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This study analyzed the ecological factors influencing soil degradation in the Atacora Mountains in northern Benin, which harbor two endemic species,Thunbergia atacorensis and Ipomoea beninensis. Data were collected along line transects from plain to summit within 22 plots of 30 m 330 m. Indicators of physical soil degradation (extent of organic layer, color of topsoil, compactness of soil, presence and extent of rills, and occurrence of sheet erosion) and factors (canopy and ground cover, topography, occurrence of flooding, and slope) were assessed. Cluster analysis identified 4 soil degradation classes: light, moderate, high, and extreme. Discriminant and multivariate variance analyses identified canopy and ground cover as the 2 main ecological drivers of soil degradation. Plant, litter, and stone cover were found to decrease as soil degradation increased. The parts of the Atacora Mountains with high elevation and steep slope were found to be less degraded than areas with low slopes, which are easily accessible for human activities. Policies to mitigate soil degradation should prioritize practices with low impact on vegetation cover and promote soil protection practices such as tree planting and mulching.
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Ecological Factors Influencing Physical Soil Degradation in the Atacora Mountain
Chain in Benin, West Africa
Author(s): Farris A. Y. Okou, Achille E. Assogbadjo, Yvonne Bachmann, and Brice Sinsin
Source: Mountain Research and Development, 34(2):157-166. 2014.
Published By: International Mountain Society
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Ecological Factors Influencing Physical Soil
Degradation in the Atacora Mountain Chain in
Benin, West Africa
Farris A. Y. Okou
*, Achille E. Assogbadjo
, Yvonne Bachmann
, and Brice Sinsin
* Corresponding author:
Laboratory of Applied Ecology, Faculty of Agronomic Sciences, University of Abomey-Calavi, 01 BP 526, Cotonou, Benin
Institute of Ecology, Evolution and Diversity, J. W. Goethe University, Max-von-Laue-Straße 13, 60438 Frankfurt am Main, Germany
Open access article: please credit the authors and the full source.
This study analyzed the
ecological factors
influencing soil
degradation in the Atacora
Mountains in northern
Benin, which harbor two
endemic species,
Thunbergia atacorensis
and Ipomoea beninensis.
Data were collected along
line transects from plain to summit within 22 plots of 30 m 3
30 m. Indicators of physical soil degradation (extent of organic
layer, color of topsoil, compactness of soil, presence and
extent of rills, and occurrence of sheet erosion) and
environmental factors (canopy and ground cover, topography,
occurrence of flooding, and slope) were assessed. Cluster
analysis identified 4 soil degradation classes: light, moderate,
high, and extreme. Discriminant and multivariate variance
analyses identified canopy and ground cover as the 2 main
ecological drivers of soil degradation. Plant, litter, and stone
cover were found to decrease as soil degradation increased.
The parts of the Atacora Mountains with high elevation and
steep slope were found to be less degraded than areas with low
slopes, which are easily accessible for human activities.
Policies to mitigate soil degradation should prioritize practices
with low impact on vegetation cover and promote soil protection
practices such as tree planting and mulching.
Keywords: Soil degradation; degradation classes; ecological
factors; Atacora Mountains; Republic of Benin; West Africa.
Peer-reviewed: February 2014 Accepted: April 2014
Soil degradation, recognized as a major process of land
degradation (Stocking and Murnaghan 2001), can be
physical, chemical, or biological and includes erosion
and loss of fertility (Snakin et al 1996). Erosive processes
transform soils into a mosaic of land surfaces (Chartier
and Rostagno 2006). According to Pickup (1985), land
surfaces represent different states of soil loss or soil gain,
each characterized by particular vegetation types and
soil characteristics. Soil degradation is a worldwide
threat that is increasing in extent and severity (Adams
and Eswaran 2000; Bai et al 2008; Kapalanga 2008). It is
seen as the outcome of the complex interplay of both
natural processes and socioeconomic factors
(particularly land use) (Blaikie and Brookfield 1987;
Stocking and Murnaghan 2001; Reynolds and Stafford
Smith 2002; Boardmana et al 2003; Geist and Lambin
2004; Foley et al 2005; Reynolds et al 2007; Haines-Young
The severity of soil degradation depends on the
sensitivity of individual elements in the ecosystem to
anthropogenic and natural disturbances. The design of
decision support systems to reverse land and soil
degradation requires, above all, sound assessments of
these systemic interdependencies (Kapalanga 2008).
Efforts must be based on a profound understanding of
soil degradation—its causes, risks, impacts, and severity,
their relationships with socioeconomic factors, and the
key ecological factors affected. However, soil degradation
processes are difficult to grasp in their totality. One of the
most common assessment methods at the field or farm
level is the use of indicators of soil degradation combined
with a classification of soils based on their erodibility
(Stocking and Murnaghan 2001, Kapalanga 2008).
Comparisons of soil classes can provide a good
understanding of ongoing processes and the factors
influencing them (Berry et al 2003; Majule 2003; De Bie
An important mountain chain in West Africa extends
from Ghana (Akwakpim Hills) to Togo (Mount Togo) and
Republic of Benin (Atacora Mountains). In Benin, the
chain reaches altitudes of 400–600 m; it is of a high
socioeconomic importance (Avohou and Sinsin 2009) and
ecological specificity, with the occurrence of 2 endemic
Beninese plant species, Thunbergia atacorensis
(Acanthaceae) and Ipomoea beninensis (Convolvulaceae)
(Akoe` gninou and Lisowski 2004). The Beninese part of
the Atacora mountain chain is undergoing soil
degradation (Adegbidi et al 1999; Mulder 2000). Soil
nutrients are lost due to, among other causes, the
hilly relief, with steep slopes.
Systems knowledge
Mountain Research and Development (MRD)
An international, peer-reviewed open access journal
published by the International Mountain Society (IMS)
Mountain Research and Development Vol 34 No 2 May 2014: 157–166 ß2014 by the authors157
There have been few studies of land degradation in the
Atacora Mountains. Saı
¨dou et al (2004) investigated
farmers’ perceptions of the causes and consequences of
land degradation at the field level and found that farmers
assessed the soil fertility on the basis of dicotyledonous
weeds, soil texture, soil color, and fauna (earthworm
casts). Tente and Sinsin (2005) found that the loss of soil
in the Atacora Mountains is more severe upslope than
downslope and greater in ploughed areas. However, much
remains to be learned about soil degradation processes in
the Atacora mountain chain, and this knowledge is
needed for effective monitoring and management. The
aim of this study is to contribute to that knowledge by (1)
analyzing physical soil degradation processes in the
Atacora Mountains using indicators and a classification
approach and (2) highlighting the main ecological factors
influencing these processes.
Study area
The study area covered the district of Toucountouna
(10u219360Nto10u44934.80N; 1u129360Eto1u38927.60E).
Four main ethnic groups dwell in the Atacora range: the
Be´tamaribe´, Wama, Natemba, and Gourmantse´ (Wala
2005). The four ethnic groups mingle, and all of them
carry out activities that both affect and depend on the
mountains, primarily farming and animal breeding, but
also fishing, hunting, and small industry including stone
crushing (INSAE 2002).
The Atacora mountain chain (Figure 1) is located in
the Sudanian West African climate zone, which is
characterized by a mean annual precipitation range of
800–1000 mm and two seasons (a dry season from
November to March and a rainy season from April to
October). However, the plains areas surrounding the
mountain chain, under the orographic influence of
mountains, show a higher annual rainfall of up to
1300 mm per year instead of the range of 800–1000 mm
observed in the common Sudanian zone (Houndenou and
Hernandez 1998; Heinrich and Moldenhauer 2002).
