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Greener
Journal of Agricultural Sciences Vol.
9(3), pp. 350-356, 2019 ISSN:
2276-7770 Copyright
©2019, the copyright of this article is retained by the author(s) DOI
Link: https://doi.org/10.15580/GJAS.2019.3.092519177
https://gjournals.org/GJAS |
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Determinants
of Productivity among Catfish Farmers in Niger State, Nigeria
Iwu, I. M.
1*; Adewole O. E. 2; Ishie, D. N. 3; Arowolo,
K. O. 4
1 & 4 Federal College of Freshwater
Fisheries Technology, P. M. B. 1500, New Bussa, Niger State, Nigeria.
2 & 3 Federal College of Animal Health
and Production Technology, Moore Plantation, Ibadan
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ARTICLE INFO |
ABSTRACT |
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Article
No.: 092519177 Type: Research DOI: 10.15580/GJAS.2019.3.092519177 |
This study was
conducted to investigate the determinants of productivity among catfish
farmers in Niger State, Nigeria. Borgu Local Government Area was purposively
selected because catfish farming is largely practiced in the area. Within
the area, data were collected with the aid of well-structured questionnaires
administered randomly to 120 fish farmers using the two-stage random
sampling technique. Descriptive statistics, Total Factor Productivity
Analysis and Ordinary Least Square Regression Model were used to isolate the
factors that affect fish farmers’ productivity in the area. Majority of the
farmers (93.33%) were males; between ages of 41-60 years (72.5%); married
(85.84%); with household size of 1 to 6 (74.17%) and had secondary education
(74.17%). Most of the respondents stocked their fish in ponds with sizes ranging
from 101M2 to 150M2 (40.83%) or 151M2
to 200M2 (39.17%). More so, 69.17% of the respondents’ ponds were hired /
leased and only 14.17% of them funded their production basically with loans.
55% of them combined personal savings and loans to fund the production
whereas the rest 37 (30.83%) made use of only personal savings. The result
showed that only 6 (5%) of the respondents had Total Factor Productivity
(TFP)<1, and only 12(10%) had TFP = 1. Majority (75) of them (62.5%) had
TFP between 1.01 and 2.00, while 27 (22.5%) had TFP>2. The result of
Double Log Production Function showed that the coefficients of pond size
(per 10M2) and quantity of feed (per 0.1 tons) were statistically
significant at 1% p>1, while that of farming experience was significant
at 5% (p>5), all with positive coefficients. The adjusted R-squared of
0.8241 explained the coefficient of variation of the catfish farmers’
productivity model. It is recommended that farmers in the study area should
be provided with more irrigation facilities in order to provide sustainable
impoundment for more ponds as well as cheap / subsidized feed, and adequate
training / extension education that could compensate for low level of
experience among majority of them. |
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Submitted: 25/09/2019 Accepted: 27/09/2019 Published: 30/09/2019 |
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*Corresponding
Author Iwu,
I. M. E-mail:
ifymimiiwu@ gmail.com |
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Keywords: |
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1.0
INTRODUCTION
Agriculture,
including fish farming is by far the largest
water user in the world today.
Vast areas
of the world are already
irrigated, and irrigation
development
continues to increase
in an attempt to
meet the world's
increasing demand for food.
Agricultural
waters
are primarily got from surface
waters. Excess waters
are released back
into many streams
and rivers.
Beyond
the sheer volume
of use, agricultural uses
ofwater are
critical because of the often significant
changes in downstream water quality due to use of agro-chemicals, erosion, and
stream diversions (which may affect water volume). In Nigeria, level of
irrigation is still low: irrigated land constitutes only 3% of total landarea,
as against 9.6% and 12.7% in South Africa and Morroco, respectively (Olasumbo,
2001).
