Earth Syst. Sci. Data, 13, 1–22, 2021
https://doi.org/10.5194/essd-13-1-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
TS1Organic matter cycling along geochemical,
geomorphic, and disturbance gradients in forests and
cropland of the African Tropics – projectCE1 TropSOC
database version 1.0
Sebastian Doetterl1,2, Rodrigue K. Asifiwe6, Geert Baert3, Fernando Bamba6, Marijn Bauters3,4,
Pascal Boeckx3, Benjamin Bukombe2, Georg Cadisch5, Matthew Cooper8, Landry N. Cizungu6,
Alison Hoyt7, Clovis Kabaseke8, Karsten Kalbitz9, Laurent Kidinda9, Annina Maier1, Moritz Mainka2,
Julia Mayrock2, Daniel Muhindo6, Basile B. Mujinya10, Serge M. Mukotanyi6, Leon Nabahungu12,
Mario Reichenbach2, Boris Rewald11, Johan Six1, Anna Stegmann2, Laura Summerauer1,
Robin Unseld2, Bernard Vanlauwe12, Kristof Van Oost13, Kris Verheyen4, Cordula Vogel9,
Florian Wilken1,2, and Peter Fiener2
1Department of Environmental System Sciences, ETH Zurich, 8092 Zürich, Switzerland
2Institute of Geography, Augsburg University, Augsburg, Germany
3Department of Green Chemistry and Technology, Ghent University, Ghent, Belgium
4Department of Environment, Forest & Nature Lab, Ghent University, Ghent, Belgium
5Institute of Plant Production and Agroecology in the Tropics and Subtropics,
University of Hohenheim, Stuttgart, Germany
6Faculty of Agricultural Sciences, Université Catholique de Bukavu, Bukavu, DR Congo
7Department ofCE2 Biogeochemical Processes, Max Planck Institute for Biogeochemistry, Jena, Germany
8School of Agriculture and Environmental Sciences, Mountains of the Moon University, Fort Portal, Uganda
9 TS2Chair of Soil Resources and Land Use, Institute of Soil Science and Site Ecology, TU Dresden, Tharandt,
Germany
10Biogeochemistry and Ecology of Tropical Soils and Ecosystems Research UnitCE3 , University of
Lubumbashi, Lubumbashi, DR Congo
11Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences
(BOKU), Vienna, Austria
12International Institute of Tropical Agriculture, Central Africa and Natural Resource Management, CGIAR,
Nairobi, Kenya
13Earth and Life Institute, UCLouvain, Louvain-la-Neuve, Belgium
Correspondence: Sebastian Doetterl (sdoetterl@usys.ethz.ch)
Received: 1 March 2021 – Discussion started: 23 April 2021
Revised: 5 July 2021 – Accepted: 7 July 2021 – Published:
Abstract. The CE4African Tropics are hotspots of modern-day land use change and are, at the same time, of
great relevance for the cycling of carbon (C) and nutrients between plants, soils, and the atmosphere. However,
the consequences of land conversion on biogeochemical cycles are still largely unknown as they are not studied
in a landscape context that defines the geomorphic, geochemical, and pedological framework in which biolog-
ical processes take place. Thus, the response of tropical soils to disturbance by erosion and land conversion is
one of the great uncertainties in assessing the carrying capacity of tropical landscapes to grow food for future
generations and in predicting greenhouse gas fluxes from soils to the atmosphere and, hence, future earth system
dynamics.
Published by Copernicus Publications.
Please note the remarks at the end of the manuscript.
2 S. Doetterl et al.: Project TropSOC database version 1.0
Here we describe version 1.0 of an open-access database created as part of the project “Tropical soil organic
carbon dynamics along erosional disturbance gradients in relation to variability in soil geochemistry and land
use” (TropSOC). TropSOC v1.0 (Doetterl et al., 2021, https://doi.org/10.5880/fidgeo.2021.009) contains spa-
tially and temporally explicit data on soil, vegetation, environmental properties, and land management collected
from 136 pristine tropical forest and cropland plots between 2017 and 2020 as part of monitoring and sampling
campaigns in the eastern Congo Basin and the East African Rift Valley system. The results of several laboratory
experiments focusing on soil microbial activity, C cycling, and C stabilization in soils complement the dataset to
deliver one of the first landscape-scale datasets to study the linkages and feedbacks between geology, geomor-
phology, and pedogenesis as controls on biogeochemical cycles in a variety of natural and managed systems in
the African Tropics.
The hierarchical and interdisciplinary structure of the TropSOC database allows linking of a wide range of
parameters and observations on soil and vegetation dynamics along with other supporting information that may
also be measured at one or more levels of the hierarchy. TropSOC’s dataCE5 mark a significant contribution
to improve our understanding of the fate of biogeochemical cycles in dynamic and diverse tropical African
(agro-)ecosystems. TropSOC v1.0 can be accessed through the Supplement provided as part of this paper or as
a separate download via the websites of the Congo Biogeochemistry Observatory and GFZ Data Services where
version updates to the database will be provided as the project develops.
1 Rationale for project TropSOC erosion globally while only representing about one third of
global cropland (Doetterl et al., 2012). An exemplary region
1.1 Changing tropical environments in Africa to observe the consequences of land use change on soil re-
sources and biogeochemical cycles in the tropical African
Tropical ecosystems provide many services of global impor- regional context is the African Great Lakes regionCE6 and,
tance. Tropical forests are among the largest terrestrial car- in particular, East African Rift Valley system along the bor-
bon (C) reservoirs and show some of the highest levels of ders between the Democratic Republic of the Congo (DRC),
biodiversity (Losos and Leigh, 2004; Pan et al., 2011). At Burundi, Rwanda, and Uganda.
the same time, tropical landscapes are among the most dy- The region is a model for the complex interplay of so-
namic regions worldwide and hotspots of modern day land cioeconomic factors and their consequences for environmen-
use change (Hansen et al., 2013) as they have to provide tal systems in the tropics. One of the highest human fertil-
food for some of the poorest yet fastest growing populations ity rates globally (e.g., recent estimates for the last decade
on the planet. In particular, the African continent is facing range from 7.3 to 7.7 children per woman in the province
huge environmental and societal challenges with a projected of South Kivu, eastern DRC) (Dumbaugh et al., 2018) is
population growth of 400 % by the end of this century (Ger- leading massive population growth in the region, largely re-
land et al., 2014), much of it happening in (sub-)tropical lying on local food and energy resources. Ridden by con-
sub-Saharan Africa. In consequence, forested landscapes in flict and open warfare in the 1990s and early 2000s, popula-
TS3 tropicalAfrica are currently facing unprecedented levels tion growth in the region is further aggravated by refugees
of land conversion and land degradation, accompanied by from remote areas settling in nearby safer, larger cities in
decreasing soil fertility (UNESCO and WHC, 2010). At the the region (Kujirakqinja et al., 2010). Consequently, mas-
same time, unlike other tropical regions of the world where sive deforestation of upland forests for firewood and cropland
deforestation is driven by the extension of commodity plan- expansion is taking place (Hansen et al., 2013), leading to
tations and commercial logging, much of the deforestation in large erosional soil fluxes and consequential soil degradation
tropical African countries is driven by smallholder farms that threatening soil quality (Karamage et al., 2016). Once con-
apply slash and burn practices for subsistence farming with version to agricultural land has taken place, soil conservation
little alternatives to provide food for their families (Curtis et measures could counteract the loss of soil quality (Veldkamp
al., 2018; Tyukavina et al., 2018). As a result, deforestation et al., 2020). However, these measures are rare in the east-
and soil degradation have accelerated greatly since the sec- ern Congo Basin due to the poverty of subsistence farmers,
ond half of the 20th century, with soil erosion in particular, socioeconomic instability, and a lack of governmental inter-
emerging as the main driver of soil degradation. vention (Heri-Kazi Bisimwa and Bielders, 2020). Soil tillage
Today, erosion rates of tropical agricultural land globally and harvesting further degrade the nutrient-containingCE7 lit-
are estimated at approx. 10.4 billion tons of soil per year and ter and topsoil layers. Consequently, fields often have to be
0.2 billion tons of C per year. Tropical agricultural soil ero- abandoned after only a few decades of use and recover poorly
sion therefore represents about half of the annual agricultural (Carreño-Rocabado et al., 2016; Ewel et al., 1991; Hattori
Earth Syst. Sci. Data, 13, 1–22, 2021 https://doi.org/10.5194/essd-13-1-2021
Please note the remarks at the end of the manuscript.
