Atmospheric carbon dioxide levels can be mitigated by sequestering carbon in
the soil. Sequestration can be facilitated by agricultural management, but
its influence is not the same on all soil carbon pools, as labile pools with
a high turnover may be accumulated much faster but are also more vulnerable to
losses. The aims of this study were to (1) assess how soil organic carbon
(SOC) is distributed among SOC fractions on a national scale in Germany,
(2) identify factors influencing this distribution and (3) identify regions
with high vulnerability to SOC losses. The SOC content and proportion of two
different SOC fractions were estimated for more than 2500 mineral topsoils
(< 87 g kg
Arable topsoils in central and southern Germany were found to contain the highest proportions and contents of stable SOC fractions, and therefore have the lowest vulnerability to SOC losses. North-western Germany contains an area of sandy soils with unusually high SOC contents and high proportions of light SOC fractions, which are commonly regarded as representing a labile carbon pool. This is true for the former peat soils in this area, which have already lost and are at high risk of losing high proportions of their SOC stocks. Those “black sands” can, however, also contain high amounts of stable SOC due to former heathland vegetation and need to be treated and discussed separately from non-black sand agricultural soils. Overall, it was estimated that, in large areas all over Germany, over 30 % of SOC is stored in easily mineralisable forms. Thus, SOC-conserving management of arable soils in these regions is of great importance.
There is increasing interest in soil organic carbon (SOC) in agricultural soils, as it contributes to soil fertility and also to the mitigation of climate change when organic carbon sequestration is enhanced (Post and Kwon, 2000). In agricultural systems the pathway of atmospheric carbon to SOC is controlled by land use and agronomic management. However, SOC comprises a large range of compounds, ranging from recently added organic matter, such as root litter and exudates, to highly condensed and transformed organic matter that may even be derived from the geogenic parent material. These different compound classes are stabilised in different ways and therefore have different turnover times (Lehmann and Kleber, 2015). Although SOC is now considered as having a continuum of turnover times, it is mostly described and modelled as consisting of different pools that vary in their turnover time (e.g. labile pool, intermediate pool and stabilised pool). The effects of land use and management are not the same for all soil organic matter compounds; they differ between SOC pools. Chimento et al. (2016) for example, found that the cultivation of perennial woody bioenergy crops increased SOC stocks compared to other bioenergy crops, but the new SOC accumulated only in the light and presumably labile particulate organic matter (POM) fraction. Poeplau and Don (2013a), on the other hand, found that cropland sites that were changed to grassland also sequestered new SOC but mainly in the more stable fractions. Therefore, the different SOC pools need to be assessed separately from the bulk SOC when discussing the influence of land use and management on stabilisation and storage of SOC.
One method for experimental quantification of the distribution of SOC among different SOC pools is fractionation. Various fractionation procedures for quantifying SOC fractions have been developed, mostly aiming at isolating fractions with differing turnover times (Poeplau et al., 2018; Zimmermann et al., 2007a). Determining the distribution of SOC among fractions with assumedly different turnover times is one step towards understanding the factors influencing SOC stabilisation. All methods for carbon fractionation are quite laborious, time-consuming and therefore expensive and not feasible for large datasets. Therefore, few studies exist on SOC fractions at a regional scale, indicating a need for the development of more efficient methods to predict carbon fractions in the assessment of large datasets. Near-infrared reflectance spectroscopy (NIRS) and mid-infrared spectroscopy (MIRS), in combination with chemometric methods, have been applied successfully to predict carbon fractions (Zimmermann et al., 2007b; Baldock et al., 2013; Cozzolino and Moro, 2006; Reeves et al., 2006). Thus, since the prediction of SOC fractions has been demonstrated to be possible using spectroscopic methods, it should also be possible to go beyond small datasets at a field scale in order to examine how SOC fractions are distributed regionally and the factors that drive this distribution.
