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Abstract

Biosphere Reserves stand as the worldwide strategy of biological conservation. However, the current global land use change involves extensive loss of vegetation cover around the reserves and increase their vulnerability and their ecological isolation. The overall objective of this study was to assess the trends of land covers change in and outside the “W” Biosphere Reserve (WBR) in Benin as well as the driving forces of land cover change in order to provide tools for its sustainable management. For this purpose, two serial times of maps from Landsat images TM 1995 and ETM+ 2006 were used to assess the rates and trends of the different land cover units from 1995 to 2006. Socioeconomic surveys based on structured interviews were conducted with 240 households in 8 villages around the reserve. Land clearing, tree logging, settlement and grazing were frequently quoted by the households as main driver forces inducing land cover change around WBR. Probability transition matrices of land cover displayed high probabilities (>0.6) in the southern part of WBR and moderate probabilities (0.3 to 0.5) in the northern part of WBR for woodland and savanna vegetation to be changed into cropland outside the reserve showing the persistence of vegetation degradation around WBR in the coming years. Our study revealed the urgent necessity of the development of conservation action planning to stop the agricultural frontline progression toward the reserve.
Environment and Natural Resources Research; Vol. 3, No. 2; 2013
ISSN 1927-0488 E-ISSN 1927-0496
Published by Canadian Center of Science and Education
87
Land Use and Land-Cover Change at “W” Biosphere Reserve and Its
Surroundings Areas in Benin Republic (West Africa)
Laurent Gbenato Houessou1, Oscar Teka1, Ismaïla Toko Imorou1,2, Anne Mette Lykke3 & Brice Sinsin1
1 Laboratory of Applied Ecology, Faculty of Agronomic Sciences, University of Abomey-Calavi, Cotonou, Benin
2 Department of Geography, Faculty of Letter, Arts and Human Sciences, University of Abomey-Calavi, Cotonou,
Benin
3 Department of Bioscience, Aarhus University, Vejlsøvej 25, DK-8600 Silkeborg, Denmark
Correspondence: Laurent Gbenato Houessou, Laboratory of Applied Ecology, Faculty of Agronomic Sciences,
University of Abomey-Calavi, Cotonou, Benin. Tel: 229-9648-5593. E-mail: houessoulaurent@gmail.com
Received: January 28, 2013 Accepted: March 3, 2013 Online Published: March 10, 2013
doi:10.5539/enrr.v3n2p87 URL: http://dx.doi.org/10.5539/enrr.v3n2p87
Abstract
Biosphere Reserves stand as the worldwide strategy of biological conservation. However, the current global land
use change involves extensive loss of vegetation cover around the reserves and increase their vulnerability and
their ecological isolation. The overall objective of this study was to assess the trends of land covers change in-
and outside the “W” Biosphere Reserve (WBR) in Benin as well as the driving forces of land cover change in
order to provide tools for its sustainable management. For this purpose, two serial times of maps from Landsat
images TM 1995 and ETM+ 2006 were used to assess the rates and trends of the different land cover units from
1995 to 2006. Socioeconomic surveys based on structured interviews were conducted with 240 households in 8
villages around the reserve. Land clearing, tree logging, settlement and grazing were frequently quoted by the
households as main driver forces inducing land cover change around WBR. Probability transition matrices of
land cover displayed high probabilities (>0.6) in the southern part of WBR and moderate probabilities (0.3 to 0.5)
in the northern part of WBR for woodland and savanna vegetation to be changed into cropland outside the
reserve showing the persistence of vegetation degradation around WBR in the coming years. Our study revealed
the urgent necessity of the development of conservation action planning to stop the agricultural frontline
progression toward the reserve.
Keywords: deforestation rate, land use and land cover change, probability transition matrices, temporal maps, W
National Park
1. Introduction
In addition to biodiversity decline and climate change, land cover/land use change is considered as an important
factor contributing to the current global change (Turner, 2002; Meyfroidt & Lambin, 2003; Verburg & Veldkamp,
2005; Lepers et al., 2005). Land cover results from a complex process and can be considered as the biophysical
state of the earth’s surface and immediate sub-surface (Turner et al., 1995), while land use refers to the
conversion or transformation of the land cover into the desired human purposes which are associated with that
cover, e.g. cropping, conservation, or settlement (Meyer & Turner, 1994).
The issue of land-cover/land use change has taken place since human beings shifted from goods harvesting in
wild into the production of its own goods to satisfy its daily requirements (Turner et al., 1990). Since then,
natural vegetation was progressively converted into agriculture land for crop production, animal grazing and
other land use types (Turner et al., 1990). Due to the rapid increasing of the population demography during the
two last centuries and subsequent land requirement for farming and urbanization, important amount of forest was
converted into anthropogenic area (Turner et al., 1990; Ouedraogo et al., 2010). The rhythm of degradation of
primary ecosystems and declined of associated biodiversity was alarming during the two last centuries (Goudie,
2006). Recently, FAO (2010) estimated the worldwide forest cover lost for about 13 million hectares per year
during the last decade 2000-2010 with a persistent high decreasing rate in sub-saharan Africa. Most primary
ecosystems are thereof been fragmented and large habitats are partitioned into smaller (Fahrig, 2003).
