Vulnerabilty of farmer's livelihood to climate variability induced hazards in Siraro District, West Arsi Zone, Oromia, Regional State, Ethiopia


Master's Thesis, 2016

112 Pages, Grade: Very Good


Table of Contents

Chapter I: Introduction
1.1Background of the study
1.2 Statement of the problem
1.3 Objectives of the Study
1.3.1 General Objective
1.3.2 Specific Objectives
1.4 Research Questions
1.5 Significance of the study
1.6 Scope of the study
1.7 Limitation of the study
1.8 Organization of the study

Chapter II: Related Literature Review
2.1 Conceptual Literature
2.2 Definitions
2.3 Conceptual framework
2.4 Empirical Literature
2.4.1 Descriptive Analysis
2.4.2 Indicators Approach
2.4.3 Econometric Approach
2.4.4 Integrated Method
2.4.5 Principal Component Analysis
2.5 Literature gaps

Chapter III: Description of the Study Area and Methods & Materials
3.1 Brief description of the study area
3.1.1 Location
3.1.2 Socioeconomic status of Siraro district Farmers
3.1.3 Demographic features
3.2 Research Methods and Materials
3.2.1 Study design
3.2.2 Data Types and Sources
3.2.3 Sampling Techniques and Sample Size Determination
3.2.4 Data Collection Methods
3.2.5. Analytical framework
3.2.6 Methods of data Analysis
3.2.7 Data validity and reliability
3.2.8 Ethical considerations

Chapter IV: Data Presentation and Analysis
4.1 Location of respondents by Agro ecology & sampled kebeles
4.2 Household Characteristics
4.2.1 Demographic characteristics
Source: Survey data, April
4.2.2 Ethnic background and Religion of household heads
4.2.3 Human Capital
4. 3 Exposure of households to climate variability induced hazards
4.3.1 Farmer’s perception on trends of intensity of climatic hazards
4.3.2 Frequencies of climate variability induced hazards experienced
4.3.3 Climate Information
4.4 Livelihood Characteristics
4.4.1 Household’s Major Livelihood Means
4.4.2 Access to Natural Capital
4.4.3 Access to Financial Capital
4.5 Access to Physical Capital
4.5.1 Access to safe water supply
4.5.2 Access to Transportation and Markets
4.5.3 Access to Social Capital
4.6. Sensitivity of farmers’ livelihoods to climatic hazards
4.7 Adaptive capacity of smallholder farmers’ livelihoods
4.7.1 Awareness and participation of farmers on climate adaptation initiatives
4.7.2 Coping mechanisms to climate variability induced hazards
4.7.3 Farmers’ medium/long term adaptation strategies to climatic hazards
4.8 Test of Independence/Goodness of Fit Test
4.8.1 Test of Association between Sensitivity and socioeconomic indicators
4.8.2 Test of Association between Adaptive Capacity and socioeconomic indicators
4.9 Multivariate Analysis of Indicators of Vulnerability
4.9.1 Determinants of Vulnerability of smallholder farmers to climatic hazards
4.9.2 Communalities of indicators
4.9.3 Total Variance Explained by Retained Components
4.9.4 Factor loadings of retained components
4.10 Influences of factors on Sensitivity and Adaptive Capacity of Livelihoods
4.10.1 Sensitivity of livelihoods to climatic hazards
4.10.2 Adaptive capacity
4.10.3. Aggregate Vulnerability and Relative Status of Farmers’ Livelihoods

Chapter V: Conclusion and Recommendations
5.1 Conclusion
5.2 Recommendations
5.2.1 Community
5.2.2 Government and NGOs








MAY, 2016



Most previous studies on vulnerability of smallholder farmer’s livelihood to climatic hazards had focused on large scale, global, regional or national levels. In addition, they gave more focuses to assessment of impacts of climatic hazards to farmers than assessment of farmer’s vulnerability to climatic hazards. Thus, this study was designed to bridge this gap by assessing vulnerability of farmer’s livelihood at household level measured as perceived sensitivity and lack of adaptive capacity to climatic hazards and by exploring sets of socioeconomic indicatorsthat significantly affect vulnerability of farmers’ livelihoods. Cross-sectional data was collected through household survey from 379 randomly selected respondents which were also complemented with qualitative assessments. Principal component analysis was used to identify major socioeconomic indicators that contribute to vulnerability of farmer’s livelihood using orthogonal axis rotation in SPSS version 20. Binary logit model was used to identify principal socioeconomic indicators that significantly influencesensitivity and adaptive capacity of livelihoods of sampled farmers. The findings revealed that households that are heterogeneous in location of residence, sources of income, ownership to animals have significant influence on perceived sensitivity of farmers to climatic hazards. However, socioeconomic indicators such as age, gender, family size, have insignificant influence on sensitivity of farmer’s livelihood to climatic hazards. Gender, marital status, educational status of household head, coping mechanisms like grain & cash saving, NRM practices has significant influence on perceived adaptive capacity of farmers. Yet, access to social interconnectedness such as idir or iqub, family relatives,and cost of drinking water has insignificant influence on perceived adaptive capacity of farmers. Currently, 59%, 38% and 3% of sampled farmers have overall vulnerability status of low, moderate and high respectively. The overall conclusion is that differences in status of vulnerability of their livelihoods are attributed to differences in their present socioeconomic attributes. Community should strengthen existing hazard coping mechanisms whilst GOs and NGOs should support farmers in diversification of income sources and restocking of animals. Active community engagement in planning and execution of disaster risk reduction strategies and practices is also indispensable.


First and foremost, I would like to thank almighty GOD for granting me the blessing, strength, and care all along my walks of life.