Nigeria is endowed with abundant water
resources (about 960km of coastline), and is believed to be the largest
consumer of fish and fish products in Africa (Oderinde, 1998) It is bounded in
the south by the Atlantic Ocean, adjacent to a coastline of over 850 km
long. The coastal zone, extending northwards to about 40
km, is characterized by enormous
water resource potential, particularly the lagoons,
creeks and estuaries. The country receives an estimated 560 xl09 M3 of
atmospheric water annually. Thus, Nigeria's land surface is well-drained by
rivers and streams, River Niger being the most prominent (Olasumbo, 2001).
According to FAO, in spite of various efforts since the 1950s,
returns on government and international aquaculture investments in Sub-Saharan
Africa appeared to be insignificant (FAO, 2004) with less than 5% of the
suitable land area being used (Kapetsky, 2004). In Nigeria for example, local
fish production has been below demand with imports accounting for about
US$48.8m in 2002 (CBN, 2004). Nigeria is one of the largest fish importers,
importing about 700,000 tonnes of fish annually to augment domestic production
of 700,000 tonnes, which constitutes 50% of the total demand (Miller and
Atanda, 2004).
The
Nigeria fishery sub- sector plays an important role in the socio – economic
development of the economy. The sector serves as an income source, facilitates
the development of cottage industries and provides employment opportunities for
the myriad of people engaged in fishery production, processing and marketing (Eyo,
1992; and Akeredolu, 1990). It equally serves as an important protein
supplement to meat protein, more so because of the persistent rise in cost of
meat ( Oladeji and Oyesola, 2002). The development of the fish industry will
increase local production of fish and save much of the foreign exchange being
used for fish importation. Specifically, it has a special role of ensuring food
security, alleviating poverty and provision of animal protein.
Fish farming is still underdeveloped in many parts of the
world, especially in developing countries. Recently, the government of Kenya
identified the enormous potential of fish farming and developed an Economic
Stimulus Programme with the main aim of increasing fish production, enhancing
food security, improving livelihoods of farmers, and providing employment for
the teeming youth population of the country (Uhuru 2010). In Nigeria, there
have been several government interventions also towards poverty alleviation
using the potentials of fish farming especially in the areas of catfish
fingerling production, grow-out production and fish processing.
According
to FAO (2007), Nigeria is the largest aquaculture producer in Africa with
production output of over 15,489 tones per annum. The fisheries sector provides
a substantial proportion of employment, especially in the rural areas: the
sector is a principal source of livelihood for more than three million people
in Nigeria (Ekunwe and Emokaro, 2009). However, one of the major challenges of
fish farming as with most other forms of production is inefficiency. Hence, FAO
(1997) asserted that increasing efficiency of resource use and productivity at
the farm level is one of the pre-requisites for sustainable aquaculture.
The total runoff in a river basin is the upper
limit to water availability and could be taken as the potential water
availability for a given basin. Borgu Local Government area plays host to the
Kainji Dam. Impoundment of the dam is so enormous that it could be easily and
rightly
deduced that the area has a fair share of the vast fishery resources in
Nigeria, including rivers, dams, streams and ponds as well as plays host to the
National Institute for Freshwater Fisheries Research (NIFFR) and the Federal
College of Freshwater Fisheries Technology (FCFFT), New Bussa. A common
observation is that the area supplies fish and fisheries products in abundance
to different parts of the country.
Unfortunately however, despite the abundant
fishery resources in Nigeria, local production has failed to meet the country’s
fish demand (FAO, 1995). Considering the perceived high level of catfish
farming going on in Borgu L G A, it is only pertinent to carry out this manner
of survey so as to improve the knowledge base, and improve on limited literature
in the subject area. A study such as this can provide some of the information
needed by policy makers to improve productivity of catfish farming in Nigeria
as a good fish farming development policy will require data from many parts of
the country (Singh, et al., 2009).
3.0.
