/
w
ECONOMIC ANALYSIS OF FACTORS
AFFECTING SUNFLOWER
PRODUCTION IN MACHAKOS DISTRICT OF KENYA:
A CASE STUDY OF KIBWEZI DIVISION
1/
A THESIS SUBMITTED TO THE UNIVERSITY OF NAIROBI
IN
PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE
OF MASTER OF SCIENCE IN AGRICULTURAL ECONOMICS
THIS THESIS HAS BEEN
th e d eg r ee o f—
....... " i n " t h b
VND A COPY MA* BE PLACED m
UNIVERSITY LIBRARY.
FEBRUARY,1990
BM 1VERS1TY VP NAinyp*
DECLARATION
This thesis is my original work and has not been presented
for a degree in any other University.
MUTUKU MUENDC KAVOI
(C a n d i d a t e )
This
thesis has been submitted for examination
approval as University Supervisors.
with
DR. M.O. ODHIAMBO
(University Supervisor)
our
-111-
A B S T R A C T
The study was motivated by the
sunflower production has
Machakos District
need to find out why
been erratic and
in spite of the joint
declining in
efforts of the
Government and Oil Crop Development Limited (OCD Ltd) to
promote its production in marginal zones of Machakos.
investigation
estimate
was
the
done
slope
through
regression
coefficient
was
done
to
correlated
find
with
out
other
how
crop
analysis
between
sunflower; and sunflower and cotton.
The
to
maize
and
Correlation analysis
sunflower
enterprises.
production
Gross
is
margin
analysis was carried out to test whether the profitability
of
sunflower
enterprise
is
different
from
the
profitabilities of other crop enterprises. The hypothesis
that farmers are offered a price which is not different
from the
The
break-even price was
results
revealed
given a special attention.
that maize
production
significant effect on sunflower production.
production
has
no
significant
effect
has no
Also cotton
on
sunflower
production and there was no correlation between sunflower
and other crop enterprises. The gross margin and breakeven
price analysis results indicated that the profitability of
sunflower-maize
enterprise
is
different
from
the
profitabilities of other crop enterprises except one and
that
the prices of the sunflower seed offered to farmers
are differed from the breakeven price computed.
iv -
To promote
that
a
sunflower production,
pricing policy
farmers’ costs
and
that
returns
takes
and
it was suggested
into
that
account
the
incorporates
all
sunflower by-products should be formulated. The extension
machinery should strengthen the liaison between research
and farmers for effective extension services
visit farmers.
seed credit and
producers.
The
should
consider reinstating
provide other input credits to sunflower
Since
intercropping,
OCD Ltd
to train and
most
research
of
efforts
the
farmers
should
go
practise
towards
developing varieties of sunflower that are high yielding,
early maturing and that are favoured by intercropping.
-V-
A C K N O W L E D G E M E N T
Space limitation does not allow me to enumerate all
who
participated
in
making
this
work
a
Nevertheless all were of indispensable
help
to
My first acknowledgment goes to Dr.
who
as
my
Thesis
first
supervisor
success.
me.
M.O.
Odhiambo
assisted
me with
total devotion and undiminishing interest throughout the
study
period.
criticisms
also
The
of my
useful
second
comments
and
constructive
supervisor Dr. S.G. Mbogoh were
immeasurably appreciated.
I would like to extend my sincere gratitude to the
University of Nairobi
for granting me
a scholarship to
undertake the study.
My
Ministry
appreciation
of
also
Agriculture
goes
and
to
my
Mr.
two
Wangeita
enumerators
of
the
John
Kithuku and Kennedy Mwanthi for their unfaltering devotion
to efficient and fast operations on a range of aspects at
the initial stages of the study.
I must also thank Ms. Wambui for typing and retyping
this work tirelessly and
finally
making
it what it is
now, a task for which she received daily renewed energies.
If anybody knows that a physical battle is won in the
spiritual realm first, it is my mum.
Special thanks go to
her for waging war against failure in the spiritual realm
- vi
through consistent and intensive intercession for me.
May
God bless her for her patience and tenacity in prayer.
I am deeply indebted to God for being omnipresent and
omnipotent
in
strength daily.
my
academic
life
and
for
renewing
my
I look back and I cannot avoid saying
'Ebenezer’ - thus far has the lord helped me.
- vn TABLE OF CONTENTS
PAGE
ABSTRACT
(1ii)
ACKNOWLEDGMENT
(v)
LIST OF TABLES
CHAPTER 1:
1.1
(ix)
INTRODUCTION
Role of sunflower and other oil crops in
Kenya’s economy.
1
1.2
Utilization of sunflower
8
1.3
Problem definition
9
1.4
Justification of the study
13
1.5
The study area
14
1.6
Objectives of the study
21
1.7
Hypotheses tested
22
CHAPTER 2:
LITERATURE REVIEW
2.1
Studies on oil crops
23
2.2
Theoretical framework
27
CHAPTER 3:
3.1
METHODOLOGY
Data and their sources
43
3.1.1
Secondary time series data
43
3.1.2
Primary data
44
3.1.3
Sampling plan and data
collection
48
3.2. Data analysis and analytical methods.
49
3.2.1
Correlation and regressionanalysis
50
V I 11
3.2.1.1
Correlation analysis
50
3.2.1.2
Regression
51
analysis
3.2.2
Gross margin (GM) analysis
62
3.2.3
Breakeven price analysis
68
3.2.3.1
3.2.3.2
CHAPTER 4:
Theoretical framework
of breakeven price
68
Method of estimating
breakeven price
71
EMPIRICAL ANALYSIS RESULTS, DISCUSSION
AND HYPOTHESIS TESTING.
4.1
Correlation analysis results
75
4.2
Regression analysis results of the
modified version of equation 3.2.
77
4.2.1
4.2.2
4.2.3
4.3
4.4
CHAPTER
Discussion,interpretations and
hypotheses testing of regression
results of the modified version
of equation 3.2
79
Regression analysis results for
modified version of equation 3.4
81
Discussion, interpretation and hypo­
thesis testing of regression results
of the modified version of
equation 3.6
84
Gross margin analysis results and hypo­
thesis testing.
Breakeven price analysis results and
hypothesis testing.
5:
85
102
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1
Summary
108
5.2
Conclusions
111
5.3
Recommendations
115
REFERENCES
118
-ix-
LIST OF TABLES
1.1
1.2
1.3
1.4
3.1
3.2
3.3
4.1
4.2
4.3
4.4
PAGE
Quantity and value of imported
vegetable
oils and fats by individual crops for 1972—
1987
Makindu
rainfall
monthly
mi 11imeters,1984-1988
Makindu monthly
temperatures
in
1984-1987.
totals
4
in
17
maximum
and minimum,
degrees centigrade,
18
Makindu temperatures in degrees centigrade,
mean monthly dry and wet bulb, 1984-1987.
19
Area under maize and sunflower
and prices
of maize and sunflower in Machakos,
1974-1987.
45
Area under food crops
Machakos, 1974-1987.
46
andsunflower
in
Area under sunflower, cotton and prices of
cotton and sunflower in Machakos
District, 1974-1987.
47
A correlation matrix of acreages under
various crop enterprises in Machakos
District.
75
Regression results for modified version of
equation 3.2.
78
Regression results for modified version
of equation 3.4.
83
Distribution
of
cropping
systems
sunflower among the sampled farmers in
Kibwezi Division.
of
86
4.5a A summary of gross margins
for the crop
enterprises among the sampled farmers
in
Kibwezi.
88
4.5b Average
gross
margin
for
sunflower
enterprise (pure stand) in Kibwezi.
89
X
4.6
4.7
4.8
4.9
Partial
gross
output
contributions
(in
Kshs.) and their respective percentages (%)
for
each
component
of
the
various
intercropped
enterprises
in
Kibwezi
Division.
90
Costs incurred in sunflower portion as a
percentage of the cost of maize-sunflower
intercrop for each input.
92
Gross margin analysis for maize- cotton,
maize-beans,
maize-cowpeas, maize-pigeon
pea and maize-sunflower
enterprises in
Kibwezi.
94
Statistical values for variables on maizecotton production in Kibwezi.
95
4.10 Statistical values for variables on
beans production in Kibwezi.
maize-
4.11 Statistical values for variables on maizecowpeas enterprise in Kibwezi.
4.12
96
96
Statistical values for variables on maizepigeonpea production in Kibwezi.
97
4.13 Statistical values for variables on maizesunflower production in Kibwezi.
97
4.14
Statistical values for variables on B-E-P
analysis for
sunflower
production
in
Kibwezi Division, Machakos District.
104
1
CHAPTER I
INTRODUCTION
1.1
Role of sunflower and other oil crops
economy
in
Kenya’s
Kenya’s supply levels of edible oils through domestic
production are inadequate to meet consumption needs of the
country.
According to the latest available statistics,
the domestic production of edible oil is estimated at
around 20,000 metric tonnes whereas the effective demand
is over 100,000 metric tonnes per annum (Zulberti, 1988).
This means a net demand deficit of over
80,000 metric
tonnes and necessitates heavy imports which cost
country valuable foreign exchange.
the
The main oil crops
produced in Kenya are sunflower, groundnuts, coconut,
rapeseed and sesame.
In spite of great demand for edible
oils, the domestic supply has remained static over many
years in Kenya.
According to the National Committee on Oil Crops
(1987), the per capita consumption of vegetable
products in Kenya has increased five times
fifteen years.
Due to the t r e m e n d o u s
population and per capita consumption,
oil
in the last
increase
in
the effective
demand deficit is likely to increase from 80,000 metric
tonnes in 1987-88 to 320,000 metric tonnes by the year
2000 [Zulberti,
1988].
Yearly importation of vegetable
oils worsens the already existing strain on the balance of
payments.
2
Most
of
the
edible
oil
used
in
Kenya
has
traditionally been imported from Malaysia in the form of
semi-refined palm oil.
deprives Kenyan
Importation
of vegetable oils
livestock farmers the benefit of the
protein meals [oil cakes of soya beans,
sunflower seed,
coconut (copra)] since they are by-products of
oil seeds
which are processed and utilised in the exporting country.
The
import
strategy
gives
Kenyans
in
no o p p o r t u n i t y
growing,
for
the
transporting
and
employment
of
processing
or handling of the crop (OCD Ltd, 1982/83).
It has been observed that prices of oil
seeds have
continuously decreased in the international markets after
the relatively high values reached during 1983/84 but
is doubtful that the prices would remain
low.
it
It is
therefore risky for a country such as Kenya to have such
heavy reliance on the international markets.
According
to the
National
Development
Plan
[1989—
1993], the domestic production of edible oils and other
food-stuffs will be stepped up during the plan period in
order to meet the country’s needs for internal
sufficiency.
in Kenya
self-
At present the production of raw materials
for edible
groundnuts, coconut,
oil
in the
form
of
sunflower,
rapeseed, cotton seed,
sesame, etc,
is not keeping pace with the requirements.
Table 1.1
shows the quantities of vegetable oils which
imports.
Kenya
3
A large part of Kenya’s edible vegetable oil imports
has mainly been composed of palm oil and
palm kernel,
soya beans, copra and other minor oils, taking on average
about 65%,
25%,
cooking oil
imports in terms of quantity and expenditure
[Opiyo, 1987].
been
5% and 5% respectively of the total
However, palm oil and palm kernel oil have
dominant.
The
of oil imports.
other
minor oils
account for 5%
Thus the production of
vegetable oils
mainly provides a substitute for palm
oil,
palm
kernel
oil and soybean oil imports.
As can be seen in
Table 1.1,
imports of palm oil
increased from 14,834.5 tonnes in 1972 to 224,463 tonnes
in 1987.
This sharp increase in imports of palm oil
necessitated
exchange
an
from
increase
Kshs.
in
expenditure
1,273,190.7
in
of
foreign
1972
to
Kshs.732,739,598 in 1987.
Increased domestic production
of vegetable oils
therefore go a
saving Kenya’s
would
scarce foreign
exchange.
long way in
4
Table 1.1
Quantity and value of imported vegetable oils and fats by individual crops for 1972-1987.
Year
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Palm
Tonnes
14834.5
16949.5
15855.3
11990.0
327235
42195.2
50095.1
46298.5
71446.4
98012.2
93055.9
71990.8
59755.7
82514.0
96434.0
224463.0
Source:
Oil
Value
(Kshs)
1273190.7
2118270.9
3011955.7
2461820.3
6159471.8
9933117.1
10790864.3
12303267.7
16403776.4
17165979.5
20187551.7
42317091.4
29308373.0
806002982
716818635
732739598
Soya Beans Oil
Tonnes
Value
(Kshs)
8247
14130
1091
5069
12840
3125
9226
594.6
210.5
2503.0
1368.0
127.5
1246.6
948.7
5082.1
46406
116537.2
191195.8
25546.5
131100.9
39458.7
120876.9
339815.4
234418.3
258455.4
1177795.6
7097886.1
17289.8
1000986.5
21006141
6922869
4611276
1’aim Kernel Oils
Tonnes
Value
(Kshs)
40324
13778
10000
13189
25307
8734
782
1.2
2.2
0.38
905.8
0.19
3.92
Copra Oil
Tonnes Value
(Kshs)
414186.2
176503.4
377791.2
301579.3
50
2053
524165
16949 364399
214743.9 22225 505763
9711 237750
24329
141
67259
364 107578
609 221005
1743.5
1863.9 5314 177694
1197.9
384578.6
0.3
392
13906
54.9 338222
67621
184 1765718
Central Bureau of Statistics, annual trade report, various issues,
(dash) means that no values were recorded.
Olive Oil Sunflower Seed Oil
Others
Tonnes Value Tonnes Value
Tonnes Value
(Kshs)
(Kshs)
(Kshs)
518
377
148
185
205
1
1.3
-
23076
20075
6891
18331
15434
973
- .
23169
-
98
2570
48
772
20
538
11
1039
2637
1
10 7785
3.54 113289
4.03 881025
0.05
2501
26
61
1
455
3430
1
604
0.5
50.6
586
396
82760
90633
124901
512
1299
26
24401
181995
34
29603
326
16791
238207
272224
819197
719730
7338
Tonnes
197883
45296
27143
30957
385977
76280
70418
47034
72023
101136
99791
72705
62306
166226
192208
395958
Total
Value
(Kshs)
1830072
2507344
3422^80
2999822
10565563
10774535
11422367
12604945
” 16773420
18574635
27481787
425737786
30939724
2794&609
725314387
739194102
5
The widening gap between
production and consumption
of edible oils calls for concerted efforts and immediate
mo bi li za ti on
of
r e so ur ce s
so as to ac h i e v e
self-
sufficiency in the shortest possible time.
Kenya has a
great potential to grow a wide range of oil
crops.
diversity of oil crops which
can grow well
The
in Kenya,
coupled with a wide range of the ecological zones, offers
a very good o p p o r t u n i t y for in cr ea si ng edib le oil
production.
Palm oil can do
along the Coastal
well only in a small region
strip.
Rapeseed does well
Valley Province, especially in the cool
Narok.
Cotton does well
Provinces.
in
Groundnuts are
provinces.
Sesame does
in Rift
highlands
of Mau
Nyanza,
Eastern and Coast
grown in
Western and Nyanza
well
in
Western,
Nyanza and
Coastal regions of Kenya.
Sunflower can do well
in a wide
range
of agro-
ecological zones, ranging from Ecozone 1 in the highland
areas up to
Ecozone 4 in the lowland grassland areas of
savanna domains in marginal areas which are found in the
Nyika plains.
marginal
Ecozones
3 and
zones, where total
4 are
rainfall
the
so
called
is low and
its
distribution and reliability are poor. They include lower
areas of Meru, Kitui, Machakos and Kajiado Districts.
sunflower does
Yet
well in these areas since it is an annual
crop which is able to tolerate fairly adverse climate and
has a very short growth cycle. However, when sunflower was
introduced in Kenya in the 1920’s, its production was
6
concentrated in high potential areas only, at least until
the early
1970’s. These areas
include Trans
Nzoia,
Kakamega, Uasin Gishu, Bungoma, West Pokot, Busia, Nakuru
and Nandi.
Yet the production
levels of sunflower
in
these areas is not enough to meet the national demand. So,
joint efforts are being made by the Oil Crop Development
Limited of the East African Industries (EAI) and the
Government of Kenya to expand domestic production of oil
crops,
particularly sunflower,
in the marginal zones of
the country. Increased domestic edible oil crop production
would reduce dependence of the country
on
imports which
could make the country vulnerable to shortages if the
supply from other countries were
impaired by factors
beyond Kenya’s control.
Since the report of "Kirby Commission"
[1977]
to
appraise and advice on accelerated oil seed production and
utilization in Kenya, there has been an extensive campaign
to expand sunflower production in the marginal
zones of
Kenya.
marginal
Sufficient
sunflower
production
in the
zones can only be achieved through offering appropriate
incentives to farmers. The incentives should result in an
increase of the sunflower Gross Margin [GM] vis-a-vis the
alternative competing crops [OCD 1982/83].
In 1982 the EAI
launched
an
ambitious Oil
Development scheme under the auspices of OCD.
Crop
The scheme
was intended to produce vegetable oils from locally grown
7
raw materials and save the country some foreign exchange.
In 1984 alone, Kenya spent Kshs.250 million, on importing
65,000 tons of vegetable oil
(mainly palm oil) for the
manufacture of edible fats, mostly the brand name "Kimbo".
[Kenya Farmer, Jan.1985).
When fully operational, the oil
crops development scheme
is expected
farmers cultivating over 400,000 ha
to involve
13,000
for local vegetable
oils and saving Kshs.500 million in foreign exchange for
Kenya.
At the same
time
the
local
v e ge ta bl e
production is expected to earn the farmers income
oil
in
excess of what subsistence production could [Opiyo,1987].
The country-wide development scheme
imported palm oil with
rape seed oil.
the
intends to replace
locally produced
sunflower and
The long-term objective is for the bulk of
sunflower crop to be grown
by
smallholders
and
especially in marginal zones of the country [Kenya Farmer,
Op.cit.].
According to the National Committee on Oil Crops
[1987], sunflower is the most important oil crop in Kenya.
Sunflower can grow relatively well
rainfall.
It
can therefore
gain
in areas with
popularity
in
low
the
marginal areas of the country, such as Kitui and Machakos
Districts.
These areas have average annual
ranging between
500mm and
750mm
and
rainfall
average
daily
temperatures of 25°C and above [Kirby Report Op.cit.].
8
1.2
Utilization of sunflower
The
most
important use
extraction of oil.
of
sunflower
is
in
the
Sunflower seed used to be the most
important product for feeding poultry in Kenya at one
time,
but this has changed due to the high demand for
edible oils in the country.
Extraction of oil for edible
purposes from sunflower seed remains the most
activity in the sunflower industry.
