Effects of Corn Silage Processing and Amino Acid Supplementation on the Performance of Lactating Dairy Cows
Bibliographic record
Abstract
This experiment was conducted to determine the effect of crop processing and amino acid supplementation on dairy cow performance. Corn silage processed (PCS) or unprocessed (UCS) was used as the main forage (45% of dry matter, DM) in a total mixed ration (TMR). Each TMR was either supplemented (AA) or not (AAO) with ruminally protected amino acids (lysine, 3 g/d and methionine, 14 g/d). Thirty-two (551 kg) Holstein cows were randomly assigned to four treatments: PCS-AA, PCS-AA0, UCS-AA, and UCS-AA0 in a 2 x 2 factorial structure. Between wk 7 and 17 of lactation, cows were fed ad libitum TMR comprising 45% of corn silage plus 1 kg of grass hay once a day. The UCS presented better fermentation characteristics than PCS. Dry matter intake (DMI) of the TMR was not affected by treatment and averaged 22.7 kg/d. Energy-corrected milk (ECM) production was 9% higher with UCS than with PCS (33.1 vs. 30.1 kg/d). Milk efficiency was therefore 6% higher with UCS than with PCS (1.43 vs. 1.35 kg ECM/kg of DMI). The concentration of major milk constituents (fat, protein, lactose, urea) was not affected by treatments. Apparent digestibility of DM, organic matter, N, starch, acid detergent fiber, and neutral detergent fiber were similar among treatments. The effective ruminal degradability of DM, starch, and protein, however, was greater with PCS than with UCS. Amino acid supplementation had no effect on milk production nor on milk constituents, whether it was used with processed corn silage or with unprocessed corn silage. These data indicate that feeding UCS resulted in a greater milk production compared with PCS. The numerically higher DMI, a potentially greater intestinal digestion of starch or the better conservation of UCS could have contributed to the greater milk production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".