Biogeosciences, 8, 1279–1289, 2011
www.biogeosciences.net/8/1279/2011/
doi:10.5194/bg-8-1279-2011
© Author(s) 2011. CC Attribution 3.0 License.
Biogeosciences
Soil carbon stock increases in the organic layer of boreal
middle-aged stands
M. Häkkinen, J. Heikkinen, and R. Mäkipää
Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, 01301 Vantaa, Finland
Received: 22 December 2010 – Published in Biogeosciences Discuss.: 7 February 2011
Revised: 25 April 2011 – Accepted: 12 May 2011 – Published: 25 May 2011
Abstract. Changes in the soil carbon stock can potentially
have a large influence on global carbon balance between ter-
restrial ecosystems and atmosphere. Since carbon sequestra-
tion of forest soils is influenced by human activities, report-
ing of the soil carbon pool is a compulsory part of the na-
tional greenhouse gas (GHG) inventories. Various soil car-
bon models are applied in GHG inventories, however, the
verification of model-based estimates is lacking. In general,
the soil carbon models predict accumulation of soil carbon
in the middle-aged stands, which is in good agreement with
chronosequence studies and flux measurements of eddy sites,
but they have not been widely tested with repeated measure-
ments of permanent plots. The objective of this study was
to evaluate soil carbon changes in the organic layer of bo-
real middle-aged forest stands. Soil carbon changes on re-
measured sites were analyzed by using soil survey data that
was based on composite samples as a first measurement and
by taking into account spatial variation on the basis of the
second measurement. By utilizing earlier soil surveys, a long
sampling interval, which helps detection of slow changes,
could be readily available.
The range of measured change in the soil organic layer var-
ied from −260 to 1260 g m−2 over the study period of 16–19
years and 23±2 g m−2 per year, on average. The increase
was significant in 6 out of the 38 plots from which data were
available. Although the soil carbon change was difficult to
detect at the plot scale, the overall increase measured across
the middle-aged stands agrees with predictions of the com-
monly applied soil models. Further verification of the soil
models is needed with larger datasets that cover wider geo-
graphical area and represent all age classes, especially young
stands with potentially large soil carbon source.
Correspondence to: R. Mäkipää
(raisa.makipaa@metla.fi)
1 Introduction
Changes in the soil carbon stock can potentially have a
large influence on global carbon balance between terrestrial
ecosystems and atmosphere. Globally soil contains three
times more carbon than atmosphere and in boreal forests
soil carbon stock is three times larger than that of vegetation
(Schimel, 1995; Goodale et al., 2002). Soil carbon stock,
and especially its topmost organic layer, in managed boreal
forests is directly (by timber harvesting and soil scarifica-
tion) and indirectly (e.g. by climate change) affected by hu-
man activities. Due to the potentially large human induced
changes in soil carbon balance, reporting of the changes in
the soil carbon stock is an essential part of the national green-
house gas (GHG) inventories. Currently, the majority of
the countries that are able to report soil carbon apply model
based approaches and only a few countries can rely on re-
peated soil measurements. In general, soil carbon models
that can also be used in the GHG reporting (Peltoniemi et al.,
2007) predict loss of carbon in regenerated young stands
and accumulation of soil carbon in the middle-aged stands
(Mäkipää et al., 1999; Peltoniemi et al., 2004; Palosuo et al.,
2008). Such a modeled pattern is in good agreement with
the chronosequence studies (e.g. Covington, 1981; Federer,
1984; Peltoniemi et al., 2004) but not confirmed with re-
peated measuments of permanent study sites (Yanai et al.,
2000).
Verification of the modeled soil carbon dynamics with em-
pirical data is essential for the development of reliable in-
ventory methods. Measuring changes in soil carbon stocks
is, however, challenging due to the fact soils are heteroge-
neous (Järvinen et al., 1993; Liski, 1995) and the rate of
change is relatively small compared to the size of the stock
(Yanai et al., 2003a; Peltoniemi et al., 2004). Due to the slow
changes, long sampling interval may be necessary in order to
Published by Copernicus Publications on behalf of the European Geosciences Union.
1280 M. Häkkinen et al.: Soil C stock increases in the organic layer
allow measurable changes to take place before re-sampling.
At the moment, the use of earlier soil data may be the only
effective way to test the soil carbon change hypothesis. An-
alyzing the change on the basis of repeated measurements of
the earlier established sample plots is often challenging due
to dissimilarities between the sampling designs applied in the
first and second inventory. In general, previous soil surveys
contain information on mean carbon stocks but they lack in-
formation on within-site spatial variation. Current sampling
can be designed to provide representative spatial informa-
tion, but following the sampling design of the first one would
improve the power of statistical testing due to correlated co-
variances of the first and second sampling.
In addition to soil heterogeneity, measuring soil changes is
also challenging due to spatial autocorrelation of soil prop-
erties. Spatial autocorrelation should be accounted for when
estimating the significance of a change in single study plots.
Positive autocorrelation enlarges the variance of the mean
of single plots but, on the other hand, it may also decrease
the estimation variances of kriging estimations. Significant
small-scale autocorrelation in soil properties has been de-
tected when the properties of mineral soil sites have been
investigated using variogram analysis and kriging (Järvinen
et al., 1993; Arrouays et al., 1997; Bruckner et al., 1999).
