Carbon Sequestration vs Agricultural Yields in Tree-Based Intercropping Systems as Affected by Tree Management
Bibliographic record
Abstract
Tree-based intercropping (TBI) may increase carbon (C) sequestration in agroecosystems, but may reduce crop yields. In this study of TBI, we used ecosys, a comprehensive mathematical model of terrestrial ecosystems, which represents interspecific competition for light, nutrients, and water, to evaluate the concurrent effects of TBI on C sequestration and crop yields in TBI experiments conducted at St. Paulin and St. Edouard in southern Quebec. Total gains in C sequestration vs. total losses in crop yields over 11 yr relative to monocropping were 682 vs. 396 g C m−2 at St. Paulin and 841 vs. 168 g C m−2 at St. Edouard. These gains and losses were generally consistent with the measurements at the two TBI sites and with those at TBI experiments under similar environmental conditions elsewhere. Gains and losses depended on competition for light by trees and crops, and so were affected by different fractions of tree foliage removal used to manage this competition in the model. The modelling protocol developed for this study provides a robust, process-based methodology to evaluate economic and environmental benefits of TBI under diverse climates, soils, and tree and crop management practices. Some of the key assumptions used to model TBI are also discussed.
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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 | 0.001 |
| 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) | 0.001 | 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".