EFFECTS OF EDUCATION ON THE AGRICULTURAL PRODUCTIVITY OF FARMERS IN THE OFFINSO MUNICIPALITY

Abstract

The study investigated the effects of education on agricultural productivity of farmers; how the varying kinds of education affect agricultural productivity; to suggest policy interventions that will facilitate the use of education to increase agricultural productivity and how educational level of farmers in the Municipality can be improved. Eight farming communities were involved in the study. They were selected based on their location in the Municipality, predominant economic activity, access to extension services and non-formal education. Data was obtained from 100 farmers in these communities and also from the Municipal Agricultural Development Unit as well as Non-formal Education Section of the Offinso Municipal Educational Directorate. The major finding in the study were that the as educational level increases, output increases with secondary school education having the highest returns on agricultural productivity. Extension service has a greater impact on agricultural productivity than formal education even though coverage is low. The study concluded that education is important to the improvement of agricultural productivity such that formal education opens the mind of the farmer to knowledge, non-formal education gives the farmer hands-on training and better methods of farming and informal education keeps the farmer abreast with changing innovations and ideas and allows farmer to share experience gained. It is recommended the government improves the quality of formal education, extension services and adult literacy classes in the Municipality. Factors that affect productivity such as transportation, access to input and credit facility to farmers should be improved.

Full text

Full Length Research Article
EFFECTS OF EDUCATION ON THE AGRICULTURAL PRODUCTIVITY OF FARMERS IN THE
OFFINSO MUNICIPALITY
*Oduro-Ofori Eric, Aboagye Anokye Prince and Acquaye Naa Aku Elfreda
Department of Planning, College of Architecture and Planning, Kwame Nkrumah University of Science and
Technology, Kumasi, Ghana
ARTICLE INFO ABSTRACT
The study investigated the effects of education on agricultural productivity of farmers; how the
varying kinds of education affect agricultural productivity; to suggest policy interventions that
will facilitate the use of education to increase agricultural productivity and how educational level
of farmers in the Municipality can be improved. Eight farming communities were involved in the
study. They were selected based on their location in the Municipality, predominant economic
activity, access to extension services and non- formal education. Data was obtained from 100
farmers in these communities and also from the Municipal Agricultural Development Unit as well
as Non- formal Education Section of the Offinso Municipal Educational Directorate. The major
finding in the study were that the as educational level increases, output increases with secondary
school education having the highest returns on agricultural productivity. Extension service has a
greater impact on agricultural productivity than formal education even though coverage is low.
The study concluded that education is important to the improvement of agricultural productivity
such that formal education opens the mind of the farmer to knowledge, non- formal education
gives the farmer hands- on training and better methods of farming and informal education keeps
the farmer abreast with changing innovations and ideas and allows farmer to share experience
gained. It is recommended the government improves the quality of formal education, extension
services and adult literacy classes in the Municipality. Factors that affect productivity such as
transportation, access to input and credit facility to farmers should be improved.
Copyright © 2014 Oduro-Ofori Eric et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original work is properly cited.
INTRODUCTION
There has been numerous studies conducted relating to
education and productivity in the agricultural sector which
have shown that there is a relationship between education and
agricultural productivity (Appleton and Balihuta, 1996;
Asadullah and Rahman, 2005; Lockheed, et al., 1980;
Pudasaini, 1983; Weir, 1999). The type of relationship that
exists between education and productivity is a matter of mixed
evidence and it may either be positive or negative, substantial
or otherwise. In 1992, the World Bank conducted a survey to
measure the relationship between farmer’s education and their
agricultural efficiency in low income countries and found out
that farmers with basic education were 8.7% more productive
than farmers with no education (Gasperini, 2000).
*Corresponding author: Oduro-Ofori Eric
Department of Planning, College of Architecture and Planning,
Kwame Nkrumah University of Science and Technology, Kumasi,
Ghana
It suggests from the finding of the World Bank that there is a
positive relationship between educational level of farmer and
productivity. In a study conducted in Nepal on the effects of
education on agriculture, it was discovered that education
enhances agricultural productivity primarily by improving
farmers' decision-making ability and secondarily by alleviating
their technical efficiency. Technical efficiency used here is the
farmer’s capability to make better choices in terms of input
and make better economically rational decisions (Pudasaini,
1983). Craig, Pardey, and Roseboom (1997) as cited in
Reimers and Klasen (2012) noted puzzling negative
coefficients for the education variables used in their studies. In
an analysis of 37 data sets from different countries by
Lockheed et al (1980), six data sets turned out to show
negative but statistically insignificant effect of education on
productivity. Hasnah et al. (2004) as cited in Asadullah and
Rahman (2005) report a significantly negative impact of
education on technical efficiency in West Sumatra-Indonesia.
In addition to the divergence in the type of relationship
between education and agriculture, there is mounting evidence
ISSN: 2230-9926
International Journal of Development Research
Vol. 4, Issue, 9, pp. 1951-1960, September,
2014
International Journal of
DEVELOPMENT RESEARCH
Article History:
Received 05th June, 2014
Received in revised form
12th July, 2014
Accepted 17th August, 2014
Published online 30th September, 2014
Key words:
Municipality,
Communities,
Investigated,
Transportation,
Agricultural.
