|Handling the changes of organic carbon
stock in mineral soils
of the Europen Union
|Vladimir Stolbovoy1, Nicola
Filippi1, Luca Montanarella1, Fabio
Mauro Piazzi2, Senthil-Kumar Selvaradjou1
|1 Land Management and Natural Hazards
Unit, Joint Research Center EC
2 Soil Department, Institute for Forestry and Environment
The interest in soil organic carbon (SOC) among
soil scientists and practitioners is quite common
due to its extensive influence on various environmental
issues pertaining to global studies. But, this
topic has recently got recognition as one among
the most priority issues, because of its implications
on the global and pan-European policies, e.g.,
SOC plays a key role in major Rio conventions,
e.g., climate change, biodiversity and Combat
Desertification. At the pan-European level SOC
takes a central stage within European Soil Thematic
Strategy issues (ESTS) (see EC COMMUNICATION (2002,
179)). The SOC is a parameter driving majority
of soil ecological functions, e.g, fertility,
buffering capacity, absorption of dangerous chemicals,
water quality, regulation of atmospheric gas composition,
etc. The decline of the SOC deteriorates soil
quality and is recognized to be a serious environment
threat indicated by European Environment Agency
(Huber 2001, 2001). In principle, the amount of
SOC is an universal indicator of the soil quality.
Hence, it is an urgent need to develop a common,
simple, transparent and cost effective method
to identify the changes of organic carbon content
in the mineral soils of the European Union. The
objective of this paper is to introduce a feasible
method for detecting the changes of organic C
in the mineral soils of EU.
A new method named “Area Frame Randomised
Soil Sampling” that focus on practical sampling
in field plots and forests was developed. This
method combines traditional soil survey composite
sampling with randomized positioning of the sampling
sites in the field. The method aims for achieving:
average SOC content; unification of the sampling
localization and the project’s consistency; pedological
substantialization; technological simplicity;
and cost effectiveness. Technically, the method
exploits ISO/FDIS 10381-1:2002(E)) and particularly
relevant to ISO 10381-4 devoted to “Sampling to
support legal or regulatory action” (standard
setting). It supplements IPCC Good Practice Guidance
A randomized sampling template is a used as a
core for this method. The template represents
a grid of 100 points resulted from ‘modified
random sampling’ with a distance threshold. Sampling
points were selected at random with a careful
attention that no points are less than 6 ‘distance
units’ (the grid step). Where ever it was not
possible to find points more distant than 6 units,
the distance threshold was progressively relaxed.
This sampling approach prevents the previous sampled
point to get too close to the subsequent ones,
failing which would lead to partially redundancy.
This occurs for example both for systematic sampling
and other sampling plans (Bellhouse, 1977, 1988).
Systematic sampling, or other sampling plans that
avoid points too close to each other, gives a
lower variance than simple random sampling (SRS).
But the application of the formulae given in the
section ‘uncertainty’ to such sampling plans generally
overestimates the variance (Wolter, 1984).
Spatial dimensions of the template are different
depending on the geographical coordinates (size)
of the field. To define the template parameters
one have to select the extrime axis X and Y values.
In our example (Figure 1) the X axis dimension
is defined as a difference between Xmax = 376255
and Xmin = 375917, which is equal to 338 m. The
Y dimension would be a difference between Ymax
= 5025669 and Ymin = 5025326 that is 343 m. Thus
the max axis dimension is 343 m, which defines
the size of the template. The grid parameters
are calculated by dividing max axis by 10, which
gives 34.3 m. The grid size defines the distances
between soil profiles and sampling points that
is shown in self explaining fashion in the Figure.
It is important to note that the location of
the soil profiles and sampling sites should be
fixed in European Coordinate Reference Systems
(CRS identifier ERTS89 Ellipsoidal CRS) (Boucher,
C., Altamini, Z., 1992). The position should be
recorded with the precision of 10m in the field
by means of Global Positioning Systems (GPS) to
be used for return visits to the sampling site.
Data can be downloaded to a portable or office
computer for registration and combination with
other layers of information for spatial analysis.mpling
As can be seen from figure 1, each sampling site
represents a soil profile surrounded by the points
at which samples should be taken for the composite
sample. The distances for the sampling points
are easy to calculate (Figure 1).
Figure 1. Parameterization of the randomized sampling
according to the field plot (red).
The crosses indicate selection of the sampling sites.
As can be seen from the Figure, the number of
points for the composite soil sampling is 25,
which is in line with the ISO recommendation.
Table 1 provides a number of the sampling sites
depending on the area of the field.
