Sparse‐Flowering Orchardgrass Represents an Improvement in Forage Quality During Reproductive Growth
Bibliographic record
Abstract
ABSTRACT Orchardgrass ( Dactylis glomerata L.) is a major component of many pastures in temperate North America. Early and profuse flowering in pastures is problematic, because livestock refuse to consume flowering stems, prompting many graziers to simply avoid using this species. The objective of this research was to determine the impact of reduced flowering on the quality of harvested forage under two harvest managements of orchardgrass. Six cultivars, three normal cultivars and three sparse‐flowering cultivars (mean panicle density of 141 vs. 61 panicles m⁻ 2 , respectively), were evaluated in field experiments at 21 locations in North America under a 3‐cut harvest management. These cultivars were also evaluated at seven locations under a 5‐cut harvest management. Sparse‐flowering cultivars averaged 9% greater crude protein (CP), 3% lower neutral detergent fiber (NDF), 2% greater NDF digestibility, and 2% greater in vitro dry matter digestibility (IVDMD) than normal cultivars. For the two digestibility measures, differential panicle density between the cultivar groups explained a significant portion of variability, indicating that the increase in forage quality was proportional to the decrease in panicle density below a threshold of about 50 panicles m⁻ 2 . Lastly, differences in regrowth forage quality between cultivar groups were smaller, less consistent, and of lesser statistical significance than for first harvest. While selection for sparse flowering in orchardgrass resulted in significant cause‐and‐effect increases in first‐harvest forage quality, these effects were too small to offset the reduced forage yield associated with the sparse‐flowering trait.
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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 | 0.001 |
| 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".