Page 1
Complexed organic matter controls soil physical properties
A.R. Dexter
a,b,c,
⁎
, G. Richard
a
, D. Arrouays
d
, E.A. Czy
ż
c,e
, C. Jolivet
d
, O. Duval
a
a
Unité de Science du Sol, INRA Orléans, Ardon, 45160 Olivet, France
b
Associate scientist, le Studium, Orléans, France
c
Institute of Soil Science and Plant Cultivation (IUNG-PIB), ul. Czartoryskich 8, 24-100 Pulawy, Poland
d
Unité InfoSol, INRA Orléans, Ardon, 45160 Olivet, France
e
Rzeszów University, Faculty of Biology and Agriculture, Aleja Rejtana 16c, 35-959 Rzeszów, Poland
Received 3 May 2007; received in revised form 21 November 2007; accepted 20 January 2008
Available online 10 March 2008
Abstract
It is shown that, for mineral soils, it is not the total amount of organic carbon (or organic matter) that controls soil physical behaviour but the
amount of complexed organic carbon (COC). We assume that this complex is formed by the association of unit mass (i.e. 1 g) of organic carbon
with
n
grams of clay. Analysis of data from two French and two Polish databases shows that, for these soils,
n
= 10. A consequence of this is that
in soils with small contents of organic carbon (OC), such as arable soils, COC is proportional to OC. However, in soils with large contents of
organic carbon, such as pasture soils, COC is proportional to the clay content. This explains why we find that soil bulk density is significantly
correlated with OC in French arable soils but with the clay content in French pasture soils. The use of COC instead of OC enables the arable and
pasture soils to be considered on the same scale.
Water retention data were fitted to a double-exponential equation which allows both the matrix and structural porosities to be estimated. It is
shown that in soils with low contents of organic carbon, the carbon content is positively correlated with the matrix porosity. In contrast, in soils
with high contents of organic carbon, the matrix porosity is constant at its maximum value and the structural porosity is not significantly correlated
with either the total organic carbon or the non-complexed organic carbon (NCOC). It is suggested that the complexed organic carbon can be
considered as being sequestered. The soil clay content can similarly be partitioned between clay that is complexed with organic carbon and clay
that is not complexed. It is shown that non-complexed clay is more easily dispersed in water than clay that is complexed with organic carbon.
These findings indicate how improved pedo-transfer functions for the prediction of soil physical properties may be produced. Such functions need
to use the values of complexed and non-complexed organic carbon and clay which must be determined by algorithms. The values produced by the
algorithms may then be used in the improved pedo-transfer functions.
© 2008 Elsevier B.V. All rights reserved.
Keywords:
Bulk density; Carbon sequestration; Clay dispersion; Matrix porosity; Organic carbon; Structural porosity; Water retention
1. Introduction
A large part of the world's organic carbon resides in the soil
(
Post et al., 1982; Eswaran et al., 1993; Batjes, 1996
). It plays
major roles in maintaining the soil physical condition,
sustaining soil microbial activity and enabling high crop yields
to be achieved and sustained (e.g.
Johnston and Poulton, 2005;
Lal, 2006, 2007
). It can also mitigate aspects of climate change
(e.g. global warming as a result of excessive concentrations of
carbon dioxide in the atmosphere) through its sequestration in
stable forms in the soil (e.g.
Sartori et al., 2006
). The literature
on soil organic carbon is huge and it is not the purpose of this
paper to attempt to review it. The purpose of this paper is
restricted to the study of some effects of soil organic carbon on
some soil physical properties.
Organic carbon occurs in the soil as organic matter (OM).
However, in this paper we shall talk mostly about organic
Available online at www.sciencedirect.com
Geoderma 144 (2008) 620
–
627
www.elsevier.com/locate/geoderma
⁎
Corresponding author. Unité de Science du Sol, INRA Orléans, Ardon,
45160 Olivet, France. Tel.: +33 2 3481 7845; fax: +33 2 3841 7869.
E-mail address:
tony.dexter@orleans.inra.fr
(A.R. Dexter).
0016-7061/$ - see front matter © 2008 Elsevier B.V. All rights reserved.
doi:
10.1016/j.geoderma.2008.01.022
Page 2
carbon (OC) because this is what people usually measure in
practice. For convenience, we assume a constant mass ratio of
(OM/OC) = 1.724 kg kg
−
1
.
Significant effects of organic matter on a range of soil physical
properties have been reported. These include compactability
(
Soane, 1990; Ball et al., 2000
), tillage (
Watts et al., 2006
), stability
in water (
Golchin et al., 1995
) and soil physical quality in general
(
Dexter, 2004a,b,c
). These effects are usually such that an increased
content of organic matter results in
“
improved
”
soil physical
properties (i.e. soil that is less compactable, more easily tilled and is
more stable in water). However, it seems that the effects on water
infiltration and hydraulic conductivity are less clear (
Nemes et al.,
2005; Mills et al., 2006
).
The soil physical properties that we consider in this paper are
bulk density, the water retention characteristic, matrix porosity
and the content of readily-dispersible clay. These properties are
critical for maintaining the soils' ability to function.
