Evaluation of Annual Companion Crops for the Establishment of Perennial Forage Crops in Eastern Canada
Bibliographic record
Abstract
The use of companion crops when establishing perennial forages is desirable as it often reduces weed growth and increases forage biomass in the seeding year. In eastern Canada, oat (Avena sativa L.) is the main species used as companion crop; although other species are used, they have not been systematically evaluated. A field study was established in 2019 at three sites in Québec, Canada, to contrast the use of six annual species as companion crops for the establishment of lucerne (Medicago sativa L.)-timothy (Phleum pratense L.) mixtures. Species evaluated include berseem clover (Trifolium alexandrinum L.), annual ryegrass (Lolium multiflorum Lamarck), forage pea (Pisum sativum L.), forage oat, Japanese millet [Echinochloa esculenta (A. Braun) H. Scholz], and sudangrass [Sorghum × drummondii (Nees ex. Steud.) Millsp. & Chase]. The control treatment consisted of the perennial species seeded without companion crop. Treatments were seeded at three dates (mid-May to early-June, mid-June to early-July, and early August) and evaluated during the seeding year based on biomass production and botanical composition. Overall, across sites, for the first two seeding dates, highest annual forage yields were observed with sudangrass, Japanese millet, and oat as companion crops. The use of these species increased yields by 1.8 to 2.5 Mg ha-1 on a dry matter basis (DM) compared to the control which yielded an average of 3.7 Mg DM ha-1. For the early August seeding, response varied significantly across sites. Annual yields were the highest with the use of oat at two sites (avg. of 2.4 Mg DM ha-1), whereas no differences between treatments were observed at the other site. Companion crop species which maximized total forage yields in the seeding year often reduced weed biomass, but also that of perennial species. The impact of treatments on the survival of perennial forages and their production during the first post-seeding year will be presented in a later publication.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".