DETERMINANTS OF COTTON PRODUCTION AMONG SMALLHOLDER
FARMERS IN KENYA: THE CASE OF MAKUENI COUNTY
KIMANI, MARTIN GITHUKU
REG.No.C154/CTYIPT/2694112011
A RESEARCH PROJECTSUBMITTED IN PARTIAL FULFILMENT FOR
THE DEGREE OF EXECUTIVE MASTER OF PUBLIC POLICY AND
ADMINISTRA TION IN THE SCHOOL OF HUMANITIES AND SOCIAL
SCIEN CES KENYATTA UNIVERSITY
NOVEMBER, 2014
KENYATIA UNIVERSITY LI RARl
DECLARATION
This project is my original work and has not been presented for a degree in any other
University.
RSl.eggniasttruarteiO..n..N.. ~.m.b.e::./.C ..T..Y../.P.T.. /.2..694112011 Date ,.~.I)}.I!If .
Name: Martin Githuku Kimani
Supervisors
We confirm that this project has been submitted by the candidate for review with our
approval as the university supervisors
Signature ~ .
Dr. Patrick Mbataru
Department of Agribusiness Management and Trade
Date ..~.':"i 1.~~.1.t.1 .
Kenyatta University
Signature .
Mr. Weldon Ngeno
Department of Public Policy and Administration
Date ~~.l.~~.I..??~r. .
Kenyatta University
Signature .
Dr. Felix Kiruthu
Chairman Department of Public Policy and Administration,
Date .
Kenyatta University
DEDICATION
This project is dedicated to my wife Emmah Wanjiru, my children Elizabeth Mumbi
and Ryan Kimani, and my brothers and Sisters. It is also dedicated to my beloved late
parents.
iii
ACKNOWLEDGEMENTS
I wish to acknowledge the support of those individuals who made it possible for a
successful completion of this work. First I give regards to the almighty God for
without you nothing would have been possible. Thanks to Dr. Patrick Mbataru, Dr
Felix Kiruthu and Mr. Weldon Ngeno for continuous guidance throughout my project.
My heartfelt appreciation goes to my family for moral and financial support,
encouragement and goodwill, which saw the successful completion of this work. I
appreciate the supportive encouragement accorded to me by my colleagues Morris
Akiri, Dr Charles Agwanda, Professor Richard Musebe and Dr Daniel Karanja as I
went through my studies.
iv
ABSTRACT
This study was carried to determine cotton production among smallholder farmers in
Makueni County. The study was based on the following objectives to: Identify the
challenges experienced by smallholder cotton farmers in Makueni County; indicate
the responses by the Government towards cotton production in Kenya and evaluate
the role of policy and institutional incentives in the production of cotton. This study
was conducted in the low rainfall cotton growing areas of Makueni, county in Kenya
because it is among the major areas of cotton production in Kenya where the cotton
yields both at county and at farm levels are well below potential. This study employed
a descriptive survey design. The target population of the study was smallholder « 1
ha) farmers who produce cotton under rain fed conditions and Extension Officers,
CODA officers and KARl Officers with a sample frame of 3455 small scale farmers.
Purposive sampling of target population in the cotton growing areas of Makueni
County was sampled. In addition, a multistage random sampling was used to select
sample of 103 cotton growers (respondents) and 12 various agricultural and cotton
growing officials. Structured and semi-structured questionnaires and interview
schedules were used to collect data from selected individual framers and interview
guide. Pilot study was done on randomly cotton growing farmers within the county.
This was mainly to verify whether the items generated by the researcher displays
stimulus homogeneity hence valid and reliable. The instrument was evaluated for
content validity that is the extent to which the questionnaire content, which includes
vocabulary, sentence structure and the questions, are suitable for the intended
respondents. The studies assessed reliability by split half method. In conclusion,
because Kenya's cotton sector is generally characterized by a large number of
smallholder farmers with a low average yield and poor quality cotton fiber outputs
making competition to be an issue particularly for small holder farmers not only in
Makueni County but the entire country. The study mainly recommends that
competitiveness of the cotton sub sector in Kenya should be addressed; at the moment
the cost of production is high due to costs of electricity and use of outdated
technology - these issues must be addressed in order to improve efficiency of
production, lower costs and improve competitiveness. The state of infrastructure must
also be addressed, as these are some of the concerns raised by the stakeholders to help
small scale farmers. The study suggests that further studies on effects of cotton
production among farmers should be carried in other parts of the country for
comparison before generalization.
v
AAACP
ACP
AGOA
ASAL
CABI
CFC
CODA
EPZA
EU
FFS
GOK
ICAC
IeM
KARl
KIPPRA
ABBREVIATIONS AND ACRONYMS
All ACP Agricultural Commodities Programme
Africa Caribbean and Pacific
African Growth and Opportunity Act
Arid and Semi-Arid Lands
Centre for Agricultural Bioscience International
Common Fund for Commodities
Cotton development Authority
Export Processing Zone Authority
European Union
Farmer Field School
Government of Kenya
International Cotton Advisory Committee
Integrated Crop Management
Kenya Agricultural Research Institute
Kenya Institute for Public Policy Research and Analysis
vi
LIST OF TABLES
Table 1: Cotton Production in bales from 2005 to 2010 11
Table 2: Distribution of the respondents 20
Table 3: Composition of the focus group discussion participants 20
Table 4: Multi-stage Sampling of Makueni County 21
Table 5: Cotton production trend in Makueni County (2005-2010) 29
Table 6: Crop enterprises among the sampled respondents 30
Table 7: Sources of Finance 34
Table 8: Factors affecting Cost of Production 35
Table 9: Cost of Cotton Production 36
vii
LIST OF FIGURES
Figure 1: Cost distribution for various activities in the production of one hectare of
cotton (Source: Gitonga et al., 2011) 12
Figure 2: Conceptual framework 16
Figure 3: Gender Analysis 26
Figure 4: Highest level of Education 27
viii
TABLE OF CONTENTS
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENTS iv
ABSTRACT v
ABBREVIATIONS AND ACRONYMS vi
LIST OF TABLES vii
LIST OF FIGURES viii
TABLE OF CONTENTS ix
CHAPTER ONE: INTRODUCTION 1
1.l Background to the Study : 1
1.2 Statement of the Problem .4
1.3 Research Questions 5
1.4 Objectives of the Study 6
1.5 Research Premise 6
1.6 Significance of the Study 6
1.7 Scope of the Study 7
1.8 Limitations of the study 7
CHAPTER TWO: LITERATURE REVIEW AND THEORETICAL
FRAMEWORK 8
2.1 Introduction 8
2.2 Empirical Review 8
ix
2.2.1 Cotton Context 8
2.2.2 Overview of Cotton production 8
2.3 Challenges on Cotton Production 9
2.3.1 Unavailability of Quality Seeds 9
2.3.2 Cotton Prices 9
2.3.3 High cost of inputs 12
2.3.4 Cotton Marketing 12
2.4 Government Response towards Cotton Production in Kenya 13
2.5 Role of Policy and Institutional Incentives on Cotton Production 14
2.5 Conceptual Framework 16
2.6 Theoretical framework 16
CHAPTER THREE: RESEARCH METHODOLOGY 19
3.0 Introduction 19
3.1 Research Design 19
3.2 Location of the study 19
3.3 Target population 19
3.5 Sample and Sampling Techniques 20
3.5.1 Sampling Technique 20
3.5 Research Instruments 21
3.6 Pilot study 22
3.6.1 Validity 22
x
3.6.2 Reliability 22
3.7 Data Collection Techniques 23
3.8 Data analysis 23
3.9 Ethical Consideration 24
CHAPTER FOUR: DATA ANALYSIS AND DISCUSSION 25
4.0 Introduction 25
4.1 Socio-economic Characteristics 25
4.1.1 Gender 26
4.1.2 Ages of the Respondents 26
4.1.3 Level of Education 27
4.1.4 Occupation 28
4.1.4 Type of Housing Structure 28
4.1.5 Size of Land Owned 28
4.1.6 Size of the Family 28
4.2 Effect of competition on cotton production 29
4.3 Marketing and Cotton Production 32
4.4 Relationship between Cost of Production and cotton productivity 33
4.4.1 Cultivation Cost 36
4.4.2 Seed/Sowing Cost 37
4.5 Effect of capacity of farmers on cotton production 37
xi
CHAPTER FIVE: SUMMARY OF FINDINGS, CONCLUSION AND
RECOMMENDATIONS 39
5.0 Introduction 39
5.1 Summary of Findings 39
5.2.1 Effect of competition on cotton production 39
5.2.2 Marketing and Cotton Production .40
5.2.3 Relationship between Cost of Production and cotton productivity .40
5.2.4 Effect of capacity of farmers on cotton production .41
5.3 Conclusion 41
5.4 Recommendations 43
5.4 Suggestions for Further Research .44
REFEREN CES 46
APPENDICES 52
Appendix I: Questionnaire 52
Section A: Socio-economic Characteristics 52
Section B: Challenges facing Cotton production 53
Appendix II: Interview Guide 56
xii
CHAPTER ONE: INTRODUCTION
1.1 Background to the Study
Cotton is an agro-industrial crop produced in both developing and developed
countries as it accounts for more than half of all fiber used in clothing and household
furnishings (Goreux, 2003). Cotton for long has significant place in the economic and
political history of the world. For example, it played immense role since the industrial
revolution of the 1ih century. Currently, it is an important cash crop to a number of
developing countries at farm and national level (Baffes, 2004).