METHODOLOGY
3.1. Study
Area: This
study was carried out in Borgu Local Government Area of Niger State. The local
government occupies a land area of about 16,000 square kilometer and lies
between longitudes 2° E and 4° E of the Greenwich Meridian and between latitudes
9° E and 11° N of the Equator. The zone experience both wet and dry season
annually. The predominant occupation of the inhabitants is farming, although
the area also harbors very large number of civil servants and traders
especially around New Bussa Metropolis.
3.2.
Sampling Technique: Borgu Local Government Area was purposively
selected as a case study because catfish farming is largely practiced in the
area. Then within the area, data were collected with the aid of well-structured
questionnaires administered randomly to 120 fish farmers using the two-stage
random sampling technique. In the two-stage sampling technique used in the
study, the sample frame was first broken into three strata according to the
three agricultural extension blocks of Niger State Agricultural Development
Programme (ADP) in Borgu Local Government Area namely; Babana, Shagunu and New
Bussa, followed by simple random sampling of fish farms from each of the three
strata. The selection was proportionate to size such that in a total of 120
fish farmers selected, 22 were from Babana, 14 were from Shagunu, while the remaining
84 were from New Bussa.
3.3.
Methods of Data Collection: Primary data (well structured questionnaire
administered to fish farmers to collect information on their socio-economic
characteristics, production inputs as well as prices of inputs and outputs) and
secondary data were used for the study.
3.4.
Methods of Data Analysis
Descriptive statistics, Total Factor Productivity Analysis and Ordinary
Least Square Regression Model were used
Total
Factor Productivity
The
equation was used to assess the level of catfish farmers’ productivity in the
study area. The equation is stated as:
Total
Factor Productivity = value of total
output / value of total input …….(1)
Double
Logarithm Regression Model: Double Logarithm form is a functional form in
which variables are transformed using the natural logarithm transformation.
Double log means the dependent and all independent variables are all logged. It
can also be called log-log form or specification. The double logarithm function
model was used to examine the factors affecting output of catfish farmers’ in
the study area. The model that was adapted from Ibitola et al (2019), with little modification to accommodate the
differences in factors, is stated thus:
LnY = 0
+1LnX1 + 2LnX2 + 3LnX3 + 4LnX4
+5LnX5 + 6LnX6 + i(2)
Where,
Y =
(catfish farmers’ Productivity = output in kg)
The
explanatory variables include:
X1 =
labour in man-days (family labour = 0, if otherwise
1)
X2 = pond
size (per 10M2)
X3 = number
of fingerlings
X4 = stocking
rate (Number of fingerlings / M2)
X5 =
quantity of feed (per 0.1 tons)
X6 =
farming experience (in years)
i
= vector of parameters to be estimated
i = error term
i
= vector of parameters to be estimated
i
= error term
4.0. RESULTS
AND DISCUSSIONS
4.1.
Socio-economic Characteristics of the Respondents
The result on Table 1 (below) shows that most of the respondents (93.33%)
were males. This agrees with the general norm as it relates to farming and as
supported by several literature that have identified that greater proportion of
women are more in the marketing and/or the processing aspects of farming (Effiong,
2016; Eyo 1983; Nwabeze and Madu, 2016). Majority of the catfish farmers
(84.66%) were still in their active working age; 14.16 % being between age of
18 and 40 and 72.5 % being between 41 and 60 years. This is a good indicator of
which policy makers can harness to boost fish production in the area. This may
not be unrelated with the fact that the local government plays host to Federal
College of Freshwater Fisheries Technology and the National Institute for
Freshwater Fisheries Research both in New Bussa as well as the presence of the
Kainji Dam and other water bodies including River Oli in the area -; the greater
concentration of the catfish farmers within New Bussa Metropolis is an
indication.
