Soil Carbon Saturation Controls Labile and Stable Carbon Pool Dynamics
Bibliographic record
Abstract
Recently, it has been suggested that soil organic C (SOC) does not always respond linearly to increasing C input, thereby limiting the rate and efficiency of C stabilization in soils. Therefore, we postulated that when a soil is exposed to a broad range of C inputs through a range of manure treatments, it will show C saturation behavior and different SOC pools will saturate at different rates. To test this, different SOC pools were isolated by physical fractionation techniques from a long‐term agricultural experiment in Lethbridge, Canada. In this experiment, manure has been applied since 1973 at rates of 0, 60, 120, and 180 Mg ha −1 yr −1 (wet weight). In the total mineral soil as well as the small macroaggregates (250–2000 μm), microaggregates (53–250 μm), and the silt plus clay fraction (<53 μm), an increase was observed in SOC contents with an increase in manure application rate to 120 Mg ha −1 yr −1 However, no additional C was sequestered when the manure application rate was augmented to 180 Mg ha −1 yr −1 , indicating C saturation in these SOC pools. Large macroaggregates (>2000 μm) were the only water‐stable aggregate fraction that increased in C content across all manure input levels. Further physical separation of macroaggregates into subpools by microaggregate isolation showed that coarse (>250 μm) particulate organic matter (POM) was the fraction that accounted most for the increase in C content of the large macroaggregates. Furthermore, the turnover of large macroaggregates increased with increasing manure applications, as indicated by decreased formation and stabilization of intramicroaggregate POM within the large macroaggregates. We conclude that as C input increases, the mineral fraction of a soil saturates and consequently additional C input will only accumulate in labile soil C pools that have a relatively faster turnover.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".