VULNERABILITY OF FARMERS TO FLOOD DISASTER

Transcription

West African Science Service Centre on Climate
Change and Adapted Land Use
VULNERABILITY OF FARMERS TO FLOOD DISASTER IN AKINYELE LOCAL
GOVERNMENT AREA, OYO STATE OF NIGERIA
A Thesis by
ADEGBILE Olusegun Adeyemi
Submitted to
West African Science Service Center on Climate Change and Adapted Land Use
Université de Lomé Togo
In partial fulfilment of the requirements for the degree of
MASTER OF SCIENCE
November, 2014
Major Subject: Climate Change and Human Security
VULNERABILITY OF FARMERS TO FLOOD DISASTER IN AKINYELE LOCAL
GOVERNMENT AREA, OYO STATE OF NIGERIA
A Thesis by
ADEGBILE Olusegun Adeyemi
Submitted to
West African Science Service Center on Climate Change and Adapted Land Use
Université de Lomé Togo
In partial fulfilment of the requirements for the degree of
MASTER OF SCIENCE
Approved by:
Chairman of Committee,
Committee Members
Director of Program
Abel Aderemi Adebayo
Egbendewe-Mondzozo Aklesso
Francis Obeng
Kouami Kokou
November, 2014
Major Subject: Climate Change and Human Security
ii
ABSTRACT
Vulnerability of Farmers to Flood Disater in Akinyele Local Government Area, Oyo State of
Nigeria. (November, 2014)
Adeyemi Olusegun Adegbile
B.S. (Agric. Economics). Obafemi Awolowo University, Ile-Ife, Nigeria.
Chairman of Advisory Committee: Prof. Abel Aderemi Adebayo
Akinyele Local Government Area, Oyo State of Nigeria is a popular place for flooding,
which has negative consequences on farmers. Thus, there is need to reduce the vulnerability
of the farmers to flood impacts. Vulnerability reduction requires adequate knowledge of the
factors, which predispose people to flood impacts. Therefore, this study assessed the
vulnerability of farmers to flood disaster in Akinyele LGA by using the MOVE framework.
Primary data were obtained through questionnaire administration, expert interview and
portable GPS device while secondary data were sourced from SRTM imagery and the Map of
Akinyele LGA. The data were analysed with Statistical and GIS tools. This study identified
two major flood plains in Akinyele LGA and Tola, Jarija, Ajeja, Alabata, Onilu, Lagbe and
Ajibode are some of the flood-prone farming communities. Furthermore, the farmers are
resource poor people, who are exposed to recurrent and damaging flood. Tola is the least
flood vulnerable village while Ajibode falls under the most vulnerable class. It is
recommended that river buffer zones should be set and enforced, major rivers should be
dredged and policies for improving income should be implemented in the LGA.
Keywords: Vulnerability, farmers, flood, disaster, Akinyele.
iii
RÉSUMÉ
La préfecture d’Akinyele, Etat d’Oyo au Nigeria, est une place réputée pour l’inondation qui a
des conséquences négatives sur les agriculteurs. Ainsi, il est nécessaire d’atténuer la
vulnérabilité des agriculteurs du milieu face aux impacts des inondations. La réduction de la
vulnérabilité requiert une connaissance des facteurs qui prédisposent les communautés aux
impacts d’inondation. Alors la présente étude évalue la vulnérabilité des agriculteurs faces aux
désastres que les inondations produisent dans la préfecture d’Akinyele par l’utilisation du cadre
conceptual de MOVE. Les données premières ont été obtenues via des questionnaires
administratifs, des interviews conduites auprès des experts et des données collectées du
dispositif portatif GPS, par ailleurs les données secondaires ont été obtenues à partir de
l’imagerie SRTM et de la carte d’Akinyele. Ces données ont été ensuite analysées avec les
outils de statistique et au moyen du GIS. La présente étude identifie deux grandes plaines
d’inondation dans les zones allant de Akinyele où Tola, Jarija, Ajeja, Alabata, Onilu, Lagbe,
et Ajibode constituant les zones de production caractérisées inondables. En outre, les paysans
sont généralement des personnes pauvres exposés le plus souvent aux inondations. Ajibode fait
partir des plus vulnérables villages alors que Tola est le moins vulnérable. Il est recommandé
que des zones tampon de la rivière soient établies et mises en vigueur, que des rivières
importantes soient draguées et que des politiques d’amélioration de revenu soient mises en
places dans la préfecture d’Akinyele.
Mots clés: Vulnérabilité, Agriculteurs, Inondation, désastre, Akinyele
iv
DEDICATION
This thesis is dedicated to the victims of the August 26th, 2011 floods in Ibadan, Oyo state,
Nigeria.
v
ACKNOWLEDGEMENTS
I am very grateful to the Almighty God for the grace given to me to be part of this
innovative MSc. programme. My appreciation goes to the West African Science Service Center
on Climate Change and Adapted Land Use for providing me with the funding for this degree.
Furthermore, I am indebted to all lecturers, who have imparted their knowledge on me,
in one way or the other, during my course work period at the University of Lome. I am
particularly grateful towards Professor Adote Blim Blivi, Prof. Kouami Kokou, Dr. Fabrice
Renaud, Dr. George Abbey, Dr. Komi Agboka, Dr Aklesso Egbendewe-Mondzozo and other
members of staff in the academic and management team of WASCAL, Université de Lomé for
their training, guidance and encouragement.
I am grateful to the mentorship of my thesis supervisor, Prof. Abel Aderemi Adebayo
of Modibbo Adama University of Technology Yola, Nigeria, as well as Mr. Idowu Amusa, Mr.
Isaac Gbiri and Mr. Femi Adetimirin of GIS Department, Federal School of Surveying Oyo,
Nigeria for their guidance during the field survey and data analysis.
I am equally indebted to my wife, Bukola Adegbile, and my son, Daniel Adegbile, for
their patience and endurance during my stay away from home.
It is my prayer that the Almighty God will reward all of you bountifully (Amen).
vi
TABLE OF CONTENTS
ABSTRACT........................................................................................................................................... iii
RÉSUMÉ ............................................................................................................................................... iv
DEDICATION ........................................................................................................................................ v
ACKNOWLEDGEMENTS ................................................................................................................... vi
TABLE OF CONTENTS ...................................................................................................................... vii
LIST OF FIGURES ............................................................................................................................... ix
LIST OF TABLES .................................................................................................................................. x
LIST OF ABBREVIATIONS ................................................................................................................ xi
CHAPTER I: INTRODUCTION ............................................................................................................ 1
1.1 Context and Justification............................................................................................................... 1
1.2 Problem Statement ........................................................................................................................ 3
1.3 Research Objective ....................................................................................................................... 4
1.4 Plan of the Thesis .......................................................................................................................... 5
CHAPTER II: LITERATURE REVIEW ............................................................................................... 6
2.1 Concepts of Disasters and Hazards ............................................................................................... 6
2.2 Concepts of Risks and Vulnerability ............................................................................................ 7
2.3 Resilience and Capacities.............................................................................................................. 8
2.4 Flood as a Disaster ........................................................................................................................ 9
2.5 Determinants of Flood Impacts ................................................................................................... 10
2.6 Vulnerability Assessment Frameworks ...................................................................................... 11
2.6.1 The Conceptual Framework to Identify Disaster Risk ......................................................... 11
2.6.2 Bohle’s Double Structure of Vulnerability .......................................................................... 11
2.6.3 The Sustainable Livelihood Framework .............................................................................. 11
2.6.4 The Pressure and Release (PAR) Model .............................................................................. 12
2.6.5 The SUST Model ................................................................................................................. 13
2.6.6 The BBC Framework ........................................................................................................... 13
2.6.7 The MOVE Framework ....................................................................................................... 14
vii
CHAPTER III: MATERIALS AND METHODS ................................................................................ 16
3.1 Study Area .................................................................................................................................. 16
3.2 Method ........................................................................................................................................ 18
3.2.1 Conceptual Framework: MOVE Framework ....................................................................... 18
3.2.2 Sampling Design Data Requirement and Method of Data Collection ................................. 20
a. Sampling Design ....................................................................................................................... 20
b. Method of Data Collection and Data Source ............................................................................ 20
3.2.3 Data Analysis ....................................................................................................................... 21
a. GIS Determination of Flood-prone Zones................................................................................. 21
b. Questionnaire Data Analysis and Indicators Development....................................................... 22
c. Determination of Vulnerability Index ....................................................................................... 24
d. Evaluation of the Vulnerability Index ....................................................................................... 26
e. Mapping Exposure, Susceptibility, Capacity and Vulnerability Indices ................................... 27
CHAPTER IV: RESULTS AND DISCUSSIONS ............................................................................... 28
4.1 Flood-Prone Farming Communities............................................................................................ 28
4.2 Correlation among Vulnerability Indicators and Weighting ....................................................... 30
4.3 Flood Exposure of the Farmers ................................................................................................... 31
4.4 Susceptibility (Fragility) of the Farmers to Flood....................................................................... 35
4.5 Capacities of the Farmers against Flood ..................................................................................... 43
4.6 Vulnerability of the Farmers to Flood......................................................................................... 52
4.7 Robustness of the Vulnerability Index ........................................................................................ 53
CHAPTER V: CONCLUSION AND POLICY RECOMMENDATIONS .......................................... 55
5.1 Conclusion .................................................................................................................................. 55
5.2 Recommendations ....................................................................................................................... 56
REFERENCES ................................................................................................................................. 57
QUESTIONNAIRE .......................................................................................................................... 60
VITA ..................................................................................................................................................... 65
viii
LIST OF FIGURES
Figure 3.1 Map of Nigeria (Left) showing the location of Oyo State and Map of Oyo Sate
(Right) showing the location of Akinyele Local Government Area (Ajadi et al, 2012) ......... 16
Figure 3.2 Map of Akinyele Local Government Area ………………………………………17
Figure 3.3 MOVE Framework by Birkmann et al (2013) ....................................................... 19
Figure 4.1: Map of Akinyele showing flood plains and major rivers (Source: Author).......... 28
Figure 4.2: Map of Akinyele LGA showing elevation and flood-prone villages .................... 29
Figure 4.3 Map of flood exposure of farming villages in Akinyele LGA ............................... 35
Figure 4.4 Flood susceptibility of villages in Akinyele LGA (Source: Author) ..................... 43
Figure 4.5 Capacity of villages against flood in Akinyele LGA ............................................. 51
Figure 4.6 Flood Vulnerability of Farming Communities in Akinyele LGA .......................... 53
Figure 4.7 Relative vulnerability index by standardisation normalisation method ................. 54
Figure 4.8 Relative vulnerability index by percentage normalisation method ........................ 54
ix
LIST OF TABLES
Table 3.1 Questionnaires responded to per village surveyed .................................................. 21
Table 4.2 Correlations among susceptibility indicators ........................................................... 30
Table 4.3 Correlation among the indicators of capacity .......................................................... 31
Table 4.4 Index of exposure based on elevation of the villages .............................................. 31
Table 4.5 Index of exposure based on closeness to river ......................................................... 32
Table 4.6 Index of exposure based on time of impact of flood on farms ................................ 33
Table 4.7 Index of exposure based on flood frequency ........................................................... 34
Table 4.8 Overall exposure of Akinyele farmers to flood disaster .......................................... 35
Table 4 9 Index of susceptibility based on average income of the farmers ............................. 36
Table 4.10 Index of susceptibility based on clay soil rate ....................................................... 37
Table 4.11 Index of susceptibility based on women land access limitation rate ..................... 38
Table 4.12 Susceptibility index based on poor human waste management rate ...................... 39
Table 4.13 Susceptibility based on surface water consumption rate ....................................... 40
Table 4.14 Susceptibility based on inadequacy of agricultural extension contact .................. 41
Table 4.15 Overall susceptibility of Akinyele farmers to flood disaster ................................. 42
Table 4.16 Index of capacity based on flood education access rate ........................................ 44
Table 417 Flood relief access capacity index .......................................................................... 45
Table 4.18 Processing facility capacity index.......................................................................... 46
Table 4.19 Geographical diversification capacity index.......................................................... 47
Table 4.20 Flood tolerant cropping capacity index ................................................................. 48
Table 4.21 Occupational diversification capacity index .......................................................... 48
Table 4.22 Social cooperation and agricultural insurance capacity index ............................... 49
Table 4.23 Overall capacity index of Akinyele farmers against flood disaster ....................... 51
Table 4.24 Vulnerability of the farmers to flood ..................................................................... 52
x
LIST OF ABBREVIATIONS
CRED- Center for Research on the Epidemiology of Disasters
DFID - Department for International Development
GPS-Global Positioning System
IPCC- Intergovernmental Panel on Climate Change
LGA – Local Government Area
NAIC- Nigerian Agricultural Insurance Corporation
NDRRMCP- National Disaster Risk Reduction and Management Council, Philippines
WHO – World Health Organization
UNOCHA – United Nations Office for the Co-ordination of Humanitarian Affairs
UNISDR- United Nations International Strategy for Disaster Reduction
xi
CHAPTER I: INTRODUCTION
1.1 Context and Justification
Flood exists at the excess extreme end of water or moisture availability continuum.
Flooding refers to a situation in which a usually dry area of land is covered by water resulting
from excessive rainfall, overflow of rivers or dams, dam burst, blockade of water ways,
earthquake or tsunamis, high tide and protective release of water from dams. The water that
occupies a usually dry large expanse of land is referred to as flood. Flood is called a hydrometeorological disaster when it results from a combination of very heavy rainfall or typhoon
and overflow of water bodies, including the oceans or seas and vulnerable features of the
community where it occurs. It could also be a cascading disaster resulting from tsunamis
waves or earthquake. With the exception of extreme events such as tsunamis, dam burst and
spring flood, floods usually occur in flood plains.
Flood plain refers to the flat part of valley, which spreads on either side of the river
channel and is sometimes covered with water when the river overflows its channel
(Summerfield, 2008). Therefore, both flood plain and the river are integral parts of the valley.
Manuamorn et al (2009) opined that flood plains have traditionally supported high population
densities due to their suitability for human settlements, economic activities and agricultural
practices. However, floods equally affect more people than any other disaster in the world.
Floods recorded the highest yearly average occurrence and number of victims between 2001
and 2011 and the highest number of occurrence in 2011 compared to other known disasters
(Guha-Sapir et al, 2012). In West Africa, epidemics (204 events), floods (167 events) and
drought (35 events) were the three most recurrent disasters among a total of 472 natural
catastrophes reported between 1980 and 2010 while Nigerian record of the same period
revealed that epidemics (49 events) and floods (35 events) were highly recurrent (UNISDR,
2014).
Flood threatens food production, food prices as well as both nutritive and microbial
quality of harvested food materials, and drives food insecurity and malnutrition. Flood
induces migration and local community conflict and impair the capability of government to
function effectively in flood-affected areas. Floods can cause chemical injury, worsen the
transmission of water-borne diseases, such as typhoid fever, cholera, leptospirosis and
hepatitis A. Equally, it can cause vector-borne diseases, such as malaria, dengue and dengue
haemorrhagic fever, yellow fever, and West Nile fever (WHO, 2013). Hence, flood can have
1
negative effects on human security - food security, economic security, health security,
environmental security, personal security, community security and political security. Flood is
a major hindrance to the achievement of the Millennium Development Goals (MDGs).
Floods and flood-related disasters are among the most damaging catastrophes in the
world. Although the number of deaths resulting from disasters are decreasing worldwide, the
number of affected people and economic impacts are increasing due to increasing number of
hazards as well as increasing exposure of people and economic activities to floods (CRED,
2013). Globally, an estimated damages of $70.72 billion were associated with hydrological
(flood-related) disasters in 2011, thereby making flood the second most damaging catastrophe
for that year while flood-related events have an annual average damage of $21.39 billion
between 2001 and 2010 (Guha-Sapir et al, 2012). In 2007, flood caused a total of £3 billion
damages in England and Whales (World Bank, 2012). Floods resulting from the Indian
Ocean Tsunamis of 2004 killed 226,000 people and caused huge livelihood losses and
property damages (Kaplan et al, 2009). Pakistan rain-induced flood of 2010 recorded $10
billion in damages and affected 20 million people, among whom 3,000 deaths were recorded
(World Bank, 2012).
In Africa, damages from flood and other hydrologic disasters amounted to $1.01
billion in 2011, with an annual average of $0.08 billion for the period between 2001 and 2010
(Guha-Sapir et al, 2012). Nigerian flood of 2012 affected 35 out of 37 states, 3,870
communities and about 7.705 million people, damaged 597,476 houses, killed 363 people and
put 387,153 internally displaced persons in camps (UNOCHA, 2012). Nigeria’s crude oil
production declined by 500,000 barrels per day due to floods in the Niger Delta (Olayinka et
al, 2013). Ibadan, the capital city of Oyo State of the country lost 100 people and incurred
property and infrastructural damages worth 30 billion naira to 2011 flood and Akinyele was
one of the most affected Local Government Areas by the flood (Taiwo et al, 2012).
Flood does not only damage properties and endanger lives of people and animals; it
equally leads to environmental degradation in the form of soil erosion, landslides, sediment
deposition and the destruction of fish spawning substrates. Recognizing the fact that the risks
associated with natural hazards and the threats to human security cannot be reduced by
focusing solely on the hazards, on one hand, while people will continue to live with changing
environmental conditions on the other hand, Birkmann et al (2013) reiterated the call made
by the Hyogo Framework of Action (2005) to build resilience by reducing vulnerability to
natural hazards.
2
Therefore, assessing the vulnerability of farmers and their communities to natural
hazards and climate change impacts, such as flood, is a worthwhile effort. Firstly, this
assessment will help in identifying farming communities and areas that are prone to flood.
Secondly, it will provide a basis for planning and help in preventing development in risky
zones. Thirdly, flood vulnerability assessment provides an understanding of the nature of
flood, flood impacts as well as the coping and adaptive capacity of affected communities,
which will help in designing the appropriate flood-related climate change adaptation policy
and strategy. Lastly, flood vulnerability assessment provides a comparison among floodprone communities via ranking, which is crucial in identifying communities whose capacity
and resilience building must be prioritised.
1.2 Problem Statement
With the on-going climate change and climate variability and their consequential
effects of increasing weather and climatic extremes, including heavy and concentrated
precipitations, coupled with other environmental woes, flooding has become a major problem
of the whole world including Nigeria (IPCC, 2012; Oyeleye and Adetunji, 2013). The total
rainfall is increasing in Ibadan, Oyo State, Nigeria (Fasinmirin et al, 2012; Amobichukwu
and Egbinola, 2013) like many other Southern parts of Nigeria. The increasing rainfall,
among other factors, has made Akinyele Local Government Area, Oyo State, a popular place
for flooding (Taiwo et al, 2012). Further increases in rainfall may make areas that are less
flood-prone to become more flood-prone. While flood-related economic damages and loss of
life are pronounced in urban and coastal areas due to the concentration of infrastructure and
people in flood hazard zones, floods in rural areas are closely linked to both agricultural
production and livelihood of rural populations (Manuamorn et al, 2009). The most severe
impact of flooding is the overall loss of livelihood for the farmers and their customers
(Howard, 2012).
The 2008 floods in Iowa, USA, accounted for an estimated $1.2 to $1.5 billion loss of
anticipated gross sales for Iowa’s crop farmers (Swenson et al, 2008). Pauw et al (2010)
estimated annual 0.7% (or US$9 million) agricultural GDP losses from flood in Southern
Malawi, where small and medium-scale farmers are worst hit by the disaster. Based on the
number of occurrence, flood is the most disastrous event in Nigeria since the 1980s
(UNISDR, 2014). The 2012 floods affected 27.9% of yam, 21.6% of cassava, 17.2% of sweet
potato, 31.4% of rice, 20.1% of maize, and 14% of sorghum producing areas along Benue
3
and Niger Rivers of Nigeria (Muhammad, 2012). The August 26th 2011 floods washed away
fish and poultry farms estimated at over 100 million naira in Ibadan City (Taiwo et al 2012).
Many studies have been carried out on flood risks, vulnerability assessment and
mapping. One of these is the study by Manuamorn et al (2009), which reported that flood is
the most frequent and most severe hazard that is affecting Thailand and its agricultural sector.
Atedhor et al (2011) noted that the perceived causes of flooding in the selected flood areas of
Benin City, Nigeria, are mainly increasing rainstorms and obstruction as well as absence of
drainage systems. Odjugo (2012) showed that the cost of damage to buildings and property
due to erosion and flooding was 3.9 billion naira. Flood claimed 14 lives between 1999 and
2010 in Benin City, Nigeria while Oyekale et al (2013) showed that the adverse impacts of
flooding reduce with ability to migrate, monthly income and possession of other secondary
occupations. Furthermore, Abah (2013) used GIS applications and modelling techniques and
showed that Makurdi town is generally susceptible to flooding while very little has been done
to steer away development from highly susceptible areas.
In Oyo State, Taiwo et al (2012) investigated the perceived causes and the
consequences of Ibadan floods and blamed hydrological factors, poor waste management,
weak institutions and inadequate flood awareness for the calamity. However, the study
concentrated on the urban parts of the City but left out the rural communities and did not
assess the relative vulnerability of the affected communities.
It is against this background that this study aims at assessing the vulnerability of
farming communities to flood in Akinyele LGA, Oyo State, Nigeria. The pertinent research
questions are as follows:

