Available online at www.jpsscientificpublications.com
Life Science Archives (LSA)
ISSN: 2454-1354
Volume – 3; Issue - 3; Year – 2017; Page: 1060 – 1072
DOI: 10.22192/lsa.2017.3.3.5
©2017 Published by JPS Scientific Publications Ltd. All Rights Reserved
Research Article
DETERMINANTS OF COCOYAM PRODUCTION AND PROFITABILITY
AMONG SMALL HOLDER FARMERS IN SOUTH EAST OF NIGERIA
C. I. Ezeano*1, C. C. Okeke
1, A. I. Onwusika
2 and N. J. Obiekwe
1,
1Department of Agricultural Economics and Extension, Nnamdi Azikiwe University, Awka, Anambra State,
Nigeria. 2Department of Agricultural Technology, Federal Polytechnic, Oko, Anambra State, Nigeria.
Abstract
The present study was designed to examine the determinant of cocoyam production and profitability
among small holder farmers in South East of Nigeria. Primary data was collected from 120 cocoyam farmers that were selected from 10 out of 14 villages for detailed study. Data collected were analyzed using
percentage, multiple regression and Net farm income analysis. Results of the data analysis showed that most
cocoyam farmers were young, educated and membership of organization. Also, the determinant factors to
cocoyam productivity were level of education, credit and membership of organization. Finally, cocoyam
farming was profitable with total cost of N408, 608, total revenue of N840,000, gross margin of N235,592
and net farm income of N431,392. It was recommended that appropriate policies that would encourage
cocoyam farmers‟ access to credit, educational programme and membership of organization should be
encourage.
Article History Received : 20.04.2017
Revised : 12.05.2017
Accepted : 05.06.2017
Key words: Cocoyam, Profitability, Small holder
farmers and South East of Nigeria.
1. Introduction Food security is a widely debated
development issue and yet remains a global
challenge, as food insecurity becomes acute
especially among vulnerable groups (marginal
population, dependent population, victims of
conflict etc) of the world (Unammah, 2003; Ojo,
2004). Food insecurity as stated by FAO (2004) is
* Corresponding author: C. I. Ezeano
Department of Agricultural Economics and
Extension, Nnamdi Azikiwe University, Awka,
Anambra State, Nigeria.
having little for healthy and productiveness or
being at risk of having little food.
The impact of food insecurity on human
and nation‟s developments are well
acknowledged. Kolawole (2009) reported that
lower per capita food intake has implications on
human welfare and productivity through its
influence on the capability of man to perform
work and the attitude of men towards work.
Furthermore, Iwundu (2009) opined that other
effects are concomitant high food prices; protests,
food riots and ever long food queues in many
countries of the world.
C. I. Ezeano/Life Science Archives (LSA), Volume – 3, Issue – 3, Page – 1060 to 1072, 2017 1061
©2017 Published by JPS Scientific Publications Ltd. All Rights Reserved
In Nigeria, food insecurity is exacerbated
by wide disparity between the nation‟s food
production and her ever growing population. This
poor performance of agriculture is most evidenced
by low standard of living of the people especially
in the rural areas (Unammah, 2003). Alleviating
food insecurity entails both physical and economic
access of food particularly staple food to the
consumers. One major staple food for more than
400 million people in both rural and urban areas of
the tropics and sub-tropics is cocoyam (GCS,
2009). Cocoyam originated from Asia and about
forty (40) species are mostly grown in West Africa
(FAO, 2008). Cocoyam, both Xanthosoma species
and Colocasia species belong to the family
(Aracea). The cocoyam species Colocasia
esculata in Sub-Sahara Africa was introduced to
this continent one thousand or more years ago
from South East Asia while cocoyam species
Xanthosoma mafafa was introduced more recently
from tropical America (Ekwe et al., 2007).
Nigeria is the largest producer of
cocoyam in the world, accounting for about 37 %
of the total world output (Echebiri, 2004; FAO,
2007). From 0.73 million metric tones in 1990,
cocoyam production in Nigeria rose to 3.89
million metric tones in 2000 and further by 30.30
% to 5.068 million metric tones in 2007 (FAO,
2008). Further estimate in Nigeria, showed a
figure of 5,387 million metric tones out of 11.77
million metric tones of world output of cocoyam
per annum since 2008 (Edet and Nsikak, 2007).
Cocoyam on a global scale is ranked 14th
as a root
and tuber crop, going by annual production figures
of 10 million tonnes (FAO, 2007). Nigeria is
currently the world‟s leading producer of cocoyam
(Okoye et al., 2009) accounting for upto 3.4
million metric tonnes annually. Nutritionally,
cocoyam is superior to cassava and yam in the
possession of higher protein, mineral, vitamin
contents and the starch is also more readily
digested (Ezedimna, 2006). It can be processed
into cocoyam flour, can be consumed in various
forms when boiled, fried, pounded or roasted and
can also be processed into chips which have a
longer shelf life (Ume et al., 2016). The leaves are
used as vegetables in preparing soup in various
parts of the world (NRCRI, 2003). It is highly
recommended for diabetic patients, the aged, and
children with allergy and for other persons with
intestinal disorders (Okoye, 2006).