The cotton industry and cotton related services play the foremost role in economy of
many countries worldwide. In Pakistan for example, cotton industry contributes 63.9
percent of the total exports earnings (Government of Pakistan, 2007). It provides raw
material to local/domestic cotton industry comprising of 503 textile mills, 1263
ginning factories, 8.1 million spindles and 2622 oil expelling units. It also yields 3.5
to 3.6 million ton of cotton seeds which contributes over 64 percent of the total
domestic edible oil production (Pakistan Cotton Ginners Association, Textile Vision
2005). On global basis, Pakistan is the fourth largest cotton producing country of the
world, after China, India and USA. Pakistan's share of total world cotton production
in 2004-05 stood at 9.47 percent (Cotton Statistical Bulletin 2006). It is the third
largest yam producer with 9 percent, second largest yam exporter with 26 percent,
third largest cloth producer with 7 percent and is also the third largest cloth exporter
with 14 percent of the world cotton production (International Cotton Advisory
Committee, 2005).
In Africa, Asia and Latin America, cotton is contributing a lot towards overcoming
food insecurity. In Africa, thirty-five of the fifty-three countries produce cotton.
Twenty-two of these countries are known for exporting it (Westlake, 2005). Ethiopia
is one of the African countries that produce and export cotton. It has an estimated area
of 2,575,810 hectares that is suitable for the cultivation of cotton (Demelash, 2006).
In Ethiopia, spinning and weaving to make cloths from cotton is perhaps as old as the
history of the country. Though written records are scarce, it is widely believed that
1
Ethiopians wore clothes woven from cotton fibers centuries ago. Still about 85% of
the total population living in rural areas of the country, satisfies a significant part of
its textile needs from the traditional non-industrial sector. Clothes that are woven
from cotton are popular also in urban areas of the country (Mulat et aI., 2004).
In Africa, cotton is typically produced by smallholders. More than two million rural
households rely on cotton production to earn their living (Baffes, 2007). In some
regions, it is the only cash crop and as such it represents the most important economic
activity. Cotton sector's share in the total merchandise exports of West and Central
Africa (WCA) ranges from 25 to 45 percent, while its share in GDP ranges from 3 to
6 percent (World Bank, 2004). Although Africa's overall share in world agricultural
trade declined from 1980 to 2005, the share of cotton trade more than doubled over
the same period (Tschirley, Poulton, and Labaste, 2009). This is due to a three-fold
increase in cotton production from 1960 to 2001. These facts explain why the
productivity and profitability of cotton production and processing are one of the key
determinants of growth and of poverty across much of the continent.
Different studies have been conducted to ascertain the factors which are responsible
for enhancing the production and ultimately benefiting the farmers. Carlos and
Octario (2008) observed that high cost of inputs, scarcity of financial resources, lack
of access to the markets and untrained farmers are responsible for the low yield per
hectare and ultimately reduction in the benefits to the farmers. Anwar, (2008)
calculated that the use of inputs has a direct bearing on the production and profit of
the farmers. He found that cultivation cost, sowing cost, seed, fertilizer, pesticide,
irrigation and labour are the important variables in production of cotton. Plant
protection and irrigation are the most important variables which affect the cost of
production.
Agriculture remains the backbone of the Kenyan Economy. It is the single most
important sector in the economy contributing approximately 25% of the GDP and
employing 75% of the national rural labor force, (Alila & Atieno, 2006). Over 80% of
2
the Kenya population live in the rural areas and derive their livelihoods directly or
indirectly from agriculture. The importance of the sector in the economy is reflected
in the relationship between its performance and that of the key indicators like GDP
and employment. Trends in the growth rates for agriculture, GDP and employment
shows that the declining trend experienced in the sector's growth is reflected in the
declines in employment and GDP as a whole (Alila & Atieno, 2006).
Since independence, Kenya's cotton-textile-apparel industry has gone through major
phases. At independence private ginners dominated the industry. Over the following
ten years the Government helped cooperative societies to buy the private ginneries
from the colonialists and instituted a regime of controlled margins and fixed farm-gate
cotton prices. In addition, it invested in a number of textile mills, which supplied the
largely private apparel (or garment) manufacturers. Under this regime (which was
also characterized by large donor support), land under cotton expanded by 180% and
processing capacity by 60% in the 1970s. However, Government and donor assistance
started declining in the mid-1980s, which saw lint production drop by 57% between
1984/85 and 1992/93 (Omollo, 2006). By the time the Government begun to liberalize
the industry in 1991 it was in tatters; cotton production had almost ground to a halt,
many ginneries had either collapsed or had excess capacity, and many textile firms
had collapsed. These were accelerated by liberalization, whose serIOUS
implementation started in 1993 and the export ban slapped on Kenyan textile products
in the USA market in 1994. These factors saw lint production drop to an annual
average of 20,000 bales, where it still remains, despite the country's large potential
estimated at 300,000 bales (Government of Kenya, 2000).
In Kenya, cotton is considered under the Kenya Vision 2030 and the government's
Medium Term Plan 2008-2013 as one of the most important industries to implement
the long term Arid and Semi-Arid Lands (ASAL) development initiatives and
industrialization strategy. It is among the few cash crops that thrive well in the fragile
ASAL regions, where few other economic opportunities exist, and hence a major
potential source of employment, income generation and food security (GoK, 2003).
The area under cotton production is estimated at 40,000 ha against a potential of
3
385,000 ha (CODA annual report 2009).Until Government initiatives to encourage
cotton growing began to take effect in 2006, national production stood at only 5,000
tonnes from 30,000 ha in 2005 (Alila & Atieno, 2006).
Within the last 20 years, there has been a significant shift in the national, regional and
international policy environment in relation to the Kenyan cotton productivity. Some
of the policies have resulted in positive, negative or have had no impact or effect
(ACTIF, 2013). The import substitution policies put in operation soon after
independence, between 1963 and 1986, coincided with the period during which the
textile industry performed well (Institute of Economic Affairs, 2009). Under the
Kenya Government's policy for addressing poverty 'Kenya Vision 2030', cotton has
been identified as a key sub-sector with the potential to benefit 8 million people in the
drier areas of the country. The Cotton Development Authority (CODA) has been set-
up to coordinate rehabilitation of the cotton sector (Gitonga et al, 2007).
During that time there was a robust and integrated cotton-textile sector with extensive
institutional support mechanisms, public sector participation and elaborate subsidy
programmes (KIPPRA, 2013). The key challenges on poor cotton productivity
revolves around high input costs including labour and energy, transport, out dated
technology, expensive credit, low investment and poor product quality and
unpredictability of prices and lack of market outlets. This has resulted in the influx of
second hand products displacing the Kenyan industry. Based on the above
background, it can be seen that smallholder farmers may be affected, as they are
producers and consumers of goods. This study will therefore identify the determinants
of cotton production among small holder farmers. Thus government need to put
proper policies that support these farmers, to ensure their overall welfare is not
compromised.
1.2 Statement of the Problem
4
Decline in agricultural production has been a major problem facing the Kenyan
economy since Independence (KIPPRA, 2013). Given the importance of agriculture
and particularly the cotton sub sector in Kenya on rural livelihoods and general well
being of the macro-economy, there is need to identify the determinants of production
in the sector for policy advice. Different studies have been conducted to ascertain the
determinants which are responsible for enhancing the production and ultimately
benefiting the farmers. Khan et al. (1986) and Hassan (1991) observed that high cost
of inputs, scarcity of financial resources, lack of access to the markets and untrained
farmers are responsible for the low yield per hectare and ultimately reduction in the
benefits to the farmers. Nabi (1991) calculated that the use of inputs has a direct
bearing on the production and profit of the farmers. He found that cultivation cost,
sowing cost, seed, fertilizer, pesticide, irrigation and labour are the important
variables in production of cotton. Plant protection and irrigation are the most
important variables which affect the cost of production. Cotton is one of the most
important sources of income for many smallholder farmers in Kenya.
Studies by Ikiara & Ndirangu (2002) and Wakhungu & Wafula (2004), suggested that
Kenyan cotton is chronically uncompetitive with examples of negative gross margins.
Another study on cotton production and marketing constraints carried out in 2005 by
KARI-Mwea, however, indicated that profit margins ranged from KES 1,614 to
12,520 per hectare, at buying prices of KES 20-25. Given that the average yield is
only 572 kg/hectare profitability would be greatly improved even with production at
50% of the yield potential of the commercial varieties. Many rural farmers in Kenya
grow cotton as a cash crop, which has the potential to provide them with a route out of
poverty. There are also policy issues which impact on the already complex situation,
such as price-setting for seed cotton, subsidies for inputs and access to input credit.
This research therefore evaluated the factors affecting cotton productivity among
smallholder farmers in Makueni county of Kenya.
1.3 Research Questions
The study was based on the following research questions
5
1. Do competitions in the sub-sector affect cotton production by small holder
farmers in Makueni County?
II. To what extent do marketing affects cotton production among small holder
farmers in Makueni County?
111. What is the relationship between cost of production and cotton productivity
among small holder farmers in Makueni County?
IV. How is cotton production affected by capacity of farmers III Makueni
County?
