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International Journal of AgriScience Vol. 3(4): 372-379, April 2013
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ISSN: 2228-6322© International Academic Journals
International Journal of AgriScience Vol. 3(4): 372-379, April 2013 372
Impact of agricultural extension services on poverty status of the palm oil
processors in Southwestern, Nigeria
Olagunju F.I. 1*, Ogunniyi L.T.1, Babatunde R.O.2, Fakayode S.B.2,Dekunle O.A.3
1Department of Agricultural Economics, Ladoke Akintola University of Technology, Ogbomoso. *Author for correspondence
(email:olagfunk@yahoo.com )
2Department of Agricultural Economics and Farm Management, University of Ilorin
3Department of Agricultural Extension, University of Ilorin
Received January 2013; accepted in revised form March 2013
ABSTRACT
This study measured the impact of extension on oil palm processing in Ondo and Osun states of Nigeria.
Specifically, the study compares the net returns of farmers visited by extension workers (FV) with those not visited
(FNV), examines the poverty profile of the palm oil processors, determines the effect of extension services on
poverty level, and determines the effects of extension on palm oil processing. A multistage sampling technique was
used. A sample of five (5) Ondo and Osun states’ Agricultural Development Programme (ADP) zones each was
purposively selected. Separate samples of 40 farmers visited by extension workers (FV) and 50 farmers not visited
by the extension workers (FNV) were chosen for each state by multistage and simple random sampling techniques.
Cross-sectional data were collected from these samples in the last quarter of 2011. Data were collected by means of
a pre - tested structured interview schedule and analysed by means of both descriptive and inferential statistics. The
adjusted R2 for the palm oil processing model was 0.72 which means that 72 percent of the variability in palm oil net
returns was explained by the independent variables and the rest was accountable for by the standard error. These are
processing experience, extraction cost, labour used and fertilizer. The average output of FV (N434, 604.84 per year)
was higher than that of FNV (N 385,231). The t-test however revealed that this difference was not statistically
significant at the 5% level. This could be inferred that FNV also enjoyed the benefits of extension services through
their interaction with FV or cooperative societies in the study areas in what could be termed as trickle down effect.
The adjusted R2 for impact of extension services on oil palm production model was 0.76. The joint effect of the
explanatory variables is significant at 1 percent level. All the explanatory variables (except processing experience
and education) had expected relationship with oil palm output. The extension services have positive effects in
reducing poverty status of the rural palm oil processors. The positive coefficient of the extension contact and its
significance implies that the greater the frequency of processor’s contact with extension workers, the more
productive the processor is. This means that the more advice, information and knowledge received by a farmer, the
greater is his productivity in the study area. These findings suggest that extension can help to close the gap between
the output attainable with existing technologies and those actually realized by the farmers
Keywords: Palm Oil processors, Extension, Regression analysis, Poverty, Processing
INTRODUCTION
Poverty involves a pronounced deprivation of well-
being. Poverty has been defined as the inability to
attain a minimum standard of living (World Bank
Report, 1990). Dudley (1975) sees poverty largely in
the light of the need for personal growth in Nigeria.
According to him, the basic needs, which any society
should provide for its members should include such
things as food, clothing, shelter, education, health,
work and mobility. According to Omotola (2008),
about 70% of the population now lives in abject
poverty. The prevalence of poverty is highly visible
in Nigeria, especially in the rural areas where
majority of the people subsist on income from
agricultural activities that are too meagre to sustain
them (The World Bank, 1996). The same report
indicates that rural areas in Nigeria accounted for 66
per cent of the incidence of poverty, 72 per cent of
the depth and 69 per cent of the extreme poor. This
portion of the population depends on agriculture for a
living. Poverty in Nigeria is increasing in hyper-
geometrical rate since 1980 (Okuneye, 2002).
Available statistics reveals that the poverty incidence
in Nigeria has been on the increase since the 1980s.
Statistics from the National Bureau of Statistics
373 International Journal of AgriScience Vol. 3(4): 372-379, April 2013
(NBS, 2007) indicates that poverty incidence in
Nigeria rose from 28.1 per cent in 1980 to 54.4
percent in 2004. With the estimated population figure
of 140 million, this translates to 74 million Nigerians
living below poverty line. While 63 percent of this
figure lives in the rural areas, 43 percent of this
number resides in the south west, Nigeria (NBS,
2007). Drawing a poverty line involves studying the
characteristics of a population in order to identify the
poor and their distinctive characteristics relative to
the rest of the population. The development of such a
profile requires first the determination of a poverty
line, which then makes it possible to classify
households into homogenous socio-economic
categories (Lariviere et al., 1998).