Available online at http://www.journalijdr.com

that the type of education used in various studies matter. It is
apparent that returns of education on agricultural productivity
vary for different educational levels (primary, secondary and
tertiary levels of education) with the returns on primary
education being the highest (Lockheed et al, 1980; Appleton
and Balihuta, 1996). In Uganda, it was realized that there are
positive externalities from schooling in the form of higher
agricultural productivity whereby other farmers benefit by
adopting technology and practices used by one educated
farmer (Appleton and Balihuta, 1996). A further study in
Ethiopia emphasized that formal education does not
necessarily affect productivity but non- formal education does
in the form of extension services and sharing of information
from farmer to farmer which has a greater influence in the
adoption of and practice of best technology (Weir, 1999). A
multiplicity of research has been conducted in Asia and in
some African countries like Kenya, Uganda, Tanzania,
Burkina Faso and Ethiopia (Appleton and Balihuta, 1996;
Weir, 1999). Some of these studies are crop specific whiles
others are general agricultural practice. That notwithstanding,
evidence from to be specific Ghana is elusive. The purpose of
this research is to ascertain the relationship between education
of farmers and agricultural productivity in the Offinso
Municipality of Ghana and to find out how education can be
used to improve agricultural productivity in Ghana.
Problem Statement
The agricultural sector employs about 50% of the population
in Ghana making it the highest employment sector in the
country. Despite that, the sector contributes to about a third of
the nation’s Gross Domestic Product. The sector is dominated
by small holder production units which are confronted by
challenges such as low productivity, low level of agricultural
production among others (National Development Planning
Commission, 2010). In terms of education, the human resource
of the sector has the lowest level of education with majority
having just up to basic education. In a study conducted by the
Cocoa Research Institute of Ghana, it was realized that 52% of
farmers had up to middle school education whiles 21.5% were
illiterates (Aneani, et al., 2012). The percentage of people
employed in the agricultural sector is not commensurate with
the percentage contribution of agriculture to the Gross
Domestic Product (GDP) of Ghana. Why the difference in
percentages? Is it that the quality of labour force is minimal to
that of other sectors? There is little evidence in Ghana to
suggest that the sector’s low education level is what affect its
contribution to GDP.
Possible causes of this problem could be attributed to the low
level of education in the sector. One might argue that this is
not the case since as stated earlier there are so many other
factors that account for low agricultural productivity such as
bad weather condition or changing trends of weather, pests
and diseases among others. But education which involves
literacy and numeracy is thought to provide people with skills,
knowledge and ability to make efficient use of their resources
in addition to innovating new ways of doing things. Even
though the agricultural sector is faced with various challenges,
there may be efficiency advantage for farmers who are better
prepared to cope with the uncertainties due to education
(Asfaw and Admassie, 2004). The study therefore seeks to
identify the effects between education of farmers on
agricultural productivity in the Offinso Municipality of Ghana.
Objectives of the Study
The objectives of the study are as follows:
 To examine the relationship between education and
productivity in the Offinso Municipality;
 To determine how the differing types of education affect
agricultural productivity;
 To propose policy intervention that facilitates the use of
education to increase agricultural productivity in Ghana;
and
 To recommend ways by which educational level of farmers
can be enhanced in the Offinso South Municipality.
Education and Agricultural Productivity
The productive value of education has two main effects on
agriculture: “worker effect” and “allocative effect” (Welch,
1970) . Worker effects is described as the situation whereby an
educated farmer, given the same number of input can produce
a greater output that is a better use of current resources. It is
seen as increased output per unit change in education holding
all other factors constant. With allocative effect, a worker is
able to acquire information about cost and characteristics of
inputs and interpret the information to make decisions that will
enhance output. Here there may be a change in input and the
farmer adopts methods which will otherwise not have been
used. In a study conducted in Nepal, India (Pudasaini, 1983), it
was discovered that the allocative effect of education on
productivity is much greater than the worker effect indicating
that a key way that education influences agricultural
productivity is by improving the ability of farmers to take
decision concerning the selection of input and the combination
of input for better output. He stated that there are three main
ways that education raises agricultural productivity:
Improvement in farmer’s skills, enhancement of farmer’s
ability to obtain, understand and utilize, new input, and
improvement in overall managerial ability.
The effect of education of agricultural productivity can also be
described as cognitive and non- cognitive as indicated by
Appleton and Balihuta, 1996. Cognitive effects of education
comprises basic literacy and numeracy that farmers achieve
from education. Literacy enables farmers to read and
understand instructions on inputs such as chemical fertilizers
and pesticides among others. Numeracy allows for calculation
of the right proportion of inputs to be combined to get the
desired output. In a research conducted on 141 villages
consisting of rice farmers within Bangladesh, it was found out
that schooling has positive effects on agriculture due to the
skills of literacy and numeracy that give the farmers better
understanding into agricultural issues (Asadullah & Rahman,
2005). With regards to non- cognitive effects, there is a change
in the attitude of farmers who attend school and this is as a
result of discipline of formal schooling in terms of punctuality,
teamwork, timeliness, adhering to schedules and so on.
