Strategizing sustainable food security in Saudi Arabia: A policy and scenario approach to agricultural resilience
Bibliographic record
Abstract
Saudi Arabia confronts major challenges in ensuring food security amid sustainability constraints that are exacerbated by freshwater scarcity and a dependency on food imports. This study seeks to holistically assess the Kingdom's agricultural landscape in light of its Vision 2030 objectives as well as broader global sustainability initiatives such as the Sustainable Development Goals. Drawing from a review of agricultural reports, including the 2015 Agricultural Census and Agricultural Production Survey Publications spanning 2018–2021, the research relies on a two-pronged methodology focused on scenario and policy analyses. By envisioning possible future agricultural scenarios grounded in present-day data and contrasting Saudi Arabia's efforts with global examples, we provide comprehensive policy and extension service recommendations. A separate focus has been placed on technological modernization and the key role of agricultural extensions in actualizing policy directives. The study culminates by discussing areas of concern for Saudi Arabia's agricultural sector, complemented with constructive suggestions for deeper research pursuits. Our findings stress the significance of water-saving technology like hydroponics and greenhouse farming for efficient Saudi agriculture. Moreover, a strengthened, science-based extension system integrating policies with global sustainability goals is vital for climate-resilient food security. This research serves as a foundation for scholars and stakeholders aiming to navigate Saudi Arabia's path toward a sustainable and resilient food future.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".