[1974]
sunflower produces edible
important
According to Gatere
oil
comparable to the
finest olive oil which lacks taste and
colour and can be
stored for long periods.
The oil
also finds application
in the manufacture of high quality
cooking and frying.
salad oil
and
in
It is also used for canning, medical
purposes and in the manufacture of soaps and cosmetics.
Sunflower oil
is extracted from the seed which contains
from 32% to 60% oil content.
Due to
its high protein
content [up to 20%], the seed is also used as poultry and
cattle feed.
According to Carter [1978], sunflower seed used to be
roasted and used for human consumption in the same way as
groundnuts are used.
was dried,
After the seed was removed, the head
then crushed and fed to cattle and poultry.
The crude protein content of the crushed head varies from
12% to 15% [Gatere, 1974].
After the seed is crushed for
oil, the residue, which has a protein content of between
35% and 54%,
product.
is used as feed cake and is the major by­
According to Gatere
[1974],
the
stalks
of
9
sunflower are processed for cellulose which is then used
for m a n u f a c t u r e of fair q u a l i t y pa pe r in some few
countries.
A major use to which the stalks are put by
small scale-farmers after drying
is the provision of fuel
for cooking and other purposes.
In some large-scale farms
green sunflower stalks are also used for silage which is
comparable in quality to maize silage.
1.3
Problem definition
Sunflower as a cash crop was introduced in Machakos
District in 1974 and production picked up very fast.
1977 the area of land under the crop was
compared to only 23.4 ha in
1974 when
in t r o d u c e d (District A g r i c u l t u r a l
Machakos).
In
18,170 ha as
the crop was
office reports,
In 1978, the area of land under the crop
dropped to 5000 ha. Since then there has been an erratic
and
general
decline
in sunflower
production
in
the
district and only 160 ha were under sunflower in the
district in 1984.
In 1985, production started picking up
again slightly, and 2520 ha were under the crop by the end
of the year.
By 1987, 5000 ha were under sunflower but
production started declining again in 1988 when 1,500 ha
were under the crop.
Sunflower was first grown in the
following Divisions of Machakos:-
Kibwezi, Yatta, Mwala,
Kijome, Mbooni, Makueni and Kangundo.
first four Divisions were still
these four divisions,
By 1986, only the
growing sunflower.
production
has been
In
low and
10
unsatisfactory.
In some of them, e.g. Yatta and Mwala,
sunflower production has been irregular. . Kilome and
Kibwezi divisions have had stable production.
since 1987, the production has been
very few farmers planted
However,
declining.
In fact
sunflower during the October-
December short rains of 1988 [OCD office, Machakos).
Both
the Government
and
the
OCD
Ltd
have
been
campaigning for extended sunflower production in Machakos.
However,
in spite of the joint efforts,
su nf lo we r
production has been erratic and generally declining. This
study was thus carried out to investigate why sunflower
production has been erratic and declining.
sunflower would entail
Production of
deploying some of the
resources
from food crop production to sunflower enterprise.
It is
not known what relationship food crop production maize in
particular has on sunflower production.
It is not known
whether production of maize would complement or compete
with sunflower production
carried
out
su nf lo we r
to
establish
production
in Machakos.
This
the
relationships
and
food
crop
study was
between
pr od uc ti on
particularly maize, through regression analysis.
Sunflower producing zones in Machakos are also cotton
growing zones.
Both crops require the same climate and
hence do relatively well in the marginal zones.
Cotton is
the alternative cash crop for farmers in these areas.
It
is suspected that if there is improvement in the marketing
arrangements for cotton and maize (the major food crop) it
11
is possible for farmers to shift from sunflower production
to cotton and maize production. This can cause a decline
in sunflower production.
relationship
between
investigated.
Therefore, the
sunflower
and
production
cotton
was
also
A regression model was run to investigate
how hectarage under sunflower responds to relative prices
of sunflower, maize and the hectarage of cotton.
Production of agricultural
phenomenon.
products
is an economic
Setting production targets without providing
sufficient incentives, such as market outlets and proper
prices for both farm
inputs and farm outputs,
yield any sufficient results.
may not
The availability of a
market is an essential precondition for production of any
commodity.
Price incentive is important because farmers’
decision to produce a commodity must be based on the
profitability
of
that
co mm o d i t y
in
relation
profitabilities of alternative
farm enterprises
1987].
to
F a r m e r s ’ decision
consequently
depends
food crops and cotton.
Machakos,
upon
the
pr o d u c e
to
[Opiyo,
sunflower
profitabilities
of other
Since sunflower was introduced in
no do c u m e n t e d work has been done on the
economics of sunflower production. This study analysed the
economics of sunflower enterprise.
profitability of
It concentrated on the
sunflower vis-a-vis
the food crops and cotton,
profitabilities of
with a view to finding out
possible policy incentives that could be used to promote
the production of more sunflower in Machakos District.
12
Since early 1987, sunflower is no longer a scheduled
crop [National Cereals and Produce Board,
1988].
As a
result, farmers have faced fluctuating prices as offered
by OCD Ltd. For instance, since 1987, the producer price
of sunflower has been changed four times [OCD Office,
Machakos].
The producer prices are usually determined by
OCD allegedly after considering the various costs incurred
at different marketing
levels,until
[Kimbo, etc.] are obtained.
consumable
products
It was hypothesised that the
OCD Ltd price incentive is not enough to induce farmers to
expanded sunflower production.
This study therefore was
carried out to estimate the total cost of production based
on the farmers resource utilization.
On that basis, the
breakeven price which an average farmer should be offered
was estimated.
The breakeven price was appraised to
determine if it was significantly
greater than or less
than the buying price of Kshs.2.85 per kg offered by OCD
Ltd. during the time this study was proposed. By the time
the study was actually carried out, the price had gone up
to Kshs.
3.00 per kg.
tested against the
is significantly
Some
of
the
new
So the breakeven price was also
price
to
ascertain whether it
less than or greater than
Kshs.
3.00.
other factors which may have worked against
increased production of sunflower were also investigated.
Such factors were: marketing arrangements, especially the
assembly of sunflower product and purchase activities and
13
distribution systems for both farm
inputs
and outputs,
and the extension services offered to farmers by the
Ministry of Agriculture and the OCD Ltd.
1.4
Justification of the study
According to the National Committee on Oil
Crops
(1987), sunflower is the most important oil crop in Kenya,
both in terms of area and output. In 1987, sunflower had
an output of 25,000 tonnes and the area under cultivation
was 50,000 ha.
Simsim is the second most important oil
crop in the country.
It had an output of 2,000 tons from
an estimated area of 2,500 ha in the same year.
most of the
oil
crops
in
Kenya
are
single
ecozone
specific, sunflower is grown in varying ecozones,
from high potential
country.
It
increase
sunflower
sufficiency.
is,
areas to the marginal
therefore,
to
ranging
zones of the
a Government
pr od uc ti on
While
levels
policy
of
to
self
Increased production would enable the nation
to avoid heavy importation of vegetable oils
from the
world market.
The
hectarage
under
sunflower
erratic and generally declining
production
has
been
since 1978, despite the
joint efforts of the Government and OCD Ltd to promote
sunflower production in Kenya.
This trend calls for the
need to study and uncover some of the problems which
thwart the efforts of the Government and OCD Ltd in the
sunflower industry.' This study comes out with suggestions
14
on ways and means to alleviate the problems frustrating
sunflower production.
It also suggests possible policy
incentives that could be used to promote the production of
more sunflower in Machakos District.
1.5
(a)
The study area
Location, climate and soils
The study was carried out in Kibwezi Division
in Machakos District of Kenya.
Kibwezi Division has
an area of 3400 square kilometres and is located at
the extreme south of Machakos
District.
It
is
bounded by Chyulu hills in the West, Athi River in
the East, Makueni
National
Park
locations:Andei.
Division
in the North and Tsavo
in the South.
Makindu,
Kibwezi
Kikumbulyu,
has
four
Ngwata and Mtito
All the locations start from Chyulu hills and
end at Athi
Division.
River
in the
Eastern
The major sunflower producing area is the
whole region bordering Chyulu Hills
locations.
side of the
in the four
Muthingiini and Mang’elete sub-locations
in Mtito Andei are the greatest sunflower producers
in the Division.
centres are
The major
sunflower
assembly
Nthongoni, Uuta, Nzeveni and Nooka.
15
The division is dissected by seasonal
rivers
which start from Chyulu hills and dra.in into Athi
River.
Most of the rivers are underground for about
halfway of the whole river course from the source.
Then they emerge to the surface downstream just
before they cross the main
road from Nairobi
to
Mombasa.
According to the Farm management handbook
of
Kenya Vol. IIC.(1982) Kibwezi Division has three
major agro-ecological zones:(i)
Lower highland zone
3-4 [LH 3-4].
LH 3 is a wheat-maize-barley zone with a short
to medium cropping season.
This zone
is not
cultivated because it is on the slopes of Chyulu
hills and is a forested area.
LH4 is a cattle-
sheep-barley zone with a short to very short and
a very short to short cropping period.
This
zone is also at the steep slopes of Chyulu hills
and is a forested area with game animals.
(ii)
Upper midland zones [UM]
Kibwezi has UM 3-4.
UM3 is a marginal coffee
zone with two short to medium cropping seasons.
UM4
is a sunflower-maize
cropping seasons.
zone with
two short
This zone is found on the
gentle slopes of Chyulu hills.
Sunflower,
16
maize and
beans are grown
in both
sub-zones.
Most of the sunflower in Kibwezi is produced in
this zone.
(iii) Lower midland zone [LM]
There is LM3 to LM6, LM3 is a cotton zone with
two short cropping seasons.
Early maturing,
dwarf sunflower varieties do very
zone.
hills.
This zone
is found at the foot of Chyulu
It is the most productive zone in terms
of maize,
sunflower
zone.
well in this
beans
and cowpeas.
in Kibwezi
LM4
is produced
The
rest
of
in this sub­
is a maize, cowpea and pigeon pea
growing zone in Kibwezi.
millet zone.
LM5 is a livestock-
LM6 and the rest of the lowlands
are ranching zones.
Kibwezi is in "Simba wa Nyika" type of grasslands and
has savanna type of climate.
600m
The altitude varies between
and 1830m above sea level.
The rainfall comes in
two maximas:- the long rains in March to May and the short
rains in October to December.
is from 600mm to 650mm.
The rainfall distribution and
reliability in this area are
equator,
The average annual rainfall
poor.
Being close to the
there is little temperature variation.
1.2 to 1.4 show the average monthly rainfall
Tables
[1984-1988]
and average monthly temperatures [1984-1987] respectively
for Makindu Meteorological
Station
in Kibwezi
Division.
17
Table
1.,2 :
Year
1984
M a k i n d u rainfall monthly
millimetres, 1984-1988
1985
1986
t o ta ls
1987
1988
Month
January
27.7
5.3
19.7
16.5
79.5
February
0.0
83.1
0.1
0.0
1.5
March
6.0
34.1
36.3
16.1
175.0
Apri 1
86.9
81.9
159.5
62.9
99.4
May
0.0
16.8
22.4
70.4
6.3
June
0.0
0.2
6.5
19.5
7.1
July
1.2
1.6
0.0
1.0
1.3
August
0.0
0.0
3.0
2.0
1.1
September
0.4
1.6
0.0
0.0
2.1
October
85.6
67.4
20.4
0.5
2.0
November
358.9
125.6
181.8
108.9
176.5
December
122.9
89.3
169.6
10.4
160.2
TOTAL
689.6
506.9
619.3
308.2
712.0
Source : Meteorological department headquarters,
rainfall section Nairobi, 1988.
18
/
Table 1.3
Makindu monthly maximum and minimum
temperatures in degrees centigrade,
1984-1987
Year
1984
1985
1986
1987
Month
Max
Min
Max
Min
Max
Min
Max
Min
January
29.9
16.7
28.7
17.6
28.5
17.7
30.2
18.0
February
32.1
17.5
30.0
18.4
31.1
17.8
32.7
17.7
March
31.9
18.4
30.7
17.8
33.1
19.3
32.0
18.9
Apri 1
30.9
18.9
29.2
18.2
32.8
19.2
29.7
19.5
May
29.3
16.9
28.2
17.0
28.5
17.9
27.0
17.6
June
28.0
14.4
26.8
15.5
27.3
16.3
26.5
15.1
July
25.9
14.8
25.6
14.2
27.3
15.5
26.1
13.9
August
26.1
14.3
26.6
14.0
27.7
15.0
27.3
13.9
September 28.8
15.5
28.5
15.7
29.6
16.4
29.0
15.3
October
28.5
17.4
29.2
16.8
31.0
17.5
30.4
18.1
November
26.5
18.1
27.8
18.3
30.6
18.6
28.3
18.2
December
26.5
17.5
27.1
18.1
30.2
19.4
26.8
18.4
Source:
Meteorological
department headquarters,
climatological section Nairobi, 1988.
19
Table
1.4
Makindu temperatures
in degrees centigrade,
mean monthly dry and wet bulb, 1984-1987
Year
1984
1985
Dry
Wet
Dry
January
25.5
18.0
February
27.1
March
1986
1987
Wet
Dry
Wet
Dry
Wet
24.3
19.7
24.2
19.0
24.2
19.7
18.0
25.1
19.7
26.7
18.9
25.8
18.7
26.0
18.8
25.7
19.6
26.6
19.5
27.6
20.2
Apri 1
26.3
20.2
25.3
19.9
25.1
19.0
26.1
20.2
May
24.5
18.7
24.0
18.9
23.2
19.4
24.5
19.8
June
23.6
17.2
22.5
17.3
22.8
17.4
23.0
18.1
July
21.7
16.6
22.1
16.7
21.3
16. 1
22.7
18.2
August
21.4
16.3
22.1
16.1
22.2
16.5
23.0
17.9
September 23.8
17.2
23.7
17.3
23.7
16.9
24.6
18.0
October
23.7
18.7
24.0
17.3
24.9
18.8
26.0
18.5
November
23.5
19.9
23.7
19.1
24.0
20. 1
25.4
19.7
December
23.2
19.8
23.5
19.8
23.0
20. 1
26.3
20.0
Month
Source:
Meteorological department headquarters,
climatological section Nairobi, 1988.
From the above tables, the maximum temperature varies
between 25°C and 33° C, while the minimum temperature
falls between 14° and 19°C.
for four years
ranges from
The Mean monthly temperature
20.4°C
to
indicating low temperature variation.
temperature range is
fairly narrow:
25.3°C,
hence
The dry bulb
it is between 21°C
and 27°C, while the wet bulb temperature range is between
16°C and 20°C.
This shows that the amount of water in the
air does not vary greatly in Kibwezi.
20
According to the Farm Management Handbook of Kenya
Vol. II C, soils in Kibwezi
range from moderate to high
fertile to low fertile soils near Athi River.
Most of the
soils between Chyulu hills and the main Nairobi - Mombasa
road are generally shallow soils.
The soils in Chyulu and
its slopes are Mol lie Andosols which are fertile and well
drained. Around Kiboko and Makindu areas, the soils are
well drained,
deep to very deep rhodic and orthic
ferralsols. Most of the soils in the other areas are an
association of well drained, moderately deep to deep dark
red to dark reddish brown Chromic Luvisols.
(b)
Land tenure, infrastructure and demography
The land tenure in the area is mainly freehold.
However,
there
are
many
cases
where
land
is
registered in the father’s name and farmed by several
sons communally.
The whole area is adjudicated.
This
the
means
that
area
has
been
surveyed,
demarcated and each land owner can be granted a
freehold title deed in respect of Land parcel
if he
pays the registration fee to the Lands Office of the
Ministry of Lands and Settlement.
The major Nairobi-Mombasa road and railway line
pass in the middle of Kibwezi dividing it into two,
Eastern portion and Western portion.
Most of the
access roads branch from this main road and end up in
Athi River at the East or at the foot of Chyulu hills
21
at the West.
These feeder roads are well murramed.
They are passable even during rainy seasons.
They
are maintained by Rural Access Roads Programme.
Both postal
adequate.
and t e le ph on e
fa c i l i t i e s
are
Electricity has reached only three market
centres so far, i.e. Mtito Andei, Makindu and Kiboko.
Plans are under way to supply other centres
electricity.
Kenya
Commercial
banking facility in the Division.
Bank
provides
The ethnic
are Kamba people.
population by 1986 was 130,000.
a
The branch started
its banking activities in January, 1989.
inhabitants in Kibwezi
with
The
The density is 40
persons per square kilometre.
1.6
Objectives of the study
The major objective of this study was to find out why
sunflower production
has been
declining
in Machakos
District despite the concerted promotional campaign by the
M i ni st ry of A g r i c u l t u r e
production,
and OCD Ltd to expand
through the determination
relationships among sunflower,
food
of
crops
its
production
(maize
in
particular) and cotton, and the relative profitability of
these crops.
The specific objectives were:(1)
to
(i)
determine
sunflower
the
production
relationship
between
and maize (the major food crop),
22
(ii) sunflower and c o t t o n
and
also assess
how
relative prices of sunflower and maize affect
sunflower production.
(2)
to estimate relative profitability of sunflower visa-vis major food crops and cotton.
(3)
to estimate the breakeven price of sunflower
produce
per kilogramme for an average small scale farmer.
(4)
to interpret and explain the observed relationships
and draw policy implications from the results.
1.7
Hypotheses tested
The following hypotheses were tested:
(1)
there is no relationship between the area under food
crops particularly maize and that
(2)
under sunflower.
there is no relationship between the area under
cotton and that under sunflower.
(3)
the profitability of sunflower production is not
different from the profitability of food crops and
cotton production in Kibwezi Division.
(4)
OCD Ltd offers a price of the sunflower produce that
is not different from the breakeven price.
23
CHAPTER 2
LITERATURE REVIEW
2.1
Studies on oil crops
Most
of
the
work
which
has
been
done
on oil
crops in Kenya is mainly in the form of surveys covering
all the oil crops in the country.
none at all has
Very little work or
been done on individual
oil
crops in
specific ecozones.
Gatere
[1974]
did some work
on
some
sunflower production and marketing in Kenya.
that the profitability of sunflower was
aspects
of
He found
relatively low,
mainly due to poor yields which in turn were due to low
levels of input utilization, especially fertilizers.
This
situation was attributed to a possible belief by farmers
that sunflower needs little fertilizer, but more important
was the fact that the sunflower prices were low and hence
returns to fertilizer in sunflower were
low.
This in
itself was thus a disincentive to production.
^ A c c o r d i n g to Gatere [1974] .average yield levels for
hybrid maize in Kenya were 2500 kg/ha which at the price
of 36 cents/kg gave Gross Margin [GM] of Kshs.900/ha. On
the other hand, average sunflower yields on the best farms
were 900kg/ha which at a price of 70 cts/kg gave a GM of
Kshs.630/ha.