Bulk density,
D
, is defined as the mass of solids per unit
volume of the soil. However, we usually consider the specific
volume, 1/
D
, which is the volume per unit mass. We do this to
be consistent because we consider other quantities such as the
contents of clay or organic carbon on the unit mass basis.
For the water retention studies, we fit the retention data to a
bi-modal distribution that gives us information about the matrix
and structural pore spaces (
Dexter et al., 2008
). This enables us
to see how the organic carbon is affecting these two components
of the soil structure and the consequent effects on water storage
in the soil.
It is known that the content of clay that is readily-dispersible
in water is smaller when the soils' content of organic carbon is
greater (
Czy
ż
et al., 2002; Czy
ż
, 2005
). However, up until now
it has not been clear exactly what part of the total clay is readily-
dispersible and what factors govern this.
In this paper, therefore, we study these soil physical
properties as functions of the content of organic carbon. This
enables us to better understand the location and role of organic
carbon in the soil.
2. Materials and methods
2.1. Databases
We have used several sources of data for this work. The
databases are in alphabetical order as follows: POLHYDRO,
POLSTAB, RMQS and SOLHYDRO. The first two of these
are maintained in IUNG-PIB, Pu
ł
awy, Poland and contain data
on Polish soils. The second two are maintained in INRA,
Orléans, France and contain data on French soils. POLHYDRO
includes values of soil particle size distribution, organic matter
content, bulk density and water retention at 11 different
suctions in the range 10
–
15,000 hPa. POLSTAB includes
values of particle size distribution, organic matter content,
chemical analysis including exchangeable cations and content
of readily-dispersible clay. RMQS includes pedological
description of the soils, particle size distribution, bulk density,
organic matter content, chemical analysis. SOLHYDRO in-
cludes values of particle size distribution, bulk density, organic
matter content and water retention at 8 different suctions in the
range 10
–
15,000 hPa.
Not everything was used in every database. For POLHY-
DRO, a sub-set of 42 arable top- and subsoil horizons was
selected (85% of the original total). For SOLHYDRO, a sub-set
of (also, coincidentally, 42) arable topsoils was selected (67% of
the original total). The selection criterion for POLHYDRO and
SOLHYDRO was that all the necessary information was
available including the condition that the bi-modal water
retention function that was used converged to give unique
values of the fitting parameters. The most common reason for
non-convergence was when an increase in pore water suction
was not accompanied by a decrease in water content. That is,
variations in the water retention between the independent
samples caused the water retention curve to be not mono-
tonically-decreasing. For RMQS, agricultural top- and subsoil
horizons were selected that were north of latitude 49° N which
gave a set of data from 614 soil sampling pits. Therefore, data
were used from a total of 908 soils (places or horizons or pits) to
give a wide range of properties.
The POLHYDRO and POLSTAB databases are indepen-
dent: that is, they are based on different soils collected by
different people for different purposes. The RMQS and
SOLHYDRO databases are similarly independent.
A summary of some of mean properties of the soils that were
used is given in
Table 1
.
2.2. Numerical and other methods
The experimental procedures will not be described in detail
here. They were more-or-less conventional but with slight
variations in detail. The POLHYDRO methods are described in
Dexter et al. (2008)
. The POLSTAB methods are described in
Czy
ż
et al. (2002)
and
Czy
ż
(2005)
. The RMQS methods are
described in the RMQS manual (
Jolivet et al., 2006
; which can
be down-loaded at
http://www.gissol.fr/programme/rmqs/
RMQS_manuel_31032006.pdf
). The SOLHYDRO methods
are described in
Bruand et al. (2004)
.
The water retention data from the POLHYDRO and
SOLHYDRO databases were measured in the range 10
b
h
b
Table 1
The data bases used and a summary of the soils that they contain
Data base
Horizon type
Number
of soils
Clay content
(g/100 g)
Organic carbon
content (g/100 g)
Mean
S.D.
Mean
S.D.
POLHYDRO
Arable top-
and subsoils
42
9.9
6.9
0.70
0.31
POLSTAB
Arable topsoils
210
7.4
6.6
1.13
0.43
RMQS
(part of)
Arable topsoils
263
20.6
5.9
1.34
0.43
Arable subsoils
255
23.3
7.1
0.55
0.22
Pasture topsoils
51
28.5
9.1
3.26
0.89
Pasture subsoils
45
30.5
10.1
1.06
0.53
SOLHYDRO
(part of)
Arable topsoils
42
24.8
11.5
1.25
0.49
Note: the values of arithmetic mean and standard deviation (S.D.) are only
indicative, and are not intended to imply that the data are normally-distributed.
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–
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Page 3
15000 hPa on the drying limbs of the water retention char-
acteristics. The data were fitted to the water retention equation
of
Dexter et al. (2008)
:
w
¼
C
þ
A
1
e
−
h
=
h
1
þ
A
2
e
−
h
=
h
2
ð
1
Þ
The fitting was done using the Marquardt
–
Levenberg
algorithm (
Marquardt, 1963
). In Eq. (1),
w
is the gravimetric
water content which is expressed as a function of soil water
suction (or matric potential),
h.