S. Doetterl et al.: Project TropSOC database version 1.0 3
et al., 2019; Heinrich et al., 2020; Kleinman et al., 1996; ity of ecosystems have, to date, not been constrained (Hahm
Lawrence et al., 2010). et al., 2014; Tang and Riley, 2015).
1.2 Tropical soils responding to disturbance 1.3 Importance and outlook of research on the future of
tropical biogeochemical cycles
With the expansion of cropland into forested landscapes,
soil erosion rates are expected to continue to increase. Soil Tropical Africa is expected to experience great changes to
erosion will undoubtedly have an impact on biogeochemi- both soil biogeochemical cycling and ecosystem-level car-
cal cycles and will change the input, storage, and exchange bon (C) fluxes between soil, plants, and the atmosphere with
of C between soils and the atmosphere, as well as the flux unknown consequences for biogeochemical cycles. Despite
of nutrients between plants and soils in tropical systems in decades of recognizing their importance, tropical soils re-
the region. To understand how tropical soils and ecosys- main among the least studied in the world (Mohr and van
tems respond to erosional disturbance, it is necessary to con- Baren, 1954; Mohr et al., 1972; Ssali et al., 1986; Juo and
sider the combined effects of climate, geology, topography, Franzluebbers, 2003). Although a more complete under-
soil formation, biological processes, and human disturbance. standing of soil–plant coupling in tropical environments is
To date, no study on the interrelationship of these controls critical, most of our process understanding of biogeochemi-
on biogeochemical cycles has been carried out in tropical cal cycling between plant and soil is still derived from tem-
ecosystems. However, studies carried out in other regions perate regions. However, due to differences in their environ-
have shown that controls on soil C dynamics, for example, mental setting and soil forming history, many tropical soil
are highly interlinked (Doetterl et al., 2015; Hobley and Wil- systems will likely react very differently to soil disturbance
son, 2016; Nadeu et al., 2015). and land conversion than temperate soil systems. For exam-
Soil redistribution as a consequence of erosion also ple, temperate ecosystems can differ fundamentally in the
changes the functionality of landscape units. For example, way nutrients cycle and in the dominating and limiting fac-
soil degradation on hillslopes is matched by a buildup of sed- tors for plant growth (Du et al., 2020). In contrast to soils in
iment deposits in valley bottoms where C and nutrient-rich the temperate zone, long-lasting chemical weathering has led
soil can be rapidly buried in subsoils under new sediments. to a massive depletion of mineral nutrients from soils in many
While this consequence of deforestation can lead to an in- tropical systems, although the remaining available nutrients
crease in the residence time of C due to slower microbial C are very efficiently recycled in natural tropical biospheres
turnover in buried soil (Doetterl et al., 2012; Alcántara et al., (Walker and Syers, 1976; Vitousek, 1984). Hence, any loss
2017), important nutrients are now lost to plants, leading to a of nutrients is therefore a critical disturbance with direct ef-
decrease in biomass productivity (Veldkamp et al., 2020) but fects on the functioning of tropical (agro-)ecosystems. Re-
also to a general degradation of tropical forest soils, lower- cent studies highlight the importance of soil degradation and
ing also microbial activity in soils (Sahani and Behera, 2001). the change in chemical soil properties that follows land con-
Soil redistribution is also known to change the temporal and version on plant communities in tropical systems (Bauters et
spatial patterns of soil weathering and affects C stabilization. al., 2021), organic matter turnover by microbial decomposers
In agricultural systems, the effects of this pressure can be (Kidinda et al., 2020; Bukombe et al., 2021), and the stabi-
observed very clearly: erosion removes weathered soil from lization of C and nutrients in soil of varying mineralogical
eroding slopes but also brings the soil weathering front into properties (Reichenbach et al., 2021).
closer contact with the C cycle (which occurs primarily in Improving our process understanding of the coupling be-
topsoils), thereby affecting carbon, nitrogen, and phospho- tween soil biogeochemistry and plant responses in the con-
rus (CNP) cycling and the stabilization of C with minerals text of tropical land use changes will help to better constrain
in these systems (e.g., Berhe et al., 2012; Park et al., 2014; and define plant–soil interactions in ecosystem and land sur-
Doetterl et al., 2016). face models. Furthermore, insight into plant–soil interactions
Concerning feedbacks on biogeochemical cycles between can help to better inform policy makers and stakeholders in
soil weathering, erosion will differ significantly not only be- improving land management practices.
tween natural and disturbed systems but also between sys-
tems with differing soil mineral reactivity. Recent advances 1.4 Objectives and framework
have shown that mineral reactivity, constrained predomi-
nantly by soil weathering and the mineralogy of the soil In the following we aim at providing an overview of the
parent material, has direct control over soil organic carbon, data collected by project TropSOC (Tropical soil organic car-
with climate exerting only indirect control through its im- bon dynamics along erosional disturbance gradients in rela-
pact on biogeochemical processes and matter fluxes (Doet- tion to variability in soil geochemistry and land use) which
terl et al., 2015; Tang and Riley, 2015). However, the exact is nowCE8 available to the research community as an open-
effects of mineralogy on the temperature sensitivity of mi- access database. We give a brief description of the project’s
crobial decomposer communities and the primary productiv- design before elaborating on the structure of the database and
https://doi.org/10.5194/essd-13-1-2021 Earth Syst. Sci. Data, 13, 1–22, 2021
Please note the remarks at the end of the manuscript.
4 S. Doetterl et al.: Project TropSOC database version 1.0
its content. Note that beyond the overview information pre-
sented here, more details on methods and sampling designs
for each assessed parameter are explained in greater detail in
the Supplement accompanying the paper and the database.
The main objective of project TropSOC was to develop
a mechanistic understanding of plant and microbial process
responses to changing soil properties in the African Trop-
ics, exemplified along land use and erosional and soil geo-
chemical gradients studied in the Congo and the Albertine
Rift. Trying to understand biogeochemical cycling affected
by human activities in tropical (agro-)ecosystems as a whole,
TropSOC had two main foci:
i. investigate how nutrient fluxes and organic matter allo-
cation between tropical soils and plants differ in relation
to the controlling factors of geochemistry, topography,
and land use;
ii. investigate how the geochemistry of soils and their par-
ent material control, interact with, or mediate the sever- Figure 1. Factorial design of project TropSOC studying biogeo-
ity of erosional disturbance on C cycling in tropical chemical cycles in Central African tropical forest and agricultural
soils. landscapes in relation to mineralogy, landform, and land cover
types.
In order to address these objectives, project TropSOC in-
vestigates effects on tropical soil biogeochemical cycling and
biological responses to variation in soil and environmental and is being confronted with rapid land conversion and forest
properties along three main vectors (Fig. 1): (i) mineralogy degradation (Hansen et al., 2013). The climate of the study
of parent material, since it may drive the geochemical fea- region is classified as tropical humid with weak monsoonal
tures ofCE9 developed soils which control soil fertility and dynamics (Köppen Af–Am) and mean annual temperatures
the potential of soils to stabilize organic matter and nutrients; (MAT) ranging between 15.3 and 19.3
◦C and mean annual
(ii) landform, since topography may influence water and soil precipitation (MAP) between 1498 and 1924 mm (Fick and
fluxes, particularly erosional soil loss on slopes and soil de- Hijmans, 2017) with high potential erosivity (Fenta et al.,
position in valleys; and (iii) vegetation and land cover, since 2017) (Fig. 2d).
it may control the input to and extraction of organic matter As a part of the East African Rift mountain system, the
from soil and respond to variation in soil properties and hy- active tectonism within the study region produced a hilly,
drology, as well as mediate the impact of rainfall to induce patchy landscape with steep slopes up to 60 % and soil parent
soil erosion. material ranging from volcanic ashes to mafic and felsic mag-
Conducted in one of the hotspots of global change – in the matic rocks, as well as a sedimentary rocks of varying geo-
Central African Congo Basin and African Great Lakes region chemistry and texture (Schlüter and Trauth, 2006) (Fig. 2a,
– the database described here is the foundation for several b).
papers published as a part of the 2021 special issue Tropical The study area is dominated by agricultural land use, with
biogeochemistry of soils in the Congo Basin and the African larger patches of protected, old-growth closed-canopy forest
Great Lakes region in SOIL Journal (Bukombe et al., 2021; in highland areas (Fig. 2c). Typical crops planted for subsis-
Kidinda et al., 2020; Summerauer et al., 2021; Reichenbach tence farming are rotations of cassava (Manihot esculenta),
et al., 2021; Wilken et al., 2021). maize (Zea mays), and a variety of legumes and vegetables.
