ASSESSMENT OF CARBON STOCK CHANGE IN FORESTS – ADOPTING IPCC
LULUCF GOOD PRACTICE GUIDANCE IN THE CZECH REPUBLIC
EMIL CIENCIALA1, VLADIMÍR HENŽLÍK2 ,VLADIMÍR ZATLOUKAL1
1
Institute of Forest Ecosystem Research, 1544, CZ–254 01Jílové u Prahy, 2Forest Management Institute, Brandýs
nad Labem, Czech Republic, [email protected]
CIENCIALA, E., HENŽLÍK, V., ZATLOUKAL, V.: Assessment of carbon stock change in forests – adopting IPCC
LULUCF Good Practice Guidance in the Czech Republic. Lesn. Čas. – Forestry Journal, 52(1–2): 17–28,
2006, 9 fig., ref. 11. Original paper. ISSN 0323–1046
This contribution describes the optional revision of the methodology to assess carbon stock change in the
Czech forests following the newly adopted Good Practice Guidance (GPG) for the LULUCF sector (IPCC
2003). The revision aims at effective utilization of available source data in the country, while minimizing
uncertainly and complying with the requirements of GPG. The key source of data on forests in the country is
that of forest management plans (FMP), which has been used for any recent national and international
statistics on the Czech forests so far. Other source of data in the country is the sample-based (statistical) forest
inventory, which ended its first cycle in 2004. The proposed revision uses FMP data to assess merchantable
timber volume in the individual years since 1990, classified by major tree species and age classes. Whole-tree
biomass and carbon stock is calculated using tree-specific expansion and conversion factors that were derived
from available tree-level data (permanent research plots or statistical forest inventory). Finally, stock change
is defined as a difference of carbon stock between subsequent years. The study presents and discusses the
optional approach of forest carbon stock change assessment in comparison with the ongoing revision of the
default method and it’s in the most recent Czech emission inventory. The preliminary results for the period of
1990 to 2003 indicate that the assessed change of carbon stock held in tree biomass was higher than
previously reported, mostly due to the in the past unaccounted components of tree biomass. The data also
suggests that the cap accountable for the Czech Republic for the optional activity of forest management under
the Kyoto Protocol Art. 3.4 would be safely exceeded, provided the trend would remain unchanged during the
1st Commitment period of the Kyoto Protocol.
Keywords: carbon accounting, forest inventory, biomass expansion factors, Kyoto Protocol
1. Introduction
The growing concern about the climate change and its impact on global economy and
environment puts pressure on developed societies to reduce their greenhouse gas (GHG)
emissions. The initial international effort at early 90s brought up the United Nations Framework
Convention on Climate Change (UNFCCC), which set up the obligation of the parties to monitor
and report their GHG emissions. In 1997, the negotiations under UNFCCC resulted in the first
international commitment to reduce emissions – the Kyoto Protocol that after a lengthy and
complicated ratification process entered in force in 2005. A specifically challenging topic of the
international negotiation was the contribution of the Land Use, Land Use Change and Forestry
(LULUCF) sector, which is an integral part of national emission inventories. This is because this
is the only sector that includes a sink component of terrestrial ecosystems as compared to
industrial categories of emission inventory that represent sources. Specific to LULUCF sector is
also the very challenging methodology of accounting. To assist the assessment of LULUCF
emission inventory, UNFCCC adopted the new comprehensive Good Practice Guidance (GPG)
for the LULUCF sector (IPCC 2003) to aid and guide preparation of the emission inventory
under both UNFCCC and Kyoto Protocol.
Emission inventory methods may differ ranging from simple approaches and default factors up to
country-specific detailed methodologies, the section of which depends on available data and
auxiliary information. Accordingly, the methods are ranked into lower or higher tiers. It is good
practice to opt for and apply higher-tier methods once the data would support them. Specifically
important is to apply the higher-tier methods for the so-called key categories, which are those
contributing most to the total emission inventory of a party, either by their level or trend. In the
Czech Republic, the only key-category of the LULUCF sector deserving such attention is
“Changes in forest and other woody biomass stocks”. In this paper, we examine an alternative
approach for this category with two different technical solutions, namely, namely the stockchange method to estimate carbon held in living biomass (IPCC 2003). The previous Czech
National Inventory Reports (NIR, FOTT et al. 2005) used the so-called default method to estimate
the emissions (sinks) for the above category. The default method applies a separate estimation of
increment and removals. This was complemented with nationally specific conversion factors
based on the study of Henzlik and Zatloukal (1994) and described e.g. in PRETEL and VACHA
(2003) and in the annual NIRs (FOTT et al. 2005). Also the default method is currently under
revision, however, it is not presented in this study.