Nevertheless, non- cognitive effect on agriculture has not been
widely studied and the inference of its effect on agricultural
productivity are few as it is assumed that change in farmer’s
behavior as a result of education make them more susceptible
to new ideas and modern practices. Education influences
agricultural productivity either directly as indicated above or
indirectly. Indirectly, with the skills derived from education,
1952 Oduro-Ofori Eric et al. Effects of education on the agricultural productivity of farmers in the offinso municipality

farmers are able to engage in activities in the non- farm sector
which serves as alternative source of income for agricultural
activities (Appleton and Balihuta, 1996; Weir, 1999). Types of
Education and Their Effect on Agricultural Productivity. The
returns to education differ with the level of education and the
type of education. A regards educational level, there are mixed
evidence from literature as to whether primary or secondary
education has the most returns to agriculture but despite that it
is generally agreed that returns on tertiary education is very
minimal or non- existence (Appleton and Balihuta, 1996;
Asadullah and Rahman, 2005; Reimers and Klasen, 2012).
This necessitates the exploration of the returns on secondary
and primary education with respect to agricultural
productivity. Lockheed, et al. (1980) argue that primary
schooling is more crucial to agricultural productivity than
secondary schooling because it gives farmer basic literacy and
numeracy. It was realized in their research that an additional
year of primary schooling increases agricultural productivity
by 7.4% which was supported by Appleton and Balihuta
(1996) who gathered that four years of primary schooling
raised productivity by 7% while completing primary schooling
increases crop production by 13%. Pudasaini (1983) also noted
that as education level increases, the rate of productivity
declines hence there is diminishing marginal productivity with
regards to education.
Nevertheless, these statements have been opposed by recent
studies conducted by Reimers & Klasen (2012) on a sample of
95 developing and emerging countries from 1961 to 2002,
who discovered that returns to secondary education exceeds
that of primary education because it is not just the ability to
read and write that gives higher agricultural productivity but
the ability to do critical thinking in addition to application of
knowledge gained. This ability is what is gained in secondary
schools. Secondary education can then be said to enhance the
allocative effect of education on agricultural productivity in
addition to indirectly contributing to productivity by providing
a means to obtain non- farm income that can be used in the
acquisition of inputs (Weir, 1999). It was realized that
generally, an increase in schooling for an additional year
increases agricultural productivity by 3.2%. Furthermore,
formal and non- formal education can be seen as
complementary in terms of enhancing agricutlural productivity
(Lockheed, et al., 1980). This means that formal schooling
alone will not boost agricultural productivity if it is not
combined with extension services and mutual learning and
sharing of ideas among farmers necessitating the need for a
combination of both to improve productivity.
Kalirajan and Shand (1985) in their study of rice farmers in the
Tamil Nadu region alluded that formal schooling does not
necessarily increase farmer productivity but rather non- formal
schooling. It was explained that an illiterate farmer is able to
learn new ideas and modern technology from a neighboring
educated farmer and from the mass media like radio and
television hence emphasis should be placed on non- formal
education like extension services rather than formal schooling.
Non- formal education which was measured in terms of
understanding, experience and extension visits led rather to
significant increase in productivity than the years of formal
schooling or educational level of a farmer. Even though their
research results cannot be generalized to all the farmers in
India, it can be said that non- formal schooling increases
agricultural productivity through the mutual learning among
farmers and extension service. An uneducated person is also
able to apply knowledge gained for increased output and
production efficiency. Social and Private Returns to Education
on Agricultural Productivity. One major factor used to
quantify the amount investment in education is whether the
returns to education is private or social. Evidence from
literature shows that social returns from education with respect
to agricultural productivity far exceeds private benefits
(Appleton and Balihuta, 1996; Weir, 1999). In a study
conducted in Ethiopia, an additional year of formal schooling
on average in the village has a much larger impact upon farm
productivity than increasing household educational attainment
by one year on average (Weir, 1999). Furthermore in Uganda,
Social returns to education exceeded individual returns due to
the fact that there was usual mutual learning among farmers.
This indicated that an educated neighbor can affect an
uneducated farmer through sharing of knowledge and ideas
hence positive externalities from schooling lead to a higher
agricultural productivity emphasizing the point of government
intervention in subsidizing educational cost in rural areas
(Appleton and Balihuta, 1996).
Determining the relationship between education and
agricultural productivity
The measurement of the relationship between education and
productivity has evolved with time with authors criticizing
previous works and finding better equations and tests to reduce
statistical and data errors to find the actual relationship that
exist between these two variables. Factors to note in this
measurement include: Sample characteristics, method of
analysis, specification of measurement of dependent,
independent and other explanatory variables (Lockheed, et al.,
1980) Studies have shown the use of production function as
the basic tool for analyzing the impact of education of
agricultural productivity (Lockheed, et al., 1980; Appleton and
Balihuta, 1996; Reimers and Klasen, 2012). Production
function relates the quantity of farm output to the level of
input that is the factors that affect production (land, labour,
capital and any other factor that seems relevant to the study).
The variables used in the production function depends on the
objectives of the study as well as the data available. The kinds
of production functions that have been commonly used include
Cobb- Douglas Production function, linear production function
and translog production function.
The Study Area
The geographical scope of this study is the Offinso Municipal
Assembly which has a total land size of 600km2 representing
about 2.5% of the total land area of the Ashanti region. It lies
within the latitude 70 15N and 60 95S and longitude 10 35W
and 10 50E. It is located in the extreme North-Western part of
Ashanti Region sharing common boundaries with Offinso
North District Assembly in the North, Afigya-Kwabre in the
East and South, Atwima-Nwabiagya and Ahafo Ano South
Municipal Assemblies in the West. The municipality has many
rivers that aid agricultural activities. These include the Offin.