The poor smallholder farmers who formed
the bulk of cocoyam producers are faced with a
number of constraints that limit their productivity
included, lack of improved varieties and cultural
practices, storage problems, increasing input costs,
land scarcity, inadequate technical know - how
among cocoyam growing farmers, poor road
network and perishability of planting material.
Furthermore, It has long been argued that limited
access of farmers to extension service, an outdated
land tenure system, climatic factors, imperfect
credit and capital market, spatial inequality
distribution of fertilizer, the high prices of other
non-fertilizer inputs and an inadequate fertilizer
supply are among other constraints to improve
fertilizer use in Nigeria (Iwueke, 1999; Ume and
Kadurumba, 2015; Ezeano et al., 2017).
Improving the productivity, profitability, and
sustainability of smallholder farming is therefore
considered the main pathway for self sufficiency
in food production and improved income.
Agricultural research and development
interventions focused on agricultural
intensification and modernizing market channels
for agricultural products can lead to agricultural
productivity growth and thereby both reduce
poverty and meet growing demands for food
(Oyinbo et al., 2013). Therefore, there is need to
appraise the socioeconomic characteristics of the
cocoyam farmers as it affects their performance in
farming and their productivity in terms of profit
accruing from cassava production in the study
area. This would lead to formulation and
implementation of polices that would enable
farmers to improve on their performances. The
specific objectives are to describe the
socioeconomic characteristics of the farmers,
determine the effects of the factors on the farmers‟
productivity and estimate the profitability of
cocoyam production in the study which is among
the cocoyam production zone of the nation.
C. I. Ezeano/Life Science Archives (LSA), Volume – 3, Issue – 3, Page – 1060 to 1072, 2017 1062
©2017 Published by JPS Scientific Publications Ltd. All Rights Reserved
Conceptual Framework
Productivity and profitability are some of
the basic concepts in economics of agricultural
production. Agricultural productivity is
synonymous with resource productivity which is
the ratio of total output to the resource or inputs
used. Output is usually measured as the market
value of final output, which excludes intermediate
products. This output value may be compared to
many different types of inputs such as labour and
land (yield). The importance of agricultural
productivity cannot be over emphasized, aside
from providing more food, increasing the
productivity of farms affects a nation‟s prospects
for growth and competitiveness on the agricultural
market, income distribution and savings, and
labour migration. Also increases in agricultural
productivity lead to agricultural growth and can
help to alleviate poverty in poor and developing
countries, where agriculture often employs the
greatest portion of the population. Low input use
and farm technology, such as improved seed and
fertilizer, are among the many reasons for low
agricultural productivity in Nigeria.
According to Obasi et al. (2013)
productivity improvements are only possible when
there is a gap between actual and potential
productivity. They suggest two types of „gaps‟ that
contribute to the productivity differential, the
technology gap and the management gap.
Extension can contribute to the reduction of the
productivity differential by increasing the speed of
technology transfer and by increasing farmers‟
knowledge and assisting them in improving farm
management practices (Iheke, 2006). The low
agricultural productivity has been attributed to low
use of fertilizer, loss of soil fertility, traditional
technology and rain fed farming system (Mbam
and Ede, 2011). Mustapha et al. (2012), noted that
productivity measurement involves the use of
basic concepts such as Average product (AP),
Marginal Product (MP), Marginal Rate of
Substitution (MRS), Elasticity of production (EP)
and Returns to scale (RTS). The three stages of
production are studied using these concepts. The
production function consists of different
functional forms. These include the Cobb Douglas
which is often used by researchers due to its
simplicity and flexibility, linear, quadratic
polynomials and square root polynomials. Others
are semi-log and exponential functional forms. On
the other hand, profitability is a measure of the
relationship between the levels of profits earned
during an accounting period and the level of
resources committed to earn those profits (FAO,
2008). It relates the level of profits to the volume
of sales or to the efficiency with which various
types of resources are managed. Thus, profit
maximization is achieved by maximizing output
from a given resource or minimizing the resources
required for a given output. Profitability is
influenced by the margins between costs and
returns per unit of production and the number of
units sold, hence it is closely tied to efficiency and
scale.
Several studies have been carried out on
the determinants of productivity and profitability
of agricultural production in Nigeria. Using
ordinary least square (OLS) criterion, Ume and
Kadurumba (2015) in the study of production
factors and farmers‟ output; using swamp rice
technology reported that age, education, labour
and cost of non-labour inputs were positively
related to output with labour input having
significant influence on output, while farm size,
years of experience and gender showed inverse
relationship with output. The study further
revealed that maize farming was profitable.
Ikwelle et al. (2011) in their study of cocoyam,
found that labour, farm size, family size, fertilizer
use, education level, and market variables were
the significant determinants of production and
profitability. The authors employed the use of Net
Farm income in their analysis. Mbam and Ede
(2011) showed that farm size and labour were the
significant determinants of rice productivity in
Anambra State. Ume et al. (2016), in the study of
determinant factors to the output of cocoyam
found that farm size and fertilizer use were the
significant determinants of output of cocoyam.