Small-scale spatial autocorrelation in the amount of carbon
has also been identified using variogram analysis (Liski,
1995; Möttönen et al., 1999; Schöning et al., 2006; Muukko-
nen et al., 2009). Spatially autocorrelated soil data have also
been studied using cross-variograms and co-kriging in many
geostatistical papers (Papritz and Flühler, 1994; Papritz and
Webster, 1995; Lark, 2002). However, these methods are not
applicable if the samples from the first inventory are com-
posite ones with unknown variances. Since spatial variation
in soil properties is widely acknowledged, the influence of
spatial pattern on estimated mean values has commonly been
reduced by taking numerous subsamples, which are analyzed
as one composite sample (e.g. Ellert et al., 2000; Smith,
2000).
The objective of this study was to evaluate soil carbon
changes in the organic layer of boreal middle-aged forest
stands. In addition, the aim was to develop methods that fa-
cilitate effective use of earlier soil inventory data that was
based on composite samples together with new data that
carry information on the spatial variation of soil properties.
The focus was on the soil organic layer, a clearly distin-
guished soil horizon of podzols, because it is the most dy-
namic part of forest soil and the most likely changes first
take place in this topmost layer. This study was restricted to
middle-aged stands for which stand development phase soil
models predict consistently a trend of increasing soil carbon
stock. However, scrutinized tests of such trends are lacking.
0 100 km
Fig. 1. Sample plots were located in the southern boreal and in the central boreal vegetation zone in Finland.
15
Fig. 1. Sample plots were located in the southern boreal and in the
central boreal vegetation zone in Finland.
2 Material and methods
2.1 Soil sampling and carbon analysis
This study was based on soil data collected from a subset of
38 sample plots from a nation-wide network of forest moni-
toring plots. A systematic network of 3000 permanent sam-
ple plots was established by the Finnish National Forest In-
ventory for the monitoring of forest ecosystems (Mäkipää
and Heikkinen, 2003), and the first soil sampling on mineral
soil sites was performed on a sub-sample of 486 plots from
the nation-wide network during 1986–1989 (Tamminen and
Starr, 1990). In 2005, soil sampling was repeated on a sub-
sample of n= 38 plots where the stand age varied between
22 and 65 years at the time of the first sampling. The 38
plots were located in Southern Finland, excluding the coastal
region (Fig. 1). The tree stand was dominated by Scots pine
(Pinus sylvestris L.) on 24 of the plots and by Norway spruce
Biogeosciences, 8, 1279–1289, 2011 www.biogeosciences.net/8/1279/2011/
M. Häkkinen et al.: Soil C stock increases in the organic layer 1281
(Picea abies (L.) H. Karst.) on 14 plots (Table 1). The sam-
ple plots divided into two or more forest patches (according
to fertility level, stand age, or management history) were not
included. The tree and stand parameters were measured on
circular plots of 300 m2 (radius 9.77 m). The fertility class
of the plots ranged from herb-rich to xeric heath forests (Ta-
ble 1). The soil type was podzol and the organic layer was
mor or moder (plots with signs of peat formation were ex-
cluded). The sample plots used in this study were a random
sample that represented intermediate age classes of conifer-
ous forest stands on mineral soils in the southern Finland.
The organic layer (excluding the litter layer) sampling in
the first soil survey was based on composite samples, com-
prising m1 = 30 sub-samples (Tamminen and Starr, 1990).
The sub-samples were combined which resulted in only one
mean value per plot and no information on variance. The
second sampling was designed to provide information on the
variation of the soil carbon stock and all sub-samples were
analyzed separately. The organic layer was sampled with
a cylinder (d = 58 mm). Above-ground parts of the living
plants as well as the litter layer were excluded from the sam-
ples. Mineral soil horizon was separated out according to vi-
sual difference between the structure of organic and mineral
soil layers of the podzolic soil. The instructions and the soil
sampling equipment used in the first and in the second sam-
pling were kept as similar as possible. Five different field
teams participated in the first sampling (Table 1), and one
person was responsible for taking the samples in the second
sampling. The means of the amount of carbon in the samples
taken by the different groups on the first sampling occasion
were tested with simple mean tests. The results of these tests
confirmed that sampling by different groups did not differ
significantly from each other.
The sampling design is presented in Fig. 2. The organic
layer samples were taken at points lying on a circle, r = 11 m,
centered on the inventory plot. Although the first and the sec-
ond round of sampling and carbon analyses were as similar
as possible, sampling could not be performed at exactly the
same points due to destruction of some of the first sampling
points by removal of soil samples. As a result, some of the
second sampling points had to be shifted. Furthermore, the
second sampling of the organic layer was designed to pro-
vide information also on the spatial within-site variation of
the soil carbon stock. In the second sampling, m2 = 40 sub-
samples were taken instead of 30 in order to also include
shorter distances between the sampling points. The organic
layer consist both partially decomposed matter whose origin
can be spotted on sight and well-decomposed organic matter,
the origin of which is not readily visible. All the 40 sub-
samples were analyzed separately.
