Changes in the spatial patterns of human
appropriation of net primary production (HANPP) in
Europe 1990-2006
Christoph Plutzar, Christine Kroisleitner, Helmut Haberl, Tamara Fetzel, Claudia Bulgheroni, Tim
Beringer, Patrick Hostert, Thomas Kastner, Tobias Kuemmerle, Christian Lauk, Christian Levers,
Marcus Lindner, Dietmar Moser, Daniel Müller, Maria Niedertscheider, Maria Luisa Paracchini, Sibyll
Schaphoff, Peter H. Verburg, Pieter J. Verkerk, and Karl-Heinz Erb
Supplementary Information
Content
1.
Abbreviations
3
2.
Methods
3
3.
4.
2.1.
Data on census statistics
3
2.2.
Construction of the land use layers
4
2.3.
Assessment of biomass flows
7
2.4.
Spatial allocation of crop yields
7
2.5.
Classification of regions
9
Results at the spatial resolution of 1 km
10
3.1.
HANPP patterns 1990
10
3.2.
HANPP patterns 2000
11
3.3.
HANPP patterns 2006
12
3.4.
HANPP change patterns 1990 – 2000, 2000 – 2006
13
Results at the NUTS2 level
14
4.1.
HANPP patterns 1990
14
4.2.
HANPP patterns 2000
15
4.3.
HANPP patterns 2000
16
4.4.
HANPP change patterns 1990 – 2000, 2000 – 2006
17
4.5.
Spatial and Temporal HANPP change patterns 1990 - 2006
18
5.
HANPP results by country
18
6.
References
21
1
Figures
Figure S 1. Regional aggregation of countries ......................................................................................... 9
Figure S 2. Pattern of HANPP, HANPPluc and HANPPharv in 1990. Left column: flows in gC/m2/yr, right
column in % of NPPpot. ........................................................................................................................... 10
Figure S 3. Pattern of HANPP, HANPPluc and HANPPharv in 2000. Left column: flows in gC/m2/yr, right
column in % of NPPpot. ........................................................................................................................... 11
Figure S 4. Pattern of HANPP, HANPPluc and HANPPharv in 2006. Left column: flows in gC/m2/yr, right
column in % of NPPpot. ........................................................................................................................... 12
Figure S 5. Spatial patterns of HANPP change between 1990 and 2000 and 2000 and 2006. Left
column (a, c): change in HANPP; right column (b, d): change in HANPPluc, in % change per 1km pixel 13
Figure S 6. Pattern of HANPP, HANPPluc and HANPPharv in 1990, Nuts2 level. Left column: flows in
gC/m2/yr, right column in % of NPPpot. .................................................................................................. 14
Figure S 7. Pattern of HANPP, HANPPluc and HANPPharv in 2000, Nuts2 level. Left column: flows in
gC/m2/yr, right column in % of NPPpot. .................................................................................................. 15
Figure S 8. Pattern of HANPP, HANPPluc and HANPPharv in 2006, Nuts2 level. Left column: flows in
gC/m2/yr, right column in % of NPPpot. .................................................................................................. 16
Figure S 9. Spatial patterns of HANPP change between 1990 and 2000, Nuts2 level. Left column (a, c):
change in HANPP; right column (b, d): change in HANPPluc, in % change per 1km pixel ...................... 17
Figure S 10. Spatial patterns of HANPP change between 2000 and 2006, Nuts2 level. Left column (a,
c): change in HANPP; right column (b, d): change in HANPPluc, in % change per 1km pixel ................. 17
Figure S 7 Spatial and temporal patterns of HANPP change between 1990 and 2006. Change in a)
HANPP and b) HANPPluc between 1990 and 2006 in % change per 1 km² pixel. Patterns of change in
c) HANPP and d) HANPPluc. Period 1: 1990 – 2000, period 2: 2000-2006. Changes smaller than 5%
were classified as stagnation................................................................................................................. 18
Tables
Tabelle S 1. Aggregation of CORINE Land cover for the construction of a consistent and
comprehensive land-use dataset ............................................................................................................ 5
Tabelle S 2. Aggregation of CAPRI items on cropland to the aggregates used in this study................... 8
Table S 3. HANPP results per country, year 1990.................................................................................. 19
Table S 4. HANPP results per country, year 2000.................................................................................. 20
Table S 5. HANPP results per country, year 2006.................................................................................. 21
For datasets and GIS layers see http://www.uni-klu.ac.at/socec/inhalt/1088.htm.