An equivalent amount of sunflower which
24
would give the same GM per ha as maize worked out to 1286
kg which under the prevailing yield levels and prices
would require 1.435 ha.
Since
the
costs of production for sunflower and
hybrid maize were not very different, the implication was
that sunflower could only begin to substitute maize where
yields of maize were substantially below the average.
Gatere [1974] anticipated that this would be in areas near
the fringes of the maize growing areas and marginal zones
where maize yields can be as low as 630 kg/ha.
smallholder farmer in the marginal
might not be so simple.
areas,
To the
the situation
Maize is basically a food crop
and is always given priority.
Sunflower therefore could
find a place as a cash crop but not to the exclusion of
maize.
In these areas the place of maize as a food crop
is always reserved out of necessity [Gatere,
1974]. The
economic choice
to produce
facing a farmer
is either
maize surplus for sale or to produce sunflower.
By the
time Gatere’s study was done, sunflower production had not
been expanded to the marginal areas of the country, such
as Kitui and Machakos.
The present study was carried out
to investigate the current situation of the economics of
sunflower in the marginal areas.
The Kirby Commission [1977] which was constituted
to
appraise and advise on accelerated oil seed production and
utilization in Kenya found that early maturing varieties
25
of sunflower had been tried with a certain degree of
success in Eastern Province.
In particular,- the cultivar
"Issanka" would survive where all other annual
crops
including Katumani (early maturing) maize variety had
succumbed to water stress, particularly
in Machakos and
Kitui Districts.
According to Thuo [1977], expansion of sunflower
production
is expected to be beneficial
to the country
because the foreign exchange position would be improved by
cutting down on the imports
found
that
substitution
especially of palm oil. He
of
im po rt ed
palm
oil
by
domestically produced sunflower seed oil would be economic
because one hectare of sunflower yields 0.525 metric tonne
of sunflower seed oil at a cost of Kshs.1 875 to the
processor.
An equal quantity of palm oil was imported by
processors at a cost of Kshs.1975.
Among the annual oil seed crops grown
in Kenya,
Pathak [1975] found that sunflower gave the best quality
edible oils.
As such,
sunflower deserves
increased
■ »
efforts in its improvement and production.
The Economics Section of National
Station,
Njoro
[1988]
did a GM analysis
Plant Breeding
for wheat
and
sunflower production in Nakuru District. It was found that
the GM/ha of sunflower was negative.
for an average
potential
areas
The breakeven yield
farmer was 30.5 bags per ha.
with
high
rainfall,
sunflower
In high
compares
26
very unfavourably with all the major crops,
like maize,
wheat and beans. This is the case in such districts like
Nakuru,
Trans Nzoia,
Uasin Gishu and parts of Kakamega
[OCD, 1986].
According to Were
[1986],
sunflower has great
potential for expanded production in all provinces in
Kenya, if marketing problems and lack of improved seed are
eliminated.
Since sunflower has 30-35% oil content for
most of the varieties grown and could be improved to 40%,
it has the potential for substituting
the palm oil and in
production of high value cake for livestock.
Kenya’s food security remains a major objective of
the Government.
According to the sessional paper No.1 of
1986, the country will strive
maize,
beans,
potatoes,
products and other
to
be self-sufficient in
vegetables, milk,
foods.
However,
as
beef and meat
farmers
are
encouraged more and more to produce cash crops, food crops
might start being substituted by cash crops.
Bazinger
[1981] recognized this problem when he conducted a survey
on tobacco production in Kunati Valley, Meru District in
Kenya.
He found that farmers were progressively reducing
the growing of maize
in favour of tobacco.
observed that relatively more
He also
production resources were
v allocated to the growing of tobacco than any other crops.
The result,
according to Bazinger, was the occurrence of
frequent shortages of foodstuffs
in the area.
It was
27
expected
in the current study that sunflower would have a
similar effect if its production
in Machakos was to be
expanded.
2.2
Theoretical framework
Oyugi
analysis
[1984]
carried out
a study using
regression
to establish the maize acreage response to
acreage under tobacco and maize prices in Migori Division
of South Nyanza district.
He found that the acreage under
tobacco affected maize acreage negatively.
On the other
hand farmers responded to maize prices positively.
meant that
tobacco production
[major food crop].
reduced maize production
The acreage under tobacco had a
negative influence on acreage under maize.
maize were competing enterprises.
used to run the model.
form:-
This
Tobacco and
Time series data were
The model was of the following
Am = f(At , Pm ) ------------- (Eq.2.1)
where
Am = Area under maize in ha.
At = Area under tobacco in ha.
Pm = Price of 90 kg - bag of maize.
In Malawi a study by Gupta(1974) as quoted by Mills
[1979]
price
where 900 farmers were surveyed following producer
increases found that
the
smallholder farmer does
respond quickly to a price change.
As to long-run supply
elasticity, as shown by acreage intentions, he found that
28
more than 60% of smallholders had intentions to plant more
of their cash crop.
The long run response was greater
than the short run response.
Gupta’s results together with
an econometric nature
1972/73],
indicated
past studies mainly of
[Dean, 1966; Gordon, 1971; Colman,
on the
whole
that
the
Malawian
smallholder farmer does take prices into account as one of
the factors determining his production or acreage.
The
response tends to have a time lag usually of one year.
support this conclusion,
Malawian studies,
Gupta
quoted
several
To
other
among them being the studies by Dean
(1966), Gordon(1971), and Colman (1973).
Dean [1966] was interested in whether or not African
farmers respond to price and wage changes in the same way
that residents of more advanced countries do.
was
concerned
only
with
to b a c c o
His study
production.
He
hypothesised that apart from the weather, variables with
income and substitution effects such as last year’s prices
of tobacco, wage rates abroad in proceeding year and price
levels of cash goods in the previous
year determine
production. With a one year time lag, he found a fairly
high response of acreage to price changes.
that the smallholder
allocator of resources,
considerations.
farmer in Malawi
He concluded
is a
rational
influenced by price and similar
29
Gordon [1971] calculated a supply response function
and production function for major cash crops in Malawi.
Acreage change was the dependent
variable
while the
independent variables were population growth,
price,
prices of c o mp et in g crops,
yield and the cost and
availability of other inputs.
Gordon related acreage
planted to the prices of the previous year, for the period
1954-67, and found for example in the case of groundnuts a
very high response of 2.3.
In other words a 1% rise in
prices evoked 2.3% rise in acreage.
Colman
[1972/3]
did a study concerning
the supply
response of cotton growers in the southern region of
Malawi.
He concluded categorically that subsistence
farmers included in his survey were price conscious and
that
a c re ag e
and
output
of
cott on
reflect
considerably the previous year’s prices.
very
An unusually
high elasticity of supply [exceeding 2.0] in response to
price had been found.
Mbogoh
[1976] examined the acreage determinants of
potato production
in Meru District.
He assumed that
potato acreage during any given season will be influenced
by the
general
level
of prices
attained
marketing of the potatoes produced
season.
during
the
during the previous
The level of prices just prior to the planting
period was crucial.
Therefore,
prices between May and
July would influence the acreage for the long rains crop
which was planted in August, while the prices between
30
December and February would influence the acreage for the
short rains crop which was planted towards
the
end of
February, in Meru District. He found that potato acreages
were inversely related to the general level of potato
prices prior to planting.
The hypothesis was that potato
acreages would be expanded if the general level of potato
prices prior to planting was high.
This hypothesis was
therefore rejected. It was postulated that since potatoes
were the main cash earner for most of the sample growers
interviewed, then one would expect them not to base their
production decisions directly on the
ruling
prices,
especially because they had few alternative enterprises.
However, the ruling prices were expected to influence
their decisions
in the long run. Since an acre of land
required a certain amount of seed potatoes,
then the
amount of potatoes retained for seed by the farmers would
depend on the planned potato acreage for the
following
season.
Similar studies
in India by George and
Mukherjee
[1988] have indicated that farm level decisions on acreage
allocations under different crops are often considered to
be influenced by changes in relative prices of different
crops.
Along with changes
in yield and cost,
price
changes influence the relative profitability of different
crop enterprises and this shift
in profitability of
different crop enterprises influences acreage adjustments
31
through increased total cropped area or shifts in the
existing cropping pattern.
Mukherjee
The study done
by
[1988] was trying to find out
George and
the
acreage
response under rice (a staple food crop in India) to the
relative price changes of coconut and rice respectively.
Time series data were used in the model.
The model used
was:Af = f (Pr> Pc ) ---------------where:
[Eq. 2.2]
A f = Area under rice in ha
Pf = Price of rice per kg
Pc = Price of coconut per kg
Most of the studies reviewed under the theoretical
framework were mainly of econometric nature and they were
intended to establish the acreage response of a particular
enterprise to
its price and the
price
of
enterprise(s). A few of them included other
explanatory variables such as population
trend and the actual
independent
growth,
and costs. However in the current study,
response of a particular enterprise to
competing
yields
the acreage
its price,
time
acreage of competing enterprise(s)
was examined.
All the models used in the quoted studies above might
have suffered from exclusion of such variables
technology,
respective
like
labour and capital used for production of the
crops
mentioned.
found that small scale
However,
Stevenson
(1971)
farmers are caught up in a low
32
technical and economic equilibrium trap.
on
supply
response
in order
to
After focusing
determine
economic
rationality of these farmers, he found that in the short
run, acreage is more responsive to prices than yield, i.e.
when prices go up, the immediate increased output realized
is due to increased acreage rather than due to technical
improvement.
Matovu
[1979]
found that technological
progress
in
Machakos for maize production in form of new varieties of
seeds and the use of fertilizers and pesticides is very
slow.
For the case of labour and capital, it was assumed
that flexibility within the farm exists.
Therefore given
the
mechanism
farm
enterprises,
the
triggering
to
reshuffle resource allocation or bring a new enterprise
into
production or to expand the production of any one of
the given enterprises is the respective price change,
ceteris paribus.
Price v a ri at io n
Traditionally,
determines
acreage
response.
acreage response to price changes has been
interpreted as the supply response to price change.
This
is because the output supplied depends on acreage under
the crop in question [Maitha, 1974].
Ne rlove
(1956,
1958)
developed
the
adaptive
expectation model which has been used for supply analysis
for agricultural products. For example, Kere (1986) used
this model
in the study of the supply responsiveness of
33
wheat farmers in Kenya. The objective of the study was to
find out how farmers in various wheat growing regions in
Kenya
respond
to
wheat
prices.
In
the
model
specification, more explanatory variables were included in
the basic Nerlovian adaptive expectation model
yield of wheat,
rainfall
etc.
employed
adaptive
expectation
the
Maitha
such as
(1974)
also
model
with
modifications, to analyse the supply response of coffee in
Kenya. Although many of the studies using Nerlovian models
introduce
important modifications
basic model
employed in most,
and
extensions,
the
is the formulation he
advanced. The basic specification of the model
is of the
form:
Xt
=
a + b Pt* +
Where X^. is the quantity
et
----------- (Eq. 2.3)
supplied or acreage under the
crop in question in period t and Pt* is the price expected
to prevail in period t.
is postulated
equation,
as
The formation of the expectation
in equation
2.4
below.
In this
the expected price in period t is revised in
proportion to the error associated with the previous level
of expectations:
P*t - P*t_i
=
B(Pt-1 - P*t-i)------- (Eq.2.4a)
or
P*t
where
=
0 < B < 1
(Cagan,1956;
the
P*t_ 1 +
B (Pt_ 1 - P*t_-|)------ (Ed- 2.4b)
is the coefficient
Nerlove, 1956).
of expectation
That is , P*t is equal to
expected price in period t-1 plus some fraction of
34
the difference between
price last period.
last
year’s actual
and expected
Nerlove (Op. cit) showed that equation
2.4 can be rewritten as:
P*t
=
BPt_ 1 +
BhPt_2
+ Bh2Pt_3 + ... ---
(Eq. 2.5)
which indicates how the expected price at time
dependent on the
actual
prices
in
t is
the previous periods
i.e t-1, t-2,...
where again:
0 < B <
h
=
1 ; and
1 - B.
Substituting eq. 2.5
in
eq. 2.3
for
P*t
applying
the Koyck’s (1954) transformation, the resulting equation
becomes:
Xt =
a(1-h) + hXt_ 1 + bBPt_ 1 +
Vt ----- (eq. 2.6)
The adaptive expectation hypothesis as given can be
used when the price at which the product will be sold is
unknown at the time the decision
is made.
Given the
biological time lag in the production of agricultural
products e.g. sunflower, this will generally be the case
in estimating
An
supply.
alternative
rationalization
of
supply
response
analysis is provided by the so called partial adjustment
model,
(Kmenta, 1971; Nerlove, 1979).
annual crop is of the form:
This model for an
35
A*t
= a + bPt_ 1 + et1
--------------
(Eq. 2.7)
where Pt-1 is actual observed price at time t-1 and A*t is
the "desired" or equilibrium area of the crop in question
to be under cultivation in time t. A*t is not directly
observable,
but it is assumed that an attempt
is being
made to bring the actual level of the area to its desired
level.
Such
an attempt is partially successful during
any one period of sunflower production.
The relationship
between the actual and the desired level of the area under
the crop
may be specified as follows:
At
~
At-1
where
= B(A*t - At-1) + et2
(Eq. 2.8)
0 < B < 1
et2
= random disturbance
B
= adjustment coefficient.
Solving (2.8) for A*t , followed by substituting for it in
(2.7) and then simplifying the resulting equation
leads
to:
At
= aB + bBPt_ 1 +'‘ (1-B)At_ 1 +
Vt ------
(Eq. 2.9)
Nerlovian models were not used in this study because
the major objective was not to establish the
"desired"
long run equilibrium acreage of sunflower, but rather to
find out
how the
acre ag es
of the c o m p e t i n g
crops
es pe ci al ly maize and cotton affect the a c re ag e of
sunflower; and
also
to find out the degree to which the
WHIYBRS1TY OI* NAIRUjn
LIBRARY
36
acreage of sunflower moves closely with the acreages of
other
enterprises.
Therefore,
a
simple
regression
analysis was carried out to establish how the acreages of
competing enterprises
influence s u n f l o w e r acreage
adjustment.
Gross margin and net return have been used as tools
for economic analysis in several
e n t e rp ri se s.
studies based on crop
For example, Vi et h and Begley
(1976)
examined the economics of wetland taro production in the
state of Hawaii.
re fl ec te d
Five typical systems of production that
the d i ff er in g
locations
and
levels of
mechanization used in the growing of taro in three major
production
centers
in the
state
were
investigated.
Interviews with 333 wetland taro growers were conducted.
The data on cultural practices,
returns were obtained from farmers.
inputs and costs and
Costs were calculated
by determining the unit’s physical inputs and multiplying
these
by the appropriate prices
Special
or
per
unit
note was made of the methods used to calculate
labour charges and machine costs.
The charge for labour
involving use of machinery was $85.50 per hour.
labour,
costs.
[family labour or hired
labour
(permanent or
casual)] was valued at $83.00 per hour.
quantified in terms of
All other
Labour was
man-hours spent on each production
activity. The labour figure of $83.00 was slightly above
the minimum wage and the charge of $85.50 was what a
machine operator could be hired for
in
rural
areas of
37
Hawaii.
Gross
return
per acre
per
crop
cycle
was
calculated from the survey data for each farmer as well as
the variable costs with and without
labour charges.
Average figures for gross returns per acre and variable
costs per acre were calculated.
Then gross margin/acre
{excluding labour charge] was obtained by subtracting
variable costs from gross
returns.
Afterwards,
labour
charges were included and the residual, which was the
return to management and working capital, was obtained.
In their study of cost of producing selected crops in
N o r t h e a s t e r n New Mexico,
Neil
separated costs of production
indirect costs.
and S w e e t s e r
into direct
[1975]
costs
and
Direct costs included those directly
applicable to the specific crop,i.e. costs of fertilizer,
seed,chemicals, fuel and lubricants, and those associated
with the use of machinery.
Indirect
associated with land, labour and
variable costs.
They
costs were those
interest charges on
noted that these indirect costs are
not commonly considered by farmers and ranchers although
they are certainly involved in the cost of producing
agricultural products.
They assumed in their study that
family labour was an indirect cost to the operator.
Any
hired labour (permanent or casual) was included as direct
cost.
The labour cost was calculated on the basis of the
number of man-hours spent on each enterprise.
They
calculated the breakeven prices for the crops dealt with.
Two sets of breakeven prices were
shown
for each crop
38
budgeted.
The first set was calculated on the basis of
"total direct cost".
per acre costs",
i.e.
The second was based on the "total
total
direct costs
plus total
indirect costs. However, in the current study, the author
calculated the breakeven price of sunflower production on
the basis of "total direct cost" only.
Fotzo
and
Winch
[1978]
carried
out
a
study
to
determine the relative cost and returns in rice production
for existing and important five rice farming systems in
North-West Province of Cameroon.
The
input-output data
used in the analysis were obtained from a field survey of
randomly selected sample of 113 households.
In the
analysis, Gross Margin, which is net return to land,
family labour, capital and management, was calculated. To
calculate average net returns for each of the five farming
systems,
total
costs were
required.
classified into four categories
namely,
Costs
were
non-labour
inputs, labour, machinery hire services and capital costs,
where
non-labour costs included seed, compound fertilizer
and pesticides.
Labour costs were expenditures on non­
family labour (hired and casual
labour).
Labour was
quantified in terms of man-hours spent on each activity.
Machine hire services costs were made up of expenditures
for hiring a tractor for land preparation, and a drilling
machine for planting. Capital costs included depreciation
and interest charges on suGh equipment items as hoes,
cutlasses,
jembes, 'harvesting knives,
etc.
These tools
39
are often used for more than one crop,
di ff i c u l t to al locate d e p r e c i a t i o n
particular enterprise.
thus making
it
c h a r g e s to any
Because of this allocation problem
and the fact that these costs account for only a minimal
proportion of the estimated expenses, one hundred percent
appropriation of the tool costs to the rice crop was
assumed because rice was the predominant crop of the
survey farmers. Two more assumptions were made in order to
value family labour and interest on capital:
that the
opportunity cost of family labour was equivalent to the
price of hired labour/hour and that opportunity cost of
operating capital was equal to 10% of the total variable
cost.
No attempt was made
in the study to value land
because land was communally owned.
subtracted from gross returns.
Then total cost was
What was obtained was net
returns accruing to land and management.
Mbogoh,
in his study entitled
"The
Economics of
production and marketing of potatoes in Meru District as
quoted previously, also calculated the gross margin, net
returns and the breakeven price of the potato enterprise.
The breakeven prices for 84
kg-bag and 94 kg-bag were
compared with the prevailing market prices respectively.
To quantify and include it in the total cost, labour was
put into two categories, i.e. family labour and hired
labour
[permanent and casual].
provided
by each
The number of man-days
labour category
per
farm
in the
production season w'as estimated from the survey data.