Sequel to habitat lost and land cover conversion in degraded ecosystem, protected areas were established all over
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88
the world to serve as representative land of biodiversity preservation for current and future generation (Pimm et
al., 2001; UNEP, 2003; IUCN, 2005). They were thought to be an effective strategy to prevent habitat destruction
and ensure ecosystems protection within their borders (Bruner, Gullison, Rice, & da Fonseca, 2001; Clerici et al.,
2007). However, due to lack of land, protected areas were reported to be more or less eroded by surrounding
forest dwellers through agricultural use, cattle herding and human settlements (Defries, Hansen, Turner, Reid &
Liu, 2007; Clerici et al., 2007; Flamenco-Sandovala, Ramos, & Masera, 2007). Wittemyer, Elsen, Bean, Burton
and Brashares (2008) observed an accelerated human population growth in Africa and Latin America around
protected areas and claimed that protected areas represent an attractive pole for human settlement by providing
access to increasingly scarce ecosystem services (e.g., NTFPs, bushmeat) and jobs deriving from the protected
areas management. The rapid increase in trends of the agricultural frontline from the communal to the borders of
protected areas is a great concern since this increase protected areas vulnerability through their ecological
isolation (DeFries, Hansen, Newton, & Hansen, 2005; Struhsaker, Struhsaker, & Siex, 2005).
In Benin, previous studies have assessed the land cover change and demonstrated its impact on natural habitats
conversion into degraded habitats. For instance Oloukoi, Mama and Agbo (2006) showed in the central part of
the country a land cover regression of about 59.4% from 1978 to 1998 and highlighted a high rate of savannah,
galleries forest, woodland conversion into cropland. Similar trends were also observed in the region of
Wari-Maro (Orekan, 2007). From the latest study which was focused on the period ranging from 1991 to 2000,
the author concluded that the region is under high vegetation cover loss with an annual deforestation rate of 8%.
However, no specific study has tackled so far the land cover/land use change around the biosphere reserve in
Benin although this remain a great concern to sustainably design conservation strategies for protected area
management (Clerici et al., 2007, Flamenco-Sandoval et al., 2007). Therefore, this study was carried out as a
case study of land cover/land use change around a protected area at “W” Biosphere Reserve located in the
uppermost northeast Benin. Since land use intensity differs around the reserve according to different cropping
systems, two study sites with different land use intensity were selected to assess the land cover/land use change
around the protected area. Specifically our study aims (i) to assess the perception of land use/land cover change
by local residents around the reserve and (ii) to quantify land cover/land use change at the out-and inside of the
reserve.
2. Method
2.1 Study Area
The study was conducted in Benin at the “W” National Park actually named “W” Biosphere Reserve since 2002
(WBR) (11º 26’-12º 26’ N; 2º17’- 3º 05’ E, Figure 1). It is part of the transboundary Biosphere Reserve over
Benin, Niger and Burkina Faso. The WBR in Benin covers 563,280 ha representing 56% of the transboundary
Biosphere’s total area. As most Biosphere Reserves, WBR core area i.e. the protected area is separate from the
communal lands (agro-system) by a buffer zone of 5 km width around the protected area. The WBR belongs to
the regional centre of sudanian endemism (White, 1983). Two zones were sampled for land use and land cover
change analysis: the first zone in the northern part (1105.4 km²) and the second zone in the southern (850.9 km²).
The cropping system in the southern part is based on cash crop production (mainly cotton) while the northern
part of the WBR is based on food crop (Table 1).
www.ccsen
Table 1.
C
Charac
t
Loc
a
Rai
n
Tempe
r
Clima
t
Act
ve
g
et
a
per
i
Ve
et
Soils
t
Main
e
g
ro
Crop
s
y
st
Figure 1.
et.org
/
enrr
C
haracteristics
t
eristics
a
tion
B
n
fall
r
ature
t
e t
y
pe
ive
a
tion
i
od
a
tion
W
ty
pes
Tr
o
c
e
thnic
up
pin
g
em
Map of the st
u
E
n
of the sample
d
B
anikoara dist
r
18
0
W
oodland, vari
o
o
pical ferrugin
o
c
rystalline roc
k
b
ro
w
Mainly base
d
sorghu
m
u
dy area sho
w
la
n
n
vironment and
N
d
zones
Zone 1
r
ic
t
(10º 94-11
º
900-1100
m
18- 35 º
C
Sudania
n
0
days (May to
o
us type of sa
v
fallows
o
us soil with
c
k
, mineral and
w
n soils and c
l
Bariba and
P
d
on cotton cro
p
m
, corn, yam,
Livestock ra
i
w
ing the clippe
n
d use and lan
N
atural Resour
c
89
º
68N & 1º99-
m
m
C
n
October)
v
annah, galler
y
c
oncretions an
d
poorly evolv
e
l
ayed soils
P
eulh
p
and second
a
and cassava.
i
sing
d areas in the
n
d cover (LUC
C
c
es Research
2º90E)
K
y
forest,
s
a
d
on pure
e
d soil,
c
a
ry on
M
n
orthern and i
n
C
) analysis
Zo
K
arimama dist
r
& 2º 2
8
600-9
17 -
Sudano
150 days (Jun
e
Woodland,
v
a
vannah, galle
r
Ferrugino
u
c
oncretions on
rock and
c
Dendi, Peulh,
Haouss
a
M
ainly based o
n
and corn, y
Livesto
c
Fishing rep
r
importa
n
n
the southern
Vol. 3, No. 2;
ne 2
r
ic
t
(11º 4-12º
4
8
-3º 28E)
00 m
m
39 ºC
-sahelian
e
to Septembe
r
v
arious type o
f
r
y forest, fall
o
u
s type with
pure crystalli
n
c
layed soils
Gourmantch
é
a
, Djerma
n
sorghum, mi
l
am, cassava,
c
k raising.