Palm oil processing is a major source of income and
employment to a large proportion of the resource
poor rural population in Nigeria especially in the
southwestern part of the country. In recent times, its
production has drastically downsized. Evidence from
(CBN/ NISER, 1992) revealed that this situation has
been brought about by a number of socio-economic
and political factors along with the technological
know how in the industry. Principal among the
factors responsible for this decline is the inefficiency
that exists in the production system for palm oil
processing. Such inefficiencies arise from high cost
of labour, lack of linking roads for transportation,
electricity, water, inadequate credit facility.
The processors in the study area process oil palm to
get palm oil, kernel and fiber. The methods of getting
these products are very tedious and laborious. This
requires substantial proportion of labour force. The
success or failure of a processing depends largely
upon how labour and other associated resources are
efficiently used. An efficient processing technique
increases the quality and quantity of food available
for consumption and trade (Ukpabi, 2004).
It is widely recognized that increasing agricultural
production is, in many parts of the developing world
an important component of a strategy to increase
incomes, reduce hunger and contribute to the
improvement in other measures of well being. Doing
so requires improvements in the productivity of
factors of production. As Birkhaeuser, et. al., (1991),
Evenson (2001) and others have argued, agricultural
extension represents a mechanism by which
information on new technologies, better farming
practices and better management can be transmitted
to farmers. Effective communication links between
researchers and extensionists are vital in the
modification of technological recommendations and
in initiating further research; such links enable new
technologies and management practices to be suited
to local ecological conditions. In addition to diffusing
new technology supplied to such institution, they
provide feed back from the farmers to the research
centers themselves (Mattia, 2003). They improve the
knowledge base of farmers through a variety of
means such as demonstration, model plots, specific
training, group meetings and so on. The participation
of extension workers in adaptive research trials
allows them to become familiar with the technologies
they are expected to promote and also helps to ensure
that the sociological dimensions of farming are not
neglected.
Extension thus has considerable potential to make a
significant contribution to agricultural growth in
Nigeria. The provision of agricultural extension
services has been justified in the literature on both
equity and efficiency grounds. In the presence of
market failures, for example externalities, limited
access to credit or non-competitive market structures,
producers will not face the correct incentives to
produce certain varieties, use new production
techniques or adopt new technologies, resulting in
production levels that are not socially optimal (Feder
et al. 2003). The need to realize this potential and to
reach the vast majority of small scale farmers
probably explains greater involvement of the public
sector in Nigeria. The structure, organization and
operation of the Training and Visit (T&V) extension
system as a statewide system are showcase of this
involvement in Nigeria. Oil palm processors also
constitute part of the target audience of the T&V
under the Ondo and Osun States’ Agricultural
Development Programme. Due to significant roles
played by the extensionist and the resources being
invested in it, it is necessary to evaluate how much
extension contributes to palm oil production and
whether the level of increase in production justifies
the investment. Pedro et. al. (2008) evaluated the
impact of agricultural extension services to grape
producers in Mendoza and Argentina on yield and
quality, they observed that areas served by extension
had highest yields and that within these areas; the
highest yields were achieved by farmers who
participated in extension activities. As a result,
extension helps to close the gap between the yields
attainable with existing technologies and those
actually realized by farmers. In this paper, focus was
made on extension services that provide extension
services to palm oil producers. Therefore, the study
sought to determine the effect of extension services
on the poverty status of palm oil producers in Ondo
and Osun states. Specifically, the study
compares the output of farmers visited by
extension workers (FV) with those not visited (FNV);
determines the effect of extension on oil
palm processing;
International Journal of AgriScience Vol. 3(4): 372-379, April 2013 374
examines the poverty profile of the oil palm
processors and
determines the effect of extension services
on poverty level.