Some impact factors are consistently reported as being important at a site scale for the distribution of SOC among different fractions or pools, one of which is land use. For western European croplands and grasslands, it was shown that a similarly high share of bulk SOC is attributed to fractions regarded as stable, while in forest soils, a higher proportion of SOC is attributed to more labile SOC fractions (John et al., 2005; Helfrich et al., 2006; Wiesmeier et al., 2014). Tillage can also have an impact on SOC pools, as some studies report higher levels of bulk SOC under no-till conditions compared with conventional tillage, with the majority of this increase occurring in the more labile carbon pools (Chan et al., 2002; Devine et al., 2014; Liu et al., 2014). This may, however, be just an effect of carbon redistribution in the soil and not lead to a net increase in SOC (Baker et al., 2007; Luo et al., 2010).
Fewer studies have examined the SOC distribution into fractions at a regional scale and even fewer have examined factors affecting the proportions of SOC distributed among different fractions or pools. Wiesmeier et al. (2014) determined the distribution of SOC fractions among 99 Bavarian soils under different land uses using the fractionation scheme devised by Zimmermann et al. (2007a), which is a combination of particle size and density fractionation. They found that approximately 90 % of the bulk SOC in cropland and grassland soils was distributed in intermediate or stabilised SOC pools, while this was only true for 60 % of the SOC found in forest soils. Therefore, those authors suggested that Bavarian soils under cropland and grassland are more suitable for long-term sequestration of additional SOC than soils under forest. They also examined controlling factors for the SOC distribution among fractions in the different land uses (Wiesmeier et al., 2014). Correlation analyses suggested that the intermediate SOC pools in croplands and grasslands were significantly correlated to soil moisture, but none of the functional SOC pools were influenced by temperature or precipitation. The particulate organic matter (POM) fraction of soils under grasslands and croplands was not significantly related to any environmental factor in that study (Wiesmeier et al., 2014). Poeplau and Don (2013a) conducted a study on 24 sites in Europe and found that SOC fractions differed in their degree of sensitivity to land-use change (LUC), with the sensitivity declining with increasing stability in the SOC fractions. Their results indicated that the afforestation of cropland shifts SOC from the more stable to the more labile fractions, while with the conversion from cropland to grassland the newly sequestered SOC is stored in the intermediate to stable pools. Rabbi et al. (2014) examined the relationships between land use, management, climate and soil properties and the stock of three SOC fractions for soils in south-eastern Australia, and observed a high impact of climate and site-specific factors (rainfall, silicon content, soil pH, latitude) and only a minor influence of land use. The dominance of site and climate variables as impact factors in that region may primarily be due to the wide range of site conditions in the area studied.
If the regional distribution of SOC fractions can be predicted using a combination of fractionation methods and NIRS and if relevant drivers for this distribution can be found, it should be possible to identify regions in Germany in which soils are most vulnerable to carbon losses. Some carbon fractions are commonly assumed to be more labile than others because they apparently have lower turnover times in the soil. The question is if it can simply be assumed that soils that contain a high percentage of those “labile” fractions are more vulnerable to carbon losses than others. On the one hand, it should be noted that for the assessment of vulnerability to carbon losses, not only the distribution of the fractions should play a role, but also the absolute amounts of carbon within the fractions. This is important as some soils may have stored a high percentage of SOC in a labile form, but the absolute amount of this SOC may be very low and thus less relevant in terms of climate change mitigation than a small percentage of light fraction that is lost from a soil rich in SOC. On the other hand, there are several regions in north-western Europe and also in northern Germany where the soils exhibit unusually high SOC content while having a high sand and low clay content (Sleutel et al., 2011). These so-called “black sands” have a poor capacity to stabilise SOC by binding onto mineral surfaces, and therefore most SOC is present in the form of POM. A great part of this land surface in northern Germany was covered by heathland and peatland until the end of the 18th century, and these soils may behave differently from other soils in terms of SOC storage and the vulnerability to carbon losses may not generally be definable via dividing SOC into fractions by density fractionation.