Which farming communities are prone to flood in Akinyele Local Government Area?

What are the capacities of the farmers’ communities against the flood?

How vulnerable are the farming communities to flood?
1.3 Research Objective
The main objective of this study is to assess the vulnerability of farming communities to
flood in Akinyele Local Government Area of Oyo State, Nigeria. The specific objectives are
to:

identify farming communities that are prone to flood;

assess the exposure and susceptibility of farmers to flood;

examine the capacities of the farmers to combat the flood; and

map the relative flood vulnerability of these farming communities.
4
1.4 Plan of the Thesis
This work is presented in five different chapters. The second chapter is the literature
review, which discussed the concepts of disasters, hazards, risk and vulnerability as well as
the concepts of resilience and capacity. It also presents the various vulnerability assessment
frameworks. The third chapter presents the materials and methods, including the study area,
materials used and the methods employed in data collection and analysis. While chapter four
presents the results and their discussions, the fifth chapter deals with the conclusion and
recommendations.
5
CHAPTER II: LITERATURE REVIEW
Vulnerability is a broad concept, which is related to other concepts, such as disasters,
impacts, hazards, risks, resilience and capacity. Furthermore, different assessment
frameworks have been used as the bases for quantifying vulnerability to hazards. Therefore,
this chapter reviews the various concepts that are interlinked with vulnerability and the
various vulnerability assessment frameworks.
2.1 Concepts of Disasters and Hazards
Disaster refers to a slow or fast destructive event that causes loss of lives, injury to
people, damage of properties, disruption of economic activities, loss of livelihood and/or
causes environmental and ecological degradation. UNISDR (2007) defined disaster as a
serious disruption of the function of a community or society, which results in widespread
damages and losses that are more than the ability of the affected community or society to
cope using its own resources. They are severe alterations in the normal functioning of a
community or a society due to hazardous physical events interacting with vulnerable social
conditions, leading to widespread adverse human, material, economic, or environmental
effects that require immediate emergency response to satisfy critical human needs and that
may require external support for recovery (IPCC SREX Glossary, 2012). According to
Estrella and Saalismaa (2012), disasters occur in the presence of one or more hazards,
vulnerability and physical hazards exposure. Therefore, disasters are the harmful
consequences of the combination of hazardous events and the vulnerable elements/features of
the society or community, which may provoke the inability of the society to cope and may
compel them to call for external assistance.
Disasters are characterized as slow or fast damaging events on the basis of duration of
the impact of the hazard. Examples of slow disastrous events are drought and coastal erosion
while fast disasters are tsunamis, cyclone, hurricane, typhoon, flood, earthquake, volcanic
eruption, mass movements and so on. On the basis of their causes, Guha-Sapir et al, 2012
classified disasters as meteorological, climatological, hydrological, geophysical and
biological. However, anthropogenic or technological classification may be added to the list to
characterize disasters such as market fires, road accidents and building collapses.
A clear distinction exists between hazards and disasters. A hazard is a dangerous
phenomenon, substance, human activity or condition that may cause loss of lives, injury or
other health impacts, property damage, loss of livelihoods and services, social and economic
6
disruption, or environmental damage (UN-ISDR Glossary, 2007). IPCC SREX Glossary
(2012) defines harzad as the potential occurrence of a natural or human-induced physical
event that may cause loss of lives, injury, or other health impacts, as well as damage and loss
to property, infrastructure, livelihoods, service provision, and environmental resources.
Estrella and Saalismaa (2012) pointed out that physical hazards, such as cyclones, flooding or
landslides do not cause disasters by themselves.
Therefore, hazards may be natural or man-induced, but disasters are purely unnatural
because the presence of humans, economic activities and infrastructure are involved in their
definition and characterization. For instance, a wildfire is a hazard as long as no man dies, no
economic activity is disrupted and no damage to infrastructure is recorded. On the other hand,
a wildfire that burns peoples’ farms is a disaster. Furthermore, hazards are potential disasters
while disasters are actualized hazards.
Disasters constitute major setbacks for the development of the world because of their
destructive impacts on gains of development. Development achievements of many years can
be lost to a single disaster within one day. According to NDRRMCP (2013), Typhoon Haiyan
claimed 36.69 billion Pesos ($1.5 billion) in damages in the Philippines. Hurricane Katrina
losses accounted for 125 billion US dollars (1.1% GDP), Japanese earthquake of 1995 costs
100 billion US dollars (3.2% GDP), Chinese flood of 1998 damages worth 30 billion US
dollars while Haiti earthquake of 2010 claimed 8 billion US dollars (Ghesquiere
and Mahul,2010).
2.2 Concepts of Risks and Vulnerability
A dichotomy exists between disaster risk and vulnerability. Risk is the combination of
the probability of an event and its potential negative consequences (UN-ISDR Glossary,
2007). Vulnerability refers to the physical, economic, social or political susceptibility of a
system or community to damage resulting from hazardous events of natural or anthropogenic
origin (Birkmann, 2006). Vulnerability is the characteristics and circumstances of a
community, system or asset that make it susceptible to the damaging effects of a hazard
(UNISDR Glossary, 2007). In other words, vulnerability is the propensity or predisposition to
be adversely affected (IPCC SREX Glossary, 2012). Vulnerability is a set of characteristics
or features of an element at risk while risk is a potential result of the combination of hazard
and vulnerability (Blaikie et al, 1994; Birkmann, 2006).
7
Vulnerability of human population and economic activities to climate risks, such as
flood hazards, depends on many factors, including the geographical location, exposure of
population and infrastructure, socio-economic and cultural conditions, political and
institutional structures as well as coping and adaptive capacity that differentiate the impacts
on people and human system (Wisner et al. 2004; Barroca et al. 2006). Because the sources
of vulnerability are greatly associated with societies’ patterns of development, small
investments in reducing risk can have disproportionately large positive impacts in protecting
communities from harm. Vulnerability is, therefore, known to change significantly with
social or habitat changes (Adelekan, 2010).
Furthermore, vulnerability is multi-dimensional, because it varies across physical
space as well as among and within social groups. It is scale-dependent and dynamic, because
its characteristics and driving forces changes over time, space and unit of analysis, including
individuals, households, regions and systems (Birkmann, 2006 and Birkmann et al, 2013).
2.3 Resilience and Capacities
Resilience is the capacity of a system or a person to deal with disturbances and the
effects of stressors. It is a concept that grew out of several schools of thought, including
ecology, psychology, socio-ecological systems research and critical infrastructures. The
socio-ecological systems and infrastructural schools of thought, view resilience as the
capacities of systems to reorganize themselves in the face of adverse events through the
processes of revolt and remember (Birkmann et al, 2013). On the other hand, capacity refers
to the assets and resources with which a community anticipates, copes, recovers and adapts to
impacts of hazards. According to Armah et al (2010), capacity is the ability to plan, prepare
for and implement adaptation plans. Therefore, capacities are the determinants of resilience.
Resilience is the ability of a system to anticipate, absorb, cope with and recover from
a hazard and maintain its essential functions, structure and interactions and includes the
degree to which it is capable of self-organisation and its capacity for learning and adaptation
(IPCC, 2012; Kaplan et al, 2009). Thus, resilience has three specific characteristics: the first
is the amount of change the system can undergo and still retain the same controls on function
and structure; the second is the degree to which the system is capable of self-organisation; the
third is the ability to build and increase the capacity for learning and adaptation. Hence,
Birkmann et al (2013) classified the limited capacities to cope or to recover as lack of
resilience but put resilience improvement under adaptation.
8
2.4 Flood as a Disaster
Flood is the water that accumulates in places that are not normally submerged.
According to Oyeleye and Adetunji (2013), flood results from continuous long lasting heavy
rains, leading to excess surface water above the capacity of available drainage system, which
is either inadequate or blocked. Guha-Sapir et al (2012) referred to flood as a hydrometeorological disaster, because heavy rainfall, snow melting, glacier outburst and over-flow
of water bodies are among its causes. Tsunamis, hurricanes, typhoons, earthquakes and dam
bursts are some of the geo-physical and mechanical phenomena that may cause flooding
(Guha-Sapir et al, 2010; Guha-Sapir et al 2012). In addition, flood produces several
deleterious effects on human life, property, economy and the environment, thus affecting
human security.
The most significant negative effects of flood on farming communities and farmers
are decline in environmental quality, outbreak of diseases, complete crop failure, damage of
seeds and loss of livestock. These impacts lead to rural-urban migration, decline in
agricultural labour and reduction in food production as well as reduction in rural household
income, which might lead to starvation (Armah et al, 2010). Empirical evidences reported by
Guha-Sapir et al (2010) and Thanh Ha et al (2011) underscore physical and psychological
health impacts of flood, which are determined by physical, social and other vulnerabilities as
well as the flood nature, including its speed of onset. In addition, Oyekale et al (2013) found
higher incidences of diseases, such as influenza, tuberculosis, typhoid fever, dysentery and
malaria as well as higher health expenditures among fishers during flooding periods in Epe,
Lagos State of Nigeria.
Furthermore, flood can render rural dwellers homeless, increase their exposure to
environmental health hazards, encourages loss of family relations and compound poverty
situation. According to Orlove et al (2004) and Oyekale et al (2013), flooding is an
environmental problem, which results in death of people and livestock, property losses,
blockage of road networks, disruption of economic activities, inundation of homes with dirty
water and refuse as well as rural migration. Lastly, flood does not only affect the victims but
also impact the national economy negatively by increasing poverty rate.
Although its negative impacts may be overwhelming, flood has its own merits.
Accumulated flood water in dams and rivers is drawn for irrigation and city water supply; it
can be used for hydro-electric power generation. Dry season valley vegetable farmers are
aware that flood is responsible for the replenishment of nutrients in the flood plains where
9
they farm. The level of flood water in rivers correlates positively with harvest for that year
(Bariweni et al., 2012; Oyeleye and Adetunji, 2013). Therefore, flood becomes a disaster
when human livelihood, infrastructure and economic activities located along flood plains are
poorly managed.
2.5 Determinants of Flood Impacts
Impacts of flood varies with location, age, sex, income level, diversification of
economic activities, social participation, marital status, household size, education, ability to
migrate, availability of efficient early warning systems and governance (Ma et al, 2007;
Oyekale et al 2013; Oyeleye and Adetunji, 2013). Poor people suffer higher negative
impacts of disasters than rich people (Kaplan et al, 2009; Oyekale et al 2013) while ability to
migrate and availability of assistance during flooding reduce the vulnerability to impacts of
flooding (Oyekale et al, 2013).
Flooding is known to create environmental problems. Environmental problems of
flooding, such as inundation of homes with flood water and debris are likely to reduce with
increasing work experience (Oyekale et al, 2013). Even though educated people are expected
to avoid water contamination and reduce the water-related problems of flooding, Oyekale et
al (2013) found that education increases the probability of water contamination and females
were more susceptible to water contamination than males during flooding in Epe, Lagos
State, Nigeria.
Disruption of economic activities and loss of livelihood are common economic
impacts of flood disaster. The rate of disruption of economic activities, as a result of flood
hazard, reduces with increasing age, work experience, availability of help during flooding
and the ability to migrate while larger household sizes and early warnings tend to encourage
economic disruption (Oyekale et al 2013). However, Orlove et al (2004) reported that rural
people, especially fishermen, do not always make use of flood warnings while Taiwo et al
(2012) asserted that the effectiveness of a particular flood warning depended on the previous
flood disaster experience of the people.
Lastly, flooding leads to property losses and livelihood insecurity. Probability of
property losses and livelihood insecurity during flooding reduces with monthly income,
economic diversification, ability to migrate and social networking (Oyekale et al 2013;
Armah et al 2010).
10
2.6 Vulnerability Assessment Frameworks
Vulnerability assessment frameworks are views of vulnerability expressed in the form
of models, which serve as guides for developing indicators for measuring vulnerability. An
indicator is a variable or tool used for quantifying a phenomenon or situation. The value of an
indicator is called index. Some of the frameworks, which have been used to develop
indicators for assessing vulnerability are described below.
2.6.1 The Conceptual Framework to Identify Disaster Risk
This framework considers disaster risk as a function of hazard, exposure, vulnerability
and capacities. In this framework, hazard is defined in terms of its probability of occurrence
and its severity while exposure includes structures, population and economy. On the other
hand, vulnerability has physical, social, economic and environmental dimensions while
physical planning, social capacity and economic capacity are included in capacity and
measures. This framework recognizes vulnerability as a component of risk and differentiate
between exposure, vulnerability and capacities (Birkmann, 2006). However, since
vulnerability is social, this framework is deficient because it separated exposure as well as
capacity and measures from vulnerability.
2.6.2 Bohle’s Double Structure of Vulnerability
This model sees vulnerability as a concept with both external and internal sides. The
internal side, coping, relates to the capacity to anticipate, cope with, resist and recover from
the impact of a hazard while the external side involves exposure to risks and shocks. The
underlying principle of the double structure is the fact that vulnerability is the result of
interaction between exposure to external stressors and the coping capacity of the affected
household, group or society (Birkmann, 2006). The strength of the double structure of
vulnerability is that it recognizes exposure and coping capacities as determinants of
vulnerability but excludes sensitivity, which addresses the impact side of the hazard.
2.6.3 The Sustainable Livelihood Framework
This framework can be seen as a model of vulnerability assessment (Birkmann, 2006).
It refers to vulnerability as shocks, trends and seasonality, which influence livelihood assets.
The livelihood assets refer to the human, natural, financial, social and physical capital of the
people. According to the framework, the livelihood assets interact with the structures and
processes, which encompass level of government, private sector, laws, policies culture and
institutions. Interactions between vulnerability, livelihood assets and transforming structures
11
and processes, through livelihood strategies, produce livelihood outcomes. The livelihood
outcomes include more incomes, improved well-being, reduced vulnerability, improved food
security and more sustainable use of natural resources.
The framework emphasises that the transforming structures in the governmental
system or private sector and respective processes (laws, culture) influence the vulnerability
context, and determine both the access to and major influences on livelihood assets of people.
The approach stresses the empowerment of local marginalised groups in order to reduce