Theoretical consideration
The production function stipulates the technical
relationships between inputs and output in any
production schema or processes. The analysis of both
technical and economic characteristics of method or
techniques that can be used in the production of
goods and services at the most economical cost and
optional combination of factors-inputs is referred to
as production theory. This theory deals with
alternative methods by which resources can be
combined in an optional manner to produce goods
and services capable of satisfying the needs to the
final consumer (Jerome and Ariyo, 2004). A
production function is a mathematical representation
of relationship that exists between the inputs and the
output in a process of production. This is
specification of the minimum quantity of inputs
required to produce a desired level of output. In
mathematical terms, this function is assumed to be
continuous and differentiable. Its differentiability
enables us to establish the rates of return. Assume an
implicit, continuous and differentiable function of the
form Q = f (Xi) where Q is the output (measured in
kg/N) and Xi are the input (labour (mandays), land
(Ha)...). Then, the production parameters of interest
are:
oductAverage
X
Xf
X
Q
AP Pr
)(
oductinalMXf
dX
dQ
MP Prarg)('
Rate of return can also be established in three main
ways; the first of these returns is the case of constant
returns. This states that each additional unit of input
results in a constant rate of increase in output. The
second of these rates of return is that of increasing
returns. This states that each additional input of
productive resources results in a larger increase in
product than the preceding unit. Actual cases of
increasing returns are not very common in
agriculture. But when such cases are observed, they
occur at relatively low levels of output, that are
characteristics of small-scale peasant farming. The
last of the three is the case of decreasing /diminishing
returns. This is the case in which each additional unit
of inputs results in a smaller increase in pro-duct than
the preceding unit. This is the case that we would
normally expect to find in the production of farm
animals and crops. It is characteristics of the stages
when optimum efficiency of production or resource
use is being approached, as well as the situation
where there exists a misallocation or over-utilization
of inputs beyond the points of technical efficiency.
Since production function relates inputs and outputs,
they are used to determine how much of an input to
produce which also implies how much of the various
inputs to use. It is conceivable that inputs such as
land, labour and capital can be quantitatively
measured without difficulty but environmental
factors and management cannot be quantitatively
determined particularly in small farmer production.
This explains why production functions are usually
defined for a particular area at a particular time for
particular level of technology and management
(Adegeye and Dittoh, 1982).
MATERIAL AND METHODS
The study was conducted in Ondo and Osun states of
Nigeria. A multistage sampling technique was used.
A sample of five (5) Ondo and Osun States’
Agricultural Development Programme (ADP) zones
each, was purposively chosen so as to concentrate on
the oil palm processors. The listing of the oil palm
processors in the chosen zones was done with the
assistance of both OSADEP and ONADEP Staffs.
The questions posed enable the compilation of two
lists for each study location that is, oil palm
processors that are visited by extension workers (FV)
and those that are not visited by the extension
workers (FNV). These lists formed the frames from
which separates samples of 40 processors from each
state visited by extension workers (FV) and 50
processors from each state not visited by the
extension workers (FNV) by a simple random
sampling technique. Cross-sectional data were
collected from these samples in the last quarter of
2010. Average values of output of farmers visited by
extension workers (FV) are compared with that of
those not visited (FNV). The deviation between the
output of FV and that of FNV implicitly represents
the index of the effect of extension implying that if
farmers were not visited by extension workers, the
output would probably be lower than those visited. A
statistical test of significance was used.
Palm oil processor response model was specified and
estimated in three functional forms.
The model was estimated by means of ordinary least
squares (OLS) method for the entire sample. The
specified model is presented implicitly as follows
),,,,
,,.,,,(
1110987
654321 XXXXX
XXXXXXfY
(1)
375 International Journal of AgriScience Vol. 3(4): 372-379, April 2013
0;0;0
;0;0;0;0
;0;0;0;0
11109
876
54321

dX
dY
dX
dY
dX
dY
dX
dY
dX
dY
dX
dY
dX
dY
dX
dY
dX
dY
dX
dY
dX
dY
Where,
Y=Net returns of palm processing (N), X1=Age of
processors (years), X2= Processing Experience
(years), X3= No of years spent in School (years), X4=
Extraction Cost (N), X5= Labour (man days),
X6=Dummy Variable: FV=1; FNV= 0,
X7=Processing Period (days), X8=Depreciation on
other capital items (N), X9= Cost of Palm Fruits (N),
X10=Other Costs (water transport, firewood etc.),
X11= Technology Used dummy 1-improved method,
0 traditional method. If the coefficient of the
dummy variable (X6) is statistically different from
zero and positive, we conclude that all other things
being equal, extension significantly increases the
productivity of FV over and above that of FNV in the
study area. This means that extension information
and other services are available only to FV and
cannot reach FNV through other means other than
contact with extension workers. In other to measure
the effectiveness of extension services, another oli
palm production response model was specified as
follows:
),,,,,
,.,,,(
11109876
54321 XXXXXX
XXXXXfY
(2)
Where, X6 = frequency of farmer contact with
extension workers. Other variables are as defined in
(1). Equation (2) is estimated by means of LS
methods for FV only. There is need to stress that the
choice of the extension variable is conditioned on the
premise that the data collected do not indicate that
more productive farmers seek out extension workers
for advice more frequently than less productive
farmers such that one can infer that extension has
increased their productivity. In other words, there is
no problem of endogeneity, it is expected that the
greater the farmer contact s with extension workers,
the more advice and information farmers will receive
and that farmers with more services will, on the
average be more productive. Furthermore, equation
(2) assumes that complementary factors such as rural
infrastructure (feeder roads), new technologies and
access to agricultural credit that might have improved
farmers ability to respond to extension messages are
equally available to all FV. Double log production
function was selected as the lead equation based on
(i) the magnitude of R2 (ii) the significance of F-
value (iii) the t-values and (iv) the appropriateness of
the signs of the regression coefficients. The F-ratio
value was statistically significant at 1% which
implies that the model is adequate for use in further
analysis.