1.4 Objectives of the Study
The study was based on the following objectives to:
I. Find out how competition in the sub-sector affects cotton production by small
holder farmers in Makueni County
11. Establish the extent to which marketing affects cotton production among small
holder farmers in Makueni County
111. Examine the relationship between cost of production and cotton productivity
among small holder farmers in Makueni County
IV. Determine how cotton production is affected by capacity of farmers III
Makueni County.
1.5 Research Premise
For the purpose of this study, the following assumptions were made.
1. There is competition in the sub-sector affecting cotton production by small
holder farmers
11. Marketing affects cotton production among small holder farmers.
111. There is a relationship between cost of production and cotton productivity
among small holder farmers
IV. Cotton production is affected by capacity of farmers
1.6 Significance of the Study
This study will generate useful information aimed at improving cotton production and
productivity in the selected counties and suggest measures for increasing adoption of
integrated crop management practices that will serve as a guide to policy prescriptions
6
aimed at revamping the cotton industry. Knowing and understanding the key
variables, which affect production of farmers, is of great importance for designing
economic policies and the ultimate implementation of vision 2030 in Kenya.
Identifying the reasons for differences in cotton production in Kenya is not only
important from a historical perspective, but also useful to evaluate the effects of
existing and new policies.
There are critical problems facing smallholder cotton producers in poor rural areas of
the country and there is need to assess the causes of success and failure of policy
interventions to support such farmers. Empirical studies in these areas are rare and
they have not been sufficiently recognized and articulated by researchers. There is
therefore need to conduct a study exploring these issues. A deeper understanding of
various policy instruments which enhance production is crucial for economic policy
making in Kenya.
1.7 Scope of the Study
This study was conducted in the low rainfall cotton growing areas of Makueni, county
in Kenya because it is among the major areas of cotton production in Kenya where the
cotton yields both at county and at farm levels are well below potential.
1.8 Limitations of the study
The study would have covered all the cotton growing areas of Kenya but cotton
growing areas are sparsely distributed. The selected counties have suitable agro-
ecological zone for cotton growing. In the selected counties farmers are sparsely
populated making it difficult to interview all cotton growers. However, farmers who
are members of cotton Farmer Field School (FFS) and who meet regularly at the
cotton FFS study plots were interviewed using questionnaire and focus group
discussion.
7
CHAPTER TWO: LITERATURE REVIEW AND THEORETICAL
FRAMEWORK
2.1 Introduction
This section reviews theoretical and empirical literature on the key study variables
with the aim of identifying research gaps. It examines literature on the concept of
cotton production and the specific areas covered here are theoretical review.
2.2 Empirical Review
2.2.1 Cotton Context
Even though cotton no longer stands among Kenya's leading cash crops, such as tea,
horticulture, sugar cane and coffee, it was once an important source of income for
rural communities in areas with low agricultural potential, as well as an important
source of raw material for a thriving national textile industry. Despite the sector's
decline in recent years, cotton is still considered one of the few cash crops with real
potential for increasing employment opportunities and food security through income
generation in the Arid and Semi-Arid Lands (ASALs) of Kenya (CODA, 2008). Thus,
revitalizing the cotton sector is one of the government's key development and
industrialization initiatives to be implemented mainly in the ASAL regions, but also
on other high potential areas for this crop, under Kenya's Vision 2030 strategic plan
and its Medium Term Plan, 2008-2012 (GOK, 2008).
2.2.2 Overview of Cotton production
Globally, over 70 countries produce and export cotton, while many developed and
developing countries depend on imports of cotton lint for their spinning and textile
industries. Eight countries, China; USA; India; Pakistan; Uzbekistan; Turkey; Brazil;
and Australia are responsible for 81% of global output, with leading African
producers that include Cameroon, Cote d'Ivoire, Chad, Benin, Togo and BukinaFarso
accounting for approximately 20% of the output (Badiane et al., 2002). The main
producers in Eastern Africa include Tanzania, Uganda, Kenya and Sudan.
In Kenya, cotton was introduced in 1901 by the British colonial administration and
promoted as a suitable cash crop in areas where other crops did not fare well (Waindi
8
and Njonge, 2005). It is one of the few cash crops suitable for marginal, low rainfall
areas, which cover about 87% of the country's landmass, and are home to about 27%
of the population (Ikiara and Ndirangu, 2002). According to the "Developing a revival
strategy for Kenya's Cotton textile industry report (OoK, 2003) the crop is grown in
Nyanza, Western, Coast, Central, Eastern and Rift Valley provinces, largely under
rain fed conditions. Irrigated cotton is produced mainly in Hola and Bura (Tana River
District) and parts of Kerio valley. About 350,000 hectares in the country are suitable
for cotton production and have the potential to yield an estimated 260,000 bales of lint
annually. However, cotton is only being cultivated on about 40,000 hectares at
present, with an annual lint production of 20,000 bales while the National demand is
140,000-250,000 annually (Bartok, 2009).
2.3 Challenges on Cotton Production
According to the World Bank (2005) and the CODA (2008), the following are some
of the key challenges responsible for the cotton sector's poor performance.
2.3.1 Unavailability of Quality Seeds
The lack of certified seed has contributed to low production of the cotton crop (Obare,
2009). A seed bulking and certified support programme was started in 2007.
However, this is currently inadequate and requires strengthening in terms of
additional trained manpower and financing. Investments especially on equipment for
the commercial production of seed by the private sector is also limited.
2.3.2 Cotton Prices
According to the Cotton Development Authority (2008), there are both international
and national price for seed cotton. The international price is influenced by the forces
of supply and demand in the international market whereas the national price is
determined by the stakeholders at the beginning of each cropping season. In 2010, the
floor (minimum) price set was Ksh 32 per kg at the buying centers. The world price of
cotton between August 2000 and September 2001 was about US $ lIkg of lint,
equivalent to about KES 26/kg of seed cotton, assuming a ginning outrun ratio of
9
approximately 33%. This price has since been on the decline, and has been declining
in real terms since 1950. Between 1950 and 1988 for instance, the price oflint fell by
60% in real terms from US$ 1.60/lbs in 1950 to 0.65/1 b in 1998.
Besides the competition from man-made fibres, the major cause of decline in world
cotton prices is the increase in international supply due to subsidies maintained by the
leading cotton producers mainly the USA, China and the EU (Kooistra, et al., 2006).
The challenge facing many cotton producers in Kenya is how to retain
competitiveness in the face of this long-term world decline, through cost
rationalization, higher production and yield enhancement strategies in the cotton sub-
sectors and other stakeholders. The pricing structure at every stage of the supply chain
should not penalize some parties at the expense of others. There must be transparency
in the pricing structures especially in spinning and weaving.
According to a report by the Institute of Economic Affairs (2009), continued synthetic
competition, diminishing world prices, introduction of cheap imports of second hand
clothes and diminished cotton profitability were amongst factors that dealt a blow
both to cotton production, and the textile and clothing industries. However, the
production is slowly picking up after years of neglect and disillusionment among
farmers. The revival of the Salawa Cotton Ginnery in Baringo district has played a
great role in revitalizing farming activities in the Kerio Valley region (Hamevik &
Bryceson, 2008). Gitonga et al. (2007) presented a five cotton production trend
between 2005 and 2010 (Table 2.1).
10
Table 1: Cotton Production in bales from 2005 to 2010
Year Production in Bales
2005 20,000
2006 50,000
2007 45,035
2008 27,194
2009 26,821
2010 58,000 ..
Source: (Gitonga et aI, 2007)
Other challenges that led to this decline included failure of the country's cotton
growing sector to provide enough raw materials to the textile mills, increasing use of
synthetic fibres, and a worsening of the operating environment manifested by high
cost of inputs and electricity and poor infrastructure. The closure of the textile and
garment industries had the following effects: Massive loss of employment estimated
at over 70,000 jobs, loss of government revenue due to closures, loss of investor
confidence. Other issues include that most of the workers who lost their jobs in the
textile industries have joined the informal activities such as trade in small enterprises,
some of which are engaged in sale of imported clothes and second-hand clothing and
shoes and in some towns like Eldoret, Thika and Kisumu where there was a
concentration oftextile activities, there has been shrinkage of the local economy, with
other business activities equally impacted negatively (EPZA, 2005).
11
2.3.3 High cost of inputs
Costs associated with spraying, weeding and harvesting contribute to the high cost of
production. Pesticide costs are high and can contribute up to 51.70% of the input costs
(Figure 1). Gross margins can range from KES 1,614 to KES 12,520 per hectare.
Besides, inadequate use of mechanization, contributes significantly to high production
costs (Gitongaet, al, 2007).
• Seed
• P{'stlcides
• labour
• EQVlpment hire'
Percentage cost KES/ha)
0.7
Figure 1: Cost distribution for various activities in the production of one hectare of
cotton (Source: Gitonga et aI., 2011).
2.3.4 Cotton Marketing
On average, cotton farmers are making a loss of KES 3 per Kg of seed cotton
produced, largely because of the high cost of production in an environment
characterized by the global decline in lint prices (Ikaria & Ndirangu, 2002). World
lint prices fell in real terms by more than 60% between 1950 and 2000. Major cost
drivers are cost of pesticides, lack of extension services and poor infrastructure. Cost
associated with pesticides constitutes about 29% of the total production cost
(Kooistra, et aI., 2006).
Market failure is manifested by the absence of fair competition among ginners in
some parts of the country and excessive competition (predatory practices) that renders
important mechanism like input credit supply schemes unfeasible in other parts.