vulnerability effectively as people and communities are examined in terms of their daily
needs, instead of implementing ready-made, general interventions and solutions, without
acknowledging the various capabilities poor people offer. The approach views vulnerability
as a broad concept, encompassing livelihood assets and their access, and vulnerable context
elements such as shocks, seasonality and trends, as well as institutional structures and
processes (DFID, 1999 and Birkmann, 2006).
Although the sustainable livelihood approach underlines the multiple interactions that
determine the ability of a person, social group or household to cope with and recover from
stresses and shocks, it remains abstract (Birkmann, 2006). In addition, this framework ignores
the negative influence of the livelihood outcomes on the environment.
2.6.4 The Pressure and Release (PAR) Model
This model defines disaster and risk as products of hazard and vulnerability. It views
vulnerability to have developed from the root causes, through the dynamic pressures, to the
unsafe conditions. The PAR approach describes how disasters occur when natural hazards
affect vulnerable people (Blaikie et al, 1994; Wisner et al, 2004; Birkmann, 2006). The
conceptual framework states that vulnerability and the development of a potential disaster are
processes, which involve increasing pressure on the one hand and the opportunities to relieve
the pressure on the other (Birkmann, 2006).
According to Wisner et al (2004) and Birkmann (2006), root causes of risks such as
limited access to power, structures and resources as well as ideologies, such as political and
economic systems interact with dynamic pressures to create unsafe conditions upon which
hazards act to produce disasters. The dynamic pressures are processes, conditions and
activities that transform and channel the effects of root causes into unsafe conditions. The
dynamic pressures include lack of local institutions, lack of training, lack of skills, lack of
local investments, lack of local markets, lack of press freedom and lack of ethical standards
12
in public life as well as macro-forces, such as rapid population growth, rapid urbanization,
arms expenditure, deforestation and decline in soil fertility. On the other hand, the unsafe
conditions encompass dangerous locations, unprotected buildings and infrastructure, risky
livelihoods with low income, epidemic diseases, rapid urbanisation and violent conflicts
(Wisner et al., 2004).
The authors further emphasise that the dynamic pressures should not be seen as being
negative but that measuring vulnerabilities should involve the identification of the underlying
causes and the driving forces in order to explain why people are vulnerable. Therefore, PAR
framework is very useful in dealing with the indirect causes of vulnerability and disaster risk.
However, the model should be used with care so that focus will not be put on the global and
national political economic forces at the expense of local conditions that create vulnerability
by contributing to dynamic pressures and unsafe conditions (Birkmann, 2006).
2.6.5 The SUST Model
Developed by Turner et al. (2003), this conceptual framework is a model for
assessing vulnerability in the global environmental change community. This model defines
vulnerability in terms of the exposure, sensitivity and resilience. Moreover, vulnerability is
viewed in the context of a joint or coupled human–environmental system (Turner et al, 2003;
Birkmann, 2006).
In contrast to the disaster risk community, this conceptual framework defines
vulnerability as a function of exposure, coping response, impact response and adaptation
response. The framework equally takes into account the interaction of the multiple
perturbations, stressors and stresses. Another important difference between the frameworks
discussed earlier and this one is that it examines vulnerability within the linked human–
environment context (Turner et al., 2003; Kasperson, 2005; Birkmann, 2006).
This framework is a good model for assessing vulnerability, because it takes into
account factors and changes outside the local environment and includes the concept of
adaptation, which is a determinant of resilience (Birkmann, 2006). However, the inclusion of
impacts under the resilience part of vulnerability should be critically considered.
2.6.6 The BBC Framework
According to Birkmann (2006), this framework is based on the works of Bogardi and
Birkmann (2004) and Cardona (1999 and 2001), thus, the name BBC. It is developed out of
the discussions on how to link vulnerability, human security and sustainable development.
13
The BBC framework stresses the fact that vulnerability analysis goes beyond the estimation
of deficiencies and assessment of disaster impacts in the past. It underlines the need to view
vulnerability within a dynamic process. That is, focusing simultaneously on vulnerabilities,
coping capacities and potential intervention tools to reduce vulnerabilities as well as on
social, environmental and economic dimensions of vulnerability, it clearly integrates the
concept of sustainable development into the vulnerability framework (Birkmann, 2006).
The BBC framework differs from risk analysis, because it focuses mainly on the
different vulnerable or susceptible and exposed elements, the coping capacity and the
potential intervention tools to reduce vulnerability. Furthermore, the BBC framework
distinguishes between the responses before disasters happen and the responses needed when
disasters occur. Thus, it is a problem-solving approach, which helps to analyse the probable
losses and deficiencies of the various elements at risk and their coping capacities as well as
the potential intervention measures, all within the three key thematic spheres (Birkmann,
2006).
Hence, the BBC framework defines vulnerability in terms of exposed and vulnerable
elements and coping capacities existing in the environmental, social and economic spheres. It
emphasises the overall reduction of disaster risks by reducing vulnerability and exposure to
hazards through various intervention strategies, such as land use changes, emission control,
early warning systems, and insurances before hazardous event as well as disaster and
emergency management during/after a disaster.
2.6.7 The MOVE Framework
The MOVE (Methods for the Improvement of Vulnerability Assessment in Europe)
framework is an integrative and holistic framework to systematize and assess vulnerability,
risk and adaptation. It is a thinking and heuristic tool, which outlines key factors and different
dimensions that need addressing when assessing vulnerability in the context of natural
hazards and climate change. The key factors of such a common framework are related to the
exposure of a society or system to a hazard or stressor, the susceptibility of the system or
community exposed, and its resilience and adaptive capacity. This approach emphasises
multiple thematic dimensions when assessing vulnerability in the context of natural and
socio-natural hazards (Birkmann et al, 2013). Thus, it bridges the gap between the different
concepts used within the disaster risk management (DRM) community and climate change
adaptation (CCA) research.
14
Therefore, the MOVE framework defines vulnerability in terms of exposure,
susceptibility/fragility and the lack of resilience. It presents the dynamic nature of
vulnerability through temporal and spatial exposure but expresses its multi-dimensional
characteristics through physical, ecological, social, economic, cultural and institutional
susceptibility (or fragility) while the lack of resilience encompasses the capacity to anticipate,
capacity to cope and capacity to recover.
The framework shows that hazards of natural and socio-natural origins interact with
societies’ vulnerability to produce risks in the form of potential economic, social and
environmental impacts. Therefore, it suggests hazard intervention, as well as vulnerability
intervention, including exposure reduction, susceptibility reduction and resilience
improvement as parts of adaptation, risk management and risk reduction strategies.
15
CHAPTER III: MATERIALS AND METHODS
3.1 Study Area
Location
This study was carried out in Akinyele Local Government Area (LGA), Oyo State,
South-western Nigeria. Akinyele LGA is a predominantly agricultural and flood-prone LGA,
which is located between geographical coordinates of latitudes range 7o 29´N to 7o 40´N and
longitude range 3o 45´ to 4o 04´. It is bordered in the north by Afijio Local Government Area,
in the north-east by Iwo Local Government Area of Osun State, in the east by Lagelu Local
Government Area, in the south by Ibadan North Local Government Area and in the west by
Ido Local Government Area (Ajadi et al, 2012).
Figure 3.1 Map of Nigeria (Left) showing the location of Oyo State and Map of Oyo Sate
(Right) showing the location of Akinyele Local Government Area (Ajadi et al, 2012)
With a total land area of about 219.2 km2, Akinyele LGA is made up of about 54
towns/villages, which are organized into 12 political (electoral) wards. These wards and their
constituent villages are presented in Fig3.2
16
Figure 3.2 Map of Akinyele Local Government Area (Source: Ajadi et al, 2012)
Geology and Soils
The soil of the area were formed from rocks such as granites, gneisses, quartz-schist,
biotite, gneisses and schist. They were formed under moist semi-deciduous forest cover and
belong to the major soil group called ferruginous tropical soil (Ajadi et al, 2012).
Climate, Vegetation and Drainage
Akinyele LGA has a tropical climate with a mean annual temperature of about 32 oC.
The relative humidity can be as high as 95% with a mean annual rainfall of 1250 mm. It is
located in the tropical rain forest agro-ecological zone of Nigeria, although most of the
forests are degraded or lost to agriculture and human settlement. Generally, the vegetation in
the area is dominated by palm trees and the area may be referred to as a “dry forest belt’’.
Akinyele is drained by Ona River (Odo Ona) and its tributaries (Ajadi et al, 2012).
Population and Economic Activities
According to the 2006 census, Akinyele LGA had a population of 211,359 with
105,897 females and 105,462 males. Based on a growth rate of 3.2% per annum in 2006, the
National Population Commission estimated the 2014 population of the LGA at 272,513
people with 136,657 females and 135,856 males. The predominant inhabitants are the
Yorubas, who represent 95% of the total population of the area. Other notable ethnic groups
are Hausa, Igbo, Edo, Fulani, Nupe, Tivs and Efiks. The major occupation of the people is
agriculture, through which crops, such as maize, cassava, oilpalm, cocoa and vegetables are
grown and livestock, such as poultry, sheep, goat and cattle are reared. This is because the
17
area has a favourable climate and soil condition. Moreover, trading and civil service work are
now competing with agriculture in the area.
3.2 Method
3.2.1 Conceptual Framework: MOVE Framework
The MOVE (Methods for the Improvement of Vulnerability Assessment in Europe)
Framework was used for this assessment due to its desirable characteristics. Firstly, it
differentiates key factors of vulnerability (Exposure, susceptibility/ fragility and lack of
resilience). Secondly, it specifies the different dimensions of vulnerability as physical,
ecological, social, economic, cultural and institutional. Thirdly, it specifies that the dynamic
nature of vulnerability could be expressed through spatial and temporal exposures. It
differentiates coping from adaptation and incorporates the concept of adaptation into disaster
risk management thereby bridging the concept and approach gaps between disaster risk
reduction and climate change adaptation communities.
Birkmann et al (2013) defines the components and sub-components of the MOVE
Model (Figure 3.3) as follows.
 Exposure - the extent to which a unit of assessment falls within the geographical
range of a hazard event.
 Susceptibility (or fragility) describes the predisposition of elements at risk to suffer
harm. The various dimensions are:
• Social dimension: propensity for human well-being to be damaged by disruption to
individual conditions (mental and physical health) and collective services (health,
education services, etc.) as well as social systems and their characteristics (e.g.
gender, marginalization of social groups);
• Economic dimension: propensity for loss of economic value from damage to
physical assets and/or disruption of productive capacity;
• Physical dimension: potential for damage to physical assets, including built-up
areas, infrastructure and open spaces;
• Cultural dimension: potential for damage to intangible values, including meanings
placed on artefacts, customs, habitual practices and natural or urban landscapes;
•Environmental dimension: potential for damage to all ecological and bio-physical
systems and their different functions, including particular ecosystem functions and
environmental services but excluding cultural values that might be attributed; and
18
Figure 3.3 MOVE Framework by Birkmann et al (2013)
•Institutional vulnerability: potential for damage to governance systems,
organizational form and function as well as guiding formal/legal and
informal/customary rules—any of which may be forced to change following
weaknesses exposed by disaster and response.
 Lack of resilience or societal response capacity is determined by limitations in terms
of access to and mobilization of the resources of a community or a socio-ecological
system in responding to an identiﬁed hazard. Resilience encompasses capacity to
anticipate, capacity to cope and capacity to recover. Thus, resilience includes preevent risk reduction activities, in-event coping strategies and post-event response
measures. This differentiates resilience from adaptation because adaptation is a long
term planned adjustments in the socio-ecological system based on the knowledge of
the hazard and its impacts. However, resilience is a sub-set of adaptation.
 Hazard means the potential occurrence of natural, socio-natural or anthropogenic
events that may have physical, social, economic and environmental impacts in a given
area and over a period of time. According to MOVE Framework, hazard interacts
with society’s vulnerability to produce economic, social and environmental risks
which must be reduced through adaptation and risk management strategies.
19
 Adaptation describes the ability of a community or a system to learn from the past
disasters and change existing practices for potential future changes in hazards as well
as vulnerability contexts. Therefore, hazard intervention and vulnerability
interventions, including exposure reduction, susceptibility reduction and resilience
improvement, are suggested as adaptation strategies (Birkmann et al, 2013).
3.2.2 Sampling Design Data Requirement and Method of Data Collection
a. Sampling Design
Purposive sampling, area sampling and simple random sampling techniques were
used in this study. Firstly, Akinyele Local Government Area (LGA) was deliberately
selected, because flood has been reported there and the LGA has a predominantly farming
populace. Secondly, area sampling was used in identifying flood-prone farming communities
from where data were gathered. Thirdly, simple random sampling was carried out to collect
information from farmers in each of the seven selected flood-prone villages.
b. Method of Data Collection and Data Source
Secondary data used in this study include Satellite Imagery (Shuttle Radar
Topographical Mapping, SRTM Data, which contains elevation), which was collected from
the Federal School of Surveying Oyo and base map of the Local Government Area (showing
road networks as well as location of villages and major rivers), which was sourced from
Department of Surveying Akinyele LGA.
Primary data included location coordinates, which are collected with portable GPS,
and farmers’ socio-economic characteristics, which are collected with the use of
questionnaires. Farmers’ socio-economic characteristics include farmers’ age, gender, farm
enterprises/crop grown, other occupation than farming, size of household, years of education,
income level, land ownership/tenancy, as well as the following.
Flood Exposure: type of flood, frequency (return period), distance of farmland to river, and
time of impact.
Susceptibility/Fragility: soil type, drinking water source, waste management, right of women
to inherit land and contact with agricultural extension service.
Capacity to Anticipate: local early warning systems, anticipatory time allowance and flood
education.
Coping/Adaptation capacities: processing facility, pre-mature harvesting, construction of
drainage channels at the edge of farms, flood tolerant crops and planting in other locations.
20
Capacity to Recover: Other economic activities, Savings and Cooperation and insurance
policies.
A total of 420 questionnaires were produced. This sample size was determined using
the following formula, which was proposed by Statistical Canada (2010).
𝑛=
𝑧 2 𝑃(1 − 𝑃)
𝑧 2 𝑃(1 − 𝑃)
𝑒2 +
𝑁
Where n = sample size
Z = a factor corresponding to the desired confidence interval
Z = 1.96 if 95% confidence interval is desired.
P = proportion of population corresponding to the population variability (P: 0<p<1) =
0.5
e = the desired margin of error = 0.05