Poverty measurement using MPCE (mean per capita
expenditure) was used to categorize the household
into poor and non-poor. Logit regression model was
used to estimate the effect of the selected variables on
farmers poverty level. The analysis of poverty was
based on the P-alpha measures proposed by Foster
Greer and Thorbeck (FGT). The use of the FGT class
of measure required the definition of poverty-line,
which was calculated on the bans of disaggregate
data on expenditure. The FGT index was based on a
single mathematics formulation as follows;
n
iTZYZ
N
P1)/(
1
(3)
Where:
Z = The poverty line (Food poverty or non food
poverty)
n = Number of individuals or household below
poverty line.
N = The number of individuals in the reference
population.
Yi = The per capita expenditure of the farming
household.
α = Degree of poverty aversion and this takes the
values of 0, 1, 2.
Pα = Weighted poverty index
This is a pre-determined and well-defined standard of
income or value of consumption. In this study, the
line was based on the expenditure of the oil palm
farm household being used as a proxy since income
data are not easy to get. A relative approach was
based on whether a farmer was defined as poor or
relative to others in the same society or economy (<
⅔, ≥ ⅔ of the population). Two third of the mean per
capita expenditure was used as the moderate poverty
line.
The mean per capita farmers expenditure was
obtained by dividing the total of all individual
expenditure by the number of processing household
size. The category of poverty line is given as; Poor <
⅔ of MPCE; Non- poor ≥ ⅔ of MPCE.
The Logit analysis is an example of a large class of
generalized linear models. This model measures the
parameters of the conditional probability of poverty
level assuming a non-normal distribution
(binomial/Bernoulli distribution) of the chance of
being poor (Ravallion, (2005). The procedure
computes a maximum likelihood estimator of β given
the non-linear probability distribution of the random
error v. palm oil processing households were
International Journal of AgriScience Vol. 3(4): 372-379, April 2013 376
classified into poor and non-poor using the poverty
line measure (Z).
 
vx
Sip
SP
Log i
'
)1(1
)1(
for
i=1…180………4
Or
)(log pit
vx
'
---------------------------------
---5
Hence,
))exp(1(
exp
*'
)( '
X
PX
--------------
----6
Where:
Si = 1 for Xi Z, 0 otherwise (poverty status of an
household).
Z = the cut off point for poverty
X = a vector of k explanatory variables (Table 1)
β = the vector of respective parameters (probability
of being poor)
P* = predicted probabilities of household being poor
The logit procedure computes a maximum likelihood
estimator of β given the non-linear probability
distribution of the random error vector v. The
expression P (Si = 1)/{1 P (Si = 1)} is the odds ratio
in favour of being poor, and the logarithm of the
expression is referred to as the logit (Gujarati, 1995).
Households below the poverty line have a probability
of being poor, Si = 0.
Table 1: Description and measurement of variables
Variables
AGE
GENDER
FORMALED
CREDITACC
EXTRACOST
FAMILYSI
FARMIEXP
INCOME
SECOCCU
EXTN
ELECTRIC
PROCESS
DEPREC
WATER
Description
Age of household head in years
Gender of household head (male=1,female=0)
Household head formal education (years spent in school)
Access to credit (yes=1,No=0)
Extraction cost (N)
Family size of the household (no)
Experience of the household head (years)
Income earned by the household head (N)
Income earned from secondary occupation(N)
No of Extension visits (no)
Availability of electricity (yes=1,no=0)
Processing period (no)
Depreciation on capital (N)
Availability of water (yes=1,no=0)
Source: Field Survey, 2011
RESULTS AND DISCUSSION
Oil Palm Processing Response Model
Equation 3 indicated that all the explanatory variables
jointly explain 72 percent of the variability in oil
palm output and their joint effect is significant. Five
of the coefficients have expected positive sign and
only seven are significant at various conventional
levels. These are farm experience, farm size, labour
used and fertilizer. The study indicated that 40
(44.4%) of the respondents were visited by the
extension workers. The frequency of farmer contact
with extension workers varied from 1 to 26 times.
