Exposure component to livelihood vulnerability
This component presents livelihood risk of rural households across the livelihood zones in view of climate variability and extreme exposure variables. In the assessment of agricultural livelihood vulnerability, indicators of the exposure component (rainfall, temperature, and frequency of drought events) at each livelihood zone were identified. These climate variability and extreme indicators of exposure component are commonly used in livelihood vulnerability exposure analyses. The identified indicators of exposure component are presented in Table 2. Accordingly, the deviation of rainfall ranges from about 105 to 194 mm in the livelihood zones. The rainfall normalized values for each livelihood zone were computed from the standard deviation of stations in the study. The results showed that Belg and Meher livelihood zones were found to be the highest level of rainfall exposure risk with the normalized values of 1.00 and 0.76 respectively. The highest range of temperature deviation (1.21 °C) was identified with the minimum as compared to the average (0.77 °C) and maximum (0.92 °C) temperatures in the livelihood zones. Similar to the rainfall, the normalized values of the mean maximum and minimum temperature for each livelihood zone were computed from the normalized standard deviation of stations in the study. Accordingly, Belg and Meher-Belg livelihood zones found to be the highest normalized average, maximum, and minimum temperature values. But, the remaining livelihood zones (CHV, SWS, and ABB) showed the lowest values except CHV at maximum temperature. The respective mean drought frequency also varies from 8.14 to 6.91 in ABB and CHV livelihood zones. Based on the method of Iyengar and Sudarshan (Eq. 6), the attached weights for rainfall, average, maximum and minimum temperature, and frequency of drought exposure events were 0.199, 0.195, 0.188, 0.214, and 0.205 respectively.
The normalized scores were also reclassified into five equal probability weight interval (20%) groups to characterize the various stages of exposure risk level to vulnerability in the livelihood zones. Accordingly, based on the weights, the overall livelihood zone level of exposure risk index (Table 2) indicates that high exposures were at Belg (0.71) and Meher (0.70). A moderate level of exposure risk index was observed at Meher-Belg (0.41) livelihood zone. The remaining livelihood zones (CHV and ABB) and SWS have low (0.35 and 0.38) and very low (0.05) exposure risk levels. In addition to the results of the meteorological data, the FGD with households reported that their exposure to the change of different climate factors increased across the livelihood zones. Specifically, there is an increasing risk to the increasing temperature and variability of rainfall in the Meher and Belg livelihood zones. The analysis of meteorological data (in the past 37 years) showed a relatively high exposure risk in Meher and Belg livelihood zones due to high rainfall variability and increase of temperature. The view of the focus group discussants were also corroborating the variability of rainfall and increasing of temperature. The climate change exposure analysis made by Tessema and Simane (2019) and Feyissa et al. (2018) show that areas with the highest deviation in rainfall and temperature as well as frequency of drought events are more exposed and vulnerable. Moreover, studies of Stroosnijder (2009) and Araya et al. (2015) identified deficit and erratic nature of rainfall resulted in the vulnerability of Eastern African countries to low agricultural productivity and food shortages.
Sensitivity component to livelihood vulnerability
Four biophysical and socioeconomic indicators (slope, soil types, population density, and accessibility of travel time to social service centers) have been identified in the analysis of livelihood sensitivity based on the standards set by different scholars which were also used by Feyissa et al. (2018), Azene et al. (2018), and Teshome (2016). Accordingly, the higher sensitivity of livelihood zones to climate variability and extremes were derived from high population density, slope and inaccessibility to social service centers, and the erodibility nature of soil types. The slope of the study region is classified into five classes ranged from 0 to 2% (least sensitive) to > 30% (most sensitive) for land degradation and soil erosion (Fig. 6). The slope is an integral part of the land surface; it influences drainage, runoff, and erosion susceptibility. The assumption is that the higher slope value linked to the probability of high susceptibility of erosion and simultaneous reduction of soil depth. As can be seen in Table 3, 14,980.5 km2 (86%) of the area was found above the slope of 8%. Thus, the changes of maximum and minimum soil depth reduction factor were observed with increasing slope gradients. Moreover, the normalized value indicated that the ABB livelihood zone was experienced a very high sensitivity slope gradient (1.00). Moderate sensitivity of the slope was in the Belg livelihood zone (0.58). Low sensitivity of slope gradients was observed at SWS, Meher, and Meher-Belg livelihood zones with normalized values of 0.33, 0.32, and 0.29 respectively (Table 6). Studies of Hurni et al. (2015) and Liu et al. (2001) revealed that the erosion-resisting capacity and depth of soil generally decrease with the increasing of slope gradient due to loose soil stability, subsequently influence of land productivity, and crop yield.
