The Tilling of Land in a Changing Climate:Panel Data Evidence from the Nile Basin of Ethiopia
Bibliographic record
Abstract
Empirical studies point to reduced tillage as a means to increase yields and reverse land degradation. A relatively neglected avenue of research concerns why farmers increase tillage frequencies. Using household plot–level panel data from the Nile Basin of Ethiopia, this article applies a random effects ordered probit endogenous switching regression model to empirically investigate the impact of weather events and other conditioning factors on farmers’ choice of tillage intensity and the effect of changing tillage frequencies on differences in farm returns. Results indicate that, while low-frequency tillage is more likely in drier areas, plot-level shocks (such as pests and diseases) are key variables in the choice of high-frequency tillage. Adoption of a low-till approach leads to increasing farm returns in low-moisture areas but high-frequency tillage provides higher returns in high-rainfall areas. Understanding how farmers’ tillage options correlate with climatic conditions and farm economies is salient for developing effective adaptation and mitigation plans
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.011 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".