The study also revealed
that most (83.84%) of the farmers were married; had family size of 1 – 6
(74.17%) and at least secondary
school level education (98.335%) with tertiary education contributing the
lesser part (24.165%) as against 74.17% from secondary school level of
education. It is not uncommon that more married people than single individuals
go into farming activities (Madu, 2016; Ibitola et. al., 2019,; Oyewo, et.
al., 2014). Married men and women take up such
responsibility, with regards to the tedious nature of farming, perhaps since
they have to meet up with their enormous family responsibilities. Married
people are also by implication older individuals in most cases and would have
gathered the required capital over the years than single individuals some of
whom will still be attending schools. On the other hand, the predominance of
the smallest household size (household size of 1 – 6 persons), in catfish
farming perhaps stems from the fact that majority of them according to the
result were non- indigenes / visitors in the area. If the indigenes dominated
the production, one would also expect a corresponding large family size owing
to the Hausa and Muslim tradition of the practice of polygamy. It is also
pertinent to note that the study has shown that catfish farming in the area is
practiced by literates – There was no respondent without any formal education
The study also revealed that the production is dominated by new entrants
(people with lesser years of experience), with 42.5% having only 1 – 5 years
and 36.67% having 5 – 10 years of experience. This is an indication that
interest in catfish farming is recently growing in the area.
Policy makers could key into this by bringing on intervention programmes
to boost catfish production in the area thereby bridging the gap of food
insecurity in Nigeria. Government intervention would be of significant
importance considering that the study also revealed that only 30.83% of the
respondents could use only personal savings to fund their production
activities. More so, most of the ponds (69.17%) were either hired or leased and
majorly, larger pond sizes
101 M2
– 150 M2 (40.83%) or 151 M2 – 2000 M2
were required.
Table
(i) Socio-economic Distribution of the Respondents
|
GENDER |
FREQUENCY |
PERCENTAGE (%) |
|
Male |
112 |
93.33 |
|
Female |
8 |
6.67 |
|
|
|
|
|
AGE |
|
|
|
18 – 40 years |
17 |
14.16 |
|
41 – 60 years |
87 |
72.5 |
|
61 years and above |
16 |
13.34 |
|
|
|
|
|
MARITAL STATUS |
|
|
|
Single |
12 |
10 |
|
Married |
103 |
85.84 |
|
Divorced |
0 |
0 |
|
Separated |
1 |
0.83 |
|
Widowed |
4 |
3.33 |
|
|
|
|
|
HOUSEHOLD SIZE |
|
|
|
1 – 6 |
89 |
74.17 |
|
7 – 12 |
21 |
17.5 |
|
13 and above |
10 |
8.33 |
|
|
|
|
|
LEVEL OF EDUCATION |
|
|
|
No formal education |
0 |
0 |
|
Primary ,, |
2 |
1.665 |
|
Secondary ,, |
89 |
74.17 |
|
Tertiary ,, |
29 |
24.165 |
|
|
|
|
|
CATFISH FARMING
EXPERIENCE |
|
|
|
1 – 5 years |
51 |
42.5 |
|
6 – 10 years |
44 |
36.67 |
|
11 – 15 years |
22 |
18.33 |
|
16 years and above |
3 |
2.5 |
|
|
|
|
|
POND SIZE |
|
|
|
< 50 M2 |
11 |
9.17 |
|
51 M2 –
100 M2 |
6 |
5 |
|
101 M2 –
150 M2 |
49 |
40.83 |
|
151 M2 –
200 M2 |
47 |
39.17 |
|
201 M2
and above |
7 |
5.83 |
|
|
|
|
|
POND OWNERSHIP |
|
|
|
Personally owned |
37 |
30.83 |
|
Hired / leased |
83 |
69.17 |
|
|
|
|
|
SOURCE OF CAPITAL |
|
|
|
Personal savings
only |
37 |
30.83 |
|
Loan only |
17 |
14.17 |
|
Personal savings
and loan |
66 |
55 |
Table ii below shows the distribution of the respondents according to
their annual farming expenditure and thus an estimate of the required capital
for one year production cycle.
