Strategizing sustainable food security in Saudi Arabia: A policy and scenario approach to agricultural resilience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it