Climate change mitigation through irrigation strategies during rice growing season is off-set in fallow season
Bibliographic record
Abstract
Non-continuous flooding irrigation practices, such as alternate wetting and drying (AWD) and mid-season drainage (MSD), have been implemented in rice agroecosystems to reduce water use and mitigate climate change. Draining fields reduces methane (CH 4 ) emissions, as soil aeration decreases the abundance and activity of soil methanogens. Mitigation effects during the growing season have been widely studied. However, there is a knowledge gap regarding potential effects these growing season practices might have on subsequent fallow season emissions. This is relevant when assessing overall annual CH 4 emissions, particularly in systems in which fallow seasons account for a significant part of these. A field experiment was implemented in the Ebro Delta region (Catalonia, Spain) with the objective of identifying potential effects of growing season AWD and MSD on CH 4 emitted during the following flooded fallow season, in comparison to continuously flooded fields. Both emissions and the structure of soil microbial communities were analyzed for rice field plots under the assessed irrigation strategies during the growing season and later for a continuously flooded mesocosm across the fallow season. Both practices achieved an average 86% decrease in CH 4 fluxes when compared to continuous flooding during the growing season. AWD resulted in the highest fallow season emissions, leading to increases in overall annual cumulative CH 4 emissions ( + 8%), global warming potential ( + 30%) and yield-scaled global warming potential ( + 70%) compared to continuous flooding. Growing season AWD decreased the relative abundance of both methanogens and methanotrophs in the fallow season. Reduced methanotroph communities might lead to lower CH 4 consumption, resulting in higher fallow season emissions and offsetting the mitigation effect achieved during the growing season. Under the studied conditions, MSD represented a more effective mitigation strategy. These results highlight the importance of considering both rice growing and fallow season when assessing climate change mitigation strategies. • AWD climate change mitigation effect is off-set in flooded fallow seasons. • MSD can be a more effective mitigation strategy, depending on system conditions. • AWD can decrease yields, increasing its annual yield scaled global warming potential. • Irrigation strategies result in legacy effect on soil microbial communities. • Assessments of rice management must consider both growing and fallow seasons.
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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 | 0.001 |
| 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".