ArticlePDF Available

Abstract and Figures

a b s t r a c t Mountain soils store huge amounts of carbon which may be highly vulnerable to the strong land use and climate changes that mountain areas currently experience worldwide. Here, we tested the Rock–Eval (RE) pyrolysis as a proxy technique to (i) quantify soil organic carbon (SOC) stocks, (ii) bring insights into SOC bulk chemistry and (iii) investigate biogeochemical stability at the landscape scale in a mountain area of the French calcareous Prealps. A total of 109 soils from 11 eco-units representing the variety of ecosys-tems of the study area were analyzed with RE pyrolysis. RE pyrolysis showed an excellent predictive per-formance (R 2 = 0.99) for SOC content even in calcareous soils. The technique revealed specific chemical fingerprints for some eco-units and soil types, with decreasing hydrogen index values from Anthroposols (425 ± 62 mg HC/g SOC) to Umbrisols, Leptosols (311 ± 49 mg HC/g SOC) and to Cambisols (278 ± 35 mg HC/g SOC), associated with an increase in SOC maturation. Newly developed RE pyrolysis indices revealed the high stability of SOC in most eco-units developed on Cambisols (acidic grasslands, alpine meadows, bushy facies) and a significantly lower stability of SOC in mountain ridges, sheepfold areas and coniferous forest soils. The persistence of SOC in this mosaic of ecosystems may depend not only on its chemistry or thermal stability, but also on local environmental factors such as climatic conditions or pH, especially for high altitude soils. Overall, RE pyrolysis appears as an appropriate tool for landscape scale carbon inven-tories and could become a standardized proxy for assessing the vulnerability of SOC stocks.
Content may be subject to copyright.
(This is a sample cover image for this issue. The actual cover is not yet available at this time.)
This article appeared in a journal published by Elsevier. The attached
copy is furnished to the author for internal non-commercial research
and education use, including for instruction at the authors institution
and sharing with colleagues.
Other uses, including reproduction and distribution, or selling or
licensing copies, or posting to personal, institutional or third party
websites are prohibited.
In most cases authors are permitted to post their version of the
article (e.g. in Word or Tex form) to their personal website or
institutional repository. Authors requiring further information
regarding Elsevier’s archiving and manuscript policies are
encouraged to visit:
Author's personal copy
Soil organic carbon quantity, chemistry and thermal stability in a mountainous
landscape: A Rock–Eval pyrolysis survey
Anaïs Saenger
, Lauric Cécillon
, David Sebag
, Jean-Jacques Brun
Irstea, UR EMGR Ecosystèmes Montagnards, 2 rue de la Papeterie, BP 76, F-38402 Saint-Martin-d’Hères, France
CNRS, UMR 6143, Morphodynamique Continentale et Côtière, Département de Géologie, Université de Rouen, F-76821 Mont Saint Aignan Cedex, France
IRD, UMR 5569, HydroSciences Montpellier, Université de Montpellier 2, F-34095 Montpellier Cedex 5, France
article info
Article history:
Received 9 July 2012
Received in revised form 19 October 2012
Accepted 19 October 2012
Available online 26 October 2012
Mountain soils store huge amounts of carbon which may be highly vulnerable to the strong land use and
climate changes that mountain areas currently experience worldwide. Here, we tested the Rock–Eval (RE)
pyrolysis as a proxy technique to (i) quantify soil organic carbon (SOC) stocks, (ii) bring insights into SOC
bulk chemistry and (iii) investigate biogeochemical stability at the landscape scale in a mountain area of
the French calcareous Prealps. A total of 109 soils from 11 eco-units representing the variety of ecosys-
tems of the study area were analyzed with RE pyrolysis. RE pyrolysis showed an excellent predictive per-
formance (R
= 0.99) for SOC content even in calcareous soils. The technique revealed specific chemical
fingerprints for some eco-units and soil types, with decreasing hydrogen index values from Anthroposols
(425 ± 62 mg HC/g SOC) to Umbrisols, Leptosols (311 ± 49 mg HC/g SOC) and to Cambisols (278 ± 35 mg
HC/g SOC), associated with an increase in SOC maturation. Newly developed RE pyrolysis indices revealed
the high stability of SOC in most eco-units developed on Cambisols (acidic grasslands, alpine meadows,
bushy facies) and a significantly lower stability of SOC in mountain ridges, sheepfold areas and coniferous
forest soils. The persistence of SOC in this mosaic of ecosystems may depend not only on its chemistry or
thermal stability, but also on local environmental factors such as climatic conditions or pH, especially for
high altitude soils. Overall, RE pyrolysis appears as an appropriate tool for landscape scale carbon inven-
tories and could become a standardized proxy for assessing the vulnerability of SOC stocks.
Ó2012 Elsevier Ltd. All rights reserved.
1. Introduction
Soil organic matter (SOM) is a key component of the global car-
bon (C) cycle. It is by far the largest terrigenous carbon reservoir,
containing three times more C than is stored in land plant vegeta-
tion and twice as much as the atmosphere (Eswaran et al., 2000).
So far, the persistence of organic matter in soils is a largely un-
known and much debated phenomenon and appears to be ecosys-
tem specific (Schmidt et al., 2011). Moreover, strong uncertainties
remain regarding the vulnerability of SOM stocks to changes in cli-
mate, land use and management (Cotrufo et al., 2011). Here, a SOC
pool is considered highly vulnerable to a disturbance if the physi-
cal, chemical and/or biological change in soil characteristics results
in a SOC loss over the long term (Grosse et al., 2011), according to
the definition of Chapin et al. (2010). Furthermore, carbon stocks of
mountain soils, although highly variable, are among the major
SOM pools, of the same magnitude as boreal and tundra soils
(Jobbagy and Jackson, 2000). These soils and their C stocks may
be particularly vulnerable to the strong land use or climate changes
that they are currently experiencing, but the huge heterogeneity of
mountain landscapes makes it difficult to generalize such state-
ments (Sjögersten-Turner et al., 2011). To date, very few studies
have addressed SOM vulnerability at the landscape scale in moun-
tain regions, as most soil C inventories focus only on stocks (e.g.
Garcia-Pausas et al., 2007; see Sjögersten-Turner et al. (2011) for
a review). Soil C inventories in mountain areas should now go a
step further, including information on SOC vulnerability to current
and future environmental changes, so as to infer its mid and long
term persistence (Messerli and Ives, 1997; Becker et al., 2007;
Schmidt et al., 2011; Sjögersten-Turner et al., 2011). Basic informa-
tion on soil C vulnerability could be accessed through the chemical
characterization of SOM and the quantification of its various stabil-
ization mechanisms. Four main mechanisms controlling SOM sta-
bility have been identified, although poorly documented in
mountain soils: (i) selective preservation of compounds chemically
resistant to decomposition (i.e. recalcitrant), (ii) physical stabiliza-
tion by surface interactions with minerals (e.g. oxides, clays), (iii)
spatial inaccessibility through aggregation, and (iv) climatic stabil-
ization due to freezing temperatures, low oxygen content and/or
waterlogging of soils (von Lutzow et al., 2006; Torn et al., 2009).
The implementation of soil C survey including information on
its vulnerability requires the use of reliable techniques adapted
to large scale monitoring, allowing for fast, easy and non-costly
0146-6380/$ - see front matter Ó2012 Elsevier Ltd. All rights reserved.
Corresponding author. Tel.: +33 4 7676 2797; fax: +33 4 7651 3803.
E-mail address: (A. Saenger).
Organic Geochemistry 54 (2013) 101–114
Contents lists available at SciVerse ScienceDirect
Organic Geochemistry
journal homepage:
Author's personal copy
handling of important sets of samples. Rock–Eval (RE) pyrolysis is a
technique that fulfills these criteria. This method was initially
developed for the oil industry and broadly used for the geochemi-
cal analyses of organic matter in sedimentary rocks (Espitalié et al.,
1977). This technique gives a rapid and thorough assessment of
carbon contents, including the apportioning of the organic and
inorganic C fractions, and information on the maturation and stoi-
chiometry of organic C (i.e. atomic H/C and O/C ratios), without any
need for preliminary sample preparation (Espitalié et al., 1977,
1985a,b; Sebag et al., 2006). Because of its simplicity and
reliability, RE pyrolysis has recently been used for a variety of
materials it had not originally been intended for, such as soils
(e.g. Disnar et al., 2003; Sebag et al., 2006; Gogo et al., 2010). In
a recent review on pioneering works on SOM, Feller et al. (2010)
pointed out the great value of RE pyrolysis for soil scientists and
Recent research has shown that the stability of SOM to thermal
treatment can be used as an overall approximation of the biogeo-
chemical stability of SOM (i.e. its persistence in ecosystems; Plante
et al., 2009, 2011). Thus, thermal analysis such as thermogravime-
try (TG, a technique which records mass changes of SOM as a func-
tion of temperature) or RE pyrolysis could be efficient proxies to
assess soil C vulnerability. But RE pyrolysis has the advantage over
TG to be rather more free from direct interfering signal of the min-
eral matrix (potential dewatering of clays and minerals) and to
provide Supplementary information on bulk SOM chemistry. Mid
infrared spectrometry (MIRS), a technique which measures absorp-
tion of soil constituents in the mid infrared region, is another pow-
erful tool providing a detailed information on the molecular
structure of SOM (Cécillon et al., 2012). However, MIRS is not rel-
evant to assess SOM stabilization processes aside from selective
preservation of biochemical components resistant to decomposi-
tion. Consequently, RE pyrolysis appears to be the only proxy tech-
nique providing reliable information on both SOM chemical
composition and stability at the same time. Furthermore, the
semi-automation of the Rock–Eval apparatus that permits the han-
dling of large numbers of samples is very suitable for monitoring
over large temporal and spatial scales.
Here, we present the results of a landscape scale soil carbon
inventory based on RE pyrolysis covering a large variety of ecosys-
tems and topographic situations in a calcareous mountain of the
French Alps. The objectives of this study were to (i) assess the accu-
racy of RE pyrolysis in measuring soil organic C (SOC) concentra-
tion in calcareous soils and quantifying SOC stocks in topsoils of
each ecosystem of the area; (ii) bring insights into SOM bulk chem-
istry using RE pyrolysis parameters linked to SOM elemental com-
position (i.e. hydrogen and oxygen indices); (iii) investigate the
stability of SOC at the landscape scale using indices derived from
RE pyrolysis (variants of R400 index; Disnar et al., 2003), and (iv)
infer the main drivers of SOC stabilization and soil C vulnerability
in each ecosystem unit.
2. Materials and methods
2.1. Study area
The study was conducted in the Vercors High Plateaus Natural
Reserve (VHPNR; Fig. 1), a protected mountainous area that is part
of the French long term ecological research site ‘‘Zone Atelier
Alpes’’ (LTER-ZAA; CNRS-Irstea). The VHPNR is located in the Ver-
cors Mountains (French calcareous Prealps; 5°42
N, 44°97
E). This area is the largest natural reserve of France, covering
17,000 ha and extending 30 km along a north–south axis. The alti-
tude ranges from 1300–2340 m above sea level (a.s.l.). The VHPNR
is subject to multiple bioclimatic conditions due to its location: its
mountainous climate is influenced by the transition between the
Northern Alps climate and the Mediterranean climate, and be-
tween continental and Atlantic influences. There is no mean annual
temperature difference between the north and south of the VHPNR,
but the northernmost part (continental influence) recorded higher
thermal amplitudes than the southernmost part (Mediterranean
influence). Mean annual temperature is +4.7 °C, with mean sea-
sonal temperature ranging from 2.3 °C in winter to +12.1 °Cin
summer. Rainfall is regularly distributed throughout the year, but
is more abundant in the central area of the VHPNR (mean annual
precipitation of 1552 mm) than the northern (1171 mm) and the
southern part (811 mm). In winter, the snow cover usually persists
from November to the end of May, with a great variability of snow
coverage and thickness depending on local geomorphology and
altitude (Bigot et al., 2010). The vegetation of VHPNR ranges from
mixed forests (mountain stage; 900–1500 m a.s.l.) to coniferous
forests and subalpine grasslands (subalpine stage; 1500–1900 m
a.s.l.) and alpine meadows (alpine stage; above 1900 m a.s.l.). Soils
of the VHPNR developed on Urgonian limestones and are generally
neutral or basic. They comprise humiferous and very shallow Cam-
bisols, Leptosols, Umbrisols and Anthroposols (FAO/IUSS/ISRIC,
2006). Detailed information on vegetation and soil types of the
study area are provided below and in Table 1 (see also the Supple-
mentary material for detailed information on each of the 109 soil
profiles). The only economic activities within the study area are
logging (low intensity) and extensive sheep grazing.
2.2. Soil sampling strategy
The VHPNR was divided into eleven eco-units, defined accord-
ing to the main types of vegetation (based on the analysis of veg-
etation maps; Véron et al., 2004), altitude and land use. The
eleven eco-units included: (i) sheepfold areas; (ii) coniferous for-
ests (subalpine stage); (iii) coniferous forests (mountain stage);
(iv) mixed forests; (v) bushy facies; (vi) xeric calcareous grass-
lands; (vii) mesic calcareous grasslands; (viii) acidic grasslands;
(ix) alpine meadows; (x) mountain ridges; and (xi) a former coal
kiln site (see Table 1 and A1 for detailed information on vegetation
and soil types of each eco-unit). A total of 109 soil profiles were
randomly sampled in summer 2009 and 2010 within all eco-units
(n= 1–34 per eco-unit, depending on its importance in the
VHPNR). In each sampling site, we established a 4 m 4 m plot
for soil sampling. Each soil sample is a composite of 8–10 subsam-
ples taken randomly from each plot. Topsoil (0–10 cm) material
was collected from the A horizon (organo-mineral layer). We only
focus on this layer because topsoil is known for its propensity to
mobilize significant amounts of C under land use or climate
changes and because soil is generally very shallow throughout
the VHPNR. The litter layer, when it was present, was removed
prior to sampling. The soil samples were sieved (<2 mm) on the
field and visible roots and animals were removed. Soil samples
were then stored at 4 °C until analysis.
