




































In ternationa l
Scholars
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African Journal of Environmental Economics and Management ISSN 2375-0707 Vol. 3 (5), pp. 231-243, October, 
2015. Available online at www.internationalscholarsjournals.org © International Scholars Journals 

 

Author(s) retain the copyright of this article. 
 
 
 

Full length Research paper 
 

Assessment of factors influencing utilization and 
conservation of forest resources in Kipini Division of 

Tana Delta District, Kenya 
 

Olunga M.A1, Kavoi M.M2and Guthiga P.M3 

 
1
Land O Lakes, Peponi Plaza, 2nd Floor, Block A, Off Peponi Road, Westlands, Nairobi, Kenya. 

2
Jomo Kenyatta University of Agriculture and Technology, P.O. Box 62000-00200, Nairobi, Kenya. 

3
International Livestock Research Institute, Nairobi, Kenya. 

 
Accepted 28 June, 2015 

 

Forest resource utilization poses a challenge to the balance between fragile ecosystems and impoverished 
populations. As population increases, the demand for forest resources and the resultant degradation are 
expected to increase. Many benefits can be derived from conservation initiatives. Yet incidences of 
destruction by local communities are prevalent. This study examined the factors that influence utilization of 
forest products and attitudes of households towards conservation. The Logit and Negative binomial models 
were used for analysis. Descriptive and factor analysis were used to assess the attitudes towards 
conservation. Data was collected from 150 households in Kipini Division of Tana Delta District, Kenya. Logit 
model results showed that income, distance to the main road, management regime and occupation of the 
household head influence use of forest products. Results from Negative Binomial regression showed that 
intensity of use of forest products is influenced by the management regime, occupation of household head, 
income and distance to the main road. The results of the descriptive and factor analysis indicated that the 
local community has negative attitude towards conservation of forests. These findings imply that 
conservation can be enhanced by creating awareness of forest benefits to the community and training them 
on energy saving charcoal burners.   
 
Key words: Forest use, intensity, attitudes, conservation, Tana Delta district. 

 
 
INTRODUCTION 
 
Natural resources form the bulk of resources that are 
important to many economies of the world in meeting 
economic and development needs. In these economies 
the majority of people are poor, they live in the rural 
areas, and are mainly dependent on agriculture or on 
natural resources and ecosystem services (World 
Resources Institute, 2005). The utilization of natural 
resources as a livelihood strategy is important especially 
to the communities residing adjacent to these resources 
 
 
 

*Corresponding author. kavoi.muendo@gmail.com 

 (Sumati, 2006). Such communities collect process 
and/or market various kinds of natural resources either as 
a predominant activity or as part of a diversified portfolio 
of livelihood strategies designed to spread and minimize 
specific risks (Norfolk, 2004). 

Forests, among the natural resources, have potentials 
and limitations for improving human welfare (Angelsen 
and Wunder, 2003).  Forests improve human welfare by 
providing a range of resources including timber, non-
timber forest resources, and recreation.  Forests also 
supplement household income thus providing safety nets 
(Neumann and Hirsch 2000; Pattanayak and Sills, 2001). 

 



Kavoi et al.           231 
 
 

 
The poor however tend to destroy the environment by 

cutting down forests; overgrazing and cultivating marginal 
lands (World Bank, 2012). 

The human activities affect soil nutrient content (Peh et 
al., 2005) which in turn affect tree growth, forest cover, 
birds and invertebrates (Peh et al., 2005; Shahabuddin 
and Kumar, 2006).  

Further, grazing, removal of dead tree branches and 
dry leaves from the ground alter the nutrient dynamics 
while constant movement of cattle and humans erode the 
top soil layer (Belsky and Blumenthal, 2002) and 
browsing by goats and sheep affect re-growth, reduce 
perennial cover and increase exotic annual cover (Yates 
et al., 2001). 

There are various management systems that can be 
used to conserve forests. These approaches take 
different organizational forms such as centralized 
management (command and control), where state 
agencies assume the lead role; decentralized 
management, where local communities are involved at 
varying levels; private management where private entities 
own and manage the resource; and co-management, 
where the state, local communities, and other actors 
share management functions, rights, and responsibilities 
(Meinzen-Dick and Pradhan, 2002; Mburu and Birner, 
2007).  

In Kenya, the majority of the closed canopy forests 
(forest reserves) are managed by the Kenya Forest 
Service (KFS) under the Ministry of Forestry and Wildlife 
Services.  

Some closed canopy forests also known as national 
parks/ national reserves are managed by the Kenya 
Wildlife Service (KWS). On the other hand forests under 
the trust lands are managed by the local county councils 
in the Ministry of Local Government holding the forests in 
trust for the local communities. Lastly, there are some 
forests that are managed by private individuals or 
organizations under private ownership. These different 
management regimes have set rules governing forest 
resource extraction and forest use. In Kipini Division, the 
conservancy/private regime had the strictest rules 
governing access and extraction of resources.  It did not 
allow neighbouring communities to extract any products 
from the forest.  

The KFS, on the other hand, allowed communities in 
the neighbourhood access to the forest and extraction of 
products. However, the extraction of products was limited 
to dead tree parts and other non-tree products. Residents 
can also secure a licence to cut live trees for poles or 
timber. In the community management regime, however, 
extraction of forest products was allowed subject to 
permission from the administrative authorities who in turn 
usually consult with the community elders before allowing 
extraction.  

Hence, in the first two regimes, a licence is required, 
while in the community regime consent from authorities 
was the main requirement.  

Problem Statement 
 
FAO (2006) estimated the rate of forest destruction at 13 
million hectares per year (for the period 1995-2005) with 
about 1.6 billion people relying on the forests, to some 
extent, for their livelihood. However, different forms of 
extraction may have different levels of impact (Shaanker 
et al., 2004; Shahabuddin and Kumar, 2006). Economies 
thus employ various resource management strategies in 
an attempt to address the challenge of balancing 
resource conservation and utilization.  

