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PAN AFRICAN INSTITUTE FOR DEVELOPMENT – WEST
AFRICA (PAID-WA),
Department of Development Studies
THE CONTRIBUTION OF COMMUNITY-BASED NATURAL
RESOURCES MANAGEMENT TO LIVELIHOODS,
CONSERVATION AND GOVERNANCE IN CAMEROON. A
COMPARATIVE ASSESSMENT OF THREE COMMUNITY
FORESTS IN FAKO DIVISION.
By
FRU Delvis NGANG
Matriculation No PAIDWA00191
A thesis submitted to the Pan African Institute for Development-West Africa
in partial fulfilment of the requirements for the award of a
Post Graduate Diploma in Development Management and Governance
Supervisors:
Asong Valentine Tellen Mbomi Elizabeth S. (Ph.D Reseacher) (Ph.D)
June 2015
i
ii
iii
iv
Dedication
This work is dedicated to Him who has kept me from falling….
and to
My mother, Nchang Margaret Ngang
My aunt, Nah Ester Landa of beloved memory and
Prof. Mbomi Elisabeth Saillieh
for their unmerited love, sacrifice, prayers, heavenly guidance and encouragement
v
Table of Contents
Certification ....................................................................................................................... i
Declaration ..................................................................... Error! Bookmark not defined.
Dedication ....................................................................................................................... iv
Table of Contents ............................................................................................................. v
Acknowledgement ............................................................................................................ x
List of Tables ................................................................................................................... xi
List of Figures ................................................................................................................ xii
List of Plates .................................................................................................................. xiv
Acronyms and Abbreviations ......................................................................................... xv
Abstract ........................................................................................................................ xvii
CHAPTER ONE
INTRODUCTION
1.1 Background of the Study ............................................................................................ 1
1.2 Statement of the problem ........................................................................................... 5
1.3 Objective of the Study ................................................................................................ 6
1.4 Hypotheses ................................................................................................................. 6
1.6 Significance of the study ............................................................................................ 7
1.6.1 Policy significance .................................................................................................. 7
1.6.2 Research significance .............................................................................................. 7
1.6.3 Community level action relevance .......................................................................... 8
1.7 Organization of the study ........................................................................................... 8
vi
1.8 Definition of Terms .................................................................................................... 8
CHAPTER TWO
LITERATURE REVIEW AND THEORETICAL FRAMWORK
2.1 Overview of forest .................................................................................................... 11
2.1.1 Extent and global distribution of forest ................................................................. 11
2.1.2 Distribution and classification of forest in Cameroon........................................... 12
2.1.4 Functions of forest ................................................................................................. 14
2.1.5 Forest use and dependence .................................................................................... 15
2.1.6 Community forestry .............................................................................................. 16
2.1.6.1 Origin and Evolution .......................................................................................... 16
2.1.6.2 Community Forestry in Cameroon ..................................................................... 18
2.1.7 Community forestry and livelihoods ..................................................................... 19
2.1.8 Community forestry and biodiversity conservation .............................................. 21
2.1.8.1 Community forestry and governance ................................................................. 22
2.2 Conceptual Framework ............................................................................................ 24
2.3 Gaps in the literature ................................................................................................ 27
CHAPTER THREE
METHODOLOGY OF THE STUDY
3.1 Models specification ................................................................................................ 28
3.2 Description of Variables in the Models .................................................................... 31
3.2.1 Independent variables ............................................................................................ 31
3.2.2 Dependent variables .............................................................................................. 31
3.3.1 Study population ................................................................................................... 31
vii
3.3.2 Sampling Techniques ............................................................................................ 31
3.3.3 Study sample and sampling intensity .................................................................... 32
3.3.4 Data collection ....................................................................................................... 33
3.4. Analytical Approach ............................................................................................... 33
3.5 Validation of the Results .......................................................................................... 34
CHAPTER FOUR
PRESENTATION AND ANALYSIS OF DATA
4.1 Socio-demographic characteristics of respondents .................................................. 35
4.2.1.1 Extent of community forest use ......................................................................... 36
4.2.1.2 Patterns of community forest use ....................................................................... 38
4.2.1.3 Socio-demographic determinants of community forest use. .............................. 43
4.2.1.4 Extent of dependence on Community Forest ..................................................... 45
4.2.2 Results of objective 2 ............................................................................................ 47
4.2.2.1 The contribution of community forestry to income ........................................... 47
4.2.2.2 The contribution of community forestry to employment ................................... 49
4.2.2.3 The contribution of community forestry to infrastructures ................................ 50
4.2.2.4. Contribution to community forestry to fuel wood availability ......................... 51
4.2.3 Results of objective 3 ............................................................................................ 52
4.2.3.1 The contribution of community forestry to forest stands ................................... 52
4.2.3.2 The contribution of community forestry to Wildlife .......................................... 54
4.2.3.3 The contribution of community forestry to environmental awareness .............. 55
4.2.3.4 The contribution of community forestry to the adoption of sustainable
exploitation practices ...................................................................................................... 56
4.2.3.5 The contribution of community forestry to forest regeneration ......................... 59
4.2.4 Results of objective 4 ............................................................................................ 60
viii
4.2.4.1 The contribution of community forestry to community participation in forest
management. .................................................................................................................. 60
4.2.4.2 The contribution to equity in forest resource benefit sharing ............................ 63
4.3 Implication of the Results ........................................................................................ 64
4.3.1 Extent of forest use, socio-demographic determinants and dependence ............... 64
4.3.2 Community forestry and livelihoods ..................................................................... 65
4.3.3 Community forestry and conservation .................................................................. 66
4.3.4 Community forestry and governance .............................................................. 66
4.4 Limitation of results ................................................................................................. 67
CHAPTER FIVE
SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS
5.1 Summary of findings ................................................................................................ 68
5.2 Conclusion ................................................................................................................ 69
5.3 Recommendations .................................................................................................... 69
5.3.1 Policy recommendations ....................................................................................... 69
5.3.2 Community forest-level recommendations ........................................................... 70
5.3.3 Research recommendations ................................................................................... 71
REFERENCES ............................................................................................................... 72
APPENDICES ................................................................................................................ 85
Appendix 3.1: Independent Variables ............................................................................ 85
Appendix 3.2: Dependent Variables .............................................................................. 86
Appendix 3.3 : Questionnaire ......................................................................................... 87
Appendix 4.1: Community forest use across socio-demographic characteristics .......... 89
Appendix 4.2: Regression analysis of the socio-demographic determinants (predictors)
of forest use .................................................................................................................... 90
ix
Appendix 4.3: Table for Multicollinearity .................................................................... 91
Appendix 4.4: Dependence on CF for household food, energy and material needs across
socio-demographic characteristics. ................................................................................ 92
Appendix 4.5: Dependence on CF for monthly income across socio-demographic
characteristics ................................................................................................................. 93
Appendix 4.6: The contribution of Community Forestry on income across socio-
demographic characteristics ........................................................................................... 93
Appendix 4.7: Contribution of CF on employment across socio-demographic
characteristics ................................................................................................................. 94
Appendix 4.8: Contribution of CF to community development infrastructure across
socio-demographic characteristics ................................................................................. 94
Appendix 4.9: Forest cover and stands across socio-demographic characteristics ........ 95
Appendix 4.10: Incidence of wildlife sightings, sounds and traces across socio-
demographic characteristics. .......................................................................................... 96
Appendix 4.11: Analysis of environmental awareness across socio-demographic
characteristics ................................................................................................................. 97
Appendix 4.12: Adoption of sustainable practices across socio-demographic
characteristics ................................................................................................................. 97
Appendix 4.13: Analysis of regeneration across socio-demographic characteristics .... 98
Appendix 4.14: Participation in forest resources management across socio-demographic
characteristics ................................................................................................................. 98
Appendix 4.15: Analysis of equity in benefit sharing across socio-demographic
characteristics ................................................................................................................. 99
x
Acknowledgement Many people have contributed to the realisation of this thesis. I will like to cease this
opportunity to extend my sincere appreciation to all those who have contributed in one
way or another to the success of this project.
I am particularly thankful to my supervisors Mr Asong Valentine Tellen and Prof.
Mbomi Elisabeth for their patience, flexibility, moral and academic support.
My sincere appreciation equally goes to Mr Defang Agbor Peter and DAP
INCORPORATED for granting me a partial scholarship for this PGD program.
I am equally indepted to Mr Azinwi G. A. for his vital comments in the final stage of
the work.
To my entire family, friends, classmates, staffs of the Pan African Institute for
Development-West Africa, Buea and all those whose names have not been mentioned
here I say thank you, merci, miya, massom, ayongne, mahoma,danke
xi
List of Tables
Table 2.1 : Distribution of forest by regions and sunregions. ..................................................... 12
Table 3.1 : Distribution of respondents ....................................................................................... 32
Table 4.1: Socio-demographic characteristics of respondents .................................................... 35
Table 4.2: Respondents dependence of forest for household food, energy and ......................... 46
Table 4.3: Respondents dependence of forest for income in study localities ............................. 47
Table 4.4: Mean distance walked to collect fuel wood before and after the introduction of CF
in Bakingili, Woteva and Bimbia-Bonadikombo ..................................................... 51
xii
List of Figures
Figure 1.1: Map of Fako Division Adapted from Ane-Anyangwe et al, 2006 .............. 3
Figure 2.1 : The World’s Forest ..................................................................................... 11
Figure 2.2 : Distribution of forest in Cameroon ............................................................ 13
Figure 2.3 : Forest Classification in Cameroon ............................................................. 14
Figure 2.4: Sustainable Livelihood Framework ............................................................ 24
Figure 4.1: Extent of community forest use in Bakingili, Woteva and .......................... 37
Figure 4.2: Patterns of Community Forest use in Bakingili, Woteva and Bimbia- ....... 38
Figure 4.3: Types of Non-Timber Forest Products exploited in Bakingili, Woteva and 40
Figure 4.4: Effects of community forestry on income in Bakingili, Woteva and Bimbia-
.................................................................................................................... 48
Figure 4.5: Effect of community forestry on employment opportunities in Bakingili, . 49
Figure 4.6: Effect of community forestry on infrastructure development in Bakingili, 50
Figure 4.7: Impact of community forestry on forest cover and stands in Bakingili,
Woteva ....................................................................................................... 53
Figure 4.8: Impact of community forestry to incidence of wildlife sightings, sounds and
traces in Bakingili, Woteva and Bimbia-Bonadikombo CFs ...................... 54
Figure 4.9: Impact of community forestry on environmental awareness in Bakingili ... 55
Figure 4.10: Impact of community forestry on the adoption of sustainable practices ... 56
Figure 4.12: Unsustainable forest practices observed in Bakingili, Woteva and ........... 58
Figure 4.11: Types of sustainable practices adopted in Bakingili, Woteva and Bimbia-
Bonadikombo CF ........................................................................................ 58
xiii
Figure 4.13: Impact of community forestry in the improvement of regeneration
activities ...................................................................................................... 59
Figure 4.14: Participation in forest management in Bakingili, Woteva and Bimbia- .... 61
Figure 4.14: Participation by women, youths and non-indigenes in forest management
in Bakingili, Woteva and Bimbia-Bonadikombo CFs ................................ 62
Figure 4.15: Changes in equity in forest benefit sharing in Bakingili, Woteva and ...... 63
Figure 4.16: Benefit sharing by gender, age group and origin in Bakingili, Woteva and
.................................................................................................................... 64
xiv
List of Plates
Plate 4.1: Firewood harvesting in Bimbia-Bonadikombo CF.........................................39
Plate 4.2: Charcoal production in Bimbia-Bonadikombo CF.........................................39
Plate 4.3: Charcoal stocked at Upper Mawon.................................................................39
Plate 4.4: Eru (Gnetum Africanum) Harvested for household consumption in
Bakingili..........................................................................................................40
Plate 4.5: Bush mangoes Ervingia spp) collection in Bamukong...................................40
Plate 4.6: Forest cleared for chopfarm in Bimbia-Bonadikombo CF..............................41
Plate 4.7: Cocoa farm in the Bakingili CF......................................................................41
Plate 4.8: Bush meat from Woteva being smoked at Bonakanda....................................41
Plate 4.9: Timber being sawn into planks in Bakingili...................................................42
Plate 4.10: Training on the sustainable harvesting of pygium carried out by PSMNR-
SWR and MOCAP in Woteva........................................................................57
Plate 4.11: The Chief of Woteva planting a tree in the Woteva CF................................60
Plate 4.12: ANAFOR-supported tree nursery in Bakingili.............................................60
xv
Acronyms and Abbreviations
ANAFOR National Forestry Development Agency
BBNRMC Bimbia-Bonadikombo Natural Resources Management Council
CAMPFIRE Community Area Management Programme for Indigenous Resources
CARPE Central African Regional Program for the Environment
CBFP Congo Basin Forest Partnership
CF Community Forest
CNBRM Community-based Natural Resources Management.
DFID Department for International Development
ERuDeF Environment and Rural Development Foundation
FAO Food and Agricultural Organization
FSC Forest Stewardship Council
FSC Forest Stewardship Council
GDP Gross Domestic Product
GDP Gross Domestic Product
GTZ German Technical Cooperation
IISD International Institute for Sustainable Development
ITTO International Tropical Timber Organization
IUCN International Union for Conservation of Nature
IUCN International Union for Conservation of Nature and Natural Resources
MINEF Ministry of Environment and Forestry
MINEF Ministry of Environment and Forestry
MINFOF Ministry of Forestry and Wildlife
MNRT Ministry of Natural Resources Tanzania
nPFEs non-Permanent Forest Estates
OFID OPEC Fund for International Development
xvi
PFEs Permanent Forest Estates
PSMNR Program for the Sustainable Management of Natural Resources- South
RECOFTC Regional Community Forestry Training Center
REDD Reducing Emissions from Deforestation and Forest Degradation
RoC Republic of Cameroon
SCBD Secretariat of the Convention on Biological Diversity
SEANN South and East Asian Countries NTFP Network
SIDA Swedish International Development Cooperation Agency
SLF Sustainable Livelihood Framework
SMP Simple Management Plan
SWCFN South West Community Forestry Network
UNESCO United Nations Education, Scientific and Cultural Organisation
WCARRD World Conference on Agrarian Reform and Rural Development
WCFSD World Commission on Forest and Sustainable Development
WCMC World Conservation Monitoring Center
WODCIG Woteva Development Common Initiative Group
WRI World Resource Institute
WWF World Wide Fund
xvii
Abstract
Community forestry has been widely paraded in academia and development circles in
Cameroon as the suitable model for pro poor and pro-forest development. More than
two decades after the introduction of this forest management model in natural resource
management policy in Cameroon, controversies about its effectiveness abound. Within
this backdrop, this study assessed forest use and dependence and contribution of
community forestry to livelihood, conservation and governance in three selected
community forest localities in Fako Division, South West Cameroon. Primary data was
obtained from a structured questionnaire administered to 295 respondents. This was
complemented by key informant interviews and field observation. The data was
analysed using descriptive and inferential statistics. The study found that 60.7% of the
population use the community forest for livelihood with statistically significant
variation (p<0.05) across the selected community forests. The forests were mostly used
for fuelwood collection, subsistence farming and NTFPs harvesting among others with
no significant variations (p>0.05) observed across the selected communities. The study
found out that community forestry has not made any considerable contribution to
income, employment, infrastructure and fuel wood availability in the selected
community forest localities, even though significant differences (p<0.05) where found
across localities. However, it was observed that community forestry has contributed
positively to forest stands, wildlife, environmental awareness, adoption of sustainable
forest exploitation practices and forest regeneration and has increased community
participation in forest decision-making and equity in the sharing of forest resource
benefits with significant variations (p<0.05) observed. The study concluded that the
community-based natural resources management model has contributed positively to
forest conservation and governance, though its contribution to livelihood is still below
expectation in the study locality. The study recommended among other policy and
further research measures that community forest management committees should
pursue value-added and other non-consumptive avenues for income generation so as to
improve the livelihood of forest dependent households.
Keywords: Community-based Natural Resources Management, Community Forest,
livelihoods, Conservation, Governance, Fako Division.
1
CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
The global forest estate is estimated at over 40 billion hectares, covering 31 percent of
the earth’s total land area (Food and Agriculture Organization (FAO), 2010a). More than
1.6 billion people in the world depend to varying degrees on forests for their livelihoods
(Secretariat of the Convention on Biological Diversity (SCBD), 2OO9) and forest play a
key role in the economic development of many countries (World Bank, 2001). Forest
supports about 65% of the world terrestrial taxa (World Commission on Forest and
Sustainable Development (WCFSD), 1999) and has the highest species diversity (Groom
et al., 2000). Forest and wooded area are essential to global ecological stability
(Agrawal, 2007).
In spite of these important socio-economic and ecological functions, loss of forest
through degradation and deforestation from anthropogenic and natural causes has
steadily increased over the years (SCBD, 2008). FAO (2010a) estimated that between
2000 and 2010, global forest loss stood at 5.2million hectares per annum, equivalent to
a loss of 140km2 a day. The negative ramifications of forest loss to the livelihood of
forest-adjacent communities, biodiversity conservation and the economic development
of forest dependent nations are obvious and have been widely documented (SCBD,
2009; Cariq, 2012; Brooks et al., 2013). In the light of these threats, forest and wooded
area have been at the crux of a multitude of conservation and poverty alleviation
policies over the years. This policies and management mechanism have gradually
moved from a post-colonial concession model to one that is inclusive of the notion of
local community participation.
Following independence, a centralized, protectionist and exclusionary approach to
forest resources management was widely practiced in developing countries (Roe et al.,
2009). But in the late 1970s, a new paradigm to forest management, variously called
social or community forestry began to emerge (FAO, 2011). This Community-based
Natural Resources Management (CBNRM) approach promoted a greater involvement
of rural communities in the management and utilization of their natural resources. This
model or its spinoffs became a buzzword in the forest development policy circles and a
2
fashionable lexicon in academia in the late 1980s (Beauchamp and Ingram, 2011). It
was hailed as a panacea and marketed by its proponents as the policy strategy for
achieving the triple objectives of livelihood improvement, forest resource conservation
and natural resource management devolution (Yufanyi Movuh et al., 2012; Oyono et
al., 2012,). In the decade that followed its inception, many developing nations jumped
on the bandwagon and adopted or experimented to some degree with this forest
management model (Nurse and Malla, 2005 in Njeumo, 2012; Beauchamp and Ingram,
2011). As a result, local communities were entrusted with the management of over 20%
(approximately 200 million hectares) of global tropical forest (International Tropical
Timber Organisation (ITTO), 2005). Decades after its adoption, the effectiveness of
Community Forestry is still debatable (Brown 2002; Oyono, 2004; Oyono et al., 2012).
While Bowler et al. (2010) and Beachamp and Ingram (2011) have presented evidence
of the effectiveness of the community forestry model in some selected developing
countries, Gilmour et al. (2004) argue that claims about the effectiveness of community
forestry are at best inconclusive.
In Cameroon, forest covers about 45.6% of the national territory and is estimated at 21,
245,000 hectares (Takem-Mbi, 2013). Cameroon’s forests support the richest flora and
fauna in continental tropical Africa with high levels of endemism, making it one of the
world’s biodiversity hotspots (Ndobe and Mantzel, 2004). According to Cerutti et al.
(2010), a majority of Cameroonians are forest dependent. Furthermore, Njuemo (2012)
posit that forest contribute 10% to the nation’s GDP (Njeumo, 2012), and commercial
logging companies provides employment to 30,000 Cameroonians. As part of the
Congo basin, Cameroon forests play a significant role in global ecological stability
(Oyono et al., 2012). Regrettably, these socio-economic and ecological functions are
under threat from high rates of deforestation (Ndobe and Mantzel, 2004; Carodenuto et
al. 2015).
In a bid to redress such and similar trends and sustainably manage its forest and other
natural resources, Cameroon enacted a Forestry, Wildlife and Fisheries Law in 1994.
This policy and legal framework among other things enshrined the concept of
community forest, granting local communities access, use, management and marketing
rights over substantial portion of the non-Permanent Forest Estates (Cameroon Ministry
of Forestry and Wildlife (MINFOF), 1998). As of 2011, 301 community forests had
been attributed in Cameroon, accounting for 4% (1 million hectare) of the country’s
3
forest estates (World Resource Institute, 2011; Yufanyi Movuh, 2013). As host to a
substantial proportion of Cameroon’s tropical and mangrove forest, the South West
Region account for 19 of these community forests. Out of these, 4 are located in Fako
Division, namely Woteva Community Forest, Bimbia-Bonadikombo Community
Forest, Etinde Community Forest and Bakingili Community Forest (South West
Community Forest Network: SWCFN, 2014).
Fako Division is located between latitude 4°28´30″ and 3°54´26″ N of the equator and
longitude 8°57´10″ and 9°30´49″ E of the Greenwich Meridian. It is bounded to the
south by the Atlantic Ocean, to the west by Ndian Division, to the north by Meme
Division and to the east by the Littoral region. Figure 1.1.
LOCATION OF CAMEROON IN
THE WORLD
LOCATION OF FAKO
DIVISION IN CAMEROON.
LEGEND
---- Sub-divisional boundary Main road Study sites
Sub-divisional headquarter Community forest
Figure 1 Figure 1.1: Map of Fako Division Adapted from Ane-Anyangwe et al, 2006
4
It encompasses six administrative units, namely Buea, Limbe I, Limbe II, Limbe III,
Tiko, Muyuka, and Idenau subdivisions and covers a total surface area of 203,876
hectare (Carodenuto et al. 2014). It has a total population of 444 269 (Orock and Lambi,
2014) consisting of the indigenous Bakweri people. Other ethnic groups include
Barondos, Bakundus, Bayanguis North westeners, Bamilekes and other immigrants
from Nigeria.
The division has the Cameroon type of climate with two seasons-one wet season from
March to November during which rains are abundant and a short dry season from
December to February. Rainfall distribution is not even. It is highest at the coast and
diminishes towards the interior of the land. Limbe receives an annual rainfall of over
5000 mm while Debunscha has an average rainfall of 10 ,000 mm. Temperatures reduce
with increase in altitude with annual average of about 26.4°C around the coast areas and
23°C around Buea. The landscape is predominantly highlands. The lowlands occur
around the coast while Mt Fako and Mount Etende are at altitudes of 4100m and 1713m
above sea level respectively. The vegetation consists of montane and sub-montane
forest, lowland forest and mangroves, and hosts a variety of wildlife species, with some
of them being endemic. The soils are ancient ferralitic, volcanic, nutrient-rich andosols,
making the area predisposed for agricultural production. As such, subsistent and cash
crop agriculture constitute the lifeblood of the local economy. Other economic activities
practiced in the division include fishing, food processing, timber extraction, market
gardening, oil refining, quarrying and tourism.
