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Record W2467194779 · doi:10.3168/jds.2015-10754

Effects of lactic acid bacteria silage inoculation on methane emission and productivity of Holstein Friesian dairy cattle

2016· article· en· W2467194779 on OpenAlexaff
J.L. Ellis, I.K. Hindrichsen, G. Klop, Robert D. Kinley, Nina Milora, A. Bannink, J. Dijkstra

Bibliographic record

VenueJournal of Dairy Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSilageMicrobial inoculantDry matterLactic acidNeutral Detergent FiberFermentationFood scienceLactobacillusAgronomyBiologyDairy cattleChemistryAnimal scienceInoculationBacteriaHorticulture

Abstract

fetched live from OpenAlex

Inoculants of lactic acid bacteria (LAB) are used to improve silage quality and prevent spoilage via increased production of lactic acid and other organic acids and a rapid decline in silage pH. The addition of LAB inoculants to silage has been associated with increases in silage digestibility, dry matter intake (DMI), and milk yield. Given the potential change in silage and rumen fermentation conditions accompanying these silage additives, the aim of this study was to investigate the effect of LAB silage inoculants on DMI, digestibility, milk yield, milk composition, and methane (CH4) production from dairy cows in vivo. Eight mid-lactation Holstein-Friesian dairy cows were grouped into 2 blocks of 4 cows (multiparous and primiparous) and used in a 4×4 double Latin square design with 21-d periods. Methane emissions were measured by indirect calorimetry. Treatments were grass silage (mainly ryegrass) with no inoculant (GS), with a long-term inoculant (applied at harvest; GS+L), with a short-term inoculant (applied 16h before feeding; GS+S), or with both long and short-term inoculants (GS+L+S). All diets consisted of grass silage and concentrate (75:25 on a dry matter basis). The long-term inoculant consisted of a 10:20:70 mixture of Lactobacillus plantarum, Lactococcus lactis, and Lactobacillus buchneri, and the short-term inoculant was a preparation of Lc. lactis. Dry matter intake was not affected by long-term or short-term silage inoculation, nor was dietary neutral detergent fiber or fat digestibility, or N or energy balance. Milk composition (except milk urea) and fat and protein-corrected milk yield were not affected by long- or short-term silage inoculation, nor was milk microbial count. However, milk yield tended to be greater with long-term silage inoculation. Methane expressed in units of grams per day, grams per kilogram of DMI, grams per kilogram of milk, or grams per kilogram of fat and protein-corrected milk yield was not affected by long- or short-term silage inoculation. However, CH4 expressed in units of kilojoules per kilogram of metabolic body weight per day tended to be greater with long-term silage inoculation. Results of this study indicate minimal responses in animal performance to both long- and short-term inoculation of grass silage with LAB. Strain and dose differences as well as different basal silages and ensiling conditions are likely responsible for the lack of significant effects observed here, although positive effects have been observed in other studies.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.246
Teacher spread0.230 · 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

Citations67
Published2016
Admission routes1
Has abstractyes

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