ISSN 2349 – 4425
AIJCSR-375
www.americanij.cam
ANALYSIS OF COSTS AND RETURN OF
PLAINTAIN PRODUCTION IN BAYELSA
STATE, NIGERIA
Ayawari, D.T. And Ugwumba, C.O.A.
Department of Agricultural Economics and Extension, Anambra State University, Igbariam Campus, P.M.B
6059, Awka Main Post Office, Anambra State, Nigeria
E-mail: [email protected]; [email protected]
Abstract
Plantain farming is a common enterprise in Bayelsa State, Nigeria. Despite this development, the farmers seem
to be operating at the subsistence level probably due to financial and production challenges. The study, hence,
examined costs and return of plantain production in the State. It specifically identified the socio-economic
factors of the farmers and their influence on net production income, determined enterprise profitability, and
identified constraints to production. Data were obtained through the administration of well-structured
questionnaire to 200 farmers selected by multistage, purposive and random sampling methods. Data collected
were analyzed using descriptive statistics and multiple regression analysis. Results showed that plantain
farming in the area was dominated (73.5%) by male farmers with average farm size of 0.5 hectare. Profitability
indicators of gross margin (N31,502,760), mean net farm income (N157,513.80) and net return on investment
(0.78) revealed that plantain production was profitable. Net farm income was significantly influenced by cost of
labour, cost of other inputs, farm size, farming experience and plantain variety. The farmers complained of
inadequate finance, scarcity of farm land and poor road infrastructure as major production problems.
Government interventions through the provision of soft loans, subsidization of inputs and improvement in rural
feeder roads construction and maintenance would minimize production constraints and enhance plantain
farmers’ income and welfare.
Keywords: Costs and return, Determinants, Plantain production, Bayelsa State, Nigeria
O.R.A. | 95 | A M E R I C A N I J
Volume 2 20145 Issue 1 DEC-JAN AIJCSR
AIJCSR-375
ISSN 2349 – 4425
INTRODUCTION
Agriculture remains the largest sector of the economy because
it employs about 70% of the nation’s population especially
www.americanij.cam
countries; Uganda is the largest producer of banana and
plantain in sub-Saharan Africa, followed by Rwanda, Ghana,
Nigeria and Cameroon.
those living in the rural areas and contributes about 40% of the
Most plantains are produced by small scale farmers in Bayelsa
Gross Domestic Product (GDP) (Central Bank of Nigeria
State. The small scale farmers are noted by Ugwumba and
(CBN), 2007). One of the major problems facing developing
Omojola (2013) to lack the financial resource to enhance their
countries in the tropics is the production of sufficient food,
productivity using improved technologies such as high
fuel, fiber and shelter for their large population. Food
yielding and disease resistant varieties of crops, fertilizer and
production, therefore is very important in the economies of
agro-chemicals, e.t.c. They attach little premium to the
tropical developing countries and agriculture provides the
foregoing with the belief that plantain can always produce for
means to increase food and fiber production ( Yuodeowei et
itself with little or no organic manure. Furthermore, other
al., 1999).
problems such as the black Sigatoka disease, is considered the
Plantain and banana belong to the family of permanent crops
with scientific name Musa spp (Phillips, 1977). They are
perennial crops with diverse cultivars that take the appearance
of trees as they mature. They are believed to have originated
in Southeast Asia but their introduction into Africa is unclear.
Plantain is a major staple food in Africa, Latin America and
Asia. It is usually cooked and not eaten raw unless it is very
ripe. It is more important in the humid lowlands of West and
Central Africa. One hundred or more varieties of plantain
grow deep in the African rainforests (International Institute of
most economically important disease of plantain worldwide,
causing yield losses up to 50%. Its major pests are the
burrowing nematode and the banana weevil (IITA, 2009).
Based on this background, this study examined costs and
return of plantain production in Bayelsa State, Nigeria. It
particularly described the socio-economic factors of the
farmers and their influence on net production income,
determined enterprise profitability, and identified constraints
to production.
MATERIALS AND METHODS
Tropical Agriculture (IITA), 2009). Plantain is an important
food crop in the sub-Saharan Africa, producing more than
25% of the carbohydrate and 10% of the calories of
approximately ten million people in the region (Swennen,
1990).
