Greenhouse gas emissions and carbon footprint under gravel mulching on China's Loess Plateau
Bibliographic record
Abstract
Abstract Gravel mulching technology has been widely verified as an effective solution to reduce evaporation and improve crop production on China's Loess Plateau, but its impacts on greenhouse gas (GHG) emissions have not been well documented. This study examined the quantification of the overall GHG emissions via estimating global warming potential (GWP), GHG intensity (GHGI), C footprint (CF), and C intensity (CI) with varying experimental treatments. A 2‐yr consecutive wheat–maize rotation field experiment was conducted through monitoring GHG emissions using a closed‐chamber method with four treatments: CK (control with no mulching), WCK (CK plus 50 mm irrigation), GM (CK plus gravel mulching), and WGM (WCK plus GM). Compared with the CK, gravel mulching significantly decreased soil CO2 emissions and increased soil CH4 uptake over both cycles, although patterns of soil N2O emissions were controversial. Mixed effects of gravel mulching and irrigation significantly minimized the GWP over both cycles. Compared with the CK, annual GHGI in the WCK, GM, and WGM treatments dramatically decreased by 35.1, 53.7, and 55.9%, respectively, over Cycle 1 and by 16.7, 19.6, and 37.2%, respectively, over Cycle 2. The average CFs in the WCK, GM, and WGM treatments over both cycles were 4.4, 35.0, and 58.7% lower than in the CK, respectively. Gravel mulching had no significant effect on the CI during Cycle 1 but did have a significant effect during Cycle 2. Thus, gravel mulching is a recommended practice to mitigate GHG emissions and enhance the crop productivity on the Loess Plateau of China.
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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) | 0.000 | 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 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".