household food insecurity and coping strategies among

HOUSEHOLD FOOD INSECURITY AND COPING STRATEGIES
AMONG SMALL SCALE FARMERS IN THARAKA CENTRAL
DIVISION, KENYA
NAME: ICHERIA BEATRICE KABUI
REG NO: H60/10722/2008
DEPARTMENT OF COMMUNITY RESOURCE MANAGEMENT AND
EXTENSION
A THESIS SUBMITTED IN FULFILMENT OF THE REQUIREMENTS
FOR THE AWARD OF THE DEGREE OF MASTER OF SCIENCE
(COMMUNITY RESOURCE MANAGEMENT AND EXTENSION) IN
THE SCHOOL OF APPLIED HUMAN SCIENCES OF KENYATTA
UNIVERSITY
MAY, 2012
i
DECLARATION
This thesis is my original work and has not been presented for a degree in any
other university or any other award.
Signature___________________
Date _________________
Name: Icheria Beatrice Kabui – H60/10722/2008
This thesis has been submitted for review with our approval as university
supervisors.
Signature___________________
Date __________________
Dr. Lucy Ngige
Community Resource Management and Extension
Kenyatta University
Signature_____________________
Dr. Alice Ondigi
School of Hospitality and Tourism
Kenyatta University
Date____________________
ii
DEDICATION
This thesis is dedicated to my mother Jeniffer Kagumo Icheria, and son Frank
Ndereba.
iii
ACKNOWLEDGEMENTS
I wish to express my sincere gratitude to my supervisors Dr. Lucy Ngige and Dr.
Alice Ondigi for their guidance, instruction and supervision of my research
concept paper, proposal and thesis. I also extend my sincere gratitude to Dr
Dorcas Mbithe for her advice and encouragement during difficult times of my
research work.
Finally, I wish to thank assistant chiefs and headmen of Tharaka Central Division
and residents for their support, co-operation and contribution to the study.
Thank you and God bless.
iv
ABSTRACT
Food insecurity is a major development problem that is caused by myriad of
factors in the global, regional, national and local spheres of human life. Several
efforts have been put in place to alleviate food insecurity globally, nationally and
even locally. Despite these efforts, the situation continues to prevail and
sometimes even increase in the contemporary human society. It is therefore
imperative that food insecurity gets addressed appropriately. Small scale farmers
play a vital role in food production especially through subsistent farming.
However, their households are major casualties of food insecurity despite their
efforts in food production. This study sought to investigate household food
insecurity and coping strategies among small scale farmers in Tharaka Central
Division of Tharaka South District, Kenya. The specific objectives of the study
were to: Establish the status of household food production among small scale
farmers in Tharaka Central Division; determine household food consumption
patterns; establish household food sources, establish the status of household food
insecurity and identify coping strategies among the households in the event of
food shortage. The research design employed in the study was cross sectional
descriptive survey which sought to obtain information that was to describe the
existing status of household food insecurity and coping strategies among the small
scale farmers. A total of 351 small scale farmers’ households were systematically
sampled from the total population of 3631 small scale farming households in the
division. Data was collected by use of structured questionnaire, observation
checklist and key informant interview guide. Data analysis was done using SPSS
(Version 11.5) computer software program. Frequency tables, pie charts, bar
graphs and line graphs are used to present the findings of the study. Mean
farmland sizes was 1.62 acres, food crops were cultivated at 95% of the total crop,
the major months of adequate and inadequate food provisioning were June to
August (40.5%) and October to January (30.2%) respectively. Crop loss was
mitigated by planting drought resistant crops. Household dietary diversity score
(HDDS) of the previous 24 hours was low (83.3%) while 50.7% had acceptable
household food consumption score (HFCS) in the previous 7 days of food
consumption. The primary source of maize was the market at 36.7%. Majority of
households (44.7%) were food insecure, 43.3% vulnerable to food insecurity and
12% food secure. Reduction in size of meals was the major coping strategy.
There were significant positive relationships between sizes of farms and sizes of
farmlands (r = 0.653, p=0.000); between HFCS and farmland size (r=0.299,
p=0.0000); significant difference between maize expected and maize harvested
(t=22.927, p=0.000). There was also significant positive association between
HDDS and HFCS (χ2=13.463, df=4 and p=0.009), sources of maize and the
statuses of household food insecurity (χ2=160.895, df= 6, p=0.000). Low food
production was precipitated by drought, food consumption patterns were mainly
characterized by low HDDS, and coping strategies were not detrimental to
livelihoods. It is recommended that the farmers’ local capacity should be
developed through community-based participatory actions; and the GOK through
the Ministry of Water and Irrigation should formulate irrigation policies and
implement them in all ASAL areas to alleviate household food insecurity.
v
TABLE OF CONTENTS
DECLARATION…………………………………………………..
i
DEDICATION……………………………………………………..
ii
ACKNOWLEDGEMENTS……………………………………….
iii
ABSTRACT…………………………………………………………
iv
TABLE OF CONTENTS…………………………………………...
v
LIST OF TABLES………………………………………………….
xi
LIST OF FIGURES ………………………………………………..
xiii
LIST OF ACRONYMS AND ABBREVIATIONS……………….
xiv
CHAPTER ONE: INTRODUCTION………………………………
1
1.1
Background to the Study……………………………………
1
1.2
Statement of the Problem…………………………………...
3
1.3
The Purpose of the Study…………………………………...
4
1.4
Objectives of the Study…………………………...................
5
1.5
Research Hypothesis………………………………………….
5
1.6
Significance of the Study……………………………….......
6
1.7
Conceptual Framework…………………………………..…
6
1.8
Operational Definition of Terms…………………………....
9
CHAPTER TWO: LITERATURE REVIEW………………………
11
2.0
Introduction……………………………………………........
11
2.1
Global Food Insecurity……………………………..……….
11
vi
2.2
Food Insecurity in Africa……………………….………...…
13
2.3
Food Insecurity in Kenya…………………………………...
14
2.4
Food Insecurity in Tharaka ………………………………...
13
2.5
Household Food Production............................………..........
17
2.6
Small Scale Farming and Household Food Insecurity...........
18
2.7
Crop Loss Mitigation...............................................................
19
2.8
Household Food Consumption Pattern………………………
19
2.9
Food Aid..................................................................................
22
2.10
Sources of Household Food…………………………………
23
2.11
Estimating Levels/Status of Food Insecurity..............................
24
2.12
Coping Strategies………………………………………………
26
2.13
Summary of the Reviewed Literature.........................................
28
CHAPTER THREE: RESEARCH METHODOLOGY…...……….
30
3.0
Introduction…………………………………………………
30
3.1
Research Design…………………………………………….
30
3.2
Study Area…………………………….……….……………
31
3.3
Population and Sample Size of the Study………………….
32
3.4
Sample Size Determination……..…………………………..
33
vii
3.5
Sampling Procedure…………………….………………….
33
3.6
Measurement of Variables……………………………......
36
3.6.1
Independent Variables…………………………………….
36
3.6.2
Dependent Variable………………………………………..
37
3.7
Research Instruments………………..……………………
38
3.8
Pre-testing Research Instruments………………………..
38
3.8.1
Reliability………………………………………………..
39
3.8.2
Validity………………………………………………….
40
3.9
Training Research Assistants……………………………
40
3.10
Data Collection Procedures………………………………
41
3.11
Ethical Considerations...………………………………….
42
3.12
Data Analysis…………………………………………….
43
CHAPTER FOUR: FINDINGS AND DISCUSSION……………..
45
4.0
Introduction…………………………………………………
45
4.1
Household Demographic Information………………………
45
4.1.1
Household Size……………………………………………..
45
4.1.2
Education Levels of Household Heads……………………
47
4.1.3
Household Type of Housing……………………………….
48
4.1.4
Household Cooking Energy……………………………….
48
viii
4.1.5
Household Main Source of Livelihood…………………….
49
4.2
Household Food Production………..…………………….….
51
4.2.1
Sizes of Household Farms and Farmlands…………………
51
4.2.2
Types of Crops Cultivated in March/May and
October/December Seasons of 2010………………………..
53
4.2.3
Amount of Harvests for Food Crops………………………
55
4.2.4
Months of Household Food Provisioning………………….
56
4.2.5
Crop Loss Mitigation………………………………………
58
4.2.6
Droughts and Flooding……………………..…………….
59
4.3
Household Food Consumption Patterns……………………
59
4.3.1
Meal Patterns among the Households……………………….
59
4.3.2
Main Foods Consumed in Meals……………………………….
61
4.3.3
Household Dietary Diversity of 24 Hour Recall……………….
62
4.3.4 The 7 Day Food Frequency………………………………………
64
4.3.5
Household Food Consumption Score (HFCS)…………………...
67
4.4
Household Food Sources…….…………………………………..
68
4.4.1
Main Sources of Food Items…………………………………….
68
4.4.2
Food Aid Support………………………………………….……
71
ix
4.4.3
Amount of Maize Received from Food Aid………………………
72
4.5
Household Food Insecurity Status………………..……………….
72
4.5.1
Household Food Insecurity Status According to HDDS……….
73
4.5.2
Household Food Insecurity Status According to HFCS…… …..
74
4.5.3
HDDS and HFCS…………………….…………………………
75
4.5.4
Statuses of Household Food Insecurity and Sources of Maize
77
4.6
Coping Strategies……………………………………….……….
78
4.6.1
Coping Strategies Commonly Used among Households…........
78
4.7
Hypotheses-Testing Results………………………………........
80
4.7.1
Relationship between Sizes of Farms and Sizes of Farmlands
80
4.7.2
Differences between Food Expected and Food Harvested… …
81
4.7.3
Relationship between the Status of HFCS and Household Size
82
4.7.4
Relationship between HFCS and Farmland Size…………... ….
83
4.7.5
Association between HDDS and HFCS…...……………….. ….
84
4.7.6
Association between Sources of Maize and the Status
of Household Food Insecurity………………………………….
85
CHAPTER FIVE: SUMMARY, CONCLUSIONS AND
RECOMMENDATIONS …………………………………………..
86
5.0
86
Introduction…………………………………………………
x
5.1
Summary……………………………………………………..
86
5.2
Conclusion……………………………………………………
87
5.3
Recommendations………………………………………….. ..
88
5.3.1
Recommendation for Policy Making…………………………
88
5.3.2
Recommendations for Practice……………………………….
90
5.4
Suggestions for Further Research……………………….........
91
REFERENCES………………………………………………………
92
RESEARCH INSTRUMENTS………………………………………
98
APPENDIX 1: Respondents’ Informed Consent……………………
98
APPENDIX 2: Questionnaire for the Household Head and
Household Principal Care Giver ……………………………..………
99
APPENDIX 3: Observation Checklist ……………..………………
115
APPENDIX 4: Key Informant Interview Guide for the District
Extension Officer and ALRMP II Manager…………………………
116
xi
LIST OF TABLES
Table 4.1
Household Size …........................................................
46
Table 4.2
Education Levels of Household Heads……………....
47
Table 4.3
Household Type of Housing.…………………………
48
Table 4.4
Household Cooking Energy………………………….
49
Table 4.5
Household Main Source of Livelihood………………
50
Table 4.6
Sizes of Household Farms ………………………..…
51
Table 4.7
Sizes of Household Farmlands………………………
52
Table 4.8
Types of Crops Cultivated..………………………….
54
Table 4.9
Months of Adequate Food Provisioning…………… .
56
Table 4.10
Months of Inadequate Food Provisioning …………. .
57
Table 4.11
Foods Consumed at Breakfast…….……………….. .
61
Table 4.12
7 Day Food Frequency….…………………….......... .
65
Table 4.13
HFCS………………………………………..……….
68
Table 4.14
Main Sources of Food Items..………………………..
69
Table 4.15
Cross-tabulation of HDDS and HFCS………………
75
Table 4.16
Cross-tabulation of Statuses of Household Food
Insecurity and Sources of Maize…………………… .
77
xii
Table 4.17
Coping Strategies Commonly Used among
Households………………………………………….
Table 4.18
Differences between Food Crops Expected
and Harvested……………………………………….
Table 4.19
Table 4.20
79
81
Relationships between the Statuses of HFCS
and Household Size…………………………………
82
Relationship between HFCS and Farmland Size…..
83
xiii
LIST OF FIGURES
Figure 1.1
A Conceptual Model Illustrating Household Food
Consumption Approach Adapted from WFP (2006)….
8
Figure 4.1
Meal Patterns among Households……………….…….
60
Figure 4.2
Dietary Diversity of 24 Hour Recall…………………..
63
Figure 4.3
Households’ Food Aid Support……………………. …
71
Figure 4.4
Amount of Maize Received from Food Aid…………..
72
Figure 4.5
Household Food Insecurity Status according to HDDS
73
Figure 4.6
Household Food Insecurity Status according to HFCS
74
xiv
LIST OF ACRONYMS AND ABBREVIATIONS
ALRMP
Arid Lands Resource Management Project II
ASAL
Arid and Semi Arid Lands
CBS
Central Bureau of Statistics
FANTA
Food and Nutrition Technical Assistance
FAO
Food and Agriculture Organization
FFW
Food for Work
GOK
Government of Kenya
HDDS
Household Dietary Diversity Score
HFCA
Household Food Consumption Approach
HFCS
Household Food Consumption Score
HFIAS
Household Food Insecurity and Access Scale
IFPRI
International Food Policy Research Institute
KARI
Kenya Agricultural Research Institute
MoA&L
Ministry of Agriculture and Livestock
MT
Metric Tonnes
NFP
National Food Policy
NFSNP
National Food Security and Nutrition Policy
xv
$
US Dollar
UNICEF
United Nations Children’s Fund
WFP
World Food Programme
WHO
World Health Organization
1
CHAPTER ONE: INTRODUCTION
1.1 Background to the Study
The World Food Summit of 1996 described food insecure households as those whose
members do not have physical and economic access to sufficient, safe and nutritious food
to meet their dietary needs and food preferences for an active and healthy life (Aiga &
Dhur, 2006). Despite the right of every man, woman and child to be free from effects of
food insecurity (including household food insecurity) being declared during the World
Food Conference of 1974 (GOK 2008a), these effects linger in the global society.
Household food insecurity is one of the major catastrophes in the Sub-Saharan Africa. In
Kenya 10 million persons and their households are highly food insecure, with 3.2 million
food insecure persons living in arid and semi-arid lands (ASALs) of the country (WFP,
2009).
The Kenya Vision 2030 and the National Food Security and Nutrition Policy (NFSNP)
stipulate that the Government of Kenya (GOK) has consistently emphasized on local food
production as one of the means of alleviating household food insecurity (GOK, 2008;
GOK, 2008b). However, despite the formulation of the strategic plans, household food
insecurity continues to persist since there is marked reliance on relief supplies by the
poor, and in Kenya, 53% of the people in rural areas are overall poor while 51% are food
poor (GOK, 2008c).
2
Household food insecurity in the country is attributed to factors such as decline in
agricultural productivity resulting from continuous land fragmentation.
Most of the
original large scale farms in Kenya have been sub-divided beyond economically
sustainable agricultural production. As a result of the fragmentations, some 89% of the
households in Kenya are living in less than 7.5 acres of land while 47 % live on farms
less than 1.5 acres (Gitu, 2004).