The samples were dried at a temperature of 35–44 ◦C,
weighed, milled and sieved to pass through a 2 mm bottom
sieve. The moisture content of the air-dried samples was de-
termined on a TGA analyzer. The total carbon concentration
was analyzed using a Leco CHN analyzer (Leco, St Joseph,
Fig. 2. Sampling design. In the first sampling, three samples were collected at each of the 10 locations (black
points in the square). In the second sampling, samples were collected at the same locations unless the first
sampling was destructive, in which case the second sampling points were shifted counterclockwise by 18
degre s. In the second sampling 4 samples were collected at each of the 10 locations. The fourth sample
point is located consecutively at one of the three grey points shown in the square, either side by side with a
black one or 0.2 m or 0.4 m away from it.
16
Fig. 2. Sampling design. In the first sampling, three samples were
collected at each of the 10 locations (black points in the square). In
the second sampling, samples were collected at the same locations
unless the first sampling was destructive, in which case the second
sampling points were shifted counterclockwise by 18 degrees. In
the second sampling 4 samples wer coll cted at each of the 10
locations. The fourth sample point is located consecutively at one
of the three grey points shown in the square, either side by side with
a black one or 0.2 m or 0.4 m away from it.
MI, USA) in the Central Laboratory of the Finnish Forest
Research Institute, which is an accredited test laboratory (in
accordance with the standard SFS-EN ISO/IEC17025).
2.2 Variogram analysis
Standard geostatistical methods were used in analyzing the
spatial autocorrelation and amounts of carbon on the plots
(see Webster and Oliver, 2001). They were performed in
the R environment (R Development Core Team, 2006) us-
ing the libraries geoR (Ribeiro Jr. and Diggle, 2001) and
gstat (Pebesma, 2004). Spatial autocorrelation was studied
using variograms. xtki shall denote the location of the i-th
sub-sample of plot k on the t-th sampling occasion, t = 1,2,
and Zt (x) shall denote the carbon concentration at the loca-
tion x at the time of the t’th sampling. Plot-specific empiri-
cal variograms were estimated from the spatially explicit ob-
servations z2ki =Z2(x2ki), k= 1,...,n, i = 1,...,m2, of the
www.biogeosciences.net/8/1279/2011/ Biogeosciences, 8, 1279–1289, 2011
1282 M. Häkkinen et al.: Soil C stock increases in the organic layer
Table 1. Mean carbon stocks of the organic layer according to the first (z1k) and the second measurement (z2k) and their standard deviations
(√Var(z1k) and √Var(z2k)).
Soil C stock, 1st Soil C stock, 2nd
Nr of site Mean Sd Mean Sd Ftypea Yearb Agec Pined Spruced Deciduousd Groupe Changef
(g C m−2) (g C m−2) (g C m−2) (g C m−2) (yr) (m2ha−1) (m2 ha−1) (m2 ha−1) (g C m−2)
134502 1976 280 2106 168 2 1986 75 7.4 22.9 0.4 1 130
136101 1580 249 1986 118 4 1986 46 12.4 2 406
176101 1972 217 1715 107 4 1986 44 14.1 2 −257
212901 2842 566 3924 309 2 1986 60 2.2 22.7 3.5 3 1082
215102 1360 164 1704 109 3 1986 75 8.7 9.0 1 344
215301 1477 229 1985 102 3 1986 65 22.9 1 508
232101 1525 275 2293 190 3 1986 42 1.7 3 767
254301 1552 186 1371 131 2 1987 75 21.8 0.3 3 −181
255101 1176 148 1371 85 4 1987 60 15.0 4.8 0.9 1 195
256501 1439 287 1765 153 3 1987 40 4.3 0.3 4 325
272103 1218 228 1514 121 2 1986 75 35.6 0.4 3 296
273101 1562 313 1791 112 2 1986 55 7.3 3 229
317304 1241 233 1945 102 4 1987 56 31.4 2.0 4 704∗
332301 1636 352 2215 180 2 1987 83 9.5 4.0 3 580
337501 1563 241 1931 142 2 1987 65 11.5 4 368
373701 1687 434 2052 153 4 1987 46 0.3 14.2 3 364
373901 917 194 1564 113 3 1987 50 2.5 1.2 3 647∗
396101 1369 298 1710 133 2 1988 75 7.0 11.4 4 342
397101 987 124 1837 88 2 1987 47 9.4 4 851∗
417702 1097 139 1792 141 4 1988 50 11.6 2 695∗
435301 1572 252 1821 169 3 1988 55 2.7 5.9 1 248
436101 1501 251 1494 145 3 1988 75 19.8 3.0 1 −6
454101 1551 217 1940 109 4 1988 55 18.3 1.4 3 389
455501 933 285 1205 130 3 1988 45 7.3 1.4 5.4 1 271
474701 1575 407 2334 220 3 1988 51 11.8 0.4 1 759
476304 1108 227 1379 91 3 1988 55 20.8 4 271
477901 1300 155 1806 119 4 1988 45 5.3 0.6 0.1 2 506
496702 1152 259 2034 233 4 1988 42 7.3 4 882
515902 1176 202 1546 118 4 1988 47 13.3 1 370
533501 1428 558 2687 210 4 1988 43 17.3 3 1260
537101 800 92 1334 67 5 1988 60 8.8 4 534∗
575501 1021 117 1249 106 4 1988 40 3.9 0.7 1.3 1 228
636303 1828 237 1879 146 4 1988 55 3.0 1.2 5 51
675704 2286 506 2447 290 5 1988 65 6.5 5 161
714304 843 104 1081 62 5 1988 70 8.6 5 238
734701 1430 145 1983 92 5 1988 65 5.5 5 553∗
735101 1624 596 1803 202 3 1988 75 19.2 1.9 4.7 5 179
794901 1582 220 1943 123 4 1989 65 5.2 1.4 2 360
a Fertility level of the site according to the Finnish site type classification (Cajander, 1949; Hotanen et al., 2008): 2= herb-rich heath forest, 3=mesic heath forest, 4= sub-xeric
heath forest, and 5= xeric heath forest.
