Ethiopia is also frequently identified as a country that is highly vulnerable to climate variability and change. The potential adverse effects of climate change on Ethiopia’s agricultural sector are a major concern, particularly given the country’s dependence on agricultural production, which is sensitive to climate change and variability. This problem calls the need to understand agroecology based vulnerability to climate change and variability to better adapt to climate risks and promote strategies for local communities so as to enhance food security. The objective of this study is to estimate and compare the level of vulnerability of smallholder farmers’ to climate change and variability from three agroecology representing Muger River sub-Basin of the upper Blue Nile basin using Livelihood Vulnerability Index. The research used quantitative and qualitative data collected through Focussed Group Discussions, key informant interviews and a questionnaire survey of 442 sampled households across three different agro-ecologies in the sub-basin. The results reveal that along with the different agro-ecological zone, households and communities experienced different degrees of climate vulnerability. These differences are largely explained by differences in exposure, sensitivity and adaptive capacity of smallholder farmers. The livelihood vulnerability analysis reveals that Kolla agroecology exhibits relatively low adaptive capacity, higher sensitivity and higher exposure to climate change and variability that is deemed to be the most vulnerable agroecology. These contributing factors to a vulnerability in Kolla agroecology are largely influenced by assets, livelihood diversification, innovation, infrastructure, socio-demographic factors, social capital, agriculture, food security, and natural disasters and climate variability. The result furthermore shows that Dega agroecology has least vulnerable owing to its higher adaptive capacity. These results suggest that designing agroecology based resilience-building adaptation strategies is crucial to reduce the vulnerability of smallholder farmers to climate change and variability.
Climate change impacts are widely observed in Africa where it has directly affected climate-dependent activities [
Like other African countries, Ethiopia is also frequently identified as a country that is highly vulnerable to climate variability and change [
According to [
It should be noted that vulnerability is defined differently in different disciplines [
While it is increasingly accepted that climate change and variability will be Ethiopian farmers’ greatest challenge, only a few studies have been undertaken in Ethiopia concerning livelihood vulnerability to climate change and variability. Most of this literature have looked into impacts of climatic variability on specific sectors such as agriculture, water resources, health, forestry, and to lesser extent socio-economic analysis [
There is emerging a consensus that livelihood vulnerability to the changing climate varies with the scale of analysis. It is noted that vulnerability assessed at the national level can conceal variations in local vulnerability [
The findings of the research can assist in identifying specific factors contribute for farmers vulnerability to climate change and useful for targeting interventions and priority setting at the agroecology level in reducing vulnerability against adverse effects of climate change and variability. The overall objective of this study is to assess and compare the level of vulnerability of smallholder farmers to climate variability and change as a result of differences in agro-ecological settings, socio-economic factors, and existing institutional capacity. The specific objective is to examine exposure, sensitivity and adaptive capacity profiles of smallholder farmers in the sub-basin.
1) Bio-Physical Setting
This study was conducted in the Muger sub-basin of the upper Blue-Nile basin. Muger sub-basin cover a total area of 8188 km2. Muger River flows from the southeast of the basin into upper blue-nile river. The altitude in Muger sub-basin ranges between 953 masl and 3550 masl. The highlands in the eastern and southern part of the sub-basin are higher in altitude, greater than 2600 meters up to 3550 meters. The lowlands along the Muger River have lower altitude less than 1700 masl [
The sub-basin has an annual rainfall varies between 833 mm and 1326 mm. Lower annual rainfall ranging from 833 mm up to 1000 mm is observed along the river and lowlands. Relatively high rainfall is found in the highlands of the sub-basin. The annual maximum and minimum temperature of the sub-basin varies between 16˚C - 31.5˚C and 3˚C -16.5˚C respectively. Temperature is higher along the river with a maximum of 28˚C - 31.5˚C and minimum of 13˚C - 16.5˚C. The sub-basin is characterized by tepid to cool moist highlands. The northwestern part of the lowlands is hot to warm moist lowlands [