The present study is part of the German Agricultural Soil Inventory. A set of
145 topsoil samples, representative of German agricultural soils, was
fractionated and used to calibrate NIRS predictions of the constituent
fractions for > 2500 sites with mineral soils all over Germany.
Additional climate, management and geographical data were gathered for all
sites, and a machine learning algorithm was employed to clarify which factors
influence the distribution of the carbon fractions. In this paper we
therefore aim to answer the following research questions:
How is SOC distributed among the fractions at a national scale? Which driving factors are relevant for this distribution? Can regions of high vulnerability to carbon losses be identified by this
predictive approach?
Germany has a total surface area of 357 000 km
Soil samples were taken in the course of the ongoing German Agricultural Soil
Inventory. By May 2017, 2900 agricultural sites (croplands and grasslands)
were sampled based on an 8 km
For this study, a representative set of calibration sites was needed to be
able to predict the carbon fractions using NIRS. Therefore, 145 calibration
sites were chosen according to the following criteria: (1) maximum difference
in NIR (near-infrared) spectra, according to the Kennard–Stone algorithm (Daszykowski et al.,
2002); (2) consistent spatial distribution within Germany; (3) the exclusion of
sites with SOC content > 87 g kg
All 2900 topsoil samples were dried and analysed for gravimetric water
content, electrical conductivity (EC), pH, SOC content (g kg
The topsoil samples (0–10 cm depth) of the selected calibration sites were
dried at 40 To obtain the fraction that contains intra-aggregate particulate organic
matter (iPOM), 20 g of soil sample were placed in a falcon tube, which was
then filled to 40 mL with sodium polytungstate (SPT) solution
(density: 1.8 g mL To obtain the particulate organic matter occluded in the aggregates (oPOM)
fraction, the falcon tube containing the pellet was again filled to 40 mL
with SPT solution. The pellet was mixed with the solution using a vortex
shaker and then ultrasonic dispersion was applied again, at
450 J mL The remaining soil pellet was assumed to contain the mineral-associated
organic matter (MOM or heavy) fraction. The pellet was washed three times
with 40 mL of distilled water, dried, weighed and milled in the same way as
the iPOM and oPOM fractions. The organic carbon (C) and total nitrogen (N)
content of the three fractions was determined through thermal oxidation by
dry combustion using an elemental analyser (LECO Corp.). One possible
limitation of the applied fractionation scheme is that pyrogenic carbon ends
up in the light iPOM and oPOM fractions although it generally has longer
turnover times than assumed for this fraction. For Germany, however, we are
confident that this does not influence the results, as pyrogenic carbon only
plays a minor role in German soils (Schmidt et al., 1999a). The fractionation
method applied is only one of several possible methods and options to
separate labile from stabilised SOC.
The carbon recovery rate of the fractionation approach was between 80 and 110 %. Recovery rates of more than 100 % can be reached if the sample that is measured for total SOC or water content and the sample that is fractionated are not exactly the same. Even through careful subsampling the samples cannot be completely homogenised concerning their carbon content. The mean carbon contents of the fractions were 34.7 % for the iPOM fraction, 27.4 % for the oPOM fraction and 1.8 % for the MOM fraction.
Basic descriptive statistics were calculated for the data on the fractionated calibration sites, including mean absolute and relative proportions of the SOC fractions divided between different land uses and soil texture classes. An ANOVA was conducted to determine whether the differences between cropland and grassland land uses were significant and to test for significant differences between soil texture classes. The Games–Howell post hoc test was used for this purpose.
