Farmers’ perceptions and capacity for 3Rs agro-waste management in a vegetable growing area of Bangladesh
Bibliographic record
Abstract
Abstract Agriculture is responsible for giving rise to huge quantities of degradable and non-degradable waste during various farming activities. A deeper understanding of farmers’ perceptions and levels of agro-waste management capacity is essential in developing locally accepted strategies for agro-waste management. This study was framed to analyze vegetable farmers’ perception and capacity for Bangladesh’s 3Rs waste management concept (reduce, reuse, and recycle). A total of 125 farmers were selected following a stratified proportionate random sampling technique and interviewed using a structured questionnaire. The findings of this study indicate that intercultural and harvesting practices produce a large variety of bio-degradable and non-degradable waste materials compared to other stages of vegetable production and marketing of produce. The overall score showed that the vegetable farmers’ have a medium (39.2%) to high (60.8%) perception of the 3Rs waste management concept, but they possessed a low perception of recycling agro-waste. However, the overall capacity score for 3Rs waste management was low (67.2%) to medium (31.2%), indicating a low capacity of vegetable growers to recycle different types of waste. This study offers suggestions for a development program that includes special training facilities for vegetable growers to strengthen their waste management capabilities based on the 3Rs concept.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".