Community Forestry was introduced into Fako Division as far back as the year 2000
(Nkemnyi et al., 2014). Like in other parts of Cameroon, this forest management model
was highly promoted in Fako Division as a successful contemporary paradigm and
implementation mechanism for sustainable forest resources management, forest
management decentralization, and livelihood improvement (BACOFMAC, 2002;
BBNRMC, 2002; WODCIG, 2012). But after more than a decade of its implementation,
questions about its effectiveness still abound in current literature. Though community
forestry in this and other parts of Cameroon have been the subject of many research
(Tekwe and Perc, 2002; Minang et al. 2007; Beauchamp and Ingram, 2011; Oyono et
al. 2012; Yufanyi Movuh and Schusser, 2012; Yufanyi Movuh, 2013), very few of
these efforts have addressed questions related to the contribution of this forest
management model to the livelihoods of forest dependent communities, forest
5
biodiversity conservation and natural resources management devolution. This research
work is an attempt to fill these knowledge gaps.
1.2 Statement of the problem
In Fako Division, montane, sub-montane, lowland and mangrove forest cover about
47.5% (96,764 hectares) of the total surface area (Carodenuto et al. 2015). In addition to
providing immense socio-economic and cultural benefits to forest fringe communities,
forest in Fako Division particularly in the Mt Cameroon region support one of the
richest flora and fauna in continental tropical Africa with high levels of endemism,
making it one of the world’s biodiversity hotspots (MINFOF, 2005). But unfortunately,
high rates of deforestation, estimated at 0.51% annually (Carodenuto et al. 2015), has
contributed in undermining the socio-economic, cultural and ecological functions of
forest in the division. Therefore, when community forestry was introduced in this area
in the wake of the rights reform of the 1990s in Cameroon, it was received with
euphoria and popular optimism (Oyono et al., 2012). It was paraded in popular
development discourse as the mechanism for simultaneously achieving the triple goals
of livelihood improvement, forest resources conservation and improved community
participation in and benefit from forest resource management (Yufanyi and Schusser,
2012). Decades after the implementation of this forest management model in the
division, controversies about its effectives abound (Oyono et al., 2012). Questions
related to the extent, patterns and socio-demographic determinants of community forest
use and the degree to which people depend on forest resources for household
consumption and income have remain largely unanswered. Grey spots still exist in
current literature on the contribution of community forestry to the livelihood parameters
of income, employment and infrastructures development in the study area. Furthermore,
very few answers exist in current literature on the conservation outcomes of community
forestry, particularly its impact on forest stands, wildlife, forest regeneration, forest
exploitation practices and environmental awareness. Moreover, it is still debatable if
community forestry has fostered community participation in natural resources
management and equity in the sharing of forest benefits in the locality. Finally, answers
as to how community forestry’s contribution to livelihoods, conservation and
governance vary across the various community forest are quasi-inexistent. FAO (2014)
has underscored the importance of this type of information for policy formulation and
forest management. This study is an attempt to fill these lacunae.
6
1.3 Objective of the Study
The main objective of this study is to assess the contribution of community forestry to
livelihoods, conservation and governance in some selected community forests in Fako
Division, South West Region of Cameroon.
To achieve this objective, the study has the following specific objectives,
a) To assess the extent, patterns and socio-demographic determinants of community
forest use and dependence in the study area.
b) To assess the contribution of community forestry on livelihood.
c) To assess the contribution of community forestry to forest resources conservation.
d) To assess the contribution of community forestry to forest resource governance.
1.4 Hypotheses
a) Hypothesis 1: Forest use, use patterns and dependence does not vary significantly
across localities.
This hypothesis has the following sub-hypotheses;
H1A: Forest use does not vary significantly across the selected community forests.
H1B: Forest use pattern does not vary significantly across the selected community
forests
H1C: Forest dependence does not vary significantly across the selected community
forests
b) Hypothesis 2: The impact of community forestry on livelihood does not vary
significantly across community forests locations.
This hypothesis has the following sub-hypotheses;
H2A: The contribution of community forestry to income does not differ
significantly across the selected community forests.
H2B: The contribution of community forestry to employment does not differ
significantly across the selected community forests
H2C: The contribution of community forestry to development infrastructure does
not differ significantly across the selected community forests
H2D: The contribution of community forestry to fuel wood availability does not
differ significantly across the selected community forests
c) Hypothesis 3: The contribution of community forestry to forest resource
conservation does not vary significantly across community forests locations.
7
This hypothesis has the following sub-hypotheses;
H3A: The contribution of community forestry to forest stands does not differ
significantly across community forest.
H3B: The contribution of community forestry to wildlife does not differ
significantly across community forest
H3C: The contribution of community forestry to environmental awareness does not
differ significantly across community forest
H3D: The contribution of community forestry to the adoption of sustainable forest
resource exploitation practices does not differ significantly across community
forest.
H3E: The contribution of community forestry to forest regeneration does not differ
significantly across community forest
d) Hypothesis 4: The contribution of community forestry to forest resource
governance does not vary significantly across community forests locations.
This hypothesis has the following sub-hypotheses
H4A: The contribution of community forestry to community participation in forest
resources management does not vary significantly across community forest.
H4B: The contribution of community forestry to equity in forest resource benefit
sharing does not vary significantly across community forest
1.6 Significance of the study
1.6.1 Policy significance
The findings of this study will provide policy-makers at the international, national and
local level with information on the socio-economic and ecological efficacy of
community-based forestry management strategies across different socio-demographic
context. This knowledge is essential in the designing of future interventions that
simultaneously addresses forest degradation and poverty reduction in forest-dependent
communities.
1.6.2 Research significance
The findings of the study will contribute to the ongoing discourse within academia on
conservation and poverty reduction in forest-dependent communities. The study will not
only help in answering present questions on the effectiveness of common pool resources
8
management policies in delivering the dual goals of conservation and improved
livelihood but will also raise other questions whose answers by subsequent research will
help extend the knowledge frontiers on natural resources management policies in the
forest subsector. In addition, the study would serve as a proxy for improving our
knowledge of community interaction with protected areas as most of the study
communities are found within the borders of the Mount Cameroon Park.
1.6.3 Community level action relevance
A comparative assessment of community forestry within the selected community forests
will provide insights on best practices and lapses, which can be harnessed by the
relevant stakeholders particularly the forest management communities for effective
implementation of the community forest management model.
1.7 Organization of the study
This study is divided into 5 chapters. Chapter one provides a brief introduction of the
study, the issues at stake, objectives of the study, hypotheses, significance of the study,
organisation of the work and definition of terms. Chapter two contains a review of
related works, a conceptual framework and gaps identified in the literature. Chapter
three focuses on the methods and materials of the study. It consists of a specification of
models, description of variables in the models, study design, analytical approach and
measures for validating the results. Chapter four consist primarily of a presentation and
discussion of the results of the study, implication of the results and limitation of the
study. Finally, chapter 5 contains the summary of findings, conclusions and
recommendations.
1.8 Definition of Terms
Community
A community is a group of people with a distinctive identity (common culture, belief,
values, language, religion and other social markers) living in a defined geographic area
(Kellert et al. 2000). Also, Uphof (1998) in Kellert et al. (2000) defines a community as
a territorially-defined social group with homogenous social structure and shared
custom.
Natural Resources
According to the United States Institute for Peace (2007), Natural Resources (NRs) are
9
materials that occur in nature and are essential or useful to humans such as water, air,
land, forest, fish and wildlife, top soils and minerals. It is defined by the World Bank
(2000) as those resources that provide fundamental life-support, in the form both
consumptive and public-good services.
Community-based Natural Resources Management (CBNRM)
According to Roe et al, (2009) community-based natural resource management
(CBNRM) is a term used to describe the management of resources such as land, forests,
wildlife and water by collective, local institutions for local benefit. Furthermore, Adams
and Hulme (2001) have defined CNBRM as a process whereby local population gain
access and use rights to, or ownership of natural resources; collaboratively and
transparently plan and participate in the management of resource use; and achieve
financial and other benefits from stewardship. CBNRM has the triple objectives of
poverty alleviation, natural resources conservation and good governance.
Livelihood
Livelihood has been defined by Chambers and Conway (1992) as comprising the
capabilities, assets and activities required for a means of living. Ellis (2000) states that
a livelihood comprises assets as capitals, access to these capitals and capital-based
activities which influenced by institutions and social relations, determine the living of
the individual or household. Furthermore, Niehof (2004) looks at livelihood as a
multifaceted concept consisting of what people do and what they accomplish by doing it
with reference to outcomes and activities.
Conservation
International Union for the Conservation of Nature (1990) defines conservation as a
process comprising the preservation, maintenance, sustainable utilization, restoration,
and enhancement of the natural environment for the benefit of present and future
generation. Furthermore, United Nations Educational, Scientific and Cultural
Organization (1986) looks at it as the maintenance of essential ecological processes and
life-support systems, preservation of genetic diversity and sustainable utilization of
species and ecosystems.
Forest
The Republic of Cameroon (RoC) (1994), defines a forest as any land covered by
vegetation with a predominance of trees, shrubs and other species capable of providing
products other than agricultural products. FAO (2010a) defines forest as land spanning
10
more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than
10 percent, or trees able to reach these thresholds in situ. It does not include land that is
predominantly under agricultural or urban land use.
Community forest
According to the Republic of Cameroon (RoC) (1994), a community forest is a forest
forming part of the non-permanent forest estate, which is covered by a management
agreement between a village community and the Forestry Administration.
Community forestry
FAO (1978) defined community forestry as any situation that intimately involves local
people in a forest activity. According to Regional Community Forestry Training
Center (RECOFTC) (2004) community forestry involves governance and
management of forest resources by communities for commercial and non-
commercial purposes, including for subsistence, timber production and collection of
non-timber forest products, wildlife protection and conservation of biodiversity and
environment, as well as for social and religious significance. Sackey (2007) defines
community forestry as a forest management approach in which local communities are
empowered and grassroots organizations strengthened and charged with the
responsibility for the stewardship, management and reaping of benefits from forests and
forest resources.
Governance
According to Hempel (1996), governance refers to the interactions among structures,
processes, rules, and traditions that determine how authority is exercised, how
responsibilities are distributed, how decisions are made, and how various actors are
involved. Governance has been defined as the norms, institutions and processes that
determine how power and responsibilities are exercised, how decisions are taken, and
how citizens participate in the management of natural resources (Department for
International Development (DFID), 2011). Environmental governance, including fair
and equitable access to natural resources, a better distribution of benefits, and a more
participatory and transparent decision‐making processes.
11
CHAPTER TWO
LITERATURE REVIEW AND THEORETICAL
FRAMWORK
2.1 Overview of forest
Forest have been variously described (MINFOF, 1994; Wass ,1995). The FAO (2010a)
defines forest as land spanning more than 0.5 hectares with trees higher than 5 meters
and a canopy cover of more than 10 percent, or trees able to reach these thresholds in
situ.
2.1.1 Extent and global distribution of forest
The world’s total forest area in 2010 was just over 4 billion hectares, covering over 31
percent of the total land area (FAO, 2010b). However, the area of forest is unevenly
distributed. The five most forest-rich countries (the Russian Federation, Brazil, Canada,
the United States of America and China) account for more than half of the total forest
area. Ten countries or areas have no forest at all and an additional 54 have forest on less
than 10 percent of their total land area. The total area of other wooded land is estimated
to be at least 1.1 billion hectares, equivalent to 9 percent of the total land area. The total
area of other land with tree cover was reported to be 79 million hectares. Figure 2.1
Figure 2Figure 2.1: The World’s Forest Source : FAO, 2010b
Forest ( > 10 percent tree cover)
Other land
Water
12
On global average, primary forests – forests of native species in which there are no
clearly visible signs of past or present human activity – are estimated to occupy 36
percent of the total forest area. Other naturally regenerated forests make up some 57
percent, while planted forests account for an estimated 7 percent, of the total forest area
(FAO, 2010). At the sub-regional level, Europe (including the Russian Federation)
accounts for 25 percent of the world’s total forest area, followed by South America (21
percent), and North and Central America (17 percent). See Table 2.1
Table 1 Table 2.1: Distribution of forest by regions and sub regions.
Region/sub region Forest area
1 000 ha % of total forest area
Eastern and Southern Africa 267 517 7
Northern Africa 78 814 2
Western and Central Africa 328 088 8
Total Africa 674 419 17
East Asia 254 626 6
South and Southeast Asia 294 373 7
Western and Central Asia 43 513 1
Total Asia 592 512 15
Russian Federation 809 090 20
Europe excl. Russian Federation 195 911 5
Total Europe 1 005 001 25
Caribbean 6 933 0
Central America 19 499 0
North America 678 961 17
Total North and Central America 705 393 17
Total Oceania 191 384 5
Total South America 864 351 21
World 4 033 060 100
Source: FAO, 2010b.
2.1.2 Distribution and classification of forest in Cameroon
Forests cover about 45.6% of Cameroon’s national territory, approximately
21,245,000 hectares (FAO, 2005 in Takem Mbi, 2013 ; ). According to CARPE
13
(2006), most of Cameroon forests form part of the Congo basin forest which is the
second largest area of dense tropical forest in the world following the Amazon basin.
Cameroon is ranked second in terms of forest cover in Africa after Democratic
Republic of Congo (Djeumo, 2001 ; Djeumo, 2011). In terms of land cover,
Cameroon forest contains 55% dense forests and 33% mixed forests, the remaining 12%
being land where forests are not the dominant vegetation (WRI, 2011). Figure 2.2
F 3Figure 2.2 : Distribution of forest in Cameroon Source : WRI, 2011.
Following the 1994 Forestry, Wildlife and Fisheries law, the Cameroon national forest
estate was subdivided and gazetted into different use categories namely the Permanent
Forest Estate (PFE) and non Permanent Forests Estates (nPFE) (MINEF, 1994). The
Permanent Forests Estates (PFE) otherwise known as protected areas are
considered to be areas belonging to the state and so closed from all unauthorised
human activities. These protected areas are divided into protected areas and
forest reserves proper (MINEF, 1994). In 2011, the PFE stood at 16.3 million ha
representing 35% of the total national land area (WRI, 2011). Within the PFE, 66% of
land cover is represented by dense forests, 11% by mixed forests, and 23% by land
where forests are not the dominant vegetation.
14
The non-Permanent Forest Estate (nPFE)—including community forests, private forest
and unclassified state forest. In 2011, the classified land area within the nPFE, although
small relative to the size of the PFE, stood at 1.1 million ha, representing 32% of the
national land area (WRI, 2011). In terms of distribution, 90% of the classified lands in
the nPFE were allocated to community forests and 10% to sales of standing volume
(SSVs). Of this value, about 41% of the land is covered by dense forests, 59% by mixed
forests, and less than 1% by land where forests are not the dominant vegetation. See
Figure 2.3 for forest classification
Zoological Gardens
Forest Plantations
Botanical Gardens
Unclassified State Forest
Community Forest
Private Forest
Game Ranches (public) Recreation Forest
Wildlife Sanctuaries Teaching and Research Forests
Buffer Zones Plant life Sanctuaries
National Parks Integral ecological reserves
Production ForestsGame Reserves
Protection ForestsHunting Areas
Permanent Forest Estate (PFE) non-Permanent Forest Estate (nPFE)
NATIONAL FOREST ESTATE
Council Forests
State Forests
PROTECTED AREAS FOREST RESERVES
4Figure 2.3: Forest Classification in Cameroon Source: WRI (2011)
2.1.4 Functions of forest
Forests have a host of ecological, socio-cultural and economic functions and provide
multiple benefits (CPF, 2011). There is mounting evidence that forest ecosystems
sequester and store high amounts of carbon (Luyssaert, 2008). Yude et al. (2011) have
shown that the world’s existing forests are a large and persistent carbon sink; they
sequestered an estimated 2.4±0.4 gigatonnes of carbon per year in the period 1990–
2007, which was more than 7 percent of total annual greenhouse gas emissions in 2004.
Forests and tree cover prevent land degradation and desertification by stabilizing soil,
reducing water and wind erosion, and maintaining water and nutrient cycling in the soil
(CPF, 2011). Forests are an important pool of biodiversity (FAO, 2011). The
importance of biodiversity and of preserving the stock of genetic diversity for
15
future food and medicinal needs and purposes are regarded as of global
importance.
Forests provide a wide range of goods, such as food, wood and fibre, spiritual fulfilment
and aesthetic enjoyment. Communities around and within forest ecosystem have been
shown to rely on forest resources for food and shelter (Le et al. , 2012). Forest are the
primary source of energy for most household in many developing countries (FAO,
2011). In Rwanda for example, more than 80% of households use fuel wood for
cooking and household heating (Ngorege and Muli, 2012 in FAO, 2011). Increasingly,
most developing countries are resorting to fuel wood for their industrial energy need
given the increase fossil fuels in the world market. Forest are central to the spiritual life
of most forest communities. The Ixtlenos community of Mexico revere the forest attach
so much spiritual importance to their forest (FAO, 2006). Some communities in
developing countries still rely heavily on forests for medicinal remedies derived from
indigenous plants (FAO, 2011). Forests provide areas of outstanding natural beauty
which provide recreational and spiritual renewal for stressed urban dwellers (FAO,
2011)
Forests are also an important sources of income for government. Cameroon’s formal
forest sector is the second largest source of export revenue in the economy after
petroleum, representing 16% of national exports earnings in 2003 (about 380 million
US dollars) and about 6% of GDP (CBFP, 2006). Non-timber Forest product is also an
important source of income (FAO, 2011). In his study of the socio-economic
importance of some selected NTFPs in, Babalola, (2011) found that the marketing of
non-timber forest products served as a major source of income and employment
to the stakeholders along the marketing chain in South West Nigeria. The global
trade in wood and non-wood products from the forest was worth over US$200 billion in
2010 (CPF, 2011).
2.1.5 Forest use and dependence
Forest use patterns and dependency of rural household have become an important
topical issue in developing economies (Sapkota and Oden, 2008). Forest like other
common pool resources are usually characterized by multiple use values such as
consumptive, recreational, environmental and spiritual with different interests of
rural households (Baland and Platteau, 1999). It is estimated that more than one-third
of the world’s population – 2.4 billion people – rely on fuel wood to prepare meals, boil
16
water and heat and light homes (FAO, 2010a). FAO (2010a) further reports that
throughout the world and particularly in developing countries, a great deal of fuel wood
is harvested – both formally and informally – from natural forests, including on public
and private forest land and land for which there is no secure tenure, and also, in some
instances, in protected areas. Forest and tress provide food (leaves, seeds, nuts, fruits,
mushrooms, honey, insects and wild animals) for millions of people and forest
ecosystem services and biodiversity are essential to agriculture (CIFOR, 2014). For
some households, forests also provide food safety nets in times of scarcity (Wunder et
al., 2014). NTFPs play a crucial role in meeting the subsistence needs of a large part of
the world’s population who live in or near forests (FAO, 2006). They provide shelter,
food and medicines on a daily basis as well as in times of crisis. The rich diversity of
medicinal plants found in forests is important for the wellbeing of millions of forest-
dependent people.
The concept of the number of “forest-dependent people” first appeared in discussions
about forestry almost two decades ago (FAO, 2014). The World Commission on Forests
and Sustainable Development (WCFSD) produced the first global estimate of the
number of forest-dependent people, suggesting that 350 million people depend almost
entirely on forests for subsistence and a further 1 billion on woodlands and trees for
their essential fuelwood, food and fodder needs (WCFSD, 1997). Shortly afterwards,
the World Bank (2001) reported that more than 25 percent of the world’s population –
an estimated 1.6 billion people rely on forest resources for their livelihoods. According
to FAO (2013), 4–5 million women in West Africa earn about 80 percent of their
income from the collection, processing and marketing of nuts harvested from naturally
occurring shea trees. Millions of people earn income – and thereby help feed their
families – by growing, harvesting, processing and selling wood as a source of domestic
energy. For poor households, NTFPs are rarely the primary source of revenue, but can
supplement income or lessen unexpected hardships such as the loss of crops (FAO,
2006).
2.1.6 Community forestry
2.1.6.1 Origin and Evolution
Community forestry (CF) came into prominence in the 1970s (Yufanyi Movuh, 2013).
By the mid-1970s it had become apparent that development strategies narrowly based
17
on industrialization were not working (FAO, 1999). Few countries had attained
significant, sustained growth in this way. Such growth as was achieved became highly
localized and all too often poorly related to people's actual needs. Growth pattern
emerged that actually worsen the improvishment of those outside the growth sector.
Development thinking and practice therefore saw the need to move towards a rural led
focus. This shift took concrete form in the World Conference on Agrarian Reform and
Rural Development (WCARRD) held by FAO in July 1979. The growing focus on rural
development did much to draw attention to the dependence of rural people on forests
and trees. At the same time, the sharply increased concern with energy supplies,
following the 1973 jump in fossil energy prices, soon drew attention to the extent to
which people in the developing world depend on wood as their main fuel for cooking
and other household needs. Apparent implications of this dependence were meeting
subsistence nutritional needs and on maintaining tree cover required for environmental
stability. Mounting concern over these overlapping problems led to a number of
initiatives, at both the national and international level, designed to meet rural needs for
fuel wood and other forest products in a more sustainable manner.
At the international level, FAO, with support from Swedish International Development
Agency (SIDA), organised a series of meetings to review existing experience and to
define what was needed. This resulted in a seminal 1978 state-of-knowledge publication
“Forestry for Local Community Development” (FAO 1978). FAO's programmes were
radically restructured to give effect to this, and FAO and SIDA launched a special
action programme to heighten awareness of the importance of “community forestry,”
and to help individual countries initiate or upgrade field programmes in this area. Also
in 1978, the World Bank issued its influential Forestry: Sector Policy Paper which
signalled a major shift in its forestry activities away from industrial forestry towards
environmental protection and meeting local needs. This shift was “to reflect the reality
that the major contribution of forestry to development will come from its impact on
indigenous people in developing countries” (World Bank 1978). A further initiative by
IDRC (Bene et al. 1977) led to the creation in 1977 of ICRAF, an organization to
promote research in “agro forestry”. A series of international meetings, notably the
1978 Eighth World Forestry Congress, which was devoted to the theme “Forests for
People”, served to give the concept of community forestry rapid and intensive exposure.