12
Studies by Ikiara and Ndirangu (2002) and Wakhungu and Wafula (2004), suggested
that Kenyan cotton is chronically uncompetitive with examples of negative gross
margins. Another study on cotton production and marketing constraints carried out in
2005 by KARI-Mwea, however, indicated that profit margins ranged from KES 1,614
to 12,520 per hectare, at buying prices of KES 20-25. Given that the average yield is
only 572 kg/hectare, profitability would be greatly improved even with production at
50% of the yield potential of the commercial varieties.
2.4 Government Response towards Cotton Production in Kenya
Under the Kenya Government's policy for addressing poverty 'Kenya Vision 2030',
cotton has been identified as a key sub-sector with the potential to benefit 8 million
people in the drier areas of the country. The Cotton Development Authority (CODA)
has been set-up to coordinate rehabilitation of the cotton sector.
The Cotton amendment Bill of 2006 provided the legal framework for Government
supported re-organization of the cotton sector. Already there has been some impact
with national production rising to 9,800 tonnes in 2006 from 5,090 tonnes in 2005.
However, this increase was mainly due to an increase in the number of producers
(hectares under cultivation) rather than any substantial increase in productivity.
Average yields remain at 400 - 600 kg/ha of seed cotton.
Available data show that the number of gazetted Export Processing Zones (EPZ) in
the country has increased gradually from 10 in 1993 to 41 in 2004 (Republic of Kenya
2005; 1995). The number of operating enterprises within the zones has also grown
from 12 in 1993 to 74 in 2004, representing an average annual increase of 47%
(Kibua & Nzioki, 2004). Local investor participation, especially in the EPZs, has been
significant. About 11% of the total EPZ firms are wholly Kenyan, while joint ventures
are 74%. According to the Export Processing Zones Authority (EPZA), the total
investment in the textiles sub-sector as at December 2003 was Ksh. 9.7 billion (US
$132.9 million). Most of the large investors are in the EPZs while a few are under the
MUB scheme.
13
2.5 Role of Policy and Institutional Incentives on Cotton Production
Developing and expanding Kenya's cotton sector is a key agricultural strategy
outlined in the national Vision 2030 plan, capable of benefiting more than 8 million
people living in the country's Arid and Semi-Arid Lands (CODA, 2008).In an effort
to revitalize the country's cotton sector, the Kenyan Ministry of Agriculture (MoA)
passed the Cotton (Amendment) Bill in 200g, which provided the legal framework to
re-organize the sector, allowing stakeholders to regulate the industry through the
Cotton Development Authority, under the supervision of the MoA (CGD Bills Digest,
2005). Although seed cotton production grew in the two years after the Bill was
adopted, this growth was largely due to a small increase in the number of producers,
rather than an increase in productivity (Gitonga et aI., 2007).
A major policy change affecting the cotton sector In recent years was the
promulgation of the 2006 Cotton (Amendment) Act, which created the Cotton
Development Authority (CODA), with the mandate of promoting and regulating the
cotton industry (Kenya Gazette Supplement, 2006). The CODA was established to
replace the Cotton Board of Kenya, moving regulation authority from the government
to industry stakeholders, including cotton growers, ginners and manufacturers (CGD,
2005).
There are policy issues which impact on the already complex situation, such as price-
setting for seed cotton, subsidies for inputs and access to input credit. Cotton farmers
are very price sensitive but attempts to control the price can have a negative impact on
the willingness of the private sector to invest in production support mechanism.
During the 2010 budget speech, the then Finance Minister Hon. Uhuru Kenyatta
doubled duty on imported second hand clothes in a move that was largely seen as
lifeline for the struggling industry. The taxman increased the charges to Sh2.1 million,
up from Sh1.3 million per container (Githae, 2012/2013) leading to increased prices
on second hand clothes.
The local cotton and textile industry danced at this development but the recent move
by the Finance Minister to reduce import duty on second hand clothes from 2 million
14
shilling to a million per container dashed their hopes. This has seen widespread sale
of second hand clothes in the country. The cotton industry lacks man power
development policy, a dynamic technology, development policy, a regulatory and
legal framework consistent with the current liberalized environment, a comprehensive
policy. There is also a glaring absence of strategic positioning policy. Thus, even as
global dynamics of the cotton-lint-textile-apparel chain governance change, there is
no strategic response in the country, with the result that the country's producers
continue suffering worsening terms of trade (The Moi University 10 year strategic
plan 2005 - 2015).
Key among policy failures in the entire cotton-lint-textile-apparel industry is the
abrupt way that liberalization was carried out. The sector was opened up completely
and suddenly, without offering players sometime for adjustments. The cotton Board
of Kenya was left without any role in the industry and yet no alternative institution
was set up to carry out crucial regulatory and coordination tasks. Besides this failure,
the industry lacks manpower development policy, a dynamic technology development
policy a regulatory and legal framework consistent with the current liberalized
environment, a comprehensive policy framework covering all links in and aspects of
cotton-lint-textile-apparel value chain and comprehensive institutional strategy
(Regional Agricultural Trade Expansion Support program, 2003).
15
2.5 Conceptual Framework
Figure 2: Conceptual framework
Independent variables Dependent variable
Macro -Economic Policy
Environment Increased out put
• Price policy Affects• Policy and institutional factors Harvested crop yield
Marketing per hectare
Technology Value of crop produced
"- per household
Competition and Cost of Production
Other Factors
• Research and Extension
Facilities
• Status of productivity
Source: Researcher (2014)
Mugenda and Mugenda (2003), define a conceptual framework as a hypothesized
model identifying the concepts under study and their relationships. In this framework,
there are certain factors that cotton production practices. These factors include but are
not limited to education, age, Gender, total land owned, land under cotton, farm
labour, and cost of inputs, cotton prices, pests and diseases. For this study, 5 variables
are considered as the independent variables. Cotton productivity among smallholder
farmers is the dependent variable that is affected by the independent variables as
illustrated in figure 2
2.6 Theoretical framework
Utility maximization approaches encompass the dual character of peasant households
as both families and enterprises and thereby take account of the consumption side of
the peasant. The seminal work by Chayanov in the 1960s emphasized the influence of
16
family size and structure on peasant economic behaviour, through the subjective
evaluation of labour within the household, in the absence of the labour market
(Chayanov, 1966).
In expanding the scope of the Chayanovian model and assuming perfect market, the
neoclassic farm household model became popular in the 1960s to explain the
behaviour of farm households in simultaneous decision making about consumption
and production. This model typically incorporates the notion of full household income
(Becker, 1965) and conceives of the household as a production unit that converts
purchased goods and services as well as its own resources into use values or utilities
when consumed. Thus the household maximizes utility through the consumption of all
available commodities, subject to full income constraints.
The model shows that if all markets exist and all goods are trade able, prices are
exogenous and production decisions are taken independently of consumption
decision. In such condition, the decision making process could be regarded as
recursive because time spent on leisure and time used in production becomes
independent; utilization of family labor will be directly linked to the market-
determined wage rate and income is singled out as the only link between production
and consumption. In the absence of labour market as in Chayanovian model, or any
missing market the decision may not be recursive because the family will be left to
decide about the percentage of its total available time to be devoted to production.
Therefore there is no reparability between consumption and production (ACTIF,
2013).
Hence the validity of recursive modelling of household resource allocation depends
on the household being a price taker and the absence of missing imperfect markets. In
reality households operating in developing countries are likely to face more than one
market imperfection which prevents first-best transactions and investments from
taking place. Empirical analysis of recursively in farm household decision making
have generally produced negative results (ACTIF, 2013).
17
Hence theoretical advances on fann household models with missing market have
opened to a new research agenda for neoclassical economists; the households'
objectives agenda is still to maximize utility from a list of consumption goods, but
subject to what may be a large set of constraints in which a missing market is yet
another constraint on the household. At the same time the task of empirical economies
has shifted to providing evidence of market efficiencies and their impact on household
production choice. Criticisms if these theoretical frameworks are particularly severe
when uncertainty and risk aversion are acknowledged (ACTIF, 2013).
18
CHAPTER THREE: RESEARCH METHODOLOGY
3.0 Introduction
This chapter outlines the methodological approach that was used in carrying out the
study. It covers the research design, study area, target population, sampling procedure
and sample size, instruments, validity of the instrument, reliability of the instrument,
procedure for data collection, data analysis and findings.
3.1 Research Design
This study employed a descriptive survey design. According to Saunders, Lewis and
Thornhill (2003) survey strategy is a deductive approach popular in business research.
The main advantage of this research design is the ability to collect large amounts of
data from sizeable population in a highly economical way.
3.2 Location of the study
This study was conducted in Makueni County of Kenya. Makueni County is located
in the Southern end of Eastern Province and covers an area of 7,965.8 km2 The
County has been subdivided into eight (8) Sub Counties, namely Makueni,
Kathonzweni, Kilungu, Mbooni East and West, Kibwezi, Nzaui and Mukaa. These
areas have been selected because of convenience and accessibility for the study. This
area was selected because of being very suitable for cotton production in addition to
cotton being the main crop enterprise. It is also agro-ecological zones suitable for
cotton production.
3.3 Target population
A target population is a group of individuals or a group of organization with some
common definitive characteristics, (Creswell, 2005). Data was collected from
smallholder « 1 ha) farmers who produce cotton under rain fed -conditions and
Extension Officers, CODA officers County Administrators and KARl Officers with a
sample frame of 3455 small scale farmers.
