This
chapter deals with the analysis and presentation of data encompassing the
socio-economic characteristics of cassava farmers, their farming system, the
cost of inputs and return gotten from the input used. It also include: the
problems and prospects of cassava production.
SOCIOECONOMIC
CHARACTERISTICS RESPONDENT
The
socioeconomic characteristics
considered in the study area are: gender, age, martial status,
educational status, farming experience and annual farm income. The
socioeconomic characteristics are based on 80 respondents in study.
SOCIOECONOMIC CHARACTERISTICS:
TABLE 1: PERCENTAGE DISTRIBUTION OF THE FARMERS BASED ON THE
SOCIOECONOMIC CHARACTERISTICS
VARIABLES
|
FREQUENCY (n=80)
|
PERCENTAGES
(100%)
|
|
Marital status
|
|||
Single
|
13
|
16.3
|
|
Married
|
52
|
65.0
|
|
Widowed
|
10
|
12.5
|
|
Divorced
|
3
|
3.8
|
|
Separated
|
2
|
2.5
|
|
Gender
|
|||
Male
|
13
|
16.3
|
|
Female
|
67
|
83.8
|
|
Age (years)
|
|||
<20
|
0
|
0.0
|
|
21-30
|
13
|
16.3
|
|
31-40
|
40
|
50.0
|
|
41-50
|
20
|
25.0
|
|
>50
|
7
|
8.8
|
|
Occupation
|
|||
Farming alone
|
32
|
40.0
|
|
Farming/ Civil service
|
15
|
18.8
|
|
Farming/Public service
|
10
|
12.5
|
|
Farming/Trading
|
23
|
28.8
|
|
Household size
|
|||
1-5
|
17
|
21.2
|
|
6-10
|
51
|
63.8
|
|
>10
|
12
|
15.0
|
|
Educational attainment
|
|||
FSLC
|
15
|
18.8
|
|
SSCE
|
12
|
15.0
|
|
NCE/OND
|
5
|
6.2
|
|
HND/ B.Sc
|
5
|
6.2
|
|
Higher degree
|
3
|
3.8
|
|
No formal education
|
40
|
50.00
|
|
Annual income
|
|||
<100,000
|
5
|
6.2
|
|
101,000-200,000
|
27
|
33.8
|
|
201,000-300,000
|
33
|
41.2
|
|
301,000-400,000
|
5
|
6.2
|
|
401,000-500,000
|
7
|
8.8
|
|
>500,000
|
3
|
3.8
|
|
Farming experience (years)
|
|||
1-5
|
13
|
16.3
|
|
6-10
|
42
|
52.5
|
|
11-15
|
15
|
18.7
|
|
>15
|
10
|
12.5
|
|
Sources: Field Survey, 2012
RELATED INFORMATION
The
result shows that majority of the respondents (65%) were married while 2.5%
were separated. This implies that married people mainly carryout the
occupation. It could also be assumed that married people were much more
involved since their children, wives and husband could form or supply part of
the farming labour.
GENDER:
The gender of the respondents was
considered to ascertain whether there is gender influence in cassava
production. The data to this effect is presented in table 1 above. From the
table above, cassava production is mainly done by female (83.8%) while only
16.3% male are involved in the production. This above shows that both male and female
are involved in cassava production in the study area.
AGE (YEARS):
The
age of the respondents were considered to know whether their age has as an
influence in cassava production enterprise. The
data to this effect presented in table1 above. A carding to the
above,50% of the respondents were between the age 31-40 years. This implies
that, the cassava farmers in the area are still within their active age of
production.
OCCUPATION:
The result above shows that majority
of the respondents (40%) has their occupation as farming alone. Whereas 12.5%
of the respondents combine farming with public service. This implies farming is
a major occupation in the study area.
HOUSEHOLD SIZE:
The
result on table 1 above also shows that majority of the household (63.8%) has
the population ranged between 6-10 persons in the study area and the least
(15%) has the population of above 10 persons. This shows that the cassava
production in the area is mainly subsistence farming in nature.