My sincere appreciation and handful gratitude goes to my advisor, Dr. Tsetadirgachew Legesse for providing me all round supports including constructive criticisms throughout the research. I am also grateful to Dr. Messay Mulugeta for his kind guidance and support that helped me a lot to undertake the research work.

My handful gratitude also goes to my friends: Yilma Tibesso, Tekalign Kalbessa, Ashenafi Teshita, Mosisa Abdisa, Abdurehman Woyessa, Million Seyoum, Alemayehu Urgi, Gezahegn Shewangizaw, Merga Gemechu, Tibesso Ifa, Adem Woya, Kumbi Tegeno, Abdi Ahmed and others for supporting me not only in the rigorous data collection processes but also for encouraging me by providing logistic supports as well. I would also like to extend my appreciations to Hunde Gudina primary school teachers, colleagues from Siraro district agriculture office, disaster preparedness and prevention office and education office, who supported me on actual data collection and in provision of necessary guidance and information. Without supports from this team of friends, I could have not managed to do this research.

My special thanks go to my mom, Demma Geriso, forher exemplary care, love and support as a mother from birth up to now. She often gives beyond her capacity to make me a dependable person with clear purpose and vision. My deepest appreciations also go to my own family, especially to my son, Nebil, and my daughter, Nanati, for their understanding and allowing me their time.

Finally, I would like to offer my special gratitude for all whose names are not listed even though you have involved directly or indirectly in this study.

List of Figures

Figure 1: Conceptual Framework

Figure 2: Physical Map of Siraro district

Figure 3: Sampling Procedure

Figure 4: Frequencies of climatic hazards

Figure 5: Trends of annual mean precipitation

Figure 6: Trends of annual mean temperature

Figure 7: Family’s bread winner

Figure 8: Trends of population received immediate food aid

Figure 9: Availability of food across months in Siraro district

Figure 10: Sensitivity of major livelihood means to climatic hazards

Figure 11: Adaptive capacity to climate variability induced hazards

Figure 12: Present Status of Vulnerability of farmers’ livelihoods

List of Tables

Table 1: Proportions of sample respondents by stratum

Table 2: Number of respondents by agro ecological zone

Table 3: Household heads’ demographic characteristics

Table 4: Ethnic background and religion of household heads

Table 5: Highest educational status of household heads & Spouses

Table 6: Exposure to climatic hazards experienced

Table 7: Household’s Major Livelihood Means

Table 8: Ownership to land

Table 9: Ownership to animal resources

Table 10: Sources of income

Table 11: Sources of food for household consumption

Table 12: Access to safe drinking water and unit cost in dry season

Table 13: Access to transportation and market

Table 14: Access to local institutional services

Table 15: Impacts of 2013/14 Mehir season flooding on livelihoods

Table 16: Farmers’ awareness and participation on climate adaptation initiatives

Table 17: Coping mechanisms to impacts of climate variability induced hazards

Table 18: Major adaptation strategies practiced

Table 19: Tests of Association b/n Sensitivity and Socioeconomic factors

Table 20: Test of association b/n adaptive capacity and socioeconomic factors

Table 21: Measure of Sampling Adequacy/MSA/

Table 22: Total Variance explained by retained factors

Table 23: Factor loadings of retained components

Table 24: Influences of socioeconomic indicators on Sensitivity and Adaptive Capacity of livelihoods

Acronyms and Abbreviation


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Chapter I:Introduction

1.1 Background of the study

Climate variability induced hazards or natural hazards such as earth quake, cyclones, drought, flood, rainstorms, etc. are the major challenges adversely impacting human population primarily due to their varying levels of vulnerability (IPCC, 2007). Available global statistics revealed that total loss as a result of climate variability induced hazards in developing countries is much higher than total loss in developed countries in spite of more exposure of the later to the hazards (Peruzzi et al., 2002). This obviously shows us that, the difference in level of vulnerability has resulted from different developmental status. Specifically in developing countries, it is attributed to over-dependence on agricultural sector, marginal climate, low adaptive capacities and existence of many other hazards (Collier, et al. 2008, and McCarthy, 2001).

When dealing with climate vulnerability analysis, one has to take both external and internal environments into account. In this case, the external environment refers to hazard analysis whereby the types, magnitudes, intensities, and consequences or impacts/damages experienced from climate variability induced hazards are analyzed whereas the internal environment refers to analysis of vulnerability explained as exposure, sensitivity and limited adapting capacity of a society to a given climate variability or sets of climate variability induced hazards (Chamber, 1989, Adger, 1996).

According to the IPCC (2007) report on the regional impacts of climate change, “Africa is the continent most vulnerable to the impacts of projected changes because of widespread poverty limits adaptation capabilities”. The importance of agricultural activities for the economies of most African countries, combined with the farming sector’s reliance on the quality of rains during the rainy season, make countries in the region particularly vulnerable to climate change. Thus, from the point of view of food security, the increasing incidence of climate variability induced hazards such as, drought, variability of rainfall, violent flooding and rainstorm represent a very serious threats to the population and their livelihoods. The climate change impact on agriculture is believed to be stronger in Sub-Saharan Africa (IPCC, 2007).

Specifically, livelihoods of smallholder famers in developing countries including Ethiopia are more vulnerable to recurrent climate variability induced hazards like late onset and/or early cessation of rainfall, flooding and rainstorms, etc. (Abate, 2009, Temesgen, 2010).It is obvious that in Ethiopia, more than 85% of people’s livelihoods entirely depend on rain fed agriculture which is vulnerable to climatic hazards and so does Siraro district’s smallholder farmers (Abate, 2009, HRF annual report, 2014).