19
3.5 Sample and Sampling Techniques
3.5.1 Sampling Technique
Multistage random sampling was used to select sample of 103 cotton growers
(respondents) and 12 various agricultural and cotton growing officials. Multistage
sampling refers to sampling plans where the sampling is carried out in stages using
smaller and smaller sampling units at each stage. This study will employ a three stage
sampling starting from the county down to the sub-counties and then at the wards.
The sample size for the questionnaire administration is represented in Table 3.1 and
the composition for the Focus group is represented in Table 3.2 and Multistage
sampling in Table 3.3
Table 2: Distribution of the respondents
Population SAMPLE FRAME SAMPLE SIZE
Farmers 3,455 91
Other Officials 15
Total 106
Source: Researcher (2014)
Table 3: Composition ofthe focus group discussion participants
Member Category Makueni
Farmer Representative 5
MoA Extension Officer 3
CODA Officer 2
County Administrators 3
KARl Officers 2
TOTAL 15
Source: Researcher (2014)
20
Table 4: Multi-stage Sampling of Makueni County
Sub-County Wards Frequency of Frames
Kalamba, 5
Makueni Mavindini 15
Kathonzweni 20
Kitise 10
Mbuvo 5
Mbooni West Kanzokea 9
Kithuki 14
Kavingoni 13
Total 91
Source: Researcher (2014)
3.5 Research Instruments
Structured and semi-structured questionnaires and interview schedules were used to
collect data from selected respondents. The questionnaires were used to collect data
from individual farmers while interviews were used to collect data from other
officials. A questionnaire is preferred because questionnaires consist of many items
combined and more reliable measure of constructs than would any single item. It
offers considerable advantages in administration: it presents an even stimulus
potentially to large numbers of people simultaneously and provides investigation with
an easy accumulation of data. Questionnaires give respondents freedom to express
their views or opinion and make suggestions while interviews provided in-depth
information mostly qualitative that may not be captured by the questionnaires. On the
other hand, interviews were conducted to investigate issues in an in depth way,
interviews also helps to discover how individuals think and feel about a topic and why
they hold certain opinions and deepen understanding and explain statistical data
where questionnaires cannot capture.
21
3.6 Pilot study
Pilot study was done on randomly cotton growing farmers within the county. This was
mainly to verify whether the instruments generated by the researcher displays
stimulus homogeneity hence valid and reliable. Piloting was also done with the
purpose of detecting any weakness and finding out if the questionnaires are clear to
the respondents. The researcher administered research instrument to a few randomly
selected cotton producers who may not be selected for the actual study. The results of
the pilot study were analyzed and used to modify and remove ambiguous items on the
instruments used in the study.
3.6.1 Validity
Validity refers to the extent to which an instrument measures what is supposed to
measure. The instrument was evaluated for content validity that is the extent to which
the questionnaire content, which includes vocabulary, sentence structure and the
questions, are suitable for the intended respondents. Content validity is done by expert
judgment. The study used validated instrument to adequately address the objectives of
the study. The researcher sought the expertise of the supervisors to check if the
instruments were viable to collect the intended data.
3.6.2 Reliability
The study assessed reliability by split half method. This method is more practical in
that it does require two administrations of the same or an alternative form test. In the
split- halves method, the total number of items was divided into halves and a
correlation taken between the two halves. This correlation only estimates the
reliability of each half of the test. The score from each half will then be compared by
assigning each test item score on a scale from 0.1 to 1.0. Next, a calculation of
correlation between the two ratings was done to determine the level of reliability. The
Cronbach Alpha coefficient of all values 0.7 and above was accepted.
22
3.7 Data Collection Techniques
The researcher personally visited the selected respondents in the sample to establish
rapport for commencement of data collection exercise. The data was collected by
distributing the questionnaires to the respondents and then collected the filled
questionnaires at a later date for data analysis. The researcher also used interview
schedules to get information from sampled key informants. In doing this the
researcher first made a courtesy call to the identified farmer representatives, MoA
Extension officers, CODA officers, KARl Officers, County Governor and Member of
County assemblies to book an appointment and agreed on a specific date and time for
the interview. Others included County Commissioner and County Administrators.
When booking the appointments, the researcher introduced himlherself, explain to
each of the respondents reason for the study.
3.8 Data analysis
Both quantitative and qualitative data were analyzed. During quantitative data
analysis, overall data quality was assessed and all the variables chronologically
arranged with respect to the questionnaire outline. This ensured that the correct code
is entered for the correct variable. The data, in form of the coded variables, was then
entered into the Statistical Package for Social Sciences, followed by data editing. This
exercise ensured that every data entered for each questionnaire in each variable is
correct. Analyzed data were then presented using tables, graphs and pie charts for
clarity easy interpretation. For qualitative data, a checklist was developed which was
the principle guide in data analysis. The checklist was clustered along main themes of
the research to ease consolidation of information and interpretation based on research
objectives. The checklist made it possible to put together scattered information under
a particular theme which in turn greatly aided in interpreting information.
23
3.9 Ethical Consideration
Ethical considerations were pertinent to this study because of the nature of the
problem, the methods of data collection and the kind of persons serving as research
participants. While carrying out this study, participants were informed of the nature of
the study and allowed to choose whether to participate or not. The researcher
therefore ensured that participants know that their involvement is voluntary at all
times. To safeguard the privacy of the participants, respondents were kept in a private
environment away from passers-by or intruders. Asking participants not to write their
names on the questionnaires during the research also helped in ensuring anonymity.
24
CHAPTER FOUR: DATA ANALYSIS AND DISCUSSION
4.0 Introduction
Content in chapter four is data analysis, presentation and interpretation of the study
findings. The subtitles in this chapter are arranged according to the objectives of this
study. The survey was carried in Makueni County particularly in eight (8) Sub
Counties, namely Makueni, Kathonzweni, Kilungu, Mbooni East and West, Kibwezi,
Nzaui and Mukaa Sub counties. This study involved randomly selecting the farmers
who were known to have been growing cotton prior to the study and those who had a
cotton crop in the field during the study period. To ensure fair representation, the
enumerators were trained on how to cover all the locations with cotton production
activities while allowing for at least a kilometer between the sampled farm(s). This
survey was supplemented by focus group discussion.
The study findings have been presented in frequency distribution tables, percentages
and explanations of the findings in between the frequency tables for further
elaboration as well the interpretation of the study results have been given alongside
the findings. All the 91 issued questionnaires from small holder cotton farmers were
receive and focus group results from 15 representatives of Extension Officers, CODA
officers, Farmer representatives, County Administrators andKARI Officers. Thus, the
questionnaire return rate was 100 per cent, which the researcher used as an excellent
response rate of the study participants. This was possible because the study applied
guided questionnaires approach to collect the data.
4.1 Socio-economic Characteristics
To establish the socio-economic characteristics of the respondents particularly the
farmers, they were asked to state their gender, ages, highest formal education attained
and the main occupation of the household head.
25
4.1.1 Gender
The household heads were asked in the questionnaires to state their gender and Figure
4.1 presents the finding.
Figure 3: Gender Analysis
86.4%
100
80
60
40
20
o
Male Female
• Frequency
Source: Researcher (2014)
As shown in Figure 4.1, above over three quarter of the household heads 86.4% were
male while 13.8% were female headed household. The researchers had the
assumption that gender of household head was an important variable to interrogate as
farmers from male-headed households are more likely to participate in cotton
production than members from female headed household. This gender differentiation
results forms biases in access to various forms of capital against female-headed
households. This assumption was however confirmed by the respondents that in
Kathonzweni Sub County, female headed households are poor and one of the
vulnerable groups relative to male-headed households.
4.1.2 Ages of the Respondents
The farmers were asked to state their ages and the minimum, the maximum and the
mean age of the household head was 20, 77 and 93 and 50 years, respectively. The
study established that younger cotton farmers tend to have a higher degree of
26
commercialization compared to older farmers. This suggests that although large
household size positively affects participation in cotton production and commercial
markets, it can negatively affect the extent of commercialization particularly due to
household age differences. Older household heads are more likely to have larger
household sizes, which despite their labour availability advantage, increases the
cotton production.
4.1.3 Level of Education
When the respondents were asked about their highest academic qualifications, results
indicated that the majority 54.0% had primary education as shown in Figure 4.2.
Figure 4: Highest level of Education
54.0%
60
50
40
30
20
10
0
Primary education Secondary
• Frequency
Post secondary
An analysis of the education level of the respondents indicated among those who had
formal education 54.0% and 30.0 % had primary and secondary school level of
education, respectively while 16.0% had post-secondary level of education. It is
generally recognized that education equips individuals with the necessary knowledge
of how to make living (Gerald, 2008). Thus, for the purpose of this study, the
researcher believes that those who are literate and have at least some education are
better able to make the transition to cash crops. This is so because it is believed that
27
Source: Researcher (2014)
producers with higher levels of education tend to have greater access to cotton
production and market information, hence expected to produce market-oriented cash
crops which agrees with the argument by (Mbataru, 2010).
4.1.4 Occupation
In terms of occupation of the household head, most of those who were interviewed
were farmers (76%) with the remaining 24% being engaged in a combination of
farming and other kinds of businesses including Government employees, business,
and teachers.
4.1.4 Type of Housing Structure
The area of the study was characterized by three types of housing structure including
permanent (24%), semi-permanent (50%) and traditional (26%). The type of housing
from tradition toward the permanent housing structures is perceived to indicate
improved livelihood and higher stands of living.
