Edited by: Francien Peterse, Utrecht University, Netherlands
Reviewed by: Philippa Louise Ascough, Scottish Universities Environmental Research Centre, UK; Stefan Doerr, Swansea University, UK
*Correspondence: Samuel Abiven
This article was submitted to Biogeoscience, a section of the journal Frontiers in Earth Science
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Pyrogenic carbon (PyC) is considered one of the most stable components in soil and can represent more than 30% of total soil organic carbon (SOC). However, few estimates of global PyC stock or distribution exist and thus PyC is not included in any global carbon cycle models, despite its potential major relevance for the soil pool. To obtain a global picture, we reviewed the literature for published PyC content in SOC data. We generated the first PyC database including more than 560 measurements from 55 studies. Despite limitations due to heterogeneous distribution of the studied locations and gaps in the database, we were able to produce a worldwide PyC inventory. We found that global PyC represent on average 13.7% of the SOC and can be even up to 60%, making it one of the largest groups of identifiable compounds in soil, together with polysaccharides. We observed a consistent range of PyC content in SOC, despite the diverse methods of quantification. We tested the PyC content against different environmental explanatory variables: fire and land use (fire characteristics, land use, net primary productivity), climate (temperature, precipitation, climatic zones, altitude), and pedogenic properties (clay content, pH, SOC content). Surprisingly, soil properties explain PyC content the most. Soils with clay content higher than 50% contain significantly more PyC (>30% of the SOC) than with clay content lower than 5% (<6% of the SOC). Alkaline soils contain at least 50% more PyC than acidic soils. Furthermore, climatic conditions, represented by climatic zone or mean temperature or precipitation, correlate significantly with the PyC content. By contrast, fire characteristics could only explain PyC content, if site-specific information was available. Datasets derived from remote sensing did not explain the PyC content. To show the potential of this database, we used it in combination with other global datasets to create a global worldwide PyC content and a stock estimation, which resulted in around 200 Pg PyC for the uppermost 2 m. These modeled estimates indicated a clear mismatch between the location of the current PyC studies and the geographical zones where we expect high PyC stocks.
Fires affect about 4.64 million km2 of biomass per year, corresponding to about 4% of the earth's vegetated surface (Randerson et al.,
PyC is found ubiquitously in the environment (Preston and Schmidt,
PyC content in the SOC has been approximated to represent between 0 and 35% of the total soil organic carbon (SOC; Forbes et al.,
Nonetheless, it is possible to distinguish three main groups of parameters likely to influence the PyC content in a given soil: (i)
In this study, we reviewed the literature reporting content of PyC in SOC and analyzed these values as functions of these three drivers (fire and land use, climatic, and pedogenic). Our aims were: (1) to calculate the PyC stocks in soils based on published data, (2) to investigate which of the three main drivers has the largest influence on the PyC content in SOC, and (3) to show a possible application of our database, by using it in combination with other global datasets to create a global estimation of PyC contents and stocks.
The database was extracted from articles selected using the keywords “black carbon,” “charcoal,” “pyrogenic organic matter,” “fire-derived carbon” associated to “soil” in Google scholar and Web of Science (last search June 2015). Since our focus was on natural fire-derived organic matter, we excluded obvious cases where PyOM was added as a soil amendment (e.g., biochar) or was found as archaeological residue (e.g., hearths). We also discarded datasets where the sampling procedure was not described, the raw data not given or PyC only qualitatively described but not quantitatively. Using these criteria, we were able to collect 569 individual values, from 55 articles.
