The quality and quantity of soil organic matter (SOM) are
key elements that impact soil health and climate regulation by soils. The
Rock-Eval® thermal analysis technique is becoming more commonly used, as
it represents a powerful method for SOM characterization by providing
insights into 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 Soil Quality Monitoring Network (RMQS – Réseau de mesures de la qualité des sols). Based on
our analyses of ca. 2000 composite surface (0–30 cm) samples collected across
mainland France, we observed a significant impact of land cover on both the SOM
thermal stability and elemental stoichiometry. Cropland soils had a lower
mean hydrogen index value (a proxy for the SOM
The fate of soil organic carbon (SOC) is crucial from both soil health and climatic perspectives. In terms of soil health, SOC plays an important functional role. Its decomposition by microorganisms provides energy to the whole soil food web as well as key nutrients to plants and soil fauna. SOC also regulates the water cycle by controlling soil structure (Rawls et al., 2003). From a climatic perspective, soils can act as a source or a sink of carbon (Amundson, 2001; Eglin et al., 2010). Maintaining or increasing SOC stocks has become a key policy issue for the coming decades (Rumpel et al., 2018) that raises a number of important scientific challenges regarding our knowledge of SOC dynamics (Dignac et al., 2017).
The evolution of SOC stocks depends on the balance between soil carbon inputs (mostly by plants) and outputs (mostly by microbial decomposition). The persistence of SOC determines soil carbon outputs; thus, estimating the biogeochemical stability of SOC to microbial decomposition (i.e. the difficulty for microorganisms to mineralize SOC) is of paramount importance to infer SOC dynamics (Schmidt et al., 2011; Lehmann and Kleber, 2015). Indeed, a better knowledge of SOC persistence would allow us to refine our estimates of the soil carbon inputs needed to maintain or enhance SOC stocks. However, estimating the biogeochemical stability of SOC is a challenging task because its turnover time encompasses a broad spectrum (ranging from days or weeks to centuries; Balesdent and Guillet, 1982), resulting from a series of interacting SOC stabilization mechanisms. Indeed, SOC can be protected from microbial decomposition due to its chemical nature (e.g. pyrogenic SOC), its interactions with soil mineral surfaces, or its spatial inaccessibility for microbes (Baldock and Skjemstad, 2000; Von Lützow et al., 2006).
Several routine techniques have been proposed to separate fractions that are labile, intermediate, or stable at various timescales (von Lützow et al., 2007; Bispo et al., 2017; Chenu et al., 2015). However, none of these techniques manage to precisely isolate homogeneous fractions with the same biogeochemical stability (von Lützow et al., 2007; Poeplau et al., 2018; Cécillon et al., 2021). Common methods include biological respiration measurements performed during laboratory incubations of soils (e.g. Collins et al., 2000) and various physical (particle size or density) and/or chemical (aqueous or organic extraction) SOC fractionation methods (von Lützow et al., 2007).
Thermal analysis methods have been used for several decades to study the characteristics of soil organic matter (SOM). Many different methods, such as thermogravimetry, differential scanning calorimetry, and evolved gas analysis, exist that measure different variables (Plante et al., 2009). A multitude of variations in temperature ramps, compositions of reaction atmosphere, and measured parameters are encountered within each class of methods. Some thermal analysis methods provide indicators that are related to SOM biogeochemical stability: the more biogeochemically stable SOM is, the more thermally stable and depleted in energy and hydrogen it is (Barré et al., 2016; Sanderman and Grandy, 2020). Among thermal analysis methods, the use of Rock-Eval® thermal analysis is becoming increasingly common to derive thermal indicators related to SOC biogeochemical stability (Gregorich et al., 2015; Saenger et al., 2015; Cécillon et al., 2018, 2021; Poeplau et al., 2019; Chassé et al., 2021).
The Rock-Eval® method was developed in the 1970s. Initially
intended for the characterization of petroleum source rocks and sediments in
order to estimate their potential for hydrocarbon extraction (Espitalié
et al., 1977), this method was then adapted to the study of SOM (Disnar et
al., 2003). This technique allows for the measurement of the organic and
inorganic carbon content of a soil sample as well as the measurement of numerous indicators of
the thermal stability and elemental stoichiometry of SOM. Espitalié et
al. (1977) showed that the Rock-Eval® hydrogen index (HI) and
oxygen index (OIre6) are a good proxies for the respective
However, to date, the different existing global soil monitoring networks
have not used thermal analysis methods to infer SOC biogeochemical
stability. Some of them have focused on SOC physical fractionation schemes,
in combination with infrared spectroscopy or environmental variables (e.g.