4.1.5 Size of Land Owned
The minimum, the maximum and the mean acreage per households was 1, 80 and
10.5acres, respectively. Among the respondents, there was no land rented in or out.
This is a continuous variable representing the size of land allocated to cotton
production in specified year by producer farmers and is measured by hectare. The
study expected that, as cultivated land size increases, provided other associated
production factors remain constant, the likelihood that the farmers produce more
cotton was high.
4.1.6 Size of the Family
In the questionnaires, the respondents were asked to indicate the size of their families.
The study revealed that there was a minimum of 2 (per family) and maximum of 15
people in a family however there was an average of 5 persons per family. This is a
continuous variable referring to farmer's access to family labour. In this study, the
researchers considered active family labour as who can participate in agricultural
activity in the household. Thus, this variable was expected to positively affect the
28
probability decision to produce cotton and the amount to be produced. This is because
cotton is a labour intensive crop, thus requires high labour and in these rural areas
there is no market for labour or if any imperfect. Thus, family labour was the main
source of labour force in cotton production in the entire Makueni County.
4.2 Effect of competition on cotton production
According to Karanja et al (2012), cotton production and yields are relatively low in
Makueni County; the amount of cotton lint marketed is often insufficient to fulfil
national demand. Therefore, many local spinners and textile mills rely on processed
cotton imports to meet their demand. The first objective of this study was therefore to
find out how competition in the sub-sector affects cotton production by small holder
farmers in Makueni County.
In a bid to establish the effect of competition on production of cotton by small scale
farmers, the study analyzed production performance in this sector for a period 2005 to
2010 and Table 4.1 presents the findings.
Table 5: Cotton production trend in Makueni County (2005-2010)
Year 2005 2006 2007 2008 2009 2010
Target ha 2000 2000 1000 3000 1000 2000
Achieved ha 300 400 400 500 667 -
Target yield per ha 2 2 2 2 2 -
Achieved yield per 0.4 0.5 0.6 0.7 1.7 -
ha
Target yield total 4000 4000 2000 6000 2000 -
Achieved yield total 120 200 240 350 1134 -
Average yield kg/ha 100 120 140 200 600 -
Average 232/ha
Table 4.1 shows that Area planted and production of cotton averaged 2000 hectares
and between 120 to 1134 metric Kilograms achieved yield respectively. Production
29
fluctuated from a low of 120 metric Kilograms in 2005 to a high of 1134 metric
Kilograms in 2009. The figures recorded in 2009 can be a result of the Land Reform
policy which dismantled the Large Scale farming community. Productivity averaged
232 Kilograms per hectare with the lowest yield of 100 Kilograms per hectare
recorded in 2005 and a high of 600 Kilograms per hectare in 2009. The high
productivity can be attributed to development of irrigation infrastructure. Summary of
growth rates and trends in production during the 2005-2010 periods of cotton grew at
an average rate of 17.06 per cent and this may be explained by the government policy.
The government during this period heavily supported small scale farmers who formed
the majority of the farming community.
Since the 1970s large scale farmers contributed more to cotton production than
smallholder farmers but production in the sector declined in the 1980s as found in
(Karanja et al 2012). This study concurs with findings during interviews with
representatives of Extension Officers, CODA officers, Farmer representatives, County
Administrators and KALRO Officers that the decline in the number of large-scale
commercial cotton growers is due to competitions and an increasing number of
farmers diversifying into more lucrative export crops such as Green grams, maize,
cowpeas and beans due to reduced viability in the cotton sector.
To confirm the existence of competition with other crops the farmers were asked to
state other crops that were preferred to cotton. They were also asked to state area
(acre)/% growing, annual yields (kg), prices per kg/ha and annual income as
compared to cotton production. Table 4.2 presents the findings.
Table 6: Crop enterprises among the sampled respondents
Type of Area (acre)/%growing Annual yields Prices per Annual income
crop (kg) kg/ba
Mi Ma Av growin mi max Ave Mi Ma Ave Min Max Ave.
n x e. g n n x
Green 0.2 3 1 78 10 720 489 20 70 52 400 2340 993
grams 5 0 0 2
Maize 0.2 10 2.6 92 8 225 775 5 26 15 750 4500 106
5 0 0 39
Cowpeas 0.2 3 0.8 - 30 450 384 10 50 20 800 9000 701,
* 5 0 0 8
Sorghum 0.2 1 0.7 16 50 540 201 - - - 400 1200 230
* 5 0 6
Pigeon 0.2 5 1.2 70 20 900 289 5 60 25 360 2430 592
peas 5 0 0
Cotton 0.2 10 2 100 80 150 412 10 32 23 104 9600 130
5 0 0 0 37
Beans 0.2 1 0.6 34 90 900 291 20 50 36 4500 859
5 360 0 4
0
No response
As shown in Table 4.2 above there is decline in cotton production as compared to
other crops like green grams and maize in terms of annual yields due market
completion however cotton remains the highest annual income earner.
This study finding acknowledges the fact that even though cotton no longer stands
among Kenya's leading cash crops, such as tea, horticulture, sugar cane and coffee, it
was once an important source of income for rural communities in areas with low
agricultural potential, as well as an important source of raw material for a thriving
national textile industry not only in Makueni County but the entire Country. Despite
31
low average yield (about .53 tonnes of seed cotton per hectare between 2000 and
2010) and poor quality cotton fiber outputs (FAOSTAT, 2012). Approximately
384,500 hectares of irrigated and rain-fed land is available for cotton production, of
which only about 10 percent is currently harvested (World Bank, 2005; CODA,
2008). Given the average yield, the national production potential for available land is
around 200,000 tonnes of seed cotton. However, the average production of 18,000
tonnes per year over the period 2005-2010 represents a mere 9 percent of this
potential.
4.3 Marketing and Cotton Production
Cotton production and yields are relatively low in Kenya; the amount of cotton lint
marketed is often insufficient to fulfil national demand. The respondents were
therefore asked to indicate where they marketed their cotton, to whom and at what
time.
The survey data indicates that majority (97.44%) of cotton producer reported that they
supplied their produce to the market. Sampled farmers were also asked to identify
their usual way of selling their produce. About 90.22% of them reported that they sell
directly to traders or purchasers at local or primary markets. Only about 9.78% of
cotton producers said they sold their produce through brokers. Similarly these
sampled farmers reported that the usual time they sell their produce was one month
after harvest, (about 33% of farmers reported). In addition, 65 percent of them replied
that they sold their cotton immediately after harvest. Only 2% of cotton farmers said
they stored their produce for two months after harvest. The reason they present is that
cotton easily be contaminated and its seeds affected if it is stored at home longer.
Thus, they prefer to sell immediately after harvest without expecting high price in the
future.
Market and price liberalization of the cotton sector in 1991, as well as the lack of
stakeholder organizations and institutional support has led to the cotton sector's
decline since early 1990s in the County. It was not until 2006 that a mechanism to set
reference prices was established. Currently, cotton general regulation requires CODA
32
to arrange a cotton stakeholder meeting to discuss and agree on a floor price for the
season.
The study (90.0%) established that international cotton prices, FOB adjustments and
transport costs, floor prices for both seed cotton and ex-ginnery lint are set each year
and was in agreement with (CODA, 2010). Since 2005, CODA had established more
than 250 authorized buying points throughout Kenya's cotton production areas in an
effort to reduce access costs and facilitate exchange between farmers, agents and
ginners. Despite all these reforms in cotton there were still challenges in marketing of
cotton in the entire Makueni County.
According to Ikiara and Ndirangu (2003), liberalization of the cotton industry in 1991
allowed cotton growers for the first time to negotiate prices directly with buyers.
Though this was first seen as an advantage to farmers, the lack of supportive market
and governmental institutions, coupled with stiff competition in an unregulated
market, led to a general decline in the prices received by producers (CGAE, 2006).
This study established that no secondary market for seed cotton exists; ginneries are
the only market and, therefore, in theory, have significant power to dictate prices paid
to local producers. The study was further informed that cotton growers have made
some attempts to organize as a way of increasing their market power, but have had
little success due middle men and brokers. On the other hand, ginneries are also
extremely dependent on local seed cotton supply, so the market relationship is one
based on interdependence that has not been fully actualized not only in Makueni
County but the entire Country.
4.4 Relationship between Cost of Production and cotton productivity
To obtain the relationship between cost of production and cotton productivity, the
respondents were asked to state where they obtained funds for cotton production and
Table 4.3 presents the findings
33
Table 7: Sources of Finance
Factors Frq. In %
Banks/ credit 67.0
Ginner 2.0
Other farm enterprises 10.0
Business 18.0
Salary/wage 55.0
Remittance 21.0 .
Others 15.0
As shown in Table 4.3 above majority 67.0% received their funding from banks and
credit from other institutions, 55.0% received funding from their own salaries and
wages and 18.0% from their businesses. According to Karanja et al (2012), cost of
cotton production affects many decisions, from applying inputs to marketing. It is
important to be able to know production costs for specific fanning operation against
the productivity in order to compare with other areas and do some benchmarking. In
conclusion the study failed to establish the clear relationship between cost of
production and productivity due to lack of compromise the data on cost of production.
For instance, there were certain inherent limitations, like seed cotton yield could not
be estimated in both Makueni County and the inputs were found to be subsidized in
different areas making it difficult to calculate the actual cost of production.
