Page 1
SOIL, 7, 305–332, 2021
https://doi.org/10.5194/soil-7-305-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
SOIL
Continental-scale controls on soil organic
carbon across sub-Saharan Africa
Sophie F. von Fromm
1,2
, Alison M. Hoyt
1,3
, Markus Lange
1
, Gifty E. Acquah
4
, Ermias Aynekulu
5
,
Asmeret Asefaw Berhe
6
, Stephan M. Haefele
4
, Steve P. McGrath
4
, Keith D. Shepherd
5
, Andrew M. Sila
5
,
Johan Six
2
, Erick K. Towett
5
, Susan E. Trumbore
1
, Tor-G. Vågen
5
, Elvis Weullow
5
,
Leigh A. Winowiecki
5
, and Sebastian Doetterl
2
1
Department of Biogeochemical Processes, Max Planck Institute for Biogeochemistry, Jena, Germany
2
Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
3
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
4
Department of Sustainable Agriculture Sciences, Rothamsted Research, Harpenden, UK
5
World Agroforestry Centre (ICRAF), Nairobi, Kenya
6
Department of Live and Environmental Sciences, University of California Merced, Merced, CA, USA
Correspondence:
Sophie F. von Fromm (sfromm@bgc-jena.mpg.de)
Received: 19 October 2020 – Discussion started: 26 October 2020
Revised: 7 May 2021 – Accepted: 12 May 2021 – Published: 29 June 2021
Abstract.
Soil organic carbon (SOC) stabilization and destabilization has been studied intensively. Yet, the
factors which control SOC content across scales remain unclear. Earlier studies demonstrated that soil texture
and geochemistry strongly affect SOC content. However, those findings primarily rely on data from temperate
regions where soil mineralogy, weathering status and climatic conditions generally differ from tropical and
subtropical regions. We investigated soil properties and climate variables influencing SOC concentrations across
sub-Saharan Africa. A total of 1601 samples were analyzed, collected from two depths (0–20 and 20–50 cm)
from 17 countries as part of the Africa Soil Information Service project (AfSIS). The data set spans arid to humid
climates and includes soils with a wide range of pH values, weathering status, soil texture, exchangeable cations,
extractable metals and land cover types. The most important SOC predictors were identified by linear mixed-
effects models, regression trees and random forest models. Our results indicate that geochemical properties,
mainly oxalate-extractable metals (Al and Fe) and exchangeable Ca, are equally important compared to climatic
variables (mean annual temperature and aridity index). Together, they explain approximately two-thirds of SOC
variation across sub-Saharan Africa. Oxalate-extractable metals were most important in wet regions with acidic
and highly weathered soils, whereas exchangeable Ca was more important in alkaline and less weathered soils
in drier regions. In contrast, land cover and soil texture were not significant SOC predictors on this large scale.
Our findings indicate that key factors controlling SOC across sub-Saharan Africa are broadly similar to those in
temperate regions, despite differences in soil development history.
1
Introduction
Soil conservation and sustainable management are crucial to
address some of the main challenges humanity is facing, such
as climate change, food security, environmental degradation
and loss of soil biodiversity. Assessing the state of soils
and their potential responses to climate and land use change
requires carefully designed sampling strategies combined
with systematic analytical and statistical analyses across
locations and scales (IPCC, 2019). One key component is
soil organic carbon (SOC). Due to its variety of sources,
transformations and stabilization mechanisms, SOC is
chemically very complex and spatially heterogeneous. This
complexity causes significant uncertainties in global climate
models (Friedlingstein et al., 2014). It also complicates
Published by Copernicus Publications on behalf of the European Geosciences Union.
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
the extrapolation of SOC to a global scale using statistical
relationships to build robust global SOC products, such as
SoilGrids and the Harmonized World Soil Database (Tifafi
et al., 2018). To improve our understanding of global C
dynamics, it is important to better understand the factors that
control SOC stabilization and destabilization in soils from
regional to global scales (Blankinship et al., 2018; Heimann
and Reichstein, 2008).
SOC-stabilizing
drivers
and
processes
have
been
intensively
studied
over
the
past
several
decades.
Dokuchaev
(1883)
and
Jenny
(1941)
shaped
the
understanding that soil properties are correlated with
(independent) variables – the so-called soil-forming factors
(Eq. 1) as follows:
s
=
f
0
(cl
, o, r, p, t
)
,
(1)
where
s
stands for any type of soil property, such as
pH, carbon content, mineralogy, etc., and is determined
by the function
f
0
of the following soil-forming factors:
cl – climate;
o
– organisms;
r
– topography;
p
– parent
material; and
t
– time. This concept is still relevant and forms
the basis for many experiments and research attempting
to understand SOC storage. However, the importance of
the individual factors of Eq. (1) at different spatiotemporal
scales remains unclear (Doetterl et al., 2015; Rasmussen
et al., 2018; Wiesmeier et al., 2019). This uncertainty
hinders implementation of Eq. (1) in Earth system models,
resulting in a gap between the theoretical understanding
of SOM dynamics and our ability to improve terrestrial
biogeochemical projections that rely on existing models
(Blankinship et al., 2018; Rasmussen et al., 2018; Schmidt
et al., 2011). Despite the long history of studying SOC
stabilization (Greenland, 1965; Oades, 1988), there still is an
increasing demand for data on SOC dynamics at landscape
to global scales (Blankinship et al., 2018), especially from
subtropical and tropical ecosystems.
SOC stabilization is commonly conceptualized as the
competition between accessibility for microorganisms versus
chemical associations with minerals (Oades, 1988; Schmidt
et al., 2011). These processes are often only considered
implicitly by models (Blankinship et al., 2018; Schmidt et al.,
2011). Instead, models commonly rely on broader variables,
such as clay content, which is used as a proxy for sorption
and other organo-mineral interactions (Rasmussen et al.,
2018; Schmidt et al., 2011). These more generic variables
integrate a variety of stabilization processes which can be
difficult to disentangle. They can differ in their relative
importance and may not adequately capture soil mineralogy
and chemistry across different ecosystems and climate zones.
Hence, improving the predictive capacity of such models
requires not only a better understanding of the factors that
control SOC dynamics but also verification (or falsification)
of those new findings in regions that are underrepresented in
field studies and models.
For example, Rasmussen et al. (2018) found that
exchangeable Ca was correlated with the quantity of SOC
in water-limited soils, while Al
ox
was a better predictor of
SOC in wet, acidic soils. However, those findings may not be
directly transferable to subtropical and tropical soils, since
they differ greatly in climate, parent material and vegetation
(Six et al., 2002b), which usually results in more weathered
and older soils compared to those in temperate regions
(Feller and Beare, 1997). This was illustrated recently in
Quesada et al. (2020), where SOC variation in highly
weathered forest soils from across the Amazon Basin was
best explained by clay content, whereas the best explanatory
variables for less-weathered soils were Al species, pH and
litter quality. Feller and Beare (1997) also found that tropical
soils, dominated by low-activity clays (i.e., 1
:
1 clays), show
a strong relationship between SOC and clay and silt content.
In addition, Barthès et al. (2008) found that sesquioxides
(Al and Fe) play an important role in SOC stabilization for
various tropical soils. However, the relationship for high-
activity clays (i.e., 2
:
1 clays) is less clear, and contrasting
trends between SOC and clay and silt content have been
reported (Feller and Beare, 1997; Six et al., 2002a). In terms
of SOC distribution across sub-Saharan Africa, Vågen et
al. (2016) showed, by using a data set similar to the one in
this paper, that SOC content was highest in equatorial and
warm temperate climates where sand content, the sum of
base concentrations and pH values were low. With regard to
land cover, it has been shown for several sites across Africa
that forests usually contained the highest amount of SOC,
whereas the differences between cropland, grassland and
shrubland were less distinct (Abegaz et al., 2016; Olorunfemi
et al., 2020; Winowiecki et al., 2016a). Cropland cultivation
decreased carbon content by 50 % compared to forested
and semi-natural plots for sites in Tanzania, regardless of
sand content and topographic position (Winowiecki et al.,
2016b). Additionally, land degradation (i.e., erosion) resulted
in decreased SOC concentrations in those ecosystems,
independent of vegetation cover (Winowiecki et al., 2016a).
To address these diverging explanations of SOC variations
at regional scales, we analyzed a comprehensive soil
data set collected across the African continent using the
Land Degradation Surveillance Framework (Vågen et al.,
2010). This data set covers a wide range of climatic and
mineralogical conditions – from very arid to humid regions,
with different pH
H
2
O
values, soil texture, weathering status,
exchangeable cations and extractable metals – allowing us
to test different parameters to explain the variation in SOC
content in subtropical and tropical soils across sub-Saharan
Africa for two distinctive depth layers (0–20 cm – topsoil;
20–50 cm – subsoil). Here, we use this continental-scale data
set to address the following research questions:
1. Which soil properties and climate parameters best
explain SOC content variation across sub-Saharan
Africa?
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
307
We
explored
the
importance
of
soil
texture,
exchangeable Ca, oxalate-extractable Al and Fe,
soil pH
H
2
O
, mean annual temperature, aridity index
(PET
/
MAP), land cover and weathering status to
explain the variation in SOC content on a continental
scale. We expect that oxalate-extractable metals, soil
texture and climate will be among the most important
predictors of SOC concentration.
2. How do geochemical controls on SOC vary between
environmentally distinct subregions?
Due to the heterogeneity of climate and soil conditions
across sub-Saharan Africa, we expect to see different
geochemical controls explaining variations in SOC
content between regions. For example, we expect
exchangeable Ca will be most important in regions that
are drier, with less weathered and alkaline soils, while
oxalate-extractable Al and Fe will mainly be important
in humid regions with highly weathered and acidic soils.
2
Methods
2.1
Study area and data collection
Soil data used in this study were collected during the
AfSIS (Africa Soil Information Service) project. In total,
18 257 soil samples were taken from 60 sentinel sites
and from two different depths (0–20 cm – topsoil; 20–
50 cm – subsoil). Samples stem from 19 countries across
sub-Saharan Africa and were collected between 2009 and
2012, following the well-established Land Degradation
Surveillance Framework (Vågen et al., 2010). The 60
sentinel sites (each 100 km
2
) were stratified across sub-
Saharan Africa according to Koeppen–Geiger zones (Vågen
et al., 2016). Within each sentinel there were 10 plots of
1000 m
2
randomized within 16 spatially stratified 1 km
2
clusters (Fig. 1). This hierarchical sampling design allows
process identification at a continental scale without losing
the ability to understand and quantify local heterogeneity
(Nave et al., 2021; Vågen et al., 2010). For more details about
sampling design and field survey, see Towett et al. (2015),
Vågen et al. (2013a) and Winowiecki et al. (2016a).
