The potential of primary forest residues as a
bioenergy source:
the technical and environmental constraints
Author: Fanny Boeraeve
Examiner: Dr. Birka Wicke
Daily supervisor: M.Sc. Vasileios Daioglou
Second reviewer: Dr. Pita Verweij
20-12-2012
Master’s Thesis | 7.5 ECTS | Ecology & Natural Resources
0
Management | Environmental Biology | Utrecht University
- Utrecht University -
The potential of primary forestry residues as a bioenergy source: the technical and
environmental constraints
Master’s Thesis: 7.5 ECTS
Utrecht, December 20th 2012
Fanny Boeraeve
(student number: 3803821)
M.Sc. student Environmental Biology
Track ‘Ecology and Natural Resources Management’
Supervison:
Examiner: Dr. Birka Wicke - Copernicus Institute, Energy and Resources
Daily supervisor: M.Sc Vasileios Daioglou - Copernicus Institute, Energy and Resources
Second reviewer: Dr. Pita Verweij - Copernicus Institute, Energy and Resources
Cover Photo: Martin Gebala, stock.xchng (EEA 2006)
1
Table of contents:
- Chapter 1 - ................................................................................................................................3
Introduction ................................................................................................................................3
- Chapter 2 - ................................................................................................................................6
Estimations of forestry residues potential ..................................................................................6
1. Selection of studies .................................................................................................................6
2.
Definitions ..........................................................................................................................7
2.2.
Definition of forestry residues ....................................................................................7
2.3.
The potential definition ..............................................................................................8
3.
Geographical scale..............................................................................................................9
4.
Methods and calculations .................................................................................................10
4.1.
Methodologies ..........................................................................................................10
4.2.
The factors influencing the estimate .........................................................................18
4.2.1. The methods complexity level ................................................................................18
4.2.2. The underlying databases .......................................................................................18
4.2.3. Assumptions ...........................................................................................................20
5.
Summary and conclusion .................................................................................................22
- Chapter 3 - ..............................................................................................................................25
Sustainability of the removal of forestry residues ....................................................................25
1.
Soil erosion .......................................................................................................................25
2.
Nitrogen balance...............................................................................................................26
3.
Carbon storage ..................................................................................................................27
4.
Biodiversity ......................................................................................................................28
5.
Summary and interrelation of the processes .....................................................................30
6.
Recommended sustainable residues extraction ................................................................31
6.2.
Current uncertainties ................................................................................................31
6.2.
Recommended harvest restrictions ...........................................................................31
6.3.
Management recommendations ................................................................................32
- Chapter 4- ...............................................................................................................................33
Conclusions and recommendations ..........................................................................................33
1.
Research questions and related hypotheses ......................................................................33
2.
Recommendations ............................................................................................................35
2.1.
Residue extraction recommendations .......................................................................35
2.2.
Further research needed ............................................................................................36
2.3.
Study limitation ........................................................................................................37
References ................................................................................................................................38
Appendices ...............................................................................................................................43
2
- Chapter 1 Introduction
Climate change, mainly resulting from the increase of atmospheric greenhouse gas
concentration is threatening the world in many aspects. The rise of global temperature is
affecting numerous species communities which see their habitat altered and their phenology
disturbed. Meteorological hazards are happening more frequently and more severely than ever
recorded, menacing many human populations (IPCC 2007). Human dependence on fossil
fuels contributes to this by releasing large amount of greenhouse gasses into the atmosphere
(Asikainen et al. 2008, IPCC 2007). In a world of increasing energy demand, facing the
consequences of the use of fossil fuels and its finite supply, the world’s population is
challenged to find alternative energy sources (Schulze et al. 2012). In this context, renewable
energies represent an interesting option as they avoid greenhouse gas emissions and show an
infinite supply (EEA 2006). The implementation of renewable energies has been considerably
deployed in recent years and has been encouraged by several governmental policies. For
instance, European countries are mandated to rely 20% of their energy sources on renewable
energy (European Union 2009) and the U.S.A. are following some strict guidelines under the
‘American Clean Energy & Security Act’ (2009).
The spectrum of renewable energy sources is wide including sources such as sun, wind, water
or biomass. Among them, biomass currently represent the largest resource of renewable
energy. Biomass can be obtained from a variety of feedstock like forests residues, agricultural
residues, energy crops or organic wastes (IPCC 2012). Because forest resources are excepted
to increase (Asikainen et al. 2008, Poudel et al. 2012, EEA 2006), forestry residues are
among the bioenergy sources showing prospects for providing a future substantial share of the
global biomass supply (Bentsen and Felby 2012, IPCC 2012). Forestry residues are
conventionally divided into three categories. Primary forest residues consist of residues
gathered from silvicultural activities such as tree thinning or logging. Secondary residues
include sawdust, barks, chips, etc. resulting from the wood industry. The third group, referred
to as ‘tertiary residues’ represent the used wood from constructions, demolition or packaging
(Röser et al. 2008, U.S. Department of Energy 2011). In particular, primary residues have
been gaining growing popularity after many studies proving their large potential as a
bioenergy source (EEA 2006, Greg and Smith 2010, Dymond et al. 2010). They have already
become an important fuel source in northern countries such as Sweden and Finland (Röser et
al. 2008).
However, uncertainties remain about the estimation of primary forestry residues’ potential as
an energy source (IPCC 2012). In the current body of literature, estimates of harvesting
residues potential vary widely (Dymond et al.2010, Bentsen and Felby 2012). Studies
assessing the potential of biomass resources rely on distinct methodologies and definitions
leading to a broad spectrum of estimated values of the available biomass for bioenergy. Some
reviews point to the wide range of estimations (Bentsen and Felby 2012, Rettenmaier 2010,
Offerman 2010) but few attempt to find explanations of these differences. The lack of
accurate estimations of the potential hampers the implementation of clear policies (Bentsen
3
and Felby 2012, Dymond et al.2010, Rosillo-Calle et al. 2007) and understanding the reasons
in the discrepancies of the estimates is of primary concern.
Besides the lack of clear potential assessment strategies, the bioenergy production from
primary forestry residues is constrained by environmental limitations (Poudel 2012, EEA
2006). Indeed, an overexploitation of forest residues could lead to soil impoverishment and
degradation which could be highly harmful to the environment and which could in turn affect
further biomass extraction and bioenergy production (Rettenmaier 2010). However, little
knowledge is shared on the extent of these collateral damages (IPCC 2012). Because the use
of forestry residues as an energy source is believed to increase rapidly in the coming decades
(Asikainen 2008), the possible environmental damages that could result from this increase are
to be addressed urgently.
The present report approaches these two issues by means of a literature review addressing the
following research questions.

How do distinct methods influence the estimation of primary forestry residues
potential as a bioenergy source?
Related hypothesis:
o If the definition of the ‘forestry residues’ differs from one study to another,
this can lead to distinct assessment of the potential.
o If the definition of the ‘potential’ differs from one study to another, this can
lead to distinct assessment of the potential.
o If the geographical scope considered for the assessment of the potential
differs from one study to another, this can lead to distinct assessment of the
potential.
o If the methods and calculations to estimate the potential differ from one study
to another, this can lead to distinct assessment of the potential.

What are the environmental impacts generated by the extraction of forestry residues
for their conversion into bioenergy?
Related hypotheses:
o If forestry residues play an important role in preventing soil erosion, their
extraction could lead to increased soil erosion.
o If forestry residues play an important role in the forest nutrient cycling, their
extraction for the use of bioenergy could endanger the ecosystem nitrogen
equilibrium.
o If forestry residues play an important role in the forest’s carbon pool, their
extraction for the use of bioenergy could lead to a global loss of carbon stock.
o If forestry residues provide habitat to some forest species (especially micro
fauna), their extraction for the use of bioenergy could threaten those species.
With the intention of answering the aforementioned questions and hypotheses, the report is
structured under chapters following the two research questions. In the second chapter, the
first research question is addressed by comparing studies according to three factors believed
to play a role in the variation of estimations: (i) the geographical scale considered by the
author, (ii) the definitions of the ‘potential’ and of ‘forestry residues’ and (iii) the methods
used to calculate the potential. These three factors are believed to be the major causes of the
4
various potential estimations available in the literature (Bentsen and Felby 2012, Rettenmaier
2010). In the third chapter, the second research question regarding the sustainability of
residues harvesting is addressed with a specific focus on soil erosion, biodiversity, carbon
stocks and dynamics and nutrients productivity. The risks engendered by the removal of
residues are investigated for each of these factors and values of sustainable amount of
removal suggested in the literature are presented. In a fourth chapter, the research questions
are answered, the hypothesis are verified and some general conclusions and recommendations
are made. Throughout the whole report, the focus is on primary forestry residues only, i.e.
residues from logging and, to a lesser extent, from thinning activities, as suggested by the
definition of Röser et al. 2008. The first chapter is mainly focused on a European scale while
the second chapter is applicable to a global scale as well as to more regional ones.
5
- Chapter 2 Estimations of forestry residues
potential
This chapter is dedicated to the first research question. Four studies have been selected and
are compared according to three factors believed to be the major causes of the various
potential estimations: (i) the geographical scale considered by the author, (ii) the definitions
of the ‘potential’ and of ‘forestry residues’ and (iii) the methods used to calculate the
potential. In a concluding section, the differences are summarised and the importance of each
factor is discussed.
1. Selection of studies
In order to have comparable studies, only assessments of European potentials were taken into
account. Moreover, only studies estimating the theoretical and the technical potentials and
using a resource-base approach were chosen. The focus is of this report is on current
assessment rather than on studies assessing future potentials. Within the studies fitting those
criteria, four have been selected because they represent a wide range of estimates and because
they rely on distinct methodologies allowing interesting comparison.
The study of Böttcher et al. (2010) is of specific interest because it relies on the recommended
standardised method from the handbook of Vis et al. (2010). This handbook is the result of a
several studies carried out by ‘Biomass Energy Europe’ which have compared many
European assessments and thereof provides the best practices for the estimation of biomass
potentials (Vis et al. 2010). This handbook recommends two types of methodologies: one
statically-based and one spatially-based. In the study of Böttcher et al. (2010), these two
methodologies lead to significant different estimations (Table 1).
The study of Verkerk et al. (2011) shows the interesting trait to rely on a model suggested by
the ‘European Forest Institute’ which has been utilized by several studies. For instance, the
report of Mantau et al. (2010), the official report of the European Commission to assess the
potential of forests, relies as well on this model. Incorporating a study based on this method
will thus give insights into the assessment provided by a widely used methodology.
The two last studies, of Asikainen et al. (2008) and Ericsson and Nilsson (2006) were chosen
because being recent and comparable with the two previous ones in terms of geographical
scope and assessment goals.
Table 1 provides the overview of the estimations the four studies provide. The conversion into
PJ/year has been done using the conversion factors supplied in Appendix 1. The spread of the
estimations is already striking.
6
Table 1: Summary of estimations of potential for forestry residues as bioernergy source available in the current
literature
Potential in PJ/yr
Reference
Asikainen et al. 2008
Verkerk et al. 2011
Bottcher et al. 2010 (statistical)
Bottcher et al. 2010 (spatial)
Ericsson and Nilsson 2006
theoretical
technical
2527.2
2390.544
3229
550.8
720
1186
621.29
590
1170
Ratio techn/th
22%
30%
37%
50%
2. Definitions
2.2.
Definition of forestry residues
The definition of forestry residues represents the basis of the study of their potential, yet no
consensus exists on a common terminology (Bentsen and Felby 2012, Röser et al.2008). This
small survey illustrates how much the definitions of forestry residues vary (Table 2). From
one study to another, different tree component are included under the term “forestry
residues”. Branches and tree tops seem to be commonly included into the potential. This is
probably because of their large amount left on site after harvesting while being of negligible
value for the timber industry, hence representing a considerable value for the bioenergy
industry (Dymond et al. 2010). The other tree components represent a less obvious value as a
bioenergy source because being less abundant and being sometimes restricted by harvest or
implementation issues. For instance, due to the scattered distribution and the small size of
needles and leaves, these may prove to be difficult to harvest. The removal of stumps involves
their extraction out of the ground which requires supplementary machinery and leads to soil
disturbance (EEA 2006). The harvesting of undergrowth tree unsuitable for the timber
industry requires their identification and their location ahead of the harvesting. A precommercial thinning, if not already implemented, is often complicated to put in practice
(Verkerk et al. 2011b). Complementary felling is defined by the additional felling assumed to
be available when the net annual increment is higher than the harvest. From this estimation,
the percentage of corresponding residues is estimated using allocation factor. The
incorporation of these extra elements into the assessment of forestry residues potential
depends on the authors assumptions and decisions and will thus always vary from one study
to another.