2.3. Basic soil characteristics
Basic soil properties were analyzed using standardized meth-
ods. Soil pH (in water) was determined on air dried samples in a
1:5 soil–water suspension using a glass electrode, according to
NF ISO 10390. Total organic carbon (TOC
) and total nitrogen were
measured by dry combustion after decarbonation according to NF
ISO 10694, using a N/C-Analyzer (Thermo Scientific, FLASH 2000
NC Analyzer, France). Bulk density of topsoil (2.5–7.5 cm) was
determined by the core method (Blake and Hartge, 1986), taking
into account the stoniness. The particle size distribution (i.e. soil
texture) was determined by wet sieving and sedimentation using
the Robinson pipette method, according to NF X31-107.
102 A. Saenger et al. / Organic Geochemistry 54 (2013) 101–114
Author's personal copy
2.4. Rock–Eval pyrolysis analysis
2.4.1. Rock–Eval for quantification of TOC and hydrocarbons
RE pyrolysis analysis was performed using a RE6 pyrolyser
(‘‘Turbo’’ model, Vinci Technologies
, France). Soils were sieved
(<2 mm), oven dried at 50 °C and homogeneously crushed to
0.25 mm prior to analysis, without any other pre-treatment. The
RE method consists of a thermal cracking of organic matter involv-
ing two successive analytical steps performed with a temperature
increase of 30 °C/min (Fig. 2). The first step consists of pyrolysis in
an oven, under an inert atmosphere (N
), of 30–100 mg of sample.
The release of pyrolysis effluents over a temperature range of 200–
650 °C are measured continuously by a flame ionization detector
(FID) for hydrocarbons and an infrared detector (IR) for oxygenated
compounds. The second step consists of the oxidation under an
oxygenated atmosphere (air) of the residual carbon over a temper-
ature range of 400–750 °C. The process results in the production of
five pyrograms which correspond to free hydrocarbons (S1 pyro-
gram, absent in our study), hydrocarbons (HC; S2 pyrogram), CO
and CO (S3 pyrograms) jointly produced during thermal cracking
of SOM under N
atmosphere, and finally to the CO and CO
and S5 pyrograms) produced during the oxidation cycle (Fig. 2).
The complete description of the method can be found in Espitalié
et al. (1985a,b) and Lafargue et al. (1998).
In this study, we only focused on total organic carbon (TOC
kg), obtained from summing all pyrograms (S1–S5) and corre-
sponding to the sum of the carbon moieties (HC, CO and CO
) re-
leased during N
pyrolysis and the oxidation stage and a detailed
analysis of the S2 pyrogram (hydrocarbons; mg HC/g sample) cor-
responding to the amount of HC released during pyrolysis (N
2.4.2. Deconvolution of S2 pyrograms
The signal of the S2 pyrogram was deconvoluted using Peakfit
software (SPSS
), which uses automatic iterations reproducing
the best adjustment to the signal (i.e. R
determination coefficient
close to 1). Deconvolution of the S2 pyrogram resulted into five to
six Gaussian signals (F1, F2, F3, F4, F5 and F6) representing the best
Fig. 1. Map of the Vercors High Plateaus Natural Reserve (VHPNR; French calcareous Prealps) and location of the 109 soil profiles.
A. Saenger et al. / Organic Geochemistry 54 (2013) 101–114 103
Author's personal copy
Table 1
Summary of the basic soil characteristics and C stocks of topsoils (0–10 cm) from the Vercors High Plateaus Natural Reserve (VHPNR), classified by eco-unit and soil type. All abbreviations are defined in the text.
Eco-unit Soil type nAltitude
C/N ratio Soil texture (%) Soil
OC stocks
0–10 cm
depth (g OC/m
Clay Silt Sand
Acidic grasslands Dystric Cambisol 4 1435–1577 5.2 ± 0.4 11.9± 1.2 43.1± 10.1 33.3± 9.8 23.6± 16.2 50 ± 16 0.71 ± 0.11 0 92.3 ± 37.2 77.8 ± 31.2 6244 ± 1808
Mesic calcareous
Lithic Leptosol
11 1361–1725 6.3 ± 0.3 13.4 ± 1.6 54.1 ± 4.1 36.4 ± 4.7 9.5 ± 3.6 10 ± 2 0.51 ± 0.06 20 163.6 ± 28.7 125.7 ± 23.1 6690 ± 1992
Lithic Leptosol
3 1464–1716 6.2 ± 0.6 15.2 ± 2.7 57.0 ± 8.9 37.9 ± 8.8 5.2 ± 4.7 12 ± 3 0.33 30 257.0± 27.0 194.5 ± 27.0 6014 ± 2628
Eutric Cambisol 15 1308–1734 6.1 ± 0.5 12.8 ± 1.6 51.1 ± 5.5 39.9 ± 6.0 9.0 ± 4.0 35 ± 15 0.62 ± 0.10 3 116.9 ± 35.7 91.7 ± 26.0 7008 ± 1683
Xeric calcareous
Lithic Leptosol
14 1455–1651 6.4 ± 0.3 12.6 ± 1.5 47.7 ± 5.6 39.1 ± 6.8 13.2 ± 4.5 8 ± 2 0.55 ± 0.08 40 170.5 ± 57.5 138.8 ± 47.3 5391 ± 2569
Eutric Cambisol 2 1610–1716 6.0 ± 0.1 13.1 ± 0.1 51.6 ± 1.8 37.5 ± 5.8 10.8 ± 4.0 25 ± 0 0.60 ± 0.11 15 112.0 ± 12.7 87.8 ± 11.7 5738 ± 1194
Bushy facies Cambisol 3 1605–1693 5.8 ± 0.7 14.3 ± 2.5 56.1 ± 3.0 37.0 ± 4.9 6.9 ± 5.3 38 ± 20 0.59 ± 0.04 10 141.7± 55.9 110.0 ± 52.4 7014 ± 1033
Mixed forests Folic Umbrisol 6 1395–1467 6.4 ± 0.2 18.5 ± 1.8 48.7 ± 7.2 44.0 ± 2.9 7.2 ± 7.3 14 ± 4 0.32 ± 0.03 35 258.0 ± 30.8 201.7 ± 23.7 5465 ± 2167
Eutric Cambisol
5 1373–1447 6.2 ± 0.3 17.1 ± 2.0 48.2 ± 1.7 44.9 ± 2.7 6.8 ± 2.7 22 ± 12 0.57 ± 0.06 20 157.1 ±41.3 124.4 ± 33.8 7013 ± 1617
Coniferous forests –
mountain stage
Leptic Cambisol 6 1377–1634 5.8 ± 0.6 18.2 ± 2.3 45.7 ± 8.2 43.5 ± 7.8 10.8 ± 8.2 18 ± 9 0.57 ± 0.09 20 141.0± 49.9 114.3 ± 41.9 5627 ± 3057
Folic Umbrisol 11 1309–1534 6.9 ± 0.4 18.4 ± 2.0 48.4 ± 6.5 48.5 ± 6.3 3.0 ± 3.6 12 ± 3 0.33 ± 0.01 55 321.2 ± 39.7 244.9 ± 32.0 4414 ± 2751
Coniferous forests –
subalpine stage
Lithic Leptosol
2 1555–1780 6.4 ± 0.2 15.5 ± 1.8 49.5 ± 2.4 35.3 ± 7.1 15.1 ± 4.7 10 ± 0 0.54
65 188.0± 22.6 149.5 ± 41.5 3478 ± 287
Leptic Cambisol 2 1518–1560 6.0 ± 0.2 19.3 ± 1.9 48.8 ± 1.9 42.6 ± 4.9 8.5 ± 6.8 16 ± 1 0.61
25 191.0± 46.7 140.2 ± 35.8 8586 ± 1303
Folic Umbrisol 4 1510–1830 6.6 ± 0.9 19.0 ± 5.6 51.0 ± 7.4 45.6 ± 8.0 3.4 ± 3.5 10 ± 0 0.33
65 296.7± 74.3 226.6 ± 52.5 3565 ± 1949
Mountain ridges Folic Umbrisol
4 1980–2341 7.3 ± 0.4 12.0 ± 0.5 44.7 ± 4.0 48.9 ± 5.3 6.3 ± 3.4 12 ± 5 0.30 ± 0.05 65 190.7 ± 61.7 148.4 ± 48.7 2674 ± 2024
Alpine meadows Lithic Leptosol
5 1937–2122 6.8 ± 0.4 12.2 ± 0.4 50.4 ± 4.8 42.0 ± 6.7 7.6 ± 2.0 9 ± 2 0.53 ± 0.00 40 136.9 ± 51.6 105.7 ± 37.7 3951 ± 1110
Eutric Cambisol 5 1980–2341 6.1 ± 0.4 12.3 ± 0.7 53.2 ± 5.6 39.3 ± 4.3 7.5 ± 5.5 20 ± 6 0.59 ± 0.03 3 117.6 ± 39.4 94.1 ± 32.1 6728 ± 2235
Former coal kiln site Lithic Cambisol
1 1664 5.9 22.7 42.7 46.5 10.8 12 0.57 0 205.0 163.8 11,685
Sheepfold area Leptic Anthroposol
6 1562–1648 6.7 ± 0.4 10.2 ± 0.6 48.8 ± 1.8 46.3 ± 3.0 4.9 ± 3.0 10 ± 0 0.54
40 293.8 ± 78.4 230.2 ± 69.7 4528 ± 1657
104 A. Saenger et al. /Organic Geochemistry 54 (2013) 101–114
Author's personal copy
fit to initial S2 curves (Fig. 3). The relative contribution of each
Gaussian to the S2 pyrogram was computed by dividing Gaussian
areas to the one of the entire S2 pyrogram (Fig. 3). Using RE
pyrolysis of pure compounds, F1 to F6 Gaussian curves of the S2
pyrogram have been attributed to organic compounds with
increasing complexity (Disnar et al., 2003; Sebag et al., 2006). F1
has a typical signal of easily decomposable (i.e. labile) fresh plant
material and soil litter, such as simple sugars. F2 has been
Fig. 2. Main steps and outputs of the Rock–Eval 6 pyrolysis.
Fig. 3. S2 pyrograms of the 11 eco-units. The central curve represents the mean of the S2 signal, the external curves represent the standard deviation. The last insert shows
the deconvolution of a S2 pyrogram, giving the relative contributions of F1–F6 hydrocarbon peaks (Gaussian elementary signals) to the S2 signal.
A. Saenger et al. / Organic Geochemistry 54 (2013) 101–114 105
Author's personal copy
attributed to rather resistant biopolymers such as lignin and cellu-
lose from litter horizons. F3 and F4 have been respectively attrib-
uted to geo-macromolecules, while F5 and F6 Gaussian curves
may be caused by the presence of charcoal and/or highly mature
SOM (Espitalié et al., 1985a,b; Disnar et al., 2003).
2.4.3. Calculation of Rock–Eval indices
Here, we hypothesized that thermal stability of SOM is related
to its biogeochemical stability. Based on previous RE studies
(Disnar et al., 2003; Hetényi et al., 2006; Sebag et al., 2006) and
following the increasing use of thermal analysis to infer SOM
biogeochemical stability (Schulten and Leinweber, 1993; Plante
et al., 2009, 2011), we propose to divide the S2 pyrogram into three
thermally defined SOM pools of increasing stability (i.e. their sus-
ceptibility to biological decomposition): (i) a thermolabile C pool
corresponding to HC compounds pyrolyzed below 380 °C (F1 and
F2 signals); and two thermostable C pools pyrolyzed above
380 °C(Disnar et al., 2003; Hetényi et al., 2006) with (ii) an inter-
mediate C pool corresponding to HC compounds pyrolyzed be-
tween 380 °C and 450 °C (F3 signal); and (iii) a more resistant C
pool pyrolyzed above 450 °C (F4, F5 and F6 signals), following
the work of Schulten and Leinweber (1993). Our conceptual
approach with three thermally defined C pools goes beyond the
classic theory with pools of varying biochemical complexity,
encompassing the three main mechanisms of SOM stabilization de-
scribed above: (i) the selective preservation of organic compounds
chemically resistant to decomposition; (ii) the protection of SOM
by the mineral matrix; and to a certain extent, (iii) the protection
of SOM within soil aggregates, considering that SOM within micro-
aggregates (20–250
m) is highly stabilized (Six et al., 2002; von
Lutzow et al., 2006).
Finally, using TOC
and the whole or deconvoluted S2 signal,
we computed several RE pyrolysis indices linked to SOM bulk
chemistry (hydrogen and oxygen indices, see below) and SOM bio-
geochemical stability (TpS2, C
and HS, see below):
– Hydrogen Index (HI; mg HC/g TOC
), corresponding to the
quantity of HC released relative to TOC
(i.e. S2/TOC
), and
known to be well correlated with the elemental H/C ratio of
SOM of the sample studied (Espitalié et al., 1977; Vandenbroucke
and Largeau, 2007).