The aim of this paper is to evaluate the approach of carbon stock estimation based of the stock
change method with its two different technical solutions, estimate the carbon stock change in
living biomass for the period of 1990 to 2003 and compare the results with the values reported in
the Czech National Inventory Reports (NIR). We provide the result interpretation and make
recommendations for the coming compilation of the LULUCF emission inventory.
2. Material and methods
2.1. Forest land
The area of forest land may differ depending on the applied definition of forest. Most commonly, forest land
corresponds to cadastral categorization in a country. Thereby, forest land also includes un-stocked areas such as
roads, cleared boundary lines, seedling nurseries etc, which mostly serves or supports forestry activities. According
to Good Practice Guidance (GPG) for the Land use, land use change and forestry (LULUCF) sector (IPCC 2003);
forest land should represent those areas that contain forest defined for the purpose of emission inventory. With
respect to the Kyoto Protocol requirements, the Marrakech Accords (7th Conference of Parties to UNFCCC – United
Nations Framework Convention on Climate Change and to its Kyoto Protocol) requires, among others, that parties
would adopt forest definition within the following parameters: minimum area of forest land of 0.05 to 1 ha with tree
crown cover (or equivalent stocking level) of more than 10–30% with trees that have potential to reach a minimum
height of 2–5 m at maturity in situ. Additionally, definition should include information on minimum width to classify
woody vegetation as forests. The most likely forest definition in the Czech Republic will use a minimum area of
0.5 ha, 10% crown cover, 2 m tree height at maturity and a minimum width of 20 meters. In the sense of these
thresholds, the only applicable forest definition in line with GPG is the stocked area including temporary clearings
resulting from basic forest management operations, such as final cut, but excluding the un-stocked areas as defined
above. Such definition of forest area differs from that commonly used for reporting the forest resource information of
the country elsewhere. The information on stocked forest area usable for emission inventory can be obtained from
the database of forest management plans (FMP), which is administered by the Forest Management Institute (FMI),
Brandys n. Labem. This information is released annually in the so-called “Green Reports” on the state of forests and
forestry published by the Ministry of Agriculture. The information is passed to the Czech Statistical Office (CSO),
which officially reports both the stocked areas and total forest land corresponding to cadastral information. The
development of total forests area in the Czech Republic since 1990 is shown Figure 1. The stocked areas are also
available on the level of major tree species and age class.
Forest area (mill. ha)
2.70
2.65
Stocked
Cadastral
2.60
2.55
2.50
90 91 92 93 94 95 96 97 98 99 00 01 02 03
19 19 19 19 19 19 19 19 19 19 20 20 20 20
Year
Figure 1. Development of forest area since 1990 in the Czech Republic: the stocked area corresponds to the forest
area definition in line with the GPG suggestions for the emission inventory, while the cadastral area is the forest area
commonly reported on Czech forests elsewhere.
2.2. Growing stock
The available data on forest growing stock in the Czech Republic for the period required for
reporting carbon stock changes (annually since 1990) are those of FMP, which are administered
centrally in FMI. Each forest stand with area above 50 ha must be managed according to its plan,
which must be updated in 10-year intervals. Smaller units also enter the central database, as they
have to follow a simplified set of management recommendations that are also updated in 10-year
intervals. FMP data contain, among others, merchantable volume under bark per tree species, age
class and area. For the purpose of regional and country-level generalization, tree species are
categorized into four major groups, namely those of beech (all broadleaved species except oaks),
oak (all oak species), pine (pines and larch) and spruce (all conifers besides pines and larch).
