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Record W2896347845 · doi:10.3168/jds.2018-15022

Short communication: Blood metabolites, body reserves, and feed efficiency of high-producing dairy cows that varied in ruminal pH when fed a high-concentrate diet

2018· article· en· W2896347845 on OpenAlexaff
S.M. Nasrollahi, A. Zali, G.R. Ghorbani, A. Kahyani, K. A. Beauchemin

Bibliographic record

VenueJournal of Dairy Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsAnimal scienceRumenDry matterChemistryDairy cattleMetaboliteForageTotal mixed rationFood scienceBiologyLactationBiochemistryIce calvingAgronomyFermentation

Abstract

fetched live from OpenAlex

Recent studies report considerable variation in ruminal pH for lactating dairy cows even when fed the same diet. We hypothesized that blood metabolites would be indicators of low ruminal pH, and hence could be used as predictors to help manage this variability. The objective of the study was to determine whether blood metabolite concentrations, body reserves, and feed efficiency were associated with ruminal pH in high-producing dairy cows fed a high-concentrate diet. Seventy-eight individually fed lactating dairy cows (days in milk=103 ± 27; body weight=638 ± 77 kg at the start; mean ± SD) were fed a diet consisting of 35% forage and 65% concentrate (dry matter basis). Cows were adapted for 14 d and then were sampled for 10 d. Ruminal pH was measured by rumenocentesis for all cows at the end of the study 4 h after feeding, and reticular pH was measured on a subsample of 14 cows via indwelling sensors for 5 consecutive days. Cows were classified according to rumenocentesis pH as high (pH ≥ 6.0; n=26), medium (5.8 ≤ pH < 6; n=21), and low (pH < 5.8; n=31). Cows were also classified according to reticular pH as high if pH <5.8 persisted <330 min/d (an average of 78 min/d; n=5) or low if duration of pH <5.8 was ≥330 min/d (an average of 920 min/d; n=9). The classification based on rumenocentesis pH revealed that serum activity of aspartate aminotransferase (AST) was greater in cows with low ruminal pH (70.7 U/L) than cows with high (56.6 U/L) and medium (59.9 U/L) ruminal pH. Also, the blood urea nitrogen concentration was greater in cows with low ruminal pH (13.6 mg/dL) than cows with medium (12.2 mg/dL) and high (12.5 mg/dL) ruminal pH. Blood albumin concentration was greater for cows with low ruminal pH than for cows with medium and high ruminal pH. The classification based on reticular pH also resulted in a trend of greater AST activity and greater blood urea nitrogen concentration in the blood of cows with low pH. Regression analysis showed high serum concentration of AST was associated with high valerate concentration in ruminal fluid (R 2 = 0.14), low rumenocentesis pH (R 2 = 0.10), and low milk fat percentage (R 2 = 0.06). Glucose, triglyceride, cholesterol, globulin, alkaline phosphates, and serum amyloid A did not differ among the different ruminal pH classes. Low pH cows (reticular and ruminal) had less backfat thickness measured via ultrasound, and cows with low ruminal pH tended to have greater milk:feed ratio. Results indicated that cows that differ in ruminal pH also had different concentrations of blood metabolites and backfat thickness, and AST activity in blood may be a plausible indicator of ruminal pH in dairy cows. Further studies on the applicability of AST in blood as a biomarker for detecting low ruminal pH in dairy cows are warranted.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.030
GPT teacher head0.257
Teacher spread0.227 · 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

Citations23
Published2018
Admission routes1
Has abstractyes

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