2. Materials and Methods
The South East Nigeria was the main
focus. The zone lies between latitude 509' and
7075'N of equator and longitude 6
085' and 8
046'
East of Greenwich Meridian. It has a total land
C. I. Ezeano/Life Science Archives (LSA), Volume – 3, Issue – 3, Page – 1060 to 1072, 2017 1063
©2017 Published by JPS Scientific Publications Ltd. All Rights Reserved
mass of 10,952.400 ha. The zone has population
of 16,381.729 people (NPC, 2006). The zone is
made up of five states viz., Abia, Anambra,
Ebonyi, Enugu and Imo States. It lies within the
rainforest and derived savanna region of the
country and bordered in the North by Benue and
Kogi States, in the West by Delta and Rivers
States, in the South by Akwa Ibom State and in
the East by Cross River State. South east states
have two major seasons in the year, the rainy
season which last from the month of April –
October and the dry season that lasts from
November to March. The temperature of the area
varies between 18 0C – 34
0C. About 60 – 70 % of
the inhabitants engage in agriculture mainly crop
farming, agricultural produce marketing and
animal rearing. Other non-agricultural activities
engaged by people for sustenance include civil
service, petty trading, vulcanizing, driving,
carpentry, mechanics and others.
Multistage random sampling technique and
purposive selection were used to select states,
agricultural zones, local government areas,
communities and respondents. In stage one, three
out of five states in South East Nigeria were
purposely selected because of high intensity of
cocoyam production (Okoye, 2006; Dimelu et al.,
2009). The selected states were Abia, Anambra
and Enugu. Stage two involved the random
selection of two agricultural zones out of three
from each state. This brought to a total of six
agricultural zones. The agricultural zones selected
were Enugu North and Enugu West for Enugu
State, Anambra and Aguata Zones for Anambra
State, while Umuahia and Ohafia Zones for Abia
State. These selected zones were further stratified
into local government areas. In the third stage, one
local government area each out of six Local
Government Areas was purposively selected from
each zone based on cocoyam production
performance. The local governments areas were;
Nsukka local government area for Enugu North,
Aninri local government area for Enugu West, Oyi
local government area for Anambra, Orumba
South for Aguata, while Ikwuano and
Umunneochi local government areas for Umuahia
and Ohafia zones respectively. In the next stage,
two communities out of four were randomly
selected from each of the local government areas,
giving a total of 12 communities. Farmers were
selected with the help of agricultural extension
agents and local leaders in the communities who
provided the sample frame, from which 10 farmers
were randomly selected from each of the
communities. This brought to a total of 120
farmers for a detail study.
For the study, both secondary and primary
data were collected. The primary data were
collected by the use of structured questionnaire
and oral interview schedule were adopted. The
sets of questionnaire were administered by
Agricultural Development Programme extension
agents, enumerators and the researchers. The
primary data collected from the farmers included;
household expenditure on planting materials and
inputs and value of planting materials and outputs.
The socio-economic characteristics of the
households were also captured ; age, level of
education, off farm income, access to credit and
membership of organization. Secondary data were
obtained from different literature sources related
to this study such as recent published and
unpublished survey articles, journals, textbooks,
internet, proceedings and other periodicals.
Method of Data Analysis
The objectives I was addressed using
percentage response, while objectives ii and iii
were captured using multiple regression and net
farm income respectively. Multiple regression can
be presented as
Y = X1 + X2 + X3 + X4 + X5 + - - - - Xn + e
Where, X1 - Age (years), X2 - Level of Education
(years), X3 - Membership of Organization
(Member; 1 otherwise, 0), X4 -Extension contact
(Access, 1; otherwise, 0), X5 - Access to credit
(N), e - error term.
Four functional forms (linear, double log,
semi double log and exponential functions) of
production function were tried and explicitly
represented as
C. I. Ezeano/Life Science Archives (LSA), Volume – 3, Issue – 3, Page – 1060 to 1072, 2017 1064
©2017 Published by JPS Scientific Publications Ltd. All Rights Reserved
Linear function:
Y = b0 + b1 x1 b2 x2 + b3 x 3 + b4 x4 + b5 x5 + ei …………….
(1)
Double log function (Cobb Douglas):
ln(y) = lnb0 + b1lnx1 + b2lnx2 + b3lnx3 + b4lnx4 + b5lnx5 + ei
…………… (2)
Semi double log function:
Y = lnb0 + b1lnx1 + b2lnx2 + b3lnx3 + b4lnx4 + b5lnx5 + ei
…………… (3)
Exponential function:
lnY = b0 + b1x1 + b2x2 + b3x3 + b4x4 + b5x5 + ei [12, 20]
................…… (4)
The choice of the best functional form was
based on the magnitude of the R2 value, the high
number of significance, size and signs of the
regression coefficients as they conform to a priori
expectation. Objective 3 was captured using net
farm income analysis.
Gross margin = G. M = TR – TVC
…….……………………………………………… 5
i.e. G.M =
m
ij
ii
n
xrQP11
11 ……...…………………………………
…………… 6
The Net farm income can be calculated by
gross margin less fixed input. The net farm
income can be expressed as thus: NFI =
kxrQPm
ij
ii
n
11
11 …….……………………………
………………… 7
Where: GM = Gross margin (N), NFI = Net
farm income (N), P1 = Market (unit) price of
output (N), Q = Quantity of output (kg), ri = Unit
price of the variable input (kg), xi = quantity of
the variable input (kg), K = Annual fixed cost
(depreciation) (N), i = 1 2 3 …….. n, j = 1 2 3
Theoretical framework of multiple regression
The multiple regression studies involve the
nature of the relationship between a dependent
variable and two or more explanatory variables.
