A global meta‐analysis of nitrous oxide emission from drip‐irrigated cropping system
Bibliographic record
Abstract
Abstract Drip irrigation is a useful practice to enhance water and fertilizer nitrogen (N) use efficiency. However, the use of drip irrigation to mitigate nitrous oxide (N 2 O) emissions in agricultural systems globally is uncertain. Here, we performed a global meta‐analysis of 485 field measurements of N 2 O emissions from 74 peer‐reviewed publications prior to March 2021, to quantify the fertilizer‐induced N 2 O emission factor (EF) of drip irrigation and examine the influencing factors of climate, crop, soil properties, and source and rate of fertilizer N application. The results showed that drip irrigation reduced ( p < 0.05) N 2 O emissions by 32% and 46% compared to furrow and sprinkler irrigation systems, respectively. The overall average EF with drip irrigation was 0.35%, being two‐thirds lower than the IPCC Tier I default value of 1% (kg N 2 O‐N/kg added fertilizer N). The EF was not significantly affected by climate, crop, soil texture, soil organic carbon content, and pH. The EF was also not significantly ( p > 0.05) affected by synthetic N fertilizer source despite a lower numerical value with enhanced efficiency than conventional fertilizers. The EF increased significantly ( p < 0.001) with N addition rate in a binomial distribution. Using the IPCC default EF overestimated N 2 O emissions inventories for drip‐irrigated cropping systems by 7614 and 13,091 Mg per year for China and the globe, respectively. These results indicate that drip irrigation should be recommended as an essential N 2 O mitigation strategy for irrigated crop production.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".