Ethnolinguistic associations and genetic diversity of rice landraces in Nagaland, India
Bibliographic record
Abstract
Societal Impact Statement Preserving and conserving crop landraces, tended by indigenous farming communities, is crucial for future food security. This research focused on rice landrace diversity in the north‐eastern Himalayan region of Nagaland, India, where Naga communities cultivate rice according to their dietary and cultural preferences. Rice diversity is closely linked to the region's ethnolinguistic and ecological variety. On‐farm conservation, in collaboration with indigenous communities, is imperative to protect these germplasm resources. Involving these communities actively in conservation efforts will safeguard their traditional knowledge, endorse sustainable farming practices, and enhance the resilience of local agricultural systems. Summary Understanding the genetic diversity and cultural significance of crop landraces is crucial for their conservation and sustainable utilization. This study focused on rice landraces from Nagaland in north‐eastern India to assess their genetic diversity and explore their associations with ethnolinguistic groups. We collected 78 rice landraces from Nagaland and a small part of Manipur and conducted microsatellite genotyping for genetic analysis. We integrated social anthropology and population genetics analyses of rice landraces to glean insights into the genetic diversity, population structure, and ethnolinguistic history of rice cultivation in Nagaland. The study revealed the rich cultural significance of rice landraces among the Nagas . Farmers practiced small‐scale subsistence farming, maintaining diverse rice landraces. Naming conventions were based on factors such as seed source, color, grain type, and ecological suitability. Rice landraces played important roles in ethnic cultures, festivals, and religious ceremonies. Genetic analysis identified significant diversity, with 277 alleles across 69 loci and a moderate gene diversity of 0.57. Two distinct sub‐populations were identified, with one dominated by Chakhesang and Angami Nagas and the other by Sümi and Lotha accessions. Differentiation was observed between lowland and upland cultivars, with one sub‐population comprising exclusively lowland varieties. Cultural factors and cultivation practices influenced population differentiation, with ethnicity and ecotype having a significant impact. The study also highlighted the correlation between ethnolinguistic differentiation and the indica–japonica structuring of rice landraces. Different ethnic groups in Nagaland had distinct cultivation practices, contributing to genetic differentiation. Overall, this research emphasizes the need to preserve rice landraces and associated traditional knowledge for future improvements and cultural heritage conservation. It provides insights into genetic diversity, cultural significance, and the relationship between genetic diversity, cultural practices, and agricultural traditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".