Thus, this study has assessed characteristics of sampled farmer households and socioeconomic indicators that characterize sensitivity of their livelihoods to climate variability induced hazards, specifically, late onset and/or early cessation of rainfall, violent flooding and violent rainstorm and indicators that characterize adaptive capacity of farmer agrarian households in Siraro district context, West Arsi zone, Oromia Regional State, of Ethiopia. The study has also assessed main indicators that influence vulnerability of household’s livelihood to climate variability induced hazards. Finally, the study has recommended future areas that need concrete actions in order to restore community’s resilience in the face of climatic hazards.

1.2 Statement of the problem

Almost more than half of Ethiopian economy is from agriculture which is by large sensitive to climate variability, timely onset, amount, duration and distribution of rainfall (CIA- World Fact Book, 2013). However, the rain-fed agricultural livelihoods of Ethiopian farmers have been adversely impacted by climate variability induced hazards such as variability of rainfall, flooding and rainstorms (Gina et al., 2008, Abate, 2009, Temesgen, 2010). However, the magnitude of impacts varies from household to household due to variations in their status of vulnerability to the inevitable climatic hazards. Hence, it is obvious that livelihoods of farmers in the study area, Siraro district where there is no access to irrigation farming, is more vulnerable to climate variability induced hazards.

Nevertheless, most previous studies have emphasized on assessment of physical aspects such as magnitude& intensity of impacts and/or losses resulted from climate variability induced hazard/s than assessment of vulnerability of the social ecosystem of the affected population such astheir major livelihood means, prior knowledge & experiences to hazard exposure, sensitivity and adaptive capacity, to one or sets of climatic hazards (V., Katharine, 2004). Few previous studies that have taken vulnerability of social ecosystem into considerations have also been done at larger scale, national, regional state and zonal levels (V., Katharine, 2004, Temesgen, 2009, Emebet, 2013). Therefore, this study assessed vulnerability of social ecosystem to sets of climatic hazards such as variability of rainfall (late onset and early cessation), flooding and violent rainstorms at local or district level. To this end, sensitivity of rain-fed agricultural livelihoods and adaptive capacity of randomly sampled farmer household heads were assessed in a face of perceived exposure to these hazards.

1.3 Objectives of the Study

1.3.1 General Objective

The overall objective of this study was to explore livelihood vulnerability contexts of smallholder farmers and to contribute to community resilience building efforts in a face of inevitable impacts resulted from climate variability induced hazards

1.3.2 Specific Objectives

1. To understand trends of frequencies of climate variability induced hazards often experienced by farmers in the study area
2. To assess sensitivity of household’s main livelihood means to climatic hazards as perceived by farmer households in the study area
3. To explore types of resilience strategies and practices that farmers are adopting as adaptation strategies to climatic hazards in the study area
4. To identify main indicators that influence vulnerability of households’ livelihoods to climatic hazards in the study area
5. To categorize present vulnerability status of households to climate variability in the study area

1.4 Research Questions

1. How are the trends of the most frequently happening climatic hazards experienced by farmer households in the study area look like over time?
2. How farmer household’s main livelihood means are sensitive to climatic hazards in the study area and why
3. How farmer households are adapting to recurrent climatic hazards in the study area
4. Which demographic and socioeconomic characteristics of farmer households can significantly influence their perceived exposure, sensitivity and adaptive capacity to climatic hazards in the study area?
5. What is the present relative status of vulnerability of households to climate variability induced hazards?

1.5 Significance of the study

In terms of major livelihood means, rain-fed farmers and pastoralist in the country are found to be most vulnerable to climatic shocks and its consequences (Keffyalew et al., 2011). Further, due to inter annual and inter decadal rainfall variability, it is difficult to detect long term rainfall trends in Ethiopia (McSweeney et al., 2008). Thus, rural rain fed agrarian households has no choice than facing the inevitable impacts from climate variability in a face of variable and unpredictable climatic conditions (Zenebe et al., 2011). Therefore, assessing socioeconomic factors that influence vulnerability (exposure, sensitivity and adaptive capacity) of rural households to climate variability at a local context in a view of short term climate variability induced problems has a considerable scientific significance. The research output can influence policy makers not only to revise or update appropriate environmental policy but also to enhance effectiveness of efforts underway in terms of preparation, coping mechanisms, and adaptation strategies and practices to future climatic shocks and impacts. In addition, uncovering of significant differences in characteristics and socioeconomic status that contributed to differences in level of vulnerability among rural households would inform other researchers as a benchmark, any interested development planners, GOs or NGOs, in design and/or implementation of disaster response plans and household resilience building initiatives.

1.6 Scope of the study

The scope of the study was limited to explorations of most frequently experienced climate variability induced hazards such as violent flooding, rainstorm and late onset of rainfall, by rural households in the study area. The study focused on the interaction of climate variability induced hazards and social ecosystem; did not cover the interaction of climate variability induced hazards with the physical ecosystem. Further, it didn’t discuss impacts that climate variability induced hazards have made to people as it highlighted few examples. This research work was done in one district only; hence, it could not be representative for all situations in Ethiopia.

1.7 Limitation of the study

This study used indicator method of livelihood vulnerability of assessment due to its convenience to objectives of the study. However, it may havelimitations as sense of subjectivities might be involved in selection of indicators used to measure determinants of vulnerability and overall vulnerability of farmers’ livelihoods. Hence, there might be other unselected indicators that may have increasing or decreasing effects to vulnerability of farmers’ livelihoods. In addition, the study didn’t addressed impacts of climate variability induced hazards comprehensively.