The techniques produce estimators of the standard
error of multiple regressions and coefficient of
multiple determinations. In implicit form, the
statement that a particular variable of interest (yi)
is associated with a set of the other variables (xi) is
given as:
yi = f (x1,x2,...., xk) ………………………………………… (8)
where, y is the dependent variable, and xi.. xk is a
set of k explanatory variables.
The coefficient of multiple determination
measures the relative amount of variation in the
dependent variable (yi) explained by the regression
relationship between y and the explanatory
variables (xi). The F-statistics tests the
significance of the coefficients of the explanatory
variables as a group. It tests the null hypothesis of
no evidence of significant statistical regression
relationship between yi and the xis against the
alternative hypothesis of evidence of significant
statistical relationship. The critical F-value has n
and n-k-1 degrees of freedom, where n is the
number of respondents and k is the number of
explanatory variables.
The standard error of regression co-
efficient is the measure error about the regression
coefficients. The z-statistics is used in testing the
null hypothesis that the parameter estimates are
statistically equal to zero against the alternative
hypothesis, the parameter estimates the
statistically different from zero. If the computed z-
value exceeds the critical value, we reject the null
hypothesis and conclude that the parameter
estimates differ significantly from zero. The
nature of the relationship between an outcome
variable (yi) and a set of explanatory variables (xi)
can be modeled using different function forms.
The four commonly used algebraic (functional)
forms are: linear, log-linear or semi-log, linear-
log, and power or double-log. The first functional
form is the linear function expressed as:
yi = bo+ bix1 + b2 + …+ Bkxk + e1
………………………………………………. (9)
C. I. Ezeano/Life Science Archives (LSA), Volume – 3, Issue – 3, Page – 1060 to 1072, 2017 1065
©2017 Published by JPS Scientific Publications Ltd. All Rights Reserved
where, the bis are the parameters to be
estimated and ei is the stochastic error term. The
elasticity estimates of the linear function are given
as bixi/yo where xiand yi are mean values of xi and
yi . the second functional form is the log-linear or
semi-log function expressed as:
yi= exp(bo+ bix1 + …+ bkxk + e1)
………………………………………………… (9)
by taking the logarithm of both sides the function
of expression can be linearized as followings:
Inyi= bo+ b1x1 + b2x2 +…+bkxk+e1)
…………………………………………… (10)
where, e is the error term. The coefficient of
elasticity given by bkxk.The third form is the
linear-log function expressed as:
exp (yi)= exp (bo+e1)[x1 b1
x2 b2
……xk bk
]
....................................... (11)
If linearized by taken the log of both sides, the above
function will become:
Yi = bo + b1 In x1 + b2 In x2 + … + bk In xk + ei
........................... (12)
The elasticity of the linear - log function is
calculated as. bk/ȳi. The fourth functional form is
the power or double - log function expressed as:
Yi = box1 bix2
b2…. Xk
bk exp
{et}………………………………… (13)
By taking the log of both sides the power
function of expression can be linearized as
follows:
In yy = bo + b1 In x1 + b2 In x2 + …………. + bk In xk + et
……… (14)
The elasticity coefficient of the power
function is defined as the beta - values of the
explanatory variables, bks.
3. Results and Discussion
The Table - 1 shows that 60 % of the
farmers fell within the age bracket of 30 – 49
years, which implied that the bulk of the farming
population were energetic, able - bodied and
active group that are not only enterprising but
would supply the much needed farm labour in
agriculture. More so, majority (66.7 %) of the
farmers had no contact with extension agent, while
33.3 % had contact. The implication was that the
farmers in the study area had poor extension
outreach and this situation had negatively
influenced agricultural productivity. Extension
services help in dissemination of innovation as
well technical assistance to the farmers in order to
improve their productivity (Ezeano et al., 2017).
The result of Table - 1 also shows that 16.7 % of
the farmers had access to credit either from formal
or informal sectors, while 83.3 % did not have
access to credit. The poor access to credit by
farmers could affect their productivity. Ume, et al
(2016) remarked that credit is important for
agricultural productivity, income generation and
household welfare. Moreso, 65 % of the farmers
were members of different organizations, while 45
% were not. Cooperative as reported by Mbam
and Ede (2011) enables members to have access to
information on improved innovations, material
inputs of the technology (fertilizer and chemicals),
credit for payment of labour, capacity building and
training for increase in farm productivity. Several
studies (Okoye et al., 2009; Eze and Akpa, 2010)
made similar findings. Table - 1 has also shown
that most (90 %) of the respondents had formal
education, while only 10 % had no formal
education. The high educational attainment is a
desirable condition for agricultural development,
since it augured well for extension services in
transferring research result for sustainable food
production (Unammah, 2003). In the same vein,
Nwaru (2004) and Oyinbo et al. (2011) opined
that educational status informed the type of job
and standard of living one had and this impacted
directly on the farmers‟ production.
