Central Role of Nitrogen Fertilizer Relative to Water Management in Determining Direct Nitrous Oxide Emissions From Global Rice‐Based Ecosystems
Bibliographic record
Abstract
Abstract The increasing atmospheric nitrous oxide (N2O) concentration stems from the development of agriculture. However, N2O emissions from global rice‐based ecosystems have not been explicitly and systematically quantified. Therefore, this study aims to estimate the spatiotemporal magnitudes of the N2O emissions from global rice‐based ecosystems and determine different contribution factors by improving a process‐based biogeochemical model, TRIPLEX‐GHG v2.0. Model validation suggested that the modeled N2O agreed well with field observations under varying management practices at daily, seasonal, and annual steps. Simulated N2O emissions from global rice‐based ecosystems exhibited significant increasing trends from 0.026 ± 0.0013 to 0.18 ± 0.003 TgN yr−1 from 1910 to 2020, with ∼69.5% emissions attributed to the rice‐growing seasons. Irrigated rice ecosystems accounted for a majority of global rice N2O emissions (∼76.9%) because of their higher N2O emission rates than rainfed systems. Regarding spatial analysis, Southern China, Northeast India, and Southeast Asia are hotspots for rice‐based N2O emissions. Experimental scenarios revealed that N fertilizer is the largest global rice‐N2O source, especially since the 1960s (0.047 ± 0.010 TgN yr−1, 35.24%), while the impact of expanded irrigation plays a minor role. Overall, this study provides a better understanding of the rice‐based ecosystem in the global agricultural N2O budget; further, it quantitively demonstrated the central role of N fertilizer in rice‐based N2O emissions by including rice crop calendars, covering non‐rice growing seasons, and differentiating the effects of various water regimes and input N forms. Our findings emphasize the significance of co‐management of N fertilizer and water regimes in reducing the net climate impact of global rice cultivation.
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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 | 0.001 |
| 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".