Our analyses built upon a subset of samples (11 %
of the total;
n
=
2002) which were originally selected
as reference samples for laboratory measurements. These
samples were used to calibrate mid-infrared spectroscopy
models (Terhoeven-Urselmans et al., 2010) and to predict
properties in the remaining 16 255 soil samples (Vågen et
al., 2016; Winowiecki et al., 2017). The calibration subset
was chosen to maximize the variation in the spectral data
using the Kennard–Stone algorithm (Kennard and Stone,
1969). More information about this approach can be found
in Terhoeven-Urselmans et al. (2010). This selection strategy
results in unequally distributed samples across 51 of the
Figure 1.
(a)
Aridity index map and sampling scheme (
n
total
=
1601). Gray triangles represent individual sentinel sites where
sample clusters were collected. The top-right inset
(b)
shows
the exact sampling points within one of the sentinel sites (Didy,
Madagascar) as an example.
60 sentinel sites yet captures the variation in the original data
set.
2.2
Sample and data processing
Soil material was air-dried and sieved to a particle size
<
2 mm in the Soil–Plant Spectroscopy Laboratory at the
World Agroforestry Centre (ICRAF) in Nairobi, Kenya.
All soil properties (except for soil texture, which was
measured at ICRAF) were analyzed at Rothamsted Research
in Harpenden, UK.
Data for soil organic carbon (SOC; weight percentage
– wt %), pH
H
2
O
, amorphous oxalate-extractable aluminum
(Al
ox
; wt %) and iron (Fe
ox
; wt %, exchangeable calcium
(Ca
ex
; centimoles per kilogram), clay
+
fine silt content
(
<
8 µm; percent), and total element concentrations (in wt %)
of Al, Ca, K, and Na, were selected in order to cover a wide
range of soil properties that have been identified to relate
to SOC stabilization mechanisms (Oades, 1988; Rasmussen
et al., 2018), while maximizing the number of samples and
minimizing the correlation among variables included in our
analysis.
SOC was calculated from the difference of total C
and inorganic C. The latter was directly measured with a
Primacs AIC100 analyzer (Skalar Analytical B.V., Breda, the
Netherlands) by treating the sample with phosphoric acid
and heating it to 135
◦
C in a closed system. Inorganic C
in the sample was converted to CO
2
and then measured
by nondispersive infrared detection (NDIR). Total C was
determined with the TruMac total N and C combustion
analyzer (LECO Corporation, St. Joseph, Michigan, USA).
Soil pH
H
2
O
was performed in a 1
:
2
.
5 soil : water suspension.
The extraction of Al and Fe with oxalic acid and ammonium
oxalate solution was done by shaking the solution for 4 h at
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
25
◦
C in the dark. Carbonate-rich samples were pretreated
with ammonium acetate at pH 5.5 to remove any CaCO
3
.
Acid-oxalate extraction in particular dissolves short-range-
order minerals such as ferrihydrite (Fe), allophane and
imogolite (Al), as well as other amorphous and organic Fe
and Al minerals (Parfitt and Childs, 1988). Hexamine-cobalt
trichloride solution was used as an extractant to determine
Ca
ex
. Aqua regia acid digestion was applied for major and
trace elements, including Al, Ca, K and Na. Although this
method does not give absolute total contents, it does give
results sufficiently close to accepted values for different soils
(McGrath and Cunliffe, 1985). Samples were digested in
tubes in time- and temperature-controlled heating blocks. All
elements were measured with inductively coupled plasma
optical emission spectrometry (ICP-OES; Optima 7300 DV,
PerkinElmer Inc., Waltham, Massachusetts, USA). Particle
size distribution was measured using a laser diffraction
particle size analyzer (LDPSA) model LA-950 (HORIBA,
Ltd., Kyoto, Japan). Each sample was shaken for 4 min in
a 1 % sodium hexametaphosphate (calgon) solution with
ultrasonic energy before measuring to disperse aggregates.
We used 8 µm as cut-off to capture all clay
+
fine silt
particles. Results were comparable to
<
20 µm (see Appendix
Fig. A1), but
<
8 µm was selected because it is more relevant
to our interest in studying the influence of smaller particles
with large surface area on SOC concentration. In addition,
particles
<
8 µm resulted in a reproducible fraction across
soil types, unlike using only clay particles
<
2 µm (Fig. A1).
Aluminum, Ca, K and Na concentrations were used to
calculate the chemical index of alteration (CIA) after Nesbit
and Young (1982), using the following equation:
CIA
=
Al
2
O
3
/
(Al
2
O
3
+
CaO
+
K
2
O
+
Na
2
O)
×
100
,
(2)
where CaO is the amount incorporated in the silicate fraction.
Correction is necessary for samples that contain carbonates
and apatite (Nesbit and Young, 1982). We adopted an
approach introduced by McLennan (1993), which assumes
that Ca is typically lost more rapidly than Na during
weathering. If a soil sample contained inorganic C (C
total
–
C
org
; used as a proxy for carbonates and apatite) and the
CaO content was greater than that of Na
2
O in the same
sample (
n
=
476), then the CaO concentration was set to
that of Na
2
O from the same sample (Malick and Ishiga,
2016). After applying the correction, no obvious correlation
remained between CIA and inorganic C (Fig. A3). The index
increases (i.e., more highly weathered soil) with the loss of
Ca
2
+
, K
+
and Na
+
.
Samples were removed that contained missing or negative
values for one or more of the abovementioned parameters.
In addition, a single sample with extraordinarily high SOC
content (
>
22 wt %) was excluded. This resulted in a total
of 1601 soil samples (out of the original 2002 samples) at
45 sentinel sites across 17 countries. Note that due to the
sample selection, not all profiles had data from both topsoil
and subsoil layers (Table B1).
The remaining soil samples (
n
=
1601) were paired
(based on longitude and latitude at the profile level) with
mean annual temperature (MAT; degrees Celsius) and mean
annual precipitation (MAP; millimeters) from the WorldClim
data set at 30 arcsec resolution (Fick and Hijmans, 2017).
Potential annual evapotranspiration (PET; millimeters) was
added from Trabucco and Zomer (2019), who calculated it
after the Penman–Monteith method, based on the WorldClim
data. Mean annual precipitation and PET were used to
calculate an annual aridity index, defined as PET
/
MAP
(Budyko, 1974). Values
>
1 indicate water-limited (dry)
regions and ratios
<
1 point to energy-limited (wet) regions.
For the monthly aridity index, we used monthly climate data
at the same spatial resolution and from the same data sources.
Land cover data was used from the collected field
data. The land cover groups were reclassified into the
following four major groups: (a) cropland (including all
cultivated plots), (b) forest, (c) grassland and (d) other
(including mainly woodland, shrubland and bushland but
also samples classified as other). A total of 10 missing
values were gap-filled from a prototype high-resolution
Africa land cover map at 20 m resolution based on
1 year of Sentinel-2A observations from December 2015
to December 2016 (http://2016africalandcover20m.esrin.esa.
int/, last access: 9 June 2020).
Due to the lack of precise data products for lithology
and soil types in sub-Saharan Africa, we did not include
these variables in our analyses. Soils at AfSIS sites
(Fig. 1) developed mainly from two parent material types,
(i) metamorphic and (ii) volcanic rocks (Hartmann and
Moosdorf, 2012; Jones et al., 2013; Schlüter, 2008), likely
modified throughout the Quaternary. (i) Metamorphic rocks
are most commonly found in West Africa, southern Africa
and Madagascar. These regions are characterized by old
cratons, except for Madagascar, which is influenced by
Mesozoic volcanism (Schlüter, 2008). Most of these soils are
classified as Ferralsols (World Reference Base, WRB, soil
classification system; Jones et al., 2013). Related AfSIS soils
from those regions are usually highly weathered with low
pH
H
2
O
values. In contrast, soils derived from (ii) volcanic
rocks are mainly found in the East African Rift System. They
are usually younger and less weathered (Buringh, 1970).
Beyond the influence of volcanic rocks, Ca
2
+
rich soils are
frequent in East Africa.
2.3
Statistical analyses
We used three different statistical approaches, including
linear mixed-effects models, regression trees and random
forests, to determine geochemical and climatic parameters
that best explain SOC variation across sub-Saharan Africa.
In brief, we used linear mixed-effects models to handle
the hierarchal sampling design of the AfSIS data set,
whereas regression trees and random forests enabled us to
account for nonlinearities within the data. More precisely,
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we used regression trees as a qualitative tool to explore and
understand the structure of the data, whereas random forests
offered more generalizable models. All statistical analyses
were performed within the R computing environment
(version 4.0.0; R Core Team, 2020). The R Markdown file
in the Supplement provides the code to reproduce all our
analyses.
Linear mixed-effects modeling was performed using the
nlme R package (Pinheiro et al., 2020) to account for
the nested sampling scheme (clusters within sites and
two sampling depths within one profile). This allows the
intercept of the regression to vary for each site, for each
cluster within the same site and for each sample within
the same profile (Harrison et al., 2018). The variance
inflation factor was used to check for multi-collinearity
among predictor variables with a threshold of
<
3
.
0 (Zuur et
al., 2010). To meet linear mixed-effects model assumptions
and to standardize variation among variables, all continuous
parameters were transformed to a normal distribution using
Box–Cox transformation, followed by standardization to a
mean of 0 and standard deviation of 1 by using the R
package bestNormalize (Peterson and Cavanaugh, 2019).
The relationship between SOC and the predictors of the
original data may not be linear.
