Assessing Livelihood Vulnerability to Climate Change Based on Livelihood Zone: Evidence from Mixed Farming System in Kembata Tembaro Zone, Southern Ethiopia

Climate change vulnerability depends upon various factors and differs between places, sectors and communities. This study is aimed at analyzing smallholder farmers’ livelihood vulnerability to climate change and variability in Kembatta Tembaro zone, Southern Ethiopia using the IPCC Livelihood Vulnerability Index approach. The approach estimates vulnerabilities by grouping nine major components into three categories of exposure, sensitivity and adaptive capacity, using data collected from 508 randomly selected farm households based on five livelihood zones. The result score of Livelihood Vulnerability Index revealed that Coffee livelihood zone with high exposure index coupled with limited adaptive capacity made it the most vulnerable among the five livelihood zones; whereas, Ginger livelihood zone with very high adaptive capacity index and low sensitivity, coupled with medium level exposure index to climate change has greatly contributed for its least vulnerability score. In line with the results, people-centered strengthening of adaptive capacity based on the local geographical and socio-economic profiles as well as widening opportunities for off-farm livelihood strategies is essential.


Introduction
Recently, climate change has become one of the most urgent challenges for today's society that seeks an integrated effort at all levels and sectors, as it is linked with the day to day livelihood systems of billions of people. The impact on each region depends mainly on the degree of vulnerability that natural ecosystems and human-made infrastructure have to changes in climate and extreme meteorological events, as well as on the coping and adaptation capacity towards new environmental conditions. Many areas in Africa are recognized as having climates that are among the most variable in the world on seasonal and decadal time scales. It is because of this reality that Africa has been identified as one of the parts of the world most vulnerable to the impacts of climate change (IPCC, 2014;Niang et.al., 2014).
Sub-Saharan Africa is vulnerable to climate change for a couple of facts inherent in the region; high natural resource and agricultural dependence; poverty (58.9% living under multi-dimensional poverty (Alkire and Housseini, 2014); inadequate and ailing infrastructure; structural challenges at policy level (Ondige et al., 2013) and limited access and use of relevant and reliable agricultural inputs (Ringler et al., 2010).
Ethiopia belongs to countries with high and stable vulnerable countries in the world. Reasons for Ethiopia's vulnerability are manifold. Adenew, (2006) states that Ethiopia's geographical location and topography, plus a low adaptive capacity make the country highly vulnerable to the adverse impacts of climate change. Recently, there is an increasing interest in using livelihoods analysis as a 'lens' through which to view a number of subjects. These subjects range from emergency response to disaster mitigation to longer term development. In order to study vulnerability and adaptation strategies of smallholder farmers to climate change, first, it is very important to identify the livelihood system of those farmers, as the concept of vulnerability is dynamic, context specific and multidimensional in nature. An understanding of people's livelihoods is essential for analysing the impact of any significant change. Seamana et.al., (2014) indicated that livelihood-based approach provides logic to know livelihoods, how they are affected by changes and shocks, and how households cope and adapt with the challenges to reduce vulnerability.
In Ethiopian context, climatic heterogeneity is a hallmark of the country, altitude determining the distribution of climatic factors (temperature and rainfall) and land suitability; and influences the crops grown, the rate of crop growth, and the natural vegetation types and their species diversity. This reality leads to have different livelihood systems across different regions and subsequent zones in the country. In describing livelihood and resilience terms, it is important to view and describe from the context of Livelihood Zone boundaries. According to FAO and ILO (2009), livelihood zones refer to zones within which people share broadly the same production system and common

. Data Sources and Methods of Data Collection
The data for this study were collected from both primary and secondary sources. Household survey questionnaires, focus group discussions and field observation were the primary data sources and zonal and district level reports were secondary sources used from the respective Agriculture and Natural Resources Offices. sectional household survey composed of both qualitative and quantitative methods was carried out using a standard structured questionnaire of both close and open ended types of questions. Through the questionnaire, farmers were asked to provide information on socio-economic characteristics, vulnerability contexts, LULCC trends, livelihood assets, climate change perceptions, adaptation and coping strategies and also institutional access and capacities.