The study investigated factors affecting cost of cotton production and Table 4.4
presents the findings.
34
Table 8: Factors affecting Cost of Production
Factors Frq. In %
Disease and pest related 70.5%
Inputs & Equipment related issue 56.7%
Market related issues 73.0%
Transportation 40.0%
Delayed payment 89.0%
Low prices 65:4%
Total
Table 9 shows that 89.0% of the farmers were affected by delayed payment. As a
result, they could not engage in farm activities in time, could not buy farm inputs in
time hence increased cost of production due price fluctuation of inputs. Market related
issues affected 73.0% of the farmers. This included lack of direct market which forced
them to use brokers and middle who greatly exploited cotton farmers particularly in
Kathonzweni in Makueni County. Other issues included cost of equipment related
issues 56.7% and low prices 65.4%.
The study endeavoured to investigate the role of various inputs, the cost benefit ratio
for the farmers and cotton productivity. They were asked to indicate the cost of all
input per acres of land in cotton production per year and Table 4.3 presents the
findings.
35
Table 9: Cost of Cotton Production
Operatio Area (acre)/%growing Annual yields Cost per kg/ha in Annual income
n (kg) KSh
Min Ma Av growin mi max Av Min Max Ave. Min Max Av
x e. g n e.
Cultivati 0.25 3 1 78 10 720 48 200 7000 5200 400 2340 99.
on 0 9 0 0
Sowing 0.25 10 2.6 92 8 225 77 500 2600 1500 750 4500 101
0 5 0 9
Fertilizer 0.25 3 0.8 - 30 450 38 100 5000 2000 800 9000 70
0 4 0 0
Plant 0.25 1 0.7 16 50 540 20 - - - 400 1200 231
Protectio 1 0
n
Labour 0.25 5 1.2 70 20 900 28 500 6000 2500 360 2430 59:
9 0
Total 0.25 10 2 100 80 150 41 400 2060 1220 104 9600 13(
Cost 0 2 0 0 0 0 0 7
/acres
The results show that in all operations, cost of production was a minimum of 4000
KShs and a maximum of 20,600 and an average of 12,200 per acre annually against
an annual average income of 13037 Ksh. The net per acre return for was an average of
412 Kgs of cotton lint and a maximum of 1500 Kgs per acre. The study further
analyzed the production cost of each Item against productivity of cotton.
4.4.1 Cultivation Cost
Table 4.3 shows that cost for the variable of cultivation is averagely 5200 KSh per
acre showing with a yield of an average of 489 Kgs. It shows that yield value per acre
varied. Therefore to increase productivity of cotton, the cultivation cost must equally
36
______ .•.•.•_ ..•.." ••. ~ a ~ ~-,
decrease. Cultivation cost was found to be highly significant and indicating a strong
impact on cotton yield.
4.4.2 Seed/Sowing Cost
The importance of seed in the cotton production was widely accepted in this study.
The data results for the in Makueni County given in Table 4.3 depicts that cotton
production on per acre basis can be increased significantly by increasing the
expenditure on seed. The average cost of sowing was 1500 KSh with a yield of 775
Kgs per acre. The expenditure on seed means use of good quality seed and improved
methods of sowing which had a strong positive relationship with productivity of
cotton.
4.4.3 Fertilizer
This was an important component of productivity as it was found to being used by the
cultivators prior to germination and for the vegetative growth of cotton plant. Farmers
used fertilizer after the germination of cotton. In Makueni County, the cost of this
variable was estimated at an average of 2000Ksh, indicating that cotton yield in this
county was responding to the increases in the use of fertilizer.
4.4.4 Plant Protection
Cotton crop is very sensitive to pests and diseases Gitonga et al (2007). In order to
control the attack of pests and diseases farmers were using heavy pesticides. So the
role of this factor was important in the cotton production. Like others factors, this
study failed to establish the actual cost of plant protection as almost all respondents
were non-committal on the actual cost.
4.5 Effect of capacity of farmers on cotton production
Cotton production depends on the capacity of farmers, especially for small farmers in
Makueni County. The study established that if cotton does not remain a financially
viable crop for farmers due to low yields as a result of soil depletion, dropping water
tables and a lack of technical know-how particularly lack of capacity, the farmers will
switch to other cash crops. This could result in market volatility and swings in supply.
37
Hence, switching from conventional methods to better cotton ensures that cultivation
stays profitable in the long term.
Through interviews, the study established that better Cotton production helps the
farmer to bring a competitively priced product to market that satisfies the consumers.
Therefore cotton farmers should have the capacity do actualize the same. Capacity
building has a positive effect on productivity in that an increase in more sustainable
farming practices leads to significant improvements in mainstream cotton production
and to less environmental and social harm for cotton farmers. Reducing the use of
water, experisive pesticides and fertilizers not only reduces the expenditures and frees
up household income, but also leads to health benefits for farmers and their families.
38
CHAPTER FIVE: SUMMARY OF FINDINGS, CONCLUSION AND
RECOMMENDATIONS
5.0 Introduction
This chapter comprises of and is organized into the following subheadings: summary
of findings, conclusions of the study, recommendations of the study and suggestions
for further studies based on the study objectives. The study was based on the
following objectives: find out how competition in the sub-sector affects cotton
production by small holder farmers; establish the extent to which marketing affects
cotton production among small holder farmers; examine the relationship between cost
of production and cotton productivity among small holder farmers and determine how
cotton production is affected by capacity of farmers in Makueni County.
5.1 Summary of Findings
5.2.1 Effect of competition on cotton production
This chapter analyzed effect of cotton production performance in Makueni County. It
started by looking at aggregate production and found out that cotton production and
yields are relatively low in the two counties; the amount of cotton lint marketed is
often insufficient to fulfil national demand. Following that the chapter characterized
effect of competition on production of cotton by small scale farmers. Results show
production performance in this sector for a period 2005 to 2010 and in Table 4.1. The
Table shows that production fluctuated from a low of 120 metric Kilograms in 2005
to a high of 1134 metric Kilograms in 2009.The figures recorded in 2009 can be a
result of the Land Reform policy which dismantled the Large Scale farming
community. Productivity averaged 232 Kilograms per hectare with the lowest yield of
100 Kilograms per hectare recorded in 2005 and a high of 600 Kilograms per hectare
in 2009Therefore it can be concluded that more than half of cotton output is produced
by smallholder farmers. The chapter then finally looked at the possible determinants
of cotton production in Makueni and possible explanations of effects of competition
where found to be price of cotton and of other competing crops.
39
5.2.2 Marketing and Cotton Production
In terms of the extent to which marketing affect, cotton production among small
holder farmers in Makueni County, the survey data indicates that majority (97.44%)
of cotton producer reported that they supplied their produce to the market. Sampled
farmers were also asked to identify their usual way of selling their produce. About
90.22% of them reported that they sell directly to traders or purchasers at local or
primary markets. Only about 9.78% of cotton producers said they sold their produce
through brokers. Similarly these sampled farmers reported that the usual time they sell
their produce was one month after harvest, (about 33% of farmers reported). In
addition, 65 percent of them replied that they sold their cotton immediately after
harvest. Only 2% of cotton farmers said they stored their produce for two months
after harvest. The reason they present is that cotton easily be contaminated and its
seeds affected if it is stored at home longer. Thus, they prefer to sell immediately after
harvest without expecting high price in the future.
5.2.3 Relationship between Cost of Production and cotton productivity
In summary concerning relationship between cost of production and cotton
productivity, some important variables were considered to determine their effects on
cotton productivity. All the variables were found positively contributing towards
higher yield of cotton in study area. From the discussion, it was concluded that there
was a dire need to fulfil the scarcity of resources for enhancing cotton production.
Among them, the major inputs include availability of quality seed, fertilizers like
DAP and Urea. The farmer's income from his cotton harvest in Makueni County was
often lower than the cost of the inputs due to low crop yields and market prices,
driving more and more farmers into debt. As cotton is a cash crop, cotton farmers are
highly dependent on volatile Kenyan markets. Cost of cotton production affects many
decisions, from applying inputs to marketing. It is important to be able to know
production costs for specific farming operation against the productivity in order to
compare with other areas and do some benchmarking. In conclusion the study failed
to establish the clear relationship between cost of production and productivity due to
lack of compromise the data on cost of production. For instance, there were certain
40
inherent limitations, like seed cotton yield could not be estimated in both Makueni
County and the inputs were found to be subsidized in different areas making it
difficult to calculate the actual cost of production.
5.2.4 Effect of capacity of farmers on cotton production
To determine how cotton production is affected by the capacity of the farmers,
majority 67.0% received their funding from banks and credit from other institutions,
55.0% received funding from their own salaries and wages and 18.0% from their
businesses to enhance their capacity to produce cotton. The study established that
better Cotton production helps the farmer to bring a competitively priced product to
market that satisfies the consumers. Therefore cotton farmers should have the capacity
do actualize the same. Capacity building has a positive effect on productivity in that
an increase in more sustainable farming practices leads to significant improvements in
mainstream cotton production and to less environmental and social harm for cotton
farmers.