To answer our first research question, i.e., which
soil properties and climate parameters best explain SOC
content, we started from a constant null model with
siteID/clusterID/plotID as random effects and then extended
the model in a step-wise manner by fitting the following
sequence of fixed effects: MAT, PET
/
MAP, depth, land
cover, clay
+
fine silt, pH
H
2
O
, CIA, M
ox
(Al
ox
+
1
/
2 Fe
ox
),
Ca
ex
, and pH
H
2
O
×
M
ox
. The order and selection of fixed
effects was predefined based on a priori knowledge from
a larger set of variables (Burnham and Anderson, 2002),
starting with large-scale climate variables and ending with
fine-scale physiochemical soil properties. The oxalate-
extractable metals Al
ox
and Fe
ox
were summed to M
ox
(Al
ox
+
1
/
2 Fe
ox
) to normalize the atomic mass difference
between Al and Fe (Wagai et al., 2020) and to account
for their similar behavior over their concentration range
(Fig. 5b). The maximum likelihood method and likelihood
ratio tests (L. ratio) were applied to evaluate model
performance and the statistical significance of the added
fixed effects (Tables B4–B9). The variation explained by
each fixed effect was obtained by calculating the marginal
R
2
(excluding the variation explained by the random effects
siteID/clusterID/plotID) for each model and subtracting
the
R
2
from the previous fitted model using the function
r.squaredGLMM from the MuMIn R package (Barton, 2020;
Nakagawa and Schielzeth, 2013). To identify how much
SOC variation is explained by climate and geochemistry
only (Legendre and Legendre, 2012), we built one model
with climate parameters (MAT and PET
/
MAP) only and
one model with geochemistry variables (clay
+
fine silt,
pH
H
2
O
, CIA, M
ox
, Ca
ex
and pH
H
2
O
×
M
ox
) only. In addition,
we analyzed the two sampling depths (0–20 and 30–
50 cm) separately to determine whether the same factors are
important for topsoil versus the deeper soil layer (Table 1).
For this model, we did not include plotID as a random effect
since each profile only contained one sample in each depth
model.
For the second research question, i.e., how geochemical
controls on SOC content vary between environmentally
distinct subregions, we grouped the data based on (a) pH
H
2
O
,
(b) wetness, (c) weathering and (d) land cover (Table 1).
Soil pH
H
2
O
and weathering data were grouped with the
number of categories chosen to maximize and equalize the
number of samples in each category and to correspond with
common pH
H
2
O
and weathering groups (Nesbit and Young,
1982). In order to take seasonality of the sites into account
separately, the data were divided into three categories based
on the number of wet months (i.e., months with P
/
PET
>
1).
Land cover was grouped based on the four predefined
categories. For each category within each subgroup, we
built a linear mixed-effects model, as previously described,
yet only included the geochemical properties (clay
+
fine
silt, pH
H
2
O
, CIA, M
ox
, Ca
ex
and pH
H
2
O
×
M
ox
) as fixed
effects, since we intended to test if the importance of
these predictors changed between environmentally distinct
subregions (Table 1). When CIA or pH
H
2
O
were used to
create the categories, they were not included as a fixed effect
in the corresponding submodels.
Regression tree (R packages rpart and rpart.plot;
Milborrow, 2019; Therneau and Atkinson, 2019) and random
forest analyses (R package ranger; Wright and Ziegler, 2017)
were conducted to identify nonlinear relationships between
SOC and any explanatory variable. This also enabled the
identification of pedogenic thresholds within the data. Each
analysis was conducted with the same explanatory variables
as for the linear mixed-effects models. However, no data
transformation was needed due to the nonlinearity of the
models.
Regression tree analysis was applied to obtain an easily
interpretable and nonlinear model for the entire data set and
for both depth layers (topsoil vs. subsoil) that best describes
the existing data (Breiman et al., 1984). Since regression
trees are known to easily overfit data, we used a grid search to
prune the model (Boehmke and Greenwell, 2020), according
to the minimum number of data points required to attempt
a split and the maximum number of internal nodes between
the root node and terminal nodes, in order to minimize the
cross-validation error (Breiman et al., 1984). The overall
performance of the regression tree analysis was tested using
a five-fold spatial cross-validation (R package mlr; Bischl et
al., 2016). Spatial partitioning was used to split the data into
five disjoint subsets, using the coordinates from each sample
and repeating the partitioning 100 times (Fig. A4). This
results in a bias-reduced assessment of model performance
(Brenning, 2012; Lovelace et al., 2019). Absolute values at
the bottom of each node indicate the predicted SOC content
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
Table 1.
Grouping variables, subgroups, number of samples and fixed effects used for the linear mixed-effects models.
Groups
Categories
n
Fixed effects
All samples
None
1601
All
Climate
Geochemistry
Depth
Topsoil (0–20 cm)
791
Geochemistry
Subsoil (30–50 cm)
810
pH
H
2
O
Strongly acidic (3.9–5.2 pH
H
2
O
)
404
Geochemistry
Moderately acidic (5.2–6.1 pH
H
2
O
)
399
Neutral (6.1–7.5 pH
H
2
O
)
398
Alkaline (7.5–9.9 pH
H
2
O
)
400
Wetness (No. of
0 wet months
572
Geochemistry
wet months
1–3 wet months
367
(P
/
PET
>
1))
4–7 wet months
662
Weathering (CIA)
Moderate (10 %–88 % CIA)
801
Geochemistry
High (88 %–100 % CIA)
800
Land cover
Cropland
429
Geochemistry
Forest
228
Grassland
242
Other
702
P – monthly precipitation (millimeters); PET – monthly potential evapotranspiration (millimeters);
CIA – chemical index of alteration (percent); fixed effects – all (i.e., mean annual precipitation (MAT), aridity
index (PET
/
MAP), depth, land cover, clay
+
fine silt, pH
H2O
, CIA, oxalate-extractable metals (M
ox
),
exchangeable Ca (Ca
ex
) and pH
H2O
×
M
ox
); climate (MAT, PET
/
MAP); and geochemistry (i.e., clay
+
fine
silt, pH
H2O
, CIA, M
ox
, Ca
ex
and pH
H2O
×
M
ox
).
(wt %) and the percentage corresponds to the relative number
of samples in this node (Fig. A6).
Random forest was used to build more generalized models
since it is an ensemble of multiple decorrelated trees.
Tuning of the model hyperparameters was done based
on spatial tuning (R package mlr; Bischl et al., 2016;
Lovelace et al., 2019). These hyperparameters included
the number of predictors used at each split, the minimum
number of observations in a terminal node and the fraction
of samples used in each tree (Probst et al., 2019). The
best hyperparameter combination search was done for the
complete data set via a five-fold spatial cross-validation with
one repetition. In each of these five spatial partitions, we ran
50 models to find the optimal hyperparameter combination
(Lovelace et al., 2019).
Partial dependence plots were used to further explore the
relationship between the predicted SOC content and the
explanatory variables of the tuned random forest models
(R package pdp; Greenwell, 2017). These plots were used
to investigate the marginal effect of individual explanatory
variables (such as Al
ox
, Ca
ex
, etc.) on the predicted SOC
content (Friedman, 2001). This allowed us to identify
thresholds within the data and provided an indication of how
important each explanatory variable was for the prediction of
SOC concentration across specific value ranges.
3
Results
3.1
Data distribution across sub-Saharan Africa
All soil and climate variables spanned at least 1 order
of magnitude (except MAT and PET), demonstrating the
diversity of this continent-wide data set. Based on skewness,
kurtosis, histograms and Shapiro–Wilk tests (data not shown
for the latter two), no variable was normally distributed
(Table 2).
In total, 429 samples were classified as cropland, 228
as forest, 242 as grassland and 702 as other land covers,
including mainly shrubland, bushland and woodland.
The SOC content decreased among those groups in the
following sequence: forest (2.69
±
1.15 wt %)
>
cropland
(2.21
±
1.68 wt %)
>
grassland
(1.77
±
1.55 wt %)
>
other
(1.35
±
1.28 wt %; Fig. 2a). Clay
+
fine silt content and
SOC showed a positive relationship across the entire data set
yet with a large spread (Fig. 2b). However, individual sites
showed contrasting correlations between SOC and clay
+
fine silt content, including none, positive and negative values
(Figs. 2c; see A5 for all individual sites).
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311
Table 2.
Summary statistics of all numerical soil and climate variables for the entire data set (
n
total
=
1601;
n
Topsoil
=
791;
n
Subsoil
=
810).
Variable
Mean
SD
P0
P25
P50
P75
P100
Skewness
Kurtosis
SOC (wt %)
1.84
1.51
0.07
0.65
1.42
2.54
9.19
1.42
2.23
MAT (
◦
C)
21.7
3.2
13.7
19.8
21.5
23.0
29.8
0.17
−
0.12
MAP (mm)
1070
487
255
648
1057
1432
2708
0.29
−
0.63
PET (mm)
1810
310
1350
1571
1759
1933
2949
1.19
1.96
PET
/
MAP
2.35
1.73
0.71
1.2
1.54
3.16
9.54
1.46
1.31
Clay
+
fine silt (%)
55.4
22.6
0.1
37.7
57.9
74.7
100.0
−
0.26
−
1.00
Al
ox
(wt %)
0.28
0.36
0.01
0.12
0.20
0.29
3.71
4.52
25.29
Fe
ox
(wt %)
0.38
0.56
0.01
0.10
0.21
0.40
4.46
3.60
14.96
Ca
ex
(cmol
+
kg
−
1
)
10.29
11.01
0.03
1.34
5.86
16.49
75.66
1.28
1.32
pH
H
2
O
6.3
1.3
3.9
5.2
6.1
7.5
9.9
0.27
−
1.11
CIA (%)
87.7
9.3
10.3
81.7
88.1
96.0
99.9
−
1.04
3.88
SD – standard deviation; P – percentile; SOC – soil organic carbon; MAT – mean annual temperature; MAP – mean annual precipitation;
PET – potential evapotranspiration; Al
ox
– oxalate-extractable Al; Fe
ox
– oxalate-extractable Fe; Ca
ex
– exchangeable Ca; CIA – chemical
index of alteration.
Figure 2.
(a)
Soil organic carbon (SOC) content (wt %) for the
different land covers, i.e., cropland, forest, grassland and other
(bushland, shrubland and woodland) by depth (0–20 cm – topsoil;
20–50 cm – subsoil).
(b)
SOC (wt %) and clay
+
fine silt content
(
<
8 µm) (percent) by depth.
(c)
SOC (wt %) and clay
+
fine
silt content (
<
8 µm) (percent) by depth for three example sites
that show contrasting trends. The gray area around fitted linear
regressions (
y
–
x
; for illustration only) in
(b)
and
(c)
shows the 95 %
confidence interval. For the relationship between SOC (wt %) and
clay
+
fine silt content (
<
8 µm) (percent) for all individual sites
(see Fig. A5).