Bayelsa State is one of the 36 states that make up the Federal
Republic of Nigeria. It occupies an area of 21,110km², and
about three-quarter of its total area lies under water. The state
is made up of 8 Local Government Areas (L.G.A). Plantain
farming is a common enterprise in the area.
Throughout history Musa spp have provided humans with
food, medicine, clothing, tools, shelter, furniture, paper and
handicrafts. It could be termed the “first fruit crop” as its
cultivation originated during a time when hunting and
gatherings were still the principal means of acquiring food.
They are rich in vitamin C, vitamin B6, minerals and dietary
fiber. They are also a rich source of energy, with carbohydrate
accounting for 22% and 32% of fruit weight for banana and
plantain respectively (IITA, 2009). According to IITA (2009),
more than 100million tonnes of banana and plantain were
produced worldwide in 2007. They are grown in nearly 130
Multistage, purposive and random sampling techniques were
used to select 200 plantain farmers for the study. Four Local
Government Areas noted for their deltaic nature were
purposely dropped at stage I. This is because they are noted
for artisanal fishing activities and lack observable evidence of
serious plantain production. At stage II, five communities
were randomly selected from each of the remaining four
LGAs to arrive at 20 communities. Stage III involved random
selection of ten farmers from each of the 20 selected
communities to arrive at the 200 respondents.
O.R.A. | 96 | A M E R I C A N I J
Volume 2 20145 Issue 1 DEC-JAN AIJCSR
AIJCSR-375
ISSN 2349 – 4425
www.americanij.cam
Data collection was through primary sources using interview
EDL = Educational level (years)
instruments, observations and memory recall. Data collection
FAE = Farming experience (years)
was for a production period of 12 months and in this case
COP = Cost of production (N)
January to December 2013. Non-parametric statistical tools
ß0 , ß1, ß2 ……ß8 = Parameters to be determined
such as means, percentages and frequency distributions were
ei = Stochastic error term.
employed to analyze data generated on socio-economic
It is hypothesized that the independent variables are not
factors; enterprise budgeting technique was used to ascertain
significant factors in the determination of the farmers’ net
enterprise profitability. The technique is given as:
production income. The data were fitted with four functional
Gross margin (GM) = TR – TVC
forms of the regression model namely linear, exponential,
NFI = GM – TFC or TR – TC
semi-log and double-log and tried with the MINITAB
NROI = NFI/TC
STATISTICs. The functional form which produced the best
Where:
output in terms of sizes, signs and number of significant
GM
= Gross margin
parameter estimates, overall significance of the regression
TR
= Total revenue
shown by F-statistic, percentage of variation in net production
TVC = Total variable cost
income determined by R2, and the existence or non-existence
NFI
= Net farm income
of autocorrelation given by the Durbin-Watson statistic was
TC
= Total cost
chosen as the lead equation. The functional forms are given as:
TFC
= Total fixed cost
Linear: NPI = ß0 + ß1AGE + ß3GEN + ß3HHS + ß4MAS +
NROI = Net returns on investment.
ß5FAS + ß6EDL+ ß7FAE + ß8COP + ei
Exponential: lnNPI = ß0 + ß1AGE + ß3GEN + ß3HHS +
The multiple regression technique was used to establish the
ß4MAS + ß5FAS + ß6 EDL+ ß7FAE + ß8COP + ei
influence of socio-economic factors of the respondents
Semi log: NPI = ß0 + ß1lnAGE + ß3lnGEN + ß3lnHHS + ß4
including age represented by AGE, gender (GEN), household
lnMAS + ß5lnFAS + ß6lnEDL+ ß7FAE + ß8lnCOP + ei
size (HHS), marital status (MAS), farm size (FAS),
Double log: lnNPI = ß0 + ß1lnAGE + ß3lnGEN + ß3lnHHS +
educational level (EDL), farming experiences (FAE) and cost
ß4lnMAS + ß5lnFAS + ß6ln EDL+ ß7lnFAE + ß8lnCOP + ei.