Values of PyC were reported as PyC mass % of the total SOC. When the stocks were reported, we calculated the concentration from the SOC and bulk density data. We chose to report the PyC content in SOC instead of stocks, because only 31% of the studies we collected reported PyC stock data or the bulk density values, which would be needed for stock estimations (Table
Pyrome | 92.2 | – | – | – | – |
Vegetation | 91.7 | – | – | – | – |
SOC [wt%] | 88.1 | 1.14 | 2.46 | 4.19 | 5.63 |
Precipitation [mm m−2 yr−1] | 86.6 | 510 | 843 | 1068 | 1618 |
Temperature [°C] | 80.2 | 7 | 10 | 10.8 | 14.8 |
pH | 52.1 | 4.8 | 5.7 | 5.9 | 6.8 |
BDD [g dm−3] | 31.1 | 0.67 | 0.94 | 0.96 | 1.25 |
Clay [wt%] | 23.1 | 8 | 19.25 | 22.2 | 30 |
Fire frequency [yr−1] or qualitative | 12.1 | – | – | – | – |
In addition to the PyC content, we collected information corresponding to the three main drivers (fire and land use, climatic, and pedogenic). These drivers-related data were extracted directly from the articles or, if not reported, derived from other sources. Table
Mean Annual Precipitation (MAP) | Join attributes by coordinates | Precipitation map | Only mean, no variability included | New et al., |
*§ |
Mean Annual Temperature (MAT) | Join attributes by coordinates | Temperature map | Only mean, no variability included | *§ | |
Köppen-Geiger Zone (KG) | Join attributes by coordinates | Köppen-Geiger map | Strongly generalized | Kottek et al., |
* |
Altitude | World Elevation Service of ESRI | USGS GTOPO 30 and SRTM 90 m | No information on relief and interpolated | ESRI, |
* |
Clay content | Join attributes by coordinates | HWSD | Interpolated and modeled data | FAO/IIASA/ISRIC/ISSCAS/JRC, |
§ |
pH | Join attributes by coordinates | HWSD | Interpolated and modeled data | § | |
Soil Organic Carbon content (SOC) | Join attributes by coordinates | HWSD | Interpolated and modeled data | § | |
Bulk Dry Density (BDD) | Join attributes by coordinates | HWSD | Interpolated and modeled data | § | |
Pyrome | Join attributes by coordinates | Pyrome dataset | Only a concept | Archibald et al., |
*§ |
Fire frequency | Join attributes by coordinates | Pyrome dataset | Data since two decades | * | |
Fire intensity | Join attributes by coordinates | Pyrome dataset | Data since two decades; resolution of acquisition and fires do not match at all. | * | |
Net primary productivity | Join attributes by coordinates | NASA npp dataset | Modeled data, derived from proxies | Zhao et al., |
* |
Land use | Join attributes by coordinates | NASA land cover dataset | Derived from proxies | Friedl et al., |
*§ |
We included the altitude using the World Elevation Service of ESRI© (ESRI,
We also included the land use (forest, grassland, agriculture, peatland, urban, shrubland) from the NASA MODIS land cover product, the mean annual precipitation (MAP) and mean annual air temperature (MAT), soil type, bulk density, sampling depth, SOC the clay content, and soil pH. All these parameters were extracted directly from the articles, or from reference datasets (Table
Soil depth was distinguished into top- and subsoil, where topsoil was defined as the uppermost 10 cm and subsoil as soil horizon below this limit. There was no significant difference (95% confidence interval) between these two soil depths so the data set was analyzed considering the whole soil profile. In order to compare continuous and discrete variables, parameters were categorized into 5–7 groups according their initial distribution.
The percentage of data available in the database as well as the median, average and the quartile values for continuous data are given for each parameter in Table
The distribution of each parameter in the dataset was compared to its worldwide distribution extracted from a reference database [i.e., soil parameters like pH, clay content, and SOC content from the harmonized world soil database (FAO/IIASA/ISRIC/ISSCAS/JRC,
A wide variety of methods exist to quantify the PyC in soil. It has been shown in the past that these methods do not always yield the same results for a given sample (Schmidt et al., Physical method: simple visual assessment (charcoal pieces counting), generally done with the naked eye or under microscope und mostly preceded by a physical separation step (for example flotation); Chemo-thermal oxidation method (CTO 375; Gustafsson et al., Dichromate oxidation method: the soil is treated with K2Cr207, a very strong oxidant, which is supposed to oxidize all labile organic carbon and the residual is considered oxidation resistant elemental carbon (OREC; Bird et al., Benzenepolycarboxylic acid (BPCA) molecular marker method, initially developed by Glaser et al. ( UV oxidation: PyC is considered as the organic residues after a strong UV irradiation treatment. Quantification is achieved by comparison of the material before and after treatment, using solid-state 13C nuclear magnetic resonance (NMR) spectroscopy (Skjemstad et al., NMR method: the PyC content in SOC is estimated directly from the NMR spectrum, using a mixing model (Nelson and Baldock,
Any other reported methods were grouped under the label “others.”
