Socio- economic Characteristics of the Respondents
This
sub-section examined the socio economic characteristics of the respondents’
based on the following: age, gender, marital status, household size,
educational qualification, annual income, farming experience, farming status,
and farm size. Data collected were analyzed and result presented in Table 1.
Table
1: Percentage Distribution of the Farmers According to Their Socio-economic
Characteristics
Characteristics Description Frequency (n=120) Percentage x
Age (years) 20-30 24 20 42
31-40 12 10
41-50 72 60
51
and above 12 10
Gender Male 57 47.5
Female 63 52.5
Marital status Single 27 22.5
Married 60 50
Separated 10 8.3
Divorced 8 6.7
Widowed 15 12.5
Household size 1-4 48 40 6
5-8 48 40
9-12 18 15
13
and above 6 5
Educational
level Non-formal 15 12.5
Primary 26 21.7
Secondary 41 34.2
OND/NCE 27 22.5
HND/B.SC 9 7.5
M.Sc 2 1.7
Annual
income
≤50,000 6 5 180,000
50,001-100,000 36 30
100,001-150,000 30 25
150,001-200,000 12 10
200,001-250,000 6 5
250,001-300,000 18 15
300,001
& above 12 10
Farming
experience
1-5 24 20 10
6-10 66 55
11-15 12 10
16-20 18 15
Farming
status full-time 84 70
Part-time
36 30
Farm
size 3-5 54 45 6
6
and above 66 55
Source:
Field Survey, 2012
Age
Table 1 shows the age distribution
of the famers. The result indicated that more than half (70%) of the
respondents are between 31-50 years of age, which is regarded as economical
active age according to FAO, (1992). At this stage in life, Anyanwu et al.
(2001) recognized that people are more likely to be energetic and have the
capacity to use innovation. This justified the findings of Ebukiba (2010), who reported that 76%
of the cassava farmers in Akwa Ibom state were aged within 31-50 years.
Gender
From the result in Table 1, it was observed that majority (52.5%) of the
farmers are female while 47.5% are male. This implies that women participate
more actively in cassava production than their men counterpart. This
collaborate the findings of Ebukiba
(2010), who reported that 60% of the cassava farmers in Akwa Ibom state were
females.
Marital status
From Table 1, it can also be observed that
most (60%) of the cassava farmers are married, 22.5% are single, 8.3% are
separated while 6.7% are divorced and 12.5% were widowed. This is an indication
that the majority of respondents that engaged in cassava farming are married.
Household size
House hold size is a very important
factor, especially in determining labour for farm work. A farmer with a large
household size has the chance of using them in their farm labour, which will
enhance his farm productivity and returns.
From the result, it was observed that the farmer had an average
household size of 6. This conforms to the findings of Oladeebo and Oluwaranti
(2012), who reported average of 8 persons per cassava
farmers in South western, Nigeria.
Educational
level
From Table 1, it was observed that
most (34.2%) of the respondents had attended secondary school education, 21.7%
of them had attended primary school and 31.7% of them had acquired post
secondary school education, while a few (12.5%) of them did not attend formal
education. By implication, a reasonable number of farmers in the area should be
able to understand the use of improved technologies and apply it to achieve
increased production. Through education, the quality of labour is improved and
with it the propensity to adopt new techniques (Tijani et al., 2006; Hyuha,
2006). Thus, cassava farmers in the study area would easily adopt new
technologies which could improve their level of profit ceteris paribus.
Annual income
Table 1 also shows the income
distribution of the respondents per annum. The result reveal an average income
of N180,000.00 per annual. The breakdown shows that most (45%) of the farmers
earned an annual income of between N100,000-N150,000, 12% earned between N150,001-N200,000,
30% of them earned above N200,000 per
annual income. Signifying that the respondents are low income earners.
Farming experience
From Table 1, it also observed that
most (55%) of farmers had been farming for between 6-10 years, while 20% of
them had been farming cassava for between 1-5 years and 15% had farmed cassava
for 16 years and above. On the average they have farmed for 10 years. This is
an indication that the farmers has been in the business of cassava farming for
quite a while in the area. This also justified the findings of Oladeebo and Oluwaranti (2012), who
reported average of 13 years farming experience for cassava farmers in South
Western, Nigeria.
Farming status
Tables 1 also showed that majority
(70%) of the respondents are full-time farmers, while the remaining (30%) are
part-time farmers. This implies that most of the cassava farmers are full-time
farmers.
Farm size
From Table 1, the result of the farm
size as held by the farmers on average was 6 ha, with majority (45%) held a
size of between 3-4 ha. This followed the study of Oladeebo and Oluwaranti (2012), who reported average of 3 ha farm
size for cassava farmers in South Western, Nigeria.
Cassava
Production System in the Study Area
The production system such as mixed
cropping, inter-cropping and mono-cropping as practiced by the farmers were
investigated. Data collected was analyzed and result presented in Table 2.