Such data are available since 1999, while for previous years, growing stock volumes were only
available for the merged categories of broadleaved and coniferous trees. Since the actual areas
under individual species was available for all years, the corresponding species volume share was
recalculated on the basis of the average growing stock per hectare and age class during the period
1999 to 2003 (
Figure 2). In this way, the volumes of broadleaves species were separated into beech and oak and
those of conifers into pine and spruce also for the period prior 1999. The aggregated growing
stock volume per species group during 1990 to 2003 is shown in Figure 3.
500
3
Merchantable volume (m /ha)
3
Merchantable volume (m /ha)
400
300
200
100
Beech
Oak
0
0
50
100
Age (years)
150
200
400
300
200
100
0
0
Pine
Spruce
50
100
Age (years)
150
200
700
600
3
Growing stock (mill. m u.b.)
Figure 2. The reported merchantable volume per hectare for broadleaved (left) and coniferous (right) tree species –
average for the years 1999 to 2003 (lines) with spread statistics indicated by over-imposed box plots.
500
400
300
200
100
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03
19 19 19 19 19 19 19 19 19 19 20 20 20 20
Beech
Oak
Pine
Spruce
Year
Figure 3. Total growing stock of merchantable timber under bark as reported from FMPs for 1990 to 2003 classified
by the major groups of species.
Also available at FMI are the data from the first cycle of the National Forest Inventory (NFI),
which was conducted during 2001 to 2004. NFI used classical sample-based approach, i.e., it
represent classical statistical inventory on tree level. The first preliminary results of NFI were
made available during 2005 (www.uhul.cz). The NFI data were not directly used in this study to
calculate carbon stock. However, a subset of NFI data from Karlovarsky region (that was made
available by FMI for testing for the authors of this study) was used to show its potential
utilization for forming the species-specific conversion and expansion factors.
2.3. Estimating carbon stock held in tree biomass
The proposed revision includes two approaches of estimating carbon stock held in tree biomass
from the available data of merchantable volume per species and age class.
The first option is to construct species-specific age-dependent biomass conversion-expansion
factors (CBEF) defined as
W
CBEFk = AB =
Vmerch
m
n
j
i
m
∑∑W
i, j
[1]
∑V j
j
where WAB (Mg) is the dry weight biomass of the considered component and Vmerch (m3) is the
merchantable tree wood volume under bark. For computation of stand level CBEFs, W and V
represents the sum of the estimated biomass and merchantable volume of m trees measured in the
given sample plot, where each tree j may contain n biomass components i. The age dependence
was built in as a functional relationship as used, e.g., by LEHTONEN et al. (2004), i.e.
CBEF = p1 + p 2 * e − Age / p 3
[2]
where p1, p2, and p3 are parameters. The biomass equations applied for individual tree species
were taken from JOOSTEN et al. (2004), CIENCIALA et al. (2005) and WIRTH et al. (2004b) for
beech, pine and spruce, respectively. Since no trustable biomass function was available for oak,
the default (IPCC 2003) constant expansion factor of 1.4 and conventional density of 0.58 t/m3
was applied for this species group. The CBEF approach requires a representative dataset of major
tree species, ideally that of the recently conducted statistical NFI. However, as these source data
were not available from FMI, we demonstrate the approach on the available database of the
permanent research plots (PRP). These tree-level data included 10.7, 6.9 and 51 thousands of
trees for beech, pine and spruce, although unfavourably distributed over age (). Because of the
limited data, specifically at younger age classes, the function of age [2] was fitted to stand data of
age above 30 years, and the parameter p3 was held constant as in LEHTONEN et al. 2004.
1000
900
0.08
800
0.05
500
800
0.12
700
0.10
600
400
300
0.03
200
0.02
200
100
0.01
100
50
100
Age (years)
150
0.00
200
0.08
500
0.04
0
0
0.14
Pine
400
0.06
300
0.04
Proportion per Bar
0.06
600
Proportion per Bar
0.07
700
Count
1000
0.09
Beech
Count
900
0.02
0
0
50
100
Age (years)
0.00
150
5000
Spruce
4000
0.09
0.08
Count
0.06
0.05
0.04
2000
0.03
Proportion per Bar
0.07
3000
0.02
1000
0.01
0
0
50
100
Age (years)
0.00
150
Figure 4. The available tree-level data from database of permanent research plots; tree distribution across age for
individual species is shown.
