content within the
individual tree
Within any particular hardwood or softwood
species, the younger “juvenile” wood produced in the
crown has characteristics that dierentiate it from the
older more “mature” wood produced below the live
crown [26]. Dierent forms of growth stress can also
leave their imprints in tree rings [27,28]. Normal stem
wood is chemically and anatomically dierent from
reaction wood, and depending on the tree species, the
same applies to sap and heartwoods, early and late
woods, and various other kinds of wood [29]. Even
growing the same species in dierent geographical
locations can result in readily detectable dierences in
wood properties [26,30]. The anatomical and chem-
ical compositional dierences arise in spite of the
cambial genotype being constant throughout the tree
[25,31]. Thus, there can be little doubt that the expla-
nation for wood variation resides in the inuence of
the microenvironment on the biochemical processes
that occur in each individual cambial derivative as it
dierentiates into a wood element [31].
Variation in phenotypic traits, whether viewed at the
level of the whole organism, tissue, individual cell or
macromolecular components, is normal to all biologi-
cal systems. This is the classical concept of G ×E=P,
where G is the genotype, E is the environment, and
P is what we see, the phenotype. Carbon content,
whether determined as total C in wood or the propor-
tion of C locked up in any particular macromolecular
class, can be viewed as the phenotype, determined by
an interaction between the genes and the environment.
Notable environmental and climatic changes have
occurred over the last century, and cambium may be
responding to those changes. On the genetic side,
selective harvesting and imposed reforestation with
“improved” trees have altered genetic diversity.
Although there have been more than two centuries of
forestry and wood properties research, evidently not
one study has addressed the question of variation in
total carbon content of wood, as it exists in the forest
in relation to changes in either G or E [8,25]. In order
to know what if any change in carbon has occurred
due to changing E and/or G, it is necessary to have
accurate baseline data. Here we provide methodology
for accurate determination of carbon in wood and
begin to establish that baseline.
2. Materials and methods
Forty-one sawn and planed blocks of kiln-dried
clear heartwood taken from mature boles of softwood
and hardwood North American species (Table 1) were
investigated for their carbon content. These blocks
were obtained from Forintek Canada Corp. (Eastern
Forest Products Laboratory, Ottawa). The dimen-
sion of each block was 6:5mm×102 mm ×63 mm
(transverse ×radial ×tangential sections, respec-
tively). Discrete early and latewood samples were
investigated in only seven of the forty-one, due to
narrowness of growth rings. The seven species in-
vestigated were Abies amabilis,Abies balsamea,
Fraxinus nigra,Fraxinus americana,Larix laric-
ina,Ulmus rubra and Tsuga canadensis. The carbon
content of anatomically characterized permineralised
wood of Araucarioxylon (Triassic, 225 million years
old) obtained from Monument Valley in Utah was
also determined [25]. Woods from freshly harvested
species were also investigated to determine how much
S.H. Lamlom, R.A. Savidge / Biomass and Bioenergy 25 (2003) 381 388 383
Table 1
Carbon and hydrogen contents of hardwood and softwood North American species ±SD
Hardwoods Softwoods
Species (kind of wooda;b;c;d;e) C% H% Species (kind of wooda;b;c;d;e)C%H%
Acer macrophyllum Pursh (jc) 49:64 ±0:27 8:52 ±0:21 Abies amabilis (Dougl.) Forbes (jc) 48:55 ±0:99 8:10 ±0:22
Acer negundo L. (mn) 49:34 ±0:53 8:13 ±0:42 Abies balsamea (L.) Mill. (jc) 50:08 ±0:45 7:69 ±0:35
Acer rubrum L. (jc) 48:64 ±0:52 8:38 ±0:36 Chamaecyparis nootkatensis (D. Don) Spach (mn) 52:84 ±0:55 8:30 ±0:15
Acer saccharum Marsh. (cw) 49:32 ±0:19 7:89 ±0:20 Juniperus virginiana L. (cw) 52:14 ±0:88 8:23 ±0:30
Alnus rubra Bong. (cw) 47:70 ±0:12 7:99 ±0:19 Larix laricina (Du Roi) K. Koch (jc) 47:21 ±0:35 7:90 ±0:18
Betula alleghaniensis Britton (cw) 46:27 ±0:33 5:56 ±2:10 Larix occidentalis Nutt. (jc) 47:60 ±0:21 7:90 ±0:20
Betula papyrifera Marsh. (jc) 48:37 ±0:21 7:87 ±0:26 Picea glauca (Moench) Voss (jc) 50:39 ±0:45 7:95 ±0:26
Carya Nutt. (cw) 48:47 ±0:41 8:02 ±0:35 Picea sitchensis (Bong.) Carr. (cw) 49:95 ±0:02 8:24 ±0:09
Fagus grandifolia Ehrh. (jc) 46:60 ±0:39 6:09 ±0:87 Pinus banksiana Lamb. (cw) 50:40 ±0:43 7:63 ±0:33
Fraxinus americana L. (jc) 48:28 ±0:36 7:90 ±0:26 Pinus contorta Dougl. (jc) 50:32 ±0:43 8:05 ±0:46
Fraxinus nigra Marsh. (mw) 47:80 ±0:48 8:02 ±0:13 Pinus ponderosa Laws. (jc) 52:47 ±0:38 8:34 ±0:34
Juglans cinerea L. (cw) 48:53 ±0:36 7:69 ±0:68 Pinus resinosa Ait. (cw) 53:28 ±0:33 8:74 ±0:07