2
1. Abbreviations
CAPRI: Common Agricultural Policy Regional Impact Assessment (Model)
CLC: Corine Land Cover
CORINE: Coordination of information on the environment
Dynaspat: DYNAmic and SPATial dimension
HANPP: Human Appropriation of Net Primary Production
HANPPharv: HANPP due to harvest
HANPPluc: HANPP due to land use and land conversions
HSMU: Homogeneous Spatial Mapping Units
LUCAS: Land Use/Cover Area frame Statistical Survey
NPP: Net Primary Production
NPPact: NPP of the actual vegetation
NPPeco: NPP remaining in ecosystems after harvest
NPPpot: NPP of the potential natural vegetation
NUTS: Nomenclature of territorial units for statistics
NUTS2: level of basic regions (Nuts0: nation state; NUTS1: major socioeconomic groups, NUTS3:
small regions)
SoEF: State of Europe’s Forests
UK: United Kingdom
WQI: Wilderness Quality Index
2. Methods
2.1.
Data on census statistics
Data on cropland as well as grazing area and yields were provided by the CAPRI (Common
agricultural Policy Regional Impact) model, which is a widely used tool for impact assessment of
agricultural policies with a focus on the EU (Leip et al., 2008), on national and subnational (NUTS-2)
level.
The economic core of CAPRI links sequentially non-linear regional programming models with a global
agricultural trade model (Britz and Witzke, 2008; Leip et al., 2008). CAPRI’s general layout is to
generate a consistent and complete data set across regional scales (Britz et al., 2011). The modelling
system is fed as far as possible with data from the Eurostat database which is regularly updated and
mostly centralized (area statistics, farm and market balances, yields, agricultural prices). If not
available, other well-documented, official and harmonised data are used from FAOSTAT, OECD or
from the Farm Accounting Data Network (FADN) (Britz and Witzke, 2008). Due to a lack of data in the
EUROSTAT data base some NUTS-2 regions, in all cases cities, were missing (Vienna, Hamburg, Berlin,
London, Bremen, Brussels).
3
Numbers on forest area and yields on regional scale (NUTS1/NUTS2/NUTS3, depending on country)
were taken from Levers et al., 2014, who used the State of European Forest Report (SoEF; FOREST
EUROPE et al., 2011) as basis. Other wooded land areas were taken from SoEF FOREST EUROPE et al.,
2011 using national numbers.
2.2.
Construction of the land use layers
The ‘Coordination of information on the environment’ (CORINE) land cover datasets (hereafter CLC;
Copernicus Programme, 2014) was used as central dataset for the spatial pattern of land use. This
dataset contains spatially-explicit datasets at the resolution of 100 x 100m, for the years 1990, 2000
and 2006). The CLC dataset for the year 2000 is the most comprehensive one, whereas for the years
1990 and 2006 serious data-gaps prevailed (e.g. data for Greece missing for the year 2000, data for
Great Britain, Sweden, and Finland missing for 1990). These data-gaps were closed by using the basic
pattern from the CORINE 2000 map for these countries. (Afterwards, these basic patterns were
modified by reconciling gridded and census data, see below).
The general pattern of unproductive areas, cropland, pastures and meadows, forest areas and inland
water bodies was derived from the CLC dataset (see Table S1.). In a first step, the 100 x 100m grid in
Boolean representation was used to calculate the fraction cover of each land use type in a 1 x 1 km
grid.
The allocation principle follows the approach described in Erb et al. (“subtractive approach”; Erb et
al., 2007): it subtracts consecutively an individual land-use layer from a basis grid, and adjusts it to
regional sum at the census level by conserving the relative pattern of the individual layers and
avoiding gridcells to surpass 100% fractional cover. The area remaining in a gridcell after this
procedure forms the new basis grid for the next layer. The following sequence of land-use allocation
was chosen: 1) inland water bodies, unproductive and wilderness, 2) infrastructure, 3) cropland, 4)
forest, 5) other wooded land, 6) grazing (meadows & pastures). The area in each gridcell that was not
allocated yet after this procedures was labelled “other land may be grazed” and added to the grazing
land layer, assuming that lands that are used but not for agriculture and forestry are subject to
sporadic grazing.