According to WFP (2009), farm family households in ASAL areas practise livestock
production to mitigate crop losses. However, low numbers of livestock and their poor
body conditions (as a result of extended trekking in search of water and pasture) has
caused a 50% decline in their value. Furthermore, these households are also depending
on undesirable mitigation strategies against their household food insecurity, such as
charcoal production, which further degrade the environment and endanger future food
production (ibid). Gitu (2004) observes that there is abandonment of indigenous drought
resistant crops in ASAL areas due to changes in food tastes and preferences constraining
drought resistant crop cultivation to mitigate crop losses.
According to FAO’s (2007) study, there are few households in developing countries
where gardens act as a major source of food to meet household consumption
requirements. A study carried out in Umbumbulu in Kwa-Zulu Natal province of South
Africa to investigate household coping strategies against food insecurity revealed that
most households obtained foods through purchases (93%), followed by own food
production (4%), gifts and payments. Households from Umbumbulu did not consume
3
sufficient food from their own production which was attributed partly to the sale of
produce to purchase other foods or the purchase of other non food goods, or the
households did not produce sufficient food for consumption (Mjonono, Ngidi &
Hendriks, 2009). Due to varying degrees of wealth among households, different coping
behaviors are adopted by households at different poverty levels (ibid).
1.2 Statement of the Problem
Like other countries in Africa, Kenya looks towards achieving the Millennium
Development Goals (MDGs) by 2015. The first goal is of alleviation of extreme poverty
and hunger and the country plans to achieve this, by reducing the proportion of people
who suffer from hunger by half by 2015, (GOK, 2008b). To achieve this, implementing
the millennium strategic plans at the grass root levels (such as divisions) is imperative.
This will ensure reduction of household food insecurity.
Household food insecurity is a critical issue in Kenya because the magnitude of
household food insecurity in the country is alarming especially in ASALs that comprise
of 88% of Kenya’s land area (Gitu, 2004). Tharaka Central Division in Tharaka South
District in the Eastern Province of Kenya is one such an ASAL area that has continued to
experience frequent household food insecurity (GOK, 2009). This is despite of national
food policy formulation of alleviating household food insecurity, especially among small
scale farmers through local agricultural food production (GOK, 2008c).
4
Small scale farmers are important players in alleviating household food insecurity by
increasing household food access, availability and utilization through their subsistent own
crop production. However, own crop production has not played a key role as the main
source of household food in Tharaka (Smucker & Wisner, 2008). Food shortages due to
high levels of household food insecurity in Tharaka predispose households to employ
adverse coping strategies (GOK, 2009).
Not much has been documented on the status of household food production, household
food consumption patterns, household sources of food, status of household food
insecurity and coping strategies among small scale farmers in Tharaka Central Division.
Due to the aforementioned observation, the study on household food insecurity and
coping strategies among small scale farmers in Tharaka Central Division was deemed
necessary.
1.3 The Purpose of the Study
The purpose of the study was to establish the status of household food insecurity and
identify coping strategies among small scale farmers in Tharaka Central Division of
Tharaka South District, Kenya.
5
1.4 Objectives of the Study
The specific objectives of the study were to:
1. Establish the status of household food production among small scale farmers in
Tharaka Central Division.
2. Determine household food consumption patterns among the small scale farmers in
Tharaka Central Division.
3. Establish household sources of food among small scale farmers in Tharaka
Central Division.
4. Establish the status of household food insecurity among small scale farmers in
Tharaka Central Division.
5. Identify coping strategies in the event of food shortage among the small scale
farmers’ households.
1.5 Research Hypotheses
Ho1. There is no significant relationship between farm size and farmland size at a
significant level of 0.05.
Ho2. There is no significant difference between food expected and food harvested at
a significant level of 0.05.
HO3. There is no significant relationship between the statuses of household food
consumption scores and household size at a significant level of 0.05.
HO4 There is no significant relationship between household food consumption score and
farmland size at a significant level of 0.05.
6
HO5.
There is no significant association between household dietary diversity score and
household food consumption score at a significant level of 0.05.
HO6.
There is no significant association between sources of maize and the status of
household food insecurity at a significant level of 0.05.
1.6 Significance of the Study
The study aimed at establishing the status of household food insecurity and coping
strategies among small scale farmers in Tharaka Central Division of Tharaka South
District. The findings of the study will be shared and discussed in Tharaka Central food
security stakeholder meetings. This will help build capacity among the small scale
farmers concerning household food insecurity and coping strategy issues. The findings
will also be shared with the Ministry of Agriculture and Livestock to provide relevant
input in policy making in the area of household food insecurity and small scale farming
practices. The findings will provide relevant data to local NGOs in planning food aid
support programmes. The findings will also contribute to the body of knowledge in the
academia and may provide insights on food security gaps for further academic research.
1.7 Conceptual Framework
The conceptual framework is based on the World Food Program’s (2006) Household
Food Consumption Approach model that uses dietary diversity, food frequency and food
sources as household proxy indicators of household food insecurity (household food
availability, access and utilization) to estimate the severity or status of household food
7
insecurity. These indicators interacted with other variables: farmland size, types of crops
cultivated, amount of harvests, months of household food provisioning, crop loss
mitigation, drought and flood occurrence, food aid and coping strategies.
8
Household Dietary Diversity
Food Consumption Groups
Household Food Frequency


Acceptable
Borderline
Food Poor

•
•
•
•
•
•
•
•
•
•
•
•
Farm size
Farmland Size
Types of Crops Cultivated
Amount of Food Expected
Amount of Food Harvested
Months of Household
Food Provisioning
Crop Loss Mitigation
Mechanisms
Droughts and Floods
Foods Consumed in 24
Hour Recall
Number of Meals in 24
Hour Recall
Frequency of Food
Consumption in the
previous 7 Days
Sources of Foods
Status of Household Food Insecurity

Food Security

Vulnerability to Food Insecurity

Food Insecurity
Food Security Groups
Household Food Sources



Food Secure
Vulnerable to Becoming
Food Insecure
Food Insecure
Figure 1.1: A Conceptual Model Illustrating Household Food Consumption Approach
Adapted from WFP (2006)
9
1.8 Operational Definition of Terms
Coping strategies: Ways of reducing impacts of a negative event once it has occurred
such as household food insecurity.
Farm family: Household whose livelihood orientation is farming.
Farmland size: Size in acres of household land under cultivation
Farm size: Size in acres of the entire household land holding
Household: A unit comprising of a group of persons living together, sharing from the
same dietary pot and same source of livelihood on a regular basis.
Household dietary diversity: The number of food groups (a grouping of food items that
have similar calorific and nutrient qualities) consumed by household members in the last
24 hours.
Household food consumption frequency: The frequency that a specific food group is
eaten at the household level in the last 7 days.
Household food consumption patterns: The patterns in terms of diversity of food
consumed and the pattern in frequency of food consumption in a household.
Household food insecurity: Inability of a household to have enough food to provide and
sustain its members’ dietary intake. Household food insecurity has three components:
Unavailability, lack of access and non utilization of food.
Household food production: Food crop cultivation and food harvests in a household
Household Food Provisioning: The presence or absence of food in a household.
Household principal care giver: The person who is either responsible or oversees food
preparation (mainly a female).
10
Small scale farmers: Farmers whose agricultural orientation is mainly subsistence and
cultivate land not exceeding 10 acres.
11
CHAPTER TWO: LITERATURE REVIEW
2.0 Introduction
This chapter discusses global, Africa, Kenya, and Tharaka household food insecurity
situations. Household food production, small scale farming and household food
insecurity, crop loss mitigation, household food consumption patterns, food aid, sources
of household food, approaches of estimating levels/status of household food insecurity,
coping strategies and the summary of the reviewed literature are also covered in the
chapter.
Sources of the literature were internet, journals, government documents,
newspapers, textbooks and the visual media.
2.1 Global Food Insecurity
Despite growing attention in the world media and expanding aid efforts by many
organisations, the world household food insecurity continues to worsen as many
communities struggle with daily hunger and starvation (Project Concern International,
2009).
A myriad of factors have been responsible for the continuing world food
insecurity. One factor is the rise in prices of the world staple foods (wheat, rice and corn).
It is established that inflation of wheat is 120% and rice is 75% (ibid). Another factor is
poverty. An estimated 100 million people have fallen into poverty in the last two years for instance in 2007, Afghanistan households were spending 75% of their income on food
(World Bank, 2008). Dependence on food imports also influences the global food
insecurity. A case in point is Haiti where over 80% of staple rice is imported. The result
of it is that over half of the country’s population is under-nourished and 24% of children
12
suffer chronic malnutrition. Fresh food exports, for instance the export of horticulture
produce from Ghana to Europe for monetary gains has resulted in the country importing a
significant proportion of its staple food such as rice, ultimately leaving the country
exposed to the spiralling world food prices. Moreover, the climate change due to global
warming has influenced world household food insecurity. El-ninos and La- ninas hamper
good crop production in Latin America and the Sub-Saharan Africa. Droughts caused by
La-ninas have caused household food insecurity especially in Ethiopia where 7 million
people are classified as food insecure and a further 10 million classified as prone to
drought, (ibid). Other factors that contribute to household food insecurity in the world
include: Shift to more non-agricultural technology, politics, environmental degradation,
insecurity and high population growth.
Several consequences of global household food insecurity have manifested themselves.
Demand for food aid is a serious consequence of the food insecurity. Each year, 10% of
Burundi’s population requires food aid, (FAO, 2008). Another consequence is poor
health status exemplified in Benin, whereby almost a quarter of children below 5 years
are underweight, (ibid). There are also increased malnutrition rates globally whereby in
2004, the global malnutrition was 15%, (WHO, 2004). World household food insecurity
has also increased poverty among the global population and there was also serious global
hunger index of 15.1% in 2010 (Grebmer, et al., 2010).
13
2.2 Food Insecurity in Africa
Various countries in Africa have experienced the devastating effects of household food
insecurity. For instance, Cameroon in West Africa, Egypt in Northern Africa, Ethiopia in
the Eastern Africa and South Africa in the extreme Southern Africa. The World Food
Programme (WFP) describes Cameroon as a food insecure country, and has further
demonstrated that food intake in households is lower now than in the early 1980s. The
result of this is that 19% of young children in the country are underweight and child
mortality rate is rising rather than falling (Oneworld.net (US), 2009).
Egypt produces half of its demand for wheat. In spite of the average food production, the
country is exposed to the escalating food prices due to its wheat imports. It is classified
as the number one importer of the produce in the world. The country also has a high
population growth rate of 2% per annum. Moreover, the desert terrain of the Sahara
limits crop production. A report by the World Bank indicates that the baladi bread
subsidy costs Egyptian government almost $ 3.5 million per annum (Oneworld.net (US),
2009).
Ethiopia experiences serious household food insecurity. Over 7 million people out of
Ethiopia’s population of 76.9 million people are classified as food insecure; and a further
10 million people are identified as prone to drought. High population growth rate in the
country increases the food insecurity further (Chu, 2009).
Although South Africa
produces bumper harvests especially in the 2007/08 season, it has been affected by high
food prices in the declining world economy. High food prices are causing hardship
14
particularly among the poorest family households who spend a huge proportion of their
income on food (Oneworld.net (US), 2009).
2.3 Food Insecurity in Kenya
Household food insecurity in Kenya is caused by inadequate farming area. It is only 18%
of Kenya’s territory which is suitable for farming. Another cause is poverty.
The
2007/08 United Nations Human Development Report noted that almost 24% of Kenyans
are living on less than one dollar a day, therefore not food sustaining (CBS, 2009).
Droughts in ASAL areas of Kenya have brought about a decline in crop and livestock
production among households in these regions. Moreover floods cause displacement of
people making them vulnerable to household food insecurity. It is estimated that the 2006
floods affected 700,000 people in the country; most of them cut off from food help due to
impassable roads (ibid). The 2008 post election violence disrupted the March/April
agricultural production. The World Food Programme reported that 50% of farmers were
not sufficiently prepared for farming due to the post election turmoil. In addition, erratic
rainfall exacerbates household food insecurity in the country.
Poor rains in 1996
prompted the GOK to declare a state of national disaster on January the 28th (IRIN
Humanitarian Report, 1997).
The GOK has assisted farmers in crop production by providing farm input subsidy, by
granting a 10% price reduction for seeds. The Citizen News reported that the government
has also imported fertilizer thus bringing down the cost from an all-time high of Ksh5,
15
500 to Ksh2, 500 per 50 kilogram (kg) bag. Successive years of drought up to 2006
compelled the WFP to provide relief support to over 3 million people in the country. The
GOK in collaboration with the WFP is also feeding 1 million people under the
Emergency Intervention Programme, while another 1 million are receiving direct
government aid (Daily Nation Correspondents, 2009).
2.4 Food Insecurity in Tharaka
The following literature review is based on the larger Tharaka District (before it was subdivided into Tharaka South and Tharaka North districts). This is because the available
literature concerning the area of study is only based on the previous district. Tharaka
District is situated in the lowlands of Meru region. It experiences bimodal rains and high
temperatures. The soil types range from sandy loamy soils to stony sandy soils. Tharaka
Central Division is situated in the marginal mixed farming livelihood zone of the district
(GOK, 2008d).
Unreliable weather is a major cause of household food insecurity in the area. In 2008,
poorly distributed rains in the district made crops perform dismally at less than 50% of
normal crop performance (ibid). Prolonged drought in the region has brought about
unsteady and low crop production. In 2005, the total cereal production was 8,014 metric
tonnes against the estimated annual demand of 16,906 metric tonnes. The failure of short
rains in the subsequent two years decreased crop output dismally. The low food
production leaves a gap of nearly 50% which exposes the area to high food prices. The
16
cost of beans escalated from KSh40 in the early 2008 to KSh80 in 2009 (GOK, 2008e).
Poor markets infrastructure hinders redistribution of food to the markets in the low
potential areas of the district. Transportation is costly and constrained by poor transport
and communication systems.
This often results in high food prices and ultimate
household food insecurity due to poorly integrated markets (GOK, 2008d).
Poor Nutritional Status is one of the effects of the household food insecurity in Tharaka.
In 2007, the district had 153 cases of underweight children attended to and 5 cases of
protein energy malnutrition (PEM) observed and attended to (GOK, 2008c). The limited
health facilities in the area are not easily accessible due to poor road infrastructure.
Moreover, the doctor to patient ratio is low – 1:100,992. Malaria is the most frequently
treated disease in the health facilities and contributes to high death rate especially among
children below five years of age. The district has a morbidity rate of 18% and about 76
children die before their fifth birthday every year. This results into overutilization of
health facilities. The district malnutrition increased from 5.7% in May 2008 to 6.2% in
June 2008 among the children between 12 to 59 months which was attributable to
household food insecurity situation in the year. It was also reported that out of the 1,027
assessed children across the region, 64 of them showed signs of malnutrition (ibid).