Average temperature and relative humidity during the
last 35 years have ranged between 25.3uC and 30.5uC and
between 26.8% (dry season) and 80.5% (wet season),
respectively. The major characteristic of the topography
is steep (30–60%) slopes, but there are also hilltops,
plateaus, and valleys. In general, hills face east or west.
East-facing hillsides are mainly exposed to the Harmattan,
a dry northeast wind from the Sahara Desert blowing
during the dry season (Jenik and Hall 1966). West-facing
hillsides are exposed to a moist monsoon from the
Atlantic Ocean (Le Barbe et al 2002).
Rocky and shallow soils are dominant throughout the
mountain chain. Sandy and clayey soils with moderate
stone content are found in seasonally wet or inundated
valleys. The combination of ecological factors explains
the diversity of vegetation patterns, consisting of shrubs,
trees, and woodland savannas dominated by Isoberlinia
doka,Daniellia oliveri,Vitex spp., Terminalia glaucescens, and
Parinari polyandra (Sieglstetter and Wittig 2002; Tente and
Sinsin 2002; Wala 2005).
Material and methods
Data collection
Two sampling sites were identified, with the help of
extension service agents and local residents. The sites
met the following criteria: ferricrete areas close to
hillsides, and accessibility during the rainy season.
Transect lines for data collection were identified based
on local knowledge about soil degradation. Within each
site, transect lines were established from plain to summit,
aiming to cover the ecological diversity of the sites. At
each topographical position (plain, downslope, hillside,
and summit) along the transect line, we installed 5 or 6
nested sample plots on the grass, shrub, and tree savanna
encountered (30 m 330 m for the woody layer and 10 m
310 m for the herbaceous layer). The vegetation
structure (number of layers, their canopy, and basal
cover) and the ground cover (litter and stone cover) were
estimated using Braun-Blanquet’s (1932) cover/
abundance scale: +5rare (less than 1%), 1 51–5%, 2 5
5–25%, 3 525–50%, 4 550–75%, and 5 575–100%.
Canopy cover is the area of ground covered by the
vertical projection of the outermost perimeter of the
natural spread of foliage of plants; basal cover is the area
of ground surface occupied by the basal portion of the
plants; ground cover is the cover litter, rocks, and gravel
on a site. In total, 22 plots were sampled. Heights of
herbaceous and shrub layers were assessed with a
decameter, and we used a clinometer for the height of
the tree layer. Visual indicators of erosion were
estimated and coded as follows:
NExtent of organic layer was assessed by vertically dropping
a metal rod on the ground and noting what kind of soil
layer (organic layer or other) it touched (Daget and
Poissonnet 1971). The rod was dropped every meter in
8 directions, until a distance of 15 m (for 4 half-median
directions) and 21 m (for 4 half-diagonal directions)
from the center of the plot was reached (Figure 2).
NColor of topsoil layer was assessed visually and coded as
15red (which indicates a mineral layer of soil) or
25black (which indicates an organic layer of soil).
NCompactness of topsoil was assessed visually and coded as
follows: 1 5loose, 2 5compact, 3 5very compact, and
NSheet erosion was assessed visually based on the presence
of sand sedimentation and coded as 1 5not visible or
NPresence and extent of rills were visually assessed and
coded using the Braun-Blanquet (1932) cover/abun-
dance scale as described above.
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FIGURE 1 Extent of the Atacora mountain chain in Benin and location of the sampling plots in the district of Toucountouna.
(Map by authors)
Mountain Research and Development
Ecological factors were assessed and coded as follows:
NTopography was assessed visually and coded as
15valley, 2 5plain, 3 5downslope, 4 5hillside,
and 5 5plateau or summit.
NFlooding during the rainy season was assessed visually
and based on local knowledge of the site status during
dry season. It was coded as 1 5dry or 2 5flooded.
NSlope angle was assessed with a clinometer and recorded
as number of degrees.
NCanopy cover and ground cover factors were derived from
the height and cover of each vegetation layer estimated
using Braun-Blanquet’s (1932) cover/abundance scale.
Data analysis
Data on 5 indicators of erosion from 22 plots were
submitted to a hierarchical cluster analysis with Ward’s
linkage method, using the software SAS 9.2 (SAS 2009) to
produce clusters of plots based on the degree of soil
degradation. Four clusters or soil degradation classes
were defined.
Next, a discriminant analysis was carried out to
determine whether and how ecological factors driving soil
degradation varied along the gradient of degradation.
The factors addressed in the analysis were topography,
flooding, slope, canopy cover factor, and ground cover
In order to identify the main ecological drivers of
soil degradation, a multivariate analysis of variance
(MANOVA) was performed. The average of measured
ecological drivers was assessed according to the
degradation stage (Azihou 2008; Azarnivand et al 2010).
As variables, canopy cover factor, ground cover factor,
and slope were used (topography and flooding were not
included in the analysis because they were qualitative
The following sections explain how canopy cover
factor and ground cover factor were computed. It is
important to know that lower values of these factors
indicate good canopy and ground cover, which are
associated with lower soil loss (De Bie 2005).
Canopy cover factor: A canopy cover protects bare soil
against erosion, but the foliage protection decreases in
the direction of higher layers in the canopy (Wischmeier
and Smith 1978; Kooiman 1987). Canopy cover factor
quantifies the degree of protection offered by the canopy
of a vegetation layer. Canopy cover factor is computed for
each vegetation layer as
CCf,x~S1{½1{(37:13H{½½4:02|EH 2
where EC is the effective canopy cover (%), and EH is the
effective canopy height (m) (Wischmeier and Smith 1978;
Kooiman 1987; Palmer 1989; Figure 3).
The EC of the vegetation layer could be defined as the
part of the remaining bare soil not covered by the canopy
of vegetation layer below and that which is covered by the
FIGURE 2 Sampling pattern for assessing the extent of the organic layer.
FIGURE 3 Canopy cover factor assessment results. The plain lines are the
representation of Equation 1. They could be used for determining the canopy
cover factor without computation of Equation 1. The dotted lines show the steps
to be followed to determine the canopy cover factor based on EC and EH.
Mountain Research and Development
canopy of the vegetation layer concerned. Assuming a
random distribution of basal cover and a nonrandom
positioning of successive canopy layers, effective canopy
cover was computed according to De Bie (2005) as:
1. Canopy cover of herbaceous layer (%) 2basal cover
2. Canopy cover of shrub layer (%) 3(1 2canopy cover
of herbaceous layer [%])
3. Canopy cover of tree layer 3(1 2canopy cover of
shrub layer [%]) 3(1 2canopy cover of herbaceous
layer [%]).
EH could be defined as the average distance traveled
by a raindrop from intercept by a plant before falling
from this plant (De Bie 2005; Jakubı´kova
´et al 2006;
Figure 4). It was computed as
EH~2=3(Ht{H1), ð2Þ
where H
5total height, and H
5height of the lower layer.
The canopy cover factor of one plot (C
) was
computed by summing all layer-specific C
as follows:
)2(X21), where Xis the number
of layers.