Vos et al., 2018; Viscarra-Rossel et al., 2019; Lugato et al., 2021;
Sanderman et al., 2021). Here, we used Rock-Eval® thermal
analysis to investigate the thermal stability and elemental stoichiometry of
topsoil samples of the first campaign of the French Soil Quality Monitoring
Network (RMQS – Réseau de mesures de la qualité des sols; GisSol;
In this study, we first aimed to verify that the Rock-Eval® method was suitable to characterize SOM on archived soil samples at the scale of a monitoring network. For this purpose, we checked if the organic and inorganic carbon yields of the Rock-Eval® thermal analysis for soil samples, calculated by comparing Rock-Eval® estimates to reference methods, were acceptable. Second, we computed several common Rock-Eval®-based indicators in order to perform an unprecedented country-wide evaluation of the thermal stability and elemental composition of the SOM. Third, thanks to the numerous environmental data available at each RMQS site, we aimed to study the relationships between land cover, climate, and soil properties and the SOC-related indicators derived from Rock-Eval® thermal analysis.
A full description of the RMQS network and the soil sampling process of its first
sampling campaign is available in Jolivet et al. (2006). Briefly, soil is
monitored at locations across the French territory on a regular, square grid with a resolution of 16 km. Sampling sites were selected, when possible, at the centre of the cell;
otherwise, an alternative site was selected within a 1 km radius of the
centre of the cell. This resulted in a total of 2170 RMQS sites in mainland
France. At each selected site, 25 topsoil samples (0–30 cm or tilled layer
depths) were taken with a spiral soil auger from a 20 m
The composite samples (5 to 10 kg of bulk soil) were air-dried at
30
Of the 2170 archived aliquots of finely ground topsoil samples from the
first RMQS sampling campaign in mainland France, 2037 were recovered and
used for this study. When necessary, the samples were manually ground again
using an agate mortar to reach the particle size requirements for
Rock-Eval® thermal analysis of soils (below ca. 250
Physical and chemical soil analyses were carried out on the 2 mm sieved
composite samples at the Laboratoire d'Analyse des Sols (INRAE, Arras,
France). Among the large set of soil properties measured, we selected the following in
this study (Jolivet et al., 2006): particle size
measurements without decarbonation, in grams per kilogram of sample (Robinson
pipette and underwater sieving, method validated in relation to standard NF
X31-107), leading to five fractions (clay:
Rock-Eval® thermal analyses on the 2037 recovered samples were carried out at the ISTeP – UMR 7193 (Sorbonne Université, Paris, France) according to the routine classically used for soil samples (Disnar et al.,
2003; Baudin et al., 2015). Approximately 60 mg of each finely ground
topsoil sample was used for the Rock-Eval® thermal analysis on
a RE6 turbo device (Vinci Technologies, 2021). For each analysis, the
sample was placed in a special high-temperature-resistant stainless-steel
pod, allowing the transport gas to pass through. It first underwent a
pyrolysis step under an inert N
Our Rock-Eval® thermal analyses campaign included duplicate
soil analyses (one every eight samples), which were performed in order to check the
reproducibility of the analyses, along with standard analyses (one every
nine samples) to check the calibration of the device and identify possible
drift in the analysis. The Rock-Eval® thermal analysis of a
soil sample measures its total organic carbon (TOCre6) and total inorganic
carbon (MinC) contents that sum to the total carbon content (see Behar et al.,
2001, for a detailed description). The organic carbon yield of
Rock-Eval® thermal analysis was defined as TOCre6
Many usual Rock-Eval® parameters were calculated from the
thermograms (Table A1 in the Appendix). First, there are parameters related to
carbon quantities: the total organic carbon (TOCre6; grams per kilogram of sample);
the total inorganic carbon (MinC; grams per kilogram of sample); the amount of
pyrolyzable organic carbon (PC; grams per kilogram of sample); the ratio of
pyrolyzable organic carbon over total organic carbon (PCM / TOCre6; no unit);
the carbon released during the first pyrolysis isotherm (PseudoS1;
grams per kilogram of sample); the carbon released as hydrocarbons during
pyrolysis except during the first isotherm (S2; grams per kilogram of sample); and the
ratio of carbon released as hydrocarbons during pyrolysis except during
the first isotherm over the pyrolyzable organic carbon (S2 / PC; no unit).