The first term,
C
, is the
asymptotic value of
w
as
h
→
∞
which can be described as the
residual water content. The second term with
A
1
and
h
1
describes
the matrix porosity, whereas the third term with
A
2
and
h
2
describes the structural porosity. The values of
A
give the
amounts of the pore spaces, whereas the values of
h
give the
effective cylindrical diameters,
δ
, of the pores through the
equation
δ
¼
4
σ
h
;
ð
2
Þ
where
σ
is the surface tension of water. Note that Eq. (2) requires
the use of a consistent set of units (e.g. S.I.). However, with Eq.
(1), we use units of hPa for
h
,
h
1
and
h
2
both for convenience in
the laboratory and also for consistency with most of the published
literature.
The values of readily-dispersible clay in the POLSTAB
database were measured using samples that had been kept moist
at their field water contents. This is important because air-
drying can significantly (and often irreversibly) change clay
dispersibility and other soil physical properties. Sub-samples
were shaken gently in de-ionized water a standard way and then
left to stand for 16 h. Samples of the suspension were then
placed in a turbidimeter to quantify the concentration of colloids
(mostly clay). These values were then compared with those
from samples for which all the soil clay had been dispersed by
high input of mechanical energy. This enabled the contents of
readily-dispersible clay to be expressed on the basis of unit mass
of original soil.
2.3. Units of measurement
In this paper, we use bulk density,
D
, in Mg m
−
3
and its
reciprocal, the specific volume, 1/
D
, in m
3
Mg
−
1
. Gravimetric
water content,
w
, is in kg kg
−
1
as are its components at
saturation,
C
,
A
1
and
A
2
. Yet, 1/
D
,
C
,
A
1
and
A
2
describe parts
of the total pore space. They are measured in the above ways
because this is conventional.
However, we wish to point out that it is better in many
ways to express everything on a common logical basis. This
can be done by quantifying every component as a volume
(e.g. of water or pores) relative to unit volume of solids. By
doing this, the denominator becomes constant and only the
components of the numerator vary. When this is done, all
the separate components of the pore space become addi-
tive. Although we continue with bulk density and gravimet-
ric water contents in this paper, we note that: the total voids
ratio,
e
total
is given by
e
total
¼
ρ
s
D
−
1
h
i
;
ð
3
Þ
and that the components
e
residual
,
e
matrix
and
e
structural
are
given by
e
residual
¼
C
ρ
s
ρ
w
;
e
matrix
¼
A
1
ρ
s
ρ
w
and
e
structural
¼
A
2
ρ
s
ρ
w
ð
4
Þ
respectively, where
ρ
s
is the density of the solid (mineral)
phase, and
ρ
w
is the density of water.
3. Results
3.1. Soil bulk density results
Analysis of the data in the RMQS data base showed that for
the arable topsoils, the specific volume (1/
D
) as a function of
OC and clay can be written as:
1
=
D
¼
a
þ
b
OC
ð
Þ þ
c
clay
ð
Þ
ð
5
Þ
Note that in Eq. (5), we use 1/
D
rather than
D
so that all the
variables used are on the same (per unit mass of dry soil) basis.
Values of the coefficients of Eq. (5) are given in
Table 2
where it can
be seen that for the arable topsoils, which have relatively small
contents of organic carbon, 1/
D
is significantly correlated with the
organic carbon content, but not with the clay content. In contrast,
for the pasture topsoils, which have relatively large contents of
organic carbon, 1/
D
is significantly correlated with the clay content,
but not with the organic carbon content. In both cases, therefore, the
specific volume depends on the quantity that is smallest.
In order to reconcile the apparently contradictory results
presented in
Table 2
, we introduce the concept of complexed
organic carbon (COC). This involves the idea that 1 g of organic
carbon is associated or complexed with
n
grams of clay. This
scenario is illustrated in
Fig. 1
where it can be seen in A(top)
that for the arable topsoils, increasing levels of OC result in
corresponding increasing levels of COC in the order COC1,
COC2, COC3. In
Fig. 1
B(bottom), which illustrates the
situation with the pasture topsoils, increasing levels of clay
content result in corresponding increasing levels of COC in the
order COC4, COC5, COC6.
Table 2
Coefficients of Eq. (5) for soils from the RMQS data base
a
b
c
Arable
0.647 (± 0.018)
0.074 (± 0.011)
N.S. (
t
= 0.83)
r
2
= 0.161
p
b
0.0001
Pasture
0.561 (± 0.070)
N.S. (
t
= 12.6)
0.099 (± 0.0034)
r
2
= 0.321
p
b
0.0001
Values in parenthesis (±) are standard errors.
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A.R. Dexter et al. / Geoderma 144 (2008) 620
–
627
Page 4
We have estimated the value of
n
by determining how the
coefficient of correlation,
r
, between 1/
D
and COC varies with
n
. We illustrate this with data from the POLHYDRO database.
The results are shown in
Fig. 2
, where it can be seen that there is
a clear maximum at
n
= 10. The POLHYDRO data were used to
illustrate this because these have a more continuous spread of
values whereas the RMQS and SOLHYDRO data tend to have
either small contents of organic carbon (arable topsoils and all
subsoils) or large contents of organic carbon (pasture topsoils).
Nevertheless, the RMQS data give a maximum correlation at
n
= 11 and the SOLHYDRO data give a maximum at
n
= 8.