The community is highly dependent on relief supplies especially in the October to
December months. There have been persistent food shortages in the area during the
mentioned months due to prolonged dry spells beginning in June to October; hence there
17
is no food cultivation during these periods (GOK, 2008e). The food aid support is by the
Catholic Diocese of Meru, the WFP, Plan Kenya and the GOK by supplying relief food,
cooking oil and food supplements to the affected (ibid).
2.5 Household Food Production
In the year 2000, the food available for Kenyans was 1965 calories per capita per day,
which was below the recommended 2250 calories per day and the source of calories
comes mainly from maize, which accounts for 36% of foodstuff. The food availability
has been declining largely because maize production was down by 44% on per capita
basis in 2000 compared to 30 years before due to local staple food production being
outstripped by a relatively high rate of population growth (Gitu, 2004). The major
cereals produced in Kenya are maize, wheat, and to a limited extent rice in higher
potential areas while traditional food crops such as sorghum, millet, cassava, vegetables,
and fruits are mainly cultivated in ASAL areas (ibid).
In normal rainfall years, the country produces about 2.7 million MT of maize, 270,000
MT of wheat, and 50,000 MT of rice while the production levels of cash crops that
contribute to food security are coffee, tea, sugar and cotton, and the annual production for
these commodities is 100,000 MT of clean coffee, 294,000 MT of processed tea, 420,000
MT of sugar and 40,000 MT of cotton lint (Gitu, 2004). Maize production during long
rains ranges from 26 to 30 million 90 kg bags out of which smallholder farms produce 75
percent whereas average maize yield is 2 MT per hectare.
Wheat production has
18
stagnated at just 270,000 MT against a rising demand currently estimated at 720,000 MT
(ibid). Rice production is mainly through irrigation in irrigation schemes in Mwea,
Ahero, West Kano and Bunyala. The average annual production of rice is estimated at
52,000 MT which accounts for 34% of national rice consumption (ibid).
In spite of the different efforts in developing sorghum and millet, mainly because of their
significance in drought prone areas, there has been a notable decrease in acreage over the
last few years from 300,000 hectares in 1996 to 260,000 hectares in 2000. Pulse
performance shown a declining trend, because of bad weather, low quality seeds, high
cost of inputs and lack of suitable varieties for marginal areas while roots and tubers
which are high in calorific value, are important food security crops but their production
has been constrained by lack of clean planting materials (Gitu, 2004).
2.6 Small Scale Farming and Household Food Insecurity
Household food insecurity is influenced among small scale farmers by continued land
fragmentation, among other factors. Most of the original large-scale farms in Kenya have
been sub-divided beyond economically sustainable crop production capacity. As a result
of the fragmentations, some 89% of the households in Kenya are living in less than 7.5
acres while 47% of households live on farms less than 1.5 acres; therefore the country is
predominantly made of small farms (93% of households in the country are of small scale
farming orientation) and it is only 10% of the households that live on lands above 7.5
acres (Gitu, 2004). This constrains large crop production among the farmers.
19
2.7 Crop Loss Mitigation
According to Rose (2008), mitigation strategies seek to minimize the potential impact of
a hazardous event that may occur. Planting of drought-resistant crops such as cassava can
reduce the shortfall of food that a household might experience in a year of low rainfall.
Effective storage also mitigates crop losses by stabilizing food supply at the household
level by smoothing seasonal food production (Thamaga-Chitsa, etal., 2004). Inadequate
post-harvest storage contributes to household food insecurity, and more so in areas with
high humidity. Crop storage efficiency depends on storage length, losses during storage
(including quality deterioration) and storage volume. Losses are largely due to pests and
oxidative damage. For storage to be effective, crop losses must be minimized. Inefficient
storage increases the likelihood of grain vermin and pest to access the stored grains
therefore increasing losses and compromising the quality and safety of the stored grain,
and again, farm family households in ASAL areas of Kenya are said to mitigate crop
losses mainly by livestock production (WFP, 2009).
2.8 Household Food Consumption Pattern
A good household consumption pattern is achieved when the consumption of food is
adequate in terms of quantity, is safe and is of good quality to make up a healthy diet
(Agriculture and Consumer Protection, 2010).
However, there are adverse dietary
changes (nutrition transition) due to changes in lifestyle, which include shifts in the
structure of the diet towards a higher energy density diet with a greater role for fat and
added sugars in foods, greater saturated fat intake (mostly from animal sources), reduced
intakes of complex carbohydrates and dietary fibre, and reduced fruit and vegetable
20
intakes (ibid).
Household food consumption patterns are influenced by household
income, food prices, intra-household preferences and beliefs, cultural practices,
geographical, environmental, social and economic factors (Agriculture and Consumer
Protection, 2010).
Household Food Consumption pattern can be measured by estimating gross household
production and purchases over a period of time, estimating growth or depletion of food
stocks held over that period of time and presuming that the food that has come into a
household’s possession and ‘disappeared’ has been consumed.
Household food
consumption can also be measured by undertaking 24 hour recalls of food consumption
for individual members of a household, and analyzing each food type mentioned for
calorific content. In such a study, respondents are required to remember the consumption
quantities for food (IFPRI, 2008).
A household food consumption pattern may encompass household dietary diversity and
household food frequency. According to GOK (2008c), dietary diversity is the number
of individual foods or food groups consumed over a fixed period of time and it is also
reflective of adequate nutrient intake. Dietary diversity encompasses nutrient adequacy
and calculation of number of different food groups rather than calculating different
individual foods - because food groups offer diversity in micro and macronutrients,
(ibid). There are 12 food groups adopted from FAO and WHO by National Food
Security and Nutrition Strategy (NFSNS) in calculating household dietary diversity score
(HDDS): cereals, roots and tubers, vegetables, fruits, meat-poultry-and-offal, eggs, fish
21
and sea food, pulses-legumes-and-nuts, milk and milk products, oil/fats, sugar and honey,
miscellaneous (ibid).
Dietary diversity as an indicator of household food insecurity is characterized by
consuming a variety of foods within and across food groups, and increased dietary
diversity has been reported in several studies to relate with adequate intake of energy and
essential nutrients, thus leading to improved overall nutritional quality of diets (Moikabi,
2011). Increase in dietary diversity is associated with high socio-economic status and
good household food security (Haddinot & Yohannes, 2002).
Household dietary
diversity score (HDDS) is the sum of the different food groups consumed, and HDDS of
24 hour recall involves the 12 food groups consumed by households and it is classified
thus: ≤3, 4 to 5 and ≥6 as lowest dietary diversity, medium dietary diversity and high
dietary diversity respectively (Kennedy, Ballard, & Dop, 2011).
Household food frequency is the frequency of consumption of food groups by household
members in the previous 7 days. Household Food Consumption Score (HFCS) is a
frequency-weighted HDDS. The HFCS is calculated using the frequency of consumption
of eight different food groups consumed by a household during the 7 days before a
survey or a study according to the following procedure by IFPRI (2008) - which uses 8
food groups in calculating HFCS: Main staples, pulses, vegetables, fruits, meat and fish,
milk, sugar, oil. HFCS is measured using standard 7 day food data by classifying food
items into food groups; summing the consumption frequencies of food items within the
same group (any consumption frequency greater than 7 is recoded as 7; multiplying the
22
value obtained for each food group by its weight for example 2, 3, 1, 1, 4, 4, 0.5 and 0.5
are weights for main staples (cereals, roots and tubers), pulses, vegetables, fruit,
meat/fish/eggs, milk, sugar and fat/oil respectively; summing the weighted food group
scores and finally recoding the variable HFCS from a continuous variable into a
categorical variable for the food consumption groups using appropriate thresholds: 0-21
as food poor, 21.5-35 as borderline and >35 as acceptable, (IFPRI, 2008). The main
advantage of using household dietary diversity and household food frequency as proxy
indicators of household food insecurity is objectivity and measurability (Aiga & Dhur,
2006).
2.9 Food Aid
Food aid to households is an important relief for emergencies during food short falls in
households and also increases access to food by households (FAO, 2008). Food aid from
various donors such as USA and EU acts as relief for emergencies during shortfalls of
food production globally (Gitu, 2004). The United States is the world’s largest food aid
donor and provides approximately half of all food aid to vulnerable populations
throughout the world; and in 2008, the US government provided more than 2.6 million
MT of food commodities worth more than $2.6 billion to 56 million beneficiaries
worldwide (USAID, 2009).
23
Most common application of food aid include: General distribution of free food to
vulnerable groups based on vulnerability criteria and needs assessment; food for work
(FFW) - if the emergency intervention is mounted rapidly enough so that it begins before
people have been badly affected by the crisis, since food for work is not an appropriate
intervention for people who are already malnourished or who lack the energy necessary
to undertake physical labour; specific feeding programmes including supplementary or
therapeutic feeding for acutely affected sub-groups, and occasionally, the strategic use of
monetization, or the sale of food aid in local markets - can be used as a means of
controlling food price hikes in the event of acute food shortages and rapidly rising prices,
particularly in urban areas or among populations that are heavily dependent on the market
for their food (Maxwell, et al., 2008).
2.10 Sources of Household Food
Food aid by food agencies such as WFP and NGOs increases access to food by
households, (Rose, 2008) and is a relief for emergencies during shortfalls of food
production among farm family households in ASAL areas (Gitu, 2004). According to
FAO’s (2007), there are few households in developing countries where gardens act as a
major source of food to meet household consumption requirements. A study carried out
in Umbumbulu in Kwa-Zulu Natal province of South Africa to investigate household
coping strategies against food insecurity revealed that most households obtained foods
through purchases (93%), followed by own food production (4%), gifts and payments.
Households from Umbumbulu did not consume sufficient food from their own
24
production which was attributed partly to the sale of produce to purchase other foods or
the purchase of non food goods, or the households did not produce sufficient food for
consumption (Mjonono, Ngidi, & Hendriks, 2009).
2.11 Estimating Levels/Status of Food Insecurity
There are various approaches of estimating levels of household food insecurity.
However, there is no single approach that is universally accepted as the standard measure
of the levels (Aiga & Dhur, 2006). Global household food insecurity levels can be
described by high food prices, high levels of malnutrition, high levels of maternal
mortality, high levels of vulnerability and high levels of poverty (UN Food Security
Taskforce, 2008).
Vulnerability, for those concerned with food insecurity, is the
probability of an acute decline in food access or consumption due to hazards in the
physical or social environment. Typical hazards include weather disturbances, such as
drought, or man-made disturbances, such as civil war or extreme price fluctuations (Rose,
2008).
One of the main problems with measuring household food insecurity is the absence of a
single indicator that could capture the definition of ‘food-insecure households’ hence, the
results of household food insecurity measurement may vary according to who conducts
each assessment (Aiga & Dhur, 2006). To contribute to efforts to standardize household
food insecurity measurement, WFP (2006) has explored the use of an indicator that could
adequately estimate the severity of household food insecurity by adopting Household
25
Food Consumption Approach (HFCA) that uses a variety of indicators and approaches to
describe multifaceted dimensions of household food insecurity and the status of
household food availability, access and utilization; and the indicators are household food
consumption pattern indicators - dietary diversity, food frequency and food sources
(ibid).
HDDS of 24 hour recall involves 12 food groups and are classified thus: ≤3, 4 to 5 and
≥6 as lowest dietary diversity, medium dietary diversity and high dietary diversity, and
are further referred to as poor, borderline and acceptable food security status respectively
(Kennedy, Ballard, & Dop, 2011).
HFCS thresholds of 7 day food frequency are
classified thus: 0-21 as food poor, 21.5-35 as borderline and >35 as acceptable (IFPRI,
2008). However, for households that consume oil and sugar nearly daily, the thresholds
for the three consumption groups are raised from 21 and 35 to 28 and 42 according to
WFP (2007) to avoid serious underestimation of food insecurity status (ibid).
A research carried out in 2005 in Darfur by WFP’s Humanitarian Practice Network
estimated the proportion of food insecure households in two steps. In the first step,
households were classified into three food consumption groups as acceptable, borderline
and food poor according to the diversity of the diet and food consumption frequency.
The other step was classification of households depending on the primary source of food,
specifically whether from food aid, and the households were classified into three food
security groups as food secure, vulnerable to becoming food insecure and food insecure.
26
This classification aimed at estimating the sustainability of the then food consumption
levels through the analysis of the primary source of food consumed (Aiga and Dhur,
2006).
A research carried out by Food and Nutrition Technical Assistance (FANTA) Project to
identify scientifically validated, easier and more user friendly approaches to measuring
the access component of household food insecurity used Household Food Insecurity and
Access Scale (HFIAS) approach by classifying households as food secure, mildly food
insecure, moderately food insecure and severely food insecure. The indicators of food
insecurity were according to household dietary diversity score and months of inadequate
household food provisioning (Swindale & Bilinsky, 2009).
2.12 Coping Strategies
Coping strategies are how households adapt to the presence or threat of food shortages,
and the person within the household who has primary responsibility for preparing and
serving meals is asked a series of questions regarding how households are responding to
food shortages (Maxwell, et al., 2008). The impact of household food insecurity can be
minimized post its occurrence through coping strategies. Coping strategies are 'ex post'
measures in that they seek to reduce the impact of a negative event once it has happened
(Rose, 2008). Among coping strategies are relying on less preferred/inexpensive food;
borrowing food, or relying on help from friends or relatives; gathering wild food, hunting
or harvesting immature crops; consuming seed stock held for the next season; sending
27
household members to eat elsewhere; limiting portion size at meal times; restricting adult
consumption in favour of small children; reducing the number of meals eaten in a day;
skipping entire days without eating and begging from neighbours or friends (Mjonono,
Ngidi & Hendriks, 2009).
Increased reliance on coping strategies is associated with lower food availability and the
higher the weighted sums of coping strategies, the more a household is food insecure,
(Maxwell, et al., 2008).
One way of calculating a weighted sum of different coping
strategies, (where the weights reflect the frequency of use by the household) is to make
the weights consecutive, so that "often" is counted as a 4, "sometimes" is counted as a 3,
"rarely" is counted as a 2, and "never" is counted as a 1. The higher the sum, the more
food insecure the household is. Calculating a weighted sum of these different coping
strategies, where the weights reflect the frequency of use and the severity of the
household's response is to ascribe a weight of 1 to the use of strategies such as eating less
preferred foods, reducing portion sizes served to household members, reducing the
quantity of food served to adults and reducing the quantity of food served to children, a
weight of 2 is ascribed to skipping meals and a weight of 3 to skipping eating all day
(ibid). Different ascribing of scores is used because coping strategies vary in severity,
and therefore, a household where no one eats for an entire day is clearly more food
insecure than one where people have simply switched from consuming rice to cassava,
(Maxwell, et al., 2008).
28
Modest dietary adjustments (eating less-preferred foods or reducing portion size) are
easily reversible strategies that do not jeopardize longer-term prospects; more extreme
behaviors (sale of productive assets) suggest more serious long-term consequences and,
many researchers have noted that as food insecurity worsens, households are more likely
to employ strategies that are less reversible, and therefore represent a more severe form
of coping and greater food insecurity (ibid). Farm family households in ASAL regions of
Kenya are depending on undesirable coping strategies to reduce the impacts of their
households’ food insecurity, such as charcoal production which degrade the environment
ultimately endangering future crop production (WFP, 2009).
