Expanding agroforestry can increase nitrate retention and mitigate the global impact of a leaky nitrogen cycle in croplands
Bibliographic record
Abstract
Abstract The internal soil nitrogen (N) cycle supplies N to plants and microorganisms but may induce N pollution in the environment. Understanding the variability of gross N cycling rates resulting from the global spatial heterogeneity of climatic and edaphic variables is essential for estimating the potential risk of N loss. Here we compiled 4,032 observations from 398 published 15 N pool dilution and tracing studies to analyse the interactions between soil internal potential N cycling and environmental effects. We observed that the global potential N cycle changes from a conservative cycle in forests to a less conservative one in grasslands and a leaky one in croplands. Structural equation modelling revealed that soil properties (soil pH, total N and carbon-to-N ratio) were more important than the climate factors in shaping the internal potential N cycle, but different patterns in the potential N cycle of terrestrial ecosystems across climatic zones were also determined. The high spatial variations in the global soil potential N cycle suggest that shifting cropland systems towards agroforestry systems can be a solution to improve N conservation.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".