Soil nutrients increase long‐term soil carbon gains threefold on retired farmland
Bibliographic record
Abstract
Abstract Abandoned agricultural lands often accumulate soil carbon (C) following depletion of soil C by cultivation. The potential for this recovery to provide significant C storage benefits depends on the rate of soil C accumulation, which, in turn, may depend on nutrient supply rates. We tracked soil C for almost four decades following intensive agricultural soil disturbance along an experimentally imposed gradient in nitrogen (N) added annually in combination with other macro‐ and micro‐nutrients. Soil %C accumulated over the course of the study in unfertilized control plots leading to a gain of 6.1 Mg C ha −1 in the top 20 cm of soil. Nutrient addition increased soil %C accumulation leading to a gain of 17.8 Mg C ha −1 in fertilized plots, nearly a threefold increase over the control plots. These results demonstrate that substantial increases in soil C in successional grasslands following agricultural abandonment occur over decadal timescales, and that C gain is increased by high supply rates of soil nutrients. In addition, soil %C continued to increase for decades under elevated nutrient supply, suggesting that short‐term nutrient addition experiments underestimate the effects of soil nutrients on soil C accumulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".