2.13 Summary of the Reviewed Literature
From the reviewed literature, it is evident that household food insecurity is a serious
problem especially in the developing countries. Among alternatives towards alleviating
household food insecurity, especially among small scale farmers is agricultural food
production. In spite of the GOK’s encouraging local food production as a means of
alleviating household food insecurity, more needs to be accomplished in order to achieve
household food security in the country. From the available literature, no in-depth studies
have tended to focus on status of food production, food consumption patterns, food
sources, status of household food insecurity and coping strategies against household food
insecurity among small scale farmers at grass root levels such as division. Due to this
observation, the study on household food insecurity and coping strategies among small
scale farmers in Tharaka Central Division of Tharaka South District, Kenya is timely.
29
This is so, especially due to the fact that small scale farmers are important players in
alleviating household food insecurity through their subsistence crop production.
30
CHAPTER THREE: METHODOLOGY
3.0 Introduction
This chapter discusses methodologies used in the study under the following areas:
Research design, study area, population and sample size, sample size determination,
sampling procedure, measurement of variables, research instruments, pre-testing research
instruments, training of research assistants, data collection procedures, ethical
considerations, and data analysis.
3.1 Research Design
Cross sectional descriptive survey design was used to undertake the study in investigating
household food insecurity and coping strategies among small scale farmers in Tharaka
Central Division of Tharaka South District, Kenya. The study was carried out in March
and April, 2011. The design was applied in the study to enable the researcher investigate
household food production, household food consumption patterns, household food
sources, household food insecurity and coping strategies against household food
insecurity. According to Mugenda & Mugenda (2003), the design enables a researcher to
investigate and describe an existing status of a behavoiur.
The design was also
considered appropriate because it allowed the use of a structured questionnaire as the
research instrument. It also produced statistical information about the existing status of
household food insecurity and coping strategies for analysis, which is supported by Olsen
& Marie (2004) who assert that the design allows the use of structured questionnaire and
also produces statistical information for analysis.
31
3.2 Study Area
Tharaka South District is an arid and semi-arid region in the Eastern part of the larger
Meru Region in the Eastern Province of Kenya. The district experiences a bimodal
rainfall pattern with annual rainfall averaging between 500 - 800mm per year (GOK,
2009). The short rain season occurs in March/May while long rains are received in the
October/December period. Generally, rains in Tharaka South are erratic. Temperatures
range between 29oC - 36oC, though at certain periods they can rise to as high as 40oC
(ibid). Food crops cultivated in the area are millet, sorghum, maize, pigeon peas, green
grams and cow peas. Cash crops are hardly cultivated but if done, they comprise cotton,
sunflower and castor.
The District borders Imenti South, Meru Central and Imenti North to the North West,
Mbeere, Maara and Meru South districts to the South. It also borders Tharaka North
District to the North. The district is divided into five administrative divisions, namely,
Tharaka South, Turima, Nkondi, Tunyai and Tharaka Central divisions. Tharaka Central
Division has administrative locations Marimanti, Gituma and Ntugi (CBS, 2010).
Tharaka Central Division covers an area of 213 square kilometres and comprises of a
total population of 16796 persons (8195 males and 8601 females). It is comprised of
3822 households of which 3631 are farm families (Ministry of Agriculture & Livestock
[MoA&L] Office, Tharaka South District, 2011). The division is sub-divided into 3
locations namely, Marimanti, Gituma and Ntugi.
Marimanti Location comprises of
32
Kamatungu, Kirangare, Kithigiri and Marimanti sub-locations.
Gituma Location
comprises of Gituma and Kaguma sub-locations; while Ntugi Location comprises of
Kanyuru and Rukenya sub-locations. The study area was chosen for the study because it
has salient characteristics of ASAL areas.
Food insecurity is one of challenges of
concern in such areas.
3.3 Population and Sample Size of the Study
The target population was small scale farmers in Tharaka Central Division of Tharaka
South District. The accessible population was the 3631 farm family households in the
division. Marimanti, Gituma and Ntugi locations have 2058, 625 and 948 farm families
respectively (CBS, 2010). The sample size was 351 farm family households. The farm
family households were focused on because they were able to reflect the situation of food
production and household food insecurity in the study area. Respondents of the study
were household heads and principal care givers of the households. Household heads
were considered as the main respondents because of their knowledge about food
production and land use. In cases where the household head was different from the
principal care giver, he/she was requested to identify the person responsible for preparing
or overseeing preparation of food for consumption, to answer questions on household
food consumption patterns and coping strategies. In some households, the household
head was the household principal care giver.
33
3.4 Sample Size Determination
The sample size of the study was 351 respondents according to Sample Size
Determination Table by Krejcie & Morgan (1970) at an alpha level 0.05 and a t value of
1.96 for a sample size derived from a population size of 4000 of categorical data (Bartlett
et al, 2001). Despite the fact that the population size of the study was 3631, it was
deemed necessary to consider deriving the sample size from 4000 population size since
according to Bartlett et al (2001), increasing sample size is vital to account for natural
attrition and uncooperative subjects because data collection method of voluntary
participation in interviews may lead to such phenomena and ultimately produce a
response rate below 100%.
3.5 Sampling Procedure
Purposive sampling as part of multi-stage sampling is used to get the location in which
units of observation (study) have the required characteristics, (Mugenda & Mugenda,
2003). It is also relevant when a researcher wishes to use cases that have the required
information with respect to the objectives of his study (ibid). Tharaka Central Division
was purposely identified from a list of five administrative divisions of Tharaka South
District because of the following reasons:
drought resistant crops such as millet, green
grams, and cowpeas are cultivated in the area. Such crops are commonly cultivated in
ASAL areas. Marimanti Town which is the headquarters of Tharaka Central Division is
a major market and therefore was considered to investigate the role it played as
households’ food source. Thirdly the area is centrally situated in Tharaka South District
34
therefore would produce reliable data about household food insecurity from a central
point in the district.
Simple random sampling was used to select five Sub-locations in the division. Eight
pieces of paper were cut, written names Kamatungu, Rukenya, Kirangare, Kithigiri,
Kaguma, Marimanti, Gituma and Kanyuru. The pieces of paper were rub-folded, put in a
container, shaken and poured on a table. Five pieces were handpicked with eyes closed.
The names on the pieces of paper were confirmed to be Rukenya, Kirangare, Kithigiri,
Kaguma and Kanyuru. Five out of the eight – more than 50% sub-locations were
selected to allow for variations in the nature of farm family households between the areas
(Saunders, 2009). The sub-locations have 435, 293, 676, 333 and 513 farm family
households respectively making a total of 2250.
Systematic random sampling was then used in acquiring the sample from the 2250 farm
family households. The farm families are the rural population households (CBS, 2010;
MoA & L, 2011). Systematic random sampling was used in order to ensure even
sampling from the homogenous population of farm family households in the rural areas.
Lists of farm family households in the sub-locations were prepared and randomized into a
list comprising all the farm family households in the selected areas. The households were
then assigned numbers 001 to 2250. The total population 2250 was divided by the
sample size 351 to get the sampling interval (K) 6. Starting point of picking sampling
units was determined by blindly picking any number from number 001 to 006. Number
35
005 was picked as the starting point. Every 6th number from the starting point was picked
to get the 351 sampling units of the study. The formula below illustrates how the 351
households were systematically sampled.
K= N/n
6=2250/351
K=sampling interval, N=population size, n=sample size
Multi-stage sampling was applied in this study by considering the above mentioned
sampling techniques to overcome the problems associated with the area’s geographically
dispersed population.
Dispersed population in a wide geographical area is a major
challenge to conduct face-to-face interviews because they are too expensive to conduct;
and it also takes a lot of time to construct a sampling frame for interviews on the entire
area (Saunders, Lewis & Thornhill, 2009).
The area ALRMP II Manager and
Agricultural Extension Officer were purposively selected as key informants because they
possessed vital information concerning household food insecurity as well as agricultural
aspects. Interviews were conducted with the two officers to get insights on household
food insecurity.
Information concerning land use such as sizes of farmlands, food
production such as types of drought resistant crops cultivated in the area was obtained
from the Agricultural Extension officer.
36
3.6 Measurement of Variables
Several variables were used in establishing household food insecurity and coping
strategies among small scale farmers in Tharaka Central Division.
3.6.1 Independent Variables
To establish the status of household food production, household sizes of farms and
farmlands, types of crops cultivated in the two rainy seasons of 2010, amount of food
expected, amount of food harvested, months of household food provisioning, crop loss
mitigation mechanisms and experience with droughts and floods were used.
The indicators of household food insecurity in this study were: dietary diversity, food
frequency and food sources. The independent variables for household dietary diversity
were number of meals in 24 hour recall. The type of foods consumed among the
households in 24 hour recall helped in determining household dietary diversity. This was
done by grouping food items into food groups. Three or less food groups were lowest
food diversity, four to five food groups were medium dietary diversity and 6 and more
food groups were highest food diversity.
Household food frequency was determined by considering the frequency of food
consumption in the previous 7 days as independent variable. It was used to establish
household food consumption frequency score (HFCS). This was done by summing the
frequency of the frequency of consumption of food items in the same food groups,
multiplying the value obtained for each food group by its weight and then summing the
37
weighted food group scores. Zero to twenty eight score was classified as poor, 28.5 to 42
borderline and 42.5 and above as acceptable household food consumption frequencies.
The independent variables of household sources of food were: market; own production;
gifts from relatives, neighbours and friends and free relief food. In order to identify
coping strategies among the households, the following variables were employed:
reduction in the number of meals per day, reduction in size of meals, restrict consumption
of adults to allow more for children, swapped consumption to less preferred or cheaper
foods, borrow food from a friend or relative, consume normal wild food, consume
immature crop, sale of milking livestock and sale of charcoal and/or firewood.
3.6.2 Dependent Variable
The dependent variable of this study was household food insecurity. There were 3
domains of the independent variable as adapted from WFP’s (2006) Household Food
Consumption Approach. They are household food security, vulnerability to household
food insecurity and household food insecurity.
38
3.7 Research Instruments
The study employed three sets of data collection instruments. Interviewer-administered
structured questionnaire, observation checklist and key informant interview guide. The
questionnaire was divided into four sections. Section A was used to collect information
on household data. The subsequent sections B, C and D were used to collect data on
household food production, food consumption/and food sources; and coping strategies
respectively.
Observation checklist comprised of 7 questions concerning: household farmland size,
types of food crops cultivated, types of house, assets, food available in household,
foodstuff sold at the nearest market, prices of foodstuff, nearest water source and
presence of water in the household.
These questions helped in depicting the
circumstances of food insecurity the households were in; and to validate the data obtained
from the respondents. The key informant interview guide questions were used to seek
insight on household food insecurity and coping strategies among the small scale farmers.
3.8 Pre-testing Research Instruments
Pre-testing the structured questionnaire was carried out among 10 households randomly
selected from Kamatungu, Marimanti and Gituma sub-locations. These sub-locations
were not considered in the main study. The first pre-test was carried out among 5
subjects drawn from Kamatungu and Marimanti in the second day of the week and the
39
second pre-test was done among 5 subjects in Gituma on the fifth day. The pre-test was
done in different locations and on different days so as ascertain the homogeneity of
responses. Comments and suggestions concerning the instrument clarity and relevance
were sought from the respondents, and relevant alterations done to enhance its validity
and reliability. Adjustments made on the questionnaire after the pre-test were increasing
response spaces and rephrasing unclear questions. This was to ensure questions which
did not elicit intended answers were corrected and made clear and that responses would
not be overcrowded or be omitted when putting them down on paper. This process was
to make sure that the questions in the instruments elicited reliable data.
3.8.1 Reliability
The reliability coefficient of the instruments was calculated using Cronbach’s Coefficient
Alpha formula. The total variance was calculated, followed by individual variances, and
then the sum of individual variances was calculated. Finally the reliability coefficient
alpha was gotten by applying the Cronbach’s formula.
N/ (N-1) (Total Variance – Sum of individual variance)/Total variance
14/ (14-1) (281.9-30.359)/281.9=0.960
N= number of questions in the instrument
A reliability coefficient of 0.80 or more implies that the items correlate well among
themselves and also there is a high degree of reliability of the data (Yu, 2010; Mugenda
& Mugenda, 2003).
40
3.8.2 Validity
Content validity was established by seeking the expertise of the study supervisors. The
supervisors ensured that correct variables relevant to the study were included in the
questionnaire.
The questionnaire was constructed and revised according to the
instructions of the experts. This is in accordance with Mugenda and Mugenda (2003),
who says content validity judgement is made better by a team of experts in the field of
the research.
3.9 Training Research Assistants
Training of research assistants is important to standardize data collection to minimize
variations in data collection procedures that may bias the results (Mugenda & Mugenda
2003). Four research assistants were trained to help collect data from the 5 sub-locations
of the study area. Each research assistant was assigned a sub-location (the researcher
conducted the study in Kanyuru). The researcher trained her research assistants by
engaging them in rehearsal sessions on question asking, probing skills and translating
questions into Kitharaka. The researcher engaged the research assistants on research
etiquette such as introducing themselves to the respondents and clarifying the purpose of
the study to respondents so as to create good rapport before embarking on the actual
study. The research assistants were also trained on how to summarize lengthy responses
into short summaries to avoid information overload and also to ensure that responses
fitted in the response spaces in the questionnaire. The importance of involving research
assistants in the study was to save on time, energy and finances of having to conduct the
41
research over a lengthy duration thereby incurring huge expenses and getting exhausted
due to fatigue.
3.10 Data Collection Procedures
The household farm head was the main respondent for questions on food production and
land use. The principal care giver, mostly a female was the main respondent in questions
dealing with food consumption and coping strategies which was in accordance with
Haddinott (2006) who says, the principal person responsible for preparing meals is asked
how much food she prepared over a period of time and how her household members are
responding to food shortage. The respondents were visited in their homes for interview
sessions conducted through the use of structured questionnaires administered by the
researcher and research assistants. There were elaborations and probing as was deemed
necessary. Interview responses were filled in the questionnaires. Observations were done
after the interview sessions. Information on observed phenomena was filled in the
observation checklist. What was observed included: sizes of the farmlands, type of food
cultivated in the season, type of houses in the homestead, household assets, types of food
available in the household, foodstuff sold at the nearest market, prices of the foodstuff at
the markets, nearest water source and presence of water in the household.
The researcher booked appointments with the area ALRMP II Manager and the area
Agricultural Extension Officer to conduct key informant interviews with them. Upon
their consent, the extension officer was visited by the researcher at his office in
42
Marimanti Town; and thereafter the area ALRMP II Manager was visited in his office for
interview. Their responses were recorded in form of notes and summaries. Counter
checking of filled questionnaires was done every day of the study by the researcher to
check for completeness and clarity of entries.
3.11 Ethical Considerations
Application and permission for authority to conduct the research was sought from the
Ministry of Higher Education, Science and Technology. A copy of the permit by the
permit was submitted to Tharaka South District Commissioner. Permission to collect
data in Rukenya, Kirangare, Kithigiri, Kaguma and Kanyuru was sought from assistantchiefs of the sub-locations. These administrators further notified headmen about the
study. These leaders created awareness to the community about the impeding field
research (especially during public meetings and during food relief supply days). This
ensured that the community appreciated the research and gave consent to get interviewed.