The intensity with which the extra components taken into account can influence the potential
estimation depends on the amount of potential energy these elements add to the estimation. It
is likely that including complementary felling, early thinning and undergrowth trees will
affect more seriously the estimate than the extra energy from leaves and stumps. Indeed,
previous studies have shown that early thinning could bring an equal amount of biomass than
residues from logging activities (U.S. Department of Energy 2011) while Böttcher et al
(2010) state that the harvesting of stumps is negligible in most European countries. Taking
this into account, Ericsson and Nilsson (2006) including both undergrowth trees and early
thinning are expected to show a higher estimation while Verkek et al. 2011, only including
leaves and needles is predicted to have a lower estimation. Indeed, the estimation of Verkerk
7
et al. 2011 is among the lowest ones, however, the estimation of Ericsson and Nilsson (2006)
is even lower (Table 1). In overall, it is assumed that the amount of biomass considered under
the term ‘residues’ increases from Verkerk, Asikainen, Böttcher to Ericsson and Nilsson,
however, this is not represented in the estimations, pointing to the importance of other factors
influencing the assessed values.
In this review, only the elements referred to as ‘residues’ were taken into account into the
potential. Certain studies assess the potential of other elements that have not been indicated in
the Table because being categorised as a separate source of biomass by the author. For
instance, Asikainen et al.(2008) and Verkerk et al.(2011a) both assess the potential of stumps,
but consider them separately from the ‘residues’.
The way these elements of residues are assessed also vary from one study to another. Some
have grouped all components together while others have calculated the potential separately
for each. Böttcher et al.(2010) as calculated the sumps and the complementary felling apart
from the branches, tree tops and leaves. Asikainen et al.(2008) has estimated a potential for
each component separately, unlike Verkerk et al.(2011a) Ericsson and Nilsson (2006) who
calculated everything together.
Table 2: residues included under the term ‘forestry residues’. ‘Undergrowth trees’ refers to tree too small for
commercial purposes, ‘early thinning’ refers to precommercial thinning and ‘complementary felling’ refers to extra
felling assumed to be available when the net annual increment is higher than the harvest.
Reference
Asikainen et al.2008
Verkerk et al.2011a
Böttcher et al.2010
Ericsson & Nilsson 2006
2.3.
branches tree top
x
x
x
x
x
x
x
x
needles/
leaves
x
x
stumps
undergr.
early
trees
thinning
Compl.
felling
x
x
x
x
x
The potential definition
The literature differentiates various types of potentials according the type of restrictions taken
into account (Röser et al. 2008). That is to say, an economic potential includes economic
constraints; a technical potential, the technical ones, etc. The classification vary form one
author to another. For instance, Bentsen and Felby (2012) suggest a categorization with the
hierarchy ‘theoretical>technical>economic>sustainable’ while the IPCC’s report (2012)
suggests a three levels categorization including the theoretical, the technical and the
economical potentials. Studies assessing different types of potential supply great differences
in their potential estimates (Dymond et al.2010, Rettenmaier 2010). For this reason, the
present work only includes two types of potential in order to allow comparison. The most
widespread estimated types of potential have been chosen for this study, namely the
theoretical and the technical potential. The theoretical potential refers to the maximum
possible energy production from residues, not including any restrictions, whereas the
technical potential refers to a more realistic estimation by including some technical
restrictions (Bentsen and Felby 2012, IPCC 2012).
However, even within one type of potential, definitions differ from one publication to another
(Röser et al.2008, IPCC 2012) which is believed to contribute to distinct estimates
(Rettenmaier et al.2010, Bentsen and Felby 2012). Among the four papers studied in this
8
research, the theoretical potential did not show distinct definition. The differences are more
striking for the technical potentials (Table 3). Some authors include different aspects within
the technical term such environmental and social ones. Moreover, each of these aspect is not
addressed in the same way; various restrictions are applied even when referring to a similar
aspect.
Inevitably, the amount of restrictions taken into account influences the final estimation.
Instinctively, it can be assumed that more restrictions are taken into account, the lower the
potential estimation will tend to be. From this postulate, the four studies could be classified
from the assumed lower estimate to the highest as follows: Verkerk, Böttcher, Asikainen,
Ericsson. As for the definition of residues, the assumed classification does not match reality,
underlying the existence of other factors influencing the potential estimations.
In addition to including various aspects and restrictions, the studies vary between each other
by the way they quantify the restrictions. One same restriction can lead to completely
different results if calculated and estimated differently. The way these restrictions are
implemented belongs to the methods used and is therefore discussed in section 2.4. which
illustrate how importance this aspect is.
Table 3: summary of the different aspects and restrictions included under the tern ‘technical potential’ in the four
publications studied.
Source
Asikainen et al.2008
Aspect
technical
Verkerk et al.2011
technical
environmental
Böttcher et al.2010
social
technical
Ericsson & Nilsson 2006
environmental
environmental
sub-aspects
site suitability to harvest
recovery rate
recovery rate
soil bearing capacity
soil productivity
soil protection
water protection
biodiversity protection
forest owneship
harvesting technique
infrastructure
accessibility
processing technique
spatial confinement
natural reserves
soil productivity
3. Geographical scale
Differences in the geographical scope impede reliable comparisons (Bentsen and Felby 2012).
Even if the four studies considered all assess European potential, it is known that European
level studies differ in the countries taking into account (Rettenmaier 2010). The studies
reviewed in this research all include the EU27 as geographical scale with the exception of
9
Ericsson and Nilsson (2006) who include the EU 15, the ACC101, Belarus and Ukraine (Table
4). In other words, their study differs from the three others by including Belarus and Ukraine
and excluding Cyprus and Malta. This results in a difference of forested area of 17 469 000 ha
(FAO 2005) which is likely to affect the estimations.
Despite this large spatial scale difference, the potential prediction of Ericsson and Nilsson
(2006) is among the lower ones (Table 1). This points to the importance of other factors
influencing the estimations which are discussed below. In the case of Ericsson and Nilsson
(2006), it is expected that that these factors lead to a strong underestimation since their study
supplied one of the lowest estimate despite including a much larger area.
In general, studies related to European assessments are likely to differ mostly across the time
scale in relation to the countries included in the European Union when the research is carried
out. For instance, the present EU consists of 12 more countries than eight years ago. Hence, a
research assessing the potential of the EU in 2004 is not comparable to a research done in
2012.
Table 4: Geographical area considered
Reference
Asikainen et al.2008
Verkerk et al.2011a
Böttcher et al.2010
Ericsson and Nilsson 2006
Geographical scope
EU27
EU27
EU27
EU15+ ACC10 + Belarus and Ukraine
4. Methods and calculations
In this section, a first part is devoted to the summaries of each of the methodologies used by
the four studies and a second part focuses on the differences between the methodologies and
on how these could affect the potential estimations.
4.1.
Methodologies
Asikainen et al.2008
The theoretical estimation of the forest residue potential was based on national round wood
removal statistics from the FAO Global Forest Resources Assessment (Appendix 2). From
those national values, the share of biomass for residues was estimated from biomass ratio
dependent on species groups (Table 5). The theoretical potential included an extra input of
residues from annual growing stock surplus. This surplus is defined as the difference between
net annual increment and fellings. 25% of the total annual change rate was estimated to be
available for energy production.
1
The ACC10 consists of: Bulgaria, the Czech Republic, Hungary, Poland, Romania, Slovakia,
Slovenia, Estonia, Latvia and Lithuania (Ericsson and Nilsson 2006).
10
Table 5: Proportion of biomass components used in the volume estimation of forest fuel potential. The components
within the red frame are the components considered as ‘forestry residues’ by the author (Modified from Asikainen
et al.2008).
The technical potential was then calculated by applying restrictions the theoretical potential.
It was assumed that 75% of the clear cut areas and 45% of the thinning areas were technically
available for supply. Recovery rates were assumed to be 65% in mechanized cutting and 50%
in manual cutting. The manual cutting was assumed to lead to lower recovery rate than
mechanized cutting because it scatters the residues while mechanized cutting can involve the
stacking of the residues. Just as the theoretical estimation, complementary felling was
assumed to bring an extra residue income. 25% of the annual change rate surplus was
assumed to be available.
Verkerk et al.2011a
Their estimation of maximum theoretical availability of forest residue biomass is based on a
spatial approach using the European Forest Information SCENario model (EFISCEN) which
simulates the global availability of the whole forest biomass (Figure1). The input data of this
model consists of national forest inventories and is summarised in Appendix 4. The model is
an area-based matrix model (van Brusselen 2012). A distinguish matrix is established for each
country (Verkerk et al.2011a), hence, the ‘forest type’ from Figure 1, in this case refers to one
country. Each matrix consists of 60 age classes and 10 volume classes (van Brusselen 2012)
and this way describes the state of the forest over age and volume classes based on the data
gathered (Verkerk et al.2011a).
11
Figure 1: EFISCEN matrix model used by Verkerk et al.2008 and Mantau 2010 to estimate forest
biomass potential as energy source. In the case of Verkerk et al.(2008) data regarding the forest types
were collected per region within each countries. Source: van Brusselen et al.2012.
The following assumptions were included in the running of the model:



Age-limits for thinning and final felling are based on conventional harvest
management that can be found in the handbooks of Nabuurs et al.2007;
The amount of harvest loss during felling was estimated from UNECE/FAO (2000)
databases;
The volume of residues was estimated from the stem wood volumes according to
allocation factors previously suggested by Vilén et al.2005, Romano et al.2009,
Mokany et al.2006 and Anderl et al.2009.
To estimate the technical potential, the constraints were quantified and combined into a
matrix layer. The resulting matrix layer was applied to a European forest map to calculate the
restriction per country. Afterwards, the overall technical potential was assessed by combining
the theoretical potential with the average reduction factor for each region. The constraints and
their extraction rates are summarised in Table 6. Datasets about soil traits, slopes and
protected areas required to implement the restrictions are listed in Appendix 5.
Explanations regarding the constraints and why they are assumed to lower the potential are
listed in Appendix 6. Only the constraint ‘private holding’ is explained here because of its less
straightforward interpretation: this constraint results from the assumption that small privately
owned forest are less likely to be harvested because of the high transaction costs the owner
would have to bear. This assumption is associated with previous studied that showed that the
size of forest holdings is positively correlated with wood harvesting. Data on size-classes of
private forests was obtained from ‘Schmithüsen and Hirsch 2009’.
12
Table 6: Constraints applied by Verkerk et al. 2011 and the corresponding assumed restriction rates. Annoted
‘1’information is based on personal communication with Karl Stampfer. (modified from Verkerk et al. 2011).
Type of constraint
Site productivity
Soil and water protection: Slope
Restriction rate
Not a constraining factor
67%on slopes up to 35%; 0% on slopes
over 35%, unless cable-crane systems are
used
Soil and water protection: Soil depth
0% on Rendzina, Lithosol and Ranker
(very low soil depth)
Soil and water protection: Soil surface texture 0% on peatlands (Histosols)
Soil and water protection: Soil compaction 0% on soils with very high compaction
risk
risk; 25% on soils with high compaction
risk; not a constraining factor on other soils
Biodiversity: protected forest areas
0%; not a constraining factor in areas with
high or very high fire risk
Recovery rate
67% on slopes up to 35%; 0% on slopes
over 35%, but 67% if cable-crane systems
are used (Cable cranes are applied in
Austria, Italy, France, Germany, Czech
Republic, Slovakia, Slovenia, Romania1)
Soil bearing capacity
0% on Histosols, Fluvisols, Gleysols and
Andosols
Forest private holding
Forest holdings < 1 ha: extraction rate of
50%; Forest holdings ≥ 5 ha: extraction
rate of 85%; Forest holdings ≥ 80 ha:
extraction rate of 96%
Böttcher et al.2010
Böttcher et al.(2010) follows the methods suggested by the BEE handbook (Vis et al.2010) in
which both a statistical and a spatially method are recommended. For each of these methods,
the potential is first calculated per country before being summed up to get the European
estimation.
1) Statistical approach
The input data was gathered from national inventories provided by EUROSTAT, UNECE,
FAO and from The Ministerial Conference on the Protection of Forests in Europe
(MCPFE)(Table 7). The corresponding Tables are listed in the Appendices.
13
Table 7: Sources of the data used by Böttcher et al.(2010) for the estimation of the potentials (Böttcher et al.2010).
The corresponding Tables are provided in the Appendices.