By 1979, field projects and programmes were already taking shape.
18
2.1.6.2 Community Forestry in Cameroon
Community forestry first made its appearance in the forest management policy scene in
Cameroon in the 1990s (Oyono et al., 2012). Driven in part by the Earth Summit of
1992, the Government of Cameroon initiated a number of environment and forest
reforms that led to the adoption of a variety of legal instruments—including the
Forestry Law of 1994 and its application decrees of 1995, the Land Use Map of 1995,
the Forestry Policy of 1995, and the Forest and Environment Sectoral Program of 2003.
These instruments set forth community based management of forest as a cornerstone in
the effort to achieve the overarching goal of “enhancing the participation of the
populations in the conservation and management of forest resources to improve their
living standards” as stipulated in the Forestry Policy of 1995. With a legal process now
enshrined in legislation, village communities could obtain and manage a forest or a
community hunting zone on the basis of an approved simple management plan (SMP)
and a duly signed final management agreement (FMA) with the government. However,
a lack of dissemination of information about CFs in rural areas made initial progress in
obtaining a CF in Cameroon extremely slow and led to a clarification of the procedures
in a Manual of Procedures and Norms for the Management of Community Forests
(MoP) in 1998, becoming a legal instrument in 2003 (Beachamp and Ingram, 2011).
The revised MoP was decreed in 2009
The first stage of the CF process is to reserve the forest. Initially, the community is
required to create a legal entity, known as a forest management institution, recognized
by Cameroon law to represent the population. The legal entity submits an application
for approval by the Ministry of Forests and Wildlife (MINFOF) to reserve the desired
forest after a series of community and legal consultations. The second stage concerns
producing a CF Simple Management Plan (SMP), including a socio-economic survey of
the community, a forest inventory comprising a timber stock assessment, planned
exploitation activities and a program of development actions to be realized with the
exploitation revenues. After the approval of the SMP, a CF management convention is
signed, serving as the contract between the state and the community, and the official
exploitation stage of the CF begins. The first CF in Cameroon started in 1997 and by
2000 there were 82 CFs (Djeumo, 2001). By early 2002, there were 138 applications
awaiting approval, 38 CFs reserved and preparing their SMPs and 24 management
conventions signed (Brown, 2002). Numbers of new CFs reached a peak in 2004. By
19
2006, 378 application files had been received by MINFOF, 78 CFs reserved and 42 had
an approved SMP and were waiting for convention signature. By mid-2010, 457 CFs
were at some stage in the process although only 20% had actually gained full CF status
(Ministry of Forestry and Wildlife, 2010). As of 2008, community forests occupied
about 621,245 hectares, representing 3.16% of the country’s total forest estate (Mbile et
al., 2009).
2.1.7 Community forestry and livelihoods
The potential livelihood outcomes of community forestry has been a topical issue in
development thinking and practices (Sunderlin et al., 2005). It is believed that such
outcomes (improved income, employment, food security, sustainable use of natural
resources base and reduced vulnerability to shocks) can in theory create incentives for
resource conservation and contribute to local economic development and poverty
reduction (WRI, 2005).
According to Mukul (2007), millions of people living in most tropical countries derive
a significant part of their livelihoods from forests. Community forestry in the onset was
crafted to maximize the benefits that forest-dependent people derived from forest (FAO,
1999). FAO (2006) reported that community forestry has contributed significantly in
improving the livelihoods of the Ixtlenos community of Mexico. According to this UN
agency, revenues from the sales of timber and non-timber forest products have
improved per capital income and community infrastructures in this forest-dependent
community. FAO (op. sit) further claims that the sales of fuelwood and charcoal from
communally managed forest have generated income for local communities in
Ougadougou and this increase in income has translated into improved livelihood
outcomes for the women involved in fuelwood trade and their households. In the Kozac
region of Turkey, pine nut from the community forest has been shown to be a major
source of income and employment, contributing to socio-economic development of the
Kozac community (FAO, 2006). A scheme between WWF and a community forest in
Southwestern Cameroon is generating revenue, employment and communiity
development from the controlled hunting wildlife (FAO, 2006). Poor people have been
shown to benefit from PES in many ways. In Zimbabwe, governement and local forest
communities are sharing the benfits from ecotoursim through the Community Area
Management Programme for Indigenous Resources (CAMPFIRE). Also payment of
carbon credits from communal forest initiatives have improve income and employment
20
in Uganda (IISD, 2005). In Ngola-Achip community forest in Eastern Cameroon,
Kenneth (2006) contend that proceeds from the sales of timber was sufficient enough to
build 72 houses and fund scholarships.
Prakash et al. (2003) investigated the impact of community forestry on livelihoods in
the middle hills of Nepal. The study found out that the impact are positive in terms of
increase in income generation opportunities, improved community infrastructure and
improved social capital for collective planning and action. The study also noted
regrettably that impacts where below their potential. In Namibia’s Khoadi community
forest conservancy for example, Jones and Mosimane (2007) report expenditure of 5 to
10 percent of gross revenues on community benefits, such as support for schools, loans
to livestock owners, and development of water points. WWF (2008) assessed the
livelihoods and conservation outcomes of community forestry in the Terai and Chure
Hills of mid- and far west Nepal. The study found out that more households are food
secure, have access to portable water and participate actively in the sustainable use of
forest resources. The study however recommend that for this gains to be sustainable,
issues of equity should be addressed. Cariq (2012) assessed the impact of community
based forest management using cases in Philippines. This study revealed a net reduction
in timber poaching and slash-and-burn agricultural, improved forest condition and
improved participation in forest management activities with the issuance of forest
tenure to communities. However, the study decried deteriorations in fauna and flora as
well as water quality and reported mixed results with respect to livelihood
improvements.
Cuny et al. (undated) in assessing local and decentralized forest management in
Cameroon, reported a significant improvement of the socio-economic conditions of
household with the advent of community forestry in Kongo village in Lomie Division
of the Eastern region of Cameroon. Nurse et al. (1995) reported improvement in forest
resources in the Kilum-Ijim forests of the North West region of Cameroon as a result of
community forest management. However, other dissenting voices have emerged, who
argue that the shift from the predominantly centralized natural resource management
towards more devolved models known as community forestry has done little for
communities and in some cases have contributed in eroding their livelihood bases.
Pokorny and Johnson (2008) reported community forestry has not met the expectations
in the Amazon region. They indexed the inadequacy of resources for overcoming the
technical, legal and financial constraints inherent the current community forestry
21
framework. Roe et al. (2006) conclude that in general, formal CBNRM programmes in
Southern Africa have not performed well at generating income at household level. The
state of livelihoods under the exercise of new community management and marketing
rights to forest in rural community has been assessed by Oyono et al. (2012). According
to the study, the rights-based reform and community forestry are not improving basic
assets and means at the household level. It also indicated that the resource base has not
changed but rather its more and more threatened by poor local level institutional
arrangements and social and bio-physical management strategies.
2.1.8 Community forestry and biodiversity conservation
Conserving biodiversity is of increasing concern to forest managers, natural resource
policy makers and many stakeholder groups (Adams and Hulme, 2001). Proponents of
CBNRM have argued that natural resource conservation can only be achieve by
strategies that emphasize the role of local residents in decision-making about natural
resources (Collomb et al, 2007). They content that local communities who are given
greater resource and governance rights improve both economically and ecologically and
ultimately develop into more resilient social-ecological systems. Others authors (IDS,
2007; Oyono et al. 2012) have retorted that the communities involved are usually
disappointed with the process. CBNRM has been criticized as an ineffective strategy –
both for conservation and development (Roe et al. 2009). ISD (2007) have argued that
community-based forest management models are not more effective than other forest
management strategies in delivering environmental benefits. Studies carried out in
Tanzania showed that there was no significant difference in taxonomy diversity and
richness between forest under community management, joint forest management and
reserves (Mgumia and Oba, 2003).
Nevertheless there are examples of impressive results. In Namibia’s communal
conservancies’ programme, for example, the contribution of CBNRM to the recovery of
wildlife populations across large parts of northern Namibia including endangered
species such as black rhino, elephants and Hartmann’s zebra is well documented. The
general trend for all these species over the past 15 years or more has been upwards
(NACSO, 2004). Durbin et al. (1997) have argued that without community forestry
species such as the black rhino would not have survived. In Tanzania, perceptual studies
on the impact of community forestry in several African countries point to some positive
outcomes. For example community forestry has been positively associated with forest
22
regeneration (Lund and Treue, 2008), reduction in unregulated levels of forest resources
harvesting (Mustalahti, 2006), reduction in encroachment of agricultural land into forest
(Sjoholm and Louno, 2002) and increase in game and wildlife numbers/diversity
(Woodcock et al., 2006). In Namibia, were communal conservancies have proliferated,
wildlife resources have recovered and illegal use of wildlife has fallen (Roe et al. 2009).
Similarly in West and Central Africa, community-based forest management have been
shown to deliver positive environmental and ecological benefits. A line transect survey
carried out in the Tayna community forest in the Democratic Republic of Congo have
shown a ten-fold significant increase in elephant encounter rate, a three-fold increase in
chimpanzee encounter rate and a two-fold increase in gorilla encounter rate (Mehlman
et al., 2006). Other positive outcomes of community-based forest management have
been reported elsewhere. The Zone Siwaa village decentralisation project in Mali
reported slowing in the degradation of natural resources, notably concerning excessive
logging and the erosion of agricultural soils (Ba, 1998). The Diaba Basin Community
Protected Area project reported signs of forest regeneration (Kaba, 2007). The Penjari
Biosphere Reserve co-management project in Benin has resulted in reductions in
poaching, illegal logging and building inside the park (GTZ, 2008).
2.1.8.1 Community forestry and governance
Community forest has been paraded in popular development discourse as an effective
policy mechanism for ensuring increase participation in forest decision making and
equity in benefit sharing (Yufanyi Movuh, 2012). Others have argued that this perhaps
is one of the greatest impacts of CBNRM far exceeding any economic or environmental
benefits (WRI, 2005 in Roe et al., 2009). A large body of literature have been dedicated
to the effectiveness of community-based forest management in delivering greater
participation and in ensuring that the benefits derived from the exploitation of forest
resources are shared equitably among community members. In the Luangwa Valley in
Zambia, Dalal-Clayton and Child (2003) have reported that community forestry has led
to a high level of participation in decision-making by villagers. Studies carried out by
Singh and Sharma (2010) in Gujarat, India revealed an improvement in women’s
participation in forest management meetings, decision-making and other forest
management activities.
However, in Botswana, there have been repeated instances of local trusts embezzling or
mismanaging revenue from wildlife-based enterprises, which Rihoy and Maguranyanga
23
(2007) attribute both to the role played by local elites and the way CBNRM has been
facilitated, with a lack of long-term investment in building local capacity. The
inequitable distribution of benefits is often associated with the domination of benefits
by well-placed local elites (Roe et al. 2009). At the local level, benefits can be
concentrated among traditional chiefs, the well-educated or the wealthy. A study of
PFM in Tanzania assessed the distribution of benefits across different wealth
categories and concluded that there were a range of barriers that prevented
greater participation in the programme by poorer members of the community.
This included among other things a more systematic exclusion of the poor from
decision making structures and processes (MNRT, 2008a).
Collomb et al. (undated) investigated the effectiveness of community-based natural
resources through the integration of governance, livelihoods and and conservation
indicators in the Capri conservancies in Namibia. The study pinpointed issues of
accountability and transparency with regards to finances and information dissemination.
In Benin there is evidence that marginal groups (women, migrants, tenant farmers) lose
out from Participatory Forest Management (Mongbo, 2008). Even though they are often
primary forest users, women usually participate much less than men in forest
management and policy decisions. Cultural, socio-economic and institutional factors
have contributed to gender inequality in the forestry sector (FAO, 2013). In Kenya,
pastoralist Group Ranches have repeatedly failed as collective resource governance
institutions, leading communities to individualize formerly communal pastures and seek
new, generally smaller collective landholding arrangements (Mwangi, 2007).
In Tanzania, there are examples of villages with sustained record of misuse of funds,
thereby undermining the potential for wildlifebased revenues to generate collective
incentives for conservation (Roe et al. 2009). Lund and Treue (2008) in their review of
community-based forest management in Mfyome village, Iringa, Tanzania, cite
examples of corrupt village government officers being ejected from management
committees after reports of embezzlement. Differences in land use and power between
ethnic groups can also result in one group succeeding in securing land rights over
another, as a result of decentralisation policies. For example, in Central Africa one of
the ethnic groups that are often disaffected by decentralisation are the Ba’aka (Joiris,
2000 in Roe et al., 2009), due to their often remote and nomadic way of living, and the
perception of pygmies as a ‘lesser’ ethnic group by many Bantu groups. In Benin,
Mongbo (2008) found that in two case-study villages the creation of community forest
24
management committees were causing friction between younger and elder members of
the community; elder members still wished to run committees using traditional ways,
which include myth and local religion, whereas younger members no longer believed in
these older traditions after a recent movement in the community towards Christianity.
The phenomenon of ‘elite capture’ where the most powerful or richest members of a
community are able to seize a disproportionate level of power and/or benefits can
constrain or undermine the intended outcomes of CBNRM
2.2 Conceptual Framework
The study builds on the renowned Sustainable Livelihood Framework developed by
DFID (1999). Figure 2.4
Figure 5Figure 2.4: Sustainable Livelihood Framework Source: DFID (1999)
Sustainable Livelihood Framework has been in vogue amongst development
practitioners and researchers since the late 1990s and indeed was a central concept of
the UK’s Department for International Development. (DFID). According to Kar (2010),
the SLF provides detailed structure for livelihood analysis. It provides an appreciation
of Households and individuals use of resources and the outcomes of such use within a
broader context of vulnerabilities and the mediating processes of policies and
institutions. More specifically, it organizes and identifies constraints and opportunities
associated with improving livelihoods and displays how they are interlinked (Carney,
25
1998). The framework is flexible in as much as its application provides a way to think
about livelihoods, and it can be modified to take the needs of a particular context into
account. The SL framework comprises several components, details of which are given
below.
The vulnerability context refers to those aspects of the external environment that
influence livelihoods and over which people have limited or no control (DFID, 1999).
These aspects of the external environment have a direct impact on the asset status of
people and the options open to them to pursue a beneficial livelihood (ibid). Five types
of livelihood assets (capitals) are recognized: natural, physical, human, financial, and
social capital. This categorization is assumed to be a settlement for the various lists of
assets identified by different researchers (Ellis, 2000). Natural capital refers to
environmental resources such as land, water, and biological resources whereas physical
capital stands for those assets created by production processes such as buildings, roads,
farm equipment, tools and irrigation canals (Ellis, 2000). Human capital refers to labor
together with its education level, skill and health (Carney, 1998). Financial capital
measures the availability of cash or the equivalent that enables people to adopt different
livelihood strategies (DFID, 1999). It can be in the form of savings, loans or other
transfers (ibid). Social capital refers to the social resources upon which people draw in
(e.g. social networks, membership in formal and informal groups, and participation in
relationships of trust, reciprocity and exchanges) (DFID, 1999). The transforming
structures and processes include the institutions, policies, and organizations that
determine access to assets, returns to livelihoods strategies, and terms of exchange
between different types of capital (DFID, 1999). Ellis (2000) considered them as critical
mediating factors that inhibit or facilitate households‟ exercise of capabilities and
choices.
The interplay of the vulnerability context, livelihoods assets, institutions and
organizations influences the adoption of particular livelihood strategies and livelihood
outcomes. In the DFID framework (DFID 1999) livelihood strategies denote the range
and combination of activities and choices that people make/undertake in order to
achieve their livelihood goals. They include productive activities, investment strategies,
reproductive choices and others (ibid). The adoption of livelihood strategies is a
dynamic process in which households combine activities to meet their various needs at
different times (Ellis, 2000). Scoones (1998) identified three broad clusters of
26
livelihood strategies: agriculture-based strategies, diversified strategies, and migration
based strategies. On the other hand, Ellis (2000) identified two broad categories: natural
resource-based activities such as collecting or gathering, crop/food cultivation, livestock
keeping/pastoralism, brick making, weaving, thatching etc; and non-natural resource-
based activities such as trade and services. However, livelihoods diversification is a
fundamental feature of livelihood strategies particularly among rural households
(Bryceson, 1999, Ellis, 1998). Livelihood diversification decisions are influenced by
vulnerability contexts such as seasonality and shocks, ownership and access to assets,
and factors related to transforming structures and processes including macro-economic
policies (e.g. structural adjustment programs) and market failures (Barrett et al., 2001,
Bryceson, 1996, Bryceson, 1999, Ellis, 2000)
The achievement or outputs of livelihood strategies are livelihood outcomes (DFID,
1999). According to Scoones (1998), establishing livelihood outcome indicators is
equivalent to elaborating what a sustainable livelihood means. Accordingly, five
important elements of sustainable livelihoods outcomes are implied: gainful
employment, poverty reduction, wellbeing/capability, adaptation and resilience, and
sustainability of the natural resource base. Therefore, a sustainable livelihood should
provide an employment that enables gaining income, consumable output, and
recognition for being engaged in something worthwhile. The livelihood outcomes,
particularly the wellbeing dimension including self-esteem, security, happiness, stress,
vulnerability, power and exclusion should be assessed as perceived by people
themselves (DFID, 1999, Scoones, 1998). The ability of a livelihood to cope with and
recover from stresses and shocks is also a central aspect of sustainable livelihoods
(Scoones, 1998).
A range of factors in the community forestry context of Cameroon and the study area
can be related to the different components of the SLF presented in Figure 2.4. A change
in the laws and policies, particularly the Forestry, Wildlife and Fisheries Law of 1994
and its accompanying degree of application granted community and community
members’ use, access and marketing rights to the livelihood asset of forest and its
related resources. With increase assess to this forest resources, community members
have indulged in several livelihood strategies such as NTFPs harvesting and
commercialization, timber exploitation, subsistence and smallholder farming, forest
regeneration, hunting, NTFPs domestication among other things. The possible outcome
of indulging in the foregoing activities could be increase in income, increased
27
wellbeing, reduced vulnerability to shocks and hazards, improved food security and a
sustainable exploitation or improvement of the forest resource base.
2.3 Gaps in the literature
Community forestry in Cameroon has been the focus of scholarly interest and has
generated a huge body of literature (Oyono, 2004; Beachamp and Ingram, 2011; Oyono
et al., 2012). However, regional disparities have been observed. Even though the South
West Region account for a considerable proportion of Cameroon’s forest estate and host
a significant number of community forests, very limited empirical works have been
carried out to here to assess the livelihood, conservation and governance outcomes of
community forestry. In the study area, available works have paid particularly attention
to decision-making and benefit sharing (Tekwe and Percy, 2001), the contribution of
forest to household income (IUCN, 2004), management plan for High Conservation
Value (FSC, 2004), community capacity for implementing Clean Development
Mechanism projects within community forests (Minang et al., 2007), community forest
model (Timko and Alemagi, 2010), the process of establishing community forest
(Yufanyi Movuh, 2013), womens participation in Prunus Africana harvesting (Abanda
and Nzino, 2014), power and conflicts in community forest (Yufanyi Movuh and
Schusser, 2014), knowledge generation (Mkemnyi et al., 2014), etc. Very few studies
have systematically assessed the forest use and dependence and the contribution of
community forestry to livelihoods, conservation and governance. Equally very little
attention has been paid to the variations in outcomes across the various communities
with community forests. This work attempts to fill these gaps.
28
CHAPTER THREE
METHODOLOGY OF THE STUDY
3.1 Models specification
Pearson’s Chi square
The Pearson’s Chi Square (χ2) test or model was employed in the study as a measure of
association or test of independence. It was used to assess variations in the sampled
population’s response (dependent variables) across and within localities and other
socio-demographic characteristics (independent variables). This model has been used
extensively in studies of this nature (Ratsimbazafy et al, 2006; Moudingo et al, 2012)
The model is explicitly stated as
x2
where χ2=chi statistics, Oi = Observed frequency on the field
Ei = Expected (theoretical) frequency,
i = the ith observation in the sample
n = The number of possible outcome of each event.
To test for association (variations), the calculated chi square was compared with
the tabulated chi square and the results interpreted as follows ;
i- When x2-calculated < x2-tabulated and or p-value > 0.05, there is no
statistically significant variation between the expected and the
observed. Therefore, the null hypothesis was accepted and we
concluded that there is no relationship or association between the
independent and dependent variable.
ii- When x2-calculated > x2-tabulated and or p-value < 0.05, there is a
statistically significant variation between the expected and the
observed. Therefore the null hypothesis was rejected and we
concluded that there is a relationship or association between the
independent and dependent variable.
This model assumed large sample i.e. a cell count should not be less than 5. In
case of smaller cell counts, Fisher’s Exact test was used.
29
Pearson’s Product Moment Correlation
The Pearsons Product Moment Correlation Coefficient was employed to assess the
strenght and direction of the association between the dependent and independent
variables determined by the Chi square test.
The model is explicitly stated as (Mbue, 2012 ; Oyuyole, 2013)
where n = number of counts
x = count of indepenedent variable
y = count of depenedent variable
Given that the correlation coefficient values lies between -1 and +1, the results
were interpreted as follows ; when
r is -1, there is a perfect negative correlation
r falls between -1 and -0.5, there is a strong negative correlation
r falls between –0.5 and 0, there is a weak negative correlation
r is 0, there is no correlation (therefore the null hypothesis is
rejected)
r falls between 0 and 0.5, there a weak positive correlation
r falls between 0.5 and 1, there is a strong positive correlation
r is 1, there is a perfect positive correlation
Binary Logistics Regression
A binary logistic regression model was employed as a test of prediction. It aimed to
assess how a respondent’s socio-demographic characteristics (independent variables)
predicts or determines his/her use of the forest (dependent variable). It specifically
assesses the odds that a respondent with a specific set of socio-demographic
characteristics will use or not use the forest.
This model has been used in similar studies (Agresti, 1996 ; Tiwara et al, 2008).