Second, there are temperature parameters related to the SOC thermal
stability. Their calculation was performed over different intervals of
integration depending on the thermogram. The upper limits of the integration
ranges were selected to exclude CO and CO
As presented above, the treatment of the five thermograms can result in the
production of a multitude of Rock-Eval® parameters. We have
decided to present the results of the following parameters in more detail:
T50_HC_PYR, T90_HC_PYR, T50_CO2_PYR,
T50_CO2_OX, the
Climate data were extracted from the French SAFRAN database
(
We calculated linear regressions without an intercept using the measurements of
the organic, inorganic, and total carbon yield of Rock-Eval® thermal analysis to verify the ability of the Rock-Eval® thermal analysis to accurately measure the carbon amount of the samples. We
chose to use no intercepts because the analysis of several empty pods only showed a
very weak signal (TOCre6
All of the samples collected from the systematic sampling grid, regardless of their land cover, were analysed using Rock-Eval thermal analysis. This included 847 croplands, 571 forests, 496 grasslands, 57 vineyards, 16 wastelands, 46 sites with little human disturbance, and 4 gardens. Considering the very small number of samples for wastelands and gardens compared with the whole set, we decided not to include them in the following statistical treatments regarding land covers. The number of samples comprising environments with little human disturbance can be considered sufficient for statistical treatment; however, these samples represent a very heterogeneous set (10 miscellaneous subclasses, such as peatlands, alpine grasslands, water edge vegetation, heath, and dry siliceous meadows). Thus, we did not consider it relevant to analyse them as a whole.
To assess the effect of land cover on the Rock-Eval® parameters, we performed pairwise comparisons of medians using non-parametric
Kruskal–Wallis tests (
Figure 1a presents TOCre6 plotted against TOCea. We observed a high
correlation (
The remaining sample selection logically showed a better agreement between
TOCre6 and TOCea, although with lower TOCre6 values on average compared with
TOCea (Rock-Eval® organic carbon yield of 0.87,
Carbon yields of Rock-Eval® thermal analysis. Panel
The summary statistics of many different Rock-Eval® temperature parameters for the 1891 RMQS topsoil samples with satisfactory Rock-Eval® organic carbon yields are compiled in Table A2 in the Appendix.
Figure 2 shows the box plots for the six selected parameters
(T50_HC_PYR, T90_HC_PYR, T50_CO2_PYR,
T50_CO2_OX, the
Effect of land cover on topsoil organic carbon thermal stability
for the RMQS topsoil (0–30 cm) samples under the four major land covers in
France: croplands, forests, grasslands, and vineyards and orchards. The individual panels show the
We observed similar results for the temperature parameters
T90_HC_PYR, T50_CO2_PYR, and T50_CO2_OX: thermal
stability was significantly higher in croplands and in vineyards and orchards
compared with forests and grasslands. Topsoil organic carbon was slightly but
significantly less thermally stable in forests than in grasslands (Fig. 2a,
b, c). Notably, three other Rock-Eval® parameters related to
SOC thermal stability in the HC_PYR thermogram
(T50_HC_PYR, the
The summary statistics of different elemental stoichiometry parameters for
the 1891 RMQS topsoil samples with satisfactory Rock-Eval® organic carbon yields are compiled in Table A2 in the Appendix. The HI,
OIre6, and
We observed significantly higher average HI values in both grasslands and forests compared with croplands and with vineyards and orchards (Figs. 3, A1b). In contrast, grasslands and forests showed smaller OIre6 values compared with croplands and with vineyards and orchards (Figs. 3, A1c).