– Oxygen Index (OI
/g TOC
), corresponding to the
quantity of oxygen released as CO and CO
during pyrolysis,
relative to TOC
and known to be well correlated with the
elemental O/C ratio of SOM of the sample studied (Espitalié
et al., 1977; Vandenbroucke and Largeau, 2007). OI
is calcu-
lated as follows, according to Lafargue et al. (1998):
¼½16=28 OI
ðÞþ32=44 OI
With OI
= 100 S3
and OI
= 100 S3
– TpS2, corresponding to the temperature at which the maximal
quantity of hydrocarbon is released during pyrolysis (maximum
of the S2 pyrogram, in °C). Thus TpS2, as an overall assessment
of the energy of the C bonds of molecules or of the C mineral
bonds, can be regarded as proxy of the energy required for
microorganisms to decompose SOM. The standard deviation of
RE parameters has been estimated as: HI: ±6%; OI
: ±10%;
TpS2: ±5 °C(Disnar et al., 2003).
index’, derived from S2 deconvoluted pyrograms, which
evaluates the proportion of the thermolabile C pool (i.e. poorly
stabilized SOC) represented by F1 and F2 Gaussians. C
is a var-
iant of the R400 index, established by Disnar et al. (2003), inte-
grating the proportion of the S2 signal approximately below
380 °C(Fig. 3). C
index is calculated as follows:
index ¼F1 þF2
F1 þF2 þF3 þF4 þF5 þF6
With components F1 to F6 obtained from the deconvolution of
S2 pyrograms.
index’, derived from S2 deconvoluted pyrograms, which eval-
uates the proportion of intermediate thermostable C pool (mod-
erately stabilized SOC) represented by the F3 Gaussian
(corresponding approximately to an integration of the S2 signal
between 380 °C and 450 °C; Hetényi et al., 2006), and calculated
as follows:
index ¼F3
F1 þF2 þF3 þF4 þF5 þF6
index’, which evaluates the proportion of the highly thermo-
stable C pool represented by the F4 to F6 Gaussians (corre-
sponding approximately to an integration of the S2 signal
above 450 °C). This highly thermostable C pool is stabilized by
several processes, such as mineral protection and/or chemical
recalcitrance to decomposition according to the results of
Schulten and Leinweber (1993).C
index is calculated as
index ¼F4 þF5 þF6
F1 þF2 þF3 þF4 þF5 þF6
– ‘HS index’, which measures the highly stabilized part of the C
pool in the fraction considered as thermostable (above
380 °C). HS index is calculated as follows:
HS index ¼C
index þC
index ¼F4 þF5 þF6
F3 þF4 þF5 þF6
These indices are summarized in Table 2.
2.5. Statistical analyses
For all investigated variables and indices, comparisons of means
between the different eco-units were performed using one-way
analysis of variance (ANOVA), after verification of the normal dis-
tribution of the data. ANOVA were followed by Bonferroni post
hoc tests, at significance levels of 0.05 (significant,
), 0.01 (very
), and 0.001 (extremely significant,
). These statisti-
cal analyses were conducted using Statistica 8.0 software (StatSoft,
2008) and the program R version 2.13 (R Development Core Team,
3. Results and discussion
3.1. Basic soil properties and soil organic carbon stocks
Basic soil characteristics and SOC stocks of topsoils (0–10 cm)
from the VHPNR are reported in Table 1. The soils are clay loam
or silty clay (fine silts predominantly). Soil pH values were quite
comparable in most eco-units, with a mean of 6.4 ± 0.5 (n= 105),
except for acidic grasslands showing significantly different pH val-
ues (5.2 ± 0.4, n= 4; one-way ANOVA: Bonferroni test, p<0.05
Table 1). This results confirmed that topsoils of the Vercors moun-
tain are largely and quickly decarbonated due to the humid climate
and the karstic relief that favors a rapid and vertical drainage
(Gobat et al., 2010). However, there are some exceptions, such as
the carbonated forest soils and mountain ridge soils exhibiting
high pH values (up to pH = 7.7). Mean soil C/N ratio decreased from
18.2 ± 2.6 under forests (mixed and coniferous, n= 36) to 12.8 ± 1.6
under grasslands (acidic, mesic, xeric and alpine meadows, n= 60),
12.0 ± 0.5 in mountain ridge soils (n= 4), and 10.2 ± 0.6 (n=6) in
106 A. Saenger et al. / Organic Geochemistry 54 (2013) 101–114
Author's personal copy
sheepfold areas soils (Table 1). The importance of these simple
indicators (pH and C/N) for SOM bulk chemistry and C stabilization
processes is discussed later (Sections 3.3 and 3.4).
Topsoil bulk density (BD) was mainly driven by soil type, with a
mean of 0.61 ± 0.14 g/cm
(n= 41) for Cambisols, 0.54 ± 0.12 g/cm
(n= 40) for Leptosols and 0.33 ± 0.09 g/cm
(n= 28) for Umbrisols
in the 0–10 cm layer (Table 1). All soils were remarkably humifer-
ous, with SOC concentration in the A (Ah, or Aho) horizon ranging
from 6.4–39.2% (as determined by an elemental analyzer; TOC
Table 1). However, the low bulk soil density, the high stone content
(Table 1) and the presence of a stony layer at depth as shallow as
10 cm tend to moderate C stocks, especially for some Umbrisols
and Leptosols under forest, yet remain particularly humiferous
(Table 1). Yet, SOC stocks in the surface layer remain relatively
important compared to those measured on CarboEurope sites in
Europe (Schrumpf et al., 2011). In the present study, Cambisols
and Leptosols stored an average of 6890 ± 2203 g OC/m
mately 69 t C/ha; n= 40) and 5848 ± 2601 g OC/m
58 t C/ha; n= 38) in the 0–10 cm layer, respectively. These values
obtained from TOC
and bulk density measurements are nearly
twice the average surface stocks assessed in plains for the same
soil types, at the same depth (Schrumpf et al., 2011). Sjögersten-
Turner et al. (2011) compiled the mean C stocks for European
grasslands and forests soils in the Alps: mean C stocks tend to in-
crease with altitude, ranging from 75 t/ha above 600 m, to 130 t/ha
between 1800 and 2200 m, in the 0–30 cm surface layer under
grasslands, and from 125 t/ha to 160 t/ha in mineral soils of forest,
down to the bedrock. In the present study, mean C stocks (0–
10 cm) are about 53 ± 30 t/ha under grasslands (n= 52) and
54 ± 26 t/ha (n= 36) under forests, which can be roughly consid-
ered the same order of magnitude as the results of Sjögersten-
Turner et al. (2011), considering the lower sampling depth of our
Folic Umbrisols (Hyperhumic, Calcaric) under forests stored
about 48 ± 25 t/ha (0–10 cm; n= 17). Although very stony, these
soils can develop in great depths, without decreases in C concen-
tration in their profile (Duchaufour, 1977). Therefore, C stocks of
such soils until bedrock must be even more important. Nonethe-
less, the persistence of these SOC stocks over time depends on
the biogeochemical stability of SOM as well as on abiotic factors
(Sections 3.3 and 3.4).
3.2. Rock–Eval TOC measurements
TOC values determined by RE pyrolysis (TOC
) show a strong
correlation (R
= 0.99) with TOC
values (Fig. 4;Table 1, see also
the Supplementary material for detailed information on each of
the 109 soil samples). However, there is a systematic divergence
between the two measurements, RE pyrolysis underestimating
TOC values (using TOC
as a reference), as already reported in pre-
vious studies (Disnar et al., 2003; Sebag et al., 2006). In the present
study, the shift is important, with TOC
values being underesti-
mated at 27.7% on average compared to TOC
. Such a difference
could be explained by a less sensitive response of the FID to
hydrogen-poor SOM compounds than to those of the IFP standard
(Disnar et al., 2003). However, this divergence between TOC
measurements can be easily solved by a linear correction of
data (i.e. TOC
= 1.294 TOC
0.197; Fig. 4) as proposed
by Disnar et al. (2003). Once the model is calibrated, results are
very satisfying with a mean error rate of 0.5% relative to the refer-
ence TOC
An interesting result is that the carbonate content of soils does
not appear to be an amplification factor of the error for TOC
increasing trend of OI
values with higher levels of CaCO
, simi-
larly to results reported by Noël, 2001; data not shown). This al-
lows us to confirm that the C of S2 plus S3 pyrograms represents
SOC accurately. Thus, RE pyrolysis can be recommended as a useful
tool allowing a fast, inexpensive and reliable measurement of SOC
concentration, after a prior calibration effort, even in carbonated
soils. However, we must mention that the initial cost for the
Table 2
Summary of RE indices.
Indices Correspondence Measurement
Total Organic Carbon g/kg TOC
= S1 + S2 + S3 + S4 + S5 (see Fig. 2 for RE pyrograms)
HI Hydrogen Index mg HC/g TOC
Oxygen Index mg O
/g TOC
= [(16/28 OI
) + (32/44 OI
)] = [(16/28 100 S3
) + (32/
44 100 S3
TpS2 S2 peak temperature °C TpS2 = maximum of the S2 pyrogram
index Proportion of the thermolabile C pool C
index = (F1 + F2)/(F1 + F2 + F3 + F4 + F5 + F6)
index Proportion of the intermediate
thermostable C pool
index = F3/(F1 + F2 + F3 + F4 + F5 + F6)
index Proportion of the highly thermostable C
index = (F4 + F5 + F6)/(F1 + F2 + F3 + F4 + F5 + F6)
HS index Highly stabilized part of the thermostable C
HS index = C
index + C
index) = (F4 + F5 + F6)/(F3 + F4 + F5 + F6)
Fig. 4. Correlation between soil organic carbon contents (%) determined by RE
pyrolysis (TOC
) or by classical combustion with an elemental analyzer (TOC
A. Saenger et al. / Organic Geochemistry 54 (2013) 101–114 107
Author's personal copy
acquisition RE6 pyrolyser is rather high compared to the one of ele-
mental CHN analyzer for example.
3.3. SOM bulk chemistry
Results for the two RE parameters associated with SOM bulk
chemistry (hydrogen and oxygen indices; HI and OI
) are pre-
sented in Table 3 (see also the Supplementary material for detailed
information on each of the 109 soil samples). Previous studies on
kerogen have established that the HI and OI
indices correlate
well with atomic H/C and O/C ratios, respectively, and that the
plot can be comparable to a classic van Krevelen diagram
(Espitalié et al., 1977; Vandenbroucke and Largeau, 2007). We
therefore used a scatter plot representation of HI and OI
examine the bulk C/O/H stoichiometry of organic matter in our soil
samples (Fig. 5). To compare SOM chemistry of our samples with
the one of pure biochemical components, we set out a reference
van Krevelen and pseudo-van Krevelen diagram (H/C vs. O/C and
HI vs. OI
, respectively; Fig. 5c) reviewing the approximate posi-
tion of the main soil organic molecules (biopolymers such as car-
bohydrates, proteins, lignins, lipids, humic and fulvic acids) and
biological compounds (leaf, wood and bark) (Visser, 1983; Preston
and Schmidt, 2006; Balaria et al., 2009; Falsone et al., 2012). Each
biochemical component is characterized by a particular location in
the diagram according to its C/O/H stoichiometry. However, the
biopolymers could vary in composition depending on their sub-
structures. Therefore, the values presented in our diagram must
be considered as approximate values that indicate bulk chemistry
of SOM (Carrie et al., 2012). Moreover, the correlations between
the H/C molar ratio and HI index, and between O/C molar ratio
and OI
index, are poorly documented. They are mainly based
on kerogen analysis, with a reduced range of HI and OI
compared to those of soil components (Vandenbroucke and Largeau,
2007), or based on the analysis of a few pure biochemical com-
pounds and biological standards which may differ from natural
components that interact with the soil matrix and are subjected
to decomposition (Carrie et al., 2012). We suggest that the relative
location of the samples in the diagram is the most important infor-
mation allowing the assessment of their bulk chemical structure
and/or molecular composition.
Furthermore, previous studies have established that OI
HI are relevant indicators of the degree of evolution or maturation
of SOM (Hetényi et al., 2006; Klavins et al., 2008; Lee, 2011; Carrie
et al., 2012). A high HI implies a major contribution of hydrogen
rich compounds (long alkyl chains such as lipids or ‘alkyl-C’, and
‘O-alkyl-C’ compounds such as cellulose; Fig. 5c), whereas a low
HI corresponds to a greater part of aromatic and dehydrogenated
structures (e.g. black carbon and humic like substances; Fig. 5c).
Hence, HI can be compared to the aromaticity ratio (aromatic-
ity = aromatics/(alkyl + O-alkyl-C + aromatic)), commonly used in
solid state
C NMR (Solomon et al., 2007), or to aromaticity index
obtained from mid infrared spectrometry (Chefetz et al., 1996;
Dick et al., 2006, 2011). OI
index is more difficult to relate to
SOM maturation. Indeed, some authors have shown that SOM mat-
uration involves a slow oxidation of plant material that results in
the enrichment of organic matter in oxygen-containing functional
groups, particularly –COOH and –OH groups (Zech et al., 1997; von
Lützow and Kogel-knabner, 2010). However, other authors noticed
deoxidation reactions during SOM maturation, such as decarboxyl-
ation or demethoxylation coupled to an aromatization of C struc-
ture (e.g. Dec et al., 2001).
We must mention that HI and OI
could be influenced by ma-
trix mineralogy and by SOC enrichment (Katz, 1983; Espitalié et al.,
1985a,b) although we consider that cracking of the mineral matrix
or carbonates in RE pyrolysis that could affect the S2 or S3 pyro-
grams is minor (contrary to TG; Plante et al., 2009, 2011). In this
Table 3
Main Rock–Eval parameters, relative contribution of F1–F6 hydrocarbon peaks (Gaussian elementary signals; %) to the S2 signal, and RE pyrolysis indices computed for each eco-unit (presented as mean ± standard deviation). All
abbreviations are defined in the text.