The soil type associations found in the study are Leptosols, Calcaric Fluvisols, Haplic Xerosols, Eutric Nitisols and Regosols, Eutric and Vertic Cambisols, Pellic and Chromic Vertisols (Fig. 7). The dominant soil types in terms of areal coverage in the region are Cambisols, Leptosols, and Regosols with 11835.99, 2176.98, and 1489.26 km2 respectively. These dominant soil types’ agricultural viability is very limited as documented in different studies. FAO (1984) for example explained that both Cambisols and Regosols have limited agricultural value due to their shallow depth as they occur dominantly on the sloppy areas that are subject to severe erosion. Leptosols are also characterized by shallow depth underlined by hard rock and with less developed soil and coarse-textured (Feyissa et al. 2018).
It is believed that the susceptibility of soil type to erosion reduces the soil depth that influences the sensitivity of agricultural practices and ultimately affected the land efficiency of production. Based on Gelagay and Minale (2016) and Hurni et al. (2015), the attached average sensitivity of soil types to erosion (erodibility) due to the inherent characteristics are 0.30, 0.25, 0.23, 0.23, 0.20, 0.20, and 0.15 for Xerosols, Nitisols, Fluvisols, Regosols, Cambisols, Leptosols, and vertisols respectively (Table 4). Therefore, those major soil types (Cambisols, Regosols and Leptosols) are accounting for about 92% of the area. These soil types are categorized under high and moderate soil depth reduction (erodibility) related to their susceptibility to erosion in the region under study. The remaining soil types such as Xerosols, Nitosols, and Fluvisols are categorized under high level of erodibility class with the average K-factors ranged from 0.23 to 0.30. Vertisols have the lowest level of erodibility class of 0.15. At the level of livelihood zones, very high sensitivity on soil type erodibility was indicated at CHV with normalized values of 1.0 while moderate sensitivity (0.5) was shown at Meher, Belg, and SWS livelihood zones. ABB and Meher-Belg livelihood zones were shown the least soil type erodibility (Table 4). Hurni et al. (2015) identified that soil depths were considered shallower in areas with soils more susceptible to erosion. Erosion sensitivity of Cambisols and Regosols are ranked as the most and intermediate susceptible soil types respectively (FAO 1991). Soil erosion in Ethiopia triggers the decline of agricultural productivity, food insecurity, and rural poverty (Gashaw et al. 2014; Lawrence et al. 2010).
The farmland suitability for agricultural practices in the study was characterized by the relative sensitivity indicators of slope, fertility, and rate of soil erosion. Such sensitivity attributes could be the source of vulnerability to the rural households mainly caused by land degradation problem. In relation to this, information obtained from the focus group discussants in ABB livelihood zone perceived that the upside down nature of farmland, cutting of trees, and grazing and marginal land conversion to farmland increases the sensitivity of the biophysical environment. Discussants further mentioned that the farming households cultivated the outrageous steeply marginal areas using a digging hoe which is impossible to plough with a pair of oxen. According to them, such biophysical sensitivity impedes the agricultural productions and threatens the lives and livelihoods of rural households. Information obtained from Zonal and District agricultural offices on the other hand showed, the problem of land degradation and soil erosions are severe because of lower rate of water permeability coupled with steep slope exacerbated rural households’ livelihood vulnerability. Indeed, MoWIE (2014) indicated that a soil erosion problem in the study area is one of the erosion hot spot areas of the Blue Nile river basin.
