Integrating indigenous traditional knowledge and ergonomic efficiency for sustainable millets farming: a study from Koraput, Odisha, India
Bibliographic record
Abstract
Millets offer significant potential for sustainable agriculture in tribal regions, yet the role of Indigenous Traditional Knowledge and the ergonomic burden on farmers, particularly women remain underexplored. This study addresses these gaps by examining the interplay between Indigenous Traditional Knowledge, physical drudgery, and millet farming practices in Koraput, Odisha. Despite wide use of ecologically sound traditional methods, farmers especially women face substantial drudgeries in post-harvest tasks such as threshing, de-hulling, and grinding. A mixed-methods approach was used to collect data from 100 randomly selected tribal farmers across three villages through structured interviews, key informant interviews, focus group discussions, and field observations. The human physical drudgery index was applied to assess ergonomic risks. Findings showed that traditional practices, while sustainable, impose very high physical strain in case of threshing (Drudgery Index = 84.63) and flour making (Drudgery Index = 71.62), ranked as the most drudgery-intensive operations. Improved tools significantly reduced the human physical drudgery index but were underutilized due to affordability, limited access, and cultural preferences. The study highlights the need to integrate validated indigenous traditional knowledge with gender-sensitive ergonomic innovations to enhance sustainability and farmer wellbeing. Policy measures should focus on self-help groups led and or custom hiring services of the machinery, expanding participatory extension, and subsidies for women-friendly equipment. Reducing drudgery while preserving traditional knowledge is key to resilient and inclusive millet-based farming systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".