The theoretical potential is calculated following the equations below. The theoretical
potential of logging residues (Equation 1) and the theoretical potential of stumps (Equation 2)
separately before being summed up (Equation 3). The potential of total logging residues
depends on the industrial removals and potential of energy stemwood removals. Each of the
factors of these equations have been calculated or measured by previous studies. The data
used for each of these factors are listed under various Tables available in the Appendices and
referred to in Table 7.
(Equation 1)
(Equation 2)
(Equation 3)
In these equations:
i = tree species/tree species groups
x= country x
y = year y
THP_LRx,y = theoretical potential of logging residues at maximum utilization rate (m3/year)
IRWremi,x,,y = industrial roundwood removals (m3/year)
THP_SWi,x,,y = theoretical stemwood potential (m3/year)
Hlx,y = harvest losses
BEFi,x,y = crown biomass expansion factor
THP_Syx = theoretical potential of stumps for energy use (m3/year)
BEFSi,x,y = stump biomass expansion factor (the factors usually range between 0.14-0.23
and do not include the stemwood)
14
NAIx,y = net annual increment of wood (m3)
THP_PFRx,y = total theoretical potential of primary forestry residues (m3/year)
In line with the reasoning for the theoretical potential, the technical potential consists of the
sum of the technical potential of logging residues and the technical potential of stumps. The
technical potential moreover includes reduction factors for different constraining criteria
(RFc1,2,…for criteria 1,2, etc.) and recovery rates (RR and RS). The restriction factors, the
recovery rates and other factors leading to further restrictions or surplus are listed, along with
their quantifications in Table 8.
(Equation 4)
(Equation 5)
(Equation 6)
TCP_PFRx,y = TCP_LRx,y + TCP_Sx,y
(Equation 7)
In these equations:
TCP_TLR = technical potential for total logging residues
IRWremi,x,,y = industrial roundwood removals (m3/year)
Hlx,y = harvest losses
BEFi,x,y = crown biomass expansion factor
TCP_SWx,y = technical stemwood potential for energy use (m3/year)
TCP_LR x, y = technical potential of logging residues (m3/year)
RFc1,2,…nx,y = reduction factors for different constraining criteria 1, 2,…
RR = recovery rate of logging residues (0-1)
RS = recovery rate of stumps (0-1)
Table 8: Properties included in the theoretical potential estimation of Böttcher et al. (2010) and their corresponding
quantifications and sources.
Property
Recovery rate of above ground forest
residues
Recovery rate of stumps
Estimated rates Source
50%
simplified from EEA 2007, Asikainen et al. 2008
20% (40% for
Finland and
Sweden)
underestimation from Asikainen et al. 2008 with
adjustement to silvicultural, harvesting practices
and species distribution per country
Surplus from complementary fellings
(including stemwood and residues)
Böttcher’s estimate, based on a guess with the
idea that parts of the compl. felling will benefit
the timber industry
Restriction to ensure sustainability
70% (of total
complementary
fellings
estimates)
5%
Surplus from unrecorded harvest
5%
Böttcher’s estimate, based on a guess
Böttcher’s estimate, based on a guess willing to
include stand biomass, dead wood, biodiversity
and carbone stock increases
15
2) Spatially-explicit method
The method consists of integrating a forest area map with the national inventories, the net
annual increments, the biomass expansion factors and several constraints.
The following datasets are being used:
 Forest map:
o Forest/non-forest map 2000, aggregated to 1km resolution; (BEE data
handbook)
 Soil maps:
o European soil database, 1km resolution: soil type , soil depth, soil texture
(BEE data handbook)
o Map on soil susceptibility to compaction, rasterized to 1km resolution (BEE
data handbook)
 Elevation:
o GTOPO30 Digital elevation model – slope data, 1km resolution (BEE data
handbook)
 Protected areas:
o World Database of Protected Areas, rasterized to 1km resolutio n
(http://www.wdpa.org/ )
o Nationally designated protected areas, rasterized to 1km resolution (European
Environment
Agency,
http://www.eea.europa.eu/data-and-maps
/data/nationally-designated-areas-national-cdda-4 )
o Natura 2000 EUNIS database, rasterized to 1km resolution (BEE data
handbook)
 Forestry statistics:
o MCPFE 2005 country-level statistics on forest area, net annual increment,
biomass and annual fellings (BEE data handbook)
o Compilation of NFI statistics on forest area and net annual increment at
regional level from the national forest inventories of the different European
countries
 Other:
o Country-level data on biomass compartments as output from EFISCEN
model
(European
Forest
Institute,
http://www.efi.int
/portal/completed_projects/efiscen/)
1. European map of semwood and average net annual increments
In this first step, a map of Europe based on remote sensing was first calibrated to forestry
statistics and then multiplied with statistical data on net annual increment (NAI) per hectare.
2. Integration of harvesting constraints
Constraints (Table 9) were integrated under a map which consisted of the representation of
local extraction rates resulting from the combination of all constraints at 1km resolution.
Constraint maps were created for stemwood, residues and stumps separetly. For area where
no constraints were applied, the maximum extraction rate of forest residues was applied
(65%).
3. Calculation of biomass expansion factors (BEFs)
The calculation of BEFs was based on MCPFE and EFISCEN data.
16
4. Applying harvesting constraints and BEFs
First, the constraint map for stemwood was multiplied with the map created in the first step to
derive the amount of stemwood potentially available for sustainable harvest. Next, this map
was multiplied with the calculated BEFs to calculate the associated biomass amount in
residues. This last map was finally multiplied to the harvesting constraints map of residues, to
derive the potentially available sustainable amount of biomass from forest residues.
Table 9:constraints and extraction rates applied
with underlying data sets
Ericsson and Nilsson 2006
The straightforward assessment of Ericsson and Nilsson relies on forest biomass growth only
including exploitable forests. Data were gathered from international statistics of the
Temperate and Boreal Forest Resource Assessment (Appendix 7). The potential harvest of
residues was deduced from a residue to stemwood ratio. This ratio was assumed to vary
according to the tree species, tree type and tree age. No ratio’s list per species, tree type or
tree age are available in the paper and the resulting assumed ratio is provided directly. For the
calculation of the theoretical potential, ratios of 0.3 and an 0.2 were hypothesised for
17
coniferous and deciduous trees respectively. As for the technical potential, also including the
constraint of nutrient depletion, ratios were presumed to be 0.15 and 0.1 respectively.
Ericsson and Nilsson do not elaborate more on how they conclude such ratios. They only
mention ‘Savolain and Berggren 2000’for the stemwood ratios but do not refer to other
studies for the restriction from nutrient depletion.
4.2.
The factors influencing the estimate
This section attempts to evaluate the reasons of the different estimations in regards to the
methodologies used (for simplification, the four studies will be referred to the names of their
first author(s), avoiding the date and the conventional ‘et al’). Comparisons are made between
the four studies, and when available, with other research from the literature. The previous
section showed that methodologies differ in essence in many aspects. More specifically, these
four research differ in complexity, in the use of various databases and in the assumptions
made by the authors. These three main factors are discussed below.
4.2.1. The methods complexity level
Some research show simple guidelines such as the ones of Ericsson and Asikaienen while the
two others show more complex methodologies. Verkerk relies on a model including mortality,
regeneration, growth and aging. Böttcher relies on complicated equations and on accurate
mapping involving many constraints. The complexity of the estimation does not necessary
mean that it is the most accurate. When requiring many data to fulfil each requirement one
has often recourse to averages and default values when data is missing at the region or the
country level. However, these proxies are frequently inaccurate (Vis et al 2010). Conversely,
the present work showed that very simplistic methodologies could lead to inaccurate
estimations. Ericsson and Nilsson’s estimation relies on a relatively randomly-set harvesting
ratio that undeniably underestimate the potential since their study embrace a wider definition
of residues (2.2.1), of geographical scope (2.3), includes fewer restrictions (2.2.2) but still
shows the lowest estimate (Table 1).
As a conclusion, it is likely that a good estimation will find a good balance between a not too
complicated method in order to minimise the use of averages and default values and a not too
simplistic method in order to avoid rough and non-representative assumptions. The decision
about the level of details to incorporate in the estimation will also depend on the aim of the
work. In general, policy makers tend to prefer simpler assessments whereas fundamental
research will favour highly detailed data and methodologies (Rosillo-Calle et al. 2007).
4.2.2. The underlying databases
Estimations can be based on distinct data which could lead to distinct estimates. First, it can
be based either on the felling’s, on the stand biomass’ or on the NAI’s statistics. Böttcher
(statistical) and Asikainen rely on felling statistics while the others rely on standing biomass
statistics. However, Verkerk and Ericsson only consider forest available for wood supply
therefore probably using input values close to the ones derived from felling statistics. This
could not be verified since felling statistics are reported in volume (Mm3) and the standing
biomass in area covered (1000 ha). It is acknowledged that felling statistics are often leading
to underestimations as not all harvests are recorded (Böttcher et al. 2010). On his side,
Böttcher (spatial) uses NAI’s statistics. This source of data represents a more accurate picture
18
of the current biomass state as it includes gross increments and natural losses and helps in
establishing sustainable harvesting rates since fellings should not exceed the NAI (Böttcher et
al. 2010).
Furthermore, data referring to the same matter can still show substantial differences if being
gathered from distinct databases. For instance, Verkerk and Ericsson both rely on standing
biomass data but have gathered the information from distinct sources. Verkerk’s estimation
depends on national inventories data while Ericsson’s estimation has leaned on international
estimation form the Temperate and Boreal Forest Resource Assessment (TBFRA 2000)
(Appendix 7). The difference between the two datasets is considerable in certain countries
such as in Portugal Finland and Poland (Table 10). The difference for Portugal is particularly
striking as Verkerk considers an area that is ten times larger than the area considered by
Ericsson. Summing up all the countries’ areas, the total difference results in more than
17.000.000 ha proving how the use of distinct datasets can influence the estimations. The
urgent need for harmonised European national inventories has already been mentioned in
previous work (Vis et al. 2010).
In this case, the data of Ericsson, shows the lowest area value therefore expected to give lower
estimates. However, the two supplementary countries that are taken into account in their study
should be considered: adding up the forested area from Belarus and Ukraine from the FAO
(2005) database, the difference of 17.653.000 is lowered and results in a difference of 184.000
ha. To sum up, when referring to the FAO (2005) data the study of Ericsson shows an study
area larger than the others of 17.469.000 ha (2.3), while when referring to the datasets used in
the respective studies, Ericsson shows a study area smaller of 184.000 ha. This clearly
underlines how the use of distinct datasets may affect the estimation of the potential.
The reliability of the datasets is believed to be more accurate for the data of TBFRA (2000)
on which Ericsson relies because consisting of national inventories that have been adjusted to
fit conventional international definitions (TBFRA 2000) whereas Verkerk’s data rely on
national inventories which have different inventory measurements and different forest
definitions (Vis et al. 2010).
This type of comparison is not valid for Böttcher and Asikainen as they both rely on the same
felling’s statistics from the Forest Resource Assessment report of the FAO (2005). However,
their potentials are highly different to each other which points to other factors than the input
datasets influencing the estimations.
19
Table 10: Comparison of the input datasets used by Verkerk and Ericsson (in 1000 ha). Data of Verkerk come
from national inventory while data of Ericsson come from TBFRA 2000. The difference consists of the subtraction
of the value from Ericsson by the value of Verkerk.
Austria
Belgium
Bulgaria
Czech Republic
Denmark
Estonia
Finland
France
Germany
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Netherlands
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
United
Kingdom
Total difference:
Verkerk
3349
587
3646
2667
473
2048
18550
13872
10382
1859
626
5408
3141
1939
71
360
6309
20267
6211
1909
1159
10476
22647
Ericsson
3352
639
3124
2559
440
1932
20675
14470
10142
1702
580
6013
2413
1686
86
314
8300
1897
5617
1706
1035
10479
21236
2202
2108
Difference Percentage
3
0%
52
8%
-522
-17%
-108
-4%
-33
-8%
-116
-6%
2125
10%
598
4%
-240
-2%
-157
-9%
-46
-8%
605
10%
-728
-30%
-253
-15%
15
17%
-46
-15%
1991
24%
-18370
-968%
-594
-11%
-203
-12%
-124
-12%
3
0%
-1411
-7%
-94
-17653
-4%
4.2.3. Assumptions
Differences also derive from the assumptions made by the authors. In the four papers studied,
only Verkerk includes restrictions for the estimation of the theoretical potential, according to
the tree age (derived from the assumption that young tree are not thinned or felled). In this
regard, the theoretical estimation of Verkerk is supposed to lead to lower estimations than the
other studies.
All four publications have relied on allocation factors (or biomass expansion factor) to
deduce from the harvesting volumes, or standing biomass, the proportion of forestry residues.