N = total number of registered farmers in the study area = 11,286 farmers
The distribution of the questionnaires responded to by farmers in the seven selected floodprone villages is presented in Table 3.1
Table 3.1 Questionnaires responded to per village surveyed
Village Surveyed
Tola
Ajibode
Ajeja
Alabata
Onilu
Lagbe
Jarija
Total
Source: Field Survey, 2014
No of Respondents
52
52
57
44
48
60
55
368
Percent
14.1
14.1
15.5
12.0
13.0
16.3
14.9
100.0
3.2.3 Data Analysis
a. GIS Determination of Flood-prone Zones
Physical development map of Akinyele was digitised using ArcGIS 10.0. Features on
the digitised map (boundary, roads, villages and location of rivers) were vectorised. The
vectorised features were combined with GPS co-ordinates to produce the Roads Networks
and Villages Maps of the Local Government Area.
To identify the flood plains of Akinyele Local Government Area (LGA), Shuttle
Radar Topographic Mission (SRTM) data was colour-ramped and used to produce the Digital
Elevation Model (DEM) of the LGA by converting it to Triangulated Irregular Network
21
(TIN). Using overlay and manipulative functions available in ArcGIS 10.0, the vectorised
villages, roads and location co-ordinates of streams and rivers were added to the SRTM and
the DEM to produce the location maps of the Major Rivers and the flood-prone villages.
b. Questionnaire Data Analysis and Indicators Development
The questionnaires containing farmers’ socio-economic data, flood exposure,
susceptibility and capacities data were analysed using frequency distribution, percentages and
descriptive statistics through SPSS Version20.
Based on the MOVE Framework of Vulnerability Assessment, relevant indicators
were developed from the frequency distribution, percentages and descriptive statistics as well
as the DEM, using Microsoft Excel Version 2013. The indicators were used to rank exposure,
susceptibility and capacities of the farmers to flood. The components and subcomponents of
vulnerability based on the MOVE Framework, the indicators that were developed for this
assessment and the justifications for selecting the indicators are presented in Table 3.2 to
Table 3.4
Table 3.2 Indicators of exposure and their justification
Subcomponents
Spatial
Temporal
Indicators
Measuring Tool
Average Elevation
Mean Elevation of the The lower the elevation,
Farming Village.
the higher the flood
exposure.
Closeness to river
Ratio of River setback
to Distance from River
Flood frequency
Probability
occurrence of flood
Time of Impact
Average time of impact The higher the value, the
on farmers’ farms
more
the
flood
vulnerability
Source: Author
22
Justification
The higher the value, the
higher the flood exposure.
of Higher
frequency
increases vulnerability.
Table 3.3 Indicators of susceptibility and their justification
Subcomponents
Ecological
Indicators
Measuring Tool
Clay Soil Rate
% of people farming on The higher the value, the
clay soil
more vulnerable.
Physical
Surface Water
Consumption rate
% of people drinking
from surface water
Surface water are most
vulnerable to flood
Environmental Poor Human Waste
Management Rate
% of Farmers
defecating in the bush
Poor waste management
increases flood
vulnerability
Cultural
Women Land
Access Limitation
Rate
% negative response to
the right of women to
inherit land in the
family/Village.
Access to land by women
reduces vulnerability
Institutional
Inadequacy of
Contact with
Agricultural
Extension services
% of Farmers lacking
seasonal contacts with
extension.
Low contacts signify
high vulnerability.
Source: Author
23
Justification
Table 3.4 Indicators of capacity and their justification
Subcomponents
Capacity to
anticipate
Indicators
Measuring Tool
Justification
Flood Education
Access Rate
Flood Forecast
Access Rate
Rate of Access to flood
education on media
% of farmers who have
access to forecast
information
The higher the access,
the higher the capacity
Lack of early warning
systems worsen impacts
Capacity to
Cope/Adapt
Adequacy of
processing facilities access to processing
facilities
Flood Relief
Access Rate
access to flood relief
materials
Inadequate processing
facilities Worsen impacts
of flood on crop produce.
Availability of relief
reduces vulnerability
Recovery
Capacity
Diversification of
Economic
Activities
% of farmers who
diversify their
economic activities
geographically and
occupationally
High degree of
diversification reduces
vulnerability
Social Cooperation
and Agricultural
Insurance Rate
% of farmers who
participate in
cooperative societies or
possess of Agricultural
Insurance policies.
Social cooperation and
agricultural insurance
reduces vulnerability to
flood
Source: Author
c. Determination of Vulnerability Index
This involves four processes which are correlation analysis, weighting, normalization
and aggregation of indicators.
Correlation Analysis
Using the approach of Damm (2010), correlation analysis was used to determine the
degree of linear relationship between the indicators before weighting/ranking them. This is
necessary because high degree of relationship between indicators may distort the
vulnerability index, and mislead the end users. Since all the indicators were quantitative,
they were analysed by using Pearson correlation. Two indicators of the same component of
vulnerability with more than 65% (r>0.65) relationship between them were carefully analysed
to consider the removal of one of them based on their importance.
24
Weighting of the of Indicators and Components Vulnerability
This refers to ranking of indicators and ranking of components of vulnerability by
assigning weights to them. Ranking indicators helps to remove distortions that may be
created by co-linearity and data sources unreliability, which will mislead end users (Damm,
2010). Also, weighting depicts the relative importance of the indicators and each component
of vulnerability in the model. Ranking may also reflect data source reliability. In this study,
the willingness of the respondents to give their personal information as well as the correlation
between indicators of the same component of vulnerability were considered in weighting the
indicators. Indicators with low correlation (r< 0.65) and very reliable data were given highest
weights while indicators with correlation above 0.65 or unreliable data were weighted 0.5
based on the approach of Damm (2010).
To rank each component of vulnerability, the importance of each component to flood
are considered. Exposure, susceptibility and capacities (resilience) are components of
vulnerability (Birkmann et al, 2013) but the most important causes of flooding are heavy and
prolonged rainfalls and river overflow while anthropogenic factors exacerbate its impacts
(Taiwo et al, 2012). Furthermore, people in low-lying areas and those close to rivers
experience longer time of flood impacts than people at higher elevation. Since elevation,
closeness to river, time of impact and flood frequency are indicators of exposure, exposure is
the most important component of flood vulnerability. Therefore, exposure is weighted 1
while susceptibility and capacity are weighted 0.5 each in this study.
Normalisation of Indicators
This process helps to give the same measurement unit or scale to the indicators so that
they can be treated mathematically. Each indicator was normalised using the standardization
method. This method is preferred to other statistical methods, because it avoids distortions
created by large extreme values (outliers) by using the mean and standard deviation in scaling
(Damm, 2010). The normalised value of an indicator (Y) for a village/town (i) is given by:
Nyi = (Yi - Ῡ)/ẟy
Nyi = Normalized value of indicator Y for village/town i.
Yi = value of indicator Y for village i.
Ῡ = mean of the values of Yi for all villages/towns.
ẟy = standard deviation of Y.
25
Aggregation of Indicators
Linear summation aggregation method was used. Index (I) of the indicator Y for a
village (i) was calculated by multiplying its weight (Wy), by its normalized value. Iyi = Wy *
Nyi.
Vulnerability Index (Iv) of each component of vulnerability (Exposure, Susceptibility and
Capacity) was computed as the arithmetic mean of the values of all indices of the component
for each farming community. Given a component of vulnerability with indicators Y,
measured for a village (i), then the Vulnerability Index (Iv) of the component of vulnerability
in that particular village (i) is given by:
Iv =
∑Iyi
∑(Wy ∗ Nyi)
=
𝑛
𝑛
Where n = number of indicators of the component of vulnerability.
Since vulnerability increases with exposure and susceptibility but reduces with
capacities, the Composited Vulnerability Index, CIv of a particular village is given by:
CIv = We*IvE + Ws*IvS – Wc * IvC
Where CIv = Composited Vulnerability Index of the village/town/area.
We = weight of exposure = 1; Ws = weight of susceptibility= 0.5
Wc = weight of capacity = 0.5
IvE = vulnerability index due to exposure of the village.
IvS = vulnerability index due to susceptibility of the village.
IvC = Index of resilience/capacities in the village.
d. Evaluation of the Vulnerability Index
Damm (2010) recommended that good modelling in quantitative vulnerability
assessment requires that the composite index should be tested to ascertain its robustness,
sensitivity and uncertainty. While robustness tests the fitness of the model, sensitivity
analysis tests its response to variations in the data fed into it and uncertainty analysis reveals
the uncertainties in the model as well as the index itself (Damm, 2010).
26
In this study, only robustness test was carried out using a different method of
normalization of indicators. The method used was the percentage normalisation method
which defines the normalised value of an indicator as:
Nyi =
Yi ∗ 100
∑ 𝑌𝑖
Where Nyi = Normalised value of an indicator Y for ith village.
Yi = value of indicator Y for ith village.
∑Yi = sum of the values of indicator Y for all the villages.
e. Mapping Exposure, Susceptibility, Capacity and Vulnerability Indices
The maps of flood exposure, susceptibility and capacity as well as the overall flood
vulnerability map of the farming villages were produced using ArcGIS 10.0. Indices of
exposure, susceptibility, capacity and vulnerability were grouped into five classes each and
displayed as colour-coded symbols on the exposure, susceptibility, capacity and vulnerability
maps of the flood-prone farming communities of Akinyele LGA.
27
CHAPTER IV: RESULTS AND DISCUSSIONS
4.1 Flood-Prone Farming Communities
The colour-ramped Shuttle Radar Topography Mission (SRTM) data (Figure 4.1) and
the Digital Elevation Model (DEM) (Figure 4.2) show that the flood plains are in the
Northwest and the Southern parts of the study area. Both Figure 4.1 and Figure 4.2 indicate
that water bodies drain out of the LGA through the North-western and the Southern parts,
making these two regions of the LGA the most flood-prone zones.
Figure 4.1: Map of Akinyele showing flood plains and major rivers (Source: Author)
Figure 4.1 shows that the prominent water bodies in the North-western part are Igbo
Olobo Stream, Alajala Stream, Ajeja Dam, Ose Stream and Shanu Bandele River. On the
other hand, Onilu River, Lagbe Stream, Gunwin River, Osun Stream, Ona River and Ajibode
River are seen to dominate the drainage system to the South. Therefore, the flood-prone
rivers are located in the North-western and Southern parts of Akinyele LGA. This also
indicates that apart from Ona River, which was reported by Ajadi et al (2012) as the drainage
28
of the LGA, the Northwest part of Akinyele is drained by a watershed different from Ona
River and its tributaries.
Figure 4.2: Map of Akinyele LGA showing elevation and flood-prone villages (Source:
Author)
Figure 4.2 shows that the lowest altitude in the LGA is about 141m above sea level
while the highest elevation is about 408m above sea level. The figure equally reveals that
Ajibode Onilu and Lagbe villages lie in the flood-prone areas of the Southern part while Tola
and Jarija are some of the flood-prone villages in the North-western part. However,
information gathered from experts’ interview and ground truthing indicated that the Southern
part of the LGA, which is predominantly urban, is more flood-prone than the North western
part, which is rural. Furthermore, experts’ interview and ground truthing confirmed that flood
episodes have been withnessed in many farming communities within the LGA, including
Jarija, Tola, Ajeja, Alabata, Lagbe, Onilu and Ajibode.
29
4.2 Correlation among Vulnerability Indicators and Weighting
Table 4.1 Correlations among exposure indicators
Elevation
Elevation
Closeness to river
Time of Impact
Flood Frequency
1
-0.206
-0.480
0.118
Closeness
to River
Time of
Impact
Flood
Frequency
1
-0.258
-0.648
1
-0.208
1
Table 4.1 records no significant linear relationship among the indicators of exposure
because the correlation coefficients are less than 0.65. Thus the linear relationship among
them is negligible and will not distort the final vulnerability index. Hence, they are all
retained for the vulnerability index computation. The elevation is from a reliable data source
and is ranked 1. The farmers relied on their eyes for measuring the distance of their farms
from the river and relied on their memory to respond to questions on time of impact and flood
frequency, hence, these three indicators are ranked 0.5.
Table 4.2 Correlations among susceptibility indicators
Indicators
AI
CSR
WLLR
SWCR
PWMR
IAEC
1
AI
0.425
1
CSR
0.006
0.466
1
WLLR
-0.080
0.116
-0.022
1
SWCR
0.205
-0.533
-0.297
-0.028
1
PWMR
0.386
-0.344
0.053
-0.364
0.600
1
IAEC
Source: Field Survey, 2014 (Note: AI= Average Income, CSR= Clay Soil Rate, WLLR =
Women Land Limitation Rate, SWCR= Surface Water Consumption Rate, PWMR= Poor
Waste Management Rate and IEC= Inadequacy of Agricultural Extension Contacts).
Similarly, Table 4.2 shows that the coefficient of correlation between any two
indicators of susceptibility is less than 0.65 and all of them are involved in the vulnerability
index computation. However, it is a taboo to most of the farmers to disclose their income and
their judgement of clay or heavy soil may not be accurate. Therefore, average income and
clay soil rate are weighted 0.5 while others are weighted 1.
Furthermore, Table 4.3 reveals that there is a very high correlation between Flood
Education Access Rate (FEAR) and Geographical Diversification Rate (GDR), indicating
that both indicators are co-linear. Yet, both indicators are retained because FEAR is a
30
measure of capacity to anticipate while GDR is a measure of capacity to recover and perform
different functions in the model. However, FEAR and GDR are weighted 0.5 each.
Table 4.3 Correlation among the indicators of capacity
FEAR FFAR FRAR
APF
GDR
FTCR
ODR
SCAIR
FEAR
1
FFAR
.a
.a
FRAR
0.446
.a
1
a
APF
0.382
.
0.608
1
GDR
-0.924
.
-.428
-0.471
1
FTCR
0.180
.
0.515
0.262
0.090
1
ODR
0.289
.
0.507
0.627
-0.146
0.457
1
SCAIR
0.238
.a
0.547
0.622
-0.331
0.052
0.592
1
Source: Field Survey, 2014 (Note: a cannot be computed because FFAR is constant. FEAR=
Flood Education Access Rate. FFAR = Flood Forecast Access Rate. FRAR = Flood Relief
Access Rate. APF = Adequacy of Processing Facility. GDR = Geographical Diversification
Rate. FTCR = Flood Tolerant Cropping Rate. ODR = Occupational Diversification Rate.
SCAIR = Social Cooperation and Agricultural Insurance Rate.)
4.3 Flood Exposure of the Farmers
Exposure refers to the extent to which a unit of assessment falls within the
geographical range of a hazard event (Birkmann et al 2013). Exposure of the farmers to flood
are indicated by both spatial and temporal indices, which are the elevation of the area,
closeness of farm to river, average time of impact of the flood on farms and flood frequency.
Table 4.4 Index of exposure based on elevation of the villages
Village Average Elevation
Tola
225.00
Ajibode
170.50
Ajeja
250.00
Alabata
250.00
Onilu
225.00
Lagbe
225.00
Jarija
195.50
Value
(Y)
4
5
3
3
4
4
5
Nyi= (Y-Ῡ)/ẟ
0.0000
1.2247
-1.2247
-1.2247
0.0000
0.0000
1.2247
Wy
1
1
1
1
1
1
1
Wy*Nyi
0.0000
1.2247
-1.2247
-1.2247
0.0000
0.0000
1.2247
The map of the flood plains of the LGA (Figure 2) shows that the farmers in the study
area are farming in flood plains and the elevation data shown in Table 4.4 indicates that
Ajibode and Jarija have the lowest elevation average (170.50m and 195.50m) with a ‘coded
exposure value’ of 5 and, hence, have the highest exposure index of 1.2247 each. This means
31
that Ajibode and Jarija are the most exposed to flood while Ajeja and Alabata are the least
exposed villages based on elevation data of the villages of Akinyele LGA.
Closeness to River
This indicator of exposure is defined by the following formula:
𝐶𝑙𝑜𝑠𝑒𝑛𝑒𝑠𝑠 𝑡𝑜 𝑅𝑖𝑣𝑒𝑟 =
River Setback X 100%
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑓𝑟𝑜𝑚 𝑅𝑖𝑣𝑒𝑟
The major river setback for buildings in Ibadan Oyo State is about 45m (Taiwo et al,
2012). The average distance of farmers’ farms from the river, the associated closeness to
river, the normalized value (N), the weight (Wy) and the Index of Exposure of the farmers
(Wy*Nyi) in each village, based on this indicator, are presented in Table 4.5.
Table 4.5 Index of exposure based on closeness to river
Village
Distance to
Name
River (m)
Tola
12.00
Ajibode
26.29
Ajeja
62.98
Alabata
22.23
Onilu
115.83
Lagbe
103.33
Jarija
17.55
Closeness to
River (Y)
375.00 %
171.18 %
71.45 %
202.45 %
38.85 %
43.55 %
256.48 %
Nyi= (Y-Ῡ)/ẟ
1.6793
0.0450
-0.7546
0.2957
-1.0160
-0.9784
0.7290
Wy
0.5
0.5