b The measurement year of the first sampling. The second sampling was conducted in 2005 of all plots.
c At the time of the second sampling in 2005.
d The basal areas of Scots pine, Norway spruce and deciduous trees on a plot measured during the first sampling.
e The field teams that collected the first samples.
f The symbol ∗ indicates a significant change (95 % confidence intervals did not intersect).
second sampling using the equation
γˆk(h)= 1|2N(h)|
∑
(i,j)∈N(h)
(z2ki−z2kj )2 (1)
where N(h) is a set of pairs (i,j) ∈ 1,2,...,m2 for which
||x2ki −x2kj || ≈ h and |N(h)| is the number of the pairs in
the set.
A spherical model was fitted to the empirical variograms.
The spherical model is defined as
γ (h)=
{
c0+c[ 3h2a − 12 (ha )3] for h≤ a,
c0+c for h>a. (2)
where c0 is the nugget variance parameter, c the sill variance
parameter, and a the range of spatial correlation. The spheri-
cal model was used because it has a well-defined range a and
it exhibits linear behavior near the origin, thus making it suit-
able for representing properties that have high short-range
Biogeosciences, 8, 1279–1289, 2011 www.biogeosciences.net/8/1279/2011/
M. Häkkinen et al.: Soil C stock increases in the organic layer 1283
variability. The experimental variograms were fitted by the
restricted maximum likelihood criterion (REML) by weights
|N(h)|/h2.
The spatial autocorrelation within a plot of 300 m2 is
strong if the nugget parameter is much smaller than the sill.
On the other hand, if the nugget parameter is approximately
same as or bigger than the sill, there is not much actual spa-
tial autocorrelation as the nugget explains most of the spatial
variability.
2.3 Plot-specific variances for the first sampling
Because the within-plot variances of the first measurements
were unknown, they were estimated on the basis of the vari-
ograms fitted to the second measurements under the assump-
tion that the variation at the time of the first measurement
was similar to that of the second one. The observations of
the first sampling occasion are plot-level averages
z¯1k = 1
m1
m1∑
j=1
Z1(x1kj ), (3)
and in the plot level analysis they are considered as predic-
tions of the unknown means
1
|Uk|
∫
Uk
Z1(x)dx (4)
over circles Uk with radii r and origins at the centre points of
the sample plots. The variances of the prediction errors can
be expressed as
Var[z¯1k− Z¯1(Uk)] =Var(z¯1k)−2Cov[z¯1k,Z¯1(Uk)] (5)
+Var[Z¯1(Uk)],
where
Var(z¯1k)= 1
m21
m1∑
i=1
m1∑
j=1
Cov[Z1(x1ki),Z1(x1kj )], (6)
Cov[z¯1k,Z¯1(Uk)] = 1
m1 |Uk|
m1∑
i=1
∫
Uk
Cov[Z1(x1ki),Z1(x)]dx, (7)
and
Var[Z¯1(Uk)] = 1|Uk|2
∫
Uk
∫
Uk
Cov[Z1(x),Z1(x′)]dxdx′. (8)
The covariances in Eqs. (6–8) were obtained from the plot-
specific variogram models fitted to the second measurement,
and the integrals were approximated by appropriately scaled
sums over dense grids discretizing the circles Uk .
2.4 Change in the carbon stocks of the organic layer
For single plots the mean carbon stocks of the second mea-
surement of the organic layer were calculated by ordinary
block kriging. The ordinary block kriging estimate over a
block Uk is a weighted average of the data,
Zˆ(Uk)=
m2∑
i=1
λiz2ki . (9)
Model-unbiasedness of the estimator 9 is ensured by the re-
striction
∑N
i=1λi = 1.
The estimation variance is
Var[zˆ2k− Z¯2(Uk)] = 2
m2∑
i=1
λiγk(x1ki,Uk) (10)
−
m2∑
i=1
m2∑
j=1
λiλjγk(‖x1ki−x1kj‖)−γk(Uk,Uk)
where
γk(x1ki,Uk)= 1|Uk|
∫
Uk
γk(‖x1ki−x‖)dx (11)
and
γ¯k(Uk,Uk)= 1|Uk|2
∫
Uk
∫
Uk
γk(‖x−x′‖)dxdx′. (12)
This variance was computed by replacing γk in (10–12) with
the variogram model fitted to the second measurement of the
k-th plot.
