Recent Insights into Molecular Breeding for High Yield Sweet Potato Cultivars
Bibliographic record
Abstract
Sweet potato is a vital staple crop with significant potential to address global food security challenges. Developing high-yield cultivars is essential to enhance productivity and meet increasing demand, and molecular breeding has emerged as a promising approach for achieving these goals. This study explores recent advancements in molecular breeding techniques applied to sweet potato, with a focus on understanding its unique genomic architecture and genetic diversity. Key methods such as marker-assisted selection (MAS), genomic selection (GS), CRISPR-based gene editing, and RNA interference (RNAi) are examined for their role in improving yield-related traits, photosynthetic efficiency, storage root development, and stress tolerance. A case study on breeding programs in China highlights successful cultivar development and lessons for global breeding efforts. This study also addresses challenges in molecular breeding, including polyploidy complexities and limitations in genomic tools, while outlining future opportunities such as the integration of artificial intelligence (AI) and international collaborations. This study emphasizes the need for targeted breeding strategies and policy support to ensure the development of resilient, high-yield cultivars capable of contributing to food security and sustainable agriculture.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".