Effects of feeding propylene glycol on dry matter intake, lactation performance, energy balance and blood metabolites in early lactation dairy cows
Bibliographic record
Abstract
The objectives of this study were to evaluate effects of feeding propylene glycol (PG) on feed intake, milk yield and milk composition, blood metabolites and energy balance in Holstein dairy cows from 1 to 63 days in milk. Thirty-two multiparous cows, blocked by lactation number, previous 305-day milk production and expected calving date, were arranged into four groups in a randomized block design. Treatments were: control, low PG, medium PG and high PG with 0, 150, 300 and 450 ml PG per cow per day, respectively. The supplement of food grade PG (0.998 g/g PG) was hand-mixed into the top one-third of the daily ration. Cows were fed ad libitum a total mixed ration consisting of forage and concentrate (50 : 50, dry matter basis). Feed intake, milk yield and milk components were not affected (P > 0.05) by PG supplementation. Overall, body weight (BW) loss tended (P < 0.08) to be linearly reduced, and energy status was linearly improved with increasing PG supplementation. Concentrations of glucose in plasma were higher for cows fed PG relative to control (55.6 v. 58.9 mg/dl) and linearly increased (P < 0.01) with increasing PG supplementation. Plasma concentrations of non-esterified fatty acids and beta-hydroxybutyrate were linearly increased, but urine acetoacetate concentration was quadratically changed with the highest for control diet and the lowest for 450 ml/day of PG. These results indicated that supplementation of PG in the early lactating cow diets had minimal effects on feed intake and milk production, but may potentially reduce contents of milk fat and milk protein. Supplementation of early lactating dairy cow diets with PG is beneficial in terms of improving energy status and reducing BW loss.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".