Carbon accumulation in agricultural soils after afforestation: a meta‐analysis
Bibliographic record
Abstract
Abstract Deforestation usually results in significant losses of soil organic carbon (SOC). The rate and factors determining the recovery of this C pool with afforestation are still poorly understood. This paper provides a review of the influence of afforestation on SOC stocks based on a meta‐analysis of 33 recent publications (totaling 120 sites and 189 observations), with the aim of determining the factors responsible for the restoration of SOC following afforestation. Based on a mixed linear model, the meta‐analysis indicates that the main factors that contribute to restoring SOC stocks after afforestation are: previous land use, tree species planted, soil clay content, preplanting disturbance and, to a lesser extent, climatic zone. Specifically, this meta‐analysis (1) indicates that the positive impact of afforestation on SOC stocks is more pronounced in cropland soils than in pastures or natural grasslands; (2) suggests that broadleaf tree species have a greater capacity to accumulate SOC than coniferous species; (3) underscores that afforestation using pine species does not result in a net loss of the whole soil‐profile carbon stocks compared with initial values (agricultural soil) when the surface organic layer is included in the accounting; (4) demonstrates that clay‐rich soils (> 33%) have a greater capacity to accumulate SOC than soils with a lower clay content (< 33%); (5) indicates that minimizing preplanting disturbances may increase the rate at which SOC stocks are replenished; and (6) suggests that afforestation carried out in the boreal climate zone results in small SOC losses compared with other climate zones, probably because trees grow more slowly under these conditions, although this does not rule out gains over time after the conversion. This study also highlights the importance of the methodological approach used when developing the sampling design, especially the inclusion of the organic layer in the accounting.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.029 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".