It is important to note however that most of the
farmers, especially those within New Bussa metropolis were having mainly two
production cycles within a year. Where that was possible, it implies that the
farmer would require about half the expenditure and then re-invest the turn
over for the second cycle of production. Majority of them (27.5 %) spent
between (₦)400, 000 to (₦)600,000 or (22.5%) between (₦)600, 000 to (₦)800, 000
annually. Using the middle value of each range as representative figure and
₦1, 100, 000 to represent above ₦1, 000, 000 , the mean annual
farming expenditure was got as ₦646, 666. 67. This shows that catfish
farming in the area is capital intensive. The mean annual farming expenditure
reported here is by far higher than ₦4178, 063. 18 mean household
expenditure reported by ibitola et al, (2019)
for maize farmers in Oyo State.
Table
(ii )Distribution of Respondents by Annual Farming Expenditure
|
EXPENDITURE (₦) |
FREQUENCY |
PERCENTAGE (%) |
|
< 200, 000 |
10 |
8.33 |
|
200, 000 – 400, 000 |
14 |
11.67 |
|
400, 000 – 600, 000 |
33 |
27.5 |
|
600, 000 – 800, 000 |
27 |
22.5 |
|
800, 000 – 1, 000, 000 |
13 |
10.83 |
|
Above 1, 000, 000 |
23 |
19.17 |
|
Total |
120 |
100 |
Table iii below shows that 10% of the respondents simply break even after
the production cycle. That is to say; they are neither losing nor gaining. Or
they simply engage in catfish farming as a way of saving money (No interest
expected). It also shows that 5 % are losing part of their investment at the
end of the production. Inexperience and unforeseen occurrences such as theft,
flooding and disease outbreak may account for that. In simple term, those 5 %
get less than I unit of output for every I unit of input. The number of catfish
farmers that are producing profitably is 62.5% + 22.5% = 85%. This 85% and
especially the 22.5% getting more than double returns on their investment could
wisely plough back part of their profit into the catfish business to bring
about expansion. Looking at the gender distribution of the TFP in Table iv, one
can easily deduce that only 3 out of the 8 females engaged in catfish farming
were producing profitably. The tedious nature of farming activities may have to
account for this as men are the stronger sex. Examining the result from the age
point of view, Table v, shows that majority of those who produced less
profitably were either the younger ones, who were likely to be less
experienced, and/or the aged ones who could likely be producing for leisure or
did not have the capacity to meet up with the labour intensive nature of
catfish farming. Nevertheless, on a general note it can be deduced from Tables
iii to Table v that catfish farming in the area is very profitable.
Table
(iii) Total Factor Productivity (TFP) Distribution of Respondents
|
TFP |
FREQUENCY |
PERCENTAGE (%) |
|
< 1 |
6 |
5 |
|
= 1 |
12 |
10 |
|
1.01 – 2 |
75 |
62.5 |
|
> 2 |
27 |
22.5 |
|
Total |
120 |
100 |
Table
(iv) Total Factor Productivity Distribution of Respondents based on Gender
|
GENDER |
TFP < 1 |
TFP = 1 |
TFP of 1.01 – 2 |
TFP > 2 |
Total |
|
Male |
5 |
8 |
72 |
27 |
112 |
|
Female |
1 |
4 |
3 |
0 |
8 |
|
Total |
6 |
12 |
75 |
27 |
120 |
Table
(v) Total Factor Productivity Distribution of Respondents based on Age
|
AGE |
TFP < 1 |
TFP = 1 |
TFP of 1.01 – 2 |
TFP > 2 |
Total |
|
18 – 40 |
2 |
2 |
9 |
4 |
17 |
|
41 – 60 |
1 |
6 |
57 |
23 |
87 |
|
61 and above |
3 |
4 |
9 |
0 |
16 |
|
Total |
6 |
12 |
75 |
27 |
120 |
Table vi below shows the results of Double Logarithm
Regression Analysis for the relationships between farmer’s socio-economic
characteristics and catfish output. It shows that years of farming experience, quantity
of feed in (per 0.1 tons) and pond size (per
10M2)) were the major socioeconomic factors that significantly
influenced catfish output at 5%, 1% and 1% respectively. The R-squared of 0.8241
indicated that the variables accounted for 82.41 percent of the variation in catfish
output. The positive coefficient of all
the variables in the model indicates that they all had direct relationship with
the inputs and the outputs used in catfish production.
