Stressed crops emit more methane despite the mitigating effects of elevated carbon dioxide
Bibliographic record
Abstract
Recent studies using single environmental variables show that under aerobic conditions terrestrial plants can emit methane (CH4). However, the effects of multiple environmental factors - as components of global climate change - on aerobic CH4 emissions have been little studied. We examined the combined effects of temperature, carbon dioxide (CO2) and watering regime on CH4 emissions from six commonly cultivated crop species: faba bean, sunflower, pea, canola, barley and wheat. Plants were grown from seeds in controlled-environment growth chambers under two temperature regimes (24°C day/20°C night and 30°C day/26°C night), two CO2 concentrations (380 and 760µmolmol-1) and two watering regimes (well watered and water stressed). Plants were grown first under 24/20°C for 1 week from sowing, and then placed under experimental conditions for a further week. After the specified time, plant growth, gas exchange and CH4 emission rates were determined. Our results revealed that higher temperature and water stress significantly enhance CH4 emissions from plants, whereas elevated CO2 had the opposite effect and partially reverses the promotive effects of these factors. We suggest that the despite the mitigating effects of rising atmospheric CO2, CH4 emission may be higher in the face of ongoing global climate change in warmer and drier environments.
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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".