Analysing the Impact of Climate Change on Cotton Productivity in Punjab and Sindh, Pakistan
Bibliographic record
Abstract
The study analyses the impact of climate change on productivity of cotton in Pakistan using the district level disintegrated data of yield, area, fertilizer, climate variables (temperature and precipitation) from 1981-2010. Twenty years moving average of each climate variable is used. Production function approach is used to analyse the relationship between the crop yield and climate change. This approach takes all the explanatory variables as exogenous so the chance endogenity may also be minimized. Separate analysis for each province (Punjab and Sindh) is performed in the study. Mean temperature, precipitation and quadratic terms of both variables are used as climatic variables. Fixed Effect Model, which is also validated by Hausman Test, was used for econometric estimations. The results show significant impact of temperature and precipitation on cotton yields. The impacts of climate change are slightly different across provinces— Punjab and Sindh. The negative impacts of temperature are more striking for Sindh. The impacts of physical variables—area, fertilizer, P/NPK ration and technology, are positive and highly significant. The results imply educating farmers about the balance use of fertilizer and generating awareness about the climate change could be feasible and executable strategies to moderate the adverse impacts of climate change to a reasonable extent.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".