3.2
Predictors of soil organic carbon
Linear mixed-effects modeling
The full linear-mixed effects model for the entire data set
had a marginal
R
2
of 0.72. The two climate parameters
(MAT and PET
/
MAP), depth, M
ox
and Ca
ex
were the
most important predictors of SOC content, based on their
marginal
R
2
. Land cover, clay
+
fine silt, pH
H
2
O
, CIA
and pH
H
2
O
×
M
ox
contributed either little or nothing to the
overall explanatory power of the model. Clay
+
fine silt
content, M
ox
and Ca
ex
were positively correlated with SOC,
whereas all other fixed effects showed negative relationships
with SOC concentration. The negative coefficient for depth
indicates that the SOC content in the subsoil layers is,
on average, lower as compared with the topsoil samples
(Fig. 3a).
The marginal
R
2
for the geochemistry model was 0.46,
which is almost the same as for the climate model
(
R
2
=
0
.
48). For the geochemistry model, the contribution of
M
ox
and Ca
ex
to explain SOC content was much higher than
in the full model (Fig. 3a). Based on variation partitioning,
27 % of the explained variation is shared between the
geochemistry model and the climate model, whereas the
variation explained by the geochemical or climate variables
alone is 19 % and 21 %, respectively (Fig. 3b).
Differences between the predictors were negligible for
the two depth models (topsoil vs. subsoil). However, the
explained variation by clay
+
fine silt was larger in the
subsoil layers compared with the topsoil layers. For Ca
ex
,
the opposite was true (Fig. 4a).
Within the pH
H
2
O
submodels, M
ox
was most important in
the strongly acidic model. The opposite was observed for
Ca
ex
(Fig. 4b), which corresponds to higher concentrations of
Ca
ex
in neutral and alkaline soils compared with moderately
and strongly acidic soils. However, Ca
ex
was also found to
have a positive relationship with SOC in acidic soils (Fig. 5;
Table B2). The direction of the correlation between clay
+
fine silt and SOC concentration was not consistent across
the four pH groups, in contrast to the other fixed effects
(Table B2). The alkaline submodel had the lowest marginal
R
2
of all pH
H
2
O
submodels, which suggests that important
predictors were missing (Fig. 4b).
Grouping by the number of wet months (wetness) showed
that M
ox
explained most of the variation in wet regions,
whereas Ca
ex
was most important in drier regions (Fig. 4c).
This corresponds to the overall distribution of M
ox
and
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
Table 3.
Marginal
R
2
for each predictor based on sequential fitting of the linear mixed-effects models of all samples (
n
Total
=
1601) for the
full, geochemistry-only and climate-only models. The sign in parentheses refers to the correlation between the predictors and soil organic
carbon. Bold values have a
p
value
<
0.05 based on likelihood ratio tests.
Figure 3.
Venn diagram illustrating the independent and shared
variation explained by the geochemistry-only and the climate-only
linear mixed-effects models.
Ca
ex
across MAP and pH
H
2
O
(Fig. 5b). The chemical index
of alteration (CIA) explained most of the variation in the
intermediate wet regions (Fig. 4c).
The high weathering model was dominated by M
ox
,
whereas the importance of M
ox
and Ca
ex
in the moderate
weathering model was similar. The other fixed effects did not
explain much of the variation in the two weathering models
(Fig. 4d).
Within the land cover models, the cropland and grassland
models had the highest marginal
R
2
and were both
dominated by M
ox
. The variation explained by Ca
ex
was
smallest for the forest model, whereas it did not change much
for the other three models (Fig. 4e).
In summary, in the linear mixed-effects models, M
ox
was
more important in wetter regions and acidic and highly
weathered soils, whereas Ca
ex
was more important in drier
regions and alkaline and less weathered soils. The other fixed
effects usually did not explain much of the SOC variation.
3.3
Regression tree and random forest
The root mean squared error (RMSE) for the topsoil
regression tree was 1.47 wt % (range
=
0.80 wt %–3.11 wt %)
and for the subsoil regression tree was 0.67 wt % (range
=
0.44 wt %–2.26 wt %); the relative RMSEs were 0.65 %
and 0.48 %, respectively. In the topsoil regression tree
(Fig. A6a) Fe
ox
, MAT and PET
/
MAP were the most
important predictors to split and explain the variation in
SOC concentration. About 23 % of the SOC data could be
explained by Fe
ox
and MAT alone. In general, higher Fe
ox
,
Al
ox
and Ca
ex
values resulted in higher SOC content. This
was equally true for the subsoil tree (Fig. A6b). While much
of the SOC variation was explained by climate parameters in
topsoils, the subsoil regression tree was more dominated by
geochemical variables, namely Fe
ox
and Al
ox
. About 40 % of
the subsoil SOC variation could be explained by Fe
ox
only.
In both trees, clay
+
fine silt content and land cover poorly
predicted SOC.
In summary, topsoil and subsoil regression trees contained
the same predictors, but climate variables played a larger role
in the topsoil regression tree, and geochemistry had a larger
influence in the subsoil regression tree. Overall, the results
showed that the explanatory variables did not differ much
between the depth intervals (topsoil vs. subsoil), while their
magnitude did.
The random forest models had a RMSE of 1.31 wt %
and a
R
2
of 0.70 for the topsoil samples, and for the
subsoil samples, they had a RMSE of 0.87 wt % and a
R
2
of 0.72. Based on the partial dependence plots (Fig. 6),
Al
ox
and Ca
ex
were important in predicting SOC over the
entire range of each variable (Fig. 6a and b). However,
in subsoils, the predictive power of Ca
ex
was reduced
(Fig. 6b). We observed a decrease in the predicted SOC with
increasing soil weathering status (CIA). However, due to the
low number of samples with CIA values below 60 %, the
relationship should be interpreted with caution in this range
(Fig. 6c). Clay
+
fine silt content had almost no effect on
SOC, with only a weak positive trend in subsoil samples
(Fig. 6d). The relationship between Fe
ox
concentration and
predicted SOC content varied with Fe
ox
concentration. At
low concentrations (
<
0
.
25 wt %), there was a strong positive
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
313
Figure 4.
Explained variation (based on marginal
R
2
) for each fixed effect, based on sequential fitting of the linear mixed-effects models
grouped by
(a)
depth (0–20 cm – topsoil; 20–50 cm – subsoil),
(b)
pH classes (3.9–5.2 pH – strongly acidic; 5.2–6.1 – moderately acidic;
6.1–7.5 – neutral; 7.5–9.9 – alkaline),
(c)
wetness (no. of wet months; P
/
PET
>
0; 0, 1–3, 4–7),
(d)
weathering (CIA – chemical index of
alteration; 10 %–88 % CIA – moderate; 88 %–100 % – high) and
(d)
land cover.
Figure 5.
(a)
Soil organic carbon (SOC) (wt %) and exchangeable Ca (Ca
ex
; centimoles per kilogram) content colored by pH classes (3.9–
5.2 pH – strongly acidic; 5.2–6.1 – moderately acidic; 6.1–7.5 – neutral; 7.5–9.9 – alkaline) with binned averages (bold squares;
n
=
20).
Note that the
x
axis is truncated for improved visualization, which removes three data points (Ca
ex
=
53
.
91, 54.58 and 75.66 cmol
+
kg
−
1
).
(b)
Al
ox
, Fe
ox
(grams per kilogram; which were combined to M
ox
, i.e., Al
ox
+
1
/
2 Fe
ox
, for the linear mixed effects models) and Ca
ex
(centimoles per kilogram) averaged content (
n
=
20) across pH
H
2
O
and mean annual precipitation (MAP; millimeters).
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
Figure 6.
Partial dependence plot for each explanatory variable of the random forest models (topsoil and subsoil). The
x
axes always
correspond to the range of the explanatory variable. Arrows indicate splitting points in the regression tree (Fig. A6). Each colored tick mark
along the
x
axes represents one sample.
relationship between predicted SOC content and Fe
ox
.
For higher concentrations, the predicted SOC content was
relatively constant (Fig. 6e). MAT correlated negatively over
the entire range with predicted SOC concentration (Fig. 6f).
For PET
/
MAP, the predicted SOC content declined sharply
as PET
/
MAP increased from 1 to 2 (transition from wet to
dry water regimes; Fig. 6g). The relationship between pH
H
2
O
and predicted SOC content was not strong (Fig. 6h). For land
cover, there was almost no difference between the classes
within the same depth layer; however, topsoils had higher
SOC content (2.2 wt %) compared with the subsoil samples
across all land covers (1.5 wt %; Fig. 6i).
4
Discussion
Climate and geochemical variables are similarly important
for explaining SOC variations across sub-Saharan Africa
(Fig. 3), which is in line with findings from a global study
(Luo et al., 2021). However, the explanatory power of climate
and geochemical variables are not independent of each other,
reflecting the overall strong interaction between climate and
geochemistry (Doetterl et al., 2015). Since it is likely that, in
the long term, climate variables have predominantly indirect
effects on SOC dynamics through their influence on soil
geochemistry, we focus our discussion on those geochemical
variables (Ca
ex
, Al
ox
and Fe
ox
) that showed the highest
explanatory power with respect to SOC content across all
models. In addition, we discuss the role of depth, clay
+
fine
silt content and land cover in explaining SOC variations on
a continental scale, since other studies have identified their
important role in SOC dynamics.
4.1
Exchangeable calcium
Strong and positive relationships emerged between Ca
ex
and SOC concentration across all models, even though
Ca
ex
concentration showed strong pH
H
2
O
and precipitation
dependence (Fig. 5). Typical Ca
2
+
sources in soils are
from (a) weathering of bedrock or surface rock formations,
(b) decomposition of Ca
2
+
-rich organic materials, (c) lateral
movement of Ca
2
+
-rich water, (d) atmospheric dust and rain
deposition or (e) anthropogenic inputs (Likens et al., 1998;
Rowley et al., 2018). Characteristically, Ca
2
+
is weathered
easily from both primary and secondary minerals (Likens
et al., 1998). This usually leads to its accumulation in
semi-arid to arid environments that are characterized by
low rates of water flow through the soil profile that drives
slow weathering rates and high pH
H
2
O
values (Fig. 4b–d).
In such environments, Ca
2
+
plays an important role as a
cation bridge that facilitates aggregate formation (Rimmer
and Greenland, 1976; Tisdall and Oades, 1982) and bonding
of clay minerals to organic matter functional groups because
of their divalent charge, relative abundance and modest
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315
hydration radius (Likens et al., 1998; Muneer and Oades,
1989). However, we found that Ca
ex
was not only important
in alkaline and less-weathered soils in dry regions but also
in acidic and more-weathered soils under wetter conditions
(Fig. 5). It is likely that the main Ca
2
+
source in those
regions derives from atmospheric deposition (Albani et
al., 2015; Goudie and Middleton, 2001) and/or biological
cycling by plants (Likens et al., 1998). This is supported
by the fact that Ca
ex
showed a stronger relationship with
SOC in topsoil than subsoil layers (Figs. 4a and 6b). Since
land cover, which is a major driver of C inputs into the
soil, did not show a strong relationship with SOC in the
models, we speculate that biological cycling of Ca
2
+
does
not play a major role in explaining the observed differences
in SOC content. Yet, further analysis with better proxies
for biological Ca
2
+
inputs is needed to test this hypothesis.