ANNUAL FARM INCOME (N):
The
result also shows that 41.2% of the farmers has annual income ranged between N201, 000-300,000, where as 3.8% has
greater then 500,000 as their annual incomes
The
farming system of the respondent was put into consideration to verify whether
there is farming system influence in cassava
production
TABLE 2. PERCENTAGE DISTRIBUTION BASED ON FARMING SYSTEM
PRACTICED
FARMING SYSTEM
|
FREQUENCY (n=80)
|
PERCENTAGE (100%)
|
Mono
cropping
|
40
|
50.0
|
Mixed
cropping
|
25
|
31.3
|
Backyard
system
|
5
|
6.3
|
Mixed
farming
|
10
|
12.5
|
Sources:
Field Survey, 2012.
Table
2 shows that, there was multiple
responses due to the fact that, many of the respondents practice mixed cropping
and as well as mono cropping system while some practice mixed farming and
backyard farming. The table shows that
50% practiced mono cropping while 12.5% practiced mixed farming.
FERTILIZER (MANURE) USED:
Fertilizer
used by the respondent was also ascertained whether there is fertilizer
influence in the output.
TABLE 3. PERCENTAGE DISTRIBUTION OF THE FARMERS BASED ON
FERTILIZER USED.
FERTILIZER(MANURE)
|
FREQUENCY (n=80)
|
PERCENTAGE (100%)
|
N.
P. K
|
50
|
57.5
|
Farm
yard manure
|
32
|
36.8
|
Never
used fertilizer
|
5
|
5.7
|
Multiple
Responses
Sources:
Field Survey, 2012.
According
to the table, 57.5% and 36.8% used fertilizer while only 5.7% of the
respondents never used fertilizer in their farm. This implies that 57.5% of the
respondents used N. P. K. fertilizer while 36.8% use manure from farms, like
goat dung, cow dung, poultry droppings. The least are 5.7% respondent that did
not make used of any fertilizer. It could also be assumed that farming without
fertilizer may be due to high fertility of their land and it is mainly
practiced by those that practice in small scale system, that also produce in
small quantity.
LAND ACQUISITION
TABLE 4: PERCENTAGE DISTRIBUTION OF THE RESPONDENTS BASED ON
LAND ACQUISITION.
LAND ACQUISITION
|
FREQUENCY (n=80)
|
PERCENTAGE (100%)
|
Inheritance
|
5
|
6.3
|
Gift
|
0
|
0.00
|
Lease
|
15
|
18.8
|
Outright
purchase
|
45
|
56.3
|
Rented
|
15
|
18.8
|
Sources:
Field Survey, 2012.
From
the above table, it was found out that majority of the farmers in the study
area (56.3%) acquired their land through outright purchase while 6.3% acquired
theirs through inheritance. The rest were by leasing (18.8%) and rented
(18.8%).
TABLE 5: PERCENTAGE DISTRIBUTION OF RESPONDENTS ACCORDING TO
FARM LAND AREA CULTIVATED
FARM SIZE (HECTARE)
|
FREQUENCY (n=80)
|
PERCENTAGE (100%)
|
<1
|
21
|
26.2
|
1-2
|
51
|
63.8
|
>2
|
8
|
10
|
Sources:
Field Survey, 2012
From
the table above, 26.2% of the farmers cultivated on less that one hectare of
land while 63.8% of the farmers cultivated between 1 and 2 hectares. Only 10%
of the respondents cultivated on above 2 hectares of land. It could also be
assumed that majority of such farmers that cultivated on less tan I hectare
(<1) of land were inherited.
AVERAGE COST OF INPUT:
TABLE 6 AVERAGE COST OF INPUT(FERTILIZER AN CASSAVA STICK)
PER HECTARE OF CASSAVA FARM.
INPUTS
|
QUANTITY
|
UNIT PRICE
|
TOTAL COST (
|
N.