4
Tabelle S 1. Aggregation of CORINE Land cover for the construction of a consistent and
comprehensive land-use dataset
CLC_CODE
111
112
121
122
123
124
131
132
133
141
142
211
212
213
221
222
223
231
241
242
243
244
311
312
313
321
322
323
324
331
332
333
334
335
411
412
421
422
423
511
512
521
522
523
999
CORINE class
Continuous urban fabric
Discontinuous urban fabric
Industrial or commercial units
Road and rail networks and associated land
Port areas
Airports
Mineral extraction sites
Dump sites
Construction sites
Green urban areas
Sport and leisure facilities
Non-irrigated arable land
Permanently irrigated land
Rice fields
Vineyards
Fruit trees and berry plantations
Olive groves
Pastures
Annual crops associated with permanent crops
Complex cultivation patterns
Land principally occupied by agriculture, with significant areas of natural vegetation
Agro-forestry areas
Broad-leaved forest
Coniferous forest
Mixed forest
Natural grasslands
Moors and heathland
Sclerophyllous vegetation
Transitional woodland-shrub
Beaches, dunes, sands
Bare rocks
Sparsely vegetated areas
Burnt areas
Glaciers and perpetual snow
Inland marshes
Peat bogs
Salt marshes
Salines
Intertidal flats
Water courses
Water bodies
Coastal lagoons
Estuaries
Sea and ocean
NODATA
Land-use class
Not used
Not used
Not used
Not used
Not used
Not used
Not used
Not used
Not used
Grassland
Not used
cropland
cropland
cropland
cropland
cropland
cropland
grassland
cropland
cropland
cropland
forest
forest
forest
forest
grassland
grassland
grassland
grassland
unproductive
unproductive
Not used (see text)
unproductive
unproductive
unproductive
unproductive
unproductive
unproductive
unproductive
unproductive
unproductive
unproductive
unproductive
unproductive
Not used
For unproductive land, the spatial extent and pattern was taken from CLC, whereas for cropland,
forestry and grazing land, the spatial pattern was reconciled with census statistics at the NUTS2 level
(Nomenclature of territorial units for statistics; EUROSTAT, 2014).
The extent of wilderness areas was calculated by delineating the 2% area with the highest wilderness
continuum values of a European wilderness map provided by the Wildland Research Institute (Fisher
et al., 2010). The area of wilderness was kept constant for all years.
5
The infrastructure layer was constructed by using a map of soil sealing (Kopecky and Kahabka 2009).
We intersected this map with the CLC in order to derive the share of vegetated and sealed areas for
each of the classes on artificial surfaces (ranging from urban fabric to artificial non-agricultural, but
vegetated areas such as sport facilities) of the CLC for the year 2006. Data gaps due to cloud cover in
the soil sealing map were filled using CLC information and the vegetation fractions were applied to
the other artificial surface areas of the CLC maps for 1990 and 2000.
In a next step of the subtractive approach we allocated cropland to the remaining areas. While we
regarded the CLC layers as authoritative for the spatial extent of cropland areas, we used the CAPRIDynaspat (http://ec.europa.eu/research/fp6/ssp/capri_dynaspat_en.htm) data for defining the
patterns. Dynaspat is – in principle –consistent with CAPRI at NUTS2, but shows small deviations with
CLC. It discerns 13 cropland commodity groups (cereals, rice, wine, oilseeds, olives, roots & tubers,
fibres, fodder crops, pulses, sugar beet, vegetables & others, fallow) which we allocated to the grid
following the above mentioned procedure. CAPRI-Dynaspat areas outside of extend the extent of CLC
cropland were clipped. Cropland areas in CLC without Capri Dynaspat information (a minority of
gridcells) were addressed by allocating information of neighbouring gridcells by Euclidian allocation.
In some regions (e.g. Mecklenburg-Vorpommern) CAPRI numbers on cropland areas were
unreasonably high, in these cases we truncated the amount using numbers derived from the CLC
data set. Priority was given to the CAPRI dataset over CLC. For a comparison between CLC results and
CAPRI see http://agrienv.jrc.it/publications/pdfs/HNV_Final_Report.pdf, pg.29, and pg.81.