0.5
0.5
0.5
0.5
0.5
Wy*Nyi
0.8397
0.0225
-0.3773
0.1479
-0.5080
-0.4892
0.3645
Table 4.5 shows that farmers in Tola, followed by their Jarija colleagues, locate their
farms closest to river with values 12m (375% closeness) and 17.55m (256.48 % closeness) to
the river respectively. This assigns exposure indices (Wy*Nyi) of 0.8397 and 0.3645 on Tola
and Jarija farmers respectively. On the other hand, Onilu and Lagbe farmers located their
farms farthest from the rivers with values 115.83m (38.85% closeness) and 103.33m (43.55%
closeness) as well as exposure indices -0.5080 and -0.4892 respectively.
32
Time of Impact
Table 4.6 Index of exposure based on time of impact of flood on farms
Village Name
Value (days)
Tola
4.3846
Ajibode
8.3654
Ajeja
6.4035
Alabata
4.0000
Onilu
5.4167
Lagbe
4.1333
Jarija
4.0000
Mean(Ῡ)
5.2434
STD (ẟ)
1.6424
Nyi= (Y-Ῡ)/ẟ
-0.5229
1.9009
0.7064
-0.7570
0.1055
-0.6759
-0.7570
Wy
0.5
0.5
0.5
0.5
0.5
0.5
0.5
Wy*Nyi
-0.2614
0.9505
0.3532
-0.3785
0.0528
-0.3379
-0.3785
According to Table 4.6, the average time of impact of the flood on farms is 5.24 days
while the standard deviation is 1.64 days. Farmers in Jarija and Alabata, with the lowest time
of impact, have the least flood exposure index (-0.3785) while Ajibode farmers, whose farms
were flooded for an average time of 8.36 days have the highest flood exposure index of
0.9505. This means that farmers in Ajibode may experience higher yield losses due to flood
than other villages while Jarija and Alabata may lose the least quantity of yield based on time
of impact of the flood.
Flood Frequency
Flood frequency refers to the number of times the flood occurs per year. Table 4.7
shows that the average frequency of the flood in Akinyele Farming villages is 0.58 per
annum. This indicates that the flood comes on an average of once in two years and the
farmers may not have ample time to recover from one flood before another strikes. Ajeja
farmers reported the lowest flood frequency (0.3732) while Lagbe Farmers reported the
highest number of flood per annum (0.9667). This imputed the lowest exposure index (0.3998) and the highest exposure index (0.7303) on Ajeja and Lagbe farmers respectively.
33
Table 4.7 Index of exposure based on flood frequency
Village Name
Value(Y)
Tola
0.4315
-0.5775
0.5
-0.2888
Ajibode
0.4556
-0.4858
0.5
-0.2429
Ajeja
0.3732
-0.7996
0.5
-0.3998
Alabata
0.5041
-0.3011
0.5
-0.1505
Onilu
0.9583
1.4286
0.5
0.7143
Lagbe
0.9667
1.4606
0.5
0.7303
Jarija
0.3927
-0.7253
0.5
-0.3627
Mean(Ῡ)
0.5832
STD (ẟ)
Nyi= (Y-Ῡ)/ẟ
Wy
Wy*Nyi
0.2626
Overall Index of Exposure
Table 4.8 summarises the index of exposure to flood for each of the farming villages
of Akinyele LGA, while Figure 4.3 presents the map of relative exposures of the villages. In
the table, Sum of Indices refers to the sum of indices of elevation, river closeness, time, and
flood frequency. On the other hand, Exposure Indices refers to the Sum of indices divided by
the number of indicators. According to the last column of the table (Exposure Indices),
Ajibode, with an index of 0.4887 is the most flood-exposed farming community, followed by
Jarija (0.2120), Tola (0.0724), Onilu (0.0648), Lagbe (-0.0242) and Alabata (-0.4015), while
Ajeja (-0.4122) is the least flood-exposed farming village. This indicates that farmers in
Ajibode village are likely to suffer more from flood-related yield losses than farmers in other
villages, while farmers in Ajeja may suffer least yield losses, based on their exposure indices.
Therefore, priority for reducing exposure of farms and agricultural activities to flood in terms
of setting and enforcing river setbacks for farming activities in Akinyele LGA should be
given to Ajibode farmers, Jarija, Tola, Onilu, Lagbe, Alabata and Ajeja in decreasing order of
need.
34
Table 4.8 Overall exposure of Akinyele farmers to flood disaster
Elevation
Index
Closeness
Index
Time
Index
Frequency
Index
Sum of
Indices
Exposure
Indices
0.0000
0.8397
-0.2614
-0.2888
0.2894
0.0724
Ajibode
1.2247
0.0225
0.9505
-0.2429
1.9548
0.4887
Ajeja
-1.2247
-0.3773
0.3532
-0.3998
-1.6487
-0.4122
Alabata
-1.2247
0.1479
-0.3785
-0.1505
-1.6059
-0.4015
Onilu
0.0000
-0.5080
0.0528
0.7143
0.2591
0.0648
Lagbe
0.0000
-0.4892
-0.3379
0.7303
-0.0968
-0.0242
Jarija
1.2247
0.3645
-0.3785
-0.3627
0.8481
0.2120
Village Name
Tola
Source: Field Survey, 2014 (Note: Exposure Indices = Sum of indices divided by the number
of indicators)
Figure 4.3 Map of flood exposure of farming villages in Akinyele LGA (source: author)
4.4 Susceptibility (Fragility) of the Farmers to Flood
Susceptibility was indicated in this study by Average Income (AI), Clay Soil Rate
(CSR), Women Land Limitation Rate (WLLR), Surface Water Drinking Rate (SWDR), Poor
35
Human Waste Management Rate (PHWMR) and Inadequacy of Agricultural Extension
Contacts (IAEC).
Average Income of the Farmers
Income has been shown by Armah et al (2010) and Oyekale et al (2013), among
others, to be one of the important determinants of the vulnerability of people and nations to
disasters. In flood-prone zones, poor people are more vulnerable to flood impacts, because
they tend to locate their economic activities in flood-prone areas and have low anticipatory,
poor coping and weak recovery capacities.
Table 4.9 presents the average annual income of the farmers in each of the floodprone villages of Akinyele LGA and the corresponding susceptibility ‘value code’ (Y),
Normalized value (Nyi= (Y-Ῡ)/ẟ), weight (Wy) and Susceptibility index (Wy*Nyi). Average
income is weighted 0.5 because the farmers in the area considered it as a table to release their
personal information such as income. The mean income, ₦ 230,929.00 per annum, indicates
that the farmers are generally poor. Farmers in Alabata village have the highest average
annual income (₦292,045.45) and the lowest flood susceptibility (-0.6944) while Ajeja
village whose farmers have the lowest average annual income (₦168,421.05) has the highest
flood susceptibility.
Table 4 9 Index of susceptibility based on average income of the farmers
Average
Value
Village Name
Income(₦)
(Y)
Nyi= (Y-Ῡ)/ẟ Wy Wy*Nyi
Tola
246,153.85
3
-0.4629
0.5 -0.2315
Ajibode
179,807.69
6
0.9258
0.5
0.4629
Ajeja
168,421.05
7
1.3887
0.5
0.6944
Alabata
292,045.45
1
-1.3887
0.5 -0.6944
Onilu
270,833.33
2
-0.9258
0.5 -0.4629
Lagbe
243,333.33
4
0.0000
0.5
0.0000
Jarija
215,909.09
5
0.4629
0.5
0.2315
Mean
230,929.00
Source: Field Survey, 2014 (Note: Value (Y) decreases with average income and indicates
that susceptibility decreases with income level).
This indicates that the propensity to suffer agricultural losses, as a result of flood is
highest in Ajeja but reduces, in order, from Ajibode, Jarija, Lagbe, Tola, to Onilu while
Alabata has the least propensity to incur flood damages. Hence, efforts and policies targeted
at reducing flood susceptibility by improving income earnings of the farmers in Akinyele
36
LGA should prioritise Ajeja village, followed by Ajibode, Jarija, Lagbe, Tola, Onilu and
Alabata villages.
Clay Soil Rate
Clay soil rate refers to the percentage of farmers whose flood-affected farms are
located on clay or heavy soils. Soil water retention capacity determines the extent of damage
to crops during flooding. Thus, crops planted on clay soils, which have the highest water
retention capacity relative to other soil types, will suffer highest damage. Note that clay soil
rate is weighted 0.5 because the farmers’ judgement of clay or heavy soils may not be
accurate.
Table 4.10 Index of susceptibility based on clay soil rate
Village Name
Value (Y)
Nyi= (Y-Ῡ)/ẟ
Wy
Wy*Nyi
Tola
92.3
0.7962
0.5
0.3981
Ajibode
96.2
1.0129
0.5
0.5065
Ajeja
47.4
-1.6988
0.5
-0.8494
Alabata
95.5
0.9740
0.5
0.4870
Onilu
75.0
-0.1651
0.5
-0.0826
Lagbe
66.7
-0.6263
0.5
-0.3132
Jarija
72.7
-0.2929
0.5
-0.1465
According to Table 4.10 Ajibode village has the highest clay soil rate (96.2%) and
susceptibility index (0.5065) while Ajeja village has the least clay soil rate (47.4%) and least
susceptibility index (-0.8494). Therefore, farmers in Ajibode may suffer the highest crop
damages while Ajeja may lose the least yield quantity due to flood compared to other
villages, based on clay soil rate. Hence, the use of drainage channels for reducing crop losses
to flood should be given urgent attention in Akinyele LGA with particular focus on Ajibode,
Alabata, Tola, Onilu, Jarija and Lagbe villages.
37
Women Land Access Limitation Rate
Land is an economic resource needed for production and the number one resource in
farming. Resource-endowed people are less susceptible to disaster impacts. Therefore, access
to and ownership of land is a measure of susceptibility of rural dwellers to flood disaster. The
most common form of land tenure in rural Southwest Nigeria is inheritance. In families
where women are not allowed to inherit land, the access of women to agricultural land is
limited. This compels such women to cultivate marginal and flood-prone soils and make them
more susceptible to flood impacts than men.
Table 4.11 Index of susceptibility based on women land access limitation rate
Village Name
Tola
Value (Y)
76.9
Nyi= (Y-Ῡ)/ẟ
-0.2450
Wy
1
Wy*Nyi
-0.2450
Ajibode
100
0.8966
1
0.8966
Ajeja
59.7
-1.0950
1
-1.0950
Alabata
95.5
0.6742
1
0.6742
Onilu
50
-1.5744
1
-1.5744
Lagbe
100
0.8966
1
0.8966
Jarija
90.9
0.4469
1
0.4469
Mean(Ῡ)
81.86
STD (ẟ)
20.23
According to Table 4.11, the average women land access limitation rate (81.86%) is
generally high in the LGA and indicates high susceptibility to flood impacts as women
farmers are most land limited in Ajibode (100%) and Lagbe (100%) villages, followed by
Alabata (95.5%), Jarija (90.9%), Tola (76.95), and Ajeja village, while the least limitation of
women access to land is recorded in Onilu village (50%). Therefore, the susceptibility of
women farmers to flood is highest in Ajibode and Lagbe (0.8966) and lowest in Onilu (1.5744), compared to Alabata, Jarija, Tola and Ajeja villages whose susceptibility values are
0.6742, 0.4469, -0.2450 and -1.0950 respectively. Hence, local policies that will improve
women access to agricultural land should be implemented in the Local Government Area,
with particular attention given to Lagbe, Alabata, Jarija, Tola and Ajeja villages accordingly.
38
Poor Human Waste Management
Poor human waste management rate refers to the percentage of the farmers that uses
bush and refuse dumping site for defecation purposes. Proper human waste management
reduces the risk of contamination of water resources with harmful micro-organisms and
worms and reduces susceptibility of people to flood-related infections, such as typhoid fever,
cholera, Taniasis and Ascariasis. Table 4.12 indicates that there is a high rate of improper
waste management practices (82.36%) among the farmers of Akinyele LGA. This signifies
that there is either an inadequacy of toilet in the study area or a very low rate of adoption of
modern human waste management system in the rural communities.
Furthermore, the Table 4.12 shows that improper human waste management practices
is most rampant in Ajeja (100%) and least rampant in Tola (61.6%). This imputes the highest
and lowest flood susceptibility on Ajeja (1.0555) and Tola (-1.2419) respectively, compared
to Alabata (95.5%), Lagbe (95%), Onilu (91.7%), Ajibode (67.3%) and Jarija (65.4%) whose
flood susceptibility, based on human waste management are 0.7863, 0.7564, 0.5590, -0.9008
and -1.0145 respectively. Therefore, proper human waste management practices should be
inculcated in the villagers and adequate facilities should be ensured to reduce the risk of
infectious diseases through flood water contamination on farmers’ farms.
Table 4.12 Susceptibility index based on poor human waste management rate
Village Name
Tola
Ajibode
Ajeja
Alabata
Onilu
Lagbe
Jarija
Mean(Ῡ)
STD (ẟ)
Value
61.6
67.3
100
95.5
91.7
95
65.4
82.36
16.71
Nyi= (Y-Ῡ)/ẟ
-1.2419
-0.9008
1.0555
0.7863
0.5590
0.7564
-1.0145
Wy
1
1
1
1
1
1
1
Wy*Nyi
-1.2419
-0.9008
1.0555
0.7863
0.5590
0.7564
-1.0145
Surface Water Consumption
Flood causes water contamination and leads to water-related diseases (Oyekale et al,
2013). However, whether people will suffer from water contamination and water-borne
diseases as a result of floods depends on the sources of their drinking water, the extent of
inundation of their homes and general waste management practices of the community. Since
39
the waste management practices in farming communities of Akinyele LGA is generally poor
(Table 4.12), the flood water is highly contaminated and the surface water resources are
highly polluted with faeces as well as harmful microbes. Hence, farmers who consume river
and stream water during farm operations are susceptible to flood-related diseases. Table 4.13
indicates that farmers in Alabata have the highest rate of surface water consumption (81.8%)
with a flood susceptibility score of 1.2197 while Lagbe has the least surface water drinking
rate (0%), with a susceptibility score of -1.3756.
On the other hand, Ajeja, Tola, Jarija, Ajibode, and Onilu have surface water drinking
rates 73.7%, 61.6%, 45.5%, 32.6% and 8.3% respectively, with susceptibility indices 0.9627,
0.5788, 0.0680, -0.3413, and -1.1123 correspondingly. This means that farmers are likely to
suffer from the drinking water-related health impacts of flood in a decreasing order from
Alabata to Ajeja, Tola, Jarija, Ajibode, Onilu and Lagbe.
Table 4.13 Susceptibility based on surface water consumption rate
Village Name
Value (Y)
Tola
61.6
Ajibode
32.6
Ajeja
73.7
Alabata
81.8
Onilu
8.3
Lagbe
0
Jarija
45.5
Mean(Ῡ)
43.36
STD (ẟ)
31.52
Nyi= (Y-Ῡ)/ẟ
0.5788
-0.3413
0.9627
1.2197
-1.1123
-1.3756
0.0680
Wy
1
1
1
1
1
1
1
Wy*Nyi
0.5788
-0.3413
0.9627
1.2197
-1.1123
-1.3756
0.0680
Hence, the farmers should be made aware of the dangers of the consumption of river
and stream water and be encouraged to carry potable water from wells/boreholes and purified
water with them when they are leaving the village to their farms so as to reduce the risk of
contaminated water infections among them.
Inadequacy of Agricultural Extension Contact
Agricultural extension agents are the media through which improved farming
practices as well as flood risk reduction strategies can be communicated to farmers. Contact
with agricultural extension services refers to the receipt of information from them through
farm visits, meetings and mass media.
40
Table 4.14 Susceptibility based on inadequacy of agricultural extension contact
Village Name
Value (Y)
Tola
7.7
Ajibode
30.8
Ajeja
47.7
Alabata
72.7
Onilu
75
Lagbe
80
Jarija
72.7
Mean(Ῡ)
55.23
STD (ẟ)
27.47
Nyi= (Y-Ῡ)/ẟ
-1.7302
-0.8893
-0.2741
0.6360
0.7198
0.9018
0.6360
Wy
1
1
1
1
1
1
1
Wy*Nyi
-1.7302
-0.8893
-0.2741
0.6360
0.7198
0.9018
0.6360
Adequate contact of extension service agents with farmers indicates a good working
relationship between them and suggests that flood risk reduction strategies will be effectively
communicated to the farmers through agricultural extension services. Inadequacy of
extension contact is defined as the percentage of the farmers who do not have access to
seasonal information from agricultural extension agents.
According to Table 4.14, 80%, 75%, 72.7%, 727%, 47.7%, 30.8% and 7.7% of the
farmers in Lagbe, Onilu, Jarija, Alabata, Ajeja, Ajibode and Tola respectively lack seasonal
contacts with extension service agents. This means that any seasonal rainfall or flood forecast
eludes these high percentages of the farming communities of Akinyele LGA and makes them
susceptible to flood-related yield losses. Furthermore, Table 4.14 indicates that Lagbe is the
most susceptible (0.9018), followed by Onilu (0.7198), Jarija and Alabata (0.6360 each),
Ajeja (-0.2741) and Ajibode (-0.8893) while Tola is the least susceptible to flood impacts,
based on this indicator. Hence, information reaching farmers through agricultural extension
service in Akinyele LGA should be improved upon so as to reduce their propensity for losses
during flooding.
Overall Susceptibility of Akinyele Farmers to Flood
Table 4.15 summarises the susceptibility of farmers in Akinyele LGA to flood
impacts based on their economic, social, environmental, ecological and institutional
conditions. According to the susceptibility indices presented in Table 4.15 and the
susceptibility map in Figure 4.4, Alabata village with an index of 0.5182 is the most
susceptible to flood while Tola (-0.4119) is the least susceptible to agricultural flood impacts.
41
Table 4.15 Overall susceptibility of Akinyele farmers to flood disaster
SUSCEPTIBILITY INDICES
Indicators/Village
Tola
-0.2315
AI
0.3981
CSR
-0.2450
WLLR
0.5788
SWCR
-1.2419
PHWMR
-1.7302
IAEC
Sum of Indices
-2.4716
Susceptibility
Indices
-0.4119
Ajibode
Ajeja
Alabata
Onilu
Lagbe
Jarija
0.4629
0.5065
0.8966
-0.3413
-0.9008
-0.8893
0.6944
-0.8494
-1.0950
0.9627
1.0555
-0.2741
-0.6944
0.4870
0.6742
1.2197
0.7863
0.6360
-0.4629
-0.0826
-1.5744
-1.1123
0.5590
0.7198
0.0000 0.2315
-0.3132 -0.1465
0.8966 0.4469
-1.3756 0.0680
0.7564 -1.0145
0.9018 0.6360
-0.2654
0.4941
3.1089
-1.9534
0.8660
0.2214
-0.0442
0.0824
0.5182 -0.3256 0.1443 0.0369
(Note: Susceptibility Indices = Sum of indices divided
by the number of indicators, AI= Average Income, CSR= Clay Soil Rate, WLLR = Women
Land Limitation Rate, SWCR= Surface Water Consumption Rate, PWMR= Poor Waste
Management Rate and IEC= Inadequacy of Agricultural Extension Contacts)
Thus, the flood impacts on the farmers in Akinyele LGA are likely to reduce in order