Juglans nigra L. (cw) 49:17 ±0:12 7:70 ±0:03 Pinus strobus L. (jc) 49:74 ±0:16 8:25 ±0:25
Platanus occidentalis L. (jc) 49:97 ±0:82 8:32 ±0:16 Pseudotsuga menziesii (Mirb.) Franco (cw) 50:50 ±0:36 8:25 ±0:10
Populus tremuloides Michx. (jc) 47:09 ±0:75 6:28 ±1:14 Thuja occidentalis L. (jc) 51:72 ±0:17 8:09 ±0:18
Populus trichocarpa Torr. & Gray (jc) 49:25 ±0:25 8:29 ±0:18 Thuja plicata Donn (mn) 51:54 ±0:38 8:16 ±0:27
Prunus serotina Ehrh. (jc) 49:53 ±0:18 8:00 ±0:34 Tsuga canadensis (L.) Carr. (jc) 50:33 ±0:32 7:63 ±0:47
Quercus alba L. (mw) 49:57 ±0:22 7:64 ±0:25 Tsuga heterophylla (Raf.) Sarg. (mw) 50:60 ±0:45 7:85 ±0:33
Quercus rubra L. (jc) 49:63 ±0:32 8:14 ±0:29 Sequoiadendron giganteum (Lindl.) Bucholz (hwc, mn) 55:16 ±0:52 8:12 ±0:07
Salix L. (cw) 49:05 ±0:58 8:26 ±0:28 Sequoiadendron giganteum (swd;mn) 54:66 ±0:27 8:50 ±0:08
Tilia americana L. (cw) 46:43 ±0:17 6:48 ±0:61 Sequoiadendron giganteum (tze;mn) 52:52 ±0:27 7:77 ±0:09
Ulmus L. (jc) 46:32 ±0:27 5:67 ±0:26
aExcepting where noted for S. giganteum, all specimens were clear heartwood.
bHeartwood was sub-divided as follows based on examination of transverse surfaces: jc, juvenile core wood containing annual rings ¿5 mm in radial width and
having pronounced curvature (i.e., obviously cut from near the pith); cw, core wood containing annual rings having some curvature and between 2–4 mm radial
width; mn, mature outer wood having non-curved narrow (¡2 mm radial width) annual rings; mw, mature outer wood having non-curved wide (¿2 mm radial
width) annual rings.
eTransitional between heartwood and sapwood.
384 S.H. Lamlom, R.A. Savidge / Biomass and Bioenergy 25 (2003) 381 388
volatile carbon exists, comparing oven-dried wood
powder with that dried at ambient temperature.
2.1. Preparation of wood for analysis
Each wood block was shaved to a depth of 1–2 mm
immediately before sampling, using a clean sharp ra-
zor blade, in order to expose a non-oxidized transverse
surface. An electric drill with small diameter (0:2mm)
bit was used to obtain wood powder, drilling into the
wood to a depth of 3–4 mm. The powder was trans-
ferred to a glass vial, capped with aluminum foil, and
dried in a vacuum desiccator over indicator silica gel.
In the case of Araucarioxylon, small pieces of solid
material previously established to be anatomically re-
solvable [25] were pulverized using mortar and pestle.
To determine the number of days needed to reach
equilibrium dryness, four species (Acer negundo,
Abies amabilis,Juniperus virginiana, and Picea
sitchensis were randomly selected. Each wood pow-
der (10 mg) in a glass vial was covered with foil,
weighed using an analytical balance, and then placed
in a vacuum desiccator over desiccant and subjected
to reduced pressure for 10 min, when the stopcock
was closed. The samples were left in the desiccator
until reweighing the following day, then returned to
the desiccator. Only 10 min of vacuum application
was used each day to minimize loss of volatile or-
ganics. The weights of all samples were observed to
stabilize before ten days; thus, in subsequent research
all samples were dried as described over 10 days be-
fore analysis. Oven-dried samples were prepared in
the same way as those dried at ambient temperature
with the exception that the wood powder was left in
C oven for one week.
2.2. Analytical method
Each sample (1 mg) of dry wood powder for
elemental analysis was weighed into a clean, dry tin
container (33 mg tin, 20 mm dia. circle crimped
into a 5 ×9 mm cup, CE Elantech, Inc.) using a Cahn
C-30 microbalance (calibrated precision 0:001 mg).
The tin containers were pre-washed with double all
glass-distilled water followed by two washes with
analytical grade acetone, then vacuum desiccated
overnight to ensure that they were completely dry.
A Carlo Erba CHN 1500 elemental analyser was
used to quantify total carbon, hydrogen and nitrogen.
Full gas chromatographic resolution of CO2,N
H2O was achieved under the following conditions:
column length, 3 m; column diameter, 6 ×4mm
(OD/ID); packing material, Porapak QS, 50–80 mesh;
UHP helium ow rate, 85 ml=min; helium reference
ow rate, 40 ml=min; gas chromatograph oven tem-
perature, 90C; lament temperature (thermoconduc-
tivity detector), 190C. In the oxidation furnace, the
combustion products passed through a 12 cm layer of
chromium trioxide (Cr2O3) followed by a 6 cm layer
of silver coated cobalt oxide separated by a few mm
of quartz (silica) wool, all packed within a vertical
clear quartz tube (45 cm long, 14 mm i.d., 18 mm
o.d., ThermoQuest). In the reduction furnace the
mixture of combustion products (CO2,N
water) passed through a second quartz tube fully
packed with metallic copper to scrub oxygen and
reduce any nitrous oxides to nitrogen (N2).
