Replacing corn silage with different forage millet silage cultivars: Effects on milk yield, nutrient digestion, and ruminal fermentation of lactating dairy cows
Bibliographic record
Abstract
This study investigated the effects of dietary replacement of corn silage (CS) with 2 cultivars of forage millet silages [i.e., regular millet (RM) and sweet millet (SM)] on milk production, apparent total-tract digestibility, and ruminal fermentation characteristics of dairy cows. Fifteen lactating Holstein cows were used in a replicated 3 × 3 Latin square experiment and fed (ad libitum) a high-forage total mixed ration (68:32 forage:concentrate ratio). Dietary treatments included CS (control), RM, and SM diets. Experimental silages constituted 37% of each diet DM. Three ruminally fistulated cows were used to determine the effect of dietary treatments on ruminal fermentation and total-tract nutrient utilization. Relative to CS, RM and SM silages contained 36% more crude protein, 66% more neutral detergent fiber (NDF), and 88% more acid detergent fiber. Cows fed CS consumed more dry matter (DM; 24.4 vs. 22.7 kg/d) and starch (5.7 vs. 3.7 kg/d), but less NDF (7.9 vs. 8.7 kg/d) than cows fed RM or SM. However, DM, starch and NDF intakes were not different between forage millet silage types. Feeding RM relative to CS reduced milk yield (32.7 vs. 35.2 kg/d), energy-corrected milk (35.8 vs. 38.0 kg/d) and SCM (32.7 vs. 35.3 kg/d). However, cows fed SM had similar milk, energy-corrected milk, and solids-corrected milk yields than cows fed CS or RM. Milk efficiency was not affected by dietary treatments. Milk protein concentration was greatest for cows fed CS, intermediate for cows fed SM, and lowest for cows fed RM. Milk concentration of solids-not-fat was lesser, whereas milk urea nitrogen was greater for cows fed RM than for those fed CS. However, millet silage type had no effect on milk solids-not-fat and milk urea nitrogen levels. Concentrations of milk fat, lactose and total solids were not affected by silage type. Ruminal pH and ruminal NH3-N were greater for cows fed RM and SM than for cows fed CS. Total-tract digestibility of DM (average=67.9%), NDF (average=53.9%), crude protein (average=63.3%), and gross energy (average=67.9%) were not influenced by dietary treatments. It was concluded that cows fed CS performed better than those fed RM or SM likely due to the higher starch and lower NDF intakes. However, no major differences were noted between the 2 forage millet 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 | 0.001 |
| 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 | 0.000 | 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".