Rice farmers’ perceptions about temperature and rainfall variations, respective adaptation measures, and determinants: Implications for sustainable farming systems
Bibliographic record
Abstract
In Pakistan, climate change is adversely affecting agricultural production and undermining the food security and subsistence of millions of farm households. Farmers’ understanding of climate change and their adaptation strategies can serve as a useful step to help minimize climate risks. This study explores farmers’ perception of and adaptation strategies to climate change and their determinants in the rice-growing zone of Punjab province, as this region of the country is highly vulnerable to climate change impacts. The multistage stratified-random sampling method was used to select 480 farmers from the four rice districts of the region, and data were collected using a structured questionnaire. Logistic regression and contingency tables are used to analyze the determinants of farmers’ adopted strategies and adaptation extent (number of adopted strategies). Results show that farmers perceived significant changes in the climate, including the rise in average summer and winter temperatures and the decline in overall precipitation. The study further found that farmers’ adopted adaptation strategies include supplementary irrigation, adjustments in rice cultivation dates, crop diversification, use of climate-smart varieties, better fertilizer management, and farm resizing. Logit model showed that farmers’ age, primary occupation, income, landholding, access to irrigation, credit, climate information, and farm advisory appeared to be the significant determinants of their adaptation decision. The adaptation extent strongly correlates with farmers’ education and access to climate information and credit services. Based on these findings, this study suggests the relevant institutions improve farmers’ access to irrigation water, credit, farm advisory, and climate information to improve their adaptation extent and hence resilience of the rice-farming system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".