For comparative purposes, a sub-set of NFI from the Karlovarsky region was also used to
complement the analysis of species-specific age-dependent CBEFs. This NFI sub-dataset was
made available the purpose of testing from the FMI (see www.uhul.cz for more information on
NFI design). Here, NFI data were screened so to select only those plots where the major species
represented at least 70% of the basal area. These tree-level data were used to apply tree level
biomass and volume equations as in the case of the PRP data and thereafter summed on plot level
to form BEF and seek its relation to age according to [2]. The dataset screened as described
above contained 22 pine plots and 366 spruce plots.
The second option to address the estimation of biomass from available data on merchantable
volume is to generate the corresponding tree distributions for the stand-based aggregates of
volume per hectare on the level of individual species and age class (Figure 5). This was
performed on the basis of the Czech growth and yields tables (ČERNY et al. 1996) and its
software derivative, growth and yield model SILVISIM (e.g., ČERNY 2005). Such disaggregated
data permit a direct application of tree-level biomass functions as above for individual species.
The input data required for generating the tree distribution include the following stand attributes:
tree species, stand age, mean height, basal area and number of trees (n) per hectare. N was
obtained from the actually reported stand volume (V) per hectare for given species, age and
reporting year by dividing V it with the mean tree merchantable volume. The generated tree
frequency distributions contain tree-level information, i. e., diameter, height and age. Such data
represent input information to the species-specific biomass functions as noted above, with
exception of oak, for which we applied the IPCC default as in the case of CBEF-approach above
for this species.
Freguency (n/ha and DBH class)
Stand age
100.0
10.0
1.0
0
10
20 30 40 50 60
Tree diameter (cm)
70
80
25
35
45
55
65
75
85
95
105
115
125
135
145
155
165
Figure 5. Frequency distribution of trees in hypothetical stands of different age – an example of Beech and the
reporting year 2003. Every distribution contains a finite number of trees (n) of certain diameter: their sum matches
the corresponding stand-level aggregated volume per hectare as reported from FMP for given year, species and age
class. For visual clarity, the y-axis is expressed on log-10 scale.
To account for the belowground biomass component, a root/shoot ratio of 0.20 was applied, a
conservative value with respect to the references summarized in GPG (IPCC 2003). Finally, total
tree biomass was converted to carbon using the commonly used standard oven-dry biomass
carbon content of 50%.
2.4. Estimating carbon stock changes
GPG (IPCC 2003) suggest two alternative methods for estimating the carbon stock change (∆C).
The so-called default is based on separate calculation of increment and removals as
∆C = ∑ ijk ⎡⎣ Aijk *(CI − CL )ijk ⎤⎦
[3]
where A is the area concerned, CI and CL represents increment and loss of carbon stock,
respectively, and the indexes i, j, k note the likely applied categorization involved in assessment
of ∆C. This approach has also been applied in the Czech NIRs until now (e.g., FOTT et al. 2005).
The proposed revision suggests utilizing the second method, based on stock change as
(
)
∆C = ∑ ijk Ct2 − Ct1 / ( t2 − t1 )ijk
[4]
where C is carbon stock at time t1 and/or t2, and the indexes i, j, k expresses the likely
categorization of the carbon pools concerned. Using the database of FMP, we assume t1 and t2 to
be the consecutive years with corresponding status of FMP database. The carbon stock change in
living biomass was estimated for the individual years of the period 1990 to 2003 on 5-year
moving average smoothed data that were calculated to suppress the inter-annual variations in the
FMP data.
3. Results
3.1. Biomass conversion-expansion factors
1.00
Beech
Pine
Spruce
3
CBEF (Mg/m )
0.85
0.70
0.55
0.40
0
50
100
Age (years)
150
Figure 6. Biomass conversion-expansion factors (CBEFs) for beech, pine and spruce and the approximated relation
to age according to the exponential decay function as used, e.g., in LEHTONEN (2004); each point represent one plot
and the estimated BEF following the [1].