Target Population and Sampling Technique
The target population of the study was smallholder farmers engaged in agricultural activities across five livelihood zones in Kembata Tembaro zone. In terms of administrative units, the study area comprised of seven districts, namely, Kedida Gamela, Kacha Bira, Angacha, Danboya, Hadero-Tunto, Tembaro and Doyogena. However, it is difficult to divide the districts proportionally to each livelihood zones, as livelihood zones do not normally follow the formal administrative boundaries. Out of the total of seven districts, five districts, namely; Doyogena, Kachabira, Hadero-Tunto, Kedida Gamela and Damboya are purposefully selected, based on representation of the typical livelihood zone settings, demographic and socio-economic attributes, geographical characteristics and previous history of the occurrence and impact of climate change (including the last three drought years). In order to determine the sample size of households, the formula set by Kotari (2004, p. 179) in the case of finite population was used and 508 farm households were randomly drawn from the selected kebeles, using the formula: n= * * * * *

Data Analysis Techniques
The formulation for Livelihood Vulnerability Index (LVI) developed for this study is based on the livelihood vulnerability analysis technique developed by Hahn et.al., (2009), with replacements of some indicators to suit the local context of the study area. It makes use of nine major components, namely; Natural Disaster, Assets and Basic Services, Land and Water, Nutrition and Health, Skill and Knowledge, Socio-economic, Biophysical, Social and Institutional and Finance and Incomes (See Annex). First, each of the sub-components is measured on a different scale and finally description of the aggregate level of the degree of vulnerability of the study area was given. Since each of the sub-components is measured on a different scale, it is first necessary to standardize each as an index as: ……………….………………………………………..…Equation (1) Where, indexSLZ refers to index standardized value for a given livelihood zone, SLZ is the original sub-component for a given livelihood zone, and Smin and Smax are the minimum and maximum values respectively for each subcomponent. After each is standardized, the sub-components were further averaged using equation (2) to calculate the value of each major component: ………..…………………………..…………..Equation (2) Where, MLZ is one of the nine major components for Livelihood Zone, indexSLZi represents the sub-components indexed by i, that make up each major component and n is the number of sub-components in each major component. Once, values for each of the nine major components for the Livelihood Zones are calculated, major components that make up each livelihood assets is averaged using the following equation (3) to obtain the Livelihood Vulnerability Index at Livelihood Zone level: ………………………………….……..…....…..… Equation (3) Which can be expressed as: Where, LVILZ: is the vulnerability index for one of the five livelihood assets of livelihood zone LZ, equals the weighted average of major components which form that livelihood asset; WMLZ: the weights of each major component, are determined by the number of sub-components that makeup each major capital. Calculating the LVI-IPCC is an alternative method for calculating LVI that incorporates the IPCC vulnerability which is used by Hahn, et.al., (2009). The LVI-IPCC diverges from LVI when the major components are combined. They are combined using the following equation;  (4) Where, CFLZ is an IPCC defined contributing factor (i.e. Exposure, Sensitivity, or Adaptive Capacity) for Livelihood Zone LZ, WMLZi is the weight of each major component, and MLZi are major components for livelihood zone LZ, indexed by i, and n is the number of major components in each contributing factor. Once, exposure, sensitivity and adaptive capacity are calculated, the three contributing factors will be combined using the formula developed by Hahn et.al., (2009): …………………………..………………..…..… Equation (5) Where, is the LVI for livelihood zone LZ, expressed using the IPCC vulnerability framework, e is the calculated exposure score for livelihood zone LZ, a is the calculated adaptive capacity score for livelihood zone LZ, and S is the calculated sensitivity score for livelihood zone LZ. And then the LVI-IPCC is scaled from -1(denoting least vulnerable) to 1 (Denoting most vulnerable). The result revealed that Cereal LZ has the highest value of Natural Disaster (0.574), which is one of the influencing factors, in addition to Infrastructure, Assets and Services for exposure of farmers to climate change. In addition, the LZ has also the highest score in Skill and Knowledge (0.593) and Social and Institutional components (0.402); indicating with relatively better access to social membership, cultural connectivity, local institutional support, access to indigenous knowledge and trust and mutual support, which contributes for its higher adaptive capacity.

