Chemical composition, ensiling characteristics and ruminal degradability of forage soybean cultivars
Bibliographic record
Abstract
A study was conducted to determine the effects of forage soybean cultivar on chemical composition, ensiling characteristics, and ruminal degradability of silage. Two cultivars of forage soybean (Kodiak and Mammouth) were field-grown, harvested at the R6 stage, and ensiled in mini-silos (n = 3) for 0, 2, 4, 8, 16 and 45 d. Two ruminally fistulated cows were used to determine in situ ruminal nutrient degradabilities of the 45-d silages. Both cultivars went through slow fermentation as indicated by a gradual decline in pH up to day 45 post-ensiling. Lactic acid concentration increased throughout ensiling and was higher for Mammouth than Kodiak except at day 45 post-ensiling. Analysis of the 45-d silages showed that Mammouth contained higher neutral detergent fiber (NDF, 490 vs. 444 g kg-1), acid detergent fiber (371 vs. 353 g kg-1) and acid detergent lignin (81 vs. 64 g kg-1) than Kodiak. However, crude protein (CP) concentration was higher for Kodiak than Mammouth. Mammouth silage had lower buffer soluble protein and higher neutral and acid detergent insoluble protein concentrations than Kodiak silage. Results of the in situ study indicated that Kodiak silage had greater ruminal dry matter (606 vs. 549 g kg-1), CP (828 vs. 752 g kg -1) and NDF (272 vs. 227 g kg-1) degradabilities than Mammouth. It was concluded that chemical composition and ruminal nutrient degradabilities of forage soybean silage were significantly influenced by cultivar. Key words: Soybean [Glycine max (L.) Merill.], silage, ensiling; forage quality, nutrient degradability
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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) | 0.000 | 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 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".