Relationships Between Food Systems, Agricultural Practices, and Food Security Amidst Climate Change in Western Bhutan
Bibliographic record
Abstract
An estimated 80% of the global population, mostly poor and vulnerable farmers, are at risk of crop failure and hunger due to climate change. Food systems are the networks for organizing food production and distribution that make meals possible at the level of the consumer, however, the networks are varied. Agricultural practices are farming methods that are used to facilitate agriculture. An economic and social condition of ready access by all household members to nutritionally adequate and safe food will ensure food security. The intricate and interconnected relationship between food systems, food security, and agricultural practices amid climate change involves significant impacts on productivity, crop yields, and food resource availability, necessitating resilient and adaptable food systems to address challenges posed by climate-induced variations and ensuring overall sustainability. The study aims to assess the relationships between agricultural practices and food systems and their implication for food security in the changing climate in the three climatic zones of Gasa, Wangdue, and Punakha. Agricultural practices in Gasa, Punakha, and Wangdue districts (Dzongkhag) were compared and relationships were drawn. The sample for the study was 360 households stratified into three climatic zones (120 households in each zone), in five sub-districts (Gewog) having 12 villages (Chiwog) taking 30 randomly selected households from every village. Household-level data was collected using the survey method by administering the pretested semi-structured questionnaire. Agriculture productivity data and climate data for the last 23 years were gathered from the National Statistical Bureau (NSB) and the National Centre for Hydrology and Meteorology (NCHM) respectively. Food systems are fragile and present agricultural practices significantly impact food security. A systematic land leasing programme (SLLP), climate-smart agriculture mass land management for mechanized farming are recommendations from this study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".