The socioeconomic sensitivity component indicators include accessibility and population density. The accessibility indicated as travel time is calculated using the cost-distance function of ArcGIS. The choices of the accessibility travel time, here used, are local settlements such as market places, schools, and health centers. The sensitivity varies among the livelihood zones in the accessibility of travel time in minutes (Fig. 8) to the service centers of market places, schools, and health institutions. Livelihood zones’ sensitivity increases as the accessibility travel time increases to the service centers such as market places, schools, and health institutions. For instance, ABB and SWS livelihood zones have a very high and moderate accessibility travel time of 1.00 and 0.53 normalized values. Therefore, these livelihood zones were highly vulnerable due to inaccessibility of rural households to social services as compared to the others. The remaining livelihood zones showed very low and low normalized values of accessibility travel time (Table 5). Population pressure sensitivity to vulnerability also varies across the livelihood zones. Thus, the average high population densities were at SWS (226.5 persons/km2), Meher-Belg (212.8 persons/km2), CHV (200.6 persons/km2), and Belg (184.6 persons/km2) livelihood zones (Table 5). The relatively high population density in these livelihood zones is also shown in Fig. 9. Hence, rural households residing in SWS, Meher-Belg, CHV, and Belg livelihood zones were highly vulnerable due to the attribute of high population density. Nyssen et al. (2015) and Dechassa et al. (2017) substantiated the impact of high population pressure for land degradation through reduced fallowing, conversion of forest and marginal steep slope areas into agricultural land. Based on the four indicators of sensitivity component, one final overall sensitivity risk levels with moderate and low sensitivity livelihood zones were identified. The attached weights to the four sensitivity component indicators were 0.24, 0.27, 0.24, and 0.24, for population density, slope, soil type erodibility, and accessibility travel time, respectively. Based on the weights, the overall sensitivity risk levels were observed as moderately sensitivity index (0.46 to 0.59) at ABB, Belg, CHV, and SWS livelihood zones. Meher and Meher-Belg livelihood zones were on the other hand indicated as low sensitivity index of 0.33 (Table 5).
Adaptive capacity component to livelihood vulnerability
The availability of adaptive capacity assets is essential that enable the ability of human systems to adjust the impact of climate variability and extreme events. The adaptive capacity component of the study is composed of fifteen variables that fall in to the different asset forms of human, social, financial, physical, and natural for each livelihood zone (see Online resource for the full visualization of vulnerability components and indicators to their functional relationships with vulnerability). The analysis denoted that different levels of adaptive capacities were indicated at each of the livelihood zone. Details of the normalized and weights attached to adaptive capacity sub-component indicators as well as the overall average adaptive capacity level classification to climate variability and extreme events for each livelihood zone were presented in Table 6 and Fig. 10.
The first major sub-component of adaptive capacity was the human capital which includes three indicators: educational level, age dependence ratio and health status of households at the livelihood zone level. These indicators exhibited variation of human capital among the livelihood zones. Meher-Belg, Meher, and Belg livelihood zones have shown better educational and health status with lower age dependence ratio. Greater age dependence ratio coupled with lower education level and health services were observed in the livelihood zones of ABB, SWS and CHV (Table 6). When all the sub-indicators were aggregated, Meher-Belg (0.68) and Meher (0.65) were at high human capital adaptive capacity levels. Belg (0.56) and ABB (0.50) were at a moderate level while CHV (0.34) and SWS (0.31) were at a low level of adaptive capacity. Similar findings made by Adu et al. (2018), Asrat and Simane (2017), and World Bank (2010) inferred that high age dependence ratio, limited access to health services, and lower level of educational level and training may stress household’s adaptive capacity and mounting their vulnerability to climate variability and extreme induced risks.