These allocation factors rely on assumptions put forward by the authors and are thus variable
from one study to another. Ericsson suggests a 30% proportion of biomass of residues for
coniferous and 20% for deciduous. These slightly similar from those of Asikainen who
assumes a 32.4% for pine trees and 21.8% for broadleaved trees (Table 5). For comparison,
the reference values suggested by the European Commission (EC 2011) of 18% for
broadleaves and 28% for pines and the allocation factor suggested by Rosillo-Calle et al.
20
(2007) is of 40%. Even if the differences fluctuate around the same percentages, these factors
have been mentioned in previous work to have a major effect on major estimates (Dymond et
al.2010) and are thus to be taken into account. Comparison with the study of Böttcher and of
Verkerk are not feasible because they do not provide the values of the factors they relied on.
Since biomass expansion factors are critical for the potential estimations, dedicated studies
should address this heterogeneity found in the literature to gain more accurate insights into the
actual allocation factors (Berndes et al.2003). In addition, assuming allocation factors
according to such large tree groups is a rough estimate as large differences of tree component
exists between tree species and development stage (Röser et al.2008).
The aforementioned differences among the estimation of theoretical potentials are directly
affecting the estimation of the technical potentials as they are calculated from the theoretical
ones. For the technical potentials, the differences are moreover influenced by the various
restrictions taken into account (Table 3). In addition, the quantification of each of the
restriction is highly dependent on the the assumptions the authors have emitted.
For instance, both Böttcher and Asikainen take into account the extra biomass available from
complementary felling. However, Böttcher assumes 70% of complementary felling to
become available for energy purposes while Asikainen assumes 25% to be available. The
recovery rates are also subject to assumption. Within the four cases studied, Asikainen and
Böttcher both refer clearly to such restrictions. Asikainen supposes that 65% of the residues
are recovered in case of mechanical harvest and 50% in case of manual harvest. As for
Böttcher, a 50% recovery rate is assumed above ground residues not making differences
between types of harvest but assuming distinct recovery rate for stumps being 20% (40% in
Finland and Sweden). In the case of Verkerk’s study, a recovery rate of 67% is assumed. As
another point of comparison, the book of Rosillo-Calle et al. (2007) refers to 25% of the
residues to be recoverable. Surprisingly, Ericsson’s research do not include recovery rates,
which is likely to influence the estimation towards an overestimation. Böttcher does also not
include a recovery rate in its spatial approach, but because applying strict restrictions and a
maximum allowed extraction rate, it is likely that his estimation does not lead to an
overestimation. The involvement of slopes or not and the quantification of their restriction
depends from one study to another. Between the four studies, only Verkerk and Böttcher
(spatial estimation) include this parameter. Both of them assume a 0% extraction rate on
slopes steeper than 35%. Above that limit, Verkerk allows a 67% extraction rate while
Böttcher relies on 65%. Verkerk and Böttcher (spatial estimation) both rely on many aspects
to include environmental restrictions. They both assume a 0% extraction within protected
areas and on peatlands. Verkerk includes the restriction of soil depth by implementing a 0%
harvest on soil types known for their shallow properties. On his side, Böttcher relies on
stricter restriction separating restrictions of soil depths from restrictions from soil types. As a
result, Böttcher estimation is much more restricted, in terms of soil type than Verkerk (Table
11).
21
Table 11: Allowance rates applied by Böttcher and Verkerk to the distinct soil types. Cells were no percentage are
shown are soil to which the author does not apply restrictions.
Böttcher
Verkerk
Histosol Fluvisol
0%
0%
-
Böttcher
Verkerk
Acrisol
33%
-
Gleysol Andosol Rendzina Lithosol Ranker
0%
0%
0%
0%
0%
0%
0%
0%
Rodzoluvisol Podzol Arenosol Planosol Xerosol
33%
33%
33%
33%
33%
-
At last, the soil compaction criteria is also addressed differently by the two authors. Both
assume a 0% extraction rate to soil known as ‘very highly susceptible to compaction’ but
Verkerk allows a 25% extraction on soils categorised as ‘highly susceptible’ while Böttcher
remain on a 0% assumption. The statistical approach of Böttcher addresses environmental
restriction in a less detailed way by assuming a general 5% restriction.
5. Summary and conclusion
The comparison of the four studies illustrates how much the assessment of forestry residues
potential can vary from one study to another (Table 1). Basic aspects such as the definition of
forestry residues or the definition of the potential considered both show no consensus
amongst the studies. Furthermore, the geographical scope varied between the studies even
though it was referred to as being similar in first instance (‘European assessment’). Even a
difference of two countries only has shown to provided considerable distinct area.
Although showing great differences, those factors showed to be of secondary importance
compared with the influence of the ‘methodology’. Particularly, the origin of the data (the
database used) and the assumptions made by the authors to quantify the restrictions seem to
have an important impact on the estimation, sometimes even overtaking the influence of the
definitions or the geographical scale. The definition of residues showed that it was dependent
on the assumed biomass behind each component. For example, while both Böttcher and
Asikainen include complementary felling, one assume a much higher biomass income from it
than the other one. Similarly, the effect of the geographical scope considered seemed mainly
depend on the databases used. While Ericsson includes more countries into his analysis, the
use of a distinct database lower this difference considerably. Another clear illustration is the
definition of the potential. In the end, the amount of restrictions taken into account influences
less the estimation than their quantification. Indeed, Verkerk and Böttcher (spatial) both
relying on many restrictions show higher technical potential than Asikainen and Ericcson
relying on a single limitation (Table 12). This is likely to be the consequence of the author
attempting to avoid overestimations while not taking many constraints into account. This
assumption can however not be generalised; Böttcher (spatial) who includes a single
sustainability criteria assumes a relatively low restriction of 5% and therefore supplies a
relatively high estimation.
As a conclusion, the potential estimated is highly dependent on the assumptions made by the
authors on how they decide to quantify certain restriction. The discrepancies between the
studies highlight the need for common and standardised guidelines towards comparable
methodologies. Further studies should aim at establishing such harmonisation. The study of
22
Vis et al. (2010) is a good example of what further studies should aim at. From a review of 17
European assessment studies, they conclude the best assessment strategies and suggest some
reference databases. Further research assessing the potential of forestry residues should rely
on such suggestions.
23
Table 12:Summary of the factors differing between the four studies and susceptible to lead to distinct potetntial estimation. NA: non applicable. Potential estimations are in PJ/year. ‘Percentage
of theoretical’ is the percentage of the theoretical potential that the technical potential represents.
Asikainen
Residue definition
Amount of restrictions
Geographical scale
result of the diff. geogr.
Databases
result of the diff datab.
Allocation factors
Complementary felling
Recovery rate
Protected area
Slope
Soil depth
Soil texture
Soil compaction
Overall sust. Restriction
Extra from unrecord.
harvest
Theoretical etimate
Technical estimate
Percentage of theoretical
2
EU27
FAO 2005
+
32.4% - 21.8%
25%
65% - 50%
NA
NA
NA
NA
Verkerk
+
7
EU27
Bottcher (stat)
++
1
EU27
Bottcher (spat)
++
6
EU27
Ericsson
+++
1
EU27
+17.469.000 ha
National
inventories
FAO 2005
FAO 2005
TBFRA 2000
+
?
70%
50%
NA
+
?
70%
0%
0%
<35°: 65%;
>35°: 0%
-17.653.000 ha
30% - 20%
NA
0%
NA
NA
<40cm: 33%
NA
NA
0% peatland
0% on highly and
very highly
sensitive
NA
?
NA
67%
0%
<35°: 67%;
>35°: 0%
0% on shallow soil
types
0% peatland
NA
NA
NA
25% on highly and
0% on very highly
sensitive
NA
NA
NA
5%
NA
NA
NA
NA
+5%
NA
NA
2527.2
550.8
22%
2390.544
720
30%
3229
1186
37%
621.29
1170
590
50%
NA
24
- Chapter 3 Sustainability of the removal of
forestry residues
In the following chapter, the second research question regarding the sustainability of residues
harvesting is addressed with a specific focus on soil erosion, biodiversity, carbon stocks and
dynamics and nutrients productivity. The risks engendered by the removal of residues are
investigated for each of these factors and values of sustainable amount of removal suggested
in the literature are presented. At the end, a summary is presented with the relations existing
among those factors.
1. Soil erosion
The numerous studies of Pimentel on soil erosion state that forestry residues play a protective
role against erosion (Pimentel et al. 1995, Pimentel 1998). Erosion results from the exposure
of soil to rain and wind and this exposure is enhanced when the cover of residues is removed
(Pimentel 1998, EEA 2006). The consequences of soil erosion are multiple: a decrease of soil
productivity, of nutrients availability, of water holding capacity and of biodiversity are the
most commonly cited examples (Pamela et al. 2009, Pimentel 1998, EEA 2006).
In the light of the above consequences, it is believed that forestry residues removal can impact
severely the soil health in terms of erosion, even if some research (Pamela et al. 2009) believe
in a minor impact only. In order to lessen the negative impacts, many advices are put forward
in the literature and are enumerated below.
In general, it is highly recommended to leave the roots and the stumps on site as this leads to
high soil disturbance and higher risks of soil erosion (Lal et al. 2011, EEA 2006, Stupak et al.
2011, Walmsley et al. 2009). Moreover, it is widely recognised that soil erosion is influenced
by the soil steepness (Pimentel 1998, EEA 2006, Lal et al. 2011). Thresholds for suitable
harvest according to slope intensity are though not commonly agreed upon. For instance, the
European Energy Agency considers slopes of more than 25° unsuitable (EEA 2006) while the
Minnoseta guidelines have set the slope limit to 35° (Lal et al. 2011). In fact, thresholds
should be site-specific and set according to the other soil characteristics (Lal et al. 2011).
Indeed, besides the influence of slopes, soil sensitivity to erosion depends on the soil type
(texture, organic composition, structure, depth, etc.) and the soil elevation (Pimentel 1998,
EEA 2006, Lal et al. 2011). Fine texture soils with low organic content and weak structural
development are believed to be subject to higher erosion rates (Pimentel 1998) and should
thus be harvested less intensely. Shallow soils where harvesting should be avoided have been
determined by the Wisconsin guidelines to be those where bedrock is within 20 inches to the
surface (Lal et al. 2011). High altitude soils (> 1500 m) have been considered unsuitable by
the European Energy Agency (EEA 2006).
25
Besides the soil characteristics, the harvesting infrastructures such as the roads and the heavy
machinery used impact negatively the soil erosion (Lal et al. 2011, EEA 2006). Consequently,
the area occupied by the infrastructures should represent only a small proportion of the total
harvesting area. Minnoseta and Wisconsin have set this limit to be 3% of the total area, while
the Missouri guidelines allow up to 10% of the area to be occupied by infrastructures and
impacted by machineries (Lal et al. 2011).
2. Nitrogen balance
In forests, nutrients are being recycled in a theoretically closed cycle. Organic nutrients are
decomposed into inorganic nutrient by fungi and saproxylic species. Inorganic nutrients are
then uptaken by the living biomass and contribute to the tree growth and thus to the
generation of new organic matter. The organic matter will then be available again to
decomposers via tree falls for example. This cycle, together with the fluxes from atmospheric
depostion and leaching results in a relatively steady state in element storage (Röser et al.
2008). However, additional fluxes from harvesting can disturb the balance of the ecosystem.
To ensure sustainability of the ecosystem, management practices need to be set in order to
result in balanced fluxes. A compensation of the removal of biomass could be done through
an input of fertilisation (Röser et al. 2008). The way each of the four fluxes are influenced by
the extraction of removal is discussed below. In the scope of this work, only the nutrient
nitrogen is discussed as it is believed to be the one critical to tree growth (Peckham and
Gower 2011, Eisenbies et al. 2009).
Harvesting
In comparison with other parts of the tree, residues, and especially foliage, needles and
stumps, contain a high percentage of nutrients and thus of nitrogen. Therefore, the removal of
residue undeniably results in a removal of nutrients (Sullivan et al. 2011, U.S. Department of
Energy 2011, EEA 2006, Eisenbies et al. 2009, Röser et al. 2008). However, the amount of
nitrogen removed from the site is difficult to estimate as it depends on the harvest practice,
the tree species and the tree age. That is to say, a more intensive harvesting will lead to a
higher loss of nitrogen (Peckham and Gower 2011, Eisenbies et al. 2009), and depending on
the tree species and the tree age, the amount of Nitrogen in the residues will be higher or
lower leading to proportional losses (Eisenbies et al. 2009, Röser et al. 2008).