Table 2: Percentage Distribution of
Respondents According to Production System
|
Production
system
|
Frequency
(n=120)
|
Percentage
|
|
Mono
cropping
|
93
|
77.5
|
|
Mixed
cropping
|
15
|
12.5
|
|
Inter-cropping
|
12
|
10.0
|
Source:
Field Survey, 2012
The result in Table 2 shows that
majority (77.5%) of the cassava farmers practiced mono cropping production
system, while few (12.5%) of them practiced inter-cropping and10% practiced inter
cropping production system in the area. This is an indication that most of the
farmers employed mono cropping production system in cassava production in the
area.
Relationship
Between Inputs and Outputs from Cassava Production
A multiple regression analysis was
employed to determine the relationship between inputs used and output from
cassava production in the area. The result of the analysis is presented in
Table 3.
Table 3: Relationship
Between Inputs and Total Outputs from Cassava Production in the Area
Variables coefficient standard error t-value
sig
Constant -18963.514 2191.241 -8.654 *
Farm
size (x1) 0.580 0.200 2.904 *
Labour
used (x2) 0.231 0.249 0.926 NS
Fertilizer
used (x3) -5.145 0.378 -13.613 *
Cassava
stem (x4) 0.939 0.262 3.583 *
Herbicide
used (x5) -5.022 0.883 -5.689 *
R2 0.833
D.W 2.356
F-statistics 113.432
Standard error 08825.27849
Source:
SPSS Analyzed Data, 2012
Table 3 shows the result of multiple
regression analysis of the relationship between inputs used and output from cassava
production in the study area. The coefficients of multiple determination (R2)
was 0.833 or 83.3%, signifying that 83.3% of total variation in dependent
variable (total output) was explained by the explanatory variables i.e. inputs
(x1-x5) included in the model.
Farm size x1: the
coefficient of farm size was positively related to total output. Indicating
that a unit increase in farm size will lead to a unit increase in total cassava
output and statistically significant at 1%. This is in conformity to a priori
expectation.
Labour used x2: the coefficient of labour used in cassava
production was positively related to total cassava output but statistically
insignificant. This implies that a unit increase in labour used in cassava
production will to a unit increase in total cassava output. This is a departure
from the a priori expectation, because increasing labour used in cassava
production will push up the cost to total cost of production and as such the a
priori expectation was not met.
Fertilizer used (x3) by the
farmers was negatively sign, but was statistically significant at 1%,
indicating an inverse relationship between the fertilizer used and the total
cassava output in the area. This is conformity to the a priori expectation, and
the law of diminishing marginal returns, which states that in all productive
processes, adding more of one factor (fertilizer) of production, while holding
all others constant ("ceteris paribus"), will at
some point yield lower per-unit returns. This is also applicable to fertility,
because aside causing soil acidity it will add additional cost to total
production cost.
Cassava stem (x4) used by
the farmers was positively related to total output and statistically significant
at 1%. This signifies that a unit increase in cassava stem will lead to a unit
increase in total cassava output. This is in line with the a priori expectation,
because more farm land will be cultivated.
Herbicide used (x5):
the coefficient of herbicide used was negatively related to the total output
but statistically significant at 1%. This implies that increasing the unit of
herbicide used in cassava production will lead to decreased output in cassava
production in the area. This is inline with the a priori expectation because
increasing herbicide used will add-up to the cost of production and decrease
returns accruing to the farmer.
Technical
Efficiency
This was
computed from the regression analysis results of inputs – outputs relationship
and the result is presented in Table 4.
Table 4: Technical Efficiency of Cassava Production
in the Area
|
Resource
|
MVP
(N)
|
MFC
(N)
|
Efficiency
Index
|
|
Farm
size (X1)
Labour
(X2)
Fertilizer
(X3)
Cassava
cuttings (X4)
Herbicide
(X5)
|
16436.3
17945.4
21050.0
4360.0
1235.4
|
1524.5
2846.7
16450.0
1405.2
1846.1
|
10.8
6.3
1.3
3.1
0.07
|
Source:
Computed Field Survey, 2012.
From the result in Table 4, it was
observed that the farmers were not efficient in the utilization of all the
specified resources as far as cassava production is concerned in the study
area. Farm size had the highest efficiency index of 10.8, followed by labour
(6.3), cassava cuttings (3.1), fertilizer (1.3) and herbicide (0.07). Farm size,
labour, fertilizer and cassava cuttings were underutilized since the efficiency
index was greater than one. This indicates that additional income can be made
from the production of cassava by using more of these inputs. There was over
utilization of herbicide since the efficiency index is less than one. Therefore
reducing the litres of herbicide used can make more income. It should be noted
that the MVP’s of all the inputs used were not negative indicating that cassava
farmers still use the resources within the economically range even though they
were not optimally used. This justifies the finding of (Ogunniyi, et al 2012), who reported that cassava farmers in Atakunmosa
Local Government Area of Osun State underutilized farm size, labour, fertilizer
and cassava cuttings, while herbicide was over-utilized.
