Options and Determinants of Rice Residue Management Practices in the South-West Region of Bangladesh
Bibliographic record
Abstract
Farmers in Bangladesh burnt an estimated 3.14 million metric tons of rice residue in 2010. Rice residue burning contributes to climate change and pollution through the release of gaseous and particulate matter. Thus, this study examines options for managing rice residue and the factors that determine its management in the south-west region of Bangladesh. Study results indicate that while straw length, low-elevation land and distance of the plot from the homestead positively and significantly influence the decision to burn rice residue, residue price has a negative effect. Farmers who burn residue enjoy a net annual benefit of USD 43-45/acre on average relative to farmers who don't burn. This benefit accrues because productivity is higher by about 9 percent in fields where burning occurs and the costs of rice harvesting, including residue burning, are lower by about 10 percent. Aggregating from our sample survey and assuming similar trends in the rest of Bangladesh, our study estimates that farmers would need to be subsidized approximately USD 2.10 million per year in order to avoid rice residue burning in Bangladesh. This amounts to approximately 4% of the subsidies currently available to farmers for fertilizer use and other purposes. Our study also proposes alternate strategies such as support for purchasing new varieties of seeds and investment in information and education to persuade farmers to move to short-straw varieties on high and medium-elevation lands. Another option might be to switch from residue burning to incorporation. Research and development efforts into shortening straw length, shortening the time-period required between harvesting and planting and new rice varieties may also help to mitigate residue burning.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".