Leaching
In general, biomass harvesting will enhance the nutrients leaching process but this is highly
dependent on the site nitrogen availability. Nitrogen saturated soil tend to show significant
nitrogen leaching whereas nitrogen limited sites do not (Röser et al. 2008). Nonetheless,
nitrogen-depleted sites are more sensitive to nitrogen loss and therefore still require attention.
Some argue that sites showing high nitrogen load could benefit from residue removal (EEA
2006, Börjesson 2000). Nitrogen availability depends partly on soil temperature; low
temperatures restrict the availability of nutrients because of the slow decomposition rates.
Thus, it can be assumed that the impact of residue removal will be greater in countries and
regions showing cooler climate. Besides soil temperature, other indicators can be referred to
assess the site sensitivity. Soil presenting low pH usually show low nutrient availability and
are therefore more responsive to biomass harvest (EEA 2006, Börjesson 2000, Röser et al.
26
2008). More shallow soils are also believed to result in greater leaching rates (Röser et al.
2008, Lal et al. 2011). To these indicators of soil sensitivity, Röser et al. (2008) add the
indicators of soil mineralogy and texture, though not making clear in which way they
influence the sensitiveness.
Atmospheric deposition
Atmospheric deposition happens on at the canopy level as well as at the ground level.
Deposition rates are influenced by many factors. For instance, it is positively correlated to
strong winds, complex landscape structure and polluted areas (Röser et al. 2008). Sitespecific deposition rates are of great interest as it gives insight into the amount of nitrogen
that could counterbalance the loss from residue removal (EEA 2006, Börjesson 2000).
Börjesson (2000) has estimated that 5% of the nitrogen present in residues could potentially
return to the forest via atmospheric deposition. However, it is questionable whether the return
is still be valid when residue are burnt away from the harvesting place.
Fertilisation
To compensate the loss of nitrogen the fertilisation is a widespread method in the U.S.A.
(U.S. Department of Energy 2011) and is highly recommended, especially for nitrogen-poor
sites (EEA 2006, Eisenbies et al. 2009, Röser et al. 2008). Many suggestions mention the use
of ashes, though no nitrogen is supplied this way since it is all transferred into gaseous nitrous
oxide (Börjesson 2000, Röser et al. 2008). The amount of fertilisers to be applied to the site is
highly dependent on the site characteristics and on its sensitivity to nitrogen and other
nutrients loss.
Recommendations
Due to the high site-specify of the nitrogen content and response to removal, estimations of
sustainable harvest rates should be based on site-specific studies (Röser et al. 2008). For
instance, Eisenbies et al.(2009), estimated from a literature review a 50% harvesting rate of
available residues in Southern yellow pine plantations of the U.S.A. In general, few similar
studies are available, and even fewer incorporate a long-term estimations (Eisenbies et al.
2009). On the other hand, since it is commonly agreed that foliage needles and stumps contain
the highest nitrogen content, it is often recommended to leave those on the site (Eisenbies et
al. 2009, EEA 2006 Börjesson 2000, Röser et al. 2008).
3. Carbon storage
The use of biomass as a source of energy is generally assumed to be carbon neutral because
the CO2 emitted during its combustion is believed to be equally absorbed by growing biomass
(Cherubini et al.2011, Schulze et al.2012, Palosuo et al.2001). This assumption however
neglects two major consequences of the use of forest residues.
First, it omits the much higher rate of CO2 emissions from biomass burning than the carbon
sequestration rate into plant biomass (Börjesson 2000). The result of this rate difference
between emission and sequestration of carbon is a temporary accumulation of CO2 into the
atmosphere that is believed to influence the climate (Körner 2003, Cherubini et al.2011, Repo
et al.2011). The impact of such accumulation is unclear as studies all show different
27
estimations of resulting emissions (IPCC 2012). Nevertheless, some studies state that even if
emission from forest residues may exceed the ones of fossil fuels in the first years, this
tendency is likely to turn around or level out with time as the emitted carbon is slowly being
re-absorbed by the growing biomass (McKechnie et al.2011, Repo et al.2011, Börjesson
2000). In order to ensure the inversion of this tendency over time, harvesting rate should not
exceed biomass growth in order to allow biomass growth to counterbalance the emission from
the use of residues. While there is a growing interest to increase harvest frequency and
intensity (Peckham and Gower 2011), studies investigating the sustainable threshold of
biomass extraction rate are crucially lacking in the literature. Some suggest that this threshold
vary between forest type showing different growth rate (Cherubini et al.2011, Peckham and
Gower2011, IPCC 2012). Therefore, further studies should focus on assessing sustainable
extraction rates of residues according to the biomass growing rate of specific forest types.
Secondly, it neglects the loss of carbon entering the soil carbon stock of the forest through
decomposition. The overall carbon balance of a forest is divided into carbon stored into the
living biomass and the carbon soil stored in the ground. Fluxes of carbon happen between
those two pools by litterfall, natural fellings, logging residues, etc. (EC 2011, Palosuo et
al.2001). The removal of residues therefore directly affect the soil carbon storage of the forest
(IPCC 2012, Repo et al.2011, Palosuo et al.2001, Peckham and Gower2011, Börjesson 2000).
Soil organic carbon is proved to be play a major role in cation exchange, water holding, soil
structure and root penetration (Pamela et al. 2009). Previous studies considering this decline
of soil carbon stock by harvesting residues, showed that these account for more than 80% of
the total carbon emissions from the use of forest residues (Palosuo et al.2001, Repo et
al.2011). In other words, the decline in soil carbon is greater than the direct emitted CO2 from
the burning of the residues. This is due to the intrinsic trait of soil humus which stores about
three times as much carbon as contained in CO2 (Körner 2003).
On the other hand, some research argues that harvesting forestry residues could be an
opportunity to improve forest health and decrease the risk of fire in forest showing biomass
overstock. A harvesting of residues through thinning operations or sustainable silviculture
practices may prove to increase the forest health by accelerating its growth and soil carbon
storage while decreasing its chances of fire to globally result in CO2 emissions reduction
(U.S. Department of Energy 2011, IPCC 2012, Pamela 2009). According to a recent research
of Poudel et al.(2012), climate change will enhance forest biomass production by 33% in the
next century, which would increase this opportunity in the future. Under the simulation of
such biomass increase, Poudel et al.(2012) proved that an increase in harvesting intensity in
two counties of Sweden (Jamtland and Vasternorrland) can be simultaneous to an increase in
standing biomass and soil carbon stocks, and to a reduction of net carbon emissions.
However, this is not taking into account the potential loss of forest growth consequent to less
nutrient available from residue harvesting, discussed in the previous sections.
4. Biodiversity
It is commonly agreed that the removal of forest residues represents a risk for the biodiversity
present in the forest (EEA 2006, IPCC 2012, Sullivan et al.2011, Dalhberg et al. 2011, U.S.
Department of Energy 2011, Lal et al. 2011, Verkerk et al. 2011b). The main reason of this is
a change of habitat. Some claim a habitat homogenisation and fragmentation resulting from
28
residue harvesting and contributing to biodiversity loss (EEA 2006, Lal et al. 2011, Pamela et
al. 2009). Overall, all studies agree that the main risk lies in habitat loss through the removal
of deadwood (IPCC 2012, EEA 2006, Lal et al. 2011, Verkerk et al. 2011b, Dalhberg et al.
2011) and some even refer to deadwood as a biodiversity indicator (MCPFE 2012, Verkerk et
al. 2011b, Schuck et al. 2005, Humphrey et al.2005a, Stockland et al. 2005, Hahn et al.
2005). On the other hand, Pamela et al. (2009), conclude from a state-of-the-art of the
literature that collecting forestry residues will have minor impact on the biodiversity
compared to other bioenergy sources because of the feedstock being located on existing forest
land.
In order to ensure sustainable wood removal, one could thus refer to this deadwood indicator.
However, this is subject to some critics because the biodiversity of an area results from
complex biotic and abiotic factors and not of a single factor such as deadwood amount
(Humphrey et al. 2005b, Lal 2011, U.S. Department of Energy 2011) and because the
amount of natural deadwood vary according to forest type (Hahn et al. 2005, Humphrey et al.
2005b).
Besides the risks of habitat removal or modification, Röser et al. (2008) mention other
dangers which are believed to have great impacts although there have been understudied.
First, the dispersal of species may be decreased due to habitat fragmentation which could lead
to population isolation, loss of genetic brewing in turn leading to higher risks of species
extinction. This aspect, though often not mentioned, probably has a great impact as insects
and other animals dependent on woody residues show short distances dispersal. Second, the
removal of residues changes the intensity of sun exposure. Many species have specific
relationships to sun exposure and could therefore be endangered. Thirdly, residue represent a
protection for underground species such as worms, which are often ignored in the current
literature. Fourthly, the harvesting of the residue often involves the stacking of the residue
into piles before transportation. These piles produce odours detectible to insects tracking
substrates to lay their eggs on. The impact of the removal of those piles for the transportation
of residues towards their burning site is certainly disastrous as it involves the extinction of
several insect generations at the same time. Last, but not least, the higher trophic effect is too
often neglected. Indeed, if insects populations are threatened, the animal populations feeding
from those, such as birds, are likely to be endangered as well.
Recommended extraction rates to ensure biodiversity vary from one study to another,
according to several assumptions made and according to the site-specificity. For instance,
Dahlberg et al. 2011, conclude from a model that an extraction rate of 70% of Swedish
residues would lead to a loss of habitat for 50% of the species present in the forest but they
assume that this would be sustainable because no Red-list species are included and because
forestry activities will increase over the years replenishing the habitats with residues.
Börjesson (2000) refers to an allowed 90% of total residues to be extracted in Sweden.
Another example is the review of Lal et al. (2011) comparing different guidelines adopted by
several countries. In Finland, 70% extraction is allowed, while in the U.S.A., according to the
State, the recommended extraction rate vary from 90% in Pennsylvania to 67% in Missouri
(Lal et al. 2011). On a European level, the study of EEA (2006) favours the retaining of 5%
of residue on site for biodiversity purposes.
29
In addition to restricted harvest rates, other recommendations are being proposed in the
literature. Some suggest to leave large diameter tree dead wood on site in order to provide a
habitat counterbalancing the loss of residues (EEA 2006). The efficiency of such techniques is
however subject to doubts since previous studies have shown that species were highly
specified to residue types and sizes (Verkerk et al. 2011b). Another recommendation made by
EEA (2006) is to avoid harvesting in areas hosting protected species from the IUCN red list
and Natura 2000. This may prove to be a more adequate suggestion than avoiding harvest in
nature reserve as previous work has shown that reserves rarely showed the highest
biodiversity (Scott et al. 2001). Another type of advice was formulated by Sullivan et al.
(2011) who showed that constructing piles of woody debris significantly helped maintaining
biodiversity on site after residue harvest.
5. Summary and interrelation of the processes
Figure 2: Effect of residue removal the balances of forest ecosystems. Arrows indicate the movement of elements,
perpendicular lines indicate an inhibition or decrease, red elements are changes resulting from the removal of
residues outside the ecosystem. a. Atmospheric deposition of carbon dioxide (CO2) and gaseous nitrogen (Ng). b.
Natural fall of residue brings organic carbon nitrogen to the soil. c. Organic matter is decomposed by fungi and
microfauna into inorganic compounds. d. Inorganic compounds are uptaken by plant’s roots and contribute to
biomass growth. e. Sun exposure decreases decomposition rate. f. Natural leaching of nutrients.
Although mentioned above separately for clarity reasons, these four processes are closely
related to each other. Figure 2 summarises the abovementioned processes and how they
interact with each other. By interacting with each other, they form a well balanced ecosystem
resulting a steady state (black arrows). The extraction of residues outside the ecosystem may
break this natural balance in several ways (red arrows) :
1. Less organic carbon and nitrogen are deposited to the ground
The consequence of a decreased input (b.) undeniably results in a decreased decomposition
(c.) and a decreased of available inorganic matter and thus of nutrient uptake (d.). This is
believed to lead to lower tree growth (Röser et al. 2008, Pamela et al. 2009).
30
2. The protective cover of residues decreases
The removal of the protective layer enhance the exposure of soil to sun, wind and rain. An
exposure to sun is believed to lead to lower decomposition rate (Röser et al. 2008).
Furthermore, the enhanced exhibition to wind and rain leads to increased erosion rates. This
in turn, positively affects the runoff of nutrients via leaching (f.). Lower nitrogen soils leads to
lower pH and acidic soil lead to less nitrogen availability starting a negative feedback loop.
Moreover, the extraction of residues consists of an extraction of many species substrates and
habitat, many of which are decomposing species (e.g. saproxylic species).The overall
consequence is a lower nitrogen availability, reinforcing the consequence mentioned in ‘1’
and affecting negatively biomass growth.