r
esent also an
n
t activity
of the reserv
e
2013
4
N
r
)
f
o
ws
n
e
é
,
l
let,
e
for
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90
2.2 Data Collection
Two types of data were collected:
2.2.1 Socio-Economic Data
A survey based on structured interviews was conducted in the southern part of the reserve in the villages of
Sampéto, Kandérou, Nipouni and Founougo (district of Banikoara) and in the northern part of the reserve in the
villages of Karimama, Kofonou, Karigui and Monsey (district of Karimama). The interviews were realized from
March to June 2008 and from February to April 2009. A total of 240 household were randomly selected in the
targeted villages i.e. 30 per villages. Apart from socio-demographic characteristics of the household (age of the
household chief, household size, household active population size, household chief education level, ethnic group,
gender, etc.), interviewees were asked the main following questions:
- Are you aware of land cover change in your village during the last ten years (1= yes, 0= no)?
- Do you clear woody vegetation during the last five year for agriculture purpose (1=yes, 0=no)?
- Do the soil fertility decrease or increase during the last five years (3= increase; 2= stable; 1= decrease)?
- What are the driving forces of land cover change?
2.2.2 Cartographic Data: Land Use/Land Cover Map Acquisition
The land use/land cover maps for the WBR and its surrounding areas were acquired at the “Centre National de
Télédetection (CENATEL)”. For this purpose, two serial times of maps i.e. land use/land cover maps from
Landsat images TM 1995 and ETM+ 2006 were used. In ArcGIS 9.3 software, we clipped the acquired maps at
the southern part of the reserve and at the northern part of the reserve. The clipped areas were selected in order to
have part of the reserve in the clipped section and one other part outside the reserve (Figure 1). In the clipped
maps we grouped the cover types: settlement, farm and fallow as farmland in order to avoid misinterpretation of
data analysis since these three covers type appear sometimes indistinct.
2.3 Data Analysis
2.3.1 Interview Data Analysis
We estimated the level of awareness of land use/land cover change as the percentage of household giving the
answers yes out of the total number of surveyed households. Frequency citation of perceived driving forces
which induce land cover change was estimated as:
FC = 100 X N/L with confidence interval (α =0.05) = 1.96 X 100 [FC(1-FC)/L]1/2 (1)
where N is the number of households who quoted a given factor as inducing land cover change and L the total
number of household. Chi-sq test was used to test whether there was association between household perception
of factors inducing land cover change and age category of the household chief, main activities, ethnic group,
level of education, and geographic location.
We used a logit instead of the probit model to assess the factors which significantly influenced the decision of
household to clear land during the last five years since the logit model is more interpretable (Long, 1997; Hurlin,
2003). The model is defined as follow:
(2)
(3)
Where the function F follows a standard logistic distribution and is expressed as:
(4)
Finally
Where β0 is the constant and β1, β2, … βk are the coefficients of the independent variables X1i, X2i, …, Xki, and
(pi/1-pi) is the odds. The model predicts the logit of the dependent variable (decision to clear land) based on the
independent variables. The logit is the napierian logarithm of the ratio of the probability pi (realization of the
event) and 1-pi (non-realization of the event).
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91
To implement the model, the investigated independent variables were: household population size (i.e. total
number of persons in the household), household active population size (i.e. total number of persons with age
ranging from 15 to 55 years old), age of the household chief, ethnic group, level of education, household main
activities and, perceived soil fertility level. The following codification was used (Table 2).
Table 2. Description of the independent variables
Variables Codes Nature Explanation and modalities
Ethnic group ETHNIE Nominal 1= Bariba; 2= Gourmantché; 3= Dendi; 4 = Haoussa,
5=Djerma; 6=Others
Household population size HOUPOP Continue Numerical Value
Household active
population size ACTPOP Continue Numerical Value
Age of the Household chief AGE Ordinal 1= <30; 2 = 30-60; 3 = >60
Level of education EDUCAT Ordinal 1 = Illiterate; 2 = Primary school; 3 = Secondary school
Household main activities ACTIVIT Nominal 1= Agriculture; 2 = Breeding; 3 = Fishing
Perceived soil fertility FERTIL Ordinal 3= Increase; 2= Stable; 1 = Decrease
The significance of the coefficient of the logistic regression was appreciated based on the chi-sq likelihood and
Wald statistics. The global logit-model significance was assessed with 2-log likelihood and the level of fitness.
2.3.2 Analysis and Quantification of Land Cover Change
The deforestation rate (r) was assessed inside and outside of the reserve both at the northern part and the
southern part of this reserve based on formula proposed by Puyravaud (2003) as follow:
(5)
Where 'r' is the deforestation rate (% of vegetation cover lost/year); A1 and A2 represent the undegraded lands
cover classes respectively for the periods 1995 and 2006. The undegraded land covers classes are represented
here by Woodland, Gallery forest, Tree and Shrub savannah and t2-t1 is the interval in years during which change
in land cover is being assessed.
