Does exposure to weather variability deter the use of productivity‐enhancing agricultural technology? Evidence from Ethiopia
Bibliographic record
Abstract
Abstract This study examines how farm households' decision to use modern agricultural inputs is influenced by the weather variability using a household panel survey merged with long‐term historical weather data from Ethiopia. As a part of an effort to fill methodological gaps that are observed and anticipated in previous similar literature, this study employs a more flexible modeling approach under a multiagricultural technologies framework. Findings suggest that weather uncertainty reduces the probability and intensity of adoption of productivity‐enhancing inputs, including chemical fertilizer and improved seed. The average partial effect for rainfall variability indicates that each additional percent of coefficient of variation of precipitation decreases the fertilizer use per hectare by, on average, 2.5%, other factors being constant. Similarly, a 1% increase in rainfall variability is associated with on average 0.6% and 4.5% decrease in probability and extent of improved seed use, respectively. We also observe that abundance rainfall during the previous production period increases the use of yield‐enhancing inputs in the current growing season. On the other hand, we observe that variability in rainfall increases the probability of the adoption of loss‐reducing agricultural technology, such as irrigation. Findings also indicate that by deterring their incentive to invest in productivity‐enhancing agricultural inputs, weather risk could confine uninsured farming households in a low productivity‐low income trap. Results also highlight the importance of policy interventions aiming to improve the risk‐bearing capacity of smallholder farmers in encouraging investment in productivity‐enhancing technologies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".