2) Socio-Economic Setting
According to the current zonal structure, the sub-basin is shared between three zones: North shoa, West shoa, and Oromia regional state of Finfine special zone. Muger sub-basin covers 15 weredas; Ejersa (Addis Alem), Walmara, Juldu, Mulo, Sululta, Adda Berga, Meta Robi, Yaya Gulelena Debre Libanos, Wichalena Jido, Ginde Beret, Kuyu, Kutaya, Gerar Jarso, Degem, and Wara Jarso . The total population of the sub-basin is 2,442,247 people [
The research design was based on multi-stage sampling procedure. In the first stage, the whole sub-basin constituting fifteen Woredas was grouped into three strata (Kolla, Woyina Dega, and Dega agro-ecological zones) based on their agro-ecological characteristics including the rainfall, soil, and topography. The intention of this grouping was to maintain the representativeness of the samples that have been selected. It helped to group Woredas’ having the same features
and characteristics into one group. Then, two woredas were randomly selected from Kolla and Dega agro-ecological zones. Similarly, two woredas were also selected from Woyina Dega agro-ecology using simple random sampling technique. In the second stage, only Peasant Associations (PAs) found in the sub-basin in each sampled Woreda were listed in consultation with agricultural experts in the area. This is mainly to exclude PAs which are not part of the sub-basin in that particular Woreda. Then, four PAs were randomly selected from each selected woredas. Finally, a total 442 sample respondents-143 from Kolla, 200 from Woyina Dega, and 99 from Dega agroecology were selected from 16 PAs using random sampling technique on the basis of probability proportional to size (PPS). The sampling frame was the list of households which was obtained from the PAs administration. Households for Focussed Group Discussions (FGDs) were also drawn from each identified woreda, and a member of the group was identified with the help of development agents working in the area.
Both quantitative and qualitative methods of data collection were used to obtain information from the selected respondents. Quantitative data were gathered using semi-structured questionnaire. Qualitative data were obtained from FGDs and key informant interview to complement the information obtained through a semi-structured questionnaire in order to have a better understanding of major indicators that farmers use to determine the level of vulnerability to climate change and variability. Questions were posed to investigate factors that contribute to lower adaptive capacity, higher sensitivity, and higher exposure that could lead to higher vulnerability. Moreover, mean monthly temperature and precipitation from 1991 to 2016 were obtained from Ethiopian metrological station found in each sampled woredas.
Methods of Data Analysis
This study employed the livelihood vulnerability index (LVI) developed by [
To calculate the LVI, we used a balanced weighted average approach where each sub-component contributes equally to the overall index through each major component which comprised a different number of sub-components [
As each sub-component was measured on a different scale, it is, therefore, necessary to standardize each as an index using the following equation;
i n d e x s r = s r − s min s max − s min (1)
where sr is the observed sub-component indicator for agroecology r and smin and smax are the minimum and maximum values, respectively. The equation for standardizing numerical values is the same as that used in constructing the Human Development Index―HDI [
Major Components | Sub-Components | Hypothesized functional relationship between indicator and vulnerability |
---|---|---|
Soil and water | Inverse of average hectare of land under SWC | A Large hectare of land under SWC and irrigation reduce vulnerability, but here an inverse is considered. |
Inverse of average hectare of land under Irrigation | ||
Percentage of households reporting land degradation by climate-related extremes during the past 20 years | A Higher percentage of households reporting land degradation increase vulnerability. | |
Agriculture | Inverse of Kilograms of total production harvested | Increased quantity of total production harvested reduces vulnerability but here an inverse is considered. |
Inverse of Percent of crop diversity | Higher crop diversity reduces vulnerability but here an inverse is considered. | |
Percent of household who do not save seeds | Higher the proportion of Households who do not save seeds, higher is the vulnerability | |
Food | Percent of household who do not save crops | Higher proportion of households who do not save crops, higher is the vulnerability |
Average number of months households trouble getting enough food (range: 0 - 12) | Higher food insecurity results in a higher vulnerability. | |
