Methane Oxidation in an Intensively Cropped Tropical Rice Field Soil under Long‐Term Application of Organic and Mineral Fertilizers
Bibliographic record
Abstract
Methane (CH4) oxidation is the only known biological sink process for mitigating atmospheric and terrestrial emissions of CH4, a major greenhouse gas. Methane oxidation in an alluvial soil planted to rice (Oryza sativa L.) under long-term application of organic (compost with a C/N ratio of 21.71), and mineral fertilizers was measured in a field-cum-laboratory incubation study. Oxidation rates were quantified in terms of decrease in the concentration of CH4 in the headspace of incubation vessels and expressed as half-life (t(1)2) values. Methane oxidation rates significantly differed among the treatments and growth stages of the rice crop. Methane oxidation rates were high at the maximum tillering and maturity stages, whereas they were low at grain-filling stage. Methane oxidation was low (t(1)2) = 15.76 d) when provided with low concentration of CH4. On the contrary, high concentration of CH4 resulted in faster oxidation (t(1)2) = 6.67 d), suggesting the predominance of "low affinity oxidation" in rice fields. Methane oxidation was stimulated following the application of mineral fertilizers or compost implicating nutrient limitation as one of the factors affecting the process. Combined application of compost and mineral fertilizer, however, inhibited CH4 oxidation probably due to N immobilization by the added compost. The positive effect of mineral fertilizer on CH4 oxidation rate was evident only at high CH4 concentration (t(1)2 = 4.80 d), while at low CH4 concentration their was considerable suppression (t(1) = 17.60 d). Further research may reveal that long-term application of fertilizers, organic or inorganic, may not inhibit CH4 oxidation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".