2.3. Calibration of the instrument
Under the described conditions, a calibration curve
generated using high purity crystalline L-leucine
(Sigma-Aldrich) yielded the linear regression
YC=6:492 ×106X+ 65809(R2=0:996), where
YCis carbon peak area and Xis unknown carbon
content in milligrams (Fig. 1). The hydrogen per-
centage was calculated from the linear regression
YH= 149:07X1:45(R2=0:989), where YHis hydro-
gen peak area and Xis unknown hydrogen content in
milligrams (Fig. 2). The linear regression for nitrogen
was YN=2:380 ×106X1578:937 (R2=0:996),
where YNis nitrogen peak area and Xis unknown ni-
trogen content in milligrams. Glucose and cellobiose
standards (Sigma-Aldrich Canada Ltd) were also in-
vestigated, and their C and H contents were found
to be in good agreement with the leucine calibration
2.4. Statistical analyses
The mean of at least three replicates per sample
and the standard deviation for those replicates were
calculated. Where the standard deviation for carbon
was greater than 0.6% (w/w), more replicates were
analysed. We tested variation in carbon contents with
S.H. Lamlom, R.A. Savidge / Biomass and Bioenergy 25 (2003) 381 388 385
Carbon Content (mg)
Integrated Peak Area Units ( × 106)
0 0.6 1.2 1.8 2.4 3
Y = (6.492 × 106)X + 65809
Fig. 1. L-Leucine calibration curve used to calculate carbon content.
Hydrogen Content (mg)
Integrated Peak Area Units ( × 10
0 0.1 0.2 0.3 0. 4 0.5
Y = 149.07X – 1.45
Fig. 2. L-Leucine calibration curve used to calculate hydrogen content.
a two-way analysis of variance (=0:001), the factors
being species of tree and method of drying (ambient
versus oven-dried).
3. Results
Analyses of heartwood of 22 hardwood species
showed that the carbon content ranged from 46.27%
to 49.97% (w/w). In contrast, it ranged from 47.21 to
55.2% in heartwood of 19 softwood species (Table 1).
Carbon content of early wood was higher than that
in corresponding late wood in all species (Fig. 3).
The data of Fig. 4indicate that carbon contents of
oven-dried woods were invariably lower than those of
ambient-temperature desiccated woods in the studied
species (two-way ANOVA: species eect: F=73:08,
P=0:000, df = 7; drying treatment: F= 131:92,
P=0:000, df = 1; interaction: F=10:30, P=0:000,
df = 7; error MS = 0:226, df = 49). Hydrogen con-
tents ranged from 5.56% to 8.32% in hardwoods and
from 7.63% to 8.74% in softwoods. Nitrogen was also
analysed, but its content never exceeded trace levels.
In most species the wood powder produced by
drilling yielded only small variation between replicate
analyses (n= 3, minimum). However, for Populus
tremuloides,Platanus occidentalis,Abies amabilis
and Juniperus virginiana standard deviations as great
as 1% were obtained. The explanation for the greater
variability between replicate samples of wood pow-
der of some species is still under investigation. In
some cases grinding wood particles into liquid nitro-
gen solved this problem. For example, in Ulmus the
carbon content was 48:11 ±2:25 for coarsely pow-
dered samples. In contrast, it was 46:32 ±0:27 for
samples nely powdered in liquid nitrogen by means
of mortar and pestle.
The carbon and hydrogen contents of Araucar-
ioxylon permineralized wood were 1:61 ±1:07%
and 0:28 ±0:10%, respectively, in agreement with
386 S.H. Lamlom, R.A. Savidge / Biomass and Bioenergy 25 (2003) 381 388
F. nigra
T. canadensis
C (% w/w)
Early wood
Late wood
Fig. 3. Carbon contents of late wood and early wood in the seven
studied species. The error bars represent standard deviations.
C (% w/w)
Desiccated wood
Oven-dried wood
Fig. 4. Carbon contents of oven-dried (93C, 1 week) versus
desiccated woods. The error bars represent standard deviations.
cellulose microbrils still being readily detected in the
material [25].
4. Discussion
Thorough investigation of the literature dealing with
carbon in wood revealed that there are actually limited
research data, and what exist are not consistent. For
instance, in a chapter of a book entitled “The chem-
istry of cellulose and wood”, Nikitin [33] reported
that completely dry wood, dried at 105C, had a very
similar elementary composition for all species, con-
taining about 49.5% C. Wenzl [13] also stated that all
species had similar elemental composition; absolutely
dry wood of any species containing about 50% car-
bon. Prakash and Murray [34] and Corder [35] stated
that although wood composition may vary from one
species to another and even within a species, depend-
ing on the section of the tree, its age, and ecologi-
cal conditions, dierent species of wood nevertheless
show remarkable uniformity in their elemental com-
position. Thus, recent research dealing with carbon in
trees has assumed the 50% value to be correct, al-
though other values have also been used. For exam-
ple, Clifton et al. [36] assigned 51.4% carbon to wood
whereas Elliott [37] stated that wood has 52% car-
bon. However, those authors gave no source for their
All past data on carbon content appear to have
been based on oven- or kiln-dried woods, but our data
on oven-dried versus desiccated woods indicate that
earlier estimates have biased downward the true car-
bon content that is locked up in forests. The two-way
ANOVA output conrmed that carbon contents sig-
nicantly varied among species as well as by method
of sample preparation; however, our data also show
that the inuence of drying (oven vs. ambient) on the
mean carbon contents depends on what species is un-
der investigation. Earlier investigations concentrated
on wood primarily as an energy resource, and it was
not uncommon for wood to be mixed with bark and
dried at high temperature, ignoring the volatile mat-
ters present in all woods. During our research, larger
wood particles were ground into a ne powder using
liquid nitrogen, and emphasis was placed on achieving
sucient homogeneity in the powder to produce small
standard deviation among replicates. Carbon and hy-
drogen percentages were analysed for both coarse
(¿1 mm) and ne (60:3 mm) powdered samples,
and higher standard deviations attended the coarse
preparations. Thus, our experience indicates that
S.H. Lamlom, R.A. Savidge / Biomass and Bioenergy 25 (2003) 381 388 387
accurate estimates can only be achieved by reducing
wood to particle sizes 60:3 mm and, ideally, much
smaller. Others evidently have made no eort to grind
wood samples to ne consistency [10], and carbon
contents having standard deviations as high as 3.2%
have been reported [11].
