Sustainability of Agricultural Practices in Various Agro-Ecological Zones of Bhutan
Bibliographic record
Abstract
Bhutan’s agriculture sector has consistently advocated sustainable agricultural practices, yet it grapples with a range of interconnected challenges. These challenges encompass limited arable land, labour shortages, human-wildlife conflicts, and inadequate irrigation facilities, outbreaks of pests and diseases, and impacts of climate change. Therefore, this study assessed the sustainability of agricultural practices at the farm level across three agro-ecological zones: dry subtropical, cool temperate, and alpine. A total of 392 households were selected through simple random sampling for face-to-face interviews. The data collection, analysis, and reporting adhered to the guidelines of Sustainable Development Goals Indicator 2.4.1. The study focused on three dimensions of sustainability, comprising eight themes and eight sub-indicators for agriculture sustainability. The sustainability status was visualized using traffic light approach: green for desirable, yellow for acceptable and red for unsustainable. Results showed social dimensions across the study areas were in a desirable status. The environmental dimension exhibited desirable status in alpine zone. However, around 60% of agricultural land in dry subtropical and 77% in cool temperate zones have acceptable fertilizer management, while about 38% and 23% respectively fall into unsustainable category. Similarly, only about 73% of agricultural areas in the cool temperate zone are in acceptable status in pesticide management, and 23% under unsustainable status. Furthermore, the economic dimension across agro-ecological zones displayed unsustainable status in farm output value with 30% of farms in cool temperate, 78% in dry subtropical, and 74% in alpine zones. Strategic interventions are imperative in addressing economic and environmental dimensions to promote resilient and sustainable agricultural practices in Bhutan and analogous regions. Findings underscore the importance of identifying that similar impacts could potentially extend to other dry subtropical regions worldwide.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".