As the oPOM fraction generally contained a small proportion of total SOC (on
average 4 %), it was not reliably predictable on its own. Therefore, it
was combined with the iPOM fraction to give a “light fraction” for the
purpose of prediction. This was done even though it is clear that iPOM and
oPOM are likely to differ in their availability for decomposition and in
their turnover times. In this case an accurate prediction of the combined
light fraction was thought to be more important and better than an inaccurate
prediction of the oPOM fraction, as this can be misleading for the readers
when displayed on a map. Soil samples were dried at 40
To improve the model accuracy a spectral pretreatment was applied, using
Savitzky–Golay first derivative smoothing (three points) and wavelength
selection from 1330 to 3300 nm, since these regions contain the main
absorbance information. The calibration set consisted of the 145 calibration
site samples, and the remaining samples were used for prediction. Partial
least squares regression (PLSR) was performed in the pls package (Mevik et
al., 2015), based on near-infrared (NIR) spectra and reference laboratory
data. A cross-validation was applied using leave-one-out to avoid over- and
under-fitting. To obtain the carbon fractions and ensure that the sum of
light and heavy fractions was equal to total SOC content, the log ratio of
the light and heavy fraction was predicted. A validation using an independent
validation set was not deemed advisable in this study as the calibration
dataset was representative of the whole area of Germany, including a diverse
set of soil types and geographical circumstances. Moreover, 145 samples are
not a large dataset for a calibration and with every split of this dataset a
large part of the variation present in German soils would be lost for the
calibration. An independent validation using the same dataset was carried
out, however, and the calibration and validation results can be found in
Table S3. Model performance was evaluated using the root mean square error of
cross-validation (RMSECV), Lin's concordance correlation coefficient (
We used the methodology as described above as NIRS is one promising method to
predict carbon fraction, which is fast, low-cost and accurate. The authors
had the following calibration results: for the prediction of carbon content in
the fractions (g kg
A total of 75 potential drivers of differences in carbon proportions in different fractions was compiled from the soil analysis data, complemented with data from a farm survey and geographical data (for a complete list of predictors, see Table S2 in the Supplement). The farm survey recorded management practices, over the 10 years prior to sampling if known by the farmer. Using this, yearly mean carbon and nitrogen inputs through plant material and organic and mineral fertilisers were calculated for each site based on the yield of the main product and on different carbon allocation functions for different crops as described in Bolinder et al. (1997). When data were missing in the survey responses, yields were calculated using regional yield estimates provided by the regional governments. Climate and site data acquired from GIS data layers completed the set of predictor variables (climate data from Deutscher Wetterdienst, normalised difference vegetation index (NDVI) data from ESA, elevation data from the Bundesamt für Kartographie und Geodäsie). For the sites in the federal states of Lower Saxony, North Rhine–Westphalia, Mecklenburg–Western Pomerania, Rhineland–Palatinate, Saxony Anhalt and Schleswig Holstein (northern Germany), the land-use history was researched using historical maps (dating back to 1873–1909), as many regions in these states are known to have a heathland or peatland legacy.
The conditional inference forest algorithm (cforest; Hothorn et al., 2006) was used to identify the most influential drivers of SOC distribution among the different fractions. Cforest is an ensemble model and uses tree models as base learners that can handle many predictor variables of different types and can also deal with missing values in the dataset (Elith et al., 2008). The cforest algorithm is similar to the better-known random forest algorithm, a non-parametric data mining algorithm that uses recursive partitioning of the dataset to find the relationships between predictor and response variables (Breiman, 2001).
Bootstrap sampling without replacement was carried out in order to prevent
biased variable importance (Strobl et al., 2007). As multicollinearity
between the predictors may result in a biased variable importance measure in
cforest algorithms (Nicodemus et al., 2010), the correlations between the
predictor variables were controlled. When the correlation between two
possible predictors was > 0.8, only the one with the broader
range of variation was kept in the dataset. Ten cforest models were created,
each containing 1000 trees and using different random subset generators. From
these models, the variable importance of predictors was extracted and the
relative variable importance was calculated and averaged over all 10 models.
Variables were considered important when their relative variable importance
was higher than
A range of soils in northern Germany, called black sands, behaved quite
differently from other soils in the country in terms of the driving factors
for SOC distribution among the fractions. Therefore, the dataset was split
into two parts for the cforest analysis, and the cforest algorithm was used
on the following:
the dataset containing only the black sands from northern Germany
( all other soils considered not to be black sands
(
All statistical analyses were conducted using the software R. Maps were generated with the software QGIS.