Results and Discussion
On the other hand, Cereal and Enset LZ registered the least score of Nutrition and Health (0.148), indicating farmers have less access to food and health. In terms of Skill and Knowledge, Pepper LZ has the least result (0.496) indicating that the LZ has relatively scored less in educational attainment, skill and knowledge and technology adoption. In terms of Biophysical assets, Pepper LZ has the highest score (0.427) indicating that the LZ registered better result in land protected from degradation, and having suitable slope (topography) of cultivated land. It is noted that Pepper LZ has the highest farm land size as compared with all LZs.
Having the highest human capital is critical in terms of building the adaptive capacity of farmers in the LZ. In the context of Assets and Basic Services, Coffee LZ has the highest result (0.429), for which access to off-farm activities contribute for registering higher assets, which helps to reduce vulnerability to climate change. With respect to Land and Water, which is the most critical component for sensitivity in all LZs, the least result is found in Ginger LZ (0.470), as there is the least farm size among the five LZs and the highest household size, which can contribute for higher sensitive to climate change.
Socio-economically, including farming experience and dependency ratio, Ginger LZ (0.372) has the highest result which can contribute for higher adaptive capacity. With regards to Assets and Basic Services, including livestock ownership, access to all weather roads, and access to veterinary services, Coffee LZ (0.429) has the highest result. This leads us to state that cash crop areas agro-ecologically located in midland areas have better result in socio-economically and having better assets and services, indicating that comparatively contributing for higher adaptive capacity. In addition, Ginger LZ has the highest result in terms of Finance and Incomes (0.334), for which the cash crop Ginger has contributed a lot, as the crop currently has higher market value, hence, influencing income from agriculture.
Better access to infrastructure, assets and basic services play critical role in reducing exposure of smallholder farmers to climate change. The highest score is registered in Coffee LZ (0.429), indicating that the LZ has better access to roads, access to climate information, type of house, ownership of pack animals and access to veterinary services; whereas Pepper LZ has the least score (0.316), indicating that the above assets and basic services are less owned and accessed for farmers, increasing their vulnerability to climate change.
The other component of LVI analyzed is access to land and water, which fall under sensitivity. It is beyond despite that land is the main source of livelihood, which has social, economic, and cultural values in smallholder agricultural livelihood system. If properly used and endowed with higher productivity and accessed with water, is capable of contributing greatly in reducing vulnerability to climate change. The analysis revealed that Maize LZ has the highest score of land and water, 0.529, indicating that the livelihood zone has better land productivity (which the flat topography of the land mainly contributes), better access to grazing land, as the livelihood zone has the second highest land size per household next to Pepper LZ, with 0.652 ha, and better access to animal forage have contributed for its highest score. On the other hand, Ginger LZ has the lowest score in Land and Water component, (0.470), indicating that it has the least average land size, 0.349 ha per household and with the highest household size of about 10 persons, which contributed for its least land holding size per household. In addition, with the continuous cultivation of the land, the productivity gets less from year to year, less access to grazing land and animal forage, which have contributed for the least score.
Among the nine components pertinent to indicate the level of vulnerability to climate change in the context of livelihood zones is socio-economic. In the assessment of sub-indicators selected for socio-economic profile is farming experience, dependency ratio and access to off-farm activity. The result revealed that Ginger LZ has the highest score, 0.372, whereas, Pepper LZ has the least score, 0.287, as compared with the five LZs. Specifically access to off-farm activity is better accessed for farmers in Ginger LZ as the livelihood zone has the highest family size of about 10. In addition, Ginger LZ has better access to small markets and towns in the nearby kebeles and the current higher market value of ginger as compared with pepper. Large family size is assumed to be the source of labor, skills and strong social capital to adapt to changing climate situation (Deressa et al., 2011) and enable a household to accomplish various agricultural tasks especially at the peak seasons.
Access to Finance and Incomes is an important indicator of vulnerability status of households, which is represented in this study through access to credit, access to remittances, farm income, access to money for emergency, subsidy from the government and access to savings. Access to and availability of financial resources and stable income support the development of adaptive capacity (Yohe and Tol, 2002;Armitage, 2005;Engle and Lemos, 2007). Most climate change adaptation measures require some level of financial sacrifice, and access to credit/funds can increase farmers' capacity to adopt coping measures to recover from climate change risks. The result revealed that Ginger LZ has the highest score, 0.334, as the current higher market value of the crop contributed for its higher value; whereas, Maize LZ has the least score, 0.247, as this LZ rely on selling green maize which has relatively lower market value as compared with ginger, contributed for its less value.