In addition, Fig. 3 highlights that the distribution of the
Rock-Eval® pseudo van Krevelen diagrams (hydrogen
index
Table 1 presents the Spearman correlation coefficient values of the
Rock-Eval® temperature and stoichiometric parameters with the
selected pedoclimatic variables. The three selected temperature parameters
(T90_HC_PYR, T50_CO2_PYR, and T50_CO2_OX) correlated
significantly and positively with the clay content and negatively with the
sand content. T90_HC_PYR and
T50_CO2_PYR also correlated positively with
silt content, although with smaller correlation coefficient values. They
strongly and positively (correlation coefficient
Regarding the indicators of SOM stoichiometry, HI and
Additionally, the correlation coefficients of TOCre6 with HI, OIre6,
Spearman correlation coefficients of the Rock-Eval® temperature and stoichiometric parameters with the following pedoclimatic variables for the RMQS topsoil (0–30 cm) samples:
TOCre6, particle size distribution, pH in water, carbonate content, cation
exchange capacity (CEC), iron oxyhydroxides, mean annual temperature (MAT)
averaged over 1969–1999, and mean annual precipitation (MAP) averaged over
1969–1999. The analysis was
limited to samples with Rock-Eval® organic carbon yields
ranging from 0.7 to 1.3. Absolute values
Figure 4 shows the point maps of the HI and T50_CO2_PYR values over the French mainland territory. The
missing topsoil samples (133 not included in the initial sample set and 146
rejected due to poor C yields) are distributed over the whole territory with
some clusters in the north of the French Alps, the north-east, Corsica,
the south-east, and in Landes. The first three clusters come from the 133
samples not included in the initial set. The Landes and south-east clusters
are from both the absent samples and the rejected samples: in
particular, the soils in Landes contain more sand on average, which is
characteristic – as stated above – of the rejected samples. Visually, we
noticed an autocorrelation of the values, with HI and T50_CO2_PYR presenting opposite trends on average (the Spearman
correlation coefficient between HI and T50_CO2_PYR is
Point maps of two Rock-Eval® parameters –
Our average, the organic carbon yield (0.86; Fig. 1a) was in line with previous studies. Indeed, Disnar et al. (2003) (0.91) and Cécillon et al. (2018, 2021) (organic carbon yield from 0.90 to 0.96 depending on the sites) obtained slightly higher yields, whereas Saenger et al. (2013) reported lower yields (0.77). However, some samples presented high discrepancies between their TOCea and TOCre6 values. Samples with a TOCre6 value strongly differing from its corresponding TOCea value were systematically reanalysed using Rock-Eval®, which confirmed their first TOCre6 measurement. The outliers with respect to the organic carbon yield were, thus, not related to a problem in their Rock-Eval® measurement. These very different values, which concern a few dozen samples, could have different origins, such as error in sample labelling, the division into aliquots, grinding, or storage conditions. Indeed, for the same sample, the powders used for the elemental analysis and the Rock-Eval® thermal analysis did not come from the same aliquot. In addition, the elemental analyses were performed shortly after sampling, whereas the samples analysed in Rock-Eval® were stored for about 15 years. Therefore, we can expect slightly better yields when elementary and Rock-Eval® analysis are performed with less time between both as well as when they are performed on the exact same powders. This is what we plan for the samples of the second RMQS sampling campaign. The very different TOCea and TOCre6 values could also be due, for some samples, to a mismeasurement of the total carbonate content, leading to a miscalculation of the inorganic and organic carbon contents. This hypothesis could be plausible, as the median value of the carbonate content was significantly higher in the rejected samples. The last hypothesis originates from the high content of sand in the rejected samples: sandy samples are more heterogeneous; thus, the material used to determine the TOCea is more likely to differ from that used to determine the TOCre6, compared with when the sand content is lower. Moreover, the physical state of organic matter in sandy soils can be different from other soils. Disnar et al. (2003) encountered “pellets” of SOM in sandy soils, which can strongly influence the TOCea and TOCre6 results.
The samples presenting a high discrepancy between TOCea and TOCre6 were not
considered further in the analysis. As stated above, we restricted our study
to the samples with an organic carbon yield ranging from 0.7 to 1.3. This
subjective threshold is a quality threshold to ensure that the samples
analysed using Rock-Eval® were the same as the samples
analysed using elemental analysis, on which all studies conducted on the
first campaign of RMQS rely. This selection only marginally improved the
average organic carbon yield (0.87; Fig. 1b), and organic carbon was still
underestimated by Rock-Eval®. Conversely, the inorganic carbon
yield was slightly overestimated (1.07; Fig. 1c). As a result, the yield of
total carbon (organic
We have observed that the thermal stability defined according to different Rock-Eval® parameters varies in French topsoils. We can investigate whether these variations are consistent with our knowledge of SOC biogeochemical stability. SOC biogeochemical stability is on average higher in croplands and vineyards than in forest or grassland soils (Poeplau and Don, 2013). Indeed, fresh organic carbon inputs to soil are usually higher in forest and grassland compared with croplands, where human exportation of biomass is higher (Murty et al., 2002). As a result, SOC fractions with a lower mean residence time in soils and a lower thermal stability can be more abundant in forests and grasslands than in croplands. For instance, several studies have reported that carbon in particulate organic matter (a relatively more labile form of SOC) contributes more to total SOC in forest and grassland compared with croplands (e.g. Guo and Gifford, 2002; Poeplau et al., 2011; Poeplau and Don, 2013; Lugato et al. have 2021). Moreover, agricultural practices may also speed up SOC mineralization, thereby further limiting the accumulation of labile SOC fractions. For instance, Balesdent et al. (1990) observed that the tillage practices lead to a significantly higher mineralization than no tillage. Combining the effects of lower carbon inputs and mineralization-enhancing practices, croplands contain less biogeochemically labile SOC on average than forests and grasslands.