The municipality experiences Wet Semi- Equatorial climatic
conditions with an annual temperature range of between 21ºC
and 32ºC. The area experiences double maxima rainfall with
an average annual rainfall of 953.40 mm. The major rainy
1953 International Journal of Development Research, Vol. 4, Issue, 9, pp. 1951-1960, September, 2014

season is usually from May to June whiles the minor rainy
seasons occurs between September and November. This
favorable climatic condition enables farming activity which is
the predominant agricultural activity in the municipality. The
soil in the area are rich in humus, very fertile and well drained
which supports the cultivation of both food and cash crops, the
main source of livelihood in the municipality.
Education
Education is seen to be every important in the Municipality
much care is given to it. There are 74 Pre- schools, 74 Primary
schools, 52 Junior High Schools, 3 Senior High Schools, 1
Midwifery Training School and 1 College of Education. The
Municipality has about 647 trained teachers and 273 untrained
teachers making a total of 932 teachers. The Municipality also
has a non- formal education sector that educates school drop-
outs and adults for free. Basic literacy and numeracy in both
English and Twi are taught for a period of 18 months each
after which a student receives a certificate of completion.
Classes are either held in the morning or evening in the
communities with a minimum of 18 students and a maximum
of 30 students
Major Economic Activities in the municipality
The predominant economic activity in the region is agriculture
which employs about 62% of the total labour force. Other
economic activities include: Commerce- 21%, Services- 15%
and Industry- 4%. Since the focus of the study is on the
relationship between education and agricultural productivity,
much emphasis will be placed on the agricultural sector. The
agricultural sector contributes about 55% of the total
household income in the Municipality from food crops and
20% from livestock. The major agricultural produce includes
food crops, cash crops and livestock. Major food crops include
plantain, maize, yam, cocoyam and vegetables such as pepper,
garden eggs and tomatoes. The industrial crops produced are
cocoa, oil palm and teak. The major method of farming is
slash and burn while the main farming practices are bush
fallowing and mixed cropping. The average farm size per
farmer is 1.0 hectares. Livestock raised in the Municipality
include sheep, goat, cattle and poultry. This is usually
subsistence based with very few large scale poultry farmers
located in Abofour and New Offinso.
MATERIALS AND METHODS
The research design used was non- experimental design
specifically correlation studies. Non- experimental designs do
not involve the manipulation of a situation or circumstance but
rather it is used to find the relationship between variables and
in comparative studies. Simple correlation measures the
degree of linear association between two variables without
stating whether there is a cause and effect relationship. To
explain and effect of the varying types of education on
productivity, the mean average output was compared for
various level and simple cross tabulations were used to
determine the relationships that exists. Due to the uneven
distribution of the data gathered Spearman’s rank Correlation
was used to give a true reflection of the relationship that exists
between education and agricultural productivity and harmonic
mean was used to reflect the true average output. Agricultural
production as stated in the literature review is a combination
of several factors such as land, labour, capital and other inputs.
And as such to determine the contribution of education on
agricultural productivity all the other factors were considered.
The 2000 population census indicated that 62% of the people
in the Municipality are farmers. Inferring from that using
economically active population of 36141, it can be concluded
that the total number of farmers in the region is 22,407.
Primary data from MADU also indicates that 55%, 25% and
20% of the population are food crop farmers, cash crop
farmers and animal producers. Therefore 75% of farmers are
into crop production hence a study population of 16,805
farmers. The sample size of 100 farmers was chosen to be
interviewed in 8 communities across the Municipality. The 8
communities from which the farmers were selected was done
purposively while the simple random sampling technique was
used to select farmers from the various communities. Primary
data was collected from farmers in the selected communities in
the district basically on production level, farm size, farm input
and equipment used, educational level and access to extension
services. The data was collected using questionnaires which
was filled by the interviewer based on the answers given by
the farmer. Primary data was also collected from the
Municipal Agricultural Development Unit on crop production
in the Municipality, extension services and any efforts put in
by MADU in the education of farmers. The data collected was
coded and analyzed using Statistical Package for Social
Scientists (SPSS) and Microsoft Excel. These software were
used to generate correlation coefficients, cross tabulation of
variables, frequencies, charts and tables to show visibly
findings identified.
Characteristic of Respondents
Hundred farmers were interviewed in the Offinso Municipality
which consist of 55% male and 45% female farmers. 31% of
the farmers have no schooling whiles 69% of farmers have
some form of formal schooling. The average age per farm
manager is 44 years whiles the average age per adult
household members who work on the farm is 48 years. Figure
1 shows the educational level of farmers by gender and it can
be seen that the male farmers have better education than the
female farmers in the Municipality because in the rural areas,
it was seen to be more beneficial to educate males than
females. The total land size cultivated is 1345.5 acres in 8
communities with the annual average income per hectare
being GHȼ530.24. Crops cultivated as shown in Figure 4.2
include food crops (eg. Maize, cassava, yam, etc), cash crops
(cocoa and oil palm) and exotic vegetables (cucumber, carrot,
cabbage and green pepper).
