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Record W2892453485 · doi:10.1093/jas/sky352

Effects of hydrolyzable tannin with or without condensed tannin on methane emissions, nitrogen use, and performance of beef cattle fed a high-forage diet1,2

2018· article· en· W2892453485 on OpenAlexafffund
Isaac Adjaye Aboagye, M. Oba, A.R. Castillo, K. M. Koenig, A. D. Iwaasa, K. A. Beauchemin

Bibliographic record

VenueJournal of Animal Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of AlbertaAgriculture and Agri-Food Canada
FundersLethbridge Research and Development Centre
KeywordsRumenTanninCondensed tanninAnimal scienceForageDry matterBeef cattleHydrolyzable TanninFermentationChemistryAnimal feedBiologyFood scienceProanthocyanidinAgronomyBiochemistryPolyphenol

Abstract

fetched live from OpenAlex

Sustainability of animal agriculture requires efficient use of energy and nitrogen (N) by ruminants fed high-forage diets. Thus, there is a need to decrease methane (CH4) emissions and prevent excessive N release into the environment. Therefore, this experiment examined the long-term effects of feeding hydrolyzable tannin (HT) with or without condensed tannin (CT) on animal performance, rumen fermentation, N use, and CH4 production in beef cattle fed a high-forage diet. A total of 75 weaned crossbred steers (292 ± 4.1 kg) were grouped by body weight (BW), housed in individual pens, and randomly assigned to 1 of 5 dietary treatments (15 animals/treatment) in a completely random design. The animals were fed a basal diet of alfalfa:barley silages (50:50; dry matter [DM] basis) with a crude protein content of 17.1% and supplemented with HT extract (chestnut, CN) or a combination (50:50) of HT and CT extracts (quebracho, Q) in a powdered form at different levels of dietary DM. The treatments for determining animal performance and N use were control (no tannin), 0.25% CN, 1.5% CN, combination of CN and Q at 0.125% each (0.25% CNQ), and CN and Q at 0.75% each (1.5% CNQ) of dietary DM. The treatments for the CH4 measurement were control, 1.5% CN, and 1.5% CNQ of dietary DM. The first 84 d of the study were used to measure animal performance, rumen fermentation, and N use, and the next 30 d were used to measure CH4 emissions with the tracer gas technique. There were no effects of treatment on DM intake (DMI), BW, average daily gain, and gain: feed (P ≥ 0.10). The plasma urea N concentration was greater (P < 0.05) for 1.5% CN and 1.5% CNQ than those fed 0.25% CNQ (120.9 and 120.4 vs. 111.7 mg/L, respectively), but not different (P > 0.05) from animals fed control or 0.25% CN (117.2 and 117.5 mg/L, respectively). Tannin inclusion did not affect rumen pH, total volatile fatty acid concentration, proportions of acetate and propionate, and total protozoa populations (P ≥ 0.16). Tannin, irrespective of type or dose, decreased (P < 0.01) ruminal ammonia concentration. Tannin type and dose did not affect (P = 0.54) daily CH4 production (154 ± 5.9 g/d) but 1.5% CNQ tended to decrease CH4 yield compared with control (20.6 vs. 22.0 g/kg DMI; P = 0.094). HT from CN alone or in combination with CT from Q can be added at a low (0.25% DM) or high (1.5% DM) level to a forage-based diet to decrease ruminal ammonia concentration in growing beef cattle fed a high-protein diet without adverse effects on animal performance. A combination of HT and CT at a concentration of 1.5% dietary DM also tended to decrease CH4 emissions without negatively affecting performance.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.414
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.254
Teacher spread0.233 · 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 teacher head, 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

Citations93
Published2018
Admission routes2
Has abstractyes

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