40
Average number of man-days for the farming system was
calculated and multiplied with Kshs.5.00, the average
rate of daily wages for farm labour based on the minimum
labour wages of 150/= per month as announced
by the
Government of Kenya on the labour day in 1975.
Ac co rd in g to M b o g o h
[1976],
the only type
of
machinery used which could be assigned a cost in potato
production was that
used
involved breaking of
practised.
in
land preparation,
which
land followed by ridging
where
Tractor ploughs,
ox-ploughs, fork-jembes or a
land master were commonly used in land preparation.
machinery was taken as
calculated.
considered.
The
hired.
Then
rest of the
small
the
costs
tools
All
were
were
not
Interest at 10% p.a. for 4 months on capital
costs per acre [costs of seed potatoes, machinery for land
preparation,
fertilizers,
Land was not valued.
Dithane
M45]
was
calculated.
The sum of capital and labour costs
per acre was taken as the Total cost of production which
was used to calculate the breakeven price for potatoes.
All the studies reviewed this far are based on mono­
cropping whereby the appropriation of both "direct costs
and indirect costs" was one hundred percent applicable to
the specific
crop.
This made
it
relatively
easy
to
estimate the profitability of the crop enterprises through
the calculation of gross margins or net return for the
specific crops because, the costs of production for each
41
enterprise accrued to each crop individually.
Also
it
facilitated the estimation of the breakeven prices for the
respective crop
enterprises.
However, according to Upton
[1973], mixed c r o p p i n g raises special
p r o b l e m s of
measurement to which no entirely satisfactory solution has
been found.
Whereas it is usually the case in Africa that
crops are interplanted
difficult,
if not
in mixtures,
impossible,
it becomes
to estimate the
very
resource
inputs used on each individual crop in the mixture.
Thus
the problem is not only that of assessing the area of land
devoted to each, but also that of allocating labour and
other inputs used in clearing,
cultivating and weeding.
One possible approach is to treat each component crop in
the mixture as though it occupied the whole area.
implies that one
This
hectare of maize, cotton and sunflower
mixed will yield the same quantities of each crop as would
three separate hectares, one of maize, one of cotton and
one of sunflower in pure stands.
allowance
for
competition
Clearly this makes no
between
the
crops
variation in their proportions in the mixture.
or
for
The total
area of crops, if the statistics are handled in this way,
would naturally be triple the actual area of land under
cultivation.
Alternatively, an attempt might be made to divide the
area under mixed crops between
the
individual
concerned on some concept of the proportion
crops
of the area
42
occupied by the respective crops.
According to Upton
( 1 9 7 3 ) ,the simplest and most ac ce pt ab le m e th od on
theoretical
grounds is to treat each crop mixture as a
particular enterprise or activity.
This approach is the
most satisfactory where there is some standardization of
crop mixtures.
In Machakos,the degree of standardization
of crop mixtures is high. More often than not, only two
crops are included in an intercropping system. Maize
normally the principal crop while
is
one of the pulses
(beans, cowpeas, pigeon peas etc.) or cotton or sunflower
is the secondary crop.
In this
study,
all
the
crop
mixtures were taken as single enterprises as was suggested
by Upton (Op.cit). Gross margin was used as a proxy for
profitability to show the performance
mixtures
of the
crop
which were captured in the survey carried out in
Kibwezi Division.
43
CHAPTER 3
METHODOLOGY
3.1
Data and their sources:
To be able to arrive at feasible economic reasons as
to why sunflower production has been erratic and generally
declining in Machakos, this study relied on both secondary
time series
3.1.1
and primary data.
Secondary time series data
The
Secondary
data
were
obtained
from
publications of the Ministries of Agriculture,
the
Finance,
Planning and National Development. More data were obtained
from
E c o n o m i c Reviews,
S t at is ti ca l
A b s t r a c t s and
Government Annual Reports. OCD offices at Nakuru and
Machakos, the National Cereals and Produce Board and the
Cotton Lint and Seed Marketing Board
office in Machakos
were of indispensable help in data collection.
The time series data on sunflower
hectarages were
available for a period of only fourteen years - from 1974
to 1987.
So were the corresponding data collected for the
other related variables.
could be
The precision of estimation
lowered by these small
number of observations
although logically correct inferences could be made.
The information contained in Tables 3.1, 3.2 and 3.3
was coded for computation purposes in order to provide the
relevant analysis and determine how production
crops and cotton affect sunflower production.
of food
44
3.1.2
Primary data
Primary
data
were
co ll e c t e d
by
using
a
structured questionnaire administered to a sample of
farmers.
The farmers were selected by the use of random
sampling method.
The first part of the questionnaire
sought information on the farm size, labour use, variable
inputs and crop enterprises in the farms.
included total
farm area,
acreage
Data collected
under
various
crops,
household size (including relatives), farm labour force,
farmer’s age, his education level and the problems
such
fa rm er s e n c o u n t e r in the farm and s u g g e s t i o n s for
solutions to the problems.
The remaining part of the questionnaire
physical input-output data by farm.
of activities,
sought the
These included: types
land preparation costs, type and quantity
of seed or planting material, type, quantity and cost of
chemical
dusts,
sprays and fertilizers,
various farm operations, such as
harvesting, threshing
and
shelling
labour
planting,
and
use in
weeding,
output
data
for crop enterprises.
The Regional and District OCD Extension Officers in
Machakos were interviewed, including the Agricultural
Extension Officer, Kibwezi Division and two OCD agents who
distribute planting seeds to the farmers and also buy the
produce from the farmers.
45
Table 3.1: Area under maize and sunflower and prices of
maize and sunflower in Machakos, 1974-1987
Year
Maize
Sunflower
ha
ha
1974
114511
23.4
28
42
1975
110017
290
65
63
1976
137115
11120
79
69
1977
128000
18170
75
80
1978
156119
5000
64
70
1979
158000
7000
48
80
1980
150000
3300
64
86
1981
137552
3400
64
90
1982
154443
153
80
95
1983
135500
225
100
130
1984
106000
160
106
175
1985
230000
2520
106
198
1986
178873
4500
112
188
1987
178000
5000
128
201
Price of
sunflower
per 40 kg
unit (Kshs.)
>
Sources:- Ministry of Agriculture
- National Cereals and Produce Board
- OCD office Nakuru
Price of
maize per
90 kg bag
(Kshs.)
46
Table 3.2:
Area under food crops and sunflower ii
Machakos, 1974- 1987
Sunflower
(ha)
Year
Food
(ha)
1974
205506
23.4
1975
205889
290
1976
234605
11120
1977
238456
18170
1978
297119
5000
1979
212252
7000
1980
254040
3300
1981
244966
3400
1982
298746
153
1983
272279
225
1984
198080
160
1985
387008
2520
1986
339003
4500
1987
338500
5000
Sum of hectares under maize , beans, cowpeas and
pigeon peas
Sources:
-Ministry of Agriculture
-OCD Ltd office, Machakos
47
Table 3.3:
Area under sunflower , cotton and prices of
cotton and sunflower in Machakos district,
1974-1987.
Year
Sunflower
ha
Cotton
ha
Price of
Cotton
1974
23.4
1134
241
28
1975
290
2223
259
65
1976
11120
7821
370
80
1977
18170
7848
463
75
1978
5000
18098
592
64
1979
7000
25000
657
48
1980
3300
26000
666
64
1981
3400
27387
703
64
1982
153
28000
796
80
1983
225
30175
888
100
1984
160
32000
925
106
1985
2520
12000
925
106
1986
4500
11000
925
112
1987
5000
25000
1110
128
Price of
Sunflower
Sources: -Ministry of Agriculture
-National Cereals and Produce Board
-OCD office Nakuru
-Cotton Lint and Seed Marketing Board office
Machakos.
48
3.1.3
Sampling plan and data collection
In the four Divisions where sunflower is produced in
Machakos District, the f a r m e r s ’ names
numbers are
and their code
kept in the OCD District Reports in Machakos
Office. The population for the study included all the
sunflower producers
in Kibwezi
Division who are
with planting seeds by the OCD Ltd.
farmers, 80
issued
Out of 646 sunflower
farmers, were sampled from the area served by
OCD Ltd.
It was considered
[approximately 13% of
that
about
80
farmers
or
so
the population] could form a fairly
representative sample of the population and
give enough
information for analysing the problem and hypotheses
testing. A sample size well above 30 and that is randomly
selected should
give results whose properties approximate
the normal distribution
Not more
characteristics
[Lawrence,1987].
than 80 farmers could be interviewed
due to
time and financial resource constraints.
There are five sunflower seed distribution centers in
the area served by OCD in Kibwezi
purposes
of this
study,
the
Division.
farmers
were
For the
clustered
according to seed distribution centres because each centre
had a list of farmers served by OCD.
Since only 80
farmers were selected from the five Centres, the selection
± . L_
procedure was designed to ensure that only 1/8
farmers were selected from each centre as follows:-
of the
49
Centre Utu/Nooka Nzeveni Makutano Iailtune Nthongoni Total
Total
number
of farmers 168
203
Number of
selected
farmers
203=25
8
168=21
8
90
92
93
646
90=11
8
92=11
8
93=12
8
80
This design ensured that
a constant
proportion of
farmers were selected from each one of the five clusters.
The code numbers of the farme rs
written down on a piece of paper.
in each
centre were
A random digit table
was used in drawing the sample in each respective centre.
All the data were collected by the author with the
help of two tr ai ne d enumerators.
interviewed once.
and May, 1989.
Each farmer was
The data were collected between March
The interviews were conducted in
the most
familiar language with the respondents. On average an
interview with the farmer took between one to one-and-ahalf hours. The primary data were used in the analysis of
gross margins.
3.2
Data analysis and analytical methods
Three analytical methods will
be applied to the
secondary and the primary data in order to generate the
results
for hypotheses testing.
(i)
These methods are:-
Correlation and regression
(ii)
Gross margin analysis
(iii)
Breakeven price analysis
analyses
50
3.2.1
Correlation and regression analysis
3.2.1.1
Correlation analysis
A simple correlation analysis will be carried out to
e s t a b l i s h how the ac re ag es of various e n t e r p r i s e s
correlate to each other.
That is, analysing the weight of
the correlation between the acreages of various crop
enterprises.
Correlation coefficient
is a parametric
measure which can be used to show the degree of linear
relationship between enterprise acreages.
It does
not
however indicate the cause and effect relationship.
The
sign of the correlation coefficient may be assumed to
indicate the production relationship between any two
enterprises. If the sign of the correlation coefficient is
positive,
it
may
be
depicting
a
complementary
relationship. A negative sign may be due to a competitive
relationship between the enterprises. A
zero correlation
coefficient may indicate that there is no correlation
between enterprises or it can be due to existence of
supplementary relationship between them.
The hypothesis that the acreage under sunflower has
no correlation with other crop enterprises can be stated
as follows:
Ho
ha
;
CAsAi
CAsAi
0
51
where CAs A^ is the population correlation coefficient
between the acreage under sunflower(As) and the acreage
under enterprise
.
It
is
sample
correlations that
are worked out in the analysis. The significance of the
obtained coefficients is
coefficient
then tested at 5 % level. If the
is not significant,
it means that there
is
zero correlation between the respective variables. Hence
the null hypothesis is not rejected.
and A.j have no correlation.
This implies that As
The analyses,
results and
tested hypothesis are in the following chapter.
3.2.1.2
Regression
analysis
One of the major objectives of this study
is
to
determine the production relationships between sunflower
and
the major competing crops
like maize
When inputs are limited in quantity,
farm
e.g cotton,
different
sunflower,
production
and cotton.
enterprises on the
maize,
possibilities
etc.
may depict
depending
on
their
technical
and economic relationships,
1978).
production possibility curve can be used to
A
( Doll and Orazem
illustrate the relationship between two enterprises on the
farm.
Those
relationships may
take
different
depending upon the particular situation.
forms
Production
possibility boundaries may be drawn for any pair of
alternative products e.g sunflower and maize,
and
cotton,
etc.
for any given
limited
sunflower
resource,
52
(Upton 1973).
The slope of the curve is a measure of the
rate at which one product can replace another and is known
as the "rate of product transformation"
,(Upton,Op.cit).
For a given limited resource, enterprises on the farm can
portray production relationships which may be competitive,
complementary, joint or supplementary.
Products are termed competitive
when the production
of one product can be increased only by reducing the
production of the other because they require the same
input at the same time.
When the production possibility
curve has a negative slope,
the products concerned are
said to be competitive,(Upton,Op.cit). On the other hand,
two products are said to be complementary if an increase
in one product causes an increase in the second product.
For example,
if one of the products like sunflower is a
cash crop, some of its cash returns might be used to
purchase inputs like
maize
fertilizer, labour, jembes, etc. for
(food crop) production. This kind of relationship
has been observed between tobacco as a cash crop and
maize,(Oyugi,1984).
When this
production possibility curve
relationship
has
occurs
a positive
the
slope.
Products are said to be supplementary if the production of
one can be increased without increasing or decreasing the
other. The production possibility curve in this case
a slope of zero,(Doll
and Orazem,1978).
products are related in such a way that
Finally,
has
joint
the production of
one inevitably leads'to the production of the other e.g
53
sunflower oil and sunflower cake.
Such
products
are
often intertwined in the production process which means
that
they
are
by-products
of
joint
production
relationship.
In this study,it is taken that farmers are endowed
with limited amount of land under which crop enterprises
are produced.
Hence, choices have to be made on how to
combine acreages allocated to maize growing for food and
and for the production of cash crops like sunflower and
cotton.
Maize is basically a food crop and is always given a
priority
(Gatere,1974).
Sunflower therefore
can
find
a
place as a cash crop but not to the exclusion of maize. In
marginal zones , the area for maize cultivation as a food
crop is always reserved out of necessity. Gatere (Op.cit)
postulated that the economic choice facing a farmer
is
either to produce maize surplus for sale or to produce
sunflower for cash. It is hypothesised that production of
sunflower may displace the surplus
produced for sale.
profitable,
However,
if
maize which can be
sunflower becomes more
its production may be expanded and this may
affect the area used for maize production as a food crop.
Thus as farmers are encouraged to expand the production of
sunflower in Machakos,
food
substituted by sunflower.
problem in Meru
crops
Bazinger
District in Kenya
might
start
(1981) observed
being
this
when he conducted a
54
survey on tobacco production
in Kunati valley.
out that, farmers were progressively
He found
reducing the area
used for growing maize as a staple food crop in favour of
tobacco(a cash crop).
This
observation
is quite
the
opposite of what Oyugi (1984) observed in Migori division
of South Nyanza.
The phenomenon observed by Bazinger (op.cit) may lead
to shortages of food crops as
relatively more production
resources are allocated to the growing of cash crops
instead of food crops. It is hypothesised in the present
study that expanded production of sunflower would have a
similar effect on the production of maize in Machakos. The
simple model which is formulated is meant to find out how
the acreage under maize (Am t ) is
acreage
under
sunflower
i.e,
related with
(As t ).
The
the
functional
relationships between the acreage under sunflower
and the
acreage under maize is assumed to be a form of productproduct relationship. The acreages and the prices of these
crops are used as variables for the specification of
the
possible functional relationships between the enterprises.
No attempt is made to an al ys e the p r o d u c t - p r o d u c t
relationship using the production possibility curves as
this approach would require more refined data.
Hence
simple regression analysis as described in the remaining
parts of this chapter was used to find out the productproduct
relationship.
55
As noted already, the acreage under maize on any
given farm is reserved out of necessity since maize is a
priority crop.
However,
in the marginal
zones near the
fringes of the maize growing areas sunflower can begin to
substitute
maize since maize yields
are substantially
below average (Gatere, 1974). Therefore, since maize( the
major staple food crop in Machakos) is a priority crop, it
is hypothesised that its production would have a negative
effect on the expanded production of sunflower. This would
mean that sunflower may compete with maize production.
Hence if it tends to displace maize, then farmers may not
be keen to
produce
such
a cash
sunflower production may decline
crop.
As
a
result,
in such zones. Therefore
in the regression model, the acreage under maize can be
taken as a variable which explains how the acreage under
sunflower is adjusted after maize is allocated a portion
of the land. The sign of the slope coefficient obtained in
such a regression between the acreage under sunflower and
the
acreage
under maize may
indicate
whether
maize
competes or complements or supplements the production of
sunflower.
The relationship is visualized to be of the
form:
As
=
f(Am )
--------------
(Eq. 3.1)
where
As = the area under sunflower,is the dependent
variable
Am = the area under maize
56
According to economic
role
in
al lo ca ti on
theory, price plays a
of
fa ctors
of
great
production
( L e f t w i t c h , 1984). The e l e m e n t a r y theory of s u pp ly
indicates that as the price of a commodity e.g sunflower
increases,
its output increases as well. According to
Maitha (1974), acreage response to price changes has
traditionally been interpreted as the supply response to
price change.
Therefore, the price of sunflower (Ps ) and
the prices of the major competing
cash crops, especially
the price of cotton (Pc )
are expected to influence the
acreage under sunflower.
Hence,
they were
included as
explanatory variables together with time trend. Thus,
equation 3.1 becomes:
As
=
f(Am> Pc , Ps , T)
---------
(Eq. 3.2)
where
Pc =
the price of cotton
Ps =
the price of sunflower
T
the time trend
=
The price of maize (Pm ) was not included in equation 3.2
inorder to avoid the possibility of m u l t ic ol1inearity
between the explanatory variables.
Cotton is the alternative cash crop available to
farmers in the marginal zones where sunflower is produced
in Machakos District.
It is assumed
here that with
improvements in production and marketing aspects (e.g
price increases, availability of credit facilities) in the
57
cotton
industry,
production
farmers would
to cotton
substantial
shift
production.
from
There
sunflower
has
been
a
acreage reduction in sunflower production in
Machakos. The area under sunflower has generally dropped
from 18170 ha in 1977 to 160 ha in 1984 (see Table 3.1).
In
contrast,
cotton has experienced persistent acreage
increase since 1974 upto 1984 (see Table 3.3).
In this
study, it is hypothesised that expanded production of
cotton (which
is the major cash crop
p r o d u c i n g zones in Machakos)
in su nf lo we r
has t e n d e d to affect
sunflower production negatively.
It is expected that the expanded acreage under cotton
has a negative significant effect on the
sunflower. This is due to the
under
hypothesised substitution
of sunflower with cotton which may
lead to the general
decline in sunflower production. Thus,
assumed that
acreage
it is strongly
due to more favourable market conditions in
cotton industry than in the other cash crop enterprises,
farmers would take cotton as a priority cash crop. As a
result, the area under cotton would always be reserved out
of necessity
among cash crops.