Eco-unit nRelative contribution of F1–F6 hydrocarbon peaks to the S2 signals
(mg HC/
(mg CO/
(mg CO
(mg O
index C
index C
index HS index
F1 F2 F3 F4 F5 F6
Acidic grasslands 4 21.1 ± 2.1 22.2 ± 0.8 19.1 ± 1.8 34.5 ± 2.2 3.2 ± 0.8 0.0 453 ± 5 277 ± 23 50 ± 11 193 ± 13 169 ± 11 0.43 ± 0.03 0.19 ± 0.02 0.38 ± 0.02 0.66 ± 0.03
Mesic calcareous
29 24.8 ± 2.0 25.3 ± 2.1 19.2 ± 2.0 27.4 ± 4.0 3.4 ± 0.7 0.0 426 ± 42 298 ± 27 47 ± 11 196 ± 10 170 ± 8 0.50 ± 0.03 0.19 ± 0.02 0.31 ± 0.04 0.62 ± 0.05
Xeric calcareous
16 24.2 ± 1.9 25.5 ± 2.9 19.9 ± 0.7 27.0 ± 4.6 3.4 ± 0.6 0.0 405 ± 46 313 ± 28 47 ± 10 193 ± 12 167 ± 11 0.50 ± 0.05 0.20 ± 0.01 0.30 ± 0.04 0.60 ± 0.03
Bushy facies 3 21.9 ± 1.7 25.0 ± 4.9 17.9 ± 3.2 31.8 ± 3.7 3.4 ± 0.4 0.0 425 ± 48 304 ± 24 51 ± 14 178 ± 7 159 ± 12 0.47 ± 0.07 0.18 ± 0.03 0.35 ± 0.03 0.66 ± 0.02
Mixed forests –
mountain stage
11 24.5 ± 1.7 25.8 ± 2.4 19.2 ± 1.3 25.2 ± 3.6 4.4 ± 0.7 0.8 ± 1.0 400 ± 43 266 ± 32 42 ± 6 198 ± 15 168 ± 11 0.50 ± 0.04 0.19 ± 0.01 0.30 ± 0.04 0.61 ± 0.04
Coniferous forests –
mountain stage
17 27.0 ± 1.7 26.5 ± 5.9 19.8 ± 1.8 21.7 ± 6.1 4.3 ± 0.8 0.6 ± 0.9 378 ± 34 284 ± 32 43 ± 8 194 ± 11 165 ± 8 0.54 ± 0.06 0.20 ± 0.02 0.27 ± 0.06 0.57 ± 0.06
Coniferous forests –
subalpine stage
8 24.8 ± 2.3 26.7 ± 4.3 19.3 ± 2.1 25.0 ± 4.7 3.7 ± 0.6 0.5 ± 0.8 405 ± 44 307 ± 37 38 ± 5 188 ± 15 159 ± 11 0.52 ± 0.05 0.19 ± 0.02 0.29 ± 0.05 0.60 ± 0.05
Mountain ridges 4 26.3 ± 1.4 26.7 ± 0.7 19.5 ± 1.2 24.0 ± 1.1 3.5 ± 0.3 0.0 352 ± 21 372 ± 7 36 ± 2 196 ± 4 162 ± 2 0.53 ± 0.02 0.20 ± 0.01 0.28 ± 0.01 0.59 ± 0.02
Alpine meadows 10 24.0 ± 1.7 23.4 ± 1.9 19.1 ± 1.1 29.8 ± 3.7 3.6 ± 0.5 0.0 449 ± 30 317 ± 23 46 ± 10 195 ± 8 168 ± 7 0.47 ± 0.03 0.19 ± 0.01 0.33 ± 0.04 0.64 ± 0.04
Former coal kiln
1 21.8 23.2 20.6 30.4 3.9 0.0 449 136 36 128 114 0.45 0.21 0.34 0.62
Sheepfold areas 6 23.6 ± 2.0 30.5 ± 3.8 21.2 ± 1.7 21.4 ± 4.0 3.4 ± 0.3 0.0 367 ± 6 425 ± 62 33 ± 2 177 ± 12 148 ± 9 0.54 ± 0.04 0.21 ± 0.02 0.25 ± 0.04 0.54 ± 0.05
108 A. Saenger et al. / Organic Geochemistry 54 (2013) 101–114
Author's personal copy
study, the S2 area vs. TOC
correlation line tends towards the ori-
gin (data not shown). Thus, there is no evident modification of HI
values due to SOC content for our sample set, as shown by Disnar
et al. (2003) for other soils.
3.3.1. Bulk SOM chemistry of Anthroposols
The detailed observation of the van Krevelen diagram shows the
highly specific chemical fingerprint of sheepfold area soils (Anthro-
posols; Fig. 5a and b). Those soils were characterized by a signifi-
cantly higher HI (425 ± 62 mg HC/g TOC
,n= 6, one-way
ANOVA: Bonferroni test, p<0.001
;Table 3) and a significantly
lower OI
(148 ± 9 mg O
/g TOC
,n= 6, one-way ANOVA: Bon-
ferroni test, p<0.001
;Table 3) than the other eco-units and soil
types (Fig. 5). These HI and OI
values are characteristic of soils
rich in lipids, proteins and fresh SOM and correspond here to an
enrichment in sheep dropping.
3.3.2. Bulk SOM chemistry of mountain ridges
Mountain ridge soils, sampled in the northernmost part, the
southernmost part and the center part of VHPNR (Fig. 1), exhibited
particularly similar HI and OI
fingerprints (372 ± 7 mg CH/g
, and 162 ± 2 mg O
/g TOC
, respectively), significantly dif-
ferent for other eco-units (ANOVA: Bonferroni test, p<0.05
depending on the different eco-units). According to the
reference van Krevelen diagram, such high HI and OI
would correspond preferentially to fresh SOM (leaf; poorly evolved
SOM), with compounds easily metabolized by microorganisms:
cellulose, carbohydrates (Fig. 5). This particular chemical finger-
print of altitude soils could be explained by (i) a slow decomposi-
tion rate due to the harsh climate of mountain ridges, and/or (ii) a
blockage of SOM maturation at an early stage due to the high pH of
those soils (Table 1). Indeed, a high content of active calcium car-
bonate (CaCO
) could promote ‘‘humification by inheritance’’
where slightly evolved biochemical components, released during
litter fragmentation (cellulose, lignins, phenolic acids), would be
directly incorporated into the clay–humus complex and closely
bound to clays by Ca
ions. This encapsulation process, specific
to calcareous soils, would lead to the formation of very stable com-
plexes, inaccessible to microorganisms and highly resistant to fur-
ther decomposition (Duchaufour, 1977; Derenne and Knicker,
2000; Gobat et al., 2010). The existence of this C stabilization pro-
cess by CaCO
for mountain ridge soils should provide to their SOM
an important stability that will be assessed in Section 3.4.
3.3.3. Bulk SOM chemistry of grasslands and forest soils
Grasslands and forests of the VHPNR were characterized by
three main soil types: Cambisols, Leptosols and Umbrisols
(Table 1). When considering chemical differences between soil
types (Fig. 5b), we observed that Cambisols under forests or grass-
lands have significantly lower HI than Leptosols, Umbrisols or
Fig. 5. Position of soil samples in the pseudo-van Krevelen diagram (HI vs. OI
) according to (a) the eco-units, (b) the main soil types; (c) location of soil molecules and
biological compounds in the van Krevelen diagram (H/C vs. O/C) and approximate correspondence in the pseudo-van Krevelen (HI vs. OI
). References: (1) Visser, 1983; (2)
Balaria et al., 2009; (3) Falsone et al., 2012; (4) Vandenbroucke and Largeau, 2007; (5) Carrie et al., 2012; (6) Lee, 2011; (7) Preston and Schmidt, 2006.
A. Saenger et al. / Organic Geochemistry 54 (2013) 101–114 109
Author's personal copy
Anthroposols, (HI: 278 ± 35 mg HC/g TOC
, one-way ANOVA: Bon-
ferroni test, p<0.01
), with a specific fingerprint of aromatic SOM
(lignin monomers, polymerized lignins, and humic like substances;
Fig. 5). Such low HI values could reflect an advanced degree of SOM
maturation for Cambisols, which could be promoted by balanced
edaphic conditions (circumneutral), with a possible formation of
complex humic like substances (Calvet, 2003) even if the presence
of such macromolecules in soils has been recently challenged
(Kelleher and Simpson, 2006), and/or stabilization of microbial
metabolites and necromass (Gobat et al., 2010). This specific aro-
matic fingerprint of Cambisols could suggest a relatively high sta-
bility of SOM for this soil type, which will be assessed in
Section 3.4.
values of Cambisols (167 ± 12 mg O
/g TOC
) did not
show statistically significant differences with Leptosols or Umbri-
sols, or depending on the type of vegetation (one-way ANOVA:
Bonferroni test, p=1.00). However, Umbrisols (mainly under for-
ests) have a lower OI
than Leptosols (mainly under grasslands)
although not significantly different (one-way ANOVA: Bonferroni
test, p>0.05, except between subalpine coniferous forests and me-
sic grasslands, one-way ANOVA: Bonferroni test, p<0.05
). Accord-
ing to the reference van Krevelen diagram, the slightly lower
values for OI
of forest SOM suggest that forest soils may be ri-
cher in aromatic compounds derived from fresh litter, such as lig-
nins (Fig. 5c).
SOM of alpine meadow soils display a chemical fingerprint sim-
ilar to grassland soils except for two samples which present a C/O/
H stoichiometry that is close to the one of mountain ridge soils.
Lastly, we noticed that the former coal kiln site presented a chem-
ical fingerprint typical of black carbon with low HI and OI
highly aromatic SOM from charcoal; Fig. 5).
3.4. SOM thermal stability
The deconvolution of the 109 S2 pyrograms into six Gaussian
distributions showed rather constant mean temperature for each
Gaussian signal: 312 ± 6 °C for F1, 369 ± 5 °C for F2, 423 ± 8 °C for
F3, 471 ± 6 °C for F4, 555 ± 9 °C for F5 and 610 ± 6 °C for F6
(Fig. 3;Table 3). These values are rigorously comparable to those
found in other studies on soil materials (Di-Giovanni et al., 1998;
Noël, 2001; Disnar et al., 2003; Sebag et al., 2006) and correspond
to the successive cracking of organic components of different ther-
mal stabilities (that may be stabilized by the three main C stabil-
ization mechanisms detailed above). Results for the RE new
indices associated with SOM biogeochemical stability (TpS2, C
and HS) are presented in Table 3 (see also the Supplementary
material for detailed information on each of the 109 soil samples).
Fig. 6 depicts C
and C
indices according to the main eco-
units. These three indices reflect the importance of the three C
pools with different biogeochemical stability. Fig. 7a and b repre-
sent HS index (reflecting the potential of soil to highly stabilize
SOM) according to the main eco-units and the main soil types.
3.4.1. SOM thermostability of sheepfold areas
Sheepfold area soils, with a very low TpS2 (367 ± 6 °C; Table 3)
were particularly rich in fresh and easily decomposable SOM which
confirms our initial findings from the pseudo-van Krevelen dia-
gram (Section 3.3.1). They also exhibited the lowest proportion
of highly protected SOM (C pyrolyzed above 450 °C according to
Schulten and Leinweber (1993)) within the thermostable C pool
(C pyrolyzed above 380 °C), with the lowest HS index (Fig. 7 and
Table 3). However, these Anthroposols had a slightly larger inter-
mediate C pool (C
index) than other soils (Fig. 6 and Table 3;
although not significantly different, Bonferroni test, p>0.05). This
indicates that SOC stocks of sheepfold areas are poorly stabilized.
Selective preservation of biochemical components resistant to
decomposition and/or SOM maturation (i.e. transition from fresh
OM of F1 and F2 clusters to humic like substances of F3), may be
the preferential way of SOM stabilization in these soils.
3.4.2. SOM thermostability of coniferous forests
We observed a similar trend for coniferous forest soils (moun-
tain and subalpine stages). In these eco-units, SOM is essentially
constituted of easily decomposable biopolymers, poorly stabilized
(high C
index, low HS index; Figs. 6 and 7;Table 3). This result
confirms that the aromatic chemical fingerprint of SOM under
Fig. 6. Proportions of labile, intermediate and passive carbon pools for each eco-unit of the Vercors High Plateaus Natural Reserve. Abbreviations: (a–c) Refer to significant
differences according to Bonferroni post hoc tests (p< 0.05).
110 A. Saenger et al. / Organic Geochemistry 54 (2013) 101–114
Author's personal copy
forests observed in the pseudo-van Krevelen diagram could thus be
dominated by high proportions of lignins and aromatic compounds
originating from fresh litter (F2 contribution). Conversely, we also
noted the presence of a F6 component for many forest soils profiles
(Table 3 and A2). The sum of the F5 + F6 contributions to S2
pyrograms of forest soils is significantly greater than those of other
eco-units (one-way ANOVA: Bonferroni test, p<0.01
), F5 + F6
reaching 8% of S2 total area for some of them. This corresponds
to a relatively important passive C pool (highly protected C) for
forest soils. Such result could be due to (i) an important black car-
bon content due to wildfires; or (ii) a high maturation of lignins
and tannins, abundant in forests soils (Section 3.3.3), challenging
the current view of an hampered SOM maturation in these
ecosystems. Indeed, the development of these soils on pure and
hard limestones with a rapid and vertical drainage is supposed to
limit pedogenesis and SOM maturation (i.e. Gobat et al., 2010).
3.4.3. SOM thermostability of mountain ridges
For mountain ridge soils, RE pyrolysis indices confirm previous
information from pseudo-van Krevelen diagram (Section 3.3.2): a
large proportion of SOM is composed of thermally labile biopoly-
mers (high C
, low C
, and low HS indices, lowest TpS2:
352 ± 21 °C; Figs. 6 and 7;Table 3). Such a high lability of SOM
(e.g. strong content in particular organic matter, POM) in the
uppermost layers of high altitude soils with remarkably long mean
residence times has been evidenced in recent studies (Wagai et al.,
2008; Leifeld et al., 2009; Budge et al., 2011; Sjögersten-Turner
et al., 2011). These observations suggest a slow litter decomposi-
tion rate and a hampered physical protection of C at higher alti-
tudes. Regarding our two hypotheses from Section 3.3.2, the
weak SOM stability for mountain ridge soils implies that active cal-
cium carbonate (CaCO
) and Ca
ions may not play an important
role of binding agents (Section 3.3.2;Duchaufour, 1977; Derenne
and Knicker, 2000; Gobat et al., 2010). Therefore, the harsh climatic
conditions (alternating freezing and thawing, strong variations of
temperature, frequent desiccation), and the high pH (Table 1)
may be the primary factors stabilizing C in this eco-unit, through
the fourth C stabilization mechanism put forward by Torn et al.
(2009) and Trumbore (2009): climatic stabilization and biotic
suppression. The persistence of SOM in mountain ridge soils is
probably poorly dependent on its chemistry or physical
stabilization, but depends on local environmental and edaphic fac-
tors. The important C content of these high altitude soils (Table 1),
which is a general trend (e.g. Sjögersten-Turner et al., 2011), raises
concerns about the future of these major C pools. Indeed, as condi-
tions may become more favorable to microbial and faunal activity
with climatic changes, large quantities of high altitude SOM cur-
rently protected by biotic suppression could be rapidly destabi-
lized (Torn et al., 2009; Schmidt et al., 2011).
