Assessing crop productivity, grain quality, and soil labile carbon and nitrogen in pea-based intercrops under low nitrogen input
Bibliographic record
Abstract
Pea-based intercrops provide nitrogen (N) benefits and often improve land productivity through functional diversification. However, their impact on grain quality and soil health remains unclear. We conducted a 2-year (2021 and 2022) intercrop study at Swift Current and Melfort, Saskatchewan, assessing productivity, grain quality, and soil water-extractable organic carbon (WEOC) and water-extractable dissolved N (WEDN). Nine treatments included pea–oat (PO) intercrops with three N rates (0, 1/4, and 1/2 of full recommended N rate for oat monocrop), pea–canola (PC) intercrops with three N rates (0, 1/4, and 1/2 of full recommended N rate for canola monocrop), and three monocrops (pea, oat, and canola). Pea monocrop received no N fertilizer, while oat and canola monocrops received the full recommended N rate. In intercrops, pea was seeded at 2/3 and the companion crop at 1/2 of their recommended rates. PO intercrops consistently produced higher energy-based yields than PC intercrops. Intercrops outperformed monocrops at Melfort but not at Swift Current. Intercropping reduced canola protein content by 6–9% and oat protein content by 6–8%, compared to monocrops. PO intercrops increased WEOC level by 5%–9% compared to monocrops. PC intercrops resulted in 10% higher WEDN than PO intercrops, attributed to a higher pea plant stand in PC. Nitrogen fertilizer rates in intercrops did not affect yields or soil labile C and N. The results showed that applying N fertilizers to pea-based intercrops did not improve productivity, but seeding rate ratio in intercrops should be finetuned based on crop competitiveness to improve overall performance.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| 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".