3.2.3 Land Cover Conversion Matrix
Based on the information of land-cover classes from the two observed periods, cross-tabular comparison using
the algorithm Intersect available in ArcGIS 9.3 were used to assess the differences in extent of each class and the
conversion that took place between the two periods. Transition matrices were elaborated for the periods 1995 to
2006 with respect to the subset areas. We determined the transition matrix separately for inside and outside the
protected area both in the northern part and southern part. Each matrix represents either the persistence area of
each land cover category during the period 1995 to 2006, or the area which was converted to another land-cover
category during the same period.
In addition to transition matrix, we determined the transition probability matrices. Values in the cells of the
transition probability matrices represented the probabilities of conversion or persistence of each land use into
another one. Probabilities values were computed following Oloukoui et al. (2006). The probability of one cell
belonging to class Ci during the initial year (1995) to be converted into class Cj during the final year (2006) was
calculated as:
P
i-j = ACi-Cj/Ai(1995) (6)
where ACi-Cj was the area of the land cover class Ci to Cj from the year 1995 to 2006 and Ai(1995) was the total
area of the land cover class Ci in the year 1995.
www.ccsen
3. Results
3.1 Local
C
More tha
n
during th
e
93.4%]) a
n
southern
a
land cove
r
(chi-sq =0
the house
h
=0.013).
O
3.2 Comm
Overall, 8
2
(95% CI:
[
settlement
significan
t
b
etween t
h
that land
c
change in
northern p
Fi
g
3.3 Facto
r
The logit
N
agelker
k
Omnibus;
agricultur
e
(Table 3).
of higher
p
was posit
i
p
opulatio
n
northern
p
maturity (
household
p
rimary c
r
et.org
/
enrr
C
ommunities’
A
n
80.4% (95%
e
last ten yea
r
n
d 73.3% (95
%
a
nd the northe
r
r
change awa
r
.259; P =0.87
9
h
old chief sig
n
O
ld chief opin
e
unities’ Perce
p
2
.5% (95% C
I
[
18.9-29.7%])
and grazing
t
difference w
a
h
e southern a
n
c
learing (Fc =
3
the southern
p
art of the rese
r
g
ure 2. Factor
s
r
s Determinin
g
model estima
t
k
e =0.66) with
Chi-sq =41.2
8
e
land was sig
n
The coefficie
n
p
robability of
i
ve. This wou
l
n
size increas
e
p
art of the res
e
i.e. able to g
e
lan
d
s with th
e
r
opland hold
b
E
n
A
wareness Le
v
CI: [75.37-85
r
preceding t
h
%
CI: [65.4-8
1
r
n part of the
r
eness with r
e
9
), and e
t
hnic
g
n
ificantly affe
c
e
d for land co
v
p
tion o
f
Drivi
n
I
: [77.6-87.3
%
of the househ
by livestock
a
s obse
r
ved b
e
n
d northe
r
n p
a
3
6.3%) and tr
e
p
art of the res
e
r
ve.
s
perceived by
g
Land Cleari
n
t
ion was glob
a
a good predi
c
8
; P <0.001).
T
n
ificantly infl
u
n
t was negati
v
land clearing.
l
d indicate th
a
e
d. As reveale
d
e
rve, when th
e
e
t married, ab
e
m or have to
b
y the house
h
n
vironment and
N
v
el o
f
Land C
o
.42%]) of the
h
e surveyed p
e
1
.2%]) of the
h
r
eserve. Rega
r
e
spect to the
m
g
roup (chi-sq
=
c
ted its opini
o
v
er change wh
i
n
g Forces Ind
u
%
]); 36.3% (95
%
old considere
d
as factors i
n
e
tween house
h
a
rt of the rese
r
e
e logging (Fc
e
rve while lan
d
respondents a
n
g by Househ
o
a
lly significa
n
c
tion (R² =63.
3
T
he household
s
u
enced by the
v
e with the soi
l
Regarding, t
h
a
t decision of
d
by our inve
e
agriculturall
y
out 20 years
find new fiel
d
h
old for the n
e
N
atural Resour
c
92
o
vers Change
respondents
w
e
riod i.e. fro
m
h
ousehold opi
n
r
dless the reg
i
m
ain activitie
s
=
6.726; P =0.1
o
n about the l
a
i
le young chie
f
u
cing Land Co
%
CI: [30.1-4
2
d
respectively
n
ducing land
h
old frequenc
y
r
ve (chi-sq =5
4
=18.1%) wer
e
d
clearing (48
s driving forc
e
o
lds around W
B
n
t (2log-likeli
h
3
0%). The coe
s
’ decision to
c
household ac
t
l
fertility sug
g
h
e household
s
land clearing
stigation, trad
i
y
active child
r
old), the chie
f
d
s to be cleare
d
e
w independe
n
c
es Research
w
ere aware of
m
1998-2008.
n
ed for chang
e
i
on of the res
e
s
(chi-sq=2.99
4
51) of the ho
u
a
nd cover cha
n
f
were not oft
e
ver Change
2
.3%]); 27.4
%
land clearing
f
cover chang
e
y
citation of fa
c
4
.26; P <0.00
)
e
m
ostly quot
e
.4%) and live
s
e
of land cove
r
B
R
h
ood =53.27;
R
fficients were
c
lear vegetati
o
t
ive populatio
n
g
esting that de
c
s
ize and main
a
increases wh
e
i
tionally in th
e
r
en (mainly b
o
f
s of the hou
s
d
for them. T
h
n
t farmers (m
a
conversion o
f
About 87.5
%
e
in land cover
e
rve, no differ
e
4
; P =0.084),
sehold chief.