It is explicitly stated as
Log ( β0 + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6 + β7X7 + β8X8 + β9X9 + 10
X10
30
Or
where
P is the probability of forest use , 1-P is the probability of non-use.
is the odds of forest use , β0 is the intercept
β1 is the regression coefficient for X1 (location)
β2 is the regression coeffcient for X2 (gender)
β3 is the regression coefficient for X3 (age group)
β4 is the regression coefficient for X4 (level of education)
β5 is the regression coefficient for X5 (primary occupation)
β6 is the regression coefficient for X5 (income level)
β7 is the regression coefficient for X5 (marital status)
β8 is the regression coefficient for X5 (duration of stay)
β9 is the regression coefficient for X5 (origin)
β10 is the regression coefficient for X5 (membership in CIG)
Dependent T Test (Paired Sample T-test) model
The paired sample t-test model compares the means between two related groups on the
same continuous, dependent variable. It was used to assess for statistically significant
differences in the means of the distance travel to collect fuelwood from the community
forest before and after the introduction of community forestry in the locality. It has been
used in comparative study of this nature (Beauchamp and Ingram, 2011).
The model is stated as (Molles, 2008).
Where
t= t-statistics
XA=Mean distance walked to carry fuelwood after CF
XB=Mean distance walked to collect fuel word before CF
= Standard error of the differences between means
31
3.2 Description of Variables in the Models
3.2.1 Independent variables
The Independent (explanatory, experimental, or predictor) variables are those socio-
demographic or institutional characteristics of the respondents which can influence,
shape, or explain the dependent (response, outcomes or criterion) variables under
consideration. In the context of this research, the independent variables includes
location, gender, age group, level of education, primary occupation, level of income,
marital status, origin and member in forest management organisation. Appendix 3.1
3.2.2 Dependent variables
The dependent (response, outcomes or criterion) variables are aspects of the study
which are shaped, influenced or determined by the independent variables. They
constitute the objects under investigation. In this study, the independent variables
includes forest use, forest dependence, income, employment opportunities, community
development infrastructures, fuel wood availability, forest cover and stands, incidence
of Wildlife sightings, sounds and traces, adoption of sustainable forest use practices,
regeneration activities, environmental awareness, participation in forest management,
equity in forest benefit sharing. Appendix 3.2
3.3.1 Study population
The study population consisted of residents aged 15 years and above living in villages
or settlements adjacent to the selected community forests under study.
3.3.2 Sampling Techniques
A multi-staged sampling procedure was employed to select respondents for the study. In
the first stage, three out of the four community forests in Fako division were randomly
selected. The chosen community forests were Bakingili Community Forest, Bimbia-
Bonadikombo Community Forest and Woteva Community Forest.
The Bimbia-Bonadikombo Community Forest was created in 2002 and located in
Limbe III, Limbe I and Tiko subdivision. It covers a surface area of 3735 hectares and
serves the following villages namely Bonangombe, Bonabile, Lifanda, Dikolo,
Chopfarm, Mbonjo, Mabeta, Bonadikombo, Ombe Native (Moliwe Hills) and
Bamukong. Woteva Community Forest is located in Buea subdivision. It was created in
2011, covering a surface area of 1865 hectares and primarily serves the Woteva village.
32
Other villages who depend on its fauna and flora resources include Bonakanda, Wonjia,
Ekona Lelu and Maungu. The Bakingili Community Forest was created in 2010. It is
located in Limbe subdivison and covers an area of 922 hectares. It primarily serves the
Bakingili Village, Wete-Wete Camp and Batoke village.
In the second stage, 9 out of the 16 villages and settlements bordering the chosen
community forest were purposefully selected based on their proximity to the forests and
geographical accessibility. These include, Bakingili, Wete-Wete camp, Woteva,
Bonagombe/Bonabile, Bonadikombo (Mile 4), Upper Mawon, Lifanda Congo, Ombe
Native and Bamukom.
In the final stage, simple random sampling was used to select respondents from
Bakingili, Wete-Wete, Woteva, Bonagombe/Bonabile, Ombe Native and Bamukom
based on a prior developed household list while in Upper Mawon, Lifanda Congo and
Bonadikombo (Mile 4), convinient or availability sampling technique was employed.
3.3.3 Study sample and sampling intensity
A total of 295 respondents from the various localities were selected for the study and
their distribution according to community forests and villages/settlements is represented
in Table 3.1.
Table 2 Table 3.1: Distribution of respondents
Community Forest Village/Settlement Count Percent
Bakingili Bakingili 73 24.75
Wete-Wete 36 12.20
Woteva Woteva 51 17.29
Bimbia-Bonadikombo
Bonagombe/Bonabile 24 8.14
Bonadikombo 34 11.53
Lifanda Congo 22 7.46
Upper Mawon 19 6.44
Ombe Native 20 6.78
Bamukom 16 5.42
TOTAL 295 100
Source: Field Work 2014
33
3.3.4 Data collection
Primary Data
Primary data were obtained from a structured questionnaire containing close-ended
questions on the respondent’s socio-demographic characteristics, use of and dependence
on forest resources and perceptions of the contribution of community forestry to
selected livelihood, conservation and governance parameters. A total of 300
questionnaires were administered. At the end of the exercise, 5 were rejected for
incomplete or imprecise answers. Furthermore, key informant interviews using an
interview guide were conducted with influential and knowledgeable members of the
community. A total of 10 key informant interviews were conducted with heads of the
village traditional councils, forest management officers, members of the forest
management committee, heads of users groups and other influential community
members. Finally, nonparticipant observations and field visits were made by the
researcher to collect relatively objective first-hand information of the state of
community development infrastructure, forest stands and regeneration activities. During
this exercise, field notes were taken.
Secondary Data
Secondary data were obtained through desktop review of community forest simple
management plans, books, journal articles, published and unpublished thesis, magazine
articles, web sites publications etc.
3.4. Analytical Approach
The quantitative data obtained from the questionnaire survey was analysed using
exploratory statistics (Boxplots, Kolmogorov-Smirnov and Shapiro-Wilk), descriptive
statistics (frequency, percentages, mean, standard deviation, standard error mean, charts
and tables) and inferential statistics (Chi square, Pearson’s correlation coefficients,
binary logistic regression coefficients and Paired sample t-test). The Pearson’s Chi
square, Pearson’s correlation, binary logistic regression and Paired sample t-test
procedures were employed as tests of association, measure of strength/direction of
association, test of prediction and test of variation. The data was analysed using IBM®
Statistical Package for Social Sciences version 20. Charts and tables were developed to
enhance illustration using Microsoft Office 2013. The qualitative data obtained from the
key informant interviews were collated for similarities and differences in response to
key questions.
34
3.5 Validation of the Results
In this study, validity of the results was a primary concern. To achieve this, the
researcher took necessary measures to ensure the reliability of the data collection
instruments, the validity of data collected, the appropriateness of data analysis
procedures and correctness in the interpretation of data analysis results.
To ensure the reliability of the data collection instruments i.e. questionnaires, pretesting
was carried out. The questionnaire were prestested in Bakingili and Bonabile. At the
end of this pre-test, some questions were added, some rephrased to reduce ambiguty,
while others were discarded completely. Also, all of the variables captured in the
questionnaires have been used extensively in studies of similar nature (WWF, 2008;
Oyono, 2011; Yufanyi Movuh, 2011; CIFOR, 2014). Finally, the variables chosen were
directly related to the objectives and hypothesis of the study.
To ensure the validity of data collected, the researcher took a series of measures. Firstly,
a face-to-face procedure was employed during questionnaire administration so that
misunderstood and ambiguous questions were clarified. Secondly, the questionnaires
were administered in a language that was most familiar to the respondents. More often,
the questionnaires were translated into Pidgin English for easier comprehension and
completion. Thirdly, the information collected were triangulated i.e. multiple
information sources were used to heighten the dependability and trustworthiness of the
data collected. Fourthly, sufficient time was allocated to data collection to avoid the
pitfalls associated with hasty data collection. Finally, inputted data were explored to
identify questionable entries, inconsistency in response, missing data and outliers using
frequency distribution and boxplots and necessary corrective measures were taken.
To ensure the appropriateness of the data analyses procedures, the data was screened for
normality and homogeneity of variance using the Kolmogorov-Smirnov and Shapiro-
Wilk tests. Given the results of the foregoing exploratory procedures, parametric test
were chosen for the analyses. Also, to ensure validity of the results of the chi-square test
for situations were cases was less than 5, the Fisher’s Exact Test was conducted.
Finally, care was taken to ensure that the results of the test were correctly interpreted.
To this effect, a list of the possible test results and their respective interpretations were
developed for the test of association and measures of strength/direction of association.
Finally, all statistics were discussed at the 0.05 level of significance. At these level, the
findings of the research can be easily generalized to the whole population
35
CHAPTER FOUR
PRESENTATION AND ANALYSIS OF DATA
4.1 Socio-demographic characteristics of respondents
In this study, 135 (45.8%) of the 295 respondents were from Bimbia-Bonadikombo, 109
(36.9%) from Bakingili and 51 (17.3%) from Woteva (Table 4.1).
Ta ble 3 Table 4.1: Socio-demographic characteristics of respondents
Characteristics Count Percent Characteristics Count Percent
Location
Age group
Bakingili 109 36.9
15-24 years 38 12.9
Bimbia-Bonadikombo 135 45.8
25-34 years 101 34.2
Woteva 51 17.3
35-44 years 63 21.4
45-54 years 38 12.9
Gender
> 55 years 55 18.6
Male 140 47.5
Female 155 52.5
Primary occupation
Agriculture 86 29.2
Level of education
Forestry 56 19.0
No formal 73 24.7
Petit trade 58 19.7
Primary 120 40.7
Fishing 30 10.2
Secondary 72 24.4
Civil service 17 5.8
University 30 10.2
Student 16 5.4
Others 32 10.8
Income Level
≤ 50000Frs 152 51.5
Marital status
50001-100000Frs 74 25.1
Single 104 35.3
100001-150000Frs 33 11.2
Married 154 52.2
≥150001Frs 36 12.2
Separated 9 3.1
Divorced 11 3.7
Longevity in area
Widowed 17 5.8
1-5 years 33 11.2
6-10 years 76 25.8
Origin
11-15 years 52 17.6
Indigene 143 48.5
Above 16 years 134 45.4 Non-indigene 152 51.5
Source: Field Work ,2014
From this total, 155 (52.5%) were female and the rest (140 or 47.5%) were male. This
female dominance in the gender distribution of the sample population is illustrative of
the population structure of Cameroon where women are slightly more than men. Thirty
eight (12.9%)of the respondents were of the 15-24 years age group, 101 (34.2%) were
of the 25-34 years age group, 63 (21.4%) were of the 35-44 years age group, 38 (12.9%)
were of the 45-54 years age group and 55 (18.6%) were above 55 years. The majority
36
(68.5%) of the respondents were below the ages of 45 years, indicative of a youthful
and productive population. Seventy three (24.7%) had no formal education, 120
(40.7%) had primary level education, 72 (24.4%) had secondary level education and 30
(10.2%) had university level education. The population is literate, since more than three
quarter (75.3%) of the respondents had some form of formal education. Eighty six
(29.2%) of the respondents had agriculture as primary occupation, 56 (19%) were into
forestry (engine saw operators, timber traders, timber haulers, NTFPs collectors and
traders), 58 (19.7%) were involved in petit trading, 30 (10.2%) were involved in fishing,
17 (5.8%) were in the civil service, 16 (5.4%) were students and 32 (10.8%) were in
other activities (seamstress, drivers, car washers, mechanics, carpenters, builders etc.).
Close to half (48.1%) of the respondents were into agriculture and forest-related
activities. Brocklesby and Ambrose-Oji (1997) have documented similar findings in the
area. The rich volcanic soils and abundant forest resources in the area lends itself to
these livelihood activities. One hundred and fifty two (51.5%) of the respondents had
income less than 50 000FCFA, 74 (25.1%) were in the 50 001-100 000FCFA income
group, 33 (11.2%) were in the 100 001-150 000FCFA income group and 36 (12.2%)
had income of 150 001FCFA and above. Most (51.5%) of the respondents were in the
lowest income category, indicative of a relatively poor population. One hundred and
four 104 (35.3%) were single, 154 (52.2%) were married, 9 (3.1%) were separated, 11
(3.7%) were divorced and 17 (5.8%) were widowed. Of these total, 33 (11.2%) had
been in the area for 1-5 years, 76 (25.85) had been in the area for 6-10 years, 52
(17.6%) had lived in the area for 11-15 years and 134 (45.4%) had lived in the area for
more than 16 years. More than half (63.1%) of the respondents have been in the area for
more than 11 years, signifying that they are knowledgeable about the trends in
livelihoods, conservation and governance in the area. One hundred and fifty two
(51.5%) were non-indigenes and 143 (48.5%) were indigenes. Most of the respondents
were non-indigenes attesting to an influx of migrants from other parts of the country for
paid employment, farming and fishing purposes (Forlemu, 2011). This influx has
resulted into an exceptional ethnic and linguistic mosaic of people.
4.2.1 Results for objective 1
4.2.1.1 Extent of community forest use
In the study, 179 (60.7%) of the 295 respondents reported using the community forest
37
for livelihood activities while the rest, 116 (39.3%) reported no use of the community
forest. (Figure 4.1).
6Figure 4.1: Extent of community forest use in Bakingili, Woteva and
Bimbia-Bonadikombo CFs
Beauchamp and Ingram (2011) have documented analogous high use of community
forest in the Melombo and Akomnyada II localities in the Eastern region of Cameroon.
This high use of the forest in the study community can be linked to limited forest
alternative livelihoods and low level of skills and academic qualification among some
of the residents (particularly in Woteva) for other form of employment. However, Ali et
al (2007) have reported low levels (less than 25%) of forest use among community
members in Northern Pakistan and linked it to the massive exodus of the young and
productive category of the population to neighboring India in search of waged
employment.
At the 95% confidence interval, forest use differed significantly across locations
(p=0.00; χ2=34.15; df=2). This differences rejected sub hypothesis H1A which
contended that forest use does not differ across selected community forest.
The studied showed that forest use was very high (82.4%) in Woteva, high (71.5%) in
Bakingili and low (43%) in Bimbia-Bonadikombo. The relative differences in
livelihood opportunities present in the selected communities can account for this
difference in forest use. In Woteva where forest use was higher (82.4%), farm and
forest related activities constitute the major livelihood activities. In Bakingili, forest use
was relatively moderate (71.5%) because in addition to farm cum forest related
activities, a significant proportion of the residents are involved in artisanal fishing and
trade. Increased opportunities for fishing, farming, petite trading and other paid
38
employment in the urban and peri-urban localities of Limbe, account for the relatively
low level (43%) of forest use in Bimbia-Bonadikombo. In his study of joint and
community-based forest management in India, Rossi (2007) observe similar difference
across several community forests in Andhra Pradash, India.
In addition, forest use differed significantly across the socio-demographic
characteristics of gender (p=0.00), age group (p=0.00), level of education (p=0.00),
primary occupation (p=0.00), level of income (p=0.00) and longevity in the area
(p=0.00) (Appendix 4.1.)
4.2.1.2 Patterns of community forest use
Among the 179 respondents who use the forest, 160 (89.4%) reported using the forest
for fuel-wood collection, 46 (25.7%) for timber exploitation, 71 (40%) for farming, 74
(41.3%) for Non-Timber Forest Products (NTFPs) harvesting, while the rest reported
using the forest for cultural rites and ceremonies (7 or 3.9%), recreation (6 or 3.4%) and
research (2 or 1.1%) (Figure 4.2)
re 7Figure 4.2: Patterns of Community Forest use in Bakingili, Woteva and Bimbia-
Bonadikombo CFs
The high use of the forests for fuel wood collection in the selected community forest is
congruent with the works of Rossi (2007). Like in most developing countries where
fuel wood constitute the dominant source of energy (OFID, 2007; FAO, 2010b), fuel
wood is extensively used in the study area for household cooking, heating and fish
smoking. Contrary to other timber and non-timber forest products, there are limited
restrictions on the collection of fuel wood for household consumption from the selected
39
CFs. Also, fuel wood is extensively harvested for charcoal production in Bimbia-
Bonadikombo and Bakingili for sale in the city of Limbe (Plat 4.1- 4.3).
Typical species of trees used for fuelwood and production of charcoal are mango wood
(Desbordesia glaucescens) matanda (Uapaca guinensis), kerosene stick (Strombosia
grandifolia), iron wood (Lophira alata), umbrella stick, etc
n
NTFPs harvesting constituted another major form of community forest use. Prakash et
al (2003), Sun (2007) and Abanda and Nzino (2014) have reported similar patterns in
Nepal, China and the highland mountains of Cameroon.
The most reported types of NTFPs were spices and condiments (22.2%), medicinal
plants (21.2%), forest fruits and nuts (19.8%), game or bush meat (15.6%), canes and
bamboos (13.7%), leaves and fodders (4.7%) and honey (2.8%) (Figure 4.3).
1Plate 4.1: Firewood harvesting in Bimbia-
Bonadikombo CF Equation
2Plate 4.2 Charcoal production in Bimbia-
Bonadikombo CF
3Plate 4.3: Charcoal stocked at Upper Mawon
40
ure 8 Figure 4.3: Types of Non-Timber Forest Products exploited in Bakingili, Woteva
and Bimbia-Bonadikombo CFs
In the study area, NTFPs are used extensively as food, medicine, livestock feed,
household construction material, etc. NTFPs like bush mangoes (Irvingia gabonensis),
Eru (Gnetum Africanum), Njangsang (Ricinodendron Heudelotti spp), bush pepper
(Piper guineensis), bush onions, alligator pepper (Aframumum spp), etc are important
parts of the local diets. NTFPs used for medicinal purposes include pygium (Prunus
Africana), yellow stick (Garcinia mannii), Bitter cola (Garcinia cola), cola (Cola
acuminata), and milk stick (Alstonia boonai). Other NTFPs of importance are rattan
(Lacosperma spp), Ngogo Leaf (Megaphrynium macrostach), bamboos (Plate 4.4 and
4.5).
Another major use of the community forest was subsistence and smallholder farming.
The use of community forest for agricultural purposes have also been documented in
5Plate 4.5: Bush mangoes (Irvingia spp)
collection in Bamukong
Equation 4 Plate 4.4: Eru (Gnetum Africanum) harvested
for household consumption in Bakingili
41
the Kilum-Ijim Ijim Community forests (Gardner, 2001) and in Tinto Community forest
(McCall and Minang, 2005) in the North West and South West regions of Cameroon
respectively. In most of the selected community forests, there are forest management
units allocated for farming purposes. Chop farms have been largely established around
and within the Keta, Lower Ngoe and Maungu Forest Management units in Bakingili,
around the southern flanks and Ekona Lelu border in Woteva and the Moliwe hills,
Likomba La Mbenge, parts of Bonadikombo and Bamukong in Bimbia-Bonadikombo
etc. Plate 4.6 and 4.7.
The forests were also used for bush meat huniting. The community forests are host to
antelope, cane rat, viper, pangolin, squirrel, Mona Monkey, Brush tail porcupine which
are valuable sources of protein for most households. (Plate 4.8).
Even though most of the CFs are short of commercial quantity of timber, available
timber species like mahogany (Ethandophama spp), iroko, Isaka, man carabot, small
Eq E 6Plate 4.6: Forest cleared for chop farm in Bimbia-
Bonadikombo CF
house hold consumption in Bakingili
uation
Equation 7 Plate 4.7: Cocoa farm in the Bakingili CF
house hold consumption in
Bakingili
uation
8 Plate 4.8: Bush meat from Woteva being smoked at
Bonakanda
42
leaf, tiger wood etc. were exploited and generally used for house construction or
transported for sales to neighboring towns. Plate 4.9
The presence of touristic sites in the community forest such as the German graves and
lava craters in Woteva, the slave port in Bimbia and lava flow traces of 1999 in
Bakingili make the community forests important destinations for tourist. Given that
most of this community forest fall within the Mt Cameroon biodiversity hotspots, they
are also used for scientific research.
9 Plate 4.9: Timber being sawn into planks in Bakingili
1923 26
52
2633
0
20
40
60
Yes No Yes No Yes No
Woteva BakingiliBimbia-Bonadikombo
Farming
9
3320
59
15
43
0
20
40
60
80
Yes No Yes No Yes No
Woteva Bakingili Bimbia-
Bonadikombo
Timber
Figure 4.4: Differences in patterns of forest use across the selected community forests.
43
No statistically significant variations in the use of the community forests for fuel wood
collection (p=0.807; χ2=0.429; df=2), NTFPs harvesting (p=0.115; χ2=4.326; df=2), far-
ming (p=0.312; χ2=2.330; df=2) and timber exploitation (p=0.861; χ2=0.300; df=2),
were observed across the selected localities (Figure 4.4). These therefore confirmed sub
hypothesis H1B that contended that forest use patterns for major forest resources does
not vary significantly across the community forests.
4.2.1.3 Socio-demographic determinants of community forest use.
A binary logistic regression analysis showed that at the 0.05 significance level, the
statistically significant socio-demographic determinants or predictors of forest use were
location (p=0.039), gender (p=0.011), primary occupation (p=0.00), level of education
(p=0.00), income level (p=0.023), longevity in area (p=0.007), origin (p=0.010) and
membership in Community Forest Management Group (p=0.025). On the other hand,
age group (p=0.682) and marital status (p=0.646) were not statistically significant
determinants of forest use (Appendix 4.2).
The binary logistic regression model for forest use in the study area was
Log (forest use) = 13.5 – 0.491 Location + 1.013 Gender – 0.88 Occupation – 1.005
Education - 0.42 Income - 0.608 Longevity + 1.367 Origin -1.935
Membership
The model correctly classified approximately 88.8% of the cases. The pseudo R2 indicate that
the model explained between 51.9% (Cox and Snell R2) and 70.3% (Nagelkelke R2) of the
variation in forest use. The Hosmer and Lameshow (X2=7.53;df=8;p=0.480) and Omnibus
Test (X2=216.01;df=8;p=0.00) tests statistics showed a high goodness-of-fit for the
model. Correlation analysis showed that the data did not violate the multicollinearity
assumptions. All of the correlation coefficients were below the threshold of 0.7
(Appendix 4.3)
The results from the regression analysis show that the respondent’s origin was the most
significant socio-demographic determinant or predictor of community forest use. After
controlling for other factors, non-indigenes were 3.706 times more likely to use the
community forest than indigenes. This is in contrast to the works of Ratsimbazafy et al
(2012) who argued that the indigenes of the Makira region in the North Eastern section
of Madagascar use the adjacent forest Makira forest more than others. The high
probability (0.78) that a forest user will be a non-indigenes observed in the study area is
44
attributed to the fact non-indigenes make up the bulk of the population in most of the
localities (Tekwe and Percy, 2001; Folemu, 2011) and also constitute the majority of
those involved in farming and forest gathering (Yaron, 1999). For example, most of the
residents of Bamukong and Mabeta, a farming settlement and fishing village located on
the fringes of the Bimbia-Bonadikombo community forest are natives of Boyo division
in the North West region and Calabar in neighboring Nigeria respectively. In Bakingili
most of the young and productive indigenous population are involved in fishing.