All continuous variables, except MAP and MAT, were grouped into five to seven groups, in order to allow the comparison of the database with to global distributions. Grouping was done according to relevant physical thresholds for each parameter and aiming for a balanced grouping, i.e., roughly the same number of points in each group. Data were log transformed to conform them to normality. These variables were tested with a One-Way ANOVA using R statistics (R Core Team,
In order to show the potential of our database, we created a linear model in combination with other existing global datasets for a global evaluation of PyC content in SOC and stocks. First, we filled the missing values in the dataset with values from the global datasets by joining the attributes in QGIS© (QGIS Development Team,
A linear model was then fitted on this extended database using R. After simplification, the model corresponded to the Equation (1):
Where PyC is the PyC content as % of SOC, clay the % of clay in soils, pH the pH of the soil, MAP the mean annual precipitation from the database, extracted as described above, MAT the mean annual temperature from the database, extracted as described above, land use the different categories of land use in the database and ε the error term, which accounts for the variability which can't be explained by the considered variables.
Together, the variables explained 33% of the total variance in the dataset (more detailed statistics are shown in Table
−4.8932 | −0.4427 | 0.0455 | 0.5346 | 2.4265 | |
(Intercept) | 2.34 | 0.56 | 4.17 | 3.70E-05 | *** |
clay 0–5% | −1.00 | 0.42 | −2.40 | 0.017 | * |
clay 5–10% | −1.04 | 0.39 | −2.71 | 0.007 | ** |
clay 10–25% | −0.87 | 0.36 | −2.41 | 0.016 | * |
clay 25–50% | −0.61 | 0.36 | −1.67 | 0.095 | · |
pH 4–5% | 0.16 | 0.28 | 0.57 | 0.568 | |
pH 5–6% | 0.48 | 0.26 | 1.84 | 0.067 | · |
pH 6–7% | 0.45 | 0.28 | 1.59 | 0.113 | |
pH >7% | 0.77 | 0.28 | 2.80 | 0.005 | ** |
MAP 0–600 | −0.38 | 0.19 | −1.96 | 0.051 | · |
MAP 601–1200 | −0.55 | 0.18 | −3.09 | 0.002 | ** |
MAP 1201–1800 | −0.76 | 0.19 | −3.90 | 0.000 | *** |
MAP 1801–2400 | 0.44 | 0.21 | 2.06 | 0.040 | * |
MAT 0–7.5° | 0.89 | 0.25 | 3.51 | 0.000 | *** |
MAT 15–22.5° | 0.77 | 0.28 | 2.75 | 0.006 | ** |
MAT 7.5–15° | 0.91 | 0.24 | 3.74 | 0.000 | *** |
MAT > 22.5° | 0.87 | 0.30 | 2.94 | 0.003 | ** |
Forest | −0.40 | 0.14 | −2.95 | 0.003 | ** |
Grass | −0.28 | 0.10 | −2.65 | 0.008 | ** |
Peat | 0.07 | 0.33 | 0.22 | 0.825 | |
Shrub land | −0.77 | 0.23 | −3.26 | 0.001 | ** |
Urban | −1.00 | 0.22 | −4.60 | 5.47E-06 | *** |
Figure
As described above, the diversity of quantification methods existing in the literature may induce bias in the collected data on PyC content in SOC. Figure
Another issue concerns the spatial representativity of the database. Figure
Figure
Surprisingly, almost no difference was observed between pyromes. Despite clear fire characteristic differences in terms of fire return period and intensity, the resulting PyC contents in SOC were very similar between the different pyromes. The only difference was observed for the zone with frequent, large and intense fires (FIL), where lower PyC contents were observed (4.8%).