Area of the field
Number of sampling sites
< 5 ha
5 - 10 ha
> 25 ha
Table 1. Number of
sampling sites depending on the field area
Pedological substantiation of the sampling
Because of the global need for the detection
of the SOC changes in soils and the necessity
for the result compatibility the widely recognized
manuals for soil sampling tend to over simplify
soil diversity. For example, the IPCC (IPCC, 2003)
suggests one standard soil depth (0-30cm) for
all soil types in different use, e.g., cropland,
pasture, forest. This brings substantial heterogeneity
in the soil parameters resulting in the need of
tremendous number of soil samples to achieve a
certain confidence of the results. To avoid this
inconvenience the proposed method recommends specifying
soils according to the use more precisely. This
approach allows reducing the sample amount and
the cost of the analysis.
The cropland-based soil profile can be schematised
by two principal horizons: topsoil (the plough
layer) and the subsoil underlying it (Figure 2a).
The plough horizon or layer indicates regular
anthropogenic perturbation and physical mixing
of soil material throughout, e.g. organic and
mineral fertilizers, application of earth, etc.
The plough horizon hosts the largest proportion
of root biomass and incorporates surface crop
residues that contribute to the change in organic
content in soils. The plough horizon is seldom
stratified due to regular tillage. The thickness
of the plough horizon is different depending on
conventional cultivation in the countries. Therefore,
it is proposed that one sample be taken from the
middle of the plough horizon, e.g., at 10-20 cm
depth if plough horizon is 30 cm thick as illustrated
in Figure 2a. An undisturbed soil sample to determine
the bulk density should also be taken at the same
Pasture soils are exposed to anthropogenic disturbances
limited to a reduction in organic input because
of biomass consumption through grazing, fertilization,
additional grass seeding, etc. The profile of
these soils has gradual change of soil characteristics
with depth in line with that of natural soils.
The good practice guidance (IPCC, 2003) suggests
detecting changes of organic carbon stock in 30
cm topsoil. The principal structure and a scheme
of soil sampling of pasture are illustrated in
Column sampling the profile at 10 cm intervals
is recommended. These samples will be combined
into one composite sample for the laboratory analysis.
‘Disturbed’ samples, taken at the three similar
sampling depths, should be combined too into a
composite sample. Sampling to determine bulk density
and for laboratory analysis is the same as for
General rules for soil sampling in forests of
Europe are specified by the ICP Manual (UNECE,
2003) and can be partly adapted, e.g., sampling
points should be 1 m distant from tree stems and
should avoid animal holes, disturbances like wind-thrown
trees and trails. However, ICP Manual centres
on monitoring of changes in the point and includes
details of litter horizon, which are unnecessary
when total organic carbon stock is considered.
As illustrated by the principal structure of
soil sampling in forests (Figure 3c) the organic
(litter) topsoil is sampled in a whole and accompanied
by indication of total thickness of the layer.
A frame of 25 by 25 cm is recommended for collecting
forest organic layer. In the field, the total
fresh weight of forest organic layer should be
determined. A sub-sample is collected for the
determination of moisture content (weight %) in
the laboratory to calculate total dry weight (kg/m2).
Mineral layers should be sampled at exactly the
same locations, i.e. sample the mineral soil underneath
the organic layer that has already been removed
for sampling. Sampling should be done at fixed
depths. The top of the mineral soil corresponds
with the zero level for depth measurements. The
entire thickness of the predetermined depth should
be sampled and not the central part of the layer
only. Auguring is preferred and pits are allowed,
especially in case of stony soils where auguring
are usually difficult and sometimes impossible.
For the determination of bulk density each mineral
layer (0-10 and 10-20 cm) of non-stony soils should
be taken from.
Figure 2. The scheme of soil profile sampling
The changes in organic carbon stock in soils
should be measurable, transparent and verifiable,
which is in line with some recommendations (IPCC,
2003). This conditions can be achieved if based
on physically measured carbon stocks prior to
(baseline occasion) and after (second occasion),
e.g. the latter can be first or second commitment
periods for the Kyoto Ptotocol (UNFCCC, 1998),
etc. Changes derived from models are complimentary
and valuable to define potential for carbon sequestration.
Area frame randomized soil sampling ensures a
reproducibility of the sampling sites. The target
is the estimate of the changes in organic carbon
stock and its standard error rather than the estimate
of organic carbon stock in soils.
The computation stems from a few parameters that
have to be measured in the field, determined in
laboratory and taken from other sources, e.g.,
cadastral information on the field location and
area. The list of necessarily parameters includes:
the carbon content in soil, the soil bulk density,
the thickness of the soil layer, the content of
coarse fragments and the area of the field. The
computation routine follows steps below:
Step 1: Calculation of soil organic carbon
density SCDsite for the sampling site:
SOC content is a soil organic carbon content,
% of mass or
Bulk Density is a soil bulk density ,
Depth is a thickness of the sampled layer
frag is volume of coarse fragments, %
of mass or
The SCDsite provides an average value for
the sampling site, which is derived from taking
a composite sample combining a number of sub-samples.