Thermal stability, as assessed using T90_HC_PYR, T50_ CO2_ PYR and T50_CO2_OX, was the highest in vineyards and orchards and in croplands
compared with forest and grassland soils (Fig. 2). These results suggest that,
overall, SOC thermal stability, as assessed using these
Rock-Eval® parameters, is related to SOC biogeochemical
stability. This is in good agreement with previous results obtained on
smaller datasets (Barré et al., 2016; Poeplau et al., 2019; Cécillon
et al., 2021). On the contrary, there was no consistent relationship between
thermal stability and expected biogeochemical stability when the thermal
stability was measured using T50_HC_PYR, the
T90_HC_PYR, T50_CO2_PYR, and T50_CO2_OX were all strongly and positively correlated with the clay content and negatively correlated with the sand content (Table 1). In a previous study, Soucémarianadin et al. (2018) did not observe any correlation between T50_CO2_OX and clay or sand content; however, their study was conducted on forest soils only and on a greatly reduced number of study sites. Soil clay fractions interact with microbial compounds, resulting in the formation of organo-mineral complexes in which SOC has a high biogeochemical stability (e.g. Lehmann and Kleber, 2015). Therefore, we can hypothesize that clay-rich soils are also richer in biogeochemically stable carbon. The positive correlation between clay content and SOC thermal stability as well as the good correlations between the CEC, which depends on the first order of the clay content, and SOC thermal stability would then be another illustration of the link between SOC thermal and biogeochemical stabilities. Iron oxides are mineral compounds that are also supposed to protect SOC from decomposition. In this respect, the inconsistent (Mehra–Jackson iron) or even negative correlations (Tamm iron) between T90_HC_PYR, T50_CO2_PYR, and T50_CO2_OX and iron oxides were not expected. These weak correlations could be attributed to the fact that the range of iron oxide contents is relatively small in our set of topsoils.
T90_HC_PYR, T50_CO2_PYR, and T50_CO2_OX were all positively correlated with pH. Such a correlation between T50_CO2_OX and pH has already been observed by Soucémarianadin et al. (2018) for a set of French forest soils. Acidity may protect SOM from degradation by microorganisms (Clivot et al., 2021), by reducing their activity, which is actually observed in low-pH bogs. Therefore, we can hypothesize that acidity slows down SOM mineralization which can favour the accumulation of labile SOC components. As these labile SOC fractions would appear thermally unstable, it would explain the positive relationship between pH and Rock-Eval® indicators of SOC thermal stability.
T90_HC_PYR, T50_CO2_PYR, and T50_CO2_OX showed
weak but significant positive correlations with MAT averaged over 1969–1999
(Table 1). Such a correlation has also been observed in Soucémarianadin
et al. (2018) for French forest soils. As soil microbial activity and, thus,
SOC mineralization increase with temperature (Rey and Jarvis, 2006), we can
expect the SOC labile fractions to be more rapidly processed at higher
temperature. This would be in line with the observed positive correlations
between MAT and the three selected thermal stability indicators. The
relatively weak (Spearman
The point map representing SOC thermal stability over mainland France (Fig. 4b) illustrates the relationships between SOC thermal stability, land cover, climate, and pedological variables. Mountainous regions (e.g. the Massif Central, Alps, and Pyrenees) dominated by forest and grassland with a low MAT and relatively high SOC contents (the latter according to Martin et al., 2011) had a lower SOC thermal stability. Plains dominated by croplands with intensive agricultural practices and with relatively low SOC contents, such as the Paris Basin, showed high SOC thermal stability. The southern part of France, which has a warmer MAT, dominant vineyard and cropland land cover, and relatively low SOC contents, also presented high SOC thermal stability. The lower SOC thermal stabilities observed in Brittany and Normandy (which are agricultural regions) could be explained by the higher proportion of livestock. Therefore, in addition to the presence of grasslands in these regions, the cultivated soils in Brittany and Normandy are more likely to receive the repeated application of exogenous organic matter.