The PyC content in SOC is also not related to the global FRI. Values range from 12.1% in regions with very long FRIs to 14.3% in regions with very frequent fires. This may be due to the resolution (1 km) of the FRI dataset, which may not capture local fire properties.
When the site has a clear reported fire history, the fire frequency has a significant effect (
The highest content of PyC in SOC was found in soils used for agriculture (16.0%; Figure
Content of PyC in SOC does not follow a consistent trend when compared to the annual precipitation (Figure
Figure
The content of PyC in SOC seems to be more directly related to the soil properties. First, PyC content in the SOC is significantly related (
Soil pH also has a statistically significant (
The overall SOC content seems to have also an influence on its relative PyC proportion (Figure
Our large collection of data is globally in line with previous estimations of the content of PyC in the SOC. We observed a mean of 13.7% of the SOC, ranging from 0 to 60%, while previous estimations ranged from 0 to 35% (Forbes et al.,
Compared to other specific identified compounds in soils, PyC seems to be a major contributor to the SOC: lignin content ranges only between 0 and 6%, with an average around 1.5% of the SOC (review of 27 studies by Thevenot et al.,
In comparison to the above mentioned compounds, fire-derived organic matter enters the soil usually only sporadically and in relative small quantities (the biomass transformation rate to PyC is estimated to be around 1–26% during a fire; Czimczik et al.,
Our analysis of the content of PyC in SOC drivers leads to an unexpected new picture on PyC distribution in soils. Fire characteristics as reported here do not seem to play a major role in the constitution of a PyC stock. Neither pyromes nor the FRI can explain the PyC content patterns. The only significant factor corresponds to very intense fires at a local scale, indicating, that fire impacts can be seen only very locally. Land use also gives an interesting picture: higher contents of PyC in the SOC in agricultural soils than in grassland and forests, respectively. On the other hand, the PyC content variations correlated very well with soil properties, i.e., higher clay and pH lead to high PyC%.
Soil properties clearly define conditions for stabilization of PyC. Higher clay content might lead to more organo-mineral interactions (Sørensen,
However, the three types of drivers we identified earlier cannot be exactly compared. Both spatial and temporal scales are problematic for information related to land use, fire characteristics, or even climate. These parameters may vary greatly over the time, particularly at the time scale we are considering here. Several authors reviewed the literature related to PyC turnover in soil and the estimates vary between 100 and 1000 years (Preston and Schmidt,
Fire frequency derived from remotely sensed satellite data cannot cover more than the last two decades, due to the availability of data (Dwyer et al.,
When the history of fire is known for a given place, the fire frequency is more relevant.
Compared to fire characteristics, land use or even climate, soil properties are relatively constant and are more integrative of the time where PyC effectively spent in the soil. It may be a reason why soil parameters appear particularly relevant for the PyC content.
Modeled PyC values are slightly lower than the literature dataset (Figure
Figure
The picture changes radically in most regions when it comes to stocks. Very high PyC stocks are found in the boreal zones. PyC contents in the SOC are low in boreal areas but SOC stocks are very important, while in some other regions PyC contents are high, for example in Australia, Africa, and the Indian sub-continent, but the SOC stocks are much lower and thus the PyC stocks are also low. In tropical regions, both the content of PyC in the SOC as well as the SOC stocks are high. Global PyC stock is estimated, based on the integration of all values to be roughly around 200 Pg.
