FACTORS INFLUENCING
HOUSEHOLDS’ PARTICIPATION IN FOREST MANAGEMENT IN THE NORTHERN REGION OF GHANA
Jamal
Mohammed
Faculty
of Business and Management Studies,
Koforidua
Technical University, Ghana
E-mail:
jamalmohammed@ktu.edu.gh
Anthony
Kofi Osei-Fosu
Kwame
Nkrumah University of Science and Technology, Ghana
E-mail:
akosei-fosu.cass@knust.edu.gh
Hadrat
Yusif
Kwame
Nkrumah University of Science and Technology, Ghana
E-mail:
hadraty@yahoo.co.uk
Submission: 09/02/2017
Revision: 20/03/2017
Accept: 11/05/2017
ABSTRACT
This
study assessed the factors influencing households’ participation in forest
management. The specific objective was to identify the determinants of
households’ participation in forest management in the Northern Region of Ghana.
The study used the cluster random sampling to sample the six (6) communities
out of nine (9) communities within the Tamale forest districts and purposively
interviewed respondents from these communities. The sample size was three
hundred and seventy (370). The logistic regression analysis result showed that,
sex, age, household size, education, benefit and location of the forest
positively influenced households’ participation in forest management. The
results showed that, all the aforementioned sociodemographic characteristics of
the respondents were significant in determining participation in forest
management. The study recommended that the government of Ghana should intensify
awareness creation and public education on the need for collaborative forest
management between local communities and forest management staff.
Keywords: Households
Participation, Forest Management, Logistic Regression
1. INTRODUCTION
The anticipated negative effects of
deforestation, environmental degradation, climate change and global warming is
a worldwide phenomenon without recourse to country specific (KAPINGA, 2015).
Bhusal (2014) reported that Reducing Emission from Deforestation and Forest Degradation, Conservation
of existing forest carbon and Enhancement of forest carbon
through sustainable forest management (REDD+) mechanisms
and policies as proposed by the United Nations is an important step puts forth
to reduce greenhouse gas emission and enhance forest
carbon storage capacity.
Onyekuru (2014) indicated that the developing countries,
especially those in Africa, are forecast to be the worst to be impacted by the
anticipated negative effects of the global environmental change. This view is
collaborated by IPCC (2014) study cited in Onyekuru (2014) that Africa warmed
by approximately 0.7°C during the 20th Century with a reduced rainfall intensively
hovering over large portions of the Sahel.
Population growth in Africa is
increasing (United Nations Population Division, 2013). This increased
population growth is applicable in Ghana and Northern Region (GBOGBO, 2011;
GSS, 2010). The increase in population growth is likely to be associated with:
a higher than the global average degree of change, high levels of dependence on
natural resources and forest goods and services, and a relatively low degree of
adaptive capacity (EASTAUGH, 2010 cited in KAPINGA, 2015).
Increases in
human population is likely to increase household size and this could lead to
increase in fuel wood dependence in Ghana and other developing countries. Kapinga
(2015) argued that, the majority of these households who depend on the forest
for their livelihood are from the rural periphery of the developing countries
and this has led to deforestation as a common feature.
The urban centres in developing
countries have served as market channels for fuel wood demand and thus
aggravates and fuels high dependency rate (MOHAMMED; OSEI-FOSU, 2016). Knight and Rosa (2012) suggest that household size has
become a dominant issue in socio-ecological literature because of the
important role demographic variables play in any societal set up.
While the household heavily depends
on the forest for fuel wood because of the size of the household, the
participation and level of management by households, communities in relation to
forests have been a subject of considerable debate in the literature.
Hagen
(2014) indicated that management of community forestry, with clear cut
policies, governance and programmes, and which are supported by the local
communities, has proven to be more successful compared with those without
community involvement even with good policies.
Rodgers
(2012) showed that, successful community forestry programmes have provided
impetus for REDD+, as regards best practices for forest governance and thus
reduces deforestation and forest degradation. In addition, the factors which
influence households’ participation should be thought through properly.
Indeed,
the Government of Ghana has recognized the contribution of the forest in terms
of income/ livelihoods of rural peoples, particularly fuel wood dependence and
the role any policy will impact on these people.
Damptey and Essel (2012) provide an account that
traditionally Ghana has been a tropical climate, with the southeast coast
comparatively warm and dry, whereas the southwest is hot and humid and the
north is hot and dry. This could be the consequences of climate change in Ghana
and other African countries and result in erratic rainfall patterns, major
flooding, rising sea levels, hot weather condition, especially in the northern
Ghana.
