Farm Households Choices of Adaptation Strategies to Climate Variability Challenges in Benishangul Gumuz Regional State, Western Ethiopia
Bibliographic record
Abstract
Climate variability and change are a serious threat to the livelihoods of rural communities because they are very sensitive to such changes. This study assesses the major adaptation strategies pursued by farm households to climate variability and change impact in Benishangul Gumuz regional state, western Ethiopia which is harshly affected by climate change stresses. The data were collected from a randomly selected 385 sample households through interview using field-based questionnaires and focus group discussions and analyzed using descriptive statistics. The results pointed out that the likelihood of households to adopt crop diversity, soil and water conservation practice, small scale irrigation, crop rotation, adjusting planting date and improved crop varieties were 54.2%, 49.8%, 47.3%, 45.3%, 44.4% and 43.5% respectively. Moreover, the results indicated that the joint likelihood of using all adaptation strategies was only 1.64% and the joint likelihood of failure to adopt all of the adaptation strategies was 2.92%. Therefore, future policy should focus on towards supporting improved extension service, offer climate related training and information especially to adaptation technologies to increase the farm households experience in adopting different strategies to the negative effects of climate variability which is a global problem of this century.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".