Forests play many important roles in the ecosystem. 
They provide direct benefits to communities around them 
and act as habitat for various plant and animal species. 
Tana delta forest is one of the most unique forests in 
Kenya. The forest is home to numerous plants and 
animal species. It is host to 350 bird species; endangered 
marine turtles; two endangered primates namely the 
Tana River Red Colobus and the Crested Mangabey 
monkey; hippopotamus; elephants and the Nile crocodile. 
There are also various fish species in the coastal waters 
and fresh water river and ponds. The forest patches are 
endowed with mangrove and tropical forests especially 
along the Tana River. The forests are therefore important 
to Kenya because they comprise lowland evergreen 
riverine tropical forest types which are rare in Kenya and 
even in Africa, due to its biodiversity (Karere et al, 2004 
and Owino et al., 2008). Further, Witu forest, in Kipini 
Division has a great potential for eco-tourism. The forest 
does not exist as one continuous forest block but as 
several blocks with one main block in Kipini location and 
several other pocket forests of different sizes in 
Kilelengwani and Ozi locations. Some of the tourist 
attractions offered by the forest include birdlife, mollusks, 
crustacea and crocodiles.  

Despite its significance, the Lower Tana River Forest 
(LTRF) complex currently faces serious threat. 
Settlement into the forest has increased significantly in 
the last one decade owing to a number of factors (Okello, 
2011).  New settlers clear the forest to make way for 
farming. At the same time the felling of trees for timber, 
building material, fuel wood and charcoal has increased 
with the increase in demand for these products (Muoria et 
al., 2002; Luke et al, 2005, Owino et al., 2008). The 
problem is that the resultant conflicts in land use between 
agriculture and forestry, and the increased extraction and 
intensity of use of tree products have complicated the 
conservation of the LTRF complex.   
The level of forest use and the degree of reliance on 
forest products differ across households. The factors that 
condition a household’s reliance on a particular economic 
activity and on forest products in particular may vary. 
Past studies (Wells and McShane, 2000; Bawa et al., 
2004 and Volker and Waibel, 2010) have pointed out that 
forest utilization is affected by factor resource endowment 
of the household, the household’s demographic and 
economic characteristics, and exogenous factors such as  



232         Afr. J. Environ. Econ. Manage. 
 
 
markets, commodity prices and technologies. Hence, 
understanding the factors that determine household’s 
activity choice and reliance on forest products is essential 
for both conservation and development-targeted policies. 
Determining the attributes of a household that are related 
to dependence on the forest will help predict which 
households are likely targets for conservation.  
 
 
RESEARCH METHODOLOGY 
 
Theoretical Framework 
 
In the study area, markets for some of the forest products 
do not exist and/ or are imperfect; and if they exist, are 
characterized by high transaction costs. Consider a farm 
household that makes production and consumption 
decisions jointly (de Janvry et al., 1991), i.e., whose 
decisions are non-separable. This means that the 
household’s decisions about production (use of inputs, 
choice of activities and desired level of production) are 
affected by the consumption decisions/characteristics 
(consumer preferences, location and demographic 
composition). Under these conditions, the household 
maximizes the utility from consumption of home 
produced, market and leisure goods subject to a 
production function and a set of constraints.   
Thus, the household’s utilitymaximization problem can be 
expressed in a utility function as: 
 
 

hiqma H,MT,C,C(UMaxU     

  (1)  

Where; Ca = consumption of home-produced goods, 

  Cm= consumption of market goods, 

 Tq = total time available to the household, 

Mi = time spent on household production and off-farm 

wage earning (household labour supply) and 
Hh = household characteristics 
 
Subject to production constraint (Equation 2), 
household’s income constraint (Equation 3), household 
total time constraint (Equation 4), market constraint 
(Equation 5) and environment constraint (Equation 6) 
expressed as: 
 
 

),,( AJKfQ       

   (2) 

YwMwJCAJKfPCP iaamm  )),,((   

   (3) 

iha MLT        

   (4) 

0ML ih        

   (5) 

0QCa        

   (6) 
Where;  
Q = the home output of both agricultural crops and forest 
products with f (.) being assumed to be increasing and 
concave in all its arguments  
J = labour 
K =capital  
A = other exogenous factors that affect production 
including property rights, local and national policy and 
technology among others 
Pm = price of market goods,  
Pa = market price of home-produced goods,  
w = wage rate and  
Y= exogenous household income from non-wage and 
non-farm sources 
 
The Lagrangian (L) equation for this optimization problem 
is given by: 

][][])}),,({[);,,(
iammia

a
hiama

MJQCCPIwMwJCAJKfPHMTCCUL  

 (7)  
The first order necessary conditions; 

0)( 


 



 PP

C
L

aaa    

    (8) 

0)( 


 



 ww

T
L

j    

    (9) 

0







w

L
f

Pa      

    (10) 
 
In equation 8, the first order necessary conditions shows 

that the price (Pa) is a function of  while in equation 9 , 
the first order necessary conditions shows that wage rate 

(w) is dependent on . This implies that as long as the 
market environment constraints are binding, market 
prices (Pa and w) cannot guide household decision-
making because their market price is zero or very low in 
value. Instead the household is guided by shadow prices 
(shown in parentheses in Equations 8 and 9). Equation 
10 also shows that the value of the marginal product of 
labour is not equal to the market wage rate. Shadow 
prices reflect the true opportunity cost and benefits. 
Households will respond to them rather than market 
prices while making utility-maximizing choices (de Janvry 
et al., 1991).  It is the sign of γ/λ and θ/λ that determine 
the size of shadow prices and the relevant wage which 
would vary by household depending on whether a 
household is self-sufficient, net seller or net buyer of a 
produce or labour (Sadouletandde Janvry, 1995). These 
variations in prices and wages are caused by transaction 
costs in buying and selling, household preferences, 
production technology and access to employment oppor- 



Kavoi et al.           233 
 
 
 
tunities. They are therefore included in the production 
function due to their influence on decision making in this 
case being maximizing utility of resource use. 