Forest area was allocated using patterns of the CLC forest layers (3.x.x, see Table S1) and area totals
from regional census statistics provided by Levers et al. (Levers et al., 2014). This dataset contains
data on forest areas and harvest at the NUTS2 and NUTS3 level, based on data from the State of
European Forests report (SoEF; FOREST EUROPE et al., 2011.
The spatial extent of other wooded land was not available in these statistics and thuswas taken from
national statistics, provided by SoEF (FOREST EUROPE et al., 2011), UNECE (UN, 2000), and FAO
(FAOSTAT, 2014). These areas were allocated to the NUTS2 level the following way: We assumed
other wooded land to prevail in several CLC layers (class 1.4.1, 2.1.1, 2.1.2, 2.1.3, 2.2.1, 2.2.2, 2.2.3,
2.3.1, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 3.2.2, 3.2.3, 3.2.4, 3.3.4, 4.1.2). A forest cover map by Schuck et al.
(2002) was used to derive the spatial pattern of this land cover type. Forest areas in census statistics
that could not be allocated to the grid layer were added to the class ‘other wooded land’. We used
fractions of the EFI forest map (ref) in suitable CORINE classes for the allocation of ‘other wooded
land’ areas.
Eventually, grazing land was allocated. In a first step, we delineated the class “meadows & pastures”
from CAPRI with the extent of the CLC class 2.3.1 (pastures), but excluded the class ‘Sparsely
vegetated areas’. CAPRI reports significantly larger pasture areas in all NUTS2 regions than these CLC
classes would contain. Thus, the remaining grassland areas were assigned to the class ‘other grazing
land’. ‘Urban grassland areas’ from CAPRI were allocated to the CLC class 1.4. (‘artificial, nonagricultural vegetated areas’).
Summing up all land use classes at the grid level showed that in many cases 100% coverage was not
reached. We defined the remaining areas (i.e. the difference of the already allocated land-use classes
and total grid-cell area) as “other land, maybe grazed” and added this to the grazing land layer,
assuming that these lands are under sporadic land uses, mostly dominated by grazing (see, e.g. Erb et
al., 2007; IIASA and FAO, 2012).
6
2.3.
Assessment of biomass flows
Basis for the HANPP assessment is the calculation of NPPpot (NPP of the potential vegetation), NPPact
(prevailing NPP of current land-use systems) and HANPPharv (biomass harvested by humans).
NPPact on forest land is assumed to be equal to NPPpot (NPPact = NPPpot). For the calculation of NPPact
on grazing land we apply two approaches: 1) on natural grasslands (based on a potential vegetation
map from Ramankutty and Foley, 1999) NPPact is assumed to equal NPPpot, and 2) on potential forest
sites NPPact is assumed to be 80% of NPPpot (NPPact = 0.8*NPPpot; Haberl et al., 2007; Erb et al., 2009).
In cases where HANPPharv on grazing land surpassed NPPact, NPPact was adjusted upwards, taking into
account that grazing harvest canmake use of NPPact a maximum of 75% of NPPact (Haberl et al.,
2007). We used data from Krausmann et al. (2008) to generate appropriate factors. NPPact on
cropland was extrapolated from HANPPharv on cropland using factors that account for non-harvested
plant parts and productivity losses during plant growth (so-called pre-harvest losses; Haberl et al.,
2007; Krausmann et al., 2008; Ciais et al., 2010). NPPact on infrastructure land was assessed in line
with the global HANPP assessment by Haberl et al. (2007): due to the lack of data, we assumed
infrastructure areas being covered on 1/3 of the surface by vegetation. The productivity of this
vegetated fraction was assumed to have a productivity equal to NPPpot, based on the considerations
that these areas are tended intensively (e.g. irrigation, fertilization, labour-intensive pest
management).
HANPPharv on cropland was calculated from CAPRI data on cropland harvest. Values were reported in
fresh weight and were converted into dry matter units using crop specific water content factors
derived by standard tables of the FAO (FAOSTAT, 2014). We created 12 aggregates (e.g. cereals, oil
crops, sugar crops) from the 36 available items (Table S2). To estimate harvest residues or other byproducts not included in statistics (e.g. straw), as well as belowground productivity, we used cropspecific factors derived from earlier studies (Krausmann et al., 2008).