For simplicity and consistency, we use the value of
n
= 10
throughout unless otherwise stated. We note that
n
is a positive,
real number. However, the use of integer values is sufficiently
accurate for most practical purposes.
The amount of complexed organic carbon can be calculated
for each soil using the following algorithm:
COC
¼
IF OC
b
clay
n
THEN OC
½
ELSE
clay
n
;
ð
6
Þ
and the amount of non-complexed organic carbon, NCOC, using
NCOC
¼
IF
C
−
COC
ð
Þ
N
0
½
THEN
C
−
COC
½
ELSE 0
½
:
ð
7
Þ
Note that COC and NCOC are in the same units as OC and
clay: i.e. g/(100 g).
For the whole of the RMQS data set used (northern France,
arable and pasture, top- and subsoils), we obtained
I
=
D
¼
0
:
638
þ
0
:
072 COC
ð
Þ
;
F
0
:
005
ð
Þ
F
0
:
004
ð
Þ
r
2
¼
0
:
354
;
p
b
0
:
0001
ð
8
Þ
which has a larger value of
r
2
than Eq. (5) even though it is
covering a wider range of soils and conditions. Any additional
term, either total clay or non-complexed clay, was not statistically
significant and did not improve the predictive power of Eq. (8).
This illustrates the dominant effect of COC in determining 1/
D
.
We can compare Eq. (8) with the equivalent equations
obtained using the other databases. With the POLHYDRO data,
we obtained:
I
=
D
¼
0
:
558
þ
0
:
097 COC
ð
Þ
;
F
0
:
014
ð
Þ
F
0
:
022
ð
Þ
r
2
¼
0
:
266
;
p
b
0
:
0001
ð
9
Þ
With the SOLHYDRO topsoil data, we obtained:
I
=
D
¼
0
:
580
þ
0
:
059 COC
ð
Þ
;
F
0
:
017
ð
Þ
F
0
:
013
ð
Þ
r
2
¼
0
:
350
;
p
b
0
:
0001
ð
10
Þ
However, it is important to note two things. Firstly, the data
in these three databases were obtained using different methods,
so comparison of the absolute values of the coefficients in Eqs.
(8)
–
(10) cannot be exact. Secondly, in the RMQS, POLHY-
DRO and SOLHYDRO data sets used for Eqs. (8)
–
(10), 64 out
of 614, 19 out of 42, and 2 out of 42, respectively, were
saturated with organic carbon (i.e. had values of OC
N
clay/10).
If all soils were non-saturated with organic carbon, then COC
would equal OC in every case and no increase in
r
2
from the use
of COC would be obtained.
3.2. Water retention results
Eq. (1) was fitted to the water retention data for arable soils
in the POLHYDRO and SOLHYDRO data sets. Here,
w
is the
gravimetric water content and the first, second and third terms
correspond to the residual water content, the matrix pore space
and the structural pore space, respectively.
Fig. 2. Values of correlation coefficient,
r
, between 1/BD and COC calculated
with different
n
for Polish arable topsoils. The largest value of the correlation
coefficient occurs at
n
= 10.
Fig. 1. The concept of complexed organic matter (COC). Clay content
(expressed as clay/
n
) is shown by the open bars and organic carbon content is
show by the hatched bars. Situations for which (clay/
n
)
N
OC are shown in
A (top) where it can be seen that, in these cases, COC is proportional to the OC
content. Situations for which (clay/
n
)
b
OC are shown in B(bottom) where it can
be seen that, in these cases, COC is proportional to the clay content.
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A.R. Dexter et al. / Geoderma 144 (2008) 620
–
627
Page 5
The characteristic pore diameters calculated using Eq. (2)
and the geometric mean characteristic pore water suctions given
from the fits of Eq. (1) were:
δ
1
= 1.6 µm and
δ
2
= 46 µm for the
POLHYDRO soils and:
δ
1
= 0.83 µm and
δ
2
= 32 µm for the
SOLHYDRO soils. Here, the suffices 1 and 2 refer to the matrix
and structural pore spaces, respectively.
Regressions between (
C
+
A
1
) and COC for the POLHYDRO
soils yielded:
C
þ
A
1
¼
0
:
088
þ
0
:
075 COC
ð
Þ
;
F
0
:
013
ð
Þ
F
0
:
019
ð
Þ
r
2
¼
0
:
52
;
p
¼
0
:
00039
ð
11
Þ
However, with OC the correlation was only
r
= 0.25.
For the SOLHYDRO arable topsoils.
C
þ
A
1
¼
0
:
106
þ
0
:
080 COC
ð
Þ
;
F
0
:
018
ð
Þ
F
0
:
014
ð
Þ
r
2
¼
0
:
68
;
p
b
0
:
0001
ð
12
Þ
In this case, the correlation with OC was almost the same
because, as described above, only 2 of the 42 soils were saturated
with organic carbon and would have different values of COC.
The data points and the regression lines are shown in
Fig. 3
.
The similarity of the coefficients in Eqs. (11) and (12), suggests
that the clay
–
carbon complex has similar properties and is
affecting soils in the same ways in both France and Poland.