H
n
ge awarenes
s
e
n aware of la
n
%
(95% CI: [2
1
f
or crop prod
u
e
in their vill
c
tors inducing
)
. It was note
w
e
d as factors i
n
s
tock grazing
(
r
change arou
n
R
² of Cox &
also globally
o
n i.e. to conv
e
n
size and per
c
c
line in soil f
e
a
ctive popula
t
e
n the househ
o
e
southern pa
r
o
ys) in the ho
u
s
ehold have t
o
h
is results into
:
a
ture children
Vol. 3, No. 2;
f
land cover c
h
%
(95% CI: [8
respectively i
e
nce were fou
n
level of educ
H
owever the a
s
(chi-sq =8.6
3
n
d cover chan
g
1
.8-33.0%]); 2
4
u
ction, tree log
age (Figure
2
land cover c
h
w
orthy to und
e
n
ducing land
c
(
Fc =26.6%) i
n
n
d the WBR
Snell =0.50;
R
significan
t
(T
e
e
rt forest cove
r
c
eived soil fe
r
e
rtility is cond
u
t
ion, the coeff
i
o
ld size and
a
r
t as well as i
n
u
sehold reach
o
share part
o
:
(i) partition
o
of the house
h
2013
h
ange
1.6 -
i
n the
n
d in
ation
ge of
3
8; P
g
e.
4
.3%
ging,
2
). A
h
ange
e
rline
c
over
n
the
R
² of
e
st of
r
into
r
tility
u
cive
i
cient
a
ctive
n
the
their
o
f the
o
f the
h
old)
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93
and/or (ii) acquisition of new land for clearing by the new independent farmers.
Table 3. Factors determining land clearing decision by the household
Variables Coefficient Wald χ² test P-value
Household population size 0.012 0.005 0.943
Household active population size 1.16 6.92 0.009
Household chief Age 0.009 0.065 0.799
Ethnic group -0.149 0.026 0.872
Level of education 0.094 0.005 0.945
Household main activities 0.505 0.2 0.655
Soil fertility -0.325 5.263 0.017
Constant 4.638 7.311 0.007
Estimated statistics:
R² = 63.3%
Omnibus Test of significant coefficients: Chi-sq = 41.282; p < 0.001
Test of Hosmer & Lemeshow: Chi-sq = 23.655; dF = 7; p = 0.001;
-2Log-likelihood = 53.277; R² of Cox & Snell = 0.50; R² of Nagelkerke = 0.66.
Global percentage of prediction = 63.3%.
3.4 Land Covers Change Dynamics in and outside the WBR from 1995 to 2006
Land cover maps (Figure 3) showed that in 1995 tree and shrub savannah (28.20%) followed by degraded
savannah (27.31%) were the most dominant land cover types in the subset region at the unprotected area in the
south of the reserve (Table 4). In 2006, in the same area, farmland became the most dominant land cover type
with 87.10% of the area at the expense of degraded savannah and tree and shrub savannah. In the inner of the
reserve in 1995 (south), land covers maps displayed a high proportion of tree and shrub savannah (54.15%) and
of woodland (30.35%). Till the year 2006, in that part of the reserve, tree and shrub savannah (56.40%) and
woodland (33.94%) remained the most dominant land cover types.
As far as the northern part of the reserve was concerned, during the year 1995, farmland displayed high
proportion (44.59%) in the subset region of the unprotected area. In 2006, the situation was almost similar with
high proportion of farmland (63.96%) comparatively to the others land covers types. In the inner of the reserve at
the northern part, tree and shrub savannah was the most dominant land cover types with 96.75% in proportion,
the situation did not sensibly change in 2006 where tree and shrub savannah still remain the most abundant land
cover type (97.03%).
Table 4. Proportion of each land cover in percentage in 1995 and 2006
Land cover types Unprotected
area (South)
Protected area
(South)
Unprotected
area (North)
Protected area
(North)
1995 2006 1995 2006 1995 2006 1995 2006
Farmland 22.52 87.10 1.65 5.30 44.59 63.96 0.02 0.11
Woodland 21.65 0.12 30.35 33.94 3.01 1.01 2.24 2.46
Gallery forest 0.32 0.33 3.15 4.16 4.66 5.46 0.27 0.30
Degraded Savannah 27.31 3.40 10.70 0.21 11.88 17.05 0.72 0.1
Tree+Shrub savannah 28.20 9.05 54.15 56.40 32.96 11.07 96.75 97.03
Opened grassland - - - - 2.8 1.45 - -
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94
Figure 3. Land use/Land cover covers maps change from 1995 to 2006 in the south and north of WBR
Legend
a- South of WBR in 1995, b- South of WBR in 2006.
c- North of WBR in 1995, d- North of WBR in 2006.
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95
Figure 4. Land covers dynamics from 1995 to 2006 at WBR and surrounding areas
Legend: F =Farmland; WL =Woodland; GF =Gallery Forest; TSS =Tree and Shrub Savannah, OG =Opened
Grassland; DeS =Degraded Savannah
a = Land cover change in the South of WBR in the unprotected area from 1995 to 2006.
b = Land cover change in the South of WBR in the protected area from 1995 to 2006.
c = Land cover change in the North of WBR in the unprotected area from 1995 to 2006.
d = Land cover change in the North of WBR in the protected area from 1995 to 2006.