P. k fertilizer
|
20
bags
|
3,000
|
60,000
|
Cassava
sticks
|
1000
bundles
|
200
|
200,000
|
Survey
Data, 2012.
The
table above, indicates that the highest cost of input N200,000 were used for
the purchase of cassava sticks while the purchases of fertilizer cost N60, 000
used in the farm.
AVERAGE
YIELD FOR CASSAVA
TABLE 7: AVERAGE YIELD AND RETURNS FOR CASSAVA PRODUCTION
PER HECTARE:
ITEM
|
YIELD
|
AVERAGE UNIT
|
TOTAL COST (
|
Cassava
tuber
|
400
tone
|
1,560
|
624,000
|
Cassava
stick
|
1800
bundles
|
200
|
360,000
|
Sources:
Survey Data, 2012.
The
above table shows the average yield for cassava farm per hectare is 1560
cassava tubers and 1800 cassava bundles of sticks. According to the table
above, during harvest period, the cassava sticks were greater in number than
the cassava tuber itself.
COST AND RETURNS OF CASSAVA PRODUCTION.
Details
of costing and economic analysis were carried out on production input. The cost
element includes: the purchase of input, land clearing, weeding, fertilizer
application and harvest of cassava tuber. The labour used was mainly hired and
family labour. The average cost per man-day was N200.
In
the study areas, no tractor was used in the land preparation. The fixed cost
consists of the depreciated value of capital equipment used in the production
such as: hoes, cutlass, tiles used for sharpening of cutlass, barrows, basins,
baskets, trucks.
TABLE 8: AVERAGE LABOUR UTILIZATION IN ONE HECTARE
OF CASSAVA FARM.
OPERATION
|
COST INPUT
|
TOTAL UNIT
|
TOTAL COST
|
Land
clearing
|
200
|
50
|
10,000
|
Planting
of cassava
|
200
|
30
|
6,000
|
Weeding
(manual)
|
200
|
60
|
12,000
|
Fertilizer
application
|
200
|
25
|
5,000
|
Harvesting
of tuber
|
200
|
18
|
3,600
|
Cultivation
|
200
|
32
|
6,400
|
Total
|
215
|
43,000
|
Source:
Survey Data 2012.
The
average labour cost used was N43,000 at
215 man days
TABLE 9:
ENTERPRISE BUDGET PER HECTARE OF CASSAVA
FARM
ITEMS
|
QUANTITY
|
UNIT COST
|
AMOUNT
|
|
1
|
Gross
revenue
|
|||
Cassava
tuber
|
400bundles
|
1560
|
624,000
|
|
Cassava
stick
|
1,800
bundles
|
200
|
360,000
|
|
Total
revenue
|
984,000
|
|||
2
|
Variable
cost
|
|||
N.P.K.
fertilizer (bag)
|
20bags
|
3,000
|
60,000
|
|
Cassava
sticks
|
1000,bundles
|
200
|
200,000
|
|
Labour
|
||||
Land
clearing (mandays)
|
50
|
200
|
10,000
|
|
Caltivaton
mandays)
|
32
|
200
|
6,400
|
|
Planting
of cassava stick
|
30
|
200
|
6,000
|
|
Weeding
(manual)
|
60
|
200
|
1,200
|
|
Fertilizer
application
|
25
|
200
|
5,000
|
|
Harvesting
of cassava tuber
|
18
|
200
|
3,600
|
|
Sub
total
|
292,200
|
|||
Miscenenous
|
3,500
|
|||
Total
variable cost (TVC)
|
295,700
|
Source:
Survey Data 2012.
The
profitability of cassava production was determined by the used of gross margin.