3. More carbon dioxide and gaseous nitrogen is released into the atmosphere
The burning of biomass results in releases of nitrogenous and carbonic gasses. These are
assumed to be partly or completely reabsorbed by tree biomass (a.). The percentage of this
recycling by living biomass is however subject to uncertainties as biomass uptake is a much
slower process than the gasses emissions from the combustion (Körner 2003, Börjesson
2000). Moreover, if the removal of residues results in decreased biomass growth as assumed
under the points 1 and 2, this may lead to lower carbon dioxide uptake (EEA 2006).
6. Recommended sustainable residues extraction
6.2.
Current uncertainties
While most authors acknowledge the environmental risks of residue removal (Verkerk et al.
2011b, Röser et al. 2008, EEA 2006, IPCC 2012, Sullivan et al.2011, Dalhberg et al. 2011,
U.S. Department of Energy 2011, Lal et al. 2011, Repo et al.2011, Palosuo et al.2001,
Peckham and Gower2011, Börjesson 2000, Eisenbies et al. 2009), many of them still
conclude that the impact is likely to be minor (EEA 2006, Eisenbies et al. 2009, Börjesson
2000, Pamela et al. 2009). For reliable results, studies should incorporate the many aspects
mentioned above and their interrelations. Because of the high spatial heterogeneity research
should carried out per regions and management strategies should follow regional
recommendations. An exemplary illustration of such study is the report of European Energy
Agency (2006) which incorporates a classification of extraction thresholds according to the
site suitability (Appendix 8). Moreover, as the processes cited above happen throughout large
time scale, studies assessing the impact should an equivalent time frame or longer. Though,
few studies are completed on long term scales.
6.2.
Recommended harvest restrictions
In general, there seems to be a common agreement that stumps, roots and foliage should be
left on site. They represent a high nutrient value and a broad species habitat and substrate.
Stumps and roots removal moreover leads to damaging erosion consequences (Röser et al.
2008, EEA 2006, Walmsley et al. 2010). Furthermore, in order to ensure the longevity of the
pre existing equilibrium, it is believed that pre existing residue should not be removed, in
other words, the extraction should only concern the extra residues resulting from the logging
activities or the precommercial thinning.
31
6.3.
Management recommendations
Some argue that harvesting should take place during the cooler period (Röser et al. 2008,
Eisenbies et al. 2009). This recommendation is made under the assumption that soil nitrogen
is less available and thus less subject to leaching at that period. Moreover, the risk of species
endangerment from the exportation of residue piles hosting insect eggs and would not take
place in this non-breeding period.
Rotation cycles should be implemented after studies investigating the time required for each
of the above-discussed processes to recover. Peckham and Gower (2011) investigate the
recovery of both carbon and nitrogen natural recovery after residue harvesting. Ideally,
harvesting should not take place before retrieving the natural state in order to prevent nutrient
depletion in the longer term.
32
- Chapter 4Conclusions and recommendations
In first instance, the research questions are answered and the related hypotheses are verified
according to the information gathered through the present literature review. In a second
section, educated recommendations based on the present work are offered to ensure
sustainability of residues extraction. The current gaps in literature impeding accurate
assessment of sustainable and technically feasible potential are presented along with the
limitations of the present work.
1. Research questions and related hypotheses
Research question 1: How do distinct methods influence the estimation of primary forestry
residues potential as a bioenergy source?
There are substantial differences among the methods used by distinct studies. The definitions,
the geographical scope, the level of details, the databases and the assumptions about the
quantifications of the restrictions are all factors influencing the final estimation. From the four
studies the present work focused on, the underlying databases and the assumptions regarding
the quantification of the restrictions appear to have the greatest influence on the estimate.
Related hypotheses:
 If the definition of the ‘forestry residues’ differs from one study to another, this can
lead to distinct assessment of the potential.
 The definition of forestry residues is believed to influence, among other factors,
the potential’s estimation. This is because depending on the definition used, the
author includes more or less biomass in his estimation. The extent to which this
influences the final outcome is dependent on the underlying assumptions
concerning the availability and the recovering rates which influence the amount
of biomass of each of the residue’s components.
 If the definition of the ‘potential’ differs from one study to another, this can lead to
distinct assessment of the potential.
 The definitions of the theoretical potential does not differ between the studies
considered in this report whereas the technical potential definitions differ in the
restrictions considered. The intensity of the influence is dependent on the
assumptions lying behind the restrictions and their quantification. In other words,
a potential considering many restrictions does not necessarily lead to a lower
estimate.
 If the geographical scope considered for the assessment of the potential differs from
one study to another, this can lead to distinct assessment of the potential.
 Relying on distinct geographical scales is likely to influence the potential
estimation if leading to large surface differences. In this case, this difference was
counterbalanced by the use of distinct database. Hence, relying on distinct
geographical scale may influence the estimation, but the final result of the
estimation is also dependent on other factors.
33

If the methods and calculations to estimate the potential differ from one study to
another, this can lead to distinct assessment of the potential.
 The application of distinct methodologies, and especially the utilisation of distinct
databases and restriction quantifications, influence greatly the potential’s
evaluation. On several occasions, these factors showed to have a stronger
influence than the factors mentioned in the previous research questions.
Research question 2: What are the environmental impacts generated by the extraction of
forestry residues for their conversion into bioenergy?
The environmental impacts resulting from the removal of forestry residues are multiple and
interdependent: biodiversity, carbon balance, soil erosion and nitrogen balance of the
ecosystem are all factors affected by the removal of residues, but the extent to which they are
affected by the removal of residues ultimately also depend on how the other factors are being
affected. Because of the interrelation between those factors, the impact on one of them often
showed to increase the impact on another one. Natural ecosystems represent a complex, but
rather steady equilibrium. The harvesting of residues means a disturbance of this natural
balance.
Related hypotheses:
 If forestry residues play an important role in preventing soil erosion, their extraction
could lead to increased soil erosion.
 The removal of forestry residues increase soil exposure to wind and rain, hence,
contribute to higher erosion rates. The consequences are multiple and include: a
decrease of soil productivity, of nutrients availability (via increased leaching), of
water holding capacity and of biodiversity. The extent to which the soil erosion
occurs following the harvest of residues is dependent on the harvest practice and
the soil characteristics. It is commonly agreed that the removal of stumps and
roots has the greatest impact.
 If forestry residues play an important role in the forest nutrient cycling, their
extraction for the use of bioenergy could endanger the ecosystem nitrogen
equilibrium.
 Removing residues from the forest involves a new flux of nitrogen outside the
ecosystem and thus unbalance the natural nitrogen cycle. The extent to which the
nitrogen cycle is endangered is unclear as the impact of nitrogen removal is sitedependent and may be counterbalanced by natural nitrogen atmospheric
deposition or by artificial fertilisation.
 If forestry residues play an important role in the forest’s carbon pool, their extraction
for the use of bioenergy could lead to a global loss of carbon stock.
 The forest’s carbon storage consist of two main pools: the soil’s carbon pool and
the carbon stored into the standing biomass. Forestry residues constitute an
important share of the soil’s carbon pool and their extraction are therefore
expected to affect this store. The impact of residues extraction on the standing
biomass is unclear and seem to depend on future biomass growth due to climate
change and on the proportion of CO2 that is returned to the biomass after the
burning of the residues.
 If forestry residues provide habitat to some forest species (especially micro fauna),
their extraction for the use of bioenergy could threaten those species.
34
 Because deadwood is a main habitat and substrate to many species, the removal
of forestry residues, depending on the harvest intensity, may decrease those
species population sizes. Besides the fact that residues are a source of deadwood
and thus of habitat, their removal may have other impacts, such as the extinction
of several generations by removing residues piles serving as insect’s nursing
home or indirect impacts on higher trophic levels.
2. Recommendations
2.1.
Residue extraction recommendations
Some advices are encountered repeatedly in the literature and should thus be interpreted as
unavoidable restrictions. It is widely documented that stumps and roots should remain on site
to (i) avoid soil erosion, (ii) maintain a proportion of the soil’s carbon pool, (iii) sustain a
balance of nitrogen in the soil and (iv) to maintain the habitat they provide to certain species.
Foliage and needles are also recommended to be left on site because of their high nitrogen
(and other nutrients) content. In addition, some authors advise a harvest during the cooler
period in order to lower the impact on nitrogen soil balance and on the biodiversity. This is of
particular relevance for countries harbouring a distinct cooler period, such as northern
countries, and when the harvest of residues involves residue stacking into piles, of which the
removal during the warm period is fatal to large species populations including several
generations.
The investigation of the two research question has allowed to estimate the most appropriate
methodology in order to assess the residue’s potential while ensuring environmental
sustainability and technical feasibility. The first research question points to a crucial lack of
commonly agreed restrictions to ensure sustainability. The factors investigated in the third
chapter all showed to be of primary importance for the environment sustainability and should
be considered when assessing the potential. These factors also showed to be highly sitedependent. Therefore, sustainable extraction thresholds should be established per site. The
third chapter also illustrated the interrelation of these factors which is thus recommended to
be included in the assessment. For instance, the impact of slopes depends on the soil type and
soil deepness. In order to incorporate the site-specificity and the overlapping of distinct
restrictions, it is assumed that a spatially-based approach would provide a most reliable
estimate. Indeed, a spatial approach, such as the ones of Böttcher et al. 2010 and Verkerk et
al. 2011 include site-specific restrictions and combine them into one ‘restriction map/matrix’
that is then applied to the ‘theoretical potential map’. The existence of spatial databases about
soil type, soil elevation, etc. allow to apply many restrictions faster than with a statistical
approach where calculations are to be made per country/region before being summed up.
Because including many restriction factors in a statistical approach is highly time-consuming,
such approach tend to include less restrictions, which presents the risk to be less
representative.
As for the quantification of each of the restriction, there exists currently no empirical
references, which hampers accurate estimations of sustainable and technically achievable
potential. When choosing from suggested extraction rates in the literature, one should
prioritise site-specific studies since ‘sustainable harvests’ have different rates in different
sites. Moreover, preference should go to studies including a long term scale to allow
35
consideration of the ecosystem’s resilience (i.e. the capacity to return to the natural state after
the harvesting). It is of primary importance to extract residues at a rate that allows the forest
biodiversity, nitrogen cycle, carbon stock and soil litter to recover. To allow this, the main
recommendation made is to leave pre-existing residues on site and to restrict the harvest to the
newly made residues.
For the particular case of biodiversity, it is recommended to avoid areas hosting rare species.
The IUCN Red List Species and, at the European level, the Natura 2000 species represent a
good overview of rare species. The use of such lists is believed to be more relevant than
restrictions to nature reserves which have been shown to host low amounts of protected
species. Moreover, it is commonly accepted that a certain amount animal’s substrates and
habitats should be left on site. Deadwood of distinct sizes and some piles of residues are often
put forward as effective solutions to maintain species diversity and sustainable abundance.
Regarding the database used, the of NAI databases shows the advantage to give insights into
the forest biomass state which can then be interpreted into corresponding carbon/biomasssustainable extraction rates. At present, it is unclear how much the removal of residues affect
the biomass and carbon content of a forest: on the one hand some studies state that it would
induce reduced tree growth and would thus decrease the carbon stock, and on the other hand,
some studies claim that this will be largely compensated by increasing carbon uptake from
increase global CO2 levels. Relying on NAI database would clarify these points and would
provide an straightforward way to ensure sustainability by preventing harvests exceeding
NAI. Furthermore, the use of felling statistics is acknowledged to lead to an underestimation
and should thus be avoided.
To sum up, it is highly recommended to leave on site: the stumps, the roots, the leaves, the
needles, some deadwood of various sizes and some piles of residues. The harvest is advised to
take place in the cooler period, and should be prevented in areas hosting protected species.
Extraction rates should be set according to many, rather than too few, factors and the spatial
variation of these factors is to be taken into account. To do so, spatially-based approaches are
recommended. These are believed to represent a more realistic potential estimation because
including many factors, their site specificity and their overlapping with other restrictions.
Lastly, it is suggested to rely on NAIs databases and to not implement harvests exceeding
NAIs, and to avoid felling statistics which are believed to lead to an underestimation.
2.2.
Further research needed
The aforementioned recommendations are however lacking theoretical knowledge on several
aspects which will impede their implementation. To allow accurate and representative
potential estimation and to allow the drawing of reliable assumptions concerning a sustainable
extraction rates, the following research are currently lacking in the literature and should be
addressed urgently:

Knowledge is acquired about which factors are limiting sustainable harvest, but little
is known about the amount of residue that should stay on site to ensure sustainability
of these factors. Research is needed to quantify accurate extraction rates to avoid
relying on rough estimations likely to lead to bias. Assessing site-recovery rates could
become introduced within national forest inventories or could be assessed by means
of remote sensing techniques in order to fill this gap in.