Statistics on forest harvest volumes were collected from national forestry reports, statistical
yearbooks and databases, and national experts and were calibrated to match national-level statistics
according to Forest Europe et al. (2011; see also Levers et al., 2014, and Verkerk et al., in prep.).
Factors from Krausmann et al. (2008) and UNECE (UN, 2000) were used to estimate the amount of
unrecovered bark and felling losses such as branches, stumps, roots and foliage left in forests. All
belowground biomass on cropland and in forests was assumed to be destroyed during harvest and
was thus included in HANPPharv, whereas roots on grazing areas were not included.
The amount of grazed biomass was derived from the CAPRI database. In the light of large
uncertainties related to grazing land we applied a plausibility cross-check using a grazing gap
approach, contrasting extrapolation of total feed demand of livestock with market feed and residues
fed to livestock to approximate grazing volumes (Wirsenius, 2003; Krausmann et al., 2008). As this
comparison yielded satisfactory results, data from CAPRI were used. Harvest on infrastructure and
built-up areas was – due to the absence of data - arbitrarily set to 50% of the NPPact in order to
account for management (i.e. gardening) on these areas. The used factor is consistent with the
factors used in global and national assessments (Haberl et al., 2007; Niedertscheider et al., 2012,
2014; Fetzel et al., 2014; Niedertscheider and Erb, 2014).
2.4.
Spatial allocation of crop yields
For the allocation of crop yields, we calculated suitability maps using a maximum entropy approach
(Phillips et al., 2006). As presence data we used the LUCAS point survey information (Tóth et al.,
2013) and aggregated this information to 12 crop types matching the CAPRI Dynaspat crop classes
7
(see Table S2). As predictors we used climatic variables (BIOCLIM, Hijmans et al., 2005), in particularly
the bioclim variables 6, 7, 10, 17 and 18, which we adjusted from the original year 2000 also to the
other years 1990 and 2006. We also used soil data (Panagos et al., 2012) from the European soil data
base and a terrain ruggedness index (Riley et al., 1999), which we derived from the SRTM30 digital
elevation model (Jarvis et al., 2008).
Tabelle S 2. Aggregation of CAPRI items on cropland to the aggregates used in this study
Capri_item
Barley
Durum wheat
Grain Maize
Oats
Other cereals
Rye and Meslin
Soft wheat
Apples Pears and Peaches
Citrus Fruits
Other Fruits
Flax and hemp
Fodder maize
Fodder other on arable land
Fodder root crops
Olives for oil
Table Olives
Other oils
Rape
Soya
Sunflower
Pulses
Paddy rice
Potatoes
Sugar Beet
Flowers
New energy crops (ligneous)
Nurseries
Other arable crops
Other crops
Other industrial crops
Other Vegetables
Tobacco
Tomatoes
Vegetables and Permanent crops
Table Grapes
Wine
HANPP - Aggregates
Cereals
Cereals
Cereals
Cereals
Cereals
Cereals
Cereals
Fruits
Fruits
Fruits
Fiber
Fodder
Fodder
Fodder
Olives
Olives
Oilseeds
Oilseeds
Oilseeds
Oilseeds
Pulses
Rice
Roots & Tubers
Sugar
Vegetables & Other Fruits
Vegetables & Other Fruits
Vegetables & Other Fruits
Vegetables & Other Fruits
Vegetables & Other Fruits
Vegetables & Other Fruits
Vegetables & Other Fruits
Vegetables & Other Fruits
Vegetables & Other Fruits
Vegetables & Other Fruits
Wine
Wine
8
2.5.
Classification of regions
The classification of regions follows the UN definition on “Composition of macro geographical
(continental) regions, geographical sub-regions, and selected economic and other groupings”
(http://unstats.un.org/unsd/methods/m49/m49regin.htm#europe), with the exception of the region
“UK and Ireland”, that was separated from the Northern Europe regions due to its marked
differences in land use and land cover (e.g. grazing dominated, forestry plays a small role).
Northern Europe
UK and Ireland
Southern Europe
Eastern Europe
Western Europe
Not considered
Figure S 1. Regional aggregation of countries
9
3. Results at the spatial resolution of 1 km
3.1.
HANPP patterns 1990
Figure S 2. Pattern of HANPP, HANPPluc and HANPPharv in 1990. Left column: flows in gC/m2/yr, right
column in % of NPPpot.
10
3.2.
