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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.

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C. I. Ezeano/Life Science Archives (LSA), Volume – 3, Issue – 3, Page – 1060 to 1072, 2017 1061


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.



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



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



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)



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



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,



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.



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



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.

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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

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