Sustainable Farming to Achieve Future-proof Food Security
Bibliographic record
Abstract
Abstract In recent years, there has been an increasing focus on changing the agri-food system, especially in terms of agricultural, food, and nutrition security. The global food system must undergo changes in social norms and technology to achieve environmental sustainability and sufficient productivity to support the growing global population. Multiple factors’ interaction is leading to transitional changes, such as decreased productivity for certain crops and a rapid increase in demand for processed foods. Therefore, the urgent transformation of global food and agricultural systems in the direction of sustainable agriculture is necessary and is one of the most challenging tasks facing humanity. Therefore, ensuring future food security in line with sustainable agriculture is of great importance. In this chapter, the authors review the literature on the disadvantages, limitations, and practical challenges of sustainable agriculture in relation to future food security. The authors also propose solutions to stabilize agricultural practices that can ensure future food security. This study shows that achieving secure future food security through sustainable agriculture is a complex process that requires comprehensive support. Various factors are involved, the most important of which is the size and management of agricultural land. To deal with these challenges based on these factors, appropriate solutions such as land consolidation, precision agriculture (PA), cropping patterns, climate-smart agriculture (CSA), and family farming should be implemented.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".