1.8 Organization of the study

This study consists of five chapters as outlined below:

The first chapter comprises introduction of the study that includes, background of the study, statement of the problem, general & specific objectives, research questions, significance of the research, scope of the research, limitations of the study, and organization of the research report in that order. This chapter explains importance of the study topic in light of existing knowledge and gaps from global to local levels. It gives brief background about the study area in view of the study topic. The chapter also explains the scope and major questions that this research wants to answer in chapter four. Finally, it highlights how the entire research report has been organized.

The second chapter presents a review of pertinent literature surrounding interaction between climate variability induced hazards or hazards and vulnerability of physical and social ecosystems with special emphasis to interaction with social ecosystem specifically about livelihoods of rural inhabitants in the study area. It provides a critical review on the key concepts related to climate variability and vulnerability of social ecosystem to the impacts resulted from climate variability. Further, issues covered in this chapter include a review of empirical studies on importance of studying vulnerability of people to climate variability induced hazards on one side and their sensitivity and adaptive capacities in the face of inevitable exposure to impacts of climate variability, specifically to rainfall variability, violent flooding and rainstorms, on the other. This chapter provides operational definitions of key concepts, terms and analytical frameworks that guided the research methods, analysis, conclusions and recommendations that come in the subsequent chapters.

The third chapter describes methods and materials of the research. It comprises the background of the study area, data collection methods, study design, sampling procedure, and data analysis methods employed in the study.

Chapter four presents the results, analysis and discussions of the study topics. Data cleaning and checking was done before going to descriptive analysis of each variable under its major category or components each relates to. This chapter provides basis for evidence based discussions of the major findings of the research. The chapter makes use of various analytical techniques inferred and conclusions made on the research questions and assumptions of the study.

Chapter five is the last part presenting the summary of the findings as conclusions, and recommendations

Chapter II: Related Literature Review

2.1 Conceptual Literature

Though vulnerability, the focus of this study, is an emerging area of academic enquiry, the field is currently fragmented and defined by competing paradigms, conflicting concepts and terminologies, incomparability of empirical studies and a lack of comparative analysis and findings (Clark et al., 2000). For instance, the top-down positivist school of thought gives emphasis to biophysical vulnerability; focus on quantification of place-based impacts on human’s life, crops, animals, etc. (Mitchell, 2001). The bottom-up constructivist school of thought on the other hand emphasizes on social vulnerability and roles of humans in mediating hazard exposure and determining whether or not it results in an impact, and have tended to focus on developing theoretical insights into the processes and interactions between human-environment ecosystem with emphasis on local level case studies (Lynn et al., 2011).

Assessing vulnerability of rural household’s major source of livelihoods, heavy dependency on rain-fed agriculture and limited adaptive capacity, remains one of the important aspects of climatic risk analysis (Thomas, 2012). Further Getnet (2010) explained that studying vulnerability of agriculture under varying geographical scales has a significant importance to provide informed scientific decisions towards making proactive adaptive measures, developing effective mitigation measures to climate variability induced hazards and ensuring sustainable agricultural development.

Vulnerability of society depends on interactions and processes between impacts of climate variability induced hazards, exposure to the climatic hazards like drought, flood, rainstorm, etc., sensitivity of the people due to differences in their livelihood situations, and their adaptive capacity to the hazards (Peruzzi et al., 2009). Therefore, exposure to a given hazard is a necessary prerequisite for a hazard to occur. However, whether that exposure to climate variability induced hazard translates into a hazard depends on the status of vulnerability of the ecosystem under consideration (Peruzzi et al., 2009).

If the natural environment is particularly sensitive and human population is of high vulnerability, i.e. low economic status with poor preparedness and few social institutions to facilitate coping and adaptive measures, then the impact will be high; If the social vulnerability is lower due to a more appropriate coping capacity, the exposure of the same nature may result in a lesser or even no impact (Peruzzi et al., 2009).

The nature of such ranges of outcome is dependent on the status of determinants of vulnerability( exposure, sensitivity and adaptive capacity) in a specified time that render the affected community, livelihoods of rural households, vulnerable or resilient to one or more sets of climatic hazards ( Lynn, 2011, and Peruzzi et al., 2009).


Vulnerability to climatic hazards is broadly defined as ‘the characteristics of a person or group and their situation that influence their capacity to anticipate, cope with, resist and recover from the impacts of a natural hazard (Wisner et al., 2004).

Cutter, Boruff, and Shirley (2008) defined social vulnerability as ‘a measure of both the sensitivity of a human population to natural hazards and their adaptive capacity to respond to and recover from the impacts of hazards.’ Similarly, the IPCC (2007) defined vulnerability as ‘the degree to which a system is susceptible to, or unable to cope with adverse effects of climate change, including variability and extremes.’Furthermore, the social vulnerability perspective points to the types of populations that might have limited access to early warning information and resources and suffer increased impacts from extreme events due to limited adaptive capacity (Parry et al., 2007). Vulnerability includes social conditions and processes that are reflected in sensitivities and adaptive capacities of impacted population in a face of exposure to climate variability. This definitions and concepts are adopted in this research.

Exposure could be expressed by percentage of respondents who perceive changes in occurrence of theclimatic hazards as compared to past days (Emebet, 2014, Mudombi, 2011). This is the specific definition of exposure considered by this study due to the fact that it has close relevance to explain local level situations compared to other definitions of exposure which are by large rely on national scales.

Sensitivity is the predisposition of community and the ecosystems to suffer impairment from the hazard or risk event depending on access to public infrastructure, nutrition, single sector based livelihoods, housing and other socioeconomic conditions it endowed with (Lynn et al., 2011). In this study, sensitivity is conceptualized as how rural households perceive dependency of their major livelihood on rain-fed agriculture or otherwise. The major bases of livelihood in this case, could be crop production, animal production, mixed agriculture or non-agricultural activities.