The type of crops grown is determined by factors such as:
regular supply of income, fertility of land, profit generated,
and farmer’s knowledge of crop and for posterity (cash crops).
Farm produce are sold at the farm, in the communities, in the
markets within the municipality and at some markets outside
the municipality such as Kumasi to get better prices. Since
most crops are perishable, they are sold right after harvest and
only maize and rice are stored in barns to be sold at a higher
price in the lean season. Oil Palm however is processed into
palm oil to earn better income. Land cultivated for farming
belonged to either family (64.22%) or rented (35.78%). With
regards to farm labour used, only few large scale farmers (5%)
1954 Oduro-Ofori Eric et al. Effects of education on the agricultural productivity of farmers in the offinso municipality

have permanently hired labor. On the other hand, farmers with
smaller farm sizes (68%) use temporarily hired labour based
on the money available whiles 6% of farmers use family
labour only and 21% of farmers do not use any labour apart
from themselves. Inputs such as fertilizer, chemicals for pest
and disease control and seedlings are available in the
municipality.
Effects of education on agricultural productivity of
farmers in the Offinso Municipality
During the data collection, it was identified that farmers were
unable to quantify their yield for the previous year but were
only able to give information on income generated from sale
of crop. Consequently, output as used in this study means
average income generated per acre of land cultivated. To
determine the effects of education on agricultural productivity
of farmers, a translog Coub Douglas Production function
(adapted from Lockheed et al, 1980) was used with value of
output as dependent variable. The independent variables
include land size cultivated last year, labour (represented by
number of adults permanently working on the farm), capital
(expenditure on purchased equipment used last year),
expenditure of purchased input used last year (fertilizer,
seedlings and agro- chemicals) and access to extension
service. This is shown below:
lnYi = α0 + α1lnLi + α2lnKi + α3lnAi + α4lnPi + α5lnEi +α6lnExti + ei
…….. (1)
Where, Y = Value of output (average income generated)
α0 = intercept (the value of Y when all other independent
variables are 0)
L = Labour (number of adults permanently working on the
farm)
K = Capital (expenditure on purchased equipment)
A = Land Size Cultivated
P = expenditure of Purchased input
E = Number of years of Schooling
Ext = Access to extension services (dummy variable)
e = Error term
From the equation, α (0, 1, 2, 3, 4, 5, 6) represents the
coefficients of the various independent variables which shows
the degree to which an independent variable affect
productivity when all other independent variables are held
constant. The regression coefficients was generated using
SPSS and is shown below.
Y= 5.805 + 0.264L + 0.309K + 0.639A + 0.016P – 0.039E + 0.036Ext
……….. (2)
Y= 5.805 + 0.264L + 0.309K + 0.639A + 0.016P – 0.041E + 0.036Ext
……….. (3)
Table 1 summarizes the regression equation and tests for the
validity of the model used. The multiple regression coefficient
(R) shows that the independent variables predict 70.8% of the
Source: Offinso Municipality Field Survey, 2014
Figure 4.1 Educational Level of Farmers by gender
Source: Offinso Municipality Field Survey, 2014
Figure 4.2. Types of Crops grown in Offinso Municipality
1955 International Journal of Development Research, Vol. 4, Issue, 9, pp. 1951-1960, September, 2014

dependent variable which is total income generated or output.
To check the extent to which the output is explained by the
independent variables chosen, the coefficient of determination
which is R2 was calculated and it indicated that 50.1% of
output is explained by the independent variable. It was also
necessary to check if the model used is suitable for the data
gathered, an F-test was conducted which gave a significance
of 0 at 95% confidence interval hence the model is a good fit
for the data. From equation (2) above, it can be seen that land
size cultivated has the highest coefficient of 0.639, meaning
that it is the highest factor that determines the agricultural
productivity of a farmer. The table 2 below shows the
regression coefficient, the standard error and statistical
significance of the variables used to predict the output. When
the p- value is less than 0.005 it can be said that the variable is
significant and any change in the variable will substantially
affect productivity. Only the farm equipment used and total
land size cultivated have a significant effect on agricultural
productivity. Since equipment use is related to type of input
used, input use can be said to affect productivity indirectly.
Table 1. Summary of Model and Statistical Significance
Multiple Regression
Coefficient (R)
R Square
(R2)
Adjusted R
Square (R2) F Sig.
0.708 0.501 0.469 15.558 0
To ascertain the effect of education on agricultural
productivity of farmers, the education coefficients which is
represented by number of years of schooling for formal
education and extension service for non- formal education,
were critically examined. From table 1, it can be seen that
formal education has negative effects on productivity but it is
not statistically significant. This means that a 1 year additional
increase in the years of schooling leads to a GHȼ0.039 which
is equivalent to 3.9 pesewas reduction in income generated
which is not substantial. This can be attributed to the low level
of literacy in the Municipality. Literacy level as used in the
analysis is the ability to read and write English. Out of the
total number of farmers interviewed 64% cannot read and
write English while 36% can read English implying that the
number of years of schooling has minimal effect on literacy
which is one of the major ways through which education
affects productivity. This is evident in equation (3), whereby
number of years of schooling is replaced with literacy and the
result shows a coefficient of -0.041. Figure 3 below shows the
interaction between educational level of farmers and literacy.
