Climate change risk perception and adaptation to climate smart agriculture are required to increase wheat production for food security
Bibliographic record
Abstract
Climate change poses a serious risk to wheat farmers in many regions of the world. The present study was conducted in the Sialkot District, Punjab, Pakistan, to investigate climate change trends during the past thirty years and to determine farmers’ knowledge and perceptions about climate change. The study also addresses the impacts of climate change on wheat production, current adaptation strategies, and limitations in adaptations to climate-smart agriculture (CSA) through a questionnaire-based survey. The historical weather data from the past thirty years indicated an increase in the mean annual minimum and maximum temperature and a decrease in annual total precipitation. Wheat productivity during the past thirty years showed an increasing trend but it was inconsistent. The respondents’ perception of climate change indicated that the literate farmers and those with broad farming experience were more knowledgeable about the climatic effects on wheat production. However, the survey results showed that the age of the farmers did not affect their perceptions. The current management practices are primarily based on prior experiences (70%) and traditional practices (30%). The standard management practices to increase farm productivity include an increase in fertilizer use (70%), a decrease in manure use (24%), and intercropping or switching to other crop cultivations (60%). The farmers stated that their reasons for limited adaptation to climate smart farm practices (CSFP) were due to their lack of knowledge and skills (86%), lack of modern technologies (74%), economic constraints (78%), politics (86%), and social influences (74%). Based on the survey results, the study suggests that addressing these gaps can increase farm-level wheat productivity to increase resilience. This can be achieved by introducing stateof- the-art farming practices through farmer training and by providing institutional services with a focus on climate-specific farm consultation services, leading to climate-smart agricultural practices for improved food security. Highlights - Literate farmers are more aware of climate change as compared to illiterate farmers. - The farmers emphasized the increase in both the summer and winter temperature. - Rainfall is identified as a major climate threat in the study area. - The farmers identified that the highest impact of climate change occurred during the harvest phase of wheat. - The farmers stated that the limited adoption of climate smart agricultural practices is due to lack of knowledge and technological, economic, and other gaps.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".