DOES ADOPTION OF MULTIPLE CLIMATE-SMART PRACTICES IMPROVE FARMERS’ CLIMATE RESILIENCE? EMPIRICAL EVIDENCE FROM THE NILE BASIN OF ETHIOPIA
Bibliographic record
Abstract
There is a paucity of information on conditioning factors that hinder or promote adoption of multiple climate-smart practices and on the synergies among such practices in increasing household resilience by improving agricultural income. This study analyzes how heat, rainfall, and rainfall variability affect farmers’ choices of a portfolio of potential climate smart practices — agricultural water management, improved crop seeds, and fertilizer — and the impact of these practices on farm income in the Nile Basin of Ethiopia. We apply a multinomial endogenous switching regression approach by modeling combinations of practices and net farm income for each combination as depending on household and farm characteristics and on a set of climatic variables based on geo-referenced historical precipitation and temperature data. A primary result of this study is that farmers are less likely to adopt fertilizer (either alone or in combination with improved varieties) in areas of greater rainfall variability. However, even when there is high variability in rainfall, farmers are more likely to adopt these two yield-increasing inputs when they choose to (and are able to) include the third part of the portfolio: agricultural water management. Net farm income responds positively to agricultural water management, improved crop variety or fertilizer when they are adopted in isolation as well as in combination. But this effect is greater when these practices are combined. Simulation results suggest that a warming temperature and decreased precipitation in future decades will make it less likely that farmers will adopt practices in isolation but more likely that they will adopt a combination of practices. Hence, a package approach rather than a piecemeal approach is needed to maximize the synergies implicit in various climate smart practices.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".