Figure 4 displayed the land cover types dynamic from 1995 to 2006. We observed that in the south of the WBR
and in the unprotected area, gallery forest was almost stable (0.01%) farmland increased for 64.6% while
woodland, degraded savannah and tree/shrub savannah decreased in area respectively for -21.5%; -23.9%; and
-19.1% showing the conversion of land cover from woody vegetation to anthropogenic vegetation (Figure 4a).
Meanwhile, in the north of WBR in the unprotected area, the same situation (i.e. conversion of forest cover to
anthropogenic vegetation) was observed (Figure 4c). However, the rate of conversion into anthropogenic
vegetation appeared as lower in the north. Farmland increased for about 64.6% in the south while in the north the
farmland increased for 19.4%.
The land cover dynamic in the protected area both in the southern part and northern part of the reserve showed
an increase in the extension of tree/shrub savannah, woodland, gallery forest and farmland. Woodland, gallery
forest, tree/shrub savannah and farmland increased respectively for 3.6%, 1.0%, 2.2% and 3.6% in the southern
of the reserve and for 0.2%, 0.03%, 0.3% and 0.1% in the northern of the reserve (Figure 4b and Figure 4d).
3.5 Deforestation Rate
The deforestation rate was higher (about 15.13%) outside the reserve at the unprotected area in the south part of
the reserve comparatively to the northern part of the reserve where the deforestation rate was about 7.63%
(Figure 5). In contrast, it is noteworthy to remark a slight increase in vegetation cover inside the reserve in the
south as well as in the north part.
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96
Figure 5. Deforestation rate outside and inside in the north and the south of WBR
Legend:
UP (south) = Unprotected area in the south of the subset zones of WBR;
P (south) = Protected area in the south of the subset zones of WBR;
UP (north) = Unprotected area in the north of the subset zones of WBR;
P (north) = Unprotected area in the north of the subset zones of WBR.
Table 5. Transition probability matrices of the land cover type from 1995 to 2006 inside and outside of the
reserve at the northern and southern of the reserve
F WL GF DeS TSS OG
Unprotected area(South)
F 0.9642 0 0.0002 0.0316 0.004 -
WL 0.9061 0.0043 0.0008 0.0888 0 -
GF 0.0119 0.0027 0.9854 0 0 -
DeS 0.9718 0.0009 0 0.0272 0 -
TSS 0.6706 0 0 0.0009 0.3286 -
Protected area (South)
F 0.0896 0 0.001 0.0045 0.9049 -
WL 0.0038 0.7494 0 0 0.2468 -
GF 0.0011 0 0.9989 0 0 -
DeS 0.0383 0.0148 0 0 0.9469 -
TSS 0.0146 0.4865 0 0 0.4989 -
Unprotected area (North)
F 0.9604 0 0.008 0 0.0209 0.0107
WL 0.3647 0.0102 0.0447 0.0079 0.5716 0.0009
GF 0.0366 0 0.9631 0 0 0.0003
DeS 0.4743 0 0.0033 0.517 0.0054 0
TSS 0.3707 0 0 0.3441 0.2849 0.0003
OG 0.3579 0 0 0 0.3056 0.3365
Protected area (North)
F 0 0.2657 0 0 0.7343 -
WL 0 0.7651 0 0 0.2349 -
GF 0.0577 0 0.9423 0 0 -
DeS 0.0119 0 0.0003 0.1437 0.8442 -
TSS 0.0009 0.0076 0 0 0.9914 -
Legend
F= Farmland; WL = Woodland; GF = Gallery Forest; DeS = Degraded Savanna; TSS = Tree and Shrub Savanna.
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97
4. Discussion
4.1 Community Perception of Land Cover/Land Use Change
The study reported that the land cover change was largely perceived by local population around the reserve. This
highlights the evidence of land conversion and land use change around the protected areas as showed previously
by DeFries et al. (2007). Among socio-demographic factors which could likely influence population perception
of land cover change, only respondent age has been found as significant. In fact, the perception of land use/land
cover change required long time experience which elder persons dispose. This might explain the significant
difference obtained with respect to the age which appears as significant factor for land use/land cover change
(LUCC) perception (Lykke, Fog & Madsen, 1999).
4.2 Land Use/Land Cover Change Dynamic
Regarding the perceived driving forces causing land cover change, 80% of the respondents opined land clearing
for crop production. From our results it can be concluded that agriculture is the main driving force for land cover
change in the region. This result is consistent with previous studies (Lambin, Geist, & Lepers, 2003; Wood,
Tappan, & Hadj, 2004) which concluded that agriculture remains the principal factor inducing land cover change
in sub-Saharan Africa. However the perceived driving forces could vary from a region to another. For instance,
Arouna, Toko, Djogbénou and Sinsin (2011) found that charcoal production represented the main activity
leading to land cover change in the centre of Benin while Lykke (2000) reported frequent intensive res and
declining rainfall as factors inducing vegetation change in a semi-arid region of Sine Saloum in Senegal.
The household active population size affected significantly the household decision to clear new land for
agriculture. Indeed, the internal growth of the household population involves more persons to be supported by
the household; so that more revenue and food production are required. As response to this requirement, farmers
often decide to clear new field in order to overcome their household charge increasing. This is in accordance
with the ndings of Orekan (2007) and Ouedraogo et al. (2010) who concluded in their study at a strong
correlation between the population growth and land degradation.