The dominant vegetation in all studied forests of the region is
characterized as tropical mountain forest (Verhegghen et al.,
2 Study and sampling design 2012; van Breugel et al., 2015). Note that while forest vege-
tation is thought to be largely spared from direct disturbance
2.1 Study area – climate, topography, land use by human activities, large mammal populations (i.e., African
The study area of TropSOC is located in the eastern part forest elephants, great apes) became extinct or largely re-
of the Democratic Republic of the Congo, Rwanda, and duced due to hunting during the 20th century resulting in a
Uganda, in the border region between the Congo and the massive increase in understory.
Nile Basin (Fig. 2). It is largely understudied (Schimel et
al., 2015) despite its great significance for the global climate
system (Jobbágy and Jackson, 2000; Amundson et al., 2015)
Earth Syst. Sci. Data, 13, 1–22, 2021 https://doi.org/10.5194/essd-13-1-2021
Please note the remarks at the end of the manuscript.
S. Doetterl et al.: Project TropSOC database version 1.0 5
Kivu, DRC. The second region is situated on felsic magmatic
and metamorphic rocks typically consisting of gneissic gran-
ites (Schlüter and Trauth, 2006) near the city of Fort Por-
tal on the foothills of the Rwenzori Mountains, Uganda. The
third region is situated on a mixture of sedimentary rocks of
varying geochemistry consisting of alternate layers of quartz-
rich sandstone, siltstone, and dark clay schists (Schlüter and
Trauth, 2006) and spread across the western province of
Rwanda in and around the district of Rusizi.
The dominant soil types of the study region are various
forms of deeply weathered tropical soils (FAO, 2014). Po-
tential ash deposition through the region’s active volcan-
ism occurs frequently, re-fertilizing soils to various degrees.
Following World Reference Base (WRB) soil classification
(IUSS, 2014), soils in the mafic region can be described as
umbric, vetic, and geric Ferralsol and ferralic vetic Nitisol.
Soils in the mixed sedimentary rock region and the felsic re-
gion can be described as geric and vetic Ferralsol. Soils in
valley bottoms can locally show gleyic features, where the
dominating soil types are variations of fluvic Gleysol.
Several striking differences in the elemental composition
of the three parent materials can be noted. In the mafic region,
bedrock is characterized by high iron (Fe) and aluminum (Al)
content, as well as a comparably high content of rock-derived
nutrients such as base cations and phosphorus (P). The fel-
sic and sedimentary rock regions are characterized by lower
contents of Fe and Al, by lower rock-derived nutrient con-
tents, and by higher Si content (Fig. 3). A specific feature
of the sedimentary site is the presence of fossil organic C
in the parent material of soils ranging between 1.29 % and
4.03 % C. Fossil organic C in these sediments is further char-
acterized by a high C : N ratio (mean± standard deviation:
153.9± 68.5), depleted in N and free of 14C (due to the high
age of sedimentary rock formation). The elemental composi-
tion of soils at a stable landscape position between the three
regions retains the geochemical features of its parent material
to some degree and illustrates the process of enrichment of
metal oxy-hydroxides and the depletion of silica as a conse-
quence of weathering. Generally, differences in the elemental
Figure 2. Overview of the study region with respect to major in- concentrations between the three regions are less pronounced
vestigated factors: soil parent material geology and geochemical re- in soil (Fig. 4) compared to differences in parent material
gions (a), slope steepness (b), land use (c), and climate (d) (data (Fig. 3). Remarkably, levels of rock-derived nutrients in soil,
sources: Farr et al., 2007; Fick and Hijmans, 2017; Friedl et al.,
2013; Dewitte et al., 2013; Dressée et al., 1949; Trabuco and Zomer while overall depleted compared to the parent material, are
et al., 2018; Verdooht and Van Ranst, 2003). comparably similar, potentially indicating biological mecha-
nisms that protect these important nutrients in the plant–soil
system against a general trend of leaching and depletion, typ-
2.2 Study area – geochemistry and soil types ical for weathered, old, and nutrient-poor tropical soils (Grau
et al., 2017 and references therein).
Within the study area three regions each representing a In summary, the study region provides a unique combina-
geochemically different parent material for soil formation tion of (i) near-pristine forest and agricultural land, (ii) steep
were determined. The first region (Fig. 2a) is predominantly terrain and heavy tropical precipitation with high erosion
situated on mafic magmatic rocks, typically mafic alkali potential, and (iii) geologically diverse parent material for
basalts (Schlüter and TrauthTS4 , 2006), resulting from ex- soil formation. These factors make the study region ideal for
tinct (Mount Kahuzi) and active (Mount Nyiragongo) vol- identifying the importance of various controls on tropical soil
canic activities between the cities of Bukavu and Goma, biogeochemical cycles.
https://doi.org/10.5194/essd-13-1-2021 Earth Syst. Sci. Data, 13, 1–22, 2021
Please note the remarks at the end of the manuscript.
6 S. Doetterl et al.: Project TropSOC database version 1.0
2.3 Overview of plots and sampling design enough sample material. The nine samples of each layer per
plot were combined to one composite sample.
Plots were established along geomorphic gradients in old- All collected composite samples were kept cooled until be-
growth closed-canopy forest, as well as in cropland, in ing brought to the laboratory (usually within 48 h). In the lab-
all three geochemical regions. Field campaigns to collect oratory, samples were oven-dried at 40 ◦C for 48–96 h and
soil and plant samples at 136 forest and cropland plots then weighed (accuracy: ±0.01 g). Derived soil parameters
along slope gradients (catena and stratified random ap- are detailed in Sect. 2.7.
proaches) and additionally within several cropped nearby
micro-catchmentsCE10 were carried out between March 2018
and July 2020. A detailed description on data quantity and 2.4.3 Forest inventory and aboveground standing
quality can be found in the metadata files accompanying the biomass
database and are briefly described in Sect. 4.1 of this publica- In 2018, full inventories of the forest tree species and stand-
tion. In order to cover potentially stable, eroding, and deposi- ing aboveground biomass (AGB) were conducted on all for-
tional landforms, topographic positions of plots ranged from est plots. The forest inventory followed an international,
plateaus (slope< 5 %), over two slope positions (slopes be- standardized protocol for tropical regions (Matthews et al.,
tween 9 % and 60 %) to valley positions (slopes< 5 %) (Ta- 2012). First, we identified, to species level, all living trees
ble 1). with a diameter at breast height (DBH; measured at 1.3 m
above ground) greater than 10 cm in each plot. Second,
2.4 Sampling design forest these identified trees were classified into the following em-
pirical DBH classes: 10–20, 20–30, 30–50, and > 50 cm.
2.4.1 Forest plot installation Third, to estimate the AGB, we constructed stand-specific
Sampling in forests followed a strict catena approach, and height diameter (H–D) allometric relationships using a rep-
plots were established following an international, standard- resentative subset of the plot-specific trees (Méchain et al.,
ized protocol for tropical regions (Phillips et al., 2016). 2017). For this, 20 % of all measured, specific trees were
Within each geochemical region, three plots covered by old- selected for height measurement across the DBH range that
growth closed-canopy tropical forest vegetation (forest that was recorded per plot. Depending on the tree abundance of
developed a complex structure characterized by large, living each DBH class, the heights of three to five individual trees
and dead trees) were established from February to June 2018 were then measured using a hypsometer (Nikon Forestry
per topographic position as field replicates representing an Pro II laser rangefinder, Nikon, Japan). AGB for each in-
area of 40 m× 40 m per plot were established. Each plot was dividual tree was then estimated using the allometric equa-
subdivided in four 20 m× 20 m subplots, and a total of 36 tion as described by Chave et al. (2014) for moist tropical
forest plots were established this way (four topographic po- forests. To estimate wood density data, we used species aver-
sitions with three replicate plots each in three geochemical ages from the DRYAD global wood density database (Zanne
regions). Note that three plots in the mafic region had to be et al., 2009). To extrapolate this information for the entire
relocated due to safety reasons after the sampling period. For plot for all our sites, we applied a stand-specific height–
an overview of forest plot sampling design, see Fig. 5a. diameter regression model, modelHD, available within the R
package BIOMASS (Méchain et al., 2017). Finally, above-
2.4.2 Sampling mineral and organic soil layers ground standing biomass carbon stock was estimated assum-
ing that all samples standing biomass has a 50 wt % share of
At the time of plot installation, four replicate soil cores per C (Chave et al., 2005). A re-census was carried out in 2020,
plot (one in each subplot) were taken in a depth-explicit way in order to detect changes in aboveground standing biomass
in 10 cm increments up to 1 m soil depth and combined as and to determine tree mortality. Tree mortality rate (λ) at
composites per plot. In addition, one soil profile pit was dug each plot was assessed following Lewis et al. (2004) using
to a depth of 100 cm in the center of one of three replicate inventories conducted in 2018 and 2020. The tree mortality
plots (Fig. 5) per topographic position in each geochemical rate was calculated for all tree stems with DBH> 10 cm in
region. These soil pits were dug and described according to every plot.