The biomass conversion-expansion factors (CBEF) estimated on the level of PRP plots were generally highest
for beech, lower for spruce and lowest for pine (
Figure 6). For all tree species, the dependence to age was weak, but significant: the estimated
regression parameters p1, p2 of Eq. 2 and coefficient of determination (r2) were 0.588, 0.246 and
0.29 for beech, 0.479, 0.117 and 0.49 for pine and 0.497, 0.2 and 0.28 for spruce, respectively.
3.2. Carbon stock and carbon stock changes
The assessed quantities of total carbon stock held in tree biomass are shown in
Figure 7. They steadily increased from about 203 Mt C (745 Mt CO2) in 1990 to 236 Mt C
(865 Mt CO2) in 2003. The difference in the two estimation approaches ranged from 0.58 to
0.98 % (mean 0.82 %) for the individual years, with lower values for the approach using CBEFs.
Obviously, different tree species contributed differently to these differences, as it is demonstrated
in
Figure 7 (left).
150
200
Spruce
Beech
100
CAB (Mt)
CAB (Mg/ha)
190
Pine
50
180
170
160
0
0
BEF
Distribution
50
100
Age
150
200
90 91 92 93 94 95 96 97 98 99 00 01 02 03
19 19 19 19 19 19 19 19 19 19 20 20 20 20
Year
Figure 7. Differences between the two approaches of aboveground biomass carbon stock (CAB) assessment, i.e.,
using either i) BEF or ii) tree distributions and direct application of biomass functions. It is demonstrated at the level
of individual tree species (left) and the reporting year 2003, and on the level of total aboveground biomass for
individual years (right) for the period of 1990 to 2003.
The stock change estimated on 5-year moving average values of total carbon stock was positive
for the whole period of 1990 to 2003 and its individual years, meaning that the forest biomass
acted as sink during this period (Figure 8; note that a negative sign is applied to these values to
indicate sink). The average annual biomass sink was -8.62 and -8.37 Mt CO2/year for the
estimation via CBEF and by tree distributions and biomass functions, respectively. The mean
difference between the two estimates was -0.25 Mt CO2/year, which was statistically insignificant
(paired t-test: t = -1.793, p = 0.096).
Carbon stock change (Mt CO2)
0
-2
-4
-6
-8
-10
-12
BEF
Distribution
90 91 92 93 94 95 96 97 98 99 00 01 02 03
19 19 19 19 19 19 19 19 19 19 20 20 20 20
Year
Figure 8. The estimated carbon stock change (sink indicated by negative values) in biomass during 1990 to 2003 by
the two different calculation procedures (BEF-aided vs. tree distributions). The solid lines indicate the average sink
values, namely -8.62 and -8.37 Mt CO2 for CBEF-aided and tree distributions approach, respectively The dashed
control line shows size of the cap (0.32 Mt C or 1.173 Mt CO2) assigned to the Czech Republic for activity of forest
management (Kyoto Protocol Art. 3.4) if elected for the first commitment period (2008–2012).
4. Discussion
4.1. Biomass conversion-expansion factors
The accuracy of carbon stock change assessment depends on accuracy of input data and on factors used in
recalculation procedure. While the accuracy of input data cannot be readily affected, the application of
suitable recalculation procedure may improve the overall assessment. Therefore, GPG (IPCC 2003)
recommends a proper application and transparent reporting of applied biomass expansion factors (BEFs).
The suggested default BEF is 1.3 for conifers and 1.4 for broadleaved trees in temperate region. However,
GPG suggests the application of region or country-specific factors once they concern a key category identified
in the national inventory compilation. While the most common perception of BEFs is a factor to just expand
volume or biomass, GPG notices that conversion-expansion factors (here denoted as CBEFs) may also be
applied, as e.g., in this study and other recently published studies on carbon inventory and related calculation
(WIRTH et al. 2004b, LEHTONEN et al. 2004). CBEFs facilitate a joint expansion and conversion, in our case
from the merchantable growing stock volume under bark to the total above ground biomass. An important
step is the estimation of the relation of CBEF to age, which is needed for application to volume data
aggregated by age classes. Since the available data from PRP plots did not contain young forest stand, the
resulting fit of the function according to Eq. 2 underestimates total biomass for individual species for young
age classes. This underestimation may not be significant considering the relatively small fraction of biomass
(non-merchantable) represented by these age classes, nonetheless the improvement may be appropriate and
easy to achieve with the data of NFI as it is indicated in
Figure 9 for pine and spruce stands (based on NFI sub-set). Obviously, a full dataset of NFI
would provide a spatially unbiased estimate of CBEF and the CBEF relation to for each major
species, together with a rigorously estimated uncertainty. Since the full set of the NFI data were
not available for this study, this could not be explored here.