Unfortunately, Ghana and other Sub-Saharan Africa (SSA)
countries have contributed little to the global climate change unlike the major
polluters like China, US and India (LEGGETT,
2011). But are likely to suffer most from the effects
of continuing climate change.
The Northern Region has twenty four
(24) forest reserves and these serve the needs of communities (SAVANNAH
FORESTRY DIVISION, 2008). Individuals and communities close to these forests
are to collaborate with forest management to safeguard the forests. However,
often there exists conflicts between forests management and local communities
and these conflicts are as a result of the degree of forest dependence by
individuals.
This hampers collaborative forests
management between local communities and forests managers at the local level. What
drives local community members towards participation and collaborative forests
management at the local level has been a considerable debate.
Households receive huge amounts of
benefit from forests within their catchment area. Any policy which is to hinder
the benefit that they receive from the forest would attract resistance. In addition, given the important role of
active participation of households’ towards community forest management in the
Northern Region of Ghana, the major factors influencing households
participation becomes a greater task for policy makers.
The
main goal of the study was to assess the factors influencing households’
participation in forest management in the Northern Region of Ghana. The study
proposes to: identify the determinants of households’ participation in the
forest management in the Northern Region of Ghana.
The
research question is: what are the determinants which affect households’
participation in the forest management in the Northern Region of Ghana? The
study hypothesises that: households’ participation in forest management is not
influenced by the location of the forest to the communities.
Damptey and Essel
(2012) have argued that traditional knowledge has been the first form of
knowledge to engineer coping to climate change consequences related to over
exploitation of the forest at the local level; a more pragmatic knowledge based
systems is required to reduce the more severe burden of climate change through
the reduction in asymmetric information between households and government
forest managers.
The study is significant in
that information obtained from this study can provide ways for policy makers to
identify a collaborative approach to saving the forest while still allowing for
some amount of fuel wood dependence.
1.1.
Theoretical
Literature Review
This study reviewed theoretical literature related to the
objective of the study to offer insight into the factors which influence
households’ participation in forest management in the Northern Region of Ghana.
Figure 1: Schematic
Presentation of the theoretical framework
Source: Engida and Mengistu
(2013)
The level of participation by the community and
households in community forest management is hinged on the biophysical,
demographic and economic dimension of the populace as illustrated in 1.
1.2.
Households
Participation in Forest Management and Determinants
Bray et al.
(2002) cited in Meshack et al. (2006) reported that Community-based forest
management (CBFM) could be a pathway to promoting a win-win situation in forest
management strategies while still maintaining some amount of benefits received
by communities from the forests and also guaranteeing a functioning ecosystem
and maintenance of forest cover.
Aabeyir et al.
(2011) suggested that achieving sustainable woodland management and controlling
and ensuring fuel wood collection in a sustainable way is viewed as a major
challenge to Ghana’s traditional energy sector. Perhaps, the drivers of
households’ participation in community forest management are vital for
sustainable fuel wood collection.
Empirical
literature has explained some of the factors which influence households’
participation in forest management and these factors are regional and location
specific. Coulibaly-Lingani et al. (2011) assert that household members’
participation in forest management is influenced to some extent by internal and
external factors.
In addition,
biophysical and economic factors have motivated households to participate in
forest management. The household characteristics are seen as important
variables which influence households’ participation in forest management and
are therefore considered in this current study.
1.3.
Households
and Forest Degradation
The
quantification of deforestation and forest degradation is a difficult issue.
The Intergovernmental
Panel on Climate Change (IPCC) indicates that,
the actual deforested estimates for tropical countries
could not be accurately ascertained. Estimates have huge margins of error (WATSON et al., 2000 cited in FREDERIC et al., 2002)
Kissinger et al.,
(2012) postulate that households and forest users will continue to depend on
the forest in the future. However, current levels of REDD+ incentives will not
be enough to deal with the actual drivers of deforestation. While the REDD+
programme is supposed to provide a pathway by ensuring sustainable management
of forests, efficiency in the fuel wood collection and a better institutional framework
for countries are major factors which influence household’s participation in
forest management. Understanding these factors is crucial to determine how
REDD+ initiative would be received by households.
