Climate smart agricultural practices and gender differentiated nutrition outcome: An empirical evidence from Ethiopia
Bibliographic record
Abstract
Since the beginning of the decade, climate resilient green economy strategies have been proposed in many African countries. One of the pillars of the strategies is the adoption and diffusion of various climate smart agricultural practices for improving crop and livestock production and farmer income while reducing greenhouse gas emissions. The effects of these innovations on household nutritional security, including gender-differentiated nutritional status, have hardly been analyzed. We examine the determinants of adoption of combinations of multiple climate smart agricultural innovations and their impact on different nutrition outcomes. We find that adoption of climate smart innovations increases dietary diversity and improves calorie and protein availability. These benefits increase with adoption of combinations of innovations, relative to adopting an innovation in isolation. Gender-disaggregation results suggest nutritional outcome differentials between male and female headed households due to both differences in household characteristics, including household resources, and differences in returns to resources. The study provides insight into the interaction between climate change adaptation and nutrition security among male and female headed households, with implication for the Sustainable Development Goals of ending hunger, achieving gender equality, and taking action on climate change.
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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 | 0.002 | 0.004 |
| 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) | 0.005 | 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".