The iPOM fraction contributed an average of 23 % to bulk SOC
(23 %
There were significant differences in the contribution of the different
fractions to bulk SOC depending on the main soil texture class (Fig. 2). In
sandy soils, the iPOM fraction contributed significantly more and the heavy
fraction contributed significantly less to bulk SOC than in other soils. For
the oPOM fraction, the difference between sandy soils and clayey, silty and
loamy soils was not significant. The absolute SOC content (g kg
Mean relative variable importance according to the conditional inference
forest (cforest) algorithm for the predicted proportion of soil organic carbon
(SOC) in the light fraction. The vertical line indicates the threshold value
of relative variable importance above which a variable was regarded as
important.
Relationship between soil organic carbon (SOC) proportion in the light fraction and influential variables. Calibration sites are shown as red dots, normal non-black sand soils as black dots and black sands as orange triangles.
With the machine-learning algorithm cforest, 75 variables that may act as
drivers for the regional distribution of SOC fractions were evaluated
(Fig. 3a). For the non-black sand soils dataset, soil texture had the highest
explanatory power in predicting the contribution of the light fraction to
bulk SOC (Fig. 4), with clay content being negatively and sand content
positively correlated with the percentage of SOC in the light fractions. The SOC
content, bulk soil C
Relationship between land-use history and
The analysis of historical land-use data of northern Germany confirmed that
the former peatland, heathland and grassland sites had a significantly higher
(
Predicted soil organic carbon (SOC) proportion range ( %) in the light fraction of soil at sites in the German Agricultural Soil Inventory.
Predicted absolute soil organic carbon (SOC) content range
(g kg
For the black sands dataset, bulk soil SOC content was the most important
driver of SOC distribution in the fractions (Fig. 3b), followed by C
Regions featuring high proportions of SOC in the light fraction (over
60 % of total SOC) nearly all lie in northern Germany (Fig. 7). Medium
proportions of SOC in the light fraction (40–60 % of total SOC) were
found in Mecklenburg–Western Pomerania and in parts of Brandenburg
(north-east Germany). Low proportions (< 40 %) of SOC in the
light fraction were found in central and southern Germany. Considering the
absolute contents of SOC in the light fraction (Fig. 8), it was obvious that
the absolute (in g kg
The relative distribution of carbon among different fractions did not differ
significantly between croplands and grasslands (Fig. 2a) in the calibration
dataset (
The significant differences observed in the absolute SOC content of fractions
between different land uses were to be expected, as grassland soils in
Germany contain on average more than twice as much SOC in the upper 10 cm as
cropland soils (42
All samples with medium or high proportions of SOC in the light fraction were
found to originate from northern Germany. This is the area in which the black
sands are present, which store large parts of their SOC in the light
fraction. Springob and Kirchmann (2002a) examined the presence of black sands
in Lower Saxony in Germany and linked it to the land-use history. In Ap
horizons of soils formerly used as heathland or plaggen, they found a high
fraction of SOC resistant to oxidation with HCl. This HCl-resistant fraction
was positively correlated with the total SOC content, but soil microbial
biomass carbon content showed a negative relationship with total SOC and,
when incubated, the specific respiration rates were lowest for the soils with
the highest SOC content (Springob and Kirchmann, 2002a). Those authors
concluded that a high proportion of the organic matter in the former
heathland soils is resistant to decomposition and suggested that low
solubility of the SOC could be responsible for its high stability. A recent
study (Alcántara et al., 2016) reported similar results for sandy soils
under former heathland, which had lower respiration rates per unit SOC and a
wider range of C
“Historical” peatlands may have lost much of their former carbon stocks for a number of reasons. Drained peatlands emit huge amounts of CO
Land-use history clearly continues to influence soil SOC dynamics, since the
light-fraction SOC proportion and the bulk soil C
The presence of black sands poses a problem for the interpretation of the SOC
fractions. In most cases, the SOC in the light fraction (iPOM
The most important driver for the SOC distribution among the fractions in non-black sand soils was the soil texture (Fig. 3a). This is well in line with the frequently reported relationship between clay content and mineral-associated (heavy-fraction) SOC, whereby clayey soils can stabilise SOC through mechanisms that protect it against microbial decay by absorption or occlusion (von Lützow et al., 2006; Six et al., 2002). The SOC that is bound to the mineral phase is mostly assigned to a conceptual stable SOC pool. The negative relationship between SOC content and percentage of SOC in the heavy fraction (Fig. 4) may indicate SOC saturation of the mineral fraction with rising SOC content, so that excess SOC can only be stored as particulate organic carbon.