Damnyag (2012)
reported that there are several major causes of loss of forest cover in Ghana
and these include: wildfires, food crop farming, fuel wood harvesting, clearing
of forests for cocoa, logging and infrastructure expansion. These causes of
loss of forest cover are attributed to the action and inaction of households in
Ghana; the Northern Region is no exception. There are many households which
live around the Tamale Forest District and ascertaining their level of
participation in forest management is considered a step in the right direction.
1.4.
Empirical
Literature Review
Engida,
and Mengistu (2013) studied the determinants of community based forest management in Alamata,
Ethiopia. Their objective was to assess the determinants of household
participation level in community forest management. They focused on identifying
the effects of demographic, bio-physical and economic factors on households’
level of participation by way of using a logistic regression model.
The results
showed that age, gender, level of education and family size were found to have
a significant effect on the level of participation. From the biophysical view
point, factors such as distance from the forest and location, a place where the
respondents were located were found to correlate with the household’s level of
participation in community forest management (CFM). The household’s economic
benefit of forest was also found to be significant.
In addition,
Kissinger et al. (2012) studied the drivers of deforestation and forest
degradation which was a synthesis report for REDD+ Policymakers. They reported
that REDD+ implementation, sustainability hinges on understanding the actual
drivers of deforestation and changing the areas which are responsible
greenhouse gas (GHG) emissions from forests. Their study brings to bear the significance
of these drivers and how it impacts on REDD+ policy development and
implementation.
They recommended
that countries should continue to be involved in the climate change
negotiations and in-country interventions to reduce deforestation and forest degradation.
Their study provided a grounding for the significance of this current study as
households in the Northern Region of Ghana continue to depend on the forest for
fuel wood.
Lastly, a study
by Coulibaly-Lingani (2011) on factors influencing people's participation in
the forest management program in Burkina Faso, West Africa has shown some
relationship with the current study. Their study used a household survey of 165
members who belonged to forest management groups.
With the
assistance of factor analysis and multiple regression procedures, they showed
that gender, household size, income source, land tenure status and technical
assistance were the factors which influenced household members’ forest
management participation. They suggested that forest management participation,
which was equitable and gender sensitive, could improve participation in forest
management.
2. METHODOLOGY
This study was undertaken in the Northern region with
special focus on the Tamale forest district. The savannah forest division
(2008) reported that the Northern region has twenty-four (24) forest reserves,
of which seventeen (17) are natural forests and seven (7) are reserves
developed through Plantation. The districts in the Northern region of Ghana are
categorized into five (5) forest districts, namely: Tamale, Yendi,
Damango-Buipe, Walewale and Bole forest districts. Each forest district spans several
politically administrative districts and serve the needs of the fringe
households (HUSSEIN et al., 2015).
Hussein et al. (2015) reported that the Tamale forest
district borders on three (3) major political districts which are Tamale
Metropolitan Area, Tolon – Kumbungu District and the Savelugu – Nanton
Districts. The Sinsablegini forest reserve is the only natural forest located
in between Tugu and Labariga whereas Tamale fuel wood plantation, Kogni
plantation, Tamale Water Works forest reserve, the Ghana Education Service
plantation and the Agriculture Department plantation are all plantation
reserves.
2.2.
Sample
and Sampling Technique
The target population was fringe households or households
living closer to the Tamale Forest District. The study adopted a purposive
sampling approach to select six (6) communities (Zoborgo, Kulaa, Tugu,
Labariga, Kogni East and Kogni West) out of the nine (9) communities (Zoborgo,
Kulaa, Moya, Zakariyili, Chepsigu, Tugu and Labariga and two communities,
namely: Kogni East and Kogni West) living around the fringes of the Tamale
forest district and also to elicit information from households living along the
only natural forest reserve (Sinsanblegbini) and Tamale fuel wood plantation.
In addition, the Sinsanblegbini forest reserve was
established to provide ecosystem services like reducing soil erosion,
plantation for fuel wood, medicinal purpose, thatch for local housing and so on
(HUSSEINI, 2015). Whereas the Tamale
fuel wood plantation was a pilot programme for fuel wood harvest. These two forests are important to this study
as the study aimed to determine the factors which influence the households’ to
participate in forest management.
2.3.
Technique
for Data Analysis
3. MODEL SPECIFICATION
In order to explain households’ participation in the
study area, a logistic regression model was used. The study regressed
household’s participation as a dependent variable as a function of the
independent variables which are socioeconomic and demographic variables as
explained in economic theory and experience. The choice of the logistic
regression model is premised on the specification of the dependent variable as
binary in nature and in outcome. This is whether selected individuals
(household heads) decide to participate actively in forest management (coded 1)
or did not participate actively in forest management (coded 0).