Our results, based exclusively on bole wood, clearly
indicate that carbon content varies among species as
well as within individual trees (for example early
vs. late woods). The carbon content of softwoods
species is generally higher than that of hardwoods,
in agreement with softwood lignin content being
approximately 10% higher than that of hardwoods
[25] of all the macromolecules making up wood,
lignin has the highest percentage carbon [25,33,38].
The carbon content of early wood is higher than that
of late wood. Late wood is quite consistently higher
in cellulose and lower in lignin than early wood
[25,30,38]. Variation between early and late wood
is expected due to the fact that wood exhibits such
chemical variability within a growth layer [30]. For
example, Andrews [32] found greater dierences in
chemical composition between early and late wood
within an annual ring, than between sapwood and
heartwood of the same Douglas-r tree.
Our ndings revealed a range from 46.27% to
55.2% in the carbon content of North American trees,
wood in mature stems of hardwood species ranging
from 46.27% to 49.97% (average 48.41%), and that
in softwood species from 47.21% to 55.20% (average
51.05%). It is probable that the higher carbon con-
tent in softwoods will be found to apply generally,
because softwoods in general have approximately
10% more lignin than hardwoods [25].
A 1% dierence in carbon content conceivably
could have a signicant impact on wood and pulp
industries in relation to allocation of carbon credits
within the Kyoto Protocol. The uncertainty (or, pre-
cision error) associated with our method was 0.5%
(0.1% weighing, 0.4% leucine standard curve), and
our observed dierences in carbon of 9% therefore
appear quite important. It could be argued that,
all other factors being equal, additional carbon
storage capacity per unit mass exists in softwood
forests. However, this would be simplistic because
many hardwood species have wood densities above
3whereas softwoods in general are well be-
low 0:6gcm
3[25,30]. Thus, high-density hardwood
species, although having lower carbon content per
unit mass than softwoods, will nevertheless contain
the greater quantity of carbon per unit volume. Even
disadvantaged hardwood species such as poplar, that
have less than 50% carbon and also have low-density
wood, if suciently fast growing conceivably could
sequester more carbon than softwoods within a grow-
ing season. To estimate carbon content of forest
stands, it is necessary to take into consideration not
only the several kinds of wood within trees [39]
but also stocking density (e.g. number of trees per
hectare by age volume class). It is apparent from such
considerations that accurate carbon inventories and
management of forest ecosystem carbon pools will
require much greater attention to detail than tradition-
ally has been addressed by foresters. It is clear that
much more research is needed, but from the preced-
ing clarication, there is no doubt that a 50% generic
value for carbon content is an oversimplication of
limited application in relation to global warming and
understanding the role of the forest as a carbon sink.
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... Increasing forestlands is an effective method to reduce CO2 in the atmosphere by converting CO2 into organic matter during photosynthesis. Since the forestlands have an important role as a carbon sink, it is essential to make carbon calculations including forest types and tree species in order to monitoring carbon balance in forestlands and performing the necessary calculations to get a better carbon inventory (Lamlom and Savidge, 2003;Malmsheimer et al., 2011). Moreover, tree components of the concerned species and their carbon concentrations should also be calculated for the sake of performing a better carbon calculation of tree species. ...
... Those reported experimental coefficients should be verified by studying tree species at local level for more precise calculations, as recommended earlier (IPCC, 2003;IPCC, 2006). A number of studies reported that carbon concentrations in carbon reservoirs vary depending on environmental factors, tree species and tree components (Laiho and Laine, 1997;Lamlom and Savidge, 2003;Bert and Danjon, 2006;Thomas and Malczewski, 2007;Çömez, 2012). ...
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In accordance with the Kyoto Protocol, countries prepare their national inventory reports (NIR) every year and present it to United Nations Secretariat of the Framework Convention on Climate Change (UNFCCC). These statements are based on AFOLU Guideline (IPCC Guidelines for National Greenhouse Gas Inventories for Agriculture, Forestry and Other Land Use). However, countries are required to produce parameters special to their own tree species in order to make more precise statements. The aim of this study was to determine to calculate both the carbon concentration of various components (needle, wood, bark, and root) of natural stone pine (Pinus pinea L.) and the weighted carbon concentration of above ground and total tree biomass. The study was conducted in natural stone pine forests in Marmara Region of Turkey. Samples were collected in 10 sampling plots that were at mature stage (dbh=20.0-51.9 cm) and had different site characteristics. Site characteristics of the sample plots were determined. Then, needle, wood, bark, and root samples were collected from 3 trees representing the top height in each sampling plot. Carbon analysis on plant samples collected from the sampling plots (10 plots × 3 replications × 4 components = 120 samples) was carried out in the laboratory. The obtained data were evaluated by using analysis of variance and Duncan test. Statistically significant differences were found between carbon concentrations of tree components (P<0.001). The lowest carbon concentrations were in needle (51.65%) and in roots (51.67%), while the highest carbon concentration was in wood (54.74 %) and in barks (54.93%). The weighted carbon concentration for natural stone pine forests were found to be 54.56% and 54.07% for the above-ground biomass and for the total tree biomass, respectively. The carbon concentrations found in this study can be used to calculate the carbon stocks stored in both trees and different components of trees in natural stone pine forests.