The respondents’ voluntary and informed consent of participation in the study was sought
before data collection, informing and clarifying to them that the study was for academic
purpose only. The respondents were also assured of the confidentiality of the information
they were to give. This was done during the visit to their homes for the study. The
researcher booked appointments with key informants prior to conducting interviews with
them and they were also informed that the purpose of the study was academic.
43
3.12 Data Analysis
Quantitative data collected was analyzed using the computer software programme
Statistical Package for Social Sciences (SPSS) Version 11.5 to make the analysis easier
and to obtain accurate results. The data collected was assembled, grouped into categories,
meanings extracted, coded and entered into SPSS and analyzed to get results. Qualitative
data obtained was organized into distinct categories, patterns and themes identified. The
data was further evaluated and analyzed to determine its adequacy, its credibility and
usefulness to objectives of the study.
SPSS (Version 11.5) was used to analyze data on 24 hour dietary recall and 7 day food
frequency to establish household dietary diversity score, household food consumption
score and main sources of household food.
Number of food groups were used to
establish 24 hour recall HDDS while the weighted factors of food groups of 7 day food
frequency were used to establish HFCS.
Household food insecurity status was
determined by considering the results of HDDS, HFCS and the primary source of
household food according to WFP (2006) and Aiga & Dhur (2006).
HDDS was established by considering 12 food groups while HFCS was established by
considering the consumption of 8 food groups: main staples (cereals, roots and tubers),
pulses, meat/fish/eggs, milk, vegetables, fruit, sugar/honey and fats/oil, and were factored
with 2, 3, 4, 4, 1, 1, 0.5 and 0.5 respectively. HFCS of 0-28, 28.5-42 and 42.5 and above
44
were considered food poor, borderline and acceptable household food security status
respectively.
Descriptive statistics such as percentages, frequencies, and the mean were used to
describe and organize both qualitative and quantitative data. Frequency tables, pie charts,
bar graphs, cross-tabulation and line graphs are used to present the findings. Pearson
Product Moment Correlation tests were used to determine the magnitude and direction of
relationships between non-categorical variables sizes of farms and farmland sizes;
statuses of HFCS and household size; and HFCS and farmland sizes. T test was done to
establish whether a significant difference existed between the amount of food expected
and amount harvested.
Chi square tests were done to establish whether significant
associations existed between HDDS and HFCS; and between sources of maize and
statuses of household food insecurity.
45
CHAPTER FOUR: FINDINGS AND DISCUSSION
4.0 Introduction
The presentation and discussion of the findings include demographic characteristics of
the households, household food production, household food consumption patterns,
household sources of food, household food insecurity status, and household coping
strategies in the event of food shortage among the small scale farmers.
4.2 Household Demographic Information
The demographic characteristics of the study included: household size, household head
education levels, type of housing, cooking energy, and sources of livelihood.
4.2.1
Household Size
Sizes of the respondents’ households are presented as follows (Table 4.1).
46
Table 4.1: Household Size
Household Size
Frequency
Percentage
1
6
1.7
2
4
1.1
3
50
14.2
4
85
24.2
5
100
28.5
6
43
12.3
7
19
5.4
8
37
10.5
9
3
0.9
10
4
1.1
351
100
Total
The total number of persons in the 351 households was 1758 with a mean of 5. Majority
of households (69.7%) had 5 or less members. According to Alem and Shumiye (2007),
a shift to smaller family size (smaller than the sample mean family size) decreases the
probability of food insecurity. Following this assertion, majority of households would be
deemed to be less food insecure because majority had 5 or less than the mean members.
The finding on household size is comparable (although slightly higher) with that of
Kenya Demographic and Health Survey (KDHS), 2008 – 2009 which reports that the
mean size of a Kenyan household is 4.2 persons (GOK, 2010b).
47
4.2.2
Education Levels of Household Heads
Table 4.2 Education Levels of Household Heads
Level of Education
Frequency
Percentage
None
126
35.9
Primary
149
42.4
Secondary
36
10.3
Post Secondary
40
11.4
Total
351
100
The household heads were of diverse levels of education: No education (35.9%), primary
level (42.4%), secondary level (10.3%) and post secondary (11.4%). It can therefore be
observed that majority of the heads (78.3%) were uneducated or of primary education
level.
The number of years spent in formal education is one of the important
determinants of increased household food production and adoption of new behaviours.
Further, education catalyses the process of information flow and leads persons to explore
as wide as possible, different pathways of getting information about agriculture and food
security (Ersado, 2001). Following this observation, there was limitation in information
flow and adoption of new food production behaviours among this group because of their
low education levels. Further GOK (2008e) indicates low literacy rates of 77% among
Tharaka District residents.
48
4.1.3 Household Type of Housing
Table 4.3: Household Type of Housing
Type of House
Modern
Semi-modern
Traditional Huts
Total
Frequency
Percentage
36
10.3
280
79.7
35
10
351
100
The houses were mostly semi-modern (79.7%) made of iron sheet roofs (90.3%), mud
walls (79.5%) and earth flour (71.5%). The shift to semi-modern housing is attributable
to the fact that the community is transitioning from grass thatches to iron sheets. The
respondents said their house walls were made of mud because it was naturally available
and less expensive compared with stones and bricks.
Floors were earthen due to
tradition. Households in rural areas of Kenya mainly have houses with floors made from
earth, sand, or dung at 71% and the housing characteristics reflect the household’s socioeconomic situation (such as ability to access food GOK (2010b). Considering their
housing characteristics, the ability to access food by the households was a bit constrained.
4.2.3
Household Cooking Energy
The respondents were asked to mention sources of their cooking energy and gave the
information in (Table 4.4).
49
Table 4.4: Household Cooking Energy
Cooking Energy
Frequency
Percentage
Firewood
254
72.4
Charcoal
53
15.1
Firewood/Charcoal
44
12.5
0
0
351
100
Others
Total
Firewood was the most common source of cooking energy (72.4%) because it was readily
available in the study area. During dry seasons trees and shrubs dry up offering firewood
to the households. Charcoal was also used at 15.1% as it was prepared from the dry
woods. The statistics of the finding is higher than the country’s statistic and lower than
the country’s rural statistic of KDHS 2008-09 Report which stipulates that the most
common cooking fuel in Kenya is wood, used by 63% of the country’s households and by
83% of its rural households (GOK, 2010b). Following the findings of the study, the
small scale farmers did not have a lot of problems in cooking food since firewood and
charcoal offered affordable sources of cooking energy.
4.2.5
Household Main Source of Livelihood
The small scale farmer households’ sources of livelihood are as shown in (Table 4.5).
50
Table 4.5: Household Main Source of Livelihood
Source of Livelihood
Frequency
Percentage
Agriculture
263
75.1
Agro-pastoralism
15
4.3
Formal Employment
54
15.4
Casual Labour
17
4.9
2
0.3
351
100
Others
Total
The findings indicate that agriculture (75.1%) was the main source of livelihood for the
households, followed by formal employment at 15.4%. Much of the food consumed in
rural households in Kenya (whose main livelihood is agriculture) is obtained from the
farm (Kaloi, et al., 2005). This was supposed to imply that the major source of food for
the households was own crop production, since their major source of livelihood was
agriculture. The finding is also comparable with that of Tharaka District Development
Plan 2008-2012 Report, which stipulates that agriculture is the major mainstay of the
economy and livelihood of the people in Tharaka District and, it is estimated that 80% of
the population depends on farming (GOK, 2008b).
51
4.3 Household Food Production
Establishing household food production involved investigating household sizes of
farmlands, types of crops cultivated in the two rainy seasons of 2010, amount of harvests,
months of household food provisioning, crop loss mitigation mechanisms and
respondents’ experience with drought and flooding.
4.2.1
Sizes of Household Farms and Farmlands
The respondents were asked to state sizes of their farms and gave the following
information presented in (Table 4.6).
Table 4.6: Sizes of Household Farms
Acreage
Farm Frequency
Percentage
≤1
61
17.3
2
96
27.4
3
60
17.1
4
68
19.4
5
49
14
7
17
4.8
351
100
Total
Majority of respondents (27.4%) possessed 2 acres of farm. This was followed by 19.4%
who owned 4 acres of land. The mean household farm size was 3.05 acres. The farm
holdings were utilized as farmlands for crop cultivation and as pasture land for livestock
52
grazing. It is estimated that 80% and 60% of Tharaka population draws their livelihood
from agriculture and livestock keeping respectively (GOK, 2009). These findings are in
agreement with a study by Gitu (2004) which observed that due to continued land
fragmentations in Kenya, some 89% of the households in the country are living in less
than 7.5 acres of farms, while 47% of households live on less than 1.5 acres. This is
comparable with the results of the study which show all respondents had farms of sizes 7
or less acres, and some 44.7% of households had 2 or less acres of land.
Table 4.7: Sizes of Household Farmlands
Acreage
Farmland Frequency
Percentage
≤1
136
38.7
2
176
50.2
3
38
10.8
4
1
0.3
7
351
100
Majority of households (50.2%) possessed 2 acres of farmland, while 38.7% owned 1 or
less acre of farmland. The mean size of household farmlands was 1.62 acres. Although
there were large potential cultivation lands, it was found that the respondents did not
want to cultivate vast farmlands which they were not capable of controlling weed
invasion and weed prevalence. For instance, on probing a respondent in Kanyuru Sublocation on why he had a farmland as small as less than acre while he owned 4 acres of
53
land, he responded thus: “What is the importance of cultivating a large portion and see,
almost everything get consumed by weeds? See my house (hut). Does it seem to belong to
a rich person with money to hire labour for weed control?” Weeds do not let crops
mature nor produce fruits. According to Alem and Shumiye (2007), small farmland size
increases vulnerability to household food insecurity because the smaller the farmland
size, the smaller the volume of crop output (if other variables are held constant).
4.2.2
Types of Crops Cultivated in March/May and October/December, 2010
The respondents were asked to give the estimates in Kgs of the crops they had expected,
harvested, sold, consumed, stored and the period the harvests lasted. They gave the
information (Table 4.8).
54
Table 4.8: Types of Crops Cultivated
Crops
Mean Amount (kg)
Expected
Harvested Sold
Consumed Stored
Duration of
post
harvest
storage
mm
mm
Od Mm od
mm
od
mm od
mm
Od
Od
Maize
350 366 270
91
160
1
123
82
40
8
4
<1
Millet
155 274 218
78
101
38 114
19
83
21
5
2
Sorghum
88
29
113
15 27
14
22
0
4
<1
Green
grams
450 200 362
96
314
74 44
12
70
10
4
<1
Pigeon
Peas
242 0
173
0
86
0
0
47
0
4
0
Cowpeas
81
39
48
28
22 27
14
10
2
1
<1
107 70
84
mm= March/May Season
82
od= October/December Season
The findings indicate that food crops were the major crops cultivated among the
households at 95% of all crop output. Cereals provided staple food while pulses could be
consumed as well as get sold for money to pay school fees and purchase clothes.
According to Rose (2008), production of staple food crops contribute to household food
availability; since when foodstuff is available in a household, it increases the chances of a
household being food secure. The types of food crops cultivated by households were
similar with those listed in GOK (2009) as being grown in Tharaka: maize, sorghum,
millet, green grams, pigeon peas, cowpeas. Moreover, Gitu (2004) stipulates that these
crops are mainly cultivated in ASAL areas. A cash crop (cotton) was cultivated along
with food crops by 5% of the households and their low cultivation was attributed to lack
55
of seed and lack of market. The type of cash crop cultivated is similar with the Tharaka
District Development Plan 2008-2012 that indicate that the main cash crop cultivated in
the area is cotton (GOK, 2009).
4.2.3
Amount of Harvests for Food Crops
The major cereals produced during March/May Season were maize and millet at a mean
of 270 kg and 218 kg respectively (Table 4.8). Maize was the primary crop cultivated
which explains that it was among crops harvested in the largest quantities during the
season. The October/December season was the most significant for analysis of food
production because it was highly reflective of the existing status of household food
availability among the small scale farmers’ households during the time of the study.
In October/December season, food crop production was much lower than the previous
season (maize and millet outputs were at a mean of 91 kg and 78 kg respectively) as
opposed to the previous season’s 270 kg and 218 kg respectively (Table 4.8). The
farmers had expected bumper harvests in the season since it was the long rains season.
However, their anticipation was not realized because of the drought which precipitated
the harvest of food quantities much lower than the previous season. The finding on
maize harvested in October/December season among the households is comparable with
that of Makueni County (which is also ASAL area) whose households had harvested a
mean of 89 kg of maize during the same season (Scribd, 2011).
56
The results on food crop harvests were attributable to bumper harvest of March/May
season (due to enough rains); and low harvests of the subsequent season due to erratic
rains experienced. According to the results, the households were deemed to be more food
secure in the March/May post-harvest period and more food insecure in the post-harvest
of October/December Season. The findings are divergent with usual expectations about
the seasons; whereby bumper harvests are expected in October/December season than
March/May season (GOK, 2009).
4.2.4
Months of Household Food Provisioning
According to FANTA (2006), months of household food provisioning are characterized
by adequate or inadequate food provisioning (GOK, 2008c).
Tables 4.9 and 4.10
illustrate the findings.
Table 4.9: Months of Adequate Food Provisioning
Month
Frequency
Percentage
June to August
142
40.5
June to September
36
10.3
June to October
17
4.8
June to November
17
4.8
June, August and February
67
19.1
June, August and May
19
5.4
May to September, January
to February
17
4.8
Other Months
36
10.3
57
Majority of households (40.5%) had enough food provisioning during the months of June
to August. June, August and February had enough food provisioning at 19.1%. The
months of enough food provisioning are immediate to post-harvest seasons. This implied
that the households’ food access and availability was good during these months.
Harvesting is done in June and January for March/May and October/December seasons
respectively. These findings support those of GOK (2008d) which indicate cultivation of
crops done during short rains boost food security in June to August in Tharaka, and those
of Long Rain Assessment Report (GOK, 2008e) that there is good food provisioning
among households in Tharaka in January and February which are the post-harvest periods
of long rains.
Table 4.10: Months of Inadequate Food Provisioning
Months
Frequency
Percentage
August to December
37
10.5
September to January
83
23.6
106
30.2
November to January
87
24.8
March to April/other
Months
122
34.8
October to January
The respondents mentioned different intervals in months of inadequate food provisioning.
October to January had the most inadequate food provisioning at 30.2%.
Other month
intervals of inadequate food provisioning were November to January at 24.8% and
58
September to January at 23.6%.
The access and availability of food among the
households was compromised because the months were too far from post-harvest
seasons. The findings are supported by the report of Tharaka District Development Plan
2008-2012 that says, there have been persistent food shortages in Tharaka in October to
December due to prolonged dry spells beginning in June which are months of no
cultivation of food (GOK, 2009).