of production (COP) on net production income. The implicit
and explicit forms of the multiple regression technique are
RESULTS AND DISCUSSION
represented as:
Cost structure for the plantain farms: The plantain farmers
NPI = f (AGE, GEN, HHS, MAS, FAS, EDL, FAE, COP, e)
incurred costs in the course of plantain production. In the short
and
run, these costs include both variable and fixed costs of
NPI = ß0 + ß1AGE + ß2GEN + ß3HHS + ß4MAS + ß5FAS + ß6
production. The variable costs involved in plantain production
EDL+ ß7FAE +
as articulated by Kainga (2012) and Kaine (2014) to include
ß8COP + ei
suckers, herbicides, labour, fertilizer, transportation and
Where:
miscellaneous costs. The fixed cost items are made up of
NPI
matchete, file, knife, hoe, spade, wheelbarrow, canoe, paddle,
= Net production income (N)
AGE = Age (years)
rope and rent on land. The overall cost structure for the
GEN = Gender (dummy: male = 1; female = 2)
plantain farmers is presented in Table 1. The total cost of
HHS = Household size (number of people living together)
production for all the farms amounted to N40,157,240. Out of
MAS = Marital status (dummy: married = 1; single = 2)
this
FAS = Farm size (ha)
N28,497,240 or 71% leaving 29% for the fixed cost items.
amount,
the
total
variable
costs
accounted
for
O.R.A. | 97 | A M E R I C A N I J
Volume 2 20145 Issue 1 DEC-JAN AIJCSR
ISSN 2349 – 4425
AIJCSR-375
www.americanij.cam
Again, cost of labour alone constituted about 54.80% of the
income. Fakayode et al. (2011) on the contrary reported a
total cost to become the most important cost of production.
negative but significant relationship of the age variable and net
farm income from plantain production in Rivers State,
Estimated profitability of the enterprise: Result of the
Nigeria.
enterprise budgeting analysis deployed to determine the
profitability of plantain production in the study area is
The estimated co-efficient of gender was positive and
presented in Table 1. The result indicate that the respondents
significant at 5% probability level. This implied that the male
realized gross margin of N31,502,760; net farm income of
plantain farmers, because they are naturally more endowed
N31,502,760 and mean net farm income of N157,513.80
with strength than the females, were able to input more energy
during the production period. The positive values of gross
and time and consequently realized better net farm income.
margin, net farm income and mean net farm income implies
The male farmers also have been proven to have more access
that the enterprise is a profitable one and worth investing in.
to credit facilities which are required to purchase modern
Plantain farming has equally been adjudged a profitable
production inputs and techniques necessary for increasing
venture in the studies conducted in Edo, Rivers and Osun
enterprise productivity and income (Ugwumba and Omojola,
States of Nigeria (Fakayode et al., 2011; Baruwa et al., 2011;
2013).
Kaine et al., 2014). In addition, net returns on investment was
0.78 for the enterprise, indicating that they returned on the
average N0.78 for every N1.00 naira invested in the business,
thus further confirming the profitability of plantain production
Farm size of the respondents exerted positive and statistically
significant influence on net production income at 5% level.
This meant that the larger the plantain farmer’s farm size the
higher the net production income earned from the enterprise.
in the study area.
The realization of higher net production income by a farmer
Estimated determinants of net production income: Result
has been associated with bigger farm size and better
of the outputs of the four functional forms of the multiple
management practices (Ugwumba and Chukwuji, 2010; Kaine
regression analysis used to predict the influence of the
and Okoje, 2014).
respondents’ socio-economic factors on net production income
is presented in Table 2. The exponential regression output
produced the highest number of significant variables and was
chosen as the lead equation for discussion. Out of the eight
predictors, four (age, gender, farm size and educational level)
were statistically significant while household size, marital
status, farming experience and cost of production were not
significant at 5% probability level.
The coefficient of educational level was positively related to
net production income. In addition, educational level had
significant effect on net production income realized by the
plantain farmers. This implied that the plantain farmers with
higher level of formal education were more likely to realize
higher net production income. This is because the higher level
of educational exposure probably enabled them easier access
to modern production inputs and technologies that assisted
The farmers’ age had positive and statistically significant
them to increase their productivity, profit and wellbeing. This
relationship with net production income at 5% probability
finding corroborates Ugwumba (2011), and Nenna and
level. This implied that the older plantain farmers were more
Ugwumba (2012) which, respectively, reported the positive
likely to realize higher net production income than the
influence of educational attainment on net farm income
younger ones.
