Alfalfa establishment with annual companion crops including Sudangrass
Bibliographic record
Abstract
When establishing alfalfa (Medicago sativa L.) stands there are two primary options to minimize weed encroachment: the use of herbicides or the use of annual companion crops. Companion crops will minimize weed development during the establishment year, provide more harvestable forage biomass, and reduce risks of erosion. In Quebec, smallgrain cereals are traditionally used as companion crops but other species such as Sudangrass and ryegrass are now also available. However, such species haven't been evaluated locally as companion crops for the establishment of alfalfa. This project had for objective to determine the effects of establishing alfalfa with various companion crop species on forage yield and quality, and alfalfa establishment and persistence, with a focus on newly available species. Sudangrass hybrids appear to be promising as companion crop for alfalfa establishment since it has a greater yield potential than traditionally used species and adapts well to drought conditions which are increasingly prevalent in southern Quebec. Results indicated that the use of a Sudangrass companion crop has a potential superior to that of oat and ryegrass to improve total seasonal forage yields during alfalfa establishment. In addition, Sudangrass was less competitive to alfalfa than oat or ryegrass and was among the most productive companion crop, resulting in high alfalfa and companion crop yields. On the other hand, Sudangrass weed suppression potential appeared to be lower then that of oat or ryegrass when weed pressure was high. In post-seeding years, the negative effect of the companion crops on yields was inconsistent. Alfalfa established with Sudangrass produced a forage of a similar average seasonal quality than the oat treatment but lower than ryegrass and soloseeded alfalfa. Finally, of all companion crop treatments evaluated, Sudangrass was less aggressive to alfalfa stands with no impact over alfalfa stands density found in subsequent years.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".