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Record W4362635245 · doi:10.3168/jds.2022-22521

Effect of low dietary concentrations of Acacia mearnsii tannin extract on chewing, ruminal fermentation, digestibility, nitrogen partition, and performance of dairy cows

2023· article· en· W4362635245 on OpenAlexafffund
Liniker N. Oliveira, Marina A.N. Pereira, Cecília D.S. Oliveira, Cássia C. Oliveira, Rayana B. Silva, R.A.N. Pereira, T.J. DeVries

Bibliographic record

VenueJournal of Dairy Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Guelph
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisConselho Nacional de Desenvolvimento Científico e TecnológicoUniversidade Federal de GoiásCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUniversity of Guelph
KeywordsAcacia mearnsiiTanninFermentationFood scienceChemistryNutrientDry matterAnimal scienceBiologyBotany

Abstract

fetched live from OpenAlex

The supplementation of dairy cows with tannins can reduce the ruminal degradation of dietary protein and urine N excretion, but high concentration in the diet can impair ruminal function, diet digestibility, feed intake, and milk yield. This study evaluated the effect of low concentrations (0, 0.14, 0.29, or 0.43% of diet in DM basis) of a tannin extract from the bark of Acacia mearnsii (TA) on milking performance, dry matter intake (DMI), digestibility, chewing behavior, ruminal fermentation, and N partition of dairy cows. Twenty Holstein cows (34.7 ± 4.8 kg/d, 590 ± 89 kg, and 78 ± 33 d in lactation) were individually fed a sequence of 4 treatments in 5, 4 × 4 Latin squares (with 21-d treatment periods, each with a 14-d adaptation period). The TA replaced citrus pulp in the total mixed ration and other feed ingredients were kept constant. Diets had 17.1% crude protein, mostly from soybean meal and alfalfa haylage. The TA had no detected effect on DMI (22.1 kg/d), milk yield (33.5 kg/d), and milk components. The proportions in milk fat of mixed origin fatty acids (16C and 17C) and the daily secretion of unsaturated fatty acids were linearly reduced and the proportion of de novo fatty acids was increased by TA. Cows fed TA had linear increase in the molar proportion of butyrate and linear reduction in propionate in ruminal fluid, whereas acetate did not differ. There was a tendency for the ratio of acetate to propionate to be linearly increased by TA. Cows fed TA had a linear reduction in the relative ruminal microbial yield, estimated by the concentrations of allantoin and creatinine in urine and body weight. The total-tract apparent digestibility of neutral detergent fiber, starch, and crude protein also did not differ. The TA induced a linear increase in meal size and duration of the first daily meal and reduced meal frequency. Rumination behavior did not differ with treatment. Cows fed 0.43% TA selected against feed particles >19 mm in the morning. There were tendencies for linear decreases in milk urea N (16.1-17.3 mg/dL), urine N (153-168 g/d and 25.5-28.7% of N intake), and plasma urea N at 6, 18, and 21 h postmorning feeding, and plasma urea N 12 h postfeeding was reduced by TA. The proportion of N intake in milk (27.1%) and feces (21.4%) did not differ with treatment. Reductions in urine N excretion and milk and plasma urea N suggest that TA reduced ruminal AA deamination, whereas lactation performance did not differ. Overall, TA up to 0.43% of DM did not affect DMI and lactation performance, while there was a tendency to reduce urine N excretion.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.286
Teacher spread0.259 · 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 designBench or experimental
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

Citations29
Published2023
Admission routes2
Has abstractyes

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