Asset | Inverse of Number of livestock owned in TLU | Higher livestock ownership and landholding size reduce vulnerability, but here an inverse is considered |
Inverse of average Ha of land holding | ||
Percent of households who do not have access to credit | A Higher proportion of households who do not have access to credit increased vulnerability. | |
Livelihood strategies | Inverse of Percent of households worked in non-farm activity | A Higher percentage of households who worked in non-farm and off-farm activity reduce vulnerability, but here an inverse is considered. |
Inverse of Percent of households worked in off-farm activities | ||
Percentage of households solely dependent on agriculture as source of income | A Higher percentage of households solely dependent on agriculture as a source of income increase vulnerability. | |
Innovation | Inverse of Percent of HH used insecticide and pesticide | A Higher percentage of households used insecticide and pesticide, fertilizer, improved seeds, and practiced irrigation reduce vulnerability, but here an inverse is considered. |
Inverse of Percent of HH used fertilizer | ||
Inverse of Percent of HH used improved seeds | ||
Inverse of Percent of HH practiced irrigation | ||
Infrastructure | Walking distance in hours to main road | Longer the distance, the higher is the vulnerability. |
Walking distance to school | ||
Walking distance to veterinary service | ||
Walking distance to market | ||
Walking distance to water sources | ||
Walking distance to health center | ||
Inverse of Percent of HH who owned mobile phone | A Higher percentage of households who used mobile phone reduce vulnerability but here an inverse is considered. |
Socio-Demographic | Percent of female head households | A Higher proportion of female members increases vulnerability. |
---|---|---|
Percentage of households where head of the household has not attended school | A Higher percentage of households has not attended school, and not owned Radio increase vulnerability. | |
Percent of households do not own Radio | ||
Age of the household head | Positive | |
Dependency ratio | Higher dependency ratio increases vulnerability. | |
Inverse of Percent of households attended agricultural training | A Higher proportion of households attended training reduce vulnerability, but here an inverse is considered. | |
Social Networks | Percent of households that have not gone to local government for assistance | A Higher proportion of households do not go to the government for assistance, borrowed money, do not help others, and receive help from others increase vulnerability. |
Percent of households borrowed money through social networks | ||
Percent of households do not help others | ||
Percent of households who received help from others. | ||
Inverse of Membership in social group | More memberships in social groups reduce vulnerability but here an inverse is considered. | |
Natural Disaster and Climate Variability | Average number of floods and drought over the past 20 years | Higher the incidence of natural disasters, higher is the vulnerability |
Percent of households that didn’t receive a warning about natural disasters | The higher proportion of households does not receive warning system the higher the vulnerability. | |
Percent of households whose family members injured or died because of climate change | Higher prop oration of households affected by climate change the higher the vulnerability. | |
Mean standard deviation of Monthly Avg. max. temperature (1991-2015) | Increasing temperature increase vulnerability. | |
Mean std. deviation of monthly Avg. minimum temperature (1991-2015) | Increasing temperature increase vulnerability. | |
Mean std. dev. of monthly Avg. Precipitation (1919-2015) | Decreasing precipitation increase vulnerability. |
M r = ∑ i = 1 n i n d e x s r i n (2)
where M r is one of the ten major components [Soil and Water, Agriculture, Food, Asset, Livelihood Strategies, Innovation, Infrastructure, Socio-Demographic, Social Networks, and Natural Disasters and Climate Variability] for agroecology r; i n d e x s r i , 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 ten major components for agroecology were calculated, they were averaged using Equation (3) to obtain the agroecology-level LVI [
L V I r = ∑ i = 1 10 w m i M r i ∑ i = 1 10 w M i (3)
where, L V I r is the Livelihood Vulnerability Index for agroecology r, equals the weighted average of the ten major components. The weights of each major component, wMi, are determined by the number of sub-components that make up each major component and are included to ensure that all sub-components contribute equally to the overall LVI [
Following from Equations (1)-(3), [
C F r = ∑ i = 1 n w m i M r i ∑ i = 1 n w M i (4)
