Economic analysis of legume-based intercropping across Canadian Prairies
Bibliographic record
Abstract
Legume-based intercropping offers a promising strategy because it may improve resource allocation and income stability for growers. Two multi-year intercrop studies were conducted in Swift Current, Melfort (Saskatchewan), Lethbridge (Alberta), and Carman (Manitoba) to assess the financial results of different intercrops, including pea ( Pisum sativum L.)–canola ( Brassica napus L.), pea–oat ( Avena sativa L.), faba bean ( Vicia faba L.)–malt barley ( Hordeum distichum L.), malt barley–pea, and corn ( Zea mays L.)–soybean ( Glycine max L.) under different nitrogen (N) fertilizer rates. Net return (NR), calculated as total revenue minus total costs, was used to compare intercrops with monocrops. Monetary returns varied by location. While pea–canola and pea–oat did poorly in semi-arid Swift Current, intercropping generally matched or outperformed monocrops and maintained income stability. Applying N fertilizer to legume-based intercrops did not enhance NRs, but enabled an 80% reduction in N application compared to monocrops. This resulted in a cost difference of $116 ha −1 , with monocrops requiring $132 ha −1 and intercrops only $16 ha −1 . Overall, intercropping improved resource use efficiency and income stability, offering farmers a viable approach to sustainable crop production under diverse growing conditions in western Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".