Based on the statistical and econometric
criteria, Cobb Douglas production function was
chosen as lead equation. The coefficient of
determination (R2) was 0.889, implying that 88.9
% of the variation in the output of the cocoyam
farmers were accounted by various inputs included
in the model, while the remaining 21.1 % were
due to error term. The statistical test of the
C. I. Ezeano/Life Science Archives (LSA), Volume – 3, Issue – 3, Page – 1060 to 1072, 2017 1066
©2017 Published by JPS Scientific Publications Ltd. All Rights Reserved
coefficient of age was negative and significant at
10 % probability level to productivity. This is in
line with Okoye and Onyenweaku (2007) who
reported that younger household heads have the
ability to comprehend new technologies and will
therefore readily adopt thus improving timeliness
of operations as well as reducing costs of
production, for higher productivity to ensued.
Nevertheless, Obasi et al. (2013) found in contrary
that age was positively related to productivity.
They opined that an increase in age of the
respondents would lead to an increase in
productivity. The increase in age could make them
to have had a mastery of the cocoyam production
activity in the aspect of management and resource
utilization as expected the coefficient of level of
education was positive in line with apriori
expectation and significant at 1 % alpha.
Education helps to enhance managerial skills,
resource management, decision making and
adaptability of an individual. These attributes
enhance individuals potentials of making informed
decisions that could optimize their output at
minimal costs (productivity) (Ume and
Kadurumba, 2015).
The coefficient of the membership of
organization was positive and significant at 5 %
risk level to productivity. Group organizations
may enable members to better smooth
consumption through their impact on income
variability, and both activities may increase on-
farm productivity and total incomes via enhanced
access to credit (Ezeano et al., 2017). Several
authors (Obeta and Nwagbo, 1999; Okoye and
Onyenweaku, 2007; Ume et al., 2016) concurred
to this assertion.
Credit coefficient was significant at 5 %
and maintained its expected positive sign, which
implies that credit is an important source of capital
which facilitates adoption for higher productivity.
This is consistent with Iheke (2010) who opined
that farmers who have better access to credit
stands a better chance of adopting technologies
faster than those who are capital -constrained.
Ume et al. (2010) and Eze and Akpan (2010) had
similar finding. Access to credit to stimulate
adoption, it is believed that access to credit
promotes the adoption of risky technologies
through relaxation of the liquidity constraints as
well as through boosting of household risk bearing
ability. This is because with an option of
borrowing a household can do away with the risk
reducing but inefficient income diversification
strategies and concentrate on more risky but
efficient investment for high productivity (Ume et
al., 2016). As expected, contact with extension
agents had positive relationship with productivity.
This suggests that cocoyam farmers experienced
higher productivity as more contacts were made
with extension agents/ services, as reported by
Nwaru and Ekumankama (2002).
Costs and returns in cocoyam productions
presented in Table - 3. In the study area, mixed
cropping was the predominant cropping pattern
although sole cropping could be cultivated
especially where other crops cannot survive. This
is particularly under fairly high shade. The food
crops usually planted in mixture were yam,
cassava, maize and stands of okra. In Nigeria, the
practice of mixed cropping is adopted as a risk
aversion strategy designed to insure against
possibilities of crop failure and heavy loses of
capital and labour inputs (Awoke, 2001).
Furthermore, mixed cropping is known to be more
profitable than sole cropping and consistent with
farmers‟ food security objectives (Okwuowulu,
2000). In this study, the emphasis was on cocoyam
as major crop. The average quantity of cocoyam
sett planted per hectare was 400kg. as shown in
Table 3. Given a cost of N300 per kilogram (kg),
expenditure on cocoyam setts for planting was
N120,000, constituted about 39.5% of the total
physical input. The high cost of planting material
(corms and cormels) (N120,000) could be
attributed to the fact that the same edible part also
served as planting material, in effect resulting in
high cost of the input (Okwuowulu, 2000). About
350kg of inorganic fertilizer costing N42000,
500kg of organic manure costing N12,000 was
applied per hectare of cocoyam enterprise. The
total cost of physical input came to N249, 000.
Labour input (family and hired) for various
farm operations was shown in Table - 3 and
included; land preparation (bush clearing,
C. I. Ezeano/Life Science Archives (LSA), Volume – 3, Issue – 3, Page – 1060 to 1072, 2017 1067
©2017 Published by JPS Scientific Publications Ltd. All Rights Reserved
stumping and mounding/ridging), planting,
fertilizer application, weeding and harvesting.
However, while bush clearing and land
preparation were predominantly male activities,
planting, fertilizer application, weeding and
harvesting were mostly undertaken by women and
children. Labour input was measured in man -
days. The hours worked by men, women and
children were converted to regular man-days using
the follow conversion factors: 1 man - day for all
activities carried out by male adult, 0.50 man -
days for all operations carried out by children (7 -
14 years) and 0.75 man - day for planting, land
preparation and fertilizer application by women. A
conversion factor of 1.00 was used for weeding
and harvesting operation by women (Ajoku,
2009).
On the average, the total amount of labour
employed per hectare was 440 man - days. A total
of 60 percent came from family labour, while 40
percent from hired labour. Hired labour was used
for most tedious operations such as land
preparation and bush clearing. Nevertheless,
family labour constituted a significant proportion
of total labour input. This could be because most
farmers used family labour since they were
financially constrained to hire labour in their farm
works (Eze and Akpa, 2010). The high cost and
scarcity of hired labour could be related to recent
unprecedented urban drift of youths witnessed in
the study areas (Eze and Okorji, 2004).
