the contribution of community-based natural...

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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

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

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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

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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

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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


4.2.3.1 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


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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

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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


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


Appendix 4.12: Adoption of sustainable practices across socio-demographic


Appendix 4.13: Analysis of regeneration across socio-demographic characteristics .... 98

Appendix 4.14: Participation in forest resources management across socio-demographic


Appendix 4.15: Analysis of equity in benefit sharing across socio-demographic


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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

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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

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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

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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

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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

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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

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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

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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.

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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

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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.

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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.


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


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.

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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


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).


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.


4.2.3.1 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


4.2.4.1 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


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


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


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


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


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.


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


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


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



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


Location Gender Age group Level of education

-0.263

1.943 2.353 1.782 1.833

0.00 0.00 0.00


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


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


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




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


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


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


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


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


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


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 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



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


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




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



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


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 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



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


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