3.4.4. SOM thermostability in other eco-units
The highest values of SOM thermostability were observed for
acidic grassland, on Cambisols, with the highest TpS2
(453 ± 5 °C), C
(0.38 ± 0.02) and HS (0.66 ± 0.03) indices (Table 3).
Alpine meadow soils, bushy facies soils and the former coal kiln
site (mainly on Cambisols) were also characterized by a high
SOM thermostability (high TpS2, C
and HS indices; Figs. 6 and 7,
Table 3). Their C
and HS indices are significantly higher than that
of mountain ridge soils, coniferous mountain soils and sheepfold
area soils (Fig. 6). Therefore, Cambisols dominating all these eco-
units are characterized by a high SOM thermostability, supporting
our assumption described above (Section 3.3.3). The high SOM
thermostability of Cambisols may be explained by a more ad-
vanced pedogenesis that enhances two mechanisms of C stabiliza-
tion: association to the mineral matrix and physical protection
within soil aggregates. From a chemical perspective, these soils
did not exhibit a significantly different chemical fingerprint (HI
or OI
indices; Fig. 5,Table 3) from other eco-units. Then, know-
ing whether selective preservation of recalcitrant organic com-
pounds plays a significant role in SOM stability in these
ecological contexts remains an open question, and would require
complementary studies using other SOM characterization tech-
niques. Regarding the coal kiln site, the high SOM stability could
be attributed to the intrinsic biochemical recalcitrance of the sam-
ple, rich in charcoal residues (which was confirmed by the SOM
aromatic chemical signature; Fig. 5). However, the F5 Gaussian sig-
nal was rather low and the F6 signal absent for this sample (Ta-
ble 3). In fact, most of the charred C ended up in the residual
carbon fraction (i.e. pyrograms S4 and S4
; see Fig. 2), and is not de-
tected in the S2 pyrogram.
Fig. 7. HS index according to (a) the eco-units, (b) the main soil types. Significant differences are given according to Bonferroni tests (p= 0.05). Vertical bars denote 0.95
confidence intervals.
A. Saenger et al. / Organic Geochemistry 54 (2013) 101–114 111
Author's personal copy
Intermediate values of SOM thermostability were observed for
subalpine grasslands and mixed forests (intermediate values for
, HS and TpS2; Figs. 6 and 7,Table 3). Here, mixed forests pre-
sented a higher thermostability than coniferous forests, and alpine
meadows showed a higher SOM thermostability than subalpine
grasslands (although differences were not significant). This last
observation is contrary to that of Leifeld et al. (2009) and Budge
et al. (2010) who found a more abundant labile soil C (i.e. unpro-
tected and uncomplexed C) in alpine grasslands compared to their
subalpine or mountain counterparts. Here, we can make the
assumption of a ‘‘grazing effect’’ to explain these differences: al-
pine meadows could benefit from greater protection of SOM as
they were less grazed, less trampled and eroded than subalpine
grasslands, which may promote C stabilization through a greater
organo-mineral association and aggregation (Ganjegunte et al.,
Overall, the eco-units can be classified according to their SOM
thermostability as follows: acid grasslands
> bushy facies
coal kiln site > alpine meadows
> mesic grasslands
> xeric
> mixed forest soils
> coniferous subalpine
> mountain ridges soils
> coniferous mountain
> sheepfold areas soils
(a–c representing the significant
differences between eco-units for the C
index according to Bonfer-
roni test, p>0.001; Fig. 6).
3.5. Stocks of the three C pools
Using information on SOM biogeochemical stability derived
from RE pyrolysis (Section 3.4) and the quantification of C stocks
(Section 3.1), we attempted to assess the vulnerability of C stocks
in the 11 eco-units of the VHPNR. Considering that the S2 pyro-
gram is a representative portion of the overall SOC, we estimated
the amount of C for each pool (labile, intermediate and passive) re-
ported to the total SOC stock (0–10 cm) for each plot (Fig. 8). Large
confidence intervals are mainly due to large uncertainties on the
SOC concentration and bulk density.
We observed that despite their intermediate values of SOM
thermostability (Section 3.4.4 and Fig. 6), grassland and mixed for-
est soils stored large amounts of highly stable C (passive C pool;
Fig. 8). Bushy facies, acidic grasslands, mesic grasslands and the
former coal kiln site presented the highest passive C stocks
(Fig. 8), suggesting a low vulnerability of SOC stocks for these
eco-units. Anyway, regarding grasslands, climate change impacts
are difficult to predict as below ground losses of SOC might be
compensated by above ground carbon gain, and depend on many
factors (Sjögersten-Turner et al., 2011). Furthermore, according to
Allewell and Bebi (2010), land use changes such as reduced grazing
pressure and pasture abandonment, can play an even more impor-
tant role than climate change on SOC stocks. Here, pasture aban-
donment that would result in an encroachment, leading to the
bushy facies, would be associated with a similar SOC storage (Ta-
ble 1) in both quantity (Table 1) and bulk chemistry (Fig. 8), except
for encroachment of xeric grasslands. In this particular case, SOC
storage would be enhanced in quantity (Table 1), alongside an in-
crease in passive SOC stocks. With respect to the former coal kiln
site, passive C stock may be even more important as most charred
materials could not be revealed in S2 pyrogram (Section 3.4.4).
Therefore, the black C fraction of soils is probably underestimated
by our approach focusing on the S2 pyrogram only. Further re-
search using the percentage of residual carbon from RE pyrolysis
(i.e. the S4, S4
and S5 pyrograms) should be carried on for a better
quantitation of this highly stable C pool as proposed by Poot et al.
(2009). However, the residual carbon probably contains significant
amounts of newly charred C that may be difficult to distinguish
from the original black C.
Conversely, mountain ridge and coniferous forests soils pre-
sented a small amount of stabilized C and important stocks of eas-
ily decomposable C (Fig. 8), indicating a high potential
vulnerability of C stocks to climate or land use changes in these
eco-units. Indeed, remobilization of labile C could be driven by
milder winter temperature and increased precipitations for alpine
soils (Sjögersten-Turner et al., 2011). On the other hand, decreased
precipitation during summer would be likely to reduce soil respi-
ration rates (Muhr and Borken, 2009).
Overall, we argue that the stability of C stocks estimated with
this approach based on RE pyrolysis indices should be comple-
mented by other analytical tests (e.g. laboratory incubations,
SOM physical fractionation) and field experiments for a better
understanding of the way these eco-units will respond to global
4. Conclusions
Rock–Eval pyrolysis seems to be an exhaustive method, provid-
ing valuable information about quantity, elemental composition,
and thermostability of SOM in an easy, rapid and inexpensive
way. The present RE approach allowed an accurate quantitation
of soil C content, even in calcareous soils. In addition, the hydrogen
and oxygen indices provided relevant insights on SOM chemistry.
The technique revealed specific SOM chemical fingerprints for soils
from different eco-units, reflecting the heterogeneity of SOM com-
position and maturation processes in this landscape mosaics. We
especially noted the high lability of SOM (potentially easily decom-
posable) in mountain ridge soils and sheepfold areas contrasting
with the apparent high maturity of SOM in Cambisols. Lastly,
new indices of thermostability, established on the shape of the
S2 pyrogram of RE pyrolysis, have been used to consider three C
pools (labile, intermediate and passive) and assess the vulnerabil-
ity of C stocks. We argue that RE pyrolysis enables the identifica-
tion of a ‘thermostable’ fraction (intermediate and passive C
pools), revealing several mechanisms of SOM stabilization: (i)
Fig. 8. Contribution of labile pool, intermediate pool, and passive pool to SOC
stocks, according to the main eco-units.
112 A. Saenger et al. / Organic Geochemistry 54 (2013) 101–114
Author's personal copy
biochemical recalcitrance, (ii) physical stabilization by adsorption
onto clays, retention by the mineral matrix, and (iii) to a certain ex-
tent, protection through spatial inaccessibility by inclusion into
aggregates (von Lutzow et al., 2006; Trumbore, 2009). Acidic grass-
lands, bushy facies and alpine meadows were eco-units showing
the highest thermostability, probably through a greater organo-
mineral association and aggregation, while coniferous forests,
mountain ridges soils and sheepfold areas soils exhibited a signif-
icantly lower thermostability (i.e. a higher labile SOC pool, more
vulnerable to disturbance such as climatic modifications). In addi-
tion, we found that the persistence of SOM may depend not only on
its chemistry or biogeochemical stability, but also on local environ-
mental and edaphic factors such as climatic conditions or pH, espe-
cially for high altitude soils. This underlines the importance of
considering climatic factors and biotic suppression as a fourth
important SOM stabilization mechanism in mountain areas. We
conclude that the Rock–Eval method is particularly appropriate
for landscape scale studies or routine analysis, and could become
a standardized proxy for soil C vulnerability assessment. This study
also suggests the need for further investigations with coupling of
RE pyrolysis to other analytical techniques (solid state NMR spec-
troscopy, FTIR spectroscopy, fractionation procedures). These com-
bined techniques would foster a better understanding of the
complexity and variability of SOC patterns in these mountain
This study was conducted on the ‘Zone Atelier Alpes’ (ZAA) Ver-
cors site, included in the Long Term Ecological Research (LTER)
Network. We would like to acknowledge the Rhône-Alpes region
for funding this project. We thank the ZAA, the ‘Réserve Naturelle
des Hauts Plateaux’ du and the ‘Parc Naturel Régional du Vercors’
for their support of our research. We also want to express our great
gratitude to Dr. Jean-Robert Disnar (ISTO Orléans) for his help with
the Rock–Eval analysis, data acquisition, and his valuable advice.
We thank all of the people who helped with the field work, and
Allison Sanders for her English revision help. Finally, we are grate-
ful to editors and the two reviewers, Alain Plante and Neal Gupta,
whose comments have substantially improved the manuscript.
Appendix A. Supplementary material
The individual basic characteristics, C stocks and Rock-Eval
parameters of the 109 topsoils (0 -10 cm) from the Natural Reserve
of the High Plateaus of Vercors (VHPNR) accompanies. Supplemen-
tary data associated with this article can be found, in the online
version, at
Associate EditorKen Peters
Allewell, C., Bebi, P., 2010. Forest development in the European Alps and potential
consequences on hydrological regime. In: Bredemeier, M. et al. (Eds.), Forest
Management and the Water Cycle: An Ecosystem-Based Approach. Ecological
Studies, vol. 212, pp. 111–126.
Balaria, A., Johnson, C.E., Xu, Z., 2009. Molecular-scale characterization of hot-
water-extractable organic matter in organic horizons of a forest soil. Soil
Science Society of America Journal 73, 812.
Becker, A., Körner, C., Brun, J., Guisan, A., Tappeiner, U., 2007. Ecological and land use
studies along elevational gradients. Mountain Research and Development 27,
Bigot, S., Rome, S., Biron, R., Laurent, J.-P., Lebel, T., Dedieu, J.-P., 2010. Geophysical
Measures on a Grassland of the High Plateaus in the Vercors Mountain (French
Prealps): Analysis of the Local and Regional Hydroclimatic Variations.
Geophysical Research Abstracts, Vienne: Autriche.
Blake, G.R., Hartge, K.H., 1986. Bulk density. In: Klute, A. (Ed.), Methods of soil
analysis. Part 1. 2nd ed. Madison, WI., pp. 363–375.
Budge, K., Leifeld, J., Hiltbrunner, E., Fuhrer, J., 2010. Litter quality and pH are strong
drivers of carbon turnover and distribution in alpine grassland soils.
Biogeosciences Discussions 7, 6207–6242.
Budge, K., Leifeld, J., Hiltbrunner, E., Fuhrer, J., 2011. Alpine grassland soils contain
large proportion of labile carbon but indicate long turnover times.
Biogeosciences 8, 1911–1923.
Calvet, R., 2003. Le Sol: Propriétés et Fonctions. Edition France Agricole et Dunod,
Paris. Tome 1&2, 235 p.
Carrie, J., Sanei, H., Stern, G., 2012. Standardisation of Rock–Eval pyrolysis for the
analysis of recent sediments and soils. Organic Geochemistry 46, 38–53.
Cécillon, L., Certini, G., Lange, H., Forte, C., Strand, L.T., 2012. Spectral fingerprinting
of soil organic matter composition. Organic Geochemistry 46, 127–136.
Chapin, F.S.I., Jorgenson, M., Kielland, K., Kofinas, G., Turetsky, M., Yarie, J., Lloyd, A.,
Taylor, D., McGuire, A., Ruess, R., Hollingsworth, T., Mack, M., Johnstone, J.,
Kasischke, E., Euskirchen, E., Jones, J., 2010. Resilience of Alaska’s boreal forest to
climatic change. Canadian Journal of Forest Research 40, 1360–1370.
Chefetz, B., Hatcher, P.G., Hadar, Y., Chen, Y., 1996. Chemical and biological
characterization of organic matter during composting of municipal solid waste.
Journal of Environmental Quality 25, 776–785.
Cotrufo, M.F., Conant, R.T., Paustian, K., 2011. Soil organic matter dynamics: land
use, management and global change. Plant and Soil 338, 1–3.
Dec, J., Haider, K., Bollag, J.-M., 2001. Decarboxylation and demethoxylation of
naturally occurring phenols during coupling reactions and polymerization. Soil
Science 166, 660–671.
Derenne, S., Knicker, H., 2000. Chemical structure and preservation processes of
organic matter in soils and sediments. Organic Geochemistry 31, 607–744.
Dick, D.P., Benvenuti Leite, S., Dalmolin, R.S.D., Cesar Almeida, H., Knicker, H., 2011.
Pinus afforestation in South Brazilian highlands: soil chemical attributes and
organic matter composition. Soils and Plant, Nutrition 68.
Dick, D.P., Knicker, H., Ávila, L.G., Inda Jr., A.V., Giasson, E., Bissani, C., 2006. Organic
matter in constructed soils from a coal mining area in southern Brazil. Organic
Geochemistry 37, 1537–1545.