About 10.9 percent of man-days were
employed in planting, fertilizer and harvesting
respectively, while 36.4 % and 27.3 % of man-
days were engaged in land preparation and hand
weeding respectively. Ajoku (2009) finding
agreed with this assertion. Nevertheless, only very
insignificant number of cocoyam farmers used
herbicides. Weeding was therefore, mostly done
manually thus raising the labour input for
weeding. Awoke (2001) invoked scarcity, high
cost, ignorant of existence and method of use of
relevant herbicide to explain the possible reasons
for limited use of herbicides among small holder
farmers in most developing counties. Limited
number of the farmers used insecticides. The same
reasons for limited use of herbicides apply to the
limited use of insecticides as well as the fact that
limited diseases and insect attacked cocoyam
farms in the survey year. These may have reduced
the need for insecticide.
Wage rate varied with the nature of the
farm operations. Land preparation (mounding and
ridge making) attracted N2, 500 per day, planting:
N800, weeding; N1000, fertilizer application;
N800 and harvesting; N1000. The total cost of
labour was N80600, which was about 19.7 percent
of total cost of production. High cost of labour
was recorded in cocoyam production in the study
area and according to Okoye et al. (2008) and
Ezedinma (2006) would continue to be inelastic
and expensive as long as agricultural activities
remained nearly zero mechanized at farm level,
urban drift of able-bodied youths and feminization
of agriculture. The depreciated value of farm
implements (machete, hoe, digger, shovel and
basket) amounted to N3, 200 per hectare. The total
cost of production was N408, 608.
A total of 2800kg of cocoyam were
harvested per hectare. At N300/kg of cocoyam;
this yielded a market value of N840, 000. Taking
away the total variable cost of N405,408. The
gross margin for cocoyam was N435, 592. The
NFI =total revenue/total cost =431392his
indicated that cocoyam production was profitable
in the study area. This collaborates with the
finding of Ajoku (2009), who obtained a similar
finding in Owerri West Local Government Area of
Imo State. The benefit cost ratio = 1:2.06. This
indicated that for every one naira spent on
cocoyam production, about N2.06 will be realized.
C. I. Ezeano/Life Science Archives (LSA), Volume – 3, Issue – 3, Page – 1060 to 1072, 2017 1068
©2017 Published by JPS Scientific Publications Ltd. All Rights Reserved
Table - 1: Distribution of Cocoyam Famers According to Socioeconomic Characteristics
Factors Frequency (n=120) Percentage
Age in Years
20 – 29 23 19.2
30 – 39 40 33.3
40 – 49 32 26.7
50 – 59 14 11.7
60 and Above 11 9.2
Extension contact
Had extension contact 40 33.3
No extension contact 80 66.7
Off Farm income
Access 100 83.3
Non Access 20 16.7
Access to Credit
Yes 20 16.7
No 100 83.3
Membership of Organization
Yes 78 65
No 52 45
Level of school (yrs)
No formal Education 12 10
Primary 55 45
Secondary 30 25
Teriaria 25 20
Source, Field, Survey, 2016
Table - 2: Multiple Regression Result Variables Cobb Douglas Exponential Linear Semi Log
Constant 597.589
(11.496)***
4.587
(16.882)***
0.246
(2.393)**
616.072
(1.957)*
Age -2.181
(-1.336)*
-0.561
(-4.502)***
-0.268
(-1.971)
-54.513
(-1.496)
Education -14.143
(4.887)***
-4.714
(-1.128)
-0.021
(-0.156)**
-0.569
(-0.022)
Off farm income 6.593
(0.346)
0.049
(3.268)***
0.008
(0.304)
25.082
(2.082)**
Organization -0.413
(2.291)**
0.133
(0.145)
-0.121
(-2.821)*
-0.157
(-0.007)
Credit 1.051
(2.098)**
0.020
(2.502)
0.006
(-1.338)*
-9.507
(-3.276)
Extension 10.410
(3.078)***
0.212
(3.359)***
0.025
(5.063)
3.200
(3.624)***
R2 0.841 0.801 0.779 0.830
F-value 15.891*** 5.587*** 5.121*** 15.021
Source; Field Survey, 2016
*, ** and *** implies significance at 10 %, 5 % and 1 % respectively
C. I. Ezeano/Life Science Archives (LSA), Volume – 3, Issue – 3, Page – 1060 to 1072, 2017 1069
©2017 Published by JPS Scientific Publications Ltd. All Rights Reserved
Table - 3: Costs and Return on Cocoyam Production Per Hectare
Item Unit Quantity Price/
Cost/Unit (N) Cost/Value
(N)
Gross revenue
Yield Kg 2800 300 840,000
Physical input cost
Cocoyam sett Kg 400 400 160,000
Fertilizer (NPK) Kg 350 6,000 42,000
Organic manure Kg 800 1,500 12,000
Transportation and other miscellaneous 35,000
Total 249,000
Labour Hired Family
Land preparation (clearing
and ridging)
Man-day 20 - 2,500 50,000
Planting Man-day 2 4 800 4,800
Fertilizer application Man-day - 6 800 4,800
Hand weeding Man-day - 15 1,000 15,000
Harvesting Man-day - 6 1000 6000
Total labour cost 80,600
Opportunity cost of capital at bank lending rate of 23% 75,808
Total variable cost 405,408
Gross margin (GM) (TR-TVC) 435,592
Depreciation of fixed assets excluding land 3,200
Total cost (TVC+TFC) 408,608
Farm income (TR-TC) 431,392
Benefit cost ratio 1:2.06
Source: Field Survey, 2015
4. Conclusion and Recommendation
The major conclusions drawn were; most
cocoyam farmers were young, educated and
membership of organization. Also, the
determinant factors to cocoyam production were
level of education, credit and membership of
organization. Finally cocoyam farming was
profitable in the study area.