Therefore,
under cotton is taken to be one of the major
explaining
the
observed trend
sunflower. The sign of the
of
the
the acreage
variables
acreage
under
coefficient of the area under
58
cotton
is assumed to depict the production
relationship
between the two enterprises. The functional
relationship
is of the form :
As
=
f(Ac )
-----------
(Eq. 3.3).
The decision on acreage allocations under different
crops (especially cash crops) depends on changes in the
prices of different crops (George and Mukherjee,
1986).
As the price of a competitive commodity increases, farmers
may
shift from the production of the less competitive to
the production of a more competitive crop whose price is
higher.
It is expected that the price of maize (Pm ) and
the price of sunflower(Ps)
would affect acreage
under
sunflower (As ) according to the simple theory of supply.
These variables
are
included
as
independent
variables
together with time trend (T) in a regression
analysis in
an attempt to explain the observed decline
in sunflower
production.
As
=
Hence, equation 3.3 becomes:
f(Ac , Pm , Ps , T)
-------
(Eq. 3.4)
Where
As = the area under sunflower, is the dependent
variable
Ac = the area under cotton
Pm = the price of maize
Ps = the price of sunflower
T
= the time trend.
4
59
The time series data from 1974 to 1987
are used to
test the functional forms in equations 3.2 and 3.4 through
regression analysis.
Various
functional relationships were
specifications of the
tried out.
is based on the assumption of a linear
between the actual numerical
The first one
relationship
values of dependent and
independent variables as shown below (eq.3.5 and 3.6).
Ast - a +
+ ^3^st + ^4^ + e
( ■
3.5)
and
Ast = a + B1Act + B2Pmt + B3Pst + B4T +
e ---
(Eq. 3.6)
where:
B1 ... , B4
=
e =
Coefficients of the respective
variables
error term
Another set of regressions tried include a log-linear
specification of eq. 3.2 and 3.4.
Douglas functional forms of the
The linearized Cobb-
models (eq.3.2 and 3.4)
were also examined.
Following
Nerlove’s
(1958)
lagged
expectation
models, a number of studies have explained current year’s
acreage adjustments through lagged responses to previous
years’ actual experiences on the farm.
Hence, farmers at
times may base their current expectation (at time t) on
their
using
previous
experiences
(at time t-1).
Therefore,
the various specifications equation 3.2 and
3.4
were also estimated with modifications to include lagged
60
variables to find out how the previous years’ experience
af fe ct s
the
current
acreage
adjust ment.
These
specifications were also tried out with absolute and
relative prices of the respective crops,
as explanatory
variables.
It
is theoretically expected that B1 and B2
equation 3.5 would be negative.
This
in
is because maize
and cotton are assumed to compete with sunflower and the
expanded production of these crops is expected to affect
sunflower production negatively.
Therefore, B1
which is
the slope coefficient in the regression of acreage on the
variable related to maize is expected to be negative.
This would depict competitive production relationship
between sunflower (cash crop) and maize (the major food
crop). In the same model, B2 the slope coefficient of the
price of cotton is expected to be negative.
because
This is
it is hypothesised that as the price of cotton
(which is a competitive cash crop to sunflower) increases,
the production of sunflower declines.
model,
Also in the same
B3, the coefficient of the price of sunflower is
expected to be positive according to the theory of supply.
B4 , the coefficient of time trend (T) is expected to be
negative to justify the observed
general
decline
in
sunflower production.
In equation 3.6, B1 i.e the slope coefficient of the
area under cotton, is expected to be negative
is assumed that
as'the production
because it
of cotton increases,
61
less sunflower would be produced as a result of the
substitution of sunflower for cotton production.
the price of ma iz e
and
it is assumed
p t is
that
if Pmt
increases, farmers would respond by allocating more land
to maize rather than sunflower which would lead to decline
in sunflower production.
negative.
Hence B2 is expected to be
B3, the coefficient of the price of sunflower
(Pst) is expected to affect the acreage under sunflower
positively according to the theory of supply.
coefficient of the time trend
(T)
B4, the
is expected
to be
negative to justify the observed decline in sunflower
production.
The results of the analysis are presented in
the next chapter. Alternative
analytical methods can be
applied to investigate the problem of sunflower production
in M a c h ak os
District
but the o b j e c t i v e s
and
the
specifications of the functional relationships may differ
from the ones set out in this study. For example, Linear
Programming as has been
Irea,1979;
Kang’e,1980)
used
before
( G e t a c h e w , 1980;
can be applied to analyse the
farming systems and establish the optimal
which sunflower may be produced.
farm plan
On the other
in
hand,
adaptive expectation models can be used to estimate the
long run desired equilibrium acreage under sunflower, and
to analyse supply responsiveness of sunflower producers in
Machakos.
62
3.2.2
Gross margin fGM) analysis
In order to measure the
su nf l o w e r
vis-a-vis
relative
a l te rn at iv e
profitability of
ma jo r
farm
crop
enterprises, Gross Margin (GM) analysis was used. GM is
defined as Gross Output less Total Variable
measured in monetary terms.
Costs, both
Since the problem under
investigation was to find out the reason for the decline
in sunflower production after it had picked up relatively
well
in the early years of its introduction in Machakos
District,
the study concentrated
in calculating GM per
hectare of sunflower, cotton and the major food crops
(i.e. maize and Beans) in Kibwezi Division. The GMs were
calculated from the primary cross sectional survey data of
the sampled farms.
These GMs were then statistically
compared with that of sunflower enterprise
in order to
arrive at some conclusion as to whether or not sunflower
production
was
production
of other crops or whether sunflower intercrops
are more
substantially
profitable
more
than other
profitable
intercrops
than
[where
sunflower is not one of the crops].
The test statistic used was the t-test as specified
in Eq. 3.7 (Wonnacott and Wonnacott, 1984).
X
-
X
j
(Eq.3.7)
!l
M
+!l
n,
63
where:x = Mean GM of sunflower or
sunflower
intercrop
—
i. L
=
O
S 1=
Mean GM of the itn enterprise
Variance of sunflower GM or sunflower
intercrop GM
0
S .j=
4-U
Variance of the iLr enterprise GM
n1 =
Sub-sample size of sunflower producers
interviewed.
n^ =
sub-sample size of i1'*1 enterprise
producers interviewed
X. L_
i = the itr crop,which can be maize,
beans, cotton or cowpea or a mixture
of two crops intercropped.
This statistic was used for both monocropped and
intercropped enterprises.
In Kibwezi, farmers have some
plots planted with maize, cotton,
sunflower as pure stands.
beans,
cowpeas and
The GM’s of these crops were
calculated from the survey data.
The mean GM/ha for each
enterprise was calculated and the results were subjected
to the test statistic.
Whereas farmers can have some plots of pure stands,
mixed cropping is very common in Kibwezi. The following
crop
mixtures were expected, maize - sunflower, maize -
cotton, maize - pigeon peas,
then sunflower - cotton
64
enterprises. Since each crop mixture was taken as a single
enterprise, it became possible to quantify the GMs/ha for
the crop mixtures.
Eq.3.8
where
intercrops,
x
while
The
was
results were
the
mean
subjected
GM/ha
for
x^ was the mean GM/ha
intercrops. With this method it is
for
to the
sunflower
the
other
possible to use Eq.3.7
to test relative profitabilities of different enterprises,
re ga rd le ss
intercropped.
of
whether
they
are
monocropped
or
65
Although the use of GM Analysis is common in economic
analysis, it has several limitations. According to Norman
(1985) the following are some of the
limitations of GM
analysis:
(1)
Gross
areas.
Margin is confined
to strictly defined cost
It does not account for fixed costs or any
changes that may occur in fixed cost structure of an
enterprise and therefore should be used with care in
farm planning.
It is therefore dangerous
in farm
planning to assume that all fixed costs will remain
constant since some of these will undoubtedly alter,
particularly when major changes of policy are being
considered.
For example,
a decision to increase
sunflower production enterprise may call
increase in number of implements,
e.g.
for an
ploughs,
Jembes, etc.
(2)
The Gross Margin of an enterprise is not necessarily
an indication that net profits are realized in the
enterprise. To arrive at the net profitability of an
enterprise,
fixed costs must
be deducted
from the
Gross Margin. Since some fixed costs, such as regular
labour, cannot be attributed to specific enterprises,
net farm profit is best assessed by adding all Gross
Margins of various enterprises
subtracting total fixed costs.
in the farm and by
66
(3)
M i s i n t e r p r e t a t i o n can easily occur unless the
components of full
examined.
Gross Margin calculation
For example,
two
have the same hectarage
are
sunflower farmers may
of sunflower,
same
yield,
seed, fertilizer and spray costs but one farmer may
use family labour while the other may use hired
labour.
The gross margin for the farmer that uses
family labour will be much higher than that of the
one who uses hired casual
labour
is not
counted
labour because
family
as a variable cost
and
therefore the costs of family labour are not deducted
from the Gross output.
as sunflower
Similarly, enterprises, such
and ma i z e
crops,
harvested
by a
contractor will, Ceteris paribus, show a lower Gross
Margin than crops h a r v e s t e d by f a r m e r ’s owned
machinery.
This is because once a farmer hires a
contractor with a tractor and a trailer to harvest
his maize, the costs will be attributed to maize
enterprise while if the farmer owns the machinery,
the tractor and the trailer can be used
enterprises as well.
except
for such
Hence
expenses
in other
the machinery
as
fuel,
attributed to a particular enterprise.
will
costs,
not
be
Therefore,
these costs are not deducted from the Gross output of
the enterprise in question.
67
In
order
to
go
around
this
especially for the labour costs,
all
problem,
labour in all
the sampled farms was to be quantified in
spent on each activity.
man-days
Both permanent and casual
labour were taken
as
Vieth and
[1984],
Begley
and
hired labour as advocated by
The
costs
were
quantified according to the average number of people
who work in the enterprise per day, and the wage rate
per worker per day was then multiplied by the average
number of days used in one enterprise.
(4)
Gross margin does not take into
account the inter­
relationships which often exist between
enterprises
such as complementary
and among
relationship,
supplementary relationship, competitive relationship,
etc.
(5)
Outputs and costs alter with scale of enterprises.
It fo ll ow s th er ef or e that if an e n t e r p r i s e
is
increased it may not necessarily maintain its Gross
Margin per hectare per man-day or per animal
be ca us e
output
proportionately.
and
costs
might
not
unit
change
This makes Gross Margins per unit
from different sizes of an enterprise not comparable.
(6)
Since outputs and costs alter with seasons due to
differences in yields, Gross Margin for the same
enterprise also changes accordingly.
68
Despite all these shortcomings, gross margin
analysis
is
an
im po rt an t
analytical
tool
in
economics. The rationale behind it is that GM is
calculated
ma r g i n s
in the short
of
run.
sunflower,
The calculated
cotton,
maize
gross
and
intercrops in this study are not necessarily
indication of the exact magnitudes of profit.
the
an
The
gross margins may not indicate the most profitable
enterprises in absolute terms.
However, GM analysis
was chosen in this study to indicate the general
performance of the enterprises in Kibwezi Division,
Machakos, in the short run.
3.2.3
Breakeven price analysis
This sub-section deals with the theoretical framework
and the method of estimating the
breakeven
price
of
sunflower production.
3.2.3.1
Theoretical framework of breakeven price
A breakeven price is the
price necessary to cover
cost of production at a certain yield level.
When product
prices are higher than the breakeven price, there is a net
profit or return to management.
The breakeven point
is
that scale of activity where income equals total cost. So,
no profit or loss is made.
Breakeven charts help in
relating costs and profits to scale of activity and they
are widely used to get a quick
idea of the
level
of
69
production or prices needed for an industry to breakeven.
They help managers to understand the effect- of volume on
profit.
Breakeven analysis can provide answers to such
questions as:
(i)
What are the likely effects of a change in the
producer price in sunflower industry?
(ii)
What are the likely effects on profit if either
fixed or variable costs of production change in
sunflower industry?
In this study, breakeven price for sunflower was
calculated on the basis of
"total
direct
costs"
of
production. These costs are directly applicable and accrue
specifically
to
sunflower enterprise.
They
costs of fertilizer, seeds, pesticides,
hired labour.
Sweetser
transport and
In their study of "costs
selected crops in
[1975]
North-Eastern
categorized
New
land,
include the
of
producing
Mexico", Neil and
family
labour
and
interest charges as indirect costs which were not included
in the calculation of breakeven price on
"total direct costs". However,
the
in this study,
basis of
family
labour was calculated and categorized as a fixed direct
cost be ca us e
it is di rectly ap plied
production. To quantify family labour,
in su nf l o w e r
it was assumed
that the opportunity cost of family labour is equivalent
to the price of hired
labour.
The opportunity
cost of
capital [interest] was assumed to be 10% [interest rate in
banks in 1988] of the total variable cost of sunflower
70
production. The interest charge was categorized as a fixed
direct cost of sunflower production since
it accrues
specifically to the enterprise.
In the pr es en t study,
categorized
as
indirect
land and m a c h i n e r y were
costs of sunflower
production.
Fotzo and Winch [1978) launched a study on "The Economics
of Rice
Production
Cameroon".
in the No rt h- We st
Province
of
In that study, they made no attempt to value
land as land was communally owned. In the present study,
there are many cases where
land
is
father’s name and farmed by several
communally.
However,
registered
sons and
land was not valued
in a
relatives
in this study
because it is an indirect cost which does not accrue
specifically to sunflower enterprise only.
Fotzo and Winch [Op cit) observed that equipment
items such as jembes, cutlasses, knives, etc., were often
used for more than one crop, thus making it difficult to
a l lo ca te
depreciation
charges
to
enterprise.
Because of this allocation
fact
these
that
costs account
any
problem
for only
proportion of the estimated expenses,
pa rt ic ul ar
they
and the
a minimal
assumed one
hundred percent appropriation of tool costs to the rice
crop because rice was the predominant crop in the area of
study. In the current study it was observed that there was
no specificity of machinery. Just like in Fotzo’s study,
jembes, knives, ploughs, etc., were used for more than one
71
enterprise.
Hence
depreciation
charges
it
was
to
di ff ic ul t
any
particular
to
allocate
enterprise.
Therefore one hundred percent depreciation of tool costs
to the sunflower crop was assumed.
This was
because
sunflower was a minor crop compared to food crops in
Kibwezi. Secondly machinery cost was an indirect cost
which did not accrue specifically to sunflower enterprise
only but to all crop enterprises in the farm as well.
3.2.3.2
Method of estimating breakeven price.
This analysis was concerned with determining the
breakeven price, i.e. the price that
should be offered in
order to recoup the total cost of production incurred by
an average small
scale sunflower producer.
The prices
of fered to farmers are d e t e r m i n e d by OCD Limited,
allegedly by considering the marketing costs incurred at
different marketing levels.
The analysis in this study
arrived at the breakeven producer price by considering the
total cost of sunflower production.
The breakeven price
so obtained was then compared
with the prevailing price offered by OCD Limited.
conclusion was then drawn after statistical testing
whether OCD Ltd offers farmers
as to
a price significantly
lower than or greater than the breakeven price.
OCD Ltd
A
If the
price is significantly lower than the breakeven
price, the farmers would be producing at a loss.
If the
OCD Ltd price is significantly greater than the breakeven
72
price then
the average farmer should be making profit by
producing sunflower.
used
The
results of this analysis were
in hypothesis testing.
For ease of analysis
calculations were done on per hectare basis.
the
At
breakeven point:-
GOi/ha = TC-j/ha
---------------- (Eq.3.8)
but
GO-i/ha =PiVi/ha
Therefore
PiY i/ha = TCi/ha. ---------------- (Eq.3.9)
and
breakeven
P
price
= TC/ha
P
is given
by
--------------- (Eq.3.10)
Y/ha
where
GO^/ha = Gross output per ha for i ^ farmer
TC^/ha
= Total cost per ha for i*^ farmer
i . L.
P^
Y^ha
= breakeven price for itn farmer
= yield or output per ha for ith farmer
P
= average breakeven price
n
=
1
= n
Tc/ha
number of farmers interviewed
n
TC^/ha
i=1
is the mean total cost
per ha for the sample
and
Y/ha
=
1
n
n
^
Y^/ha is the mean yield per ha
i=1
for the sample
73
The hypothesis tested was that OCD Ltd buys sunflower
produce at a price which
breakeven price.
is not
different
from
the
The test statistic was as specified in
Eq.3.11 (Lawrence,1987):
t
(Eq.3.11)
CT
___ P_
n]
n-T
where:-
P
P0
Sample breakeven price
=
Price offered by OCD Ltd
0*_ =
p
Standard deviation of price computed
from the sample of sunflower producers
n
sample size
=
The calculated sample breakeven price was tested
against Kshs.2.85/kg and Kshs.
3.00/kg which were the
prices of sunflower offered to farmers by the time this
study
was
proposed
and
the
time
it
undertaken. The results are given in the
was
actually
next chapter.
74
%
CHAPTER 4
EMPIRICAL ANALYSIS RESULTS. DISCUSSIONS AND
HYPOTHESIS
TESTING
In this chapter, the production relationships between
sunflower and
maize;
and
sunflower
Machakos District were investigated.
and
cotton
in
A correlation matrix
was tabulated to show how the acreages under sunflower
move together with the acreages under the other competing
enterprises. Equations 3.2 and 3.4 were used to determine
the relationship
maize
between production of sunflower and
(the major staple food crop
pr od uc ti on of s u n f l o w e r
specifications of the
and
linear
in Machakos);
cotton.
and
Alternative
model were tried out as
suggested in chapter 3. Linearized Cobb-Douglas, loglinear, and direct linear forms of each of the two model
specifications were estimated with non-lagged or
independent variables
or a mixture of both.
lagged
The various
specifications were also tried with absolute and relative
prices of the enterprises in consideration. Equations 3.2
and 3.4
were
modified to include
some of the
explanatory variables
when lagged
once.
lagged
values because
gave better results
This was because current acreage
adjustment was found to be explained better by lagged
responses to previous
farm.
year’s actual
experiences on
the
Therefore they were estimated as modifications
of
equations 3.2 and 3.4 respectively.
75
Estimates
of
the structural
parameters
were
obtained through the application of the least squares
estimation technique. The validity and the reliability of
the
regression
equation estimates were
assessed on the
basis of the goodness of fit as measured by the R2 and
consistency of signs for various variables.
The results
and the discussions of the correlation analysis and
best functional specifications of the regression
the
analysis
obtained are presented in the subtopics 4.1 and 4.2.
4.1
Correlation analysis results
This
subsection discusses
the
and the coefficients obtained .
correlation
analysis
The aim of the analysis
was to show how the acreage under sunflower moves together
with
the
a c r e ag es
enterprises
of the
ot he r
competing
crop
(see Table 4.1).
