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Record W4206932889 · doi:10.21203/rs.3.rs-1017842/v1

Effect of Commercial Slow-Release Urea Product on in Vitro Rumen Fermentation and Ruminal Microbial Community Using RUSITEC Technique

2021· preprint· en· W4206932889 on OpenAlexafffund
Yongmei Guo, Ling Xiao, Long Jin, Sumei Yan, Dongyan Niu, Wenzhu Yang

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of CalgaryAgriculture and Agri-Food Canada
FundersAlberta Agriculture and Forestry
KeywordsRumenPropionateDry matterFermentationChemistryAnimal scienceSoybean mealOrganic matterUreaNeutral Detergent FiberFood scienceBiologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Background The objectives of this study were to determine the effect of commercial slow-release urea (SRU) on in vitro fermentation characteristics, nutrient digestibility, gas production, microbial protein synthesis and bacterial community using rumen simulation technique (RUSITEC). The experiment was a completely randomized design with four treatments and four replications of each treatment. Treatments were: control diet (no SRU addition), control diet plus 0.28% SRU (U28), or plus 0.56% SRU (U56), and control diet that was modified for substituting with 0.35% SRU for equavelant soybean protein (MU35; dry matter [DM] basis). The experiment consisted of 8 days of adaptation and 7 days of data and sample collection. Rumen inoculum was obtained from three ruminally fistulated Angus cows fed the same diet to the substrate incubated. Results Digestibility of DM, organic matter (OM), crude protein (CP), fibre and starch was not affected, but daily production of gas (P < 0.07) and methane (P < 0.05) was quadratically changed with increasing SRU supplementation. The increase of SRU addition did not affect fermentation pH and total volatile fatty acid (VFA) production, whereas linearly (P < 0.01) decreased proportion of propionate, and linearly (P < 0.01) increased acetate to propionate ratio and ammonia nitrogen (N) concentration. The microbial N efficiency also linearly (P < 0.03) improved with increasing supplementation of SRU. In comparison with control diet, the dietary substitution of SRU for part of soybean meal increased (P < 0.05) the digestibility of DM, OM and CP and decreased (P < 0.02) the total gas production. The total VFA production and acetate to propionate ratio did not differ between control and MU35, whereas the proportion of butyrate was lower (P < 0.05) and that of branched-chain VFA was greater (P < 0.05) with MU35 than control diet. Total and liquid-associated microbial N production as well as ammonia N concentration were greater (P < 0.03) with MU35 than control diet. Observed OTUs, Shannon diversity index, and beta diversity of the microbial community did not differ among treatments. Taxonomic analysis revealed no effect of adding SRU on the relative abundance of bacteria at the phylum level, while at the genus level, the impact of SRU addition on microbial community was greater with MU35 either for liquid associate bacteria or feed particle-associated bacteria. Conclusions Supplementation of a dairy cow diet with SRU showed potential of increase in ammonia N concentration and microbial protein production, and change fermentation pattern to more acetate production. Adding SRU in dairy cow diet also showed beneficial effect on improving digestibility of OM and fibre. The results suggest that SRU can partially substitute soybean meal in dairy cow diet to increase microbial protein production without impairing rumen fermentation.

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)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.067
GPT teacher head0.375
Teacher spread0.308 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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