Imperfections in the market here imply missing labor or 
credit markets.  Rural labor markets are not completely 
developed. Although some labor transactions occur, the 
marginal value product of labor deviates from the market 
wage, implying that production and consumption 
decisions are non-separable. The marginal value product 
of labor is equated to a shadow wage that depends on 
household characteristics (household size and years of 
formal education of the household head) and other utility-
related variables (collection time, distances to the forest 
and accessibility of the forest products).  
 
 

Model Specification: Use of Forest Products 
 
Use of forest products in this study refers to extraction of 
products from the forests e.g. fuel wood, medicinal herbs 
and thatching grass. To assess the use of products, 
respondents were asked whether they obtained any 
products from the forests in their neighborhoods or not. 
Therefore the response variable in this case was binary 
choice that is a "Yes" if the household collected products 
from the forest and "No" if it did not. The three most 
commonly used approaches to estimate such binary 
dependent variable regression models are (1) the linear 
probability model (LPM), (2) the logit, and (3) the probit. 
They are applicable in a wide variety of fields (Gujarati, 
2004).  

The LPM is not used in empirical research because it 
violates a major rule of probabilities that requires that the 
sum of the probabilities be equal to unity (Wooldridge, 
2002). The logit and probit models, however, guarantee 
that the estimated probabilities lie between the logical 
limit of 0 and 1 (Wooldridge, 2002). Hence, the Logit and 
the probit models are the most frequently used models 
when the dependent variable is dichotomous (Maddala, 
2001; Gujarati, 2004). Gujarati (2004) argues that the 
Probit and Logit models are quite similar. They generate 
predicted probabilities that are almost identical. Aldrich 
and Nelson (1984) indicate that in practice these models 
yield estimated choice probabilities that differ by less than 
0.02.  
The main difference between the logit and probit models 
is in the nature of their distribution which is captured by 
Cumulative Distribution Function (CDF). Probit has a 
normal distribution while logit has a logistic distribution 
which has slightly fatter tails than the normal distribution. 
The choice of probit versus logit regression therefore 
depends largely on the distribution assumption one 
makes. In practice many researchers choose the logit 
model because of its comparative mathematical simplicity 
(Kirui, 2011). 
In this study, a logistic regression model is used to 
assess factors affecting the use of forest products by 
households. 

Logistic Regression 
 
Following Maddala (2001), the probability, p, that a 
household uses forest products is given by:  

e
z

e
z

P





1

      

   (11) 
Central to the use of logistic regression is the logit 
transformation of p given by Z 

 

2

1
ln 










p
p

Z     

    (12) 
Where;  

    adfZZ ,,     

    (13) 
Z is a latent variable that takes the value of 1 if the 
household used forest products and 0 otherwise, f  is a 
vector of farmer characteristics, d is a vector of farm level 
variables, a is a vector of asset endowment variables, 
and ε is the stochastic term assumed to have a logistic 
distribution. The empirical model estimated contains the 
following variables (letters in parenthesis indicate related 
category variables from the conceptual model):  
1) Farmer specific variables (f) = age, gender  
2) Farm specific variables (d) = distance to the forest 
from household, household size   
and distance to market  
3) Asset endowment variables (a):  
i. Financial asset (income)  
ii. Human capital (education)  
iii. Social capital (group member)  
Based on the above equation, the logistic regression 
model estimated in implicit functional form becomes; 
 
Use of forest products (Z) = f (lnage, distance from forest, 
farm size, household size, lnincome, market distance, 
occupation, education, group membership and regime) + 
e (14) 
 
 

Intensity of use of forest products 
 
In order to assess the factors affecting the degree of use 
of forest products, this study specified the dependent 
variable as the number of head-loads harvested by a 
household in 2010/11. It therefore used the Poisson and 
the negative binomial regression models to isolate the 
determinants of the degree of use because the 
dependent variable is a count data variable. These count 
variable models are suitable for dependent variables that 
are countably finite. Count data are non-normal and 
hence are not well estimated by OLS regression 
(Maddala, 2001). The key models normally used to 
analyze count data include the Poisson Regression 
Model (PRM), the Negative Binomial Regression Model  



234         Afr. J. Environ. Econ. Manage. 
 
 
 
(NBRM), the Zero Inflated Poisson (ZIP) and the Zero 
Inflated Negative Binomial (ZINB). Poisson and negative 
binomial regression models are frequently used in 
estimating models with nonnegative integer dependent 
variables (Greene, 2008). The ZIP and ZINB regression 
models are specifically used to account for the frequency 
of zero counts (i.e. when there are more zeros than 
would be expected in either a Poisson or Negative 
Binomial Model). The study identified only few zero 
counts therefore never warranted the need for ZIP and 
ZINB. The results of NBRM is discussed in this study 
since the response variables were nonnegative integers 
and the Poisson regression model, which was the first 
stage in analyzing count data, displayed over-dispersion.  

Pearson chi-square ratio test (Pearson chi-square 
divided by degrees of freedom) was conducted to check 
whether Poisson model fitted the data well. Under this 
test, under-dispersion or over-dispersion occurs when the 
ratio is less than 0.8 or greater than 1.2 respectively. In 
that case, the negative binomial is recommended 
(Greene, 2008). In this study, the test detected over-
dispersion hence negative binomial regression model 
(NBRM) was applied. NBRM model has the additional 
advantage in that it relaxes the Poisson regression 
model’s assumption of equivalence of mean and 
variance.  
 
Following Greene (2008), the negative binomial model is 
written as:  
 

)'exp(),xy(E ii      

  (15)  
The model requires that; 

       XXxiyi
 expexp1var   

  (16) 
Where X’ is a vector of explanatory variables similar to 
those included in the model, the β refers to the variable 
coefficients and α is the constant.  
Hence the estimated NBRM is specified as:  
Number of head-loads (Z) = f (lnage, gender of 
household head, regime, distance from forest, household 
size, lnincome, occupation, lnfsize, education, group 
membership) + e   (17) 
 
Attitude towards Forest Conservation 
 
To analyse the peoples’attitude towards an issue, two 
main approaches used include descriptive statistics and 
inferential statistics. The descriptive statistics includes 
summing up of the responses and obtaining a 
scoreorusing the percentage of respondents in a given 
Likert scale category (Shibia, 2010)or scale averages for 
the particular question responses (Dolisca et al., 2007 
and Rishi , 2007). The second method uses factor 
analysis (Dolisca et al., 2007).  Some studies use a 

combination of descriptive statistics and inferential 
approaches (Dolisca et al., 2007). 