FAO guidelines (FAO, 2006).
Leaf litter (L horizon) and partially decomposed organic 2.4.4 Canopy leaves
material in O horizons were sampled at eight points along
the border and in the center of each forest plot (Fig. 5a). To assess plant functional traits (leaf nitrogen, phospho-
At each sampling point, the thickness of the L and O hori- rus, potassium, magnesium, and calcium content) of living
zon layer wasCE11 measured with a ruler and then sampled canopy leaves (see Sect. 2.7), we sampled, at the beginning
within a 5 cm× 5 cm square. When the litter layer was too of the weak dry season (December–February), sun-exposed
thin (= no closed coverage of forest floor with litter), the shoots from the outer canopy of selected tree species that
sampling square was expanded to a 10 cm× 10 cm to retrieve collectively make up 80 % of the standing basal area per plot
Earth Syst. Sci. Data, 13, 1–22, 2021 https://doi.org/10.5194/essd-13-1-2021
Please note the remarks at the end of the manuscript.
S. Doetterl et al.: Project TropSOC database version 1.0 7
Figure 3. Chemical composition of unweathered rock samples representing the parent material for soil formation in three studied geo-
chemical regions (mean± standard error). Panel (a) shows the distribution and concentration of rock-derived aluminum (Al), iron (Fe), and
manganese (Mn) and total silica content (Si). Panel (b) shows the distribution and concentration of rock-derived calcium (Ca), potassium
(K), magnesium (Mg), sodium (Na), and phosphorus (P). Note the difference in scale on the y axis between panels (a) and (b).
Table 1. Topographic information of TropSOC plots across different geochemical regions and land use. Slope and altitude are displayed as
minimum and maximum values. Each topographic position per geochemical region contains the range between three and seven field replicate
plots.
Felsic region (Uganda)
Forest plots Cropland plots
Topographic position Plateau Sloping Valley Plateau Sloping Valley
Slope (%) 3–5 9–55 3 1–5 7–50 1–5
Altitude (m) a.s.l 1304–1306 1271–1420 1272–1277 1507–1797 1466–1830 1587–1768
Mafic region (DRC)
Forest plots Cropland plots
Topographic position Plateau Sloping Valley Plateau Sloping Valley
Slope (%) 3 11–60 1–2 0–5 8–43 0–3
Altitude (m) a.s.l 2208–2227 2188–2248 2181–2310 1477–1731 1486–1774 1505–1708
Mixed sedimentary region (Rwanda)
Forest plots Cropland plots
Topographic position Plateau Sloping Valley Plateau Sloping Valley
Slope (%) 3 9–60 1 3–5 8–50 2–5
Altitude (m) a.s.l 1908–1939 1891–2395 1882–1889 1719–1837 1565–1952 1556–1758
https://doi.org/10.5194/essd-13-1-2021 Earth Syst. Sci. Data, 13, 1–22, 2021
8 S. Doetterl et al.: Project TropSOC database version 1.0
Figure 4. Soil chemical composition of subsoil in stable, old-growth closed-canopy forests (no erosion) in the three investigated geochemical
regions (mean± standard error). The data illustrate the convergence of elemental concentrations between the three regions as a result of
weathering and soil development. Abbreviations explained in Fig. 3. Note the difference in scale on the y axis between panels (a) and (b).
Figure 5. Overview of forest (a) and cropland (b) plot sampling design. Forest plots were subdivided into four 20 m× 20 m subplots, and
one soil profile pit was established per topographic position in each geochemical region for one of three replicate plots.
with the help of trained tree climbers and following a sam- 2.5 Sampling design cropland
pling protocol described in Pérez-Harguindeguy et al. (2016).
For every tree species, we selected at least 3 individual trees, 2.5.1 Cropland plot installation
and a minimum of 5 and maximum of 17 trees per plot were Plots on cropland were established following a stratified ran-
sampled for mature, healthy-looking (=without signs of her- dom approach using the same slope classification and selec-
bivory) individual canopy leaves. Where sampling of outer tion criteria as for forest sites. However, cropland plots be-
canopy leaves was physically not feasible, partially shaded longing to the same geochemical region and topographic po-
leaves situated below the uppermost canopy were sampled. sition were not connected along a hillslope catena. On crop-
land, only fields that were currently covered by cassava were
sampled. Cassava fields were chosen since cassava is one of
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S. Doetterl et al.: Project TropSOC database version 1.0 9
the most important food crops in the region and is harvested mentary rocks: latitude −2.460503◦, longitude 29.095251◦).
for both tubers and leaves. Rotations of cassava, maize, An additional weather station was installed in the mafic re-
pulses, and vegetables are common throughout the area, and gion near a cropland catchment (latitude −2.583984◦, lon-
two harvests are possible per year. The main varieties of cas- gitude 28.715298◦) which was selected for high-resolution
sava on our sites were Mwabailon, Nabiombo, Mwamizinzi, erosion monitoring (see Wilken et al., 2021). Furthermore,
Sawasawa (in eastern DRC), Bukalasa, Shayidire, Gitamisi, a meteorological station in the city of Bukavu (latitude
Amaduda (in Rwanda), Sambati, and Mubalaya (in Uganda). −2.499979◦, longitude 28.845009◦) and one in Lukananda
Only fields without soil protection measurements (i.e., ter- (latitude −2.344073◦, longitude 28.750937◦) were put into
raced systems) were sampled. For an overview of forest plot operation. All stations collected data at a temporal resolution
sampling design, see Fig. 5b. of 5 min on precipitation, air temperature, relative humid-
ity, and air pressure. Additionally, global radiation and wind
2.5.2 Soil sampling speed were measured at the Bukavu and Lukananda stations.
Soil sampling was carried out in the same way as for forest 2.6.2 Litterfall sampling
soils with the exception that only two cores were combined
per plot taken within a 3 m× 3 m area to create depth-explicit Litterfall was assessed following a standardized protocol
composite samples. A total of 100 cropland plots were sam- to measure tropical forest carbon allocation and cycling
pled this way (Fig. 5) with 3–7 field replicate plots per topo- (Matthews et al., 2012). At each of our 36 forest soil sam-
graphic position (plateaus, slopes, valleys) in each geochem- pled plots, 10 litter traps were installed and distributed evenly
ical region. No organic litter layers (L and O horizons) were and systematically per plot. These had a diameter of 60 cm
present in cropland, and no soil profile description was car- each and were installed at a height of 1.0 m above ground.
ried out. Derived soil parameters are detailed in Sect. 2.7. Litter samples were collected every two weeks for the pe-
riod between August 2018 and February 2020 and later ag-
2.5.3 Biomass and crop yield gregated to assess seasonal and annual variability in litter
productivity and quality (see Sect. 2.4). Collected litter in-
As part of the regional stratified random sampling design cluded all organic residues collected by the traps. Larger
for cropland plots (see cropland plot installation), biomass dead animals and woody material> 2 cm in diameter were
from different cassava varieties was collected for 65 plots discarded. After sampling, material from all 10 traps per plot
out of the 100 sampled cropland plots. Biomass was sam- was mixed to obtain a composite sample. These composite
pled shortly before harvest, approximately at the time of the samples were taken to the laboratory the day of sampling,
plant tuber’s maximum development. The timing of harvest oven-dried at 70 ◦C for 72 h, and subsequently weighed (dry
differed between 12 and 24 months after planting depending weight, accuracy:±0.01 g). Data are provided as megagrams
on the variety and season. Within each plot, a 3 m× 3 m sam- per hectare per day (Mg ha−1 d−1) per plot and as the sum
pling area was chosen close to the center of each field, and all of total litter production per plot, aggregated at the seasonal
cassava plants in this area were counted and harvested. The and annual level. The considered seasons were categorized
biomass of all plants was separated into leaves, stems, and based on the average precipitation for each period: weak dry
tubers. These parts were then weighed separately and indi- season (December–February), strong wet season (March–
vidually at the time of sampling (i.e., in a field moist state). May), strong dry season (June–August), and weak wet sea-
son (September–November).