where C F r is an IPCC-defined contributing factor (exposure, sensitivity, or adaptation capacity) for agroecology r, M r i is the major components for agroecology r indexed by i, w M i is the weight of each major component, and n is the number of major components in each contributing factor. Once exposure, sensitivity, and adaptation capacity were calculated, the three contributing factors were combined using Equation (5):
L V I - I P C C r = ( e r − a r ) * s r (5)
where L V I - I P C C r is the LVI for agroecology r expressed using the IPCC vulnerability framework, e r is the calculated exposure score for agroecology r (equivalent to the natural disaster and climate variability major component), a r is the calculated adaptation capacity score for agroecology r (weighted average of the Assets, livelihood strategies, Innovations, Infrastructures, socio-demographic, and social networks), and s r is the calculated sensitivity score for agroecology r (weighted average of the Soil and Water, Agriculture, and food). The LVI-IPCC was scaled from −1 least vulnerable) to 1-most vulnerable [
Finally, this research was framed in the lens of vulnerability framework developed by Turner and his colleague’s [
Adaptive capacity, exposure, and sensitivity are the key factors that determine the vulnerability of households and communities to the impacts of climate variability and change [
For this study, adaptive capacity is represented by asset, livelihood strategies, innovation, availability of infrastructure, socio-demographic, and social networks. Wealth enables communities to absorb and recover from losses more quickly due to insurance, social safety nets, and entitlement programs [
Access to agricultural inputs is identified as an indicator of innovation. For instance, [
[
The literacy rate is another important factor contributing to adaptation to climate change. It shows the degree to which the community can have access to the right kind of knowledge in understanding changes in the environment and the management practices required to deal with them. [
Sensitivity is the degree to which a system is affected, either adversely or beneficially, by climate change stimuli. In this study, three indicators were considered that may have an influence on the sensitivity of the farming community in the study area. These includes: soil and water, agriculture and food. Thus, it is hypothesised that smaller SWC, irrigation, and higher perception of land degradation increases sensitivity of smallholder farmers’ to climate change and variability. In addition, smaller amount of total production harvested, less crop diversity, and larger households who do not save seed increases sensitivity. On the same vein, high prevalence of food insecurity has a negative impact on sensitivity to climate change and variability.
Exposure is the nature and degree to which a system is exposed to climate variations [
The result of Vulnerability analysis for all the three agro-ecologies is reported in two parts. First, the results obtained from the assessment of individual major components and subcomponents contributions to each of the major components for each agroecology are presented. Second, the estimated values for the different dimensions (sensitivity, exposure, and adaptive capacity) of the climate vulnerability index are presented. The LVI provides information of which components determine vulnerability. The LVI-IPCC indicates which of the three factors (exposure, adaptive capacity and sensitivity) influences the most when determining the vulnerability.
LVI results
Overall, Kolla agroecology has a higher LVI than Woyina Dega and Dega (0.5991; 0.5118; 0.4801, respectively), indicating relatively greater vulnerability to climate change and variability impacts. The spider diagram in
and water component. The next sections present the details of sub-components and major components that could contribute to exposure, sensitivity and adaptive capacity for each agroecology.
Exposure: Natural disaster and climate variability
The natural disasters and climate variability component are made up of six sub-components. In terms of natural disasters and climate variability, the analysis reveals that Kolla agroecology is found to be more vulnerable (0.4916) whereas Dega agroecology is found to be least vulnerable (0.3386) (
Agro-Ecology | Kolla | Woyina Dega | Dega | ||
---|---|---|---|---|---|
Major components | Sub-component | Explanation of Sub-Components | Index | Index | Index |
Soil and Water | Average hectare of land under SWC | Inverse of Average hectare of land under SWC | 0.8422 | 0.8699 | 0.8419 |
Average Ha of land under Irrigation | Inverse of Average Ha of land under Irrigation | 0.9888 | 0.9476 | 0.8809 | |
Percent of households reporting land degradation by climate-related extremes during the past 20 year | Percentage of households reporting land degradation by climate-related extremes during the past 20 years. | 0.6084 | 0.675 | 0.4545 | |
Agriculture | Total production harvested in Kilogram | Inverse of Kilograms of total production harvested | 0.9339 | 0.8168 | 0.8018 |