From the figure above it can be seen that only 9.09% of
farmers who have attained primary school education and
47.83% who have had Middle School or Junior Secondary
School education can read and write English. A major effect of
education on agriculture is the cognitive effect whereby a
farmer acquiring basic literacy and numeracy can read
instructions on fertilizer, pesticides and weedicides and can
calculate the right mix of input to enhance productivity
(Appleton and Balihuta, 1996). Since 57% of farmers
interviewed have up to 10 years of education which has not
had a significant effect on the literacy of the farmers, majority
of farmers are not able to apply lessons learnt in the classroom
in their agricultural activity. Although formal education has
negative but statistically insignificant effect on agricultural
productivity, it can be used to indirectly improve productivity.
Education is said to have allocative effect whereby a worker is
able to acquire information about cost and characteristics of
inputs and interpret the information to make decisions that will
enhance output (Welch, 1970).
Table 2. Showing results of Multiple Regression
Independent Variables Regression
Coefficient
Std.
Error t Sig.
(p- value)
(Constant) 5.805 0.396 14.667 0
Number of years of
schooling -0.039 0.087 -0.451 0.653
Number of farm
workers 0.264 0.178 1.477 0.143
Total cost of equipment 0.309 0.097 3.184 0.002
Total Land size
Cultivated 0.639 0.1 6.379 0
Total Cost of purchased
input 0.016 0.05 0.313 0.755
Dummy variable for
extension 0.036 0.187 0.19 0.85
*Dependent variable is total income generated
Source: Author’s Own Construct, 2014
Figure 3. Relationship between Literacy and Educational Level
1956 Oduro-Ofori Eric et al. Effects of education on the agricultural productivity of farmers in the offinso municipality

The type of equipment used which is directly related to the
type of input use has a substantial effect on agricultural
productivity. Therefore, an improvement in education can
enhance agricultural productivity through improvement of
farmer’s ability to make decisions concerning choice of farm
equipment and input to boost output. Conversely, extension
service had a coefficient of 0.036 which also implies that
access to extension services once a year increase productivity
by GHȼ0.036 or 3.6 pesewas which is positive but not
statistically significant. The level of significance is low
because only 36% of farmers interviewed have extension
services.
Types of Education and their effect on Agricultural
productivity
Formal Education
Formal education has been grouped in to Primary education (1
to 6 years of schooling), Middle School/ JSS (7 to 10 years of
schooling), Secondary school (10 to 13 years of schooling)
and Tertiary (above 13 years of schooling). About 11 percent
of the respondents have attained primary education, 46 percent
have middle school educations, another 11 percent have
attained secondary education while only one percent has
tertiary education. About 31 percent of the respondents have
never been to school. According to Welch (1970), the
productive value of education has two main effects which is
the worker effect (how much one is able to produce more
given the same input) and the allocative effect (acquiring
knowledge to change combination of inputs to enhance
output). The allocative effect of education has already been
examined above. To determine the worker effect of formal
education on agricultural productivity, the mean outputs of the
various educational levels are compared as shown in table 4.
Table 4. Comparing Means of Educational Levels
Education level *Mean Output per
Annum (GHȼ) Frequency Mean
farm size
Primary 568.77 11 7.36
Middle School 503.60 46 4.25
Secondary 829.23 11 5.99
Tertiary 931.00 1 5
No School 487.66 31 6.24
Source: Author’s Own Construct, 2014
*Mean used in this study is harmonic mean because the data contains some values
that are much higher than the rest and using harmonic mean gives a better
representation of the average.
From table 4 it can be seen that tertiary education has the
highest mean average income per hectare with no schooling
being the lowest. Taking the individual components of
education, it can be seen that the higher the education the
higher the output gained. This emphasizes Reimers and Klasen
(2012) discovery that returns to secondary education is higher
than primary education because secondary education gives the
farmers better ability to think critically and take decisions that
have positive effect on productivity in the face of other
agricultural challenges such as changing seasons and
inadequate funds for input and hired labour. Primary school
output is higher than that of Middle school because about 52%
of farmers with the highest level of education being Middle
school/ JSS cannot read and write English. The overall effect
of the various levels of education is determined by using no
schooling as control group and replacing the number of years
of schooling with dummy variables for primary, middle
school, secondary and tertiary education. The equation is
shown below.
lnYi = α0 + α1lnLi + α2lnKi + α3lnAi + α4lnPi + (α5lnPrimi +
α6lnMidi + α7lnSeci + α8lnTeri)+ α9lnExti + ei …….. (3)
Y= 5.805 + 0.264L + 0.309K + 0.639A + 0.016P + (0.014Prim
+ 0.033Mid + 0.284Sec - 0.226Ter) + 0.036Ext ……….. (4)
Where,
Prim = 1 to 6 years of schooling
Mid = 7 to 10 years of schooling
Sec = 11 to 13 years of schooling
Ter = above 13 years of schooling
Looking at equation (4), it can be seen that an additional year
of primary, middle school and secondary education leads to
increases productivity with secondary education giving the
highest returns to education. This finding matches that of
Reimers and Klasen (2012) who also discovered that returns to
secondary education is higher than primary education because
the ability of farmers to make better decisions and choices
about combinations of inputs to obtain maximum output is
developed. Another reason for secondary schooling yielding
the highest returns is that about 50% of these farmers studied
Agriculture which gives them better knowledge than other
farmers. However an additional year of tertiary schooling has
a negative effect on productivity. This confirms findings made
by Pudasaini (1983) which that as education level increases
beyond a certain, the rate of productivity declines hence there
is diminishing marginal productivity with regards to
education. Interaction between educational level of farmers
and other variables. To determine the interaction between the
educational level of farmers and the other variables used to
predict productivity (Land, Labour, Purchased input and
equipment), educational level of famers is cross tabulated with
land size cultivated, type of equipment used, and type of input
used and utilization of input.