Ground cover factor: A high ground cover fraction (litter,
basal cover, and stones) provides a protection against
eroding rainfall and reduces runoff velocity while
increasing infiltration (Wischmeier and Smith 1978). As
shown in Figure 5, canopy cover factor decreases sharply
as the ground cover increases. Ground cover factor (C
values were computed for each plot according to the
ground surface roughness as
CGf ~½exp{(b|ground cover(%),ð3Þ
where bis ground surface roughness, with a value of 0.05
recorded for rough field surfaces and 0.04 for smooth
field surfaces (De Bie 2005). Ground cover percentage was
computed as the sum of the percentages of litter, basal
cover, and stones (De Bie 2005).
Soil degradation classes
Cluster analysis of the 22 study plots, based on the severity
of the erosion indicators, resulted in 4 clusters. An R-
squared value of 96.8% was sufficient to obtain distinct
Cluster 1 was composed of 10 plots. They were
characterized by a low level of soil compaction. Soil
compaction limits water infiltration (few rills, no visible
sheet erosion). On the topsoils of these plots, a black
organic layer covered the entire surface; no reddish soils
were observed. Based on these characteristics, these soils
were characterized as having a light degree of erosion (to
allow for the possibility of other types of physical soil
degradation not observed during this study).
Cluster 2 contained 6 plots showing a great
disturbance of the soil structure. The topsoil of these
plots was ferricrete, rich in iron, and hard (Duchaufour
1952). The organic layer remained only on small patches
(around 40% of rod contacts). Exposure of red soils was
obvious. The presence of this ferricrete layer reduced the
depth to which roots could grow. The thickness of the
organic layer rarely exceeded 10 cm. There was no visible
evidence of sheet erosion, and the mean rill cover was
very low (0.5%) because of the high level of compaction.
Due to the degree of surface sealing, the level of
degradation assigned to this cluster of plots was extreme.
FIGURE 4 Determination of the effective canopy height of a tree. (Based on
´et al 2006)
FIGURE 5 Ground cover factor assessment results (bis ground surface
roughness). The plain lines are the representation of Equation 3. They could be
used for determining the ground cover factor without computati on of Equation 3.
The dotted lines show the steps to be followed to determine the ground cover
factor based on the ground cover value.
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Clusters 3 and 4 contained 3 plots each. The plots of
cluster 3 were characterized by compact red soils, with a
thin clay crust on the surface. Sheet erosion occurred on
these soils, and rills covered on average 7% of the surface.
Organic layers remained as thin patches (around 48% of
rod contacts). The soils of cluster 4 were red and very
compact. They looked like ferricrete but remained
friable. Sheet erosion occurred here too. Rills covered a
larger surface than on the other soils (around 45%),
whereas the thin patches (never more than 5 cm thick) of
organic layer were less extended (only 23% of rod
contacts). The degree of degradation of cluster 3 was
considered moderate, and that of cluster 4 was high.
Table 1 summarizes the erosion indicator values for
each plot cluster.
Ecological drivers of soil degradation
The main result of discriminant analysis was the
projection of soil degradation classes in the canonical
system axes based on ecological factors. Discriminant
analysis summarizes all the information relative to the
ecological drivers of degradation on many axes. In our
case, the first 2 axes were significant and explained
97.12% of the initial information relative to the
ecological drivers of degradation for each plot.
Correlation between ecological drivers and axes 1 and 2
(Table 2) showed that the canopy cover (0.87) and the
ground cover (0.99) were highly positively correlated with
the first axis. Slope (20.90) and topography (20.93) were
highly negatively correlated with the first axis. The only
occurrence of flooding was positively correlated with the
second axis (0.75). In other words, axis 1 corresponded to
the axis of high values of canopy and ground cover
factors, low slope values, and low elevation values. Axis 2
represented the gradient of occurrence of flooding.
Extremely, highly, and moderately degraded soils were
highly positively correlated with axis 1, while lightly
degraded soils were highly negatively correlated with the
same axis (Figure 6). Information on axis 1 indicated that
extremely, highly, and moderately degraded soils were
found on low slopes at low elevations. They were
characterized by high values for C
and C
, which
correspond to poor soil protection and high soil loss.
Lightly degraded soils were found on steep slopes at high
elevations. They were characterized by dense canopy
cover and extensive ground cover.
Extremely degraded soils were highly positively
correlated with axis 2. However, highly, moderately, and
lightly degraded soils were negatively correlated with axis
2. Based on the ecological factors explained by the axis 2,
we concluded that extremely degraded soils were often
flooded during the rainy season, while lightly, highly, and
moderately degraded soils were not.
Table 3 summarizes the ecological characteristics of
the 4 degradation classes represented by the plot clusters.
An analysis of variance was conducted between the mean
values of some studied factors to find the reason for the
differences in severity of soil degradation. The results
provided by an F-test were significant (p,0.01) for all of
the studied factors except slope. Canopy cover and
ground cover factors were the 2 main ecological drivers of
soil degradation.
This study identified 4 soil degradation classes in the
Atacora mountain chain in northern Benin. They
represent the different steps of forest soil degradation
identified by Duchaufour (1952). According to
Duchaufour, 2 factors—organic layer removal due to
erosion, and hardening and desiccation due to solar
radiation—explain all of the differences observed along
TABLE 1 Erosion indicator values for the 4 plot clusters.
Erosion indicator
Cluster 1 Cluster 2 Cluster 3 Cluster 4
Mean SD Mean SD Mean SD Mean SD
Extent of organic layer (%
%)100.00 0 40.42 10.6 48.89 2.93 23.61 10.48
Presence of rills (%
%of area) 1.95 4.59 0.50 0 7.00 6.92 45.83 14.43
Color of topsoil Black Red Red Red
Compactness of soil Loose Hard Compact Very compact
Occurrence of sheet
Not visible Not visible Visible Visible
TABLE 2 Correlation between ecological factors and the 2 axes.
Axis 1 Axis 2
Canopy cover factor 0.87 20.14
Ground cover factor 0.99 0.02
Slope angle (%) 20.90 20.05
Topography 20.93 0.26
Flooding during rainy season 0.65 0.75
Mountain Research and Development
the gradient of tropical forest soil degradation. The 4 soil
types identified were:
1. Lightly degraded soils: These are used as control plots
for the observation of degradation processes because
they present no obvious signs of soil degradation.
2. Moderately degraded soils: These appear when ero-
sion, due to anthropogenic and ecological drivers,
removes the organic layer, exposing the red layer. This
layer is more compact and often forms a thin layer of
curled plates called still depositional crust (Valentin
and Bresson 1992).
3. Highly degraded soils or lateritic carapace: These
occur when the red layer is hardened by desiccation.
Great variations in temperature (17.4–37.1uC) and
relative humidity (23–82%) in the study area facilitate
the desiccation process.
4. A hard surface called ferricrete occurs when iron and
aluminum oxides are dissolved during the rainy season
and migrate to the surface during the dry season
(Duchaufour 1952; Bockheim and Gennadiyev 2009;
Yuangen et al 2009). Compaction and surface sealing
reduce infiltration and hydraulic conductivity, which
increases surface water runoff (Frisby and Pfost 1993;
Horton et al 1994; Radcliffe and Rasmussen 2000) and
lengthens runoff periods (Hinckley et al 1983). The
presence of water on extremely degraded soils during
the rainy season can be explained by soil surface
sealing. According to Bilong et al (1992), the erosive
force of water could break up ferricrete into loose
soil and then facilitate regeneration/reversal of the
soil degradation process and recolonization by
This study often found lightly degraded soils at high
elevations with high slope angles. Similar results were
found by Majule (2003) on the slopes of Mount
Kilimanjaro, where soils in the lower zones were more
FIGURE 6 Projection of soil degradation classes in the system axes based on ecological factors.