1.2
NFI
PRP
Data source
NFI
PRP
1.0
3
3
CBEF (Mg/m )
1.0
CBEF (Mg/m )
1.2
Data source
0.8
0.6
0.4
0
0.8
0.6
35
70
105
Age (years)
140
175
0.4
0
35
70
105
Age (years)
140
175
Figure 9. The relation of CBEF to age for pine (left) and spruce (right) using the PRP data and a sub-set of NFI plots.
The combined data permit a fully flexible fit across the full age span (solid line) as compared to the fit with one
parameter held constant (dashed line).
4.2. Carbon stock and carbon stock changes
The two alternative approaches of the stock change method resulted in insignificant quantities of
the assessed carbon stock (less than 1%). The differences increased to about 3% once the
comparison refers to the assessed stock change. This actually demonstrates the weakness of the
stock change method, namely that the assessed stock change in individual years is very sensitive
to small changes in the stocks. GPG (IPCC 2003) suggests that the stock change method might be
very sensitive in those cases, when large stocks are used to detect small changes. Actually, the
approach utilizing CBEFs proved to be more robust and the year-to year differences were not as
large as those estimated by the approach of biomass functions applied on fictive tree distributions
in stands of particular age. The analysis of the factors contributing to this observation cannot be
provided in this material, but it apparently is the effect of shifting areas and age structure of
individual species in 1997 and 1998. It maybe concluded that the advantage of the approach with
tree-level biomass functions applied on generated tree distributions is counterbalanced by
additional uncertainties associated with disaggregating the age-class aggregated data by
SILVISIM model. This uncertainty is difficult to assess, but could potentially be evaluated on the
observed stand and tree level data. The approach utilizing CBEFs directly on the aggregated data
by age-classes is more transparent and simpler to evaluate in terms of uncertainties, which will be
the subject of our next study.
With respect to the data reported in the Czech National Inventory Report so far (FOTT et al. 2005),
the assessed carbon sink by the stock change is about twice as large. However, it must be noted
that a new revision of the default method is currently also ongoing. The revision will, among
others, utilize a more suited set of allometric equations and expansion factors, revise the other
expansion factors involved and apply the actual representation of the four major tree species by
age classes. The preliminary assessment of emissions associated with the category “Changes in
Forest and Other Woody Biomass Stocks” by the revised default method indicates a sink of -6.7
Mt CO2 annually for the period of 1990–2003. This would be about 20 and 22% less than the
assessment by the two stock change method approaches. Worth noting is the effect of root/shoot
factor (R), which within the stock-change method simply adds the corresponding quantity of CO2
sink. Once discounted in the stock change method calculations, the assessment of carbon sink in
biomass would be -7.18 and -6.98 Mt CO2 per year. R acts differently within the default method,
because the calculation procedures for increment and the harvested volumes differ.
The objectivity of the stock-change method depends on the assumption that the data collected in
subsequent years are assessed using the same procedures. Obviously, FMP data do not represent
the same type of objective information as the statistical inventory and in-situ tree level
measurements of NFI. However, since the repeated NFI is not available yet, FMP will remain the
basis for the carbon stock change assessment in the country. Comparing the two major methods
to estimate carbon stock change, the stock-change method might represent a more objective and
less uncertain approach as compared to the default method, which requires more information to
be known. The by far most important information for the default method is increment, the
estimation of which currently depends on growth and yield tables and hence remains uncertain.
Secondly, the necessary expansion factors related to increment are not available, and the IPCC
(2003) default values must be used in connection with the default method. Still, the default
method may represent a more attractive solution as compared to the stock change method,
because it discerns emissions by removals (increment) and sources (harvest and other loss),
whereas the stock change method gives only the net change of emissions associated with forest
biomass.
