Improving crop yield, N uptake and economic returns by intercropping barley or canola with pea
Bibliographic record
Abstract
Two field experiments were conducted from 2009 to 2011 on a Gray Luvisol (Typic Haplocryalf) loam at Star City, Saskatchewan, Canada, to determine the effectiveness of intercropping barley or canola with pea in improving crop yield, total N uptake, seed quality, Land Equivalency Ratio (LER) and economic returns compared to barley, canola or pea grown as monocultures. Average seed yields of barley-pea or canola-pea intercrops were usually greater than those of barley, canola or pea as sole crops. In intercrops, application of N fertilizer increased seed yield of barley or canola but had only slight beneficial effect on the combined seed yield of both crops together. The LER values for intercrops were usually much greater than 1, suggesting less land requirements of intercropping systems than monoculture for the same seed yield. Net returns were lowest for barley as sole crop. Without applied N, net returns were slightly lower for barley-pea intercrop and slightly greater for canola-pea intercrop than pea as a sole crop. Generally, protein concentration in canola or barley seed was higher and oil concentration in canola seed was lower in intercrop combinations compared to sole crops. Response trends of total N uptake in seed or straw were usually similar to that of seed or straw yield. In conclusion, intercropping barley or canola with pea improved yield, N uptake and net returns, suggesting the potential of barley-pea or canola-pea intercrops and pea for organic farming systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".