Reconciling climate change mitigation, biodiversity conservation and rice production through changes in water management strategies
Bibliographic record
Abstract
Tackling climate change while enhancing biodiversity without compromising production is a main goal in agricultural policy. In rice farming, water-saving irrigation techniques alternative to permanent flooding are necessary to face water scarcity and have proven effective in reducing greenhouse gas (GHG) emissions, yet potential trade-offs with biodiversity conservation are often overlooked. Here we used a field-scale experiment to compare the effects of water management strategies representing a water use gradient on i) GHG emissions, ii) the diversity of aquatic macroinvertebrate and vertebrate (fish and amphibians) communities, and iii) crop productivity. Reduced methane emissions were observed in rice fields with lowest water use when compared to fields permanently flooded, yet the effect on aquatic biodiversity and crop yield was the opposite. Through this holistic assessment approach, we were able to identify an intermediate rice water-saving irrigation strategy that conciliates climate change mitigation, biodiversity conservation and crop production in rice agrosystems.
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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 | 0.001 |
| 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".