2.5.4 Land use history and management assessment
2.6.3 Belowground standing root biomass
Farmers were sent a questionnaire to collect information on
the land use and management history of sampled fields fol- For all soil-sampled forest plots, standing root biomass and
lowing McCarthy et al. (2018). This questionnaire was com- fine root production were assessed from September 2018 to
pleted for a corresponding total count of 87 out of the 100 December 2019. Sampling took place once per season within
sampled cropland plots. this period (one coring every 3 months), and a total of three
rainy seasons and three dry seasons in 2018 and 2019 were
2.6 Monitoring design covered. Each plot was divided into four equally sized sub-
plots of 20 m× 20 m. Prior to deciding the root sampling
2.6.1 Micrometeorological data strategy and size of depth intervals, root distribution was as-
Three weather stations (ATMOS 41, Meter, Germany) were sessed using soil profiles that were dug in the plot centers
installed in August 2018, one in each geochemical region for soil classification purposes. This assessment revealed that
of project TropSOC close to the investigated forest cate- roots mostly dominated the organic horizons and the upper
nae (mafic: latitude −2.324457◦, longitude 28.740818◦; fel- 50 cm of mineral soil (data not shown).
sic: latitude 0.561767◦, longitude 30.356808◦, mixed sedi-
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10 S. Doetterl et al.: Project TropSOC database version 1.0
Belowground standing root biomass was sampled using a Basic chemical parameters
soil core sampler (Vienna Scientific Instruments, Austria).
Two cores were sampled per subplot in which undisturbed – Soil pH (KCl)
soil cores were divided into five depth layers: one organic
soil layer (O horizon), and four mineral soil layers from 0– – Soil potential cation exchange capacity and its base sat-
10, 10–20, 20–30, and 30–50 cm. After transport to the lab- uration
oratory, each sample was rinsed inside a 2 mm sieve; roots – Soil effective cation exchange capacity and its base sat-
were separated into fine roots (≤ 2 mm diameter) and coarse uration
roots (> 2 mm diameter) using calipers. In addition, fine and
coarse roots were separated into living and dead roots based – Main elemental composition of bulk soil (Al, Fe, Mn,
on criteria such as color, root elasticity, and the degree of Si, Ti, Zr, P) and the total reserve in base cations (Ca,
cohesion of cortex, periderm, and stele; i.a. roots were con- Mg, Na, K) in rock parent material, soil, litter, and veg-
sidered living when root steles were bright and resilient (Os- etation samples
tonen et al., 2005). The dry mass of isolated roots per plot
was assessed after previously having dried the root samples – Pedogenic oxide concentration (Al, Fe, Mn)
at 70 ◦C for 72 h. Data are provided as milligrams per cubic Available nutrients
centimeter (mg cm−3) per plot per sampling date and are also
aggregated at the seasonal and annual level. – Dissolvable soil organic nitrogen and carbon
2.6.4 Fine root net primary production – Plant available phosphorus in soil
Fine root net primary productivity was assessed using the Organic matter characteristics
ingrowth net method following Ohashi et al. (2016). Two
net sheets (polyester mesh aperture size 2 mm, 10 cm wide, – Total and organic carbon and nitrogen content in rock
20 cm high) were installed per subplot in a regular pattern parent material, soil, litter, and vegetation samples
with a distance of approximately 1 m between the two nets. – Bulk soil radiocarbon signature
Each net was vertically inserted in the top 20 cm of soil start-
ing from the surface of the mineral layer. Nets were sampled – C : N ratio in soil, litter, and vegetation samples
every 3 months after installation and seasonally fourCE12
times a year from September 2018 to December 2019. Data – Soil carbon stabilization mechanisms
are provided as grams per square meter (g m−2) and grams
per square meter per day (g m−2 −1
Microbial activity
d ) of total fine root pro-
duction per plot over a certain period of time, and they are – Heterotrophic soil respiration (including isotopic signa-
also provided aggregated at the seasonal and annual level. ture of respired gas)
2.7 Chemical and physical analyses – Microbial biomass during incubation
A wide range of chemical and physical parameters were as- – Extracellular enzyme activity during incubation
sessed for the sampled soil and plant material with the aim
to characterize (i) indicators of soil redistribution, (ii) the de- Soil redistribution
gree of soil weathering, and (iii) the physical structure of soil, – 239+ 240 Pu activity
as well as (iv) soil fertility and (v) soil organic carbon charac-
teristics, in order to link them to (vi) functional traits of the All of the parameters listed above have been measured in
sampled biomass, (vii) biomass production, and (viii) land soil for three depth layers (0–10, 30–40, 60–70 cm) repre-
management. For a full overview of all assessed parameters senting distinct sections of the soil profile. Physico-chemical
including their assessment methods, please consult the meta- key properties of the remainder of the soil samples in other
data accompanying the database. soil layers have been assessed using mid-infrared spec-
Among others, key measured parameters encompass the troscopy and predicted following the workflow of Summer-
following: auer et al., 2021). An overview of chemical and physical key
Basic physical parameters soil parameters is provided in Appendix Table A1. Note that
all physico-chemical soil properties and the corresponding
– TS5Soil bulk density mid-infrared data are part of the central African spectral li-
– Soil texture brary (Summerauer et al., 2021), which minimizes the need
for future traditional soil analyses.
– Soil water holding capacity
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Please note the remarks at the end of the manuscript.
S. Doetterl et al.: Project TropSOC database version 1.0 11
2.8 Milestones reached In summary, TropSOC’s first results demonstrate that even
in deeply weathered tropical soils, parent material has a long-
Overall a total of approximately 2100 soil and rock sam- lasting effect on soil chemistry that can influence and con-
ples were collected, of which about 10 %–30 % were used trol microbial activity, the size of subsoil C stocks, and the
for yet more detailed analyses in different experiments by turnover of C in soil. Soil parent material and the resulting
our group (see below). Additionally, 6000 above- and below- soil chemistry need to be taken into account in understanding
ground biomass and litter samples were taken during several and predicting C stabilization and turnover in tropical forest
sampling and monitoring campaigns at forest and cropland soils. Given the investigated rates of erosion on cropland, our
sites. Several thousand near-infrared and mid-infrared (NIR-
1 findings confirm the threat of large losses of organic matterMIR) spectra in the wavenumber range 600 to 7500 cm− leading to a sharp decline in soil fertility with little potential
(wavelength 1333.7–16666.7 nm) were collected across the for soils to recover from nutrient losses naturally on decadal
sampled plant and soil samples and were used to train cal- or centennial timescales. TropSOC highlights that consider-
ibration models for each property to predict spatially and ing feedbacks between geochemistry and topography to un-
depth-explicit soil parameters in relation to soil fertility, derstand the development of soil fertility in the African Great
carbon stocks, and carbon stabilization using partial least Lakes regions can significantly improve our insights into the
square regressions following the workflow of Summerauer role of tropical soils to reach several key sustainable devel-
et al. (2021). Furthermore, since 2018, continuous monitor- opment goals, such as climate mitigation, zero hunger, and
ing has been carried out for the installed weather stations, to help raise awareness of the need to maintain sufficient
and vegetation dynamics in tropical forests have been as- soil resources for future generations. Future work realized in
sessed from August 2018 until December 2019. Water and project TropSOC based on the database will provide further
heat fluxes between soil and atmosphere are monitored using insights into biomass and plant trait responses to soil geo-
several weather stations and soil probes. chemistry in forests, as well as cassava yield responses and
Analyses conducted on collected samples so farCE13 con- SOC dynamics in cropland along the investigated geomor-
tributed to scientific advances realized throughCE14 phic and geochemical gradients across the region.
