Climate change perception, adaptation, and constraints in irrigated agriculture in Punjab and Sindh, Pakistan
Bibliographic record
Abstract
Abstract Pakistan's irrigated agriculture suffers from climate change due to its high exposure to climate extreme events and the low adaptation of its farming systems. Understanding the human aspects of adaptation decisions in a vulnerable climatic environment is integral for policymakers who want to enhance farmers’ adaptive capacity. This study investigates how farmers perceive climate change and what adaptation strategies they consider. Furthermore, we assess the enabling and constraining factors influencing farmers’ adaptation decisions. We conducted in-person interviews with 800 farmers across Pakistan's irrigated districts of the Punjab and Sindh provinces. We used a standardized questionnaire to gather primary cross-sectional data, which we analyzed with descriptive statistics. The results show that farmers in the Indus Plain have noticed changes in climate extremes along with longer summer and shorter winter seasons during the last ten years. Most farmers are aware of adaptation options and have already applied some measures. However, the dominant adaptation strategies differ between regions. The farmers in Punjab have primarily adopted crop and farm management practices, while farmers in Sindh have focused on implementing irrigation measures. In both provinces, farmers regarded rainwater harvesting as the least adopted strategy due to perceived lower effectiveness and practical challenges. The main constraints in the region are a lack of financial resources, water scarcity, and poor soil fertility. Farming decisions are primarily influenced by the availability of financial capital, and specific challenges such as variable rainfall patterns and rising temperatures. Our findings can help policymakers design better policy instruments that account for farmers’ perceptions, motivations, and constraints and are thus more effective in promoting sustainable farming practices in Pakistan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".