Altering undigested neutral detergent fiber through additives applied in corn, whole barley crop, and alfalfa silages, and its effect on performance of lactating Holstein dairy cows
Bibliographic record
Abstract
OBJECTIVE: We hypothesized that silage additives may alter the undigested neutral detergent fiber (uNDF) content through ensiling. Therefore, urea and formic acid were applied to corn, whole barley crop (WBC) and alfalfa to change uNDF content of the ensiled forages. METHODS: Six experimental diets at two groups of high uNDF (untreated corn and alfalfa silages [CSAS] and untreated whole barley and alfalfa silages [BSAS]) and low uNDF (urea-treated corn silage+untreated alfalfa silage [CSUAS], urea-treated whole barley silage+untreated alfalfa silage [BSUAS], untreated corn silage+formic acid-treated alfalfa silage [CSASF], and untreated whole barley silage+formic acid-treated alfalfa silage [BSASF]), were allocated to thirty-six multiparous lactating Holstein dairy cows. RESULTS: The untreated silages were higher in uNDF than additive treated silages, but the uNDF concentrations among silages were variable (corn silage<barley silage<alfalfa silage). Dry matter intake was not influenced by the reduction of uNDF or physical NDF supply source from corn to WBC silages (p>0.05). Milk yield tended to increase in the cows fed high uNDF diets than those fed low uNDF (p = 0.10). The cows fed diet based on urea-treated corn silage had higher milk yield than those fed other silages (p = 0.05). The substitution of corn silage with the WBC silage tended to decrease milk production (p = 0.07). Changing the physical source of NDF supply and the uNDF content from the corn silage to the WBC silage caused a significant increase in ruminal NH3-N concentration, milk urea-N and fat yield (p< 0.05). The cows fed diets based on WBC silage experienced greater rumination time than the cows fed corn silage (p<0.05). CONCLUSION: Administering additives to silages to reduce uNDF may improve the performance of Holstein dairy cows.
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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 | 0.000 | 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 | 0.001 |
| 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 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".