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Record W3159788294 · doi:10.3168/jds.2020-19932

Effect of solid feed level and types of roughage on passage kinetics of milk replacer, concentrate, and roughage in veal calves

2021· article· en· W3159788294 on OpenAlexaff
Sanne van Gastelen, A.J.W. Mens, G.P. Binnendijk, Christopher D. Powell, W.J.J. Gerrits

Bibliographic record

VenueJournal of Dairy Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Guelph
FundersWageningen University and ResearchMinisterie van Landbouw, Natuur en Voedselkwaliteit
KeywordsStrawHayDry matterAnimal scienceAlfalfa hayFecesRumenChemistryBody weightBiologyFood scienceAgronomyFermentationEndocrinology

Abstract

fetched live from OpenAlex

This study aimed (1) to provide estimates of total mean retention times of milk replacer (MR), concentrates, and roughage in veal calves fed a mixed diet; (2) to determine the effect of level and type of solid feed (SF) on passage kinetics of MR, concentrates, and roughages in veal calves; and (3) to compare passage kinetics in veal calves using the fecal excretion curves of indigestible markers and a noninvasive 13 C tracer breath test approach to determine whether the latter technique can serve as an alternative. At the start of the trial, 48 Holstein-Friesian calves (6 wk of age; 68 ± 7.7 kg of body weight; BW) were assigned to 1 of 4 dietary treatments (for statistical analysis, only 39 calf observations were used). Three treatments contained chopped wheat straw as roughage in the SF mixture in a concentrate:roughage ratio of 90:10 (dry matter basis). The SF level was 20 g/kg of metabolic BW per day (low straw), 30 g/kg of metabolic BW per day (middle straw), or 40 g/kg of metabolic BW per day (high straw). The fourth treatment (high hay) contained long perennial ryegrass hay as roughage in the SF mixture in a concentrate:roughage ratio of 70:30 (dry matter basis, at 40 g/kg of metabolic BW per day). The quantity of MR was fixed for the high straw treatment, whereas the amount of MR for the other treatments during the adaptation period was adjusted based on a pair gain strategy (i.e., exchanging ration components but keeping similar net energy). At the end of the adaptation period, calves ranged from 12 to 15 wk of age with an average BW of 123 ± 8.6 kg. Passage kinetics of concentrates were estimated by measuring 13 C enrichment excess of CO 2 in breath from a pulsed-dose of [1- 13 C]octanoate. Passage kinetics of roughage, concentrates, and MR were also estimated using fecal excretion curves obtained after ingestion of chromium-mordanted roughage, Yb 2 O 3 , and Co-EDTA, respectively. We conclude that [1- 13 C]octanoate cannot serve as a measure for oro-duodenal transit of concentrates because of unrealistic estimates. Based on the fecal excretion curves, we concluded that the total mean retention time of MR (i.e., time to peak; the moment that the excretion curve reaches peak concentration) was, on average, 12.4 h, and that the passage kinetics of MR was not affected by the level or type of SF. The mean retention time of concentrates was shorter (21.4 h) than that of both straw (59.1 h) and hay (36.8 h), and was not affected by the level or type of SF. Also, the mean retention time of the slowest compartment (i.e., the rumen) was shorter for concentrates (39.6 h) than that of straw (110.0 h) and hay (59.2 h). Contrary, the passage of roughage was affected by level and type of SF. Long hay increased time to peak by 22.3 h and decreased ruminal mean retention time by 50.8 h relative to chopped straw, indicating that the passage rate of long hay is faster than that of chopped straw. We conclude that the level and type of SF only affects the passage kinetics of roughage and not that of MR and concentrates.

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)0.001
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.272
Teacher spread0.251 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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