Enhancing food and income security of rural families through production, processing and value addition of regional staple food grains
Bibliographic record
Abstract
This paper is based on a research project on local crops being currently implemented by the M.S. Swaminathan Research Foundation with the support of the Canadian International Food Security Research Fund (CIFSRF). The primary research problem is how far the local crops could be leveraged to enhance food security through participatory research interventions aimed at increased productivity and profitability and how far value chain built on these crops could enhance the income to poor farm families. Examining the extent of re-tooling of the interventions to make them women-centric for reducing their drudgery in cultivation and post-harvest processing and how enhanced consumption of these crops would improve the nutritional status are other aspects of the research problem. The important group of local crops being studied are three of the six small millets or the ‘nutri-cereals’, namely, finger millet (Eleusine coracana), little millet (Panicum sumatrense) and foxtail millet (Setaria italica). These grains are nutritionally superior to other grains for their higher levels of calcium, iron, fibre, certain limiting essential amino acids and vitamins and also nutraceutically in view of their low glycaemic index and higher anti-oxidant activity. The research examines how participatory seed selection could be used to increase productivity and how integrated method from use of quality seed to better crop management and introduction of a value chain approach could contribute to improved availability of nutritionally superior food and increased income. The study could successfully induct locally suited simple machineries to eliminate the drudgery of women in post-harvest processing of these grains, how drudgery reduction promotes consumption of these grains and how it promotes village level value addition of grain. These interventions could establish tangible benefit to the communities in terms of food availability, improved nutrition and income and better conservation of local genetic diversity of these crops.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".