It could be that the older farmers had
realized by catfish farmers in Anambra State and production
accumulated more capital and skills through long years of
output of palm oil farmers in Delta Central Agricultural Zone
farming experience that enabled them to produce more output
of Delta State, Nigeria.
per given input, hence better productivity and net production
O.R.A. | 98 | A M E R I C A N I J
Volume 2 20145 Issue 1 DEC-JAN AIJCSR
ISSN 2349 – 4425
AIJCSR-375
Further result of the multiple regression analysis revealed a
Table 1: Estimated enterprise budget for plantain
production
2
co-efficient of multiple determination (R ) of 85.2%. This
Variable
implied that 85.2% of the variations in net production income
Total Revenue
Variable Cost
Suckers used (700)
Herbicides
Labour
Fertilizer
Transportation
Miscellaneous
Total variable cost (TVC)
Fixed cost (annual depreciation values)
Machete
Knife
File
Hoes
Spade
Wheelbarrow
Rent on land
Canoes
Paddle
Rope
Total fixed cost
Total cost (TC=TVC+TFC)
Gross margin (GM=TR-TVC)
Net farm income (NFI=TR-TC)
Mean Net farm income (MNFI = NFI/n)
Net return on investment
(NROI = NFI/TC)
realized by the respondents was explained by the independent
variables while the remaining 14.8% was due to error. The Fstatistic value of 42.57 was significant and confirmed the
overall significance of the regression analysis. Also the
Durbin-Watson value of 1.71 indicated the absence of
autocorrelation among observations of the independent
variables.
Constraints to plantain production: Distribution of the
respondents according to problems militating against plantain
production in the area is shown in Table 3. The result
indicated that high cost of labour with a score of 89%
constituted the major constraint to plantain production in the
area. This was followed by transportation (80%), water logged
land (74%), poor storage facilities (63%), inadequate capital
(62%) and poor extension service (58%).
www.americanij.cam
Amount
(N)
60,000,000
Percentage(%)
4,666,200
600,000
22,000,000
480,520
400,256
350,270
28,497,240
11.60
1.50
54.80
1.20
1.00
0.90
71.00
900,000
20,000
40,000
720,000
840,000
1,800,000
4,000,000
3,000,000
320,000
20,000
11,660,000
40,157,240
31,502,754
31,502,760
157,513.80
2.20
0.00
0.10
1.80
2.10
4.50
10.00
7.50
0.80
0.00
100
0.78
Fertilizer and
Table 2: Determinants of net production income
chemicals ranked last in the production of plantain in the study
Predictor
Exponential
Linear
Semi-log
Double-log
area with 23%.
Constant
4.5868 (40.66)
-35839 (0.82)
514107 (1.52)
5.3227 (11.32)
AGE
0.0066 (2.32)**
-24 (-0.02)
-9446 (-0.64)
0.1484 (0.86)
GEN
0.0993 (3.39)**
11474 (1.01)
-8.449 (-1.56)
0.0772 (0.98)
NHS
-0.0096 (-0.77)
6203 (1.29)
7444 (1.80)***
0.0558 (0.92)
Plantain production is a profitable enterprise in the study area
MAS
-0.0327 (-1.11)
7636 (0.67)
38220 (0.63)
0.0062 (0.07)
since the farmers returned 78kobo for every investment of
FAS
0.5739 (7.11)**
36171 (11.71)**
4173 (7.10)**
0.8299 (9.66)**
FAE
0.0047 (1.47)
939 (0.76)
-53003 (-1.31)
0.0082 (1.65)
EDL
0.0037 (1.90)**
1675.6 (2.20)**
5532 (1.61)
-0.6086 (2.12)**
realized without the production problems militating against the
COP
0.00004 (0.08)
-0.1893 (-0.88)
1172 (0.02)
-0.0074 (-1.36)
enterprise especially transportation challenges, water-logged
R2
85.2%
90.6%
79.2
81.3%
Adjusted R
83.2%
89.3%
76.4
78.1%
F-Statistic
42.57
70.76
28.14
50.32
Durbin-Watson statistic
1.71
1.66
2.13
1.83
CONCLUSION AND RECOMMENDATIONS
100kobo made. Better net production income would be
2
nature of the soil, poor storage facilities and inadequate
capital. Government interventions through the provision of
soft loans, subsidization of inputs and improvement in rural
feeder roads construction and maintenance would minimize
production constraints and enhance plantain farmers’ income
and welfare.