The confidence intervals of the first and the second mea-
surement were calculated from the equations
z¯1k±s0.95
√
Var(z¯1k) and zˆ2k±s0.95
√
Var(zˆ2k), (13)
where s0.95 is the statistic from Student’s t distribution at the
95 % confidence level. The confidence intervals of the first
and the second means of each plot were compared with each
other. If they did not intersect, the change was considered
statistically significant.
3 Results
3.1 Variogram analysis
In most of the studied plots, spatial autocorrelation in soil
carbon stock of organic layer was found on the basis of the
variograms (Fig. 3), which indicates that soil spatial varia-
tion needs to be taken into account when plot-wise mean car-
bon stocks are estimated. The estimated range parameter was
constant in two plots and in one plot (number 496702), where
the estimated range was 0.70 m, the spatial variation seemed
to be very small-scale – this plot could also be considered to
have a constant variogram. In these cases, spatial pattern
does not influence on mean estimates of plot-wise carbon
stocks of organic layer. In 17 cases out of 35 where spatial
autocorrelation in soil carbon of organic layer was found, it
seemed to disappear at distances shorter than 7 m (range pa-
rameter < 7 m). On the other hand, the estimated range pa-
rameter was larger than the radius of the soil sampling plots
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1284 M. Häkkinen et al.: Soil C stock increases in the organic layer
0 5 10 15 20
0
50
00
00
15
00
00
0
49 37
35
17
22
43
49
78 59
58
27
83
223
a = 4.59 c0 = 335000 c = 756000
Plot 134502
0 5 10 15 200
e+
00
4e
+0
5
8e
+0
5
16
31 26
11
19
16 12
6
12
11 23
32 25
a = 7.73 c0 = 211000 c = 377000
Plot 136101
0 5 10 15 200
e+
00
4e
+0
5 17 27 20
2
6
13
24
24
35
28
18
9 8
a = 976.8 c0 = 361000 c = 5529000
Plot 176101
0 5 10 15 200
e+
00
3e
+0
6
6e
+0
6
52 40
40
8
7
42
50
96 70
51
5
79
239
a = 7.43 c0 = 3078000 c = 499000
Plot 212901
0 5 10 15 200
e+
00
4e
+0
5
8e
+0
5
53
39
29
16
24
41 50 74
62 65
23
82 222
a = 3.02 c0 = 140000 c = 308000
Plot 215102
0 5 10 15 200
e+
00
4e
+0
5
8e
+0
5
50 42 41
14 15
43 47
78 59 58
29
82 222
a = 10.61 c0 = 241000 c = 184000
Plot 215301
0 5 10 15 20
0
10
00
00
0
20
00
00
0
52
41
39
8
8 38
57
88 74 50
9
82
234
a = 2.7 c0 = 926000 c = 410000
Plot 232101
0 5 10 15 200
e+
00
4e
+0
5
8e
+0
5
50 45
38
7
7
40
53 95
72
49
6
82
235
a = 2.57 c0 = 488000 c = 147000
Plot 254301
0 5 10 15 20
0
40
00
00
10
00
00
0
50 41 31
19 19
39
54 74 69
52
27
82
223
a = 5.12 c0 = 30000 c = 262000
Plot 255101
0 5 10 15 20
0
50
00
00
15
00
00
0
48
38 35
19
22
41
49 77 61
56
30 85
218
a = 6.54 c0 = 180000 c = 811000
Plot 256501
0 5 10 15 200
e+
00
4e
+0
5
8e
+0
5
44
16 15
60 69
16 21
97 42
20
120
34 226
a = 7.72 c0 = 399000 c = 163000
Plot 272103
0 5 10 15 200
e+
00
4e
+0
5
8e
+0
5
49
37
33
20
26 51 48
57
47 56
76 97 183
a = 16.77 c0 = 387000 c = 119000
Plot 273101
Distance (m)
Va
rio
gr
a
m
0 5 10 15 200
e+
00
3e
+0
5
6e
+0
5
50 42
39
9
9
40
51 93 72 51
5
82 236
a = 10.23 c0 = 178000 c = 277000
Plot 317304
0 5 10 15 20
0
10
00
00
0
50 44
38
8
8
41
51
93
72
53
3 82
236
a = 8.14 c0 = 684000 c = 610000
Plot 332301
0 5 10 15 200
e+
00
4e
+0
5
8e
+0
5
49
43
38
10
10
39
52
92 70
52
9
81
234
a = 4.92 c0 = 216000 c = 572000
Plot 337501
0 5 10 15 20
0
50
00
00
15
00
00
0
50
42
39
9
9
40 51 93 72
51
5
82
236
a = 13.96 c0 = 407000 c = 774000
Plot 373701
0 5 10 15 200
e+
00
4e
+0
5 48 38
34
19 23