Adaptive capacity ranges from capacity of the society to immediately respond to existing hazard or coping capacity to practicing long-term strategies and capacities and institutional and governance services: access to disaster preparedness and early warning information, medical & social services, social and material security status, etc. among others (IPCC, 2001). In this study, adaptive capacity is explained by present climate change adaptation practices and potential adaptation strategies sought by sampled rural household heads. However, the three determinants of vulnerability of agrarian livelihood (exposure, sensitivity and adaptive capacity) are intrinsically linked to one another (Emebet, 2014). i.e. exposure of rural livelihood to high frequencies and intensities of climate variability induced hazards, highly affects its sensitivity. The same author noted that higher adaptive capacity reduces the potential damage from higher exposure whereby simultaneously results in low sensitivity and vice versa. Hence, total vulnerability is the sum of sensitivity and lack of adaptive capacity (Emebet, 2014).

2.3 Conceptual framework

Different authors in climate adaptation field of study uses different approaches. These include, socioeconomic approach to asses vulnerability of people’s livelihoods, that is human centric; biophysical approach which is place-based impact assessment and an integrated approach that combines both (Temesgen et al., 2009, cited by Emebet, 2014). This particular study however, focuses on human centric approach as it align with objectives of the study. Thus, The Sustainable Livelihood Conceptual Framework shown in figure 1 was adopted.

The Sustainable Livelihoods Framework used to analyze the vulnerability context in the study area by aligning the socio demographic and socioeconomic characteristics with the five capitals stated in the framework. Because, the framework portrays the target community, having varying livelihood status, as functioning in the context of varying vulnerability to various forms of climate variability induced hazards. This context ultimately influences the livelihood strategies that are open to people in pursuit of their self-defined beneficial livelihood outcomes (Christensen and Pozarny, 2008).

Livelihood Strategies comprise the range and combination of activities and choices that people undertake in order to achieve their livelihood outcomes; These activities and choices have to be understood as a dynamic process in which people combine them to meet their various needs at different times and on different geographical or economical levels, whereas they may even differ within a household (Christensen and Pozarny, 2008).

Figure 1: Conceptual Framework

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Fig.1:Sustainable Livelihood Framework;Adapted from Christensen and Pozarny, 2008)

Livelihood outcomes are the achievements of livelihood strategies, such as increased well-being or reduced vulnerability; e.g. better resilience through increase in asset holding status, improved food security and a more sustainable use of natural resources or become vulnerable due to depletion of assets, increased prevalence of malnutrition, inappropriate use of natural resources, etc.(Kollmair and Gamper, 2002).

The vulnerability context forms the environment in which people exist and gain importance through direct impacts upon people’s sensitivity which in turn depend on frequency of their exposure to climate variability incidents/hazards, where they live, their entitlement to productive & other resources, availability of food at least at household level, and the extent to which they can adapt to climate variability induced hazards despite the inherent and present livelihood situation differences they have (Christensen and Pozarny, 2008). Vulnerability varies widely across people, whether in the same geographical location or not; it comprises sensitivity of households due to their demographic, socioeconomic, governance, access to infrastructure and institutional services among others (Kollmair and Gamper, 2002). National policies, local infrastructure and institutional services and processes that also shape the livelihoods have pivotal importance to effectively determine access, terms of exchange between different types of capitals: human, physical, natural, social, and financial which in turn returns to any given livelihood strategy (Christensen and Pozarny, 2008).

2.4 Empirical Literature

Different researchers used different approaches that range from simple descriptive analysis to multivariate logit models.

2.4.1 Descriptive Analysis

Perceptions of indigenous people in Jema’a of Kaduna State in Nigeria, if climate is changing or not and adaptation strategies they practiced in response to the climate change, were examined using descriptive analysis (Ishaya and Abaje, 2008). The results of their study revealed that indigenous people perceived that climate has of course changed and threatened health, food supply, biodiversity loss, and fuel wood availability more than on businesses. They also noted in their conclusion that lack of awareness and knowledge of climate change scenarios, improved seeds, water for irrigation, knowledge of modern adaptation strategies, and capital are the major hindering factors to the adoption of modern techniques of climate change mitigation in the area.

The vulnerability of society to climate change impact is a function of several biophysical and socioeconomic factors. Taking the overall economic share of agriculture, especially its prominent role in the livelihood of poor households and the scarce distribution of irrigation schemes and watershed management practices into consideration, it is quite correct to conclude that Ethiopia is among the most vulnerable countries to current and future inevitable impacts of climate change or its variability (DRMFSS Strategic paper, 2014).

Despite the fact that Ethiopia is known as “Water Tower of Africa”, currently only 4 to 5 percent of its cultivated land is under irrigation out of 30% potential (DRMFSS Strategic paper, 2014). Hence, agriculture is by large relies on timely onset, amount, duration, and distribution of rainfall and ultimately does the livelihood of famers. Over 90% of the food supply comes from rain fed subsistent agriculture and rainfall failure means loss of major livelihood source that always accentuate food deficit (Adgolign, 2006 cited by Abate, 2009). Similarly, about 40 percent of Ethiopia’s small farms have a size of 0.4 hectares or less and yields on such farms are on average 50 percent lower than on large farms (DRMFSS Strategic paper, 2014).