– the creation of a data frame of reference samples for
calibration used in the newly developed soil spectral li- 3 Structure of TropSOC project database (TropSOC
brary for central Africa (Summerauer et al., 2021) v1.0)
– an investigation of the role of geochemistry and geo- 3.1 Database hierarchy
morphic position for soil organic matter stabilization
mechanisms and patterns of soil organic carbon (SOC) Datasets are given as tab-delimited .csv files. For each .csv
stocks in tropical rainforests (Reichenbach et al., 2021) file the metadata describing data structure and assessment
methods are given in a .pdf file of the same name. Moreover,
– an investigation of the role of geochemistry and geo- additional .pdf files for each main section of the database
morphic position on the heterotrophic soil respiration (basic information, forest, cropland, and microscale mete-
(Bukombe et al., 2021), as well as the role of adapta- orology) are given, providing an overview of the structure
tions of microbial communities and their strategies to within each section. Note that the “basic information” sec-
access nutrients along the investigated forest gradients tion of the database provides the linkages between individual
(Kidinda et al., 2020) data points, e.g., from soil analysis and the location and/or
soil depths where these samples were acquired (for linkages,
– an assessment of the suitability and the application of see also Fig. 6).
radioisotope 239+240Pu inventories for studying soil ero-
sion processes in tropical forests and cropland (Wilken
et al., 2021) 3.2 Database infrastructure
3.2.1 Basic information
– soil fractionation and incubation experiments encom-
passing cropland soils along geomorphic and geochem- The database comprises basic information of all plots and
ical gradients (unpublished). single point sampling positions where data were collected
during project TropSOC. An overview of the structure of the
As CE15part of this paper, the entirety of TropSOC’s data database is presented in Appendix Table A2. The basic infor-
is available as an open-access database with extensive meta- mation of the database is structured in the following way.
data documenting experimental approaches, framing of the Part 1. This is the location and basic background informa-
analyses, data quality, and methodology. An overview of all tion for all plots and points where data were collected. Data
datasets presented in this database is given in Appendix Ta- can be found in file 11_plots_points.csv, with descriptions
ble A2. given in 11_plots_points.pdf.
https://doi.org/10.5194/essd-13-1-2021 Earth Syst. Sci. Data, 13, 1–22, 2021
Please note the remarks at the end of the manuscript.
12 S. Doetterl et al.: Project TropSOC database version 1.0
Figure 6. Overview of linkages between datasets in the TropSOC database v1.0. Note that for each data .csv file a .pdf file is given detailing
the metadata of the respective data sheet.
Part 2. This includes the sample identifier for the data file 11_plots_points.pdf for an overview of the structure
database’s internal connection between location of plots, of the plots ID and 12_sample_identifier.pdf for an overview
points, and soil data from different soils depths, as well as of the structure of the sample ID.
vegetation data. Data are stored in 12_sample_identifier.csv,
with descriptions given in 12_sample_identifier.pdf.
The key element to link all data tables for which data 3.2.2 Forest
were collected and analyzed is the plot ID and its deriva- TropSOC’s forest data consist of seven parts (Table A2 for
tive, the sample ID. This identifier allows us to link the overview) structured as paired .csv–.pdf files containing the
results from sample analysis with the locations given in data (.csv) and accompanying metadata (.pdf) describing pa-
11_plots_points.csv. This results in a n:1 connection between rameters and methods. Additionally, an overview of all col-
12_sample_identifier.csv and 11_plots_points.csv. See meta- lected forest data is given in file 2_forest.pdf.
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S. Doetterl et al.: Project TropSOC database version 1.0 13
Part 1. Above- and belowground vegetation data were ac- Part 1: locations of meteorological stations.CE16 This in-
quired in 2018, 2019, and 2020 at all forest plots, comprising cludes coordinates, elevations, and contact addresses for the
13 datasets (dataset files 2.1.1–2.1.13). respective data (dataset file 4.1).
Part 2. Mineral soil layer data were acquired in 2018 at all Part 2: daily meteorological data. Six meteorological sta-
forest plots, comprising three data sets (dataset files 2.2.1– tions recorded precipitation, air temperature, relative humid-
2.2.3). ity, air pressure, solar radiation, and wind speed (dataset file
Part 3. Organic soil layer data were acquired in 2018 at all 4.2).
forest plots, comprising one dataset (dataset file 2.3). Part 3: high-resolution 5 min triggered precipitation data.
Part 4. The 239+240Pu soil inventory was carried out in Precipitation was recorded at the time of tipping bucket tilt
2018. In contrast to parts 1 to 3 of the forest data, Pu data at a resolution of 5 min (dataset file 4.3).
represent individual points and do not follow the plot concept
in a strict manner (dataset file 2.4).
4 Database status
Part 5. Soil experiments were carried out from 2018 to
2020, comprising three datasets with results from laboratory 4.1 TropSOC v1.0
soil incubation and fractionation experiments and additional
data from soil sample analyses (dataset files 2.5.1–2.5.3). The current version, v1.0, of TropSOC includes several thou-
Part 6. Parent material elemental composition was ana- sand individual plant and soil samples collected across 136
lyzed based on unweathered rock samples taken within plots sites, spanning cropland and forests in the East African Rift
or from nearby road cuts and mines surrounding the study Valley system and a large variety of parameters. A total of
sites (dataset file 2.6). 36 .csv data sheets are available that give all analyses com-
Part 7. Soil profile descriptions were done in soil pits at the pleted for specific samples. Data sheets are structured ac-
center of plots following WRB-FAO soil description (dataset cording to the descriptions given in Sect. 3 and described
file 2.7). and elaborated on in the accompanying metadata files. The
current distribution of data points across the various levels
of the database hierarchy is shown in Table 2. All individual
3.2.3 Cropland data entries present in the database have passed quality con-
trol completed by experts who were involved in the creation
TropSOC’s cropland data consist of the following seven of the data. When applicable, reports on the quality assess-
parts (Table A2 for overview), structured as paired .csv– ment of each parameter can be found in the metadata .pdf
.pdf files containing the data (.csv) and accompanying meta- files accompanying the .csv files.
data (.pdf) describing parameters and methods. Additionally,
an overview of all collected cropland data is given in file 4.2 Accessing TropSOC v1.0, asking questions, and
3_cropland.pdf. reporting issues to its hosting platform CBO
Part 1. Biomass and management data were acquired in
65 and 87 out of 100 sampled cropland plots respectively, Users may access the TropSOC database v1.0 and its sup-
comprising two datasets (dataset files 3.1.1–3.1.2). porting information through the Supplement provided as
Part 2. Data on mineral soil layers were acquired in 2018 part of this submission. Version 1.0 of the database is
for 100 cropland plots and comprising three datasets (dataset also available through the data download section of the
files 3.2.1–3.2.3). Congo Biogeochemistry Observatory (CBO) (https://www.
Part 3. Pu soil inventory was carried out in 2018. In con- congo-biogeochem.com/data, last access: 10 August 2021)
trast to parts 1 and 2 of the cropland data, Pu data represent and the PANGEA open-access environmental data reposi-
individual points and not plots and were sampled across sev- tory. CBO is a consortium of multinational researchers from
eral catchments (dataset file 3.3). Africa, Europe, and the United States who study biogeo-
Part 4. For soil experiments, this part of the database com- chemical cycles and atmosphere–plant–soil interactions in
prises two datasets with results from laboratory soil incuba- tropical Africa with a focus on the Congo Basin and the
tion and fractionation experiments and additional data from African Great Lakes region (Doetterl et al., 2020). The ded-
soil sample analyses (dataset files 3.4.1–3.4.2). ication of young African researchers to understand and pre-
serve the threatened natural resources of their home coun-
tries is paired with the resources of some of the most ex-
3.2.4 Meteorological data perienced and largest research groups focusing on African
tropical forest and agroecosystems. Founded in 2018 by sci-
The meteorological data comprises four parts (Table A2 for entists of several African and European institutions and sup-
overview), structured as paired .csv–.pdf files containing the ported by multinational organization such as the Consulta-
data (.csv) and accompanying metadata (.pdf) describing pa- tive Group for International Agricultural Research’s Interna-
rameters and methods. tional Institute of Tropical Agriculture (CGIAR-IITA) and
https://doi.org/10.5194/essd-13-1-2021 Earth Syst. Sci. Data, 13, 1–22, 2021
Please note the remarks at the end of the manuscript.
14 S. Doetterl et al.: Project TropSOC database version 1.0
Table 2. Overview of the current number of data points in TropSOC v1.0 on plant, soil, and meteorology and their affiliation to the hierar-
chical levels of forest and cropland. Numbers in tables refer to the number of data entries at the lowest available aggregation level (= highest
resolution of data). For details on parameters, see the corresponding metadata descriptions. Note that collected weather station data in the
felsic (Uganda) and mixed sediment region (Rwanda) represent both cropland and forest, while separate stations were available for the two
land cover classes in the mafic region (DRC). Abbreviations: SOM= soil organic matter.
