Suitability of legume cover crop-winter wheat intercrops on the semi-arid Canadian Prairies
Bibliographic record
Abstract
Farmers on the Canadian prairies are interested in including legume cover crops in their cropping systems to reduce fertilizer inputs and improve farm sustainability. A field study was conducted to determine the merits of establishing alfalfa (Medicago sativa L.), red clover (Trifolium pratense L.) or Austrian winter pea (Pisum sativum L.) cover crops in fall or in spring with winter wheat (Triticum aestivum L.). Spring-planted legumes emerged well within the winter wheat crop, but their growth was limited under these semi-arid conditions. Fall-planted red clover had low plant densities following winter in two of three experiments and fall-planted winter pea reduced winter wheat yield by 23 to 37% compared with the no cover crop control. In contrast, fall-planted alfalfa exhibited good winterhardiness, provided some weed suppression without reducing winter wheat yield, caused only a slight reduction in soil water content, and contributed an extra 18 to 20 kg ha-1 of available soil N at the time of seeding the following spring crop. Additionally, fall-planted alfalfa increased the yield of succeeding canola (Brassica napus L.) in unfertilized plots in two of three experiments. Further research is warranted to better understand the agronomic and economic benefits of alfalfa-winter wheat intercrops under a wider range of environmental conditions.Key words: Cover crops, intercropping, relay crops, soil nitrogen, soil conservation
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".