The Potential Role of Precision Agriculture in Building Sustainable Livelihoods and Farm Resilience Amid Climate Change: A Stakeholders’ Perspective from Southern Punjab, Pakistan
Bibliographic record
Abstract
This study explores the potential role of precision agricultural technologies (PATs) in enhancing the physical, natural, human, financial, and social capitals of farming communities in the southern Punjab region of Pakistan, specifically focusing on the districts of Bahawalpur, Rahim Yar Khan, Dera Ghazi Khan, and Multan. A stratified random sampling method with proportional allocation was employed to gather insights from four heterogeneous key stakeholder groups, including progressive farmers, researchers, extension agents, and academicians, yielding a total sample of 287 respondents. A structured questionnaire utilizing a five-point Likert scale was administered, allowing the respondents to assess the perceived potential impacts of the PATs on various livelihood assets. The findings reveal that while stakeholders recognized some potential for PATs to improve physical assets, natural resources, and human capital, the overall perceived impact remained limited across all dimensions. The highest-rated potential impact was noted in crop diversity, with an average score of 2.26 in the physical capital category. In the category of natural capital, precise plant protection practices were rated the highest, with an average score of 2.31 that showed little potential change. A reduction in labor displacement issues and generating skilful employment resources, with average scores of 2.12, were rated the highest in the human capital category. A slight increase in family income, with an average score of 2.28, was observed in the financial capital category, highlighting cautious optimism among respondents. Additionally, reducing family problems and social issues, with an average score of 2.20, was rated the highest, leading to a minimal perceived change in social capital, indicating a need for integrated approaches to foster stronger community ties. The results underscore the necessity for targeted interventions that combine technological adoption with community engagement to enhance the overall resilience of farming systems. This research contributes valuable insights into adopting PATs and their implications for sustainable livelihoods, emphasizing the importance of aligning technological advancements with the unique needs of farming communities in the face of a changing climate.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".