... This may be partly due to an increasing investment in fibres as trees grow, with greater lignin accumulation in fibre cell walls (Hietz et al., 2017;Ma et al., 2018;Plourde et al., 2015;Rungwattana & Hietz, 2018). WCC increases with increasing lignin concentrations across species (though not necessarily within species; , so that an increase in carbon-rich structural compounds such as lignin with tree size may also generate an increase in WCC (Gao et al., 2016;Lamlom & Savidge, 2003;Ma et al., 2018;. Other size-related changes in carbon-based wood chemistry may also influence total WCC (Lachenbruch et al., 2011) such as non-structural carbohydrates (NSC) that are known to vary during the reproductive period such as masting (Sala et al., 2021), and volatile carbon compounds that tend to increase as trees allocate more resources to plant defence (Gao et al., 2016). ...
... To cover the tree size range for each species, we took wood cores in at least five individuals randomly sampled within each of the following five diameter classes: 8-10, 10-15, 15-20, 20-30 and >30 cm DBH (237 trees in total). Since WCC may vary between heartwood and sapwood (Lamlom & Savidge, 2003;Martin et al., 2015Martin et al., , 2018, we considered (a) heartwood as the wood core section c. 1 cm from the pith for 107 sampled individuals, and (b) sapwood as the wood core section at approximately 20% of the radial distance from the bark for 180 sampled individuals. Fifty individuals shared both types of wood (see Figure S1). ...
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1. There is increasing evidence that intraspecific trait variation plays a role in governing rates of ecosystem functioning. While wood traits such as wood specific gravity (WSG) and wood carbon concentration (WCC) are key drivers of forest aboveground carbon (AGC) stocks, the sources of intraspecific variation in these wood traits and the consequences of this variation on AGC are poorly known, especially in the tropics. 2. Here, we investigated intraspecific variation in wood specific gravity (WSG) and wood carbon concentration (WCC) from 556 individual trees belonging to 15 species that well characterize different successional stages of seasonal evergreen forests in Southeast Asia. Specifically, we tested the contribution of individual or species characteristics (tree size, growth rate and regeneration guilds) and local environmental conditions (topographic wetness index and successional stages) to intraspecific variation in WSG and WCC, and assessed the consequences of intraspecific variation in these wood traits on AGC estimates in 14 permanent forest plots established along a successional gradient in Khao Yai National park, Thailand. 3. We found that tree size was the main driver of intraspecific variation in WSG and WCC as tree sizes increased from 10−100 cm in diameter, WSG increased by 7.3%, while WCC increased by 2.4% in heartwood, 1.6% and 2.7% in sapwood without and with volatile carbon included. There was no effect of the topographic wetness and other local environment condition in wood traits led to a slight overestimation of AGC in young secondary forests (+0.09 to +1.29%) and a small underestimation in older forests (‐0.86 to ‐2.87%), but overall AGC estimates (13 of 14 forest plots) remained within error margins (the 95% interval). 4. Our study provides evidence that tree size variation translates into intraspecific variability in wood traits, whereas local environmental conditions related to topography successional stages had no effect on wood trait variability. While size‐dependent variation in WSG and WCC have largely been undocumented and thus ignored in forest carbon assessment approaches, we highlight that it has a limited impact on AGC estimates, indicating that it does not invalidate current forest carbon stock estimation approaches.
... The carbon storage indicators comprise carbon content in living above-and belowground tree biomass as a fraction of biomass for conifers (51%) and broadleaves (48%); cf. Lamlom and Savidge (2003), in deadwood (from natural mortality and harvest residue) and in four harvested wood products (HWP). Following Blattert and Lemm (2018) and Thrippleton et al. (2021a), the harvested volume was separated for broadleaved and coniferous species in the four HWP pools (i) sawn timber, (ii) wood-based panels, (iii) paper and paperboard, and (iv) energy wood, each with individual decay rates. ...
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Climate-adaptive forest management aims to sustain the provision of multiple forest ecosystem services and biodiversity (ESB). However, it remains largely unknown how changes in adaptive silvicultural interventions affect trade-offs and synergies among ESB in the long term. We used a simulation-based sensitivity analysis to evaluate popular adaptive forest management interventions in representative Swiss low- to mid-elevation beech- and spruce-dominated forest stands. We predicted stand development across the twenty-first century using a novel empirical and temperature-sensitive single-tree forest stand simulator in a fully crossed experimental design to analyse the effects of (1) planting mixtures of Douglas-fir, oak and silver fir, (2) thinning intensity, and (3) harvesting intensity on timber production, carbon storage and biodiversity under three climate scenarios. Simulation results were evaluated in terms of multiple ESB provision, trade-offs and synergies, and individual effects of the adaptive interventions. Timber production increased on average by 45% in scenarios that included tree planting. Tree planting led to pronounced synergies among all ESBs towards the end of the twenty-first century. Increasing the thinning and harvesting intensity affected ESB provision negatively. Our simulations indicated a temperature-driven increase in growth in beech- (+ 12.5%) and spruce-dominated stands (+ 3.7%), but could not account for drought effects on forest dynamics. Our study demonstrates the advantages of multi-scenario sensitivity analysis that enables quantifying effect sizes and directions of management impacts. We showed that admixing new tree species is promising to enhance future ESB provision and synergies among them. These results support strategic decision making in forestry.