In addition to the active population size, soil fertility depletion affected significantly the household decision for
land clearing in the region. This could be explained by the traditional cropping system consisting of slash and
burn cultivation. After land clearing, farmers exploited the arable land for a period and when the soil fertility is
decreasing, they moved to another place to clear. However, due to the rapid population growth and the increasing
demand of land for agriculture purpose, most farmers complained nowadays that they have to stay for a long
period in the same land since they cannot go over the limit of the reserve. While considering the growth of the
household and the limit imposed by the reserve, it can be deduced in a near future that the problem of cropping
land will be a great issue in the region as many farmers yet complained for land scarcity for clearing.
With respect to land cover dynamics, our findings showed the conversion of land cover from forest cover to
anthropogenic vegetation made of farmland (fallow, field and agglomeration) in the communal land around the
reserve both in the north and south. However, the land cover degradation during the study period appeared more
much important in the south than in the north. As it was estimated the deforestation rate in the south was nearly
twice of that observed in the north (15.13% in the south vs 7.63% in the north). This could be explained by the
type of crops produced in the two regions. In the south part of the reserve, cash crop (mainly cotton) is the most
cultivated while, food crops (sorghum and maize) are the most practiced in the north. Cotton crop represents the
top cash crops in Benin country and the government encourages it production by providing to the farmers
technical support such as fertilizers, pesticides and tractors to improve their capacity. This national agricultural
policy results in the increasing of cropland in the expense of forest cover as it was in the south of the reserve.
Therefore incentive policies for improving crop production may result in the forest cover degradation as
previously showed by Gray (2005) and Ouedraogo et al. (2010). The driving forces of land cover and land use
change can be globally mapped in two groups (Figure 6). Direct factors such as socio-economic activities,
population growth and natural ecological factors related to the ecosystem and the indirect factors related to the
policy decision at local, national and regional level. Some policies decisions such as economic incentives price
of a given crop could influence indirectly the pressure on land cover. This is the specific case of cotton crop in
communal lands around the “W” biosphere reserve in Benin. Moreover, institutional factors, such as land tenure
and legislation, can contribute in land use/land cover change (Reid et al., 2000). Hence, we concluded that
factors inducing land cover change are as well of at local, national and international level.
www.ccsen
4.3 Land
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e
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ontrast to ou
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a
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s
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y
provides kno
w
h
owed a rapid
e
deforestatio
n
e
the agricult
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e
rtility impro
v
in the intenti
o
c
learing for a
g
for the “W”
E
n
Fig
u
Cover Transit
i
sition matrix,
o
utside the re
s
n
al vegetation
r
as it was o
b
reserve and i
n
e
ir livelihood
south than i
n
were higher i
n
M
aps
the satellite i
m
a
during the s
a
me pixels are
on the perio
d
m
e time, far
m
r
expectation,
pressure is
h
r
ve. A possib
l
m
age processi
n
h
is issue. Unf
o
a
nd rapid land
c
p
ortant pressu
r
ar
k managers
s
n
of agricult
u
t
ility based on
w
ledge of lan
d
degradation
o
n
rate is lower.
u
ral frontline
p
v
ement coul
d
h
o
n to increase
t
g
riculture pur
p
biosphere re
s
n
vironment and
N
u
re 6. Driving
f
i
on Matrices
the probabil
i
s
erve. Thus, f
u
outside the r
e
b
served betwe
n
crease anthro
(Vodouhê, C
o
n
the north si
n
n
the south th
a
m
ages data pr
o
a
me or consec
u
sometimes
m
d
where the s
a
m
land seemed
t
gallery forest
s
h
igh outside t
h
l
e argumentat
i
n
g and analysi
o
rtunetaley, su
c
c
over convers
i
r
e and the e
c
s
hould theref
o
u
re frontline
t
sustainable a
g
d
cover/land u
s
o
f land cover
o
This highlig
h
p
rogression t
o
h
elp increasin
g
t
heir producti
o
p
ose. It appea
r
s
erve conser
v
N
atural Resour
c
98
f
orces to land
c
i
ties of the di
u
ture scenari
o
e
serve into an
t
en 1995 and
o
pogenic press
u
o
ulibaly, Gre
e
n
ce the
p
roba
b
a
n in the north
.
o
cessing and i
n
u
tive year to
a
m
isinterpreted
d
a
tellite image
s
t
o slightly inc
r
s
were almost
h
is reserve. I
n
i
on must be l
i
s. Time series
c
h data are la
c
i
on into anthr
o
c
ological isola
o
re implement
t
owards the
r
g
roforestry p
r
a
s
e change in a
o
utside the re
s
h
ts the necessi
t
o
ward the par
k
g
crop produc
t
o
n. As shown
i
r
s then that p
o
v
ation. Tempo
c
es Research
c
over change
fferent land
c
o
s of land co
v
t
hropogenic v
e
2006. This si
u
res on the
r
e
e
ne, & Sinsin
,
b
ilities of the
.
n
terpretation i
a
ccurately est
a
d
ue to the abs
e
s
have been
t
r
ease inside t
h
stable from
1
n
the same ti
m
i
nked to the
m
satellite ima
g
c
king in our c
o
o
pogenic vege
t
tion of the r
e
conservation
r
eserve. This
ctises.