Di-Giovanni, C., Disnar, J.R., Bichet, V., Campy, M., Guillet, B., 1998. Geochemical
characterization of soil organic matter and variability of a post detrital organic
supply (Chaillexon lake, France). Earth Surface Processes and Landforms 23,
Disnar, J.R., Guillet, B., Keravis, D., Di-Giovanni, C., Sebag, D., 2003. Soil organic
matter (SOM) characterization by Rock–Eval pyrolysis: scope and limitations.
Organic Geochemistry 34, 327–343.
Duchaufour, P., 1977. Pédologie. 1. Pédogénèse et Classification, Tome 1, 477 p.
Espitalié, J., Laporte, J.L., Madec, M., Marquis, F., Leplat, P., Paulet, J., Boutefeu, A.,
1977. Méthode rapide de caractérisation des roche mères, de leur potentiel
pétrolier et de leur degré d’évolution. Revue de l’Institut Français du Pétrole 32,
Espitalié, J., Deroo, G., Marquis, F., 1985a. La pyrolyse Rock–Eval et ses applications.
2ème partie. Revue de l’Institut Français du Pétrole 40, 755–784.
Espitalié, J., Deroo, G., Marquis, F., 1985b. La pyrolyse Rock–Eval et ses applications.
1ère partie. Revue de l’Institut Français du Pétrole 40, 563–579.
Eswaran, H., Reich, P.F., Kimble, J.M., Beinroth, F.H., Padmanabhan, E.M.P., 2000.
Global carbon stocks. In: Lal, R., Kimble, J.M., Eswaran, H., Stewart, B.A. (Eds.),
Global Change and Pedogenic Carbonate. CRC Press, Boca Raton, FL, pp. 15–25.
Falsone, G., Celi, L., Caimi, A., Simonov, G., Bonifacio, E., 2012. The effect of clear
cutting on podzolisation and soil carbon dynamics in boreal forests (Middle
Taiga zone, Russia). Geoderma 177–178, 27–38.
FAO/IUSS/ISRIC, 2006. W.R.B. (World Reference Base for Soil Resources), Rapport
F.A.O. No. 103, Rome.
Feller, C., Brossard, M., Chen, Y., Landa, E.R., Trichet, J., 2010. Selected pioneering
works on humus in soils and sediments during the 20th century: a retrospective
look from the International Humic Substances Society view. Physics and
Chemistry of the Earth, Parts A/B/C 35, 903–912.
Ganjegunte, G., Vance, F., Preston, C., Schuman, C., Ingram, L., Stahl, P., Welker, J.,
2005. Soil organic carbon composition in a northern mixed-grass prairie: effects
of grazing. Soil Science Society of America Journal 69, 1746–1756.
Garcia-Pausas, J., Casals, P., Camarero, L., Huguet, C., Teresa Sebastia, M., Thompson,
R., Romanyà, J., 2007. Soil organic carbon storage in mountain grasslands of the
Pyrenees: effects of climate and topography. Biogeochemistry 82, 279–
Gobat, J., Aragno, M., Matthey, W., 2010. Le Sol Vivant: Bases de Pédologie, Biologie
des Sols. Ed. Presses Polytechniques et Universitaires Romandes, 3ème éd., 817
Gogo, S., Laggoun-Défarge, F., Delarue, F., Lottier, N., 2010. Invasion of a Sphagnum-
peatland by Betula spp and Molinia caerulea impacts organic matter
biochemistry. Implications for carbon and nutrient cycling. Biogeochemistry
106, 53–69.
Grosse, G., Harden, J., Turetsky, M., McGuire, a.D., Camill, P., Tarnocai, C., Frolking, S.,
Schuur, E.a.G., Jorgenson, T., Marchenko, S., Romanovsky, V., Wickland, K.P.,
French, N., Waldrop, M., Bourgeau-Chavez, L., Striegl, R.G., 2011. Vulnerability of
high-latitude soil organic carbon in North America to disturbance. Journal of
Geophysical Research 116, 1–23.
Hetényi, M., Nyilas, T., Sajgó, C., Brukner-Wein, A., 2006. Heterogeneous organic
matter from the surface horizon of a temperate zone marsh. Organic
Geochemistry 37, 1931–1942.
Jobbagy, E.G., Jackson, R.B., 2000. The vertical distribution of soil organic carbon and
its relation to climate and vegetation. Ecological Applications 10, 423–436.
Katz, B.J., 1983. Limitations of ‘‘Rock–Eval’’ pyrolysis for typing organic matter.
Organic Geochemistry 4, 195–199.
A. Saenger et al. / Organic Geochemistry 54 (2013) 101–114 113
Author's personal copy
Kelleher, B.P., Simpson, A.J., 2006. Humic substances in soils: are they really
chemically distinct? Environmental Science & Technology 40, 4605–4611.
Klavins, M., Sire, J., Purmalis, O., Melecis, V., 2008. Approaches to estimating
humification indicators for peat. Mires and Peat 3, 15p.
Lafargue, E., Marquis, F., Pillot, D., 1998. Rock–Eval 6 applications in hydrocarbon
exploration, production and soil contamination studies. Revue de l’Institut
Français du Pétrole 53, 421–437.
Lee, H.-T., 2011. Analysis and characterization of samples from sedimentary strata
with correlations to indicate the potential for hydrocarbons. Environmental
Earth Sciences 64, 1713–1728.
Leifeld, J., Zimmermann, M., Fuhrer, J., Conen, F., 2009. Storage and turnover of
carbon in grassland soils along an elevation gradient in the Swiss Alps. Global
Change Biology 15, 668–679.
Messerli, B., Ives, J., 1997. Mountains of the World. A Global Priority. Parthenon,
New York, London, 495p.
Muhr, J., Borken, W., 2009. Delayed recovery of soil respiration after wetting of dry
soil further reduces C losses from a Norway spruce forest soil. Journal of
Geophysical Research 114.
Noël, H., 2001. Caractérisation et calibration des flux organiques sédimentaires
dérivant du bassin versant et de la production aquatique (Annecy, le Petit Lac) –
Rôles respectifs de l’Homme et du Climat sur l’évolution des flux organiques au
cours des 6000 dernières ann. PhD, Université d’Orléans, 279 p.
Plante, A.F., Fernández, J.M., Leifeld, J., 2009. Application of thermal analysis
techniques in soil science. Geoderma 153, 1–10.
Plante, A.F., Fernández, J.M., Haddix, M.L., Steinweg, J.M., Conant, R.T., 2011.
Biological, chemical and thermal indices of soil organic matter stability in four
grassland soils. Soil Biology and Biochemistry 43, 1051–1058.
Poot, A., Quik, J.T.K., Veld, H., Koelmans, A., 2009. Quantification methods of black
carbon: comparison of Rock–Eval analysis with traditional methods. Journal of
Chromatography A 1216, 613–622.
Preston, C.M., Schmidt, M.W.I., 2006. Black (pyrogenic) carbon: a synthesis of
current knowledge and uncertainties with special consideration of boreal
regions. Biogeosciences 3, 397–420.
R Development Core Team, 2008. R: A Language and Environment for Statistical
Computing. R Foundation for Statistical Computing. Vienna, Austria. <http://>.
Schmidt, M.W.I., Torn, M.S., Abiven, S., Dittmar, T., Guggenberger, G., Janssens, I.,
Kleber, M., Kögel-Knabner, I., Lehmann, J., Manning, D.A.C., Nannipieri, P., Rasse,
D.P., Weiner, S., Trumbore, S.E., 2011. Persistence of soil organic matter as an
ecosystem property. Nature 478, 49–56.
Schrumpf, M., Schulze, E.D., Kaiser, K., Schumacher, J., 2011. How accurately can soil
organic carbon stocks and stock changes be quantified by soil inventories?
Biogeosciences 8, 1193–1212.
Schulten, H.-R., Leinweber, P., 1993. Influence of the mineral matrix on the
formation and molecular composition of soil organic matter in a long-term,
agricultural experiment. Biogeochemistry 22, 1–22.
Sebag, D., Disnar, J.R., Guillet, B., Di Giovanni, C., Verrecchia, E.P., Durand, A., 2006.
Monitoring organic matter dynamics in soil profiles by ‘‘Rock–Eval pyrolysis’’:
bulk characterization and quantification of degradation. European Journal of
Soil Science 57, 344–355.
Six, J., Conant, R.T., Paul, E.A., Paustian, K., 2002. Stabilization mechanisms of soil
organic matter: implications for C-saturation of soils. Plant and Soil 241, 155–
Sjögersten-Turner, S., Alewell, C., Cécillon, L., Hagedorn, F., Jandl, R., Leifeld, J.,
Martinsen, V., Schindlbacher, A., Sebastià, M.-T., Van Miegroet, H., 2011.
Mountain soils in a changing climate; vulnerability of carbon stocks and
ecosystem feedbacks. In: Jandl, R. et al. (Eds.), Soil Carbon in Sensitive European
Ecosystems: From Science to Land Management. Wiley-Blackwell, Oxford, pp.
Solomon, D., Lehmann, J., Kinyangi, J., Amelung, W., Lobe, I., Pell, A., Riha, S., Ngoze,
S., Verchot, L., Mbugua, D., Skjemstad, J., Schafer, T., 2007. Long-term impacts of
anthropogenic perturbations on dynamics and speciation of organic carbon in
tropical forest and subtropical grassland ecosystems. Global Change Biology 13,
StatSoft, 2008. STATISTICA (Data Analysis Software System), Version 8.0. StatSoft,
Tulsa. Maisons-Alfort, France.
Torn, M.S., Swanston, C.W., Castanha, C., Trumbore, S.E., 2009. Storage and turnover
of organic matter in soil. In: Senesi, N., Xing, B., Huang, P.M. (Eds.), Biophysico-
Chemical Processes Involving Natural Nonliving Organic Matter in
Environmental System. John Wiley & Sons, Inc., Hoboken, NJ, USA, pp. 215–
Trumbore, S., 2009. Radiocarbon and soil carbon dynamics. Annual Review of Earth
and Planetary Sciences 37, 47–66.
Vandenbroucke, M., Largeau, C., 2007. Kerogen origin, evolution and structure.
Organic Geochemistry 38, 719–833.
Véron, F., Bornard, A., Bernard Brunet, C., Bernard Brunet, J., Favier, G., Dorée, A.,
2004. Dynamiques respectives des pelouses et de la pinède de Pins à crochets
(Pinus uncinata Miler ex Mirbel in Buffon) sous l’influence du pâturage ovin.
Conséquences pour la gestion de la biodiversité dans la réserve naturelle des
Hauts-Plateaux du Vercors. Rapport d’étude MEED/Irstea (Cemagref), 157 p.
Visser, S.A., 1983. Application of Van Krevelen’s graphical–statistical method for the
study of aquatic humic material. Environmental Science & Technology 17, 412–
von Lützow, M., Kogel-knabner, I., 2010. Response to the concept paper: ‘‘What is
recalcitrant soil organic matter?’’ by Markus Kleber. Environmental Chemistry
7, 333–335.
von Lutzow, M., Kögel-Knabner, I., Ekschmitt, K., Matzner, E., Guggenberger, G.,
Marschner, B., Flessa, H., 2006. Stabilization of organic matter in temperate
soils: mechanisms and their relevance under different soil conditions – a
review. European Journal of Soil Science 57, 426–445.
Wagai, R., Mayer, L.M., Kitayama, K., Knicker, H., 2008. Climate and parent material
control on soil organic matter storage, dynamics, and partitioning into physical
fractions in undisturbed tropical forest soils. Geoderma 147, 23–33.
Zech, W., Senesi, N., Guggenberger, G., Kaiser, K., Lehmann, J., Miano, T.M., Miltner,
A., Schroth, G., 1997. Factors controlling humification and mineralization of soil
organic matter in the tropics. Geoderma 79, 117–161.
114 A. Saenger et al. / Organic Geochemistry 54 (2013) 101–114
... At higher scales, some studies focus on functional compartments, i.e. the coarse and fine free particulate OM (cPOM, fPOM) as well as the mineral associated OM (MAOM) fractions. The former usually display a low thermostability, corresponding to the most labile C pools, while the latter exhibits a high thermostability, i.e. a more stable C pool (Saenger et al., 2013(Saenger et al., , 2015Gregorich et al., 2015). The literature mainly emphasizes the role of OM stability on C sequestration in soils, as controlled by environmental drivers (plant cover, land use, climate). ...
... Both indices are derived from integrated S2 areas between specific bounds (200-400 °C, 400-460 °C, and above 460 °C), usually interpreted as specific thresholds in thermal stability of organic compounds, separating thermally labile, resistant, and refractory C pools (fig. SM1 and SM2;Disnar et al., 2003;Sebag et al., 2006;Saenger et al., 2013Saenger et al., , 2015. Both indices are calculated from S2 thermograms (i.e., hydrocarbon compound released by thermal cracking under pyrolytic conditions) that seem little or not sensitive to interference with mineral matrix and catalytic reactions. ...
... 1-2, Tab. 1) related to decomposition and changes in chemical composition of soil OM. The results are in agreement with previous Rock-Eval ® studies (e.g., Di-Giovanni et al., 1998;Disnar et al., 2003;Sebag et al., 2006Sebag et al., , 2016Saenger et al., 2013). ...