The positive correlation between the soil C
The fact that land use is an important driver for the distribution of SOC
among the fractions is mainly due to the fact that in the dataset containing
all non-black sand sites, topsoils under grassland store a significantly
higher share of SOC in the light fraction than topsoils under cropland. This
is in line with higher inputs of roots, which make up part of the light
fraction, into grassland topsoils. The higher proportion of SOC in the light
fraction was also noted in the calibration dataset (
Apart from texture, C
The influence of soil type is mainly due to the Podzol soils storing a much higher proportion of bulk SOC in the light fraction than all other soil type classes (Fig. 6). Podzols often develop on sandy soils and therefore do not have a high capacity for SOC stabilisation in the heavy fraction (Sauer et al., 2007).
In the dataset containing only the black sands, soil total SOC content was
the most important driver for the SOC distribution among the fractions, with
increasing light fraction with increasing SOC content (Fig. 4). On the one
hand, this could indicate the saturation of the heavy fraction at high SOC
contents, which would lead to further storage in the light fraction only, as
already mentioned above for non-black sand soils. Another possible
explanation is that those soils with the highest SOC content in the dataset
are degraded peatlands, in which a high percentage of the SOC ends up in the
light fraction. On former heathlands, the soil total SOC content is also
quite high compared with that in other sandy soils and the light fraction is
mainly built up from
There is a close link between land-use history as peatland and heathland and
soil the C
In black sands, there was a significant negative relationship between soil temperature and the light-fraction SOC proportion, but this was not found for the other soils (Fig. 4). A negative relationship was observed between soil bulk density and the proportion of SOC in the light fraction, which was evidently due to the low density of the light fraction affecting overall soil bulk density (Fig. 4).
Even though the land-use history was part of the dataset, and we could link
several of the important driving factors to a history as peatland or
heathland, the cforest algorithm did not identify the land-use history as an important driver for the SOC distribution into fractions. This was the case
because we did not have detailed land-use history data for all sites. But
even when running the cforest algorithm only for those sites with a known
land-use history, it was not selected as an important driver. This is probably
due to the fact that at the time of the land survey in 1873–1909 some of the
former heathland and peatland sites had already been cultivated. Therefore,
the land-use history would not prove to be a reliable indicator. We confirmed
this by referring to an older land survey, dating back to 1764–1785. For
sites that exhibited typical black sand features (e.g. high SOC proportions
in light fractions, high sand content and a high C
For a soil to be definitively identified as being vulnerable to SOC losses, it not only needs to have a high proportion of bulk SOC in the light fraction but also a high absolute SOC content in this fraction. The map in Fig. 8 shows the absolute SOC content of the light fraction at sites of the German Agricultural Soil Inventory. Comparing Figs. 7 and 8, it is evident that sites which store a high proportion of their SOC in the light fraction generally also have high absolute SOC content in the light fraction. This implies that those sites are really the most vulnerable to SOC losses, as they not only have high proportions of SOC in the light fraction but also the highest absolute SOC content in the light fractions to lose. As the SOC in former peatland soils has been shown to be easily mineralised (Bambalov, 1999), the management of such sites should be aimed at stabilising the SOC stocks and preventing further degradation of the peat. When there is a heathland history, it can be assumed that the SOC in the light fraction is quite stable but that does not imply that freshly added litter will also be stable. In fact, it is quite likely that it will not be stable if no heathland vegetation is planted. This implies that the SOC stocks on these sites will decline when the resistant litter is not replenished.