HANPP patterns 2000
Figure S 3. Pattern of HANPP, HANPPluc and HANPPharv in 2000. Left column: flows in gC/m2/yr, right
column in % of NPPpot.
11
3.3.
HANPP patterns 2006
Figure S 4. Pattern of HANPP, HANPPluc and HANPPharv in 2006. Left column: flows in gC/m2/yr, right
column in % of NPPpot.
12
3.4.
HANPP change patterns 1990 – 2000, 2000 – 2006
Figure S 5. Spatial patterns of HANPP change between 1990 and 2000 and 2000 and 2006. Left
column (a, c): change in HANPP; right column (b, d): change in HANPPluc, in % change per 1km pixel
13
4. Results at the NUTS2 level
4.1.
HANPP patterns 1990
Figure S 6. Pattern of HANPP, HANPPluc and HANPPharv in 1990, Nuts2 level. Left column: flows in
gC/m2/yr, right column in % of NPPpot.
14
4.2.
HANPP patterns 2000
Figure S 7. Pattern of HANPP, HANPPluc and HANPPharv in 2000, Nuts2 level. Left column: flows in
gC/m2/yr, right column in % of NPPpot.
15
4.3.
HANPP patterns 2000
Figure S 8. Pattern of HANPP, HANPPluc and HANPPharv in 2006, Nuts2 level. Left column: flows in
gC/m2/yr, right column in % of NPPpot.
16
4.4.
HANPP change patterns 1990 – 2000, 2000 – 2006
Figure S 9. Spatial patterns of HANPP change between 1990 and 2000, Nuts2 level. Left column (a,
c): change in HANPP; right column (b, d): change in HANPPluc, in % change per 1km pixel
Figure S 10. Spatial patterns of HANPP change between 2000 and 2006, Nuts2 level. Left column (a,
c): change in HANPP; right column (b, d): change in HANPPluc, in % change per 1km pixel
17
4.5.
Spatial and Temporal HANPP change patterns 1990 - 2006
Figure S 11 Spatial and temporal patterns of HANPP change between 1990 and 2006. Change in a)
HANPP and b) HANPPluc between 1990 and 2006 in % change per 1 km² pixel. Patterns of change
in c) HANPP and d) HANPPluc. Period 1: 1990 – 2000, period 2: 2000-2006. Changes smaller than 5%
were classified as stagnation.
5. HANPP results by country
18
Table S 3. HANPP results per country, year 1990
1990
Country
Region
Austria
Belgium
Bulgaria
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Netherlands
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
United Kingdom
Total
W
W
E
E
N
N
N
W
W
S
E
BI
S
N
N
W
W
E
S
E
E
S
S
N
BI
[MtC/y]
NPPpot
55.75
19.99
60.37
47.72
24.78
22.61
181.80
330.07
217.25
65.94
46.56
44.05
182.43
35.93
37.50
1.73
20.94
174.97
51.78
137.92
30.48
14.37
277.51
241.86
135.99
2460.32
[MtC/y]
NPPact
51.89
19.83
52.84
45.88
24.39
18.11
171.78
304.27
218.25
53.67
43.91
37.61
142.79
29.17
29.07
1.55
23.91
155.02
41.57
106.76
24.89
13.65
230.26
228.45
117.91
2187.41
[MtC/y]
NPPeco
34.14
8.05
34.33
20.23
7.66
13.93
150.11
154.96
96.75
43.40
15.89
22.23
97.98
22.16
15.37
0.68
7.10
77.40
32.96
63.26
15.15
10.15
179.96
198.90
54.74
1377.50
19
[MtC/y]
HANPPluc
3.87
0.17
7.53
1.84
0.39
4.50
10.02
25.80
-1.00
12.27
2.65
6.44
39.64
6.76
8.43
0.17
-2.96
19.96
10.21
31.15
5.60
0.72
47.25
13.42
18.07
272.91
[MtC/y]
HANPPharv
17.75
11.77
18.50
25.65
16.72
4.18
21.67
149.30
121.50
10.27