of importance from Alabata (0.5182) to Lagbe (0.1443), Ajeja (0.0824), Jarija (0.0369),
Ajibode (-0.0442), Onilu (-0.3256) and Tola (-0.4119). Therefore, policies and strategies for
reducing susceptibility of farmers to flood negative impacts − in terms of the improvement of
farmers’ income, drainage construction and management training, improvement of women
access to agricultural land by inheritance, discouragement of surface water consumption,
proper human waste management practices and the improvement of the access of the farmers
to agricultural extension services − should be implemented in the local government area.
Furthermore, such flood susceptibility reducing policies should give priority to Alabata,
Lagbe, Ajeja and Jarija villages, which have high flood susceptibility, than Ajibode, Onilu
and Tola whose susceptibility indices are low.
42
Figure 4.4 Flood susceptibility of villages in Akinyele LGA (Source: Author)
4.5 Capacities of the Farmers against Flood
Armah et al (2010) defined capacity as the ability to plan, prepare for and implement
adaptation plans while Birkmann et al (2013) recognised the capacity to anticipate, capacity
to cope and capacity to recover. Therefore, capacity refers to the set of resources at the
disposal of a community to anticipate, cope, recover and adapt to impacts of hazards. The
anticipatory, coping/adaptive and recovery capacities of the farmers include premature
harvesting processing and storage, construction of emergency and temporary drainage
channels at the edge of flooded farms, flood-tolerant perennial cropping, planting in other
locations, which are not flood-prone, among others. However, their capacities were analysed
using the most important indicators, including flood forecast access rate (FFAR), flood
education access rate (FEAR), flood relief access rate (FRAR), adequacy of processing
facilities (APF), Geographical Diversification Rate (GDR), Flood Tolerant Cropping Rate
(FTCR), Occupational Diversification Rate (ODR) as well as Social Cooperation and
Agricultural Insurance Rate (SCAIR).
43
Flood Forecast Information Access
Flood forecast is key to flood risk reduction in every community. The farmers
revealed that there is no specific flood forecasting system in the LGA. Furthermore, no river
water level gauge is noticed in the major rivers like Orogun, Ona River and Ajeja Dam
during ground truthing. However, some of the farmers only observe river water levels during
heavy and prolonged raining days but this has never been accurate for timely farm operation
flood risk reduction decisions to be taken before the occurrence of the floods. Therefore, the
farmers’ access to flood forecast is very poor, indicating a very low risk reduction capacity.
Flood Education Access Rate is weighted 0.5 (Table 4.16) because it has high correlation
with Geographical Diversification Rate and may distort the final capacity index and
vulnerability index.
Flood Education Access
Table 4.16 Index of capacity based on flood education access rate
Value (Y)
Nyi= (Y-Ῡ)/ẟ
Wy
Wy*Nyi
100
0.8918
0.5
0.4459
Ajibode
96.2
0.7349
0.5
0.3674
Ajeja
66.7
-0.4830
0.5
-0.2415
Alabata
100
0.8918
0.5
0.4459
Onilu
41.7
-1.5152
0.5
-0.7576
Lagbe
53.3
-1.0363
0.5
-0.5181
Jarija
90.9
0.5161
0.5
0.2580
Mean(Ῡ)
78.40
STD (ẟ)
24.22
Village Name
Tola
Flood education and awareness program are key to flood risk prevention and
mitigation. Farmers who have adequate flood education will be able to reduce their losses and
recover faster than those without adequate flood education. According to Table 4.16, most of
the farmers (78.40%) in the surveyed villages said that they have received flood education
and awareness programs on the radio or television at least once. This means that most of the
farmers are aware of the risks involved in farming very close to rivers. The relative capacity
indices, based on flood education rate shown in Table 4.16 indicate that Tola and Alabata
44
have the highest flood education capacity to reduce the risk of agricultural losses to flood
than other villages while Onilu has the least know-how on flood risk reduction in farming.
Hence, flood education capacity building programmes and policies should give priorities to
Onilu, Lagbe and Ajeja farmers whose flood education level are lower than other villages.
Flood Relief Fund
Flood relief fund in terms of cash, food and seed are some of the aid supplied to flood
affected communities by Nigerian government and Non-Governmental Organizations during
and after 2011 floods. However, Table 4.17 shows that only 16.20% of the flood affected
farmers in Akinyele Local Government Area received one form of flood relief or the other
and this indicates a low coping and recovery capacity of the farmers.
Table 417 Flood relief access capacity index
Village Name
Value (Y)
Tola
76.9
Ajibode
5.8
Ajeja
0
Alabata
4.5
Onilu
8.4
Lagbe
0
Jarija
18.2
Mean(Ῡ)
16.26
STD (ẟ)
27.44
Nyi= (Y-Ῡ)/ẟ
2.2096
-0.3810
-0.5924
-0.4284
-0.2863
-0.5924
0.0708
Wy
1
1
1
1
1
1
1
Wy*Nyi
2.2096
-0.3810
-0.5924
-0.4284
-0.2863
-0.5924
0.0708
Furthermore, Tola farmers have comparably high access to flood relief materials than
other farming communities in the Local Government Area while Ajeja and Lagbe have the
lowest access to flood relief. Hence, improving flood relief access of the farmers in the local
government area, with particular attention to Lagbe, Ajeja, Alabata, Ajibode and Onilu
compared to Jarija and Tola, is necessary.
45
Adequacy of Processing Facilities
Table 4.18 Processing facility capacity index
Village Name
Value (Y)
Tola
38.5
Ajibode
7.7
Ajeja
5.3
Alabata
11.4
Onilu
16.7
Lagbe
6.7
Jarija
54.5
Mean(Ῡ)
20.11
STD (ẟ)
18.98
Nyi= (Y-Ῡ)/ẟ
0.9687
-0.6541
-0.7805
-0.4591
-0.1799
-0.7067
1.8116
Wy
1
1
1
1
1
1
1
Wy*Nyi
0.9687
-0.6541
-0.7805
-0.4591
-0.1799
-0.7067
1.8116
Availability and access to processing facilities are very important in mitigating the
losses of flood-intolerant crops such as cassava and yam. Adequate processing facilities are
necessary because farmers tend to harvest their cassava prematurely to reduce losses to flood.
According to Table 4.18, only 20.11% of the farmers have adequate access to processing
facilities while about 80% of them declared that the processing equipment available are
inadequate for reducing losses during flooding. This imputes very low coping and adaptive
capacity on majority of the farmers. The table also indicates that 54.5%, 38.5%, 16.7%,
11.4%, 7.7%, 6.7% and 5.3% of the farmers have adequate access to processing facility in
Jarija, Tola, Onilu, Alabata, Ajibode, Lagbe and Ajeja respectively. Thus, Jarija has the
highest processing facility capacity index (1.8116) to reduce flood yield losses while Ajeja
has the least processing facility capacity to reduce flood yield losses. Hence, processing
facility capacity building responses to flood should prioritise Ajeja, Lagbe, Ajibode, Alabata,
Onilu, Tola and Jarija in their reducing order of need.
Diversification of Economic Activities
Economic diversification can be geographical or occupational (Armah et al, 2010).
Geographical diversification refers to farming in other locations while occupational
diversification connotes possession of other economic activities apart from farming. Among
farmers in Akinyele LGA, enterprise diversification, the practice of planting more than one
crop type and rearing of more than one animal species, is exhibited in addition to
geographical and occupational diversification. Individuals who have diversified their
economies geographically and occupationally have higher capacity to cope with and adapt to
flood impacts than others (Armah et al, 2010).
46
Geographical Diversification
Table 4.19 Geographical diversification capacity index
Village Name
Value (Y)
Tola
15.4
Ajibode
15.4
Ajeja
84.2
Alabata
4.5
Onilu
75
Lagbe
86.7
Jarija
18.2
Mean(Ῡ)
42.77
STD (ẟ)
37.08
Nyi= (Y-Ῡ)/ẟ
-0.7381
-0.7381
1.1171
-1.0320
0.8691
1.1846
-0.6626
Wy
0.5
0.5
0.5
0.5
0.5
0.5
0.5
Wy*Nyi
-0.3690
-0.3690
0.5586
-0.5160
0.4345
0.5923
-0.3313
Table 4.19 shows that Lagbe farmers have the highest geographical diversification
capacity (0.5923) with 86.7% of them planting in other locations that are not flood-prone in
the LGA while Alabata farmers have the least geographical diversification capacity (-0.5160)
to reduce the crop losses to flood with only 4.5% of them planting in other areas. Therefore,
the encouragement to build capacity by diversifying farming enterprises geographically
should be directed mostly at Alabata, Ajibode, Tola, and Jarija farmers compared to Onilu,
Ajeja and Lagbe farmers who have higher geographical diversification capacities.
Flood Tolerant Cropping
Flood tolerant crops such as oilpalm, plantain, banana and cocoa cope better with
flood and reduce farmers’ losses during floods than most food crops. Table 4.20 presents the
flood tolerant cropping capacity of the farmers in each village of Akinyele Local Government
Area Table 4.20 shows that the flood tolerant cropping rate of the farmers is generally low
with an average of 8.36%. This means that only 8.36% of the farmers in the flood-prone parts
of the LGA cultivate crops such as oilpalm, plantain, cocoa and banana, which are able to
tolerate flood and reduce the income losses of the farmers during flooding. Among the
farming communities that are flood-prone, Ajeja, with a flood tolerant cropping rate of
26.3%, has the highest capacity index (1.5356) while Alabata, Onilu, Lagbe and Ajibode,
with a 0% flood tolerant cropping rate, have the lowest capacity index (-0.7152), based on
this indicator. Apart from Ajibode, which is a village whose farmers are not allowed to
cultivate perennial crops, farmers in other villages of the LGA should be encouraged to
47
improve on their cultivation of the flood tolerant species, especially on agricultural lands,
which are close to rivers so as to reduce their losses to flood.
Table 4.20 Flood tolerant cropping capacity index
Village Name
Value (Y)
Tola
23.1
Ajibode
0
Ajeja
26.3
Alabata
0
Onilu
0
Lagbe
0
Jarija
9.1
Mean(Ῡ)
8.36
STD (ẟ)
11.68
Nyi= (Y-Ῡ)/ẟ
1.2617
-0.7152
1.5356
-0.7152
-0.7152
-0.7152
0.0636
Wy
1
1
1
1
1
1
1
Wy*Nyi
1.2617
-0.7152
1.5356
-0.7152
-0.7152
-0.7152
0.0636
Occupational Diversification
Occupational diversification reduces the sensitivity of people to negative impacts of
flood (Armah et al, 2010). This is because people with diversified occupations have other
sources of income that are, perhaps, less vulnerable to disaster impacts. Generally, Table 4.21
shows that 55.75% of the farmers of the LGA have other occupations apart from farming.
This means that about 44.25% of them have no occupation apart from farming, implying that
they have no other income generating activity to sustain their livelihood, if there is complete
crop failure due to flood. On village basis, 81.5%, 76.9%, 61.5%, 60.0%, 54.4%, 33.3% and
22.7% of the farmers in Jarija, Tola, Ajibode, Lagbe, Ajeja, Onilu and Alabata respectively
have other economic activities apart from farming.
Table 4.21 Occupational diversification capacity index
Village Name
Value (Y)
Tola
76.9
Ajibode
61.5
Ajeja
54.4
Alabata
22.7
Onilu
33.3
Lagbe
60.0
Jarija
81.5
Mean(Ῡ)
55.76
STD (ẟ)
21.45
Nyi= (Y-Ῡ)/ẟ
0.9859
0.2678
-0.0633
-1.5414
-1.0471
0.1978
1.2003
48
Wy
1
1
1
1
1
1
1
Wy*Nyi
0.9859
0.2678
-0.0633
-1.5414
-1.0471
0.1978
1.2003
From Table 4.21, the occupational diversification capacity index of the farmers is
highest in Jarija (1.2003), followed, in decreasing order by Tola (0.9859), Ajibode (0.2678),
Lagbe (0.1978), Ajeja (-0.0633), Onilu (-1.0471) and Alabata (-1.5414). Therefore, there is
need to improve the income generating capacities of the farmers by encouraging them to
participate in other income generating activities, such as agricultural inputs supply, produce
marketing and petty trading to improve their coping, adaptive and recovery capacities against
flood.
Participation in Cooperative Societies and Agricultural Insurance
Cooperative societies are the institutions through which farmers and other economic
groups combine their economic and financial resources for the purpose of inputs
procurement, produce marketing and micro loans sourcing. Armah et al (2010) reported that
farming groups play vital roles in reducing flood vulnerability. On the other hand,
agricultural insurance refers to the insurance cover provided for Nigerian farmers against
natural hazards such as fire, lightning, windstorm, flood, drought, pests and diseases, invasion
of farm by wild animals as well as accidents, so as to keep the farmers in business (NAIC,
2013). Possession of agricultural insurance policy in Nigeria also connotes assess to microbank loans. Therefore, cooperative societies and investment in agricultural insurance boost
farmers’ recovery capacity. The higher the percentage of farmers that participate in
cooperative societies or invest in agricultural insurance, the higher the recovery capacity and
the lower the vulnerability of the farming population to flood disaster.
Table 4.22 Social cooperation and agricultural insurance capacity index
Village Name
Tola
Ajibode
Ajeja
Alabata
Onilu
Lagbe
Jarija
Mean(Ῡ)
STD (ẟ)
Value (Y)
92.3
84.6
36.8
27.3
66.7
60
100
66.81
27.62
Nyi= (Y-Ῡ)/ẟ
0.9229
0.6440
-1.0868
-1.4309
-0.0041
-0.2468
1.2017
Wy
1
1
1
1
1
1
1
Wy*Nyi
0.9229
0.6440
-1.0868
-1.4309
-0.0041
-0.2468
1.2017
Table 4.22 presents the capacity index of the farmers based on social cooperation and
agricultural insurance rates. The table shows that the participation of the farmers in
agricultural and other cooperative societies plus their investment rate in agricultural insurance
49
is generally high with a mean of 66.8% but highly varied with a standard variation of
27.62%. This means that about 66.8% of the farmers in Akinyele LGA participate in either
cooperative societies or invest in agricultural insurance.
Table 4.22 further reveals that Jarija village, with a cooperative and insurance rate of
100%, has the highest capacity index of 1.2017, followed by Tola (0.9229), Ajibode (0.6440),
Onilu (-0.0041), Lagbe (-0.2468) and Ajeja (-1.0868) while Alabata has the lowest capacity
index of -1.4309, given its lowest social cooperation and agricultural insurance rate of
27.3%. Therefore, agricultural cooperatives and agricultural insurance should be made
popular among the farmers in Alabata, Ajeja, Lagbe and Onilu villages, whose capacity
indices measured based on this indicator are low, so as to improve their recovery capacity
against flood disaster.
Overall Capacity of Akinyele Farmers against Flood Disaster
Capacity index of a village is the arithmetic mean of the indices of the capacity
indicators for the village. The overall capacity of the farmers against flood is summarised in
Table 4.23 and Figure 4.5. The capacity index presented in the last row of the table shows
that Alabata (-0.6636) has the lowest mean of the capacities to anticipate, cope with and
recover from flood disaster followed by Onilu (-0.3651), Lagbe (-0.2842), Ajibode (-0.1200),
Ajeja (-0.0958) and Jarija (0.6107) while Tola has the highest capacity to combat flood
disaster. In other words, Alabata, Onilu, Lagbe, Ajibode and Ajeja are more unlikely to
anticipate, cope with and recover from flood than Jarija and Tola.
Therefore, flood anticipatory, coping and recovery capacity building strategies and
policies, such as improvement of flood education access, improvement of flood relief access,
increase in processing facility capacity, encouragement of crop cultivation in less flood-prone
areas, cultivation of flood tolerant crops, combining farming with agricultural input supply
and produce marketing as well as improvement of farmers participation in co-operative
societies and investment in agricultural insurance should be prioritised in Alabata, Onilu,
Lagbe, Ajibode and Ajeja than in Jarija and Tola.
50
Table 4.23 Overall capacity index of Akinyele farmers against flood disaster
Indices per Village
Ajeja
Alabata
Onilu
-0.2415
0.4459
-0.7576
-0.5924 -0.4284
-0.2863
-0.7805 -0.4591
-0.1799
0.5586
-0.5160
0.4345
1.5356
-0.7152
-0.7152
-0.0633 -1.5414
-1.0471
-1.0868 -1.4309
-0.0041
Indicators
Tola
Ajibode
Lagbe
Jarija
0.4459
0.3674
-0.5181
0.2580
FEAR
2.2096
-0.3810
-0.5924
0.0708
FRAR
0.9687
-0.6541
-0.7067
1.8116
APF
-0.3690
-0.3690
0.5923
-0.3313
GDR
1.2617
-0.7152
-0.7152
0.0636
FTCR
0.9859
0.2678
0.1978
1.2003
ODR
0.9229
0.6440
-0.2468
1.2017
SCAIR
Sum of
Indices
6.4256
-0.8401 -0.6704 -4.6451
-2.5557
-1.9891
4.2748
Capacity
Index
0.9179
-0.1200 -0.0958 -0.6636
-0.3651
-0.2842
0.6107
(Note: Capacity Index = sum of indices divided by
number of indicators, FEAR= Flood Education Access Rate, FRAR= Flood Relief Access
Rate, APF= Adequacy of Processing Facilities, GDR= Geographical Diversification Rate,
FTCR = Flood Tolerant Cropping Rate, ODR= Occupational Diversification Rate, SCAIR =
Social Cooperation and Agricultural Insurance Rate).
Figure 4.5 Capacity of villages against flood in Akinyele LGA (Source: Author)
51
4.6 Vulnerability of the Farmers to Flood
The composite vulnerability indices of the farming villages are summarised in Table
4.24 and figure 4.6. Among the components of vulnerability, exposure is given a weight of 1
while each of susceptibility and capacity is ranked 0.5. This is because the components of
exposure in this study are hydrological factors while the components of susceptibility and
capacity are anthropogenic factors. Furthermore, hydrological factors are more important
causal factors of flood than human factors (Taiwo et al, 2012)
