Forage Potential of Intercropping Berseem Clover with Barley, Oat, or Triticale
Bibliographic record
Abstract
Intercropping berseem clover (Trifolium alexandrinum L.) with silage cereals may increase forage yield and quality. Berseem clover was intercropped with barley (Hordeum vulgare L.), oat (Avena sativa L.), or triticale (×Triticosecale rimpaui Wittm.) at 30, 60, 90, 120, and 240 cereal plants m−2 at Edmonton, Alberta, from 1998 to 2001. Cereals dominated Cut 1 (silage‐stage) yield, and berseem clover dominated regrowth yield. As cereal density decreased from 240 to 60 plants m−2, Cut 1 yield decreased from 10.5 to 9.3 Mg ha−1 dry matter (DM), berseem clover percentage of Cut 1 increased from 5 to 14%, and berseem clover regrowth yield (Cut 2) increased from 1.8 to 3.0 Mg ha−1 DM. Total season intercrop yields with barley or oat at 60 plants m−2 were ≥yields with 240 plants m−2. Total season intercrop DM yields did not differ among the three cereal species in 3 of 4 yr. Triticale intercrops had advantages of greater Cut 1 yield and greater berseem clover percentage in Cut 1. Barley intercrops had advantages of greater Cut 2 yield and greater total season protein yield. Greater Cut 2 yield with barley intercrops was related to earlier silage‐stage (Cut 1) harvest date. Intercropping berseem clover with reduced seeding rates of cereals improved Cut 1 forage quality. When berseem clover was 20% of Cut 1 yield, neutral detergent fiber was 25 to 45 g kg−1 less than with cereals alone. The crude protein of berseem clover regrowth averaged 210 g kg−1, providing high quality late‐season forage.
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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".