Table 5: Analysis of cost and returns of cassava production perhectare
Items Unit Quantity Unit Price Total
A. Revenue
Cassava tubes tonnes 14.5 25,000 362,500.00
Cassava Stems tonnes 7 4,500 31,500.00
Total
Revenue 394,000.00
B. Variable Cost
Inputs
Cassava Stems tonnes 4 4,500 18,000.00
(Cuttings)
Fertilizers Bag 3 5,000
15,000.00
Herbicide litre 1.5 2200 2,200.00
Total Cost 35,200.00
C. Cost of
Labour Mandays
Land preparation
(Clearing, ploughing &
harrowing) 18 750 15,000.00
Planting mds 10
750 7,500.00
Weeding mds 30
750 30,000.00
Harvesting mds 10 750
7,500.00
Transportation 50,000.00
Miscellaneous cost 20,000.00
Total cost 130,000.00
D.
Total Variable Cost 165,200.00
Fixed Cost
Depreciation
on farm tools (hoes, machetes) @ 10 5,600.00
Depreciation
on land @ 5% 25,000.00
E. Total Fixed
Cost 32,800.00
Total variable cost (TVC) = B + C
= 165,200.00
Gross margin = TR – TVC = A – D =
228,800.00
Total cost = TFC + TVC = E + D 198,200.00
Benefit Cost Ration (TR/ TC) = 2.0:1.0
Source: Computed
From Field Survey, 2012
GM = TR -
TVC
394,000 – 162,200
= N228,800
II = GM - TFC
228,800 – 30,600
= N198,200.00
BCR = TR
TC
= 394,000
198,200 =
From the result in Table 5, Total Cost
of producing cassava per hectare was N198,200.00,
the Total Revenue was N394,000.00 and
the Gross Margin was N228,800.00. The
profit of N198,200.00, implies that
cassava production in the area was profitable. Also the Benefit Cost Ratio was N2.0, indicating that for every N1.0 expended in cassava production, N1.0k was realized as a profit. This
follows the findings of Ebukiba
(2010) who reported BCR of N1.9:1.0 for cassava farmers in Akwa Ibom state.
Constraints
Militating Against Cassava Production
Factors that militate against the
production of cassava in the area were investigated. Data on factors such as
lack of ready market, lack of access to credit facility, lack of storage
facilities, high cost of transportation, lack/ adequate of improved varieties,
high cost of labour, high cost of fertilizer, inadequate supply of fertilizer,
land fragmentation, poor extension services, problems of pests and diseases and
poor road network were collected, analyzed using mean score obtained from a
4-point likert scale and result presented in Table 6.
Table 6: Mean Score Distribution of Respondents
According to Constraints Militating Against Cassava Production
|
Constraints
|
Mean
score (xs)
|
Decision
|
|
Lack
of ready market
|
2.5
|
Accepted
|
|
Lack
of access to credit facilities
|
3.8
|
Accepted
|
|
Poor
storage facilities
|
2.8
|
Accepted
|
|
High
cost of transportation
|
3.2
|
Accepted
|
|
Lack/
inadequate improved varieties
|
2.0
|
Rejected
|
|
High
cost of labour
|
3.4
|
Accepted
|
|
Inadequate
supply of fertilizer
|
3.5
|
Accepted
|
|
Land
fragmentation
|
2.2
|
Rejected
|
|
Poor
extension services
|
3.6
|
Accepted
|
|
Problems
of pests & diseases
|
2.9
|
Accepted
|
|
Poor
road network
|
3.0
|
Accepted
|
Source: Field
Survey, 2012
From the result in Table 6, the
following were identified by the farmers as factors that militate against
cassava production in the area. These are: lack of access to credit facilities (3.8),
lack of ready market (2.5), poor storage facilities (2.8), high cost of
transportation (3.2), high cost of labour (3.4), inadequate supply of
fertilizer (3.5), poor extension services (3.6), problems of pests and diseases
(2.9) and poor road network (3.0). This follows the findings of Ebukiba (2010), who reported that
cassava farmers in Akwa Ibom state face problems such as inadequate capital, lack
technical, lack of government support, lack of improve cuttings and poor market,
among others.
Test of Hypotheses
HO1:
There is no significant relationship between the inputs used and the total
output from cassava production in the area.
F-cal
= R2 (N-K)
1-R2 (K-1)
Where;
R2
= co-efficient of determination
N
= sample size
K
= number of variables
F-cal = 0.833 (120-5) = 95.80
1-0.833 (5-1) 0.668
F-cal
= 95.13
V2
= N-K = (120-5) = 115
V1
= K-1 = 5-1 = 4
F-tab
at 0.05level of significant = 2.25, at 0.01 level of significant = 3.12
Decision Rule
If
F-cal > F-tab, reject the null hypothesis otherwise accept the alternative.
Since F-cal > F-tab at both 0.05 and 0.01 level of significance, the
alternative hypothesis was accepted that there is significant relationship
between inputs used and the outputs from cassava production in Ndokwa West Local
Government Area of Delta state.