Dataset Information

Exploring households' resilience to climate change-induced shocks using Climate Resilience Index in Dinki watershed, central highlands of Ethiopia.

ABSTRACT: This study assessed households' resilience to climate change-induced shocks in Dinki watershed, northcentral highlands of Ethiopia. The data were collected through a cross-sectional survey conducted on 288 households, three focus group discussions, and 15 key informant interviews. The Climate Resilience Index (CRI) based on the three resilience capacities (absorptive, adaptive and transformative) frame was used to measure households' resilience to climate change-induced shocks on an agro-ecological unit of analysis. A principal component analysis (PCA) and multiple regression analysis were used to identify determinant factors and indicators to households' resilience, respectively. Findings indicate that the indexed scores of major components clearly differentiated the study communities in terms of their agro-ecological zones. Specifically, the absorptive capacity (0.495) was the leading contributing factor to resilience followed by adaptive (0.449) and transformative (0.387) capacities. Likewise, the Midland was relatively more resilient with a mean index value of 0.461. Both the PCA and multiple regression analysis indicated that access to and use of livelihood resources, such as farmlands and livestock holdings, diversity of income sources, infrastructure and social capital were determinants of households' resilience. In general, it might be due to their exposure to recurrent shocks coupled with limited adaptive capacities including underdeveloped public services, poor livelihood diversification practices, among others, the study communities showed minimal resilience capacity with a mean score of 0.44. Thus, in addition to short-term buffering strategies, intervention priority focusing on both adaptive and transformative capacities, particularly focusing on most vulnerable localities and constrained livelihood strategies, would contribute to ensuring long-term resilience in the study communities.

SUBMITTER: Asmamaw M

PROVIDER: S-EPMC6615616 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Exploring households' resilience to climate change-induced shocks using Climate Resilience Index in Dinki watershed, central highlands of Ethiopia.

Asmamaw Mengistu M Mereta Seid Tiku ST Ambelu Argaw A

PloS one 20190709 7

This study assessed households' resilience to climate change-induced shocks in Dinki watershed, northcentral highlands of Ethiopia. The data were collected through a cross-sectional survey conducted on 288 households, three focus group discussions, and 15 key informant interviews. The Climate Resilience Index (CRI) based on the three resilience capacities (absorptive, adaptive and transformative) frame was used to measure households' resilience to climate change-induced shocks on an agro-ecologi ...[more]

PMID: 31287852

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Project description:Watershed resilience is the ability of a watershed to maintain its characteristic system state while concurrently resisting, adapting to, and reorganizing after hydrological (for example, drought, flooding) or biogeochemical (for example, excessive nutrient) disturbances. Vulnerable waters include non-floodplain wetlands and headwater streams, abundant watershed components representing the most distal extent of the freshwater aquatic network. Vulnerable waters are hydrologically dynamic and biogeochemically reactive aquatic systems, storing, processing, and releasing water and entrained (that is, dissolved and particulate) materials along expanding and contracting aquatic networks. The hydrological and biogeochemical functions emerging from these processes affect the magnitude, frequency, timing, duration, storage, and rate of change of material and energy fluxes among watershed components and to downstream waters, thereby maintaining watershed states and imparting watershed resilience. We present here a conceptual framework for understanding how vulnerable waters confer watershed resilience. We demonstrate how individual and cumulative vulnerable-water modifications (for example, reduced extent, altered connectivity) affect watershed-scale hydrological and biogeochemical disturbance response and recovery, which decreases watershed resilience and can trigger transitions across thresholds to alternative watershed states (for example, states conducive to increased flood frequency or nutrient concentrations). We subsequently describe how resilient watersheds require spatial heterogeneity and temporal variability in hydrological and biogeochemical interactions between terrestrial systems and down-gradient waters, which necessitates attention to the conservation and restoration of vulnerable waters and their downstream connectivity gradients. To conclude, we provide actionable principles for resilient watersheds and articulate research needs to further watershed resilience science and vulnerable-water management.