In this study, a combination of descriptive and 
inferential statistics was used to examine household’s 
attitudes towards conservation. Descriptive statistics 
(percentages and mean scores) were used to describe 
respondents’ attitude towards forest conservation. On the 
other hand, factor analysis was used to identify latent 
dimensions underlying the different variables that 
measured respondents’ attitudes towards conservation. 
Responses to twelve five-point Likert-type scale items 
were subjected to a principal component factor analysis 
with Varimax rotation. The factors were subjected to the 
Kaiser-Meyer-Oklin and Bartlett’s test (KMO and 
Bartlett’s test) to determine the sampling adequacy. 
According to the test, samples that score above 0.7 are 
considered reliable for policy-related decision-making 
while those below 0.7 are considered unreliable.  The 
above procedures were adopted for this study and used 
to discuss the attitude towards forest conservation. 
 
Data and Variables 
 
This study used data collected from households in Kipini 
Division of Tana Delta District. The division has three 
locations namely Kipini, Ozi and Kilelengwani. Each 
location is further divided into two sub-locations. Each 
sub-location has several villages of varying household 
populations.  
Multi-stage sampling technique was used to select a 
representative sample from the population for interviews.  
First, the three locations were purposively selected. This 
was because each location represented a different forest 
management regime. A list of all villages in each location 
was then obtained with the help of the local 
administrators.The villages were clustered into two 
categories based on proximity to the forest. Six of the 
villages selected were close to the forest (distance of 0-
5km) while the other four villages were far from the forest 
(distance 6-10km). A total of ten villages out of seventy 
villages were selected. A list of all households in the 
selected villages was then drawn. 
The population sizes of each of the locationswereused to 
arrive at the number of households interviewed in each 
location. Hence the study sampled the respondents from 
the locations using thepopulation proportions.The division 
statistics based on the 2009 census estimates showed 
that Kipini location had approximately 4000 households 
while Kilelengwani location had approximately 2500 
households and Ozi location had approximately 400 
households. This procedure resulted in 72 households in 
Kipini location, 48 households in Kilelengwani location 
and 30 households in Ozi location.  Overall 150 
respondents/ households were interviewed.  

Data was collected in each of the households through 
personal interviews using a pre-tested questionnaire. The 
household head or spouse was selected forinterviewin  



Kavoi et al.           235 
 
 
each case. The data collected included household/ 
respondent characteristics and location characteristics. 
From this data the following model variables were 
obtained:  
Use of forest products: this was a binary choice variable 
(1=a household extracts products from the forest 
and0=otherwise) that established whether a household 
collected any products from the forest or not. It covered 
the period between June 2010 and July 2011. 

Age: is a continuous variable and was measured in 
years. Age was expected to have a negative effect on 
use of forests considering majority of respondents were 
in their youthful stage. 
Distance from forest edge:this was a continuous variable 
measured in kilometers. The proximity to the forests 
makes households inherently dependent on them due to 
accessibility and availability of the resources. Distance 
from forest affects access to the forest for getting forest 
products and also increases costs in terms of time spent 
to walk to the forest and to gather fuel wood and other 
products in the forest. This might result in a larger 
proportion of the population of a village using fuel wood 
from the forest. Distance from the forest edge was 
expected to negatively influence respondents’ decision to 
use resources.  
Land size: this was a continuous variable measured in 
acres. Land holding is a form of physical capital that a 
household possesses. It is expected that the likelihood of 
resource use would be less for households with large 
tracks of land. Households with more land are likely to 
have access to farm fuel in the form of crop residue or 
fallow/unutilized land areas/ area of land under trees or 
grow trees on their land and therefore they are likely to 
be less dependent on fuel wood from the forest.  

Household size: refers to the number of members in a 
given household. Household size was expected to 
positively influence use of forest products. Households 
with many members are expected to need more fuel 
wood for cooking and for construction of houses.  
Income: this variable forms part of the financial capital 
owned by a household from all possible income 
generation sources that they were engaged in including 
remittances. It was measured as total income earned by 
household from various sources in a year (June 2010 - 
July 2011).  

Households working in non-farm jobs such as business 
or salaried employment should depend less on the forest 
for their income and will consequently need to clear less 
forest to meet their needs. 

When households diversify their income generating 
activities other than depending on forest products their 
tendency to rely on forests are likely to decline. This can 
slow down economic pressure to extract products from 
forests for sale to support their families; or generate 
resources that can be used to purchase inputs such as 
fertilizers; labor saving technologies or investments in 
activities that promote sustainable practices in natural 
resources management. 

Distance to market: was measured in kilometres. 
Respondents in villages closer to market places are more 
likely to allocate labor to the extraction of forest products, 
such as medicinal herbs, seeds and nuts for sale in the 
local market. Hence households living close to a market 
have greater incentives to extract and sell forest products 
as their income source. Thus market distance was 
expected to negatively influence the use of forest 
products. 
Education: was measured as the number of years a 
respondent spent in formal schooling. The number of 
years of education of a respondent influences their level 
of understanding and decision making ability. A 
respondent with more years of formal education is likely 
to have access to alternative employment opportunities 
hence reducing the level of dependency on forest due to 
possibility of access to alternative fuel sources.  
Regime: was measured as a categorical variable as 
earlier defined. The regimes influence the level of 
resource use through lack of enforcement and ill-defined 
property rights and corrupt governance structures. 
Therefore regime was expected to negatively influence 
use of forest products. 
 