Mountain Research and Development
degraded (in physical and chemical terms) than soils in the
middle and upper zones. This is an interesting result, as
slope and elevation have also been recognized as increasing
runoff velocity and therefore posing a greater erosion risk
(Stocking and Murnaghan 2001; Saı
¨dou et al 2004; Tente
and Sinsin 2005; Inkpen and Stephenson 2006). Bonan
(2002) and Molinar et al (2001), on the other hand, found
that slope can act as a buffer, depending on aspect and
initial stability of the soil. The presence of an organic layer
rich in humus improves the soil structure and enhances the
ability of the soil to hold moisture (Pidwirny and Jones
2010), and the lightly degraded soils of the study area are
characterized by extensive coverage of organic layer. This
explains the low level of degradation of these areas despite
the fact that they are found at high elevations with high
slope angles.
The increase of canopy and ground cover factors
indicates the decrease in plant, litter, and stone cover as
soil degradation worsens. Chapman (1948) established
that vegetation canopies change the drop size and
distribution of rain and that splash detachment under
dense canopy is different from that under sparse canopy.
Plant, litter, and stone cover protect the soil from
raindrop impact. Splash tends to slow the movement of
surface runoff and increase the infiltration rate and water-
holding capacity of the soil (Molinar et al 2001; Bonan
2002). According to Bonan (2002), the erosion factor used
to estimate total erosionlosses in forests drops from 0.36 in
soil with no litter cover to 0.003 with 100% litter cover.
Chartier and Rostagno (2006) found that the rate of soil
erosion increased in northeastern Patagonian rangelands
when plant and litter cover decreased. They identified 90%
plant and litter cover as a site conservation/soil erosion
threshold. We found that in the Atacora Mountains, C
and C
values above 0.5 indicated degraded soils or soils
with high risk of erosion. Plant and litter also reduce soil
evapotranspiration rates and reduce soil insolation
(Molinar et al 2001; Kohutiar 2008).
We examined only ecological drivers because they
are the key elements of an environment threatened by
anthropogenic activities and other natural processes
that lead to soil degradation. The identification of the
most sensitive ecological factors will help decision
makers to identify activities that have the greatest
impact on these factors, thus contributing most to soil
degradation. For instance, we found that plant, litter,
and stone cover, if removed, are main ecological drivers
of soil degradation processes. Activities such as shifting
cultivation, overgrazing, and lopping of trees for fodder
during the dry season, which remove the vegetation or
vegetation cover and leave the soil bare, are threatening
activities. Moreover, the local population should be
trained to identify the different soil degradation classes
and their characteristics in order to enhance their
capacity to monitor soil degradation closely on their
Elevation and slope angle can also be seen as
protecting soil because the higher and steeper the site,
the more difficult it is for human beings to reach it
and disturb it by their activities. Nevertheless, the
TABLE 3 Ecological characteristics of the 4 degradation classes represented by the plot clusters.
Degree of degradation
Light Moderate High Extreme
FvalueMean SD Mean SD Mean SD Mean SD
0.49 0.09 0.51 0.03 0.72 0.09 0.61 0.10 8.98 ***
0.09 0.05 0.45 0.03 0.73 0.09 0.71 0.08 142.02 ***
Slope angle
3.85 2.50 0.80 0.29 2 0 0.75 0.27 4.96 ns
Topography Plain,
plateau, or
Valley or plain Valley or plain Plain
during rainy
Dry Dry Dry Dry or flooded
*** P#0.001; ns 5no significant difference.
Mountain Research and Development
protection of steep hillsides and summits should be
prioritized in management policies by tree planting
and other management practices based on local
Physical soil degradation processes in the Atacora
Mountains are very complex. This study examined various
indicators of land degradation at the field level in
northern Benin and found that:
NSoils of the Atacora mountain chain can be classified
in 4 soil degradation categories (light, moderate, high,
and extreme).
NSoils at high elevations with high slope angles were less
degraded than those in lower zones.
NPlant, litter, and stone cover are the main ecological
factors affecting soil degradation; they protect the soil,
and their decrease leads to an increase in degradation.
For management purposes, plant, litter, and stone
cover are indicators that could be used for monitoring of
land degradation. Policies to mitigate soil degradation
have to prioritize practices with low impact on vegetation
cover and promote soil protection practices such as tree
planting and mulching. Large-scale studies integrating
remote-sensing data and geographic information systems
are needed to investigate in detail the erosion risk status
of the entire mountain chain.
The authors are indebted to the UNDESERT project (EU FP7 243906)
‘‘Understanding and Combating Desertification to Mitigate Its Impact on
Ecosystem Services,’’ funded by the European Commission, Directorate
General for Research and Innovation, Environment Program, for financial
support. Our acknowledgment also goes to Amanda Morgan, An ne
Zimmermann, Belarmain Fandohan, Romain Gle
¨, and two anonymous
reviewers, whose useful comments helped improve the quality of this
Adams CR, Eswaran H, editors. 2000. Global Land Resources in the Context of
Food and Environmental Security. New Delhi, India: Soil Conservation Society of
Adegbidi A, Burger K, Gandonou E, Mulder I. 1999. Farmers’ Perceptions and
Sustainable Land Use in the Atacora, Benin. CREED Working Paper 22. London,
United Kingdom: International Institute for Environment and Development.
Akoe`gninou A, Lisowski S. 2004. Notulae florae beninensis 2. Un ipomoea
(Convolvulaceae) nouveau et un thunbergia (Acanthaceae) nouveau du Be
Systematics and Geography of Plants 74(2):337–340.
Avohou HT, Sinsin B. 2009. The effects of topographic factors on aboveground
biomass production of grasslands in the Atacora Mountains in northwestern
Benin. Mountain Research and Development 29(3):250–254.
Azarnivand H, Jafari HJ, Nikoo S, Chahouki MaZ, Malekian A. 2010. Study of
effective ecological factors on distribution of vegetation types (case study:
southern margin of Haj Aligholi Kavir, Damghan). Desert 15:1–4.
Azihou FA.2008. Ecological Factors Associated with the Spatial Isolation of
Individual Trees of the Gregarious Species Isoberlinia doka Craib and Stapf in
Benin. Abomey-Calavi, Benin: University of Abomey-Calavi, Department of
Natural Resources Management, Faculty of Agronomic Sciences.
Bai ZG, Dent DL, Olsson L, Schaepman ME. 2008. Proxy global assessment of
land degradation. Soil Use and Management 24(3):223–234.
Berry L, Olson J, Campbell D. 2003. Assessing the Extent, Cost and Impact of
Land Degradation at the National Level: Findings and Lessons Learne d from
Seven Pilot Case Studies. Rome, Italy: Global Mechanism of the United Nations
Convention to Combat Desertification.
Bilong P, Eno Belinga S, Volkoff B. 1992. Iron Crusted Landscape Evolution
and Soil in Central African Rain Forest. Position of the Mottled Clay Soils. Paris,
France: Gauthier-Villars.
Blaikie P, Brookfield H. 1987. Land Degradation and Society. New York:
Routledge Kegan & Paul.