Crop diversity | Inverse of Percent of sown area under all crops divided by number of total crops | 0.6721 | 0.6546 | 0.7069 | |
Percent of household who do not save seeds | Percent of household who do not save seeds | 0.5175 | 0.395 | 0.091 | |
Food | Percent of household who do not save crops | Percent of household who do not save crops | 0.6573 | 0.345 | 0.0708 |
Number of months households trouble to get enough food | Average number of months households trouble getting enough food (range: 0 - 12) | 0.2532 | 0.1041 | 0.0816 | |
Asset | Number of livestock in TLU | Inverse of average Number of livestock | 0.8876 | 0.2357 | 0.7565 |
Average Ha of land holding | Inverse of average Ha of land holding | 0.8577 | 0.266 | 0.7468 | |
Percent of households who do not have access to credit | Percent of households who do not have access to credit | 0.5664 | 0.795 | 0.707 | |
Livelihood Strategy | Percent of households who work in non-farm activity | Inverse of Percent of households who work in non-farm activity | 0.9226 | 0.8772 | 0.7920 |
Percent of households who worked in off-farm activities | Inverse of Percent of households who worked in off-farm activities | 0.9167 | 0.8163 | 0.8535 | |
Percentage of households who solely dependent on agriculture as source of income | Percentage of households who solely dependent on agriculture as source of income | 0.8602 | 0.70 | 0.6465 | |
Innovation | Percent of HH used insecticide and pesticide | Inverse of Percent of HH used insecticide and pesticide | 0.8773 | 0.8197 | 0.8684 |
Percent of HH used fertilizer | Inverse of Percent of HH used fertilizer | 0.5793 | 0.5348 | 0.5103 | |
Percent of HH used improved seeds | Inverse of Percent of HH used improved seeds | 0.7688 | 0.8230 | 0.7279 | |
Percent of HH practiced irrigation | Inverse of Percent of HH practiced irrigation | 0.9286 | 0.7246 | 0.5723 | |
Infrastructure | Distance to the main road | Walking distance in hours to main road | 0.3763 | 0.2140 | 0.0953 |
Distance to school | Walking distance to school | 0.2176 | 0.1579 | 0.1433 | |
Distance to veterinary service | Walking distance to veterinary service | 0.2439 | 0.2053 | 0.2154 |
Distance to market | Walking distance to market | 0.3130 | 0.2174 | 0.1617 | |
---|---|---|---|---|---|
Distance to water sources | Walking distance to water sources | 0.1812 | 0.1644 | 0.1040 | |
Distance to health center | Walking distance to health center | 0.2704 | 0.2500 | 0.2296 | |
HH owned mobile phone | Inverse of Percent of HH owned mobile phone | 0.7647 | 0.7435 | 0.5657 | |
Socio-Demographic | Percent of female head households | Percent of female head households | 0.1049 | 0.0850 | 0.2222 |
Household had not attended school | Percentage of households where head of the household had not attended school | 0.5804 | 0.5600 | 0.4646 | |
Households do not own Radio | Percent of households do not own Radio | 0.6923 | 0.2750 | 0.0809 | |
Age of the household head | Number of years of age of the household head | 0.4059 | 0.3482 | 0.4423 | |
Dependency ratio | Dependency ratio | 0.9763 | 0.8934 | 0.9649 | |
Households attended agricultural training | Inverse of Percent of households attended agricultural training | 0.6413 | 0.6098 | 0.7443 | |
Social Network | Households that have not gone to local government for assistance | Percent of households that have not gone to local government for assistance | 0.8811 | 0.8900 | 0.9595 |
Households borrowed money through social networks | Percent of households borrowed money through social networks | 0.1259 | 0.025 | 0.1818 | |
Households who do not help others | Percent of households who do not help others | 0.6434 | 0.9050 | 0.6566 | |
Households who received help from others | Percent of households who received help from others | 0.3566 | 0.0600 | 0.1717 | |
Membership in social groups | Inverse of Membership in social groups | 0.7974 | 0.6753 | 0.6189 | |
Natural Disaster and Climate Variability | Number of floods and drought over the past 20 years | Average number of floods and drought over the past 20 years | 0.468 | 0.412 | 0.288 |
Households that didn’t receive a warning about natural disasters | Percent of households that didn’t receive a warning about natural disasters | 0.4476 | 0.595 | 0.5656 | |
Households whose family members injured or died because of climate change | Percent of households whose family members injured or died because of climate change | 0.4663 | 0.035 | 0.0101 | |
Mean standard deviation of monthly Avg. max temperature (1991-2015) | 0.5608 | 0.4596 | 0.3967 | ||
Mean std. deviation of monthly Avg. minimum temperature (1991-2015) | 0.2953 | 0.7092 | 0.2597 | ||
Mean std. dev. of monthly Avg. Precipitation (1919-2015) | 0.7113 | 0.6059 | 0.5115 |
maximum monthly temperature and precipitation. This implies that high temperature and high rainfall will cause failure to crops grown.