 
EMPIRICAL RESULTS AND DISCUSSIONS 
 

 
Descriptive Statistics Results 
 
The results of the descriptive statistics (Table 4.1) show 
that age of the respondents ranged from 20 to 100 years, 
with the mean age being 44 years and the mean 
household size was 4 members (Olunga, 2013). The 
overall mean of fuelwood head-loads collected was 178 
per year and the regime with the largest mean of head-
loads was community regime (309 head-loads/year) while 
that with the lowest mean of head-loads was the KFS 
regime (112 head-loads). Mean years of formal education 
was 6.8. Of the interviewed households, 117 (78% 
percent) were males while 33 (22 percent) were females. 
Mobile phones were owned by 78 respondents (52 
percent). 

Distance to the main road was on average, 23.8 
kilometers indicating that most households were located 
in the interior. The mean number of forest benefits known 
to respondents was about 11. Of the 150 respondents, 
125 (83.3 percent) were practicing farming as their main 
occupation. Results also showed that 72 (48 percent) of 
the respondents belonged to a group(s) that engage in 
conservation activities. Household mean income per 
annum was Ksh. 21814.06. The respondents in the KFS 
regime had the highest incomes compared to those from 
the community and Private/ Conservancy regime.  

There were significant differences between variable 
responses in the three regimes with respect to farmer-
specific, farm-level and asset endowment characteristics 
(Olunga, 2013). Specifically, there were significant differ- 



236         Afr. J. Environ. Econ. Manage. 
 
 
 

Table 4.1 Summary statistics of variables used in the Poisson regressions Model. 

 
  Private KFS Community Overall 

Variable Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Dev 

Gender of household head 
(1=male 0=female) 

0.8 0.38 0.7 0.44 0.8 0.4 0.8 0.42 

Age of household head 
(years) 

44 11.75 45.1 11.21 41.1 17.65 44 12.71 

Household size (count) 4.3 2.15 3.9 1.98 3.7 2.66 4 2.17 

Forest benefits known 
(count) 

8.8 3 12.3 5 9.9 5 10.7 5 

Group membership (1=Yes, 
0=No) 

0.6 0.5 0.4 0.5 0.3 0.49 0.5 0.5 

Group members with farm 
forest (count) 

0.48 0.5 0.29 0.46 0.19 0.4 0.34 0.48 

Total income of household 
(‘000) 

27.1 34.0 22.4 33.2 9.5 13.0 21.8 31.5 

Total land size (acres) 7.9 4.78 9.6 4.1 5.8 3.63 8.3 4.47 

Main occupation of 
household head 

0.9 0.32 0.8 0.41 0.8 0.37 0.8 0.37 

Distance from forest (km) 3.3 2.06 4 2.37 1.9 0.58 3.4 2.17 

Use forest products (1=Yes, 
0=No) 

0.5 0.5 0.4 0.49 1 0.2 0.5 0.5 

Distance to the market (km) 13.6 6.65 8.3 5.97 1.5 0.51 9 7.03 

Distance to main road (km) 28.7 5.18 18.9 5.05 27.3 1.51 23.8 6.62 

Education (Years) 6.2 4.21 7.8 3.84 5.4 4.26 6.8 4.14 

Quantity of fuelwood head-
loads collected per annum 
(’00) 

2.04 3.14 1.12 4.12 3.09 3.66 1.78 3.77 

Land under trees (acres) 1.1 1.38 1.4 1.51 1 1.21 1.2 1.42 

Own mobile phone  
(1=Yes, 0=No) 

0.5 0.5 0.6 0.49 0.3 0.49 0.5 0.5 

 

Source: Authors’ Computation, 2013. 

 
 
rences in number of forest benefits known to 
respondents,distance to the main roadand market and 
ownership of land. When comparisons were made 
between different regimes, significantdifferences were 
observed in farm sizes, distance to the main road, the 
forest and the market, years of education and land 
ownership among respondents in the 
conservancy/reserve regime and those from the KFS 
regime. Significant differences were also observed when 
the respondents from the community regime and the 
conservancy regime were compared. These differences 
were significant in the case of incomes, farm sizes and 
distances to the market. 
 
Factors Affecting the use of Forest Products 
 
In order to assess the use of forest products by the 
households, respondents were asked whether they ever 
extracted products from the forest between June 2010 
and July 2011 (Figure 4.1).  Although forests were within 
the reach of most households (from .01km to 10 km 

distance) only 51% of the Householdssurveyed extracted 
products from them (Olunga, 2013). The mean number of 
years for formal education was six among those who 
extracted products and seven years among non-
extractors of forest products. 

The use of the forest products differed among the 
different regimes. The community regime had the highest 
percent of extractors of forest products (96%) followed by 
Private (48%) and the KFS regime (38%).   
The forest products used by the households were fuel 
wood, charcoal, poles, medicinal herbs and thatching 
grass (Table 4.2). Of all the users of forest products, 94% 
used fuel wood while other products were used by less 
than 40% of the households. The least extracted product 
was thatching grass which was used by 5% of the 
households. 

In order to examine factors explaining the use of forest 
products, a binary dependent variable that takes the 
value of 1 if the respondent extracted a product from the 
forest and 0 otherwise was used to fit a logit regression 
model. The results of the fitted regression model are  



Kavoi et al.           237 
 
 
 

Figure 4.1. Use (Extraction) of forest products by households (N=77).  
 

 
 

Source: Authors’ Computation, 2013. 
 
 
 
 

Table 4.2. Types of forest products extracted by households.  
 

Products extracted      Number of  users 
Percent among product 
users 

Fuelwood 72 93.5 
Charcoal  13 16.9 
Medicinal herbs 14 18.2 
Poles 27 35.1 
Grass 4 5.2 
Source: Authors’ Computation, 2013. 

 
 
 
shown in Table 4.3. The Likelihood Ratio (LR) test 
statistic shows that the model fitted the data well (p-value 
= 0.0003).  