4.2.5 Crop Loss Mitigation
There were various mechanisms employed by households in mitigating crop loss due to
erratic rains or pest infestation. Maize, millet and sorghum potential loss was reduced by
planting drought resistant varieties. For example 85% of households cultivated drought
resistant varieties of maize. Pest control was practised by spraying crops on the farm
(79.8%) and dusting foodstuff (85%) with pesticides so as to reduce the amount of crop
destruction on the farm and to prevent post harvest foodstuff loss respectively. Some of
pests that infested crops on farms were chaffer grabs, termites, suckers and they cut
young crop shoots, cut maize stems and sucked crop fruits respectively. The common
pests that invaded foodstuff in stores were great grain borer (Osama meaning ‘destroyer’)
and moths. They bored and disintegrated foodstuff into pieces and into powder form.
59
4.2.6 Droughts and Flooding
When asked if they had experienced drought(s) in the past two rainy seasons, all the
respondents said yes and no for flooding. The respondents indicated that in spite of
cultivating droughts resistant crops, the preceding drought was so severe that their crops
dried immaturely thus constraining their harvests. This exposed them into vulnerability to
household food insecurity. Droughts increase a community’s vulnerability to household
food insecurity (Rose 2008).
4.3 Household Food Consumption Patterns
Household food consumption patterns were investigated by asking household principal
caregivers (mainly female) food consumption questions.
4.3.1
Meal Patterns among the Households
The study sought information concerning meal patterns by asking the respondents to
mention foods their households had consumed during different meals. The information
on the meal patterns is illustrated in (Figure 4.1).
60
Figure 4.1: Meal Patterns among the Households
The highly consumed meal was breakfast by 85.4% of the households, followed by
supper at 72.9%. Lunch was consumed by 35.6% of the households. The consumption of
breakfast was to gain energy to start up their day and consumption of supper was to
replenish the lost energies during day time.
There was positive implication of
consumption of breakfast among the households because it was highly consumed.
Breakfast is the first meal taken after rising from a night's sleep, most often eaten in the
early morning before undertaking the day's work. Nutritional experts have regarded
breakfast as the most important meal of the day, because people who skip breakfast are
disproportionately likely to have problems with concentration, metabolism and weight
(Wikipedia, 2011).
61
Many households skipped lunch due to the impacts of food shortage precipitated by the
drought during October/December rain Season.
This finding corroborates with
Reliefweb (2011) findings that the population that is highly and moderately food insecure
and unable to meet a significant proportion of their food needs in Kenya rose to 2.4
million people in January 2011, from 1.6 million in December 2010. This is due to the
impacts of failed rains on crop production (close to 80% October/December crop was
lost) in ASAL areas. Skipping lunch was a coping mechanism against household food
insecurity.
4.3.2 Main Foods Consumed in Meals
The main foods taken at breakfast are presented in (Table 4.11), and the main foods
consumed in different meals are also discussed.
Table 4.11: Foods Consumed at Breakfast
Foods
Frequency
Percentage
Githeri
153
43.7
Traditional ugali
19
5.4
Tea
70
19.9
Porridge/gruel
54
15.2
Other foods
4
1.2
None
51
14.5
Total
300
85.4
62
The main foods consumed at breakfast were githeri by 43.7% of the households, tea by
19.9%, traditional ugali (cereal flour mixed with green vegetables) by 5.4%,
gruel/porridge by 15.2% and other foods at 1.2%. Githeri and traditional ugali eaten
during breakfast were the remnants of the preceding night’s supper. The types of food
for breakfast were different from the normal Kenyan breakfast menu. The traditional
Kenyan breakfast menu comprises tea and chapatti, mandazi (local pastry), bread spread
with margarine (KenyaZone.com, 2011).
The main food consumed for lunch and supper was githeri by 35% and 65% respectively.
For mid-morning snack and afternoon snack, the main food was porridge/gruel taken by
20.2%, 28.7% respectively. The major ingredient in the githeri was maize which is
Kenya’s staple food according to GOK (2008c).
4.3.3 Household Dietary Diversity of 24 Hour Recall
There is no international consensus on which food groups to include in the scores of
HDDS and therefore this study adopts 12 food groups proposed by FAO, WHO and
FANTA (2006) in calculating HDDS: cereals, roots and tubers, vegetables, fruits, meatpoultry-and-offal, eggs, fish and sea food, pulses-legumes-and-nuts, milk and milk
products, oil/fats, sugar and honey, miscellaneous (GOK, 2008c). HDDS of 24 hour
recall 12 food groups are thus: 3 or less food groups, 4 to 5 food groups and 6 or more
food groups are classified as lowest dietary diversity, medium dietary diversity and high
dietary diversity. There are no established cut-off points in terms of number of food
groups to indicate adequate or inadequate dietary diversity for the HDDS and, so, it is
63
recommended to use the mean score or distribution of scores for analytical purposes
(Kennedy, Ballard & Dop, 2011).
Figure 4.2: Dietary Diversity of 24 Hour Recall
The HDDS of the previous 24 hours was generally poor with 83.3% of households
having consumed 1 to 3 food groups which was low, 16.2% had consumed medium
dietary diversity of 4 and 5 food groups and 0.3% more than 5 food groups which was
high dietary diversity according to HDDS thresholds by Kennedy et al (2011). These
findings are different with the findings of Integrated Smart Survey carried in Makueni
County, Kenya in April 2011 which found that Makueni’s HDDS was as follows: low
dietary diversity of 3 or less food groups was 11.5%, medium dietary diversity of 4 to 5
food groups was 20.3%, and high dietary diversity of 6 or more food groups was 68.2%
(Scribd, 2011). Both studies were conducted in March/April 2011 and April 2011, and
both areas are ASALs.
64
4.3.4 The 7 Day Food Frequency
The 7 day food frequency of the study adopts the quantitative aspect of food consumption
pattern by IFPRI (2008) that uses 8 food groups - main staples, pulses, vegetables, fruit,
meat and fish, milk, sugar and oil. Respondents were asked how many times their
households had consumed the food groups in the previous 7 days, and their responses
were as shown in (Table 4.12).
65
Table 4.12: The 7 Day Food Frequency
Food Type
Total Consumption
by Households (%)
Frequency of Consumption
Households (%)
0
1
2
Maize
96.6
0.6
4.8
Pulses
94.6
5.4
0
Milk
54.4
45.6 0
Millet
54.1
45.9
Fats/oils
50.1
Honey/sugar
3
4.8
4
by Food
Adequacy
(%)
5
Yes
No
4.8 10.5 74.4 30.9 65.7
14.8 20.3 15.1 44.4
5.1 89.5
0.3
0
0.3 53.8
5.1 49.3
5.1
0
0
0 49.0
0.3 53.8
49.9
0
4.8
4.8
0 40.5 14.8 35.3
49.6
50.4
0
5.1
0
0 44.5 15.1 34.5
Banana
38.5
61.5 33.6
0
0
4.9
0
0 38.5
Cowpeas
leaves
35.1
64.9 35.1
0
0
0
0
0 35.1
Rice
31.6
68.4 26.5
0
5.1
0
0
5.1 26.5
Cabbage
30.2
69.8
5.1 25.1
0
0
0 10.3 19.9
Wheat
25.4
84.6
5.1
10
5.7
0.3
4.3
0.3 25.1
80 20.0
0
0
0
0
0 20.0
0 19.9
Eggs
20
Finger millet 19.9
80.1
4.8
5.1
0
0 10.0
Sorghum
15.1
84.9
5.1
0
0
0 10.0 10.0
Red meat
15.1
84.9 15.1
0
0
0
0
0 15.1
Mango
14.8
85.2 14.8
0
0
0
0
4.8 10.0
5.1
Poultry meat
9.7
90.3
9.7
0
0
0
0
0
9.7
Fish
9.6
90.4
0
4.8
4.8
0
0
4.8
4.8
Kales
4.9
95.1
0
4.9
0
0
0
0
4.9
66
Results show that maize was widely consumed by the majority of (96.6%) households
during the past one week. This was because it was available and culturally acceptable as
an ingredient of githeri - the main staple food among the households (74.4% consumed it
5 or more times). This finding is in agreement that maize is the main staple food of
Kenya and averages over 80% of total cereals consumed and 41% source of the daily
calorie (Kaloi, Tayebwa & Bashaasha, 2005). However, only 30.9% of households
indicated that it was adequate for their household consumption.
Pulses, milk (in
tea/porridge) and millet followed suit at 94.6%, 54.4% and 54.1% respectively. Despite
the fact that many households consumed these food items, 89.5%, 49.3% and 53.8% said
that the quantity of these items were inadequate respectively. Pulses and milk were good
sources of proteins for household members.
Main vegetables consumed among the
households were cowpeas leaves and cabbage by 35.1% and 30.2% of households
respectively. Some respondents (35.1%) and 19.9% respectively said the vegetables were
not adequate for their household consumption. Main fruits consumed were banana and
mango by 38.5% and 14.8% of households respectively. Although bananas were the most
common and affordable fruits in the markets, all the respondents said they were not
adequate.
Generally, cereals were the main food consumed among the households. However, their
quantities were inadequate.
The results on the consumption of cereals were in
concurrence with those of Makueni County where the major food group consumed were
cereals by 90.3% of households, (Scribd, 2011). Inadequate quantities of food would
67
predispose household members to nutritional deficiencies, which are said to be prevalent
in Kenya as energy, protein, iron and vitamin A deficiencies (GOK, 2008c).
4.3.5 Household Food Consumption Score (HFCS)
Household Food Consumption Score (HFCS) is a frequency-weighted HDDS (IFPRI,
2008). The HFCS is calculated using the frequency of consumption of 8 different food
groups consumed: main staples, pulses, vegetables, fruits, meat and fish, milk, sugar, oil.
HFCS is measured using standard 7 day food data by classifying food items into food
groups; summing the consumption frequencies of food items within the same group (any
consumption frequency greater than 7 is recoded as 7; multiplying the value obtained for
each food group by its weight. Thus 2, 3, 1, 1, 4, 4, 0.5 and 0.5 are weights for main
staples (cereals, roots and tubers), pulses, vegetables, fruit, meat/fish/eggs, milk, sugar
and fat/oil respectively. Then summing the weighted food group scores is done, and
finally recoding the variable HFCS from a continuous variable into a categorical variable
for the food consumption groups using appropriate thresholds: 0-28 food poor, 28.5-42
borderline and above 42 acceptable, according to (WFP, 2007; IFPRI, 2008).
The
limitation of the findings on HFCS is that, weighting of food groups was done without
considering their adequacy.
68
Table 4.13: HFCS
Profile
HFCS
Frequency
Percentage
Poor
0-28
93
26.5
Borderline
28.5-42
80
22.8
Acceptable
>42
178
50.7
351
100
Total
The findings indicate that 26.5% of households had poor HFCS of 0 to 28 and majority
(50.7%) had acceptable HFCS of above 42 in the previous 7 days of household food
consumption. This means that the overall HFCS was relatively good. The findings on
HFCS are attributable to high consumption of cereals and pulses as illustrated in Table
4.12. These results are supportable by upward consumption trend of world cereals from
2006/07 to 2009/10 as reported in Economic Review of Agriculture (GOK, 2010c).
4.4 Household Food Sources
The principal caregiver, mainly the female was asked to respond to questions concerning
the main sources of household food.
4.4.1 Main Sources of Food Items
The main sources of food items in a household are as presented in (Table 4.14).
69
Table 4.14: Main Sources of Food Items
Food Type
Pulses
Total
Consumption
by
Households
(%)
Main Sources of Food Items (%)
Gifts
Market Own
Production relatives,
neighbours
friends
from Free
food
and
94.6
50.1
34.5
0
10.0
Honey/sugar 49.6
49.6
0
0
0
Banana
38.5
38.5
0
0
0
Maize
96.6
36.7
18.3
5.7
35.9
Rice
31.6
31.6
0
0
Millet
54.1
30.2
19.1
4.8
0
Cabbage
30.2
30.2
0
0
0
Cowpeas
leaves
35.1
30.2
4.9
0
0
Fats/oils
50.1
25.4
0
0
24.7
Wheat
25.4
25.1
0.3
0
0
Red meat
15.1
15.1
0
0
0
Finger
millet
19.9
10.0
10.0
0
0
Milk
54.4
9.7
44.7
0
0
Sorghum
15.1
5.1
10.0
0
0
Eggs
20.0
5.1
14.8
0
0
Poultry
meat
9.7
4.8
4.8
0
0
Fish
9.6
4.8
4.8
0
0
Kales
4.9
4.8
0
0
0
Mango
14.8
4.8
5.2
4.8
0
relief
70
The findings show that the households’ main source of all food items was from markets
as illustrated in Table 4.14. Maize was mainly sourced from markets at 36.7% and from
free relief food at 35.9%; and it was the main cereal consumed (96.6%) among the
households. Millet was mainly sourced from markets at 30.2% and own production at
19.1%. Sourcing millet mainly from markets was attributed to depletion of millet stock
from stores due to a period of non crop production from January and February.
The
findings are further supported by the result of probing respondents on their main source
of all their household food and 86.9% said it was markets. This implies that the
households did not consume sufficient food from their own production, because
according to Mjonono, et al (2009), small scale farmers are major potential contributor to
household food security (they consume sufficient food) through own crop production.
These findings are divergent from Kaloi, et al (2005) and Gitu (2004) points of view that
much of the food consumed in rural households in Kenya is obtained from the farm and
very little is purchased from the market and, on the average 30% of the food consumed
by rural households is purchased while 70% is derived from own farm production. This
contradiction is due to the seasonality of the study (drought period).
However, the
findings tend to corroborate with the findings about Makueni County in April 2011 that
showed 64.5% of households’ main source of food was market (Scribd, 2011). Food
consumption and food sources are likely to vary depending on the proximity of the
harvest (Aiga & Dhur, 2006).
71
4.4.2 Food Aid Support
To get insightful information concerning salient sources of household food, respondents
were asked whether their households were getting food assistance through food aid.
Their responses are as shown on (Figure 4.3).
Received =55.3%
Not received =44.7%
Figure 4.3: Households’ Food Aid Support
Majority of households (55.3%) had received food aid in a period of less than a month
and the major food commodity received was maize. They mentioned Catholic Diocese of
Meru and Plan International as some of the organizations that supplied the food aid. This
agrees with GOK (2008a) which stipulates that some of organizations that provide food
aid support in Tharaka are Plan International, GOK and WFP through the Catholic
Diocese of Meru. Food aid support is a source of relief for emergency in natural disasters
and slow-onset crises (Maxwell et al, 2008).
72
4.4.3 Amount of Maize Received from Food Aid
Maize was the major food item received by households from food aid.
Figure 4.4: Amount of Maize Received
Five kilograms of maize was received by 5.1%, 10kg by 35.3%, 15 kg by 4.9% and 20 kg
by 10%. Of all the maize commodity received, 50.1% was consumed in the households
while 5.1% was shared with kin. The maize support helped in increasing household
access to cereal food group as Rose (2008) observes that food aid increases household
access to food.
4.5 Household Food Insecurity Status
Household food insecurity status was established by considering the results of HDDS and
HFCS. Further the statuses of household food insecurity were cross-tabulated with
sources of maize to establish their interaction. The findings are presented hereunder.