Boardmana J, Poesen J, Evans R. 2003. Socio-economic factors in soil erosion
and conservation. Environmental Science & Policy 6:1–6.
Bockheim JG, Gennadiyev AN. 2009. The value of controlled experiments in
studying soil-forming processes: A review. Geoderma 152(3–4):208–217.
Bonan G. 2002. Ecological Climatology. Cambridge, United Kingdom:
Cambridge University Press.
Braun-Blanquet J.1932. Plant Sociology. The Study of Plant Communities. New
York: McGraw-Hill.
Chapman G. 1948. Size of rain drops and their striking force at the soil surface
in a red pine plantation. Eos, Transactions of the American Geophysical Union
Chartier MP, Rostagno CM. 2006. Soil erosion thresholds and alternative
states in northeastern Patagonian rangelands. Rangeland Ecology Management
Daget P, Poissonnet J. 1971. Une me
´thode d’analyse phytosociologique des
prairies. Annales Agronomiques 22:5–41.
De Bie CAJM. 2005. Assessment of soil erosion indicators for maize-based
agro-ecosystems in Kenya. Catena (Giessen) 59(3):231–251.
Duchaufour P. 1952. La de
´gradation des sols forestiers en Co
ˆte d’Ivoire. Revue
Forestie`re Franc¸aise 12:789–797.
Foley JA, Defries R, Asner GP, Barford C, Bonan G, Carpenter SR, Chapin FS,
Coe MT, Daily GC, Gibbs HK, Helkowski JH, Holloway T, Howard EA, Kucha rik
CJ, Monfreda C, et al. 2005. Global consequences of land use. Science 309:
Frisby J, Pfost D. 1993. Soil Compaction: The Silent Thief. Columbia, MO:
University of Missouri–Columbia Extension.
Geist HJ, Lambin EF. 2004. Dynamic causal patterns of desertification.
BioScience 54:817–829.
Haines-Young R. 2009. Land use and biodiversity relationships. Land Use
Policy 26:178–186.
Heinrich J, Moldenhauer K-M. 2002. Climatic and anthropogenic induced
landscape degradations of West African dry savanna environments during the
later Holocene. Quaternary International 93–94:127–137.
Hinckley BS, Iverson RM, Hallett B. 1983. Accelerated water erosion in off-
road vehicle use areas. In: Webb RH, Howard GW, editors. Environmental
Effects of Off-Road Vehicles, Impacts and Management in Arid Regions. New
York: Springer-Verlag, pp 81–94.
Horton R, Ankeny MD, Allmaras RR. 1994. Effects of compaction on soil
hydraulic properties. In: Soane BD, Ouwerkerk CV, editors. Soil Compaction in
Crop Production. Amsterdam, Netherlands: Elsevier, pp 141–165.
Houndenou C, Hernandez K. 1998. Modification of the rainy season in the
Atacora Mountain region (1961–1990). A drought situation in northwestern
Benin. Se´cheresse 9(1):23–34.
Inkpen RJ, Stephenson W. 2006. Statistical analysis of the significance of site
topography and erosion history on erosion rates on intertidal shore platforms,
Kaikoura Peninsula, South Island, New Zealand. Geomorphology
INSAE [Institut National de la Statistique et de l’Analyse Economique du
Be´nin]. 2002. Troisie
`me recensement ge
´ral de la population et de l’habitat:
Cahier des villages et quartiers de ville, De
´partement de l’ATACORA. Cotonou,
´nin: INSAE.
Mountain Research and Development
´kova´ A, Janecek M, Tippl M. 2006. Field determination of the specific
input characteristics to calculate the value of C factor of time-variable crops for
the revised universal soil loss equation (RUSLE). Soil and Water Research 1(1):
Jenik J, Hall JB. 1966. The ecological effect of the Harmattan wind in the
Djebobo massif (Togo Mountains, Ghana). Journal of Ecology 54(3):767–779.
Kapalanga TS. 2008. A Review of Land Degradation Assessment Methods.
Report of the Land Restoration Training Programme. Reykjavk, Iceland : Soil
Conservation Service of Iceland.
Kohutiar J. 2008. Soil sealing—A prospect of starvation (for soil bugs) and
flooding (for us). Unpublished paper presented at the Green Week Conference,
3–6 June. Brussels, Belgium: European Commission.
accessed on 4 August 2012.
Kooiman A. 1987. The Factor C: Relations between Landcover and Landuse,
and Aspects of Soil Erosion, Specifically for the Upper Komering Catchment,
South Sumatra, Indonesia. New York, NY: International Typeface Corporation.
Le Barbe L, Lebel T, Tapsoba D. 2002. Rainfall variability in West Africa during
the years 1950–90. Journal of Climate 15:187–202.
Majule AE. 2003. Impacts of Land Use/Land Cover Changes on Soil
Degradation and Biodiversity on the Slopes of Mount Kilimanjaro, Tanzania.
Land Use Change, Impacts and Dynamics Project Working Paper. Nairobi,
Kenya: LUCID Project, International Livestock Research Institute.
Molinar F, Galt D, Holechek J. 2001. Managing for mulch. Rangelands
Mulder I. 2000. Soil Degradation in Benin: Farmers’ Perceptions and
Responses [PhD dissertation]. Amsterdam, Netherlands: Tinbergen Institute,
Vrije Universiteit Amsterdam.
Palmer M. 1989. The Relationship between Land Cover, Land Use and Land
Degradation. Enschede, Netherlands: International Institute for Geo-Information
Science and Earth Observation, University of Twente.
Pickup G. 1985. The erosion cell—A geomorphic approach to landscape
classification in range assessment. Australian Rangeland Journal 7:114–121.
Pidwirny M, Jones S. 2010. Introduction to lithosphere—Introduction to soils.
In: Pidwirny M, Jones S, editors. Fundamentals of Physical Geography.
Okanagan, Canada: University of British Columbia, Okanagan, www.; accessed 4 August 2012.
Radcliffe DE, Rasmussen TC. 2000. Soil water movement. In: Sumner ME,
editor. Handbook of Soil Science. Boca Raton, FL: CRC Press, pp 87–128.
Reynolds JF, Maestre FT, Kemp PR, Stafford-Smith DM, Lambin E. 2007.
Natural and human dimensions of land degradation in drylands: Causes and
consequences. In: Canadell JG, Pataki DE, Pitelka LF, editors. Terrestrial
Ecosystems in a Changing World. Berlin and Heidelberg, Germany: Springer,
pp 247–258.
Reynolds JF, Stafford Smith DM. 2002. Global Desertification: Do Humans
Create Deserts? Berlin, Germany: Dahlem University Press.
¨dou A, Kuyper TW, Kossou D, Tossou RC, Richards P. 2004. Sustainable
soil fertility management in Benin: Learning from farmers. NJAS Wageningen
Journal of Life Sciences 52(3/4):349–369.
SAS. 2009. SAS/STAT User’s Guide. Cary, NC: SAS Institute.
Sieglstetter R, Wittig R. 2002. L’utilisation des ligneux sauvages et son effet
sur la ve
´tation de la re
´gion D’atakora (Be
´nin nord-occidental). Etudes de la
Flore et de la Ve´ge´tation du Burkina Faso et des Pays Avoisinants 7:23–30.