High Ca
2
+
concentrations in acidic soils can also be derived
from the development of those soils from Ca
2
+
-rich parent
material which are out of equilibrium with modern climate
conditions (Slessarev et al., 2016).
In conclusion, the important role of Ca
ex
in our data set
was most pronounced in dry regions dominated by alkaline
and less weathered soils. However, it also played a role in
explaining the SOC variation in wetter regions and more
acidic soils, which supports the overall importance of Ca
ex
in stabilizing SOC.
4.2
Oxalate extractable Al and Fe
Similar to Ca
ex
, short-range-order minerals (M
ox
, Al
ox
and
Fe
ox
) showed a positive and strong correlation with SOC
content across all models. The relationship was strongest
in wet regimes with acidic and highly weathered soils
(Figs. 4b–d and 5b). Hydrous oxides of Al and Fe are usually
highly reactive because of their large specific areas with a
high proportion of reactive sites (Parfitt and Childs, 1988).
This results in the adsorption of organic matter to Fe and Al
oxides and the formation of stable soil aggregates (Tisdall
and Oades, 1982). In humid regions, high rates of mineral
weathering may release Fe, Al and Si faster than crystalline
minerals can precipitate (Rasmussen et al., 2018). Therefore,
Fe
ox
and Al
ox
are usually found to be important in SOC
stabilization in humid and acidic soils (Eusterhues et al.,
2003; Kramer and Chadwick, 2018).
In our study, short-range-order minerals were also
identified to play an important role for SOC stabilization in
soils of sub-Saharan Africa. However, even though Al
ox
and
Fe
ox
showed similar trends in their concentrations (Fig. 5b),
we observed diverging behavior in their predictive power
of SOC in the regression trees (Fig. A6) and the random
forests (Fig. 6a and e). For example, Fe
ox
was one of the
most important explanatory variables in the regression tree
and partial dependence plots, although only within a very
narrow range and at low Fe
ox
concentrations (Fig. 6e),
whereas Al
ox
was important over the entire range (Fig. 6a).
Inagaki et al. (2020) showed that higher amounts of soil
organic matter were co-localized with Fe in drier regions
compared to sites with higher rainfall, whereas the content
of Al
ox
co-localized with organic matter was not affected by
precipitation changes. This may be linked to the different
oxidation levels of Fe. At higher precipitation levels, Fe
oxides can be reduced, resulting in a release of associated
SOC to the aqueous phase (Berhe et al., 2012; Chen et al.,
2020; Thompson et al., 2011). This mechanism is probably
responsible for the low correlation between SOC and high
Fe
ox
concentrations in our data (Fig. 6e), pointing to the fact
that Fe
ox
can act as pedogenic threshold, depending on its
oxidation level in the soil system.
In summary, short-range-order minerals also play an
important role in SOC stabilization across sub-Saharan
Africa, similar to other regions. However, Al
ox
and Fe
ox
do
behave differently in explaining SOC content, even though
they showed covariance in terms of their concentrations.
Since we only have data for acid-oxalate extraction, we
cannot speculate further about their diverging behavior in the
models.
4.3
Depth
For the depth models, predictor differences were small
between topsoil (0–20 cm) and subsoil (20–50 cm) samples
(Figs. 4a and 6). This may reflect the large depth increments
for each of the two sampling depths, which may also
explain the overall small explanatory power of depth in the
linear-mixed effects model (Fig. 3a). Since the identified
SOC-controlling factors were similar for both depth layers
(Fig. 4a), differences in SOC content were likely driven by
the fact that subsoil samples usually contain less SOC due to
lower C inputs at greater depth (Jobbágy and Jackson, 2000).
Soil erosion at some sites (data not shown) might also dilute
differences between the two depth layers, since water and
wind can permanently remove surface soil.
4.4
Clay
+
fine silt content
Clay
+
fine silt content (
<
8 µm) did not emerge as
an important predictor of SOC concentration within our
different models (Figs. 3, 4 and 5e). This is in contrast to
some earlier studies that indicated that total clay content
explains a large proportion of SOC storage and stabilization
due to the sorption of soil organic matter to surfaces
of clay minerals and building of aggregates (Amelung et
al., 1998; Kahle et al., 2002). The relationship between
SOC and total clay content is used in various models to
describe the turnover and storage of SOC. However, this
simplified correlation may not account for the different
stabilization mechanisms related to various clay minerals,
e.g., 1
:
1 vs. 2
:
1 clay minerals (Oades, 1988). Past research
has yielded contradictory results on whether clay content
explains SOC variation in subtropical and tropical soils or
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
not. For example, Bruun et al. (2010) showed, for various
tropical soils, that clay mineralogy, Fe
ox
and Al
ox
are better
explanatory variables for SOC content than clay content
alone (
<
2 µm). In contrast, Quesada et al. (2020) found a
strong relationship between clay and SOC content for highly
weathered soils in the Amazon Basin that are dominated
by 1
:
1 clay minerals, such as kaolinite, whereas soils in
the same system, dominated by 2
:
1 clay minerals, showed
stronger relationship between SOC and Al species. In a
comparison between tropical and temperate soils, Six et
al. (2002b) found that less C was associated with the clay and
silt fraction (
<
20 µm) in tropical soils than in temperate soils.
Even though these studies used various cut-offs to define
the clay (
<
2 µm), clay
+
fine silt (
<
8 µm) and clay and
silt fraction (
<
20 µm), they all illustrate that the relationship
with SOC can be complex in subtropical and tropical soils.
Due to the broad spatial scale, soils in the AfSIS data set
contain different clay minerals (Butler et al., 2020). No clear
relationship between clay
+
fine silt content (
<
8 µm) and
SOC concentration was observed in the models, although
the raw data indicate an overall positive trend between
clay
+
fine silt content (
<
8 µm) and SOC concentration
(Fig. 2b). This positive relationship does not hold across
all sites (Figs. 2c and A5). Variable relationships with SOC
(Table B2) may explain the low predictive power of clay
+
fine silt content in this data set. Instead, variables that better
capture the different behavior of clay-sized minerals, e.g.,
Ca
ex
, Fe
ox
and Al
ox
, are likely more suitable soil parameters
to explain the variation in SOC content – even in highly
weathered soils across sub-Saharan Africa. This is supported
by the fact that a clay
+
fine silt-only model resulted in a very
small
R
2
(0.01 – linear mixed-effects model; 0.12 – random
forest; Table B3).
4.5
Land cover
The effect of land cover on SOC content was generally
small in our models, even in topsoils (Fig. 6i). Similar
findings were recently encountered in a global study (Luo
et al., 2021). One possibility may be that the relatively large
0–20 cm depth interval might dilute differences that could
be more marked in the top few centimeters. However, we
did observe differences in SOC content across land cover
classes, with forests containing the highest amount of SOC
– especially in topsoils (Fig. 2a). Croplands had higher
SOC content than grasslands, which is opposite of what is
commonly observed in temperate regions (Prout et al., 2020).
Another possible explanation for the absence of land cover
as an important predictor in our models, is that we lacked the
detailed data necessary to disentangle the impacts of different
practices and land use history. The land cover class cropland
contained a wide variety of cultivated plots, while more
detailed information about land management practices was
missing. This is particularly important since prior research
in other regions showed that SOC stock changes in tropical
cropland soils may be driven by C inputs (Fujisaki et al.,
2018b). Additionally, historical land use may even play a
more important role in explaining current stocks compared
to recent land use (Vågen et al., 2006).
Furthermore, land cover may covary with other parameters
(temperature, precipitation and geochemistry) to such a
degree that it is not an explanatory variable. This might
be the reason why the submodels grouped by land cover
did not show a clear pattern (Fig. 4e). However, the land-
cover-only models resulted in small
R
2
(0.01 – linear mixed-
effects models; 0.10 to 0.16 – random forest), which suggests
that land cover is a poor predictor for our SOC data at this
large spatial scale (Table B3). This may be due to the high
variation in SOC content within the different land cover
classes (Fig. 2a). Land use changes and their impact on soil
physico-chemical properties are scale dependent and likely
to be more distinct at smaller scales (Holmes et al., 2004,
2005). For example, land management and land degradation
(i.e., erosion) are known to impact SOC stocks at regional
scales in sub-Saharan Africa (Winowiecki et al., 2016a).
Future studies are needed to better understand the impacts
of land management and carbon storage potential in soils
across sub-Saharan Africa at different scales (Fujisaki et al.,
2018a; Vanlauwe et al., 2015). Overall, our data for sub-
Saharan Africa suggests that SOC content on a continental
scale is better explained by stabilization potential in soils
(climate, geochemistry) than by different aboveground C
inputs (vegetation).
5
Conclusions
We used a continental-scale data set from sub-Saharan Africa
to test relationships between SOC content, various soil
properties and climate variables in order to address our core
research questions.
1.
Which soil properties and climate parameters best
explain SOC content variation across sub-Saharan
Africa?
Parameters similar to temperate regions best explain the
variation in SOC content in tropical and subtropical
soils under various climate conditions across sub-
Saharan Africa, namely Ca
ex
, M
ox
(Al
ox
and Fe
ox
)
and PET
/
MAP. At this large spatial scale, climate
and geochemical parameters are equally important and
share some of the explained SOC variation. However,
land cover and clay
+
fine silt content did not explain
much of the variation in SOC content, in contrast to
some findings from other regions and studies.
The selected climatic and geochemical parameters,
which can be seen as proxies for most of the soil-
forming factors, explain about two-thirds of SOC
variation across sub-Saharan Africa. The remaining
third likely reflects those soil-forming factors that were
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
317
not or only poorly represented within our selected
variables, namely organisms, relief and time. However,
given the large spatial scale of the study, even such
additional information is unlikely to explain all of the
SOC variation measured.
2.
How do geochemical controls on SOC vary between
environmentally distinct subregions?