The oil palm processors (FV) are being visited once
every two weeks. The average output in naira of FV
(N434,604.84 per year) was higher than that of FNV
(N385,231). The t-test however revealed that this
difference was not statistically significant at the 5%
level. This could be inferred that FNV also enjoyed
the benefits of extension services through their
interaction with FV or cooperative societies in the
study areas in what could be termed as trickle down
effect. Equation 3 shows the double log regression
results for the palm oil production response model in
(1).
***ln542.0ln192.0ln194.0ln186.0**ln484.09137.1ln 54321 xxxxxY
(0.967) (0.321) (0.058) (0.197) (0.164) (0.104)
i
xxxxxx
***-1.3589ln***-0.8751ln***1.3589ln-ln046.0*ln329.0***ln540.0 11109876
(0.347) (0.282) (0.275) (3.267) (2.582) (4.012)
72.0
2R
453.14F
Standard error in parenthesis, ***- significant at 1%, **- significant at 5%, *- significant at 10%
International Journal of AgriScience Vol. 3(4): 372-379, April 2013 377
The positive sign of the dummy variable (X6) and its
non significance at all conventional levels mean
that visit by extension workers has positive impact on
oil palm processing but the effect was not significant
between FV and FNV. This suggests the possibility
of diffusion of extension services to FNV through
some social interactions in the cooperative societies
they belong. This thereby support the result of the t-
test reported earlier.
Impact of Extension Services on oil palm Production
Model
Y is the dependent variable which is the net returns
of oil palm produced measured in Naira and it
depends on the independent variables that are:
X1=Age of farmers (years); X2= Processing
Experience (years); X3= No of years spent in School
(years); X4= Extraction Cost (N); X5= Labour (man
days); X6=Dummy Variable: FV=1; FNV= 0;
X7=processing period and X8=Depreciation on other
capital items (N).
Equation (4) shows the empirical results for oil palm processing response model in (2) as follows:
***ln574.0***ln402.0ln194.0**ln186.0ln237.01937.2ln 54321 xxxxxY
(0.549) (0.196) (0.258) (0.167) (0.104) (0.156)
i
xxx
876 ln175.0*ln286.0ln389.0
(4)
(0.321) (0.148) (0.365)
76.0
2R
576.18F
Standard error in parenthesis; ***- significant at 1%; **- significant at 5%; *- significant at 10%
The explanatory power of the model is 76% and the
joint effect of the explanatory variables is significant
at 1 percent level. The significant variables include
processing experience (X2), extraction cost (X4),
labour (X5) and processing period (X7). All the
explanatory variables (except processing experience
and education) had expected relationship with oil
palm processing. The positive coefficient of the
extension contact and its significance implies that the
greater the frequency of farmer’s contact with
extension workers, the more productive the farmer is.
This means that the more advice, information and
knowledge received by a farmer, the greater is his
productivity in the study area. This suggests that
extension has been contributing effectively in terms
of advisory services and education of farmers to oil
palm processing in the study area.
Level of Poverty among Oil Palm Processing
Households
Table 3 shows the distribution of income of the oil
palm processors at different level per annum. The
poverty line is that level of welfare which
distinguishes poor households from non-poor
households (Mukherjee and Benson 2003). As there
is no clear consensus in the literature about when a
household or an individual should be defined poor,
the poverty line set for this study follows income
poverty line measures. The relative poverty line was
thus defined based on total income for the
households. For the households sampled, the value of
the poverty line is N107, 920.83 per annum and
consequently, the farm households that earned less
than half the average income or that, earn incomes
which fall below 50% of the mean income and such
were considered to be poor (Olubanjo 1998 and
Olagunju, et. al., 2012). Out of the one hundred and
eighty respondents, 54.2% were non poor. Only
about 10 % of the palm oil processors visited by
extension workers were poor while about 35% of
processors not visited by the extension workers were
poor.
