Tree‐Based Intercropping in Southern Ontario, Canada
Bibliographic record
Abstract
Tree‐based intercropping (TBI) integrates tree production within annual grain cropping. The system is widely used in tropical regions, but is not common in temperate regions. This study evaluates the annualized return from TBI systems in southern Ontario, Canada against annual grain crop production. The TBI systems include hybrid poplar, Norway spruce, and red oak. The annualized return for all TBI systems is less than for annual cropping using the base prices; however, when tree prices are high and grain prices low the hybrid poplar TBI system has a higher return than annual cropping. Grants for planting trees, technologies to reduce the cost of establishing and maintaining trees, and improving the returns from tree production will be required for producers in temperate regions to adopt TBI systems. Un système de cultures intercalaires (SCI) intègre la production d’arbres dans la culture annuelle de céréales. Ce système est largement utilisé dans les régions tropicales, mais peu courant dans les régions tempérées. La présente étude évalue le rendement annualisé des SCI dans le sud de l’Ontario, au Canada, par rapport à celui de la production annuelle de céréales. Les SCI intègrent le peuplier hybride, l’épinette de Norvège et le chêne rouge. Le rendement annualisé des SCI est inférieur à celui des cultures annuelles si l’on utilise les prix de référence. Toutefois, lorsque les prix des arbres sont élevés et que les prix des céréales sont faibles, le SCI qui intègre le peuplier hybride obtient un rendement supérieur à celui des cultures annuelles. Pour que les agriculteurs des régions tempérées adoptent les SCI, il faudra offrir des subventions à la plantation d’arbres, offrir des technologies qui permettront de réduire les coûts de plantation et d’entretien des arbres et améliorer les rendements/revenus de la production d’arbres.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".