In dry regions with alkaline and less-weathered
soils, Ca
ex
explained most of the SOC concentration
variation, whereas M
ox
was more important in wetter
regions with acidic and highly weathered soils. Still,
Ca
ex
remained important in acidic and more weathered
soils and in wetter regions. Fe
ox
, as a predictor of
SOC content, was only important at low concentrations
in moderately weathered and wet soils. This observed
trend suggests that Fe
ox
can play an important role
in pedogenic thresholds in various soils across sub-
Saharan Africa.
Overall, a combination of PET
/
MAP, Ca
ex
and M
ox
seems to be an appropriate set of variables to explain the
SOC content variation on a continental scale across sub-
Saharan Africa. This does not imply that other variables,
such as clay
+
fine silt content and land cover are
not good predictors on a regional scale, as shown by
previous studies. However, the variables identified by
this study showed a consistent predictive power of SOC
content across various climate regions.
Future studies on large-scale SOC stabilization should
consider measuring these soil properties to include them
in models. This would likely improve the predictive
capacity of these models and contribute to closing
the gap between our theoretical understanding of SOC
concentration across large scales and our ability to
improve terrestrial biogeochemical model projections.
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
Appendix A
The figures and tables on the next two pages all belong to
the same topic. They show the results for the different cut-
offs we used to identify the best cut-off to be used for soil
texture. We looked at and tested for
<
2,
<
8 and
<
20 µm. In
the end, we decided to use <8 µm because we wanted to stay
as close as possible to
<
2 µm. However, we could not use
<
2 µm due to some reproducibility issues for duplicates. The
differences between
<
8 and
<
20 µm are negligible.
Figure A1.
Scatterplot of duplicate measurements for the particle size distribution data.
(a)
Duplicate 1 and 2
<
2 µm.
(b)
Duplicate 1 and 2
<
8 µm.
(c)
Duplicate 1 and 2
<
20 µm.
Table A1.
Correlation coefficient between SOC and particle size data
<
8 and
<
20 µm for all samples (
n
=
1601), topsoil (0–20 cm;
n
=
791)
and subsoil (20–50 cm;
n
=
810).
Samples
<
8 µm
<
20 µm
All
0.32
0.41
Topsoil
0.37
0.46
Subsoil
0.43
0.49
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
319
Figure A2.
(a)
Soil organic carbon (SOC) content (wt %) and clay
+
fine silt content
<
8 µm (percent) by depth.
(b)
SOC content (wt %)
clay
+
fine silt content
<
20 µm (percent) by depth.
Table A2.
Summary table of
R
2
for the different models (linear mixed-effects model and random forest) for the two different explanatory
variables (
<
8 and
<
20 µm) for all samples (
n
=
1601), topsoil (0–20 cm;
n
=
791) and subsoil (20–50 cm;
n
=
810).
Model
Linear
Random
Random
mixed-effects
forest
forest
model
(topsoil)
(subsoil)
Clay
+
fine silt
<
8 µm
0.01
0.12
0.12
Clay and silt
<
20 µm
0.03
0.17
0.19
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
Figure A3.
Scatterplot of inorganic carbon (C
total
–C
org
; wt %), the uncorrected chemical index of alteration (CIA; percent)
a
) and the CIA
(percent) correct for carbonates and apatite after Nesbit and Young (1982)
(b)
. See Sect. 2 for more details.
Figure A4.
Spatial visualization of selected training (blue) and test (orange) observations for spatial cross-validation of two repetitions from
the topsoil samples. Note: each dot may represent multiple samples.
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321
Figure A5.
Soil organic carbon (SOC; wt %) and clay
+
fine silt content (percent) by depth for each sampling site that contained more
than one sample per depth layer (0–20 cm – topsoil; 20–50 cm – subsoil). The gray area around fitted linear regressions represents the 95 %
confidence interval.
Figure A6.
Regression tree for
(a)
topsoil (0–20 cm) and
(b)
subsoil (20–50 cm). Splitting values are always in the units of the parameter
used for the split (for units, see Table 1). Absolute values in the boxes indicate the predicted soil organic carbon (SOC) content (wt %). The
percentage corresponds to the relative number of samples.
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
Appendix B
Table B1.
Overview of sample distribution used in this study across geographical regions, countries, sites, depths and land cover.
Region
Country
Site
Depth
Land cover
Topsoil
Subsoil
Forest
Cropland
Grassland
Other
East
TZA
5
61
54
6
16
13
80
ETH
4
179
165
3
153
56
132
KEN
3
131
153
5
4
55
220
UGA
2
99
101
0
90
29
81
MDG
2
161
175
206
86
20
24
West
NGA
5
16
19
1
15
5
14
MLI
3
11
14
1
9
6
9
CMR
1
8
6
2
10
2
0
GIN
2
12
8
1
9
1
9
NER
1
13
11
0
12
0
12
GHA
1
1
0
1
0
0
0
South
ZAF
3
11
11
0
0
7
15
MOZ
2
7
6
0
4
3
6
BWA
3
29
26
0
2
11
42
ZMB
2
10
9
1
2
13
3
AGO
4
36
44
1
14
17
48
ZWE
2
6
8
0
3
4
7
TZA – Tanzania; ETH – Ethiopia; KEN – Kenya; UGA – Uganda; MDG – Madagascar; NGA – Nigeria; MLI – Mali;
CMR – Cameroon; GIN – Guinea; NER – Niger; GHA – Ghana; ZAF – South Africa; MOZ – Mozambique;
BWA – Botswana; ZMB – Zambia; AGO – Angola; ZWE – Zimbabwe.
Table B2.
Marginal
R
2
for each fixed effect based on sequential fitting of the linear mixed-effects models for the different submodels (depth,
pH classes, number of wet months, weathering and land cover). The sign in parentheses refers to the correlation between the fixed effect and
soil organic carbon, respectively. Bold values have a
p
value
<
0.0001 based on likelihood ratio test.
Submodel
Clay and fine silt
pH
H
2
O
CIA
M
ox
Ca
ex
pH
H
2
O
×
M
ox
Depth
Topsoil
0.02
(
−
)
0.04 (
−
)
0.01
(
−
)
0.29
(
+
)
0.09
(
+
)
0.05
(
−
)
Subsoil
0.08
(
+
)
0.01 (
−
)
0.00 (
−
)
0.27
(
+
)
0.03
(
+
)
0.05
(
−
)
pH classes
Strongly acid
0.00 (
−
)
–
0.00 (
−
)
0.54
(
+
)
0.02
(
+
)
–
Moderately acid
0.04 (
−
)
–
0.04
(
−
)
0.37
(
+
)
0.06
(
+
)
–
Neutral
0.05 (
+
)
–
0.16
(
−
)
0.19
(
+
)
0.13
(
+
)
–
Alkaline
0.02 (
−
)
–
0.00 (
−
)
0.07
(
+
)
0.10
(
+
)
–
No. of
0
0.04
(
−
)
0.00 (
−
)
0.02 (
−
)
0.10
(
+
)
0.18
(
+
)
0.01 (
−
)
wet months
1–3
0.07
(
−
)
0.01 (
−
)
0.25
(
−
)
0.18
(
+
)
0.14
(
+
)
0.00
(
−
)
4–7
0.00 (
−
)
0.00 (
−
)
0.00 (
−
)
0.32
(
+
)
0.14
(
+
)
0.01
(
−
)
Weathering
Moderate
0.05
(
−
)
0.01 (
−
)
–
0.19
(
+
)
0.17
(
+
)
0.02
(
−
)
High
0.00 (
−
)
0.01 (
−
)
–
0.40
(
+
)
0.05
(
+
)
0.01
(
+
)
Land cover
Cropland
0.00 (
−
)
0.00 (
−
)
0.09
(
−
)
0.31
(
+
)
0.11
(
+
)
0.03
(
−
)
Forest
0.01 (
−
)
0.06 (
−
)
0.00 (
−
)
0.19
(
+
)
0.01
(
+
)
0.07 (
−
)
Grassland
0.05 (
−
)
0.09
(
−
)
0.06
(
−
)
0.22
(
+
)
0.11
(
+
)
0.04
(
−
)
Other
0.03
(
−
)
0.00 (
−
)
0.02
(
−
)
0.20
(
+
)
0.09
(
+
)
0.03 (
−
)
CIA – chemical index of alteration; M
ox
– oxalate-extractable metals (Al
ox
+
1
/
2
Fe
ox
).
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
323
Table B3.
Summary table of
R
2
for the different models (linear mixed-effects model and random forest) with different explanatory variables
(clay
+
fine silt, land cover, clay
+
fine silt and land cover and full) included for the entire data set. The
R
2
in parentheses for the linear
mixed-effects models refer to the conditional
R
2
, which include the variation explained by the random effects (siteID/clusterID/plotID).
Model
Linear mixed-
Random
Random
effects model
forest
forest
(topsoil)
(subsoil)
Clay
+
fine silt
0.01 (0.72)
0.12
0.12
Land cover
0.01 (0.75)
0.10
0.16
Clay
+
fine silt and land cover
0.02 (0.72)
0.22
0.26
Full
0.71 (0.94)
0.70
0.72
Table B4.
Analysis of variance (ANOVA) summary for linear mixed-effects analyses with the entire data set (
n
=
1601), including all
predictors and geochemistry-only and climate-only predictors. Fixed effects were added using a step-wise method. The first entry (
∼
1)
refers to the constant null model, respectively.
d
f
AIC
BIC
logLik
Test
L. ratio
p
value
All predictors
∼
1
5
2993.22
3020.11
−
1491.61
n/a
n/a
n/a
MAT
6
2969.00
3001.27
−
1478.50
1 vs. 2
26.23
<
0
.
0001
. . .
+
PET
/
MAP
7
2932.50
2970.15
−
1459.25
2 vs. 3
38.50
<
0
.
0001
. . .
+
Depth
8
2414.21
2457.24
−
1199.11
3 vs. 4
520.29
<
0
.
0001
. . .
+
Land cover
11
2416.06
2475.22
−
1197.03
4 vs. 5
4.15
0.2454
. . .
+
Clay
+
fine silt
12
2340.40
2404.94
−
1158.20
5 vs. 6
77.65
<
0
.
0001
. . .
+
pH
H
2
O
13
2342.00
2411.92
−
1158.00
6 vs. 7
0.40
0.5281
. . .
+
CIA
14
2248.88
2324.18
−
1110.44
7 vs. 8
95.13
<
0
.
0001
. . .
+
M
ox
15
1915.32
1995.99
−
942.66
8 vs. 9
335.56
<
0
.