Soil organic matter (OM) is a complex heterogeneous mixture: resulting from decomposition and organo-mineral interactions, it challenges characterization in terms of composition and biogeochemical stability. From this perspective, the Rock-Eval® method is a rapid and efficient thermal analysis, which combines quantitative and qualitative information on soil OM, including several parameters related to thermal stability. This approach has already been used to monitor changes in OM properties at landscape, cropland, and profile scales. This study aims to assess the stability of soil organic matter pools by characterizing grain-size fractions from forest litters and topsoils using Rock-Eval® thermal analyses. Organic and topsoil samples were selected from a beech forest located in Normandy (France), whose management has been documented for the last 200 years. Fractionation by wet sieving was used to separate large debris (>2000 μm), coarse (200–2000 μm), and fine particulate organic matter (50–200 μm) in organic samples, and coarse (200–2000 μm), medium (50–200 μm), and fine (<50 μm) fractions in topsoils. Rock-Eval® was able to provide thermal parameters sensitive enough to study fine-scale soil processes. In organic layers, quantitative and qualitative changes are all explained by progressive decomposition of labile organic compounds from plant debris to the finest organic particles. On the other hand, the grain size fractions of the topsoil display different characteristics: indeed, the coarse organo-mineral fractions show high C contents, but with a different composition and a higher thermal stability and degree of decomposition than the plant debris forming the organic layers. These results are consistent with previous studies concluding that the microbial activity is more effective in this fraction. The finest fractions of topsoil reveal low C contents and the highest thermal stability, but also a low degree of decomposition, which can be explained by stronger interactions with the mineral matrix. Therefore, it is suggested that the dynamics of OM present in the different size fractions be interpreted in the light of a plant-microbes-soil continuum. Finally, three distinct thermal stability C pools are highlighted through the grain-size heterogeneity of soil OM: free-coarse organic matter (large debris, coarse and fine particles), weakly-protected organic matter in (bio)aggregates (coarse fraction of topsoil), and stabilized organic matter in fine fractions of topsoil, the latter resulting from interactions inside organo-mineral complexes. These results allow Rock-Eval® thermal parameters to be used in order to empirically illustrate the conceptual models emphasizing the role of drivers played by the gradual decomposition and protection of the most thermally labile organic constituents.
... The first large-scale soil monitoring projects with focus on soil organic carbon (SOC) were initiated (e.g., RMQS in France, Arrouays et al., 2003 One promising analytical technique in SOM research is Rock-Eval® thermal analysis. A timeefficient and inexpensive method, it can be used on large sample sets to quantify soil organic and inorganic carbon and characterize SOC bulk chemistry and thermal stability (Saenger et al., 2013;Gregorich et al., 2015;Sebag et al., 2016;Soucémarianadin et al., 2018). Moreover, a strong empirical link exists between parameters obtained with this method and in situ observed SOM biogeochemical stability (Barré et al., 2016;Poeplau et al., 2019). ...
... Simultaneous and continuous detection of effluents generates five thermograms in total that describe the evolution of carbon containing gases (HC, CO and CO2) during the analysis. A large number of Rock-Eval® parameters can be calculated from the five thermograms (Behar et al., 2001;Saenger et al., 2013;Sebag et al., 2016;Cécillon et al., 2018Cécillon et al., , 2021Khedim et al., 2021). Parameters obtained with this method are characteristic of the SOM and its interaction with the soil mineral matrix, since soil samples are analysed with no previous isolation of SOM or removal of carbonates. ...
... This correction can yield some negative values for the CO_PYR and CO2_PYR thermograms of soil samples with very low SOC content (data not shown). For the HC_PYR thermogram we also determined three parameters reflecting a proportion of thermally resistant or labile hydrocarbons: a parameter representing the proportion of hydrocarbons evolved between 200 and 450 °C (thermolabile hydrocarbons, TLHC index, unitless; modified from Saenger et al., 2013Saenger et al., , 2015, as described by Cécillon et al. (2018); a parameter representing the preservation of thermally labile hydrocarbons (I index, unitless; after Sebag et al., 2016); and a parameter representing the proportion of hydrocarbons thermally stable at 400 °C (R index, unitless; after Sebag et al., 2016). We also considered the hydrogen index (HI, mg HC g −1 C) and oxygen index (OIRE6, mg O2 g −1 C) that respectively describe the relative elemental hydrogen and oxygen enrichment of soil organic matter (see e.g. ...
Soils store twice the amount of carbon that is found in atmosphere and vegetation combined. They act as a buffer between solid earth and atmosphere and exercise a major control on the atmospheric concentration of CO2 through the release or sink of greenhouse gases. Organic carbon in soils in the form of organic matter is essential to soil health and fertility, to nutrient availability and water quality. The performance of the most valuable tool at our disposal for understanding and predicting the evolution of this reservoir, soil organic carbon (SOC) dynamics models, is currently limited by a missing key: the ability to estimate the proportion of SOC that will remain unchanged over projection-relevant timescales. This important amount of carbon present in soils for centuries or millennia, and therefore considered “stable”, can vary greatly from one location to another. The goal of my thesis was to explore a new approach based on thermal analysis and machine learning, to characterise SOC, estimate the proportion of “stable” carbon in soil samples, and use this information to improve the accuracy of SOC dynamics models. In a second step, I focused on the thermal analysis technique in the heart of this approach to understand better the important information it offers, based on model laboratory experiments. Finally, the main results of my thesis consist of a complete and validated operational approach improving the accuracy of SOC models with a clear and significant value for “climate-smart” soil management, while the experimental part offers new insights into the working principle, limitations and possibilities of the thermal analysis technique at the heart of this approach.
... Due to the increasing demands for rapid and quantitative assessments of soil organic matter quality, ther-mal analysis techniques are a unique means to characterize the complete continuum that comprises soil organic matter. Among the most common thermal techniques, Rock-Eval pyrolysis [60,61] has been increasingly applied to geologically recent sediment and soils [58,[62][63][64][65]. Details of the application of Rock-Eval to soils are provided elsewhere [58,62,64,66,67]. ...
... Disnar et al. [58] provided essential information on the amount and composition of tropical SOM. In addition to infor-mation on the abundance of SOM, Rock-Eval provides insight into the composition of SOM and even into its structure [58,65]. In a recent review on pioneering works on SOM, [53] pointed out the great value of RE pyrolysis for soil scientists. ...
Full-text available
In strongly weathered tropical soils, humus and humic substances (HSs) appear to play an important role in soil fertility because they represent the dominant reservoir and source of plant nutrients. As the refractory organic carbon form of soil, HSs play a vital role in the atmospheric CO 2 sequestration. Detailed classification of humus forms in tropical ecosystems and the dynamics and function of humus are still poorly understood. Nevertheless, in tropical environment many studies indicated that it is very difficult to differentiate between tropical humus, at least in normally drained soil. Moders, mulls, and Amphimull are the dominant humus forms in the topsoil of tropical environment. Knowing the mechanisms of formation, the dynamics and the methods of characterization of humus in tropical zones are a scientific challenge. This chapter aims to share recent findings from a broad humus in tropical soil and research related to this theme.
... One promising analytical technique in SOM research is Rock-Eval® thermal analysis. A time-efficient and inexpensive method, it can be used on large sample sets to quantify soil organic and inorganic carbon and characterize SOC bulk chemistry and thermal stability (Gregorich et al., 2015;Saenger et al., 2013;Sebag et al., 2016;Soucémarianadin et al., 2018a). Moreover, a strong empirical link exists between parameters obtained with this method and in situ observed SOM biogeochemical stability (Barré et al., 2016;Poeplau et al., 2019). ...
... Simultaneous and continuous detection of effluents generates five thermograms in total that describe the evolution of carbon containing gases (HC, CO and CO 2 ) during the analysis. A large number of Rock-Eval® parameters can be calculated from the five thermograms (Behar et al., 2001;Saenger et al., 2013;Sebag et al., 2016;Cécillon et al., 2018Cécillon et al., , 2021Khedim et al., 2021). Parameters obtained with this method are characteristic of the SOM and its interaction with the soil mineral matrix, since soil samples are analysed with no previous isolation of SOM or removal of carbonates. ...
Soil sampling depths strongly vary across soil studies. Stocks of elements (such as C, N) or organic matter in a soil layer can be simply calculated from stocks measured in its sublayers. This calculation is less obvious for other soil characteristics, such as soil organic carbon (SOC) persistence, complicating the comparison of results from different studies. Here, we tested whether Rock-Eval® parameters of a soil layer, characterizing soil organic matter and its biogeochemical stability, can be determined using Rock-Eval® data measured on its sublayers. Soil samples collected in 10 plots located in eight French forest sites, taken up at two different depths (0–30 cm, 30–50 cm), and their mixtures were analysed with Rock-Eval®. Expected values for the Rock-Eval® parameters of the soil mixtures were calculated either: (1) as the weighted mean of Rock-Eval® parameters measured on the two sublayers, or (2) based on a signal reconstructed as the weighted mean of Rock-Eval® thermograms recorded on the two sublayers. Our results showed a good agreement between measured and expected Rock-Eval® parameter values. However, when the clay content strongly differed between the two soil sublayers, the amount of pyrolyzed hydrocarbons measured on the soil mixtures was slightly lower than expected. We conclude that it is reasonable to calculate Rock-Eval® parameters of a soil layer, from the Rock-Eval® signature of its sublayers. Our findings facilitate the harmonization of Rock-Eval® data from large scale soil studies using different sampling depths.
... The Rock-Eval® device has also been used and developed for many different matrices to detect and quantify the total organic carbon (TOC) and the mineral carbon (MINC) contents. This analytical technique has been used increasingly in other geoscience applications including: (1) the characterization of organic matter in soils (e.g., Di-Giovanni et al., 2000;Disnar et al., 2003;Hetényi et al., 2005;Sebag et al., 2006;Saenger et al., 2013); (2) the study of recent lacustrine sediments (e.g., Campy et al., 1994;Di-Giovanni et al., 1998;Jacob et al., 2004;Sanei et al., 2005); (3) the evaluation of recent marine sediments (e.g., Peters and Simoneit, 1982;Hussain and Warren, 1991;Calvert et al., 1992;Ganeshram et al., 1999;Tribovillard et al., 2008Tribovillard et al., , 2009); (4) the study of past climate changes and global carbon cycle (e.g. Baudin et al., 2007Baudin et al., , 2010Rohais et al., 2019), and many other applications. ...
In this work, artificial thermal degradation experiments using the Rock-Eval® device were performed on selected polymer microsphere samples (PE, PP, PE100, PA6, PA11, PFA and PET). The main idea of this work is first to create a database of different polymer standard responses using the specific Rock-Eval® FID/IR peak signals. Several specific Rock-Eval® parameters are now defined to characterize each polymer family. For instance, each polymer is characterized by specific quantified parameters like Total HCpolymer, Total COpolymer, TotalCO2polymer, Tpeakpolymer, among others. This study attempts to demonstrate if this quick thermal degradation method can be also used to characterize the plastic contents (detection, type, and quantity) in sedimentary samples. Results indicate that each investigated polymer shows specific Rock-Eval® parameters that can be considered as useful characteristics of polymer families (mainly Tpeakpolymer, TOCpolymer, PCpolymer, RCpolymer, total HCpolymer, total COpolymer and total CO2polymer parameters). Samples containing different mineral matrices (e.g. sand, shale, marl and carbonate) were also mixed with polymers at different concentrations varying between 0.2 and 4.2 wt%. These composite samples were also analyzed in order to evaluate their thermal degradation comparing their specific Rock-Eval® FID/IR signatures. For example, most composite samples show an excellent linear correlation between TOC, PC, RC, total HC, CO and CO2 parameters versus the amount polymer at different concentrations. Although more work is still needed, a methodology is here proposed to distinguish and quantify the presence of plastics in the environment applying the proposed polymer Rock-Eval® database.
... Le pyrogramme montre une proportion de CO labile très importante par rapport au CO réfractaire. Ce profile bimodale est semblable à celui d'un sol avec des horizons supérieurs régulièrement alimenté en matière organique fraiche et avec une humification rapide comme sous forêt à basse altitude (Nyilas & Imre, 2009;Saenger et al., 2013). Le ratio entre HI et OI indique un environnement de formation eutrophe (Díez et al., 2017). ...
This work focuses on the characterization of sewage sludges (DSS) and explores several bioremediation strategies. The objective is to find a process to reuse the nutrients (mostly P and N) contained in sewage sludges in agriculture while limiting negative externalities. The first strategy aims to rid the sewage sludges of heavy metals by using the mobilization, translocation and immobilization capacity of certain fungi which, under certain conditions, precipitate oxalates crystals of different metals. The second strategy attempts to explore the abundantly documented ability of basidiomycetes to concentrate heavy metals in there carpophores can be used in bioremediation. The third strategy explores the possibility of recovering nutrients, here phosphorus, from sludges by using the ability of certain bacteria to solubilize phosphates. The various analyzes (XRF, MEB-EDX and Rock-Eval) showed a high content of calcium, silicon, iron, phosphorus in the major elements, a strong presence of zinc, copper, chromium, lead and cadmium in the trace elements, as well as a high content of labile and refractory organic compounds. SEM-EDX analyzes showed a correlation between the presence of iron and phosphorus within the same aggregates, confirming that the phosphorus in the sewage sludges is probably mainly present as iron phosphate. The first strategy test showed a correlation between the presence of sludges and the precipitation of oxalate crystals, but these containing mostly calcium and only a few low detectable traces of other metallic elements. The second test remained in the protocol state although many preliminary steps have already been completed. These preliminary tests verify the selected basidiomycetes optimal conditions for fruiting, which are two strains of Pleurotus ostreatus and a strain of Agaricus bisporus, as well as the affinity of the strains with sewage sludges The tests relating to the third strategy revealed a good phosphates solubilizing capacity in sludge for two strains of bacteria : a Bacillus strain, already known for its capacity to solubilize tricalcium phosphate, and a paenibacillus polymyxa strain, isolated from sewage sludges. The third strategy seems promisingand would allow extraction of the solubilized nutrients by simple phases separation.