Correlations
N
=
A ft
ASt
A ft
1.000
A st
-0.0566
A ct
0.0936
Amt
0.9035**
T
0.6934*
14
1-tailed
>
o
r+
Table 4.1 A correlation matrix of acreages under various
crop enterprises in Machakos district
Amt
T
1.000
-0.3281
1.000
0.0292
0.0739
1.000
-0.2468
0.5973
0.6013
Signif:
* -
0.01
** - 0.001
1.000
76
Where
A^t
=
area under food crops (maize, beans,
cowpeas pigeon peas).
Ast
=
area under sunflower.
Act
=
area under cotton.
Amt
=
area under maize.
T
=
Time trend.
The correlation analysis results gave a correlation
coefficient of 0.0292 between sunflower and maize,
which
is not significant at any conventional
This
coefficient
linearity
is positive depicting
i.e.
a complementary
level.
a positive degree of
relationship between the
two enterprises as is also shown
in the
regression
analysis results in Table 4.2. A corresponding
interval is -0.49 < p < 0.54.
which therefore shows
population
The interval includes zero
that there
is no correlation
between the two enterprises in the population.
The correlation coefficient between maize and cotton
acreages is 0.0739 which depicts a complementary but
insignificant
relationship.
The corresponding population
correlation interval is -0.45 < p < 5.8
zero and hence shows that there
which
includes
is zero correlation
between the two enterprises.
Sunflower
and
cotton
have
a
correlation
coefficient of -0.3281 which is not significant at any
conventional
level.
This coefficient
is negative which
depicts a negative degree of linearity i.e. a competitive
77
relationship
be tween
corresponding
population
-0.73 < p < 0.25
the
two
en te rp ri se s.
correlation
The
interval
is
which includes zero and therefore shows
that there is no correlation between the two enterprises.
Sunflower
and
food
crops
in
general
have
a
correlation coefficient of -0.0566. The corresponding
population correlation interval is -0.56 < p < 0.49
which
includes zero and hence we can also conclude again that
there is no correlation between the two enterprises.
On the overall,
analysis
the
results
indicate that there
of
the
correlation
is no correlation
between
sunflower and other crop enterprises in Machakos. However,
the acreage under maize is positively and significantly
correlated with the acreage under food crops perhaps
because maize is the major staple food crop in Machakos.
Also, the correlation of food crops
correlated with time trend
is significantly
which means that, the acreage
under food crops has been increasing with time.
4.2
Regression analysis results of the
modified
version of equation 3.2
This sub-section deals with the regression results of
modified version of equation 3.2.
of the
The direct linear
form
regression estimate of modified equation 3.2 was
found to have the best fit when the hectarage
under
sunflower was regressed on lagged hectarage of maize,
price of cotton, lagged price of sunflower and time trend.
78
The price of sunflower was lagged once (i.e. one year’s
lag). Thus
the modified functional relationship from
equation 3.2 was of the form:
Ast “ f(Am ’ pc ’ pst-1’ ^
which when specified
Ast =
in
[Eq.4.1]
linear form becomes :
a + B1Amt + B2Pct + B3Pst-1 + B4T +
where:
Ast
= Hectarage under sunflower at time t
is the dependent
variable.
Am =
Hectarage under maize at time t.
Pc =
Price of cotton at time t.
Ps t _1 =
V
Table 4.2
price of sunflower at time t-1.
a =
constant term.
T =
time trend.
B1 ,...,B4 =
The results
were
coefficients.
as shown in Table 4.2.
Regression results for
equation 3.2
Variables
parameters
Constant term
a
modified version of
Amt
B1
BCt
B2
-655.08
S.E.
7689.30
42.22
t
(-0.085)
(0.43)
(-0.10)
B4
-1666.57
21.81
78.42
1447.23
1
D - W = 1.97
T
229.96
CM
CM
= 0.62
18.20
pst— 1
B3
00
Coefficients
R2
e -- (Eq.4.2)
(2.93)* (-1.15)
* significant at 5 per cent.
79
4.2.1 Discussion, Interpretations and hypotheses testing
of regression results of the modified version of
equation 3.2
Equation 3.2 was used to analyse
relationship between sunflower and maize.
of
this
eq uation
we re
at te mp te d
the
production
Various trials
with
absolute
prices,relative prices and lagged prices of the respective
crop enterprises.
The model
was also tried with
lagged
variables unlagged variables and a mixture of both. It was
finally
estimated with hectarage of maize,
cotton, lagged price of sunflower and
price of
time trend because,
these variables with lagged sunflower price response to
previous year’s actual experience on the farm were found
to have the best explanation for current year’s sunflower
acreage adjustments.
Therefore,
current
on their previous year’s actual
expectations
farmers
base
their
sunflower price following Nerloves lagged expectation
models.
The results of the estimate of modified equation
3.2 show that
sunflower price
it was only
lagged
one
significant at 5 per cent.
B3 , the coefficient
year
(Pst_ ^ ),
which
was significant at 10 per cent.
the
regression equation
By extrapolation,
results of Durbin Whatson
(D-W) statistic test
for serial
were
sample size was small.
was
The other coefficients where
insignificant. The F-value for
correlation
of
the
carried
inconlusive although the
80
From the results of the estimation of the equation
4.2 it is found
that lagged sunflower price (Ps^_ 1 ) is
significant in explaining the area under sunflower.
variable had the
apriori
expected
po si ti ve
This
sign,
suggesting that if the price of sunflower in the previous
year ( i.e Pst_.|)
were
under sunflower in the
be
expected to
increased by a shilling, the area
current
year (i.e Ast )
increase approximately by
230 ha.
would
The
elasticity of the area under maize with respect to price
change was found to be 2.28.
The estimated slope coefficient
and maize
between
sunflower
B1 of the variable of the area under maize was
positive
but
insignificant.
This implies that the
production of maize has positive
but
insignificant
effect on sunflower production. These results could mean
that as
more of maize
also produced.
some
small
is produced, more
Thus suggesting that maize production to
extent
is facilitated by the production of
sunflower in a complementary
manner.
that some farmers may be using
This
part of
returns from sunflower to produce maize
results
sunflower is
have no significant effect.
would mean
their
even though
cash
the
The coefficient of
the price of cotton i.e B2 was found to be negative but
insignificant. This would mean that as the price of cotton
increases, the acreage under sunflower would have an
insignificant decline. The coefficient of time trend i.e
81
B4 was found to be negative meaning that sunflower has
had a general
decline even though the
results are
not
significant.
The
regression
model
discussed
above
generated
results used to test the hypothesis that there is no
relationship between the area under sunflower
area under maize.
The null hypothesis was:
H0 :
This
and the
B, = 0
hypothesis
was
tested
against
an
alternative
hypothesis that there is a relationship between the area
under sunflower and the area under maize.
HA :
B,
That is:-
* 0
The t - s t a t i s t i c for the c o e f f i c i e n t
production in the regression model was
insignificant at 5 per cent level.
that B 1
is not
statistically
of maize
positive but
The results implied
different from
Thus, the null hypotheses was accepted.
zero.
This suggests
that maize production does not affect the production of
sunflower in Machakos.
4.2.2 Regression analysis results, for modified version of
equation 3.4
This sub-section
results for modified
gives
version
estimate for the model
the
regression
analysis
of equation 3.4.
The best
was found to be of the direct
82
linear form
variables.
with both lagged and non-lagged independent
When equation 3.6
was estimated with lagged
variables to find out how these responses to previous
year’s experience affects the current acreage adjustment
for sunflower, the R-square was found to be 0.64.
The
price of sunflower (Ps t ) lagged once was found to be
significant at 5 per cent while the other coefficients
were insignificant.
were
Modified forms of the same equation
further estimated with one or two
variable(s)
remained
lagged once while
un la gg ed
as
per
the
the
other
independent
variable(s)
original
linear
specification.
Out of all the trial regressions made, the modified
forms of equation 3.6 had the highest R-square (0.69) when
this equation was estimated with lagged price of sunflower
while
leaving
(hectarage
unlagged.
the
ot he r
of cotton,
This
independent
price
implies that
of maize,
variables
time
trend)
farmers base their current
sunflower acreage adjustment on the previous year’s price
experience with sunflower more than on the previous year’s
price of maize and hectarage under cotton.
implicit functional relationship is as shown
4.3)
The estimated
in (equation
below:
Ast =
f<Act' Pm f Pst-1’ T >-
which when specified in
-------- [Eq.4.3]
linear form becomes:
83
As t = a + B.,Ac t + B2 Pmt + B3 Ps t _ 1 + B4 T + e --------( E q . 4 . 4 )
Where: Ast = Hectarage under sunflower at time t
is
the
dependent
variable.
Act = Hectarage under cotton at time t.
pmt = Pr^ce °f maize at time t.
Pst-1 = Price of sunflower at time t-1.
T = Time trend,
a = constant term.
B1,...,B4 =
coefficients.
The results are presented in Table 4.3.
Table 4.3
Regression
results for modified version of
equation 3.4
Variables
parameters
Constant term
a
Coefficients
232.535
S.E
405.583
t
R2
(0.573)
= 0.692
D-W = 2.432
B1
-1.708
1.582
v 2
Pst-1
b3
l
b4
-11.212
27.772
-30.528
7.848
8.457
111.322
(-1.079) (-1.429)
^Significant at 5 per cent
3.284)* (-0.274)
84
4.2.3 Discussion, interpretation and hypothesis testing of
regression results of the modified version of
equation 3.4
The results of the estimate of the modified version
of equation 3.6 show that the coefficients had the apriori
expected signs.
except
for
Ho wever,
The t-values were
sunflower
the
significant.
price
F-value
for
not
lagged
the
one
total
significant,
year (Pst_ 1).
equation
was
Although the sample size was small, the
extrapolation
results
statistic test was
showed that D u r b i n -W ha ts on
inconclusive.
Since the coefficient of the lagged sunflower price
(Pst_l) was statistically significant at 5 per cent, this
means that if the previous years’ sunflower price were to
be increased by 1 shilling, the farmers would respond by
increasing the
hectarage under sunflower in Machakos by
277.72 ha in the following year. The percentage change in
the area under
sunflower arising from 1 percent change of
the price of sunflower was found to be 5.4.
This suggests
that there
response
sunflower
is
price
a significant
change.
The
positive
coefficient
of
to
cotton
production was not statistically significant although it
had the
apriori
that
production
the
expected negative sign.
relationship
between
cotton is competitive but insignificant'
This implies
sunflower
and
85
Re g r e s s i o n re su lt s of the mo di fi ed v e r s i o n of
equation 3.6 were used to test the hypothesis that there
is no relationship between the area under cotton and the
area under sunflower. The alternative hypothesis was that
the area under cotton has a relationship with the area
under sunflower.
The t-statistic for the
coefficient of
the area of land under cotton was not significant at 5 per
cent
level.
The
statistically
results
different
hypothesis was accepted.
no significant
4.3
implied
from zero.
that
B 1 was
Hence,
not
the null
Cotton production has therefore
effect upon the production of sunflower.
Gross margin analysis results and hypothesis
testing:
This sub-section deals with the presentation of gross
margin analysis results for the crop enterprises of the
sampled farmers
in Kibwezi
Division.
farmers gave the area of their
but not hectares as suggested
In the
land in terms
survey,
of acres
in chapter 3. Therefore,
gross margin calculations were expressed as GM/acre.
Gross margin which is gross output less variable costs was
used as a proxy for profitability to show the performance
of crop enterprises. The results for the gross margin
analysis were used for testing the hypothesis that
maize-
sunflower enterprise is significantly less profitable than
the other crop enterprises in Kibwezi.
86
The results of the survey
sunflower is grown as an
revealed that most of the
intercrop,
mostly with maize.
Only 7.5% of the farmers had grown sunflower as a monocrop
(see Table 4.4)
Table 4.4 Distribution of cropping systems of sunflower
among the sampled
farmers
in Kibwezi Division
Enterprise
SunflowerMaize
intercrop
Number of
farmers
62
Percentage
77.5%
Source:
SunflowerBeans
intercrop
Sunflower
Monocrop
number of farmers
who had not grown
sunflower
Total
6
5
80
7
8.75%
7.5%
6.25%
100%
Author’s survey, 1989.
Five of the survey farmers had not grown sunflower within
the
time the survey was carried out.
Table 4.5a shows a summary of the performance in
terms of gross margins of crop enterprises studied in
Kibwezi. Taking per acre gross margin as an indicator of
profitability, sunflower (pure stand) enterprise is found
to be the least profitable of all
the pure stands and
mixed enterprises. Maize-sunflower enterprise is found to
be the least profitable of the mixed enterprises. The
average yield of sunflower (pure stand) was found to be
267 kg/acre.
The average yield
ranges from
400kg/acre
87
in marginal zones to 1000kg/acre in
as given
by OCD Ltd.
Kibwezi was
high potential areas
Therefore the yield of sunflower in
remarkably below
the
average
OCD
gross
Ltd
standards.
Consequently,
the
margin
Kshs.484.30/acre
in this area (see Table 4.5b) was
of
also
low when compared with the estimated gross margin of Kshs.
880/acre for an average small scale
OCD Ltd.
producer, as given by
88
Table 4.5a A summary of gross margins for the crop enterprises
among the sampled farmers in Kibwezi.
Pure stands
Enterprise
Maize
Gross
Margins
(Kshs/acre) 677.883
Number of
Observations 12
Beans
Cotton
2221.220 3982.900
Mixtures
Sunflower Cowpeas Pigeonpeas
484.317
N/A
N/A
Maize-
Maize-
Maize-
Cotton
Beans
cowpeas
Maize-
Maize-
Pigeonpeas Sunflower
3388.584 2079.886 1548.781 1333.278
1184.732
Sunflower8eans
1918.214
t
1C
3
6
0
0
31
Source:Author’s survey, 1989.
Where: N/A : Not Available because cowpeas and pigeon peas are not grown as pure stands.
55
33
48
62
7
89
Table 4.5b
Average gross margin for sunflower
(pure stand) in Kibwezi.
enterprise
Enterprise
Cost Item
Sunflower pure stand
Gross output(GO)
Kshs/acre
840.25
Operating capital (VC)
Kshs/acre
Ploughing
58.33
Planting seeds
24.75
Ferti1izer
0.00
Pesticides
42.78
Hired labour
79.26
Threshing/winnowing/bagging
136.67
T ransport
14.16
Total VC
355.95
Gross
margin (Kshs/acre)
(GO-VC)
484.30
However, due to data limitations as is observed in
the case of pure stands
in Table
4.5a,
analysis was based on sunflower intercropped
The average gross margin for
was
most
with
maize-sunflower
used as the standard for comparing
margins of the other enterprises.
of
the
maize.
intercrop
the average gross
90
Table 4.6: Partial gross output contributions (in Kshs.) and their respective
percentages (*) for each component of the various intercropped
enterprises in Kibwezi division.
•Intercropped
enterprises
MaizeCotton
G0.
(Kshs/acre)
1197.107
1201.242
1291.954
1243.583
1149.018
25*
48*
72*
79*
70*
GOj
(Kshs/acre)
3585.337
1296.035
497.606
326.474
495.550
*G0:
contribution
75*
52*
28*
21*
30*
231.780
459.838
*Gross output
contribution for
maize
Total variable
cost (Kshs/acre)
for the intercropped
enterprises
1393.770
MaizeBean
417.378
MaizeCowpeas
240.764
MaizePigeonpeas
SOURCE: Author’s survey, 1989.
Where: G0# : Gross output contribution for maize component.
GOj : Gross output contribution for the secondary crop (cotton, beans,
cowpeas, pigeonpeas or sunflower), in the r" intercrop.
The
gross
outputs
for
ma iz e
decomposed to show the contributions
.intercrops
of each
were
component
in the mixed enterprises(see Table 4.6 above). The partial
gross outputs and the total variable costs for the various
enterprises are shown in Table 4.6.
It can be observed
that the partial gross output of maize is lowest in the
maize-sunflower enterprise. The partial gross outputs for
maize-pulse enterprises are relatively higher perhaps
because of the complementary
relationship between maize
and pulses brought about by the nitrogen fixing activity
Maize
Sunflower
91
of the pulses. Maize and sunflower are both tall monocots
and
possibly compete for sunlight, water and mineral
salts and this could
result
in depressed
partial
gross
output for maize. However, there is not much observed
numerical difference between the partial gross outputs per
acre of maize in all the various enterprises although the
percentage contribution varies a lot due to variation of
outputs for the respective intercropped
enterprises.
From the last two rows of Table 4.6 it can be deduced that
as the total variable cost for maize intercrop
increases,
the partial gross output per acre of the secondary crop
also increases but with an exception of maize-sunflower
enterprise. This suggests that the variable costs of
producing maize intercrops is directly proportional to the
gross output contribution of the secondary crop given that
the partial gross output per acre of maize
is almost
constant in all enterprises. For example it was found from
the survey data that the greater part of the high costs of
pesticides, labour and transport in maize-cotton intercrop
were incurred in the cotton portion of the enterprise.
However,
the
gross
portion is still
the
output contribution of the cotton
highest and this
gross output and gross margin
intercrop
influences the
for the maize-cotton
making it the highest paying
enterprise.
In maize-sunflower enterprise, farmers incurred high
costs of planting seeds, pesticides, labour and transport
in sunflower portion of the enterprise. This is shown in
92
Table 4.7 where the cost incurred in sunflower portion was
segregated and expressed as a percentage of the cost of
maize-sunflower enterprise for each
ploughing,
planting
input.
The cost of
and weeding for sunflower
portion
was
assumed to be half of the cost of ploughing, planting
and
weeding
for maize-sunflower enterprise respectively
because they were
common
costs.
The other costs were
directly calculated from the data
since
they
accrue
specifically to sunflower portion of maize-sunflower
intercrop.
Table 4.7
Costs incurred in sunflower portion as a
percentage of the cost of maize-sunflower
intercrop for each input.
Inputs
Percentage
1. Ploughing
50%
2. Planting seeds
65%
3. Pesticides
72%
4. Labour
65%
SOURCE: Author’s survey, 1989.
The
percentage
gross output
co n t r i b u t i o n
for
sunflower in maize-sunflower enterprise is only 30%. The
average output of sunflower in maize-sunflower intercrop
was found to be 178.2 kg/acre.
The price offered to
farmers for sunflower produce was Kshs. 2.85 per kilogram.
These results
indicate that the cost of producing
93
sunflower is high while the yield and the prices are very
low. While the low partial gross output of sunflower pulls
down the gross output of maize-sunflower enterprise,
the
high v a r i a b l e costs incurred in s u n f l o w e r portion
increases the variable cost of maize-sunflower enterprise
hence leading to the lowest gross margin
of all
the
enterprises.
Table
4.8
below shows the gross margins
for the
frequent or most common crop enterprises in Kibwezi. Most
of the enterprises were found to be mixtures of two crops
where maize was the primary crop.
examination of the results
A cross-sectional
indicates that the cost
of
ploughing is rather low. This'was because the majority of
the farmers own ox-ploughs and hence few farmers hire for
ploughing services.