The two main contributing factors for a higher vulnerability to natural disasters and climate variability for woyina Dega are a higher percentage of the household did not receive a warning about impending natural disaster such as drought and floods (Woyina Dega 59.5 percent, Dega 56.56 percent, and Kolla 44.76 percent) and mean std. deviation of monthly average minimum temperature (2.485545). A significant number of farmers in all three agroecology did not receive any warning about impending natural disaster such as floods or droughts, however, the problem is most prevalent in the woyina Dega agroecology where about 59.5 percent of the sample reported a lack of information about impending disasters and are therefore unable to adequately prepare for them. This result indicates that broadcasting early warning is more limited to Kolla and Dega agroecology and not available to a remote area of Woyina Dega agroecology. This may imply that early warning systems and community preparedness plans may help communities to prepare for extreme weather events. It is also noted that seasonal weather forecasts distributed through local farming associations may help farmers adjust the time for their plantings and prevent diversion of scarce water resources for irrigation during severe drought.
Sensitivity: Soil and water, agriculture, and food
Land degradation has become one of the most important environmental problems in the Muger river sub-basin, mainly due to soil erosion and nutrient depletion. Although the study does not show much difference in the soil and water vulnerability of the three agro-ecologies, the vulnerability of soil and water component was lowest in Dega (0.7258) and highest in Woyina Dega (0.8308). The majority of the households in Woyina Dega (67.5%) and Kolla (60.83%) reported that their land has been degraded due to climatic events, such as flash floods, landslides, and erosions. Lack of efficient agricultural practice to preserve topsoil, lack of proper terrace system for farming and practice of occasional slash and burn has made topsoil prone to degradation which potentially would make households in Woyina Dega more vulnerable. These facts provide enough reasons to make a claim that the households in Woyina Dega are highly vulnerable in terms of soil and water component. One way ANOVA analysis reveals that hectare of land under soil and water conservation measure is significantly different across the three agro-ecologies (
On the same vein, the inferential analysis shows that hectare of land under irrigation is significantly different among the three agro-ecologies (
Variable | F-test | Significance level |
---|---|---|
Hectare of land under irrigation | 63.209* | 0.000 |
Crop Diversity index | 2.710*** | 0.068 |
Hectare of land with soil and water conservation measure | 2.532*** | 0.081 |
*, ***: Significant at 10% and 1%, respectively.
net sown area in Kolla agroecology gives an indication of the higher dependence on rainfall.
As seen in
Food is another component that has a high effect on a vulnerability in Kolla, with a value of 0.4553. The results reveal this high value is presumably due to the fact that Kolla households struggled about 2.53 months per year to find adequate food for their families as compared to 1.04 months in woyina Dega and 0.8163 month in Dega. The result further shows that a higher percentage of Kolla households (65.73%) reported that they do not store crops compared to woyina Dega (34.5) and Dega (7.08). The main lesson drawn from this point is that farmers in Kolla agroecology are more likely food insecure that could aggravate their vulnerability to the changing climate. This suggests that adaptation options designed to reduce the adverse effect of climate change and variability in Kolla agroecology should give priority to food security.
Adaptive capacity: Asset, Livelihood strategies, innovation, infrastructure, Scio-demographic, and social networks
The fifth component that mainly affects the vulnerability of Kolla agroecology is an asset with a value of 0.7706. This high value is presumably due to the fact that Kolla agroecology has lower livestock ownership and smaller landholding as compared to Dega and Woyina Dega agroecology. One way ANOVA analysis reveals that there exists a significant difference of livestock ownership and size of landholding among the three agro-ecologies (
Kolla agroecology, with an index value of 0.8998 on livelihood strategies have a higher effect on vulnerability, than in Dega and Woyina Dega. This value came as a result of three main factors. The first is that a higher percentage of Kolla households reported relying solely on agriculture for income as compared to Woyina Dega and Dega households (
With an index value of 0.7885, innovation is the high influencing component on a vulnerability in Kolla than the rest two agro-ecologies (
Contributing factors | Major component | Kolla | Woyina Dega | Dega |
---|---|---|---|---|
Adaptive capacity | Asset | 0.7706 | 0.4322 | 0.7368 |
Livelihood strategies | 0.8998 | 0.7978 | 0.7640 | |
Innovation | 0.7885 | 0.7255 | 0.6697 | |
Infrastructure | 0.3382 | 0.2789 | 0.2164 | |
Socio-Demographic | 0.5669 | 0.4619 | 0.4865 | |
Social Networks | 0.5609 | 0.5111 | 0.5177 | |
Sensitivity | Soil and water | 0.8131 | 0.8308 | 0.7258 |
Agriculture | 0.7078 | 0.6221 | 0.5332 | |
Food | 0.4553 | 0.2246 | 0.0762 | |
Exposure | Natural disasters and climate variability | 0.4916 | 0.4695 | 0.3386 |
LVI | 0.5991 | 0.5118 | 0.4801 |
it is even more difficult and expensive to transport produce to the market. Similarly, the percentage of farmers with some irrigation on their land varies between agro-ecologies.
