Enhancing sweet potato yield: An overview of strategies for high-yield and sustainable production
Bibliographic record
Abstract
Sweet potato ( Ipomoea batatas. L), the third most important root crop globally, offers significant nutritional value and high yield potential., making it a critical crop for food security, particularly in developing regions. However, its production is influenced by various factors. As the global population grows and dietary demands shift toward more sustainable and nutritious food sources, there is an increasing need to enhance the productivity of sweet potato cultivation. This review provides a comprehensive overview of strategies to improve sweet potato yield, with a focus on practical agricultural approaches. Key aspects such as the selection of high-yield, disease-resistant varieties are emphasized. Soil management practices, including optimal soil conditions, preparation techniques, and nutrient management, are demonstrated. Planting techniques, including optimal timing, plant density, and propagation methods, are also highlighted. Effective irrigation and water management strategies for different growth stages are crucial and addressed in detail. Pest and disease management is addressed through integrated approaches, while weed control strategies emphasize sustainable practices. Nutrient management and fertilization are addressed, comparing organic and synthetic options. Finally, the review offers guidelines on harvesting handling to reduce losses and maximize yield. In summary, integrating genetic improvement with effective agricultural practices can significantly enhance sweet potato production. However, future research should prioritize developing new varieties with higher yield potential and greater tolerance, ultimately improving the marketability and economic viability of sweet potato farming.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".