73
4.5.1
Household Food Insecurity Status According to HDDS
Household food insecurity status according to HDDS is illustrated on (Figure 4.5).
Acceptable – 0.3%
Borderline – 16.2%
Food Poor – 83.3%
Figure 4.5: Household Food Insecurity Status According to HDDS
The findings indicate that majority of households (83.3%) had low HDDS of between 1
and 3 food groups, 16.2% had medium HDDS of 4 and 5 food groups and 0.3% had high
HDDS of 6 and above food groups in the previous 24 hours; with the mean food groups
of 3. This translates into the status of household food insecurity being 83.3% food poor,
16.2% borderline and 0.3% acceptable. Tharaka South District is one of the areas
classified as moderately food insecure according to Kenya Food Security Update (2009)
that classified status of food insecurity in Kenya – generally food secure, moderately food
insecure, highly food insecure and extremely food insecure (WFP, 2009).
74
4.5.2
Household Food Insecurity Status According to HFCS
Household food insecurity status according to HFCS is illustrated hereunder (Figure 4.6).
Figure 4.6: Household Food Insecurity Status According to HFCS
The 7 day food frequency showed that the status of household food insecurity was not
desperate with slightly higher than half of the households (50.7%) having acceptable
HFCS of more than 42. A good proportion of households (26.5%) had poor HFCS of less
than 28. The classification of household food insecurity status was thus: 26.5% food
poor, 22.8% borderline and 50.7% acceptable according to household food frequency.
These findings are comparable with the results of Mwingi by Kaloi, et al (2008) which
indicate households found to be food insecure in the district were 38%.
75
4.6.3
HDDS and HFCS
The findings of cross-tabulating HDDS and HFCS are as shown (Table 4.15).
Table 4.15: Cross-tabulation of HDDS and HFCS
% of Households
Categories of HFCS
Poor =
0 – 28
Categories
of HDDS
Low = ≤3
Medium
= 4 &5
Borderline = Acceptable=
28.5 – 42
≥42
Total
Frequency 85
72
136
293
HDDS
29%
24.6%
46.4%
100%
HFCS
91.4%
90%
76.4%
83.5%
Frequency 8
8
41
57
HDDS
14%
71.9%
100%
10%
23%
16.2%
0
1
1
HFCS
High
≥6
Total
14%
8.6%
= Frequency 0
HDDS
0%
0%
100%
100%
HFCS
0%
0%
0.6%
0.3%
Frequency 93
80
178
351
HDDS
26.5%
22.8%
50.7%
100%
HFCS
100%
100%
100%
100%
χ2=13.463, df=4 and p=0.009.
44.7%=Food Insecure
43.3%=Vulnerable to Food Insecurity
12= Food Secure
76
The households that had low HDDS and poor HFCS were 85, low HDDS/borderline
HFCS were 72. The cut offs for the household food insecure households was determined
by adding the frequency (n=85) and frequency (n=72) to get n=157 which is, 44.7% of
households classified as food poor. Those that had low HDDS/acceptable HFCS were
136, medium HDDS/poor HFCS were 8 and medium HDDS/borderline HFCS were 8.
These frequencies were summed up and their percentage calculated to establish
households’ vulnerability to food insecurity (borderline). The households at borderline
were 43.3%. The households that had medium HDDS and acceptable HFCS were 41.
Neither did any household have high HDDS and poor HFCS, nor high HDDS and
borderline HFCS and only one household had high HDDS and acceptable HFCS.
Frequency (n=41) and frequency (n=1) were summed up to get n=42. Therefore 42
(12%) households’ food security was acceptable.
The analysis of household food insecurity status was in accordance with an analysis by
WFP’s Humanitarian Practice Network’s study carried out in Darfur in 2005 for
emergency food security and nutrition assessment that first classified households into
three food consumption groups (‘acceptable’, ‘borderline’ and ‘poor’) according to the
diversity of the diet and consumption frequency (Aiga & Dhur, 2006). The classification
of the households in the study area according to status of household food insecurity was
thus:
44.7% food poor, 43.3% borderline food security and 12% acceptable food
security.
This translates into 44.7% households were food insecure, 43.3% were
vulnerable to food insecurity while 12% were food secure according to WFP (2006).
77
4.5.4 Statuses of Household Food Insecurity and Sources of Maize
The interaction between the statuses of household food insecurity and sources of maize
were established by cross-tabulating the variables (Table 4.16).
Table 4.16: Cross-tabulation of statuses of household food insecurity and sources of
maize
Status of Food Insecurity
Food Insecure
Sources of Maize
Market Own
Production
Gifts
from Free
Relatives and Relief
Friend
Food
Total
48
3
0
106
157
1.9%
0%
67.5%
100%
46
20
17
152
30.3%
13.2%
11.2%
100%
20
0
3
42
Percentage 45.2%
47.6%
0%
7.1%
100%
Frequency
69
20
126
351
19.7
5.7
35.9%
100%
Frequency
Percentage 30.6%
Vulnerable to Frequency 69
Food
Percentage 45.4%
Insecurity
Food Secure
Total
Frequency
19
136
Percentage 38.7%
χ2=160.895, df=6, p=0.000
Maize was selected as an indicator for sources of food because it was the main staple
food among the small scale farmers’ households. Majority of food insecure households
(n=106) sourced maize from free relief food. This category received food aid because
they were likely to be poor therefore could not afford to purchase maize from the market.
This proposition is supported by (GOK, 2008c) which stipulates that limited accessibility
78
of food by food insecure households is linked to poverty (whereby about half of the
Kenyan population fall below the poverty line), and inadequate incomes coupled with
low employment rates.
Majority of households vulnerable to food insecurity also sourced their maize from
market (n=69), while the main source of maize for the food secure households was own
production (n=20) and the market (n=19). Farming (own food production) did not act as
the main source of food among majority of the households because their crops did not
mature up to yield enough food for sustained consumption. The drought experienced in
October/December rain season of 2010 caused this. These findings are supportable by
the findings which showed that low crop production reduced the availability of food for
consumption and exposed farmers in Umbululu into getting food from other sources,
such as purchases (Mjonono, et al., 2009).
4.7 Coping Strategies
Coping strategies used among households during food shortages were as shown below
(Table 4.17).
4.6.1
Coping Strategies Commonly Used among Households
Assessing the magnitude of a coping strategy entails measuring the frequencies of the
strategy by ascribing weights, summing up the weights and then putting the result as a
score (Maxwell, 2008). Weights 0, 1, 2, 3 and 4 were ascribed for this study as never,
79
hardly, sometimes, often and always respectively. The weights were multiplied by the
percentage of their frequencies and then were summed up to get scores of every coping
strategy.
Table 4.17: Coping Strategies Commonly Used among Households
Coping Strategy (in the
previous 7 days)
Total
Weights
Relative Frequency %
Never Hardly Sometimes Often Always
Reduction in size of 0
meals
4.9
39.7
35.4
20
270.5
Reduction in the number 0.3
of meals per day
9.7
35
40.2
14.8
259.5
Consume immature crop
20
59.7
10.3
0
170.3
Restrict consumption of 29.4
adults to allow more for
children
10.3
45.4
14.9
0
145.8
Swapped consumption 25.4
to less preferred or
cheaper foods
24.6
39.4
0.6
10
145.2
Borrow food from a 14.9
friend or relative
34
51.1
0
0
136.2
Consume normal wild 24.9
food
25.1
45.1
4.9
0
130
Sale
of
livestock
milking 40.3
15.1
30
14.6
0
118.9
Sale of charcoal and/or 55.9
firewood
19.3
20.0
4.8
0
73.7
10
Reduction in size of meals had the highest score of 270.5. It was followed by reduction
in the number of meals per day at 259.5 and consumption of immature crop at 170.3.
80
Other coping strategies employed by the households are shown above (Table 4.17).
Some of these coping strategies are similar with the coping strategies identified by Wiley
(2007) among Tharaka District households, which were: seeking assistance for food from
relatives and neighbours, sale of livestock and collecting bush food by poor households.
The findings therefore are implicative that small scale farmers in Tharaka Central
Division relied on a variety of coping strategies to counter their household food
insecurity; which is in agreement that increased reliance on coping strategies is associated
with lower food availability (Mjonono, et al., 2009).
4.7 Hypotheses-Testing Results
The findings on the hypotheses testing were established by carrying out 2 tailed Pearson
correlation tests, 2 tailed t test and 2 tailed chi square tests.
4.7.1
Relationship between Sizes of Farms and Sizes of Farmlands
Ho1. There is no significant relationship between farm size and farmland size at a
significant level of 0.05.
The hypothesis stating that there was no significant relationship between farm size and
farmland size was tested by carrying out 2 tailed Pearson Moment correlation test. The
test showed that there was significant relationship (positive correlation) between sizes of
farms and sizes of farmlands of r = 0.653 and p=0.000. This means that the more farm a
household owned, the larger its farmland. The null hypothesis was rejected.
81
4.7.2 Differences between Food Expected and Food Harvested
Ho2. There is no significant difference between food expected and food harvested at
a significant level of 0.05.
The null hypothesis stating that there was no significant difference between food
expected and food harvested at a significant level of 0.05 was tested by carrying out a 2
tailed t test on food crops expected and harvested as shown (Table 4.18).
Table 4.18: Differences between Food Crops Expected and Harvested
Food Crop Expected Factor
Versus Harvested
Mean
Difference
Standard
Error t value p
value
Difference
Maize
27.625
283.6
12.374
22.927 0.000
Millet
3.111
195.53
6.781
28.832 0.000
Sorghum
69.507
78.64
15.390
5.110
Green grams
103.515 247.57
13.010
19.029 0.000
Cow peas
124.936 68.75
5.530
12.341 0.000
0.000
A 2 tailed t test on food expected and harvested in October/December season showed a
significant difference of 22.927 at a p value of 0.000 on maize. This shows that there was
significant difference between maize expected and maize harvested during the season.
Millet, sorghum, green grams and cowpeas also showed significant differences at similar
p value with maize. Following these results, the null hypothesis was rejected. The
decline in the amount of harvest during the season is comparable with that of Makueni
County whose households indicated a decline in the amount of harvest during the season
82
as compared with the previous season (Scribd, 2011).
The decline predisposed the
households into vulnerability to food insecurity.
4.7.3
Relationship between the Status of HFCS and Household Size
HO3. There is no significant relationship between the status of household food
consumption score and household size at a significant level of 0.05.
The null hypothesis stating that there was no significant relationship between the status of
HFCS and household size at a significant level of 0.05 was tested by carrying out Pearson
correlation test.
Table 4.19: Relationships between the Statuses of HFCS and Household Size
HFCS/
Pearson Correlation (r)
P Value
Poor
-0.123
0.239
Borderline
0.491
0.000
Acceptable
-0.313
0.000
Household Size
A 2 tailed Pearson correlation test on the relationships between the statuses of HFCS and
household size revealed different coefficients. The relationship between poor HFCS and
household size was not significant.
The correlation between borderline HFCS and
household size revealed a positive relationship of r=0.491 at a p=0.000.
acceptable HFCS revealed a negative correlation of r=0.313 at p=0.000.
While
83
The relationship between borderline and household size was positively significant
implying that the more persons per household, the more vulnerable to food insecurity it
was.
An overall 2 tailed Pearson correlation was also carried out on HFCS and
household size and it revealed a negative correlation of r=-0.476 at a p value of 0.000;
meaning that the more persons a household had, the poorer the status of HFCS therefore
the more food insecure it was. Thus the null hypothesis was rejected. These findings
were in corroboration with Alem and Shumiye (2007) report which observed that the
smaller a family size, the more acceptable its household food consumption.
4.7.4 Relationship between HFCS and Farmland Size
HO4 There is no significant relationship between household food consumption score and
farmland size at a significant level of 0.05.
The null hypothesis stating that there was no significant relationship between HFCS and
farmland size at a significant level of 0.05 was tested by conducting Pearson correlation
test. The results are as shown (Table 4.20).
Table 4.20: Relationships between HFCS and Farmland Size
HFCS/Farmland Size
Pearson Correlation (r)
P Value
Poor
0.163
0.118
Borderline
0.533
0.000
Acceptable
-0.030
0.690
84
The relationship between borderline and farmland size was significant at a correlation of
r=0.533 at a p value of 0.000, meaning that the larger the farmland size of a household,
the better was their HFCS. There was no significant relationship between acceptable
HFCS and farmland size. An overall 2 tailed Pearson correlation test was done on HFCS
and farmland size, and the correlation obtained was r=0.299 at a p=0.000. This meant
that the more the farmland size a household possessed, the more improved was its HFCS,
and therefore the more food secure it was deemed to be. Following this result, the null
hypothesis was rejected.
4.7.5 Association between HDDS and HFCS
HO5.
There is no significant association between household dietary diversity score and
household food consumption score at a significant level of 0.05.
The null hypothesis stating that there was no significant association between HDDS and
HFCS at a significant level of 0.05 was tested by carrying out 2 tailed Chi square test as
shown in (Table 4.15). The test showed a significant association between HDDS and
HFCS of χ2=13.463, df=4 and p=0.009. In view of these findings, the null hypothesis
was rejected. This meant that the higher the HDDS, the more acceptable the HFCS. It is
ordinary to expect that households with acceptable HFCS would also have high and
medium HDDS; which is supportable by IFPRI (2008) assertion that HFCS is a
frequency-weighted HDDS.
85
4.7.6 Association between Sources of Maize and the Status of Household Food
Insecurity
HO6.
There is no significant association between sources of maize and the status of
Household food insecurity at a significant level of 0.05.
A 2 tailed Chi square test was carried out to test the null hypothesis stating that there was
no significant association between sources of maize and the status of household food
insecurity at a significant level of 0.05. The results are as shown in (Table 4.16). A 2
tailed Chi square test showed that there was significant relationship between sources of
maize and the status of household food insecurity of χ2=160.895, df= 6 and p=0.000.
Thus the null hypothesis was rejected.
86
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.0 Introduction
This chapter highlights the summary, conclusion, recommendations of the study and
suggestions for further research.
5.1 Summary
i.
The major source of livelihood for the small scale farmers’ househods in Tharaka
Division was agriculture.
ii.
Food crops such as maize, millet, green grams were the major crops cultivated,
and potential crop loss was mitigated by planting drought resistant crop varieties.
Many households had enough food provisioning during the months of June to
August while in October to January food provisioning was inadequate.
iii.
The most highly consumed meal in the previous 24 hours was breakfast followed
by supper. Lunch was the most skipped meal. Generally, the HDDS in the
previous 24 hours was low. Maize was the main food item consumed among
households during the past one week and the major HFCS was acceptable.
iv.
The main source of household food was market and the households that were
receiving food aid got maize as the major food commodity.
v.
The main household food security status was found to be poor according to
HDDS, with slightly more than half of all households having acceptable
household food security according to HFCS. The classification of household food
87
insecurity status by combining HDDS and HFCS showed that majority of
households were food insecure.
vi.
The main coping strategies employed by the households in the case of food
shortages were reduction in size of meals, reduction in the number of meals per
day and consumption of immature crop.
vii.
