Intercropping winter cereals in Kura clover for spring forage production
Bibliographic record
Abstract
Intercropping winter cereals into Kura clover (Trifolium ambiguum M. Bieb.) has the potential to improve forage nutritive value without reducing yields. The objective of this research was to compare forage yield and nutritive value of cereal–legume mixtures to cereals or Kura clover grown in monoculture and harvested at two different maturity stages in spring. Winter rye (Secale cereale L.), winter wheat (Triticum aestivum L.), and winter triticale (×Triticosecale Wittmack) were sown in autumn at two locations as monocultures and into herbicide suppressed and non-suppressed Kura clover and harvested for forage the following spring. Harvested at the cereal crop boot stage at Arlington, forage yields averaged 4.7, 4.8, 5.3, and 1.7 Mg ha −1 and 3.5, 3.7, 4.1, and 2.6 Mg ha −1 at Lancaster for mixtures with non-suppressed Kura clover, suppressed Kura clover, monoculture cereals, and monoculture clover, respectively. At the milk stage, yields across all mixture treatments increased by 46% to 115% compared to the boot stage at Arlington and by 88% to 101% at Lancaster. Kura clover in mixtures increased crude protein by 34% to 46% and in vitro true digestibility by 3% to 6%, neutral detergent fiber digestibility by 0% to 6%, and reduced neutral detergent fiber concentration by 8% to 18% relative to cereal monocultures. The nutritive value of mixtures and monoculture Kura clover was always greater than that of monoculture cereals. Winter cereals can be successfully managed with Kura clover for forage production by maximizing nutritive value with boot stage harvests or achieving higher yields by harvesting at the milk stage.
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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 | 0.001 | 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 | 0.001 |
| 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".