Regressions between
A
2
and COC or OC were not
statistically significant with either the Polish or the French
soils. This shows that the organic matter in the soil (expressed as
COC) affects the smallest soil pores (residual plus textural), but
has no effect on the larger structural pores. There was also no
significant correlation between
A
2
and NCOC.
3.3. Readily-dispersible clay
It is possible to quantify the amounts of complexed and non-
complexed clay in a similar way to the amounts of complexed
and non-complexed carbon given by Eqs. (6) and (7), above.
The amount of complexed clay, CC, is given by
CC
¼
IF
n
OC
b
clay
½
THEN
n
OC
½
ELSE clay
½
ð
13
Þ
and the amount of non-complexed clay, NCC, is given by
NCC
¼
IF clay
CC
ð
Þ
N
0
½
THEN clay
CC
½
ELSE 0
½
ð
14
Þ
We might expect that the NCC would be more readily-
dispersible in suspension than the total clay content, clay. We have
tested this hypothesis using the POLSTAB database which
contains measurements of the contents of readily-dispersible clay
as a measure of the stability (or instability) of soils in water. This
method is used in Poland instead of the more usual method of wet-
sieving of aggregates because in the typically sandy soils of Poland,
aggregates are often not sufficiently strong to collect and handle.
Use of the POLSTAB database enabled us to examine
correlation coefficients,
r
, between the amount of readily-
dispersible clay, RDC, g/(100 g soil), and different measures of
the clay content. Correlation between RDC and total clay gave
r
= 0.43. However, we get a different picture if we consider the
Fig. 3. Values of
C
+
A
1
as a function of COC for Polish (A) and French (B)
arable topsoils. As described in the text and shown in Eqs. (11) and (12), the
intercepts and slopes are not significantly different for these two data sets.
Fig. 4. Values of the coefficient of correlation,
r
, between measured readily-
dispersible clay and calculated non-complexed clay as a function of
n
. Also
shown is the line corresponding to the case in which the contents of organic
carbon have been randomized, thereby eliminating effects of correlations with
measured carbon contents.
Fig. 5. Differentials of the coefficient of correlation,
r
, between readily-
dispersible clay and non-complexed clay as a function of
n
.
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A.R. Dexter et al. / Geoderma 144 (2008) 620
–
627
Page 6
amount of clay that is not complexed with organic carbon, NCC.
We note that here we are studying the correlation coefficient,
r
,
between one part of the total clay (RDC) and another part of the
total clay (NCC) as a function of
n
. A graph of
r
against
n
rises
progressively as
n
increases as shown in
Fig. 4
. With each
increase in
n
, the number of soils with a finite amount of NCC
decreases progressively. When
n
= 0, all the clay in all 210 soils is
predicted to be NCC and
r
= 0.43, as described above. When
n
= 6, 10 and 14, for example, only 106, 49 and 29 of the soils
are predicted to have finite amounts of NCC and can be used to
study the correlation between RDC and NCC. When there is only
one soil remaining with finite NCC, then the coefficient of
correlation between RDC and NCC is
r
= 1.0, although this
situation is not shown in
Fig. 4
.
The question arises as to how we know that the top curve in
Fig. 4
is due to interactions with the organic matter. To test this, we
destroyed any direct interactions or correlations by using
randomised organic matter contents together with the measured
values of total clay and contents of readily-dispersible clay. This
produced lower values of
r
which are represented by the
horizontal line in
Fig. 4
. Different values were obtained depending
on the set of random numbers that was used in the simulations. We
conclude that the difference between the upper and lower curves
in
Fig. 4
is due to interactions involving the organic matter.
The slope of the top curve in
Fig. 4
is not constant. Therefore, we
produced its differential, d
r
/d
n
, which is shown in
Fig. 5
. This
shows a clear maximum at
n
= 10 which again supports the
conclusion that 1 g of organic carbon is complexed with 10 g of clay.
NCC was calculated for each soil on the basis of
n
= 10. This
gave a correlation coefficient between RDC and NCC of
r
= 0.53. We then prepared a sub-set of these data for which
NCC was greater than 0. We then obtained the following
regression equation for readily-dispersible clay:
RDC
¼
0
:
286
þ
0
:
064NCC
;
g
=
100g
;
F
0
:
123
ð
Þ
F
0
:
011
ð
Þ
r
¼
0
:
79
;
p
b
0
:
0001
ð
15
Þ
This illustrates the strong effect of the complexing between
clay and organic carbon on clay dispersion from the soils in the
POLSTAB database.
4. Discussion
4.1. Implications for soil bulk density
Eq. (5) with the data in
Table 2
shows that the reciprocal of
the soil bulk density (i.e. the specific volume) is positively
correlated only with the content of organic carbon in soils of
small carbon content and only with the content of clay in soils of
large carbon content. It has been shown in Section 3.1 that these
findings can be reconciled if we introduce the concept of
complexed organic carbon (COC). This involves the assump-
tion that within the soil, unit mass (e.g. 1 g) of organic carbon is
complexed with
n
grams of clay. This situation is illustrated in
Fig. 1
, where it can be seen that the amount of COC is governed
either by the content of organic carbon, OC, or value of (clay/
n
),
whichever is the smaller. With the Polish and French data sets
used here, we find that
n
= 10, to within experimental error.