Project description:BackgroundFollowing global pledges to reduce greenhouse gas (GHG) emissions by 30% by 2030 compared to the baseline level of 2020, improved quantification of GHG emissions from developing countries has become crucial. However, national GHG inventories in most Sub-Saharan African countries use default (Tier I) emission factors (EFS) generated by the Intergovernmental Panel on Climate Change (IPCC) to estimate enteric CH4 emissions from animal agriculture. The present study provides an improved enteric CH4 emission estimate (Tier II) based on animal energy requirements derived from animal characteristics and performance data collected from about 2500 cattle in 480 households from three smallholder farming systems to represent the common dairy farming in the central highlands of Ethiopia. Using average seasonal feed digestibility data, we estimated daily methane production by class of animal and farming system and subsequently generated improved EF.ResultsOur findings revealed that the estimated average EF and emission intensities (EI) vary significantly across farming systems. The estimated value of EF for adult dairy cows was 73, 69, and 34 kg CH4/cow/year for urban, peri-urban, and rural farming systems, respectively. Rural dairy farming had significantly higher emission intensity (EI) estimated at 1.78 CO2-eq per kg of fat protein-corrected milk (FPCM) than peri-urban and urban 0.71 and 0.64 CO2-eq kg-1 FPCM dairy farming systems, respectively. The EF estimates in this study are lower than the IPCC's (2019) default value for both stall-fed high-productive and dual-purpose low-productive cows.ConclusionsThe current findings can be used as a baseline for the national emission inventory, which can be used to quantify the effects of future interventions, potentially improving the country's commitment to reducing GHG emissions. Similarly, this study suggests that increased animal productivity through improved feed has a considerable mitigation potential for reducing enteric methane emissions in smallholder dairy farming systems in the study area.

Project description:BackgroundCommunity-based Health Insurance (CBHI) is a voluntary prepayment mechanism that guarantees the provision of basic healthcare services without financial barriers to underserved segments of the population in developing countries. The Government of Ethiopia launched the CBHI program to protect the community from high out-of-pocket health expenditure and improve health service utilization a decade ago. However, to improve the quality of healthcare services delivery in health facilities and cover the changing costs of healthcare, the government should revise the contribution of the CBHI scheme. Therefore, we determined the willingness to pay for a CBHI scheme and associated factors among rural households of Lemu and Bilbilo district, South Central Ethiopia.MethodsWe conducted a community-based cross-sectional study design to assess willingness to pay for the CBHI scheme and its associated factors among households in Lemu and Bilbilo districts, South Central Ethiopia. We used a double bounded contingent valuation method to elicit households' willingness to pay for the CBHI scheme. Data were coded, cleaned, entered into Statistical Package for Social Science (SPSS) version 25, and exported to STATA 16 for analysis. A logistic regression analysis was conducted to determine the presence of statistically significant associations between the willingness to pay for the CBHI scheme and independent variables at a p-value <0.05 and Adjusted odds ratio (AOR) values with 95% CI. Finally, we checked the fitness of the model using Hosmer and Lemeshow's goodness-of-fit test.ResultsOf the 476 study participants, 82.9% (95% CI: 79.2%, 86.01%) were willing to pay for the CBHI scheme and only 62% of them can afford the average amount of 358.32ETB ($7.68) per household per annum. Primary education (AOR = 3.17; 95% CI: 1.74-5.80), secondary and above education (AOR = 4.13; 95% CI: 1.86-9.18), large family size (AOR = 2.75; 95% CI: 1.26-5.97), monthly income of 500-1000ETB (AOR = 3.75; 95% CI: 1.97-7.13) and distance to public health facilities (AOR = 2.14, 95% CI: 1.04-4.39 were significantly associated with willingness to pay for the CBHI scheme.ConclusionIn this study, around 83% of respondents were willing to pay for the CBHI and meet the government expectation for 2020. The study also revealed that educational status, family size, monthly income, and distance from the health facilities were significant factors associated with WTP for the CBHI scheme. In addition, we found that a large number of the respondents couldn't afford the average amount of money that the participants were willing to pay for the CBHI scheme. So, the government should consider the economic status of the communities while revising the CBHI scheme premium not to miss those who cannot afford the contribution.

Dataset Information

Exploring households' resilience to climate change-induced shocks using Climate Resilience Index in Dinki watershed, central highlands of Ethiopia.

Publications

Exploring households' resilience to climate change-induced shocks using Climate Resilience Index in Dinki watershed, central highlands of Ethiopia.

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