Long-term retention of carbon from litter decay in diverse agricultural soils in Canada and New Zealand
Bibliographic record
Abstract
Identifying the primary regulators of plant litter decay in agricultural soils is important for understanding ecosystem function now and for managing post-harvest litter in a warmer world. We conducted a litter decay study at 11 sites in Canada and New Zealand with diverse soils and climates. We incorporated 13C-labelled barley litter into the surface 10 cm of soil and monitored amount remaining over 8–10 years; at five sites litter was also applied to the soil surface to simulate no-tillage. Our objectives were to evaluate litter decay as influenced by soil type, tillage practice, and environmental conditions, and quantify the most important factors controlling C retention in soil. Loss of C via decomposition occurred quickly – more than half was lost within 1 year and only about 5–12% remained at the end of the experiment. A double exponential decay model, based on temperature and developed from the first 5 years of the study, accurately described litter decay, but only after re-tuning using measurements from the full experimental period. Including precipitation in the model further improved its fit. Soil properties exerted minimal discernible influence on the amount of litter C remaining, implying that properties such as the amount and surface area of clay minerals, were less important than climate in limiting litter decay or enhancing retention of C in soil. Comparison of litter application treatments showed that no-till slowed decay of plant litter, but only for a short time (∼1 yr) and only in environments with a mean annual precipitation of <1000 mm. These findings have implications for the role of soils in climate mitigation. If only 10% of added plant litter C remains in soil beyond a few years, regardless of climate, residue placement, or soil type, then rates of soil gain are limited without substantive increases in residue inputs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".