The results of this study indicate that the prevailing 
management regime influences use of forest products 
(Olunga, 2013). Results show that belonging in the 
Private/ Conservancy regime or KFS regime reduces the 
likelihood of using forest products other factors constant. 
The households that are close to the KFS or the 
conservancy/private forest regime are less likely to use 
forest products relative to the households in the 
community regime. These findings show that regime 
management plays a critical role in determining the 
decision to use forest products. Indeed, the type of 
management regime determines the scope of monitoring 
and enforcement of rules. 

The study findings also reveal that there is a negative 
and significant relationship between distance to main 
road and likelihood of using forest products. This 
suggests that the further the distance to the main road, 
the less likely the use of forest products. Infrastructure is 
expected to influence the ease of accessing places and 

facilities such as markets. Therefore, if distances to such 
facilities are large, the likelihood of extracting forest 
products for sales may be less. 
The results further showed that the main occupation of 
the household head influences the use of forest products. 
The households practising farming as the main 
occupation were more likely to use the forest products 
than those whose main occupation was non-farm. In 
addition, the results showed that income influences the 
use of forest products. Increase in income has positive 
influence on likelihood of forest product use. This finding 
is in line with the findings of Hedge and Enters (2000) 
that indicated that higher income groups utilize more 
forest resources than the lower income groups when no 
forest use restrictions are in place. However, it is in 
contrast with findings of most past studies (Cavendish, 
2000; Sanders and Zeller, 2004; Shackleton and 
Shackleton, 2006; Wambua, 2008) which suggest that as 
incomes increase the likelihood of dependence on forests 
declines. The probable reason for the positive 
relationship is the lack of alternatives for fuel wood in the 
study area due to remoteness. Households have to cover  



238         Afr. J. Environ. Econ. Manage. 
 
 
 

Table 4.3Factors affecting the use of forest products: Logistic regression model.  
 

Dependent Variable  
(1=Use 0=non use) Coefficient P-Value 

Marginal Effects 
Coefficient P-Value 

Gender   0.44 0.415 0.08 0.411 
Natural logarithm of household size 0.05 0.920 0.01 0.920 
Education (Years of formal education) 0.01 0.834 0.00 0.834 
Natural logarithm of distance to forest 0.25 0.218 0.05 0.207 
Natural logarithm of income 0.09 0.049

b
 0.02 0.043 

Natural Log of farm size  0.15 0.708 0.03 0.707 
Natural log of distance to main road -1.57 0.084

c
 -0.29 0.075 

Regime     
Private/ Conservancy -4.07 0.000

a
 -0.46 0.000 

KFS -5.16 0.000
a
 -0.67 0.000 

Occupation  1.30 0.032
b
 0.24 0.022 

Constant 6.33 0.062   

Number of observations    150    

Wald chi2(9)    32.64    

Prob> chi2      0.0003    

Pseudo R2        0.2306    

Log pseudo-likelihood  -79.9588    
 
Source: Authors’ Computation, 2013. 
Note: p-value significance level a refers to 1%, b refers to 5% and crefers to 10% 

 
 
 
 
 

Table 4.4. Determinants of intensity of use of forest products: Poisson and Negative binomial regression results. 
 

Dependent Variable= 
Quantity of fuelwood head-
loads collected Poisson Regression 

Negative Binomial 
Regression 

 Coefficient P-value Coefficient P-value 

Log of age -0.45 0.106 -0.11 0.739 

Gender -0.28 0.152 -0.19 0.325 

Occupation 0.36 0.132 0.72 0.000
a
 

Household size -0.07 0.095 -0.06 0.348 

Log of income 0.03 0.044 0.05 0.018
b
 

Log of farm size 0.20 0.041 0.12 0.198 

Log of collection frequency 0.58 0.000 0.94 0.000
a
 

Log of distance to forest 0.27 0.006 0.25 0.089
c
 

Regime     

Conservancy/Private -0.20 0.395 -0.48 0.082
c
 

KFS -0.75 0.003 -0.77 0.003
a
 

Constant 4.09 0.000 1.38 0.218 

Number of observations 

150 
508.58 
Mean 
-432.19 
0.000 
-0.387 

Wald chi2(10) 

Dispersion 

Log pseudo-likelihood 

Prob> chi2 

Lnalpha 
 

Source: Authors’ Computation, 2013. 
Note: p-value significance level a refers to 1%, b refers to 5% and c refers to 10% 

 
  



Kavoi et al.           239 
 
 
 

Table 4.5. Attitude towards forest conservation.  
 

 Percent of households within the response  

Attitudinal views/Dimensions                 
Strongly 
Disagree Disagree Neutral Agree 

Strongly 
Agree 

Importance of forest conservation  18 46 2 20 14 
Tree nursery mgt and farm forestry  6 20.7 6 54.7 12.7 
Methods of fuelwood conservation  5.3 26.7 8.7 50 9.3 
Honey production techniques  11.3 46 5.3 34 3.3 
Interaction with forest officers  4 38.7 12.7 36 8.7 
Community surveillance  7.3 28.7 6 52.7 5.3 
Consultations on forest related activities  3.3 34 6 45.3 11.3 
Partnerships with other stakeholders  11.3 36.7 9.3 41.3 1.3 
Confidence in future user rights  12.7 23.3 18.7 38 7.3 
Interest in knowledge acquisition  2.7 3.3 4 74.7 15.3 
General support for conservation activities  1.3 9.3 8.7 66.7 14 
Labour and  monetary contribution  8 28 10 50.7 3.3 

 

Source: Authors’ Computation, 2013. 

 
 

 
long distances to get kerosene or cooking gas, making 
use of firewood more attractive.  
 
