Background of the study
is a way of life for an estimated 120 million people worldwide of which 50 million
are from sub-Saharan Africa (CAADP policy brief No.6, March 2012).
Kenya arid and semi-arid Lands (ASALS) make up make up 83% of the country and
home for 10% of the total population (Kirbride & Grahn 2008:8).Livestock is
the main source of livelihood for this population. Livestock in pastoral areas
is estimated to be worth US$800 million per year (AUIBAR in IIED and SOS Sahel
livestock products contributes to 24% of total agricultural GDP. Over 70% of
the country’s livestock and 75% of the wildlife are found in the ASALs (Orindi
et al. 2007). Despite this, the pastoral households have been sidelined
economically and politically for decades. Pastoralist areas have the highest
incidences of extreme poverty and the least access to basic services in the
as many other sub Saharan countries, prone to variety of natural disasters like
drought, agricultural pests, floods that affect livelihoods and cause food insecurity.
Pastoral livelihoods are increasingly under pressure which may cause resource depletion,
diminishing resilience, loss of livestock and shrinking rangelands (UNOCHA
2007). In the 2011 drought World Bank indicates that estimated livestock
mortality was about 10-15 percent above normal in the affected areas, which was
equivalent to 5 per cent of Kenya’s livestock population (World Bank, 2011).The
situation calls for the need to understand the nature and extend of the food
insecurity problem as they have a great impact on economic performance and
livelihoods of the affected communities
households have developed different strategies to cope/reduce the risks/shocks
that affect them Strategies are based resource endowment or access to external
assistance (Maharjan and Chhetri, 2006).
is the Ability to maintain a certain level of wellbeing and withstand shocks
and stresses (DFID 2011).despite the heavy capital investment by the government
in agriculture food security still poses as a serious threat in the country it
is important to determine the key factors of household resilience to food
insecurity in order to better address the adverse effect of climate shocks.
1.2 Statement of the problem
insecurity poses a serious threat to human survival in the SSA for this reason
it has remained a major global development agenda, as in the SDG 2 which is to end
hunger, achieve food security and improved nutrition, and promote sustainable
effects of food insecurity have been observed in Kenya and are likely to
persist with increasing population pressure and changing consumption
preferences despite many interventions it is still an issue of major concern,
Particularly Baringo County, the study area, has high prevalence of food
engage in different and multiple coping strategies .Household coping strategies
attempt to minimize the effect of the shocks by maximizing the limited
resources Coping strategies may be intended to sustain lives rather than
improve the food security status. The strategies are region specific and when
effective the resilience of the households is improved. Based on this the study
will screen the coping strategies and measure the household resilience to food
Many studies that have
been done on food security (Nyariki, 2002; Pinstrup, 2009; Barret, 2010;
Headey & Ecker, 2013) focus on definition, causes and
Other studies have
outlined measures of attaining food security and coping mechanisms among
households households (Shariff & Khor, 2008; Yengoh et. al., 2010; Burchi, 2011; Gupta et. al., 2015)
.This study seeks to build on the available literature by determining status in
pastoral households and also linking the aspect of resilience and food
General objective the study
study aims to analyze the determinants of household resilience to food
insecurity among pastoralists and agro pastoralists in Mogotio sub county,
characterize food insecurity status among households in Baringo country.
analyze food insecurity coping strategies employed by households in the study
determine household resilience to food insecurity in Baringo county
CHAPTER TWO: LITERATURE REVIEW
Measurement of resilience
to the fact that resilience is unobservable, few studies have quantitatively
assessed it. Alinovi et al (2008) modeled resilience a latent variable, while
keil et.al(2008) used an observable variable as a proxy.
to Allinovi et al household resilience is determined by social safety nets, access
to public services, assets, income and food access, stability and adaptive capacity.
They then use the 6 factors to compute a resilience index
(2006) uses an approach based on the idea that resilient households have the
ability to smooth their consumption by reducing asset stock or other coping
strategies and non resilient ones cope by reducing consumption in order to
maintain their assets’
(2012) uses an ordered probit to identify and analyze determinants of
resilience to climate change impacts.
found farmers with better investments in natural resource management , access
to markets , better social networks , access to credit , preparedness , saved
liquid assets , access to irrigation, and a better level of education, have
greater levels of resilience.
CHAPTER THREE: METHODOLOGY
chapter presents the theoretical, and empirical framework used in the study. It
further presents a description of the study area, sampling design and data
study specifically draws from the Sustainable Livelihoods Approach (SLA)
developed by DFID.
SLA provides a framework for assessing how
people go about maintaining their livelihood household livelihoods’
sustainability depends on households’ ability to cope with shocks and stresses
,A household’s livelihood is said to be sustainable if it can cope with and
recover from shocks ,maintain or enhance its capabilities and assets, while not
undermining the natural resource base.
3.2 Empirical Framework
1: To characterize food insecurity status among households
The household food
insecurity access scale (HFIAS) will be used to determine food insecurity status.
This is a nine-item food insecurity scale used to calculate HFIAS score-a
continuous measure of the degree of food insecurity in the past 30days
2: To analyse the food insecurity coping strategies employed by households in
the study area
coping strategies will be analyzed using descriptives and a coping strategies index
(CSI) will be created.For each coping strategy a degree will be set based on
its severity. The coping strategies will be ranked based on the average weight
3: To determine household resilience to food insecurity in the study area
probit regression model will be used to identify and analyze the determinants
of household resilience to food insecurity. The level of resilience will be
classified into 3 categories based on speed of bouncing back after a shock
measured in the number of agricultural seasons.
Fast :households that are go back to normal in the next agricultural season
one or 2 seasons to get back to normal
households that were unable to bounce back after 2 seasons
Is the level of resilience and involves
the explanatory variables determining time taken to bounce back
is the disturbance term
3.3 Study Area
will be conducted in mogotia Sub-County, Baringo County. The region was
selected as it is in the Arid and Semi-Arid Lands (GOK 2015)
,with high poverty incidences. The
residents in this county practice pastoralism and agro-pastoralism as source of
livelihood.the area receives an average
rainfall of 500mm per annum which is highly variable and unreliable.
3.4 Sample Size Determination
to Kothari (2004), the following formula will used to determine sample size.
n = the
sample size to be determined
p = the
estimated proportion of the target population that has an attribute the study
is interested in
Weighting variable computed as (1 – p)
e = the
acceptable error (precision)
proportion of the population that is food insecure is unknown and therefore p
will be set at 0.5 since this proportion will be statistically sufficient and
reliable. This will lead to q of (1 – 0.5). According to Kothari (2004), an
error less than 10% is usually acceptable and hence this study will assume an
error of 0.08.
at least 150 questionnaires will be targeted during data collection.
3.5 Sampling Design
sampling technique will used in the study. Mogotio Sub County in Baringo County
will be purposively selected. Simple random sampling will used to select
locations in the Sub County where four locations will selected. Systematic
random sampling will be used to select households where every fourth household
will be selected from either side of the road in the villages.
3.7 Data Collection
will use primary data to be collected using semi structured questionnaires.
Secondary data will be obtained from publications, seasonal annual reports of
the county, and relevant government ministries documents and will be used for
3.8 Data Analysis
entered using Statistical Package for Social Sciences (SPSS) while Stata will
be used for analysis in both descriptive statistics and econometric models. For
the descriptive statistics, frequency distributions, percentage and means will
be used to present the results.
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