Measuring and Understanding Carbon Storage in Afforested Soils by Physical Fractionation
Bibliographic record
Abstract
Forested ecosystems have been identified as potential C sinks. However, the accuracy of measurement and understanding of the underlying mechanisms for soil organic C (SOC) storage in forested ecosystems needs to be improved. The objective of this study was to use aggregate and soil organic matter (SOM) fractionation techniques to identify SOC pools that preferentially stabilize SOC in the long term and elucidate SOC sequestration mechanisms in forested soils. At two sites (Wildlife area, Ohio and Kemptville, Ontario) representing two different soils (Hapludalf and Hapludoll), we sampled soils under agriculture, afforestation, and forest and separated them into aggregates. Different size classes of intra‐aggregate particulate organic matter (iPOM) fractions were isolated by density flotation, dispersion, and sieving. At both sites, aggregation and whole SOC content were greater in the forested than in the agricultural ecosystems. The greater aggregation in forested ecosystems resulted in greater iPOM C concentrations, especially the iPOM C fractions associated with microaggregates (53–250 μm) and microaggregates occluded within macroaggregates (mM) (250–2000 μm). The sum of C in these fractions (microaggregate protected C) was 468 ± 29, 696 ± 171, 673 ± 70 g C m −2 in the agricultural, afforested, and forested soils at Kemptville, respectively. The difference in the microaggregate protected C between the agricultural and the afforested soils accounted, on average, for 20% of the difference in whole SOC stocks between the soils. We conclude, SOC is stabilized for a relatively longer term within microaggregates formed in afforested and forest systems. Therefore, we suggest a new fractionation scheme to isolate this microaggregate associated SOC for assessing the impact of land use, land management, and climate change on C storage.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".