Intensity of use of ForestProducts among 
Households 
 
The factors that influence the extent to which households 
use forest products was assessed by estimating both 
Poisson and Negative Binomial regression models 
(Olunga, 2013). The dependent variable was the quantity 
of fuel wood collected (measured by the number of head-
loads). The results of both models are presented in Table 
4.4. The mean deviance and the Pearson chi-square ratio 
(the Pearson chi-square value divided by its degrees of 
freedom) were used to assess the degree of fit of the 
Poisson model. The estimated Deviance and Pearson 
ratios are shown below:  
Deviance/df = 4167.773/141= 29.56  
Chi-square/df =4382.065/141= 31.08  

From these results, both ratios are significantly greater 
than 1 indicating that there is evidence of over-
dispersion. Hence the Poisson model does not fit the 
data well. Consequently the discussion below is based on 
the results of negative binomial regression model. The 
Likelihood Ratio test of the model (NBRM) has a p-value 
of 0.000 showing that the model fits the data well.  

As hypothesized the management regime influences 
the intensity of use of forest products. The relationship 
between the regimes and the quantity of forest product 
used is not only negative but also statistically significant. 
This implies that the expected number of head-loads of 
fuel wood decreases by 0.48 and 0.77 for the private and 
the KFS regimes, respectively. The decrease in expected 
number of head-loads of fuel wood collected is lower in 
the private regime probably because of challenges of 
monitoring the expansive borders by an inadequate 
number of staff. The hypothesis that management regime 
does not influence the intensity of use of forest products 

was therefore rejected. Results also show that household 
income influences the level of use of forest products. An 
increase in household income by ten percent increases 
the expected number of head loads by 5% holding other 
model variables constant. This result corroborates that of 
Hedge and Enters (2000) who found that the higher 
income groups utilize more forest resources than the 
lower income groups when no forest use restrictions are 
in place. However as in the case of the binary model 
results, the findings contradicts those of other studies 
(Cavendish, 2000;Shackleton and Shackleton, 2006 and 
Wambua, 2008) which reveal that as incomes increase 
the likelihood of dependence on forests declines.  

Table 4.4 also shows that distance to the forest 
increases the expected number of head loads of fuel 
wood collected. Hence, there is higher use of forest 
products among households that are further away from 
the forest edge. If the distance to the forest were to 
increase by a kilometre, the expected number of head-
loads would increase by 0.27, holding other factors 
constant. This finding however contradicts those of other 
studies on forest products extraction by households 
(Thapa and Chapman, 2010 and Hedge and Enters, 
2000). Their studies found that the closer households are 
to the forest the greater the probability of extracting 
products. Fuel wood is a basic need for households and 
cannot be easily substituted with other sources of energy 
in remote villages due to distances and costs to access 
alternatives. Thus households are willing to travel as far 
as it takes to collect fuel wood. Another possible reason 
for going to further distances to collect forest resources is 
that people in that area may not easily identify the 
residence of people from far. Although the reporting may 
be done, apprehending the person may not be as easy 
as a resident whose home is known.  
The main occupation of the household head also 
significantly influences the quantity of fuel wood 
collected. The expected number of head-loads of fuel  



240         Afr. J. Environ. Econ. Manage. 
 
 
 

Table 4.6. Results of exploratory factor analysis.  
 

Factor and item description Factor loading 

Factor 1:Education and knowledge on conservation  

We have been educated on importance of forest conservation .792 

We have received training on tree nursery development and farm forests use .808 

We have been informed on use of fuel conservation methods to conserve forests .794 

We are confident of land-use rights in the long term .639 

  

Factor 2:Interaction and application of knowledge  

We have changed our honey production techniques to minimize tree species losses .982 

There is consultation regarding forest related activities and forest conservation .981 

  

Factor 3:Social and economic commitment  

There is surveillance between community and forest guards regarding forest use .629 

There is partnership between the community and other stakeholders on forest conservation .879 

Am willing to invest my resources in terms of time and finances to protect forest destruction .540 
  

Factor 4:Personal initiative  

Am interested in knowing more about what to do regarding forest conservation .822 

Am willing to support conservation practices that will ensure forest protection .826 

  

Factor 5:Consultation and goal achievement  

We have interaction with forest guards thus conservation is now achievable .902 
 

 

Source: Authors’ Computation, 2013. 

 
 
wood collected is higher by 0.72 for households whose 
main occupation is farming relative to those who have 
other activities as the main occupation. 
 
 
Attitude towards Forest Conservation 
 
In order to assess households’ attitude towards forests 
conservation, respondents were asked a series of 
questions that cover different aspects relating to forest 
conservation. These were in Likert scale format with the 
scale ranging from strongly disagree and strongly agree 
(i.e. on a scale of 1 for strongly disagree to 5 for strongly 
agree). Points were added from each statement and 
divided by the highest sum to calculate a score in 
percentage terms. The mean score of all respondents 
was 54.07±10.30. If a respondent scored above the 
mean score then they were considered to have a positive 
attitude based on the stated scale range 1-5 with 5 being 
strongly agree (positive statement). For purposes of 
statistical analysis respondents with neutral and negative 
attitudes were grouped together. The finding wasthat only 
10% of the respondents had a positive attitude and 56% 
were on the borderline. The results of the analysis of 
responses to the statement are as shown in Table 4.5 
(Olunga, 2013). The results show that 20% of the 
respondents received some form of education and 

training on forest conservation and 50% had been 
educated on fuel wood conservation methods. The 
communities’ time value for conservation activitieswas 
low. Results also show that 48% of the respondents 
attended meetings and were enrolled in groups focusing 
on environmental conservation as also indicated in Table 
4.1. 
Regarding the stakeholder involvement in forest 
conservation initiatives and management 41% of the 
respondents knew about the existence of partnerships 
between the local communities, the KFS and the NGO’s 
working with farmers within the area while 53% were 
aware of existence of forest surveillance in the 
community. Almost one-half (45%) of the respondents 
indicated that consultations among stakeholders on forest 
related activities was a positive contributor to forest 
conservation. However, studies conducted in other 
countries reveal that, where community members and 
other stakeholders are involved in environment 
management, the laid down strategies can be achieved 
given the local area conditions (Rishi, 2007). 