There were significant relationships between farm size and farmland size, status
of household food consumption score and household size, household food
consumption score and farmland size at a significant level of 0.05. There was
also significant difference between food expected and food harvested at a
significant level of 0.05.
Further, the hypotheses testing results showed
significant associations between household dietary diversity score and household
food consumption score, sources of maize and the status of household food
insecurity at a significant level of 0.05.
5.2 Conclusion
i.
The status of food production was lower than expected and was exacerbated by
frequent droughts. This had exposed households to food insecurity.
ii.
Food consumption patterns were mainly characterized by low HDDS but majority
of households had acceptable HFCS.
iii.
The small scale farmers depended mainly on markets as their main source of
household food as opposed to usual expectation that own crop production would
88
be the lead source. This means own crop production played a supplementary role
in food access.
iv.
Majority of households were in the status food insecurity.
v.
Among the main coping strategies identified were reduction in size of meals and
reduction in the number of meals per day. These coping strategies were not
detrimental to the small scale farmers’ livelihoods; therefore the households were
resilient to food insecurity.
vi.
All the hypotheses were rejected because they all showed significant
relationships, differences and associations among the tested variables.
5.3 Recommendations
Several recommendations of dealing with household food insecurity in Tharaka Central
Division are proposed herein.
They focus on means of improving household food
production, means of improving household food consumption patterns, means of
improving food access through food purchases, means of reducing the status of
household food insecurity and means of improving the use of less drastic coping
strategies in cases of household food insecurity.
5.3.1
i.
Recommendations for Policy Making
Household food production among small scale farmers in Tharaka Central
Division were influenced by several factors. Small farmland sizes in the study
area were influenced by the high cost of production such as the cost of weed
control. It is therefore important for agricultural extension officers in the area to
89
create awareness and empower the small scale farmers on the need to use
herbicides that kill weeds in large scale rather than over relying on manual
methods of weed control. This will enable cultivation of vast farmlands for
improved crop production.
ii.
Tharaka Central Division is frequently afflicted by droughts causing poor crop
production. It is for this reason that the GOK through the Ministry of Water and
Irrigation should create irrigation policies and implement these policies in all
ASAL regions in Kenya to ensure sustainable crop production.
iii.
Household food consumption patterns were poor because of several factors such
as lack of a variety of food items for consumption. Good market infrastructure
for cash crops that thrive in the area should be made available by the government
and the private sector through constructing cotton ginneries and ensuring good
market capital for cotton, sunflower and castor. The GOK should also supply the
farmers with cash crop seeds to enable them grow the crops. In this way, the
households would be economically empowered to purchase variety of food items
for improved HDDS and HFCS.
90
5.3.2 Recommendations for Practice
i.
The households in collaboration with the government and the local the NGOs
should plan, source and implement irrigation projects so as to improve household
crop production when rains are erratic.
This would mitigate crop loss and
minimize the use of coping strategies.
ii.
The main source of food among the small scale farmers was market while own
food production played a secondary role. The small scale farmers should invest in
education to improve their literacy levels and also access formal employment for
improved capacity and better food purchasing powers from the markets. This is
because majority of them had no education or were of primary level and only a
few whose livelihood source was employment.
iii.
Household food insecurity prevalence among the small scale farmers was found
to be high. To alleviate the situation, development of local capacity through
community-based participatory actions is suggested as a means of improving
program outcomes as well as promoting human rights of household food security.
Apart from providing food relief responses, the GOK together with food relief
stakeholders should lay out sustainable food policies, implement them to the letter
and conduct capacity building with the small scale farmers through arranging and
conducting training seminars and sessions to equip the community with
appropriate household food security information.
91
5.4 Suggestions for Further Research
The following further researches are recommended, based on the findings of the study on
household food insecurity and coping strategies among small scale farmers in Tharaka
Central Division.
i.
A similar study could be done covering a wider geographical region in Arid and
Semi-Arid Lands.
ii.
A comparative study in relation to food security could be done covering both
harvest and post-harvest seasons in the study area.
92
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98
RESEARCH INSTRUMENTS
APPENDIX 1
Respondents’ Informed Consent
My name is Beatrice Kabui Icheria. I am a master’s student at Kenyatta University
carrying an academic research entitled Household Food Insecurity and Coping Strategies
among Small Scale Farmers in Tharaka Central Division of Tharaka District, Kenya. The
purpose of this study is academic; and I wish to interview you on the same. I am kindly
requesting for your cooperation during the interview session. I further wish to clarify that
the information you give for this interview will be confidential and anonymous.
99
APPENDIX 2
Questionnaire for the Household Head and Household Principal Care Giver for the
Study of Household Food Insecurity and Coping Strategies among Small Scale
Farmers in Tharaka Central Division of Tharaka South District, Kenya.
Division _____________ Sub-location ____________ Household Code _______
(A). HOUSEHOLD DATA
1. Household Size: How many people live in this household and share meals?
Age
group
Name and
personal ID
Relationship
to Household
Age
Head
Years Months
Under 1.
5
years 2.
3.
4.
5 to 5.
18
6.
7.
8.
9.
Over
18
10.
11.
12.
13.
14.
Sex Main
occupation
Education
level
100
2. Wealth of household: Does your household own the following items?
Code
Item
1
Type of house (modern, semi-modern,
traditional huts, shanty)
2
Type of
house wall (mud, stone,
concrete, brick, timber, other)
3
House roof (grass thatch, iron sheets,
asbestos, tile, other)
4
House floor (earth, cement, cow dung
and mud, other)
5
Mobile transport assets (bicycle,
motorcycle, vehicle, ox/donkey-cart,
other)
6
House lighting (kerosene, solar power,
electricity, light from firewood, others)
7
Cooking energy (firewood, charcoal,
kerosene, cooking gas, electricity, other)
8
Bedding (timber bed, raft bed, mattress,
palm mat, reed mat, skin mat)
9
Livestock (cows, goats, sheep, poultry)
Response
Source of Sources of income in the last 3 months Tick
income
(3 most important sources)
a. Sale of livestock
b. Sale of livestock product
c. Sale of fish
d. Sale of ration food
e. Sale of own crop
f. Wage/casual labour
g. Salary
101
h. Sale of charcoal/firewood
i. Weaving
j. Others (specify)
Source of Please indicate the main source of
livelihood livelihood for the household
a. Pastoralism
b. Agriculture
c. Agro-pastoralism
d. Formal employment
e. Casual labour
f. Fishing
g. Trading
h. Others (specify)
Tick
102
(B). HOUSEHOLD FOOD PRODUCTION:
3. Size of farm in acres ______________
4. Size of the farmland (area under cultivation) in acres _______________
5. Types of crops cultivated in March /May Season:
March/May Season
Crops
Food
Crops
Expected Harvested Sold
Maize
Millet
Sorghum
Finger
millet
Others
(specify)
Green
grams
Pigeon
Peas
Cowpeas
Others
(specify)
Cash
Crops
Amount (kg)
Cotton
Sunflower
Castor
Consumed Stored Duration
of
post
harvest
storage
103
Others
(specify)
i.
Did you harvest what you expected? _______________________________
ii.
If not, why? __________________________________________________
6. Types of crops cultivated in October/December Season:
October/December
Season
Expected Harvested Sold Consumed Stored Duration
of
post
harvest
storage
Crops
Food
Crops
Maize
Millet
Sorghum
Finger
millet
Others
(specify)
Green
grams
Pigeon
Peas
Cowpeas
Others
(specify)
Cash
Crops
Amount (kg)
Cotton
Sunflower
104
Castor
Others
(specify)
(i) Did you harvest what you expected? ________________________________
(ii) If not, why? ___________________________________________________
(iii) Besides farm produce, how else do you provide food for your family?
_____________________________________________________________________
7. How do you mitigate crop losses?
Code Mitigation Strategy
previous month)
(in
the Relative Frequency
Never Hardly Sometimes Often Always
1
Planting of cassava
2
Katumani variety of millet
3
Katumani variety of maize
4
Katumani variety of pigeon peas
5
Kaguru variety of sorghum
6
Crop on the farm spray
7
Dusting foodstuff with pesticides
8
Goat, cattle and sheep rearing
9
Poultry keeping
10
Employment
activities
in
non-farm
105
11
Others (specify)
8. Crop pest and disease
i.
Which pests invade crops in your farm? _________________________________
ii.
How do they affect the crop? _______________________________________
_________________________________________________________________
iii.
Which pests invade grains in your store? ________________________________
iv.
How do they affect the grains? _______________________________________
v.
Mention signs of/or crop diseases in your farm? __________________________
vi.
In what way do they affect crop? ______________________________________
_________________________________________________________________
9. Droughts and Floods:
i.
Have you experienced drought(s) in the recent two crop production seasons?
ii.
If
yes,
in
what
way
did
the
drought(s)
affect
crop
production?
________________________________________________________________
iii.
Mention any flood episode that you have experienced in the recent two crop
production seasons. ________________________________________________
10. Household food provisioning:
(i) Which months do your household have enough food? ______________________
(ii) Which months when your household do not have enough food? ______________
106
(C). HOUSEHOLD FOOD CONSUMPTION (to be answered by the household
principal care giver)
11. 24 Hour Dietary Recall for Dietary Diversity
(i) Beginning from morning to evening yesterday, please mention all foods and drinks
your household members consumed.
(ii) What amounts of foods and drinks did your household members consume?
Meal
Age
Household Dish
group members’
codes
Under 1
5 yrs
2
B
3
r
4
e
a
k
f
5
5-18
yrs
6
7
8
a
9
s
t
Over 10
18 yrs
11
12
13
14
S
Under 1
Ingredients
Adequate
Yes (1)
No
(2)
107
n
5 yrs
2
a
3
c
4
k
5-18
years
5
6
7
8
9
Over
18
10
11
12
13
14
u
Under 1
5 yrs
2
n
3
c
4
L
h
5-18
yrs
5
6
7
8
9
Over
18
10
11
12
13
108
14
n
Under 1
5 yrs
2
a
3
c
4
S
k
5-18
yrs
5
6
7
8
9
Over
18
10
11
12
13
14
u
Under 1
5 yrs
2
p
3
p
4
S
e
r
5-18
yrs
5
6
7
8
9
Over 10
18 yrs
11
109
12
13
14
n
Under 1
5 yrs
2
a
3
c
4
S
k
5-18
yrs
5
6
7
8
9
Over 10
18 yrs
11
12
13
14
12. 7 Day Food Frequency and Main Food Sources
(i) How many times does your household consume the following foods?
(i) What are the sources of these foods, and does the household get enough of it?
110
Food type
1 Maize
2 Sorghum
3 Wheat
4 Rice
5
Finger
millet
6 Arrow root
7 Irish potato
8 Cassava
9
Sweet
potato
10
Honey/sugar
11.Fats/oils
12 Other
carbohydrate
(specify)
13 Milk
14 Red meat
15 Poultry
meat
16 Fish
Frequency of consumption per week
Main
Source of
food
None Once
(1 – 7)
Twice 3
times
4
5 and
times more
times
Enough
1= yes
2=no
111
17 Eggs
18 Pulses
(beans,
pigeon peas,
green grams,
cow peas)
19 Nuts
20 Other
proteins
(specify)
21 Kales
(sukuma
wiki)
22 Spinach
23 Cabbage
24 Cow peas
leaves
25 Carrot
26 Other
vegetables
(specify)
27 Mango
28 Paw paw
29 Banana
30 Oranges
31 Guava
32 Other fruit
(specify)
112
Codes for main source of food: 1=Market 2=Own production 3=Gifts from relatives,
neighbours and friends 4=Food-for-work 5=Free relief food 6=Wild food 7=Other
(specify)
13. Food Aid Support:
i.
Have you received food aid in the last three months? (please circle)
1 = yes
2 = no
ii.
If yes, when?
1 = less than 1 month
2 = 1 and 2 months
3= over 2 months
iii.
Food commodities received in the last distribution, quantity received, how it was
utilized and duration each food commodity lasted.
Food Aid Commodity
Code
1
2
3
4
5
6
Commodity
Quantity Resold
Bartered Shared
in
the for other with kin
(Kgs)
market
item
Saved
for
seed
Consumed
by
household
members
Duration
(days) each
food
commodity
lasted
113
(D) COPING STRATEGIES:
14. Has your household done any of the following in the previous 7 days? Tick
appropriately
Code
Coping Strategy
previous 7 days)
(in
the
1
Reduction in the number of
meals per day
2
Skip food consumption for an
entire day
3
Reduction in size of meals
4
Restrict consumption of adults
to allow more for children
5
Feed working members at the
expense of non-working
6
Swapped consumption to less
preferred or cheaper foods
7
Borrow food from a friend or
relative
8
Purchase food on credit
9
Consume normal wild food
10
Consume immature crop
11
Consume dead animals (cows,
goats and others)
12
Consume taboo foods (acacia
pod, bitter fruits)
13
Food consumption of seed
stock
14
Send household members to
eat elsewhere (women groups’
tea parties, schools, churches)
Relative Frequency
Never Hardly Sometimes Often Always
114
15
Withdraw
school
children
16
Begging or engaging
degrading jobs
17
Individual migration out of the
area
18
Household migration out of
the area
19
Sale of farm implements
20
Sale of milking livestock
21
Sale of household assets
22
Disintegration of families
23
Abandonment of children or
elderly
24
Sale of
firewood
25
Others (specify)
charcoal
from
in
and/or
115
APPENDIX 3
Observation Checklist for the Study of Household Food Insecurity and Coping
Strategies among Small Scale Farmers in Tharaka Central Division of Tharaka
South District, Kenya.
Division _____________ Sub-location ____________ Household Code __________
1. Size of farmland ______________
2. Type of food cultivated in the season:
i.
Millet____ Sorghum______ Maize______ Any other Cereal_______
ii.
Green grams_____ Pigeon Peas____ Cowpeas____ Beans___ Other ____
iii.
Cash Crops _____________________________________________
3. Type of house ________________________________________________
4. Household assets _____________________________________________
5. Types of food available in household _______________________________________
________________________________________________________________________
________________________________________________________________________
6. Foodstuff sold at the nearest market _______________________________________
________________________________________________________________________
________________________________________________________________________
7. Prices of the foodstuff___________________________________________________
________________________________________________________________________
7. Nearest water source ____________________________________________________
8. Presence of water in the household ________________________________________
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APPENDIX 4
Key Informant Interview Guide for the District Extension Officer and ALRMP
Manager for the Study of Household Food Insecurity and Coping Strategies among
Small Scale farmers in Tharaka Central Division of Tharaka South District, Kenya.
1. What organizations in collaboration with your department are involved in helping
small scale farmers in Tharaka Central Division achieve food access for their
households?
2. What help did they render the community?
3. Has the help provided had positive impacts towards achieving household food
security?
4. What challenges have you experienced during the implementation of the food
assistance plans?
5. What would you recommend as a sustainable solution to household food
insecurity among the small scale farmers in Tharaka Central Division?
6.
Please mention coping strategies against hunger among household of the
division.
7. If you can, please tell me the range and average farmland size in Tharaka South
District and/ or Tharaka Central Division.
8. What types of drought resistant crops are cultivated in Tharaka Central Division?