Testing for soil carbon saturation behavior in agricultural soils receiving long-term manure amendments
Bibliographic record
Abstract
Feng, W., Xu, M., Fan, M., Malhi, S. S., Schoenau, J. J., Six, J. and Plante, A. F. 2014. Testing for soil carbon saturation behavior in agricultural soils receiving long-term manure amendments. Can. J. Soil Sci. 94: 281–294. Agricultural soils are typically depleted in soil organic matter compared with their undisturbed counterparts, thus reducing their fertility. Organic amendments, particularly manures, provide the opportunity to restore soil organic matter stocks, improve soil fertility and potentially sequester atmospheric carbon (C). The application of the soil C saturation theory can help identify soils with large C storage potentials. The goal of this study was to test whether soil C saturation can be observed in various soil types in agricultural ecosystems receiving long-term manure amendments. Seven long-term agricultural field experiments from China and Canada were selected for this study. Manure amendments increased C concentrations in bulk soil, particulate organic matter+sand, and silt+clay fractions in all the experiments. The increase in C concentrations of silt+clay did not fit the asymptotic regression as a function of C inputs better than the linear regression, indicating that silt+clay did not exhibit C saturation behavior. However, 44% of calculated C loading values for silt+clay were greater than the presumed maximal C loading, suggesting that this maximum may be greater than 1 mg C m−2for many soils. The influences of soil mineral surface properties on C concentrations of silt+clay fractions were site specific. Fine soil particles did not exhibit C saturation behavior likely because current C inputs were insufficient to fill the large C saturation deficits of intensely cultivated soils, suggesting these soils may continue to act as sinks for atmospheric C.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".