5.3 Conclusion
For objective one, competition has seriously affected cotton productivity, therefore
this study concurs with findings during interviews with representatives of Extension
Officers, CODA officers, Farmer representatives, County Administrators and
KALRO Officers that the decline in the number of large-scale commercial cotton
growers is due to competitions and an increasing number of farmers diversifying into
more lucrative export crops such as Green grams, maize, cowpeas and beans due to
reduced viability in the cotton sector. The negative effect of competition in the sub-
sector on cotton production by small holder farmers in Makueni County in not an
exception because Kenya's cotton sector is generally characterized by a large number
of smallholder farmers with a low average yield and poor quality cotton fiber outputs
making competition to be an issue particularly for small holder farmers not only in
Makueni County but the entire country.
For objective two, the study concludes that there is a need to find further mechanisms
of mitigating challenges of cotton and its effect on cotton production. This is because
41
there have been reforms and despite all these reforms in cotton there were still
challenges in marketing of cotton in the entire Makueni County. Since 2005, CODA
had established more than 250 authorized buying points throughout Kenya's cotton
production areas in an effort to reduce access costs and facilitate exchange between
farmers, agents and ginners. Even though cotton growers negotiated prices directly
with buyers, lack of supportive market coupled with stiff competition in an
unregulated market, led to a general decline in the prices received by producers. Due
to lack of secondary market for seed cotton, ginneries are the only market and
therefore in theory, they have significant power to dictate prices paid to local
producers. The study was further informed that cotton growers have made some
attempts to organize as a way of increasing their market power, but have had little
success due middle men and brokers. On the other hand, ginneries are also extremely
dependent on local seed cotton supply, so the market relationship is one based on
interdependence that has not been fully actualized not only in Makueni County but the
entire Country.
Objective three regarding the relationship between cost of production and
productivity, the study concludes that there is a need to fulfil the scarcity of resources
for enhancing cotton production. Among them, the major inputs include availability
of quality seed and fertilizers. There are many factors that affect the production of
cotton. In this study, some important variables were considered to determine their
effects on cotton productivity. All the variables were found positively contributing
towards higher yield of cotton in study area. The study revealed that the yield and
profitability increases as the size of holding increases. The large farmers in the study
area were found more technology as well as resource oriented. The cost of production
for farmers is higher resulting in low yield and low profit. The study reveals that
farmers, who are already resource deficient, cannot bear the burden of increasing cost
of inputs. To address this issue, provision of subsidized inputs for this category of
farmers is the need of the hour which will help not only to enhance cotton
productivity, profitability and improve the living standards of farmers.
42
On objective four, improving capacity of the farmers is key to the better cotton
production, however, the production process is incomplete if the better produced
cotton does not enter the supply chain or if it cannot be traced through the various
stages of processing and manufacture into the finished product. That means it is
necessary to create a series of linkages between the actors in the chain - from farmers
to ginners and from ginners to suppliers. Based on the study findings, policy failure is
also a key issue characterizing the Cotton-textiles sub-sector; the way in which
liberalization was carried out completely and suddenly without prior consultation with
stakeholders, there were no complimentary polices for the stakeholders to cope with
effects of liberalization and no time allowed for adjustment. With the abolishment of
cooperative societies an privatization of state owned corporations, the Cotton Board
of Kenya was left without any role in the industry and no alternative institutions were
set up to deal with issues of regulation and coordination in the industry.
5.4 Recommendations
Based on study findings, the study made the following recommendations:
In response to objective one, competitiveness of the cotton sub sector in Kenya should
be addressed; at the moment the cost of production is high due to costs of electricity
and use of outdated technology - these issues must be addressed in order to improve
efficiency of production, lower costs and improve competitiveness. The state of
infrastructure must also be addressed, as these are some of the concerns raised by the
stakeholders. Taxes and ad-hoc charges, which raise costs of production, must also be
lowered to improve the enabling environment for the sub sector.
For objective two, lack or inadequate availability of quality seeds of cotton locally is
an obstacle to improve yields and income for rural livelihood in the project target area
and cotton production zones in general which has far reaching impact on the
marketing of cotton. Therefore provision of quality seeds and seed multiplication
initiative needs to be expanded to cater for the high demand for cotton seed not only
in Makueni but entire Country
43
Concerning objective three, the cost of production for small farmers is higher
resulting in low yield and low profit. The study reveals that the small farmers, who
are already resource deficient, cannot bear the burden of increasing cost of inputs. To
address this issue, provision of subsidized inputs for this category of farmers is the
need of the hour which will help not only to enhance cotton productivity, profitability
and improve the living standards of the small farmers.
In objective four Improving capacity of the farmers is key to the better cotton
production but if the better produced cotton does not enter the supply chain or not
traced through the various stages of processing and manufacture into the finished
product that sells to customers; it is necessary to create a series of linkages between
the actors in the chain - from farmers -to ginners and from ginners to suppliers.
Finally innovative strategies are required to encourage more participation of women
particularly where cotton production is largely perceived (culturally) to be exclusively
for men with women being engaged in production of food crops. With the high
incidences of migration of men to the cities (urban centres mainly Nairobi) among
others, women (and youth) are now taking over as the head of households
5.4 Suggestions for Further Research
Based on the study findings, the researcher suggested that further studies on effects of
cotton production among farmers should be carried in other parts of the country for
comparison before generalization. This study examined determinants of cotton
production among smallholder farmers, but did not quantify the welfare effects of
government policies on cotton producing households. There is also need to study the
relationship between farmers' responsiveness to policies and welfare affects in future
studies.
44
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Sl
APPENDICES
Appendix I: Questionnaire
Section A: Socio-economic Characteristics
1. Age of respondent _
2. Gender: Male [ ] Female [
3. Highest formal education attained by the respondent (circle answer): non-formal
Education [ ] primary [ ] secondary [ ] post-secondary [ ]
Other (specify) [ ]
4. Main occupation ofthe household head /respondent (circle answer):
Farmer [ ] Business [ ] Teacher [ ] Government Employee [
NGO employee [ ] other (specify) =6 --------------------
5 Type of Housing Structure
Permanent [ ]
5 Size of Land------
b) Family Size _
Semi-permanent [ Traditional Hut [ ]
7 In the table shown below please list and rank in order of importance 5 major crop
enterprises that you had planted last time you grew cotton
(year )
Crop Area Annual Price per Annual Ranking
Enterprise (Acre) yield kg/unit income
52
Section B: Challenges facing Cotton production
Where do you obtain funds for cotton production?
Banks/ credit [ ] ginner [ ]Other farm enterprises [ ] Business
Salary/wage [ ] Remittance [ ] Others (name )--------------------
5. What are the challenges that affect the production of cotton in your area?
Disease and pest related { }
Inputs & Equipment related issue { }
Market related issues { }
Transportation { }
Delayed payment . { }
Low prices { }
Others .
In your opinion what are the facing cotton production in this area?
Section C: Intervention Measures
6. What do you think should be done to improve cotton yield per ACRE?
53
9. Please list any development programme/activity being carried out in your area to
tackle the cotton production constraints
1. -----------------------------------------------------------------------------------------------------
11. ------------------------------------------------------------------------------------------------
Ill. -----------------------------------------------------------------------------------------------
IV. ----------------------------------------------------------------------------------------------------
10. Where do you get advice on better cotton farming practices?
Government extension officer=l, neighbours=2, NGOs=3, private companies=4,
none=5, others (specify) -------------=6
11. Please suggest your perceived methods for improving interactions/relations
between you and other cotton stakeholders as indicated below.
I. Extensi on 0fficers-------- ---------- --------- -------- --------------------------------- ------
54
11 Flesearcllers----------------------------------------------------------------------------------
iii Other cotton farmers-----------------------------------------------------------------------
IV. Cotton buyers------------------------------------------------------------------------------
V. Cotton ginneries----------------------------------------------------------------------------
VI. Cotton Development Authority----------------------------------------------------------
55
Appendix II: Interview Guide
Evaluating factors affecting cotton production in selected counties in Kenya
Focus group discussion checklist for farmers, extension officers & other actors
1. What are the major income generating activities of farmers
2. Generate a list of major cotton production constraints by consensus and provide
a matrix scoring for the constraints.
3. What is the rate accessibility to improved cotton production inputs, and
mechanisms for improving accessibility?
4. What tools and equipment are bottlenecks in cotton production?
5. State the support required for cotton marketing with respect to transportation,
storage issues, price! payment (promptness, amounts), governance in
smallholder farmer organizations, emerging issues
Aspect Required Remarks
support
Transportation
Storage
Cotton price
Payment promptness
Payment amount
56
Governance in smallholder farmer
organizations
6. What are the policy and regulatory issues touching on: production, post-
harvest handling, marketing, environment and labour regulations
57
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RE: RESEARCH AUTHORIZATION
Following your application for authority to carry out research on
'(Determinants of cotton production among smallholder farmers in Kenya:
The case of Makueni County," I am pleased to inform you that you have
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ending 31st December, 2014.
You are advised to report to the County Commissioner and the County
Director of Education, Makueni County before embarking on the research
project.
On completion of the research, you are expected to submit two hard copies
and one soft copy in pdf of the research report/thesis to our office.
S~SSEIN
FOR: SECRETARY/CEO
Copy to:
The County Commissioner
Makueni County.
The County Director of Education
Makueni County. --- .. ~..~.-
National Commission for Science, Technology and Innovation is ISO 9007: 2008 Certified
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IEB,C REVIS,ED MAKUENI CONSTITUENCY COUNTY ASSEMBLY WARDS
LegendD Constituency Boundaryo Subfocations Boundaries
Wardc=J Woteo Emali Mulala
·0Kathonzwenio Kitis€ Kithuki
D Nguu Masumba
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