Snakin VV, Krechetov PP, Kuzovnikova TA, Alyabina I, Gurov AF, Stepichev
AV. 1996. The system of assessment of soil degradation. Soil Technology 8:
Stocking M, Murnaghan N. 2001. Handbook for the Field Assessment of Land
Degradation. Sterling, VA: Earthscan.
Tente B, Sinsin B. 2002. Diversite
´et structure des formations arborescentes
du secteur perma-toucountouna dans la chai
ne de l’Atacora (Be
´nin). Etudes
de la Flore et de la Ve´ge´tation du Burkina Faso et des Pays Avoisinants
Tente B, Sinsin B. 2005. Recherche sur les Facteurs de la Diversite´ Floristique
des Versants du Massif de l’Atacora: Secteur Perma-Toucountouna (Be´ nin)
[PhD dissertation]. Abomey-Calavi, Benin: Universite
´d’Abomey-Calavi, Ecole
Pluridisciplinaire ‘‘Espaces, Cultures et De
Valentin C, Bresson LM. 1992. Morphology, genesis and classification of
surface crusts in loamy and sandy soils. Geoderma 55:225–245.
Wala K. 2005. La Ve´ge´ tation de la Chaıˆne de l’Atacora au Benin: Diversite´
Floristique, Phytosociologique et Impact Humain [PhD dissertation]. Lome
Togo: Universite
´de Lome
´, Faculte
´des Sciences.
Wischmeier WH, Smith DD. 1978. Predicting Rainfall Erosion Losses—A Guide
to Conservation Planning. Agriculture Handbook 537. Washington, DC: US
Department of Agriculture, Science and Education Administration.
Yuangen Y, Shirong L, Zhisheng J. 2009. Laterization and its control to gold
occurrence in Laowanchang gold deposit, Guizhou Province, southwest China.
Journal of Geochemical Exploration 100:67–74.
Mountain Research and Development
... Into the mountainous Atacora region, previous study in Ref. [23] had examined various indicators of land degradation and found that soils could be classified into 4 soil degradation categories i.e. light, moderate, high, and extreme degradation. However, nothing is known about the impacts of soil degradation classes on vegetation. ...
... Local knowledge on soil erosion was On the basis of physical soil degradation indicators (extent of organic layer, color of topsoil, compactness of soil, presence and extent of rills, and occurrence of sheet erosion) each plot was classified visually into specific soil degradation classes. Physical soil degradation in the study area falls into four grades, namely light, moderate, high and extreme soil degradation classes described in [23]. The characteristics of each class are summarized in Table 1. ...
... This finding could be explained by the fact that the soil aggregate stability is closely related to soil organic matter composition [35], biological activity [36], infiltration capacity [37], water absorption and retention in the biomass and upper rhizosphere [38,39] and erosion resistance [37]. Physical soil degradation on the hillsides of Atacora mountain was characterized by the removal of the organic layer and the modification of soil structure leading to the occurrence of ferricrete (extremely degraded soils) [23]. Soil degradation had resulted in soil loss, nutrient depletion, changes in soil structure, and soil hardening that limited plant root system penetration. ...
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Atacora mountain is a particular ecosystem of West Africa where soil degradation occurs. The present study assessed the impacts of physical soil degradation on vegetation in the Beninese portion of this mountain chain. Phytosociological surveys were carried out along line transects from plain to summit within 22 plots of 30 m x 30 m. Based on indicators of physical soil degradation each plot was classified into one soil degradation class (Light, Moderate, High or Extreme). Impacts on plant diversity were assessed by comparing the floristic composition of soil degradation classes with the index of similarity of Jaccard. Variations between soil degradation classes of species richness, species chorological types, species life forms and species dispersal were also tested using a discriminant analysis combined with ANOVA. The Multi-Response Permutation Procedures analysis was used to pairwise compare the soil degradation classes based on the cover data of the species lists. All soil degradation classes were dissimilar, depending on the floristic composition. Discriminant analysis and ANOVA performed on biodiversity indicators had shown that species richness, and the number of regional species, phanerophytes and sarcochory decreased along the increasing degradation gradient in contrast to the number of species with wide distribution, therophytes and sclerochory. With regard to vegetation structure, the results had shown that only moderately and highly degraded soils presented the similar vegetation type. Physical soil degradation induced modification of floristic composition, phytodiversity loss and modification of vegetation structure. These results showed that the soil degradation gradient corresponds to a vegetation disturbance gradient.
... Fields and fallows cover decreases from flat and gentle to steep and very steep parts of the study area, while woodlands cover increases. These findings are in accordance with the work of (Okou et al., 2014) who argue that higher slope constitutes a natural protection against land degradation in this region. A research conducted inside and around Bukit Barisan Selatan National Park located on the Indonesian island of Sumatra leads to a similar conclusion: conversion to arable land of lowland forests often located on gentle slopes is much higher and faster than that of hill forests commonly found on steep slope areas Kinnaird et al. (2003). ...
... In our study, the vegetation cover map is extracted from the NDVI MODIS product that has a low resolution of 5600 m. Recall that vegetation cover is a key driver of erosion patterns, as shown in various studies on regional erosion assessment for mountainous regions (Bou Kheir et al., 2006;Okoth, 2003;Okou et al., 2014;Vrieling et al., 2006). The (low) resolution of the vegetation cover map used in our model seems to be insufficient to accurately estimate the local vegetation cover variation, thus reducing the erosion risk map precision. ...
Effective soil and water management strategies require regional-scale assessment of erosion risk in order to locate prioritized area of intervention. Our study focuses on the Atacora mountain and surrounding areas (covering more than 18% of the total land area of Republic of Benin) which face a serious erosion threat despite their ecological and economic importance. To appraise the level of soil erosion risk of large area, we rely on the Instituto Nacional para la Conservación de la Naturaleza (ICONA) erosion model and use data from geographic information system (GIS). The erosion risk model requires four main inputs, namely, information on slope, lithofacies, land use and vegetation cover. The slope layer computed from ASTER digital elevation model (DEM) and the lithofacies layer inferred from digital pedogeological map are combined to draw soil erodibility map. To build soil protection map, we use land use/land cover layer extracted from LANDSAT 7 ETM + images in addition to vegetation cover layer derived from MODIS NDVI product. The final erosion risk map (with a resolution of 1 arc second) is obtained by overlapping erodibility and soil protection maps. We find that 21.8%, 58.5%, and 19.5% of the study area presents very low to low, medium, and high to very high level of erosion risk, respectively. Moreover, our findings are aggregated at the district-level (administrative unit). We observe that erosion risk is more acute in Boukoumbe district. Kerou, Kobli and Natitingou districts are mildly affected by erosion risk, while Kouande, Materi, Pehunco, Tanguieta and Toucountouna districts face a low risk. Ultimately, the proposed erosion risk map can help researchers and decision makers design and implement effective soil and water management interventions in the study area.
... According to Wilson et al. (2004), topography explains between 26% and 64% of the variation of soil moisture. In the BRP, the landscape physiography (which includes topography, slope aspect) and substrate conditions are important because they determine the drainage patterns (Okou et al. 2014), and define the distribution and structuring of the plant communities (Assédé et al. 2015;Diwediga et al. 2015). Thus, soil moisture regimes could be the main factors discriminating the plant associations. ...