Although Kolla households have higher vulnerability score for the use of insecticide and pesticide, chemical fertilizer, and irrigation practice of the innovation indicators, percent of households used improved seeds has been found to be higher in Dega. In Woyina Dega agroecology, only 21.5 percent of households used improved seeds to enhance crop production as compared to 37.37 percent and 30 percent in Dega and Kolla households respectively (
Infrastructure development is another important component that determines the level of vulnerability of smallholder farmers in the study area. The result indicates that access to major indicators of infrastructure significantly varies across agro-ecologies at less than 1% significance level except for distance to the health center (
Variable | F-test | Significance level |
---|---|---|
Number of total livestock in TLU | 49.071*** | 0.000 |
Educational status of the household head in year | 2.974* | 0.052 |
Age of the household heads in year | 7.821*** | 0.000 |
Total crops harvested in kilogram | 58.179*** | 0.000 |
Estimated annual income from non-farm activity in birr | 8.093*** | 0.000 |
Estimated annual income earned from off-farm activity | 1.334 | 0.265 |
Sex of the household head | 6.189*** | 0.002 |
The distance to all-weather roads from your home in walking hours | 65.955*** | 0.000 |
The distance of your home to the nearest school | 9.383*** | 0.000 |
The distance to veterinary service from your home | 3.473** | 0.032 |
The distance to health services from your home | 2.215 | 0.110 |
The distance to water source from your home | 6.840*** | 0.001 |
The distance to saving and credit institution | 44.573*** | 0.000 |
The distance to market from your home | 36.996*** | 0.000 |
*, **, ***: Significant at 10%, 5% and 1%, respectively.
that Kolla households have higher vulnerability score (0.3382) than Woyina Dega and Dega households on the infrastructure component (0.2789, 0.2164 respectively) (
The socio-demographic component has higher vulnerability effect in Kolla (0.5669) than Dega (0.4865) and Woyina Dega (0.4619). The ANOVA analysis reveals that sex of the household head and age of the household head are statistically significant (P < 1%) among the three agro-ecologies (
The social network is an important component that determines vulnerability of farmers in the study site. The results reveal that households that have not gone to local government for assistance, households borrowed money through social networks, households who do not help others, households who received help from others, and household heads membership in social groups are found to be an important indicators that explain the social network component [
Livelihood Vulnerability Index-IPCC Results
Agro-ecology | IPCC contributing factors to vulnerability | |||
---|---|---|---|---|
Exposure | Sensitivity | Adaptive capacity (inverse) | LVI-IPCC | |
Kolla | 0.4916 | 0.6842 | 0.36326 | 0.0878 |
Woyina Dega | 0.4694 | 0.6009 | 0.40011 | 0.04164 |
Dega | 0.3386 | 0.4912 | 0.43412 | −0.04692 |
low values of exposure relative to adaptive capacity yield negative vulnerability scores. Sensitivity acts as a multiplier, such that high sensitivity in an agroecology for which exposure exceeds adaptive capacity will result in a larger positive LVI-IPCC vulnerability scores [
It is apparent from
On the other hand, biophysical vulnerability is exacerbated by relatively low soil fertility due to land degradation by soil erosion, diminishing water resources and increasing trends of environmental hazards like drought and floods. All these factors lead to deterioration of agroecology thereby compromising their ability to provide ecosystem services leading to farmers’ vulnerability as also reported by [
The result further reveals that Dega agroecology is least vulnerable study site owing to its lowest sensitivity and exposure and highest adaptive capacity. The higher adaptive capacity of the households in Dega can be explained by the fact that there exists improved infrastructure and institutional services (i.e., access to credit, extension service, and market facilities), higher asset possession, diversified livelihood strategies, and high access to innovations. It is also noted that Dega agroecology has successful and endured local institutions that create relationships with a common purpose and promote shared interest. From the above indicators considered in the sensitivity analysis, the Dega agroecology is less vulnerable because of the better size of land under small-scale irrigation and large size of land under soil and water conservation measures.