Gender emerged as the second most significant determinant or predictor of forest use. It
was observed that being a woman increases the likelihood of forest use by a factor of
2.761, holding every other variable constant. Analogous findings have been advanced
by Abanda and Nzino (2014) in a study of gender disparity in NTFPs resource use in
the Mount Cameroon Region. The study argued that women were more involved in the
NTFPs value chains than men. This high probability (0.7) of female forest use can be
linked to the fact that women by the virtue of their household roles are often primary
forest users (FAO, 2013). Increasingly, women are taking on productive roles in their
households which more often are linked to forest resource exploitation (Jagger and
Angelson, 2011). It could also be because of their limited assets and skills. Women find
it more difficult to enter better paid occupations (Abanda and Nzino, 2014).
The income level of the respondents was shown to be the third most significant
determinant of forest use. With every other factor held constant, respondents in the ≤ 50
000frs income group were 0.632 times more likely to use the community forest than
those in the 50 001-100 000frs income category, were 1.264 times more likely to use
the forest than those in the 100 001-150 000frs income category and 1.896 times more
likely to use the community forest than those in the ≥150 001frs group. Prakash et al
(2003) have also documented an inverse relationship or negative correlation between
income levels and forest use in the middle hills of Nepal. Lower levels of income and
high forest use have been reported by Jodha (1992) and Iyenger and Shukla (1999) in
Sapkota and Oden (2008). The heighten use of the forest by the low income strata of
the population can be explain in that most poor household rely exclusively on forest and
other common pool resources for their livelihoods.
The fourth most significant predictor of forest use was location. Residents in Bimbia-
Bonadikombo were 0.583 and 1.166 times less likely to use the community forest those
in Bakingili and Woteva respectively, controlling for other variables. Alternative
45
nonfarm and non-forest livelihood opportunities that abound in Bimbia-Bonadikombo
explain the small probability (0.37) of a forest user being from this locality. Brocklesby
and Ambrose-Oji (1997) have stated different forest use in forest dependent
communities in the mount Cameroon region.
The respondent’s primary occupation emerged as the fifth most significant determinants
of forest use. Those involved in forestry (loggers, NTFPs collectors, fuelwood and
charcoal production) were 0.432 times more likely than those whose primary
occupation was petite trading, 0.864 times more likely than those involved in fishing,
1.296 times more likely than those in the civil service, 1.728 times more likely than
students and 2.16 times more likely than those involved in other activities (tailoring, car
washing, driving, carpentry, mechanic etc), ceteris paribus. How close a respondent’s
primary occupation is forest dependent will determine his/her probability or likelihood
of using the forest.
The respondent’s level of education was the sixth most significant determinant or
predictor of forest use. Respondents with university level education were 0.402 times
less likely to use the forest than those with secondary school level education, 0.804
times less likely than those with primary level education and 1.206 times less likely to
use the community forest than those with no formal education. This negative correlation
or inverse relationship between educational level and income have been put forward by
other studies (Sapkota and Oden, 2008; Rossi 2009). Those with higher educational
attainment prefer white collar jobs and trade than menial farm and forest related jobs in
the community forests.
4.2.1.4 Extent of dependence on Community Forest
All of the 179 community forest user reported using the forest and associated resources
for household consumption. Of this total, 13 (7.3%) reported a dependence on the
community forest for 1-30% of their household food, energy and material needs, 71
(39.6%) reported a dependence of 31-60% while 95 (53.1%) reported a dependence of
61-100% of the above household needs (Table 4.2).
This clearly indicated that a high dependence on forest for livelihood in the study area.
Similar findings have been shown by Sapkota and Oden (2008) who argued that
livelihood activities of forest fringed communities are invariably tied to the forest and
its associated resources.
46
4 Table 4.2: Respondents dependence of forest for household food, energy and material
Proportion of household Needs Counts Percent Level of dependence
1-30% 13 7.3 Low
31-60% 71 39.6 Moderate
61-100% 95 53.1 High
Source: Field Work (2014)
At the 0.05 significance level, dependence on community forest for household
consumption did not vary significantly with location (p=0.963; χ2=0.602; df=4). This
confirmed sub hypothesis H1C that posited that dependence on forest does not vary
significantly across community forest.
The similarity of forest dependence for household food, fuel, fibre and other materials
can be explain in that even though there are heterogeneity (in terms of culture,
preference, ethnicity, income level, education, political ideologies) in the sampled
population, they have similar needs. Sapkota and Oden (2008) have documented similar
levels of forest dependence and homogeneity among forest user groups in the Teria
communities in Nepal.
However, forest dependence vary by gender. This is consonant with Bwalya (2013).
Women constituted the bulk of those who depend on forest for livelihood. This is so
because a large percentage of these women live in rural areas and an even higher
percentage (92%) live off the land and its associated resources. Furthermore, rural
women are the main consumers of natural resources. They gather firewood, leaves,
fruits, bark, and small animals that go into the meals of their families; they are the
custodians of traditional pharmacopoeia and harvesters of forest products for craft
work.
Furthermore, it was observed that forest dependence for household food, energy and
material needs differed significantly with gender (p=0.002), age group (p=0.00),
primary occupation (p=0.00), level of income (p=0.015), marital status (p=0.00) and
longevity in the area (p=0.009) of respondents (Appendix 4.4)
Table In addition to depending on the community forest for some proportion of their household
food, energy and material need, 56 (31.3%) of the total 179 forest users reported using
the forest resource for commercial purposes. Of this total, 3 (5.3%) reported that
proceed from the sales of these forest products accounted for 1-30% of their total
47
monthly income, 21 (36.8%) reported that sales of forest resources represented 31-60%
of their total monthly income while the rest (33 or 57.9%) reported a monthly income
proportion of 61-100% (Table 4.3).
Table 5 Table 4.3: Respondents dependence of forest for income in study localities
The results show that an dependence on the forest for income range between moderate
and high whereby an overwhelming majority (54 or 94.7%) of the 57 respondents who
used the community forest for commercial purpose depend on it for 31-100% of their
monthly income. Similar findings have been documented by (Yufanyi Movuh, 2012)
who reported that the sales of timber and non-timber products constitute a major source
of income stream for most households in the area.
At the 0.05 level of significance, dependence on forest for income did not vary across
locations ((p=0.816; χ2=1.559; df=4; r=). This confirmed sub hypothesis H1C. This
similarity in forest dependence for income has also observed by Reddy and Chakravarty
(1999) and Sapkota and Oden (2008).
However, forest dependence for income differed with gender (p=0.014; χ2=8.554; df=2)
and level of income (p=0.014; χ2=15.953; df=6) (Appendix 4.5).
4.2.2 Results of objective 2
4.2.2.1 The contribution of community forestry to income
In total, 53 (17.9%) of the 295 respondents perceived an increase in community
members’ income under community forestry. On the other hand, 36 (12.3%) perceived
a decrease, 159 (53.9%) perceived no change while the rest (47 or 15.9%) did not know
(Figure 4.5).
The results show that community forestry has had no significant change in the income
of community members. This is in conformity with the works of Minang et al (2007),
Mbile et al (2009) and Oyono et al (2012) that postulated that forest management devol-
Proportion of Total Income Counts Percent Level of dependence
1-30% 3 5.3 Low
31-60% 21 36.8 Moderate
61-100% 33 57.9 High
Source: Field Work (2014)
48
r e 9Figure 4.5: Effects of community forestry on income in Bakingili, Woteva and Bimbia-
Bonadikombo.
ution has not contributed significantly in improving basic assets and means at the
household level in the Bimbia-Bonadikombo area and in four selected areas in
Cameroon (i.e. Lomie/Dja, Ocean, Mt Cameroon and Mount Oku) respectively.
However, Beauchamp and Ingram (2011) argues that community forestry has benefited
forest dependent communities economically. A reason why community forestry has had
no marked changes in the income in the study localities could be associated to the fact
that most of the community forest entrusted to communities in this area by the state
were highly degraded with little or no commercially exploitable quantity of timber and
non-timber products. Also, most community forests have been concerned with acquiring
government approval and regeneration rather than on income generation.
At the 95% level of confidence, respondents perception of the effect of community
forestry on income did not differ across locations (p=0.152; χ2=9.412; df=6; r=0.007).
This confirmed sub hypothesis H2A. The fact that all of the community forests have
similar resources and challenges account for the similarities in the impact on income.
However, significant differences in the responses were found with gender (p=0.001),
age group (p=0.00), level of education (p=0.039), primary occupation (p=0.00), level of
income (p=0.00), marital status (p=0.00), and longevity in area (p=0.018) (Appendix
4.6).
49
4.2.2.2 The contribution of community forestry to employment
Out of the 295 respondents, 28 (9.4%) reported an increase in employment
opportunities with community forestry, 63 (21.3%) reported a decrease, 153 (51.8%)
reported no change while the rest (51 or 17.2%) did not know (Figure 4.6).
10Figure 4.6: Effect of community forestry on employment opportunities in Bakingili,
Woteva and Bimbia-Bonadikombo.
The majority (153 or 51.8%) of the respondents reported that community forestry has
had no change in employment opportunities in the study area. This is congruent with the
works of Angu (2006) that argue that in over 5 years of community forestry less than
6.8% of the total population of 1050 have benefited from direct employment in the
Ngola-Achip forest communities in the eastern region of Cameroon. This contrast
sharply with the works of Cuny et al (undated) who argue that with the creation of the
Kongo Community Forestry, employment opportunities significantly improved in the
Kongo village in the Eastern region of Cameroon. According to the study, most of the
local populations were employed by the timber exploiters that were granted
concessionary rights to the forest. In the communities where community forestry has
improved on employment opportunities, most of these new jobs have been provided by
timber loggers. Opportunities for commercial timber exploitation in the study
communities are very limited.
At the 95% confidence interval, significant differences existed in the respondents’
perceptions of the effect of community forestry on employment opportunities across
location (p=0.001; χ2=25.123; df=6; r=-0.002). This finding nullified sub hypothesis
H2B.
50
The absence of employment opportunities under community forestry was highly
reported (75 or 49%) or more evident in Bimbia-Bonadikombo as compared to
Bakingili (59 or 38.6%) and Woteva (19 or 12.4%). This is due to the fact that the
Bimbia-Bonadiikombo CF is managed by a very few powerful elites whose
preoccupations are not geared towards local development or job creations (Tekwe and
Percy, 2001).
Significant differences were also found with level of education (p=0.040), level of
income (p=0.014), marital status (p=0.036), and longevity in area (p=0.00) (Appendix
4.7).
4.2.2.3 The contribution of community forestry to infrastructures
In total, 74 (25.1%) of the 295 respondents reported improvement in community
development infrastructure as a results of community forestry, 192 (65.1%) reported no
improvement while the rest (29 or 9.8%) did not know (Figure 4.7).
Fi 11Figure 4.7: Effect of community forestry on infrastructure development in Bakingili,
Woteva and Bimbia-Bonadikombo
The results show that community forestry has not led to any significant improvement in
community development infrastructures. This finding are in conformity with Maharjan
et al (2009) and Oyono et al (2011). On the contrary, similar studies (Cuny et al,
undated; Angu, 2006) undertaken in the eastern regions of Cameroon show that
community forestry has improved development infrastructures such as health centers,
schools, churches and water supply facilities, considerably in the Ngola-Achip
localities.
51
At the 95% confidence interval, respondents perception of the effect of community
forestry on community development infrastructure differed significantly with location
(p=0.001; χ2=25.125; df=6; r=-0.275). This was in contrast with sub hypothesis H2C.
Most (96 or 50%) of the 192 respondents who reported no improvement in community
development infrastructures under community forest management were from Bimbia-
Bonadikombo (96 or 50%) as compared to 79 (41.1%) form Bakingili and 17 (8.9%)
from Woteva. This blatant absence of development infrastructure from community
forestry in Bimbia-Bonadikombo was perfectly summarized by a local of Bonagombe
who remarked that “after more than ten years of community forestry in the localities, all
we can show for it are the two pit toilets constructed at the government primary school
(Matute, per. Com.). On the contrary, proceeds from the sales of illegal logs seized and
timber from the forest have been used in the construction of the Woteva Community
Hall and plantain suckers’ propagator.
Significant variations were also found with age group (p=0.02), level of education
(p=0.00), primary occupation (p=0.00), level of income (p=0.00), longevity in the area
(p=0.00) and origin (p=0.004). No difference were found with gender (p=0.209) and
marital status (p=0.201) (Appendix 4.8).
4.2.2.4. Contribution to community forestry to fuel wood availability
The 160 respondents who used the community forest for fuel wood collection reported a
statistically significant ((p=0.00; t=8.855; df=159; r=0.764) increase in the averaged
distanced walked to collect fuel wood after the introduction of community forestry.
According to them, the mean distance increased from 2.99 ± 1.99 km to 3.69 ± 1.2 km;
a mean increase of 0.697 ±0.99 km .Table 4.4.
Tabl 6 Table 4.4: Mean distance (km) walked to collect fuel wood before and after the introduction of
CF in Bakingili, Woteva and Bimbia-Bonadikombo
Distance
Walked N Means
Std.
Deviation
Mean
difference
Std
Deviation t-value df Sig
Before CF 16
0
2.99 1.237 -0.697 0.999 8.855
15
9 0.00
After CF 3.69 1.539
Source: Field Work 2014
The observed increase in the mean distance trekked by residents to collect fuelwood
after the introduction of community forestry clearly demonstrated that forest
52
management devolution has not improved fuelwood availability in the study
communities. This is in contradiction to Rabindra (1999) that reported an increased in
fuelwood with community forestry in the Gaukhureshwor community forests,
Kavrepalanchwok district of Nepal. The reduced availability of fuelwood around the
fringes of the community forest has caused many residents to penetrate further into the
forest in search of this essential household commodity. The results have been increased
tree felling and habitat fragmentation.
However, differences in the mean distance trekked to collect fuelwood were observed
among the selected community forests. It was observed that the increase in average
distance trekked to collect fuelwood was highest in Bimbia-Bonadikombo, followed by
Bakingili and tailed by Woteva (Table 4.5).
Source: Field Work 201
This clearly invalidated the study’s sub hypothesis H2D that the impact of community
Forestry on fuel wood availability does not vary across community forests. The ever-
increasing demand for fuel wood and charcoal by residents around the fringes of the
Bimbia-Bonadikombo CF, explains the scarcity of fuel wood around the borders of
forest.
4.2.3 Results of objective 3
4.2.3.1 The contribution of community forestry to forest stands
In the study area, 103 (34.9%) of the 295 respondents reported that forest stands have
witnessed a minor increase with community forestry, 45 (14.2%) reported a major
increase, 65 (22%) reported a major decline, 62 (21%) reported a minor decline while
the rest, 20 (6.8%) reported no change (Figure 4.8).
The results showed that forest stands have increased in the context of community
forestry. This is congruent with the findings of Lupala (2009) who recorded increased
Table 4.5: Mean distance (km) walked to collect fuel wood before and after CF by
localities
Location
Bakingili Bimbia-Bonadikombo Woteva
Before CF 3.34 3.54 1.54
After CF 3.91 4.74 1.76
Difference 0.57 1.2 0.22
53
forest covers and stands in the context of participatory forest management in the Babati
district in Tanzania. On the contrary, Carodenuto et al (2015) argue that forest covers
and by extension in Fako divison has reduced significantly over the last ten years.
e 12Figure 4.8: Impact of community forestry on forest cover and stands in Bakingili, Woteva
and Bimbia-Bonadikombo.
At the 0.05 significance level, significant differences in respondents perception of
changes in forest stands were found with location (p=0.019; χ2=18.271; df=8; r=0.114).
This rejected the study’s sub hypothesis H3A that changes in forest stands does not
differ across community forest.
Decline in forest covers and stands were above average in (39 or 60%) in Bimbia-
Bonadikombo, below average (21 or 32.3%) in Bakingili and low (5 or 7.7%) Woteva.
Decrease in forest stand were highest in Bimbia-Bonadikombo CF because this forest is
subjected to higher pressures from the surrounding population than in Bakingili and
Woteva. Also, the size of the forest makes regular patrol and surveillance to check
illegal forest exploitation difficult. According to Carodenuto et al (2015), changes in
forest cover and by extension forest stands was more acute in Bimbia-Bonadikombo
than in any part of Fako Division.
Moreover, differences in perception of change in forest stands were also found between
and across the socio-demographic characteristics of gender (p=0.01), age group
(p=0.000), level of education (p=0.001), primary occupation (p=0.01), level of income
(p=0.002), marital status (p=0.018) and origin (p=0.018) of respondents (Appendix 4.9).
54
4.2.3.2 The contribution of community forestry to Wildlife
In the study, 121 (40%) of the 295 respondents reported an increase in the incidence of
wildlife sightings, sounds and traces with community forestry, 116 (39.4%) reported a
decrease, 38 (12.8%) reported no change while 20 (6.8%) did not know (Figure 4.9).
F 13Figure 4.9: Impact of community forestry to incidence of wildlife sightings, sounds and
traces in Bakingili, Woteva and Bimbia-Bonadikombo CFs
An increase in the incidence of wildlife sightings, sounds and traces reported indicate
that community forestry has contributed to some extent to wildlife conservation. This is
line with Forlemu (2011) who argue that participatory forest management has resulted
in the conservation of endangered and endemic fauna species in the Mt Cameroon
region. According to Chief Woloko Leiti, head of the Woteva traditional council and
forest management officer of the Woteva Community Forest, sights, traces and sounds
of some endangered species such as the drill (Papio leucophaeus), chimpanzee (Pan
troglodytes) and Bush Pig (Hylochoeru smeinertzhagent) are becoming more frequent
(Woloko, per. com).
At the 95% level of confidence, significant difference in the incidence of wildlife
sightings, sounds and traces was found with location (p=0.00; χ2=36.243; df=6; r=0.08).
Decreases in the incidence of wildlife sightings, sounds and traces were high (68 or
58.6%) in Bimbia-Bonadikombo as compared to Bakingili (32 or 27.6% and Woteva
(16 or 13.8%). This findings abrogated sub hypothesis H3B.
Woteva CF and Bakingili CF have witnessed limited decrease in wildlife as compared
to Bimbia-Bonadikombo. As buffer community forests to the Mt Cameroon National
55
Park, the former have benefitted from increased surveillance against illegal poaching
from the staffs of MINFOF and PSMNR-SW
Significant differences were also found with age group (p=0.00), level of education
(p=0.00), primary occupation (p=0.00), level of income (p=0.00), marital status
(p=0.00), and longevity in area (p=0.001) of respondents (Appendix 4.10).
4.2.3.3 The contribution of community forestry to environmental awareness
Out of the 295 respondents, 200 (67.8%) reported an increase in environmental
knowledge and awareness of the importance of forest resources conservation with the
advent of community forestry. On the other hand, 38 (12.8%) reported no change, 32
(10.8%) reported a decrease while the rest, 25 (8.6%) did not know. Figure 4.10.
14Figure 4.10: Impact of community forestry on environmental awareness in Bakingili Woteva and Bimbia-Bonadikombo
This clearly indicated that community forestry has greatly increased environmental
awareness and community members’ understanding of the importance of the sustainable
use of the forest and its related resources. Similar arguments have been advanced by
Nkemnyi et al (2014). This findings strongly reflect the views of Mr Mulema, former
forest management officer of the Bimbia-Bonadikombo community forest that “even
though the Bimbia-bonadikombo community forest has gradually lost its luster and its
contribution to the livelihood of the community is debatable, nobody can dispute the
fact that it has contributed in raising people’s awareness of the importance of natural
resources conservation (Mulema, per. com).
56
At the 95% level of confidence, perceptions on changes in environmental awareness
differed significantly with location (p=0.005; χ2=18.65; df=6; r=0.086). This rejected
sub hypothesis H3D.
Most of the 200 respondents who reported an increase in environmental awareness were
found in Bakingili (86 or 43%) as compared to Bimbia-Bonadikombo (75 or 37.5%)
and Woteva (38 or 19.5%).
Though the establishment of community forests were preceded by a long period of
consultation between stakeholders and education of the local masses on the rational of
community forestry, more was carried out in one than others.
In addition, differences were found within and across the socio-demographic
characteristics of gender (p=0.00), age group (p=0.00), primary occupation (p=0.00),
level of income (p=0.00), marital status (p=0.00) and origin (p=0.016) (Appendix 4.11).
4.2.3.4 The contribution of community forestry to the adoption of
sustainable exploitation practices
In the study localities, 176 (60%) of the 295 respondents reported that forest resource
users have adopted sustainable resource exploitation and farming practices with the
advent of community forestry. However, 89 (30%) reported that sustainable resource
exploitation farming practices have not been adopted while the rest (30 or 10%) did not
know. Figure 4.11.
176
89
300
40
80
120
160
200
Yes No Don’t know
Adoption of sustainable practices
Co
un
ts
F15Figure 4.11: Impact of community forestry on the adoption of sustainable practices
57
The results showed that sustainable forest resource exploitation practices have been
adopted in the study localities since the introduction of community forestry. This
finding is in line with Nkeng et al (2010) and Eben (2014) who noted an improvement
in the methods used in the exploitation on non-timber forests products and Prunus
Africa in particularly in the study area. The improved adoption of sustainable
exploitation can be explained by the numerous sensitization and training workshops and
field demonstration carried out by MINFOF through the Programme for the Sustainable
Management of Natural Resources-Southwest, Mount Cameroon Prunus Africana
Association (MOCAP), non-governmental and community organization (Plate 4.10)
Of the 178 respondents who reported the adoption of sustainable practices, agroforestry
(30 or 71%), collection of dead branches only (18 or 32.1%), sectional harvesting (15 or
32.6%), picking only of fallen fruits (13 or 48.1%), selective hunting (8 or 38.2%), and
cut-and-replant (18 or 40.9%) was cited by respondents as the most adopted sustainable
practices for farming, fuel wood collection, medicinal plants harvesting, forest fruits
collection, wildlife hunting and timber exploitation respectively. Figure 4.12
Of the 89 respondents who reported that sustainable practices have not been adopted,
slash-and-burn (36 or 40%), forest clearance (16 or 17.8%) and non-respect of quotas
(14 or 15.6%) were the most cited unsustainable practices. Figure 4.13.
