Proteolysis in alfalfa silages made from different cultivars
Bibliographic record
Abstract
During silage fermentation, proteolysis reduces the nutritional value of N, particularly in alfalfa. This study evaluated the proteolysis of 27 alfalfa cultivars seeded in triplicate in 2 consecutive years. Forage from each plot was wilted to a targeted dry matter (DM) content of 250 g kg–1 and ensiled in laboratory silos. Minisilos were made with spring growth and summer regrowth in the first year of production for the first seeding, and with the first 2 yr of production for the second seeding, giving 3 harvest years altogether. Concentrations of NPN, NH3, FAA, DM, TN, and pH were measured in silages, whereas RUP concentration was evaluated in forages. Low NPN concentration indicates low proteolysis during silage fermentation. Spring growth and summer regrowth were analyzed separately by ANOVA as a completely randomized block design replicated over 3 harvest years. A principal component analysis was then performed on the ANOVA means. Silage DM was used as covariate when it was deemed appropriate. There was a significant variation among cultivars for NPN concentration in silages made of summer regrowth; it varied from 612 to 717 g kg–1 of TN. Concentration of NPN was significantly lower for cultivars WL 225, Rangelander, Iroquois, and WL 222, and higher for cultivars Oneida VR, Arrow, Vernal, Spredor 2, and Armor. Oneida VR and Rangelander had respectively high and low silage NPN concentration in both spring growth and summer regrowth. From cultivars with low silage NPN concentration, Rangelander was the only one with high forage RUP concentration in both spring growth and summer regrowth; proteins in this cultivar would be more resistant to microbial degradation during ensiling and in the rumen. Key words: Non protein nitrogen; proteolysis; alfalfa silage; 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 | 0.000 |
| 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".