Overall, the key observation here is even if the existing development interventions have helped farmers to reduce the adverse effect of climate change and variability, the benefit of agroecology specific interventions to reduce farmers’ vulnerability are still not fully realized. It is this problem that makes Kolla agroecology the most neglected area by development interventions for unjustified reasons. This suggests that development interventions should target their efforts to reduce farmers’ sensitivity and enhance adaptive capacity so as to reduce vulnerability to climate change and variability specific to the agro-ecologic context.
This paper has aimed to address a gap in differences of smallholder farmers’ vulnerability to climate change among different agroecology by using empirical data to assess the exposure, adaptive capacity, and sensitivity. Though significant attention has been given to assessing vulnerability at the national level, fewer papers have looked vulnerability across varying agro-ecology. Through LVI developed by Hahn and his colleagues, the research demonstrates empirically the differences in exposure, sensitivity, and adaptive capacity of farmers across three Argo-ecologies.
The results reveal that Kolla agroecology is found to be the highest exposure and sensitive to climate stress and have the most limited adaptive capacity. Its higher sensitivity to extreme climate events is probably because of small land under irrigation, low level of crop diversity, and high level of food insecurity in the area. The result further points out that Kolla agroecology has the limited adaptive capacity to adapt to the changing climate is due to the combined effect of limited livelihood options, underdeveloped infrastructure, low access to the most important socio-economic factors including asset ownership, and weak social cohesion. This will lead to the conclusion that a moderate climate change will disrupt the livelihoods of smallholder farmers in this agroecology. In contrary, Dega agroecology has lower exposure and sensitivity, and greater adaptive capacity as compared to the other two agroecology and this could be attributable to higher asset ownership, developed infrastructure, more diversified livelihood options, access to innovation, and relatively well-developed social networks. Although the aggregate sensitivity is higher in Kolla agroecology, land degradation problem is found to be more pronounced in woyina Dega.
Several important policy implications can be drawn from this analysis. Feasible interventions to reduce vulnerability and ameliorate the impact of climate change revolve around promoting small-scale irrigation and crop diversification that would later or sooner help to increase food security. In line with this, it is, therefore, imperative to ensure access to alternative sources of income through non-farm and off-farm activities, improving infrastructure, and increase vulnerable farmers’ asset base thereby increase their adaptive capacity to withstand the vagaries of the climate variability risk. This result also suggests that more emphasis needs to be given to investing in social capital formation by involving and building good relationships with smallholder farmers who can then take care of and obtain benefits from it to reduce their vulnerability to climate change and variability. Reducing land degradation problem using soil and water conservation measures will also help to reduce the sensitivity of farmers in Woyina Dega agroecology. Overall, it is imperative to give a closer attention in planning adaptation options to reduce current and future vulnerability based on agroecology and socio-economic context.
As often stated in climate change theory, vulnerability is a function of three contributing factors via adaptive capacity, sensitivity, and exposure [
We wish to express our profound gratitude to Addis Ababa University; the German Academic Exchange Service (DAAD), Germany; African Climate change Fellowship Program; and International Development Research Centre (IDRC) for their financial support in accomplishing this paper. We also want to thank Catharine Ronald for her rigors comments and suggestions on the manuscript.
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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
All data collection, data analysis and write-up of the study were supported by Addis Ababa University, African climate change fellowship program, and DAAD in-country scholarship program.
Abayineh Amare and Belay Simane generated the idea and designed the study. Abayineh carried out the data collection, data analysis, and write-up. Belay provided statistical assistance and read and revised the manuscript. .Both authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Amare, A. and Simane, B. (2017) Climate Change Induced Vulnerability of Smallholder Farmers: Agroecology-Based Analysis in the Muger Sub-Basin of the Upper Blue-Nile Basin of Ethiopia. American Journal of Climate Change, 6, 668-693. https://doi.org/10.4236/ajcc.2017.64034