Measuring the extent and level of poverty among the
palm oil processors, Table 4 revealed that poverty
incidence was most noticed among non extension
users of processing palm oil, palm oil processors with
no formal education, married households and
household with farm size of between 1-2 ha. As a
whole, the incidence of poverty in the study area was
0.458 implying that 45.8% of the sampled farm
households were actually poor. This proportion
invariably agreed with the earlier estimation of the
proportion of poor farm households (i.e 45.8%) in the
sample based on the poverty line definition. The
value P1(poverty depth) across economic
characteristics of the respondents was 0.120 implying
that the poor palm oil processors require 12.0% of the
poverty line to get out of poverty. Palm oil processors
with no formal education and farm size (1-2ha)
require 8% and 9% respectively getting out of
poverty.
The P2 (poverty severity) across all sampled palm oil
processors was 0.044, thus poverty severity among
poor oil palm household is 4%. The severity was
more expressed by non extension users, processors
with no formal education, the married respondents
and respondents with farm size that is between 1-2ha.
International Journal of AgriScience Vol. 3(4): 372-379, April 2013 378
Table 3: Distribution of Oil palm Processors’ Income
Income (Naira)
Frequency
T %
≤ 300,000
12
6.67
300,001-600,000
65
36.11
600,001-900,000
87
48.33
≥ 900,001
16
8.89
Total
180
100.00
Source: Field Survey Data, 2011
Table 4: Poverty Levels among the Palm oil Processors
Household
Characteristics
Poverty level and extent across socio-economic correlates
Po P1 P2
Incidence of Poverty Poverty
Poverty depth Severity
All households
0.458 0.120 0.044
Extension Users
0.104 0.028 0.010
Non Ext. Users
0.354 0.092 0.034
Total
0.458 0.120 0.044
EEducational status
No formal
0.292 0.082 0.032
Primary
0.139 0.034 0.011
Secondary
0.027 0.004 0.001
Total
Marital status
Married
Widowed
Divorced
0.458 0.120 0.044
0.256 0.062 0.021
0.139 0.035 0.013
0.063 0.023 0.010
Total
0.458 0.120 0.044
Farm Size
1-2ha
0.319 0.091 0.035
3-4ha
0.139 0.029 0.009
Total
0.458 0.120 0.044
Source: Field Survey Data, 2011
CONCLUSION AND RECOMMENDATIONS
This paper has evaluated the impact of the provision
of agricultural extension services for oil palm
processing in Ondo and Osun states. Based on the
major findings of the study, the following
conclusions and recommendations are made: The
study has shown that extension services perform the
function of getting the farmers into a frame of mind
and attitude conducive to acceptance or adoption of
technological change. This function is achieved by
educating the farmers on the newly developed
technology and to convince them of the viability of
the new technology in agriculture. This thus increase
palm oil production in the study area, as the output
of farmers visited by extension workers were higher
than those of farmers not visited by extension
workers although the difference was not statistically
significant. In addition, the extension services have
positive effects in reducing poverty status of the rural
palm oil processors.
The greater the frequency of farmer contact with
extension workers the more productive the farmer is.
The findings of the study confirmed with those of
Birkhaeuser et. al (1991). This study suggest that to
boost the effectiveness of extension services, efforts
needs to be directed at increasing the frequency of
extension visits to farmers and more farmers need to
be reached. The implication of this suggestion is that
more funds need to be provided to improve
agricultural extension services to palm oil processors.
However, since the public sector could not single
handedly provide the necessary funds, private
individuals and organizations need to be encouraged
to participate in extension funding. This is calling on
community associations, farmers union and farmer’s
379 International Journal of AgriScience Vol. 3(4): 372-379, April 2013
congress to assist in funding and promoting extension
services in oil palm processing in the study area.
REFERENCES
Adegeye AJ, Dittoh JS (1982) Essentials of
Agricultural Economics. Impact Publishers.
Ibadan pp 113-116.
Birkhaeuser D, Evenson R, Feder G (1991) The
Economic Impact of Agricultural Extension: A
Review. Economic Development and Cultural
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... Armando [8] reported that Organic Producers and Processors Association of Zambia (OPPAZ) have contributed to poverty alleviation among smallholder farmers in Zambia by raising their income through the premium generated from the sale of organic products. [9] in their study titled impact of extension services on poverty status of palm oil processors in Southwest Nigeria reported that out of 180 respondents sampled, 54.2% were non poor and only small amount (10%) of the palm oil processors visited by extension agents were poor. [10] in their work titled household poverty and its effect on child labour use among palm oil processors in Abia State reported that within the group of households whose children engage in child labour activities, less than 28% are living below poverty threshold compared to about 18% and 22% whose children do not engage in child labour activities. ...
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