1 = participate
Dependent variable = household’s
participation 0 = not participate
Independent variables = (socio-demographic
characteristics)
Therefore, the probability of a household participating
in forest management, Pr (Y;=1) is a joint probability likelihood function
evaluated at Xi β, where Xi is a matrix of independent variables and β is the
coefficient of the independent variable.
Following Tafere
(2013), the estimated logistic regression model is as follows:
HPFM = β0 + β1 AG + β2 SX + β3 MS+ β4 ED + β5 HS + β6 BD + β7 LO +
ε
Where HPFM-
refers to Household Participation in Forest Management; AG – Age of the
individual (household head); SX – Sex of respondent; ED – Education of
individual; HS – indicating the number of people in the household; BD – Benefit
derived from the forest; LO – Is the location /place where the household lives
close to the forest and MS – Marital status of respondents.
Participation in
forest management has to do with the involvement of fringe forest community
members and forest management in a collaborative decision towards protecting
livelihood and forest functioning (MUSYOKI et al., 2016). Participation
according Buttoud (1999) classifications included: auto-mobilization,
functional, passive and active (COULIBALY-LINGANI et al., 2011).
In the case of
auto-mobilisation, the stakeholders take centre stage in decision making and
this could be providing relevant information to community members. Whereas, in
functional participation, decision making is ‘double-edged’ where community members
contribute to discussions. In the case of active participation, decision making
is ‘bottom up’ approach and involves direct involvement of community members
and that of major players in the forestry sector. However, with passive
participation, community members or participants are direct receivers of
information without partaking in decision making (AGARWAL, 2001).
3.2.
Variable
Coding
3.3.
Expectation
The a prior expectations are that the age of the
respondent should have a positive relationship with the household participation
in forest management; sex and marital status of the respondents could have a
positive or negative relationship with household participation in forest management;
education, household size, benefit from the forest and location of the forest
are expected to have a positive relationship with household participation in
forest management.
This study discusses the results and findings as well as
linkage to empirical literature. The first results relate to the
sociodemographic characteristics of households within the selected communities
under study, which is shown in Table 1.
Table 1: Sociodemographic
characteristics of Households
|
Variable |
Total n=
370 n
(%) |
Participants
in forest management n=
83 |
Non-participant
in forest management n=
287 |
P-value |
|
Sex Male Female |
146(39.5) 224(60.5) |
2(2.4) 81(97.6) |
144(50.2) 143(49.8) |
<0.001 |
|
Age |
|
46.0 ± 3.7 |
39.8 ± 6.9 |
<0.001 |
|
Occupation Semi-skilled Unskilled |
111(30) 259(70) |
38(45.8) 45(54.2) |
73(25.4) 214(74.6) |
<0.001 |
|
Household
Size |
|
4.3 ± 1.6 |
3.5 ± 1.4 |
<0.001 |
|
Education No
formal Formal |
277(74.9) 93(25.1) |
43(51.8) 40(48.1) |
234(81.5) 53(18.5) |
<0.001 |
|
Monthly
Income |
|
374.3 ± 147.0 |
316.5 ± 153.5 |
<0.002 |
|
Marital
Status Married Unmarried |
296(80) 74(20) |
79(95.2) 4(4.8) |
217(75.6) 70(24.4) |
<0.001 |
Categorical
data are presented as frequencies (outside parentheses) and percentage (inside
parentheses). Continuous data are presented as mean ± standard deviation.
Source: Field data, 2016
Individuals who
participated in forest management were significantly (p< 0.001) older (46.0
± 3.7 years), had a larger household size (4.3 ± 1.6) and earned a greater
monthly income (Gh₵ 374.3 ± 147.0) compared to the individuals who did not
participate in forests management. The majority of the participants were
females (81 (97.6%)) and married (79(95.2%)).
A slightly
smaller minority of the respondents were illiterate (40(48.1%)). From Table 1,
it is evident that for all the socioeconomic characteristics there were
statistically significant differences between participants and non-participants
in forest management. This result is similar to other studies such as Ngang
(2015) Tafere (2013) and Bwalya (2011).
3.5.
