Soil organic carbon in tree-based intercropping systems of Quebec and Ontario, Canada
Bibliographic record
Abstract
ABSTRACT M.Sc.Amanda D. BambrickNatural Resource Sciences Tree-based intercropping (TBI) is an agroforestry system where a crop, generally an annual, is planted between established tree rows. TBI systems have a greater potential for carbon storage than conventional cropping systems because carbon is stored in the biomass of growing trees and trees provide additional carbon inputs (leaves, roots) that contribute to the soil organic carbon (SOC) pool. Differences in the litter quality and amount of litter deposited in the tree row versus the intercropped space are expected to generate spatial heterogeneity in the SOC pool. The objectives of this work were to evaluate the spatial variability of the SOC pool in TBI systems, to compare SOC stocks in the TBI system with a nearby conventional agroecosystem, and to describe the SOC dynamics in a TBI system using the ecosys model. Research sites included in this study were 4-year old TBI sites at St. Paulin and St. Edouard (Quebec, Canada), an 8-year old TBI site in St. Remi, Quebec, and a 21 year old TBI site in Guelph, Ontario, Canada. Spatial heterogeneity in SOC pools due to the presence of trees was observed in two of the four sites, but obscured by field variability at one site and even distribution of leaf litter associated with large trees at the oldest TBI site. The SOC pool increased in older TBI sites, relative to the nearby conventional agroecosystem, but the magnitude of SOC change was affected by the land use history. A simulation of changes in SOC using the ecosys environmental model predicted a 5.0% decrease in SOC pools twenty-one years after the site was converted to TBI, while field experiments showed a 12% increase in the SOC pool compared to the conventional agroecosystem. A spatial algorithm that describes the distribution of trees and crops in TBI systems would improve ecosys model predictions. Overall, field results suggest that the trees growing in TBI systems will increase SOC levels after a n
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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".