Of the 80 farmers
interviewed,
not
even a single one indicated to have applied fertilizer.
Hence the cost of fertilizer utilization is conspicuously
zero in the results.
94
Table 4.8 Gross margin analysis for maize-cotton, maize-beans, maize-cowpeas, maizepigeon pea and maize-sunflower enterprises in Kibwezi
CROP ENTERPRISES
Cost Items
Maize-Cotton Maize-Beans Maize-Cowpeas Maize-Pigeon peas Maze-Sunflower
Yield/ha
Gross OutputfGO]
4782.355
[Kshs/Acre]
2497.264
1789.545
1570.058
1644.568
36.419
26.737
14.848
48.425
31.774
2. Planting Seeds
5.706
71.682
22.742
12.885
31.506
3. Fertilizer
0.000
0.000
0.000
0.000
0.000
4. Pesticides
252.558
6.335
15.979
6.495
14.556
1032.587
312.624
187.194
163.975
66.500
0.000
0.000
0.000
43.782
1393.770
417.378
240.764
231.780
459.838
3388.584
2079.886
1548.781
1338.276
1184.370
Operating Capital (VC)
Kshs/Acre
1. Ploughing
5. Hired Labour
6. Transport
Total VC
Gross
Margin
(GO-VC)
SOURCE : Author’s survey, 1989.
338.22
95
Table 4.9
Statistical values for variables on maize-cotton production in Kibwezi.
Variables
Mean
Minimum
Land[acre]
2.150
1
Gross Output
[Kshs.] per acre
4782.355
Operating Capital
[Kshs.] per acre
Gross Margin
[Kshs.] per acre
Maximum
Standard Deviation
Number of Observation
6
1.21
31
1372.50
12727.50
2270.671
31
1393.771
44.70
4569.50
1136.289
31
3383.584
1309.50
8376.60
2016.533
31
Source: Calculations by the author from the survey data, 1989.
Table 4.9 above shows the gross margin results for
Maize-Cotton enterprise in Kibwezi. Relatively few MaizeCotton growers were captured in the farm survey. This was
because the selected farmers were found to have been
situated
in Ag ro -E co lo gi cal
Zone
UM 3-4 which
is
predominantly a Sunflower-Maize zone where the climate is
not favorable for cotton. However, Maize-Cotton enterprise
was found to be the most profitable of all enterprises,
perhaps because farmers use more chemical inputs than in
all other enterprises and planting
seeds of cotton are
given free of charge.
Table 4.10 shows the Gross Margin Analysis results
for Maize-Beans in Kibwezi. The maximum operating capital
for the enterprise was found to be Kshs. 2086/acre while
the minimum was zero. This means that all the costs of
production for some farmers are implicit,
i.e. they use
their own seeds, family labour, own ex-plough, etc.
96
Table 4,10
Statistical values for variables on maize-beans production in Kibwezi.
Variables
Mean
Minimum
Maximum
Land [acres]
2.645
0.5
7
1.62
55
Gross Output
[Kshs.] per acre
2497.264
670.00
7470.00
1336.714
55
Operating capital
[Kshs.] per acre
417.378
0
2086.00
512.827
55
Gross Hargin
[Kshs] per acre
2079.886
90.00
6370.00
1264.152
55
Standard deviation Number of Observations
Source: Author's survey, 1989.
Table 4.11 below shows the results of Gross Margin
Analysis for Maize-Cowpea enterprise
in Kibwezi . Few
farmers grow cowpeas in UM 3-4 zone because the climate is
not favorable for it.
Table 4,11 Statistical values for variables on maize-cowpeas enterprise in Kibwezi •
Variables
Mean
Minimum
Maximum
Land[acre]
1.863
0.5
6
1.174
33
1789.545
495.00
3600.00
705.344
33
1110.00
314.277
33
3562.50
811.674
77
Gross Output
[Kshs] per acre
Operating capital
[Kshs] per acre
Gross Margin
[Kshs]per acre
240.764
1548.781
0
-390.50
Standard deviation
Number of Observations
Source: Author’s survey, 1989.
Therefore
re la ti ve ly
few
gr owers
of M a i z e - C o w p e a
enterprise were captured in the farm survey.
The minimum
operating capital was found to be zero which means that
97
some of the farmers do not use chemical inputs and hired
labour. They also do not hire ploughing services nor do
they buy planting seeds. They use own planting seeds, own
ox-plough and family labour.
Table 4.12 Statistical values for variables on maize-pigeon pea production in Kibwezi,
Variables
Mean
landfacre]
3.375
0.5
12
Gross Output
[Kshs.] per acre
1570.058
585.00
Operating capital
[Kshs] per acre
231.760
Gross Margin
[Kshs.] per acre
1338.278
Minimum
Maximum
Standard Deviation
Number of Observations
2.598
48
3546.00
668.242
48
0
1259.00
336.572
48
73.00
3546.00
720.995
48
Source : Author's survey, 1989.
Table 4.12 above shows the Gross Margin Analysis
results
for Maize--Pigeonpea
enterpri se
in
Kibwezi.
The
minimum operating capital was found to be zero which
depicts that some of the farmers use own inputs.
Table 4.13
Statistical values for variables on maize--sunflower production in Kibwezi
Variable
Landfacre]
Mean
Minimum
Maximum
Standard Deviation
Number of
Observations
2.161
0.5
6.0
1.108
62
Gross Output
[Kshs.] per acre
1644.568
90.00
3782.00
747.623
62
Operating caoital
[Kshs.Jper acre
459.836
21.0
1603.00
7G£
OC'T
J w w .L J L
62
1185.732
-525.00
3022.00
723.568
62
Gross Kargin
[Kshs] per acre
Source : Author’s survey, 1989.
98
Table 4.13 above shows the results of the Gross
Margin Analysis for Maize-Sunflower enterprise in Kibwezi.
Of all the five maize intercrops examined in Kibwezi,
Maize-Sunflower enterprise is the least profitable.
The results in the summary Table 4.5a were used to
test the hypothesis that the profitability of maizesunflower
intercrop
is
not
different
from
the
profitability of maize-pulse intercrops and maize-cotton
intercrop. Statistical tests were carried out for:
(i)
Maize-Sunflower enterprise and MaizeBeans enterprise;
(ii)
Ma-ize-Sunf lower
Maize-Cowpea
(iii)
enterprise
and
enterprise;
Maize-Sunflower
enterprise and Maize-
Pigeonpea enterprise;
(iv)
Maize-Sunflower
enterprise and
Maize-
Cotton enterprise.
The test statistic used was the t-test as specified
in
Eq.3.8 (Wonnacott and Wonnacott) in Chapter 3; and which
is repeated below:
X1 "
Xi
ss 2 1 + s 2
n1
where
X.|
=
ni
is the mean Gross Margin for maize-sunflower
enterprise.
99
X.j
=
the Mean Gross M a r g i n
alternative enterprise.
for
the
ith
n^ = sample size for Maize-Sunflower enterprise.
n.j =
sample size
enterprise.
for
the
S1 =
standard deviation
enterprise.
S.j =
standard deviation
enterprises.
ot he r
for
for
respective
Maize-Sunflower
other
respective
The true population mean for maize-sunflower enterprise
was taken to be
U1, while
was taken to be the true
population mean for the other respective enterprises.
The null
hypothesis was that the p r o f i t a b i l i t y of
sunflower - maize enterprise
is not different from the
profitability of the other crop enterprises, i.e.
Hq
This
:
was tested
U1
against
that the profitability of
different
=
U1
an
alternative
hypothesis
sunflower - maize enterprise is
from the profitability
of
the
other
crop
enterprise, i.e.
Ha
The critical
:
U,
t
t-values
U,
for the tests were
available in the student’s t table.
not
This was because the
observed degrees of freedom from the survey data lay
within a gap which had no values
in the table.
So,
interpolation of the degrees of freedom was done.
The
null hypothesis could be accepted if
-tc < t < tc
100
where:-
t = was the calculated value
t_ = was the critical value.
If on the other hand t> tc or t
< -tc , the null
hypothesis could be rejected in favour of the alternative
hypothesis.
The t-values for the various
tests were
calculated as follows:(i) For Maize-Sunflower vs Maize-Beans
1184.732 - 2079.886
I723.5682 + 1264.1522
N
62
55
t = -4.623
The critical
calculated
t c value was -1.658.
t-value is less than
the critical
i.e. t < -t„,the mean gross margin
Since the
tc value,
for Maize-Sunflower
enterprise
is significantly less than that of Maize-Beans
enterprise
at 5 percent.
Hence
the null hypothesis was
rejected.
(ii)
For Maize-Sunflower vs Maize-Cowpeas
1184.732-
1548.781
t
723.5682 + 811,6742
33
N
62
t = -2.160
tc= -1.662
The calculated t-value is less than tc , i.e., t <-tc ,
hence the mean gross margin for Maize-Sunflower enterprise
is significantly less than the mean gross margin for
101
Maize-Cowpea enterprise.
Therefore,
the
null
hypothesis
was rejected in favour of the alternative hypothesis.
(iii)
For Maize-Sunflower vs Maize-Pigeon peas
1184.732 - 1338.278
N
1723.5682 + 720.9952
62
48
t = -1.106
tc = -1.659
The calculated t-value for this test was between
and
tc , i.e.,
-tc < t < t-.
Thus,
significant at 5 per cent level.
-t_
it was
not
This meant that there
was no difference between the mean gross margins for
Maize-Sunflower
enterprise.
en te rp ri se
Therefore,
and
the null
Maize-Pigeon
hypothesis
pea
for this
particular case was accepted.
(iv) For Maize-Sunflower vs Maize-Cotton
1184.732 - 3388.584
t
723.56862
+
2016.5332
31
t = -5.898
tc = -1.662
The
calculated t-value for this test was less than
the critical tc
value, i.e. t < -tc .
This meant that the
mean gross margin for Maize-Sunflower enterprise was
statistically less than the mean gross margin for MaizeCotton enterprise.
percent.
The result was
significant
at 5
Therefore the null hypothesis was rejected in
favour of alternative hypothesis.
102
Having performed tests for the difference between the
means of gross margins for maize-sunflower enterprise
against maize-pulse and maize-cotton enterprises stepwise,
the null hypothesis for each test except one (for MaizePigeon pea) was rejected.
However, for the test against
Maize-Pigeon pea, although the t-value showed that MaizeSunflower enterprise is less profitable than Maize-Pigeon
pea enterprise,
the results were not significant at 5%
level. These results were confirmed by the gross margin
analysis of the major crop enterprises where Maize-Cotton
emerged with the highest gross margin per acre followed by
Maize-Beans,
Maize-Cowpea,
Maize-Pigeon
pea
and
Maize-
Sunflower in the descending order.
4.4
Break-even price analysis results and hypothesis
testing
The
break-even
price
for sunflower
calculated on the basis of Direct
enterprise
Costs
of
was
Maize
-
Sunflower enterprise.
These Direct Costs are the costs
which
to
are
applicable
specifically.
Maize-Sunflower
To get the total
production the author
enterprise
cost of sunflower
segregated those costs which accrue
to sunflower production only from the Maize-Sunflower
enterprise.
Such costs included the costs of seeds,
fertilizer,
pe sticides,
threshing/ winnowing/bagging,
birdsc ar in g,
and transport.
harvesting,
These costs
were incurred on sunflower portion only in the Maize-
103
Sunflower enterprise.
The other costs were common to
sunflower and maize as a single enterprise.
These costs
included
ploughing,
the
costs
planting and weeding.
of
land
preparation,
These costs were calculated as the
cost of hired labour and the cost of family labour for
maize-Sunflower enterprise.
Then they were apportioned
into two and it was assumed that the cost
incurred on
sunflower or maize in these activities would be a half of
the cost of Maize-Sunflower enterprise.
Hired
labour used was
costed on the
basis
of
activities undertaken and was included as apportioned cost
of sunflower production.
The quantification for family
labour was similar to that of hired labour, i.e. according
to the number of man-days contributed by family labour in
each activity for Maize-Sunflower enterprise, after which
it was apportioned into two.
It was assumed that family
labour faced similar activities,
planting, weeding, harvesting,
(i.e. land preparation,
etc) as hired
labour.
Family labour was priced as if it were hired labour for
each respective activity.
Then family labour was included
as a fixed cost.
Variable costs of sunflower production
for each
farmer were calculated and summed up together.
Then
interest charge was computed on the basis of 10% of the
variable costs for four months and was included as a fixed
cost.
All the costs were categorized as variable costs or
fixed costs depending on the survey data (See Table 4.14).
104
Table 4.14
Statistical values for variables on B-E-P
analysis for sunflower production in
Kibwezi division, Machakos district
VARIABLES COSTS
FIXED COSTS
Hired
Seed cost ploughing Hired labour
services
(kg/acre) (Xshs/acre) (Kshs/acre) (Xshs/acre) (Kshs/acre) (Kshs/acre) (Kshs/acre)
Gross output Fertilizer Pesticides Transport
Variables
Mean
178.185
0
10.429
Minimum
70
0
12.4
Maximum
560
0
245
0
62
Standard
Deviation
108.257
Number of
Observations
62
Source : Author’s survey, 1989.
B-E-P - Break-even price
Own
Ploughing
Family labour Interest
services
(Kshs/acre) (Kshs/acre
(Kshs/acre) B-E-
43.782
21.689
17.258
221.417
125.121
439.731
10.489
5.872
5
11
150
0
0
8.25
0.83
1.69
280
33
215
730.75
25.92
13.62
41.28
47.752
3.363
53.490
187.640
67.527
276.768
62
62
62
62
62
62
62
210
1458
6.985
2.618
105
The Total Cost for each farmer was obtained from the
sum of Variable Cost and Fixed Cost.
Equation 3.11 was
not used to calculate the breakeven price as was suggested
in chapter 3.
It
was suspected that dividing
TC/acre
with Y/acre which were both estimates computed from survey
data
would depress the average breakeven price (P).
was because both TC/acre and Y/acre have
t h e r e f o r e wh en the division
breakeven
errors and
is done the
price would have more
resulting
pronounced
Therefore, the break-even price for each
computed individually for all the farmers.
This
errors.
farmer was
Then the mean
break-even price for the sample was calculated as follows:
TC
[Eq. 4.5]
j
Pi
Yi
n = 62
TC j
[Eq. 4.6]
i = 1
where:
±.l.
P j = is the Break-even price for iLr farmer
^L
TC
j
= Total Cost per acre for itn farmer
Y.j = Output/acre of sunflower produce for ijth
farmer.
n
= sample size of sunflower producers
p = Mean Break-even price for the sample
size.
The mean break-even price (P) for the sample size was
found to be Kshs.5.872 per kg of sunflower produce.
The
results of this analysis were used to test the hypothesis
106
that the OCD Ltd
buys sunflower produce at
a price which
is not different from the break-even price, i.e.
Ho
: Po = P
This hypothesis was tested against the alternative
hypothesis
that the
OCD
Ltd
buys
sunflower produce at
a price which is different from the break-even price, i.e:
: p0 1 P
ha
where PQ was the price of sunflower offered by OCD Ltd to
the farmers. By the time the study was proposed, PQ was
equal to Kshs. 2.85 per kg. When the Survey was carried
out PQ was equal to Kshs. 3.00 per kg. So the breakeven
price was tested against these two prices.
statistic used was as specified
The test
in Chapter 3, Equation
3.11, which is repeated below:
t
=
PQ - P
cr_
p
vl n-1
The t-value for the test was calculated as follows:
(i) Testing the breakeven price vs Kshs. 2.85.
t
2.850 - 5.872
2.618
=
\l 62-1
t = - 9.015
(ii) Testing the breakeven price vs Kshs. 3.00
t
=
3.00
2.618
\l
t = -8.568
6 2 -7 "
- 5.872
107
The null hypothesis (H_)
could be accepted if -t_
< t < t_
U
w
C
where tc is the critical t-value and t is the calculated
value.
However, HQ could be rejected if t < -tc or t>tc .
The critical t -value at 5 per cent level was -1.671.
calculated
t-values
The
are less than the critical -t_,
i.e.
v
t<-tc .
The results reveal that
the
prices offered by OCD
Ltd to the farmers in 1988 and 1989 consecutively
significantly
Therefore the
less than
the computed
price which is
even
was
accepted
price.
hypothesis that OCD Ltd buys sunflower
produce at a
price
break-even
are
not
different
from
the
rejected. So hypothesis
break­
was
as true.
It was found from the survey data that there is very
low chemical
input
use
in Kibwezi.
97.5% of the
interviewed farmers do not apply fertilizer,
80% do not
apply pesticides and 86.3% have never had any agricultural
extension services.
108
CHAPTER 5
SUMMARY. CONCLUSIONS AND RECOMMENDATIONS
5.1
Summary
The main objective of this study was to look into
sunflower cultivation and investigate why its production
has had erratic and general decline in Machakos District.
The
investigation
production
was done
through
the
analysis
of
relationships among sunflower and food crops
and cotton, and the relative profitability of these crops.
Certain
results
and conclusions
were
reached.
The
specific objectives were:(1)
to
(i)
(ii)
determine the production relationships between:
sunflower and maize (the major food crop)
sunflower and cotton and
also
how
relative
prices of sunflower and maize affect sunflower
production;
(2)
to determine relative profitability of sunflower visa-vis major food crops and cotton;
(3)
to estimate the break-even
price
of
sunflower per
kilogramme for an average small scale farmer;
(4)
to interpret and explain the observed relationships
and then draw policy implication from the results.
The hypotheses tested were:
(1)
there is no relationship between the area under food
crops particularly maize and that under sunflower.
109
(2)
there is no relationship between the area under
sunflower and that under cotton.
(3)
the profitability of sunflower production is not
different from the profitability of food crops and
cotton production in Kibwezi Division.
(4)
OCD Ltd offers a price of the sunflower produce that
is not different from the breakeven price.
Both primary and secondary data were used in the
study.
The primary data were obtained from a survey of 80
randomly selected farmers.
Secondary data were Obtained
from Ministry of Agriculture, Machakos District, Ministry
of Agriculture Library at Kilimo
Survey and Statistical
House, Nairobi, Economic
Abstracts and OCD offices
in
Nakuru and Machakos and NCPB in Nairobi.
Two
estimated
re g r e s s i o n
m o de ls
through
special
sciences (SPSS).
the
were f o r m u l a t e d
programme
for
and
social
The first regression model examined the
production relationship between sunflower production and
maize production.
The second
regression model
examined
the production relationship between sunflower and cotton
production
[the other
major
producing zones in Machakos].
cash
crop
in
sunflower
A correlation matrix was
tabulated to show how sunflower is correlated with other
crop enterprises.