Land Size
Relating educational level to average land size cultivated
shows primary school leavers having the largest land size
among the others. It was found out in the studies that highest
educational level attained does not affect the size of land
cultivated but rather factors such as tribe, resources
availability and age rather determine the size of land
cultivated.
Type of equipment and farm input used
Farmers in the Municipality still use the traditional tools which
are cutlass and hoe for farming. However, about 88% use the
Knapsack sprayer with weedicides, herbicides and other
insecticides to control weeds, pests and diseases on their
farms. Only 18% of farmers use pumps for irrigation purposes
and this only applies to farmers who grow vegetables such as
tomatoes, pepper, okro, garden eggs and the exotic vegetables
like carrot, cabbage and cucumber. About 41% use fertilizer
either organic or inorganic fertilizer purchased from chemical
1957 International Journal of Development Research, Vol. 4, Issue, 9, pp. 1951-1960, September, 2014

shops on their farms. Currently, the choice of input or
equipment is not determined by one’s educational level
because about 57% of farmers without schooling also use
these input and equipment.
Utilization of farm input
Educational level has minimal effect on how farm inputs such
as fertilizer and agrochemicals are used. This is because 18.9%
of farmers use inputs based on instructions from extension
officers; 11.3% of farmers use inputs based on knowledge
gained from friends and colleague farmers; 19.3% of farmers
use inputs based on farmer’s own discretion; 5.2 % of farmers
use inputs based on instructions from chemical shop and 0.9%
of farmers use inputs based on read instructions. These reasons
cut across the various educational levels.
Savings and Access to credit
The different levels of education also do not have any
significant relationship with savings and access to credit
facilities. This is because 63% of farmers save of which 20%
have had no schooling. In addition, 70% of those who save
and have had no schooling save in the bank. Access to credit
facilities from this study is determined by one joining an FBO,
saving at a bank, and from the relationship one has with
friends and family not one’s educational level.
Alternative occupation
Education level of farmers can have an indirect relationship
with productivity through provision of alternate source of
income to fund agricultural activities. This was examined by
Appleton and Balihuta (1996) and Weir (1999) who stated that
with the skills derived from education, farmers are able to
engage in activities in the non- farm sector to gain alternative
source of income for agricultural activities. It was discovered
in the Offinso Municipality that 42% of farmers have
alternative source of income with Trade being the main
source. Out of this 6% have primary education, 17% have
Middle School/ JHS, 5% have secondary schooling and 14%
have no schooling. The average income obtained from the
farms of those who have alternative occupation is much lesser
than that of those whose sole occupation is agriculture and this
can be ascribed to small farm sizes as well as inadequate time
to pay attention to farming activities. An interesting finding
was that farmers with secondary schooling who have other
occupations have the highest average output of GHȼ1289.40.
Therefore, education which enables farmers with skills to
work in the non- farm sector also has minimal effect on
agricultural productivity for low levels of education but with
secondary education, one is able to better manage time and
other resources to improve productivity.
Non- formal Education
Non- formal education will take into consideration extension
services and adult literacy classes. 2 out of the 100 farmers
educated had 1 year of adult literacy classes had the ability to
read and write only Twi. The effect of adult literacy classes on
agricultural productivity cannot be examined because it has
had no effect on the farming activities of the farmers
interviewed. The focus in this section is on extension services.
About 36% of the farmers interviewed have access to
extension services in the Offinso Municipality. According to
the farmers some of the services delivered include the
provision on knowledge on: row planting, pests and disease
control, farm management, fertilizer application, harvesting,
good farming practices, How to Save; and provision of input
such as fertilizer, seedlings and chemicals for pests and
disease control. Comparing the mean annual income per acre
of farmers with extension services (GHȼ 540.28) to that of
farmers without extension services (GHȼ524.76), it was
discovered that farmers who have extension services have
output that is 10% higher than that of those with no extension
services implying that extension service helps improve
agricultural productivity. This is as a result of the services
provided by extension officers listed above. An interesting
finding on the field was that some farmers even though receive
extension service have some perceptions on the use of
weedicides and fertilizer on the land. Such farmers who
constituted about 10% for weedicide use and 13% for fertilizer
application, explained that application of fertilizer reduces the
fertility of the land in the long run and after about 10 years, the
land will no longer be able to produce on its own but will be
completely reliant on fertilizer and output will be low. To
remedy this situation some farmers use poultry droppings, cow
dung and urea.
How inputs are utilized is also very crucial to the allocative
effect of education (Welch, 1970). About 32% of farmers use
inputs as learnt from extension officers, 12 percent use as
learnt from other farmers, 34% use inputs based on own
knowledge, 10% inquire from chemical shop and 1percent
read instructions on the container. This shows that when
farmers are given information on the right methods of
utilization of input, they will take decisions about how to
combine the inputs in other to get an increase in output. About
72% of farmers that have extension services save while 58%
of farmers without extension services save. Among the things
taught by extension officers are the importance of saving and
where to access credit facilities from. This implies that farmers
with extension services save and have greater chance of
getting access to credit facilities as well as opportunity for
reinvestment.
