Climate change adaptation: a study of multiple climate-smart practices in the Nile Basin of Ethiopia
Bibliographic record
Abstract
Improving farm-level use of multiple climate change adaptation strategies is essential for improving household food security, particularly against a backdrop of a high risk of climatic shocks. However, the empirical foundation for understanding how farm households choose multiple climate-smart practices is far from being established. In this paper, the effects of household, farm and climatic factors on farmers’ decisions to use multiple adaptation practices are analysed. A survey of 921 farm households and 4312 farm plots combined with historical climate data in the Nile Basin of Ethiopia is explored using multivariate and random effect ordered probit econometric models. Results show agricultural production can be characterized by complementarities between adaptation practices. This result is important to designing packages of adaptation practices. The econometric results confirm that social capital, tenure security and climatic shocks are important determinants of the choice of the type and number of adaptation practices. The results suggest the need for carefully designing combinations of adaptation strategies based on agro-ecological conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".