Seeding patterns and companion grasses affect total forage yield and components of binary red clover-grass mixtures
Bibliographic record
Abstract
Lafrenière, C. and Drapeau, R. 2011. Seeding patterns and companion grasses affect total forage yield and components of binary red clover-grass mixtures. Can. J. Plant Sci. 91: 91-97. Red clover (Trifolium pratense L.) is the most popular seeded legume for forage production in northern Quebec and Ontario because of the poorly drained soils that prevail in these regions. The objective of this experiment was to determine which seeding patterns [mixed within a row (MR), single alternate rows (1+1), or double alternate rows (2+2)] and which companion grasses (smooth bromegrass, Bromus inermis L.; tall fescue, Festuca arundinacea Schreb.; orchardgrass, Dactylis glomerata L.; or timothy, Phleum pratense L.), in association with red clover, were best for sustaining the total forage yield of a red clover-grass mixture and how these factors affect red clover and grass components. Test plots were established at Kapuskasing, ON, and Normandin, QC. At each site, two independent seedings were performed and harvested over 3 yr following the seeding year. Total dry matter (DM) yield and contribution of red clover grasses and weeds to total forage yield were measured. The MR pattern gave higher yield over both alternate seeding patterns by nearly 0.5 Mg DM ha-1 at the Normandin site and 1.0 Mg DM ha-1 at the Kapuskasing site. Neither the seeding pattern nor the companion grass species improved significantly the contribution of red clover to total forage yield beyond the second production year even though there were differences between sites. Environmental conditions, principally high precipitation in the fall, and maturity stage at harvest resulted in major differences between sites. Results from this experiment showed that tall fescue and orchardgrass could be good alternatives to timothy or bromegrass in association with red clover given that plots were still productive in the third production year and invasion by weeds was lower.
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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 | 0.000 | 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) | 0.000 | 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".