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Aims: The importance of phytosociology very early led the scientists to wonder about the structure of the basic units of different vegetation types. This study aims to: i) differentiate the plant associations in Biosphere Reserve of Pendjari (BRP), ii) determine the ecological characteristic of their habitat and iii) develop their distribution map on different soil types. Study area: The BRP, located in the Sudanian Zone of Benin. Methods: 202 phytosociological relevés were sampled according to the Braun-Blanquet method in the BRP. Ordination was performed using Detrended Correspondence Analysis to evaluate the vegetation patterns. Soil parameters were used to characterize the vegetation types. Results: The numerical analysis of 202 plots and 249 plant species showed two major floristic groups that correlated with moisture gradient: drylands versus wetlands. The dryland group was a mixture of woodland and shrub savanna, the dominant ecosystems of the study area. The wetland group encompassed species primarily from riparian forest, tree savanna and grass savanna on floodplains. Syntaxonomical analysis of the dryland group showed rocky and gravelly soil associations (Burkeo africanae-Detarietum microcarpi) and soils associated with or without fine gravels associations (Andropogono gayani-Terminalietum avicennioidis, Andropogono gayani-Senegalietum dudgeonii and Terminalietum leiocarpae). Syntaxonomical analysis of the wetland group showed riparian forest associations on sandy-clay soil (Coletum laurifoliae, Borassetum aethiopi and Hyparrhenio glabriusculae-Mitragynetum inermis) and floodplain associations on silt-clay soil (Terminalio macropterae-Mitragynetum inermis, Brachiario jubatae-Terminalietum macropterae, Sorghastro bipennati-Vachellietum hockii). Conclusions: Eleven new associations were identified from this study. If the distribution of plant associations was determined by different soil properties, the soil humidity would be one of the main ecological factors determining the establishment of plant species and thus plant association development.
... The land is a fixed asset, one of the factors of production and property that determines farmland size and agricultural productivity (FFP, 2016). Anthropogenic and natural driven-factors hardly let the land rest to provide fertility to the biosphere (Farris et al., 2014). Unlike the former, the latter gradually and unnoticeably affects the fertility of land through degradation. ...
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Acquisition of land for farming activities is hitting hard on the interested smallscale farmers in Nigeria while the available land faces climate change influenced degradation. This current research verified how farmers are coping across four rain-fed (Benue, Edo, Ondo and Niger) states using 1600 copies of pretested questionnaire administered through the multistage procedure. The retrieved copies were coded and analyzed descriptively (frequency and percentage) and inferentially (Chi-Square) at p < 0.05 levels of significance. The results showed that most farmland sizes were three plots, and adaptation of soil management techniques was observed in both Benue and Ondo States, where there were significant (p < 0.05) differences in the farmers' responses to indicate possible differences in policy could assist the two elements of quests. The farmers’ responses indicated addressing of land degradation significantly (p < 0.05), but not with application of fertilizers except in Ondo State (χ= 14.413, p = 0.108 > 0.05). The farmers responsively planted trees with the same reasons in Ondo State (χ= 4.813, p = 0.850 > 0.05). The farmers hardly exercised zero tillage except in Edo State, while the Ondo State's farmers had no different (χ= 4.813, p = 0.850 >0.05) opinion. Farmers' responses on farmland size, soil management techniques, and land degradation were positive but significantly different. Therefore, the investigation deduced that states with significantly different responses would require different decisions, agricultural policies, and adaptation strategies to lessen and ease the effects of climate change on the land of such states.
... . 이러한 산림황폐화의 주요 원인 으로는 1990년대 중후반 '고난의 행군' 시기 동안 식량난 을 극복하기 위한 다락밭, 비탈밭 건설과 무분별한 남벌 등이 제기되었다 (Hwang, 1997 (Okou et al., 2014;Monsieurs et al., 2015;Yu et al., 2016 (Chun, 1990;Cho, 2005; Kim Ilsung (1948.9.9.-1994 Kim Jongil (1994Jongil ( .7.9.-2011 Kim Jongun (2011.12.18.-Recnet) Table 4). ...
North Korea has experienced floods and sediment-related disasters annually since the 1970s due to deforestation. It is of paramount importance that technologies and trends related to forest restoration and soil erosion control engineering be properly understood in a bid to reduce damage from sediment-related disasters in North Korea, and to effect national territorial management following unification. This paper presents a literature review and bibliometric analysis including 146 related articles published in North Korea. First, we analyzed the textual characteristics of the articles. We then employed the VOSviewer software package to classify the research topic and analyzed this topic based on the time change. The results showed that articles on the topic have consistently increased since the 1990s. In addition, research related to soil erosion control engineering has been classified into four subjects in North Korea: (i) assessment of hazard area on soil erosion and soil loss, sediment related-disasters; (ii) hydraulic and hydrologic understanding of forests; (iii) reasonable construction of soil erosion control structures; and (iv) effects and management plan of soil erosion control works. The proportion of research related to the (ii) hydraulic and hydrologic understanding of forests had been significant during the reign of Kim Ilsung. However, the proportion of research related to the (i) assessment of hazard area on soil erosion and soil loss, sediment-related disasters, increased during the reign of Kim Jongil and Kim Jongun. Using these results, our analysis indicated that an interest in and need for soil erosion control engineering in North Korea has continually increased. The results of this study are expected to serve as a basis for preparing forestry cooperation between North and South Korea, and to serve as essential data for better understanding soil erosion control engineering in North Korea.
... Soil fertility decline is a key factor in soil degradation and is probably a major cause of decreasing crop yields. The severity of soil degradation depends on the sensitivity of individual elements in the ecosystem to anthropogenic and natural disturbances like land use change and land conversion (Okou Farris et al., 2014). ...
... Cultivated area that is generally located in the middle and lower places, however, did not show the same trend ( Figure 1). This result seems to be similar with the result of Okou et al. (2014) who found that high slope angles in high elevation often result in lightly degraded soil. This condition is allegedly due to the effect of high dense of canopy cover in protected forest that decreases kinetic power of rainfall that will damage the soil through soil erosion process. ...
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Degraded lands are getting extensive worldwide. Even its existence has projected as a solution to fulfill agricultural land scarcity to meet the global demands of food and other agricultural goods, the rate of its extension should be inhibited. Some factors play important role. This research was aimed to find the explanation about how degraded land, biophysical and social factors are related. Research site was located in Lekso Watershed, East Java, Indonesia. Land degradation is assessed by evaluation of the critical land status based on procedure established by Indonesia’s Ministry of Forestry in form of Regulation No. P.32/Menhut-II, 2009.A series of field survey using secondary data obtained from GIS tool performed to collect data for quantify the critical land status. Social factors in this study were limited on people perception, awareness and participation. These data collected by in-depth interview to the respondents. Site of presented respondent selected with purposive sampling, while the respondents in each site selected with stratified random sampling method. The research revealed that surface cover demonstrated high correlation and regression toward critical and very critical land (average r = -0.9822, R2= 0.9648). However, slope steepness located in high altitude showed a contrary trend in which increasing slope steepness decreased the number of total moderate, critical and very critical lands. The functional area of this location as protected forest gave a good surface cover on the steep slope and resulted on small area of degraded land. On the other side, negative perception about cultivation on forest and steep slope resulted in positive correlations with the area of very critical land (r = 0.6710 for cultivated forest, and r = 0.9113 for cultivated steep slope). Moreover, people awareness about flood, landslide and drought gave a negative correlation (r = -0.6274) with critical and very critical area. At last, people participation on farmers’ organization could not be used to elucidate the range of degraded land as the participation in this context did not include the competency building about soil and water conservation values.