2.4.2 Indicators Approach

As evidenced by literature several factors affect vulnerability of smallholder farmers to climate variability. Some of the factors are within the farmer’s control while others are beyond the farmer’s control. The factors can be subdivided into socio-economic, cultural, institutional factors and demographic characteristics (Legesse and Drake, 2005). Further, a number of authors also suggested indicators for measuring and understanding social vulnerability. For instance, Legislative Cooperation Secretariat/LeCo Secretariat (2005) suggested levels of income, unemployment, pension contributions, illiteracy and malnutrition among children as indicators for measuring social vulnerability while Cannon (2000) suggested, livelihood resilience, self-protection, societal protection, social capital, class or income group, gender, ethnicity, type of state, civil society, and science and technology.The roots of vulnerability extend to social structures and settlement and development patterns which ultimately affect access to resources, power, information, and networks. Hence, they concluded that understanding the relationships and dimensions of socially vulnerable populations will facilitate the formulation of policies to reduce vulnerability among these populations (Fothergill and Peek, 2004; cited in LegCo Secretariat, 2005).

In this study, the indicators approach of vulnerability analysis was deployed. This study has adopted various indicators used by various researchers to assess vulnerability of people in general with specific application to rural households who are making their livelihoods from rain fed agriculture. Thus, sustainable livelihoods approach is used to explain how the changes in perception of households to impacts of climate variability induced hazards, their sensitivity and adaptive capacity matters their overall vulnerability to climatic hazards. The study assessed the varying access of rural households to a bundle of capitals: Human, Social, Natural, Financial and Physical that determine the overall level of household’s vulnerability. These are then linked to disturbances such as trends in rainfall variability, such as late onset of precipitation, early secession, low amount or distribution of rainfall, etc. Linking household’s access to resources and exposure to shocks and trends in climate is important because climate risks are basically determined by the interaction between hazards and vulnerability (Legesse, and Drake, 2005).The roles of human capital: knowledge, skills, and experience and social capital: trust, relationships, support networks/interconnectedness, local institutions and knowledge transfer systems have an indispensable contribution in determining social vulnerability to climate change impacts (Gamble et al., 2008). Communities with educated populations of productive age have a better chance of responding to climate risk management strategies, coping with severe weather events (Wall and Marzall, 2006). Besides, Gamble et al. (2008) considered how institutions affect society’s ability to respond to climate variability induced changes, and they provided examples of institutions while Agrawal (2008) and Ogden and Innes (2009) described factors that limit adaptive capacity in local institutions and networks in their study of 30 forest practitioners in the rural Yukon region of Canada.

The study conducted by Hulme et al. (2000) showed that rainfall decreases ‘significantly’ in June-July-August (JJA) over parts of the Horn of Africa which is the main crop cultivation season in the region which Ethiopia is part of it. However, it was acknowledged that the level of information and knowledge on climate variability induced impacts in several sectors of East Africa is exceedingly patchy, generally poor to moderate only (Thornton et al., 2006). In particular, there has been little discussion combining both climate change impact on agriculture and subsistent agricultural systems (Morton, 2007).

Ethiopia has been confronted by a wide range of climate change induced hazards. Millions of Ethiopians have been affected by drought and flood in the last decades; For instance, the number of people who suffered from drought was never been below 1.5 million between 2000 and 2007 every year; the peaked one was 14 million in 2003; The floods of 2006 were the most disastrous affecting about 1.7 million persons (DRMFSS Strategic paper, 2014).

It is obvious that Ethiopian agro ecosystem is sensitive to rainfall variability and low adaptive capacity to respond to and/or withstand damages, even a slight change in climate will have a large impact on the socio-economic activity of the country (Abate, 2009, Fraser, 2007).Further, a study conducted by (Abate 2009) at West Arsi zone, where Siraro district is one of his target districts in the zone, revealed that there were high cases of rainfall variability such as, prolonged drought, delayed onset of rainfall, erratic rainfall, low precipitation, heavy and unseasonal rainfalls causing violent floods. The involved respondents conform overall increasing of temperature and downward trend of precipitation that had imposed challenges on household’s livelihoods (Abate, 2009). Existence of repeated serious climatic impacts in West Arsi zone in general, both in mid and lowlands, and Siraro district in particular, caused food deficits, malnutrition cases, educational dropouts, increased susceptibility to diseases and failure to fulfill financial requirements, and lack of agricultural inputs (Abate, 2009).

Households characterized by lack of farm land, have large family size including children and elders, women headed households were found more vulnerable to climate variability impacts than their counter parts (Abate, 2009).

It was noted that rural households who use saving (crop, forage, livestock, cash, and other assets), on-farm diversification (planting Enset, eucalyptus, vegetables, etc.), social interconnectedness between lowlanders and mid-lowlanders or highlanders, access to loans/credits, changing cropping season and sowing of early maturing crops, engaging in off-farm activities and external supports from government and non-government agencies as coping strategies to climate variability induced hazards (Abate, 2009).

In considering indicators for well-being, acknowledging that communities will experience climate impacts at local and regional levels is very important. I.e. Communities and regions will differ in geographic and biological vulnerability to climate impacts. Although vulnerability analysis tends to be done on a regional scale, vulnerability exists at finer scales too (Gamble et al., 2008).

In addition, (Gamble et al. 2008) suggested that people within communities will experience climate impacts differently; some people may be more at risk to climate impacts and related stresses, including the poor, the elderly, people living alone, people in poor health, indigenous populations, and people with limited power and rights; They suggested that planners and decision makers take into account the social and spatial differentiation of climate impacts and ability people to adapt.

For instance, Hurricane Katrina made clear impacts felt in one community ripple throughout the region and nation. Many of the persons made homeless in New Orleans resettled in Baton Rouge, Lafayette, and Houston, creating stresses on those communities (Cutter et al., 2009).

