Crop productivity and adaptation to climate change in Pakistan
Bibliographic record
Abstract
How effective adaptation practices in response to climate change are is a crucial question confronting farmers across the world. Using detailed plot-level data from a specifically designed survey conducted in 2013, this paper investigates whether there are productive benefits for farmers who adapt to climate change in Pakistan. The impact of implementing on-farm adaptation strategies is estimated for three of the most important crops grown across Sindh and Punjab provinces: wheat, rice, and cotton. This study finds that there exists significant positive benefits from adaptation for most of the farmers in the sample. For those that actually adapted, productive benefits are positive for wheat and cotton, but not significantly different from zero for rice. For those that did not adapt, the gains from adapting to climate change for all crops are predicted to be large. These findings provide evidence that the use of strategies to adapt to climate change can have a positive impact on food security. The large estimated gains for non-adapters, however, point to the existence of barriers to the adoption of these strategies. Policies aimed at reducing these barriers would be likely to both increase short term production of households and enable them to better prepare for the potential impacts of 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".