Some farmers are in Farmer- based Organizations whereby
they meet and learn together concerning crop production,
growing of seedlings, joint harvesting, and joint weeding
among others. About 50% of farmers who have extension
services are in FBO’s and these are mainly made up of cocoa
farmers. These farmers attend meetings and are taught how to
nurse and transplant cocoa seedlings, pruning, pests and
disease control, harvesting and drying of cocoa. There are also
some crop specific FBO’s which include Rice Growers
Association, Exotic Vegetable Growers Association and Maize
Growers Association. Extension services provided in the
Municipality has a stronger effect on the productivity of
farmers in terms of savings, utilization of inputs and sharing of
knowledge among farmers than formal education.
Informal Education
Informal education used in this study describes the
“neighbourhood effect” of education whereby farmers share
ideas among each other concerning crop production. 62% of
1958 Oduro-Ofori Eric et al. Effects of education on the agricultural productivity of farmers in the offinso municipality

farmers share ideas with other farmers while 38% do not. Of
the total number of farmers, 28% do not have access to
extension services, are not members of FBO’s and do not
share ideas with other farmers. Their average output is
GHȼ475 which is much lesser than the general average of
GHȼ530. It can be seen that, the sharing of knowledge among
farmers has contributed greatly to the utilization of farm input
such as fertilizer, weedicide and chemicals for pests and
disease control. The low coverage of extension service offered
in the Municipality also contributes to the increase in the
sharing of knowledge among farmers because that is the
readily available source of knowledge in the communities.
Farmers share knowledge on how to control pests and
diseases, types and quality of seeds to use for planting,
farming practices, harvesting, marketing among others.
Recommendations
The following recommendations have been suggested to help
improve upon education and agricultural productivity in the
Offinso Municipality.
Strengthening of extension services in the Municipality
Since extension services contribute more to agricultural
productivity, government investment in agriculture should be
channeled towards the provision of better extension services.
The Ministry of Food and Agriculture should transfer more
extension officers to the Municipality and provide them with
motorbikes to facilitate easy movement among communities
most especially to the hinterlands. Some farmers also refuse to
patronize the extension services provided but have problems
with pests and disease management on their farms. Extension
officers should be trained to practice evidence- based teaching
whereby things taught will be practiced on a sample farm with
community members monitoring progress so that when other
farmers see the results, they will change their perceptions and
apply the lessons taught. Also individuals in communities who
are respected and acknowledged by community members can
be trained and used as advisors to farmers so that they can be a
link between other farmers and extension officers.
Government investment in other sectors that affect
agriculture
For productivity to increase in agriculture, there need for a
right mix of all the factors that affect productivity and the
government in investing in agriculture must consider this. The
Ministry of Agriculture should consider subsidization of input
and equipment used for agricultural purposes. The Municipal
Assembly should make sure that roads leading to farming
areas are frequently graded and bridges built over streams to
enable easy movement of produce from the farms to the
markets.
Enhancing the quality of formal schooling
From the data analyzed above, it was realized that 90.91% of
the farmers who have had Primary schooling cannot read or
write English whiles 52.17% who have had up to Middle
School or JSS education cannot read and write English. This is
a very serious situation which raises concerns about the quality
of education in the Offinso Municipality in times past and
indicates that the returns (both private and social returns) to
education is very low. Supervision at the primary and JHS
level has to be strengthened by the Municipal Educational
Directorate to ensure that resources are not being wasted and
that students are understanding and are able to apply what they
have been taught and teachers are teaching properly. Also, the
curriculum in basic school already has agriculture incorporated
into it but the link between literacy and agriculture as well as
the how education can enhance agriculture should be made
known to students so that should they pursue agriculture as an
occupation, they can apply knowledge gained in school.
Ways by which educational level of farmers can be
improved
The literacy level of farmers in the Municipality is very low
even with 57% of farmers having obtained basic education
which necessitates the improvement of the educational level of
farmers in the Municipality to reap the benefits that education
has on agricultural productivity. The channel through which
this can be done is through adult literacy classes. It was found
out during the study that only five communities in the
Municipality have access to adult literacy classes; two of
which are in the Municipal Capital, New Offinso. The
locations are changed every 2 to 4 years which is the duration
of the study. Adult literacy classes is a great channel that can
be exploited because enhanced literacy gives farmers
confidence in decision making and enables them read
instructions, gives a better understanding of issues confronting
agriculture. The Non- formal section of the Educational
Directorate can train basic school teachers and other literates
in the communities to hold the classes and teach the illiterates
so that more farmers will learn basic literacy and numeracy to
enhance their agricultural activities.
Conclusion
Education is said to be one of the factors that affect
agricultural productivity. After the study in the Offinso
Municipality, it was unraveled that education indeed has an
effect on agricultural productivity but this effect has been
minimized due to the low literacy level, low educational level
of farmers in the Municipality as well as low level of provision
of extension services. Also, the farmers faced other factors that
magnify the effects of education such as transportation,
resources availability and cost of farm equipment and input
which minimized the effect of education on agricultural
productivity of farmers.
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