According to ISO 10381-4 at least 25 sub-samples
should be obtained (see Figure 2).
Step 2: Calculation of mean (arithmetic average)
soil carbon density ()
SCDsite is as indicated in Equation 1,
n is a number of sampled sites within the
Step 3: Calculation of reference soil organic
carbon stock (SOCrefstock) for the field:
indicated in Equation 2,
Ap is an area of the field.
Step 4: Calculation of changes (ΔSOCstock)
in organic carbon stock in soils:
SOCrefstock is as indicated in Equation
SOCnew is a new soil organic carbon stock
(second occasion), which is computed similar to
forg is C applied with organic fertilizers,
flim is C applied with lime.
Uncertainty is a parameter associated with the
result of measurement that characterizes the dispersion
of the values that could be reasonably attributed
to the measured quantity (IPCC, 2003) defines.
The uncertainty of the changes in organic carbon
stock in soils can be characterized by standard
error of the changes value that can be computed
by the steps below:
Step 5: Calculation of standard error
for mean soil carbon density :
is the average
of for the
sites sampled in the field ,
n is a number of sampling sites within the field.
Step 6: Calculation of standard error of
the changes of organic carbon stock
in the field:
is as indicated
in Equation 5,
Ap is an area of the field.
Step 7: The overall result in weight of
SOC and its standard error is:
Expressing the result inaccuracy in terms of standard
error allows to avoid the normality assumptions.
Table 2 shows average SCD and SOCstock
for three tested plots in Piemonte. The test was
done twice on each plot to simulate second occasion
sampling and followed the sampling protocol (Stolbovoy
et al., 2005). The highest SCD (12.91 KgC/m2)
was observed for pasture, which was explained
by the increase of C content in soil with height.
Forest soils have the lowest SCD (4.12
KgC/m2), which might be due to the
forest planting on poor dehumified soils. In this
case, one can assume that forest planting will
stimulate recovering of carbon content in soils.
Cropland soils have some medium SCD (9.03
KgC/m2). This data can suggest that
establishing of grassland on cropland might lead
to increase of carbon accumulation in soil of
Piemonte. Actually, this conclusion is in line
with general analysis for carbon sequestration
options in soil of Europe.
Second sampling illustrates distribution of SCD
similar to that of the first sampling. The difference
for both samplings varies from 14.5 tC/ha for
pastures to 5.2 tC/ha for forests.
% of the first sampling
Table 2. Parameters
of carbon content in soils of the tested cropland
(Repeated area frame randomized soil sampling)
The cost efficiency is a critical issue for the
carbon detection in soils. Figure 3 provides tentative
cost analysis of the carbon determination in the
laboratory depending on the area of the plot.
The calculation is done for two common European
conditions: 1) average carbon sink in agricultural
soils is 1.5 tC/ha; 2) - the lab cost of carbon
determination is 16 euro. As can be seen, the
lab cost decreases with the increase of the field
size: e.g., the lab cost to detect 1 tC in the
field having 1 ha is nearly 50 euro; this cost
is less than 2 euro if the area of the plot is
about 50 ha.
Figure 3. Dependence of the lab cost for carbon determination
on area of the field
(area frame randomized soil sampling).
A new area frame randomized soil sampling to
detect the changes of organic carbon stock in
mineral soils is developed. The field-testing
shows the following advantages of the method:
instrumental (GPS) positioning of the sampling
sites, uniform design of the sampling strategy
ensuring project’s consistency, pedological substantialization
of the sampling scheme, technological simplicity
and cost effectiveness. First field-testing of
the Area Frame Randomised Soil Sampling illustrates
that the changes ranged from 11 to 14 % of initial
C stock can be verified for the tested plots.
This difference illustrates that the method allows
achieving the reliable reproducibility of the
second occasion measurements. In addition, the
method allows to check applicability of specific
carbon sequestration measures for the field, e.g.,
cropland conversion to grassland can be verified
for the 20yr period (approximate sink would be
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 If no-till and non-plough crops are adopted the soil profile
turns to have gradual changes of soil characteristics
with depth. For this case soil sampling should
follows that of pasture.
 SCD refers to carbon concentration or related
to a layer of soil, e.g., 0-0.3, 0-05, 0-
1.0, 0-2.0 m. The SCD should not be confused
with carbon content in soils, which is fraction
of carbon by
weight of soil expressed in per cent or .
This equation describes the changes of organic
carbon stock due to sequestration from the atmosphere.