... Aubier et duramen sont formés de cernes et ceux-ci sont eux-mêmes formés de bois de printemps qui est mis en place en début de saison et de bois d'été qui est mis en place en fin de saison. Le bois d'été est plus dense que le bois de printemps pour les feuillus et les résineux (Fries and Ericsson, 2009;Kuo and Wang, 2019;Zhang, 1997), moins efficace dans le transport de l'eau (Domec and Gartner, 2002), a un taux de carbone plus élevé (Lamlom and Savidge, 2003) et leur structure cellulaire est différente (Björklund et al., 2017). Au sein même du bois d'été et du bois de printemps d'un même cerne, des variations de propriétés comme le module d'élasticité peuvent être observées le long du cerne (Cramer et al., 2005). ...
Dans un contexte de renouvellement de l'industrie chimique et de recherche de nouveaux débouchés pour la foresterie, les extractibles deviennent des molécules de plus en plus intéressantes, tant écologiquement que financièrement parlant. Afin d'évaluer la pertinence de ces molécules comme nouvelle ressource pour la chimie et potentiel débouché pour la foresterie, il est nécessaire de faire une évaluation préalable de la ressource. Ceci nécessite de connaître le volume des compartiments riches en extractibles, particulièrement les écorces et les nœuds. La présente étude s'intéresse donc à la modélisation des volumes d'écorce et de nœuds. Elle se concentre spécifiquement sur deux régions françaises, le Grand Est et la Bourgogne-Franche-Comté et six essences d'importance, Abies alba, Picea abies, Pseudotsuga menziesii, Quercus robur, Quercus patraea, Fagus sylvatica.Cette étude est rendue possible grâce à l'utilisation d'une grande base de données comprenant des mesures d'épaisseur d'écorce pratiquées à différentes hauteurs sur la tige de nombreux arbres. D'autre part de nouveaux échantillonnages ont eu lieu ce qui a permis d'obtenir, grâce à l'utilisation d'un scanner à rayon X, une image informatique des nœuds et d'en mesurer précisément le volume.Afin de modéliser la quantité d'écorce disponible trois types de modèles ont été construits, des modèles de prédiction du volume d'écorce, des modèles de prédiction de la surface d'écorce le long de la tige et des modèles de prédiction de l'épaisseur d'écorce à 1m30. Les premiers ont permis d'atteindre une racine de l'erreur quadratique moyenne relative (RMSErel) comprise entre 16.7 % et 27.5 % en fonction des espèces.L'étude portant sur les modèles de surface d'écorce a permis de mettre en évidence la possibilité d'utiliser un modèle indépendant du diamètre-sur-écorce mais que les modèles utilisant en entrée cet variable sont encore plus précis. Le RMSErel atteint par ces modèles de surface d'écorce varie entre 23 et 38 % en fonction de l'espèce et du modèle considéré. Ce travail a montré l'importance de l'utilisation de l'épaisseur d'écorce à 1m30 comme donnée d'entrée. Celle-ci n'étant aujourd'hui que rarement mesurée, elle a aussi été modélisée à partir du D130. Cela a permis de mettre en évidence une influence de l'altitude sur l'épaisseur d'écorce à 1m30 pour trois espèces : Abies alba, Picea abies, Fagus sylvatica. Les modèles obtenus atteignent un RMSErel allant de 26.8 % à 36 % en fonction de l'espèce considérée.Enfin, les volumes de nœuds ont commencé à être étudiés. Bien que ce travail n'ai pas été entièrement mené, il montre déjà l'importance de produire de nouveaux modèles de volume de nœuds. De plus leur quantité dans le bois semble, à ce stade de l'étude, trop peu importante pour dégager de grandes ressources en extractible, malgré leur grande richesse intrinsèque. Leur intérêt pourrait donc plus se trouver dans l'extraction de molécules spécifiques.
... We estimated carbon in standing live and dead trees using species specific allometric equations from Ung et al. (2008) to calculate biomass and then applying a carbon factor of 0.5 (Lamlom and Savidge, 2003). The biomass of standing dead trees was further reduced by applying a species and decay class specific density reduction factor (Harmon et al., 2011) and a decay class specific structural reduction factor (Domke et al., 2011). ...