The respondents’ personal commitment to forest 
conservation was also considered in the study. Majority 
of respondents (75%) expressed their interest in learning 
about forests conservation. Overall, 67% of the 
respondents were willing to support efforts to protect the 
forest and about one-half of the respondents (51%) indi- 



Kavoi et al.           241 
 
 
 
cated that they would invest their time and finances in 
conservation efforts.  
Factor analysis was used to identify latent dimensions 
underlying the different variables that measured 
respondents’ attitudes. Responses to the 12 five-point 
Likert-type scale items were subjected to principal 
component factor analysis. Factor analysis was selected 
to create measurement scales. In order to develop these 
scales, exploratory factor analysis with Varimax rotation 
was employed. The objective was to obtain fewer 
dimensions that reflected the relationships among these 
inter-related variables. An Eigen-value greater than one 
rule was applied in identifying the number of factors. The 
variables that had large loadings on the same factors 
were grouped together. Factor loadings value of 0.50 and 
above is normally considered good and significant. The 
analysis produced a solution with five factors that 
accounted for 75.2% of the total explained variance as 
shown in Table 4.6.  

The Kaiser's overall measure of sampling adequacy 
obtained was 0.68, which borders on the recommended 
threshold of 0.7 suggesting that the data is marginally 
appropriate for factor analysis.  

Four attitude variables concerning education and 
knowledge of conservation were loaded on factor 1 with 
the cross-correlation coefficients of 0.792, 0.808, 0.794 
and 0.639. This factor accounted for 28.7% of the total 
variance and was termed ‘education and knowledge of 
conservation’ because these variables involve awareness 
of conservation practices by local people.  

Higher scores and positive responses on this factor 
revealed a general need for promoting education on 
conservation practices. 
Factor 2 had cross-correlation coefficients of 0.982 and 
0.981. Because these variables imply application of 
acquired knowledge and interaction among stakeholders, 
factor 2 was then labeled ‘interaction and knowledge 
application and accounted for 15.7% of the total variance.  
Three attributes (namely, surveillance, partnership and 
investment) were loaded on Factor 3 with cross-
correlation coefficients of 0.629, 0.879 and 0.540. These 
attributes focused on social and economic issues. Hence 
Factor 3 was termed ‘social and economic commitment’. 
It accounted for 11.4% of the total variance.  

Factor 4 had cross correlation coefficients of 0.822 and 
0.826 and these variables were labeled ‘personal 
initiative’ and it accounted for 10.8% of the total variance 
and the fifth factor which represented the  achievement of 
the goal on conservation had a cross correlation 
coefficient of 0.902. It was termed ‘consultation and goal 
achievement’ and it accounted for 8.4% of the total 
variance. The cumulative percent of variance for all the 
factors explained was 75.2.  
 The findings on the attitude questions show that 
majority of the respondents had a negative attitude 
towards conservation (90%). The findings suggest the 
need for information on conservation; desire to know 

more (75%); willingness to support conservation activities 
(66%), and labour and financial contributions (51%). 
 
 
CONCLUSIONS AND RECOMMENDATIONS 
 
The purpose for this study was to determine the factors 
influencing the decision to use  forest products and the 
attitudes of farmers on forest conservation. Regression 
techniques were used to examine decision to use and the 
degree of use of forest products.  The logit regression 
model was used to assess the factors influencing the use 
of forest products whereas negative binomial model was 
used to examine the factors affecting the intensity of use 
of forest products.The studyalso used descriptive 
analysis and exploratory factor analysis to assess the 
attitudes of households on conservation.The data used 
was collected through personal interviews using 
pretested questionnaires from 150 households in Kipini 
division of Tana Delta district. The area was purposively 
selected to represent the three existing regimes (i.e KFS, 
community and private management systems) that 
govern forest product utilization. 

The study found that the factors explaining the use of 
forest products include household income, distance from 
the household to the forest, regime and occupation of the 
households head. The study particularly found that the 
prevailing management regime had an inverse 
relationship with the decision to extract products from the 
forest.  The farther a household was from the forest in the 
KFS or Private regime the lower the likelihood of 
extraction of forest products. The null hypothesis that 
prevailing management regime does not influence use of 
forest products was rejected. The study concluded that 
prevailing management regime have a significant role in 
determining extraction of forest products. 
The study further found that occupation of the household 
head, household income, distance from household to the 
forest and prevailing regime explain the intensity of use of 
forest products. Specifically, the study found that 
prevailing management regime had an inverse 
relationship with the expected number of fuel wood head-
loads collected by a household in the KFS and the private 
regimes compared to the community regime. It was 
concluded that the type of management system used in 
the conservation of forests affects the level of forest 
product utilization by the surrounding community.  

The attitude of the community members towards 
conservation was mostly negative. This finding was 
contrary to our expectations.  It is probably because as 
results indicate, most household heads do not have 
education on importance of forest conservation and also 
did not regularly attend forest conservation meetings 
organized by local leaders, NGOs and environmental 
agencies. 

The distance to the forest edge was found to be a 
significant contributor to the use of forest products indi- 



242         Afr. J. Environ. Econ. Manage. 
 
 
 
cating that households that extract forest products come 
from far and wide. This finding suggests the need for 
more effective monitoring and control of forest borders. 
The number of KFS and KWS ground staff needs to be 
increased to effectively deal with illegal utilization of the 
forest. An alternative approach may include community 
policing and community forest associations using youths 
to assist with guarding the forest borders.   
The majority of households relied on farming as the main 
occupation which was found to have a significant effect 
on utilization of forest products. To minimize on the 
dependence on forests there is need to invest in 
sensitization and training on commodity value chains 
which could boost income. Secondly, there is need for 
provision of accessible credit to households for crop 
intensification.  This will ensure that households have 
increased food supply and also increased crop residue to 
use as fuel wood instead of relying on the forests all-
year-round as well as reducing expansion of agricultural 
land into forest demarcated areas.The youth and women 
development enterprise fund created by the government 
is a positive approach toward development. However the 
access of these funds is limited in most cases by lack of 
awareness by households and bureaucracies. 
Diversification of activities that can help generate income 
in the area would also act as an incentive to reduce 
reliance on forest products.  
 
 
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http://data.worldbank.org/
http://www.wri.org/