42
47
77
62 57
29
82
222
a = 5.82 c0 = 345000 c = 134000
Plot 373901
0 5 10 15 200
e+
00
4e
+0
5
8e
+0
5
41
19
18
60
64
18
22
92
45 22
118
38
222
a = 11.07 c0 = 542000 c = 148000
Plot 396101
0 5 10 15 200
e+
00
2e
+0
5
4e
+0
5
50
42
39
9
9
40
51
93
72
51
5
82
236
a = 2.35 c0 = 23000 c = 273000
Plot 397101
0 5 10 15 200
e+
00
4e
+0
5
8e
+0
5
47 39 38
9
9 39
46
91
65
50
5
78
224
a = 0 c0 = 579000 c = 0
Plot 417702
0 5 10 15 20
0
50
00
00
15
00
00
0
29
21
16
18
18
23 22
12
17
23
26
17
33
a = 3.25 c0 = 273000 c = 750000
Plot 435301
0 5 10 15 200
e+
00
4e
+0
5
8e
+0
5
49
43
38
10
10
42
52
89
73
50
9
83
231
a = 5.82 c0 = 462000 c = 343000
Plot 436101
0 5 10 15 200
e+
00
4e
+0
5 50 44
38
8
8
41
51
93
73
51
4
82
236
a = 834.2 c0 = 382000 c = 4462000
Plot 454101
0 5 10 15 20
0
40
00
00
10
00
00
0
48 40
36
16
15
40
53 80
72
53
14
86 226
a = 9.63 c0 = 312000 c = 410000
Plot 455501
Distance (m)
Va
rio
gr
a
m
Fig. 3. Plot-specific empirical variograms of carbon concentration (dots), numbers of pairs of observations contributing to each estimated
value (attached to the dots), spherical models fitted to the empirical variograms (solid lines), and the parameter values of the models (a = range,
c0 = nugget, c = sill).
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M. Häkkinen et al.: Soil C stock increases in the organic layer 1285
0 5 10 15 20
0
10
00
00
0
25
00
00
0
34
28
234
5
24
26 53 44 30
3
45
145
a = 7.07 c0 = 581000 c = 1341000
Plot 474701
0 5 10 15 200
e+
00
3e
+0
5
6e
+0
5
50 36
33
22 25
42 53 78
59
49
48
111
173
a = 11.61 c0 = 152000 c = 230000
Plot 476304
0 5 10 15 200
e+
00
4e
+0
5
8e
+0
5
50
43
38
9 9
39
52
94
71
51
5
82
236
a = 1.83 c0 = 49000 c = 482000
Plot 477901
0 5 10 15 20
0
10
00
00
0
25
00
00
0
45
36
28
27
37 43
47 60
52
58
54
85 207
a = 0.7 c0 = 0 c = 2021000
Plot 496702
0 5 10 15 200
e+
00
4e
+0
5
8e
+0
5
51 46 35
8
8
41
49
96
73
51
3
82 236
a = 5.89 c0 = 480000 c = 35000
Plot 515902
0 5 10 15 20
0
10
00
00
0
25
00
00
0
46 35 31
25
30
43 39
77
63
52 42
81
216
a = 15.31 c0 = 1364000 c = 410000
Plot 533501
0 5 10 15 20
0
10
00
00
25
00
00
44
22
22 47
61
27
29 77
45
37
99
56
214
a = 2.21 c0 = 43000 c = 125000
Plot 537101
0 5 10 15 200
e+
00
3e
+0
5
6e
+0
5
49
44
39
8
8
42 50
93 73 51
4
82
236
a = 0 c0 = 414000 c = 0
Plot 575501
0 5 10 15 20
0
40
00
00
10
00
00
0
50
42
39
9
9
40
51
93 73
50
5
82 236
a = 4.45 c0 = 642000 c = 143000
Plot 636303
0 5 10 15 200
e+
00
2e
+0
6
4e
+0
6
49 44
39
8
9 42 54
95 71
46
10
104
208
a = 4.53 c0 = 266000 c = 3293000
Plot 675704
0 5 10 15 200
e+
00
2e
+0
5
49
45
38
8
8
42 50 93 73
52
3
82 236
a = 4.65 c0 = 57000 c = 91000
Plot 714304
0 5 10 15 200
e+
00
2e
+0
5
4e
+0
5
36 30 25
5
7
34
36
52
44
32
4
48
142
a = 3.74 c0 = 96000 c = 219000
Plot 734701
Distance (m)
Va
rio
gr
a
m
0 5 10 15 200
e+
00
4e
+0
6
50 42
399
10
39
52
92
72
50
9
81 234
a = 13.6 c0 = 459000 c = 1819000
Plot 735101
Distance (m)
Va
rio
gr
a
m
0 5 10 15 200
e+
00
4e
+0
5
8e
+0
5
36 31 24
5
7
30
36 58 46
28
4 52
137
a = 6.71 c0 = 313000 c = 262000
Plot 794901
Fig. 3. Continued.
(11 m) in 7 of the 38 plots, and in 2 plots the range was larger
than the diameter of the plot indicating that scale of spatial
variation may be larger than we were able to evaluate with
this data. Due to observed within-site spatial variation, mean
carbon stocks of the organic layer in this study were esti-
mated by kriging, which yields more realistic estimates than
calculation of simple averages.