Table 4.24 Vulnerability of the farmers to flood
Villages
Exposure
Susceptibility
Capacity
Vulnerability
0.4887
-0.0442
-0.12
0.5266
Ajibode
-0.0242
0.1443
-0.2842
0.1901
Lagbe
-0.4015
0.5182
-0.6636
0.1894
Alabata
0.0648
-0.3256
-0.3651
0.0846
Onilu
0.212
0.0369
0.6107
-0.0749
Jarija
-0.4122
0.0824
-0.0958
-0.3231
Ajeja
0.0724
-0.4119
0.9179
-0.5925
Tola
Minimum
-0.4122
-0.4119
-0.6636
-0.5925
Maximum
0.4887
0.5182
0.9179
0.5266
Source: Field Survey, 2014 (Note: Vulnerability Index = Exposure Index + 0.5(Susceptibility
Index – Capacity Index)
Table 4.24 reveals that Ajibode is the most vulnerable to flood-related agricultural
impacts with a vulnerability index of 0.5266, followed by Lagbe (0.1901) and Alabata
(0.1894) while Tola is the least vulnerable to flood-related yield losses with an index of 0.5925 followed by Ajeja (-0.3231) and Jarija (-0.0749). Figure 4.6 displays the relative
vulnerability of the villages. It is worth noting that farming villages with high exposure, low
susceptibility but high capacities, such as Tola and Jarija, have the lowest agricultural
vulnerability to flood while places with low exposure, high susceptibility and low capacities,
such as Alabata, have high agricultural vulnerability to flood. Furthermore, Ajibode, with
very high exposure but low susceptibility and low capacities, possesses the highest
vulnerability to agricultural flood losses. Thus, villages with low capacities tend to have high
vulnerabilities while those with high capacities tend to be less vulnerable. Therefore while
reducing exposure and susceptibility of the farmers to flood, the capacity building should be a
priority in Ajibode, Lagbe, Alabata, Onilu and Ajeja villages.
52
Figure 4.6 Flood Vulnerability of Farming Communities in Akinyele LGA (Source: Author)
4.7 Robustness of the Vulnerability Index
Figure 4.7 and Figure 4.8 compare the relative vulnerability indices of the farming
communities, using the standardisation normalisation method, with the result of the
percentage normalisation method. In both cases, Ajibode, Lagbe, Onilu and Alabata are
among the villages with high flood vulnerability while Tola, Jarija and Ajeja have low
vulnerability. Thus, the two approaches show similar patterns. However, Alabata, one of the
villages in the highly vulnerable class under the standardisation normalisation method (Figure
4.7), emerged as one of the very highly vulnerable farming community under the percentage
normalisation method (Figure 4.8). This agrees with the report of Damm (2010) that different
methods of normalisation of the indicators could produce slightly differences in the relative
vulnerability indices.
53
Figure 4.7 Relative vulnerability index by standardisation normalisation method
Figure 4.8 Relative vulnerability index by percentage normalisation method
54
CHAPTER V: CONCLUSION AND POLICY RECOMMENDATIONS
5.1 Conclusion
Flooding, one of the effects of increasing trends of climate-induced changes in
rainfall, is becoming a serious problem to farming communities of Akinyele Local
Government Area, Oyo State of Nigeria. Therefore, this study assessed the vulnerability of
the farmers to flood disaster in Akinyele LGA by using primary and secondary data. Primary
data used in this study included farmers’ socio-economic characteristics and the flood-related
data obtained through the use of expert interviews and structured questionnaires as well as
geographic co-ordinates of points obtained by using portable GPS. On the other hand, the
secondary data employed for this study were Map of Akinyele LGA and SRTM data.
Using Geographic Information System techniques, two major flood plains were
identified in Akinyele LGA: one in the North-western part and the other in the Southern part,
thereby making these two regions the flood-prone areas of the LGA. Similarly, Ajibode
River, Ona River, Ajeja Dam, Shanu Bandele River, River Asani, onilu River, Gunwin River,
Osun Stream and Lagbe Stream are flood-prone water bodies while Tola, Jarija, Ajeja,
Alabata, Onilu, Lagbe and Ajibode are some of the flood-prone farming communities.
With the use of SPSS and Microsoft Excel softwares, relevant indicators were used to
rank the flood exposure, flood susceptibility and capacity against flood in the farming
communities. Generally, the farmers in the identified flood-prone villages of Akinyele LGA
are exposed to highly recurrent floods, which return once in every two years and have an
average duration of 5 days on farms. On the bases of the landscape and the characteristics of
the flood, Ajibode village, with its lowest elevation and the longest time of impact of the
flood on farms, is the most flood-exposed farming community while Alabata and Ajeja
villages are the least flood-exposed farming communities, compared to others.
Based on the socio-economic characteristics of the farmers and the ecological
characteristics of the farming villages, Alabata is shown to be the most susceptible to flood
impacts, followed by Lagbe, Ajeja, Jarija, Ajibode and Onilu villages while Tola is the least
susceptible to flood damages.
Fortunately, the farmers employ several strategies to combat flood in the LGA. These
include premature harvesting and processing, construction of drainage channels at the edge of
flooded farms and flood-tolerant cropping as well as planting in other locations and
55
diversification of economic activities. However, the capacity indices of the farming villages
and the capacity map show that the farmers have very limited capacity to anticipate, cope
with, recover from and adapt to flood disaster. Tola and Jarija have very high relative
capacities, Ajeja, Lagbe and Ajibode exhibit low relative capacities while Alabata and Onilu
have very low relative capacities against flood.
Tola village, which has moderate exposure, very low susceptibility but very high
capacity indices is the least flood vulnerable village while Ajibode village (most exposed,
with low susceptibility and low capacity) is the most vulnerable. Lagbe village (moderately
exposed, with moderate susceptibility and low capacity) and Alabata village (least exposed,
most susceptible, with very low capacity) fall under the second most vulnerable village class.
Therefore, villages with low capacities tend to have high flood vulnerability.
5.2 Recommendations
The government should dredge the major rivers in the LGA as well as set and enforce
river buffer zones for farm lands in the LGA with priorities for implementation placed on
Ajibode, Jarija, Tola, Onilu, Lagbe, Alabata and Ajeja villages in decreasing order of
necessity. Such river setbacks should include the major water bodies such as Ajibode River,
Ona River, Ajeja Dam, Shanu Bandele River, River Asani, among others and only floodtolerant crops such as oilpalm, plantain, banana and cocoa should be encouraged on farm
lands, which are adjacent to the buffer zones.
Government should legislate and enforce the right of women to inherit land.
Agricultural extension services should improve their service delivery to farmers and
encourage them to improve on their participation in other income generating activities such
as input supply, produce processing and marketing. Public wells and toilets should be
provided in villages and people should be made to understand the need to use these facilities
rather than surface water consumption and the bush respectively.
It is necessary to improve farmers’ access to flood education, seasonal flood forecasts
and boost the processing machine capacity of the villages. Specifically, river gauge should be
installed in the major rivers to improve the farmers’ access to flood warnings. Besides,
farmers should be encouraged to diversify their income earning activities to flood tolerant
crops, especially on lands that are close to rivers, cultivate in less flood-prone areas and
invest in agro-allied occupations. In addition, farmers should be motivated to improve their
participation in agricultural co-operative societies and investment in agricultural insurance.
56
REFERENCES
Abah R. C. (2013). An Application of Geographic Information System in Mapping Flood Risk Zones in
A North Central City in Nigeria. African Journal of Environmental Science and Technology Vol 7(6),
Pp365-371.
Adelekan O. I. (2011). Vulnerability Assessment of an Urban Flood in Nigeria: Abeokuta Flood 2007.
Natural Hazards 56:215-231.
Ajadi O. K., Olaniran D. H., Alabi M. F. and Adejumobi O. D. (2012). Incidence of Malaria among
Various Rural Socio-economic Households. Greener Journal of Medical Sciences, Vol. 2(3), pp. 51-63.
Amobichukwu C. A. and Egbinola N. C. (2013). Climate Variation Assessment Based on Rainfall and
Temperature in Ibadan, South-Western, Nigeria. Journal of Environment and Earth Science Vol. 3,
No.11,
Armah A. F., Yawson D. O., Yengoh G. T., Odoi J. O. and Afrifa E. K. A.(2010). Impact of Floods
on Livelihoods and Vulnerability of Natural Resource Dependent Communities in Northern Ghana.
Water Vol 2, pp 120-139; doi:10.3390/w2020120
Atedhor O. G., Odjugo A. O.P and Uriri E. A. (2011). Changing Rainfall and Anthropogenic-Induced
Flooding: Impacts and Adaptation Strategies in Benin City, Nigeria. Journal of Geography and
Regional Planning Vol. 4(1), pp. 42-52.
Bariweni P., Tawari C. and Abowei J. (2012). Some Environmental Effects of Flooding in the Niger
Delta Region of Nigeria. International Journal of Fisheries and Aquatic Sciences. 1(1): 35-46.
Barroca B., Bernardara P., Mouchel J. M. and Hubert G. (2006). Indicators for Identification of Urban
Flood Vulnerability. Nat Hazards Earth Syst Sci 6: 553–558.
Birkmann J. (2006). Measuring Vulnerability to Promote Disaster-resilient Societies: Conceptual
Frameworks and Definitions. AutoPDF V7 22/8 09:37 UNU, PMU: WSL 03/08/2006 PMU: WSL(W)
15/8/06 pp. 7–54 1589_01 (p. 9)
Birkmann J., Alexander D., Barbat A.H., Cardona O.D.; Carreno M.L.; Keiler M.; Kienberger S.;
Pelling M.; Schneiderbauer S.; Wele T. and Zeil P. (2013). Framing Vulnerability, Risk and Societal
Responses: The MOVE Framework. Natural Hazards 67: 193-211, DOI 10.1007/s11069-013-05585.
Blaikie P.; Cannon T.; Davis I.; and Wisner B. (1994). At Risk: Natural Hazards, People’s Vulnerability
and Disasters. 1st edition, London: Routledge.
CRED (2013). EM-DAT- The International Disaster Database. http://www.emdat.be/natural-disasterstrends. Data sourced on Feb 20th, 2013.
Damm M. (2010). Mapping Socio-ecological Vulnerability to Flooding. United Nations University,
Institute of Environment and Human Security, Graduate Research Series Vol. 3.
DFID (1999). Sustainable Livelihood Guidance Sheets. Department for International Development.
Data sourced from http://www.ennonline.net/resources/667 on Feb 28th, 2014.
Estrella M. and Saalismaa N. (2012). Ecosystem-based Disaster Risk Reduction (ECO-DRR): An
Overview. In: The Role of Ecosystems in Disaster Risk Reduction, Renaud, Sudmeier-Rieux and
Estrella (eds), United Nations University Press.
Fasinmirin J. T., Alli A. A., Oguntunde P. G. and Olufayo A. A. (2012). Implications of Trends and
Cycles of Rainfall on Agriculture and Water Resource in the Tropical Climate of Nigeria. Hydrology
for Disaster Management.
57
Ma D., Chen J., Zhang W., Zheng L. and Liu Y. (2007). Farmers’ Vulnerability to Flood Risk: A Case
Study in the Poyang Lake Region. Journal of Geographical Sciences DOI: 10.1007/s11442-007-0269-5
Ghesquiere F. and Mahul O. (2010). Financial Protection of the State against Natural Disasters: A
Primer. Policy Research Working Paper 5429. The World Bank.
Guha-Sapir D., Vos F., Below R, with Ponserre S (2012). Annual Disaster Statistical Review 2011: The
Numbers and Trends. Brussels: CRED.
1.
Guha-Sapir D., Jakubicka T., Vos F., Phalkey R., and Marx Michael (2010). Health Impacts of
Floods in Europe: Data Gaps And Information Needs From A Spatial Perspective. A MICRODIS
Report.rof
Howard H. (2012). Vulnerability Assessment and Climate Adaptation Strategies to Reduce the Impacts
of Flooding on Urban Agriculture. Focus: The Intervale Farms, Burlington Vermont.
IPCC SREX Glossary (2012). http://ipcc-wg2.gov/SREX/images/uploads/SREX-Annex_FINAL.pdf
Kaplan M., Renaud F. G. and L¨uchters G. (2009). Vulnerability Assessment and Protective Effects of
Coastal Vegetation during the 2004 Tsunami in Sri Lanka. Nat. Hazards Earth Syst. Sci., 9, 1479–1494.
Kasperson R. (2005). Human Vulnerability to Global Environmental Change: The State of Research.
Presentation at the Fifth Annual IIASA-DIPRI Forum: Integrated Disaster Risk Management,
Innovations in Science and Policy, 14-18 Sep, 2005, Beijing.
Manuamorn P. O., Lotsch A. and Dick W. (2009). Assessment of Innovative Approaches to Flood Risk
Management and Financing in Agriculture: The Thailand Case Study. The Commodity Risk
Management Group Agriculture and Rural Development, The World Bank.
Muhammad S. S. (2012). The Impact of the 2012 Floods On Agriculture And Food Security in Nigeria
Using GIS. Presentation at United Nations International Conference on Space-based Technologies for
Disasters Management. Nov 8th, 2012.
NDRRMCP (2013). Effects of Typhoon Yolanda (Haiyan). Sit Report No 76. National Disaster Risk Reduction and
Management Council, Philippines.
NAIC (2013). Nigerian Agricultural Insurance Corporation http://www.naic.com.ng Data retrieved on
28th-29th, may 2013.
Odjugo, P. A. O. (2012). Valuing the Cost of Environmental Degradation in the face of Changing
Climate: Emphasis on Flood and Erosion in Benin City, Nigeria. African Journal of Environmental
Science and Technology Vol. 6(1), pp. 17-27.
Olayinka N. D., Nwilo, C. P. and Adzandeh E. A. (2013). From Catchment to Reach: Predictive
Modelling of Floods in Nigeria. FIG Working Week 2013 Environment for Sustainability Abuja,
Nigeria, 6 – 10 May 2013.
Orlove B. S., Broad K. and Petty A.M (2004). Factors that influence the use of Climate Forecasts:
Evidence from the 1997/98 El Nino Event in Peru.
Oyekale A. S., Oladele, O. I. and Mukela, F. (2013). Impacts of Flooding on Coastal Fishing Folks
and Risk Adaptation Behaviours in Epe, Lagos State. African Journal of Agricultural Research Vol.
8(26), pp. 3392-3405.
Oyeleye O. and Adetunji M. (2013). Evaluation of the Causes and Effects of Flood in Apete, Ido
Local Government Area, Oyo State, Nigeria. Civil and Environmental Research Vol 3(17).
58
Pauw K., Thurlow J. and Seventer D. V. (2010). Droughts and Floods in Malawi. Assessing the
Economy-wide Effects. International Food Policy Research Institute.
Statistical Canada (2010). Survey Methods and Practices. Ministry of Industry, Canada.
Summerfield Michael A (2008). Flood plain. Microsoft® Student 2009 [DVD], Microsoft
Corporation.
Swenson D., Eathington L. and Meghan O’Brien (2008). Economic Impacts of the 2008 Floods
in Iowa. Regional Capacity Analysis Program (ReCAP), Iowa State University Extension.
Taiwo J. O., Ajayi O., Agbola S. B. and Wahab W. B. (2012). The August 2011 Flood in Ibadan,
Nigeria: Anthropogenic Causes and Consequences. Int Journal Disaster Risk Sci. 3(4), 207-217.
Thanh Ha. Le Thi, Quang La Ngoc, Bich Huu Tran, Hanh Duc Thi Tran and Guha-Sapir D. (2011).
Impacts of Flood: Epidemiologic Evidence from Hanoi, Vietnam. Global Health Action 4:6356
Turner B. L., Kasperson R. E., Matson P.A., McCarthy J.J., Corell R.W., Christensen L., Eckley, N.,
Kasperson, J.X., Luers, A., Martello, M.L., Polsky, C., Pulsipher, A. and Schiler, A. (2003). A
Framework for Vulnerability Analysis in Sustainability Science. Proceedings of the National Academy
of Sciences, 100(14): 8074-8079.
UNISDR Glossary (2007). www.unisdr.org/we/inform/terminology.
UNISDR (2014). African Disaster Statistics: Natural Disaster Occurrence Reported between 1980 and 2010.
www.preventionweb.net Data retrieved on Feb 20th, 2014.
UNOCHA (2012). Nigeria: Humanitarian Dashboard- Floods (As of 26 Nov, 2012). United Nations
Office for the Co-ordination of Humanitarian Affairs.
WHO (2013). Floods and Communicable Diseases. www.who.int/hat/techguidance/ems/flood_cds.
Data sourced on May 20th, 2013.
Wisner B., Blaikie P., Cannon T. and Davis I (2004). At Risk: natural hazards, peoples vulnerability
and disasters, 2nd edn. Routledge, London.
World Bank (2012). Turn Down the Heat: Why a 40C Warmer World Must be Avoided. The World
Bank.
59
QUESTIONNAIRE
QUESTIONNAIRE ON THE VULNERABILITY OF FARMERS TO FLOOD DISASTER
IN AKINYELE LOCAL GOVERNMENT
Study Objective: This study is aimed at assessing vulnerability of farming communities to
flood disaster. It is part of the requirement for the award of MSc. in Climate Change and Human
Security. The information obtained through this study is not meant for any political nor
governmental purposes but for the purpose of MSc Research of the Researcher. You are assured
of the confidential treatment of the valuable information you supply to me.
Researcher’s Name: Adeyemi Olusegun ADEGBILE
Researcher’s Affiliation: West African Science Service Center on Climate Change and
Adapted Land
Use, University of Lome, Togo.
PRELIMINARY
Name of Village ………………….
Ward Number and/or Name……………………….
Date of Interview……………………………….
SOCIO-ECONOMIC CHARACTERISTICS
Age: 18-25
51-55
26-30
56-60
31-35
60-65
Gender: Male
36-40
41-45
46-50
Above 65
Female
Marital Status: (a) Single Mother (b) Married (c) Widowed (d) Divorced (e) Separated
(f) Never Married
Household Size………………………
Highest Education Status (a) Primary (b) Secondary (c) NCE/OND (d) HND/BSc (f)
PGD/MSc
What is/are the Source of land you use for Farming?