The
coefficient of farming experience (0.1772) indicates that the number of years
of farming experience was a significant factor, positively related to catfish output.
Hence an additional year of experience in farming increased the output of a catfish
farmer by approximately 17.72kg. Okoye et al.,(2009) opined that greater
experience enables a farmer to adopt new technologies or to take risk whereas Adah,
Olukosi, Ahmed, and Balogun (2007) sees it that farming experience improves
decision making and enhances better farm management.
Labour
input follows the expected positive sign though with insignificant coefficient
of 0.1033. It therefore implies that an increase in labour by one man-day will
lead to an increase in catfish output by 10.33kg. Singh (2007) also identified
labour as a major determinant of fish productivity in West Tripura. The main
aspects of labour identified were in feed formulation, excavation works,
harvesting and haulage. The farmer in putting additional one man- day may have
to consider the cost of the labour and the price of 10.33kg of the catfish.
Pond size had a significant positive relationship with output at 5%. This
relationship could have been negative if the ponds were underutilized. The sign
of the coefficient suggested that an additional 10M2 stocked by the farmer
would increase output by 53.12kg.
Quantity of feed (per 0.1 tons) had
significant positive relationship with output with coefficient of 0.6878. This
implies that additional 100kg or 0.1 tons will increase catfish output by
68.78kg. Iwu et al (2015) as well as
Singh (2007) also identified that quantity of feed had significant positive
effect on technical efficiency of fish farmers in the same study area. Stocking
rate and number of fingerlings had insignificant positive relationship with
output. That is to say the more the number of fingerling or stocking rate the
greater the output. However, it is important to note that this relationship
with stocking rate can only go on up to the point that the ponds are not
overstocked. Suffice it to say that the catfish farmers in the area are not
overstocking their ponds.
The
R and adjusted R-squared were 0.8337 and 0.8241 respectively, indicating that
82.41% of the catfish farmers’ resources were used for productive farm
activities.
Table
(vi) Result of the Double Logarithm Production Function Analysis of Catfish
Output
|
Variables |
Coefficient |
Standard error |
P > / t / |
|
Constant |
0.1467 |
0.2085 |
0.659 |
|
Pond size(M2) |
0.5312 |
0.5311 |
0.000 |
|
Years of experience |
0.1772 |
0.0764 |
0.007 |
|
Quantity of feed (kg) |
0.6878 |
0.7137 |
0.000 |
|
Labour (Man -days) |
0.1033 |
0.0529 |
0.416 |
|
No. of fingerlings |
0.0744 |
0.0525 |
0.233 |
|
Stocking rate (no. /M2) |
0.0345 |
0.0498 |
0.178 |
CONCLUSION AND
RECOMMENDATIONS
Number of
years of experience as well as pond size and quantity of feed have been
identified as the major determinants of catfish output (having significant
positive effect on catfish output). Stocking rate, number of fingerlings
stocked and quantity of labour also had positive but insignificant effect on
catfish output in the study area.
It is therefore recommended that adequate
training and extension programmes be organized and funded in fish farming
communities in Nigeria to compensate for inadequate experience among some
farmers. Furthermore, provision of subsidies, irrigation facilities and accessible
credit facilities may help to enable farmers reduce the challenge they are
facing in acquiring more ponds and that of high feed cost.
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Cite this Article: Iwu, IM; Adewole OE; Ishie, DN; Arowolo, KO
(2019). Determinants of Productivity among Catfish Farmers in Niger State,
Nigeria. Greener Journal of
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