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Record W4388529056 · doi:10.1093/jas/skad281.558

PSVIII-A-4 Evaluation of Blood Parameters Associated to Environmental Stress in Grazing Beef Heifers with Divergent Residual Feed Intake

2023· article· en· W4388529056 on OpenAlexaffabout
Maria Camila Londono-Mendez, Sergio Lasso, Carolyn Fitzsimmons, Graham Plastow, Edward W. Bork, J. A. Basarab, Gleise da Silva

Bibliographic record

VenueJournal of Animal Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Alberta
Fundersnot available
KeywordsNEFAResidual feed intakeAnimal scienceRumenBeef cattleGrazingHeat indexHaptoglobinLeptinFatty acidChemistryBiologyFeed conversion ratioAgronomyHeat stressFood scienceBiochemistryEndocrinologyBody weightFermentationObesity

Abstract

fetched live from OpenAlex

Abstract There is considerable interest in improved feed efficiency to enhance sustainability in beef production, but a lack of understanding exists on the interaction between residual feed intake (RFI) and environment while grazing. Increasing variation in environmental conditions has been documented in western Canada with summers becoming warmer. This study evaluated blood parameters and rumen temperature (RT) of grazing beef heifers with divergent residual feed intake (RFI) during summer (July to August) of 2022. Forty-four crossbred beef heifers (358 ± 4.78 kg body weight; approximately 14 months of age) previously tested for RFI in drylot and classified as more (n = 21; LOW-RFI = -0.9 ± 0.70) or less feed efficient (n = 23; HIGH-RFI = 1.3 ± 1.00) were grazed at 2.72 AUM/ha over 7 wk in Alberta, Canada. Rumen temperatures were automatically recorded throughout the study using the Smart Rumen Bolus by Moonsyst. Plasma was collected every 14 ± 1 d for 42 d to determine concentrations of blood urea nitrogen (BUN), non-esterified fatty acids (NEFA), insulin-like growth factor 1 (IGF-1), β-Hydroxybutyric acid (BHBA), leptin (LEP), free triiodothyronine (fT3), haptoglobin (HP), heat shock protein 70 (HSP70), and gamma-aminobutyric acid (GABA). Environmental conditions were assessed by calculating the Climate Comprehensive Index using temperature, wind speed, solar radiation, and humidity data from a weather station within 1 km of the grazed pastures. Daily weather conditions were considered to impose risk to cause mild, moderate, severe, and extreme stress for 8, 21, 8, and 1 days, respectively. Plasma and RT data were analyzed as a completely randomized design with repeated measures. An RFI × day interaction was observed for BUN (P = 0.018). LOW-RFI had greater (P = 0.02) BUN on d 42 compared with HIGH-RFI heifers (42.5 vs. 32.6 mg/dL, respectively), while GABA tended to be greater (P ≤ 0.09) for HIGH-RFI heifers on d 0 and 42. Free T3 concentrations were greater (P = 0.04; 8.54 vs. 7.78 pmol/L, respectively), whereas HP was less (P = 0.01) in LOW-RFI heifers. However, HP concentrations were below the threshold for inflammation throughout the study. There was also an effect of day (P < 0.01) for fT3, IGF-1, LEP, NEFA, HSP70, and BHBA. Leptin and HSP70 concentrations were greatest on d 14 and 28, whereas BHBA was the greatest (P < 0.01) and IGF-1 the least on d 42 (P < 0.01). An RFI × hour interaction was detected for RT (P = 0.0003), where HIGH-RFI heifers had greater RT throughout the day. In summary, weather conditions in western Canada changed blood parameters of replacement beef heifers during summer. Feed efficient heifers had decreased RT and greater plasma concentrations of fT3, which could be associated with metabolic homeostasis and regulation of thermogenesis.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.028
GPT teacher head0.252
Teacher spread0.224 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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