GM=
TR-TVC
Profit=
GM-TFC
GM=
Gross Margin
TR=
Total revenue
TVC=
total variable cost
Total
Fixed cost
GM=
984,000-295,700=688,300
Less
fixed cost
Depreciation
on hoe 83.3
Depreciation
on cutlass 167
Depreciation on barrow 1167
Depreciation
on basin 500
Total
1,917.3
Cost
of land = N10,000
Total
fixed cost= 11,917.3
Since
profit =GM-TFC
Profit
(N)= 688,300-11917.3
= 676,382.7
This
implies that cassava production is a very profitable business in the study
area. This is tested using Z-test statistics as presented in appendix I. where
Z-cal> Z-tab. Therefore the cassava producers in the area are advice to
continue, for the business is viable
TABLE 10: COEFFICIENT OF DETERMINATION OF MULTIPLE
REGRESSION.
VARIABLES
|
Semi-log
|
Double log
|
Linear
|
Exponential
|
Constants
|
1.754*
|
-1.999
|
1.948
|
2.257
|
(0.531)*
|
(0.797)**
|
(0.850)**
|
(0.190)*
|
|
Sex
|
-0.668
|
0.148
|
-0.623
|
-0.662
|
(0.238)*D
|
(0.365)A
|
(0.380)D
|
(0.121)*D
|
|
Farming
experience
|
0.049
|
-0.598
|
-0.352
|
0.361
|
S(0.159)A
|
(0.221)*D
|
(0.242)D
|
(0.081)*A
|
|
Level
of education
|
0.009
|
0.689
|
0.849
|
0.029
|
(0.123)A
|
(0.161)*A
|
(0.162)*A
|
(0.071)A
|
|
Marital
status
|
0.255
|
-0.604
|
0.749
|
0.028
|
(0.142)*A
|
(0.200)*D
|
(0.206)*A
|
(0.083)A
|
|
Household
size
|
-0.338
|
1.477
|
-0.588
|
0.228
|
(0.201)***D
|
(0.244)*A
|
(0.310)***D
|
(0.038)*A
|
|
Occupation
|
0.160
|
0.043
|
0.049
|
-0.081
|
(0.079)**A
|
(0.111)A
|
(0.125)A
|
(0.043)***D
|
|
Farm
size
|
0.072
|
0.340
|
0.172
|
-0.111
|
(0.076)**A
|
(0.16)***A
|
(0.181)*A
|
(0.066)***D
|
|
R
|
0.852
|
0.943
|
0.749
|
0.970
|
R2
|
0.726
|
0.889
|
0.701
|
0.942
|
Standard
error of estimate
|
0.30353
|
0.44297
|
0.46896
|
0.17421
|
F-ratio
|
27.219
|
82.028
|
93.515
|
166.464
|
Durbin
Watson
|
1.405
|
1.706
|
1.862
|
0.941
|
Data
Analysis, 2012.
TABLE 11: FITNESS TEST:
VARIABLES
|
LINEAR
|
SEMI-LOG
|
DOUBLE LOG
|
EXPONENTIAL
|
R
|
0.749
|
0.852
|
0.943
|
0.970
|
R2
|
0.701
|
0.726
|
0.889
|
0.942
|
%
|
70.1
|
72.6
|
88.9
|
94.2
|
Adjusted
R2
|
0.791
|
0.699
|
0.878
|
0.936
|
SEE
|
0.46896
|
0.30353
|
0.44297
|
0.1742
|
%
|
46.9
|
30.35
|
44.3
|
17.42
|
Durbin
Watson
|
1.862
|
1.405
|
1.706
|
0.941
|
F-ratio
|
93.515
|
27.219
|
82.028
|
166.464
|
Sign.
F-ratio
|
0.000
|
0.0000
|
0.000
|
0.000
|
Data
Analysis, 2012.
The
result of the regression analysis shows that exponential analysis, having the
highest R2 (94.2%), the lowest value of standard error of estimates
(17.42%), the lowest value Durbin Watson (0.941) and the highest F-ratio (166.464) which is significant at 1%
level of significance and five coefficients of the variables were significant
at 1%, 5% and 10% level of significance and having four variables meeting the a
prori expectation, is thus selected to explain the relationship between the
socio-economic characteristics of the farmers and the farmers’ output valued in
Naira.