36




The current body of literature includes little long term studies assessing the long term
impacts of forestry removals. Such studies are of primary concern to provide
sustainable harvest rates.
Although it is suggested to rely on NAI statistics, the conversion from NAI into
biomass data is lacking increment-biomass informations, impeding the biomass
expansion factors to be determined accuratly. Because this type of statistics is known
to provide more accurate data on how to sustainably harvest a forest, this gap is
necessary to be addressed.
Despite that researchers are advised to rely on spatially-based assessment
methodologies, the available spatial data are often of poor accuracy; soil and forest
maps are of poor resolutions. Future effort should be put in place to improve those
datasets.
National inventories are not comparable to each other because relying on distinct
methodologies and assumptions. Further studies should be carried out in an attempt to
provide harmonised databases by adjusting country-level inventories to international
datasets.
2.3.
Study limitation
The present work, although carried out within a certain time restriction is believed to provide
a good overview of the present literature concerning the assessment of European forestry
residue potential and on which factors to integrate to sustainably harvest residues. However,
this time limitation prevented the comparison of more studies in the scope of the first research
question, and restricted the amount of environmental factors addressed within the third
chapter. Regarding the first research question, it is likely that more robust conclusions could
be drawn from comparing a larger amount of studies. As for the second research question, it
has to be kept in mind that water protection is often hampered by the removal of residues and
that other nutrients play an important role within ecosystems (e.g. K+ and Ca2+) although these
have not been addressed in this study.
Moreover, the feasibility of forestry residues cannot be assessed if ignoring the social and
economic factors. Social factors can influence the potential with, for example, restrictions
from private forest holdings, or from a non acceptation of alternative energy sources by the
public. The economical factor is undeniably to be considered, to ensure that a sustainable
harvest would be economically viable. Those two factors are to be acknowledged even before
considering the potential as they lay at the basis of forestry residues harvesting feasibility.
37
References
American Clean Energy & Security Act (2009) Authenticated U.S. Government Information
GPO, H.R. 2454, Calendar No. 97, pp. 1428.
Asikainen, A., Liiri, H., Peltola, S., Karjalainen, T., Laitila, J. (2008) Forest energy potential
in Europe (EU 27) Working Papers of the Finnish Forest Research Institute, 69, pp.33.
Available at: http://www.metla.fi/julkaisut/workingpapers/2008/mwp069.htm
Bentsen, N., S., Felby, C. (2012) Biomass for energy in the European Union – a review of
bioenergy resource assessments, Biotechnology for Biofuels, 5, pp. 10
Berndes, G., Hoogwijk, M., van den Broek, R. (2003) The contribution of biomass in the
future global energy supply: a review of 17 studies, Biomass and Bioenergy, 25, pp. 1-28.
Böttcher, H., Dees, M., Fritz, M. S., Goltsey, V., Gunia, K., Huck, I., Linder, M., Paappanen,
T., Pekkanen, J. M., Ramos, C. I. S., Schneider, U. A., Thebaud, A., TorénChalmers, C. J. M.,
van Brusselen. J., Vesterinen, P., Vis, M. W., Woynowski, A., (2010) Biomass Energy
Europe: Illustration Case for Europe, D6.1, pp. 168.
Börjesson, P. (2000) Economic valutation of the environmental impact of logging residue
recovery and nutrient composition, Biomass and Bioenergy, 19, 137-152.
Cherubini, F., Peters, G. P., Berntsen, T., Stromman, A. H., Hertwich, E. (2011) CO2
emissions from biomass combustion for bioenergy: atmospheric decay and constribution to
global warming, Global Change Biology Bioenergy, 3, 413-426.
Dahlberg, A., Thor, G., Allmér, J., Jonsell, M., Jonsson, M., Ranius, T. (2011) Moddelled
impact of Norway spruce logging residue extraction on biodiversity in Sweden, NRC
Research Press, 41, 1120-1232.
Dymond, C.C., Titus, B.D., Stinson, G., Kurz, W.A. (2010) Future quantities and spatial
distribution of harvesting residue and dead wood from natural disturbances in Canada, Forest
Ecology and Management, 260, pp. 181–192
EC (2011) Soil organic matter management across the EU – best practices, constraints and
trade-offs, Chapter 9: the use of forestry residues.
EEA (2006) How much bio energy can Europe produce without harming the environment?
European Environmental Agency Report, 7/2006, pp72
Eisenbies, M. H., Vance, E. D., Aust, W. M., Seiler, J. R. (2009) Intensive Utilization of
harvest residues in Southern Pine plantations: quantities available and implications for
nutrient budgets and sustainable site productivity, Bioenergy Resource, 2, 90-98.
Ericcson and Nilsson (2006) Assessment of the potential biom ass supply in Europe using a
resource-focused approach, Biomass and Bioenergy, 30, pp. 1-15.
38
European Union (2009) directive 2009/28/EC of the European Parliament and of the Council
of 23 April 2009 on the promotion of the use of energy from renewable sources and amending
and subsequently repealing Directives 2001/77/EC and 2003/30/EC, Pottering, H. -G., Necas,
P., Official Journal of the European Union, L 140/16 EN, pp. 47.
FAO (2005) Global Forest Resources Assessment 2005, Progress towards sustainable forest
management, FAO Forestry Paper, 147, pp. 350.
Gregg, J. S., Smith, S. J. (2010) Global regional potential for bioenergy from agricultural and
forestry residue biomass, Mitigation Adaptation Strategy Global Change, 15, pp. 241-262.
Hahn, K., Christensen, M. (2005) dead wood in European forest reserve – a reference for
forest management, Monitoring and indicators of forest biodiversity in Europe – from ideas to
operationality, section 1, EFI proceedings, 51, pp. 181-195.
Humphrey, J.W., Watts, K. (2005a) Biodiversity indicators for UK managed forests:
development and implementation at different spatial scales, Monitoring and indicators of
forest biodiversity in Europe – from ideas to operationality, section 1, EFI proceedings, 51,
pp. 79-91.
Humphrey, J. W., Sippola, A.-L., Lempérière, G., Dodelin, B., Alexander, K.N.A., Butler, J.
E. (2005b) deadwood as an indicator of biodiversity in European forests: from theory to
operational guidance, Monitoring and indicators of forest biodiversity in Europe – from ideas
to operationality, section 3, EFI proceedings, 51, pp. 193-220.
IPCC (2007) Contribution of Working Group II to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, Parry, M.L., Canziani, O.F., Palutikof, J.P., van
der Linden, P.J., Hanson C.E, Cambridge University Press
IPCC (2012) Renewable Energy Sources and Climate Change Mitigation Special Report of
the Intergovernmental Panel on Climate Change, Cambridge University Press, pp. 1088.
Lal, P., Alavalapati, J. R.R., Marinescu, M., Rao Matta, J., Dwivedi, P., Susaeta, A. (2011)
Developing sustainability indicators for woody biomass harvesting in the United States,
Journal of Sustainable Forestry, 30, 736-755.
Mantau, U., Saal, U., Prins, K., Steierer, F., Lindner, M., Verkerk, H., Eggers, J., Leek, N.,
Oldenburger, J., Asikainen, A., Anttila, P. (2010) Final Report: real potential changes in
growth and use of EU forests, EUwood, Hamburg/Germany, pp. 160.
McKechnie, L., Colombo, S., Chen, J., Mabee, W., Maclean, H. L. (2011) Forest bioenergy or
forest varbon? Assessing trade-offs in greenhouse gas mitigation with wood-based fuels,
Environmental Sciences, Technologies, 45, 789-795.
MCPFE (2012) The Ministerial Conference on the Protection of Forests in Europe, Forest
Europe Growing Life, Sustainable Forest Management Indicators, accessed online the
10.12.13 http://www.foresteurope.org/sfm_criteria
39
Offermann, R., Seidenberger, T., Thrän, D., Kaltschmitt, M., Zinoviev, S., Miertus, S. (2011)
Assessment of global bioenergy potentials, Mitigation Adaptation Strategy Global Change,
16, pp. 103-115
Paluoso, T., Wihersaari, M., Liski, J. (2001) Net Greenhouse gas emissions due to Energy use
of forest residues – impact of soil carbon balance, Woody Biomass as an Energy Source –
Chanllenges in Europe, EFI proceedings, 39, pp.176.
Pamela, R., Williams, D., Inman, D., Aden, A., Heath, G., A. (2009) Environmental and
sustainability factors associated with next generation biofuel in the U.S.: what do we really
know? Environmentql Science and Technology, 43, pp. 4763-4775.
Peckham, S., Gower, S. (2011) Simulated long term effects of harvest and biomass residue
removal on soil carbon and nitrogen content and productivity for two Upper Great Lakes
forest ecosystems, Global Change Biology Bioenergy, 3, 135-147.
Pimentel, D. Harvey, C., Resosudamo, P., Sinclair, K., Kurz, D., McNair, M., Crist, S.,
Shpritz, L., Filtron, L., Saffouri, R., Blair, R. (1995) Environmental and economic costs of
soil erosion ans conservation benefits, Science, 267, pp. 1117-1123.
Pimentel, D., Kounang, N. (1998) Ecology of soil erosion in ecosystems, Ecosystems,1, pp.
416-426.
Poudel, B. C., Sathre, R., Bergh, J., Gustavsson, L., Lundstrom, A., Hyvonen, R. (2012)
Potential effects of intensive forestry on biomass production and total carbon balance in
north-central Sweden, Environmental Science and Policy, 15, pp. 106-124.
Repo, A., Tuomi, M., Liski, J. (2011) indirect carbon dioxide emissions from producing
bioenergy from forest harvest residues, Global Change Biology Bioenergy, 3, pp. 107-115.
Rettenmaier, N., Schorb, A., Koppen, S., Berndes, G., Christou, M., Dees, M., Domac, J.,
Aleftheriadis, I., Goltsev, V., Kajba, D., Kunikowski, G., Lakida, P., Lehtonen, A., Lindner,
M., Pekkanen, M., Roder, J., Chalmers, J. T., Vasylyshyn, R., Veijonen, K., Vesterinen, P.,
Chalmers S. W., Zhelyezna, T. A., Zibtsev, S. (2010) Status of Biomass Resource
Assessments, Biomass Energy Europe, Version 3, D 3.6, pp.205.
Röser, D., Asikainen, A., Stupak, I., Pasanen, K;, (2008) Sustainable Used of Forest Biomass
for Energy: A synthesis with Focus on the Baltic and Nordic Region, Science and Business,
pp. 258.
Rosillo-Calle, F., de Groot, P., Hemstock, S. L., Woods, J. (2007) The Biomass Assessment
Handbook : Bioenergy for a Sustainable Environment, Earthscan, pp. 260.
Schuck, A., Meyer, P., Menke, N., Lier, Lindner, M. (2005) Forest biodiversity indicator:
dead wood – a proposed approach towards operationalising the MCPFE indicator, Monitoring
and indicators of forest biodiversity in Europe – from ideas to operationality, section 1, EFI
proceedings, 51, pp. 49-70.
40
Schulze, E.-D., Körner, C., Law, B. E., Haberl, H., Luyssaert, S. (2012) Large-scale
bioenergy from additional harvest of forest biomass is neither sustainable nor greenhouse gas
neutral, Global Change Biology Bioenergy, 10.1111, pp. 6.
Scott, J. M., Davis, F. W., McGhie, R.G., Wright, G., Groves, G., Estes, J. (2001) Nature
reserves: do they capture the full range os America’s biological diversity? Ecological
applications, 11(4), pp. 999-1007.
Stockland, J. N., Tomter, S. M., Söderberg, U. (2005) Development of dead wood indicators
for biodiversity monitoring: expersionces from scandinavia, Monitoring and indicators of
forest biodiversity in Europe – from ideas to operationality, section 1, EFI proceedings, 51,
pp. 207- 230.
Stupak, I., Lattimore, B., Titus, B. D., Smith, C., T. (2011) Criteria and indicators for
sustainable forest fuel production and harvesting: a review of the current standards for
sustainable forest management, Biomass and Bioenergy, 35,pp. 3287-3308.
Sullivan, T., P., Sullivan, D., S., Lindgren, P. M. F., Ransome, D. B., Bull, J. G., Ristea, C.
(2011) bioenergy or biodiversity? Woody debris structures and maintenance of red-backed
voles on clearcuts, Biomass and Bioenergy, 35, pp. 4390-4398.