28.02
15.38
44.81
7.02
13.70
0.87
16.81
77.61
8.62
43.51
9.73
3.50
50.30
29.55
63.17
809.92
[MtC/y]
HANPP
21.62
11.94
26.04
27.49
17.11
8.68
31.69
175.10
120.50
22.54
30.67
21.82
84.45
13.78
22.13
1.05
13.84
97.57
18.82
74.66
15.33
4.22
97.55
42.97
81.25
1082.82
Table S 4. HANPP results per country, year 2000
2000
Country
Austria
Belgium
Bulgaria
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Netherlands
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
United Kingdom
Total
W
W
E
E
N
N
N
W
W
S
E
BI
S
N
N
W
W
E
S
E
E
S
S
N
BI
[MtC/y]
NPPpot
59.34
19.88
55.32
53.62
28.21
25.48
180.58
366.22
228.50
69.89
51.72
42.33
172.96
37.76
39.78
1.74
20.95
197.64
53.29
136.76
32.94
15.24
280.29
258.01
134.17
2562.61
[MtC/y]
NPPact
54.54
21.40
45.20
44.36
26.07
19.81
170.98
351.05
232.34
57.01
42.07
36.92
148.59
30.88
28.75
1.57
24.47
159.47
46.00
100.29
25.58
14.24
242.36
242.97
119.85
2286.77
[MtC/y]
NPPeco
36.34
7.92
40.61
26.33
9.59
15.56
143.79
182.49
89.79
47.11
22.00
19.58
107.73
20.15
16.99
0.88
7.30
95.66
34.48
86.08
18.50
11.45
192.63
205.21
57.80
1495.98
20
[MtC/y]
HANPPluc
4.80
-1.52
10.12
9.26
2.13
5.67
9.60
15.16
-3.84
12.87
9.65
5.40
24.37
6.88
11.03
0.17
-3.52
38.17
7.29
36.47
7.36
1.00
37.93
15.04
14.33
275.84
[MtC/y]
HANPPharv
18.20
13.48
4.58
18.03
16.48
4.25
27.19
168.57
142.55
9.90
20.07
17.34
40.86
10.73
11.76
0.69
17.17
63.81
11.52
14.21
7.08
2.80
49.73
37.76
62.05
790.78
[MtC/y]
HANPP
22.99
11.96
14.71
27.30
18.61
9.92
36.79
183.73
138.71
22.78
29.71
22.74
65.23
17.61
22.79
0.86
13.65
101.98
18.81
50.68
14.44
3.79
87.66
52.80
76.37
1066.62
Table S 5. HANPP results per country, year 2006
2006
Country
Austria
Belgium
Bulgaria
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Netherlands
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
United Kingdom
Total
W
W
E
E
N
N
N
W
W
S
E
BI
S
N
N
W
W
E
S
E
E
S
S
N
BI
[MtC/y]
NPPpot
60.68
20.42
63.08
55.48
26.34
25.64
179.43
361.92
234.08
71.08
54.58
43.78
183.14
39.49
41.87
1.83
21.08
192.30
54.81
162.13
34.14
15.63
286.08
256.02
140.10
2625.14
[MtC/y]
NPPact
55.27
21.32
53.90
47.42
25.99
20.73
168.74
339.05
235.77
57.36
49.22
37.67
155.61
31.90
28.53
1.61
22.79
157.99
47.41
123.95
28.39
14.45
243.96
240.63
125.57
2335.23
[MtC/y]
NPPeco
36.34
7.92
40.61
26.33
9.59
15.56
143.79
182.49
89.79
47.11
22.00
19.58
107.73
20.15
16.99
0.88
7.30
95.66
34.48
86.08
18.50
11.45
192.63
205.21
57.80
1495.98
[MtC/y]
HANPPluc
5.41
-0.90
9.18
8.07
0.35
4.91
10.69
22.87
-1.69
13.72
5.36
6.11
27.53
7.59
13.34
0.23
-1.71
34.31
7.40
38.17
5.75
1.19
42.12
15.39
14.53
289.90
[MtC/y]
HANPPharv
18.92
13.40
13.28
21.09
16.39
5.17
24.96
156.56
145.98
10.25
27.22
18.09
47.88
11.75
11.54
0.72
15.49
62.33
12.93
37.87
9.89
3.00
51.33
35.42
67.77
839.25
[MtC/y]
HANPP
24.33
12.50
22.47
29.16
16.75
10.08
35.65
179.43
144.29
23.97
32.58
24.20
75.41
19.34
24.88
0.95
13.77
96.64
20.33
76.04
15.64
4.19
93.45
50.81
82.30
1129.15
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