IPCC's Vulnerability Index (LVI-IPCC): Compare and Contrast
The LVI-IPCC was computed by grouping the nine major components into three categories namely; exposure (made up of two major components), sensitivity (one major component) and adaptive capacity (six major components) are represented in the vulnerability table as shown in Table ---. Index values should be interpreted as relative values to be compared within the study sample only. The LVI-IPCC is on a scale from -1 (least vulnerable) to 1 (most vulnerable). The overall LVI-IPCC result shows that Coffee LZ is the highest vulnerable to climate change, whereas, Ginger LZ is the least vulnerable. Very high exposure coupled with limited adaptive capacity made Coffee LZ as the most vulnerable among the five LZs. In the context of Ginger LZ, very high adaptive capacity and low sensitivity, coupled with medium level exposure to climate change has greatly contributed for its least vulnerability score.
To compare the findings of the research with those researches undertaken agro-ecologically, farmers in midland agro-ecological zone, but with respective specific assets, capabilities and access and ownership of intangible resources, livelihood zones located within the same agro-ecology are both highly vulnerable (Coffee LZ) and less vulnerable (Ginger LZ). On the other hand, Simane et.al., (2016) reported that both farmers in the Dega (Highland) and Kolla (Lowland) agroecological zones were more vulnerable than those in the Weyna Dega (Midland) agroecological zone.

. Vulnerability Mapping
The vulnerability mapping is an important tool that helps to take effective response actions to the adverse impacts of climate change through identification of vulnerable areas. The knowledge of vulnerability to climate change can assist decision makers in recommending adaptation measures and prioritizing resource allocation for specific areas as well as determining investments for adaptation to future impacts of climate change.

Exposure Index
Two components; namely, Frequency of Natural Disaster and Climate Variability and Infrastructure, Assets and Basic Services constitute the exposure contributing factor. The analysis result shows that Coffee LZ has the highest exposure result (0.090); indicating that the LZ has the highest score in terms of Natural Disaster and low score in Assets and Services, whereas, Maize LZ and Pepper LZ have very low levels of the score, the least being that of Pepper LZ, scoring 0.077 and that of Maize LZ is 0.080, indicating that the LZs have implications in terms of achieving higher results in Natural Disaster and lower score in Assets and Services. On the other hand, Cereal and Enset and Ginger LZs fall under medium level of exposure. The more access to social membership, the higher adaptive capacity

Cultural connectivity
The more access to cultural connectivity, the higher adaptive capacity

22
Trust and mutual support The more social help at times of shocks (Covariate and idiosyncratic shocks), the higher adaptive capacity

10
Local institutional support The more access to get support from local institutions (iddir, equb,, etc), the higher adaptive capacity 8

Access to indigenous knowledge
The more access to indigenous Knowledge (coping and adaptation), the higher adaptive capacity

Traditional Weather Prediction
The more access to get traditional weather prediction, the higher adaptive capacity 20 Financial 15 Finance and incomes (9)

Access to credit
The more access to credit services, the higher adaptive capacity