Unpacking Climate Impacts and Vulnerabilities of Cotton Farmers in Pakistan: A Case Study of Two Semi-arid Districts
Bibliographic record
Abstract
This paper aims to contribute to the understanding of climate risks and vulnerability facing cotton farmers in semi-arid regions of Pakistan. Given the ever-increasing climate change impacts on cotton production in Pakistan, especially in semi-arid regions where water scarcity puts additional pressure on water sensitive agricultural livelihoods, we have conducted this study to identify climate risks facing cotton farmers in two semi-arid districts of Punjab province (average annual contribution to total cotton production is 80%), based on various climate indicators (such as temperature, rainfall and climate extremes.). A mix of qualitative and quantitative methods has been used to explore factors of vulnerability and comparative vulnerabilities. In the cotton production stage, we found that vulnerability to climate change decreases with increase in the size of the landholding, mainly because large farmers have more financial resources at their disposal to deal with adverse climate impacts, such as crop damages and losses. Adaptive capacity, on the other hand, is found to be one of the significant factors determining the overall vulnerability at the household level, as level of exposure and sensitivity do not differ across different sized households. In other words, indicators of adaptive capacity, such as access to financial resources, diversified livelihoods and access to weather information plays a major role in reducing vulnerability against 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 | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 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) | 0.002 | 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".