... Ecological features and human disturbances were recorded at each sampling site. first, vegetation canopy cover, indicating the surface covered by the vertical projection of the all tree foliage present in a given plot, was recorded according to Braun-Blanquet method used in several studies (Diwediga et al., 2015;Folega et al., 2012;Okou et al., 2014). Then, based on the occurrence (presence/absence) without any intensity gradient, the footprints of tree logging, cattle grazing, and wildfire were recorded as human disturbances. ...
Quantification of carbon and nitrogen in soils in relation to ecological, landform and management factors over river basins is essential to understand landscape ecosystem functions and efforts to manage land restoration and the reduction of greenhouse gasses emissions. Therefore, this research aimed at providing distribution of the potential storage in soil organic carbon (SOC) and total nitrogen (TN) within the multifunctional landscapes of the Mo river basin in Togo. We (1) quantified the potential storages of SOC and TN under different land use/cover types, landscape positions, and land management regimes; and (2) highlighted the relationships among these soil chemical properties, in-situ ecological conditions, and other hypothesized controlling factors. We used soil data from 75 sample sites to determine the quantity of SOC and TN at two depths (0 - 10 cm and 10 - 30 cm). In-situ ecological variables were collected simultaneously during soil sampling. Spatial information on biophysical conditions of the study sites were obtained from satellite images and most updated global topographic and soil databases. The results showed that SOC and TN varied significantly according to land cover types, soil depths, topographical positions and land protection regime. Generally, forests and woodland contain highest SOC (4 %) and TN (0.3 %). Agricultural fields (fallowed and cultivating farms) exhibited the lowest values of SOC and TN, except in some selected farm sites where these chemicals are still high. Topsoil layer (0 - 10 cm) contribute up to 60 % of the total nutrient contents in soils. The sequential multivariate statistical approach unpacked and quantified the effects of inter-dependent ecological, management and landform drivers on the two important soil chemical properties (SOC and TN). The findings from this study could contribute to the improvement of national program for assessing of greenhouse gases induced by land conversions. Based on this case-based finding in contextualization with related studies, we discussed on its implications for sustainable landscape restoration and climate change mitigation.
Soil erosion is a danger that threatens the world today and the basis of the fight against erosion must be sought in the role human. The aim of this study is determine a logical relationship between natural and planted forests conditions with soil erosion risk classes in the kasilian watershed. This basin is located in the Hyrcanian vegetation area on the northern slopes of the Alborz Mountains in northern Iran. In this research, the erosion risk map was prepared using the ICONA model and RS/GIS techniques and it was adapted to the physical realities of the area. The results showed that human interventions and pressures have reduced habitat good species percentage in the downstream areas in the northern part and upstream areas near the forest-rangeland boundary in the southern part. Also, the choice of species was incorrect in some planted forest. Therefore, high erosion risk class is clearly seen in these areas. There is a low erosion risk class (19.3%) in natural forest and a very low erosion risk class (2.73%) in plantation forest. The main reason for the high percentage of very low erosion risk class in planted forests can be due to the presence of 70–80% of canopy, which is a combination of 90% of broadleaf plants with 10% of conifers. These results are consistent with the realities in the study area. The ICONA model and RS/GIS techniques can be used as a reliable framework for erosion risk assessment.
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The objectives of this study were to establish the slope criteria and analyze the forest land restoration plan in North Korea. Reviewing the literature of the countries, the relationships among the climate, erosion controls, and slope criteria with forest restoration programmes, implemented were analyzed. Comparison of forest land management policies was conducted between South and North Korea. The soil erosion controls using biological barriers were at 15{\sim}20^{\circ}slopes in arid climate regions and 25{\sim}30^{\circ} in humid climate regions. In the case of South Korea, an afforestation policy from the "Act on Clearance Project of Slash-and Burn Agriculture" of 1966 was enforced on mountains with slope greater than 20^{\circ}, however, at present, the "Marginal Cropping Land Policy" recommends cropping lands with slope bigger than 8.5^{\circ} to forest land. In 1961, in "Land Reclamation of One Million Hectare", North Korea reclaimed additional cropping lands with slope bigger than 8.5^{\circ}, and currently, the "Act on Forestry" states to enforce reforestation with slope bigger than 20^{\circ}. This study recommends that South Korea aids for forest land restoration in North Korea based on the different stages of their development on reconciliation and cooperation between South and North Korea.
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To determine specific characteristics necessary for the computation of the C factor in RUSLE for timevariable crops, measurements were carried out in fields with selected agricultural crops grown by conventional practices. Sloping plots on an experimental area in Třebsin locality and farm fields were used to measure surface runoff and soil loss by erosion in conditions of natural and simulated rainfall. Basic characteristics to compute the C factor were determined in the particular growth phases of selected crops – sunflower, flax, poppy and rape. Effective root mass, canopy cover and fall height of rain drops were measured.
The perception of farmers from the Atacora and Savè regions of Benin was studied about the causes and consequences of land degradation and corrective actions for sustaining soil fertility. Research methods in this diagnostic study included group discussions, using non-standardized unstructured interviews and participant observations. Farmland degradation leading to declining yields, and land tenure arrangements were identified as the main constraints on the sustainability of agriculture. In both regions the farmers stated that climatic changes (less and more irregular rainfall), run off, erosion, and overexploitation of farmlands caused land degradation. Soil fertility status was assessed on the basis of dicotyledonous weeds, soil texture and colour, and soil fauna (earthworm casting activity). Farmers have adapted their cropping systems to the local environment by developing traditional and new strategies and activities that could contribute to maintain or enhance crop productivity. These strategies include animal manure, inorganic fertilizer, crop rotation, a five-year fallow, extensive cropping systems with cassava or egusi melon, and emigration. Land tenure arrangements between landlords and migrants affect strategies that can be applied to maintain soil fertility. The importance of building mutual trust and the need to experiment with different land tenure arrangements are indicated. A framework for interactive research where knowledge is collectively generated is proposed in order to test the effectiveness and applicability of some of these local innovations not yet well understood by conventional science.
Virtually every element of the Earth’s landscape is to some extent the product of erosion. Denudation and sedimentation are inevitable geologic phenomena that man may regard as either benevolent or pernicious depending on the place and time. Erosion of upstream fertile lands, for example, leads to enrichment of the prolific Nile and Mesopotamian floodplains, nuturing the first agriculturalists (Moss and Walker, 1978). From another point of view, modern agricultural researchers commonly estimate that only 0.1 to 0.8 mm year−1 of soil removal can be sustained indefinitely without loss of productivity on croplands (U.S. Department of Agriculture, 1975). Yet erosion of thin topsoils, rapid sedimentation of streams and lakes, and gullying of landscapes are in some areas esthetic and economic problems of massive proportions. Figure 5–1, for example, illustrates a Mojave Desert hill slope where erosion has become a serious problem following slope modifications by vehicles. The problem, however, is not erosion per se, but rather erosion rates, and the adjective “accelerated” is commonly applied to erosion rates considered to be significantly greater than “natural.”
In this paper, the literature is reviewed in order to present data and relationships showing the effects of compaction on soil hydraulic properties and water flow, as well as numerically modeled water flow for some management systems. The objectives are to: review some fundamental principles of water retention and transmission; highlight observations of soil hydraulic properties and hydrologic components for various compactive and tillage situations; report results of modeling efforts for predicting the hydrology of soils; and suggest future research directions. -from Authors