The second major sub-component of adaptive capacity was the social capital. Variation of social capital capacity was observed among the livelihood zones (Table 6). Accordingly, in the informal insurance organization (Iddir/Kire) participation, Meher, ABB, CHV, and Meher-Belg livelihood zones revealed the highest normalized values. In the labor sharing organizations (Mahiber, Wenfel, and Debbo/Jiggie), Meher, ABB, and Meher-Belg livelihood zones denoted the highest normalized values. Moreover, CHV, SWS, and Meher livelihood zones were also the highest normalized values. When all indicators were aggregated, Meher (0.84), CHV (0.65), Meher-Belg (0.62), and ABB (0.52) were indicated from a very high to moderate level of adaptive capacity. On the other hand, Belg (0.19) and SWS (0.15) showed a very low level of adaptive capacity. Community institutions such as informal insurance organizations (Idir/Kire), labor sharing organizations (Mahiber, Wenfel and Debbo/Jiggie), and informal rotating savings association (Equib) are vital for linking ideas and resources, and increasing the adaptive capacity of people in time of immediate-induced disasters (Gebreegziabher et al. 2018; Arega 2013; World Bank 2010; Walker et al. 2001).
The third major sub-component of adaptive capacity in the study was that the physical capital comprises producer goods and infrastructure. Producer goods include the availability of technology components (farm inputs such as fertilizers, improved seeds, and extension services) and farm equipment (plowing instruments and farm animals). The infrastructure is the travel time access including access to road, school, and health services (Table 6). These sub-component indicators of physical capital showed variation among the livelihood zones. Hence, the normalized values of Meher-belg, Meher, and Belg livelihood zones exhibited the highest in the availability of technology components and farm equipment. Better access to travel time also observed in CHV and Meher livelihood zones. When all the indicators of physical capital were aggregated, Meher (0.72), Meher-Belg (0.60), and CHV (0.57) were at high and moderate adaptive capacity levels. However, Belg (0.41), SWS (0.29), and ABB (0.00) were divulged low and very low adaptive capacity levels. In relation to the results, Gebreegziabher et al. (2018) and World Bank (2010) noted that limited access to physical infrastructure and unavailability of producer goods were matters that constrained to reduce sensitivity and to improve adaptive capacity of rural households.
The fourth major sub-component of adaptive capacity was the financial capital. In this study, the financial capital includes the livestock per capita owned (in TLU), diversity of income sources (sales of crop, livestock, firewood, and charcoal), safety nets, agricultural labor, paid employment in Towns (causal labor), and institutional access to saving and credit services (Table 6). The result showed that the livelihood zones of Meher-Belg, Meher, and Belg exhibited very high normalized values of livestock (in TLU) per capita owned, which means the adaptive capacity level is higher. The normalized values of Meher and Meher-Belg livelihood zones showed very high and high diversity of income sources respectively. When all the financial capital indicators were aggregated, Meher-Belg (0.69), Meher (0.64), and Belg (0.60) were relatively at high adaptive capacity levels. SWS (0.37) and CHV (0.33) were the lowest adaptive capacity levels while ABB (0.09) was at very low adaptive capacity level.
The fifth major sub-component of adaptive capacity was the natural capital. In this study, the natural resource capital involves the availability and access of water and forest resources, farmland size, perceived farmland degradation, and soil fertility across the livelihood zones. Consequently, CHV, SWS, and Belg livelihood zones showed higher adaptive capacity with better access either from rivers and streams or piped clean water. However, CHV, SWS, and Meher-Belg livelihood zones indicated the lowest adaptive capacity owing to the relative high utilization of forest as energy sources. Enhanced adaptive capacity with higher farmland size owned by the households was observed in livelihood zones of CHV, ABB, and SWS. This happens because higher farmland size provides an opportunity for crop diversification, soil conservation practices, and increasing crop yield which was also reported by O’Brien et al. (2006); Teshome (2016); Asrat and Simane (2017); and Tessema and Simane (2019). The average farmland size owned by households across the livelihood zones ranges from 1.44 ha at CHV to 0.69 ha at Meher-Belg. The average farmland size owned by a household was 0.96 ha, which is found to be almost equal to the regional average (1.0 ha) (Bluffstone et al. 2008; Arega 2013) and below the national (1.22 ha) level average farmland size owned (MoANR 2016; CSA 2012). Households’ high level of adaptive capacity in the perceived farmland degradation and soil fertility was observed in CHV SWS and Meher livelihood zones. As indicated in USAID (2009), CHV SWS and Meher livelihood zones were consists of moderately fertile soil with alluvial, sandy, sandy clay, and sandy loam. It is apparent that when all the natural capital indicators were aggregated, CHV (0.75) was at high adaptive capacity levels. SWS (0.57), Meher (0.55), and Belg (0.55) were at moderate adaptive capacity levels. ABB (0.40) and Meher-Belg (0.13) were at low and very low adaptive capacity levels.
