Evidence for the primacy of living root inputs, not root or shoot litter, in forming soil organic carbon
Bibliographic record
Abstract
Summary Soil organic carbon (SOC) is primarily formed from plant inputs, but the relative carbon (C) contributions from living root inputs (i.e. rhizodeposits) vs litter inputs (i.e. root + shoot litter) are poorly understood. Recent theory suggests that living root inputs exert a disproportionate influence on SOC formation, but few field studies have explicitly tested this by separately tracking living root vs litter inputs as they move through the soil food web and into distinct SOC pools. We used a manipulative field experiment with an annual C 4 grass in a forest understory to differentially track its living root vs litter inputs into the soil and to assess net SOC formation over multiple years. We show that living root inputs are 2–13 times more efficient than litter inputs in forming both slow‐cycling, mineral‐associated SOC as well as fast‐cycling, particulate organic C. Furthermore, we demonstrate that living root inputs are more efficiently anabolized by the soil microbial community en route to the mineral‐associated SOC pool (dubbed ‘the in vivo microbial turnover pathway’). Overall, our findings provide support for the primacy of living root inputs in forming SOC. However, we also highlight the possibility of nonadditive effects of living root and litter inputs, which may deplete SOC pools despite greater SOC formation rates.
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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 | 0.001 |
| 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".