The impact of adaptation practices on crop productivity in northwest Ethiopia: an endogenous switching estimation
Bibliographic record
Abstract
Climate change and variability adversely affect smallholder farmers in developing countries, including Ethiopia. In response, farmers are adopting various adaptation strategies. However, there is a paucity of studies examining whether or not these responses benefit farmers in increasing crop productivity. Cognizant of this fact and its policy importance, this study empirically analyzes the impact of adaptation strategies on crop productivity in northwest Ethiopia. We collected data through household survey questionnaire, focus group discussion and key informant interview. We also analyzed time-series climate data to see how crop yield responds to climate variability. The empirical model employs the endogenous switching regression. Climate information and distance to market are validated as instrumental variables. The model revealed that farmers who adopted adaptation strategies would have gained lower yield if they had not adopted them; and those who did not adopt a strategy would have gained higher yield than if they had. Improved seed, contact with development agents (DAs), urea, compost and rainfall are significantly associated with the likelihood of increasing yield. The results also show systematic difference where age is inversely related with adapters and vice versa for non-adapters. Hence, adaptation interventions should consider these heterogeneities.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".