0001
. . .
+
Ca
ex
16
1678.09
1764.14
−
823.04
9 vs. 10
239.23
<
0
.
0001
. . .
+
pH
H
2
O
×
M
ox
17
1599.15
1690.59
−
782.58
10 vs. 11
80.93
<
0
.
0001
Geochemistry only
∼
1
5
2993.22
3020.11
−
1491.61
n/a
n/a
n/a
Clay
+
fine silt
6
2979.20
3011.47
−
1483.60
1 vs. 2
16.03
0.0001
. . .
+
pH
H
2
O
7
2980.12
3017.77
−
1483.06
2 vs. 3
1.07
0.3000
. . .
+
CIA
8
2882.13
2925.16
−
1433.07
3 vs. 4
99.99
<
0
.
0001
. . .
+
M
ox
9
2515.81
2564.22
−
1248.91
4 vs. 5
368.32
<
0
.
0001
. . .
+
Ca
ex
10
2249.95
2303.73
−
1114.97
5 vs. 6
267.86
<
0
.
0001
. . .
+
pH
H
2
O
×
M
ox
11
2170.66
2229.82
−
1074.33
6 vs. 7
81.29
<
0
.
0001
Climate only
∼
1
5
2993.22
3020.11
−
1491.61
n/a
n/a
n/a
MAT
6
2969.00
3001.27
−
1478.50
1 vs. 2
26.23
<
0
.
0001
. . .
+
PET
/
MAP
7
2932.50
2970.15
−
1459.25
2 vs. 3
38.50
<
0
.
0001
MAT – mean annual temperature; PET – potential evapotranspiration; MAP – mean annual precipitation; CIA – chemical index of
alteration; M
ox
– oxalate-extractable metals (Al
ox
+
1
/
2
Fe
ox
); Ca
ex
– exchangeable calcium; n/a – not applicable; df:– degree of
freedom; AIC – Akaike information criterion; BIC – Bayesian information criterion; logLik – log likelihood; L.ratio – likelihood
ratio.
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Table B5.
ANOVA summary for linear mixed-effects grouped by depth (
n
Topsoil
=
791;
n
Subsoil
=
810). Fixed effects were added using a
step-wise method. The first entry (
∼
1) refers to the constant null model, respectively.
d
f
AIC
BIC
logLik
Test
L. ratio
p
value
Topsoil
∼
1
4
1440.72
1459.42
−
716.36
n/a
n/a
n/a
Clay
+
fine silt
5
1418.88
1442.25
−
704.44
1 vs. 2
23.84
<
0
.
0001
. . .
+
pH
H
2
O
6
1408.74
1436.78
−
698.37
2 vs. 3
12.14
0.0005
. . .
+
CIA
7
1350.41
1383.12
−
668.20
3 vs. 4
60.33
<
0
.
0001
. . .
+
M
ox
8
1148.87
1186.26
−
566.44
4 vs. 5
203.54
<
0
.
0001
. . .
+
Ca
ex
9
1016.14
1058.20
−
499.07
5 vs. 6
134.73
<
0
.
0001
. . .
+
pH
H
2
O
×
M
ox
10
967.11
1013.84
−
473.55
6 vs. 7
51.03
<
0
.
0001
Subsoil
∼
1
4
1460.72
1479.51
−
726.36
n/a
n/a
n/a
Clay
+
fine silt
5
1373.42
1396.91
−
681.71
1 vs. 2
89.30
<
0
.
0001
. . .
+
pH
H
2
O
6
1372.98
1401.16
−
680.49
2 vs. 3
2.44
0.1180
. . .
+
CIA
7
1373.42
1406.30
−
679.71
3 vs. 4
1.56
0.2123
. . .
+
M
ox
8
1188.60
1226.18
−
586.30
4 vs. 5
186.82
<
0
.
0001
. . .
+
Ca
ex
9
1135.71
1177.99
−
558.86
5 vs. 6
54.89
<
0
.
0001
. . .
+
pH
H
2
O
×
M
ox
10
1106.11
1153.09
−
543.06
6 vs. 7
31.60
<
0
.
0001
MAT – mean annual temperature; PET – potential evapotranspiration; MAP – mean annual precipitation; CIA – chemical
index of alteration; M
ox
– oxalate-extractable metals (Al
ox
+
1
/
2
Fe
ox
); Ca
ex
– exchangeable calcium; n/a – not applicable;
df:– degree of freedom; AIC – Akaike information criterion; BIC – Bayesian information criterion; logLik – log likelihood;
L.ratio – likelihood ratio.
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Table B6.
ANOVA summary for linear mixed-effects grouped by pH
H
2
O
(
n
strongly acidic
=
404;
n
moderately acidic
=
399;
n
neutral
=
398;
n
alkaline
=
400). Fixed effects were added using a step-wise method. The first entry (
∼
1) refers to the constant null model, respectively.
d
f
AIC
BIC
logLik
Test
L. ratio
p
value
Strongly acidic (3.9–5.2 pH)
∼
1
5
909.23
929.23
−
449.61
n/a
n/a
n/a
Clay
+
fine silt
6
909.32
933.33
−
448.66
1 vs. 2
1.91
0.1673
. . .
+
CIA
7
911.31
939.32
−
448.65
2 vs. 3
0.01
0.9293
. . .
+
M
ox
8
712.18
744.19
−
348.09
3 vs. 4
201.13
<
0
.
0001
. . .
+
Ca
ex
9
690.68
726.69
−
336.34
4 vs. 5
23.51
<
0
.
0001
Moderately acidic (5.2–6.1 pH)
∼
1
5
876.39
896.34
−
433.20
n/a
n/a
n/a
Clay
+
fine silt
6
864.42
888.36
−
426.21
1 vs. 2
13.97
0.0002
. . .
+
CIA
7
849.82
877.74
−
417.91
2 vs. 3
16.60
<
0
.
0001
. . .
+
M
ox
8
734.60
766.51
−
359.30
3 vs. 4
117.22
<
0
.
0001
. . .
+
Ca
ex
9
679.03
714.93
−
330.51
4 vs. 5
57.57
<
0
.
0001
Neutral (6.1–7.5 pH)
∼
1
5
785.87
805.80
−
387.93
n/a
n/a
n/a
Clay
+
fine silt
6
772.22
796.14
−
380.11
1 vs. 2
15.65
0.0001
. . .
+
CIA
7
686.06
713.97
−
336.03
2 vs. 3
88.16
<
0
.
0001
. . .
+
M
ox
8
620.16
652.06
−
302.08
3 vs. 4
67.90
<
0
.
0001
. . .
+
Ca
ex
9
581.03
616.91
−
281.52
4 vs. 5
41.13
<0.0001
Alkaline (7.5–9.9 pH)
∼
1
5
688.71
708.67
−
339.36
n/a
n/a
n/a
Clay
+
fine silt
6
679.07
703.02
−
333.53
1 vs. 2
11.64
0.0006
. . .
+
CIA
7
681.04
708.98
-333.52
2 vs. 3
0.02
0.8765
. . .
+
M
ox
8
651.45
683.38
−
317.72
3 vs. 4
31.59
<
0
.
0001
. . .
+
Ca
ex
9
592.58
628.51
−
287.29
4 vs. 5
60.87
<
0
.
0001
MAT – mean annual temperature; PET – potential evapotranspiration; MAP – mean annual precipitation;
CIA – chemical index of alteration; M
ox
– oxalate-extractable metals (Al
ox
+
1
/
2
Fe
ox
); Ca
ex
– exchangeable
calcium; n/a – not applicable; df:– degree of freedom; AIC – Akaike information criterion; BIC – Bayesian
information criterion; logLik – log likelihood; L.ratio – likelihood ratio.
https://doi.org/10.5194/soil-7-305-2021
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
Table B7.
ANOVA summary for linear mixed-effects grouped by the number of wet months (P
/
PET
>
1;
n
0
=
572,
n
1
−
3
=
367,
n
4
−
7
=
662). Fixed effects were added using a step-wise method. The first entry (
∼
1) refers to the constant null model, respectively.
d
f
AIC
BIC
logLik
Test
L. ratio
p
value
0 (no. of wet months)
∼
1
5
1016.28
1038.03
−
503.14
n/a
n/a
n/a
Clay
+
fine silt
6
989.89
1015.98
−
488.94
1 vs. 2
28.40
<
0
.
0001
. . .
+
pH
H
2
O
7
990.41
1020.85
−
488.20
2 vs. 3
1.48
0.2245
. . .
+
CIA
8
980.65
1015.44
−
482.32
3 vs. 4
11.76
0.0006
. . .
+
M
ox
9
934.82
973.96
−
458.41
4 vs. 5
47.82
<
0
.
0001
. . .
+
Ca
ex
10
840.40
883.89
−
410.20
5 vs. 6
96.42
<
0
.
0001
. . .
+
pH
H
2
O
×
M
ox
11
840.08
887.92
−
409.04
6 vs. 7
2.33
0.1272
1–3 (no. of wet months)
∼
1
5
933.01
952.53
−
461.50
n/a
n/a
n/a
Clay
+
fine silt
6
912.86
936.29
−
450.43
1 vs. 2
22.15
<
0
.
0001
. . .
+
pH
H
2
O
7
910.07
937.41
−
448.04
2 vs. 3
4.79
0.0287
. . .
+
CIA
8
811.91
843.16
−
397.96
3 vs. 4
100.16
<
0
.
0001
. . .
+
M
ox
9
708.70
743.85
−
345.35
4 vs. 5
105.21
<
0
.
0001
. . .
+
Ca
ex
10
618.44
657.49
−
299.22
5 vs. 6
92.26
<
0
.
0001
. . .
+
pH
H
2
O
×
M
ox
11
599.70
642.66
−
288.85
6 vs. 7
20.74
<
0
.
0001
4–7 (no. of wet months)
∼
1
5
1,489.12
1,511.60
−
739.56
n/a
n/a
n/a
Clay
+
fine silt
6
1487.46
1514.44
−
737.73
1 vs. 2
3.66
0.0558
. . .
+
pH
H
2
O
7
1488.86
1520.32
−
737.43
2 vs. 3
0.61
0.4355
. . .
+
CIA
8
1486.23
1522.19
−
735.11
3 vs. 4
4.63
0.0315
. . .
+
M
ox
9
1339.02
1379.48
−
660.51
4 vs. 5
149.21
<
0
.
0001
. . .
+
Ca
ex
10
1256.20
1301.15
−
618.10
5 vs. 6
84.82
<
0
.