110
Gross Margin Analysis results were used to measure
relative profitability of maize-sunflower enterprise visa-vis alternative major farm crop enterprises, i.e. maizepulse enterprises and maize-cotton enterprise. The Break­
even Price Analysis was used to compute the price which
would enable the farmers recoup the cost of sunflower
production.
The results of the regression analyses revealed that
maize production has a p o s i t i v e
sunflower production.
relati on sh ip with
The results of the t-test showed
that the coefficients of the area of
land under maize
production in
the regression equation was statistically
insignificant
at
production has
5%
level.
However,
sunflower
insignificant negative relationship with
cotton production, which is the other major cash crop
in
the sunflower producing zones in Machakos, at 5% level.
The correlation analysis showed that sunflower has no
correlation with the other crop enterprises.
The Gross Margin
Analysis
revealed
that
Maize-
Sunflower enterprise was the least profitable of the five
major crop enterprises in Kibwezi, Machakos District.
test was statistically significant at 5 per cent
and therefore the null
The
level
hypothesis that the profitability
of maize-sunflower enterprise is not different from the
profitability of other crop enterprises in the
area was
rejected. However, in the stepwise testing, the mean gross
111
margin for Maize-Sunflower vis-a-vis Maize-Pigeon pea
enterprise was not statistically significant at 5% level,
and hence there was no difference for this case.
The break-even price analysis
revealed
that the
price/kg of sunflower produce offered to farmers by OCD
Ltd
was significantly less than the
computed from the survey
that
OCD
Ltd
buys
data.
sunflower
break-even price
Therefore the hypothesis
produce
at a price that
is different from the break-even price was
5.2
Conclusion
From
the
results
of
conclusions can be made.
maize
pr od uc ti on
has
out
from
the
bulk of the sunflower
the
study,
a
number
of
The first conclusion is that
no nega ti ve effect
production of sunflower
found
accepted.
upon
in Machakos District.
OCD
the
It was
office in Machakos that the
in the district
short rains of October-December.
is grown
in the
The survey data of the
selected farmers showed that some of the returns from
sunflower are used to meet the cost of hired labour in the
farms or school fee payment or family amenities after
selling the produce in February-March. These
results
suggest that s u n f l o w e r pr od uc ti on c o m p l e m e n t s the
production of food crops and also that the returns from
the previous year’s produce is utilized by the farmers the
following year. Another point is that sunflower is often
112
intercropped with maize.
Therefore increased production
of sunflower leads to more maize being produced since more
Maize-Sunflower intercropping will be done.
Sunflower and
maize do not compete for most of the resources since
resources are often allocated to sunflower and maize as
one enterprise.
These results are important in
help
to
displaces
nullify
the
the sense that they
the fear that growing of sunflower
growing
of
food
crops.
Therefore, the
apriori assumption that sunflower production has had
erratic general decline because it competes with food crop
production
was disapproved.
Therefore,
there should
be
other reasons why sunflower production has been declining
as
discussed at the end of this
sub-section.
The
second conclusion is that cotton production has a negative
effect upon the production of sunflower.
Cotton is the
other major cash crop in sunflower producing areas in
Machakos. The survey data of the sampled farmers showed
that planting seeds for cotton are given to farmers free
of charge while pesticides are given to farmers on credit.
It was also found out that cotton payments are delayed by
4-6 months. However, according to the Cotton Lint and Seed
Marketing Board office in Machakos,
the price of cotton
went up by 20% (Grade 1) in 1987. Also it was hinted that
negotiations are under way
to
reduce
the
period
of
payments of cotton from 4-6 months to one month. In the
113
sunflower industry, farmers used to be given planting
seeds on credit up to 1987. According to the OCD office
and Machakos Co-operative Society Branch,
Kambu,
the
planting seeds were then being sold to the farmers on cash
payment at Kshs. 33 per 2 kg-bag. In 1989, the price of
the planting seeds was raised to Kshs. 36 per 2 kg-bag. As
a result of the scrapping off of the seed credit in 1989,
only 190 kg of planting seeds was sold to farmers in
October-December short rains [OCD office,
Machakos].
These results may suggest that there is improvement in the
m a r k e t i n g a r r a n g e m e n t for cotton.
Thus,
there are
incentives for farmers to produce cotton while in the
sunflower industry incentives are being scrapped off.
incentives
in the Cotton Industry potentially
The
create
a
favourable economic environment for farmers to shift from
sunflower production to cotton production.
However, the results of this study reveal that cotton
production has not had significant effect upon the decline
of sunflower production.
Another
co nc lu si on
was that
Maize-Sunflower
enterprise was significantly less profitable than the
other crop enterprises examined.
Also the price offered
to the farmers was found to be significantly less
the computed Break-even price.
than
These two conclusions
reveal that the state of farm incentives in sunflower
industry in Kibwezd, Machakos District is too weak to
I
114
encourage its production.
Farmers do not break-even
the sunflower production.
Price
incentive
in
is important
because farmers’ decision to produce Maize-Sunflower
enterprise is based on how they view the profitabilities
of alternative crop enterprises.
production has had problems,
The reason why sunflower
appears to be that Maize-
Sunflower enterprise is the least profitable.
Therefore,
when farmers are making decisions on what to produce,
there are some other priority crop
enterprises which are
more profitable, hence leaving sunflower-maize enterprise
as the last choice.
Since the farmers cannot recoup the cost of sunflower
production because they do not break-even,
enthusiastic to grow it.
Hence a decline in production.
It was also ob served
that
utilization of chemical inputs.
farms
there
is very
low
97.5% of the interviewed
do not apply fertilizer,
pesticides and
they are not
80% do not apply
86.3% have never had any agricultural
extension services.
In 1987, 86.3% of the farmers were
given sunflower planting seeds on credit but in
1989, the credit was scrapped off.
So the
1988 and
farmers had to
buy planting seeds directly from OCD. It would thus appear
that
all
these
factors
have
led
to
the
decline
sunflower production and also to the observed low
of output/acre.
in
levels
115
5.3
Recommendations
On the basis of the results of this
study,
the
following recommendations are made:
(i)
To
promote sunflower production the Ministry of
Agriculture should formulate
pricing policies
that take into account the farmer’s costs and
returns in the growing of sunflower.
pricing policy should incorporate
all
The
the by­
products of sunflower seed instead of one by­
product only. The sunflower cake, oil and other
products of sunflower crop should be considered
while calculating the price for sunflower crop.
The price should be reviewed frequently and
adjusted to be in line with
the
break-even
price.
(ii)
Alternatively,
since Maize-Sunflower enterprise
has the lowest per acre Gross Margin of the crop
enterprises considered, a price review programme
for sunflower would be extremely
e x t e n d i n g the area under the
useful
crop.
in
This
suggestion is very important in the light of the
significant low price which prevails
sunflower industry.
in the
116
(iii)
The Ag ri cu lt ur al
E x te ns io n
Division of the Ministry
strengthen
and Services
of Agriculture
should
the liaison between Research and
Farmers and try to reach as many farmers as
possible. From the survey data, only 13.7
per
cent of the farmers in the sample had received
agricultural
extension services
Government and OCD extension
directed to attend to oil
crops.
in
1988.
The
agents should be
crops like all other
Farmers should be given
up-to-date
information concerning sunflower production as
well as food crops.
Emphasis should be laid on
increased input use for increased yields and
profits.
The
farmers should be taught about
inputs use,
and these
shou ld
be
readily
available.
(iv)
The OCD Ltd should consider reinstating seed
credit and also providing other input
for sunflower production as they
If not so, there is a
the farmers might
where they are
For example
planting
the
credits
used to do.
possibility that most of
shift to
cotton production
getting seeds free of charge.
in 1988, only 190 kg of sunflower
seeds were bought by the farmers in
whole of Machakos District after the seed
credit had
been scrapped off.
Scrapping off of
the seed credit appears to have
disincentive
to farmers.
been a great
117
(v)
Most of the farmers in Machakos are small scale
producers who practise mainly intercropping to
maximize profits
reduce risks
per given acre of
land and
in their farm ventures.
Research
efforts therefore should go towards developing
varieties of
sunflower that are high yielding
and are f a v o u r e d
by
intercropping.
Such
varieties should be early maturing.
Now
that sunflower production was found
complementing
to
be
food crop production, the new direction of
research should be to find out the optimal farm plan for
su nf lo we r producers
in both pure stands and mi xe d
sunflower
The
enterprises.
gross
margins
for
sunflower
enterprise and sunflower-maize enterprise were found to be
relatively low. Therefore, the analytical work to be done
in future should be directed towards maximizing the gross
margins for the crop enterprises,
so that the
level
at
which sunflower enterprises should enter into the basis of
the cropping system can be established.
118
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-1 2 4 -
APPENDIX
A QUESTIONNAIRE FOR FARMERS.
-p
-12 5
C O N F I D E N T I A L
UNIVE R S I T Y OF NAIROBI
DEPARTMENT OF AGRICULTURAL ECONOMICS
QUESTIONNAIRE FOR FARMERS
INTERVIEWER*S NAME
.............
RESPONDENT'S N A M E ( F A R M E R ) ......
AGE
% NAME OF THE FARM
A
......
......
SUB-LOCATION
......
VILLAGE
......
NEAREST PRIMARY SCHOOL
......
DATE OF INTERVIEW
......
INFORMATION ABOUT LAND RESOURCE
Land use
Acres
Type of ownership
01
Size of your farm
07 = Owned
02
Size of other farms
elsewhere
08 = Rented
03
Total area of land
09 = 0 ; her
Acres
10 = Total
Indica
O 1*
Land for grazing of
all farmers
11) Status of land
(Read Out)
05
Total area 0 f land
cultivated 0 f a n
farm (s)
01 = U n a djudicated
02 = Surveyed
• • • • • •
Area of arable land
not used at all
03 = Communal land
•
06
1
0*4 = Title deed
• • • • •
-1 2 6 -
B
INFORMATION ABOUT LABOUR
01
Number of people living in your h o m e ...............
02
Number of your people available for farm w o r k .....
Number of hired labourers and their wage rates/day
Permanent
Casual
03
Number
.................
0*t
Wage rate/day
.......................................
Husband
Wife(s)
..................
Children
Relatives
Hired
Labou
Permanent Casua
Number
Number of hrs
worked/day
OS
Average number of hours worked/day by family members and
hired labour.
Labour nrofile in a year - Average number of labourers per
month.
1
2
3
Jan. Feb. |Mar.
A
S
7
May. Jun. Jul.
k
Apr.
8
Aug.
9
Sept.
10
11
12
Oct.
Nov.
Dec.
*
Family
Permanent
Casual
06
Do you experience labour shortage in any period(s)
Within the year?
01
07
=
If yes
Yes,
02
=
No
in w h i c h m o n t h s
............................
(Jar..... 1,
Feb. = 2
Dec.~
l;
-1 2 7 -
08
In which farm operations and which enterprise do you
experience labour shortage - (Rank the operations as
follows
planting = 0 1 , weeding = 0 2 , haversing = 03 etc
and the corresponding enterprises.
Operation
Crop enterprises
09
Which enterprise has the highest labour d e m a n d ? .................
10
How do you ease the labour shortage - by employing casual
labourers = 1 , leaving the problem unsolved = 0 2 ,
Mwethya = 03, any other specify = 01* ...........................
11
.12
Do you employ children as labourers?
01 = Yes,
02 = N o ......
If yes, what is the rate of payment/child/day? K s h .............
Do you pay your labourers in kind 01 = Yes, 02 = N o ............
j
13
If yes,
in what form and how do you value it per day/labourer?
v
Form .
Value/day/labpurer
Adults
C
Children
INPUT USE AND SUPPORTING I N S T I T U T I O N S .
In this section of the questionnaire,
use the following
initials for the following variables:
- S = Sunflower,
M = Maize,
C = Cotton,
H = Hired labour,
E = Eeans,
F = 'Family labour.
P = Cow-peas
-1 2 8 -
E N T E R P R I S E
INPUT
S
Did you apply fertilizer to any of
the following enterprises? 01 = Yes
02 = No
Amounts
bags
kg
Did you apply pesticides to any of
the following crops? 03 = Yes,
01* = No
Amount in litres
Where did you get the planting seeds
for the crops from? 05 = own seed,
06 = OCD,
07 = Co-operative,
08 = Market,
09 = F r i e n d s / R e l at i v e s , 10 = other
_________________________________________________
CREDIT
Did you take credit for any of the
following enterprise in 1 9 8 8
11 = Yes,
12 = No
Form of credit?
12 = Funds,
l1* = Planting seeds, 15 = Fertilizer,
16 = Other specify
What was the value of the credit?
Kshs .
Where did you get the credit from?
17 = Co-operative society,
18 = OCD
19 = Friends/Relatives,
20 = Cotton Lint Marketing Board,
21 = Financial Institutions,
22 = Other specify
M
C
B
P
-1 2 9 -
8
Is fertilizer
2k
9
available anytime you need .it 23 = Yes,
= N o .....................
If you borrowed credit,
for what purpose? 25 = Hiring labour,
26 = Buying Planting seeds,
27 = Family use,
29 = Any other
s p e c i f y ...............................................................
/ 10
11
What were the terms of r e p a y m e n t ? ................................
Are you satisfied with the present arrangement of the *
credit,
D
30 = Yes,
31 = N o .......................................
MARKETING OF COTTON AND SUNFLOWER
S
QUESTIONS
Where do you sell your sunflower/cotton
produce?
01 = OCD,
02 = Cotton- Lint
Marketing Board, 03 = F r i e n d s / R e l a t i v e s ,
0*+ = Co-op.
society, Other - specify
What is the form of payment?
05 = cheque,
06 = cash,
07 =other
specify
Is there any delay in payments?
08 = Yes,
If yes,
after how long are you paid
10 = Years,
13
09 = No.
11 = Months,
12 = Weeks,
= days
Indicate the number of years
months or weeks or days.
or
C
-1 3 0 -
EXTENSION SERVICES
.QUESTION
■INDICATE
Are you ever given any Agricultural Advisory
Services?
01 = Yes,
02 = No.
If yes from who? 03 =0CD,
Agriculture
0^ = Ministry of
05 = other specify
How frequent are the visit? 06 = Twice a
week,
year,
07 = once per month,
08 = once per
09 = other specify
How do you rate the advice given?
09 = not useful,
understand,
10 = d i f ficult to
11 = useful,
12 = very useful
OTHER RELATED ISSUES
INDICATE
QUESTIONS
How do you use the income you get from
sunflower?
01 = For food crop production,
02 = Cotton Production, 03 = Family use
05 = Food Purchase
^ - rees
How do you use the income you get from
cotton? 05 = Food Production,
06 = Family use, 07 = Fees.
09 = Food Purchase08 = Other specify
In production of sunflower,
do you
substitute any food crops?
09 = Yes,
10 = No.
If yes, which one? 1 1
= Eeans,
-IJ-. =. Piffeon, Dea 5 ,___12 = Mai
zr
12
=Cowpeas
QUESTIONS
5
INDICATE
Why do you substitute the food crop in
Question 4?
15 = it is less yielding
than' sunflower,
intensive,
16 = is more labour
17 = is not well favoured by
the climate here,
6
Do you plant Katumani Maize?
19 = Yes,
7
18 = Other specify
20 = No.
If no, why don't you plant it?
21 = Less yielding, 22 = seed not
available,
8
Then what type of Maize do you plant?
2h
9
23 = Other specify
=
Local,
25 = Other specify
Why do you plant that maize?
26 = High yielding,
available,
G
27 = Planting seed
28 = Other specify
PROBLEMS ENCOUNTERED BY THE FARMER IN PRODUCTION
List three of the critical problems you encounter in the
production of the following crops:CASH CROPS
Sunflower
FOOD CROPS
Maize
(1)
......................................................................
(1)
..................................................
(2)
......................................................................
(2)
..................................................
(3)
.................................
(3)
........................
Cotton
Beans
( 1 ) ....................................................................................
(1)
........................................................
(2)
(2)
..................................................
(3)
........................
.................................................. ...................
( 3 ) ............. ....................
Cow-peas
INFORMATION ABOUT VARIABLE COSTS OF CROP E N T E R P R I S E S .
ENTERPRISE
ACTIVITIES
COSTINGS
M+
S+
1
C+
B+
P+
■1
Acreage (Area planted)
Acres , ha
Ploughing
Oxen:
Number
Hours worked
Total cost
(K s h s )
Cost/acre
Tractor:
Number
Hours Worked
Total cost
(Kshs )
cost/acre
Furrowing
Oxen:
Number
Hours worked
Total cost
(K s h s )
■
Cost/acre
Planting seeds
Seed rate (acres/kg)
cos t/kg
Total amount of seed
used
(kg)
Total cost (kshs )
Cost/acre
■
-1 3 3 -
ENTERPRISE
S+
Planting
Number of labourers: F;
H*
Days worked
Payment rate/labourers
/day
F
H
Total cost (kshs)
cost/acre
Fertilizer Type
Number of bags used
cost / bag
Total cost
Cost/acre
Manure:
Tonnes used
(Kg) used
cos t/tonne
Total cost
(Ksh$
cOst/acre
Fertilizer- manure
application
Number of labourers:
F:
H:
Days workeu:
F:
H:
Payment rate/
Labourers /day
IT•
K:
Total cost
cost/acre
M+
C+
COSTINGS
B+
P+
-1 3 4 -
ENTERPRISE
K+
COSTING
C+
B+
Weeding
Number of labourers F:
H
Days worked
F
H
Payment rate/labourers/
day
F:
H:
Total cost
Cost/acre
Bird scaring (where
applicable e.g sunflower)
Number of labourers F:
H:
Days worked
F:
H:
Payment rate/labourers/
day
F:
H:
Total Cost
Cos t/acre
10
P e s t i c i d e s : Type
Application
rate
Number of
litres used
cost/litre
Total cost
Cost/acre
11
Harvesting: Number of
Laboureres F :
E:
Days worked
F:
.
H:
Payment rate/labourers/
day
F:
&NlVERSi TY OP N A!RQ6t
'i') \ it i
P+
I
-135
ENTERPRISE COSTING
S+
M+
•C +
B+
P+
Total cost
cost/acre
Transport cost (where
applicable e.g for the
sunflower produce cost/
Acre
Total,
cost/acre (All
activities)
Interest on capital where
applicable e.g sunflower
enterprise or intercrop
(10$ of total variable
cost,
for J+ months)
1
Total cost/acre for
sunflower or sunflower
intercrop
I
1
INFORMATION ABOUT ENTERPRISE GROSS OUTPUTS
ENTERPRISES
ITEM
S+
Total output in 90 Kg
bag for M, B, and P
But ^Okg bag for s and
185 kg per bag for C
Total output in kg
Output/acre
(Kg/acres)
Price/kg
Gross output/acre
M+
C+
B+
P+