3.2 Soil carbon stock and stock change in the organic
layer
We calculated simple mean stock estimates of the first and
second measurements and the difference between them based
on the empirical variances of the plot-specific stock estimates
(Table 1). The mean carbon stock of the organic layer in the
middle-aged stands (40–84 yr) of boreal forests during the
second measurement was 1852±67 g m−2. The mean car-
bon stock of the organic layer of all the 38 plots in the first
measurement, 16–19 yr earlier, was 1444± 95 g m−2. The
mean change of all the 38 plots was 412± 44 g m−2. The
amount of carbon was increased on 35 of the 38 plots (Ta-
ble 1). However, the increase was statistically significant on
only 6 plots (Fig. 4 and Table 1). The measured change was
larger in younger stands (Fig. 5).
4 Discussion
The average rate of increase in the carbon stock of the or-
ganic layer (23± 2 g C m−2 yr−1) measured with repeated
sampling in these managed boreal forests of intermediate
age classes was higher than that reported earlier on the ba-
sis of chronosequency studies. Soil carbon accumulation
of 8 g C m−2 yr−1 in the organic layer was reported in the
chronosequence of windthrow pits in Alaska (Bormann et al.,
1995), and long-term accumulation of the organic layer with-
out fire resulted in an increase of 5 g C m−2 in Sweden (War-
dle et al., 2003). Peltoniemi et al. (2004) measured and sim-
ulated 64 sites in boreal coniferous stands in Finland and
obtained a 4.7±1.4 g C m−2 yr−1 increase in carbon in the
simulations, and a 4.2± 1.2 g C m−2 yr−1 increase in the
www.biogeosciences.net/8/1279/2011/ Biogeosciences, 8, 1279–1289, 2011
1286 M. Häkkinen et al.: Soil C stock increases in the organic layer
Fig. 3. The amount of carbon at the time of the first and the second sampling with 95 % confidence intervals,
connected with a line which describes the magnitude of the change.
Fig. 4. The rate of soil carbon change in the organic layer in relation to stand age.
17
Fig. 4. The amount of carbon at the time of the first and the sec-
ond sampling with 95 % confidence intervals, connected with a line
which describes the magnitude of the change.
organic layer in measured chronosequency data. Their study
sites represented a wide range of age classes, while this study
was restricted to stands of intermediate age classes where
a significant increase in the soil carbon stock was expected
on the basis of the earlier simulation studies. Increase in
the soil carbon stock in over 20 yr-old stands has been con-
sistently predicted with several models (e.g. Mäkipää et al.,
1999; Chertov et al., 2001; Yanai et al., 2003b; Peltoniemi
et al., 2004). In addition, we observed that the rate of soil car-
bon change tends to decrease with stand age (Fig. 5), which
agrees with the understanding of soil carbon dynamics based
on modeling where the rate of soil change is driven by an-
nual biomass and litter production that decrease after a fast
growth period and canopy closure.
The measured soil carbon sequestration in the middle-
aged stands is indirectly supported by the CO2 flux data
of eddy covariance measurement sites (e.g. Valentini et al.,
2000; Kolari et al., 2004). After early development of a
stand and closure of the canopy they show only minor vari-
ation in the gross primary production (GPP), however, the
total ecosystem respiration tends to decrease with stand age,
which is an indication of soil carbon accumulation (Kolari
et al., 2004). The measured rate of soil carbon sequestration
in the organic layer (23±2 g C m−2 yr−1) is relatively slow
in comparison to stand scale carbon sequestration of 192 and
323 g C m−2 yr−1 measured on 40- and 75-yr-old stands, re-
spectively (Kolari et al., 2004). Thus, in the middle-aged
stands soil organic layer may contribute to less than 10 % of
the forest carbon sink, but in the old-growth stands, where
carbon sink is measured to be 240 g C m−2 yr−1, soil carbon
sequestration continues and living trees have only a minor
role (some 40 g C m−2 yr−1) (Luyssaert et al., 2008). Ac-
cording to the earlier model predictions supported by our cur-
rent results, one may interpret that a change in the age class
distribution from a predominance of younger age classes to
middle-aged and mature forests may result in an increase in
the soil carbon stock of organic layer.
Fig. 3. The amount of carbon at the time of the first and the second sampling with 95 % confidence intervals,
connected with a line which describes the magnitude of the change.
Fig. 4. The rate of soil carbon change in the organic layer in relation to stand age.
17
Fig. 5. The rate of soil carbon change in the organic layer in relation
t stand age.
The mean carbon stock of the organic layer measured in
the stands of 40–80 yr (1850±70 g m−2) is consistent with
the large data set of 1248 sample plots in Southern Finland
(Tamminen, 1991), where the mean carbon stocks of stand
development classes 2, 3, and 4 (from young to mature) were
1450, 1630, and 1740 g C m−2, respectively (assuming the
inverse of the van Bemmel factor, 1.72, for converting the
organic matter content to the carbon concentration). In this
study, the average carbon stock of organic layer carbon in-
creased by almost 30 % from 1444 to 1852 g m−2 in less
than 20 yr. This accumulation of the soil organic layer takes
place after a remarkable decline in the soil carbon stock after
a regeneration of the stands and a release of carbon during
early years of succession; Kolari et al. (2004) measured a net
carbon source of 400 g C m−2 yr−1 on clear-cut site where
the size of the organic layer carbon stock was similar to this
study.