Plant–soil observations Plots Bulk soil samples Bulk vegetation Incubated SOM fractionated Plots with
(0–100 cm soil depth, samples (above/ soil layers soil layers vegetation
10 cm increments) belowground) assessments
Forest 36 916 1437/4374 112 145 40
Cropland 100 1190 132/66 131 159 65
Total 136 2106 1569/4400 243 304 105
Meteorological observations Stations Precipitation Air temperature Relative humidity Global radiation Wind speed
Felsic region 1 541 541 541 0 0
Mafic region (forest) 1 674 858 860 860 644
Mafic region (cropland) 3 1310 1310 1312 709 650
Mixed sediment region 1 90 520 565 0 0
Total 6 2615 3229 3278 1569 1294
the World Agroforestry Centre (CGIAR-ICRAF), CBO has cessing the dataset and using it for one’s own research, users
become an important scientific network in tropical Africa commit to cite the original paper provided here in addition
for studying biogeochemistry in soils and sediments, creat- to the version number, DOI, and any description provided to
ing synergies between key local institutions and international future versions of the database (see Sect. 6 for details).
researchers which are crucial for the implementation of re-
search in remote and difficult to access environments. Re-
search at CBO is funded and supported by German, Belgian, 5 Database governance and participation
US, and Swiss research foundations and linked to research
institutes at Ghent University, Augsburg University, Florida TropSOC is a community effort with multiple contributors
State University, ETH Zurich, the University of Louvain, and operating at different levels (Fig. 7). Governance of Trop-
the Max Planck Society. SOC is required in order to ensure continuity of services and
Users are encouraged to provide feedback and corrections to plan for the future evolution of this data repository. Study-
to existing data if problems are discovered by contacting ing the rapid environmental changes to the African Trop-
CBO (contact@congo-biogeochem.com) or the correspond- ics is a central research objective for the scientists of the
ing author of this paper (sdoetterl@usys.ethz.ch). Correc- Congo Biogeochemistry Observatory (CBO) making it the
tions will be implemented in consecutive versions of the ideal body to govern future versions of TropSOC. The gov-
database that can be downloaded via the CBO site. ernance structure of TropSOC is briefly described in Fig. 7.
While the TropSOC core team is responsible for the orig-
4.3 Consecutive database versioning and archiving inal version of the database, its maintenance, management,
and archiving, scientists involved in the CBO oversee the es-
Updated versions of the database will be periodically re- tablishment of cooperative agreements in the long term and
leased following either substantial changes or new peer- act as a steering committee for modifications to TropSOC
reviewed publications using the dataset. The versions of suggested by the research community. The main role of the
these official releases are tracked using an associated ver- steering committee is to determine the feasibility of major
sion number, e.g., TropSOC v1.0, and so on. These offi- changes to TropSOC proposed by the community and to co-
cial releases will be archived at ETH Zurich’s Research ordinate activities that would build upon TropSOC or con-
Collection via ETH’s Soil Resources Group (https://soilres. tinue similar research work within the framework of CBO.
ethz.ch/, last access: 10 August 2021) and CBO’s data stor- Although the structure of TropSOC is oriented around in-
age (https://www.congo-biogeochem.com/data, last access: dividual and research projects, the nature of scientific re-
10 August 2021) with a dataset DOI issued for each release search is often more group-focused. For example, teams of
via ETH Zurich so that users may revert back to the earlier researchers generally work together to seek out funding and
version if so required. These archived releases will be main- to conduct research. Thus, in some cases a group or team
tained in perpetuity to facilitate reproduction of any analyses of individuals may seek to utilize or modify TropSOC for
conducted using a past version of the database. When ac- their purposes. Such groups can petition the scientific steer-
Earth Syst. Sci. Data, 13, 1–22, 2021 https://doi.org/10.5194/essd-13-1-2021
S. Doetterl et al.: Project TropSOC database version 1.0 15
repositories. As detailed above, TropSOC is an open-source
project that provides several ways for participation. Any-
one may share the TropSOC dataset provided they do so
in accordance with the Creative Commons Attribution 4.0
International Public License (https://creativecommons.org/
licenses/by/4.0/legalcode, Creative Commons Corporation,
2021) and by citing the corresponding references of the orig-
inal database description and future modifications under their
separate DOI.
Figure 7. A simplified depiction of the TropSOC governance. The
scientific steering committee (SCS) is responsible for approving
major management decisions. The TropSOC core team as data 7 Conclusions and outreach
maintainers are responsible for implementing broader changes to-
gether with new data contributors. All interested scientists are wel- The TropSOC database is an attempt to gather the data used
come to contribute data to future versions of the database or access
the data for their own research. in individual studies in one place and in the same format to
facilitate comparisons and synthesis activities. TropSOC is
unique in that it includes measurements and monitoring data
ing committee (SCS) to be formally designated a CBO mem- of bulk soil and vegetation responses in the African tropical
ber group. Approved organizations should nominate a mem- context for the first time on carefully selected and compara-
ber to serve on the steering committee. ble land use, geomorphic, and geochemical gradients on the
Interested researchers are also invited to contribute data to landscape scale. Building on the data gathered along these
future versions of TropSOC in order to grow the database. gradients during several years of field activities and carrying
Anyone can be a data contributor provided they agree to the out numerous lab experiments to investigate the impact of
terms of use and follow the proper steps for contributing soil geochemistry and land degradation on biogeochemical
data to TropSOC. If such suggestions arise, the CBO steering cycles in tropical plant–soil systems, TropSOC is the largest
committee together with the TropSOC core team are respon- integrative project database on plant–microbial–soil systems
sible for approving the suggested changes and additions to in the Congo Basin to date. TropSOC’s open-access database
the database. Upon approval, the TropSOC core team will structure and participatory approach makes it a suitable tool
coordinate with the new data contributors to implement the for scientists to study experimentally defined soil disturbance
suggested data additions. In the case of organizations or in- and plant responses, as well as to test some of the assump-
dividuals making larger changes or additions to TropSOC, tions behind modeling biogeochemical cycles in land surface
a designated data maintainer from new contributor groups models. Furthermore, we hope to encourage the community
is required to coordinate the technical aspects of the imple- to increase the effectiveness of that investment and to use the
mentation of changes together with the TropSOC core team. TropSOC database as a repository to increase the impact of
Within the pool of data contributors, individuals with signif- their own research results. As such, TropSOC is an interac-
icant experience working with TropSOC may be designated tive database that is open for contributions. In addition, Trop-
by either the steering committee or database maintainers as SOC now manages one of the largest topically structured soil
expert reviewers. These individuals are tasked to assist main- and plant sample archives for tropical eastern Africa with
tainers and oversee peer review and quality assessment of several thousand samples and more than three tons of plant
contributed new entries. and soil material stored at ETH Zurich. Subsamples of all the
above are available upon request to interested researchers.
Finally, we hope that work based on the TropSOC database
6 Data availability can help to provide answers on the role and magnitude of
geochemistry, as well as soil mobilization, in controlling bi-
All data presented in this study is part of the publica- ological processes and fluxes of carbon and nutrients in the
tion and added as a Supplement consisting of data tables tropics in order to better constrain soil processes in mod-
(.csv) and accompanying metadata descriptions (.pdf files). els ranging from profile to global scales (Todd-Brown et al.,
In addition, the database and its metadata are archived 2013). Reducing the uncertainties associated with our under-
and published in the open-access environmental and geo- standing of tropical (agro-)ecosystems in diverse but rapidly
science data repository GFZ Data Services, accessible changing landscapes is one of the most pressing issues for
at https://doi.org/10.5880/fidgeo.2021.009 (Doetterl et al., securing the future well-being of hundreds of millions of
2021). Additionally, the database is accessible via the web- people and to constrain land loss in an area that is home to
site of the Congo Biogeochemistry Repository (https://www. some of the last and most fragile populations of great apes in
congo-biogeochem.com/dataTS6 ). Updated versions of the the wild. Further, elucidating the gravity of the consequences
database will be made available as version updates in both for soil functioning that can be observed in TropSOC’s study
https://doi.org/10.5194/essd-13-1-2021 Earth Syst. Sci. Data, 13, 1–22, 2021
Please note the remarks at the end of the manuscript.
16 S. Doetterl et al.: Project TropSOC database version 1.0
area can contribute to reducing the large uncertainty associ-
ated with terrestrial biogeochemical processes in models. Fi-
nally, our hope is that studies based on TropSOC’s data will
also help to raise further awareness for the necessity of cre-
ating the socioeconomic fundamentals for sustainable land
management in tropical Africa.
Earth Syst. Sci. Data, 13, 1–22, 2021 https://doi.org/10.5194/essd-13-1-2021
S. Doetterl et al.: Project TropSOC database version 1.0 17
Appendix A
Table A1. Basic chemical and physical soil parameters aggregated at land use and geochemical regions. Displayed are average values
and standard deviations taken over 10 soil increments at 10 cm from 0–100 cm soil depth derived from NIR-MIR spectral data, calibrated
on samples from three depth increments (0–10, 30–40, and 60–70 cm). See metadata files 223_soil_spec.pdf and 323_soil_spec.pdf for
details. Abbreviations: CEC= potential cation exchange capacity; ECEC= effective cation exchange capacity; Si= silica; Al= aluminum;
Fe= iron; Mn=manganese; SOC= soil organic carbon; SON= soil organic nitrogen; P= phosphorus; TRB= total reserve in base cations;
BD= bulk density. All assessment methods are explained in the corresponding .pdf metadata files accompanying the database.
