Agronomic Traits of Cassava and Their Genetic Bases: A Focus on Yield and Quality Improvements
Bibliographic record
Abstract
Cassava ( Manihot esculenta Crantz) is a key food and industrial crop in the global tropics, valued for its high adaptability to marginal soil conditions and the starch-rich nature of its roots. As the global population continues to grow and climate change becomes more severe, the scientific community is seeking to address the challenges of food security and agricultural sustainability by improving cassava production and processing quality. This paper reviews the research progress of cassava agronomic traits and genetic basis in recent years, with special attention paid to the mining of genetic diversity, improvement of agronomic traits and application of modern biotechnology in cassava breeding. Studies have shown that the combination of traditional selective breeding, molecular marker-assisted selection (MAS), gene editing and other technologies has greatly improved the cassava root yield and starch quality. In addition, the implementation of precision agronomic technology and smart agriculture provides new possibilities for optimizing cassava production management and improving its environmental adaptability. The paper also discusses the direction of future cassava research, including further development of genetic resources, improving cassava's resilience to environmental changes and its role in the global food system.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".