Based on a large data collection from the literature, we propose here the first global estimation of PyC content as a function of SOC in soils. Based on this estimation, we are able to identify hotspots of PyC presence. Some of these locations are not surprising, for example where chernozems or mollisols can be found. More unexpected are locations such as tropical forests, which seem to yield high contents of PyC in SOC as well. The high clay and low SOC contents would explain these patterns. It does not seem that one simple rule can explain high levels of PyC in soils, but rather a conjunction of soil properties (pH or clay, both parameters do not need to be met) and ecosystem properties (large biomass in tropical forests, frequent fires on easy fragmentable grass material in central Asia). This would also indicate that the qualitative properties related to this PyC (chemical functions, physical structure) might also vary greatly with the region. On the other hand, we can identify zones where in absolute numbers only very little PyC can be found, despite frequent fires or apparent other favorable conditions for high PyC content. This is the case for large parts of Australia, where stocks are largely limited through the small overall SOC stocks, or boreal forests. Both regions were used frequently in previous studies. As for the hotspots, it is difficult to identify a unified explanation for these low values. There is a need to selectively identify the main missing drivers at a regional scale. The example of boreal forests is particularly interesting. This ecosystem is prone to fire, and decomposition rate should be highly reduced by low temperature and high moisture content. From this point of view, it is comparable to high latitude soils. However, the literature dataset and the global evaluation indicate rather low PyC contents in the SOC, compared to other climatic zones. This can only be explained by parameters we did not take into account in our analysis, such as lateral transport in the landscape, the combustion of the PyC by successive fires (although estimated to have only small influence; Santín et al.,
Since the variation range of SOC is much larger than the PyC one (in particular, our estimation tends to reduce this range), the largest projected PyC stocks appear in zones that are not directly related to fire or PyC stabilization parameters. The largest stocks are in high latitude soils where it is probable that little PyC content is present per unit of SOC, but where large stocks of SOC are stored. Tropical forests would be the location where both content and stocks are within the highest on the planet.
Our model has also a series of limitations. First, it explains only 33% of the total variance. This rather low power can be explained by different reasons: the time and space scale mismatch between the parameters and the PyC content dynamics (see above), the location of the sampling places or the method multiplicity. There are important differences in the location of the original sites from the literature and the global evaluation of PyC by our model. Most of our data come from Europe, East Australia and Northern America, while larger contents in the SOC are expected in boreal forest and central Asian steppes and larger stocks in high latitude soils. A direct consequence of this bias is that our evaluation rather tends to underestimate the content of PyC in SOC overall. Some zones are not explored at all, including for example most of Africa, southern Asia or central Russia, locations where high PyC contents in the SOC would be expected.
The diversity of methodological approaches may also be an issue. Hammes et al. (
Based on a large literature database, we assessed the content of PyC in SOC, investigated a variety of drivers related to PyC production and ecosystem properties to explain these contents. Then we used these in combination with several other datasets to model the distribution of PyC on a global scale. Our key achievements and findings are as follows:
We have produced the first unified database of published PyC measurements; PyC represents on average around 14% of the SOC, corresponding to one of the largest identified groups of chemical compounds in soil; High soil pH and clay content are the most significant parameters explaining a high PyC content in the SOC; PyC production parameters, such as fire or land use, do not well explain PyC content patterns. There is a temporal mismatch between the time scales over which PyC is expected to vary and the time scales over which it (and its related variables) are observed. There is a spatial mismatch between the regions with expected high PyC stocks and those, which are actually studied. There are still many limitations to overcome, if we want to improve our global picture of PyC, for example data scarcity in remote locations, resolution and derivation of global datasets or quantification method comparability.
The database is available in the Supplementary Material and can be used for other studies. We want to encourage scientists to improve the database, expand it with other variables or find new ways of filling the gaps and missing values.
MR and SA conceived the paper structure. MR collected the database. MR, SA, and RP contributed to the data analysis. MR and SA wrote the manuscript and all authors contributed to the writing of the manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We thank S. Archibald for providing the fire dataset. We also thank the reviewers for their valuable comments. This research is supported by the University of Zurich, through the URPP GCB (University Research Priority Program Global Change and Biodiversity).
The Supplementary Material for this article can be found online at: