Spatial and temporal variation of black cotton soil organic carbon in Guinean forest zone in West Africa

Abstract

The overall objective of the research was to generate soil organic carbon (SOC)
reference data for the benefit of the REDD+ initiatives. In this study, SOC was derived from direct
measurements of organic matter (OM) content in soil. Six hundred and seventy five soil samples
were collected to 30 cm depth in black cotton soil and across three vegetation types including
undisturbed forest, degraded forest and fallow in a Guinean forest zone in West Africa. The
samples were analysed for bulk densities and for soil OM using loss-on-ignition method. Between
12% and 21% OM per soil mass was found at all layers, 0–10, 10–20 and 20–30 cm, suggesting
that black cotton soil was organic soil. OM and C contents and SOC were higher in the upper soil
layer and decreased with depth. The highest values of these soil factors were detected in
undisturbed forest. The low variation of these soil factors within each vegetation type and their
fairly homogeneous spatial distribution across vegetation types confirmed that soils in degraded
forest and fallow reached equilibrium, considering undisturbed forest as reference. The lowest
bulk density (BD) was found in the top 10 cm layer of the soil depth. There were no significant
differences between the mean values of BD observed at the same horizon across vegetation types.

Full text

Tropical Ecology 58(4): 823-832, 2017 ISSN 0564-3295
© International Society for Tropical Ecology
www.tropecol.com
Spatial and temporal variation of black cotton soil organic carbon in
Guinean forest zone in West Africa
CEDRIC A. GOUSSANOU1*, SABIN GUENDEHOU1,2 & BRICE SINSIN1
1Laboratory of Applied Ecology, Faculty of Agronomic Science, University of Abomey-Calavi,
01 PO Box 526 Cotonou
2Benin Centre for Scientific Research and Innovation, 03 BP 1665, Cotonou, Bénin
Abstract: The overall objective of the research was to generate soil organic carbon (SOC)
reference data for the benefit of the REDD+ initiatives. In this study, SOC was derived from direct
measurements of organic matter (OM) content in soil. Six hundred and seventy five soil samples
were collected to 30 cm depth in black cotton soil and across three vegetation types including
undisturbed forest, degraded forest and fallow in a Guinean forest zone in West Africa. The
samples were analysed for bulk densities and for soil OM using loss-on-ignition method. Between
12% and 21% OM per soil mass was found at all layers, 0–10, 10–20 and 20–30 cm, suggesting
that black cotton soil was organic soil. OM and C contents and SOC were higher in the upper soil
layer and decreased with depth. The highest values of these soil factors were detected in
undisturbed forest. The low variation of these soil factors within each vegetation type and their
fairly homogeneous spatial distribution across vegetation types confirmed that soils in degraded
forest and fallow reached equilibrium, considering undisturbed forest as reference. The lowest
bulk density (BD) was found in the top 10 cm layer of the soil depth. There were no significant
differences between the mean values of BD observed at the same horizon across vegetation types.
Key words: Benin, bulk density, carbon stock, loss-on-ignition, soil pool, tropical forest.
Soil organic carbon (SOC) is an important carbon
pool, which accounts for about three times the
amount of carbon in vegetation (IPCC 2007; Kumar
et al. 2013; Lal 2004; Wang et al. 2004). Its influence
on the global climate change is also important
because the processes leading to carbon accu-
mulation in soil, including litter fall and decom-
position, are influenced by climatic conditions
(Bargali et al. 2015; Salgado et al. 2015; Thomas et
al. 2014). Very little attention has been given to this
carbon pool in tropical forest in Africa (Henry et al.
2009) in order to acknowledge the issue of the global
carbon cycle (Guendehou et al. 2013). Our assess-
ment suggests that the main cause of this lack of
attention is related to the difficulty in implementing
large scale sampling for measurements and modelling
soil carbon flux in tropical forest ecosystems.
Due to this lack of data, most countries in Africa
are not able, at the moment, to report to the United
Nations Framework Convention on Climate
Change (UNFCCC), soil organic carbon using
country-specific data. The default data from the
2006 IPCC Guidelines (IPCC 2006) these countries
use are not always representative of their national
circumstances. Soils are classified as either mineral
or organic types depending on the amounts of
organic matter they contain. Organic soils contain
approximately 12 to 20% organic matter by mass
(Brady & Weil 1999) and all other soils are
classified as mineral soil types (IPCC 2006). Both
organic and inorganic forms of carbon are found in
soils, but given that the organic carbon is more
subject to modifications, a lot of attention is given
to changes in soil organic carbon stocks (IPCC 2006).
*Corresponding Author; e-mail: cedricgoussanou@gmail.com

824 BLACK COTTON SOIL ORGANIC CARBON IN WEST AFRICA
Fig. 1. Location of the study area.
Two approaches, including direct measurements
and modelling are used to estimate SOC and its
change in forest ecosystems. These approaches are
complementary as results from direct measure-
ments are usually used to validate predictions from
modelling. Soil sampling is a laborious and time
consuming activity especially the first direct
measurements of soil carbon which require large
scale sampling to detect and account for the large
variability in SOC and to provide guidance on
future sampling approach.
The need for more reliable soil carbon
monitoring system has increased as developing
countries have started to construct their forest
reference emission levels/forest reference levels
(FREL/FRL) as part of the REDD+ process related
to mitigation actions in the forest sector. At the
moment, countries that have submitted their
FREL/FRL to the UNFCCC planned to include soil
pool in future submissions as part of the stepwise
approach when reliable data become available.
Volkoff et al. (1999) carried out an assessment of
SOC in Benin and determined carbon stock average
for some soil types according to soil depth (0–20 cm,
0–50 cm and 0–100 cm) and its variability using
historical database developed in 1960–1970. Azocli
et al. (2015) determined carbon content under
cropland and forest. These studies on soil carbon in
Benin did not address temporal and spatial
variation in carbon stock and it was not clear
whether they were conducted in natural forest. The
overall objective of this study was to generate SOC
reference data for the benefit of REDD+ initiatives.
The specific objectives were to assess the spatial
(vertical and horizontal) distribution of the SOC
and to study its temporal dynamic in semi-
deciduous forests. It also intends to verify the
hypothesis that after a long period of disturbance
soil carbon reaches equilibrium.
The study was conducted in the Lama forest
reserve, a semi-deciduous forest ecosystem located
in southern Benin (Nagel et al. 2004) between 6°55′
and 7°00′N and 2°04′ and 2°12′E (Fig. 1). The forest
covers an area of 16,250 ha including 4777 ha of

GOUSSANOU, GUENDEHOU & SINSIN 825
natural forest entirely protected referred to as the
‘Noyau Central’. The climate in the study area is
classified as tropical moist according to the
definition of climate regions by the IPCC (IPCC
2006). The monthly average temperatures vary
from 25 to 29 °C and the mean annual rainfall is
1200 mm. Monthly rainfall exceeds 100 mm except
for January, February and March, which are the
warmest months. Two rainy seasons occur between
mid-March and mid-July and between mid-
September and mid-November.
The soil in the area is a hydromorphic clayey
vertisol of black cotton soil (40–60% of clay)
characterized by poor drainage and a pH range of
5–5.5 in the 0–30 cm horizon (Küppers et al. 1998).
Von Bothmer et al. (1986) described the soil in Lama
forest as rich in calcium (Ca) and magnesium (Mg)
due to a “granito-gneissic” parent material from the
secondary and tertiary ages. The mean altitude in
the forest is 60 m above sea level. During the rainy
season, the swelling of the clay result in mud on the
forest floor.
The vegetation types include an undisturbed
forest, a degraded forest and fallow (Bonou et al.
2009). The land classification was based on the
extent of historical deforestation activities that
have affected this natural forest. Between 1946 and
1987, 9000 ha of the natural forest was converted to
cropland (Emrich et al. 1999). The undisturbed
forest refers to the part of the study area that has
remained intact while degraded forest and fallow
refer to areas that were subjected to low
perturbations and severe disturbances respectively.
Since the interruption of agricultural activities in
1987, protection measures including some
afforestation activities, through tree plantation,
have taken place in areas previously disturbed.
Applying the default transition period of 20 years
(IPCC 2006), degraded forest and fallow that were
reconverting from cropland to forest land have been
classified under categories degraded and fallow. In
1986, the areas reported by Von Bothmer et al.
(1986) were 3784 ha for undisturbed forest, 5827 ha
for degraded forest, 5800 ha for fallow land and 840
ha for plantation forest. The undisturbed and
degraded forests are dominated by tree species such
as Afzelia africana (Sm.), Ceiba pentandra (L.),
Diospyros mespiliformis (Hochst. Ex A.DC.),
Dialium guineense (Wild), Mimusops andongensis
(Hiern.), Celtis prantlii Priemer ex Engl.,
Holarrhena floribunda (G. Don) Durand and
Schinz, Malacantha alnifolia (Baker) Pierre,
Drypetes floribunda (Müll. Arg.) Hutch., and
Cynometra megalophylla (Harms). The fallow is
characterized by open canopy forests contain
dominant species such as Anogeissus leiocarpa
((DC.) Guill. & Perr.), Lonchocarpus sericeus (Poir.)
Kunth, Albizia zygia (DC.) J.F.Macbr. and Ficus sur
(Forssk.). The dominance of the tree species was
determined based on the importance value index
(Goussanou et al. 2016). The plantation is composed
of species such as Tectona grandis L.f. and Gmelina
arborea Roxb.
The plots used in this study were those
established for biomass measurement by
Goussanou et al. (2016) i.e. forty-five permanent
plots of 50×50m square distributed proportionately
in the area of each vegetation type of the Lama
forest. According to the distribution, 20, 10 and 15
plots were installed in undisturbed forest, degraded
forest and fallow respectively. More details on plots
establishment can be found in Goussanou et al.
(2016).
Within each plot, five sample points were used.
Samples were taken at each corner and in the
centre using a soil corer (2 cm diameter and 30 cm
length). The top-most loose litter layer was
discarded. Soil samples were taken vertically from
surface up to a depth of 30 cm and divided into three
layers: 0–10 cm, 10–20 cm and 20–30 cm and put in
plastic bags. This depth was considered sufficient
because the organic carbon in the top 30 cm layer is
often the most chemically decomposable, and the
most directly affected by natural and anthropogenic
disturbances (IPCC 2006), the sampling was
limited to this depth. In total 15 samples were taken
in a plot. This amounted to 675 samples for all plots.
Fresh mass of each sub-sample was taken in the
field using electronic hand scale (with an accuracy
of 0.001g) before taking them to laboratory.
Sub-samples were oven-dried at 50 °C to
constant weight (during 72h) to get rid of humidity
and determine water content (Eq. 1). An
assumption was made that drying samples at this
moderate temperature would minimize the loss of
material, in particular, of volatile organic
compounds, likely to occur at higher temperatures.
Oven-dried samples were reweighed with an
electronic scales (Ohaus Pionneer Analytical Model
scale with an accuracy of 0.001g) to determine the
dry mass and then the bulk density taking into
account the known volume of the soil corer used to
collect the sample (Eq. 2).
In order to determine the organic matter content
in soil sub-samples, the method of loss-on-ignition
(LOI) was carried out (Ghabbour et al. 2014;
Hoogsteen et al. 2015). Composite samples (i.e. mix

826 BLACK COTTON SOIL ORGANIC CARBON IN WEST AFRICA
Table 1. Mean bulk density of soil depth across vegetation types; bulk density range includes all measurements
without modification.
Vegetation types
Soil depth
(cm)
Number
of soil samples
BD (g cm–3)
Range
Mean (standard deviation)
CV (%)
Undisturbed forest
0–10
100
0.97–1.35
1.15 (0.10)
8.51
10–20
100
1.11–1.5
1.30 (0.11)
8.51
20–30
100
1.26–1.64
1.42 (0.10)
7.15
Degraded forest
0–10
50
1.04–1.28
1.15 (0.07)
6.42
10–20
50
1.18–1.47
1.31 (0.10)
7.54
20–30
50
1.29–1.58
1.41 (0.10)
7.41
Fallow
0–10
75
0.99–1.25
1.15 (0.09)
7.46
10–20
75
1.18–1.42
1.33 (0.06)
4.38
20–30
75
1.30–1.51
1.42 (0.06)
4.14
of the five sub-samples taken from the same soil
depth within the same plot) per depth and per plot
were used for the LOI. Composite samples were used
in order to reduce the number of individual samples
to analyse. An assumption was made that composite
samples do not affect the accuracy of the organic
matter measurement. In this study, 5g of each oven-
dried sub-sample were placed in a ceramic crucible,
previously dried, and combusted at 550 °C for
4 hours in a muffle furnace (Nabertherm GmBh LV
5/11/B180) as described by Wright et al. (2008).
Following the ignition in the muffle furnace, the
crucible containing the residue composed of ash was
weighed and the mass of ash was determined by
subtracting the mass of the empty crucible. During
the ignition, it was assumed that all the organic
material was combusted.
The water content of the sub-samples was
determined using Eq. (1).
    
 (Eq. 1)
Where  (g/g) is the water content of sub-
sample, (g) = mass of wet sub-sample measured
in the field, (g) = oven-dry mass of sub-sample.
The bulk density of sub-samples was estimated
from Eq. (2)
   
 (Eq. 2)
Where  (g cm–3) is the bulk density of the sub-
sample,  = volume of the sub-sample, derived from
the dimensions of the corer ( = 31.4 cm3), M2 as
defined in Eq. (1)
The organic matter content of the samples was
estimated using Eq. (3)
    
 (Eq. 3)
Where  (g/g soil) is the soil organic matter
content of the sub-sample,  = mass of ash (g) after
ignition at 550 , M2 as defined in Eq. (1)
The soil organic carbon stock was computed by
applying Eq. (4)
          
   (Eq.4)
Where  (t C ha–1) is the soil organic carbon
stock in the depth increment ,  = depth
increment (10 cm),  = proportion of coarse-
fragment free soil, in this study,  = 0, 
= conversion factor to convert soil organic matter to
carbon;  = 0.58 (IPCC 2006; Sakin 2012; Tesfaye
et al. 2016).
Statistical parameters including mean,
standard deviation, and coefficient of variation
were assessed using the statistical computing
software R (R Development Core Team 2012). The
analysis of variance (ANOVA) was also performed
to determine the variation of bulk density, and SOC
according to soil depth and across vegetation types.
The comparison with existing data was carried out
to assess the deviation of the results from similar
previous studies.
A mapping of spatial distribution of SOC
according to vegetation type was developed using
ArcGIS 10 and the most recent vegetation map of
the Lama forest (Bonou et al. 2009). Mean SOC
values were assigned to each vegetation type
classes (Fig. 2).
In all vegetation types, the lowest bulk
densities (BD) were found in the upper soil layer, 0–
10 cm, and the highest values in the deep layers,
20–30 cm (Table 1) suggesting that BD increases
with depth. The variation in BD was more
pronounced (higher coefficient of variation) in the
upper layers (0–10 cm) in the undisturbed forest
than in the other vegetation types (Table 1). Analysis

GOUSSANOU, GUENDEHOU & SINSIN 827
Fig. 2. Spatial distribution of SOC in lama forest
reserve.
of variance (F2,126=100.56, P < 0.001) revealed a
significant variation of BD between depths (Table
1). There were no significant differences between
the mean values of BD observed at the same horizon
across vegetation types (F2=0.05, P = 0.952). For
example, the mean BD observed in 0–10 cm layer
(1.15 g cm–3) was identical for undisturbed forest,
degraded forest and fallow and the mean BD in 10–
20 cm in undisturbed forest were only 0.8–2.3%
lower than in degraded forest and fallow.
For all vegetation types, the OM, carbon
content (CC) and SOC showed a decreasing trend
from the upper layer of soil to the 30 cm depth
(Table 2, Figs. 3 and 4). The soil factors were
significantly different by vegetation types
(F2,126=6.41, P = 0.002) and depth (F2,126=109.81,
P < 0.001) namely between undisturbed forest and
fallow (Table 1). The highest values of OM content,
carbon content and carbon stock were detected in
undisturbed forest. However, low variation in these
factors within and across vegetation types was
observed (Table 2).
In undisturbed forest, the most distant points
from each other were located in plots P1 (2°4´50´´E,
6°58´35´´N) and P13 (2°09´54´´E, 6°5740´´N) and the
closest points in plots P18 (2°07´59´´E, 6°57´30´´N)
and P21 (2°07´47´´E, 6°57´37´´N). The assessment
showed that OM content (g g–1) in plot P13 was only
1.17 times higher than in plot P1 and that in plot P21
it was 1.01 times higher than in plot P18. Similar
observations were made in the other vegetation
types. Considering the plot P20 (2°08´02´´E,
6°57´06´´N) closest to the centre of the forest and
located in undisturbed forest as reference point, we
assessed the variation of OM content according to
the distance for all other plots compared to the
reference point. The OM in P20 is 0.57 to 0.99 higher
Fig. 3. Distribution of soil organic carbon according to
vegetation type for soil depth 30 cm.
than that in other plots. The variation indicated
fairly homogeneous spatial distribution of OM. P20
has the highest value of OM and the observations
indicated that OM decreases as the distance
increases from the center to the periphery of the
forest (Fig. 3). The estimated carbon stock in each
layer 0–10, 10–20 and 20–30 cm in undisturbed
forest was higher than in degraded forest which is
also higher than in the fallow (Table 3, Figs. 3
and 4).
The bulk densities found in this study (Table 1)
were within the range (1–2 g cm–3) reported in other
studies on vertisol in the tropics (Jewitt et al. 1979;
Seyoum 2016; Virmani et al. 1982). The increasing
trend in bulk density with soil depth was also in line
with findings from Návar & Synnott (2000); Osborne
et al. (2011); Dengiz et al. (2012) who reported soil
compression and compaction due to overburden as
causes of this trend. However, the black cotton soil in
the Lama forest has not been subject to land use
activities since the interruption of deforestation and
agricultural activities in the year 1987. One possible
explanation of this trend could be the higher water
content in the upper layer of the soil. As indicated
above, soil in the study area is characterized by a
poor drainage and water hardly infiltrates in the
deeper layers. The lack of significant effect of
vegetation types on bulk density (low variation
across vegetation) may be interpreted by the fact that
black cotton soil properties have reached equilibrium

828 BLACK COTTON SOIL ORGANIC CARBON IN WEST AFRICA
Table 2. Organic matter content (g g–1 soil), organic carbon content (g g–1 soil) and soil organic carbon stock
(t C ha–1) of soil depth across vegetation types. Organic matter content was derived from loss on ignition; carbon
content was estimated using the conversion factor 0.58. Stand. dev. is Standard deviation and CV is the coefficient
of variation.
Vegetation types
Undisturbed forest
Degraded forest
Fallow
Soil depth (cm)
0–10
10–20
20–30
0–10
10–20
20–30
0–10
10–20
20–30
Water
Content
(g g–1)
Range
0.17–0.33
0.15–0.29
0.16–0.31
0.16–0.32
0.18–0.29
0.17–0.29
0.19–0.31
0.16–0.27
0.18–0.26
Mean
0.27
0.24
0.23
0.27
0.24
0.23
0.24
0.23
0.22
Stand.
dev.
0.05
0.04
0.04
0.06
0.04
0.04
0.04
0.03
0.03
CV (%)
18.10
15.92
15.85
21.15
16.23
16.14
17.58
14.58
12.28
Organic
Matter
(g g–1)
Range
0.14–0.25
0.12–0.21
0.10–0.18
0.17–0.23
0.14–0.18
0.10–0.17
0.17–0.22
0.10–0.18
0.08–0.16
Mean
0.21
0.16
0.14
0.20
0.16
0.14
0.19
0.14
0.12
Stand.
dev.
0.03
0.02
0.02
0.02
0.02
0.02
0.01
0.02
0.02
CV (%)
13.91
14.02
14.99
8.36
10.22
15.42
7.05
14.63
17.28
Carbon
Content
(g g–1)
Range
0.08–0.20
0.07–0.15
0.06–0.13
0.10–0.13
0.08–0.11
0.06–0.10
0.10–0.13
0.07–0.10
0.05–0.09
Mean
0.12
0.10
0.09
0.12
0.09
0.08
0.11
0.09
0.07
Stand.
dev.
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
CV (%)
19.33
18.02
19.70
8.36
10.22
15.42
7.05
11.31
13.77
Soil
organic
carbon
stock
(t C ha–1)
Range
11.09–17.46
10.14–14.44
8.66–14.11
11.21–14.60
10.23–13.48
8.8–13.42
10.03–14.51
9.05–13.32
8.01–12.50
Mean
13.61
12.32
11.66
13.27
11.82
10.74
12.68
11.41
10.27
Stand.
dev.
1.44
1.14
1.31
0.97
1.20
1.44
1.14
1.23
1.22
CV (%)
10.59
9.24
11.25
7.32
10.12
13.38
9.01
10.74
11.91
Table 3. Distribution of total soil organic carbon stock
across vegetation types for soil depth 30 cm.
Vegetation types
Carbon
stock
(t C ha–1)
Area
(ha)
Total carbon
stock
(t C)
Undisturbed forest
37.59
2076.73
78 064.281
Degraded forest
35.83
1075.67
38 541.256
Fallow
34.36
1624.6
55 821.256
Total
172 426.793
as result of less perturbation over three decades (De
Blécourt et al. 2013).
The SOC stock reported in this study for the
three vegetation types (Table 3) was in the lower
end of the range 34.3–59 t C ha–1 in tropical climate
(Table 4). Because the carbon stock of the
undisturbed forest (37.59 t C ha–1, Table 3) was
derived for native vegetation, in this study it was
considered as reference soil carbon stock in line
Table 4. Soil organic carbon stock (t C ha–1) in Lama
forest reserve and comparison with other published
data.
Some studies on
vertisol
Climate
Carbon
stock t C
ha–1
Soil
depth
(cm)
This study
Tropical
36.12
0–30
Volkoff et al. (1999)
Tropical
59.00
0–20
Lal (2002)
Tropical
62.00
0–100
Tsai et al. (2010)
Tropical
88.60
0–30
Ngo et al. (2013)
Tropical
34.30
0–20
Brahim et al. (2014)
Tropical
45.60
0–30
Venkanna et al. (2014)
Tropical
49.63
0–60
with the IPCC Guidelines (IPCC 2006). However,
data in Table 3 indicated large variation of SOC
across the tropics. Because of this variation, it is
difficult to identify the appropriate SOC stock
determined elsewhere that could be applied as
default in another region.

GOUSSANOU, GUENDEHOU & SINSIN 829
The decrease in OM content, C content and C
stocks from upper soil layer was consistent with
findings from Bessah et al. (2016), IPCC (2006),
Morisada et al. (2004), Muñoz-Rojas et al. (2012), Su
et al. (2006), Vanguelova et al. (2013). The first
explanation of the decrease of these values with
depth was the mineralization of organic matter
(Kadlec et al. 2012). Lama forest, as semi-deciduous
forest, loses large proportion of leaves in dry season
to limit water requirement, this results in a large
amount of litterfall between 6.2 and 9.0 tdm ha–1
yr–1 (Attignon et al. 2004; Djego 2006). As this litter
comes from the aboveground biomass, its de-
composition results in higher OM and C contents in
upper horizons and could explain the decrease
observed between the soil depths. In addition,
Guendehou et al. (2014) reported that decomposition
of litter chemical components (e.g. acid-hydrolysable)
may be affected by the formation of stable complexes
in black cotton soil. Such soil texture constitutes a
physical barrier from to decomposed material deposit
underground, as decomposers are limited in
transporting of OM into deep soil leading to
discrepancies with depth (Schmidt et al. 2011; Six &
Paustian 2014).
The present study also suggests that vegetation
types influenced spatial variation of soil factors as
reported in Bessah et al. (2016) and Assefa et al.
(2017). This finding suggest effects of plant species
on carbon input as pointed out by Sariyildiz et al.
(2015) and in the Lama forest, plant communities
vary with vegetation types and explains the
difference in soil patterns. This results in litter
amount and the decomposition of litter (Attignon et
al. 2004; Djego 2006) leading to modification of soil
chemical properties and by the way OM and SOC
(Guendehou et al. 2014; Guo et al. 2016).
The long-term changes in vegetation is known to
affect SOC chemical composition and dynamics in
forest chronosequence (Guo et al. 2016; Lawrence et
al. 2015; Lv & Liang 2012; Wang et al. 2011; Xiao et
al. 2016). Historical deforestation activities in the
Lama forest converted 9000 ha of undisturbed forest
to cropland between the years 1946 and 1987. The
former croplands changed to degraded forest or
fallow over three decades since 1987 onwards and no
further changes in land-uses have occurred since the
interruption of these activities so far. The low
variation of OM, C contents and C stock per ha
within each vegetation type and the fairly
homogeneous spatial distribution of these soils
factors across vegetation types confirmed that black
cotton soils in degraded forest and fallow have
reached equilibrium, if we consider undisturbed
semi-deciduous forest as reference. The default time
period assumed for carbon stocks to come to
equilibrium was 20 years (IPCC 2006). The
mechanism leading to SOC equilibrium after forest
recovery was facilitated by the forest management
activities especially enrichment by some tree species
planting (Terminalia superba, Khaya grandifoliola,
Khaya senegalensis, Holoptelea grandis and Afzelia
africana) in former croplands (Djodjouwin et al.
2011, 2012). For instance, Hombegowda et al. (2016)
demonstrated the rebound of SOC level back to
undisturbed forest level when cropland was replaced
by agroforestry systems. The study has also shown
low value of carbon in periphery than forest interior
(Fig. 4) suggesting an edge effect on SOC.
The study confirmed that black cotton soil
properties decrease from the upper layer of soil and
varies according to vegetation types suggesting the
plant species effects. Additionally, absence of
disturbance leads SOC to evolve at its normal rate
after a given period. The data reported in this study
are reference data for reporting soil carbon pool
under international agreements such as REDD+
and greenhouse gas inventories for national
communications and biennial update reports of the
UNFCCC. It contributes to database on tropical
soils.
Acknowledgements
This study was conducted as part of the project
“Pilot site: quantification and modelling of forest
carbon stocks in Benin” funded by the Global
Climate Change Alliance and the European Union
(Nº 00009 CILSS/SE/UAM-AFC/2013). We thank
the Permanent Interstates Committee for Drought
Control in the Sahel (CILSS) and the Regional
Centre AGRHYMET for the technical assistance
provided during the implementation phase of the
project. We thank the Editor and two anonymous
reviewers for thoughtful comments that improved a
previous version of our manuscript.
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(Received on 13.04.2017 and accepted after revisions, on 13.02.2018)