Organic Carbon Accumulation in Topsoil Following Afforestation with Willow: Emphasis on Leaf Litter Decomposition and Soil Organic Matter Quality
Bibliographic record
Abstract
Short-rotation intensive cultures (SRICs) of willows can potentially sequester carbon (C) in soil. However, there is limited information regarding the factors governing soil organic C (Corg) accumulation following afforestation. The objectives of this study were to: (i) determine whether willow leads to Corg accumulation in the topsoil (0–10 cm) two to six years after establishment in five SRICs located along a large climatic/productivity gradient in southern Quebec, and (ii) assess the influence of leaf litter decomposition and soil organic matter (OM) quality on Corg accumulation in the topsoil. Topsoil Corg concentrations and pools under SRICs were, on average, 25% greater than reference fields, and alkyls concentrations were higher under SRICs. On an annualized basis, Corg accumulation rates in the topsoil varied between 0.4 and 4.5 Mg ha−1 yr−1. Estimated annual litterfall C fluxes were in the same order of magnitude, suggesting that SRICs can accumulate Corg in the topsoil during early years due to high growth rates. Leaf litter decomposition was also related to Corg accumulation rates in the topsoil. It was positively correlated to growing season length, degree-days, and growing season average air and topsoil temperature (r > 0.70), and negatively correlated to topsoil volumetric water content (r = −0.55). Leaf litter decomposition likely occurred more quickly than that of plants in reference fields, and as it progressed, OM became more decay resistant, more stable and accumulated as Corg in the topsoil.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".