nd around the
s
erve during o
t
y to define be
s
k
. Practices s
u
t
ivity so that t
h
i
n this study a
c
o
pulation gro
w
ral maps an
a
c
over units to
v
er change wi
l
e
getation if th
e
t
uation may i
serve since h
u
,
2009). The
s
different land
s often limite
d
a
blish land co
v
e
nce of time
s
t
aken
t
o well
h
e reserve for
1
995 to 2006
o
m
e, farmland
m
isinterpretati
o
g
es data analy
s
o
ntext. Nonet
h
t
ation around
t
e
serve as de
m
strategies ou
t
might be po
s
“W” biosphe
r
u
r study peri
o
s
t manageme
n
u
ch as agricult
u
h
e farmers do
c
tive populati
o
w
th could be
v
a
lysis allowe
d
Vol. 3, No. 2;
be converte
d
l
l result in a
r
e
land use ch
a
nvolve the lo
u
man being re
l
s
ituation wou
l
cover unit’s
t
d
by the absen
v
er maps (Rue
s
eries imager
y
control each
that period. I
n
o
utside the re
s
seemed to sli
g
o
n of the dif
f
s
is during the
h
eless, data sh
o
t
he reserve. T
h
m
onstrated ear
l
t
side the reser
v
s
sible throug
h
r
e reserve in B
o
d while withi
n
n
t practices in
o
u
ral intensific
n’t need to e
x
o
n growth res
u
v
iewed as pre
d
detecting c
h
2013
d
into
r
apid
a
nges
ss of
l
y on
l
d be
t
o be
ce of
lland
y
data
pixel
n
this
s
erve
g
htly
f
erent
same
o
wed
h
is in
l
y by
v
e to
h
the
enin.
n
the
o
rder
ation
x
pand
u
lts in
ssure
h
ange
www.ccsenet.org/enrr Environment and Natural Resources Research Vol. 3, No. 2; 2013
99
occurring in land cover during the period 1995 and 2006. However, future scenarios based on annualized
transition matrices appear as more precise for understanding LUCC change dynamics, and will enable to assess
the land cover change in long period. Therefore further studies based on the simulation of land cover change in
and around the reserve are required.
Acknowledgments
This research was funded by the SUN project (Sustainable Use of Natural vegetation in West Africa) (EU FP6
INCO-dev 031685). We are grateful to Belarmain Fandohan for comments and corrections provided on the early
version on this manuscript. We thank Oloukoï Joseph for sharing ideas with us during the course of this
manuscript writing.
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Appendix 1. Land cover unit matrix of transition from 1995 to 2006 inside and outside the reserve at the
northern and southern part of the reserve
F WL GF DeS TSS OG Area 1995
Unprotected area(South)
F 10353.98 0 2.48 339.42 42.53 - 10738.41
WL 9353.52 43.97 8.2 917.12 0 - 10322.82
GF 1.79 0.41 148.08 0 0 - 150.28
DeS 12652.82 12.12 0 354.63 0 - 13019.58
TSS 9015.18 0 0 11.53 4416.95 - 13443.67
Area 2006 41377.29 56.5 158.76 1622.7 4459.48 47674.73
Protected area (South)
F 177.84 0 1.9 8.98 1795.7 - 1984.41
WL 43.08 8509.91 0 0 2802.33 - 11355.32
GF 1.28 0 1176.74 0 0 - 1178.02
DeS 100.97 39.14 0.12 0 2498.26 - 2638.49
TSS 294.92 9856.53 0 0 10108.23 - 20259.68
Area 2006 618.09 18405.58 1178.76 8.98 17204.52 37415.93
Unprotected area (North)
F 23875.53 0 198.1 0 520.64 265.26 24859.53
WL 196.49 5.51 24.11 4.25 308.02 0.46 538.84
GF 106.54 0 2804.4 0 0 0.8 2911.74
DeS 3006.74 0 21.07 3277.34 34.44 0 6339.59
TSS 6381.32 0 0 5923.48 4904.07 5.22 17214.09
OG 536.37 0 0 0 458.01 504.28 1498.66
Area 2006 34102.99 5.51 3047.68 9205.07 6225.18 776.02 53362.45
Protected area (North)
F 0 3.78 0 0 10.45 - 14.24
WL 0 978.12 0 0 300.24 - 1278.35
GF 8.84 0 144.26 0 0 - 153.09
DeS 4.88 0 0.11 59.12 347.34 - 411.46
TSS 51.6 422.39 0 0 54861.27 - 55335.27
Area 2006 65.32 1404.29 144.37 59.12 55519.3 57192.4
Legend: F=Farmland; WL =Woodland; GF =Gallery Forest; DeS =Degraded Savannah; TSS =Tree and Shrub
Savannah.
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Perceptions of socio-economic determinants of the vegetation degradation were studied in the district of Djidja, located in the Sudano-Guinean area in Benin. The socio-economic surveys aimed to collect perceptions of farmers, ranchers, loggers and charcoal producers on socio-economic determinants of degradation of vegetation cover. These perceptions have been analysed by factorial analysis. By decreasing order of importance, agriculture, logging, charcoal production, hunting and farming have been recognized by local actors, as key socio-economic factors, determining vegetation degradation. However, a cross-reading of all stakeholders' perceptions displayed an opposition of perceptions between farmers and breeders. The interplay between socio-economic determinants renders tedious the identification of the core element, responsible of the degradation of vegetation cover.
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