Innovative Breeding Techniques for Cassava: The Role of Doubled Haploids and Genetic Engineering
Bibliographic record
Abstract
Cassava ( Manihot esculenta Crantz) is a crucial crop for food security in tropical and subtropical regions. However, its genetic improvement is hindered by its long breeding cycle and heterozygous nature. This study explores innovative breeding techniques, focusing on the role of doubled haploids (DH) and genetic engineering in accelerating cassava breeding. Doubled haploid technology, which enables the rapid production of homozygous lines, has been successfully applied in various crops and holds promise for cassava improvement. Techniques such as gynogenesis, another culture, and interspecific pollination are discussed for their potential to induce DHs in cassava. Additionally, advancements in genetic engineering, including CRISPR/Cas9 and other gene-editing tools, are examined for their role in enhancing DH production and incorporating desirable traits. The integration of these innovative techniques could significantly shorten the breeding cycle and improve cassava's adaptability to changing environmental conditions. This study highlights the current state of DH and genetic engineering technologies, their applications in cassava breeding, and future directions for research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".