5. Conclusions
This paper explored two approaches of the stock change method to assess the stock change
associated with tree biomass and forests. Although the stock change method based on FMP data
shown here may be a defendable approach, it lacks representative biomass conversion and
expansion factors and provides data of unknown accuracy, although presumably unchanged
during the reported period. A more significant improvement of the forest carbon stock change
assessment in the Czech Republic may be expected once the second NFI cycle will be performed
in the country. Until then, the assessment of carbon stock change will rely on FMP data. NFI data
may and should be utilized to construct suitable allometric relationships and biomass conversion
and expansion factors. This is actually needed for both the default and the stock change method.
6. Acknowledgements
Many thanks belong to the organizers of the international conference “Climate Change – Forest
Ecosystem & Landscape, where this study was presented. The authors also gratefully
acknowledge the support of the Czech Science Foundation (GAČR), Grant number 526/03/1021
(CzechRECAF), and of the Czech Ministry of Environment (VaV/640/18/03 – CzechCARBO).
References
1. ČERNÝ, M., 2005: Use of the growth models of main tree species of the Czech Republic in combination with the
data of the Czech National Forest Inventory. In NEUHÖFEROVÁ, P. (ed) The growth functions in forestry. Korf´s
growth function and its use in forestry and world reputation. Kostelec nad Černými lesy, Prague. Czech University of
Agriculture Prague, ISBN 80-213-1331-5 (in Czech). – 2. ČERNÝ, M., PAŘEZ, J., MALÍK, Z., 1996: Growth and yield
tables of the main tree species of Czech Republic (spruce, pine, beech, oak). IFER, Jílové u Prahy, Czech Republic,
245 pp. (in Czech). – 3. CIENCIALA, E. ČERNÝ, M, TATARINOV, F., APLTAUER, A., EXNEROVÁ, Z., 2006: Biomass
functions applicable to Scots pine. Trees – Structure and Function, 20, p. 483–495. – 4. FOTT, P., PRETEL, J., VÁCHA,
D., NEUŽIL, V., BLÁHA, J., HAVRÁNEK, M., 2005: National greenhouse gas emission inventory: Report of the Czech
Republic (Reported inventory 2003). Report of the Czech Hydrometeorological Institute, 117 pp. – 5. HENZLIK, V.,
ZATLOUKAL, V., 1994: Estimation of the total greenhouse gas emissions from forestry in the Czech Republic. Report
of the Czech Ministry of Agriculture. – 6. IPCC, 2003: Good Practice Guidance for Land Use, Land-Use Change and
Forestry. Institute for Global Environmental Strategies (IGES), Hayama, Japan, ISBN 4-88788-003-0. – 7. JOOSTEN,
R., SCHUMACHER, J., WIRTH, C., SCHULTE, A., 2004: Evaluating tree carbon predictions for beech (Fagus sylvatica
L.) in western Germany. Forest Ecology and Management, 189, p. 87–96. – 8. LEHTONEN, A., MAKIPAA, R.,
HEIKKINEN, J., SIEVANEN, R., LISKI, J., 2004: Biomass expansion factors (BEFs) for Scots pine, Norway spruce and
birch according to stand age for boreal forests. Forest Ecology and Management, 188, p. 211–224. – 9. PAŘEZ, J.,
ŽLÁBEK, I., KOPŘIVA, J., 1990: Basic technical units of determining timber volume in the logging fund of the main
forest species. Lesnictví, 36(6): 479–508 (in Czech). – 10. WIRTH, C., SCHULZE, E. -D., SCHWALBE, G., TOMCZYK, S.,
WEBER, G., WELLER, E., 2004a: Dynamik der Kohlenstoffvorräte in Wäldern Thüringens. Thüringer Ministerium für
Landwirtschaft, Naturschutz und Umwelt, Mitteilungen 23/2004, 308 pp. (In German). – 11. WIRTH, C.,
SCHUMACHER, J., SCHULZE, E-D., 2004b: Generic biomass functions for Norway spruce in Central Europe – a metaanalysis approach toward prediction and uncertainty estimation. Tree Physiology, 24, p. 121–139.