Based on the study the following
recommendations were proffered; there is need to
ensure farmers‟ access to credit through micro
finance and other financial institutions. Also,
farmers should be encouraged to form themselves
into groups such as co-operative societies to
enable them strengthen their bargaining ability,
especially during credit negotiation and
production input procurement at lower price.
Furthermore, adult education, workshops and
seminars should be organized by the concerned
government agencies in other to improve
farmers‟ efficiency and effectiveness.
5. References
1) Ajoku, G. (2009). Economic of cocoyam
production in Owerri West Local
Government Area. HND (Higher National
Diploma) Project, Federal College of
Agriculture, Ishiagu, Ivo L.G.A.
2) Awoke, M. U. (2001). Resource use
efficiency in multiple cropping system by
small -holder farmers in Ebonyi state of
Nigeria”. A Ph.D. thesis of Department of
Agricultural Economics and Extension of
Enugu state University of Science and
Technology.
3) Echebiri, R. N. (2004). Socio-economics
factors and resource allocation in cocoyam
production in Abia State. Nigeria Journal
of Agricultural Economics, 7(5): 14 – 26.
4) Edet, J. U and Nsikak, A. A. E. (2007).
Cocoyam farms in Akwa Ibom, Nigeria: A
stochastic production function frontier
C. I. Ezeano/Life Science Archives (LSA), Volume – 3, Issue – 3, Page – 1060 to 1072, 2017 1070
©2017 Published by JPS Scientific Publications Ltd. All Rights Reserved
approach. Mendel Agric Journal, 6 (2): 15
– 26.
5) Ekwe, K. C., Nwosu, K. I., Ekwe, C. C and
Nwachukwu, U. I. (2007). Examining the
under exploited values of cocoyam
(Cococasia and Xanthomonas species) for
the enhanced household food security,
nutrition and economy in Nigeria, pp. 65 –
75.
6) Eze, C. T and Akpa, C. E. (2010). Analysis
of technical efficiency of National Fadama
II facility on arable crop farmers in Imo
State, Nigeria. Nigeria Agricultural
Journal, 41 (1): 109 - 115.
7) Eze, C. C and Okorji, E. C. (2004).
Cocoyam production by women farmers
under improved and local technologies in
Imo State, Nigeria. Africa Journal of
Science, 5 (2): 114 – 117.
8) Ezeano, C. I., Ume, S. I., Okeke, C. C and
Gbughemobi, B. O. (2017). Socio-
economic Determinant Factors to Youths
Participation in Broilers Production in Imo
State of Nigeria. International Journal of
Research & Review, 4 (1): 136.
9) Ezedinma, F. O. (2004). Prospect of
cocoyam in food system and economy of
Nigeria. 1st National Workshop on
Cocoyam. National Root Crop Research
Institute, Umudike.
10) Ezedinma, F. O. (2006). Production cost in
the cocoyam based cropping systems of
South Eastern Nigeria. RCMP Research
Monograph. No 6 Resource and crop
Management program. IITA, Ibadan
Nigeria.
11) FAO. (2007). FAOSTAT, statistics
division of the food and agriculture
organization.
12) FAO. (2008). Food and agriculture
organization production year book, Rome,
Italy.
13) Mbam and Edeh. (2011). Determinants of
farm productivity among small holder rice
farmers in Anambra State, Nigeria. The
Journal of Animal and Plant Sciences, 9
(3): 1187 - 1191.
14) Mustapha, S. B., Undiandeye, U. C.,
Sanusi, A. M and Bakari, S. (2012).
Analysis of adoption of improved rice
production technologies in Jeer local
government area of Borno state, Nigeria.
International Journal of Development and
Sustainability, 1 (3): 1112 - 1120.
15) NPC (National Population Commission).
(2006). Population census of Federal
Republic of Nigeria: Analytical report at
the national level. National Population
Commission, Abuja.
16) NRCRI. (2003). Annual report of National
Root Crop Research Institute, Umudike,
Umuahia.
17) Nwaru, J. C. (2004). Rural credit market
and resource use in arable crop production
efficiency in Imo State of Nigeria. Ph.D
Thesis, Michael Okpara University of
Agriculture, Umudike Abia State.
18) Nwaru, J. C and Ekumankama, O. O.
(2002). Economic of resources use by
women arable crop farm in Abia state.
Research Report submitted to the Senate
Grant Committee, Michael Okpara
University of Agriculture, Umudike,
December, 40 pp.
19) Iheke, R. O. (2006). Gender and resource
use efficiency in rice production system in
Abia State. M.Sc. Thesis, Michael Okpara
University of Agriculture, Umudike of
Abia State.