Factors
Influencing households’ participation in forest management
Table 2: Results of the
Multivariate Logistic Regression Analysis
|
Variable |
β |
OR
(95% CI) |
SE |
P-value |
|
Sex |
3.555 |
34.984
(11.138-109.884) |
0.584 |
0.000* |
|
Age |
0.151 |
1.163 (1.100-1.230) |
0.028 |
0.000* |
|
Education |
1.581 |
4.860 (1.315-17.960) |
0.667 |
0.018* |
|
Household
Size |
0.658 |
1.931 (1.318-2.831) |
0.195 |
0.001* |
|
Benefit
from forest |
1.115 |
3.050 (1.035-8.990) |
0.552 |
0.043* |
|
Location
of forest |
2. 648 |
1.071 (1.019-1.265) |
0.674 |
0.000* |
|
Marital
Status |
-1.660 |
0.190 (0.059-0.612) |
0.597 |
0.005* |
|
Constant |
-13.942 |
|
1.877 |
0.000 |
OR= Odds
Ratio; CI = Confidence Interval; *Significant variables; SE= Standard Errors
Source: Field data, 2016
The odds ratio
for age shows that older people were more likely to participate in forest
management. The result for sex indicates
that men were more likely to participate in forest management than women. Marital status indicated that married people
were less likely to participate in forest management.
Larger household
size was associated with the likelihood of participation in forest management.
Respondents who derived benefits from the forest were more likely to
participate in forests management. The
closer the location of the forest to the household the more likely respondent
would participate in forest management.
The study of
Tafere (2013) supports the findings of this current study in relation to age,
household size, and location of the forest. However, his study differs with
this current study in relation to sex (gender) in forest management.
The a prior
expectation was that, age was to have a positive relationship with
participation in forest management and this result was obtained from the data.
With respect to sex, the expectation was that, it could be positive or
negative, the result showed positive with participation in forest management.
Education (formal education), household size, location and benefit were
expected to have a positive relation with participation in forest management,
and these results were obtained from the data.
The study
rejected the null hypothesis postulated that, households’ participation in
forest management was not influenced by the location of the forest to the
community. The study accepted the alternative hypothesis and that, the location
of the forest influences households’ participation in forest management. This
result regarding the hypothesis is supported by both Alhassan (2010) and Lise
(2000).
3.6.
Deforestation
and Forest Degradation Awareness
The results of
study revealed that 222(60%) of the households indicated there was no
deforestation at all while 148(40%) believed deforestation existed. The
majority 222(60%) believed that deforestation could be stopped if policy
encouraged forest management through community participation. In addition, as
many as 296(80%) of the households have rated their access to the forest
products to be reduced.
Interestingly,
the majority of respondents, 259(70%) did not know the role of forest in
climate change. What was obvious from the study was that 222(60%) of the
respondents would receive a compensation by payments or by alternative sources
of livelihood to stop harvesting products from forest reserves.
4. CONCLUSIONS AND RECOMMENDATIONS
Households’
participation in forest management in the Tamale forest district was shown to
be influenced by sociodemographic characteristics of the respondents. This
study involved a sample size of three hundred and seventy (370) respondents.
The sociodemographic variables which were sex, age, household size, education,
and marital status were all significant in influencing participation in forest
management.
In addition, the
results obtained from the logistic regression analysis showed that sex, age,
household size, benefit and location of the forest were factors influencing
households’ participation in forest management in the Tamale forest district.
Many households responded that deforestation was not going on even though they
agreed that their access to the forest was being reduced. Yet they believed
that, deforestation could stop if there was an active policy to encourage
forest management through community participation.
It was obvious
from the study that households were dependent on the forest for resources with
the harvest of fuel wood given a special focus as it contributed to their
energy needs. The current arrangement of the management of the forest was seen
to be inefficient and households felt that they were marginalized and left out
from getting a share of the benefits. Respondents in the study area did not see
deforestation as an issue because of their dependence on the forest and would
stop if there was an alternative livelihood source.
The study
recommended that any government policy framework must incorporate the
sociodemographic variables which were significant. These variables if they are
carefully incorporated in policy formulation could influence households’
participation in cooperative forest management to reduce deforestation and
forest degradation in the study area. Public education in forest management,
forest provision of carbon sinks and the need for community’s participation
could enhance sustainable forest and sustainable development.
5. LIMITATION OF THE STUDY
This particular study is limited in scope to the selected
communities and also in terms of methodology. Therefore, this study does not
lay claim to generalization of its findings. However, the results is
generalized to the selected districts.
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