10Plate 4.10: Training on the sustainable harvesting of pygium carried
out by PSMNR-SWR and MOCAP in Woteva
house hold consumption in Bakingili
uation
58
e 16Figure 4.12: Types of sustainable practices adopted in Bakingili, Woteva and Bimbia-Bonadikombo
CFs
17Figure 4.13: Unsustainable forest practices observed in Bakingili, Woteva and Bimbia-
Bonadikombo CFs
At the 95% confidence interval, adoption of sustainable practices differed significantly
across community forests (p=0.00; χ2=40.421; df=4; r=0.079). The observed differences
in the adoption of sustainable forest resource exploitation practices across community
forest, rejects the study’s sub hypothesis H3E that the impact of community forestry on
the adoption of sustainable forest resources exploitation practices does not vary across
localities.
Adoption of sustainable practices was high (77.1%) in Bakingili, above average in
Woteva (66.6%) and low (43%) in Bimbia-Bonadikombo. The adoption of sustainable
practices were highest in Bakingili and Woteva because they are small and closely knit
59
communities that permit reinforcement of good practices among community members
as compared to the large and peri-urban/urban nature of Bimbia-Bonadikombo.
In addition, significant difference in adoption of sustainable practices were observed
with level of income (p=0.023; χ2=14.795; df=6; r=0.129. Appendix 4.12
4.2.3.5 The contribution of community forestry to forest regeneration
In the study area, 187 (63.4%) of the 295 respondents reported that regeneration
(afforestation and reforestation) have been carried out with the advent of community
forest. On the other hand, 78 (26.4%) of the respondents reported no such activities
while the rest (30 or 10.2%) did not know. Figure 4.14.
i18Figure 4.14: Impact of community forestry in the improvement of regeneration
activities in Bakingili, Woteva and Bimbia-Bonadikombo CFs
The findings showed that Community forestry has improved on forest regeneration
activities. This finding is in conformity with the works of Gardner (2001), Roe et al
(2009) and Nurse et al (2011). One of the areas where community forests in area have
been highly involved in is tree planting. In 2014, over 20 000 endangered trees have
been planted under the Environment and Rural Development Foundation (ERuDef)
Program for the Conservation of Threatened Trees in the Bakingili and Woteva CFs.
In Bimbia-Bonadikombo, a highly used practice is cut-and-replant whereby forest users
are oblige to plat and tagged two trees for every one they fell. Also tree nursery
development has been carried out extensively in the area with the assistance of National
Forestry Development Agency (ANAFOR). Plate 4.11 and 4.12
60
Equati on 11Plate 4.11: The chief of Woteva planting a treei 12Plate 4.12: ANAFOR-supported tree nursery
in the Woteva CF in Bakingili
At the 95% level of confidence, respondent’s perception of regeneration activities did
not vary significantly among locations (p=0.509; χ2=3.299; df=4). This finding
confirmed sub hypothesis H3F.
These similarities can be explained by the fact that reforestation and afforestation has
been a major preoccupation and activity of the selected community forest management
organizations.
No significant differences were also found with gender (p=0.199), age group
((p=0.086), primary occupation (p=0.346), level of income (p=0.061), marital status
(p=0.479), longevity in area (p=0.894) and origin (p=0.026). However significant
differences were found with level of education (p=0.012). Appendix 4.13
4.2.4 Results of objective 4
4.2.4.1 The contribution of community forestry to community participation
in forest management.
In total, 190 (64.4%) of the 295 respondents reported that community participation in
forest management decision-making has improved with the creation of the community
forest, 76 (25.7%) reported no improvement, while the rest (29 or 9.8%) did not know.
Figure 4.15.
61
e 19Figure 4.15: Participation in forest management in Bakingili, Woteva and Bimbia-
Bonadikombo CFs
The results showed that community forestry has improved on the participation of
community members in the management of forest resources. This is findings echoed the
work of Ongie (2012) but contradicts Yufanyi Movuh and Schusser (2012) that
contends that the poor and other traditionally marginalized groups have been sidelined
in decision making in forest resources management by powerful elites and politicians.
The Community consultation has been a regular practice in most community forests.
Out of the 190 respondents who reported an increase in participation, 93 (48.9%) cited a
moderate increase in participation by women, 63 (38.8%) reported a high increase in
participation while the rest, 29 (15.2%) reported a minimal increase. With regards to
participation by youths, 95 (50%) reported a high increase in participation, 76 (40%)
reported a moderate increase while the rest, 19 (10%) reported a minimal increase.
Finally, 69 (36.3%) reported a minimal increase in participation by non-indigene, 67
(35.2%) reported a moderate increase while the rest, 54 (28.4%) reported a high
increase. See Figure 4.16.
62
20
Figure 21Figure 4.16: Participation by women, youths and non-indigenes in forest management in Woteva
Bakingili and Bimbia-Bonadikombo CFs
At the 0.05 significance level, participation in forest management varied with location
(p=0.008; χ2=14.171; df=4; r=0.031). Most of the 76 respondents who reported no
improvement in participation were from Bimbia-Bonadikombo (40 or 52.6%), followed
by Bakingili (28 or 36.8%) and Woteva (8 or 10.5%).
Variations in participation observed rejected the study’s sub hypothesis H4A that the
impact of community forestry on community participation does not vary across
localities. Participation was relatively high in Bakingili and Woteva, because these are
small and closely knit communities where community members can be easily
mobilized. In Bakingili for example there is a practice call big upside whereby the
entire community members assemble at the chief courtyard and matters concerning the
community and forests are openly debated. The study also show that participation in
forest management decision-making has improved for traditionally marginalized groups
notably women, youths and non-indigenes. This is in line with Vyaman (2009),
Shyamsundar (2011) and SWCFN (2015). Increasingly, these traditionally marginalized
groups are represented on the management board or committees of these community
forests.
Variations in perception of participation were observed with gender (p=0.004), age
group (p=0.00), level of education (p=0.023), primary occupation (p=0.021), level of
income (p=0.00), longevity in area (p=0.00), and origin (p=0.008). Appendix 4.14
63
4.2.4.2 The contribution to equity in forest resource benefit sharing
In the study, 80 (27.1%) of the 295 respondents reported that community forestry has
improved equity in forest resource benefit sharing. On the other hand, 191 (64.7%)
reported no improvement while the rest, 24 (8.2%) do not know. Figure 4.17
e 22Figure 4.17: Changes in equity in forest benefit sharing in Bakingili, Woteva and
Bimbia-Bonadikombo CFs
The results show that equity in forest resource benefits sharing has not improved with
the advent of community forestry. This corroborates the findings of Oyono et al (2012)
and Yufanyi Movuh and Schusser (2012). The results show that those who have
benefited more are men, indigenes and the older generations. Similar findings have been
documented by Rabindra (1999), Niesenbaum (2005) and McDermott and
Schreckenberg (2009). Unless benefits are shared equitably, Community Forestry will
only contribute to the reproduction of rural poverty and lead to division and disharmony
among those affected.
Of the 191 respondents who reported no improvement in equity in forest resources
benefit sharing with community forestry, 160 (83.8%) reported that men have benefited
more than women, while 31 (16.2%) reported that women have benefited more than
men. Also, 148 (77.9%) said it older people have benefited more than youths while 37
(19.4%) said it has benefited youths more than the aged people. Finally, 83 (43.4%)
reported that it has benefited indigenes more than non-indigenes while 103 (56.6%) said
it has benefitted non-indigenes more than indigenes. Figure 4.18.
64
e 23Figure 4.18: Benefit sharing by gender, age group and origin in Bakingili, Woteva and
Bimbia-Bonadikombo CFs
However, at the 95% level of confidence, equity in forest resources benefit sharing
differed with location (p=0.00; χ2=55.659; df=4; r=-0.170). Most of the 191 respondents
who reported no improvement in the equitable distribution of the benefits of forest
resources were from Bimbia-Bonadikombo (104 or 54.4%) as compared to Bakingili
(73 or 38.3%) and Woteva (14 or 7.3%).
The differences in equity observed nullified the study’s sub hypothesis H4B that the
impact of community forestry on equity in forest resource benefit sharing does not vary
across community forests. Incidence of embezzlement, mismanagement and elite
capture of the Bimbia-Bonadikombo CF explains to some extent why improvement in
equity was lowest in that community forest.
Differences in the respondents perception of equity in benefit sharing were also
observed with level of education (p=0.049), primary occupation (p=0.00) and origin
(p=0.007). Appendix 4.15
4.3 Implication of the Results
4.3.1 Extent of forest use, socio-demographic determinants and dependence
The study showed that the majority of the respondents use the community forest for
livelihood amidst variations among the selected community forest localities. The
implications of such high forest use are many. At the policy level, measures aimed at
alleviating poverty in these and similar forest-fringe communities should focus on
65
mechanisms or activities geared at improving forest resources base, improving access to
these resources and ensuring wise and sustainable use. At the community forest level,
alternatives to forest resources such the promotion of home garden, livestock production
(conventional and unconventional) which will reduce anthropogenic pressure on this
valuable resources. At the household level, such measures will reduce community
member’s dependence on community forest. Furthermore, given the variation in forest
use observed among the selected community forest, policies and interventions should be
elaborated taken into consideration the local specificities.
The results highlighted the fact that the community forests were mostly used for
fuelwood collection. This calls into question the basis for the ongoing efforts of the
community forest management committee which lay particular emphasis on the planting
of trees for the timber. While this is a laudable activity, it should be accompanied by the
planting of fast growing fuelwood species in the buffer zones which will simultaneously
provide fuelwood to the people and reduce direct access to the forest.
The study also found that forest use is determine by a host of socio-demographic
characteristics of the respondents such as location, gender, level of education, primary
occupation, income level, longevity in the area and origin. Therefore poverty alleviation
projects within this localities should not be elaborated in the traditional one-size-fits-all
approach but should be devised in cognizance of this forest use determinants. For
example measures should be targeted more on women with primary level education,
making less than 50000FRS a month, living in Woteva than other women with other
socio-demographic characteristics.
Furthermore, the results show that there is high dependence on forest for food, energy,
material and income among forest users with no variation among and within the
selected community. This implies that similar measures aimed at reducing forest
dependence are applicable across and within the selected forest communities.
4.3.2 Community forestry and livelihoods
The results showed that community forestry has not fully contributed in improving the
livelihood of community members when compared to its contribution before
gazzetation. Its contribution to income, employment, infrastructure and fuel wood
availability have been negligible. This finding challenges the popular conception that
66
the devolution of natural resources management to local community inexorably leads to
improved livelihood for community members. It contributes to a growing body of
literature in Cameroon that argue that community forestry in its present dispensation
cannot fully play its role of poverty alleviation. It also adds to those dissenting voices in
various quarters who are of the opinion that community forestry has simply moved
management rights from the state to a handful of powerful elites whose motives are far
from communitarian.
The results also showed that despite the fact that community forestry has not
contributed significantly to livelihoods, there are variations among the selected
community forests. Some communities have fared better than others with the advent of
this forest management model. Therefore measures aimed at fostering the contribution
of this forest management model should be context-specific.
4.3.3 Community forestry and conservation
The most significant contribution of community forestry has been shown to be in the
domain of forest resource conservation. The study showed that the transfer of forest
stewardship has led to an increase in forest stands, wildlife, environmental awareness,
adoption of sustainable forest resources exploitation practices and forest regeneration.
Therefore, more of the Permanent Forest Estate (PFE) should be put under community
management or in some state-community partnership. This inclusion of riverine
communities in the management of common pool resources in Cameroon can contribute
in preventing what Harding called “the tragedy of the commons”.
This also supports literature that postulates that conservation efforts that does not
include the priorities and needs of the local communities nor creates avenues for
meaningful participation by these community members is bound to be ineffective.
Therefore conservation policies should systematically be constructed in cognizance of
the needs and priorities of the local communities and the specificities within and across
communities.
4.3.4 Community forestry and governance
The study showed that community forestry has made significant inroads with respect to
participation in decision making and equity in benefit sharing in the selected community
forests localities. The study also unmasked differences in participation and equity in
67
benefits sharing among the selected communities and along gender, age and ethnic
lines. Therefore, policies aimed at fostering a greater participation in forest decision
making and increased equity in forest resource benefit sharing should identify the socio-
cultural and contextual factors that circumscribe traditionally marginalized social strata
from being a part of forest management devolution.
4.4 Limitation of results
The results of the study are based on data obtained from a questionnaire. The accuracy
of this information is a challenge because it is reliant on the sincerity of the respondents.
The lack of baseline economic and forestry-related data precluded this study from
tracking direct temporal changes within the selected community forests. The long recall
period used in the study also limited the accuracy of the results. For example, in
Bimbia-Bonadikombo and Bakingili, respondents were required to recall what it was
before 2002 and compare with the present situation. Also, the results were advanced on
the assumption that no other factor except community forest management had an impact
on the dependent variables under study. No quantitative information was used to assess
forest stands. The heavy reliance on respondent’s views on changes in this ecological
parameters limits the accuracy of the results.
68
CHAPTER FIVE
SUMMARY OF FINDINGS, CONCLUSION AND
RECOMMENDATIONS
5.1 Summary of findings
The study sought to assess the contribution of community-based natural resources
management to livelihood, conservation and governance in three selected community
forests in Fako Division. The findings revealed that forest use was high and varied
significantly across the selected community forests. The forests were mostly used for
fuelwood harvesting, farming, NTFPs gathering and timber exploitation. A binary
logistic regression analysis showed that origin, gender, income level, location, primary
occupation and level of education of respondents were the statistically significant socio-
demographic determinants or predictors of forest use. Equally, dependence on forest
resources for household food, energy, and material consumption and monthly income
were considerable in the study area with no variations observed across the selected
community forests.
With regards to the contribution of community forestry to livelihoods, the study found
out that this forest management model has had no significant impact on income,
employments, development infrastructures and fuelwood availability. However,
variations in the impact of community forestry were found with employment,
community development infrastructure and fuelwood availability varied across the
selected community forests. The study also showed that significant contribution has
been made by community forestry to forest cover and stand, wildlife, environmental
awareness, adoption of sustainable exploitation practices and forest regeneration.
However differences in the impact of community forestry were observed with all the
dependent variables except forest regeneration. Finally, it was observed that community
forestry has contributed significantly to community participation in forest management
decision-making, while no improvement was observed with equity in forest resources
benefit sharing. The impact of community forestry on participation and equity varied
across the selected community forests.
69
5.2 Conclusion
On the bases of the above findings, the study concluded that community-basedNatural
Resources Management has made significant contribution to forest conservation and
governance. Though the impact on community members’ livelihoods has been minimal,
it has not contributed in undermining the livelihood bases of forest-dependent
households. The impact of community forestry has taken varying trajectories in the
selected community forests due to their different institutional, contextual and
geographical specificities. However, the study posits that definite judgements should
not be passed hastily since most of the community forests have only been existing for
short period and most have had to grapple with in-house fighting, limited financial and
material resources and inadequate human capacity.
5.3 Recommendations
On the bases of the study’s finding, the following policy, forest-level and research
recommendations are made.
5.3.1 Policy recommendations
a. A new land tenure policy (Secure tenure)
Although the Cameroon Land Ordinance No. 74-1 of July 6, 1974 maintains that the
State is the guardian of all lands, traditional authorities continue to exercise de facto
rights over land. This uncertain and colonial-like land tenure situation, couple with the
provisions of the new forestry law that grants communities use and assess right to
forest for a specified duration (25 years) makes the local stakeholders unable to fully
embrace participatory forestry. There is therefor need for a new land tenure policy that
augments the deficiencies of the existing legal mechanisms.
Secure tenure rights are particularly important for forestry and agroforestry compared
with agriculture because of the relatively long period that may be required to realize
benefits.
b. Forest extension policy
Just like in the agricultural sector where a strong agricultural extension policy
framework exists, a similar mechanism should be elaborated and geared towards the
needs of forest dependent communities. Within the context of community forestry, such
a policy will enable forest users, forest management officers and Village Forest
70
Management Committees to be trained on forest exploitation, development and
management techniques and processes.
c. Quota policy
Government should institute policies that will require a certain proportion of the
representative of traditionally marginalized population (women, youths and non-
indigenes) on the board or supreme organ of the community forest management
organization. This will ensure that that the needs and expectations of this important
social stratum are also incorporated into forest management and development agenda.
5.3.2 Community forest-level recommendations
a. Second generation community forestry
In the first decades of community forestry, the priorities of the management team have
largely been in securing administrative authorization, settling boundary and internal
disputes and acquiring requisite community forest management organizational skills
and competence. Community forests should move to a second generation community
forestry whose principal mission will be in addition to consolidating previous gains,
actively pursue income generation, infrastructure development and employment
creations. For this to be achieved, forest management organization should be
transformed into veritable Community Forest Enterprise (CFE) which are small for-
profit entity managed by local communities responsible for the production, processing
and sale of timber and non-wood forest products.
b. Carbon sequestration and REDD+ financing mechanism
Given that most of the community forests have limited commercially exploitable
quantities of timber and non-timber forest products, they should actively pursue carbon
sequestration and trade initiatives under the emerging global market in carbon trading
either through the REDD+ mechanisms or other voluntary market schemes. This non-
consumptive use of the forest can be a potential stream of income for undercapitalized
community forests in the locality.
c. Community-wide patrol and surveillance
Illegal timber and non-timber forest products exploitation is one of the major threats to
the sustainability of community forests. Given that communities lack the man-power
and or technical capacity to effectively patrol the community forest on a regular bases
and MINFOF patrols are one-off and sporadic, there is a need for a community-wide
71
patrol team, to be composed of members of all the households in the community. This
community-level control and monitoring brigade will serve as a deterrent to illegal
exploiters of forests resources.
d. Training in value addition and forest alternative livelihood activities.
The forest management organization should train forest users on value adding activities
so as to increase the price of forest resources. Pre-market processing of timber and non-
timber products to add value could significantly enhance returns. Also farmers should
be trained in forest alternative livelihoods such as conventional and non-conventional
livestock production, plantain suckers multiplication and commercialization and
domestication of some non-timber forest products etc. This will significantly reduce the
pressure off forests and its associated resources.
e. Planting of fast growing fuel wood tress
Given that the community members used the forest mostly for the collection of fuel
wood, the forest management committees should embark on a vast program of fuel
wood tree planting. This will reduce the pressure off areas reserved for conservation
purposes. A notable tree species that cannot only provide high energy fuel but equally
enhances soil fertility is the techtonia specie. It can be grown on farmlands or in a
fuelwood plantation.
5.3.3 Research recommendations
a. Cover and stand change analysis using remotely sensed and field data
Even though respondent’s perception of forest covers and stands is a widely accepted
method for assessing ecological change in forest communities (Poteete and Ostrom,
2002), it should be complemented with methods of ecological change analyses. In this
regard, remotely sensed imagery and plot survey data should be acquired and studied to
give an accurate picture of the trajectory of forest cover and stands in the area.
b. Baseline research should be conducted
Finally, a socio-economic and ecological survey should be carried out in the area to
serve as a baseline for comparison in the future. This baseline, though mid-term, will
serve to assess the contribution of community forestry at the end of the lease period. It
will provide an accurate from which the management agreement between government
and communities can be renewed or cancelled.
72
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APPENDICES
Appendix 3.1: Independent Variables
Name Definition Study assumption Similar studies used Type Categories and codes for
data input and analysis
Location Geographic position of the CF and
place of resident of respondents
Location of a CF can influence the
extent to which people use and depend
on a CF
Adhikari et al. (2007) Nominal
variable
Woteva = 1 Bakingili=2
Bimbia-Bonadikombo =3
Gender Represents the sex of the
respondent
Gender will influences an individual's
use and dependence on natural
resources
Timko (2015) Nominal
variable
Male=1
Female =2
Age group A measure of the age of the
respondents
Different age group’s use and
dependence on natural resources vary
and they relate and perceive policy
impact in varied ways
Rossi (2002) Ordinal
variable
15-24 years =1 25-34 years=2
35-44 years =3 45-55 years=4
≥ 55 years = 5
Level of
education
A measure of the amount of
schooling of the respondents
Education level mediates a respondents
perception of the impact of a particular
policies strategy
Acharya (2000);
Bandyopadhyay and
Shyamsundar (2004).
Ordinal
variable
No formal education =1
Primary education = 2
Secondary education = 3
University education = 4
Primary
occupation
Refers to the main source of
income and livelihood
The occupation of an individual
influence his/her use or dependence on
natural resources
Timko (2015) Nominal
variable
Farming = 1 Forestry=2
Petty trading =3 Fishing 4
Civil service=5 Students=6
Others = 7
Marital Status Captures the matrimonial situation
of the respondents
The marital status of a respondents will
more or less influence his/her
dependence on natural resources
Ekindi (2010) Nominal
variable
Single= 1 Married 2
Separated =2 Divorced 4
Widowed=5
Longevity in the
area
Amount of years that respondent
have stayed in the locality
Duration of stay in a locality will
influence perception of resource change
or policy impact
Gilmour et al.(2004) Ordinal
variable
1-5 years = 1 6-10 years=2
11-15 years= 3 ≥ 16 years = 4
Origin Measured the ancestral link of the
respondent to the study area
Participation and benefit in natural
resources management is determine
ethnicity
Ekindi (2010); Djomo
(2011)
Nominal
variable
Indigene = 1
Non-indigene=2
Membership in
community
forest CIG
Measures a respondent affiliation
to the common initiative group that
manages the community forest
Membership in forest management
committee or forest user group mediates
use of the forest for livelihood
Kar (2010) Nominal
variable
Member = 1
Non-member = 2
Source : Field Work 2014
86
Appendix 3.2: Dependent Variables
Name Definition Similar studies used Variable
type
Categories and codes for data input
and analysis
Forest use Denotes the utilization of CF for household consumption and
income
Kenneth (2006);
Maharjan et al. (2009); Nominal
None-use=0
Use = 1
Forest dependence Measured the degree to which a respondent rely on forest
resource for household consumption and income Le et al. (2012) Ordinal
1-30% (Low)=1 31-60% (Moderate)=2
61-100% (High) = 3
Income A measure of livelihood contribution of CF and denotes
monetary benefits made from the exploitation of forest
Niesenbaum et al.
(2005) ; Ali et al.
