Water-saving irrigation can mitigate climate change but entails negative side effects on biodiversity in rice paddy fields
Bibliographic record
Abstract
Tackling climate change while enhancing biodiversity without compromising production is a main goal in agricultural management. In rice farming, water-saving irrigation techniques alternative to permanent flooding have been globally adopted to face more severe and frequent droughts and have proven effective in reducing greenhouse gas emissions. Yet potential trade-offs with other global concerning environmental issues such as 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 (continuous flooding as the lowest intensity water use management; mid-season drainage (MSD) as medium intensity; and alternate wetting and drying (AWD) as the highest intensity management) on i) greenhouse gas emissions, ii) the abundance and diversity of freshwater biological communities, and iii) crop yield. While a positive climate change mitigation effect was observed under water-saving practices (92.5 % and 67.3 % methane emission decreases for AWD and MSD, respectively, when compared to continuous flooding), these resulted negative for biodiversity conservation. Even though AWD decreased species richness only at the richness peak, a strong negative effect was observed on the abundance of aquatic organisms (decapods, heteropterans, odonates and amphibians). Grain yield decreased 12.9 % with AWD management as opposed to continuous flooding but did not vary under MSD. Even though wider adoption of water-saving strategies might help achieving climate mitigation goals while maintaining yields, negative effects on biodiversity should be addressed to preserve highly diverse communities of aquatic organisms and the broad range of ecosystem services they provide. These results point towards marked trade-offs among different agri-environmental issues, therefore, we advocate for more integrative solutions that account for potential side effects when designing alternative water management plans.
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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 | 0.000 | 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) | 0.001 | 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".