Resilient Local Food Systems and Capabilities for Sustainable Development in Uttarakhand
Bibliographic record
Abstract
This paper explores how resilient, locally rooted food systems in Uttarakhand can serve as a foundation for achieving nutrition, livelihood, and ecological security in alignment with the Sustainable Development Goals (SDGs). Drawing on Amartya Sen’s Capability Approach and recent literature on transformative resilience, the study argues that food resilience in mountain regions must go beyond short-term adaptation to build systems that expand human freedoms and capabilities. Using a combination of secondary data, regional policy analysis, and studies from Uttarakhand, the paper identifies key challenges, including the marginalization of traditional crops in food provisioning schemes, limited mountain-specific agricultural support, and institutional fragmentation across food, nutrition, and climate programs. At the same time, promising models—such as women-led food enterprises, local millet procurement, and climate-adaptive cropping practices—demonstrate how capability-enhancing interventions can promote both nutrition security and systemic resilience. The findings underscore the need for a more integrated and regionally sensitive policy framework that recognizes the ecological, cultural, and economic value of traditional food systems. The paper suggests policy focus areas that need to be undertaken by various key stakeholders to improve and attain sustainable development goals and capabilities of the region. By supporting traditional local food and advancing institutional support for agroecology, local procurement, and gender-inclusive governance, Uttarakhand can move toward a food system that not only withstands shocks, but also prosper. Cite as: Sharma, D. (2025). Resilient Local Food Systems and Capabilities for Sustainable Development in Uttarakhand. Journal of Agricultural Science, 17(9), 1-18.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".