We know, of course, that the bulk density of soils is a
function not only of the soil composition but also of manage-
ment factors such as compaction by machinery, tillage and
cropping. However, the results presented here do not take
account of these factors and show that of the soil properties, it is
the content of COC rather than total OC in the soil that better
correlates with the value of the bulk density.
4.2. Implications for soil sampling
Consider a soil that behaves as in Eq. (8). Substitution of some
values shows that a change of content of COC by 1% (i.e. a
change of 1 g COC per 100 g dry soil), will cause a corresponding
change in the height (of a typical topsoil) of 10%. In this way, the
gain or loss of organic carbon can cause swelling or shrinkage of
soil. This is another component of the changes in soil density in
addition to the swelling and shrinking caused by water and the
increases in density caused by compaction.
These density changes can have effects on the measured depths
of soil features such as the boundary between the topsoil and the
subsoil. This observation supports the proposal of
Ellert and
Bettany (1995)
that depths in soils should be measured on the
basis of mass of dry soil per unit area and not in units of length.
4.3. Implications for soil water retention
These results show that soil organic matter, expressed as
COC, influences the matrix pore space in soil but not the
structural pore space as suggested originally by
Pereira et al.
(2007)
. This is in contrast with compression or compaction of
soil which has been found to occur at the expense of the
structural pore space (
Dexter et al., 2008; Pereira et al., 2007
).
This finding supports the view that all the different terms in
Eq. (1) have different physical meanings which respond
differently to differing situations. This is in contrast with
other water retention equations, such as the
van Genuchten
(1980)
equation, in which the fitting parameters
α
,
n
and
m
interact strongly and cannot be uniquely identified with physical
features in the soil. These interactions have been quantified and
discussed by
Dexter et al. (2008)
.
4.4. Implications for the soil clay
The amounts of complexed and non-complexed clay were
calculated as shown in Eqs. (13) and (14). We have tested the
hypothesis that NCC is more easily dispersed in water than CC
using the POLSTAB database. This was done using correlation
coefficients.
The results show clearly that the complexed clay is less readily-
dispersible in water and that it is the non-complexed clay that is
preferentially-dispersed and which contributes strongly to mea-
sured values of readily-dispersible clay. The transition between
non-complexed and complexed clay occurs at around
n
= 10.
For more than one-half of the soils, some RDC was measured
even when NCC = 0. This suggests that a spectrum of binding
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A.R. Dexter et al. / Geoderma 144 (2008) 620
–
627
Page 7
energies is involved in the complexing of organic carbon and
clay. No sharp cut-off in RDC was observed when clay
b
n
OC.
The above results were obtained with only very small inputs
of mechanical energy (a gentle shaking in water). We conjecture
that with greater energy inputs, successively smaller values of
n
would be obtained corresponding to the progressive dispersion
of more strongly bound (complexed) clay. Therefore, we could
not have predicted beforehand that these measurements of
readily-dispersible clay would give the same value of
n
as the
bulk density and water retention experiments, described above.
4.5. Implications for carbon sequestration
We might expect that complexed organic carbon would be
more stable (i.e. less labile) than non-complexed organic carbon.
We therefore suggest that for carbon sequestration in arable soil,
the carbon should be complexed. This means that the maximum
amount, CMAX, that can be stored in arable soil is
CMAX
¼
clay
=
n
:
ð
16
Þ
This maximum amount has been called the
“
capacity factor
”
by
Hassink (1997)
and
Carter et al. (2003)
, although they related the
organic carbon to the sum of clay + silt instead of just the clay, as
used here. We have used the clay fraction because many studies of
soil organic matter and its interactions emphasize the key role of
clay (e.g.
Oades, 1988
) and because it is used as the factor that
physically protects organic matter from microbial decomposition in
the RothC model for the dynamics of soil organic matter (
Coleman
et al., 1997
). Eq. (16) is consistent with all the observations and
results presented above. However, it would be useful to test this
relationship with soils with different clay mineralogy.
The carbon stored within microaggregates and which is
associated with the matrix porosity is relatively insensitive to
soil management practices, such as tillage, as described by
Tisdall and Oades (1982)
. In contrast, carbon contents greater
than the capacity factor, as occur in the RMQS pasture topsoils,
have been found to be mainly in the form of particulate organic
matter (POM) by
Carter et al. (2003)
but also partly in the form
of polymeric decomposition products by
Puget et al. (1995)
.
This non-complexed and non-protected organic matter is
sensitive to soil management practices in arable soils and is
susceptible to decomposition. However, it seems that it can
persist for longer times or even to accumulate in soils that are
not disturbed, such as pasture soils (
Soussana et al., 2004
).
Data for RMQS topsoils are plotted in
Fig. 6
. It can be seen that
most of the points for arable soils fall below the saturation line.
Also, most of the points for pasture soils fall above the saturation
line. We did not take account of changes of land use which might
account for the small number of soils being on the opposite side of
the saturation line in comparison with the majority of soils having
the same current land use. Most of the arable soils fall well below
the saturation line which implies that they could potentially hold
more organic carbon in complexed form.
