Vulnerability of Farmers to Crop Farming Risks in Ethiopia: An Integrated Vulnerability Analysis Approach Using Social-Ecological System Framework
Bibliographic record
Abstract
Crop production under a smallholder system is challenged by several (a)biotic risks those resulted in livelihood insecurity. This study assesses farmers’ perceived vulnerability level to the crop farming risks and identifies its determinants using an integrated vulnerability analysis approach. Survey data collected from 393 sample households in West Shewa Zone, Ethiopia, were analyzed using PCA and ordered probit regression. Results indicate that 13 percent of the sampled households are highly vulnerable, 73.5 percent are moderately vulnerable and 13.5 percent are less vulnerable where 77 percent of the highly vulnerable groups faced more than 3 months lean season and 72 percent of the less vulnerable groups faced less than 3 months of lean season. Moreover, farming experience and education level of household head, livestock owned, farm size, on-farm diversification, access to credit, small scale irrigation, off-farming income, extension contact, and social capital are significantly affecting the perceived vulnerability level. These calls for need-based government and/or non-government intervention plans focusing on improving rural infrastructure and facilities and devising an effective and responsive institutional setup for enhancing the responsive capacities of smallholder farmers in the short-run and minimizing the likelihood of exposure and sensitivity in the long-run.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".