Selective agricultural and environmental practices to sustain food production and mitigate climate change
Bibliographic record
Abstract
Semi-arid and arid agricultural regions with growing populations and extreme weather vulnerability face major sustainable food production challenges. In this study, we conducted a narrative literature review of 163 published scientific articles and documented environmental case studies. The collected references were carefully analyzed and summarized to synthesize current knowledge on agricultural and environmental practices. This review evaluates climate-resilient farming methods, integrated soil fertility management, and resource-efficient irrigation systems. It combines evidence on the effectiveness of organic amendments, conservation tillage, and crop diversification in maintaining soil health, reducing greenhouse gas emissions, and improving yield stability under climate stress. Results show that adopting these practices can increase crop yields by 15–35%, raise soil organic carbon by up to 25%, lower synthetic fertilizer use by 40%, and cut greenhouse gas emissions from agriculture by 10–20%, while also supporting biodiversity and decreasing environmental impacts. These findings provide practical recommendations for policymakers and practitioners to develop region-specific adaptation strategies that protect food production, conserve natural resources, and enhance resilience against future climate challenges. Our synthesis highlights the most effective strategies for integrating sustainable practices into food systems, providing a comprehensive overview that can guide future research, policy development, and practical implementation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".