Effects of Cultivars on Ensiling Characteristics, Chemical Composition, and Ruminal Degradability of Pea Silage
Bibliographic record
Abstract
A study was conducted to determine the effects of cultivar on ensiling characteristics, chemical composition and ruminal nutrient degradability of pea (Pisum sativum L.) silage. The cultivars evaluated were Lenca (L), Carneval (C), and Delta (D). Peas were field-grown and forage was harvested and ensiled in mini-silos for 0, 2, 4, 8, 16, and 70 d. The ensiled forage of all cultivars went through a rapid fermentation with a sharp reduction in pH during the first 2 days of ensiling. Extensive proteolysis took place between 0 and 2 d as indicated by a reduction in true protein and neutral detergent insoluble protein (NDICP) and an increase in nonprotein nitrogen. Chemical analysis of the 70 d silage showed that cultivar L contained higher neutral detergent fiber (NDF) and acid detergent fiber and lower starch levels than C and D. Crude protein was highest for C (20.5% DM), intermediate for D (19.0% DM) and lowest for L (17.9% DM). Distribution of protein fractions showed that L contains lower soluble protein and higher NDICP levels than the other two pea cultivars. However, no difference in acid detergent insoluble protein levels was observed between the three cultivars. Results of the in situ incubation experiment indicated that L had lower ruminal DM (69.2 vs 74.0%) and CP (84.1 vs 90.6%) degradabilities than C or D. However, ruminal degradability of NDF was similar among the three cultivars (average of 32.9%). It was concluded that chemical composition and ruminal nutrient degradability of pea silage are significantly influenced by cultivars.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".