The potential additional amount of stable (i.e. complexed)
organic carbon, PAOC, that could be stored in arable soil can be
estimated by the difference between the maximum possible (Eq.
(16)) and the actual amount:
PAOC
¼
IF CMAX
COC
ð
Þ
N
0
½
THEN CMAX
COC
½
ELSE 0
½
ð
17
Þ
More than this could be stored in soil that is never disturbed,
as described above. However, we conjecture that most of this
could be lost by decomposition relatively rapidly (years rather
than decades) if the soil is disturbed, for example by tillage.
5. Conclusions
A simple algorithmic model for the amount of complexed
organic matter in soils explains why soil bulk density is
correlated with organic carbon content in soils of low organic
matter content whereas it is correlated with the clay content in
soils of high organic matter content. Regressions show that this
model is optimized when 1 g of organic carbon is complexed
with 10 g of clay, giving the value of
n
= 10. Use of this value of
n
in algorithms enables the amounts of complexed and non-
complexed organic matter in a soil to be estimated.
Although 10 may seem a large value for
n
, it should
considered in relation to its definition.
If we assume a mass ratio of OM/OC = 1.724, then we find
that 1 g OM can complex 5.8 g clay. Then, if we assume that the
density of OM = 1 Mg m
−
3
and that the density of a common
clay mineral (illite) = 2.75 Mg m
−
3
, we can see that unit volume
of OM is involved in complexing approximately 2 unit volumes
of clay. It is easier to envisage this 2:1 volume ratio in the soil.
A new function for soil water retention was fitted to data sets
for arable topsoils from both Poland and France. Adjustable
parameters in this model correspond to the matrix porosity and the
structural porosity. It is shown that the complexed organic matter
is correlated with the matrix porosity but not with the structural
porosity. The non-complexed organic matter is not correlated
significantly with either the matrix or the structural porosity.
The part of the clay in soil that is readily-dispersible in water
is found to correlate most strongly with the portion of the clay
Fig. 6. Data for RMQS topsoils from northern France. Arable soils are shown as
circles and pasture soils are shown as triangles. The line is the
saturation line
calculated using Eq. (17) with
n
= 10.
Y
values below the line are contents of
complexed organic carbon (COC), and heights about the line are contents of
non-complexed organic carbon (NCOC).
626
A.R. Dexter et al. / Geoderma 144 (2008) 620
–
627
Page 8
that is not complexed with organic matter (again giving the
optimum value of
n
= 10).
Considerations of the capacity of soils to form complexes
with organic matter enable the potential of soils to sequester
additional organic carbon in stable form to be estimated. This,
however, needs further experimental testing.
The content of complexed organic carbon in soils, determined
by the simple algorithmic method, correlates better with the soil
physical properties considered (bulk density, soil structure, water
retention and content of readily-dispersible clay than the total
carbon content. We therefore recommend that the complexed
organic carbon should be evaluated for potential use in pedo-
transfer functions for the prediction of soil physical properties.
We believe that further valuable insight could be obtained
from study of the water retention characteristics of pasture
topsoils. Unfortunately, we do not have such a data set.
The use of complexed organic carbon instead of total carbon
leads to equations that have both greater predictive power and
greater parsimony.
Acknowledgements
A.R.D. would like to thank the Le STUDIUM organization
(Orléans) for the financial support that has made it possible to
work for a period in INRA, Orléans. E.A.C. and G.R. would
like to thank the POLONIUM programme for the possibility to
have scientific exchanges of visits between INRA, Orléans and
IUNG-PIB, Pu
ł
awy. The authors would like to thank Manuel
Martin of the Unité InfoSol, INRA, Orléans for his help with the
RMQS data base. The RMQS is supported by a scientific
interest group named GISSOL (
http://www.gissol.fr
).
References
Ball, B.C., Campbell, D.J., Hunter, E.A., 2000. Soil compactability in relation to
physical and organic properties at 156 sites in U.K. Soil Tillage Res. 57,
83
–
91.
Batjes, N.H., 1996. Total carbon and nitrogen in soils of the world. Eur. J. Soil
Sci. 47, 151
–
163.
Bruand, A., Duval, O., Cousin, I., 2004. Estimation des propriétés de retention
en eau des sols à partir de la base de données SOLHYDRO. Étude Gest. Sols
11 (3), 323
–
332.
Carter, M.R., Angers, D.A., Gregorich, E.G., Bolinder, M.A., 2003.
Characterizing organic matter retention for surface soils in eastern Canada
using density and particle size fractions. Can. J. Soil Sci. 83, 11
–
23.
Coleman, K., Jenkinson, D.S., Crocker, G.J., Grace, P.R., Klír, J., Körschens,
M., Poulton, P.R., Richter, D.D., 1997. Simulating trends in soil organic
carbon using RothC-26.3. Geoderma 81, 29
–
44.
Czy
ż
, E.A., 2005. Ilo
ś
ciowa i przestrzenna charakterystyka podatno
ś
ci na
destrukcj
ę
gleb u
ż
ytków rolnych w Polsce. Quantitative and spatial
characteristics of the sensitivity of Polish agricultural soils to destruction.
In
ż
. Rol. 3 (63), 15
–
23 (in Polish with English summary).
