Genetic structure and diversity of indigenous rice (Oryza sativa) varieties in the Eastern Himalayan region of Northeast India
Bibliographic record
Abstract
The Eastern Himalayan region of Northeast (NE) India is home to a large number of indigenous rice varieties, which may serve as a valuable genetic resource for future crop improvement to meet the ever-increasing demand for food production. However, these varieties are rapidly being lost due to changes in land-use and agricultural practices, which favor agronomically improved varieties. A detailed understanding of the genetic structure and diversity of indigenous rice varieties is crucial for efficient utilization of rice genetic resources and for developing suitable conservation strategies. To explore the genetic structure and diversity of rice varieties in NE India, we genotyped 300 individuals of 24 indigenous rice varieties representing sali, boro, jum and glutinous types, 5 agronomically improved varieties, and one wild rice species (O. rufipogon) using seven SSR markers. A total of 85 alleles and a very high level of gene diversity (0.776) were detected among the indigenous rice varieties of the region. Considerable level of genetic variation was found within indigenous varieties whereas improved varieties were monoporphic across all loci. The comparison of genetic diversity among different types of rice revealed that sali type possessed the highest gene diversity (0.747) followed by jum (0.627), glutinous (0.602) and boro (0.596) types of indigenous rice varieties, while the lowest diversity was detected in agronomically improved varieties (0.459). The AMOVA results showed that 66% of the variation was distributed among varieties indicating a very high level of genetic differentiation in rice varieties in the region. Two major genetically defined clusters corresponding to indica and japonica groups were detected in rice varieties of the region. Overall, traditionally cultivated indigenous rice varieties in NE India showed high levels of genetic diversity comparable to levels of genetic diversity reported from wild rice populations in various parts of the world. The efforts for conservation of rice germplasm in NE India should consider saving rice varieties representing different types with specific emphasis given to sali and jum types. The protection against the loss of vast genetic diversity found in indigenous rice varieties in NE India is crucial for maintaining future food security in the changing world.
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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 | 0.000 | 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".