Soil organic carbon sequestration by shelterbelt agroforestry systems in Saskatchewan
Bibliographic record
Abstract
Carbon (C) sequestration through the implementation of agroforestry practices is identified as one of the major strategies in the reduction of carbon dioxide (CO2) emissions from the agricultural sector. The objective of this study was to examine the soil organic carbon (SOC) sequestration potential of major shelterbelt species, including green ash (Fraxinus pennsylvanica), hybrid poplar (Populus spp.), Manitoba maple (Acer negundo), white spruce (Picea glauca), Scots pine (Pinus sylvestris), and caragana (Caragana arborescens), ranging in age from 5 to 63 yr. Soil samples (0–50 cm) were collected for six major shelterbelt species and adjacent agricultural fields, and SOC concentration was determined. Shelterbelts had a significantly higher amount of SOC compared with adjacent agricultural fields, with an average difference of 18.6 Mg C ha−1 in the top 50 cm soil. An additional 3–8 Mg C ha−1 was contained in the tree litter layer. Younger shelterbelts (age less than 20 yr) tended to lose SOC in the early years of shelterbelt establishment. However, the SOC accrual was positively related to shelterbelt stand age. Besides stand age, other shelterbelt stand characteristics, including tree height and diameter, crown width, and amount of surface litter, were also positively correlated with the increase in SOC concentration. The findings of this study support the hypothesis that shelterbelts can lead to a significant amount of SOC sequestration in agroecosystems.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".