(2007) ;
Nominal Increase in income=1 Decrease in income=2
No change in income =3
Employment A measure of livelihood contribution of CF and denotes
remunerated work opportunities Prakash et al (2003) Nominal
Increase in employment = 1 Decrease in
employment = 2 No change in employment
=3
Development
infrastructures
A measure of livelihood impact of CF and denotes any
constructed works (roads, halls, schools, electricity etc)
Sun (2007) and Vyamana
(2009) Nominal
Improvement =1
No improvement =2
Fuel wood
availability
A measure of livelihood and defined here as the average
distance walk to collect fuel wood from CF
Bandyopadhyay and
Priya (2004) Scale
Forest cover and
stands
A measure of the conservation impact of CF and evaluates
changes in forest structures
Sreedharan and
Dhanapal, 2005;
(Aggarwal et al. 2006).
Ordinal Major Increase= 1 Minor Increase = 2
No change = 3 Major decrease= 4
Minor decrease= 5
Incidence of Wildlife
sightings, sounds and
traces
A measure of the conservation impact of CF and assess changes
in wildlife population
Mehlman et al.( 2006);
Ongie (2013) Nominal
Increase incidence=1 Decrease incidence =2
No change in incidence = 3 Don’t know= 4
Adoption of
sustainable practices
A proxy for the conservation impact of CF and assess
community members adoption of sustainable forest exploitation
practices
Sjoholm and Louno
(2002); Mustalahti
(2006); Roe et al. (2009)
Nominal Yes= 1 No=2
Don’t know= 3
Regeneration It is an indicator of forest conservation under CF and measures
the incidence of reforestation and afforestation activities
Kaba (2007); Lund and
Treue (2008) Nominal
Yes= 1 No =2
Don’t know= 3
Environmental
awareness
It is an indicator of forest conservation and it assess changes in
community member's knowledge of the importance of forest
protection
Ekindi (2011); Ongie
(2013) Nominal
Increase=1 Decrease=2
No change=3 Don’t know = 4
Participation It is an indicator of forest governance and measures community
members inclusion in forest management decision-making
Singh and Sharma (2010)
Yufanyi Movuh (2012) Nominal
Increase in participation=1 Decrease in
participation=2 No change in participation=3
Equity It also an indicator of governance and measures changes in
fairness in the sharing of forest resources benefits
Roe et al.( 2009); FAO
(2014) Nominal
Increase in equity = 1 Decrease in equity = 2
No change in equity = 3 Don’t know =4
Source: Field Work 2014
87
Appendix 3.3 : Questionnaire
A. Socio-demographic profile of respondents
1. Location: Bakingili Woteva Bimbia Bonadikombo
2. Gender: Male Female
3. Age group: 15 – 24 25 – 34 35 – 44 45 – 54 ≥ 55
4. Level of education: No formal Primary Secondary University
5. Occupation: Agriculture Forestry Business Wage labour
Fishing Livestock Student Others
6. Income level: ≤ 50,000 50,001–100,000 100,001–150,000 ≥150,000
7. Marital status: Single Married Separated Divorced Widowed
8. Duration of stay in the area: 1 – 5 6 – 10 11 – 15 ≥ 16
9. Origin: Indigene Non-indigene
10. Member of FUG: Yes No
B. Forest use and dependence
1. Do you use the community forest? Yes No
If yes, what do you use the forest for? Timber exploitation NTFPs
Cultural rites Farming Recreation
2. What Non-Timber Forest Products (NTFPs) do you harvest from the community
forest? Fuel wood Medicinal plants and barks Forest fruits
Spices and condiments Bush meat
3. What do you harvest this forest resources for? Consumption Sales
If for consumption, forest products account for what proportion of your
household food consumption: ………..%
If for sales, forest activities account for what proportion of your income
?......................%
C. Livelihoods Outcomes
1. Has the advent of the community forest affected income from the sales of forest
products in the area? Yes No Don’t know
- If yes how? Increase Decrease
2. Has the advent of the community forest affected employment opportunities in
the area? Yes No Don’t Know
- If yes how? Increase Decrease
3. Has the advent of community forest led to the improvement of community
infrastructures? Yes No Don’t know
4. Distanced walked to collect fuel food? Before CF……..After CF……..
D. Conservation or ecological outcomes
1. How has forest cover change over the past years 20 years? Major decline
Minor Decline No change Minor increase Major increase
88
2. Has CF improved the incidence of fauna sightings? Yes No Don’t know
3. Has the advent of community forest improved the adoption of sustainable forest
practices? Yes No Don’t know
- If yes, what are the current fuelwood practices? Collection of fallen
Branches Harvesting from mature trees Cutting of dry branches
- If yes, what are the current prunus Africana harvesting practices? Sectional
harvesting Above ground harvesting Seasonal harvesting
Harvesting from mature trees
- If yes, what are the current fruit harvesting practices? Mature fruits only
Limited amount Fallen fruits Right moment
- If yes, what are the current farming practices? Agroforestry Manure
application Zero tillage slash and burn
- If yes, what are the current hunting practices? Selective trapping
Hunting of mature specie Non-female Seasonal hunting
4. Has the advent of the community forest improve community’s member’s
awareness of the importance of forest and the need for its conservation?
Yes No Don’t Know
5. Has the advent of CF, led to afforestation and reforestation in the study area?
Yes No Don’t Know
E. Governance outcome
6. Has participation in decision-making of forest resource management change
with the advent of CF? Yes No Don’t know
- If yes, participation by men? Increase Decrease
- If yes, participation by women? Increase Decrease
- If yes, participation by youths? Increase Decrease
- If yes, participation by non-indigenes? Increase Decrease
7. Has fairness in the sharing of forst benefits improved with CF? Yes No
Don’t know
- If yes who has benefited more than the others
Women Men
Aged Youths
Indigenes Non-indigene
89
Appendix 4.1: Community forest use across socio-demographic characteristics
QU
ES
TIO
NS
RE
SP
ON
SE
Bak
ing
ili
Bim
bia
-Bon
adik
om
bo
Wo
tev
a
To
tal
Mal
e
Fem
ale
15-2
4 y
ears
25-3
4 y
ears
35-4
4 y
ears
45-5
4 y
eas
≥ 5
5 y
ears
No
form
al e
du
Pri
mar
y
Sec
on
dar
y
Un
iver
sity
Ag
ricu
ltu
re
Fo
rest
ry
Pet
it tra
din
g
Fis
hin
g
Civ
ilse
rvic
e
Stu
den
t
Oth
ers
≤ 5
00
00F
CF
A
50001-1
00000F
CF
A
100001-1
50000F
CF
A
>1
500
01F
CF
A
Sin
gle
Mar
ried
Sep
erat
ed
Div
orc
ed
Wid
ow
ed
1-5
yea
rs
6-1
0 y
ears
11-1
5 y
ears
≥ 1
6 y
ears
Indig
ene
No
n-i
nd
igen
e
Yes 79 58 42 179 67 112 28 35 42 28 46 60 90 27 2 85 55 21 5 1 8 4 101 47 20 11 55 96 6 8 14 11 53 32 83 89 90
No 30 77 9 116 73 43 10 66 21 10 9 13 30 45 28 1 1 37 25 16 8 28 51 27 13 25 49 58 3 3 3 22 23 20 51 54 62
x²-statistics
P value
F
Exact P Value
Rp
df
95%
Significance
Socio-demographic characteristics
Location Gender Age group Level of education Primary Occupation Level of Income Marital Status Longevity in area Origin
Do you use
the
community
forest?
34.150 18.356 47.155 77.368 177.750 16.060 6.977 13.061 0.238
0.00 0.00 0.00 0.00 0.00 0.001 0.137 0.005 0.595
0.00 0.00 0.00 0.00 0.001 0.140 0.005 0.634
18.520 47.202 80.097 217.600 15.588 6.828
0.018 -0.249 -0.25 0.485 0.65 0.20 0.15 -0.083 0.031
x² Tabulated
2 1 4 3 6 3 4 3
ss ss ss ss ss ss ns ss
1
2.92 6.314 2.132 2.353 1.943 2.353 2.132 2.353 6.314
12.685 0.28334.51
0.00
ns
Source: Field Work 2014
90
Appendix 4.2: Regression analysis of the socio-demographic determinants (predictors) of
forest use
Variables Estimates Std. Errors Wald df Sig. Odd Ratios Exp
(β)
Location -.540 0.327 2.73 1 0.099 0.583
Gender 1.016 .401 6.41 1 .011 2.761
Agegroup .077 0.187 0.17 1 .682 1.08
Educ -.981 0.241 16.51 1 .000 0.375
Occupation -.859 0.137 39.13 1 .000 0.423
Incomelevel -.458 .202 5.14 1 0.023 0.632
Status .115 0.251 0.21 1 .646 1.122
Longevity -.664 0.247 7.24 1 0.007 0.515
Origin 1.310 0.508 6.65 1 .010 3.706
Membership -1.885 0.841 5.02 1 0.025 0.152
Constant 9.074 2.155 17.74 1 0.00 8725.831
Hosmer and Lameshow Test (X²=7.534;df=8;p=0.480) Omnibus Test (X²=216.015;df=10;p=0.00); Cox and Snell R²=0.519; Nagelkerke R²=0.703
Source: Field Work 2014
91
Appendix 4.3: Table for Multicollinearity
Lo
cati
on
Gen
der
Ag
e g
rou
p
Lev
el o
f
edu
cati
on
Pri
ma
ry
occ
up
ati
o
n
Inco
me
lev
el
Ma
rita
l
sta
tus
Lo
ng
evit
y
Ori
gin
Mem
ber
shi
p i
n C
IG
Location r 1 -.034 -.046 -.054 -.074 -.120* -.044 .060 -.106 -.313**
Sig.
.564 .429 .359 .205 .039 .456 .308 .068 .000
Gender r -.034 1 .100 -.117* -.114* .011 .130* .014 .178** -.018
Sig. .564
.088 .044 .050 .850 .026 .812 .002 .752
Age group r -.046 .100 1 -.288** -.285** .018 .555** .336** .005 -.205**
Sig. .429 .088
.000 .000 .755 .000 .000 .934 .000
Level of
education
r -.054 -.117* -.288** 1 .427** .282** -.124* -.290** .224** .276**
Sig. .359 .044 .000
.000 .000 .034 .000 .000 .000
Primary
occupation
r -.074 -.114* -.285** .427** 1 .199** -.203** -.250** .305** .447**
Sig. .205 .050 .000 .000
.001 .000 .000 .000 .000
Income level r -.120* .011 .018 .282** .199** 1 .164** -.299** .300** .057
Sig. .039 .850 .755 .000 .001
.005 .000 .000 .330
Marital status r -.044 .130* .555** -.124* -.203** .164** 1 .235** .075 -.111
Sig .456 .026 .000 .034 .000 .005
.000 .198 .057
Longevity r .060 .014 .336** -.290** -.250** -.299** .235** 1 -.352** -.139*
Sig. .308 .812 .000 .000 .000 .000 .000
.000 .017
Origin r -.106 .178** .005 .224** .305** .300** .075 -.352** 1 .159**
Sig. .068 .002 .934 .000 .000 .000 .198 .000
.006
Membership
in CIG
r -.313** -.018 -.205** .276** .447** .057 -.111 -.139* .159** 1
Sig. .000 .752 .000 .000 .000 .330 .057 .017 .006
* Correlation is significant at the 0.05 level ** Correlation is significant at the 0.01 level r=correlation coeff.
Source: Field Work 2
92
Appendix 4.4: Dependence on CF for household food, energy and material needs across socio-demographic characteristics.
QU
EST
ION
S
RE
SPO
NSE
Bak
ingi
li
Bim
bia-
Bon
adik
ombo
Wot
eva
Tota
l
Mal
e
Fem
ale
15-2
4 ye
ars
25-3
4 ye
ars
35-4
4 ye
ars
45-5
4 ye
as
≥ 5
5 ye
ars
No
form
al e
du
Prim
ary
Seco
ndar
y
Uni
vers
ity
Agr
icul
ture
Fore
stry
Petit
trad
ing
Fish
ing
Civ
ilser
vice
Stud
ent
Oth
ers
≤ 5
0000
FCFA
5000
1-10
0000
FCFA
1000
01-1
5000
0FC
FA
>150
001F
CFA
Sing
le
Mar
ried
Sepe
rate
d
Div
orce
d
Wid
owed
1-5
year
s
6-10
yea
rs
11-1
5 ye
ars
≥ 16
yea
rs
Indi
gene
Non
-indi
gene
1-30% 6 5 2 13 0 13 5 0 0 0 8 4 5 4 0 9 0 0 0 0 4 0 8 5 0 0 5 0 0 0 8 0 0 6 7 3 10
31-60% 32 22 17 71 30 41 5 23 17 17 9 24 32 12 3 30 21 14 3 1 0 2 40 11 13 7 17 51 1 1 1 5 28 6 32 39 32
61-100% 42 30 23 95 36 59 18 11 26 11 29 32 52 11 0 46 34 8 1 0 4 2 52 31 7 5 32 46 5 7 5 6 26 20 43 46 49
x²-statistics
P value
F
Exact P Value
Rp
df
x²-tabulated
Significance
Proportion of
household
food and
energy
consumption
from CF
39.858
2
2.132 2.92 1.86 1.943 1.782
0.000
ss ns
1.943 1.86 1.943 2.92
ns ss
Socio-demographic characteristics
Location Gender Age group Level of education Primary Occupation
13.580 71.920 17.164 4.505
x² Tabulated
x²=chi square Rp=Pearson's correlation coifficient P=Probability df=Degree of freedom ss=statistically significant ns=non-significant F =Fisher exact test statistics
Level of Income Marital Status Longevity in area Origin
0.602 8.519 42.054 8.761
ssss ns ss ss
6 8 6
0.000 0.009
0.00 0.201 0.00 0.04 0.00 0.006
0.105
4 2 8 6 12
0.963 0.002 0.000 0.155 0.015
0.612 9.993 47.347 8.019 30.946 12.376 46.755 16.891 4.429
0.98 0.006 0.111
Source: Field Work 2014
93
Appendix 4.5: Dependence on CF for monthly income across socio-demographic characteristics
QU
EST
ION
S
RE
SPO
NSE
Bak
ingi
li
Bim
bia-
Bon
adik
ombo
Wot
eva
Tota
l
Mal
e
Fem
ale
15-2
4 ye
ars
25-3
4 ye
ars
35-4
4 ye
ars
45-5
4 ye
as
≥ 5
5 ye
ars
No
form
al e
du
Prim
ary
Seco
ndar
y
Uni
vers
ity
Agr
icul
ture
Fore
stry
Petit
trad
ing
Fish
ing
Civ
ilser
vice
Stud
ent
Oth
ers
≤ 5
0000
FCFA
5000
1-10
0000
FCFA
1000
01-1
5000
0FC
FA
>150
001F
CFA
Sing
le
Mar
ried
Sepe
rate
d
Div
orce
d
Wid
owed
1-5
year
s
6-10
yea
rs
11-1
5 ye
ars
≥ 16
yea
rs
Indi
gene
Non
-indi
gene
1-30% 2 0 1 3 3 0 0 0 3 0 0 0 3 0 0 1 2 0 0 0 0 0 0 3 0 0 0 3 0 0 0 0 0 0 3 0 3
31-60% 8 6 7 21 8 13 1 7 9 3 1 6 11 2 2 12 4 3 1 0 1 0 15 3 0 3 6 15 0 0 0 1 6 0 14 15 6
61-100% 12 10 11 33 7 26 6 5 9 5 8 15 14 3 1 18 8 2 1 0 3 1 25 4 1 3 8 19 0 1 5 5 5 3 20 19 14
x²-statistics
P value
F
Exact P Value
Rp
df
x²-tabulated
Significance ns ss ns ns ns
x²=chi square Rp=Pearson's correlation coifficient P=Probability df=Degree of freedom ss=statistically significant ns=non-significant F =Fisher exact test statistics
0.055 0.347 0.097 -0.175 0.087 -0.117 0.208 -0.114 -0.066
x² Tabulated
4 2 8 6 10 6 8 6 2
2.132 2.92 1.86 1.943 1.782 1.943 1.86 1.943 2.92
ns ss ns ns
7.547 10.521 5.282 7.356 11.787 6.533 5.189 5.143
0.917 0.014 0.14 0.527 0.84 0.042 0.39 0.498 0.056
Socio-demographic characteristics
Location Gender Age group Level of education Primary Occupation Level of Income Marital Status Longevity in area Origin
Proportion of
monthly
income from
CF
1.599 8.554 13.265 5.563 5.481 15.953 6.219 6.313 5.704
0.816 0.010 0.103 0.474 0.857 0.014 0.399 0.389 0.058
1.463
Appendix 4.6: The contribution of Community Forestry on income across socio-demographic characteristics
QUE
STIO
NS
RESP
ONS
E
Baki
ngili
Bim
bia-
Bona
diko
mbo
Wot
eva
Total
Male
Fem
ale
15-2
4 ye
ars
25-3
4 ye
ars
35-4
4 ye
ars
45-5
4 ye
as
≥ 5
5 ye
ars
No
form
al ed
u
Prim
ary
Seco
ndar
y
Uni
vers
ity
Agr
icultu
re
Fore
stry
Petit
trad
ing
Fish
ing
Civi
lserv
ice
Stud
ent
Oth
ers
≤ 5
0000
FCFA
5000
1-10
0000
FCFA
1000
01-1
5000
0FCF
A
>150
001F
CFA
Sing
le
Mar
ried
Sepe
rated
Div
orce
d
Wid
owed
1-5
year
s
6-10
yea
rs
11-1
5 ye
ars
≥ 16
yea
rs
Indi
gene
Non
-indi
gene
Increase 15 27 11 53 35 18 2 12 15 13 11 16 26 8 3 20 14 9 0 1 2 7 13 23 5 12 9 32 1 1 10 3 12 13 25 24 29
Decrease 18 13 5 36 15 21 7 2 5 7 15 10 17 6 3 9 14 4 1 0 5 3 25 4 7 0 14 16 1 3 2 2 9 7 18 12 24
Unchange 62 75 22 159 61 98 26 60 27 18 28 38 64 37 20 50 26 27 21 7 9 19 91 35 13 20 55 88 5 7 4 17 51 23 68 80 79
Don’t Know 14 20 13 47 29 18 3 27 16 0 1 9 13 21 4 7 2 18 8 9 0 3 23 12 8 4 26 18 2 0 1 11 4 9 23 27 20
x²-statistics
P value
F
Exact P Value
Rp
df
x²-tabulated
Significance
x² Tabulated
6 3 12 9 18 9 12 9 3
40.113
0.00
35.86
0.00
20.603
0.01
5.221
0.156
ss ss ss
1.734 1.833 1.782
0.119
x²=chi square Rp=Pearson's correlation coifficient P=Probability df=Degree of freedom ss=statistically significant ns=non-significant F= Fisher's exaxt test statistics
19.911
0.018
-0.082
1.833
ss ns
2.353
-0.094
ss ss ss
Longevity in area Origin
0.154
ns
0.158
9.082
0.165
16.88
0.001
74.696
0.00
16.189
0.04
72.86
0.00
17.648 69.170 36.023 38.410 5.251
Primary Occupation Level of Income Marital Status
What effect
has the CF
had on income
of community
members. 9.412 16.918 63.913
0.007 0.078 -0.271
0.152 0.001 0.00 0.039
-0.124
Socio-demographic characteristics
Location Gender Age group Level of education
-0.263
1.943 2.353 1.782 1.833
0.00 0.00 0.00
Source: Field Work 2014
94
Appendix 4.7: Contribution of CF on employment across socio-demographic characteristics
QU
EST
ION
S
RE
SPO
NSE
Bak
ingi
li
Bim
bia-
Bon
adik
ombo
Wot
eva
Tota
l
Mal
e
Fem
ale
15-2
4 ye
ars
25-3
4 ye
ars
35-4
4 ye
ars
45-5
4 ye
as
≥ 5
5 ye
ars
No
form
al e
du
Prim
ary
Seco
ndar
y
Uni
vers
ity
Agr
icul
ture
Fore
stry
Petit
trad
ing
Fish
ing
Civ
ilser
vice
Stud
ent
Oth
ers
≤ 5
0000
FCFA
5000
1-10
0000
FCFA
1000
01-1
5000
0FC
FA
>150
001F
CFA
Sing
le
Mar
ried
Sepe
rate
d
Div
orce
d
Wid
owed
1-5
year
s
6-10
yea
rs
11-1
5 ye
ars
≥ 16
yea
rs
Indi
gene
Non
-indi
gene
Increase 9 7 12 28 15 13 5 8 3 6 6 5 17 5 1 11 7 2 3 0 2 3 15 7 5 1 11 14 0 0 3 3 9 3 13 16 12
Decrease 25 33 5 63 28 35 10 15 18 11 9 25 18 15 5 16 15 9 7 2 4 10 34 9 4 16 19 35 4 1 4 1 24 16 22 28 35
Unchange 59 75 19 153 73 80 14 54 34 18 33 35 65 38 15 51 26 32 13 10 7 14 74 43 21 15 52 77 4 10 10 21 40 22 70 73 80
Don’t Know 16 20 15 51 24 27 9 24 8 3 7 8 20 14 9 8 8 15 7 5 3 5 29 15 3 4 22 28 1 0 0 8 3 11 29 26 25
x²-statistics
P value
F
Exact P value
Rp
df
95%
Significance
14.548 32.118
0.702
What effect
has the CF
had on
employment
opportunities
in the
community?