2.4.3 Econometric Approach

Vulnerability of livelihoods was studied in a view of losses of welfare attributed to climatic hazards (Hoddinott et al., 2003). They explained vulnerability in three categories: vulnerability as expected poverty; vulnerability as low expected utility; and vulnerability as uninsured exposure to risk. Similarly, Temesgen et al. (2009) assessed farmers' vulnerability to climate extremes in Ethiopia. They estimated the probability of the income of households falling below a poverty line, and classify vulnerable households as those with more than 50 per cent probability of falling below the poverty line, 2USD per day.

Sufficient evidence shows that the average temperature rise in Africa is faster than the global average and is likely to persist in the future (Hulme et al., 2000). The warming is definitely hazardous for agricultural activities in the African continent as many of the crops are grown close to the thermal tolerance limits (Collier et al., 2008). The warming of few degrees and increase in frequency of extreme weathers will consequently strongly influences the agricultural production and make the society victim of the events and decreases the future adaptive capacities (Collier et al., 2008). There are many tries to make a monetary valuation of climate change impacts. One study for example estimates that African farmers, on rain-fed land, will lose $28 per hectare per year for each 1oc rise in global temperatures (Robert Mendelsohn cited in La Fleur et al., 2008).

Likewise, vulnerability assessment of smallholder farmers in the Nile river basin of Ethiopia was conducted by focusing on economic aspect of risk assessment (Moss, Brenkert, and Malone, 2001). They used “expected poverty” as an indicator of vulnerability which was limited to economic aspect of the complex field of study. Further, the studies focused on higher scales, national levels, which could not clearly inform about local contexts and communities due to heterogeneous nature of the ecosystems impacted within a country, Ethiopia.

2.4.4 Integrated Method

Impacts resulted from a system’s sensitivity and exposure to climate variability induced hazards depend on a system’s willingness or ability to apply adaptive strategies to adapt and to reduce its vulnerability (Burton et al., 2002). In line with this notion, the study conducted by Ford et al. (2006) focused on integrating social, physical, and health sciences and local and indigenous knowledge in climate change vulnerability and adaptation research, particularly at the local level, for the Inuit in the Arctic region of Canada. The interaction between human communities and landscapes from local to global scales will shape climate change effects (Liverman and Merideth, 2002). In considering the relationships among society and climate and vulnerability, Liverman and Merideth (2002) reviewed five elements: demography, economy, land, water, and institutions and values. They suggest the importance of considering social context and the differentiation of vulnerability across the population.

Vulnerable groups migrate from stricken areas to more hospitable ones, taking their health, economic, and educational needs and problems with them across both national and state lines (Gamble et al., 2008: 123). They also asserted that populations in certain geographic regions may be more vulnerable to human health and welfare impacts associated with climate change; geographic regions may be vulnerable because of their baseline climate, elevation, proximity to coasts or rivers, natural resource availability, and infrastructure connected to natural resources such as drinking water wells (Gamble et al., 2008).

In Africa, farm-level climate adaptation measures were analyzed using a multinomial logit approach to data from a cross-sectional survey of over 8000 farms from 11 African countries(Hassan and Nhemachena, 2008). The result of the analysis indicated that mono-cropping is the agricultural practice most vulnerable to climate change in Africa.Sub-Saharan African countries, including Ethiopia, have given an overriding emphasis on agriculture sector as a core sector to solve major socioeconomic and environmental challenges such as low agricultural productivity, degradation of natural resources, high population growth, high unemployment, and extreme poverty among others and bring sustainable development to the continent(Hassan and Nhemachena, 2008 as cited by Abate,2009). Nevertheless, the sector has been consistently challenged by climate change and/or climate variability. Hence, the more the frequency and severity of the impacts of climate change, the more vulnerability of the fragile socioeconomic status of the continent, nation or local community would be (Adger et al., 2003, McCarthy, 2001, Ngaira, 2007, and Collier et al., 2008).

In Ethiopia, (Temesgen et al. 2009) have conducted an integrated quantitative vulnerability assessment for seven Regional States of the total eleven regions by using biophysical and social vulnerability indices of Ricardian approach. The study had found that decline in precipitation and increase in temperature are both damaging to Ethiopian subsistence agriculture. The results of the study have further pointed out that Oromia Regional State, where Siraro district is one of the most vulnerable districts in the region, if not in the country, vulnerable to climate change impacts.

2.4.5Principal Component Analysis

Principal component analysis(PCA) is a form of analysis useful to reduce sets of indicators to scientifically significant ones only.Principal component extraction factor analysis is useful method to identify factors influencing smallholder farmers’ perceptions of sources of risks (Legesse and Drake, 2005). Multinomial logistic regression analyses were used to study the relationships of the identified principal components to perceived frequencies of occurrences and consequences of various sources of risks. Logistic regression analyses revealed that smallholders’ perceived risks were determined by different factors such as asset endowments, location settings and livelihood diversification strategies employed by farmers. Legesse and Drake (2005) also noted that qualitative information obtained from village or religious leaders, informal peers and neighbors, has a stronger influence on perceptions of the farmers than information obtained from the key informants, extension workers. Binary logistic regression analysis was applied to identify principal components that significantly influence status of exposure, sensitivity and adaptive capacity of farmers’ main livelihood means to climatic hazards. Besides, multinomial regression analysis was employed to understand the influences of total scores of exposure, sensitivity and adaptive capacity of farmers as indicators of overall vulnerability of farmers’ livelihoods.

