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Record W4386087260 · doi:10.3168/jds.2023-23402

Dose response to postruminal urea in lactating dairy cattle

2023· article· en· W4386087260 on OpenAlexaff
K. Nichols, Rainer Rauch, Louis Lippens, Dave J Seymour, J. Martín‐Tereso

Bibliographic record

VenueJournal of Dairy Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsCanadian Nutrition Society
Fundersnot available
KeywordsRumenLatin squareUreaDry matterAnimal scienceSilageTotal mixed rationDairy cattleHayForageChemistryLactationDigestion (alchemy)StrawNeutral Detergent FiberBiologyFermentationAgronomyFood scienceBiochemistryIce calvingPregnancyChromatography

Abstract

fetched live from OpenAlex

Inclusion of urea in dairy cattle diets is often limited by negative effects of high levels of feed urea on dry matter intake (DMI) and efficiency of rumen N utilization. We hypothesized that supplying urea post-ruminally would mitigate these limitations and allow greater inclusion of urea in dairy cattle diets. Four rumen-fistulated Holstein-Friesian dairy cows (7 ± 2.1 lactations, 110 ± 30.8 d in milk; mean ± standard deviation) were randomly assigned to a 4 × 4 Latin square design to examine DMI, milk production and composition, digestibility, rumen fermentation, N balance, and plasma constituents in response to 4 levels of urea continuously infused to the abomasum (0, 163, 325, and 488 g/d). Urea doses were targeted to linearly increase the crude protein (CP) content of total DMI (diet plus infusion) by 0, 2, 4, and 6% and equated to 0, 0.7, 1.4, and 2.1% of expected DMI, respectively. Each 28-d infusion period consisted of a 7-d dose step-up period, 14 d of adaptation, and a 7-d measurement period. The diet was fed ad libitum as a total mixed ration [10.9% CP, 42.5% corn silage, 3.5% grass hay, 3.5% wheat straw, and 50.5% concentrate (dry matter basis)] and was formulated to meet 100, 82, and 53% of net energy, metabolizable protein, and rumen-degradable protein requirements, respectively. Linear, quadratic, and cubic effects of urea dose were assessed using polynomial regression assuming the fixed effect of treatment and random effects of period and cow. Dry matter intake and energy-corrected milk yield responded quadratically to urea dose, and milk urea content increased linearly with increasing urea dose. Apparent total-tract digestibility of CP increased linearly with increasing urea dose and ruminal NH 3 -N concentration responded quadratically to urea dose. Mean total VFA concentration was not affected by urea dose. The proportion of N intake excreted in feces decreased linearly and that excreted in urine increased linearly in response to increasing urea dose. The proportion of N intake excreted in milk increased linearly with increasing urea dose. Urinary urea excretion increased linearly with increasing urea dose. Microbial N flow responded cubically to urea dose, but the efficiency of microbial protein synthesis was not affected. Plasma urea concentration increased linearly with increasing urea dose. Regression analysis estimates that when supplemented on top of a low-CP diet, 179 g/d of post-ruminal urea would maximize DMI at 23.4 kg/d, corresponding to a dietary urea inclusion level of 0.8% of DMI, which is in line with the current recommendations for urea inclusion in dairy cattle diets. Overall, these results indicate that post-ruminal delivery of urea does not mitigate DMI depression as urea dose increases.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.291
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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