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Record W2776056334 · doi:10.4236/ajcc.2017.64034

Climate Change Induced Vulnerability of Smallholder Farmers: Agroecology-Based Analysis in the Muger Sub-Basin of the Upper Blue-Nile Basin of Ethiopia

2017· article· en· W2776056334 on OpenAlex
Abayineh Amare, Belay Simane

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAmerican Journal of Climate Change · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsnot available
FundersAddis Ababa UniversityDeutscher Akademischer AustauschdienstInternational Development Research Centre
KeywordsAgroecologyLivelihoodAdaptive capacityVulnerability (computing)Climate changeFood securityGeographyAgricultureAgricultural diversificationDiversification (marketing strategy)Vulnerability assessmentSocial vulnerabilityCapital assetAgroforestrySocioeconomicsEnvironmental scienceBusinessEcologyPsychological resilienceEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.306
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it