Diffusive CH <sub>4</sub> Emissions From Agricultural Ditches Overshadow CH <sub>4</sub> Sinks by Upland Fields
Bibliographic record
Abstract
Abstract Ditches are potentially important sources of methane (CH 4 ) in agricultural regions, but their CH 4 emissions are largely unknown due to data scarcity. Here, we investigated CH 4 concentrations and diffusive fluxes across different ditches in the North China Plain (NCP), an extensive upland agricultural region with maize‐wheat rotations, and well‐constructed ditch systems. Based on intensive monthly and extensive regional surveys, we found that (mean ± SD) CH 4 concentrations (11.42 ± 37.69 μmol L −1 ) and fluxes (344.7 ± 1,198.1 μmol m −2 h −1 ) in the agricultural ditches (ADs) showed high variability, primarily driven by spatial and temporal heterogeneity in nutrient and carbon inputs. On average, CH 4 concentrations and fluxes were 3–12 times higher than those in the nearby agricultural‐rural ditches (3.80 μmol L −1 , 99.8 μmol m −2 h −1 ) and rivers (0.92 μmol L −1 , 47.1 μmol m −2 h −1 ). Dissolved organic carbon (DOC) and ammonium (NH 4 + –N) were primary drivers of CH 4 emissions in the ADs, highlighting the key role of nutrient and carbon inputs from surrounding fields. The annual diffusive CH 4 emission from ADs in the NCP was estimated to be 1,836.3 ± 311.6 Gg CH 4 yr −1 and 68.1 ± 7.3 Gg CH 4 yr −1 based on the mean and median CH 4 fluxes, respectively, acting as a significant source of CH 4 emissions, despite large uncertainty. This emission overwhelmingly offsets the CH 4 uptake by soils (i.e., −9.2 Gg CH 4 yr −1 ) in the NCP, highlighting the necessity of including CH 4 emissions from ADs in estimating CH 4 budget from upland agricultural regions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".