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Record W4399746779 · doi:10.3390/ani14121804

Effect of 3-Nitropropionic Acid at Different Doses on In Vitro Rumen Fermentation, Digestibility, and Methane Emissions of Grazing Yak and Cattle

2024· article· en· W4399746779 on OpenAlexaff
Wei Guo, Ying Zhang, Mi Zhou

Bibliographic record

VenueAnimals · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of ChinaGuizhou UniversityLanzhou University
KeywordsRumenPropionateIncubationDry matterFermentationYAKAnimal scienceRuminantGrazingChemistryFatty acidBiologyFood scienceBiochemistryAgronomyPasture

Abstract

fetched live from OpenAlex

3-nitropropionic acid (3NPA) has been proposed as an useful modifier to mitigate ruminal enteric methane emissions. However, few studies investigated the effects of 3NPA on ruminal fermentation characteristics of grazing ruminants in vitro. Rumen fluid from grazing yak and cattle were collected and incubated with additions of 0, 8, and 16 mM 3NPA. The total gas production, CH4 production, and dry matter digestibility significantly decreased with increasing 3NPA doses in both ruminant species (p < 0.05) and methane production decreased to almost 100% in cattle at 8 mM NPA but not yak, while H2 accumulation showed an opposite trend. The total fatty acid (TVFA) production, acetate concentration, and propionate concentration in cattle decreased as 3NPA doses increased at 12 and 24 h incubation. For yak, the H2 accumulation reached its apex at 8 mM NPA (p < 0.05). The TVFA in yak decreased significantly with increasing 3NPA doses at 12 and 72 h incubation. Moreover, the acetate concentration and propionate concentration in yak decreased as 3NPA doses increased at 12 and 24 h incubation. Overall, these findings demonstrated that 3NPA could be used as a strategy to mitigate methane emissions; although, it negatively affected the dry matter degradability in vitro.

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 score0.445
Threshold uncertainty score0.124

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.278
Teacher spread0.261 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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