Swath grazing potential of spring cereals, field pea and mixtures with other species
Bibliographic record
Abstract
There is little information on the relative suitability of cereal species and field pea or their mixtures for winter swath grazing. The objective of this study was to compare the swath grazing potential of small-grain cereal and field pea (Pisum sativa L.) monocultures, their mixtures, and mixtures with other species, by evaluating forage yield in the fall and changes in nutritive value due to weathering from fall until spring. The monocultures and mixtures were seeded in early summer and swathed in late September with conventional farm equipment for 3 yr. Dry matter yield was measured by harvesting a subplot (1.22 × 3.62 m) across each plot prior to swathing. A cross-section sample of swath was taken for quality determination immediately after swathing, in late November and April. In vitro digestible organic matter (IVDOM), protein, and neutral (NDF) and acid detergent fiber (ADF) concentrations were measured for each sampling time. Generally, barley (Hordeum vulgare L.), oat (Avena sativa L.) and field pea monocrops produced similar yields, and mixtures did not out-yield monocrops. Yield of the barley/oat mixture was more stable from year to year than the respective monocrops. Addition of field pea to cereals in mixtures increased crude protein slightly and reduced NDF over cereal monocrops, but field pea mixtures did not improve nutritive value compared with the field pea monocrop. In one year with severe weathering conditions, field pea and field pea mixtures lost nutritive value faster than cereal monocrops initially, but had nutritive value similar to the cereal monocrops by April. Mixtures and monocrops lost nutritive value at a similar rate due to weathering. Added costs of growing mixtures rather than monocrops were not offset by superior yield, nutritive value, or resistance to weathering. Key words: Winter swath grazing, cereal and field pea mixtures, weathering, nutritive value
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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 | 0.001 | 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".