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Wildfire is a natural disturbance in many forested biomes, with the loss of carbon to the atmosphere and mortality of trees actively sequestering carbon of global concern as a contribution to climate change. Natural regeneration is often successful at reestablishing a forest in ecosystems adapted to fire, but there is increasing concern that the changing size, frequency and severity of wildfire is causing regeneration failures or inadequate densities of trees that sequester and store carbon following these disturbances. It remains unclear whether the action of planting trees accelerates carbon storage following fire compared to forests established through natural regeneration. The central interior of British Columbia recently experienced multiple years of record-breaking fire activity. Rehabilitation planting focused on reestablishing trees in the managed forest but was also prescribed in previously unmanaged forests to initiate carbon sequestration. Planting is often accompanied by other stand treatments such as salvage harvesting or snag removal and debris clearing to ensure planter safety. Here, we determine carbon recovery and stores in 21 wildfires across a chronosequence from the early 1960s to 2015. We measured above and belowground carbon pools to determine the effect of time since fire and planting treatments on carbon. Tree planting did not increase total ecosystem carbon over time, but rather decreased carbon through the loss of dead wood from site preparation. All carbon pools were affected by time since fire except the mineral soil pool, which was best predicted by soil clay content and coarse fragments positive effects. Live tree carbon increased over time, with more stored in planted stands over 60 years compared to stands that were not planted. Projecting growth to 100 years since fire suggests we may see increasing divergence in carbon stores in planted stands over a full fire-return interval, but these differences remain relatively small [mean (sd): 140.8 (19.6) Mg⋅ha–1 in planted compared to 136.9 (27.5) Mg⋅ha–1 in not-planted stands], with 1.4 Mg⋅ha–1 year–1 sequestered in not-planted compared to 1.5 Mg⋅ha–1 year–1 in planted stands. To meet carbon objectives, replanting trees on average sites in burned forests of BC’s central interior would require preserving the carbon legacy of fire, including dead wood.
Biodegradable waste is generated either in nature, which naturally uses it to its advantage and there are no negative effects on the environment, or by human activity, in which it is necessary to regulate its generation and management. Biodegradable waste generated by human activity is any waste that undergoes aerobic or anaerobic digestion. Biodegradable waste is most often generated in agriculture, forestry, food industry, and from everyday used items that are produced by the public. It is important to sort them right at the source and then hand them over for processing to the facility that is designated for this purpose (composting plant, incinerator, anaerobic digestion technology, etc.). This will prevent the disposal of biodegradable waste in landfills, where they are a source of greenhouse gas (methane) and chemical and biological pollutants in landfill leachate.
An essential stage in woody plant ontogeny (heartwood (HW) formation) determines tree resistance to weather conditions, wood quality (moisture, colour, resistance to biodegradation), and regulates the proportion of functionally active sapwood (SW) in the total trunk biomass. In this study, the patterns of HW formation depending on tree age and cambial age within the same tree were studied in the North-West of Russia in Scots pine in a lingonberry pine forest. It is shown that HW either repeats the trunk profile or shows a maximum proportion on average at the height of 1.5 m. Models using the square root transformation and logarithm transformation have been proposed to predict the number of annual rings in HW depending on the cambial age. Multiple regression is proposed to predict the radial width in HW. Validation of the developed models on random trees gave a good result. HW formation begins at the age of 17–18 years and continues at the rate of 0.3 rings per year for 20–30-year-old trees, 0.4–0.5 rings per year for 70–80-year-old trees, and about 0.7 rings per year for 180-year-old trees. The lifespan of xylem parenchyma cells ranged from 10–15 years in 20-year-old trees to 70 years in 180-year-old trees. At the age of the previous felling (70–80 years) the HW area in the trunk biomass is about 20%, and in 180-year-old pine forests, it increases to 50%. These data can be used to assess the role of old-growth forests in carbon sequestration.
To highlight the ecosystem value of trees in the urban environment an EU's funding instrument for the environment and climate action – LIFE Project – was undertaken involving four Mediterranean cities as study cases: Thessaloniki in Greece, Cascais in Portugal, and Perugia and Bologna in Italy. The methodology utilized to evaluate plant carbon storage and shade areas highlighted the usefulness of gathering data about both the current urban green asset in the Project's investigated areas and future potential performances of the same asset to evaluate its efficiency in the forthcoming decades (2030–2050). The lower future potential of some cities for CO 2 storage can be attributed to having older 'green assets' in comparison to other municipalities. A projection over the next 30 years on the basis of census results in the 4 studied areas highlighted the potential of some green areas characterised by the presence of efficient carbon storage tree species (high-growing broadleaf plants) while still small in size. The main objective of this study was to present both the current urban green asset carbon storage potential in the studied areas and also its future potential efficiency. Another aim of the work was to develop tree growth curves in urban areas, not only to estimate tree volumes but also to estimate the present and future shade areas related to woody plants' presence. With regard to CO 2 storage simulation, the research highlighted the potential of green areas in Perugia that present tree species particularly efficient at carbon storage yet still small in size. Conversely, the lowest value presented by the species recorded in Cascais was influenced by the predominance of Cupressus sempervirens and Pinus pinea : two species not particularly efficient and large in size with average DBH values above 50 cm.
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Woody tissue carbon (C) concentration is a key wood trait necessary for accurately estimating forest C stocks and fluxes, which also varies widely across species and biomes. However, coarse approximations of woody tissue C (e.g., 50%) remain commonplace in forest C estimation and reporting protocols, despite leading to substantial errors in forest C estimates. Here, we describe the Global Woody Tissue Carbon Concentration Database (GLOWCAD): a database containing 3,676 individual records of woody tissue C concentrations from 864 tree species. Woody tissue C concentration data—i.e., the mass of C per unit dry mass—were obtained from live and dead woody tissues from 130 peer-reviewed sources published between 1980–2020. Auxiliary data for each observation include tissue type, as well as decay class and size characteristics for dead wood. In GLOWCAD, 1,242 data points are associated with geographic coordinates, and are therefore presented alongside 46 standardized bioclimatic variables extracted from climate databases. GLOWCAD represents the largest available woody tissue C concentration database, and informs studies on forest C estimation, as well as analyses evaluating the extent, causes, and consequences of inter- and intraspecific variation in wood chemical traits.