Mountain grasslands contain large stocks of soil organic carbon (SOC), of which a good part is in labile particulate form. This labile SOC may be protected by cold climate that limits microbial activity. Strong climate change in mountain regions threatens to destabilize these SOC stocks. However, so far the climate response of SOC stocks in mountain grasslands remains highly uncertain, under either warming or cooling conditions. To overcome this knowledge gap, we studied the effect of pedoclimatic regime changes on topsoil (0–10 cm) SOC in two complementary experiments: 3 °C of warming or cooling by reciprocal transplanting to an alpine (2450 m a.s.l.) and a subalpine (1950 m a.s.l.) grassland and 1 °C of warming by open-top chambers in the same grasslands. Topsoil SOC stocks were higher at the alpine site than at the subalpine site, and the biogeochemical signature of the soil organic matter (SOM) also differed between the two study sites. SOM was O-enriched, H-depleted, and more thermally stable at the warmer subalpine site. After three years, abrupt warming by transplanting tended to decrease topsoil SOC content. The remaining SOC was characterized by a more thermostable signature. This result suggests the preferential depletion of labile SOC upon experimental topsoil warming. Cooling did not modify overall SOC content but uphill transplanted topsoils showed a more thermolabile biogeochemical signature. In contrast, open-top chamber warming of alpine and subalpine topsoils caused limited changes to SOC stocks and SOM biogeochemical signature, possibly because the induced pedoclimatic change was more limited and more gradual compared to the warming by transplantating which reduced the annual snow cover period by around 60 days and increased cumulative degree days by a factor of ten as compared to the OTC-induced warming. Gradual temperature changes may take longer to become effective than a shock transplant treatment. We conclude that SOC in mountain grassland topsoils can be highly reactive to climate shocks.
Full-text available
The quality and quantity of soil organic matter (SOM) are key elements of soil health and climate regulation by soils. The Rock-Eval® thermal analysis technique is increasingly used as it represents a powerful method for SOM characterization by providing insights on bulk SOM chemistry and thermal stability. In this study, we applied this technique on a large soil sample set from the first campaign (2000–2009) of the French monitoring network of soil quality: RMQS. Based on our analyses on ca. 2000 composite surface (0–30 cm) samples taken all over mainland France, we observed a significant impact of land cover on both SOM thermal stability and elemental stoichiometry. Cropland soils had a lower mean value of hydrogen index (a proxy for SOM H / C ratio) and a higher thermal stability than grasslands and forests. Regarding the oxygen index (a proxy for SOM O / C ratio), we observed significant differences in values for croplands, grasslands and forests. Positive correlations between the temperature parameters on the one hand and the clay content and pH on the other hand highlight the protective effect of clay on organic matter and the impact of pH on microorganisms mineralization activity. Surprisingly, we found weak effects of climatic parameters on the thermal stability and stoichiometry of SOM. Our data suggest that topsoil SOM is on average more oxidized and biogeochemically stable in croplands. More generally, the high number and even repartition of data on the whole French territory allow to build a national interpretative referential for these indicators in surface soils.
Les dynamiques de constitution des stocks de matière organique des sols, leur variabilité et leurs relations avec les autres composantes biotiques et abiotiques des écosystèmes sont insuffisamment connues à l’échelle du paysage. Or cette compréhension des interdépendances entre le système pédologique et les autres compartiments environnementaux est cruciale pour prévoir les changements globaux et mesurer l’impact des activités anthropiques. Les milieux de montagnes présentent dans ce contexte des spécificités fortes dues au fait de leur importante variabilité spatiale, des stocks élevés de matière organique contenus dans leurs sols et de leur vulnérabilité aux changements globaux. Par ailleurs, ils subissent intensément les effets du changement climatique. Pour toutes ces raisons, ils offrent un grand intérêt pour l’étude temporelle des dynamiques écosystémiques.En étudiant des sols de montagne aux situations environnementales contrastées, nous avons tenté de comprendre quelles étaient les dynamiques qualitative et quantitative de la matière organique au cours du temps. Pour cela, cette thèse s’est appuyée sur (i) l’étude de sols récemment formés le long de chronoséquences, (ii) de climatoséquences pour l’examen de la variabilité de la matière organique à l’échelle du paysage dans des sols ayant suivis des trajectoires différentes et enfin (iii) sur des simulations expérimentales de changement pédoclimatique pour analyser la réactivité du carbone organique stocké à la surface de sols de prairies de haute altitude.Nos résultats ont mis en évidence, indépendamment des conditions locales de chaque site, un schéma commun d’accumulation de la matière organique lors des phases initiales de formation d’un nouvel écosystème terrestre après retrait glaciaire en différents points du monde. Cette accumulation est affectée par le temps et accélérée par un climat plus chaud. La végétation contribue alors largement à l’incorporation de matière organique relativement labile dans ces sols nouvellement formés. Dans les écosystèmes plus évolués, la stabilisation environnementale par le climat structure en partie la variabilité quantitative et qualitative de la matière organique des sols en montagne à l’échelle du paysage. L’importance de cette structuration par le climat est plus forte dans les horizons de surface des sols qu’en profondeur. La stabilisation environnementale par le climat maintient dans les sols de montagne du carbone organique particulièrement réactif en préservant de la dégradation une matière organique vulnérable à haute altitude. En simulant un réchauffement de 3°C par transplantation d’un sol alpin à l’étage subalpin, nous avons effectivement démontré le rapide relargage de ce carbone organique labile.Ces résultats offrent un éclairage nouveau, à la fois sur la nature de la matière organique des sols de montagne, et aussi sur sa dynamique à-travers le temps et l’espace. La stabilité de la matière organique dans les sols est bien une fonction de l’écosystème.
Full-text available
Like many African countries, Madagascar has been counting on the exploitation of its mineral resources for several years. Considered as a biodiversity hotspot, the country must be able to combine the exploitation of its natural resources with the preservation of its environment. The sustainable management of vegetation cover is therefore a big challenge faced by mining projects. In the southeast of the country, QIT Madagascar Minerals (QMM) is exploiting ilmenite and zircon. Since the mine is located almost entirely within the remaining forest area of the region, restoration is no longer up for discussion. The objective of this work was therefore to propose an innovative restoration strategy adapted to the conditions of Mandena, integrating the valorization of both a by-product of the mine (topsoil) and the target native plants. The results obtained during this study highlighted three major points: (i) topsoil under Erica floribunda has interesting characteristics, although it has a strongly acidic pH. A stabilization by addition of vermicompost was thus necessary in order to optimize the development of plant species, here Mimosa latispinosa, (ii) Mimosa latispinosa, in addition to its strong adaptive capacity, is also able to improve the physicochemical and microbiological properties of the degraded soils. Three years of colonization by this species would bring the characteristics of degraded soils closer to those of forest soils. It would therefore be judicious to use it as a facilitator of plant succession and, (iii) among the 9 target plants species studied, six turned out to have interesting microbiological characteristics and could be used in restoration. Among them, three (3) pioneer species (M. latispinosa, V. caudata and M. spathulata) and three post-pioneer species (V. emirnense, P. ornifolia and H. verrucosa). The results of this research work have improved our understanding of the functioning of the Mandena ecosystem and provide a scientific basis for optimizing the mine's restoration programs.
Full-text available
Precise determination of changes in organic carbon (OC) stocks is prerequisite to understand the role of soils in the global cycling of carbon and to verify changes in stocks due to management. A large dataset was collected to form base to repeated soil inventories at 12 CarboEurope sites under different climate and land-use, and with different soil types. Concentration of OC, bulk density (BD), and fine earth fraction were determined to 60 cm depth at 100 sampling points per site. We investigated (1) time needed to detect changes in soil OC, assuming future re-sampling of 100 cores; (2) the contribution of different sources of uncertainties to OC stocks; (3) the effect of OC stock calculation on mass rather than volume base for change detection; and (4) the potential use of pedotransfer functions (PTF) for estimating BD in repeated inventories. The period of time needed for soil OC stocks to change strongly enough to be detectable depends on the spatial variability of soil properties, the depth increment considered, and the rate of change. Cropland sites, having small spatial variability, had lower minimum detectable differences (MDD) with 100 sampling points (105 ± 28 kg C m<sup>−2</sup> for the upper 10 cm of the soil) than the grassland (206 ± 64 kg C m<sup>−2</sup>) and forest (246 ± 64 kg C m<sup>−2</sup>) sites. Expected general trends in soil OC indicate that changes could be detectable after 2–15 years with 100 samples if changes occurred in the upper 10 cm of stone-poor soils. Error propagation analyses showed that in undisturbed soils with low stone contents, OC concentrations contributed most to OC stock variability while BD and fine earth fraction were more important in upper soil layers of croplands and in stone rich soils. Though the calculation of OC stocks based on equivalent soil masses slightly decreases the chance to detect changes with time at most sites except for the croplands, it is still recommended to account for changing bulk densities with time. Application of PTF for the estimation of bulk densities caused considerable underestimation of total variances of OC stocks if the error associated with the PTF was not accounted for, which rarely is done in soil inventories. Direct measurement of all relevant parameters approximately every 10 years is recommended for repeated soil OC inventories.
Mme E. LALLIER-VERGES Dir. de thèse Dir. de Recherche au CNRS, Orléans M. M. MAGNY Rapporteur Dir. de Recherche au CNRS, Besançon M. C. DI GIOVANNI Rapporteur Maitre de Conf., Tours Mme F. GASSE Examinateur Dir. de Recherche au CNRS, CEREGE, Aix-Marseille M. J.L. de BEAULIEU Examinateur Dir. de Recherche au CNRS, Marseille-St Jérôme) M. J-R. DISNAR Invité Dir. de Recherche au CNRS, Orléans M. F. BERTHIER Invité Ingénieur BRGM M. G. GUILLAUMET école doctorale Professeur, Orléans
As the largest pool of terrestrial organic carbon, soils interact strongly with atmospheric composition, climate, and land cover change. Our capacity to predict and ameliorate the consequences of global change depends in part on a better understanding of the distributions and controls of soil organic carbon (SOC) and how vegetation change may affect SOC distributions with depth. The goals of this paper are (1) to examine the association of SOC content with climate and soil texture at different soil depths; (2) to test the hypothesis that vegetation type, through patterns of allocation, is a dominant control on the vertical distribution of SOC; and (3) to estimate global SOC storage to 3 m, including an analysis of the potential effects of vegetation change on soil carbon storage. We based our analysis on >2700 soil profiles in three global databases supplemented with data for climate, vegetation, and land use. The analysis focused on mineral soil layers. Plant functional types significantly affected the v...
Rock–Eval 6 analysis, a well established screening tool for petroleum geochemistry, is being increasingly used to characterise the varying species of organic matter (OM) in the bulk samples of recent aquatic sediments. This is particularly important due to recent scientific attention on the role of OM in biogeochemical distribution of environmentally hazardous compounds (e.g., trace metals) in recent sediment archives. Rock–Eval’s automated use, low sample volume requirements and its high analytical accuracy and precision makes it an ideal tool for relatively rapid screening of OM in sediment cores. However, to date, there has been no broad scale standardisation to determine what may be contributing to each signal (e.g., S1, S2, S3, RC). We have selected a wide variety of representative, pure biochemicals (proteins, lipids, carbohydrates and lignins) and biological standards (phytoplankton, copepods, tree bark and conifer needles) to better understand the Rock–Eval 6’s measured organic matter parameters in the unconventional environmental samples. These data have been corroborated with organic petrographical and elemental (CHNS/O) data. Our results show that small organic molecules (<500 Da) are largely responsible for the S1 hydrocarbon peak while lipids and aquatic biological standards are contributing most in the S2 signal, and in particular the more labile “S2a” signal. Furthermore, carbohydrates, lignins and terrigenous plant standards are most responsible for the S3 signal. We also note that the S3 signals (CO/CO2 ratios: OICO, OICO2 and OIRE6) are the best discriminants for the source of OM. Finally, step wise pyrolysis of biological standards coupled with elemental analysis (CHNS/O) suggests that S2 and, to a lesser extent, S3 (S3CO and/or S3CO2), would be most responsible for metal-binding elements such as S and N, with implications for element biogeochemical cycles.
The various ecosystem functions of soil organic matter (SOM) depend on both its quantity and stability. Numerous fractionation techniques have been developed to characterize SOM stability, and thermal analysis techniques have shown promising results to describe the complete continuum of SOM in whole soil samples. However, the potential link between SOM thermal stability and biological or chemical stability has not yet been adequately explored. The objective of this study was to compare conventional chemical and biological methods used to characterize SOM stability with results obtained by thermal analysis techniques. Surface soil samples were collected from four North American grassland sites along a continental mean annual temperature gradient, each with a native and cultivated land use. Soil organic C concentrations ranged from 6.8 to 33 g C kg−1 soil. Soils were incubated for 588 days at 35 °C, and C mineralization rates were determined periodically throughout the incubation by measuring CO2 concentration using an infrared gas analyzer (IRGA) to calculate biological indices of SOM stability. Hot-water extractable organic C (HWEOC) contents were determined before and after incubation as chemical indices. Finally, samples from before and after incubation were analyzed by simultaneous thermal analysis (i.e., thermogravimetry (TG) and differential scanning calorimetry (DSC)) to determine thermal indices of SOM stability. Long-term incubation resulted in the mineralization of up to 33% of initial soil C. The number of days required to respire 5% of initial soil organic carbon (SOC), ranged from 27 to 115 days, and is proposed as a standardized biological index of SOM stability. The number of days was greater for cultivated soils compared to soils under native vegetation, and generally decreased with increasing site mean annual temperature. HWEOC (as % of initial SOC) did not show consistent responses to land use, but was significantly lower after long-term incubation. Energy density (J mg−1 OM) was greater for soils under native vegetation compared to cultivated soils, and long-term incubation also decreased energy density. The temperatures at which half of the mass loss or energy release occurred typically showed larger responses to land use change than to incubation. Strong correlations demonstrated a link between the thermal and biogeochemical stability of SOM, but the interpretation of the thermal behavior of SOM in bulk soil samples remains equivocal because of the role the mineral component and organo-mineral interactions.
Boreal forests are one of the most important terrestrial carbon sink, and a large portion of C is allocated in soil for long-term storage. However forest harvesting may quickly affect soil carbon stocks and dynamics, especially where organic substances drive the soil-forming processes, such as in Podzols. To evaluate the effects of clear cutting on carbon dynamics and podzolisation process over a short time period, a pristine boreal forest (Komi Republic, Russian Federation) and a recently clear cut site (5 year-old) were selected. Soils are polygenic: podzolisation occurs within the clay-depleted eluvial horizon, formed by a previous lessivage process. Because podzolisation can start only after the eluvial horizon has reached a sort of threshold, bisequal soils allow to individuate comparable pedogenic conditions prior to anthropogenic disturbances.