Purchased
Inherited
Rented/hired
What is the range of your monthly income?






Less than 10,000
10,000-20,000
21,000-30,000
31,000-40,000
41,000-50,000
Above 50,000
60
What is the range of your annual income?
Below 100,000
100,000-200,000
200,000-300,000
300,000-400,000
400,000-500,000
Above 500,000
FLOOD EXPOSURE
Have you experienced flood/farm water lodging before? Yes
No
Which year/years did you experience flood/farm water lodging?
Which of the following types of flood did you experience on your farm? (a) River overflow
(River flood) (b) Farm water-lodging
How many days did it take the flood water to disappear from your farm?
……………………………
How close to a river/stream is your farm? …………….. Meters
How often do you experience flood in your farm/village? (a) Annually (b) Once in 2 years
(c) once in 3 years (d) 1 in 4 years (e) 1 in 5 years (f) 1 in 10 years (h) 1 in 15 years (i) 1 in 20
years
Total Expenditure on Farm Activities: (a) Weekly ……………….. (b) Monthly
Total………………………………… (b) Annual Total ………………………
SUSCEPTIBILITY
How often does agricultural extension services visit your farm?







Weekly
Monthly
1 in 6 months
Annually
1 in 5 years
Others (specify)
Not at all
Do you receive any flood education or awareness program on radio or Television? Yes
……… No ………
Are women allowed to inherit land in this town? Yes ……………
………………………..
61
No
Which of the following is/are the source(s) of your drinking water? Tick as many as
appropriate.





Pure water
Well water
Borehole water
River water
Stream water
On which type of soil is your farm affected by flood located?



Clay/heavy soil
Silt/medium soil
Sandy/light soil
Where do you defecate? Tick as many as applicable





In the bush
Near the dung hill (refuse dumping ground)
Pit toilet in my house
Public toilet
Water closet in my house
Is there a public toilet in this town? Yes ……….. No……….. If yes, how many public
toilets are available? ………………………………….
CAPACITIES
Were you warned by radio/television/Extension Services that there will be flood? Yes ….
No……. If yes, how long did it take the flood to arrive after the flood warnings? (Use the
following table)
Flood
Warnings
Radio
Television
Agric Extension
Time interval Between Flood Warnings and Occurrence of Flood
Months
Weeks
Days
Hours
Is there any local signs/means of predicting flood in your village or town? Yes ……….
No……………….. If yes, please, specify these signs and the time interval it took the flood to
come after the observation of these signs. (Use the table below).
Local
Signs
Flood Time interval Between Sign Observation and Occurrence of Flood
Months
Weeks
Days
Hours
Did you receive any assistance during and after the flood? Yes…………
62
No………
If yes What kind of assistance did you receive from the government to reduce suffering
from
the
flood?
…………………………………………………………………………………………………
…………………………………………
What
kind
of
Assistance
did
you
receive
…………………………………………………………………..
from
NGOs?
Is there a processing facility for your crop in your town/village? Yes ………. No…………
Is the processing facility adequate/sufficient? Yes ………. No…………
Are you able to process all your farm produce within your village/town? Yes ……….
No…………
Is there a market for your produce within your village or town? Yes ………. No…………
If yes, do you sell all your produce in your town/village? Yes ………. No…………
Do you transport part/all your produce to another town’s market to sell? Yes ……….
No…………
If
yes
why?
…………………………………………………………………………………………………
………………………………………….
Which of the following did you do to reduce your losses due to flood?







pre-mature harvesting
construction of drainage channels at the edge of farms,
changing planting date
flood resistance crops
planting/rearing animals/fishes in other locations that are not flood prone,
migration
None of the above
Which of the following do you have in personally as a farmer? (Tick as many as applicable)


Processing facility
Storage facility
Which of the following do you invest in personally as a farmer? (Tick as many as
applicable)



Agricultural Insurance
Farmers’ cooperative society
Other co-operative societies
Savings: (a) Weekly…………………… (b) Monthly …………………………….
Annual Total………………………
(C)
Other Economic Activities: (a) Civil service (b) Trading (c) Artisan
Were you able to do the second season planting after the flood? Yes …………….. No
………………….
63
How long did it take you to
……………………………. months
go
back
into
farming
after
the
flood?
Please, choose the type of crops or animals that you are rearing and specify the size of
land and/or number of the animals as well as your sales.
Crops/Animal
Land Area/No of Weekly
Animals/chickens Sales(₦)
Maize
Cassava
Water melon
Melon (Egusi)
Beans/Soybean
Vegetables
Oilpalm
Cashew
Cocoa
Plantain
Mango
Banana
Poultry Farm
Sheep and goat
Cattle
Fish Farming
Other
crops/animals
64
Monthly Sales Annual Sales
(₦)
(₦)
VITA
Adeyemi Olusegun was born to the family of Adegbile in Ilora, Afijio Local
Government Area of Oyo, Nigeria. He has a Bachelor of Agriculture Degree in Agricultural
Economics from Obafemi Awolowo University, Ile-Ife, Nigeria in 2007 and received an MSc.
in Climate Change and Human Security from the West African Science Service Center on
Climate Change and Adapted Land Use, Université de Lomé, Togo in 2014.
His research interests are in climate change and sustainable development, sustainable
farming and energy systems, land use/land cover changes as well as disaster risk and
vulnerability reduction
65

VULNERABILITY OF FARMERS TO FLOOD DISASTER

Transcription

Similar documents

perception of social vulnerability in relation to flood situation in

What you have told us

excess flood faq

FL20 CS - Solas Ray Lighting

LPRCA Factsheet - Long Point Region Conservation Authority