The overall attached weights based on the method of Iyengar and Sudarshan (Eq. 5), for human, social, physical, financial, and natural capitals are 0.27, 0.19, 0.16, 0.18, and 0.20 respectively. Based on the weights, one final overall adaptive capacity index is also present in Fig. 6 and Table 6, which suggests differences across the livelihood zones of the study. Among the livelihood zones, Meher (0.68) was the highest adaptive capacity index to climate variability and extreme events. Meher-Belg (0.54), CHV (0.53), and Belg (0.46) were at moderate adaptive capacity. SWS (0.39) and ABB (0.30) were at low adaptive capacity. The low level of adaptive capacity to the impact of climate variability and extremes was perhaps associated with the low level human, physical, and financial capitals.
The spider diagram (Fig. 10) also illustrated similar results in which the contribution of each livelihood capital indicators to the overall adaptive capacity component also spatially varies across the livelihood zones. In the diagram, the social capital contributes very high (0.84) normalized score to the adaptive capacity of Meher livelihood zone. High (0.6–0.8) normalized scores to the adaptive capacity of Meher and Meher-Belg livelihood zones were contributed from human, social, physical, and financial capitals. Similar normalized scores were also contributed from social and natural capitals to CHV and the financial capital to Belg livelihood zones. On the other hand, low (0.2–0.4) and very low (0.0–0.2) normalized score contributions to the adaptive capacity were also observed from the livelihood capitals. As a result, very low normalized scores to the adaptive capacity of ABB livelihood zone contributed from physical and financial capitals while social and natural capitals contributed to Belg and Meher-Belg livelihood zones respectively. Similarly, low normalized scores to the adaptive capacity of SWS livelihood zone contributed from all the livelihood capitals except natural capital while human and financial capital contributed to CHV livelihood zone.
Vulnerability index: exposure, sensitivity, and adaptive capacity components
Vulnerability was found to stem from a number of component factors. Rural households’ livelihood vulnerability index per livelihood zones in the study was determined by the composite indicators of exposure, sensitivity, and adaptive capacity components (Table 7 and Fig. 11). Hence, both Belg and Meher found to be the highest exposure livelihood zones to vulnerability with an aggregated normalized exposure value of 0.71. Equally, SWS, ABB, Belg, and CHV livelihood zones showed moderate level of sensitivity to vulnerability (Table 7). Therefore, the highest level of exposure and sensitivity combined with a low level of adaptive capacity increased the vulnerability of SWS, ABB, and Belg livelihood zones as also reported by Tessema and Simane (2019) and Gebreegziabher et al. (2018). Vis-à-vis to this, Fig. 11 exhibits the livelihood zones’ vulnerability index based on the normalized values of exposure, sensitivity, and adaptive capacity level. Thus, livelihood zone of Belg (0.75) was at a high level of vulnerability. ABB (0.57) and CHV (0.45) were at a moderate level of vulnerability. The SWS (0.37) was at a low vulnerability level. Meher-Belg (0.22) was the least vulnerable livelihood zone perhaps due to a high level of adaptive capacity such as infrastructure, asset accumulation, and social networks. High level of exposure to climate variability (rainfall and temperature) and extremes (drought) increases the vulnerability of rural households across livelihood zones. Likewise, the biophysical and socioeconomic sensitivity to vulnerability is exacerbated by topography/slope, poor soil fertility and high erodibility, population pressure, and increasing trends of environmental hazards like climate variability and droughts. The exposure to climate variability and extremes, biophysical and socioeconomic sensitivity with low adaptive capacity, lead communities to livelihood vulnerability across the livelihood zones are also reported by Feyissa et al. (2018), Callo-Concha and Ewert (2014), and Deressa et al. (2008).
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