0.001 0.883 0.090
x²=chi square Rp=Pearson's correlation coifficient P=Probability df=Degree of freedom ss=statistically significant ns=non-significant
ss ns ns ss ns ss ns ss
x² Tabulated
6 3 12 9 18 9 12
ns
39
1.943 2.353 1.782 1.833
Marital Status Longevity in area Origin
1.734 1.833 1.782 1.833 2.353
-0.002 0.014 -0.079 0.146 0.050 -0.057 -0.083 0.060 0.080
0.040 0.201 0.014 0.166 0.000
25.123 0.656 18.956 18.346 20.513 21.348 16.573 26.285
19.439
1.416
Socio-demographic characteristics
Location Gender Age group Level of education Primary Occupation
22.538
Level of Income
0.681 17.638 22.742 20.81 1.424
0.001 0.885 0.078 0.04 0.201 0.014 0.199 0.002 0.698
Source: Field Work 2014
Appendix 4.8: Contribution of CF to community development infrastructure across socio-demographic characteristics
QU
EST
ION
S
RE
SPO
NSE
Bak
ingi
li
Bim
bia-
Bon
adik
ombo
Wot
eva
Tota
l
Mal
e
Fem
ale
15-2
4 ye
ars
25-3
4 ye
ars
35-4
4 ye
ars
45-5
4 ye
as
≥ 5
5 ye
ars
No
form
al e
duca
tion
Prim
ary
Seco
ndar
y
Uni
vers
ity
Agr
icul
ture
Fore
stry
Petit
trad
ing
Fish
ing
Civ
ilser
vice
Stud
ent
Oth
ers
≤ 5
0000
FCFA
5000
1-10
0000
FCFA
1000
01-1
5000
0FC
FA
>150
001F
CFA
Sing
le
Mar
ried
Sepe
rate
d
Div
orce
d
Wid
owed
1-5
year
s
6-10
yea
rs
11-1
5 ye
ars
≥ 16
yea
rs
Indi
gene
Non
-indi
gene
Yes 17 26 31 74 41 33 4 26 22 13 9 27 31 13 3 26 19 19 4 0 1 5 40 17 13 4 23 46 0 3 2 4 18 10 42 42 32
No 79 96 17 192 88 104 29 66 31 24 42 42 81 50 19 55 36 35 25 4 11 26 105 51 13 23 67 97 8 8 12 10 55 39 88 95 97
Don’t know 13 13 3 29 11 18 5 9 10 1 4 4 8 9 8 5 1 4 1 13 4 1 7 6 7 9 14 11 1 0 3 19 3 3 4 6 23
x²-statistics
P value
F
Exact P value
Rp
df
95%
Significance
37.065
0.00
3.10
0.206
19.421
0.013
18.682
0.004
72.453
0.00
25.253
0.00
10.267
0.173
61.524
0.00
11.335
0.003
ss ss ns ss ss
x²=chi square Rp=Pearson's correlation coifficient P=Probability df=Degree of freedom ss=statistically significant ns=non-significant F=Fishers exact test statistics
0.004
x² Tabulated
4 2 8 6 12 6 8 6 2
2.132 2.92 1.86 1.943 1.782 1.934 1.86 1.943 2.92
ss ns ss ss
11.073
-0.275 0.103 -0.052 0.244 0.234 0.170 0.021 -0.299 0.169
Has CF
improved on
community
development
infrastructures
in the
community?
42.510 3.133 18.086 21.060 109.822 26.834 11.004 99.079
0.000 0.209 0.021 0.002 0.000 0.000 0.201 0.000
Socio-demographic characteristics
Location Gender Age group Level of education Primary Occupation Level of Income Marital Status Longevity in area Origin
Source: Field Work 2014
95
Appendix 4.9: Forest cover and stands across socio-demographic characteristics Q
UE
STIO
NS
RE
SPO
NSE
Bak
ingi
li
Bim
bia-
Bon
adik
ombo
Wot
eva
Tota
l
Mal
e
Fem
ale
15-2
4 ye
ars
25-3
4 ye
ars
35-4
4 ye
ars
45-5
4 ye
as
≥ 5
5 ye
ars
No
form
al e
duca
tion
Prim
ary
Seco
ndar
y
Uni
vers
ity
Agr
icul
ture
Fore
stry
Petit
trad
ing
Fish
ing
Civ
ilser
vice
Stud
ent
Oth
ers
≤ 5
0000
FCFA
5000
1-10
0000
FCFA
1000
01-1
5000
0FC
FA
>150
001F
CFA
Sing
le
Mar
ried
Sepe
rate
d
Div
orce
d
Wid
owed
1-5
year
s
6-10
yea
rs
11-1
5 ye
ars
≥ 16
yea
rs
Indi
gene
Non
-indi
gene
Major Decline 21 39 5 65 19 46 11 11 17 7 19 13 38 10 4 14 15 19 4 0 4 9 21 26 10 8 12 43 3 1 6 6 19 10 30 21 44
Minor Decline 33 21 8 62 34 28 7 33 13 7 2 10 18 25 9 14 9 8 14 10 4 3 34 14 6 8 32 27 2 0 1 12 13 12 25 38 24
No change 5 10 5 20 13 7 2 4 11 0 3 11 4 3 2 4 6 4 1 0 1 4 14 4 0 2 12 8 0 0 0 1 9 4 6 12 8
Minor Increase 35 44 24 103 50 53 13 40 16 21 13 27 40 24 12 35 18 21 7 7 3 12 63 17 7 16 36 56 3 3 5 11 25 21 46 50 53
Major Increase 15 21 9 45 24 21 5 13 6 3 18 12 20 10 3 19 8 6 4 0 4 4 20 13 10 2 12 20 1 7 5 3 10 5 27 22 23
x²-statistics
P value
F
Exact P value
Rp
df 4
95%
Significance
x² Tabulated
64.591
0.019 0.011 0.00 0.01 0.01 0.002 0.00 0.247 0.018
-0.036 -0.092 -0.077 0.067 0.077 -0.064
0.019 0.011 0.000 0.289
18.477 13.272 29.855 54.081 33.154 45.242 14.184 11.981
Origin
How has
Community
Forestry
affected forest
cover and
stands?
18.277 13.155 63.649 31.888 52.490 31.142 47.765 14.900 11.496
0.114 -0.121 0.045
Location Gender Age group Level of education Primary Occupation Level of Income Marital Status Longevity in area
Socio-demographic charcateristics
2.132
0.170
8 16 12 24 12 16 12 4
0.001 0.001 0.002 0.00
ns
1.86 2.132 1.746 1.782 1.711 1.782 1.746 1.782
ss ss ss ss
x²=chi square Rp=Pearson's correlation coifficient P=Probability df=Degree of freedom ss=statistically significant ns=non-significant CF=Community Forest F= Fishers Exact Test Value
ss ssss ss
Source: Field Work 2014
96
Appendix 4.10: Incidence of wildlife sightings, sounds and traces across socio-demographic characteristics. Q
UE
STIO
NS
RE
SPO
NSE
Bak
ingi
li
Bim
bia-
Bon
adik
ombo
Wot
eva
Tota
l
Mal
e
Fem
ale
15-2
4 ye
ars
25-3
4 ye
ars
35-4
4 ye
ars
45-5
4 ye
as
≥ 5
5 ye
ars
No
form
al e
duca
tion
Prim
ary
Seco
ndar
y
Uni
vers
ity
Agr
icul
ture
Fore
stry
Petit
trad
ing
Fish
ing
Civ
ilser
vice
Stud
ent
Oth
ers
≤ 5
0000
FCFA
5000
1-10
0000
FCFA
1000
01-1
5000
0FC
FA
>150
001F
CFA
Sing
le
Mar
ried
Sepe
rate
d
Div
orce
d
Wid
owed
1-5
year
s
6-10
yea
rs
11-1
5 ye
ars
≥ 16
yea
rs
Indi
gene
Non
-indi
gene
Increase 59 31 31 121 67 54 12 40 34 12 23 42 43 28 8 45 28 13 12 10 6 7 75 18 16 12 56 55 0 5 5 14 28 26 53 66 55
Decrease 32 68 16 116 50 66 23 47 25 13 8 26 40 34 16 26 15 36 12 6 9 12 46 34 14 22 40 61 8 5 2 14 41 22 39 54 62
Unchanged 13 22 3 38 16 22 2 12 1 0 23 2 26 8 2 12 6 5 6 0 1 8 17 19 2 0 4 24 1 0 9 3 5 2 28 12 26
Don’t know 5 14 1 20 7 13 1 2 3 13 1 3 11 2 4 3 7 4 0 1 0 5 14 3 1 2 4 14 0 1 1 2 2 2 14 11 9
x²-statistics
P value
F
Exact P value
Rp
df
95%
Significance
95.764
Socio-demographic characteristics
x²=chi square Rp=Pearson's correlation coifficient P=Probability df=Degree of freedom ss=statistically significant ns=non-significant CF=Community Forest F= Fishers Exact Test Value
Location Gender Age group Level of education Primary Occupation Level of Income Marital Status Longevity in area Origin
35.460 51.899 27.284
0.084
0.008 0.127 0.156 0.126 0.137 -0.018 0.201 0.124 0.085
0.000 0.000 0.000 0.000 0.001
6.641
3
1.943 2.533 1.782 1.833 1.734 1.833 1.782 1.833 2.353
6 3 12 9 18 9 12 9
How has
Community
Forestry
affected
incidence of
wildlife
sightings,
sounds and
traces?
0.000 0.133 0.000
5.538 32.09 49.038
x² Tabulated
36.243 5.603 116.429 30.688 45.422
38.784 50.348 28.237 6.643
0.133 0.00 0.00 0.00 0.00 0.00 0.001 0.084
ss ns ss ss ss ss ss ss ns
Source: Field Work 2014
97
Appendix 4.11: Analysis of environmental awareness across socio-demographic characteristics Q
UE
STIO
NS
RE
SPO
NSE
Bak
ingi
li
Bim
bia-
Bon
adik
ombo
Wot
eva
Tota
l
Mal
e
Fem
ale
15-2
4 ye
ars
25-3
4 ye
ars
35-4
4 ye
ars
45-5
4 ye
as
≥ 5
5 ye
ars
No
form
al e
duca
tion
Prim
ary
Seco
ndar
y
Uni
vers
ity
Agr
icul
ture
Fore
stry
Petit
trad
ing
Fish
ing
Civ
ilser
vice
Stud
ent
Oth
ers
≤ 5
0000
FCFA
5000
1-10
0000
FCFA
1000
01-1
5000
0FC
FA
>150
001F
CFA
Sing
le
Mar
ried
Sepe
rate
d
Div
orce
d
Wid
owed
1-5
year
s
6-10
yea
rs
11-1
5 ye
ars
≥ 16
yea
rs
Indi
gene
Non
-indi
gene
Increase 86 75 39 200 92 108 25 60 48 28 39 50 77 51 22 62 45 36 13 17 11 16 107 43 18 32 62 120 2 2 14 27 54 34 85 89 111
Decrease 8 22 2 32 29 3 0 25 3 3 1 3 17 10 2 4 3 1 16 0 0 8 29 3 0 0 25 5 0 0 2 3 6 6 17 24 8
Unchanged 9 23 6 38 12 26 5 14 10 5 4 11 18 5 4 11 4 13 1 0 4 5 11 19 5 3 11 19 7 0 1 1 9 9 19 18 20
Don’t know 6 15 4 25 7 18 8 2 2 2 11 9 8 6 2 9 4 8 0 0 1 3 5 9 10 1 6 10 0 9 0 2 7 3 13 12 13
x²
P value
F
Exact P value
Rp
df
95%
Significance
33.92118.101 10.4246.932141.54663.965
0.000
56.720
0.000
10.636
0.287
93.232
0.000 0.000 0.000 0.643
x²=chi square statistics Rp=Pearson's correlation coifficient F=Fisher's Exact Test Statistics P=Probability df=Degree of freedom ss=statistically significant ns=non-significant CF=Community Forest
ss ss ns ss ss ss ns ss
0.0150.005
0.322 0.000 0.000 0.000 0.625
Location Gender Age group Level of education Primary Occupation Level of Income Marital Status Longevity in area Origin
10.300
0.086 0.087 -0.016 -0.086 0.039 0.069 0.095 0.101 -0.049
18.653 31.722 58.867 10.367 95.433 64.366 143.296 7.121
0.016
x² Tabulated
6 3 12 9 18 9 12 9 3
1.943 2.533 1.782 1.833 1.734 1.833 1.782 1.833 2.353
How has
Community
Forestry
affected
environmental
awareness
and
importance of
forest
resource
conservation?
0.005 0.000 0.000
Source: Field Work 2014
Appendix 4.12: Adoption of sustainable practices across socio-demographic characteristics
QU
EST
ION
S
RE
SPO
NSE
Bak
ingi
li
Bim
bia-
Bon
adik
ombo
Wot
eva
Tota
l
Mal
e
Fem
ale
15-2
4 ye
ars
25-3
4 ye
ars
35-4
4 ye
ars
45-5
4 ye
as
≥ 5
5 ye
ars
No
form
al e
duca
tion
Prim
ary
Seco
ndar
y
Uni
vers
ity
Agr
icul
ture
Fore
stry
Petit
trad
ing
Fish
ing
Civ
ilser
vice
Stud
ent
Oth
ers
≤ 5
0000
FCFA
5000
1-10
0000
FCFA
1000
01-1
5000
0FC
FA
>150
001F
CFA
Sing
le
Mar
ried
Sepe
rate
d
Div
orce
d
Wid
owed
1-5
year
s
6-10
yea
rs
11-1
5 ye
ars
≥ 16
yea
rs
Indi
gene
Non
-indi
gene
Yes 84 58 34 176 76 100 27 53 38 19 39 48 74 38 16 58 34 29 21 7 12 15 99 41 16 20 65 93 3 6 9 16 46 34 80 89 87
No 13 65 11 89 49 40 9 36 18 17 9 22 34 23 10 20 16 23 8 5 3 14 42 27 8 12 30 47 3 2 7 11 24 14 40 40 49
Don’t know 12 12 6 30 15 15 2 12 7 2 7 3 12 11 4 8 6 6 1 5 1 3 11 6 9 4 9 14 3 3 1 6 6 4 14 14 16
x²
P value
F
Exact P value
Rp
df
95%
Significance ss ns ns ns ns ss ns ns ns
x²=chi square statistics Rp=Pearson's correlation coifficient F=Fisher's Exact Test Statistics P=Probability df=Degree of freedom ss=statistically significant ns=non-significant CF=Community Forest
x² Tabulated
4 2 8 6 12 6 8 6 2
2.132 2.92 1.86 1.943 1.782 1.943 1.86 1.943 2.92
14.341 6.189 17.020 12.232 9.822 3.974 0.805
0.00 0.180 0.073 0.333 0.149 0.051 0.229 0.683 0.695
0.079 -0.067 -0.004 0.132 0.059 0.129 0.073 -0.045 0.035
Socio-demographic characteristics
Location Gender Age group Level of education Primary Occupation Level of Income Marital Status Longevity in area Origin
Has
community
forestry
fostered the
adoption of
sustainable
practices?
40.421 3.429 13.699 6.399 18.634 14.705 11.311 4.126 0.792
0.00 0.187 0.090 0.380 0.098 0.023 0.183 0.660 0.673
41.557 3.434
Source : Field Work 2014
98
Appendix 4.13: Analysis of regeneration across socio-demographic characteristics Q
UE
STIO
NS
RE
SPO
NSE
Bak
ingi
li
Bim
bia-
Bon
adik
ombo
Wot
eva
Tota
l
Mal
e
Fem
ale
15-2
4 ye
ars
25-3
4 ye
ars
35-4
4 ye
ars
45-5
4 ye
as
≥ 5
5 ye
ars
No
form
al e
duca
tion
Prim
ary
Seco
ndar
y
Uni
vers
ity
Agr
icul
ture
Fore
stry
Petit
trad
ing
Fish
ing
Civ
ilser
vice
Stud
ent
Oth
ers
≤ 5
0000
FCFA
5000
1-10
0000
FCFA
1000
01-1
5000
0FC
FA
>150
001F
CFA
Sing
le
Mar
ried
Sepe
rate
d
Div
orce
d
Wid
owed
1-5
year
s
6-10
yea
rs
11-1
5 ye
ars
≥ 16
yea
rs
Indi
gene
Non
-indi
gene
Yes 75 79 33 187 94 93 24 68 35 21 39 56 72 42 17 63 31 35 15 12 9 22 101 47 20 19 65 95 7 10 10 21 45 35 86 88 99
No 25 39 14 78 37 41 9 27 22 8 12 16 28 24 10 18 15 16 11 5 5 8 40 17 6 15 31 39 2 1 5 8 23 14 33 45 33
Don’t know 9 17 4 30 9 21 5 6 6 9 4 1 20 6 3 5 10 7 4 0 2 2 11 10 7 2 8 20 0 0 2 4 8 3 15 10 20
x²
P value
F
Exact P value
Rp
df
95%
Significance
x²=chi square statistics Rp=Pearson's correlation coifficient F=Fisher's Exact Test Statistics P=Probability df=Degree of freedom ss=statistically significant ns=non-significant CF=Community Forest
ns ns ns ss ns ns ns ns ns
-0.020 0.019
x² Tabulated
4 2 8 6 12 6 8 6 2
2.132 2.92 1.86 1.943 1.782 1.943 1.86 1.943 2.92
5.509
0.536 0.119 0.131 0.007 0.256 0.080 0.617 0.894 0.063
5.557
0.509 0.119 0.086 0.012 0.346 0.061 0.479 0.894 0.062
Has CF forster
regeneration
(reforestation
and
afforestation)
3.299 4.259 13.846 16.381 13.321 12.059 7.542 2.262
3.149 4.244 12.273 16.381 14.740 11.030 5.982 2.424
0.044 0.106 0.010 0.122 0.019 0.087 -0.028
Socio-demographic characteristics
Location Gender Age group Level of education Primary Occupation Level of Income Marital Status Longevity in area Origin
Source: Field Work 2014
Appendix 4.14: Participation in forest resources management across socio-demographic characteristics
QU
EST
ION
S
RE
SPO
NSE
Bak
ingi
li
Bim
bia-
Bon
adik
ombo
Wot
eva
Tota
l
Mal
e
Fem
ale
15-2
4 ye
ars
25-3
4 ye
ars
35-4
4 ye
ars
45-5
4 ye
as
≥ 5
5 ye
ars
No
form
al e
duca
tion
Prim
ary
Seco
ndar
y
Uni
vers
ity
Agr
icul
ture
Fore
stry
Petit
trad
ing
Fish
ing
Civ
ilser
vice
Stud
ent
Oth
ers
≤ 5
0000
FCFA
5000
1-10
0000
FCFA
1000
01-1
5000
0FC
FA
>150
001F
CFA
Sing
le
Mar
ried
Sepe
rate
d
Div
orce
d
Wid
owed
1-5
year
s
6-10
yea
rs
11-1
5 ye
ars
≥ 16
yea
rs
Indi
gene
Non
-indi
gene
Yes 69 89 32 190 82 108 28 65 37 23 37 44 77 53 16 55 35 31 21 16 11 21 83 51 30 26 65 94 8 11 12 30 43 38 79 86 104
No 28 40 8 76 36 40 7 19 26 7 17 21 35 8 12 20 19 15 7 0 5 10 44 21 1 10 27 43 1 0 5 3 18 14 41 35 41
Don’t know 12 6 11 29 22 7 3 17 0 8 1 8 8 11 2 11 2 12 2 1 0 1 25 2 2 0 12 17 0 0 0 0 15 0 14 22 7
x²-statistics
P value
F
Exact P value
Rp
df
95%
Significance ss ss ns
0.008
x² Tabulated
4
1.86 1.943 1.782 1.943
2 8 6 12 6 8 6 2
2.132 2.92 1.86 1.943 2.92
-0.010 -0.089 -0.232 -0.124 0.088 -0.143
9.765
0.008
ssss ss ss ss ss
x²=chi square Rp=Pearson's correlation coifficient P=Probability df=Degree of freedom ss=statistically significant ns=non-significant CF=Community Forest F=Fishers Exact statistics
Socio-demographic characteristics
Location Gender Age group Level of education Primary Occupation Level of Income Marital Status Longevity in area Origin
Has CF
improved
community
participation
in forest
management ?
14.171 10.792 30.746 14.663 23.882 28.523 11.559 26.472
0.008 0.004 0.000 0.023 0.021 0.000 0.172 0.000
0.000
9.672
0.031 -0.167 -0.019
0.004 0.014
15.473 29.032
0.040
30.82
0.000
9.588
0.241
27.54813.465
0.008
36.101
0.00
10.783
Source: Field Work 2014
99
Appendix 4.15: Analysis of equity in benefit sharing across socio-demographic characteristics Q
UE
STIO
NS
RE
SPO
NSE
Bak
ingi
li
Bim
bia-
Bon
adik
ombo
Wot
eva
Tot
al
Mal
e
Fem
ale
15-2
4 ye
ars
25-3
4 ye
ars
35-4
4 ye
ars
45-5
4 ye
as
≥ 5
5 ye
ars
No
form
al e
duca
tion
Pri
mar
y
Sec
onda
ry
Uni
vers
ity
Agr
icul
ture
For
estr
y
Pet
it tr
adin
g
Fis
hing
Civ
ilser
vice
Stu
dent
Oth
ers
≤ 5
0000
FC
FA
5000
1-10
0000
FC
FA
1000
01-1
5000
0FC
FA
>15
0001
FC
FA
Sin
gle
Mar
ried
Sep
erat
ed
Div
orce
d
Wid
owed
1-5
year
s
6-10
yea
rs
11-1
5 ye
ars
≥ 1
6 ye
ars
Indi
gene
Non
-indi
gene
Yes 29 17 34 80 38 42 12 16 23 13 16 26 38 13 3 42 20 11 2 0 3 2 46 22 5 7 27 42 3 1 7 4 19 17 40 50 30
No 73 104 14 191 93 98 23 74 37 22 35 43 72 52 24 39 33 41 26 15 13 24 95 44 27 25 66 102 5 10 8 25 50 32 84 80 111
Don’t know 7 14 3 24 9 15 3 11 3 3 4 4 10 7 3 5 3 6 2 2 0 6 11 8 1 4 11 10 1 0 2 4 7 3 10 13 11
x²
P value
F
Exact P value
Rp
df
95%
Significance ss ns ns ss ss ns ns ns ss
x²=chi square statistics Rp=Pearson's correlation coifficient F=Fisher's Exact Test Statistics P=Probability df=Degree of freedom ss=statistically significant ns=non-significant CF=Community Forest
x² Tabulated
4 2 8 6 12 6 8 6 2
2.132 2.92 1.86 1.943 1.782 1.943 1.86 1.943 2.92
12.120 12.271 57.202 7.355 7.428 6.187 9.933
0.00 0.617 0.134 0.049 0.000 0.277 0.432 0.396 0.007
-0.170 0.029 -0.075 0.177 0.318 0.093 -0.042 -0.109 0.119
Socio-demographic characteristics
Location Gender Age group Level of education Primary Occupation Level of Income Marital Status Longevity in area Origin
Has
community
forestry
improved
equity in
benefit
sharing?
55.659 1.171 11.602 11.774 50.707 7.562 7.459 5.813 9.933
0.00 0.585 0.170 0.067 0.000 0.262 0.488 0.444 0.007
51.836 1.054
Source: Field Work 2014