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Annual carbon uptake by the 1.24 million ha of plantation forest in New Zealand was calculated. The plantation forests of New Zealand stored approximately 4.5 ± 0.8 million tonnes C in the year between 1 April 1988 and 1 April 1989, increasing total plantation carbon storage to approximately 88 million tonnes C in April 1989. Without harvest, the average annual carbon uptake of the New Zealand plantation estate between 1988 and 1989 would have been approximately 6.4 tonnes C/ha. Plantation roundwood removals were equivalent to 2.7 tonnes C/ha, so that average carbon storage was approximately 3.6 tonnes C/ha. The annual storage of carbon in the New Zealand plantation estate in 1988-89 was equivalent to approximately 70% of total New Zealand fossil fuel emissions, but was <0.1% of total global fossil fuel emissions. The high annual rate of carbon uptake by the New Zealand plantation estate is a consequence of a large area of new plantings initiated in the 1970s and 1980s. Without continued new plantings, the net annual rate of carbon uptake by New Zealand plantation forests will rapidly approach zero. -from Authors
1 General Concepts of Juvenile Wood.- 1.1 General Concepts.- 1.2 What Is Juvenile Wood?.- 1.3 The Causes of Juvenile Wood.- 1.4 Importance and Characteristics of Juvenile Wood.- 1.5 Literature on Juvenile Wood.- 1.6 Summary.- 2 Characteristics of Juvenile Wood.- 2.1 General Concepts.- 2.2 Juvenile Compared with Mature Wood.- 2.2.1 Effects upon Wood Uniformity.- 2.3 Characteristics of Conifers.- 2.3.1 Specific Gravity Within and Among Species.- 2.3.2 Variation in Other Wood Properties Within and Among Species.- 2.4 Characteristics of Hardwoods.- 2.4.1 Specific Gravity in Hardwoods.- 2.4.2 Other Wood Properties.- 2.5 Summary.- 3 Occurrence of Juvenile Wood.- 3.1 General Concepts.- 3.2 Where Does Juvenile Wood Occur?.- 3.3 Radial Development in the Conifers.- 3.3.1 Estimation of the Juvenile Wood Zone in Conifers.- 3.3.2 Radial Development of Specific Gravity.- 3.3.3 Radial Development of Other Wood Properties.- 3.4 Radial Development in the Hardwoods.- 3.5 Development with Tree Height.- 3.5.1 In the Conifers.- 3.5.2 In the Hardwoods.- 3.6 Summary.- 4 Characteristics Affecting Juvenile Wood.- 4.1 Relationship to Reaction Wood.- 4.2 Genetics of Juvenile Wood.- 4.3 Relationship to Growth Rate.- 4.4 General Sampling Methods for Different Characteristics.- 4.5 Summary.- 5 Changing Juvenile Wood.- 5.1 General Concepts.- 5.2 Response to Genetic Manipulation.- 5.3 Response to Silvicultural Treatments.- 5.3.1 The Effect of Spacing and Thinning.- 5.3.2 The Effect of Fertilization, Irrigation and Site.- 5.4 Response to Geographic Location, Seed Source and Species.- 5.5 Response to Other Environmental Factors.- 5.6 Summary.- 6 Predictions of Mature and Total Tree Wood Properties From Juvenile Wood.- 6.1 General Concepts.- 6.2 Juvenile to Mature Wood Correlations.- 6.2.1 Predictions Across the Tree Bole.- 6.2.2 Predictions Along the Bole.- 6.2.3 Predictions For the Whole Tree.- 6.3 Summary.- 7 The Importance of Juvenile Wood.- 7.1 General Concepts.- 7.2 In Conifers.- 7.2.1 Utilization in the Hard Pines, Douglas-fir, Larches etc.- Utilization of Top Wood.- 7.2.2 Utilization in the Spruces, Firs, Cypresses etc..- 7.3 In Hardwoods.- 7.3.1 Utilization.- Diffuse-Porous Species.- Ring-Porous Species.- 7.4 Summary.- 8 Use of Juvenile Wood.- 8.1 General Concepts.- 8.2 Juvenile Wood for Pulp and Paper.- 8.3 Juvenile Wood for Solid Wood Products.- 8.4 Future Juvenile Wood Supplies and Utilization.- 8.5 Summary.- 9 Unusual Wood Properties Near the Tree Center.- 9.1 General Concepts.- 9.2 Heartwood.- 9.3 Growth Stresses.- 9.4 Summary.- References.- Species Index.
We consider two general issues, the effects of global change on ecosystems and the effects of ecosystem changes on the climate system. To understand many ecosystem responses to climate change, we consider them in the context of other components of global change such as increases in the atmospheric concentration of CO2. We also consider the ecological consequences of tropical deforestation and the eutrophication of Northern Hemisphere areas with nitrogen in agriculture fertilizers and in acid precipitation as examples of ecosystem changes influencing climate systems. While the primary focus of this section is on terrestrial ecosystems, we end the section with a brief discussion of climate change and marine ecosystems. -from Authors
This volume comprises results of a technological and economic studies undertaken to show the possibility of southern pine utilization as an alternative energy resource. The report is presented under subheadings: the raw material; direct combustion; gasification; pyrolysis; other processes (liquefaction, hydrolysis and fermentation, prehydrolysis); industrial use of wood energy; economics. Refs.