Geochemical region Mafic Felsic Mixed sedimentary rocks
Land use Forest Cropland Forest Cropland Forest Cropland
n= 169 n= 370 n= 201 n= 239 n= 174 n= 305
Soil chemistry
pH (KCl) 3.92± 0.45 4.21± 0.32 4.96± 0.64 5.00± 0.44 3.48± 0.35 4.14± 0.42
CEC (me 100 g−1) CE17 34.14± 4.89 21.26± 7.46 15.24± 5.37 26.33± 6.69 14.71± 11.50 19.02± 9.17
Share of bases in CEC (%) 13.21± 14.16 13.90± 10.04 59.92± 20.87 52.72± 12.75 5.66± 11.68 18.58± 17.65
ECEC (me 100 g−1) 9.12± 3.55 4.90± 3.00 10.43± 5.40 13.74± 3.93 5.53± 2.49 6.49± 4.63
Share of bases in ECEC (%) 46.08± 18.66 48.69± 15.67 81.72± 20.67 91.74± 16.45 9.94± 15.83 41.36± 23.13
Si (%) 12.41± 1.36 11.88± 2.18 19.35± 2.83 16.35± 1.88 18.99± 5.46 15.59± 1.84
Al (%) 9.02± 1.11 6.37± 2.39 2.81± 1.11 4.08± 1.29 3.10± 2.92 3.20± 1.97
Fe (%) 10.32± 1.67 10.98± 2.58 3.50± 1.84 5.05± 1.68 5.65± 3.54 5.77± 1.71
Mn (%) 0.25± 0.07 0.19± 0.10 0.14± 0.11 0.26± 0.10 0.25± 0.09 0.08± 0.12
SOC (%) 2.79± 1.55 2.12± 1.24 1.17± 1.25 2.14± 1.45 2.87± 1.82 2.49± 1.42
SON (%) 0.28± 0.14 0.18± 0.10 0.12± 0.12 0.22± 0.12 0.15± 0.14 0.20± 0.12
SOC /SON (–) 9.09± 6.94 15.2± 7.89 12.30± 8.78 11.67± 14.07 38.13± 46.07 20.52± 9.07
Total P (%) 0.20± 0.07 0.12± 0.06 0.12± 0.06 0.30± 0.10 0.07± 0.07 0.10± 0.08
TRB (%) 0.56± 0.22 0.18± 0.19 0.60± 0.27 1.03± 0.30 0.09± 0.17 0.21± 0.30
Soil physics
BD (g cm−3) 1.20± 0.14 1.28± 0.16 1.64± 0.16 1.41± 0.16 1.43± 0.34 1.42± 0.19
Clay (%) 54.79± 11.79 64.76± 13.00 41.45± 11.44 35.17± 11.26 39.60± 14.77 43.12± 11.40
Silt (%) 13.94± 2.29 11.01± 3.28 10.23± 3.70 14.42± 3.76 21.73± 13.03 14.45± 5.20
Sand (%) 31.39± 10.20 24.84± 9.55 51.08± 10.52 48.81± 8.11 39.10± 18.69 41.50± 9.15
https://doi.org/10.5194/essd-13-1-2021 Earth Syst. Sci. Data, 13, 1–22, 2021
Please note the remarks at the end of the manuscript.
18 S. Doetterl et al.: Project TropSOC database version 1.0
Table A2. Structure of the TropSOC database. For each topic a .pdf file is given that contains an overview of the available data on soil,
vegetation, and weather collected for the investigated forest and cropland plots. Each dataset then comprises a data-containing .csv file and
an additional metadata-containing .pdf file of the same name.
0_intro_structure.pdf
TS7 Introduction and structure of the database
1 Basic information 1_basic_information.pdf
1.1 Location and basic background information for all plots and points where 11_plots_points.csv/pdf
data were collected 12_sample_identifier.csv/pdf
1.2 Database internal connection between location of plots and points and soil
data from different soil depths
2 Forest 2_forest.pdf
2.1 Vegetation 211_forest_invent.csv/pdf
2.1.1 Forest inventory 212_ forest_invent_agg.csv/pdf
2.1.2 Forest inventory aggregated 213_fresh_leaves.csv/pdf
2.1.3 Fresh leaves chemistry 214_fresh_leaves_agg.csv/pdf
2.1.4 Fresh leaves chemistry aggregated at species level 215_litter.csv/pdf
2.1.5 Litter fall 216_litter_seasonal.csv/pdf
2.1.6 Litter fall aggregated to seasonal values 217_litter_annual.csv/pdf
2.1.7 Litter fall aggregated to annual values 218_root_biomass.csv/pdf
2.1.8 Root biomass 219_root_ biomass_ seasonal.csv/pdf
2.1.9 Root biomass aggregated to seasonal values 2110_root_biomass_annual.csv/pdf
2.1.10 Root biomass aggregated to annual values 2111_ root_ prod.csv/pdf
2.1.11 Root productivity 2112_root_prod_seasonal.csv/pdf
2.1.12 Root productivity aggregated to seasonal values 2113_ root_ prod_ annual.csv/pdf
2.1.13 Root productivity aggregated to annual values 221_soil_carbon.csv/pdf
2.2 Mineral soil layers 222_soil_ phy_ chem.csv/pdf
2.2.1 Soil carbon and nitrogen including organic matter fractions 223_soil_spec.csv/pdf
2.2.2 Physicochemical soil properties from laboratory analyses 231_soil_organic_layer.csv/pdf
2.2.3 Physicochemical soil properties from NIR-MIR spectroscopy 241_pu_inventory.csv/pdf
2.3 Organic soil layers 251_incubation.csv/pdf
2.4 Pu soil inventory 252_microbiology.csv/pdf
2.5 Soil experiments 253_c14.csv/pdf
2.5.1 Incubation experiments 261_ rocks.csv/pdf
2.5.2 Microbial biomass and enzyme experiments 271_profiles.csv/pdf
2.5.3 14C data from bulk soil and CO2 measurements
2.6 Parent material
2.7 Soil profile descriptions
3 Cropland 3_cropland.pdf
3.1 Biomass and management 311_biomass.csv/pdf
3.1.1 Biomass yield based on plot data 312_ management.csv/pdf
3.1.2 Land management data 321_soil_carbon.csv/pdf
3.2 Mineral soil layers 322_soil_phy_chem.csv/pdf
3.2.1 Soil carbon and nitrogen including organic matter fractions 323_soil_spec.csv/pdf
3.2.2 Physicochemical soil properties from laboratory methods 331_pu_inventory.csv/pdf
3.2.3 Physicochemical soil properties from NIR-MIR spectroscopy 341_incubation.csv/pdf
3.3 239+240Pu soil inventory 342_c14.csv/pdf
3.4 Soil experiments
3.4.1 Incubation experiments
3.4.2 14C data from bulk soil and CO2 measurements
4 Meteorological data 4_meteo.pdf
4.1 Locations of meteorological stations 410_meteo_locations.csv/pdf
4.2 Daily meteorological data from six meteorological stations 420_ meteo_daily.csv/pdf
4.3 High-resolution 5 min triggered precipitation data 430_meteo_pcp_tig.csv/pdf
Earth Syst. Sci. Data, 13, 1–22, 2021 https://doi.org/10.5194/essd-13-1-2021
Please note the remarks at the end of the manuscript.
S. Doetterl et al.: Project TropSOC database version 1.0 19
Sample availability. Remaining soil and plant samples are logged Financial support. This research has been supported by the
and barcoded at the Department of Environmental Science at ETH Deutsche Forschungsgemeinschaft (grant no. 387472333).
Zurich, Switzerland. As long as idle sample mass remains avail-
able, samples for independent research and to stimulate collabo-
ration with the CBO network and the TropSOC project group are Review statement. This paper was edited by Jocelyn Lavallee
available upon request. The authors cannot guarantee that they can and reviewed by Yao Zhang and Rose Abramoff.
revisit the study sites in order to provide additional sample material
in future campaigns. Samples will be given to researchers free of
charge. Sample preparation and transport are subject to a handling
fee.
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CE1 Capitalization is not required after an en dash since it is only further information and “project” is not part of the name
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