Thus,
the estimated regression model is resulted below:
Y
= 2.257 -0.662x1+ 0.361x2+
0.029x3+ 0.028x4+ 0.228x5-
(0.190)
(0.121) (0.081) (0.071)
(0.083) (0.038)
0.081x6
– 0.111x7 + et
(0.043) (0.066)
The
R2 value of 0.942 shows that the regression equation explains 94.2%
of the total variation in the total output by the respondents: u, sex, farmers
experience (years), level of education, marital status, household size, number
of occupation and farm size, explained about 94% of the variations in the farm
output. This implies that only 15% of the variation was due to stochastic
disturbances; Hence the socio-economic characteristics have a heavy positive
influence on the independent variable (input).
The low value of Durbin Watson shows
the absence of autocorrelation indicating that all the relevant variables are
included.
The regression coefficient shows
negative values on the sex (x1) and statistically significant at 1%
level of significance. This implies that the sex of the farmers does effect the
level of output from the farm.
Farming experience (x2)
was positive and significance at 10% level of significance, this implies that
the higher the farming experience of a farmer, the greater his/her output and
thus conforms with the a prior
expectation. Also the coefficient of level of education(x3)
had positive value that the farmers with higher level of education had positive
value and hence conform with the a priori expectation that the farmers with
higher level of education has the higher output from their farm.
The result above shows that the
marital status (x4) had positive relationship with the level of
output meaning that, the married people engaged into farming business mostly.
Also the coefficient of household
size (x5) had positive value and statically significant at 1% level
significance. This also conform with the a priori expectation and shows that
the higher the member of household of a farmer, the higher their level of
output.
The occupation (x6) had a
negative relationship and statistically significant at 10% level of
significance. And the farm size also has the negative relationship with the
level of output but significant at 10% level of significance. This implies that
even if your hectares of land is high but not fertile, the output cannot
increase ceteris paribus.
The overall statistical reliability
of the regression was shown by the low level of standard error of the estimate
and the F-ratio (166.464) which was significant at 1% level of
significance.
HYPOTHESIS TESTING:
This
was tested using F-test statistics
F-cal = R2 (N-K)
(1-R2) (K-1)
where
R2= 0.942
N= 80
K= 8
F-cal = 0.942 (80-8)
(1-0.942) (8-1)
= 0.942 (72)
0.058 (7)
= 67.824
0.406
F-cal
= 167.05
F-
tab at 0.05 = 2.09
F-
critical df1 = k-1 =8-1=7 df2= n-k =80-8=72
Decision
rules, if F-cal > F –tab, reject null hypothesis and accepts alternative.
Therefore since F-cal (9167.05)> F-tab (2.09) we reject null hypothesis and
accept the alternative hypothesis that socio-economic characteristics of
cassava farmers in Ihiala Local Government Area has positive relationship with
the level of output (N).
Identify
problems and prospects:
TABLE 12: PERCENTAGE DISTRIBUTION OF THE PROBLEM MILITATING
AGAINST CASSAVA PRODUCTION IN THE STUDY AREA.
PROBLEMS
|
FREQUENCY (n=180)
|
PERCENTAGES (100%)
|
Lack
of fund
|
25
|
21.4
|
Inadequate
farm land
|
20
|
17.1
|
Inadequate
transportation
|
12
|
10.3
|
Scarcity
of cassava cultivars
|
5
|
4.3
|
Inadequate
of labour supply
|
7
|
6.0
|
Poor
extension services
|
10
|
8.5
|
Unavailability
of fertilizer
|
13
|
11.1
|
Pest
and diseases
|
25
|
21.4
|
Multiple
Responses
Sources:
Field Survey,2012.
Despite the fact that they benefit
from cassava production, they suffer some difficulties, which in one way or the
other militated against maximum production of cassava. According to the
respondents, 21.4% of the problems military against cassava production is lack of funds and problem of pest and
diseases while 17% is lack of inadequate farm land to the will farmers and
others as presented in table 12 above.
RELATED INFORMATION