Source: Computed from field survey data, 2012. Notes: ** =
Significant at 5% level. Values in parentheses are t-ratios.
O.R.A. | 99 | A M E R I C A N I J
Volume 2 20145 Issue 1 DEC-JAN AIJCSR
ISSN 2349 – 4425
AIJCSR-375
www.americanij.cam
Table 3: Constraints to plantain production
Variable
Labour
Transportation
Land
Storage
Finance
Extension
service
Fertilizer &
chemicals
Frequency
177
160
148
125
123
115
53
Ranking
1st
2nd
3rd
4th
5th
6th
7th
[5]
Kaine, A.I.N and Okoje L.J D (2014).
Estimation of costs and return of plantain
production in Orhionw on Local Government Area,
Edo State, Nigeria. Asian Journal of Agriculture
and Rural Development, 4(2), 162-168.
References
[6]
Kainga, P.E. and Seiyabo, I.T. (2012).
Economics of plantain production in Yenagoa Local
[1]
Baruwa, O.I, Masuku, M.B and Alimi T.
(2011). Economic analysis of plantain production in
Government Area of Bayelsa State. Journal
of
derived savannah zone of osun state, Nigeria. Asian
Agriculture
and
Social
Research,
12(1),
114-123.
Journal of Agricultural Sciences, 3(5), 401 – 407.
[7]
[2]
Nenna, M. G. and Ugwumba, C. O. A.
Central Bank of Nigeria (CBN) (2007).
(2012). Influence of socio-economic variables on
Domestic output. CBN Statistical Bulletin. Abuja,
palm oil production in Delta Central Agricultural
Nigeria: CBN Publication.
Zone of Delta State, Nigeria. Journal of Raw
Material Research, 7(1&2). 1-9.
[3]
Fakayode, B.S, Rahji, M.A.Y, Ayinde, O and
Nnom, G.O (2011). An economic assessment of
plantain production in Rivers State, Nigeria.
International
Journal
of
[8]
Phillips, T.A. (1977). An agricultural hand
book. London, UK: Longman Group Ltd.
Agricultural
Economics and Rural Development, 4(2) xxxx.
[9]
Ugwumba, C.O.A. and Chukwuji C.O
(2010). The economics of catfish production in
Anambra
[4]
International
Institute
of
Tropical
Agriculture (IITA) (2014). Banana and plantain
State,
Nigeria:
A
profit
function
Approach. Journal of Agriculture and Social
Sciences (JASS), 6, 105-109.
crops. Retrieved from http://www.iita.org
O.R.A. | 100 | A M E R I C A N I J
Volume 2 20145 Issue 1 DEC-JAN AIJCSR
ISSN 2349 – 4425
AIJCSR-375
[ 10 ] Ugwumba,
C.O.A.
(2011).
www.americanij.cam
Technical
efficiency and profitability of catfish production in
Anambra
State,
Department
of
Nigera.
Ph.D
Agricultural
Dissertation,
Economics
and
Extension, Delta State University, Abraka.
[ 11 ] Ugwumba, C.O.A. and Omojola, J.T (2012).
Socio-economic determinants and profitability of
yam production in Ipao-Ekiti, Nigeria. Journal of
Sciences and Multidisciplinary Research, 4, 96-103.
[ 12 ] Ugwumba, C.O.A. and Omojola, J.T (2013).
Determinants of loan repayment of livestock
farmers under the Supervised Agricultural Credit
Guarantee Scheme (S.A.C.G.S) in Etche Local
Government
Area
of
Rivers
state,
Nigeria.
Agricultural Advances, 2(6) 165-172.
[ 13 ] Youdeowei, A., Zedinma, F.O.C. and Onazi,
O.C. (1999). Introduction to tropical agriculture.
UK: Longman Ltd.
O.R.A. | 101 | A M E R I C A N I J
Volume 2 20145 Issue 1 DEC-JAN AIJCSR