Effect of post-harvest practices on greenhouse gas emissions in rice paddies: flooding regime and straw management
Bibliographic record
Abstract
Abstract Aims To assess 1) the effect of the combination of flooding (winter flooding vs. non-winter flooding; WFL vs NWF) and timing of straw incorporation (early vs late straw incorporation; ESI vs LSI) in the post-harvest of paddy agrosystem, on a year-round global balance of greenhouse gases (GHG) exchanges, i.e. methane (CH 4 ), carbon dioxide (CO 2 ) and nitrous oxide (N 2 O); 2) the impact on the net ecosystem carbon balance (NECB) and 3) the resulting net global warming potential (GWP). Methods A field experiment was conducted with fortnightly samplings of main GHG emissions. Effect of the studied factors on GHG emissions was seasonally assessed. The net GWP is estimated from the balance between GHG (CH 4 and N 2 O) and NECB. Results NWF-LSI reduced net GWP by 206% compared to conventional post-harvest management (WFL-ESI). NECB was similar in all treatments. Avoiding winter flooding reduced CH 4 emissions significantly in the post-harvest and next growing seasons, while delay straw incorporation prevented CH 4 and CO 2 emissions during post-harvest. None of the treatments increased N 2 O emission. Environmental implications of post-harvest management options are discussed. Conclusions Post-harvest management affects net GWP of the paddy rice cultivation by modifying GHG emissions in post-harvest and next growing season without compromise sequestration C budget. The combination of non-winter flooding and late straw incorporation strategies were more effective in reducing both CH 4 and CO 2 emissions, due to avoiding higher temperatures at the time of the straw incorporation during post-harvest and increasing soil Eh conditions at the following growing season.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".