Czy
ż
, E.A., Dexter, A.R., Terelak, H., 2002. Content of readily-dispersible clay
in the arable layer of some Polish soils. In: Pagliai, M., Jones, R. (Eds.),
Sustainable Land Management
–
Environmental Protection. Advan. Geoe-
cology, vol. 35, pp. 115
–
124.
Dexter, A.R., 2004a. Soil physical quality. Part I: theory, effects of soil texture,
density and organic matter, and effects on root growth. Geoderma 120,
201
–
214.
Dexter, A.R., 2004b. Soil physical quality. Part II: friability, tillage, tilth and
hard-setting. Geoderma 120, 215
–
225.
Dexter, A.R., 2004c. Soil physical quality. Part III: unsaturated hydraulic
conductivity and general conclusions about S-theory. Geoderma 120,
227
–
239.
Dexter, A.R., Czy
ż
, E.A., Richard, G., Reszkowska, A., 2008. A user-friendly
water retention function that takes account of the textural and structural pore
spaces in soil. Geoderma. 143, 243
–
253.
Ellert, B.H., Bettany, J.R., 1995. Calculation of organic matter and nutrients
stored in soils under contrasting management regimes. Can. J. Soil Sci. 75,
529
–
538.
Eswaran, H., van den Berg, E., Reich, P., 1993. Organic carbon in soils of the
world. Soil Sci. Soc. Am. J. 57, 192
–
194.
Golchin, A., Clarke, P., Oades, J.M., Skjemstad, J., 1995. The effects of
cultivation on the composition of organic matter and the structural stability
of soils. Aust. J. Soil Res. 33, 975
–
993.
Hassink, J., 1997. The capacity of soils to preserve organic C and N by their
association with clay and silt particles. Plant Soil 191, 77
–
87.
Jolivet, C., Boulonne, N., Ratié, C., 2006. Manuel du réseau de mesures de la
qualité des sols. Unité InfoSol, INRA, Orléans, France. (ISBN 2-7380-1235-3),
190 pp.
Johnston, A.E., Poulton, P.R., 2005. Soil organic matter: its importance in
sustainable agricultural systems. Proc. Int. Fert. Soc. 565, 1
–
46 (ISBN 0
85310 202 3).
Lal, R., 2006. Enhancing crop yields in the developing countries through
restoration of the soil organic carbon pool in agricultural lands. Land
Degrad. Dev. 17, 197
–
209.
Lal, R., 2007. Anthropogenic influences on world soils and implications for
global food security. Adv. Agron. 93, 69
–
93.
Marquardt, D.W., 1963. An algorithm for least squares estimation of non-linear
parameters. J. Soc. Ind. Appl. Math. 11, 431
–
441.
Mills, A.J., Fey, M.V., Grongoft, A., Petersen, A., Medinski, T.V., 2006.
Unravelling the effects of soil properties on water infiltration: segmented
quantile regression on a large data set from south-west Africa. Aust. J. Soil
Res. 44, 783
–
797.
Nemes, A., Rawls, W.J., Pachepsky, Y.A., 2005. Influence of organic matter on
the estimation of saturated hydraulic conductivity. Soil Sci. Soc. Am. J. 69,
1330
–
1337.
Oades, J.M., 1988. The retention of organic matter in soils. Biogeochemistry 5,
35
–
70.
Pereira, J.O., Défossez, P., Richard, G., 2007. Soil susceptibility to compaction
by wheeling as a function of some properties of a silty soil as affected by the
tillage system. Eur. J. Soil Sci. 58, 34
–
44.
Post, W.M., Emanuel, W.R., Zinke, P.J., Stangenberger, A.G., 1982. Soil carbon
pools and world wildlife zones. Nature 298, 156
–
159.
Puget, P., Chenu, C., Balesdent, J., 1995. Total and young organic matter
distributions in aggregates of silty cultivated soils. Eur. J. Soil Sci. 46,
449
–
459.
Sartori, F., Lal, R., Ebinger, M.H., Parrish, D.J., 2006. Potential soil carbon
sequestration and CO
2
offset by dedicated energy crops in the U.S.A. Crit.
Rev. Plant Sci. 25 (5), 441
–
472.
Soane, B.D., 1990. The role of organic matter in soil compactability. Soil Tillage
Res. 16, 179
–
201.
Soussana, J.F., Loiseau, P., Vuichard, N., Ceschia, E., Balesdent, J., Chevalier,
T., Arrouays, D., 2004. Carbon cycling and sequestration opportunities in
temperate grasslands. Soil Use Manage. 20, 219
–
230 (Supplement).
Tisdall, J.M., Oades, J.M., 1982. Organic matter and water-stable aggregates in
soils. J. Soil Sci. 33, 141
–
163.
van Genuchten, M.Th., 1980. A closed-form equation for predicting the
hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44,
892
–
898.
Watts, C.W., Clark, L.J., Poulton, P.R., Powlson, D.S., Whitmore, A.P., 2006.
The role of clay, organic carbon and long-term management on mouldboard
plough draft measured on the Broadbalk wheat experiment at Rothamsted.
Soil Use and Manage. 22, 334
–
341.
627
A.R. Dexter et al. / Geoderma 144 (2008) 620
–
627
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