TBFRA (2000) Forest Resources of Europe, CIS, North America, Australia, Japan and New
Zealand, UN-ECE/FAO Contribution to Global Forest Resources Assessment 2000, Main
Report, Geneva Timber and Forest Study Papers, no. 17, ECE/TIM/SP/17, pp. 467.
U.S. Department of Energy (2011) U.S. Billion-Ton Update: Biomass Supply for a Bioenergy
and Bioproducts Industry, Perlack, R. D., Stokes, B., J., ORNL/TM-2011/224. Oak Ridge
National Laboratory, Oak Ridge, TN, pp. 227.
van Brusselen, J., Eggers, J., Karjalainen, T., Linder, M., Nabuurs, G.-J., Pussinen, A.,
Schelhaas, M.-J., Schuck, A., Thürig, E., Verkerk, H., van der Werf, B., Zudin, S., (2012)
European Forest Institute website, EFISCEN : Modelling approach, accessed on line on the
28/11/2012, http://www.efi.int/portal/virtual_library/databases/efiscen/modelling_approach/
Verkerk, P. J., Vis, P. Eggers, J., Linder, M., Asikainen, A., (2011a) The realisable potential
supply of woody biomass from forests in the European Union, Forest Ecology and
Management, 261, 2007–2015
Verkerk, P. J., Linder, M., Zanchi, G., Zudin, S. (2011b) Assessing impacts of intensified
biomass removal on deadwood in European forests, Ecological Indicators, 11, 27-35.
Vis, M. W., van den Berg, D., Anttila, M.P., Böttcher, H., Dees, M., Domac, J., Eleftheriadis,
I., Gecevska, V., Goltsev, V., Gunia, K., Kajba, D., Koch, B., Koppen, S., Kunikowski,
Lehtonen, A. H. S., Ledus, S., Lemp, D., Lindner, M., Mustonen, J., Paappanen, T.,
Pekkanen, J. M., Ramos, C. I. S., Rettenmaier, N., Schneider, U. A., Schorb, A., Segon, V.
(2010) Harmonization of biomass resource assessments, Best Practices and Methods
Handbook, Biomass Energy Europe, 1, pp. 220.
41
Walmsley, J., D., Godbold, D. L. (2010) Stump harvesting for bioenergy – a review of the
environmental impacts, Forestry, 83, pp. 17-38
42
Appendices
Appendix 1: Conversion factors used for the conversion of original estimates into a singular unit
Conversion factor
1kg wet weight= 10,06MJ
1kg dry weight= 15,48MJ
Reference
Böttcher et al. 2010
Böttcher et al. 2010
Average wood density= 0,4t/m3
de Wit et al. 2010
Appendix 2: Extent of forests and other wooded land in 2005 in Europe (Global Forest Resources Assessment
2005).
Land area
Country/area
1000 ha
Albania
Andorra
Austria
Belarus
Belgium
Bosnia and Herzegovina
Bulgaria
Channel Islands
Croatia
Czech Republic
Denmark
Estonia
Faeroe Islands
Finland
France
Germany
Gibraltar
Greece
Holy See
Hungary
Iceland
Ireland
Isle of Man
Italy
Latvia
Liechtenstein
Lithuania
Luxembourg
Malta
Monaco
Netherlands
Norway
Poland
Portugal
Republic of Moldova
Romania
Russian Federation
San Marino
Serbia and Montenegro
Slovakia
Slovenia
Spain
Sweden
Switzerland
The former Yugoslav Republic of Macedonia
Ukraine
United Kingdom
Total Europe
Other
wooded
land
Forest
% of
land
area
Other land
with
Total
tree
cover
Inland
water
Total
area
1000 ha 1000 ha 1000 ha 1000 ha 1000 ha
794
29,0
261
1.685
-
135
16
35,6
-
29
-
0
2.875
45
3.862
46,7
118
4.293
-
113
8.386
7.894
38,0
914
11.940
-
12
20.760
667
22,0
27
2.334
-
25
3.053
2.185
43,1
549
2.339
-
47
5.120
3.625
32,8
27
7.411
-
36
11.099
1
4,1
0
18
0
n.s.
19
2.135
38,2
346
3.111
-
62
5.654
2.648
34,3
0
5.080
96
159
7.887
500
11,8
136
3.607
-
66
4.309
2.284
53,9
82
1.873
-
284
4.523
n.s.
0,1
-
140
-
0
140
22.500
73,9
802
7.145
177
3.367
33.814
15.554
28,3
1.708
37.748
269
140
55.150
11.076
31,7
-
23.819
-
808
35.703
0
0
0
1
0
0
1
3.752
29,1
2.780
6.358
-
306
13.196
0
0
0
n.s.
-
0
n.s.
1.976
21,5
0
7.235
95
92
9.303
46
0,5
104
9.875
8
275
10.300
669
9,7
41
6.179
-
138
7.027
3
6,1
0
54
0
n.s.
57
9.979
33,9
1.047
18.385
-
723
30.134
2.941
47,4
115
3.149
29
255
6.460
7
43,1
0
9
-
0
16
2.099
33,5
77
4.092
62
262
6.530
259
87
33,5
1
170
-
0
n.s.
1,1
0
32
-
0
32
0
0
0
n.s.
n.s.
0
n.s.
365
10,8
0
3.023
0
765
4.153
9.387
30,7
2.613
18.625
-
1.751
32.376
9.192
30,0
-
21.437
-
640
31.269
3.783
41,3
84
5.283
-
48
9.198
329
10,0
31
2.928
-
96
3.384
6.370
27,7
258
16.359
-
852
23.839
808.790
47,9
74.185
805.875
4.698
18.690
1.707.540
n.s.
1,6
0
6
-
0
6
2.694
26,4
808
6.698
269
17
10.217
1.929
40,1
-
2.879
32
93
4.901
1.264
62,8
44
706
24
13
2.027
17.915
35,9
10.299
21.730
-
655
50.599
27.528
66,9
3.257
10.377
1.353
3.834
44.996
1.221
30,9
67
2.667
-
174
4.129
906
35,8
82
1.543
-
40
2.571
9.575
16,5
41
48.319
907
2.435
60.370
2.845
11,8
20
21.223
24
203
24.291
1.001.394
44,3
100.925
1.157.788
8.044
37.611
2.297.719
43
Appendix 3: Removals of wood products between 1990 and 2005 in Europe (FAO, Global Forest Resources
Assessment 2005).
Country/area
Albania
Andorra
Austria
Belarus
Belgium
Bosnia and Herzegovina
Bulgaria
Channel Islands
Croatia
Czech Republic
Denmark
Estonia
Faeroe Islands
Finland
France
Germany
Gibraltar
Greece
Holy See
Hungary
Iceland
Ireland
Isle of Man
Italy
Latvia
Liechtenstein
Lithuania
Luxembourg
Malta
Monaco
Netherlands
Norway
Poland
Portugal
Republic of Moldova
Romania
Russian Federation
San Marino
Serbia and Montenegro
Slovakia
Slovenia
Spain
Sweden
Switzerland
The former Yugoslav Republic of Macedonia
Ukraine
United Kingdom
Total Europe
1990
2000
2005
Total
Total
Total
Industrial
roundwood
Wood
fuel
1000
m³ o.b.
1000
m³ o.b.
1000
m³ o.b.
1000
m³ o.b.
1000
m³ o.b.
% of
growing
stock
626
157
168
24
144
-
-
-
-
-
0,2
-
17.318
16.834
20.127
15.858
4.269
1,7
-
7.367
8.568
7.323
1.245
0,6
4.352
3.526
4.368
3.768
600
2,5
4.773
4.326
4.139
2.993
1.146
1,1
3.400
3.778
4.200
3.075
1.125
0,7
-
-
-
-
-
-
2.287
4.062
4.950
3.662
1.288
1,4
13.030
15.860
17.274
16.317
957
2,3
2.023
2.099
1.807
900
907
2,4
3.206
11.164
9.602
7.502
2.100
2,1
-
-
-
-
-
-
47.203
60.603
64.295
59.095
5.200
3,0
55.621
58.330
51.475
33.443
18.032
2,1
42.177
48.818
60.770
54.497
6.273
-
-
-
-
-
-
-
2.979
2.221
1.842
438
1.404
1,0
-
-
-
-
-
-
5.945
5.902
5.528
3.421
2.107
1,6
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
1.789
2.778
2.819
2.797
22
4,3
-
-
-
-
-
-
9.877
10.031
9.600
3.800
5.800
0,7
4.820
11.574
11.500
10.580
920
1,9
21
21
21
16
5
1,2
3.651
6.171
7.727
5.881
1.846
1,9
-
230
139
135
4
0,5
0
0
0
0
0
0
-
-
-
-
-
-
1.518
1.147
1.200
860
340
1,8
12.475
10.304
9.219
7.631
1.588
1,1
23.617
29.882
33.015
31.692
1.323
1,8
11.922
10.590
11.123
10.433
690
3,2
-
62
65
31
34
0,1
17.218
14.285
17.300
11.418
5.882
1,3
336.527
152.316
180.000
129.400
50.600
0,2
-
-
-
-
-
-
3.806
3.002
2.600
1.301
1.299
0,8
5.545
6.150
6.732
6.372
360
1,4
2.978
2.547
3.153
2.622
531
0,9
18.517
17.965
17.689
15.741
1.948
2,0
58.140
70.570
76.780
68.740
8.040
2,4
5.345
6.421
6.958
5.664
1.294
1,5
-
927
927
162
765
1,5
13.590
12.231
14.820
6.660
8.160
0,7
7.152
8.471
8.895
8.630
265
2,6
44
Appendix 4: Forest inventory data used in the EFISCEN model (Verkerk et al.2011)
Appendix 5: Datasets on which Verkerk et al. (2011) relied on to apply restrictions to estimate the technical
potential.
Constraint
Site productivity
Soil surface texture
Soil depth
Soil bearing capacity
Soil susceptibility to compaction
Slope
Natura 2000 sites
Fire weather index
Reference of dataset
EC, 2006b
EC, 2006b
EC, 2006b
EC, 2006b
Housková 2008
USGS 1996
EC 2009b
M. Moriondo pers. commun.
45
Appendix 6: Explanations of the constraints taken into account by Verkerk et al. (2011).
Appendix7: Forest available for wood supply. Data used by Ericsson and Nilsson (2006) (TBFRA 2000).
46
Appendix 8: Sustainable extraction thresholds according to site suitability (EEA 2006).
Appendix 9: Table 14 from Böttcher et al.(2010) report. List of net annual increment used for the potentials
estimations.
47
Appendix 10: Table 15 of Böttcher et al.(2010) report. Determination of harvesting loss fraction used for the
potential estimates.
48
Appendix 11: Table 16 of Böttcher et al.(2010) report. Determination of harvesting loss fraction used for the
potential estimates.
49
Appendix 127: Table 17 of Böttcher et al.(2010) report. Trends of removals of wood products partly derived from
FAO (2010).
50
Appendix 13: Table 18 of Böttcher et al.(2010) report. Trends in removals of wood products from forest in 2005 –
EU 27 – based on FRA 2010 Annex 3 Table 13 and other data sources.
51
Appendix 84: Table 19 of Böttcher et al.(2010) report. Growing Stock on Forest and Other Wooded Land by forest
type and country [1000 ha] Source: MCPFE 2005.
52
Appendix 159: Table 20 of Böttcher et al.(2010) report. Industrial roundwood consumption, Forest Product
Statistics from the Forestry and Timber Section, UNECE.
53
Appendix 16: Table 24 of Böttcher et al.(2010) report. Area of Forest and Other Wooded Land 2005 [1000 ha]
Source: MCPFE 2007
54
Appendix 10: Thesis schedule
Task
Tot.
hours
We. 46: 13-16 November Week 47: 19-23 November Week 49: 26-30 November
Tues. Wed. Thur. Fri.
Mon. Tues. Wed. Thur. Fri.
Mon. Tues. Wed. Thur. Fri.
Week 50: 3-7 December
Mon. Tues. Wed. Thur. Fri.
Week 51: 10-14 December Week 52: 17-21 December
Mon. Tues. Wed. Thur. Fri.
Mon. Tues.
Weekly meetings
6
Carry out preliminary
literature review
Write introduction
9
14
Draw hypotheses
1
Literature review
48
Write 1st RQ
64
Edit 1st draft
2
Submit 1st draft
0
Edit 1st draft
24
Write 2nd RQ
56
Edit 2nd Draft
8
Submit 2nd Draft
0
Edit 2nd Draft
24
Submit last version
0
Total time (not including
meetings)
256
Total ECTS
9 (256/28)
55
56