20) Iheke, R. O. (2010). Market access,
income diversification and welfare status
of rural farm households in Abia State,
Nigeria. Nigeria Agricultural Journal, 4
(2): 13 - 18.
C. I. Ezeano/Life Science Archives (LSA), Volume – 3, Issue – 3, Page – 1060 to 1072, 2017 1071
©2017 Published by JPS Scientific Publications Ltd. All Rights Reserved
21) Ikwelle, M. C., Ezulike, T. O and Eke
Okoro, O. N. (2003). Contribution of roots
and tubers to the Nigeria economy.
Proceeding and Triennial Symposium of
the International Society for Tropical Root
Crops. Africa branch held at IITA Ibadan
on November 12 – 16th 2003; pp. 14 – 18.
22) Information and Communication Support
for Agricultural Growth in Nigeria
(ICSAGN). (2009). Cocoyam Production
in Nigeria.
23) Iwueke, C. C. (1999). Appraisal of yam
minisett technique by farmers in
Southeastern States of Nigeria.
Appropriate Agricultural Technology for
Research – Poor Farmer. A publication of
the Nigerian National Farming System,
Research Network Workshop held at
Calabar, Cross River Sate, August 14-16.
24) Iwundu, C.L. (2009): Achieving food
security in country. Daily Trust
Newspaper, 19 February 2009.
25) Obasi, P. C., Henri-Ukoha A., Ukewuihe I.
S. and Chidiebere - Mark N. M. (2013).
Factors Affecting Agricultural Productivity
among Arable Crop Farmers in Imo State,
Nigeria. American Journal of
Experimental Agriculture, 3(2): 443 - 454.
26) Obeta, M. E and Nwagbo, E. C. (1999).
The adoption of agricultural innovations in
Nigeria. A case study of an improved IITA
cassava technology packages in Anambra
State, National Farming System Research
of Nigeria.
27) Okoye, B. C. (2006). Efficiency of small
holder cocoyam farmers in Anambra State,
Nigeria. M.Sc. Thesis Department of
Agricultural Economics. Michael Okpara
University of Agriculture, Umudike,
Nigeria.
28) Okoye, B. C., Okoye, A. C., Okoroafor, O.
N and Amaefula, A. B. (2009). Adoption
analysis of improved cocoyam production
process and storage technology across
gender in Enugu North agricultural zone of
Enugu State. Proceedings of the 45th
Annual Conference of the Agricultural
Society of Nigeria, “Abuja 2009”. Pp. 619
– 623.
29) Okoye, B. C and Onyenweaku, C. E.
(2007). Economic efficiency of small
holder cocoyam farmers in Anamabra
State, Nigeria: A Trans-log stochastic
frontier cost function approach. Mendwell
Journals, 4: 535 - 546.
30) Okwuowulu, P. A. (2000): Evaluation of
performances of cocoyam (Colocasia sp.
and Xanthosoma sp.) and sweet potato
(Ipoma batata) in sole and intercropping
system with rice and maize. Unpublished
Ph.D. Thesis, University of Nigeria,
Nsukka.
31) Onwueme, I. C and Singha, T. I. (1994).
Field crop production: Tropical Africa
CTA Ed. The Netherlands.
32) Oyinbo, O., Damisa, M. A and Ugbabe, O.
O. (2011). An assessment of the
profitability of smallscale cassava
production in Edo State: A guide to policy.
33) Unammah, R. P. A. (2003). Agricultural
technology generation and transfer
strategies for food securities. Proceedings
of the 16th
Annual zonal Research and
Extension, farmers input linkage system
(REFILS) Workshop South East/South
South zones of Nigeria. 19th
– 23
November.
34) Ume, S. I., Onunka B. N., Nwaneri T. C
and Okoro, G. O. (2016). Socio-economic
Determinants of Sweet Potato Production
among Small Holder Women Farmers in
Ezza South Local Government Area of
Ebonyi State, Nigeria. Global Journal of
Advance Research, 3 (9): 972 – 883.
C. I. Ezeano/Life Science Archives (LSA), Volume – 3, Issue – 3, Page – 1060 to 1072, 2017 1072
©2017 Published by JPS Scientific Publications Ltd. All Rights Reserved
35) Ume, S. I., C. I. Ezeano, B. N. Onunka and
T. C. Nwaneri. (2016). Socio-economic
determinant factors to the adoption of
cocoyam production technologies by
small holder farmers in South East Nigeria.
Indo - Asian Journal of Multidisciplinary
Research, 2 (5): 760 – 769.
36) Ume, S. I and Kadurumba, C. (2015).
Production factors and Farmer‟s output in
using swamp rice Technology in Ivo l. G.
A, Ebonyi State. Advances in Agriculture,
Science and Engineering Research, 5 (8):
1781 - 1790.
Access this Article in Online
Quick Response Code
Website www.jpsscientificpublications.com
DOI Number DOI: 10.22192/lsa.2017.3.3.5
How to Cite this Article:
C. I. Ezeano*, C. C. Okeke, A. I. Onwusika and N. J. Obiekwe. 2017. Determinants
of Cocoyam Production and Profitability among Small Holder Farmers in South
East of Nigeria. Life Science Archives, 3 (3): 1060 – 1072.
DOI: 10.22192/lsa.2017.3.3.5