Higher HI and lower OIre6 values were observed in forests and grasslands compared with croplands and vineyards. This trend has also been observed in previous studies (Disnar et al., 2003; Saenger et al., 2013; Sebag et al., 2016). This confirms that HI and OIre6 can be good proxies for SOC biogeochemical stability. Indeed, as previously observed, biogeochemically stable SOC is more oxidized and H depleted (Barré et al., 2016; Poeplau et al., 2019; Cécillon et al., 2021).
The pseudo van Krevelen diagrams (Fig. 3) show a high variability in the
SOM elemental stoichiometry presented correlation patterns with land cover, climate, and pedological variables that were similar to those observed for SOM thermal stability. As shown in Table 1, HI and OIre6 are negatively and positively correlated with pH, respectively, as previously observed by Soucémarianadin et al. (2018) in French forest soils. This would be in line with acidity slowing down the mineralization of H-enriched labile SOC fractions (Clivot et al., 2019). The negative correlation between clay content and HI could be explained by the fact that the presence of clays can promote the protection of microbially processed H-depleted SOM. Similar to what was observed for SOM thermal stability, relationships between elemental stoichiometry and climate variables are weak, probably because climate plays a role in both soil carbon inputs and outputs in opposite ways (climate conditions enhancing SOC mineralization usually also enhance fresh SOM inputs).
The point map of HI in mainland France (Fig. 4a) illustrates the effect of land cover, climate, and pedological variables on SOM elemental stoichiometry. Regions dominated by grassland and forest (Fig. 4d), such as mountainous regions, the Landes forest, or the forest-dominated eastern part of France, are characterized by a relatively H-enriched SOM. Conversely, regions with a high MAT and dominant cropland, vineyard, and orchard land covers are characterized by a relatively H-depleted SOM. Both point maps of thermal stability and HI (Fig. 4) also illustrate the relationships previously observed between these Rock-Eval® parameters (Barré et al., 2016; Cécillon et al., 2021).
This study is an unprecedented effort to carry out widespread thermal analysis
measurements on a national soil quality monitoring network. It demonstrated
that Rock-Eval® may be used as a rapid and cost-effective
method to assess the thermal stability and elemental stoichiometry of SOM on
national soil monitoring networks. The very satisfying organic and inorganic
carbon yields could make Rock-Eval® thermal analysis a very
suitable tool for research work in carbonate soils or even for routine soil
analysis if commercial laboratories take advantage of the method. Our
results highlighted the influence of land cover and pedoclimatic variables
on SOM thermal stability and elemental stoichiometry. They suggested that
some Rock-Eval® temperature parameters describing SOC thermal
stability (T90_HC_PYR, T50_CO2_PYR, and T50_ CO2_OX) could
be used as reliable proxies for SOC biogeochemical stability, whereas other
parameters (T50_HC_PYR, the
Effect of land cover on topsoil organic carbon stoichiometry for
the RMQS topsoil (0–30 cm) samples under the four major land covers in
France: croplands, forests, grasslands, and vineyards and orchards. The individual panels show the
Score of the 2037 samples on axes 1 and 2 of the principal
component analysis on 11 pedoclimatic parameters: clay, total silt and total
sand contents, pH in water, water content, carbonate content, mean annual
temperature, mean annual precipitation, Tamm and Mehra–Jackson iron
oxyhydroxide contents, and the
Description of the Rock-Eval® parameters and their calculation.
Continued.
Minimum, maximum, mean, first quartile, third quartile,
median, and standard deviation values of the Rock-Eval® parameters for the RMQS topsoil (0–30 cm) samples limited to those with
Rock-Eval® organic carbon yields ranging from 0.7 to 1.3
(
Continued.
Data on basic soil properties are freely available from the GisSol
Dataverse website:
AAD, LC, and PB carried out the sample collection and ensured that samples were properly ground. FS and FB produced the Rock-Eval® thermal analyses. DA, AB, LB, CJ, MPM, CR, and NPAS provided the detailed pedoclimatic data. NPAS produced the point maps. AAD processed and interpreted the data with contributions from all co-authors. AAD, PB, and LC wrote the manuscript with contributions from all co-authors.
The contact author has declared that none of the authors has any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The École Normale Supérieure of Paris is gratefully acknowledged for financially supporting Amicie Delahaie's PhD thesis.
This research has been supported by ADEME (grant no. 2003C0017).
This paper was edited by Jocelyn Lavallee and reviewed by two anonymous referees.