Taking together all the important explanatory variables discussed above, regions in which the SOC can be classified as mostly labile were identified. These were soils with a high proportion of light fraction and without a heathland history. Such soils are mainly located in northern Germany and many of those have a peatland history (Fig. 7). These soils can be seen as vulnerable to losses of a high proportion of their SOC in the topsoil easily and rapidly. Loss of SOC could occur, for example, through a change in management that reduces carbon inputs to the soil and therefore fails to maintain the light fraction, for example a land-use change from grassland to cropland (Poeplau et al., 2011) or reduced input of organic fertilisers or crop residues (Dalal et al., 2011; Srinivasarao et al., 2014). Losses of SOC could also occur due to higher temperatures, which could lead to enhanced microbial activity and therefore enhanced mineralisation of SOC in the light fraction (e.g. Knorr et al., 2005). Former peatland soils may already lose significant parts of their SOC (Leiber-Sauheitl et al., 2014; Tiemeyer et al., 2016).
Regions with soils with a high proportion of stable SOC are located mainly in
central and southern Germany (Fig. 7). In these regions, soils consistently
store over 60 % of their SOC in the heavy fraction, in which the SOC is
bound mostly to the mineral surfaces of clay minerals. Thus, these soils have
the lowest vulnerability to losing their SOC, as losses mostly occur from the
light fraction. However, even in these regions up to 40 % of bulk SOC is
stored in the light fraction, and this may be lost. Therefore, apparent lower
vulnerability does not mean that SOC-conserving soil management is not needed
in these regions. It should be noted that the quality of the SOC in the light
fraction is probably not the same in all soils, land use (history) and
climate regions. Therefore, the vulnerability and turnover time of the light
fraction may also vary considerably within different regions. This can be
seen in the light-fraction C
Using the combination of SOC fractionation and prediction with NIRS, it is generally possible to identify regions that are more or less vulnerable to SOC losses. The results must be assessed with care, however, as phenomena like a non-labile light fraction in black sands can hamper the interpretation. It is therefore advisable to look at different driving factors when classifying sites as more vulnerable than others. Moreover, special soil phenomena are to be assessed separately from non-black sand soils, as the driving factors for the fractions distribution may vary considerably.
The identification of the distribution of SOC fractions in German soils
allowed a clear identification of regions where the SOC in agricultural soils is most
vulnerable to being lost. The cforest analysis provided indications of the
factors driving the distribution of SOC into the different fractions. It was
found that soil texture, bulk soil SOC content, the bulk soil C
The German Agricultural Soil Inventory is an ongoing project and the dataset cannot be published before its completion. For further information please contact Axel Don (ak@thuenen.de).
The supplement related to this article is available online at:
The authors declare that they have no conflict of interest.
This study was funded by the German Federal Ministry of Food and Agriculture in the framework of the German Agricultural Soil Inventory. We thank the field and laboratory teams of the German Agricultural Soil Inventory for their thorough and persistent work with the soil samples. Special thanks go to Anita Bauer for her support with the SOC fractionation. We also want to thank Catharina Riggers, Florian Schneider and Christopher Poeplau for valuable comments and discussion of a previous version of this paper. We thank Norbert Bischoff, Jochen Franz, Andreas Laggner, Lena Liebert and Johanna Schröder. Our thanks also go to the Bundesamt für Kartographie und Geodäsie and the Deutscher Wetterdienst for providing geodata and climate data, respectively, and to the Landesamt für Geoinformation und Landesvermessung Niedersachsen and the Landesamt für innere Verwaltung – Koordinierungsstelle für Geoinformationswesen for providing data on historical land use. Edited by: Asmeret Asefaw Berhe Reviewed by: A. Peyton Smith and one anonymous referee