2.5 Literature gaps

Though many global studies have been carried out to identify and quantify vulnerability of the households to impacts of climate variability induced hazards, they have limited regional and local specificity and thus, have failed to address local abilities to adapt to climate variability induced impacts (Temesgen et al., 2009). This is because, envisaging vulnerability of subsistent farmer households to impacts of climate variability at either global or regional levels, top-down approach, is a very difficult task, remote to address household level conditions and associated decisions. These could be attributed to lack of standard definitions, absence of benchmark data, local specificity of some hazards, disparity in capability of households to integrate on-farm and off-farm activities, no or limited capacity of local credit services & insurance institutions and infrastructure, etc. among others at local level (Lynn et al.,2011). However, this study took as many locally available factors as possible as indicators of vulnerability of rain fed dependent livelihoods of rural farmers, specifically demographic, socioeconomic, and institutional factors that are assumed to underpin variability in exposure, sensitivity, and adaptive capacity of rural households to the climate variability induced hazards.

In contrast to past studies that had by large focused on hazard analysis: hazard magnitude & impact assessments, i.e. quantification of specific climatic hazard in space and time and understanding the type and size of damage brought by that particular climatic hazard. Many authors have acknowledged as their study was highly aggregated and further study is needed at local levels, particularly at district and/or kebele levels. This is one of the gaps that this study wanted to address.

Chapter III: Description of the Study Area and Methods & Materials

3.1 Brief description of the study area

3.1.1 Location

Siraro district is located at 70 17’ N latitude and 380 06’ E longitudes in West Arsi zone of Oromia national regional state. Loke Heda town is the capital of the district, found approximately at 60 & 310 kilometers from Shashemene & Addis Ababa respectively. The district is found in the Great Rift Valley system having a total area of 674.62km² or 63,710 hectares (Siraro district profile report, 2013). According to this report, geographically, the district is sharing borders with SNNPR-Halaba district in the north, Shala district in the east and in south and west Boricha and Fango districts of SNNPR respectively. Administratively Siraro district is divided into 32 kebeles, (28 rural and 4 towns). Fig.2

The Altitude of the district ranges from 1500- 2300 meters above sea level having the land feature of plain (68% ), and mountainous and hilly(32%), ( Siraro district profile report, 2013). Siraro district has two agro ecological zones, lowland and mid lowland. Out of the 28 rural kebeles of the district, 12 kebeles are situated in the lowland which is inhabited by 39.65% (10, 781 households) of the total household inhabited in the district (27, 191 households), whereas the remaining 16 kebeles are under mid lowland agro ecological zone inhabited by a total of 16,410 households, i.e. 60.35% of total households in the district. Siraro district is among the districts most impacted by climate variability induced hazards in the region. The mean annual temperature of the district is found between 13-25º C. However, there is aslight variation of temperature from month to month.

Figure: 2. Map of the study area/location

Abbildung in dieser Leseprobe nicht enthalten

Fig. 2: Map of study area, Siraro district Source: CSA 2010 and field survey 2015

3.1.2 Socioeconomic status of Siraro district Farmers

The overwhelming majority of the people (about 92%) living in the district is dependent on rain-fed subsistence agriculture; and hence agriculture remains the main livelihood activity and the back bone of the area’s economy. The remaining 8% lives on small trade and service sectors (Siraro District Socio-Economic Profile, 2013). Siraro district is characterized with having no irrigation agriculture, no permanent river except Bilate River, which is found at south west periphery of the district, hot spot for climate variability, recurrent drought, erratic rainfall and food insecurity, etc. among others (Siraro district disaster preparedness and response office situation assessment report, 2011).

The area is also generally characterized as having underdeveloped social and economic infrastructure where there is insufficient access to electricity, school, transport and road networks (Siraro district Socio-Economic Profile Report, 2013). Access to safe water supply is a dream than reality for most of the inhabitants (Siraro CFDA, area strategic plan, 2011-2013). Currently, coverage of potable water in the district is as low as 36% ( Siraro district water office, 2015). Food insecurity and child malnutrition are very common phenomenon almost every year (Siraro DPPC report, 2013). Mothers and young girls have to walk a distant place to fetch water or should rely on manmade ponds and other undeveloped water sources (Siraro CFDA, area strategic plan, 2011-2013).

3.1.3 Demographic features

According to Siraro district Finance and Economic Development Office/FEDO/ data (2014), the district has a total of 204,579 population estimated from CSA census out of which over 92% (188, 213 persons) reside in rural areas; hence, urban population is only 8% (16,366 persons). The average family size and sex ratio is estimated to be 6 persons per household & 99 males per 100 females respectively (Siraro district FEDO data, 2014).

3.2 Research Methods and Materials

3.2.1 Study design

Cross sectional/mixed research designhas been applied. Quantitative aspects of the design was used to capture and analyse measurable variables while qualitative aspect of the design had been used to select appropriate indicators to measure exposure, sensitivity and adaptive capacity of rural households to climatic hazards and thereby guide design of quantitative survey questionnaire. In addition secondary information that reveals some important data including historic events of climatic hazards faced by the rural households have been captured. Furthermore, qualitative aspect of the design helped validation of findings of quantitative design that eventually helped me to give reliable research conclusion and recommendations.


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Vulnerabilty of farmer's livelihood to climate variability induced hazards in Siraro District, West Arsi Zone, Oromia, Regional State, Ethiopia
Population and Socioeconomic Development Planning
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vulnerabilty, siraro, district, west, arsi, zone, oromia, regional, state, ethiopia, thesis
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Jarso Wakeyo Utala (Author), 2016, Vulnerabilty of farmer's livelihood to climate variability induced hazards in Siraro District, West Arsi Zone, Oromia, Regional State, Ethiopia, Munich, GRIN Verlag,


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