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Record W4406961285 · doi:10.3168/jds.2024-25454

Associations of serum fatty acids, serum urea nitrogen, and ruminal ammonia nitrogen with residual feed intake in lactating dairy cows

2025· article· en· W4406961285 on OpenAlexafffund
W.M. Coelho, H.F. Monteiro, C.C. Figueiredo, B. Mion, J.E.P. Santos, R.S. Bisinotto, Francisco Peñagaricano, Payam Vahmani, E.S. Ribeiro, F.S. Lima

Bibliographic record

VenueJournal of Dairy Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Guelph
FundersUniversity of California, DavisNational Institute of Food and AgricultureOntario Agri-Food Innovation AllianceFoundation for Food and Agriculture ResearchUniversity of FloridaU.S. Department of Agriculture
KeywordsUrea nitrogenUreaNitrogenChemistryRumenAmmoniaAnimal scienceVolatile fatty acidsResidual feed intakeFood scienceDairy cattleBiochemistryBiologyBody weightFeed conversion ratioFermentationEndocrinologyOrganic chemistryCreatinine

Abstract

fetched live from OpenAlex

Feed efficiency is critical in dairy farming, affecting production costs and environmental sustainability. The development of the trait residual feed intake (RFI) has provided an opportunity to select dairy cows that are more efficient in converting nutrients into milk. Note that RFI requires individual daily intake records, which are typically collected on a limited number of research farms. In this context, the identification of biomarkers that can be used to identify and select more feed-efficient cows is of great interest. As such, this study aimed to identify ruminal and serum biomarkers associated with RFI in mid-lactation Holstein cows. A selected subset of 24 out of 454 Holstein cows was used in this study. This subset was strategically selected to represent extremes of least feed-efficient (LFE; n=12, RFI=2.44) and most feed-efficient (MFE; n=12, RFI=−2.69) cows with no difference in the 3 energy sinks, namely BW change, metabolic BW, and energy secreted in milk. Rumen fluid and serum samples were collected between 60 and 90 DIM. Rumen fluid samples were collected using an oro-esophageal tubing procedure. Serum samples were used to measure fatty acids using a 2-step assay. The fatty acid methyl ester was assessed using solid-phase extraction and quantified using the chromatographic peak area and internal standard-based calculations. Ruminal ammonia nitrogen was measured using a phenol-hypochlorite assay, and serum urea was measured using a commercial ELISA kit. Cows in the MFE group had higher ruminal ammonia nitrogen concentrations than cows in the LFE group. There were no differences in serum urea concentration between MFE and LFE cows. Serum fatty acid concentrations differed between groups, with myristic acid (C14:0), palmitic acid (C16:0), cis -heptadecenoic acid ( cis -9–17:1), stearic acid (C18:0), and total SFA having greater concentrations in the MFE group than in the LFE group. The total PUFA concentration was lower in the MFE group than in the LFE group. A model incorporating C14:0, C16:0, palmitoleic acid ( trans -9-C16:1), anteiso -heptadecanoic acid plus palmitoleic acid (C17:0+ trans -13-C16:1), oleic acid ( cis -9-C18:1), cis -vaccenic acid ( cis -11-C18:1), petroselinic acid ( cis -12-C18:1), C18:0, linoleic acid (C18:2n-6), dihomo-γ-linolenic acid (C20:3n-6), cis -MUFA, n-6 PUFA, total PUFA, total SFA, and other or unknown fatty acids was used to assess goodness-of-fit for RFI and showed an adjusted R 2 of 0.74. When ruminal ammonia nitrogen was added to the previous model, the adjusted R 2 improved to 0.84. Our findings provide evidence that ruminal ammonia nitrogen and serum fatty acids are associated with RFI, thus suggesting that these metabolites might be helpful in identifying more feed-efficient dairy cows.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.021
GPT teacher head0.260
Teacher spread0.239 · 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 designObservational
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
Published2025
Admission routes2
Has abstractyes

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