At the plot scale, the block kriging estimates and variances
of the carbon amounts showed a significant change only on
6 of the 35 sites where the amount of carbon had increased.
In general, changes smaller than one third of the stock were
not significant and many large changes were also not signifi-
cant due to large within-site variation (Fig. 4). This result is
consistent with earlier findings which indicate that the detec-
tion of a change in soil carbon by re-measuring a single plot
is challenging or impossible due to soil heterogeneity (large
spatial variation) and the small temporal changes relative to
the large total amount of carbon in forest soil (Yanai et al.,
2000; Conen et al., 2003; Smith, 2004; Mäkipää et al., 2008).
The amount of carbon appeared to have decreased on 3 plots
(Table 1, Fig. 5), two of which were relatively fertile Norway
spruce stands thinned some 5 years before the first sampling,
and one had a management history with a lower basal area
of the trees (harvesting of seedling trees), which evidently
resulted in a decreased input of litter from the trees.
Biogeosciences, 8, 1279–1289, 2011 www.biogeosciences.net/8/1279/2011/
M. Häkkinen et al.: Soil C stock increases in the organic layer 1287
Possible measurement errors in this study are related to
mislocated sample plots or sampling points within a plot in
the second measurement, differences caused by the sampling
practices of measurement groups, or inaccuracy in taking
soil samples or in the carbon analysis. The exact location
of the sample plots was known at both measurement times
due to permanent marks of the sample plot centers, detailed
descriptions of the location of the sampling points, very exact
instructions and equipment. Similarly, the measurement er-
ror between the sample coordinates of the sampling points
and the exact locations where they were taken was small,
within 10 cm. The field personnel should not have signifi-
cantly affected the results as they used the same precise in-
structions for both measurements. Furthermore, the different
field groups were tested to ensure that they were consistent
with each other in terms of their sampling practices. The
carbon concentrations and moisture of the soil samples were
measured in an accredited laboratory and are considered very
reliable.
5 Conclusions
We found spatial autocorrelation in the carbon stock of
the organic layer, that has already been demonstrated in
many other studies for a range of soil properties (Järvinen
et al., 1993; Hokkanen et al., 1995; Liski, 1995; Bruckner
et al., 1999; Möttönen et al., 1999; Muukkonen et al., 2009).
Therefore, the spatial structure of the data was considered in
the analysis of the soil data. The spatial variation of the soil
carbon stock was taken into account on the basis of spatial
sampling performed during the second measurement (mean
and variance of carbon stocks of second sampling were es-
timated by kriging) with the assumption that the within-plot
variation in soil carbon was the same in the first sampling.
This approach provided a realistic basis for the plot-scale
analysis of the soil carbon changes that have to build on
soil inventories where composite samples are used in the first
measurements. With spatial sampling in the second measure-
ment, we were able to assess the total variation and to gain
a more reliable assessment of the significance of the change
than with only composite samples. Since the carbon stock
changes are small in comparison to the size of the soil stock,
the time period needed to detect a change could be tens of
years. Therefore, in the analysis of soil carbon changes, we
must be able to use the data from earlier soil surveys. Re-
gional soil surveys provide a considerable amount of data
based on composite sampling (e.g. Arrouays et al., 2001;
Coomes et al., 2002; Tremblay et al., 2002; Jones et al., 2005;
Lettens et al., 2004), and this could be utilized in assessing
changes after re-measuring the same plots. In addition, it is
common in forest soil surveys that soil samples cannot be
collected at every planned point because of natural or other
obstacles. The methods applied in this study can also take
into account sampling with an unequal number of soil sam-
ples per plot.
We measured carbon sink in the soil organic layer, which
may represent some 10 % of the overall carbon sink in the
middle-aged boreal forest stands. Soil carbon sequestration
in the boreal forests of this age class has earlier been reported
on the basis of the various soil carbon models (e.g. Mäkipää
et al., 1999; Peltoniemi et al., 2004; Palosuo et al., 2008)
that are also applied in the large scale forest carbon invento-
ries including national GHG reporting under the UNFCCC.
Our analysis of the empirical data from the southern boreal
zone indicates that the model predicted soil carbon trend in
the middle-aged stands complies with measurements. How-
ever, further verification of the models is needed with larger
datasets that represent all age classes and a wider geographi-
cal area.
Acknowledgements. This study was co-funded by the Eu-
ropean Commission through the Forest Focus pilot project
“Monitoring Changes in the Carbon Stocks of Forest Soils”
(http://www.metla.fi/hanke/843002). We would like to thank
Mikko Peltoniemi and Markku Tamminen for their help with the
databases and Pekka Tamminen for the design and execution of the
first soil sampling.
Edited by: J. Leifeld
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