Household Food Diversity and Food Habits in Changing Climate of Western Bhutan
Bibliographic record
Abstract
Household food diversity index (HFDI) is qualitative measure of food consumption that reflects household access to a variety of food groups. Food habit is the way people eat food which is influenced by various factors. Impacts of climate change poses a threat on food diversity and food habit and food security in agrarian Bhutanese. The study aims to analyze if household food diversity and food habits are affected by climate change in the three ecological zones. Household food diversity and food habits in Gasa, Punakha and Wangdue Phodrang districts (Dzongkhags) were compared and relationships were drawn. Household level data were collected using survey method from 368 randomly selected households, stratified into three agroecological zones, by administering pretested semi-structured questionnaire. The survey questions were designed using guidelines of Food and Agricultural Organization (FAO). Food components consumed in the last 24 hours were recorded and grouped into 10 food groups. Food diversity indices are computed at the levels of household, Chiwog (village), Gewog (block), Dzongkhag (district), and at the whole study area. Spearman’s correlation tests were used to evaluate relationship between household food diversity Index (HFDI) and Food habit with Climate Change and Elevation. Kruskal Wallis tests ascertained association among the same four sets of variables with three Dzongkhag (district) as independent variable. In both sets of tests, the relationships were statistically significant. Climate change is affecting food diversity and food habits in the three agroecological zones. Introducing mass potato cultivation in Gasa, less water intensive rice variety in Punakha, and high yielding Jersey cows for dairy are recommended for food diversity enhancement in the study areas. Preserving traditional food culture like Aoolay from Gasa, and conserving biodiversity will contribute to mitigate impacts of climate change on food habits to achieve food security.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".