0001
. . .
+
pH
H
2
O
×
M
ox
11
1237.14
1286.58
−
607.57
6 vs. 7
21.06
<
0
.
0001
MAT – mean annual temperature; PET – potential evapotranspiration; MAP – mean annual precipitation; CIA – chemical index
of alteration; M
ox
– oxalate-extractable metals (Al
ox
+
1
/
2
Fe
ox
); Ca
ex
– exchangeable calcium; n/a – not applicable; df:–
degree of freedom; AIC – Akaike information criterion; BIC – Bayesian information criterion; logLik – log likelihood; L.ratio
– likelihood ratio.
SOIL, 7, 305–332, 2021
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
327
Table B8.
ANOVA summary for linear mixed-effects grouped by weathering (
n
moderate
=
801;
n
high
=
800). Fixed effects were added using
a step-wise method. The first entry (
∼
1) refers to the constant null model, respectively.
d
f
AIC
BIC
logLik
Test
L. ratio
p
value
Moderate weathering (10-88 % CIA)
∼
1
5
1535.35
1558.78
−
762.67
n/a
n/a
n/a
Clay
+
fine silt
6
1495.43
1523.54
−
741.71
1 vs. 2
41.92
<
0
.
0001
. . .
+
pH
H
2
O
7
1487.13
1519.93
−
736.56
2 vs. 3
10.30
0.0013
. . .
+
M
ox
8
1352.69
1390.18
−
668.35
3 vs. 4
136.44
<
0
.
0001
. . .
+
Ca
ex
9
1169.17
1211.35
−
575.59
4 vs. 5
185.52
<
0
.
0001
. . .
+
pH
H
2
O
×
M
ox
10
1151.67
1198.53
−
565.84
5 vs. 6
19.50
<
0
.
0001
High weathering (88-100 % CIA)
∼
1
5
1536.25
1559.67
−
763.13
n/a
n/a
n/a
Clay
+
fine silt
6
1538.15
1566.26
−
763.07
1 vs. 2
0.10
0.7483
. . .
+
pH
H
2
O
7
1535.93
1568.72
−
760.96
2 vs. 3
4.22
0.0400
. . .
+
M
ox
8
1343.70
1381.17
−
663.85
3 vs. 4
194.23
<
0
.
0001
. . .
+
Ca
ex
9
1248.82
1290.99
−
615.41
4 vs. 5
96.87
<
0
.
0001
. . .
+
pH
H
2
O
×
M
ox
10
1215.27
1262.12
−
597.64
5 vs. 6
35.55
<
0
.
0001
MAT – mean annual temperature; PET – potential evapotranspiration; MAP – mean annual precipitation; CIA – chemical
index of alteration; M
ox
– oxalate-extractable metals (Al
ox
+
1
/
2
Fe
ox
); Ca
ex
– exchangeable calcium; n/a – not applicable;
df:– degree of freedom; AIC – Akaike information criterion; BIC – Bayesian information criterion; logLik – log likelihood;
L.ratio – likelihood ratio.
https://doi.org/10.5194/soil-7-305-2021
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328
S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
Table B9.
ANOVA summary for linear mixed-effects grouped by land cover (
n
Cropland
=
429;
n
Forest
=
228;
n
Grassland
=
242;
n
Other
=
702). Fixed effects were added using a step-wise method. The first entry (
∼
1) refers to the constant null model, respectively.
d
f
AIC
BIC
logLik
Test
L. ratio
p
value
Cropland
∼
1
5
942.57
962.88
−
466.28
n/a
n/a
n/a
Clay
+
fine silt
6
942.77
967.13
−
465.38
1 vs. 2
1.80
0.1794
. . .
+
pH
H
2
O
7
943.73
972.16
−
464.86
2 vs. 3
1.04
0.3085
. . .
+
CIA
8
911.72
944.21
−
447.86
3 vs. 4
34.01
<
0
.
0001
. . .
+
M
ox
9
817.96
854.51
−
399.98
4 vs. 5
95.77
<
0
.
0001
. . .
+
Ca
ex
10
755.49
796.11
−
367.75
5 vs. 6
64.46
<
0
.
0001
. . .
+
pH
H
2
O
×
M
ox
11
736.80
781.48
−
357.40
6 vs. 7
20.69
<
0
.
0001
Forest
∼
1
5
627.98
645.13
−
308.99
n/a
n/a
n/a
Clay
+
fine silt
6
626.06
646.64
−
307.03
1 vs. 2
3.92
0.0477
. . .
+
pH
H
2
O
7
615.79
639.79
−
300.89
2 vs. 3
12.27
0.0005
. . .
+
CIA
8
614.94
642.38
-299.47
3 vs. 4
2.85
0.0915
. . .
+
M
ox
9
556.77
587.64
−
269.39
4 vs. 5
60.17
<
0
.
0001
. . .
+
Ca
ex
10
538.35
572.64
−
259.17
5 vs. 6
20.42
<
0
.
0001
. . .
+
pH
H
2
O
×
M
ox
11
532.33
570.05
−
255.16
6 vs. 7
8.02
0.0046
Grassland
∼
1
5
570.23
587.68
−
280.12
n/a
n/a
n/a
Clay
+
fine silt
6
561.06
581.99
−
274.53
1 vs. 2
11.18
0.0008
. . .
+
pH
H
2
O
7
542.45
566.88
−
264.23
2 vs. 3
20.60
<
0
.
0001
. . .
+
CIA
8
484.66
512.57
−
234.33
3 vs. 4
59.79
<
0
.
0001
. . .
+
M
ox
9
430.95
462.35
−
206.47
4 vs. 5
55.71
<
0
.
0001
. . .
+
Ca
ex
10
381.49
416.38
−
180.75
5 vs. 6
51.45
<
0
.
0001
. . .
+
pH
H
2
O
×
M
ox
11
352.66
391.04
−
165.33
6 vs. 7
30.83
<
0
.
0001
Other
∼
1
5
1313.24
1336.01
−
651.62
n/a
n/a
n/a
Clay
+
fine silt
6
1291.22
1318.54
−
639.61
1 vs. 2
24.02
<
0
.
0001
. . .
+
pH
H
2
O
7
1293.10
1324.98
−
639.55
2 vs. 3
0.12
0.7294
. . .
+
CIA
8
1277.31
1313.75
−
630.66
3 vs. 4
17.79
<
0
.
0001
. . .
+
M
ox
9
1146.62
1187.60
−
564.31
4 vs. 5
132.70
<
0
.
0001
. . .
+
Ca
ex
10
1020.27
1065.81
−
500.13
5 vs. 6
128.35
<
0
.
0001
. . .
+
pH
H
2
O
×
M
ox
11
1011.66
1061.75
−
494.83
6 vs. 7
10.61
0.0011
MAT – mean annual temperature; PET – potential evapotranspiration; MAP – mean annual precipitation; CIA – chemical
index of alteration; M
ox
– oxalate-extractable metals (Al
ox
+
1
/
2
Fe
ox
); Ca
ex
– exchangeable calcium; n/a – not applicable;
df:– degree of freedom; AIC – Akaike information criterion; BIC – Bayesian information criterion; logLik – log likelihood;
L.ratio – likelihood ratio.
SOIL, 7, 305–332, 2021
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S. F. von Fromm et al.: Continental-scale controls on soil organic carbon
329
Code availability.
The code used in this paper is available as an R
markdown file (pdf) in the Supplement.
Data availability.
The soil properties data set used in this study is
available from the authors upon reasonable request and under the
following DOI: https://doi.org/10.34725/DVN/66BFOB (Vågen et
al., 2021). Field data (i.e., land cover) for the sampling locations
can be found in Vågen et al. (2013b). The climate data used
(MAT, MAP and PET) can be downloaded from the sources
cited (WorldClim, Fick and Hijmans, 2017, and Trabucco and
Zomer, 2019). Land cover data used for gap-filling can be retrieved
from http://2016africalandcover20m.esrin.esa.int/ (ESA, 2017).
Supplement.
The supplement related to this article is available
online at: https://doi.org/10.5194/soil-7-305-2021-supplement.
Author contributions.
The conceptualization of the study for this
paper was done by SFvF, AMH, AAB, SET and SD, with input from
EA, SMH, SPM, KDS, JS, TGV and LAW. The data curation and
investigation and collection of resources were done and provided by
GEA, EA, SMH, SPM, KDS, AMS, EKT, TGV, EW and LAW. The
formal analysis, methodology and visualization for the paper was
performed by SFvF, with substantial input from AMH, ML, SD and
SET as well as feedback from all authors. SFvF wrote the initial
draft and all authors were involved in the review and editing of the
paper.
Competing interests.
Sebastian Doetterl and
Asmeret Asefaw Berhe are the liaison editors of the special issue
“Tropical biogeochemistry of soils in the Congo Basin and the
African Great Lakes region”, and Johan Six is an executive editor
of the
SOIL
journal. However, none of them were involved in the
reviewing process of this paper. All other authors declare that they
have no conflict of interest.
Special issue statement.
This article is part of the special issue
“Tropical biogeochemistry of soils in the Congo Basin and the
African Great Lakes region”. It is not associated with a conference.
Acknowledgements.
Sophie F. von Fromm has received funding
from the International Max Planck Research School for Global
Biogeochemical Cycles. Susan E. Trumbore and Alison M. Hoyt
acknowledge support from the European Research Council
(Horizon 2020 Research and Innovation Program; grant no. 695101;
14Constraint). Sebastian Doetterl has received supportive funds
from the DFG Emmy Noether Group “TropSOC” (project
no. 387472333). The analytical data used in the study were
produced by the Chemical and Biological Assessment of AfSIS
soils project, which is funded by the Biotechnology and Biological
Sciences Research Council (BBSRC)/Global Challenges Research
Fund (GCRF; grant no. BBS/OS/GC/000014B). Steve P. McGrath
and Stephan M. Haefele have partly been funded by the Institute
Strategic Program (ISP) grant (Soils to Nutrition – S2N; grant
no. BBS/E/C/000I0310). The original field surveys and sample
analysis costs at ICRAF were covered by the AfSIS Phase I project
funded by the Bill and Melinda Gates Foundation (grant no. 51353).
Sophie F. von Fromm thanks Jörg Matschullat for proofreading
earlier versions of the paper.
Financial support.
The article processing charges for this open-
access publication were covered by the Max Planck Society.
Review statement.
This paper was edited by Marijn Bauters and
reviewed by two anonymous referees.
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